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kaggle-notebooks-outputs-filtered-44

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{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"#LAB 04\n#Take an image of a “Car” and a “Cup”. Perform following tasks:\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-01-28T13:55:30.986851Z","iopub.execute_input":"2022-01-28T13:55:30.987382Z","iopub.status.idle":"2022-01-28T13:55:30.991793Z","shell.execute_reply.started":"2022-01-28T13:55:30.987323Z","shell.execute_reply":"2022-01-28T13:55:30.990826Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"**Read both images**","metadata":{}},{"cell_type":"code","source":"import cv2\nimport matplotlib.pyplot as plt\n\npath =\"../input/car-and-cup/deep/car.jpg\"\ncar = cv2.imread(path)\nplt.imshow(car)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:30.994187Z","iopub.execute_input":"2022-01-28T13:55:30.995046Z","iopub.status.idle":"2022-01-28T13:55:31.336255Z","shell.execute_reply.started":"2022-01-28T13:55:30.994774Z","shell.execute_reply":"2022-01-28T13:55:31.335329Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3768164290>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"path =\"../input/car-and-cup/deep/cup.jpg\"\ncup = cv2.imread(path)\nplt.imshow(cup)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:31.337634Z","iopub.execute_input":"2022-01-28T13:55:31.337978Z","iopub.status.idle":"2022-01-28T13:55:32.035244Z","shell.execute_reply.started":"2022-01-28T13:55:31.337918Z","shell.execute_reply":"2022-01-28T13:55:32.034103Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f37680c8990>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Resize image to 256 by 256**","metadata":{}},{"cell_type":"code","source":"car = cv2.resize(car,(256,256))\nprint(car.shape)\nprint('Image 1')\nplt.imshow(car)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:32.037595Z","iopub.execute_input":"2022-01-28T13:55:32.038238Z","iopub.status.idle":"2022-01-28T13:55:32.281763Z","shell.execute_reply.started":"2022-01-28T13:55:32.038195Z","shell.execute_reply":"2022-01-28T13:55:32.280653Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"(256, 256, 3)\nImage 1\n","output_type":"stream"},{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3763fc0790>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Resize image to 256 by 256**","metadata":{}},{"cell_type":"code","source":"path =\"../input/car-and-cup/deep/cup.jpg\"\ncup = cv2.imread(path)\n\ncup = cv2.resize(cup,(256,256))\nprint(cup.shape)\nprint('Image 2')\nplt.imshow(cup)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:32.283534Z","iopub.execute_input":"2022-01-28T13:55:32.284513Z","iopub.status.idle":"2022-01-28T13:55:32.559715Z","shell.execute_reply.started":"2022-01-28T13:55:32.284462Z","shell.execute_reply":"2022-01-28T13:55:32.559085Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"(256, 256, 3)\nImage 2\n","output_type":"stream"},{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3763f46890>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"****#Converting car image to gray scale****","metadata":{}},{"cell_type":"code","source":"gray_car = cv2.cvtColor(car,cv2.COLOR_BGR2GRAY)\nplt.imshow(gray_car)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:32.560761Z","iopub.execute_input":"2022-01-28T13:55:32.562199Z","iopub.status.idle":"2022-01-28T13:55:32.789687Z","shell.execute_reply.started":"2022-01-28T13:55:32.562149Z","shell.execute_reply":"2022-01-28T13:55:32.788838Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3763ecd710>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"gray_cup = cv2.cvtColor(cup,cv2.COLOR_BGR2GRAY)\nplt.imshow(gray_cup)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:32.791417Z","iopub.execute_input":"2022-01-28T13:55:32.791990Z","iopub.status.idle":"2022-01-28T13:55:33.016112Z","shell.execute_reply.started":"2022-01-28T13:55:32.791928Z","shell.execute_reply":"2022-01-28T13:55:33.015166Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3763ebff50>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Normalizing car image**","metadata":{}},{"cell_type":"code","source":"import numpy as np\nnormalize = np.zeros((250,250))\nnorm_car = cv2.normalize(gray_car,normalize,0,255,cv2.NORM_MINMAX)\nplt.imshow(norm_car)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:33.017241Z","iopub.execute_input":"2022-01-28T13:55:33.017495Z","iopub.status.idle":"2022-01-28T13:55:33.383201Z","shell.execute_reply.started":"2022-01-28T13:55:33.017465Z","shell.execute_reply":"2022-01-28T13:55:33.382468Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3763dc39d0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"normalize = np.zeros((250,250))\nnorm_cup = cv2.normalize(gray_cup,normalize,0,255,cv2.NORM_MINMAX)\nplt.imshow(norm_cup)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:33.385227Z","iopub.execute_input":"2022-01-28T13:55:33.385594Z","iopub.status.idle":"2022-01-28T13:55:33.611102Z","shell.execute_reply.started":"2022-01-28T13:55:33.385561Z","shell.execute_reply":"2022-01-28T13:55:33.610160Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3768272d90>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"** contrast, energy and mean of both images**","metadata":{}},{"cell_type":"code","source":"min_car = np.min(gray_car)\nmin_cup = np.min(gray_cup)\nmax_car = np.max(gray_car)\nmax_cup = np.max(gray_cup)\n# #formula for the contrast is as following\ncont_car = (max_car-min_car)/(max_car+min_car)\ncont_cup = (max_cup-min_cup)/(max_cup+min_cup)\nprint('Contrast of car is : ',cont_car)\nprint('Contrast of cup is : ',cont_cup)\nprint(plt.bar(['car','cup'],[cont_car,cont_cup]))","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:33.612340Z","iopub.execute_input":"2022-01-28T13:55:33.612545Z","iopub.status.idle":"2022-01-28T13:55:33.770630Z","shell.execute_reply.started":"2022-01-28T13:55:33.612519Z","shell.execute_reply":"2022-01-28T13:55:33.770026Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Contrast of car is : 0.95689654\nContrast of cup is : 4.521739\n<BarContainer object of 2 artists>\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: overflow encountered in ubyte_scalars\n import sys\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"car_std = np.std(car)\ncup_std = np.std(cup)\nprint(car_std,cup_std)\nprint(print(plt.bar(['car','cup'],[car_std,cup_std])))","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:33.771753Z","iopub.execute_input":"2022-01-28T13:55:33.772230Z","iopub.status.idle":"2022-01-28T13:55:33.950151Z","shell.execute_reply.started":"2022-01-28T13:55:33.772189Z","shell.execute_reply":"2022-01-28T13:55:33.949449Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"41.566963167083884 69.2213395474125\n<BarContainer object of 2 artists>\nNone\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**mean of both images**","metadata":{}},{"cell_type":"code","source":"mean1= car.mean()\nmean2 = cup.mean()\nprint(mean1,mean2)\nprint(print(plt.bar(['car','cup'],[mean1,mean2])))","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:33.951252Z","iopub.execute_input":"2022-01-28T13:55:33.951967Z","iopub.status.idle":"2022-01-28T13:55:34.114031Z","shell.execute_reply.started":"2022-01-28T13:55:33.951904Z","shell.execute_reply":"2022-01-28T13:55:34.113163Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"36.609893798828125 219.0826670328776\n<BarContainer object of 2 artists>\nNone\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"energy_dif = (car_std) - (cup_std)\ncont_dif = (cont_car) - (cont_cup)\nmean_dif = (mean1) - (mean2)\nprint(energy_dif)\nprint(cont_dif)\nprint(mean_dif)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:55:56.443394Z","iopub.execute_input":"2022-01-28T13:55:56.443667Z","iopub.status.idle":"2022-01-28T13:55:56.449764Z","shell.execute_reply.started":"2022-01-28T13:55:56.443638Z","shell.execute_reply":"2022-01-28T13:55:56.448896Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"-27.654376380328614\n-3.5648425\n-182.47277323404947\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**detection of edge**","metadata":{}},{"cell_type":"code","source":"car_edg = cv2.Canny(gray_car, threshold1=30, threshold2=100)\nplt.imshow(car_edg)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:56:13.757087Z","iopub.execute_input":"2022-01-28T13:56:13.757519Z","iopub.status.idle":"2022-01-28T13:56:13.992088Z","shell.execute_reply.started":"2022-01-28T13:56:13.757487Z","shell.execute_reply":"2022-01-28T13:56:13.990891Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3763dba390>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"cup_edg = cv2.Canny(gray_cup, threshold1=30, threshold2=100)\nplt.imshow(cup_edg)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:56:37.098463Z","iopub.execute_input":"2022-01-28T13:56:37.098749Z","iopub.status.idle":"2022-01-28T13:56:37.271314Z","shell.execute_reply.started":"2022-01-28T13:56:37.098718Z","shell.execute_reply":"2022-01-28T13:56:37.270321Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f3762539790>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
0086/397/86397804.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e68c0a21", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:53.568472Z", "iopub.status.busy": "2022-01-28T13:57:53.560348Z", "iopub.status.idle": "2022-01-28T13:57:53.577309Z", "shell.execute_reply": "2022-01-28T13:57:53.578036Z", "shell.execute_reply.started": "2022-01-28T13:54:59.235256Z" }, "papermill": { "duration": 0.074649, "end_time": "2022-01-28T13:57:53.578355", "exception": false, "start_time": "2022-01-28T13:57:53.503706", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/tmdb-movie-metadata/tmdb_5000_movies.csv\n", "/kaggle/input/tmdb-movie-metadata/tmdb_5000_credits.csv\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np \n", "\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a5dd30e3", "metadata": { "papermill": { "duration": 0.053594, "end_time": "2022-01-28T13:57:53.681680", "exception": false, "start_time": "2022-01-28T13:57:53.628086", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "id": "eeb55963", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:53.791153Z", "iopub.status.busy": "2022-01-28T13:57:53.790424Z", "iopub.status.idle": "2022-01-28T13:57:55.036877Z", "shell.execute_reply": "2022-01-28T13:57:55.037456Z", "shell.execute_reply.started": "2022-01-28T13:54:59.246397Z" }, "papermill": { "duration": 1.303946, "end_time": "2022-01-28T13:57:55.037654", "exception": false, "start_time": "2022-01-28T13:57:53.733708", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_movies.csv')\n", "credits= pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_credits.csv')" ] }, { "cell_type": "code", "execution_count": 3, "id": "ca44f665", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:55.147032Z", "iopub.status.busy": "2022-01-28T13:57:55.145899Z", "iopub.status.idle": "2022-01-28T13:57:55.169381Z", "shell.execute_reply": "2022-01-28T13:57:55.168742Z", "shell.execute_reply.started": "2022-01-28T13:54:59.933933Z" }, "papermill": { "duration": 0.081861, "end_time": "2022-01-28T13:57:55.169535", "exception": false, "start_time": "2022-01-28T13:57:55.087674", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>budget</th>\n", " <th>genres</th>\n", " <th>homepage</th>\n", " <th>id</th>\n", " <th>keywords</th>\n", " <th>original_language</th>\n", " <th>original_title</th>\n", " <th>overview</th>\n", " <th>popularity</th>\n", " <th>production_companies</th>\n", " <th>production_countries</th>\n", " <th>release_date</th>\n", " <th>revenue</th>\n", " <th>runtime</th>\n", " <th>spoken_languages</th>\n", " <th>status</th>\n", " <th>tagline</th>\n", " <th>title</th>\n", " <th>vote_average</th>\n", " <th>vote_count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>237000000</td>\n", " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n", " <td>http://www.avatarmovie.com/</td>\n", " <td>19995</td>\n", " <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n", " <td>en</td>\n", " <td>Avatar</td>\n", " <td>In the 22nd century, a paraplegic Marine is di...</td>\n", " <td>150.437577</td>\n", " <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n", " <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n", " <td>2009-12-10</td>\n", " <td>2787965087</td>\n", " <td>162.0</td>\n", " <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n", " <td>Released</td>\n", " <td>Enter the World of Pandora.</td>\n", " <td>Avatar</td>\n", " <td>7.2</td>\n", " <td>11800</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " budget genres \\\n", "0 237000000 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... \n", "\n", " homepage id \\\n", "0 http://www.avatarmovie.com/ 19995 \n", "\n", " keywords original_language \\\n", "0 [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":... en \n", "\n", " original_title overview \\\n", "0 Avatar In the 22nd century, a paraplegic Marine is di... \n", "\n", " popularity production_companies \\\n", "0 150.437577 [{\"name\": \"Ingenious Film Partners\", \"id\": 289... \n", "\n", " production_countries release_date revenue \\\n", "0 [{\"iso_3166_1\": \"US\", \"name\": \"United States o... 2009-12-10 2787965087 \n", "\n", " runtime spoken_languages status \\\n", "0 162.0 [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso... Released \n", "\n", " tagline title vote_average vote_count \n", "0 Enter the World of Pandora. Avatar 7.2 11800 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.head(1)" ] }, { "cell_type": "code", "execution_count": 4, "id": "a49df63b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:55.283435Z", "iopub.status.busy": "2022-01-28T13:57:55.282744Z", "iopub.status.idle": "2022-01-28T13:57:55.287699Z", "shell.execute_reply": "2022-01-28T13:57:55.287192Z", "shell.execute_reply.started": "2022-01-28T13:54:59.956766Z" }, "papermill": { "duration": 0.065669, "end_time": "2022-01-28T13:57:55.287842", "exception": false, "start_time": "2022-01-28T13:57:55.222173", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>movie_id</th>\n", " <th>title</th>\n", " <th>cast</th>\n", " <th>crew</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>19995</td>\n", " <td>Avatar</td>\n", " <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n", " <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " movie_id title cast \\\n", "0 19995 Avatar [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"... \n", "\n", " crew \n", "0 [{\"credit_id\": \"52fe48009251416c750aca23\", \"de... " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "credits.head(1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "7b9f623a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:55.394293Z", "iopub.status.busy": "2022-01-28T13:57:55.393621Z", "iopub.status.idle": "2022-01-28T13:57:55.397651Z", "shell.execute_reply": "2022-01-28T13:57:55.397029Z", "shell.execute_reply.started": "2022-01-28T13:54:59.982850Z" }, "papermill": { "duration": 0.058958, "end_time": "2022-01-28T13:57:55.397796", "exception": false, "start_time": "2022-01-28T13:57:55.338838", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(4803, 20)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.shape\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "ed128678", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:55.502946Z", "iopub.status.busy": "2022-01-28T13:57:55.502303Z", "iopub.status.idle": "2022-01-28T13:57:55.504766Z", "shell.execute_reply": "2022-01-28T13:57:55.505295Z", "shell.execute_reply.started": "2022-01-28T13:54:59.992236Z" }, "papermill": { "duration": 0.057758, "end_time": "2022-01-28T13:57:55.505472", "exception": false, "start_time": "2022-01-28T13:57:55.447714", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(4803, 4)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "credits.shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "049253cf", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:55.613634Z", "iopub.status.busy": "2022-01-28T13:57:55.612960Z", "iopub.status.idle": "2022-01-28T13:57:55.642218Z", "shell.execute_reply": "2022-01-28T13:57:55.642753Z", "shell.execute_reply.started": "2022-01-28T13:55:00.006974Z" }, "papermill": { "duration": 0.087593, "end_time": "2022-01-28T13:57:55.642929", "exception": false, "start_time": "2022-01-28T13:57:55.555336", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies = movies.merge(credits,on='title')" ] }, { "cell_type": "code", "execution_count": 8, "id": "61508ff3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:55.753696Z", "iopub.status.busy": "2022-01-28T13:57:55.752749Z", "iopub.status.idle": "2022-01-28T13:57:55.774737Z", "shell.execute_reply": "2022-01-28T13:57:55.774062Z", "shell.execute_reply.started": "2022-01-28T13:55:00.054979Z" }, "papermill": { "duration": 0.08144, "end_time": "2022-01-28T13:57:55.774889", "exception": false, "start_time": "2022-01-28T13:57:55.693449", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>budget</th>\n", " <th>genres</th>\n", " <th>homepage</th>\n", " <th>id</th>\n", " <th>keywords</th>\n", " <th>original_language</th>\n", " <th>original_title</th>\n", " <th>overview</th>\n", " <th>popularity</th>\n", " <th>production_companies</th>\n", " <th>...</th>\n", " <th>runtime</th>\n", " <th>spoken_languages</th>\n", " <th>status</th>\n", " <th>tagline</th>\n", " <th>title</th>\n", " <th>vote_average</th>\n", " <th>vote_count</th>\n", " <th>movie_id</th>\n", " <th>cast</th>\n", " <th>crew</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>237000000</td>\n", " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n", " <td>http://www.avatarmovie.com/</td>\n", " <td>19995</td>\n", " <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n", " <td>en</td>\n", " <td>Avatar</td>\n", " <td>In the 22nd century, a paraplegic Marine is di...</td>\n", " <td>150.437577</td>\n", " <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n", " <td>...</td>\n", " <td>162.0</td>\n", " <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n", " <td>Released</td>\n", " <td>Enter the World of Pandora.</td>\n", " <td>Avatar</td>\n", " <td>7.2</td>\n", " <td>11800</td>\n", " <td>19995</td>\n", " <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n", " <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>300000000</td>\n", " <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n", " <td>http://disney.go.com/disneypictures/pirates/</td>\n", " <td>285</td>\n", " <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n", " <td>en</td>\n", " <td>Pirates of the Caribbean: At World's End</td>\n", " <td>Captain Barbossa, long believed to be dead, ha...</td>\n", " <td>139.082615</td>\n", " <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n", " <td>...</td>\n", " <td>169.0</td>\n", " <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n", " <td>Released</td>\n", " <td>At the end of the world, the adventure begins.</td>\n", " <td>Pirates of the Caribbean: At World's End</td>\n", " <td>6.9</td>\n", " <td>4500</td>\n", " <td>285</td>\n", " <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n", " <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 23 columns</p>\n", "</div>" ], "text/plain": [ " budget genres \\\n", "0 237000000 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... \n", "1 300000000 [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"... \n", "\n", " homepage id \\\n", "0 http://www.avatarmovie.com/ 19995 \n", "1 http://disney.go.com/disneypictures/pirates/ 285 \n", "\n", " keywords original_language \\\n", "0 [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":... en \n", "1 [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na... en \n", "\n", " original_title \\\n", "0 Avatar \n", "1 Pirates of the Caribbean: At World's End \n", "\n", " overview popularity \\\n", "0 In the 22nd century, a paraplegic Marine is di... 150.437577 \n", "1 Captain Barbossa, long believed to be dead, ha... 139.082615 \n", "\n", " production_companies ... runtime \\\n", "0 [{\"name\": \"Ingenious Film Partners\", \"id\": 289... ... 162.0 \n", "1 [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"... ... 169.0 \n", "\n", " spoken_languages status \\\n", "0 [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso... Released \n", "1 [{\"iso_639_1\": \"en\", \"name\": \"English\"}] Released \n", "\n", " tagline \\\n", "0 Enter the World of Pandora. \n", "1 At the end of the world, the adventure begins. \n", "\n", " title vote_average vote_count movie_id \\\n", "0 Avatar 7.2 11800 19995 \n", "1 Pirates of the Caribbean: At World's End 6.9 4500 285 \n", "\n", " cast \\\n", "0 [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"... \n", "1 [{\"cast_id\": 4, \"character\": \"Captain Jack Spa... \n", "\n", " crew \n", "0 [{\"credit_id\": \"52fe48009251416c750aca23\", \"de... \n", "1 [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de... \n", "\n", "[2 rows x 23 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.head(2)" ] }, { "cell_type": "code", "execution_count": 9, "id": "67dcb0d7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:55.881179Z", "iopub.status.busy": "2022-01-28T13:57:55.880430Z", "iopub.status.idle": "2022-01-28T13:57:55.883567Z", "shell.execute_reply": "2022-01-28T13:57:55.883033Z", "shell.execute_reply.started": "2022-01-28T13:55:00.084077Z" }, "papermill": { "duration": 0.057662, "end_time": "2022-01-28T13:57:55.883699", "exception": false, "start_time": "2022-01-28T13:57:55.826037", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#removing columns -\n", "# budget\n", "# homepage\n", "# id\n", "# original_language\n", "# original_title\n", "# popularity\n", "# production_comapny\n", "# production_countries\n", "# release-date(not sure)" ] }, { "cell_type": "code", "execution_count": 10, "id": "f398bed7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:56.006745Z", "iopub.status.busy": "2022-01-28T13:57:56.006034Z", "iopub.status.idle": "2022-01-28T13:57:56.023555Z", "shell.execute_reply": "2022-01-28T13:57:56.024061Z", "shell.execute_reply.started": "2022-01-28T13:55:00.091321Z" }, "papermill": { "duration": 0.08792, "end_time": "2022-01-28T13:57:56.024249", "exception": false, "start_time": "2022-01-28T13:57:55.936329", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 4809 entries, 0 to 4808\n", "Data columns (total 23 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 budget 4809 non-null int64 \n", " 1 genres 4809 non-null object \n", " 2 homepage 1713 non-null object \n", " 3 id 4809 non-null int64 \n", " 4 keywords 4809 non-null object \n", " 5 original_language 4809 non-null object \n", " 6 original_title 4809 non-null object \n", " 7 overview 4806 non-null object \n", " 8 popularity 4809 non-null float64\n", " 9 production_companies 4809 non-null object \n", " 10 production_countries 4809 non-null object \n", " 11 release_date 4808 non-null object \n", " 12 revenue 4809 non-null int64 \n", " 13 runtime 4807 non-null float64\n", " 14 spoken_languages 4809 non-null object \n", " 15 status 4809 non-null object \n", " 16 tagline 3965 non-null object \n", " 17 title 4809 non-null object \n", " 18 vote_average 4809 non-null float64\n", " 19 vote_count 4809 non-null int64 \n", " 20 movie_id 4809 non-null int64 \n", " 21 cast 4809 non-null object \n", " 22 crew 4809 non-null object \n", "dtypes: float64(3), int64(5), object(15)\n", "memory usage: 901.7+ KB\n" ] } ], "source": [ "movies.info()" ] }, { "cell_type": "code", "execution_count": 11, "id": "46a65bb8", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:56.134495Z", "iopub.status.busy": "2022-01-28T13:57:56.133686Z", "iopub.status.idle": "2022-01-28T13:57:56.142111Z", "shell.execute_reply": "2022-01-28T13:57:56.141526Z", "shell.execute_reply.started": "2022-01-28T13:55:00.132264Z" }, "papermill": { "duration": 0.066285, "end_time": "2022-01-28T13:57:56.142251", "exception": false, "start_time": "2022-01-28T13:57:56.075966", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies = movies[['movie_id','title','overview','genres','keywords','cast','crew']]" ] }, { "cell_type": "code", "execution_count": 12, "id": "0823c622", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:56.263338Z", "iopub.status.busy": "2022-01-28T13:57:56.262401Z", "iopub.status.idle": "2022-01-28T13:57:56.267232Z", "shell.execute_reply": "2022-01-28T13:57:56.266670Z", "shell.execute_reply.started": "2022-01-28T13:55:00.143161Z" }, "papermill": { "duration": 0.07332, "end_time": "2022-01-28T13:57:56.267379", "exception": false, "start_time": "2022-01-28T13:57:56.194059", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>movie_id</th>\n", " <th>title</th>\n", " <th>overview</th>\n", " <th>genres</th>\n", " <th>keywords</th>\n", " <th>cast</th>\n", " <th>crew</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>19995</td>\n", " <td>Avatar</td>\n", " <td>In the 22nd century, a paraplegic Marine is di...</td>\n", " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n", " <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n", " <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n", " <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>285</td>\n", " <td>Pirates of the Caribbean: At World's End</td>\n", " <td>Captain Barbossa, long believed to be dead, ha...</td>\n", " <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n", " <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n", " <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n", " <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>206647</td>\n", " <td>Spectre</td>\n", " <td>A cryptic message from Bond’s past sends him o...</td>\n", " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n", " <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n", " <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n", " <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>49026</td>\n", " <td>The Dark Knight Rises</td>\n", " <td>Following the death of District Attorney Harve...</td>\n", " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n", " <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n", " <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n", " <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>49529</td>\n", " <td>John Carter</td>\n", " <td>John Carter is a war-weary, former military ca...</td>\n", " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n", " <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n", " <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n", " <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " movie_id title \\\n", "0 19995 Avatar \n", "1 285 Pirates of the Caribbean: At World's End \n", "2 206647 Spectre \n", "3 49026 The Dark Knight Rises \n", "4 49529 John Carter \n", "\n", " overview \\\n", "0 In the 22nd century, a paraplegic Marine is di... \n", "1 Captain Barbossa, long believed to be dead, ha... \n", "2 A cryptic message from Bond’s past sends him o... \n", "3 Following the death of District Attorney Harve... \n", "4 John Carter is a war-weary, former military ca... \n", "\n", " genres \\\n", "0 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... \n", "1 [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"... \n", "2 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... \n", "3 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam... \n", "4 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... \n", "\n", " keywords \\\n", "0 [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":... \n", "1 [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na... \n", "2 [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name... \n", "3 [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,... \n", "4 [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":... \n", "\n", " cast \\\n", "0 [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"... \n", "1 [{\"cast_id\": 4, \"character\": \"Captain Jack Spa... \n", "2 [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr... \n", "3 [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba... \n", "4 [{\"cast_id\": 5, \"character\": \"John Carter\", \"c... \n", "\n", " crew \n", "0 [{\"credit_id\": \"52fe48009251416c750aca23\", \"de... \n", "1 [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de... \n", "2 [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de... \n", "3 [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de... \n", "4 [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de... 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"7eb9de80", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:56.496182Z", "iopub.status.busy": "2022-01-28T13:57:56.495452Z", "iopub.status.idle": "2022-01-28T13:57:56.599570Z", "shell.execute_reply": "2022-01-28T13:57:56.598887Z", "shell.execute_reply.started": "2022-01-28T13:55:00.186104Z" }, "papermill": { "duration": 0.160499, "end_time": "2022-01-28T13:57:56.599725", "exception": false, "start_time": "2022-01-28T13:57:56.439226", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.duplicated().sum()" ] }, { "cell_type": "code", "execution_count": 15, "id": "093aea28", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:56.716009Z", "iopub.status.busy": "2022-01-28T13:57:56.708191Z", "iopub.status.idle": "2022-01-28T13:57:56.722144Z", "shell.execute_reply": "2022-01-28T13:57:56.721529Z", "shell.execute_reply.started": "2022-01-28T13:55:00.300014Z" }, "papermill": { "duration": 0.069637, "end_time": "2022-01-28T13:57:56.722305", "exception": false, "start_time": "2022-01-28T13:57:56.652668", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 16, "id": "0aba4f38", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:56.837307Z", "iopub.status.busy": "2022-01-28T13:57:56.836621Z", "iopub.status.idle": "2022-01-28T13:57:56.843484Z", "shell.execute_reply": "2022-01-28T13:57:56.844027Z", "shell.execute_reply.started": "2022-01-28T13:55:00.316949Z" }, "papermill": { "duration": 0.068425, "end_time": "2022-01-28T13:57:56.844205", "exception": false, "start_time": "2022-01-28T13:57:56.775780", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "movie_id 0\n", "title 0\n", "overview 0\n", "genres 0\n", "keywords 0\n", "cast 0\n", "crew 0\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 17, "id": "610b4e99", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:56.981470Z", "iopub.status.busy": "2022-01-28T13:57:56.980442Z", "iopub.status.idle": "2022-01-28T13:57:56.985631Z", "shell.execute_reply": "2022-01-28T13:57:56.986462Z", "shell.execute_reply.started": "2022-01-28T13:55:00.334565Z" }, "papermill": { "duration": 0.086192, "end_time": "2022-01-28T13:57:56.986699", "exception": false, "start_time": "2022-01-28T13:57:56.900507", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.iloc[0].genres" ] }, { "cell_type": "code", "execution_count": 18, "id": "b1475e3f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:57.115342Z", "iopub.status.busy": "2022-01-28T13:57:57.114624Z", "iopub.status.idle": "2022-01-28T13:57:57.118743Z", "shell.execute_reply": "2022-01-28T13:57:57.119448Z", "shell.execute_reply.started": "2022-01-28T13:55:00.342937Z" }, "papermill": { "duration": 0.06195, "end_time": "2022-01-28T13:57:57.119624", "exception": false, "start_time": "2022-01-28T13:57:57.057674", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import ast" ] }, { "cell_type": "code", "execution_count": 19, "id": "c32d1e01", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:57.250370Z", "iopub.status.busy": "2022-01-28T13:57:57.248942Z", "iopub.status.idle": "2022-01-28T13:57:57.251537Z", "shell.execute_reply": "2022-01-28T13:57:57.252342Z", "shell.execute_reply.started": "2022-01-28T13:55:00.355185Z" }, "papermill": { "duration": 0.073937, "end_time": "2022-01-28T13:57:57.252586", "exception": false, "start_time": "2022-01-28T13:57:57.178649", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def con(obj):\n", " L= []\n", " for i in ast.literal_eval(obj):\n", " L.append(i['name'])\n", " return L" ] }, { "cell_type": "code", "execution_count": 20, "id": "21c5b345", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:57.384587Z", "iopub.status.busy": "2022-01-28T13:57:57.383519Z", "iopub.status.idle": "2022-01-28T13:57:57.648691Z", "shell.execute_reply": "2022-01-28T13:57:57.648144Z", "shell.execute_reply.started": "2022-01-28T13:55:00.366309Z" }, "papermill": { "duration": 0.335983, "end_time": "2022-01-28T13:57:57.648843", "exception": false, "start_time": "2022-01-28T13:57:57.312860", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies['genres']=movies['genres'].apply(con)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0132d1bb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:57.769148Z", "iopub.status.busy": "2022-01-28T13:57:57.768489Z", "iopub.status.idle": "2022-01-28T13:57:57.771473Z", "shell.execute_reply": "2022-01-28T13:57:57.772084Z", "shell.execute_reply.started": "2022-01-28T13:55:00.646953Z" }, "papermill": { "duration": 0.064811, "end_time": "2022-01-28T13:57:57.772257", "exception": false, "start_time": "2022-01-28T13:57:57.707446", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"space war\"}, {\"id\": 3388, \"name\": \"space colony\"}, {\"id\": 3679, \"name\": \"society\"}, {\"id\": 3801, \"name\": \"space travel\"}, {\"id\": 9685, \"name\": \"futuristic\"}, {\"id\": 9840, \"name\": \"romance\"}, {\"id\": 9882, \"name\": \"space\"}, {\"id\": 9951, \"name\": \"alien\"}, {\"id\": 10148, \"name\": \"tribe\"}, {\"id\": 10158, \"name\": \"alien planet\"}, {\"id\": 10987, \"name\": \"cgi\"}, {\"id\": 11399, \"name\": \"marine\"}, {\"id\": 13065, \"name\": \"soldier\"}, {\"id\": 14643, \"name\": \"battle\"}, {\"id\": 14720, \"name\": \"love affair\"}, {\"id\": 165431, \"name\": \"anti war\"}, {\"id\": 193554, \"name\": \"power relations\"}, {\"id\": 206690, \"name\": \"mind and soul\"}, {\"id\": 209714, \"name\": \"3d\"}]'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.iloc[0].keywords" ] }, { "cell_type": "code", "execution_count": 22, "id": "633aa256", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:57.885064Z", "iopub.status.busy": "2022-01-28T13:57:57.884328Z", "iopub.status.idle": "2022-01-28T13:57:58.316929Z", "shell.execute_reply": "2022-01-28T13:57:58.316330Z", "shell.execute_reply.started": "2022-01-28T13:55:00.655346Z" }, "papermill": { "duration": 0.490506, "end_time": "2022-01-28T13:57:58.317106", "exception": false, "start_time": "2022-01-28T13:57:57.826600", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies['keywords']=movies['keywords'].apply(con)" ] }, { "cell_type": "code", "execution_count": 23, "id": "75e16d5d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:58.428922Z", "iopub.status.busy": "2022-01-28T13:57:58.427972Z", "iopub.status.idle": "2022-01-28T13:57:58.446513Z", "shell.execute_reply": "2022-01-28T13:57:58.447031Z", "shell.execute_reply.started": "2022-01-28T13:55:01.095505Z" }, "papermill": { "duration": 0.076641, "end_time": "2022-01-28T13:57:58.447203", "exception": false, "start_time": "2022-01-28T13:57:58.370562", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " 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\"de...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>49529</td>\n", " <td>John Carter</td>\n", " <td>John Carter is a war-weary, former military ca...</td>\n", " <td>[Action, Adventure, Science Fiction]</td>\n", " <td>[based on novel, mars, medallion, space travel...</td>\n", " <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n", " <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " movie_id title \\\n", "0 19995 Avatar \n", "1 285 Pirates of the Caribbean: At World's End \n", "2 206647 Spectre \n", "3 49026 The Dark Knight Rises \n", "4 49529 John Carter \n", "\n", " overview \\\n", "0 In the 22nd century, a paraplegic Marine is di... \n", "1 Captain Barbossa, long believed to be dead, ha... \n", "2 A cryptic message from Bond’s past sends him o... \n", "3 Following the death of District Attorney Harve... \n", "4 John Carter is a war-weary, former military ca... \n", "\n", " genres \\\n", "0 [Action, Adventure, Fantasy, Science Fiction] \n", "1 [Adventure, Fantasy, Action] \n", "2 [Action, Adventure, Crime] \n", "3 [Action, Crime, Drama, Thriller] \n", "4 [Action, Adventure, Science Fiction] \n", "\n", " keywords \\\n", "0 [culture clash, future, space war, space colon... \n", "1 [ocean, drug abuse, exotic island, east india ... \n", "2 [spy, based on novel, secret agent, sequel, mi... \n", "3 [dc comics, crime fighter, terrorist, secret i... \n", "4 [based on novel, mars, medallion, space travel... \n", "\n", " cast \\\n", "0 [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"... \n", "1 [{\"cast_id\": 4, \"character\": \"Captain Jack Spa... \n", "2 [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr... \n", "3 [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba... \n", "4 [{\"cast_id\": 5, \"character\": \"John Carter\", \"c... \n", "\n", " crew \n", "0 [{\"credit_id\": \"52fe48009251416c750aca23\", \"de... \n", "1 [{\"credit_id\": 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{}, "output_type": "execute_result" } ], "source": [ "movies.cast[0]" ] }, { "cell_type": "code", "execution_count": 25, "id": "1e7b1620", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:58.688622Z", "iopub.status.busy": "2022-01-28T13:57:58.687886Z", "iopub.status.idle": "2022-01-28T13:57:58.693471Z", "shell.execute_reply": "2022-01-28T13:57:58.692913Z", "shell.execute_reply.started": "2022-01-28T13:55:01.129169Z" }, "papermill": { "duration": 0.068363, "end_time": "2022-01-28T13:57:58.693630", "exception": false, "start_time": "2022-01-28T13:57:58.625267", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def convert(obj):\n", " L=[]\n", " counter=0\n", " for i in ast.literal_eval(obj):\n", " if counter != 3 :\n", " L.append(i['name'])\n", " else:\n", " break\n", " return L\n", " " ] }, { "cell_type": "code", "execution_count": 26, "id": "f666b956", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:57:58.816559Z", "iopub.status.busy": "2022-01-28T13:57:58.815452Z", "iopub.status.idle": "2022-01-28T13:58:02.224316Z", "shell.execute_reply": "2022-01-28T13:58:02.223611Z", "shell.execute_reply.started": "2022-01-28T13:55:01.141756Z" }, "papermill": { "duration": 3.47395, "end_time": "2022-01-28T13:58:02.224465", "exception": false, "start_time": "2022-01-28T13:57:58.750515", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies.cast = movies.cast.apply(convert)" ] }, { "cell_type": "code", "execution_count": 27, "id": "9a6cecf9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:02.357565Z", "iopub.status.busy": "2022-01-28T13:58:02.356647Z", "iopub.status.idle": "2022-01-28T13:58:02.361661Z", "shell.execute_reply": "2022-01-28T13:58:02.361017Z", "shell.execute_reply.started": "2022-01-28T13:55:04.551973Z" }, "papermill": { "duration": 0.08161, "end_time": "2022-01-28T13:58:02.361801", "exception": false, "start_time": "2022-01-28T13:58:02.280191", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>movie_id</th>\n", " <th>title</th>\n", " <th>overview</th>\n", " <th>genres</th>\n", " <th>keywords</th>\n", " <th>cast</th>\n", " <th>crew</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>19995</td>\n", " <td>Avatar</td>\n", " <td>In the 22nd century, a paraplegic Marine is di...</td>\n", " <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n", " <td>[culture clash, future, space war, space colon...</td>\n", " <td>[Sam Worthington, Zoe Saldana, Sigourney Weave...</td>\n", " <td>[{\"credit_id\": 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Attorney Harve...</td>\n", " <td>[Action, Crime, Drama, Thriller]</td>\n", " <td>[dc comics, crime fighter, terrorist, secret i...</td>\n", " <td>[Christian Bale, Michael Caine, Gary Oldman, A...</td>\n", " <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>49529</td>\n", " <td>John Carter</td>\n", " <td>John Carter is a war-weary, former military ca...</td>\n", " <td>[Action, Adventure, Science Fiction]</td>\n", " <td>[based on novel, mars, medallion, space travel...</td>\n", " <td>[Taylor Kitsch, Lynn Collins, Samantha Morton,...</td>\n", " <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " movie_id title \\\n", "0 19995 Avatar \n", "1 285 Pirates of the Caribbean: At World's End \n", "2 206647 Spectre \n", "3 49026 The Dark Knight Rises \n", "4 49529 John Carter \n", "\n", " overview \\\n", "0 In the 22nd century, a paraplegic Marine is di... \n", "1 Captain Barbossa, long believed to be dead, ha... \n", "2 A cryptic message from Bond’s past sends him o... \n", "3 Following the death of District Attorney Harve... \n", "4 John Carter is a war-weary, former military ca... \n", "\n", " genres \\\n", "0 [Action, Adventure, Fantasy, Science Fiction] \n", "1 [Adventure, Fantasy, Action] \n", "2 [Action, Adventure, Crime] \n", "3 [Action, Crime, Drama, Thriller] \n", "4 [Action, Adventure, Science Fiction] \n", "\n", " keywords \\\n", "0 [culture clash, future, space war, space colon... \n", "1 [ocean, drug abuse, exotic island, east india ... \n", "2 [spy, based on novel, secret agent, sequel, mi... \n", "3 [dc comics, crime fighter, terrorist, secret i... \n", "4 [based on novel, mars, medallion, space travel... \n", "\n", " cast \\\n", "0 [Sam Worthington, Zoe Saldana, Sigourney Weave... \n", "1 [Johnny Depp, Orlando Bloom, Keira Knightley, ... \n", "2 [Daniel Craig, Christoph Waltz, Léa Seydoux, R... \n", "3 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\"Philippe Rebours\"}, {\"credit_id\": \"5592b317c3a36877470012af\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483232, \"job\": \"CG Supervisor\", \"name\": \"Michael Takarangi\"}, {\"credit_id\": \"5592b345c3a36877470012bb\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483233, \"job\": \"CG Supervisor\", \"name\": \"David Weitzberg\"}, {\"credit_id\": \"5592b37cc3a368775100113b\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483234, \"job\": \"CG Supervisor\", \"name\": \"Ben White\"}, {\"credit_id\": \"573c8e2f9251413f5d000094\", \"department\": \"Crew\", \"gender\": 1, \"id\": 1621932, \"job\": \"Stunts\", \"name\": \"Min Windle\"}]'" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.crew[0]" ] }, { "cell_type": "code", "execution_count": 29, "id": "03932266", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:02.615625Z", "iopub.status.busy": "2022-01-28T13:58:02.614689Z", "iopub.status.idle": "2022-01-28T13:58:02.618036Z", "shell.execute_reply": "2022-01-28T13:58:02.617520Z", "shell.execute_reply.started": "2022-01-28T13:55:04.591682Z" }, "papermill": { "duration": 0.075934, "end_time": "2022-01-28T13:58:02.618187", "exception": false, "start_time": "2022-01-28T13:58:02.542253", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def director(obj):\n", " L= []\n", " for i in ast.literal_eval(obj):\n", " if i['job'] == 'Director':\n", " L.append(i['name'])\n", " break\n", " return L" ] }, { "cell_type": "code", "execution_count": 30, "id": "5644bd92", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:02.762178Z", "iopub.status.busy": "2022-01-28T13:58:02.761180Z", "iopub.status.idle": "2022-01-28T13:58:06.499085Z", "shell.execute_reply": "2022-01-28T13:58:06.498382Z", "shell.execute_reply.started": "2022-01-28T13:55:04.602614Z" }, "papermill": { "duration": 3.815533, "end_time": "2022-01-28T13:58:06.499235", "exception": false, "start_time": "2022-01-28T13:58:02.683702", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies.crew=movies.crew.apply(director)" ] }, { "cell_type": "code", "execution_count": 31, "id": "d7233be1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:06.635268Z", "iopub.status.busy": "2022-01-28T13:58:06.634400Z", "iopub.status.idle": "2022-01-28T13:58:06.638549Z", "shell.execute_reply": "2022-01-28T13:58:06.638016Z", "shell.execute_reply.started": "2022-01-28T13:55:08.426992Z" }, "papermill": { "duration": 0.08327, "end_time": "2022-01-28T13:58:06.638685", "exception": false, "start_time": "2022-01-28T13:58:06.555415", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", 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"2022-01-28T13:58:07.192419Z", "iopub.status.idle": "2022-01-28T13:58:07.389505Z", "shell.execute_reply": "2022-01-28T13:58:07.388781Z", "shell.execute_reply.started": "2022-01-28T13:55:08.526939Z" }, "papermill": { "duration": 0.270791, "end_time": "2022-01-28T13:58:07.389652", "exception": false, "start_time": "2022-01-28T13:58:07.118861", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies.genres=movies.genres.apply(space)\n", "movies.keywords=movies.keywords.apply(space)\n", "movies.cast=movies.cast.apply(space)\n", "movies.crew=movies.crew.apply(space)\n" ] }, { "cell_type": "code", "execution_count": 36, "id": "288ec210", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:07.532480Z", "iopub.status.busy": "2022-01-28T13:58:07.531801Z", "iopub.status.idle": "2022-01-28T13:58:07.535954Z", "shell.execute_reply": "2022-01-28T13:58:07.535389Z", "shell.execute_reply.started": "2022-01-28T13:55:08.749631Z" }, "papermill": { "duration": 0.087902, "end_time": "2022-01-28T13:58:07.536112", "exception": false, "start_time": "2022-01-28T13:58:07.448210", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>movie_id</th>\n", " <th>title</th>\n", " <th>overview</th>\n", " <th>genres</th>\n", " <th>keywords</th>\n", " <th>cast</th>\n", " <th>crew</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>19995</td>\n", " <td>Avatar</td>\n", " <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n", " <td>[Action, Adventure, Fantasy, ScienceFiction]</td>\n", " <td>[cultureclash, future, spacewar, 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Attorney...</td>\n", " <td>[Action, Crime, Drama, Thriller]</td>\n", " <td>[dccomics, crimefighter, terrorist, secretiden...</td>\n", " <td>[ChristianBale, MichaelCaine, GaryOldman, Anne...</td>\n", " <td>[ChristopherNolan]</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>49529</td>\n", " <td>John Carter</td>\n", " <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n", " <td>[Action, Adventure, ScienceFiction]</td>\n", " <td>[basedonnovel, mars, medallion, spacetravel, p...</td>\n", " <td>[TaylorKitsch, LynnCollins, SamanthaMorton, Wi...</td>\n", " <td>[AndrewStanton]</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " movie_id title \\\n", "0 19995 Avatar \n", "1 285 Pirates of the Caribbean: At World's End \n", "2 206647 Spectre \n", "3 49026 The Dark Knight Rises \n", "4 49529 John Carter \n", "\n", " overview \\\n", "0 [In, the, 22nd, century,, a, paraplegic, Marin... \n", "1 [Captain, Barbossa,, long, believed, to, be, d... \n", "2 [A, cryptic, message, from, Bond’s, past, send... \n", "3 [Following, the, death, of, District, Attorney... \n", "4 [John, Carter, is, a, war-weary,, former, mili... \n", "\n", " genres \\\n", "0 [Action, Adventure, Fantasy, ScienceFiction] \n", "1 [Adventure, Fantasy, Action] \n", "2 [Action, Adventure, Crime] \n", "3 [Action, Crime, Drama, Thriller] \n", "4 [Action, Adventure, ScienceFiction] \n", "\n", " keywords \\\n", "0 [cultureclash, future, spacewar, spacecolony, ... \n", "1 [ocean, drugabuse, exoticisland, eastindiatrad... \n", "2 [spy, basedonnovel, secretagent, sequel, mi6, ... \n", "3 [dccomics, crimefighter, terrorist, secretiden... \n", "4 [basedonnovel, mars, medallion, spacetravel, p... \n", "\n", " cast crew \n", "0 [SamWorthington, ZoeSaldana, SigourneyWeaver, ... [JamesCameron] \n", "1 [JohnnyDepp, OrlandoBloom, KeiraKnightley, Ste... [GoreVerbinski] \n", "2 [DanielCraig, ChristophWaltz, LéaSeydoux, Ralp... [SamMendes] \n", "3 [ChristianBale, MichaelCaine, GaryOldman, Anne... [ChristopherNolan] \n", "4 [TaylorKitsch, LynnCollins, SamanthaMorton, Wi... [AndrewStanton] " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.head()" ] }, { "cell_type": "code", "execution_count": 37, "id": "347f46ad", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:07.684590Z", "iopub.status.busy": "2022-01-28T13:58:07.658251Z", "iopub.status.idle": "2022-01-28T13:58:07.741383Z", "shell.execute_reply": "2022-01-28T13:58:07.740674Z", "shell.execute_reply.started": "2022-01-28T13:55:08.779315Z" }, "papermill": { "duration": 0.147268, "end_time": "2022-01-28T13:58:07.741536", "exception": false, "start_time": "2022-01-28T13:58:07.594268", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "movies['tags'] = movies.overview + movies.genres + movies.keywords + movies.cast + movies.crew" ] }, { "cell_type": "code", "execution_count": 38, "id": "6d9b61a7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:07.866651Z", "iopub.status.busy": "2022-01-28T13:58:07.864710Z", "iopub.status.idle": "2022-01-28T13:58:07.870108Z", "shell.execute_reply": "2022-01-28T13:58:07.869543Z", "shell.execute_reply.started": "2022-01-28T13:55:08.853173Z" }, "papermill": { "duration": 0.070521, "end_time": "2022-01-28T13:58:07.870257", "exception": false, "start_time": "2022-01-28T13:58:07.799736", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df= movies.drop(columns = ['overview','keywords','genres','cast','crew'])" ] }, { "cell_type": "code", "execution_count": 39, "id": "396a0942", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:08.018843Z", "iopub.status.busy": "2022-01-28T13:58:08.006795Z", "iopub.status.idle": "2022-01-28T13:58:08.022615Z", "shell.execute_reply": "2022-01-28T13:58:08.021513Z", "shell.execute_reply.started": "2022-01-28T13:55:08.863013Z" }, "papermill": { "duration": 0.092004, "end_time": "2022-01-28T13:58:08.022794", "exception": false, "start_time": "2022-01-28T13:58:07.930790", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df.tags = df.tags.apply(lambda x: ' '.join(x))" ] }, { "cell_type": "code", "execution_count": 40, "id": "72beb7b2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:08.162025Z", "iopub.status.busy": "2022-01-28T13:58:08.154872Z", "iopub.status.idle": "2022-01-28T13:58:08.164581Z", "shell.execute_reply": "2022-01-28T13:58:08.163962Z", "shell.execute_reply.started": "2022-01-28T13:55:08.897657Z" }, "papermill": { "duration": 0.082459, "end_time": "2022-01-28T13:58:08.164723", "exception": false, "start_time": "2022-01-28T13:58:08.082264", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df.tags = df.tags.apply(lambda x : x.lower())" ] }, { "cell_type": "code", "execution_count": 41, "id": "c05cb0c5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:08.285931Z", "iopub.status.busy": "2022-01-28T13:58:08.285298Z", "iopub.status.idle": "2022-01-28T13:58:10.061759Z", "shell.execute_reply": "2022-01-28T13:58:10.062345Z", "shell.execute_reply.started": "2022-01-28T13:55:08.914522Z" }, "papermill": { "duration": 1.838683, "end_time": "2022-01-28T13:58:10.062552", "exception": false, "start_time": "2022-01-28T13:58:08.223869", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import nltk" ] }, { "cell_type": "code", "execution_count": 42, "id": "04df8e6b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:10.185455Z", "iopub.status.busy": "2022-01-28T13:58:10.184560Z", "iopub.status.idle": "2022-01-28T13:58:10.186742Z", "shell.execute_reply": "2022-01-28T13:58:10.187347Z", "shell.execute_reply.started": "2022-01-28T13:55:10.696797Z" }, "papermill": { "duration": 0.065067, "end_time": "2022-01-28T13:58:10.187524", "exception": false, "start_time": "2022-01-28T13:58:10.122457", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from nltk.stem.porter import PorterStemmer\n", "ps = PorterStemmer()" ] }, { "cell_type": "code", "execution_count": 43, "id": "7ad8bceb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:10.307783Z", "iopub.status.busy": "2022-01-28T13:58:10.307119Z", "iopub.status.idle": "2022-01-28T13:58:10.311379Z", "shell.execute_reply": "2022-01-28T13:58:10.311889Z", "shell.execute_reply.started": "2022-01-28T13:55:10.706560Z" }, "papermill": { "duration": 0.066342, "end_time": "2022-01-28T13:58:10.312077", "exception": false, "start_time": "2022-01-28T13:58:10.245735", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def stem(text):\n", " m= []\n", " \n", " for i in text.split():\n", " m.append(ps.stem(i))\n", " \n", " return ' '.join(m)" ] }, { "cell_type": "code", "execution_count": 44, "id": "0e8e9a74", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:10.430679Z", "iopub.status.busy": "2022-01-28T13:58:10.430042Z", "iopub.status.idle": "2022-01-28T13:58:21.569464Z", "shell.execute_reply": "2022-01-28T13:58:21.568885Z", "shell.execute_reply.started": "2022-01-28T13:55:10.722006Z" }, "papermill": { "duration": 11.199741, "end_time": "2022-01-28T13:58:21.569613", "exception": false, "start_time": "2022-01-28T13:58:10.369872", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df.tags= df.tags.apply(stem)" ] }, { "cell_type": "code", "execution_count": 45, "id": "c97cefcd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:21.696351Z", "iopub.status.busy": "2022-01-28T13:58:21.695231Z", "iopub.status.idle": "2022-01-28T13:58:21.699795Z", "shell.execute_reply": "2022-01-28T13:58:21.699288Z", "shell.execute_reply.started": "2022-01-28T13:55:21.847708Z" }, "papermill": { "duration": 0.072836, "end_time": "2022-01-28T13:58:21.699940", "exception": false, "start_time": "2022-01-28T13:58:21.627104", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>movie_id</th>\n", " <th>title</th>\n", " <th>tags</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>19995</td>\n", " <td>Avatar</td>\n", " <td>in the 22nd century, a parapleg marin is dispa...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>285</td>\n", " <td>Pirates of the Caribbean: At World's End</td>\n", " <td>captain barbossa, long believ to be dead, ha c...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>206647</td>\n", " <td>Spectre</td>\n", " <td>a cryptic messag from bond’ past send him on a...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>49026</td>\n", " <td>The Dark Knight Rises</td>\n", " <td>follow the death of district attorney harvey d...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>49529</td>\n", " <td>John Carter</td>\n", " <td>john carter is a war-weary, former militari ca...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " movie_id title \\\n", "0 19995 Avatar \n", "1 285 Pirates of the Caribbean: At World's End \n", "2 206647 Spectre \n", "3 49026 The Dark Knight Rises \n", "4 49529 John Carter \n", "\n", " tags \n", "0 in the 22nd century, a parapleg marin is dispa... \n", "1 captain barbossa, long believ to be dead, ha c... \n", "2 a cryptic messag from bond’ past send him on a... \n", "3 follow the death of district attorney harvey d... \n", "4 john carter is a war-weary, former militari ca... " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 46, "id": "07b434d9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:21.823908Z", "iopub.status.busy": "2022-01-28T13:58:21.823021Z", "iopub.status.idle": "2022-01-28T13:58:21.827698Z", "shell.execute_reply": "2022-01-28T13:58:21.827155Z", "shell.execute_reply.started": "2022-01-28T13:55:21.861206Z" }, "papermill": { "duration": 0.069486, "end_time": "2022-01-28T13:58:21.827868", "exception": false, "start_time": "2022-01-28T13:58:21.758382", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'in the 22nd century, a parapleg marin is dispatch to the moon pandora on a uniqu mission, but becom torn between follow order and protect an alien civilization. action adventur fantasi sciencefict cultureclash futur spacewar spacecoloni societi spacetravel futurist romanc space alien tribe alienplanet cgi marin soldier battl loveaffair antiwar powerrel mindandsoul 3d samworthington zoesaldana sigourneyweav stephenlang michellerodriguez giovanniribisi joeldavidmoor cchpounder wesstudi lazalonso dileeprao mattgerald seananthonymoran jasonwhyt scottlawr kellykilgour jamespatrickpitt seanpatrickmurphi peterdillon kevindorman kelsonhenderson davidvanhorn jacobtomuri michaelblain-rozgay joncurri lukehawk woodyschultz petermensah soniaye jahnelcurfman ilramchoi kylawarren lisaroumain debrawilson chrismala taylorkibbi jodielandau julielamm cullenb.madden josephbradymadden frankietorr austinwilson sarawilson tamicawashington-mil lucybri nathanmeist gerryblair matthewchamberlain paulyat wraywilson jamesgaylyn melvinlenoclarkiii carvonfutrel brandonjelk micahmoch hanniyahmuhammad christophernolen christaoliv aprilmariethoma bravitaa.threatt colinbleasdal mikebodnar mattclayton nicoledionn jamieharrison allanhenri anthonyingrub ashleyjefferi deanknowsley josephmika-hunt terrynotari kaipantano loganpithy stuartpollock raja garethruck rhiansheehan t.j.storm jodietaylor aliciavela-bailey richardwhitesid nikiezambo julenerene jamescameron'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tags[0]" ] }, { "cell_type": "code", "execution_count": 47, "id": "9ab01717", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:21.952331Z", "iopub.status.busy": "2022-01-28T13:58:21.951583Z", "iopub.status.idle": "2022-01-28T13:58:21.958204Z", "shell.execute_reply": "2022-01-28T13:58:21.957297Z", "shell.execute_reply.started": "2022-01-28T13:55:21.874121Z" }, "papermill": { "duration": 0.071293, "end_time": "2022-01-28T13:58:21.958370", "exception": false, "start_time": "2022-01-28T13:58:21.887077", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "\"captain barbossa, long believ to be dead, ha come back to life and is head to the edg of the earth with will turner and elizabeth swann. but noth is quit as it seems. adventur fantasi action ocean drugabus exoticisland eastindiatradingcompani loveofone'slif traitor shipwreck strongwoman ship allianc calypso afterlif fighter pirat swashbuckl aftercreditssting johnnydepp orlandobloom keiraknightley stellanskarsgård chowyun-fat billnighi geoffreyrush jackdavenport kevinmcn tomholland naomieharri jonathanpryc keithrichard leearenberg mackenziecrook gregelli davidbaili martinklebba davidschofield laurenmah vanessabranch angusbarnett gilesnew reggiele dominicscottkay takayofisch davidmeuni ho-kwants andybeckwith peterdonaldbadalamentiii christophers.capp keithrichard hakeemkae-kazim ghassanmassoud goreverbinski\"" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tags[1]" ] }, { "cell_type": "code", "execution_count": 48, "id": "3df184a4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:22.086368Z", "iopub.status.busy": "2022-01-28T13:58:22.085603Z", "iopub.status.idle": "2022-01-28T13:58:22.088598Z", "shell.execute_reply": "2022-01-28T13:58:22.087939Z", "shell.execute_reply.started": "2022-01-28T13:55:21.888531Z" }, "papermill": { "duration": 0.066437, "end_time": "2022-01-28T13:58:22.088742", "exception": false, "start_time": "2022-01-28T13:58:22.022305", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "cv = CountVectorizer(max_features=5000,stop_words='english')" ] }, { "cell_type": "code", "execution_count": 49, "id": "4dc29e83", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:22.259581Z", "iopub.status.busy": "2022-01-28T13:58:22.254208Z", "iopub.status.idle": "2022-01-28T13:58:23.173072Z", "shell.execute_reply": "2022-01-28T13:58:23.172391Z", "shell.execute_reply.started": "2022-01-28T13:55:21.899595Z" }, "papermill": { "duration": 1.023163, "end_time": "2022-01-28T13:58:23.173214", "exception": false, "start_time": "2022-01-28T13:58:22.150051", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "vector = cv.fit_transform(df['tags']).toarray()" ] }, { "cell_type": "code", "execution_count": 50, "id": "7b56b6f2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:23.297034Z", "iopub.status.busy": "2022-01-28T13:58:23.296345Z", "iopub.status.idle": "2022-01-28T13:58:23.299745Z", "shell.execute_reply": "2022-01-28T13:58:23.299154Z", "shell.execute_reply.started": "2022-01-28T13:55:22.859464Z" }, "papermill": { "duration": 0.067491, "end_time": "2022-01-28T13:58:23.299883", "exception": false, "start_time": "2022-01-28T13:58:23.232392", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vector" ] }, { "cell_type": "code", "execution_count": 51, "id": "c3cd0b1a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:23.433130Z", "iopub.status.busy": "2022-01-28T13:58:23.432447Z", "iopub.status.idle": "2022-01-28T13:58:23.452365Z", "shell.execute_reply": "2022-01-28T13:58:23.452839Z", "shell.execute_reply.started": "2022-01-28T13:56:01.719893Z" }, "papermill": { "duration": 0.092341, "end_time": "2022-01-28T13:58:23.453028", "exception": false, "start_time": "2022-01-28T13:58:23.360687", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['000',\n", " '007',\n", " '10',\n", " '100',\n", " '11',\n", " '12',\n", " '13',\n", " '14',\n", " '15',\n", " '16',\n", " '17',\n", " '18',\n", " '18th',\n", " '19',\n", " '1930',\n", " '1940',\n", " '1950',\n", " '1960',\n", " '1960s',\n", " '1970',\n", " '1970s',\n", " '1980',\n", " '1990',\n", " '19th',\n", " '19thcenturi',\n", " '20',\n", " '20th',\n", " '24',\n", " '25',\n", " '30',\n", " '3d',\n", " '40',\n", " '50',\n", " '60',\n", " '70',\n", " 'aaron',\n", " 'aaroneckhart',\n", " 'aarontaylor',\n", " 'aasifmandvi',\n", " 'abandon',\n", " 'abduct',\n", " 'abigailbreslin',\n", " 'abil',\n", " 'abl',\n", " 'abov',\n", " 'abus',\n", " 'academi',\n", " 'accept',\n", " 'access',\n", " 'accid',\n", " 'accident',\n", " 'accompani',\n", " 'accomplish',\n", " 'account',\n", " 'accus',\n", " 'ace',\n", " 'achiev',\n", " 'act',\n", " 'action',\n", " 'activ',\n", " 'activist',\n", " 'actor',\n", " 'actress',\n", " 'actual',\n", " 'adam',\n", " 'adambrodi',\n", " 'adamgoldberg',\n", " 'adamlefevr',\n", " 'adamsandl',\n", " 'adamscott',\n", " 'adamshankman',\n", " 'adapt',\n", " 'add',\n", " 'addict',\n", " 'adewaleakinnuoye',\n", " 'adjust',\n", " 'admir',\n", " 'admit',\n", " 'adolesc',\n", " 'adopt',\n", " 'ador',\n", " 'adrianmartinez',\n", " 'adrienbrodi',\n", " 'adult',\n", " 'adulteri',\n", " 'adulthood',\n", " 'advanc',\n", " 'adventur',\n", " 'adventure',\n", " 'advertis',\n", " 'advic',\n", " 'advis',\n", " 'affair',\n", " 'affect',\n", " 'afghanistan',\n", " 'africa',\n", " 'african',\n", " 'aftercreditssting',\n", " 'afterlif',\n", " 'aftermath',\n", " 'ag',\n", " 'agbaj',\n", " 'age',\n", " 'agediffer',\n", " 'agency',\n", " 'agenda',\n", " 'agent',\n", " 'aggress',\n", " 'ago',\n", " 'agre',\n", " 'ahead',\n", " 'aid',\n", " 'aidanquinn',\n", " 'ail',\n", " 'aim',\n", " 'air',\n", " 'airplan',\n", " 'airport',\n", " 'al',\n", " 'alanalda',\n", " 'alanarkin',\n", " 'alancum',\n", " 'alanrickman',\n", " 'alantudyk',\n", " 'alaska',\n", " 'albert',\n", " 'albertbrook',\n", " 'albertfinney',\n", " 'alcohol',\n", " 'alecbaldwin',\n", " 'alessandronivola',\n", " 'alex',\n", " 'alexandersiddig',\n", " 'alexapenavega',\n", " 'alexborstein',\n", " 'alexisbledel',\n", " 'alfredhitchcock',\n", " 'alfredmolina',\n", " 'alfrewoodard',\n", " 'ali',\n", " 'alic',\n", " 'alice',\n", " 'alicedrummond',\n", " 'aliciasilverston',\n", " 'aliciawitt',\n", " 'alien',\n", " 'alieninvas',\n", " 'alienlife',\n", " 'alilart',\n", " 'aliv',\n", " 'alive',\n", " 'allancordun',\n", " 'allen',\n", " 'allencovert',\n", " 'alleong',\n", " 'alli',\n", " 'allianc',\n", " 'allisonjanney',\n", " 'allow',\n", " 'alon',\n", " 'alongsid',\n", " 'alpacino',\n", " 'alreadi',\n", " 'alter',\n", " 'altern',\n", " 'alway',\n", " 'alzheimer',\n", " 'amanda',\n", " 'amandapeet',\n", " 'amandaseyfri',\n", " 'amateur',\n", " 'amaz',\n", " 'ambassador',\n", " 'amberheard',\n", " 'ambit',\n", " 'ambiti',\n", " 'ambul',\n", " 'ambush',\n", " 'america',\n", " 'american',\n", " 'americanfootbal',\n", " 'amid',\n", " 'amidst',\n", " 'amnesia',\n", " 'amp',\n", " 'amus',\n", " 'amusementpark',\n", " 'amyadam',\n", " 'amypoehl',\n", " 'amyryan',\n", " 'amysedari',\n", " 'amysmart',\n", " 'anagastey',\n", " 'analyst',\n", " 'anarchiccomedi',\n", " 'ancient',\n", " 'anderson',\n", " 'andi',\n", " 'andiemacdowel',\n", " 'andreamartin',\n", " 'andrebraugh',\n", " 'andrew',\n", " 'andrewdali',\n", " 'android',\n", " 'andydick',\n", " 'andygarcía',\n", " 'andyricht',\n", " 'andysamberg',\n", " 'andyserki',\n", " 'angel',\n", " 'angelabassett',\n", " 'angeles',\n", " 'angelinajoli',\n", " 'angelo',\n", " 'anger',\n", " 'angle',\n", " 'angri',\n", " 'angusmacinn',\n", " 'ani',\n", " 'anim',\n", " 'animalattack',\n", " 'animalhorror',\n", " 'animals',\n", " 'anjelicahuston',\n", " 'ann',\n", " 'anna',\n", " 'annafari',\n", " 'annakendrick',\n", " 'annalevin',\n", " 'annapaquin',\n", " 'annasophiarobb',\n", " 'anndowd',\n", " 'anne',\n", " 'annebancroft',\n", " 'annefletch',\n", " 'annehathaway',\n", " 'annehech',\n", " 'annemoss',\n", " 'annetteben',\n", " 'anni',\n", " 'annikaperga',\n", " 'announc',\n", " 'annual',\n", " 'anonym',\n", " 'anoth',\n", " 'answer',\n", " 'ant',\n", " 'antholog',\n", " 'anthoni',\n", " 'anthonyanderson',\n", " 'anthonyhopkin',\n", " 'anthonymacki',\n", " 'anthropomorph',\n", " 'anti',\n", " 'antihero',\n", " 'antonicoron',\n", " 'antoniobandera',\n", " 'antonyelchin',\n", " 'anyon',\n", " 'anyth',\n", " 'apart',\n", " 'apartheid',\n", " 'apartment',\n", " 'ape',\n", " 'apocalyps',\n", " 'apocalypt',\n", " 'appar',\n", " 'appear',\n", " 'appoint',\n", " 'appreci',\n", " 'apprentic',\n", " 'approach',\n", " 'april',\n", " 'arab',\n", " 'archaeologist',\n", " 'architect',\n", " 'arci',\n", " 'arctic',\n", " 'area',\n", " 'arena',\n", " 'argument',\n", " 'arielwint',\n", " 'arigraynor',\n", " 'aris',\n", " 'aristocrat',\n", " 'arm',\n", " 'armi',\n", " 'arminmueller',\n", " 'armor',\n", " 'armsdeal',\n", " 'armstrong',\n", " 'army',\n", " 'arnold',\n", " 'arnoldschwarzenegg',\n", " 'arrang',\n", " 'arrest',\n", " 'arriv',\n", " 'arrog',\n", " 'art',\n", " 'arthur',\n", " 'arthurj',\n", " 'arthurtovey',\n", " 'artificialintellig',\n", " 'artist',\n", " 'ash',\n", " 'ashley',\n", " 'ashleyjudd',\n", " 'ashtonkutch',\n", " 'asia',\n", " 'ask',\n", " 'aspir',\n", " 'assassin',\n", " 'assault',\n", " 'assign',\n", " 'assist',\n", " 'assistant',\n", " 'associ',\n", " 'assum',\n", " 'asteroid',\n", " 'astronaut',\n", " 'asylum',\n", " 'athlet',\n", " 'atomicbomb',\n", " 'attack',\n", " 'attempt',\n", " 'attend',\n", " 'attent',\n", " 'attitud',\n", " 'attorney',\n", " 'attract',\n", " 'audienc',\n", " 'audit',\n", " 'august',\n", " 'aunjanueelli',\n", " 'aunt',\n", " 'austin',\n", " 'australia',\n", " 'australian',\n", " 'author',\n", " 'autism',\n", " 'auto',\n", " 'avaacr',\n", " 'aveng',\n", " 'avoid',\n", " 'await',\n", " 'awaken',\n", " 'award',\n", " 'away',\n", " 'awkward',\n", " 'awri',\n", " 'awry',\n", " 'azizansari',\n", " 'babe',\n", " 'babi',\n", " 'baby',\n", " 'bachelor',\n", " 'backdrop',\n", " 'background',\n", " 'bad',\n", " 'bag',\n", " 'bahama',\n", " 'bail',\n", " 'bailey',\n", " 'balanc',\n", " 'ball',\n", " 'ballet',\n", " 'band',\n", " 'bandit',\n", " 'banish',\n", " 'bank',\n", " 'banker',\n", " 'bankrobberi',\n", " 'baptist',\n", " 'bar',\n", " 'barbarahershey',\n", " 'barbrastreisand',\n", " 'bare',\n", " 'bargain',\n", " 'barn',\n", " 'barney',\n", " 'baron',\n", " 'barri',\n", " 'barrylevinson',\n", " 'barrypepp',\n", " 'barryshabakahenley',\n", " 'bas',\n", " 'base',\n", " 'basebal',\n", " 'basedoncomicbook',\n", " 'basedongraphicnovel',\n", " 'basedonnovel',\n", " 'basedonplay',\n", " 'basedonstagemus',\n", " 'basedontrueev',\n", " 'basedontruestori',\n", " 'basedontvseri',\n", " 'basedonvideogam',\n", " 'basedonyoungadultnovel',\n", " 'basement',\n", " 'basketbal',\n", " 'batman',\n", " 'battl',\n", " 'battle',\n", " 'battlefield',\n", " 'bay',\n", " 'bdwong',\n", " 'beach',\n", " 'bear',\n", " 'beast',\n", " 'beat',\n", " 'beaubridg',\n", " 'beauti',\n", " 'beautiful',\n", " 'beautifulwoman',\n", " 'beauty',\n", " 'becam',\n", " 'becaus',\n", " 'beccasweitz',\n", " 'becom',\n", " 'becominganadult',\n", " 'bed',\n", " 'bedroom',\n", " 'beer',\n", " 'befor',\n", " 'befriend',\n", " 'began',\n", " 'begin',\n", " 'behavior',\n", " 'belief',\n", " 'believ',\n", " 'bell',\n", " 'bella',\n", " 'belong',\n", " 'belov',\n", " 'ben',\n", " 'benaffleck',\n", " 'benchaplin',\n", " 'bend',\n", " 'benedictcumberbatch',\n", " 'benedictwong',\n", " 'benfost',\n", " 'bengazzara',\n", " 'beniciodeltoro',\n", " 'benjamin',\n", " 'benjaminbratt',\n", " 'benkingsley',\n", " 'benmendelsohn',\n", " 'bennett',\n", " 'benstil',\n", " 'bent',\n", " 'benwhishaw',\n", " 'berlin',\n", " 'bernardhil',\n", " 'bernardle',\n", " 'berniemac',\n", " 'best',\n", " 'bestfriend',\n", " 'bet',\n", " 'beth',\n", " 'bethgrant',\n", " 'betray',\n", " 'bettemidl',\n", " 'better',\n", " 'betti',\n", " 'beverli',\n", " 'beverlyd',\n", " 'bibl',\n", " 'big',\n", " 'bigger',\n", " 'biggest',\n", " 'biker',\n", " 'bikini',\n", " 'billcamp',\n", " 'billcobb',\n", " 'billduk',\n", " 'billhad',\n", " 'billi',\n", " 'billionair',\n", " 'billmoseley',\n", " 'billmurray',\n", " 'billnighi',\n", " 'billnunn',\n", " 'billpaxton',\n", " 'billpullman',\n", " 'billybobthornton',\n", " 'billyburk',\n", " 'billyconnolli',\n", " 'billycrudup',\n", " 'billycryst',\n", " 'billydeewilliam',\n", " 'billyslaught',\n", " 'billywest',\n", " 'billyzan',\n", " 'biographi',\n", " 'bird',\n", " 'birth',\n", " 'birthday',\n", " 'bisexu',\n", " 'bishop',\n", " 'bit',\n", " 'bite',\n", " 'bitter',\n", " 'bizarr',\n", " 'black',\n", " 'blackmag',\n", " 'blackmail',\n", " 'blackpeopl',\n", " 'blade',\n", " 'blakeclark',\n", " 'blame',\n", " 'blend',\n", " 'blind',\n", " 'bliss',\n", " 'block',\n", " 'blond',\n", " 'blood',\n", " 'bloodi',\n", " 'bloodsplatt',\n", " 'bloodthirsti',\n", " 'blow',\n", " 'blue',\n", " 'blythedann',\n", " 'board',\n", " 'boardingschool',\n", " 'boat',\n", " 'bob',\n", " 'bobbalaban',\n", " 'bobbergen',\n", " 'bobbi',\n", " 'bobbycannaval',\n", " 'bobbyfarrelli',\n", " 'bobbymoynihan',\n", " 'bobclendenin',\n", " 'bobgunton',\n", " 'bobhoskin',\n", " 'bobodenkirk',\n", " 'bobstephenson',\n", " 'bodi',\n", " 'body',\n", " 'bodyguard',\n", " 'bokeemwoodbin',\n", " 'bold',\n", " 'bollywood',\n", " 'bomb',\n", " 'bond',\n", " 'bone',\n", " 'bonniehunt',\n", " 'book',\n", " 'border',\n", " 'bore',\n", " 'boredom',\n", " 'born',\n", " 'boss',\n", " 'boston',\n", " 'bound',\n", " 'bounti',\n", " 'bountyhunt',\n", " 'box',\n", " 'boxer',\n", " 'boy',\n", " 'boydbank',\n", " 'boydholbrook',\n", " 'boyfriend',\n", " 'boys',\n", " 'braddourif',\n", " 'bradgarrett',\n", " 'bradleycoop',\n", " 'bradleywhitford',\n", " 'bradpitt',\n", " 'bradwilliamhenk',\n", " 'brain',\n", " 'brand',\n", " 'brave',\n", " 'braveri',\n", " 'brazil',\n", " 'brazilian',\n", " 'break',\n", " 'breckinmey',\n", " 'brendanfras',\n", " 'brendangleeson',\n", " 'brendansextoniii',\n", " 'brentbrisco',\n", " 'brentspin',\n", " 'brettcullen',\n", " 'brettratn',\n", " 'brian',\n", " 'briancox',\n", " 'briandennehi',\n", " 'briandepalma',\n", " 'briandoyle',\n", " 'brianj',\n", " 'briano',\n", " 'bride',\n", " 'bridg',\n", " 'bridgettewilson',\n", " 'brief',\n", " 'brielarson',\n", " 'brien',\n", " 'brilliant',\n", " 'bring',\n", " 'brink',\n", " 'britain',\n", " 'british',\n", " 'britishsecretservic',\n", " 'brittanymurphi',\n", " 'broadway',\n", " 'broke',\n", " 'broken',\n", " 'brook',\n", " 'brooklyn',\n", " 'brothel',\n", " 'brother',\n", " 'brotherbrotherrelationship',\n", " 'brothers',\n", " 'brothersisterrelationship',\n", " 'brought',\n", " 'brown',\n", " 'bruce',\n", " 'brucecampbel',\n", " 'brucedern',\n", " 'brucegreenwood',\n", " 'brucemcgil',\n", " 'brucesp',\n", " 'brucewilli',\n", " 'brutal',\n", " 'bryan',\n", " 'bryancranston',\n", " 'bryansing',\n", " 'brycedallashoward',\n", " 'bu',\n", " 'buck',\n", " 'bud',\n", " 'buddi',\n", " 'buddycomedi',\n", " 'budget',\n", " 'build',\n", " 'building',\n", " 'built',\n", " 'bullet',\n", " 'bulli',\n", " 'bumbl',\n", " 'burglar',\n", " 'buri',\n", " 'burn',\n", " 'burtreynold',\n", " 'bush',\n", " 'busi',\n", " 'business',\n", " 'businessman',\n", " 'bust',\n", " 'butler',\n", " 'buy',\n", " 'byrn',\n", " 'cabin',\n", " 'cal',\n", " 'california',\n", " 'callumkeithrenni',\n", " 'calvin',\n", " 'came',\n", " 'camera',\n", " 'cameron',\n", " 'cameronbright',\n", " 'camerondiaz',\n", " 'camgigandet',\n", " 'camp',\n", " 'campaign',\n", " 'campbel',\n", " 'campu',\n", " 'canada',\n", " 'canadian',\n", " 'cancer',\n", " 'candid',\n", " 'cannib',\n", " 'capabl',\n", " 'capit',\n", " 'captain',\n", " 'captiv',\n", " 'captur',\n", " 'car',\n", " 'caraccid',\n", " 'caraseymour',\n", " 'carchas',\n", " 'carcrash',\n", " 'card',\n", " 'care',\n", " 'career',\n", " 'careymulligan',\n", " 'caribbean',\n", " 'carjourney',\n", " 'carl',\n", " 'carlagallo',\n", " 'carlagugino',\n", " 'carlo',\n", " 'carlosalazraqui',\n", " 'carmen',\n", " 'carmenelectra',\n", " 'carol',\n", " 'carolineaaron',\n", " 'carolinegoodal',\n", " 'carolkan',\n", " 'carrac',\n", " 'carri',\n", " 'carrie',\n", " 'carriefish',\n", " 'carter',\n", " 'cary',\n", " 'caryelw',\n", " 'case',\n", " 'caseyaffleck',\n", " 'cash',\n", " 'casino',\n", " 'cast',\n", " 'castl',\n", " 'cat',\n", " 'cataclysm',\n", " 'catastroph',\n", " 'catch',\n", " 'cateblanchett',\n", " 'catherinekeen',\n", " 'catherineo',\n", " 'catherinezeta',\n", " 'cathey',\n", " 'cathol',\n", " 'catholic',\n", " 'caught',\n", " 'caus',\n", " 'cave',\n", " 'cedrictheentertain',\n", " 'celebr',\n", " 'celiaimri',\n", " 'celiaweston',\n", " 'cellphon',\n", " 'cemeteri',\n", " 'center',\n", " 'central',\n", " 'centuri',\n", " 'century',\n", " 'certain',\n", " 'chadmichaelmurray',\n", " 'chain',\n", " 'chainsaw',\n", " 'challeng',\n", " 'champion',\n", " 'championship',\n", " 'chanc',\n", " 'chance',\n", " 'chancekelli',\n", " 'chang',\n", " 'channingtatum',\n", " 'chao',\n", " 'chaos',\n", " 'chapter',\n", " 'charact',\n", " 'character',\n", " 'characters',\n", " 'charg',\n", " 'charismat',\n", " 'charl',\n", " 'charlesd',\n", " 'charlesdurn',\n", " 'charlesnapi',\n", " 'charless',\n", " 'charli',\n", " 'charlie',\n", " 'charliesheen',\n", " 'charlizetheron',\n", " 'charlotterampl',\n", " 'charltonheston',\n", " 'charm',\n", " 'chase',\n", " 'chazzpalminteri',\n", " 'cheat',\n", " 'cheechmarin',\n", " 'cheerlead',\n", " 'chef',\n", " 'chelcieross',\n", " 'chemic',\n", " 'cher',\n", " 'chevychas',\n", " 'chicago',\n", " 'chicken',\n", " 'chief',\n", " 'child',\n", " 'childabus',\n", " 'childhood',\n", " 'children',\n", " 'chill',\n", " 'chimcbrid',\n", " 'china',\n", " 'chines',\n", " 'chip',\n", " 'chiwetelejiofor',\n", " 'chloëgracemoretz',\n", " 'chloësevigni',\n", " 'choic',\n", " 'choos',\n", " 'chosen',\n", " 'chri',\n", " 'chrisbrown',\n", " 'chriscolumbu',\n", " 'chriscoop',\n", " 'chriselli',\n", " 'chrisevan',\n", " 'chrishemsworth',\n", " 'chrisklein',\n", " 'chrismessina',\n", " 'chrismil',\n", " 'chriso',\n", " 'chrisparnel',\n", " 'chrispenn',\n", " 'chrispin',\n", " 'chrispratt',\n", " 'chrisrock',\n", " 'christ',\n", " 'christian',\n", " 'christianbal',\n", " 'christianslat',\n", " 'christinaappleg',\n", " 'christinaricci',\n", " 'christinebaranski',\n", " 'christma',\n", " 'christmas',\n", " 'christmasparti',\n", " 'christoph',\n", " 'christopherguest',\n", " 'christopherlambert',\n", " 'christopherle',\n", " 'christopherlloyd',\n", " 'christophermcdonald',\n", " 'christophermeloni',\n", " 'christophermintz',\n", " 'christopherplumm',\n", " 'christopherwalken',\n", " 'christophwaltz',\n", " 'chronicl',\n", " 'church',\n", " 'cia',\n", " 'ciaránhind',\n", " 'cillianmurphi',\n", " 'cinema',\n", " 'circl',\n", " 'circu',\n", " 'circumst',\n", " 'citi',\n", " 'citizen',\n", " 'city',\n", " 'civil',\n", " 'civilian',\n", " 'civilwar',\n", " 'claim',\n", " 'clair',\n", " 'clairedan',\n", " 'claireforlani',\n", " 'clan',\n", " 'clancybrown',\n", " 'clark',\n", " 'clarkgregg',\n", " 'clash',\n", " 'class',\n", " 'classic',\n", " 'classmat',\n", " 'classroom',\n", " 'claudevandamm',\n", " 'cleaduval',\n", " 'clean',\n", " 'clear',\n", " 'clerk',\n", " 'client',\n", " 'cliffcurti',\n", " 'cliftoncollinsjr',\n", " 'climat',\n", " 'climb',\n", " 'clinteastwood',\n", " 'clinthoward',\n", " 'cliveowen',\n", " 'clock',\n", " 'clone',\n", " 'clorisleachman',\n", " 'close',\n", " 'closer',\n", " 'clown',\n", " 'club',\n", " 'clue',\n", " 'coach',\n", " 'coast',\n", " 'cocain',\n", " 'code',\n", " 'codycameron',\n", " 'coffin',\n", " 'cohen',\n", " 'col',\n", " 'cold',\n", " 'coldwar',\n", " 'cole',\n", " 'colehaus',\n", " 'colinfarrel',\n", " 'colinfirth',\n", " 'colinhank',\n", " 'colinsalmon',\n", " 'collaps',\n", " 'colleagu',\n", " 'collect',\n", " 'collector',\n", " 'colleencamp',\n", " 'colleg',\n", " 'college',\n", " 'collid',\n", " 'collin',\n", " 'collis',\n", " 'colmfeor',\n", " 'colmmeaney',\n", " 'colonel',\n", " 'coloni',\n", " 'color',\n", " 'coma',\n", " 'combat',\n", " 'combin',\n", " 'come',\n", " 'comeback',\n", " 'comedi',\n", " 'comedian',\n", " 'comedy',\n", " 'comfort',\n", " 'comic',\n", " 'coming',\n", " 'comingofag',\n", " 'comingout',\n", " 'command',\n", " 'commando',\n", " 'commerci',\n", " 'commit',\n", " 'common',\n", " 'commun',\n", " 'compani',\n", " 'companion',\n", " 'company',\n", " 'compet',\n", " 'competit',\n", " 'competition',\n", " 'complet',\n", " 'complex',\n", " 'complic',\n", " 'compos',\n", " 'compton',\n", " 'comput',\n", " 'concern',\n", " 'concert',\n", " 'condit',\n", " 'confess',\n", " 'confid',\n", " 'conflict',\n", " 'confront',\n", " 'confus',\n", " 'congress',\n", " 'conman',\n", " 'connect',\n", " 'connel',\n", " 'connienielsen',\n", " 'connieray',\n", " 'connor',\n", " 'conquer',\n", " 'conradvernon',\n", " 'consequ',\n", " 'consequences',\n", " 'consid',\n", " 'conspir',\n", " 'conspiraci',\n", " 'constantli',\n", " 'construct',\n", " 'consum',\n", " 'contact',\n", " 'contain',\n", " 'contemporari',\n", " 'contend',\n", " 'contest',\n", " 'continu',\n", " 'contract',\n", " 'control',\n", " 'controversi',\n", " 'convent',\n", " 'convict',\n", " 'convinc',\n", " 'cook',\n", " 'cool',\n", " 'cooper',\n", " 'cop',\n", " 'cope',\n", " 'core',\n", " 'coreyburton',\n", " 'coreystol',\n", " 'corner',\n", " 'corpor',\n", " 'corps',\n", " 'corrupt',\n", " 'cost',\n", " 'costum',\n", " 'couldn',\n", " 'count',\n", " 'countri',\n", " 'country',\n", " ...]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cv.get_feature_names()" ] }, { "cell_type": "code", "execution_count": null, "id": "0189a01a", "metadata": { "papermill": { "duration": 0.061562, "end_time": "2022-01-28T13:58:23.575813", "exception": false, "start_time": "2022-01-28T13:58:23.514251", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 52, "id": "8a9c1b09", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:23.702371Z", "iopub.status.busy": "2022-01-28T13:58:23.701686Z", "iopub.status.idle": "2022-01-28T13:58:23.704439Z", "shell.execute_reply": "2022-01-28T13:58:23.703836Z", "shell.execute_reply.started": "2022-01-28T13:55:22.905224Z" }, "papermill": { "duration": 0.067938, "end_time": "2022-01-28T13:58:23.704581", "exception": false, "start_time": "2022-01-28T13:58:23.636643", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.metrics.pairwise import cosine_similarity" ] }, { "cell_type": "code", "execution_count": 53, "id": "db73974b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:23.832607Z", "iopub.status.busy": "2022-01-28T13:58:23.831906Z", "iopub.status.idle": "2022-01-28T13:58:26.074897Z", "shell.execute_reply": "2022-01-28T13:58:26.075839Z", "shell.execute_reply.started": "2022-01-28T13:55:22.910704Z" }, "papermill": { "duration": 2.310346, "end_time": "2022-01-28T13:58:26.076185", "exception": false, "start_time": "2022-01-28T13:58:23.765839", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "similarity = cosine_similarity(vector)" ] }, { "cell_type": "code", "execution_count": 54, "id": "d5f531f7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:26.209811Z", "iopub.status.busy": "2022-01-28T13:58:26.209150Z", "iopub.status.idle": "2022-01-28T13:58:26.213414Z", "shell.execute_reply": "2022-01-28T13:58:26.212871Z", "shell.execute_reply.started": "2022-01-28T13:55:25.127785Z" }, "papermill": { "duration": 0.069905, "end_time": "2022-01-28T13:58:26.213562", "exception": false, "start_time": "2022-01-28T13:58:26.143657", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(4806, 4806)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "similarity.shape" ] }, { "cell_type": "code", "execution_count": 55, "id": "37a98a4a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:26.348232Z", "iopub.status.busy": "2022-01-28T13:58:26.347202Z", "iopub.status.idle": "2022-01-28T13:58:26.350267Z", "shell.execute_reply": "2022-01-28T13:58:26.350766Z", "shell.execute_reply.started": "2022-01-28T13:55:25.137282Z" }, "papermill": { "duration": 0.073009, "end_time": "2022-01-28T13:58:26.350958", "exception": false, "start_time": "2022-01-28T13:58:26.277949", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['title'] == 'Avatar'].index[0]" ] }, { "cell_type": "code", "execution_count": 56, "id": "bb97fdcc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:26.489629Z", "iopub.status.busy": "2022-01-28T13:58:26.488806Z", "iopub.status.idle": "2022-01-28T13:58:26.492106Z", "shell.execute_reply": "2022-01-28T13:58:26.492615Z", "shell.execute_reply.started": "2022-01-28T13:55:25.153453Z" }, "papermill": { "duration": 0.079095, "end_time": "2022-01-28T13:58:26.492782", "exception": false, "start_time": "2022-01-28T13:58:26.413687", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[(1216, 0.27028123880866767),\n", " (582, 0.23162743094465488),\n", " (2333, 0.22996655275195),\n", " (3730, 0.22498852128662872),\n", " (507, 0.22438727760202976)]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(list(enumerate(similarity[0])),reverse = True,key = lambda x :x[1])[1:6]" ] }, { "cell_type": "code", "execution_count": null, "id": "c0e62211", "metadata": { "papermill": { "duration": 0.061764, "end_time": "2022-01-28T13:58:26.616613", "exception": false, "start_time": "2022-01-28T13:58:26.554849", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 57, "id": "327e0ade", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:26.744106Z", "iopub.status.busy": "2022-01-28T13:58:26.743144Z", "iopub.status.idle": "2022-01-28T13:58:26.749184Z", "shell.execute_reply": "2022-01-28T13:58:26.749755Z", "shell.execute_reply.started": "2022-01-28T13:55:25.173193Z" }, "papermill": { "duration": 0.071774, "end_time": "2022-01-28T13:58:26.749935", "exception": false, "start_time": "2022-01-28T13:58:26.678161", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def recommend(movie):\n", " index = df[df['title'] == movie].index[0]\n", " distances = similarity [index]\n", " movies_list = sorted(list(enumerate(distances)),reverse = True,key = lambda x :x[1])[1:6]\n", " \n", " for i in movies_list:\n", " print(df.iloc[i[0]].title)" ] }, { "cell_type": "code", "execution_count": 58, "id": "29d4910f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T13:58:26.877920Z", "iopub.status.busy": "2022-01-28T13:58:26.876955Z", "iopub.status.idle": "2022-01-28T13:58:26.890733Z", "shell.execute_reply": "2022-01-28T13:58:26.891241Z", "shell.execute_reply.started": "2022-01-28T13:55:25.185692Z" }, "papermill": { "duration": 0.079273, "end_time": "2022-01-28T13:58:26.891438", "exception": false, "start_time": "2022-01-28T13:58:26.812165", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Dark Knight\n", "Batman Begins\n", "Batman Returns\n", "Batman\n", "Batman Forever\n" ] } ], "source": [ "recommend('The Dark Knight Rises')" ] }, { "cell_type": "code", "execution_count": null, "id": "dd41b868", "metadata": { "papermill": { "duration": 0.062626, "end_time": "2022-01-28T13:58:27.016138", "exception": false, "start_time": "2022-01-28T13:58:26.953512", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 45.862979, "end_time": "2022-01-28T13:58:27.888654", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T13:57:42.025675", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/397/86397858.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**Introduction(giriş) To Python**\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport seaborn as sns #visualization tool ","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:52:09.729001Z","iopub.execute_input":"2022-01-28T13:52:09.729360Z","iopub.status.idle":"2022-01-28T13:52:10.893007Z","shell.execute_reply.started":"2022-01-28T13:52:09.729324Z","shell.execute_reply":"2022-01-28T13:52:10.891859Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('../input/pokemon-challenge/pokemon.csv')","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:52:13.459852Z","iopub.execute_input":"2022-01-28T13:52:13.460243Z","iopub.status.idle":"2022-01-28T13:52:13.489456Z","shell.execute_reply.started":"2022-01-28T13:52:13.460193Z","shell.execute_reply":"2022-01-28T13:52:13.488627Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"data.info() #first info at the data. Whats give us; columns names, data typse info, #which data types where used","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:52:20.365363Z","iopub.execute_input":"2022-01-28T13:52:20.366468Z","iopub.status.idle":"2022-01-28T13:52:20.398292Z","shell.execute_reply.started":"2022-01-28T13:52:20.366422Z","shell.execute_reply":"2022-01-28T13:52:20.397006Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 800 entries, 0 to 799\nData columns (total 12 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 # 800 non-null int64 \n 1 Name 799 non-null object\n 2 Type 1 800 non-null object\n 3 Type 2 414 non-null object\n 4 HP 800 non-null int64 \n 5 Attack 800 non-null int64 \n 6 Defense 800 non-null int64 \n 7 Sp. Atk 800 non-null int64 \n 8 Sp. Def 800 non-null int64 \n 9 Speed 800 non-null int64 \n 10 Generation 800 non-null int64 \n 11 Legendary 800 non-null bool \ndtypes: bool(1), int64(8), object(3)\nmemory usage: 69.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"data.corr() \n#it means correlation map in brief we can say 'revelance(alaka,uygunluk)' \n#think the house, if the number of rooms increases, the cost of the house also increases.this is a right proportion(orantı)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:52:23.344368Z","iopub.execute_input":"2022-01-28T13:52:23.344884Z","iopub.status.idle":"2022-01-28T13:52:23.375419Z","shell.execute_reply.started":"2022-01-28T13:52:23.344851Z","shell.execute_reply":"2022-01-28T13:52:23.374530Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":" # HP Attack Defense Sp. Atk Sp. Def \\\n# 1.000000 0.097712 0.102664 0.094691 0.089199 0.085596 \nHP 0.097712 1.000000 0.422386 0.239622 0.362380 0.378718 \nAttack 0.102664 0.422386 1.000000 0.438687 0.396362 0.263990 \nDefense 0.094691 0.239622 0.438687 1.000000 0.223549 0.510747 \nSp. Atk 0.089199 0.362380 0.396362 0.223549 1.000000 0.506121 \nSp. Def 0.085596 0.378718 0.263990 0.510747 0.506121 1.000000 \nSpeed 0.012181 0.175952 0.381240 0.015227 0.473018 0.259133 \nGeneration 0.983428 0.058683 0.051451 0.042419 0.036437 0.028486 \nLegendary 0.154336 0.273620 0.345408 0.246377 0.448907 0.363937 \n\n Speed Generation Legendary \n# 0.012181 0.983428 0.154336 \nHP 0.175952 0.058683 0.273620 \nAttack 0.381240 0.051451 0.345408 \nDefense 0.015227 0.042419 0.246377 \nSp. Atk 0.473018 0.036437 0.448907 \nSp. Def 0.259133 0.028486 0.363937 \nSpeed 1.000000 -0.023121 0.326715 \nGeneration -0.023121 1.000000 0.079794 \nLegendary 0.326715 0.079794 1.000000 ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>#</th>\n <th>HP</th>\n <th>Attack</th>\n <th>Defense</th>\n <th>Sp. Atk</th>\n <th>Sp. Def</th>\n <th>Speed</th>\n <th>Generation</th>\n <th>Legendary</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>#</th>\n <td>1.000000</td>\n <td>0.097712</td>\n <td>0.102664</td>\n <td>0.094691</td>\n <td>0.089199</td>\n <td>0.085596</td>\n <td>0.012181</td>\n <td>0.983428</td>\n <td>0.154336</td>\n </tr>\n <tr>\n <th>HP</th>\n <td>0.097712</td>\n <td>1.000000</td>\n <td>0.422386</td>\n <td>0.239622</td>\n <td>0.362380</td>\n <td>0.378718</td>\n <td>0.175952</td>\n <td>0.058683</td>\n <td>0.273620</td>\n </tr>\n <tr>\n <th>Attack</th>\n <td>0.102664</td>\n <td>0.422386</td>\n <td>1.000000</td>\n <td>0.438687</td>\n <td>0.396362</td>\n <td>0.263990</td>\n <td>0.381240</td>\n <td>0.051451</td>\n <td>0.345408</td>\n </tr>\n <tr>\n <th>Defense</th>\n <td>0.094691</td>\n <td>0.239622</td>\n <td>0.438687</td>\n <td>1.000000</td>\n <td>0.223549</td>\n <td>0.510747</td>\n <td>0.015227</td>\n <td>0.042419</td>\n <td>0.246377</td>\n </tr>\n <tr>\n <th>Sp. Atk</th>\n <td>0.089199</td>\n <td>0.362380</td>\n <td>0.396362</td>\n <td>0.223549</td>\n <td>1.000000</td>\n <td>0.506121</td>\n <td>0.473018</td>\n <td>0.036437</td>\n <td>0.448907</td>\n </tr>\n <tr>\n <th>Sp. Def</th>\n <td>0.085596</td>\n <td>0.378718</td>\n <td>0.263990</td>\n <td>0.510747</td>\n <td>0.506121</td>\n <td>1.000000</td>\n <td>0.259133</td>\n <td>0.028486</td>\n <td>0.363937</td>\n </tr>\n <tr>\n <th>Speed</th>\n <td>0.012181</td>\n <td>0.175952</td>\n <td>0.381240</td>\n <td>0.015227</td>\n <td>0.473018</td>\n <td>0.259133</td>\n <td>1.000000</td>\n <td>-0.023121</td>\n <td>0.326715</td>\n </tr>\n <tr>\n <th>Generation</th>\n <td>0.983428</td>\n <td>0.058683</td>\n <td>0.051451</td>\n <td>0.042419</td>\n <td>0.036437</td>\n <td>0.028486</td>\n <td>-0.023121</td>\n <td>1.000000</td>\n <td>0.079794</td>\n </tr>\n <tr>\n <th>Legendary</th>\n <td>0.154336</td>\n <td>0.273620</td>\n <td>0.345408</td>\n <td>0.246377</td>\n <td>0.448907</td>\n <td>0.363937</td>\n <td>0.326715</td>\n <td>0.079794</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Description of what we use inside the code:\n#gives us the ratio(oran) between featurs. The closer this number gets to 0, the less the ratio, and \n#ratio, and the closer it gets to 1, the higher the ratio.\n#Turkhis\n# data.corr() bize feturlar arası orantıyı veriri bu sayı 0'a yaklaşırsa orantı yok\n#1'e ne kadar yaklaşırsa orantı o kadar fazla ve var demektir\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:52:31.780532Z","iopub.execute_input":"2022-01-28T13:52:31.780957Z","iopub.status.idle":"2022-01-28T13:52:31.784241Z","shell.execute_reply.started":"2022-01-28T13:52:31.780926Z","shell.execute_reply":"2022-01-28T13:52:31.783614Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"* **heatmap** içinde **data.corr** yani üstteki görüntüsü kötü olan kısımı alır\n* **annot=True** demek tabloda yazan 0.1 1.0 gibi sayıların yazılması demektir.\n* **linewidths=0.5** the thickness of the line between two squares\n* **fmt = 1.f** demek 0 dan sonra yazdırıcağı değer 1 tane olsun demek 2 dersek 2 tane olur\n* **figsize = 18x18**'lik bir alan olmasını sağlar yazamsak figurun sizeı default değer çıkardı\n","metadata":{}},{"cell_type":"code","source":"f,ax = plt.subplots(figsize=(15, 15))\nsns.heatmap(data.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:52:49.762192Z","iopub.execute_input":"2022-01-28T13:52:49.762495Z","iopub.status.idle":"2022-01-28T13:52:50.581904Z","shell.execute_reply.started":"2022-01-28T13:52:49.762463Z","shell.execute_reply":"2022-01-28T13:52:50.581251Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x1080 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"data.head(7)\n#give us top 7 sample","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:53:33.350355Z","iopub.execute_input":"2022-01-28T13:53:33.350645Z","iopub.status.idle":"2022-01-28T13:53:33.366249Z","shell.execute_reply.started":"2022-01-28T13:53:33.350614Z","shell.execute_reply":"2022-01-28T13:53:33.365202Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" # Name Type 1 Type 2 HP Attack Defense Sp. Atk Sp. Def \\\n0 1 Bulbasaur Grass Poison 45 49 49 65 65 \n1 2 Ivysaur Grass Poison 60 62 63 80 80 \n2 3 Venusaur Grass Poison 80 82 83 100 100 \n3 4 Mega Venusaur Grass Poison 80 100 123 122 120 \n4 5 Charmander Fire NaN 39 52 43 60 50 \n5 6 Charmeleon Fire NaN 58 64 58 80 65 \n6 7 Charizard Fire Flying 78 84 78 109 85 \n\n Speed Generation Legendary \n0 45 1 False \n1 60 1 False \n2 80 1 False \n3 80 1 False \n4 65 1 False \n5 80 1 False \n6 100 1 False ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>#</th>\n <th>Name</th>\n <th>Type 1</th>\n <th>Type 2</th>\n <th>HP</th>\n <th>Attack</th>\n <th>Defense</th>\n <th>Sp. Atk</th>\n <th>Sp. Def</th>\n <th>Speed</th>\n <th>Generation</th>\n <th>Legendary</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>Bulbasaur</td>\n <td>Grass</td>\n <td>Poison</td>\n <td>45</td>\n <td>49</td>\n <td>49</td>\n <td>65</td>\n <td>65</td>\n <td>45</td>\n <td>1</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>Ivysaur</td>\n <td>Grass</td>\n <td>Poison</td>\n <td>60</td>\n <td>62</td>\n <td>63</td>\n <td>80</td>\n <td>80</td>\n <td>60</td>\n <td>1</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>Venusaur</td>\n <td>Grass</td>\n <td>Poison</td>\n <td>80</td>\n <td>82</td>\n <td>83</td>\n <td>100</td>\n <td>100</td>\n <td>80</td>\n <td>1</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>Mega Venusaur</td>\n <td>Grass</td>\n <td>Poison</td>\n <td>80</td>\n <td>100</td>\n <td>123</td>\n <td>122</td>\n <td>120</td>\n <td>80</td>\n <td>1</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>Charmander</td>\n <td>Fire</td>\n <td>NaN</td>\n <td>39</td>\n <td>52</td>\n <td>43</td>\n <td>60</td>\n <td>50</td>\n <td>65</td>\n <td>1</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>6</td>\n <td>Charmeleon</td>\n <td>Fire</td>\n <td>NaN</td>\n <td>58</td>\n <td>64</td>\n <td>58</td>\n <td>80</td>\n <td>65</td>\n <td>80</td>\n <td>1</td>\n <td>False</td>\n </tr>\n <tr>\n <th>6</th>\n <td>7</td>\n <td>Charizard</td>\n <td>Fire</td>\n <td>Flying</td>\n <td>78</td>\n <td>84</td>\n <td>78</td>\n <td>109</td>\n <td>85</td>\n <td>100</td>\n <td>1</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.columns\n#give us columns names","metadata":{"execution":{"iopub.status.busy":"2022-01-28T13:53:53.107474Z","iopub.execute_input":"2022-01-28T13:53:53.107928Z","iopub.status.idle":"2022-01-28T13:53:53.115260Z","shell.execute_reply.started":"2022-01-28T13:53:53.107884Z","shell.execute_reply":"2022-01-28T13:53:53.114396Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"Index(['#', 'Name', 'Type 1', 'Type 2', 'HP', 'Attack', 'Defense', 'Sp. Atk',\n 'Sp. Def', 'Speed', 'Generation', 'Legendary'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"**MATPLOTLİB**\nMatplot is a python library that help up to plot data. The easiest and basic plots are line, scatte and histogram plots.\n\nLine plot is bette when x axis is time.\nScatter is better when there is correlation between two features.\nHistogram is better when we need to see distribution(dağılım) of numerical data.(Diyelim HP(can) featurunda iki tane 8 değeri var bunları histogram ile görselleştiririz.)","metadata":{}}]}
0086/398/86398079.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
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" } }, "cell_type": "markdown", "id": "d3eb794f", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "papermill": { "duration": 0.020374, "end_time": "2022-01-28T14:01:49.071074", "exception": false, "start_time": "2022-01-28T14:01:49.050700", "status": "completed" }, "tags": [] }, "source": [ "![268-2683578_uber-wallpaper.jpg](attachment:b66a66a4-4c44-4aae-89dd-0302f141843c.jpg)" ] }, { "cell_type": "markdown", "id": "2321c028", "metadata": { "papermill": { "duration": 0.01838, "end_time": "2022-01-28T14:01:49.108745", "exception": false, "start_time": "2022-01-28T14:01:49.090365", "status": "completed" }, "tags": [] }, "source": [ "# Seaborn visualization on UBER data_set\n", "**Proccess we did on this data_set:**\n", "1. want to know how much distance taxis take for services\n", "2. Statue of prices\n", "3. Is there any relation between incresing distance with price?\n", "4. Desire each destination for having more taxi\n", "5. burstness of price statue" ] }, { "cell_type": "markdown", "id": "de08dbf2", "metadata": { "papermill": { "duration": 0.01858, "end_time": "2022-01-28T14:01:49.146562", "exception": false, "start_time": "2022-01-28T14:01:49.127982", "status": "completed" }, "tags": [] }, "source": [ "# 1)Import Libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "4f40af2e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:01:49.188452Z", "iopub.status.busy": "2022-01-28T14:01:49.187201Z", "iopub.status.idle": "2022-01-28T14:01:50.356343Z", "shell.execute_reply": "2022-01-28T14:01:50.355578Z", "shell.execute_reply.started": "2022-01-28T13:25:15.109901Z" }, "papermill": { "duration": 1.191682, "end_time": "2022-01-28T14:01:50.356536", "exception": false, "start_time": "2022-01-28T14:01:49.164854", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "74e80329", "metadata": { "papermill": { "duration": 0.018128, "end_time": "2022-01-28T14:01:50.393205", "exception": false, "start_time": "2022-01-28T14:01:50.375077", "status": "completed" }, "tags": [] }, "source": [ "# 2)Import Data_Set" ] }, { "cell_type": "code", "execution_count": 2, "id": "5ada3124", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:01:50.433968Z", "iopub.status.busy": "2022-01-28T14:01:50.433248Z", "iopub.status.idle": "2022-01-28T14:01:53.174295Z", "shell.execute_reply": "2022-01-28T14:01:53.173681Z", "shell.execute_reply.started": "2022-01-28T13:25:17.091846Z" }, "papermill": { "duration": 2.762932, "end_time": "2022-01-28T14:01:53.174458", "exception": false, "start_time": "2022-01-28T14:01:50.411526", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "Df=pd.read_csv('../input/uber-lyft-cab-prices/cab_rides.csv')" ] }, { "cell_type": "markdown", "id": "3390fa55", "metadata": { "papermill": { "duration": 0.01855, "end_time": "2022-01-28T14:01:53.211620", "exception": false, "start_time": "2022-01-28T14:01:53.193070", "status": "completed" }, "tags": [] }, "source": [ "# 3)Data Overview" ] }, { "cell_type": "code", "execution_count": 3, "id": "6679c6cf", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:01:53.267097Z", "iopub.status.busy": "2022-01-28T14:01:53.266216Z", "iopub.status.idle": "2022-01-28T14:01:53.289872Z", "shell.execute_reply": "2022-01-28T14:01:53.290540Z", "shell.execute_reply.started": "2022-01-28T13:45:07.883620Z" }, "papermill": { "duration": 0.059956, "end_time": "2022-01-28T14:01:53.290758", "exception": false, "start_time": "2022-01-28T14:01:53.230802", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>distance</th>\n", " <th>cab_type</th>\n", " <th>time_stamp</th>\n", " <th>destination</th>\n", " <th>source</th>\n", " <th>price</th>\n", " <th>surge_multiplier</th>\n", " <th>id</th>\n", " <th>product_id</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.44</td>\n", " <td>Lyft</td>\n", " <td>1544952607890</td>\n", " <td>North Station</td>\n", " <td>Haymarket Square</td>\n", " <td>5.0</td>\n", " <td>1.0</td>\n", " <td>424553bb-7174-41ea-aeb4-fe06d4f4b9d7</td>\n", " <td>lyft_line</td>\n", " <td>Shared</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.44</td>\n", " <td>Lyft</td>\n", " <td>1543284023677</td>\n", " <td>North Station</td>\n", " <td>Haymarket Square</td>\n", " <td>11.0</td>\n", " <td>1.0</td>\n", " <td>4bd23055-6827-41c6-b23b-3c491f24e74d</td>\n", " <td>lyft_premier</td>\n", " <td>Lux</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.44</td>\n", " <td>Lyft</td>\n", " <td>1543366822198</td>\n", " <td>North Station</td>\n", " <td>Haymarket Square</td>\n", " <td>7.0</td>\n", " <td>1.0</td>\n", " <td>981a3613-77af-4620-a42a-0c0866077d1e</td>\n", " <td>lyft</td>\n", " <td>Lyft</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.44</td>\n", " <td>Lyft</td>\n", " <td>1543553582749</td>\n", " <td>North Station</td>\n", " <td>Haymarket Square</td>\n", " <td>26.0</td>\n", " <td>1.0</td>\n", " <td>c2d88af2-d278-4bfd-a8d0-29ca77cc5512</td>\n", " <td>lyft_luxsuv</td>\n", " <td>Lux Black XL</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.44</td>\n", " <td>Lyft</td>\n", " <td>1543463360223</td>\n", " <td>North Station</td>\n", " <td>Haymarket Square</td>\n", " <td>9.0</td>\n", " <td>1.0</td>\n", " <td>e0126e1f-8ca9-4f2e-82b3-50505a09db9a</td>\n", " <td>lyft_plus</td>\n", " <td>Lyft XL</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>693056</th>\n", " <td>0.91</td>\n", " <td>Uber</td>\n", " <td>1543456028123</td>\n", " <td>Beacon Hill</td>\n", " <td>Haymarket Square</td>\n", " <td>7.0</td>\n", " <td>1.0</td>\n", " <td>d1a50035-184d-4e63-8aa1-813b497e293e</td>\n", " <td>9a0e7b09-b92b-4c41-9779-2ad22b4d779d</td>\n", " <td>WAV</td>\n", " </tr>\n", " <tr>\n", " <th>693057</th>\n", " <td>1.61</td>\n", " <td>Uber</td>\n", " <td>1543456028123</td>\n", " <td>Haymarket Square</td>\n", " <td>Theatre District</td>\n", " <td>17.0</td>\n", " <td>1.0</td>\n", " <td>0f13e495-cd0a-4b87-8219-b9a4ae06ece7</td>\n", " <td>6c84fd89-3f11-4782-9b50-97c468b19529</td>\n", " <td>Black</td>\n", " </tr>\n", " <tr>\n", " <th>693058</th>\n", " <td>1.61</td>\n", " <td>Uber</td>\n", " <td>1543456028123</td>\n", " <td>Haymarket Square</td>\n", " <td>Theatre District</td>\n", " <td>22.0</td>\n", " <td>1.0</td>\n", " <td>16ccd6b9-a294-41c1-9827-0e44bd92db15</td>\n", " <td>6f72dfc5-27f1-42e8-84db-ccc7a75f6969</td>\n", " <td>UberXL</td>\n", " </tr>\n", " <tr>\n", " <th>693059</th>\n", " <td>1.61</td>\n", " <td>Uber</td>\n", " <td>1543728484149</td>\n", " <td>Haymarket Square</td>\n", " <td>Theatre District</td>\n", " <td>14.0</td>\n", " <td>1.0</td>\n", " <td>178bfa35-7df5-4ef8-a6eb-42a65f95bcce</td>\n", " <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n", " <td>UberPool</td>\n", " </tr>\n", " <tr>\n", " <th>693060</th>\n", " <td>1.61</td>\n", " <td>Uber</td>\n", " <td>1543728484149</td>\n", " <td>Haymarket Square</td>\n", " <td>Theatre District</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>50a7b6be-ed2a-4a11-8d36-73bd977ad66a</td>\n", " <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n", " <td>Taxi</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>693061 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " distance cab_type time_stamp destination source \\\n", "0 0.44 Lyft 1544952607890 North Station Haymarket Square \n", "1 0.44 Lyft 1543284023677 North Station Haymarket Square \n", "2 0.44 Lyft 1543366822198 North Station Haymarket Square \n", "3 0.44 Lyft 1543553582749 North Station Haymarket Square \n", "4 0.44 Lyft 1543463360223 North Station Haymarket Square \n", "... ... ... ... ... ... \n", "693056 0.91 Uber 1543456028123 Beacon Hill Haymarket Square \n", "693057 1.61 Uber 1543456028123 Haymarket Square Theatre District \n", "693058 1.61 Uber 1543456028123 Haymarket Square Theatre District \n", "693059 1.61 Uber 1543728484149 Haymarket Square Theatre District \n", "693060 1.61 Uber 1543728484149 Haymarket Square Theatre District \n", "\n", " price surge_multiplier id \\\n", "0 5.0 1.0 424553bb-7174-41ea-aeb4-fe06d4f4b9d7 \n", "1 11.0 1.0 4bd23055-6827-41c6-b23b-3c491f24e74d \n", "2 7.0 1.0 981a3613-77af-4620-a42a-0c0866077d1e \n", "3 26.0 1.0 c2d88af2-d278-4bfd-a8d0-29ca77cc5512 \n", "4 9.0 1.0 e0126e1f-8ca9-4f2e-82b3-50505a09db9a \n", "... ... ... ... \n", "693056 7.0 1.0 d1a50035-184d-4e63-8aa1-813b497e293e \n", "693057 17.0 1.0 0f13e495-cd0a-4b87-8219-b9a4ae06ece7 \n", "693058 22.0 1.0 16ccd6b9-a294-41c1-9827-0e44bd92db15 \n", "693059 14.0 1.0 178bfa35-7df5-4ef8-a6eb-42a65f95bcce \n", "693060 NaN 1.0 50a7b6be-ed2a-4a11-8d36-73bd977ad66a \n", "\n", " product_id name \n", "0 lyft_line Shared \n", "1 lyft_premier Lux \n", "2 lyft Lyft \n", "3 lyft_luxsuv Lux Black XL \n", "4 lyft_plus Lyft XL \n", "... ... ... \n", "693056 9a0e7b09-b92b-4c41-9779-2ad22b4d779d WAV \n", "693057 6c84fd89-3f11-4782-9b50-97c468b19529 Black \n", "693058 6f72dfc5-27f1-42e8-84db-ccc7a75f6969 UberXL \n", "693059 997acbb5-e102-41e1-b155-9df7de0a73f2 UberPool \n", "693060 8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a Taxi \n", "\n", "[693061 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Df.head(-10)" ] }, { "cell_type": "markdown", "id": "b55e02bb", "metadata": { "papermill": { "duration": 0.019188, "end_time": "2022-01-28T14:01:53.330536", "exception": false, "start_time": "2022-01-28T14:01:53.311348", "status": "completed" }, "tags": [] }, "source": [ "# 4)Distance spectrum" ] }, { "cell_type": "code", "execution_count": 4, "id": "4a51abaf", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:01:53.374193Z", "iopub.status.busy": "2022-01-28T14:01:53.373394Z", "iopub.status.idle": "2022-01-28T14:01:54.152051Z", "shell.execute_reply": "2022-01-28T14:01:54.152549Z", "shell.execute_reply.started": "2022-01-28T13:25:18.943474Z" }, "papermill": { "duration": 0.803045, "end_time": "2022-01-28T14:01:54.152750", "exception": false, "start_time": "2022-01-28T14:01:53.349705", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x7f18a654fe90>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.displot(data=Df, x='distance', bins=60, color='blue', height=8)" ] }, { "cell_type": "markdown", "id": "db987ee0", "metadata": { "papermill": { "duration": 0.022406, "end_time": "2022-01-28T14:01:54.195950", "exception": false, "start_time": "2022-01-28T14:01:54.173544", "status": "completed" }, "tags": [] }, "source": [ "# 5)Price View" ] }, { "cell_type": "code", "execution_count": 5, "id": "e3b3ed2b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:01:54.244531Z", "iopub.status.busy": "2022-01-28T14:01:54.243748Z", "iopub.status.idle": "2022-01-28T14:01:56.986265Z", "shell.execute_reply": "2022-01-28T14:01:56.986830Z", "shell.execute_reply.started": "2022-01-28T13:27:00.817180Z" }, "papermill": { "duration": 2.77002, "end_time": "2022-01-28T14:01:56.987007", "exception": false, "start_time": "2022-01-28T14:01:54.216987", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='price', ylabel='Density'>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20, 10))\n", "sns.kdeplot(data=Df, x='price', color='purple', weights=50,levels=10, thresh=10 )" ] }, { "cell_type": "markdown", "id": "e72cdbb7", "metadata": { "papermill": { "duration": 0.022108, "end_time": "2022-01-28T14:01:57.031722", "exception": false, "start_time": "2022-01-28T14:01:57.009614", "status": "completed" }, "tags": [] }, "source": [ "# 6)Relation between distance and price" ] }, { "cell_type": "code", "execution_count": 6, "id": "cbfa8a3a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:01:57.089435Z", "iopub.status.busy": "2022-01-28T14:01:57.088216Z", "iopub.status.idle": "2022-01-28T14:01:59.686930Z", "shell.execute_reply": "2022-01-28T14:01:59.687479Z", "shell.execute_reply.started": "2022-01-28T13:26:43.468115Z" }, "papermill": { "duration": 2.633261, "end_time": "2022-01-28T14:01:59.687659", "exception": false, "start_time": "2022-01-28T14:01:57.054398", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='distance', ylabel='price'>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20, 10))\n", "sns.scatterplot(data=Df, x='distance', y='price', sizes=10, ci=100, x_bins=10, y_bins=10, n_boot=1000, color='blue' )" ] }, { "cell_type": "markdown", "id": "81335cc3", "metadata": { "papermill": { "duration": 0.030612, "end_time": "2022-01-28T14:01:59.747871", "exception": false, "start_time": "2022-01-28T14:01:59.717259", "status": "completed" }, "tags": [] }, "source": [ "# 7)Destination count" ] }, { "cell_type": "code", "execution_count": 7, "id": "edaae5fd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:01:59.810484Z", "iopub.status.busy": "2022-01-28T14:01:59.809736Z", "iopub.status.idle": "2022-01-28T14:02:02.899071Z", "shell.execute_reply": "2022-01-28T14:02:02.899573Z", "shell.execute_reply.started": "2022-01-28T13:26:23.696186Z" }, "papermill": { "duration": 3.122548, "end_time": "2022-01-28T14:02:02.899756", "exception": false, "start_time": "2022-01-28T14:01:59.777208", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='destination', ylabel='price'>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20, 10))\n", "sns.violinplot(data=Df, x='destination', y='price', gridsize=100, cut=10)" ] }, { "cell_type": "markdown", "id": "4e8e38e4", "metadata": { "papermill": { "duration": 0.032808, "end_time": "2022-01-28T14:02:02.965538", "exception": false, "start_time": "2022-01-28T14:02:02.932730", "status": "completed" }, "tags": [] }, "source": [ "# 8)Heatmap for Price" ] }, { "cell_type": "code", "execution_count": 8, "id": "ccdc2a9a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:03.038419Z", "iopub.status.busy": "2022-01-28T14:02:03.037698Z", "iopub.status.idle": "2022-01-28T14:02:03.249638Z", "shell.execute_reply": "2022-01-28T14:02:03.248918Z", "shell.execute_reply.started": "2022-01-28T13:46:42.246109Z" }, "papermill": { "duration": 0.251666, "end_time": "2022-01-28T14:02:03.249796", "exception": false, "start_time": "2022-01-28T14:02:02.998130", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>destination</th>\n", " <th>Back Bay</th>\n", " <th>Beacon Hill</th>\n", " <th>Boston University</th>\n", " <th>Fenway</th>\n", " <th>Financial District</th>\n", " <th>Haymarket Square</th>\n", " <th>North End</th>\n", " <th>North Station</th>\n", " <th>Northeastern University</th>\n", " <th>South Station</th>\n", " <th>Theatre District</th>\n", " <th>West End</th>\n", " </tr>\n", " <tr>\n", " <th>name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Black</th>\n", " <td>19.941394</td>\n", " <td>20.093579</td>\n", " <td>23.733181</td>\n", " <td>22.831083</td>\n", " <td>22.339558</td>\n", " <td>17.365883</td>\n", " <td>18.286259</td>\n", " <td>20.770755</td>\n", " <td>22.591374</td>\n", " <td>18.972113</td>\n", " <td>19.267195</td>\n", " <td>20.070369</td>\n", " </tr>\n", " <tr>\n", " <th>Black SUV</th>\n", " <td>29.574510</td>\n", " <td>29.736906</td>\n", " <td>32.426083</td>\n", " <td>31.517861</td>\n", " <td>33.310474</td>\n", " <td>27.759199</td>\n", " <td>28.422583</td>\n", " <td>31.071885</td>\n", " <td>31.398170</td>\n", " <td>28.887582</td>\n", " <td>29.142330</td>\n", " <td>30.158193</td>\n", " </tr>\n", " <tr>\n", " <th>Lux</th>\n", " <td>17.484561</td>\n", " <td>17.253652</td>\n", " <td>21.248067</td>\n", " <td>20.330094</td>\n", " <td>19.337640</td>\n", " <td>14.387679</td>\n", " <td>15.962286</td>\n", " <td>18.191187</td>\n", " <td>19.498244</td>\n", " <td>14.753513</td>\n", " <td>17.351856</td>\n", " <td>17.416804</td>\n", " </tr>\n", " <tr>\n", " <th>Lux Black</th>\n", " <td>22.135322</td>\n", " <td>22.321748</td>\n", " <td>27.596861</td>\n", " <td>26.054098</td>\n", " <td>25.485365</td>\n", " <td>19.157426</td>\n", " <td>20.320684</td>\n", " <td>23.735963</td>\n", " <td>24.960070</td>\n", " <td>19.992623</td>\n", " <td>22.238839</td>\n", " <td>22.691457</td>\n", " </tr>\n", " <tr>\n", " <th>Lux Black XL</th>\n", " <td>31.590409</td>\n", " <td>31.152215</td>\n", " <td>36.524209</td>\n", " <td>35.123302</td>\n", " <td>35.433227</td>\n", " <td>28.811548</td>\n", " <td>29.757906</td>\n", " <td>33.321369</td>\n", " <td>34.295515</td>\n", " <td>29.051171</td>\n", " <td>30.985667</td>\n", " <td>31.765086</td>\n", " </tr>\n", " <tr>\n", " <th>Lyft</th>\n", " <td>9.243392</td>\n", " <td>9.476673</td>\n", " <td>11.074139</td>\n", " <td>10.682670</td>\n", " <td>10.568260</td>\n", " <td>8.120836</td>\n", " <td>8.599672</td>\n", " <td>9.761549</td>\n", " <td>10.353391</td>\n", " <td>8.522096</td>\n", " <td>9.405075</td>\n", " <td>9.499647</td>\n", " </tr>\n", " <tr>\n", " <th>Lyft XL</th>\n", " <td>15.016374</td>\n", " <td>14.984802</td>\n", " <td>18.240689</td>\n", " <td>17.343208</td>\n", " <td>16.882575</td>\n", " <td>12.587046</td>\n", " <td>13.531038</td>\n", " <td>15.512438</td>\n", " <td>16.735972</td>\n", " <td>12.766745</td>\n", " <td>15.007166</td>\n", " <td>15.064486</td>\n", " </tr>\n", " <tr>\n", " <th>Shared</th>\n", " <td>5.870058</td>\n", " <td>6.100495</td>\n", " <td>7.226751</td>\n", " <td>6.823770</td>\n", " <td>6.264578</td>\n", " <td>4.923500</td>\n", " <td>5.327711</td>\n", " <td>6.093817</td>\n", " <td>6.640900</td>\n", " <td>5.201405</td>\n", " <td>5.864427</td>\n", " <td>6.016121</td>\n", " </tr>\n", " <tr>\n", " <th>UberPool</th>\n", " <td>8.873203</td>\n", " <td>8.727313</td>\n", " <td>9.969077</td>\n", " <td>9.298192</td>\n", " <td>8.845568</td>\n", " <td>7.922491</td>\n", " <td>8.231758</td>\n", " <td>8.412373</td>\n", " <td>9.561860</td>\n", " <td>8.297559</td>\n", " <td>8.547092</td>\n", " <td>8.338316</td>\n", " </tr>\n", " <tr>\n", " <th>UberX</th>\n", " <td>9.724510</td>\n", " <td>9.894783</td>\n", " <td>10.981276</td>\n", " <td>10.523960</td>\n", " <td>10.751449</td>\n", " <td>8.514261</td>\n", " <td>8.848181</td>\n", " <td>9.727763</td>\n", " <td>10.547920</td>\n", " <td>8.855120</td>\n", " <td>9.482317</td>\n", " <td>9.315296</td>\n", " </tr>\n", " <tr>\n", " <th>UberXL</th>\n", " <td>15.623529</td>\n", " <td>15.605742</td>\n", " <td>17.884607</td>\n", " <td>17.227728</td>\n", " <td>16.934750</td>\n", " <td>13.173634</td>\n", " <td>14.133057</td>\n", " <td>15.745707</td>\n", " <td>17.182313</td>\n", " <td>13.877887</td>\n", " <td>15.300933</td>\n", " <td>15.430613</td>\n", " </tr>\n", " <tr>\n", " <th>WAV</th>\n", " <td>9.724510</td>\n", " <td>9.894696</td>\n", " <td>10.981276</td>\n", " <td>10.523960</td>\n", " <td>10.751449</td>\n", " <td>8.514261</td>\n", " <td>8.847779</td>\n", " <td>9.727763</td>\n", " <td>10.547920</td>\n", " <td>8.855120</td>\n", " <td>9.482317</td>\n", " <td>9.315296</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "destination Back Bay Beacon Hill Boston University Fenway \\\n", "name \n", "Black 19.941394 20.093579 23.733181 22.831083 \n", "Black SUV 29.574510 29.736906 32.426083 31.517861 \n", "Lux 17.484561 17.253652 21.248067 20.330094 \n", "Lux Black 22.135322 22.321748 27.596861 26.054098 \n", "Lux Black XL 31.590409 31.152215 36.524209 35.123302 \n", "Lyft 9.243392 9.476673 11.074139 10.682670 \n", "Lyft XL 15.016374 14.984802 18.240689 17.343208 \n", "Shared 5.870058 6.100495 7.226751 6.823770 \n", "UberPool 8.873203 8.727313 9.969077 9.298192 \n", "UberX 9.724510 9.894783 10.981276 10.523960 \n", "UberXL 15.623529 15.605742 17.884607 17.227728 \n", "WAV 9.724510 9.894696 10.981276 10.523960 \n", "\n", "destination Financial District Haymarket Square North End North Station \\\n", "name \n", "Black 22.339558 17.365883 18.286259 20.770755 \n", "Black SUV 33.310474 27.759199 28.422583 31.071885 \n", "Lux 19.337640 14.387679 15.962286 18.191187 \n", "Lux Black 25.485365 19.157426 20.320684 23.735963 \n", "Lux Black XL 35.433227 28.811548 29.757906 33.321369 \n", "Lyft 10.568260 8.120836 8.599672 9.761549 \n", "Lyft XL 16.882575 12.587046 13.531038 15.512438 \n", "Shared 6.264578 4.923500 5.327711 6.093817 \n", "UberPool 8.845568 7.922491 8.231758 8.412373 \n", "UberX 10.751449 8.514261 8.848181 9.727763 \n", "UberXL 16.934750 13.173634 14.133057 15.745707 \n", "WAV 10.751449 8.514261 8.847779 9.727763 \n", "\n", "destination Northeastern University South Station Theatre District \\\n", "name \n", "Black 22.591374 18.972113 19.267195 \n", "Black SUV 31.398170 28.887582 29.142330 \n", "Lux 19.498244 14.753513 17.351856 \n", "Lux Black 24.960070 19.992623 22.238839 \n", "Lux Black XL 34.295515 29.051171 30.985667 \n", "Lyft 10.353391 8.522096 9.405075 \n", "Lyft XL 16.735972 12.766745 15.007166 \n", "Shared 6.640900 5.201405 5.864427 \n", "UberPool 9.561860 8.297559 8.547092 \n", "UberX 10.547920 8.855120 9.482317 \n", "UberXL 17.182313 13.877887 15.300933 \n", "WAV 10.547920 8.855120 9.482317 \n", "\n", "destination West End \n", "name \n", "Black 20.070369 \n", "Black SUV 30.158193 \n", "Lux 17.416804 \n", "Lux Black 22.691457 \n", "Lux Black XL 31.765086 \n", "Lyft 9.499647 \n", "Lyft XL 15.064486 \n", "Shared 6.016121 \n", "UberPool 8.338316 \n", "UberX 9.315296 \n", "UberXL 15.430613 \n", "WAV 9.315296 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "H=Df.pivot_table(index='name', columns='destination', values='price')\n", "H" ] }, { "cell_type": "code", "execution_count": 9, "id": "b8075f7e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:03.325773Z", "iopub.status.busy": "2022-01-28T14:02:03.325083Z", "iopub.status.idle": "2022-01-28T14:02:03.728683Z", "shell.execute_reply": "2022-01-28T14:02:03.728128Z", "shell.execute_reply.started": "2022-01-28T13:46:46.432130Z" }, "papermill": { "duration": 0.445655, "end_time": "2022-01-28T14:02:03.728840", "exception": false, "start_time": "2022-01-28T14:02:03.283185", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='destination', ylabel='name'>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(H)" ] }, { "attachments": { "11cab442-ed6f-43a2-8661-d245bb7e12a1.jpg": { "image/jpeg": 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" 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26.434298, "end_time": "2022-01-28T14:02:04.713784", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:01:38.279486", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/398/86398180.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "fb69d9e9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:50.312875Z", "iopub.status.busy": "2022-01-28T14:02:50.310584Z", "iopub.status.idle": "2022-01-28T14:02:51.662587Z", "shell.execute_reply": "2022-01-28T14:02:51.663095Z", "shell.execute_reply.started": "2022-01-28T14:01:36.849202Z" }, "papermill": { "duration": 1.369186, "end_time": "2022-01-28T14:02:51.663389", "exception": false, "start_time": "2022-01-28T14:02:50.294203", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np \n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 2, "id": "5ac12688", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:51.685454Z", "iopub.status.busy": 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<tr>\n", " <th>3</th>\n", " <td>M</td>\n", " <td>11.420</td>\n", " <td>20.38</td>\n", " <td>77.58</td>\n", " <td>386.1</td>\n", " <td>0.14250</td>\n", " <td>0.28390</td>\n", " <td>0.241400</td>\n", " <td>0.10520</td>\n", " <td>0.2597</td>\n", " <td>...</td>\n", " <td>14.91</td>\n", " <td>26.50</td>\n", " <td>98.87</td>\n", " <td>567.7</td>\n", " <td>0.2098</td>\n", " <td>0.86630</td>\n", " <td>0.68690</td>\n", " <td>0.25750</td>\n", " <td>0.6638</td>\n", " <td>0.17300</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>M</td>\n", " <td>20.290</td>\n", " <td>14.34</td>\n", " <td>135.10</td>\n", " <td>1297.0</td>\n", " <td>0.10030</td>\n", " <td>0.13280</td>\n", " <td>0.198000</td>\n", " <td>0.10430</td>\n", " <td>0.1809</td>\n", " <td>...</td>\n", " <td>22.54</td>\n", " <td>16.67</td>\n", " <td>152.20</td>\n", " <td>1575.0</td>\n", " <td>0.1374</td>\n", " <td>0.20500</td>\n", " <td>0.40000</td>\n", " <td>0.16250</td>\n", " <td>0.2364</td>\n", " <td>0.07678</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>554</th>\n", " <td>B</td>\n", " <td>12.880</td>\n", " <td>28.92</td>\n", " <td>82.50</td>\n", " <td>514.3</td>\n", " <td>0.08123</td>\n", " <td>0.05824</td>\n", " <td>0.061950</td>\n", " <td>0.02343</td>\n", " <td>0.1566</td>\n", " <td>...</td>\n", " <td>13.89</td>\n", " <td>35.74</td>\n", " <td>88.84</td>\n", " <td>595.7</td>\n", " <td>0.1227</td>\n", " <td>0.16200</td>\n", " <td>0.24390</td>\n", " <td>0.06493</td>\n", " <td>0.2372</td>\n", " <td>0.07242</td>\n", " </tr>\n", " <tr>\n", " <th>555</th>\n", " <td>B</td>\n", " <td>10.290</td>\n", " <td>27.61</td>\n", " <td>65.67</td>\n", " <td>321.4</td>\n", " <td>0.09030</td>\n", " <td>0.07658</td>\n", " <td>0.059990</td>\n", " <td>0.02738</td>\n", " <td>0.1593</td>\n", " <td>...</td>\n", " <td>10.84</td>\n", " <td>34.91</td>\n", " <td>69.57</td>\n", " <td>357.6</td>\n", " <td>0.1384</td>\n", " <td>0.17100</td>\n", " <td>0.20000</td>\n", " <td>0.09127</td>\n", " <td>0.2226</td>\n", " <td>0.08283</td>\n", " </tr>\n", " <tr>\n", " <th>556</th>\n", " <td>B</td>\n", " <td>10.160</td>\n", " <td>19.59</td>\n", " <td>64.73</td>\n", " <td>311.7</td>\n", " <td>0.10030</td>\n", " <td>0.07504</td>\n", " <td>0.005025</td>\n", " <td>0.01116</td>\n", " <td>0.1791</td>\n", " <td>...</td>\n", " <td>10.65</td>\n", " <td>22.88</td>\n", " <td>67.88</td>\n", " <td>347.3</td>\n", " <td>0.1265</td>\n", " <td>0.12000</td>\n", " <td>0.01005</td>\n", " <td>0.02232</td>\n", " <td>0.2262</td>\n", " <td>0.06742</td>\n", " </tr>\n", " <tr>\n", " <th>557</th>\n", " <td>B</td>\n", " <td>9.423</td>\n", " <td>27.88</td>\n", " <td>59.26</td>\n", " <td>271.3</td>\n", " <td>0.08123</td>\n", " <td>0.04971</td>\n", " <td>0.000000</td>\n", " <td>0.00000</td>\n", " <td>0.1742</td>\n", " <td>...</td>\n", " <td>10.49</td>\n", " <td>34.24</td>\n", " <td>66.50</td>\n", " <td>330.6</td>\n", " <td>0.1073</td>\n", " <td>0.07158</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.2475</td>\n", " <td>0.06969</td>\n", " </tr>\n", " <tr>\n", " <th>558</th>\n", " <td>B</td>\n", " <td>14.590</td>\n", " <td>22.68</td>\n", " <td>96.39</td>\n", " <td>657.1</td>\n", " <td>0.08473</td>\n", " <td>0.13300</td>\n", " <td>0.102900</td>\n", " <td>0.03736</td>\n", " <td>0.1454</td>\n", " <td>...</td>\n", " <td>15.48</td>\n", " <td>27.27</td>\n", " <td>105.90</td>\n", " <td>733.5</td>\n", " <td>0.1026</td>\n", " <td>0.31710</td>\n", " <td>0.36620</td>\n", " <td>0.11050</td>\n", " <td>0.2258</td>\n", " <td>0.08004</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>559 rows × 31 columns</p>\n", "</div>" ], "text/plain": [ " diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n", "0 M 17.990 10.38 122.80 1001.0 \n", "1 M 20.570 17.77 132.90 1326.0 \n", "2 M 19.690 21.25 130.00 1203.0 \n", "3 M 11.420 20.38 77.58 386.1 \n", "4 M 20.290 14.34 135.10 1297.0 \n", ".. ... ... ... ... ... \n", "554 B 12.880 28.92 82.50 514.3 \n", "555 B 10.290 27.61 65.67 321.4 \n", "556 B 10.160 19.59 64.73 311.7 \n", "557 B 9.423 27.88 59.26 271.3 \n", "558 B 14.590 22.68 96.39 657.1 \n", "\n", " smoothness_mean compactness_mean concavity_mean concave points_mean \\\n", "0 0.11840 0.27760 0.300100 0.14710 \n", "1 0.08474 0.07864 0.086900 0.07017 \n", "2 0.10960 0.15990 0.197400 0.12790 \n", "3 0.14250 0.28390 0.241400 0.10520 \n", "4 0.10030 0.13280 0.198000 0.10430 \n", ".. ... ... ... ... \n", "554 0.08123 0.05824 0.061950 0.02343 \n", "555 0.09030 0.07658 0.059990 0.02738 \n", "556 0.10030 0.07504 0.005025 0.01116 \n", "557 0.08123 0.04971 0.000000 0.00000 \n", "558 0.08473 0.13300 0.102900 0.03736 \n", "\n", " symmetry_mean ... radius_worst texture_worst perimeter_worst \\\n", "0 0.2419 ... 25.38 17.33 184.60 \n", "1 0.1812 ... 24.99 23.41 158.80 \n", "2 0.2069 ... 23.57 25.53 152.50 \n", "3 0.2597 ... 14.91 26.50 98.87 \n", "4 0.1809 ... 22.54 16.67 152.20 \n", ".. ... ... ... ... ... \n", "554 0.1566 ... 13.89 35.74 88.84 \n", "555 0.1593 ... 10.84 34.91 69.57 \n", "556 0.1791 ... 10.65 22.88 67.88 \n", "557 0.1742 ... 10.49 34.24 66.50 \n", "558 0.1454 ... 15.48 27.27 105.90 \n", "\n", " area_worst smoothness_worst compactness_worst concavity_worst \\\n", "0 2019.0 0.1622 0.66560 0.71190 \n", "1 1956.0 0.1238 0.18660 0.24160 \n", "2 1709.0 0.1444 0.42450 0.45040 \n", "3 567.7 0.2098 0.86630 0.68690 \n", "4 1575.0 0.1374 0.20500 0.40000 \n", ".. ... ... ... ... \n", "554 595.7 0.1227 0.16200 0.24390 \n", "555 357.6 0.1384 0.17100 0.20000 \n", "556 347.3 0.1265 0.12000 0.01005 \n", "557 330.6 0.1073 0.07158 0.00000 \n", "558 733.5 0.1026 0.31710 0.36620 \n", "\n", " concave points_worst symmetry_worst fractal_dimension_worst \n", "0 0.26540 0.4601 0.11890 \n", "1 0.18600 0.2750 0.08902 \n", "2 0.24300 0.3613 0.08758 \n", "3 0.25750 0.6638 0.17300 \n", "4 0.16250 0.2364 0.07678 \n", ".. ... ... ... \n", "554 0.06493 0.2372 0.07242 \n", "555 0.09127 0.2226 0.08283 \n", "556 0.02232 0.2262 0.06742 \n", "557 0.00000 0.2475 0.06969 \n", "558 0.11050 0.2258 0.08004 \n", "\n", "[559 rows x 31 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Import the data 'breast-cancer-dataset' from Kaggle\n", "\n", "cancer_data = pd.read_csv('../input/breast-cancer-dataset/breast-cancer.csv')\n", "cancer_data = cancer_data.drop(['id'], axis=1)\n", "\n", "#take last 10 row of the data for the final test result as the untouched final test set\n", "final_test_cancer_data = cancer_data[-10:]\n", "cancer_data_train = cancer_data[:-10]\n", "\n", "cancer_data_train" ] }, { "cell_type": "code", "execution_count": 3, "id": "9d88c64d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:51.784459Z", "iopub.status.busy": "2022-01-28T14:02:51.783849Z", "iopub.status.idle": "2022-01-28T14:02:52.272670Z", "shell.execute_reply": "2022-01-28T14:02:52.272043Z", "shell.execute_reply.started": "2022-01-28T14:01:38.224120Z" }, "papermill": { "duration": 0.503037, "end_time": "2022-01-28T14:02:52.272813", "exception": false, "start_time": "2022-01-28T14:02:51.769776", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Plotting the correlation between the features of the breast cancer datasets\n", "\n", "\n", "#negative_correlation = cancer_data.corr().values <0\n", "sns.heatmap(cancer_data_train.corr(), annot=False)\n", "#sns.pairplot(cancer_data_train)\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "id": "7ca64e0f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:52.303339Z", "iopub.status.busy": "2022-01-28T14:02:52.302660Z", "iopub.status.idle": "2022-01-28T14:02:52.374357Z", "shell.execute_reply": "2022-01-28T14:02:52.373713Z", "shell.execute_reply.started": "2022-01-28T14:01:38.751266Z" }, "papermill": { "duration": 0.091881, "end_time": "2022-01-28T14:02:52.374498", "exception": false, "start_time": "2022-01-28T14:02:52.282617", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>radius_mean</th>\n", " <th>texture_mean</th>\n", " <th>perimeter_mean</th>\n", " <th>area_mean</th>\n", " <th>smoothness_mean</th>\n", " <th>compactness_mean</th>\n", " <th>concavity_mean</th>\n", " <th>concave points_mean</th>\n", " <th>symmetry_mean</th>\n", " <th>fractal_dimension_mean</th>\n", " <th>...</th>\n", " <th>radius_worst</th>\n", " <th>texture_worst</th>\n", " <th>perimeter_worst</th>\n", " <th>area_worst</th>\n", " <th>smoothness_worst</th>\n", " <th>compactness_worst</th>\n", " <th>concavity_worst</th>\n", " <th>concave points_worst</th>\n", " <th>symmetry_worst</th>\n", " <th>fractal_dimension_worst</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>...</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " <td>569.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>14.127292</td>\n", " <td>19.289649</td>\n", " <td>91.969033</td>\n", " <td>654.889104</td>\n", " <td>0.096360</td>\n", " <td>0.104341</td>\n", " <td>0.088799</td>\n", " <td>0.048919</td>\n", " <td>0.181162</td>\n", " <td>0.062798</td>\n", " <td>...</td>\n", " <td>16.269190</td>\n", " <td>25.677223</td>\n", " <td>107.261213</td>\n", " <td>880.583128</td>\n", " <td>0.132369</td>\n", " <td>0.254265</td>\n", " <td>0.272188</td>\n", " <td>0.114606</td>\n", " <td>0.290076</td>\n", " <td>0.083946</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>3.524049</td>\n", " <td>4.301036</td>\n", " <td>24.298981</td>\n", " <td>351.914129</td>\n", " <td>0.014064</td>\n", " <td>0.052813</td>\n", " <td>0.079720</td>\n", " <td>0.038803</td>\n", " <td>0.027414</td>\n", " <td>0.007060</td>\n", " <td>...</td>\n", " <td>4.833242</td>\n", " <td>6.146258</td>\n", " <td>33.602542</td>\n", " <td>569.356993</td>\n", " <td>0.022832</td>\n", " <td>0.157336</td>\n", " <td>0.208624</td>\n", " <td>0.065732</td>\n", " <td>0.061867</td>\n", " <td>0.018061</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>6.981000</td>\n", " <td>9.710000</td>\n", " <td>43.790000</td>\n", " <td>143.500000</td>\n", " <td>0.052630</td>\n", " <td>0.019380</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.106000</td>\n", " <td>0.049960</td>\n", " <td>...</td>\n", " <td>7.930000</td>\n", " <td>12.020000</td>\n", " <td>50.410000</td>\n", " <td>185.200000</td>\n", " <td>0.071170</td>\n", " <td>0.027290</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.156500</td>\n", " <td>0.055040</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>11.700000</td>\n", " <td>16.170000</td>\n", " <td>75.170000</td>\n", " <td>420.300000</td>\n", " <td>0.086370</td>\n", " <td>0.064920</td>\n", " <td>0.029560</td>\n", " <td>0.020310</td>\n", " <td>0.161900</td>\n", " <td>0.057700</td>\n", " <td>...</td>\n", " <td>13.010000</td>\n", " <td>21.080000</td>\n", " <td>84.110000</td>\n", " <td>515.300000</td>\n", " <td>0.116600</td>\n", " <td>0.147200</td>\n", " <td>0.114500</td>\n", " <td>0.064930</td>\n", " <td>0.250400</td>\n", " <td>0.071460</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>13.370000</td>\n", " <td>18.840000</td>\n", " <td>86.240000</td>\n", " <td>551.100000</td>\n", " <td>0.095870</td>\n", " <td>0.092630</td>\n", " <td>0.061540</td>\n", " <td>0.033500</td>\n", " <td>0.179200</td>\n", " <td>0.061540</td>\n", " <td>...</td>\n", " <td>14.970000</td>\n", " <td>25.410000</td>\n", " <td>97.660000</td>\n", " <td>686.500000</td>\n", " <td>0.131300</td>\n", " <td>0.211900</td>\n", " <td>0.226700</td>\n", " <td>0.099930</td>\n", " <td>0.282200</td>\n", " <td>0.080040</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>15.780000</td>\n", " <td>21.800000</td>\n", " <td>104.100000</td>\n", " <td>782.700000</td>\n", " <td>0.105300</td>\n", " <td>0.130400</td>\n", " <td>0.130700</td>\n", " <td>0.074000</td>\n", " <td>0.195700</td>\n", " <td>0.066120</td>\n", " <td>...</td>\n", " <td>18.790000</td>\n", " <td>29.720000</td>\n", " <td>125.400000</td>\n", " <td>1084.000000</td>\n", " <td>0.146000</td>\n", " <td>0.339100</td>\n", " <td>0.382900</td>\n", " <td>0.161400</td>\n", " <td>0.317900</td>\n", " <td>0.092080</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>28.110000</td>\n", " <td>39.280000</td>\n", " <td>188.500000</td>\n", " <td>2501.000000</td>\n", " <td>0.163400</td>\n", " <td>0.345400</td>\n", " <td>0.426800</td>\n", " <td>0.201200</td>\n", " <td>0.304000</td>\n", " <td>0.097440</td>\n", " <td>...</td>\n", " <td>36.040000</td>\n", " <td>49.540000</td>\n", " <td>251.200000</td>\n", " <td>4254.000000</td>\n", " <td>0.222600</td>\n", " <td>1.058000</td>\n", " <td>1.252000</td>\n", " <td>0.291000</td>\n", " <td>0.663800</td>\n", " <td>0.207500</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " radius_mean texture_mean perimeter_mean area_mean \\\n", "count 569.000000 569.000000 569.000000 569.000000 \n", "mean 14.127292 19.289649 91.969033 654.889104 \n", "std 3.524049 4.301036 24.298981 351.914129 \n", "min 6.981000 9.710000 43.790000 143.500000 \n", "25% 11.700000 16.170000 75.170000 420.300000 \n", "50% 13.370000 18.840000 86.240000 551.100000 \n", "75% 15.780000 21.800000 104.100000 782.700000 \n", "max 28.110000 39.280000 188.500000 2501.000000 \n", "\n", " smoothness_mean compactness_mean concavity_mean concave points_mean \\\n", "count 569.000000 569.000000 569.000000 569.000000 \n", "mean 0.096360 0.104341 0.088799 0.048919 \n", "std 0.014064 0.052813 0.079720 0.038803 \n", "min 0.052630 0.019380 0.000000 0.000000 \n", "25% 0.086370 0.064920 0.029560 0.020310 \n", "50% 0.095870 0.092630 0.061540 0.033500 \n", "75% 0.105300 0.130400 0.130700 0.074000 \n", "max 0.163400 0.345400 0.426800 0.201200 \n", "\n", " symmetry_mean fractal_dimension_mean ... radius_worst \\\n", "count 569.000000 569.000000 ... 569.000000 \n", "mean 0.181162 0.062798 ... 16.269190 \n", "std 0.027414 0.007060 ... 4.833242 \n", "min 0.106000 0.049960 ... 7.930000 \n", "25% 0.161900 0.057700 ... 13.010000 \n", "50% 0.179200 0.061540 ... 14.970000 \n", "75% 0.195700 0.066120 ... 18.790000 \n", "max 0.304000 0.097440 ... 36.040000 \n", "\n", " texture_worst perimeter_worst area_worst smoothness_worst \\\n", "count 569.000000 569.000000 569.000000 569.000000 \n", "mean 25.677223 107.261213 880.583128 0.132369 \n", "std 6.146258 33.602542 569.356993 0.022832 \n", "min 12.020000 50.410000 185.200000 0.071170 \n", "25% 21.080000 84.110000 515.300000 0.116600 \n", "50% 25.410000 97.660000 686.500000 0.131300 \n", "75% 29.720000 125.400000 1084.000000 0.146000 \n", "max 49.540000 251.200000 4254.000000 0.222600 \n", "\n", " compactness_worst concavity_worst concave points_worst \\\n", "count 569.000000 569.000000 569.000000 \n", "mean 0.254265 0.272188 0.114606 \n", "std 0.157336 0.208624 0.065732 \n", "min 0.027290 0.000000 0.000000 \n", "25% 0.147200 0.114500 0.064930 \n", "50% 0.211900 0.226700 0.099930 \n", "75% 0.339100 0.382900 0.161400 \n", "max 1.058000 1.252000 0.291000 \n", "\n", " symmetry_worst fractal_dimension_worst \n", "count 569.000000 569.000000 \n", "mean 0.290076 0.083946 \n", "std 0.061867 0.018061 \n", "min 0.156500 0.055040 \n", "25% 0.250400 0.071460 \n", "50% 0.282200 0.080040 \n", "75% 0.317900 0.092080 \n", "max 0.663800 0.207500 \n", "\n", "[8 rows x 30 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cancer_data.describe()" ] }, { "cell_type": "code", "execution_count": 5, "id": "eded5edd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:52.414400Z", "iopub.status.busy": "2022-01-28T14:02:52.402362Z", "iopub.status.idle": "2022-01-28T14:02:52.430406Z", "shell.execute_reply": "2022-01-28T14:02:52.429833Z", "shell.execute_reply.started": "2022-01-28T14:01:38.855304Z" }, "papermill": { "duration": 0.045914, "end_time": "2022-01-28T14:02:52.430546", "exception": false, "start_time": "2022-01-28T14:02:52.384632", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " 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<th>4</th>\n", " <td>20.290</td>\n", " <td>14.34</td>\n", " <td>135.10</td>\n", " <td>1297.0</td>\n", " <td>0.10030</td>\n", " <td>0.13280</td>\n", " <td>0.198000</td>\n", " <td>0.10430</td>\n", " <td>0.1809</td>\n", " <td>0.05883</td>\n", " <td>...</td>\n", " <td>22.54</td>\n", " <td>16.67</td>\n", " <td>152.20</td>\n", " <td>1575.0</td>\n", " <td>0.1374</td>\n", " <td>0.20500</td>\n", " <td>0.40000</td>\n", " <td>0.16250</td>\n", " <td>0.2364</td>\n", " <td>0.07678</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>554</th>\n", " <td>12.880</td>\n", " <td>28.92</td>\n", " <td>82.50</td>\n", " <td>514.3</td>\n", " <td>0.08123</td>\n", " <td>0.05824</td>\n", " <td>0.061950</td>\n", " <td>0.02343</td>\n", " <td>0.1566</td>\n", " <td>0.05708</td>\n", " <td>...</td>\n", " <td>13.89</td>\n", " <td>35.74</td>\n", " <td>88.84</td>\n", " <td>595.7</td>\n", " <td>0.1227</td>\n", " <td>0.16200</td>\n", " <td>0.24390</td>\n", " <td>0.06493</td>\n", " <td>0.2372</td>\n", " <td>0.07242</td>\n", " </tr>\n", " <tr>\n", " <th>555</th>\n", " <td>10.290</td>\n", " <td>27.61</td>\n", " <td>65.67</td>\n", " <td>321.4</td>\n", " <td>0.09030</td>\n", " <td>0.07658</td>\n", " <td>0.059990</td>\n", " <td>0.02738</td>\n", " <td>0.1593</td>\n", " <td>0.06127</td>\n", " <td>...</td>\n", " <td>10.84</td>\n", " <td>34.91</td>\n", " <td>69.57</td>\n", " <td>357.6</td>\n", " <td>0.1384</td>\n", " <td>0.17100</td>\n", " <td>0.20000</td>\n", " <td>0.09127</td>\n", " <td>0.2226</td>\n", " <td>0.08283</td>\n", " </tr>\n", " <tr>\n", " <th>556</th>\n", " <td>10.160</td>\n", " <td>19.59</td>\n", " <td>64.73</td>\n", " <td>311.7</td>\n", " <td>0.10030</td>\n", " <td>0.07504</td>\n", " <td>0.005025</td>\n", " <td>0.01116</td>\n", " <td>0.1791</td>\n", " <td>0.06331</td>\n", " <td>...</td>\n", " <td>10.65</td>\n", " <td>22.88</td>\n", " <td>67.88</td>\n", " <td>347.3</td>\n", " <td>0.1265</td>\n", " <td>0.12000</td>\n", " <td>0.01005</td>\n", " <td>0.02232</td>\n", " <td>0.2262</td>\n", " <td>0.06742</td>\n", " </tr>\n", " <tr>\n", " <th>557</th>\n", " <td>9.423</td>\n", " <td>27.88</td>\n", " <td>59.26</td>\n", " <td>271.3</td>\n", " <td>0.08123</td>\n", " <td>0.04971</td>\n", " <td>0.000000</td>\n", " <td>0.00000</td>\n", " <td>0.1742</td>\n", " <td>0.06059</td>\n", " <td>...</td>\n", " <td>10.49</td>\n", " <td>34.24</td>\n", " <td>66.50</td>\n", " <td>330.6</td>\n", " <td>0.1073</td>\n", " <td>0.07158</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.2475</td>\n", " <td>0.06969</td>\n", " </tr>\n", " <tr>\n", " <th>558</th>\n", " <td>14.590</td>\n", " <td>22.68</td>\n", " <td>96.39</td>\n", " <td>657.1</td>\n", " <td>0.08473</td>\n", " <td>0.13300</td>\n", " <td>0.102900</td>\n", " <td>0.03736</td>\n", " <td>0.1454</td>\n", " <td>0.06147</td>\n", " <td>...</td>\n", " <td>15.48</td>\n", " <td>27.27</td>\n", " <td>105.90</td>\n", " <td>733.5</td>\n", " <td>0.1026</td>\n", " <td>0.31710</td>\n", " <td>0.36620</td>\n", " <td>0.11050</td>\n", " <td>0.2258</td>\n", " <td>0.08004</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>559 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " radius_mean texture_mean perimeter_mean area_mean smoothness_mean \\\n", "0 17.990 10.38 122.80 1001.0 0.11840 \n", "1 20.570 17.77 132.90 1326.0 0.08474 \n", "2 19.690 21.25 130.00 1203.0 0.10960 \n", "3 11.420 20.38 77.58 386.1 0.14250 \n", "4 20.290 14.34 135.10 1297.0 0.10030 \n", ".. ... ... ... ... ... \n", "554 12.880 28.92 82.50 514.3 0.08123 \n", "555 10.290 27.61 65.67 321.4 0.09030 \n", "556 10.160 19.59 64.73 311.7 0.10030 \n", "557 9.423 27.88 59.26 271.3 0.08123 \n", "558 14.590 22.68 96.39 657.1 0.08473 \n", "\n", " compactness_mean concavity_mean concave points_mean symmetry_mean \\\n", "0 0.27760 0.300100 0.14710 0.2419 \n", "1 0.07864 0.086900 0.07017 0.1812 \n", "2 0.15990 0.197400 0.12790 0.2069 \n", "3 0.28390 0.241400 0.10520 0.2597 \n", "4 0.13280 0.198000 0.10430 0.1809 \n", ".. ... ... ... ... \n", "554 0.05824 0.061950 0.02343 0.1566 \n", "555 0.07658 0.059990 0.02738 0.1593 \n", "556 0.07504 0.005025 0.01116 0.1791 \n", "557 0.04971 0.000000 0.00000 0.1742 \n", "558 0.13300 0.102900 0.03736 0.1454 \n", "\n", " fractal_dimension_mean ... radius_worst texture_worst \\\n", "0 0.07871 ... 25.38 17.33 \n", "1 0.05667 ... 24.99 23.41 \n", "2 0.05999 ... 23.57 25.53 \n", "3 0.09744 ... 14.91 26.50 \n", "4 0.05883 ... 22.54 16.67 \n", ".. ... ... ... ... \n", "554 0.05708 ... 13.89 35.74 \n", "555 0.06127 ... 10.84 34.91 \n", "556 0.06331 ... 10.65 22.88 \n", "557 0.06059 ... 10.49 34.24 \n", "558 0.06147 ... 15.48 27.27 \n", "\n", " perimeter_worst area_worst smoothness_worst compactness_worst \\\n", "0 184.60 2019.0 0.1622 0.66560 \n", "1 158.80 1956.0 0.1238 0.18660 \n", "2 152.50 1709.0 0.1444 0.42450 \n", "3 98.87 567.7 0.2098 0.86630 \n", "4 152.20 1575.0 0.1374 0.20500 \n", ".. ... ... ... ... \n", "554 88.84 595.7 0.1227 0.16200 \n", "555 69.57 357.6 0.1384 0.17100 \n", "556 67.88 347.3 0.1265 0.12000 \n", "557 66.50 330.6 0.1073 0.07158 \n", "558 105.90 733.5 0.1026 0.31710 \n", "\n", " concavity_worst concave points_worst symmetry_worst \\\n", "0 0.71190 0.26540 0.4601 \n", "1 0.24160 0.18600 0.2750 \n", "2 0.45040 0.24300 0.3613 \n", "3 0.68690 0.25750 0.6638 \n", "4 0.40000 0.16250 0.2364 \n", ".. ... ... ... \n", "554 0.24390 0.06493 0.2372 \n", "555 0.20000 0.09127 0.2226 \n", "556 0.01005 0.02232 0.2262 \n", "557 0.00000 0.00000 0.2475 \n", "558 0.36620 0.11050 0.2258 \n", "\n", " fractal_dimension_worst \n", "0 0.11890 \n", "1 0.08902 \n", "2 0.08758 \n", "3 0.17300 \n", "4 0.07678 \n", ".. ... \n", "554 0.07242 \n", "555 0.08283 \n", "556 0.06742 \n", "557 0.06969 \n", "558 0.08004 \n", "\n", "[559 rows x 30 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features_data = cancer_data_train.drop(['diagnosis'], axis=1)\n", "target_data = cancer_data_train['diagnosis']\n", "#print(target_data.unique())\n", "\n", "features_data\n", "#target_data.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "f5223a8d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:52.455114Z", "iopub.status.busy": "2022-01-28T14:02:52.454530Z", "iopub.status.idle": "2022-01-28T14:02:52.461488Z", "shell.execute_reply": "2022-01-28T14:02:52.461984Z", "shell.execute_reply.started": "2022-01-28T14:01:38.896655Z" }, "papermill": { "duration": 0.020742, "end_time": "2022-01-28T14:02:52.462159", "exception": false, "start_time": "2022-01-28T14:02:52.441417", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "((391, 30), (168, 30), (391,), (168,))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train, X_test, y_train, y_test = train_test_split(features_data, target_data, test_size = 0.3, random_state=123)\n", "\n", "X_train.shape, X_test.shape, y_train.shape, y_test.shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "f03bee52", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:52.491845Z", "iopub.status.busy": "2022-01-28T14:02:52.490918Z", "iopub.status.idle": "2022-01-28T14:02:52.502878Z", "shell.execute_reply": "2022-01-28T14:02:52.503330Z", "shell.execute_reply.started": "2022-01-28T14:01:38.907688Z" }, "papermill": { "duration": 0.030206, "end_time": "2022-01-28T14:02:52.503496", "exception": false, "start_time": "2022-01-28T14:02:52.473290", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(solver='liblinear')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_model = LogisticRegression(solver='liblinear')\n", "#knn_model = KneighborsClassifier()\n", "\n", "\n", "\n", "log_model.fit(X_train, y_train)\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "0b539eae", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:52.529589Z", "iopub.status.busy": "2022-01-28T14:02:52.528990Z", "iopub.status.idle": "2022-01-28T14:02:52.543306Z", "shell.execute_reply": "2022-01-28T14:02:52.542680Z", "shell.execute_reply.started": "2022-01-28T14:01:55.848174Z" }, "papermill": { "duration": 0.028826, "end_time": "2022-01-28T14:02:52.543463", "exception": false, "start_time": "2022-01-28T14:02:52.514637", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model_Score : 0.9821428571428571\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : B, | Predicted Result : B\n" ] } ], "source": [ "y_pred = log_model.predict(X_test)\n", "#print(y_test.values)\n", "#print(y_pred)\n", "\n", "accuracy = log_model.score(X_test, y_test)\n", "print('Model_Score : ', accuracy)\n", "for i in range(len(y_pred)):\n", " if i <20:\n", " print('Actual Result : {}, | Predicted Result : {}'.format(y_test.values[i], y_pred[i]))\n", " \n" ] }, { "cell_type": "code", "execution_count": 9, "id": "b829118c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:02:52.575651Z", "iopub.status.busy": "2022-01-28T14:02:52.575019Z", "iopub.status.idle": "2022-01-28T14:02:52.580453Z", "shell.execute_reply": "2022-01-28T14:02:52.579737Z", "shell.execute_reply.started": "2022-01-28T14:01:59.025445Z" }, "papermill": { "duration": 0.025275, "end_time": "2022-01-28T14:02:52.580643", "exception": false, "start_time": "2022-01-28T14:02:52.555368", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Actual Result : B, | Predicted Result : B\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : B, | Predicted Result : B\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : M, | Predicted Result : M\n", "Actual Result : B, | Predicted Result : B\n" ] } ], "source": [ "#Using the untouched test data set\n", "final_feature = final_test_cancer_data.drop(['diagnosis'], axis=1)\n", "actual_diagnosis = final_test_cancer_data['diagnosis']\n", "#print(actual_diagnosis)\n", "\n", "final_predictions = log_model.predict(final_feature)\n", "\n", "for i in range(len(final_predictions)):\n", " print('Actual Result : {}, | Predicted Result : {}'.format(actual_diagnosis.values[i], final_predictions[i]))" ] }, { "cell_type": "code", "execution_count": null, "id": "6472abe7", "metadata": { "papermill": { "duration": 0.011532, "end_time": "2022-01-28T14:02:52.605009", "exception": false, "start_time": "2022-01-28T14:02:52.593477", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 11.699162, "end_time": "2022-01-28T14:02:53.327392", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:02:41.628230", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/398/86398279.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d594ae22", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:04:36.266100Z", "iopub.status.busy": "2022-01-28T14:04:36.264254Z", "iopub.status.idle": "2022-01-28T14:04:36.287443Z", "shell.execute_reply": "2022-01-28T14:04:36.288295Z", "shell.execute_reply.started": "2022-01-27T05:20:26.110619Z" }, "papermill": { "duration": 0.06081, "end_time": "2022-01-28T14:04:36.288789", "exception": false, "start_time": "2022-01-28T14:04:36.227979", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/billboard-the-hot-100-songs/charts.csv\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" ] }, { "cell_type": "markdown", "id": "6e59e4b1", "metadata": { "papermill": { "duration": 0.030464, "end_time": "2022-01-28T14:04:36.351238", "exception": false, "start_time": "2022-01-28T14:04:36.320774", "status": "completed" }, "tags": [] }, "source": [ "**Importing useful libraries**" ] }, { "cell_type": "code", "execution_count": 2, "id": "5292ee8b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:36.414324Z", "iopub.status.busy": "2022-01-28T14:04:36.413555Z", "iopub.status.idle": "2022-01-28T14:04:37.594878Z", "shell.execute_reply": "2022-01-28T14:04:37.595474Z", "shell.execute_reply.started": "2022-01-27T05:20:26.14784Z" }, "papermill": { "duration": 1.214524, "end_time": "2022-01-28T14:04:37.595733", "exception": false, "start_time": "2022-01-28T14:04:36.381209", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "91bd7cc7", "metadata": { "papermill": { "duration": 0.02965, "end_time": "2022-01-28T14:04:37.655731", "exception": false, "start_time": "2022-01-28T14:04:37.626081", "status": "completed" }, "tags": [] }, "source": [ "**Readind The Dataset**" ] }, { "cell_type": "code", "execution_count": 3, "id": "b21352da", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:37.721708Z", "iopub.status.busy": "2022-01-28T14:04:37.720942Z", "iopub.status.idle": "2022-01-28T14:04:38.333246Z", "shell.execute_reply": "2022-01-28T14:04:38.332440Z", "shell.execute_reply.started": "2022-01-27T05:20:27.118777Z" }, "papermill": { "duration": 0.647328, "end_time": "2022-01-28T14:04:38.333441", "exception": false, "start_time": "2022-01-28T14:04:37.686113", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df=pd.read_csv(\"/kaggle/input/billboard-the-hot-100-songs/charts.csv\")\n", "df.shape\n", "g=df" ] }, { "cell_type": "markdown", "id": "b9604cbe", "metadata": { "papermill": { "duration": 0.029696, "end_time": "2022-01-28T14:04:38.396818", "exception": false, "start_time": "2022-01-28T14:04:38.367122", "status": "completed" }, "tags": [] }, "source": [ "This data set contains 330087 entries\n", "and 7 columns(attributes)(features)" ] }, { "cell_type": "code", "execution_count": 4, "id": "cc11ac47", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:38.462453Z", "iopub.status.busy": "2022-01-28T14:04:38.461334Z", "iopub.status.idle": "2022-01-28T14:04:38.493386Z", "shell.execute_reply": "2022-01-28T14:04:38.493891Z", "shell.execute_reply.started": "2022-01-27T05:20:27.781914Z" }, "papermill": { "duration": 0.067043, "end_time": "2022-01-28T14:04:38.494120", "exception": false, "start_time": "2022-01-28T14:04:38.427077", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>rank</th>\n", " <th>song</th>\n", " <th>artist</th>\n", " <th>last-week</th>\n", " <th>peak-rank</th>\n", " <th>weeks-on-board</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2021-11-06</td>\n", " <td>1</td>\n", " <td>Easy On Me</td>\n", " <td>Adele</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2021-11-06</td>\n", " <td>2</td>\n", " <td>Stay</td>\n", " <td>The Kid LAROI &amp; Justin Bieber</td>\n", " <td>2.0</td>\n", " <td>1</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2021-11-06</td>\n", " <td>3</td>\n", " <td>Industry Baby</td>\n", " <td>Lil Nas X &amp; Jack Harlow</td>\n", " <td>3.0</td>\n", " <td>1</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2021-11-06</td>\n", " <td>4</td>\n", " <td>Fancy Like</td>\n", " <td>Walker Hayes</td>\n", " <td>4.0</td>\n", " <td>3</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2021-11-06</td>\n", " <td>5</td>\n", " <td>Bad Habits</td>\n", " <td>Ed Sheeran</td>\n", " <td>5.0</td>\n", " <td>2</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>330082</th>\n", " <td>1958-08-04</td>\n", " <td>96</td>\n", " <td>Over And Over</td>\n", " <td>Thurston Harris</td>\n", " <td>NaN</td>\n", " <td>96</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330083</th>\n", " <td>1958-08-04</td>\n", " <td>97</td>\n", " <td>I Believe In You</td>\n", " <td>Robert &amp; Johnny</td>\n", " <td>NaN</td>\n", " <td>97</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330084</th>\n", " <td>1958-08-04</td>\n", " <td>98</td>\n", " <td>Little Serenade</td>\n", " <td>The Ames Brothers</td>\n", " <td>NaN</td>\n", " <td>98</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330085</th>\n", " <td>1958-08-04</td>\n", " <td>99</td>\n", " <td>I'll Get By (As Long As I Have You)</td>\n", " <td>Billy Williams</td>\n", " <td>NaN</td>\n", " <td>99</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330086</th>\n", " <td>1958-08-04</td>\n", " <td>100</td>\n", " <td>Judy</td>\n", " <td>Frankie Vaughan</td>\n", " <td>NaN</td>\n", " <td>100</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>330087 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " date rank song \\\n", "0 2021-11-06 1 Easy On Me \n", "1 2021-11-06 2 Stay \n", "2 2021-11-06 3 Industry Baby \n", "3 2021-11-06 4 Fancy Like \n", "4 2021-11-06 5 Bad Habits \n", "... ... ... ... \n", "330082 1958-08-04 96 Over And Over \n", "330083 1958-08-04 97 I Believe In You \n", "330084 1958-08-04 98 Little Serenade \n", "330085 1958-08-04 99 I'll Get By (As Long As I Have You) \n", "330086 1958-08-04 100 Judy \n", "\n", " artist last-week peak-rank weeks-on-board \n", "0 Adele 1.0 1 3 \n", "1 The Kid LAROI & Justin Bieber 2.0 1 16 \n", "2 Lil Nas X & Jack Harlow 3.0 1 14 \n", "3 Walker Hayes 4.0 3 19 \n", "4 Ed Sheeran 5.0 2 18 \n", "... ... ... ... ... \n", "330082 Thurston Harris NaN 96 1 \n", "330083 Robert & Johnny NaN 97 1 \n", "330084 The Ames Brothers NaN 98 1 \n", "330085 Billy Williams NaN 99 1 \n", "330086 Frankie Vaughan NaN 100 1 \n", "\n", "[330087 rows x 7 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 5, "id": "07267833", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:38.559495Z", "iopub.status.busy": "2022-01-28T14:04:38.558693Z", "iopub.status.idle": "2022-01-28T14:04:38.573430Z", "shell.execute_reply": "2022-01-28T14:04:38.572785Z", "shell.execute_reply.started": "2022-01-27T05:20:27.811233Z" }, "papermill": { "duration": 0.048606, "end_time": "2022-01-28T14:04:38.573596", "exception": false, "start_time": "2022-01-28T14:04:38.524990", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>rank</th>\n", " <th>song</th>\n", " <th>artist</th>\n", " <th>last-week</th>\n", " <th>peak-rank</th>\n", " <th>weeks-on-board</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2021-11-06</td>\n", " <td>1</td>\n", " <td>Easy On Me</td>\n", " <td>Adele</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2021-11-06</td>\n", " <td>2</td>\n", " <td>Stay</td>\n", " <td>The Kid LAROI &amp; Justin Bieber</td>\n", " <td>2.0</td>\n", " <td>1</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2021-11-06</td>\n", " <td>3</td>\n", " <td>Industry Baby</td>\n", " <td>Lil Nas X &amp; Jack Harlow</td>\n", " <td>3.0</td>\n", " <td>1</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2021-11-06</td>\n", " <td>4</td>\n", " <td>Fancy Like</td>\n", " <td>Walker Hayes</td>\n", " <td>4.0</td>\n", " <td>3</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2021-11-06</td>\n", " <td>5</td>\n", " <td>Bad Habits</td>\n", " <td>Ed Sheeran</td>\n", " <td>5.0</td>\n", " <td>2</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2021-11-06</td>\n", " <td>6</td>\n", " <td>Way 2 Sexy</td>\n", " <td>Drake Featuring Future &amp; Young Thug</td>\n", " <td>6.0</td>\n", " <td>1</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2021-11-06</td>\n", " <td>7</td>\n", " <td>Shivers</td>\n", " <td>Ed Sheeran</td>\n", " <td>9.0</td>\n", " <td>7</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2021-11-06</td>\n", " <td>8</td>\n", " <td>Good 4 U</td>\n", " <td>Olivia Rodrigo</td>\n", " <td>7.0</td>\n", " <td>1</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2021-11-06</td>\n", " <td>9</td>\n", " <td>Need To Know</td>\n", " <td>Doja Cat</td>\n", " <td>11.0</td>\n", " <td>9</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2021-11-06</td>\n", " <td>10</td>\n", " <td>Levitating</td>\n", " <td>Dua Lipa</td>\n", " <td>8.0</td>\n", " <td>2</td>\n", " <td>56</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date rank song artist \\\n", "0 2021-11-06 1 Easy On Me Adele \n", "1 2021-11-06 2 Stay The Kid LAROI & Justin Bieber \n", "2 2021-11-06 3 Industry Baby Lil Nas X & Jack Harlow \n", "3 2021-11-06 4 Fancy Like Walker Hayes \n", "4 2021-11-06 5 Bad Habits Ed Sheeran \n", "5 2021-11-06 6 Way 2 Sexy Drake Featuring Future & Young Thug \n", "6 2021-11-06 7 Shivers Ed Sheeran \n", "7 2021-11-06 8 Good 4 U Olivia Rodrigo \n", "8 2021-11-06 9 Need To Know Doja Cat \n", "9 2021-11-06 10 Levitating Dua Lipa \n", "\n", " last-week peak-rank weeks-on-board \n", "0 1.0 1 3 \n", "1 2.0 1 16 \n", "2 3.0 1 14 \n", "3 4.0 3 19 \n", "4 5.0 2 18 \n", "5 6.0 1 8 \n", "6 9.0 7 7 \n", "7 7.0 1 24 \n", "8 11.0 9 20 \n", "9 8.0 2 56 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 6, "id": "24d62ce6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:38.639668Z", "iopub.status.busy": "2022-01-28T14:04:38.638940Z", "iopub.status.idle": "2022-01-28T14:04:38.652559Z", "shell.execute_reply": "2022-01-28T14:04:38.651871Z", "shell.execute_reply.started": "2022-01-27T05:20:27.824581Z" }, "papermill": { "duration": 0.047861, "end_time": "2022-01-28T14:04:38.652743", "exception": false, "start_time": "2022-01-28T14:04:38.604882", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>rank</th>\n", " <th>song</th>\n", " <th>artist</th>\n", " <th>last-week</th>\n", " <th>peak-rank</th>\n", " <th>weeks-on-board</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>330077</th>\n", " <td>1958-08-04</td>\n", " <td>91</td>\n", " <td>The Purple People Eater Meets The Witch Doctor</td>\n", " <td>Joe South</td>\n", " <td>NaN</td>\n", " <td>91</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330078</th>\n", " <td>1958-08-04</td>\n", " <td>92</td>\n", " <td>Bird Dog</td>\n", " <td>The Everly Brothers</td>\n", " <td>NaN</td>\n", " <td>92</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330079</th>\n", " <td>1958-08-04</td>\n", " <td>93</td>\n", " <td>Are You Really Mine</td>\n", " <td>Jimmie Rodgers</td>\n", " <td>NaN</td>\n", " <td>93</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330080</th>\n", " <td>1958-08-04</td>\n", " <td>94</td>\n", " <td>She Was Only Seventeen (He Was One Year More)</td>\n", " <td>Marty Robbins</td>\n", " <td>NaN</td>\n", " <td>94</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330081</th>\n", " <td>1958-08-04</td>\n", " <td>95</td>\n", " <td>Little Mary</td>\n", " <td>Fats Domino</td>\n", " <td>NaN</td>\n", " <td>95</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330082</th>\n", " <td>1958-08-04</td>\n", " <td>96</td>\n", " 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], "text/plain": [ " date rank song \\\n", "330077 1958-08-04 91 The Purple People Eater Meets The Witch Doctor \n", "330078 1958-08-04 92 Bird Dog \n", "330079 1958-08-04 93 Are You Really Mine \n", "330080 1958-08-04 94 She Was Only Seventeen (He Was One Year More) \n", "330081 1958-08-04 95 Little Mary \n", "330082 1958-08-04 96 Over And Over \n", "330083 1958-08-04 97 I Believe In You \n", "330084 1958-08-04 98 Little Serenade \n", "330085 1958-08-04 99 I'll Get By (As Long As I Have You) \n", "330086 1958-08-04 100 Judy \n", "\n", " artist last-week peak-rank weeks-on-board \n", "330077 Joe South NaN 91 1 \n", "330078 The Everly Brothers NaN 92 1 \n", "330079 Jimmie Rodgers NaN 93 1 \n", "330080 Marty Robbins NaN 94 1 \n", "330081 Fats Domino NaN 95 1 \n", "330082 Thurston Harris NaN 96 1 \n", "330083 Robert & Johnny NaN 97 1 \n", "330084 The Ames Brothers NaN 98 1 \n", "330085 Billy Williams NaN 99 1 \n", "330086 Frankie Vaughan NaN 100 1 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail(10)" ] }, { "cell_type": "code", "execution_count": 7, "id": "857ff316", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:38.722297Z", "iopub.status.busy": "2022-01-28T14:04:38.721457Z", "iopub.status.idle": "2022-01-28T14:04:38.839047Z", "shell.execute_reply": "2022-01-28T14:04:38.839702Z", "shell.execute_reply.started": "2022-01-27T05:20:27.844719Z" }, "papermill": { "duration": 0.154668, "end_time": "2022-01-28T14:04:38.839920", "exception": false, "start_time": "2022-01-28T14:04:38.685252", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "date 0\n", "rank 0\n", "song 0\n", "artist 0\n", "last-week 32312\n", "peak-rank 0\n", "weeks-on-board 0\n", "dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 8, "id": "8f074267", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:38.916446Z", "iopub.status.busy": "2022-01-28T14:04:38.915293Z", "iopub.status.idle": "2022-01-28T14:04:38.919383Z", "shell.execute_reply": "2022-01-28T14:04:38.919941Z", "shell.execute_reply.started": "2022-01-27T05:20:27.90695Z" }, "papermill": { "duration": 0.047734, "end_time": "2022-01-28T14:04:38.920178", "exception": false, "start_time": "2022-01-28T14:04:38.872444", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df['last-week'].fillna(0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 9, "id": "e1e0d7f1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:38.990252Z", "iopub.status.busy": "2022-01-28T14:04:38.989532Z", "iopub.status.idle": "2022-01-28T14:04:39.109038Z", "shell.execute_reply": "2022-01-28T14:04:39.108375Z", "shell.execute_reply.started": "2022-01-27T05:20:27.916008Z" }, "papermill": { "duration": 0.155982, "end_time": "2022-01-28T14:04:39.109201", "exception": false, "start_time": "2022-01-28T14:04:38.953219", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "date 0\n", "rank 0\n", "song 0\n", "artist 0\n", "last-week 0\n", "peak-rank 0\n", "weeks-on-board 0\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 10, "id": "74fa6215", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:39.181158Z", "iopub.status.busy": "2022-01-28T14:04:39.180420Z", "iopub.status.idle": "2022-01-28T14:04:39.189706Z", "shell.execute_reply": "2022-01-28T14:04:39.190311Z", "shell.execute_reply.started": "2022-01-27T05:20:27.978896Z" }, "papermill": { "duration": 0.048806, "end_time": "2022-01-28T14:04:39.190524", "exception": false, "start_time": "2022-01-28T14:04:39.141718", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>rank</th>\n", " <th>song</th>\n", " <th>artist</th>\n", " <th>last-week</th>\n", " <th>peak-rank</th>\n", " <th>weeks-on-board</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>330082</th>\n", " <td>1958-08-04</td>\n", " <td>96</td>\n", " <td>Over And Over</td>\n", " <td>Thurston Harris</td>\n", " <td>0.0</td>\n", " <td>96</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330083</th>\n", " <td>1958-08-04</td>\n", " <td>97</td>\n", " <td>I Believe In You</td>\n", " <td>Robert &amp; Johnny</td>\n", " <td>0.0</td>\n", " <td>97</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330084</th>\n", " <td>1958-08-04</td>\n", " <td>98</td>\n", " <td>Little Serenade</td>\n", " <td>The Ames Brothers</td>\n", " <td>0.0</td>\n", " <td>98</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330085</th>\n", " <td>1958-08-04</td>\n", " <td>99</td>\n", " <td>I'll Get By (As Long As I Have You)</td>\n", " <td>Billy Williams</td>\n", " <td>0.0</td>\n", " <td>99</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>330086</th>\n", " <td>1958-08-04</td>\n", " <td>100</td>\n", " <td>Judy</td>\n", " <td>Frankie Vaughan</td>\n", " <td>0.0</td>\n", " <td>100</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date rank song \\\n", "330082 1958-08-04 96 Over And Over \n", "330083 1958-08-04 97 I Believe In You \n", "330084 1958-08-04 98 Little Serenade \n", "330085 1958-08-04 99 I'll Get By (As Long As I Have You) \n", "330086 1958-08-04 100 Judy \n", "\n", " artist last-week peak-rank weeks-on-board \n", "330082 Thurston Harris 0.0 96 1 \n", "330083 Robert & Johnny 0.0 97 1 \n", "330084 The Ames Brothers 0.0 98 1 \n", "330085 Billy Williams 0.0 99 1 \n", "330086 Frankie Vaughan 0.0 100 1 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "markdown", "id": "9bbfed59", "metadata": { "papermill": { "duration": 0.032665, "end_time": "2022-01-28T14:04:39.256369", "exception": false, "start_time": "2022-01-28T14:04:39.223704", "status": "completed" }, "tags": [] }, "source": [ "grouping the artisst and summing the number of weeks in top 100" ] }, { "cell_type": "code", "execution_count": 11, "id": "22c6ef92", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:39.327250Z", "iopub.status.busy": "2022-01-28T14:04:39.324905Z", "iopub.status.idle": "2022-01-28T14:04:39.330253Z", "shell.execute_reply": "2022-01-28T14:04:39.329607Z", "shell.execute_reply.started": "2022-01-27T05:20:27.991573Z" }, "papermill": { "duration": 0.041359, "end_time": "2022-01-28T14:04:39.330424", "exception": false, "start_time": "2022-01-28T14:04:39.289065", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "a=df.groupby(['artist','song'])" ] }, { "cell_type": "code", "execution_count": 12, "id": "3e344e43", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:39.412569Z", "iopub.status.busy": "2022-01-28T14:04:39.411826Z", "iopub.status.idle": "2022-01-28T14:04:39.918000Z", "shell.execute_reply": "2022-01-28T14:04:39.917426Z", "shell.execute_reply.started": "2022-01-27T05:20:28.002885Z" }, "papermill": { "duration": 0.553373, "end_time": "2022-01-28T14:04:39.918210", "exception": false, "start_time": "2022-01-28T14:04:39.364837", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='artist,song'>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a['weeks-on-board'].sum().sort_values(ascending=False)[:10].plot(kind='bar')" ] }, { "cell_type": "markdown", "id": "c456a33a", "metadata": { "papermill": { "duration": 0.034075, "end_time": "2022-01-28T14:04:39.986992", "exception": false, "start_time": "2022-01-28T14:04:39.952917", "status": "completed" }, "tags": [] }, "source": [ "Now we get the artist peak rank " ] }, { "cell_type": "code", "execution_count": 13, "id": "aaa33611", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:40.070957Z", "iopub.status.busy": "2022-01-28T14:04:40.062460Z", "iopub.status.idle": "2022-01-28T14:04:42.216568Z", "shell.execute_reply": "2022-01-28T14:04:42.217137Z", "shell.execute_reply.started": "2022-01-27T05:20:28.455454Z" }, "papermill": { "duration": 2.195756, "end_time": "2022-01-28T14:04:42.217357", "exception": false, "start_time": "2022-01-28T14:04:40.021601", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='artist'>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,10))\n", "df.groupby(['artist'])['peak-rank'].min().sort_values()[:100].plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 14, "id": "b3f9b2da", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:42.300357Z", "iopub.status.busy": "2022-01-28T14:04:42.299596Z", "iopub.status.idle": "2022-01-28T14:04:42.345448Z", "shell.execute_reply": "2022-01-28T14:04:42.344863Z", "shell.execute_reply.started": "2022-01-27T05:20:30.442525Z" }, "papermill": { "duration": 0.089444, "end_time": "2022-01-28T14:04:42.345618", "exception": false, "start_time": "2022-01-28T14:04:42.256174", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "10205" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df.artist.unique())" ] }, { "cell_type": "markdown", "id": "33a24947", "metadata": { "papermill": { "duration": 0.038354, "end_time": "2022-01-28T14:04:42.423459", "exception": false, "start_time": "2022-01-28T14:04:42.385105", "status": "completed" }, "tags": [] }, "source": [ "1. lets analyse the most apperad artist on the data set" ] }, { "cell_type": "code", "execution_count": 15, "id": "5e310a6b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:42.550807Z", "iopub.status.busy": "2022-01-28T14:04:42.550011Z", "iopub.status.idle": "2022-01-28T14:04:42.558504Z", "shell.execute_reply": "2022-01-28T14:04:42.558996Z", "shell.execute_reply.started": "2022-01-27T05:20:30.477702Z" }, "papermill": { "duration": 0.097232, "end_time": "2022-01-28T14:04:42.559216", "exception": false, "start_time": "2022-01-28T14:04:42.461984", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Taylor Swift 1023\n", "Elton John 889\n", "Madonna 857\n", "Drake 787\n", "Kenny Chesney 769\n", " ... \n", "RiceGum Featuring Alissa Violet 1\n", "XXXTENTACION Featuring PnB Rock & Trippie Redd 1\n", "Justin Bieber Featuring Burna Boy 1\n", "Justin Bieber Featuring BEAM 1\n", "Frankie Vaughan 1\n", "Name: artist, Length: 10205, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['artist'].value_counts()" ] }, { "cell_type": "markdown", "id": "6e3a21a4", "metadata": { "papermill": { "duration": 0.038556, "end_time": "2022-01-28T14:04:42.636521", "exception": false, "start_time": "2022-01-28T14:04:42.597965", "status": "completed" }, "tags": [] }, "source": [] }, { "cell_type": "code", "execution_count": 16, "id": "a15325eb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:42.717666Z", "iopub.status.busy": "2022-01-28T14:04:42.716650Z", "iopub.status.idle": "2022-01-28T14:04:42.774369Z", "shell.execute_reply": "2022-01-28T14:04:42.774849Z", "shell.execute_reply.started": "2022-01-27T05:20:30.51049Z" }, "papermill": { "duration": 0.100153, "end_time": "2022-01-28T14:04:42.775081", "exception": false, "start_time": "2022-01-28T14:04:42.674928", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(1023, 7)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=df[df['artist']=='Taylor Swift']\n", "gg=df\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 17, "id": "336bd3b7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:42.858361Z", "iopub.status.busy": "2022-01-28T14:04:42.857368Z", "iopub.status.idle": "2022-01-28T14:04:42.871139Z", "shell.execute_reply": "2022-01-28T14:04:42.871652Z", "shell.execute_reply.started": "2022-01-27T05:20:30.540683Z" }, "papermill": { "duration": 0.057588, "end_time": "2022-01-28T14:04:42.871858", "exception": false, "start_time": "2022-01-28T14:04:42.814270", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>rank</th>\n", " <th>song</th>\n", " <th>artist</th>\n", " <th>last-week</th>\n", " <th>peak-rank</th>\n", " <th>weeks-on-board</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>536</th>\n", " <td>2021-10-02</td>\n", " <td>37</td>\n", " <td>Wildest Dreams (Taylor's Version)</td>\n", " <td>Taylor Swift</td>\n", " <td>0.0</td>\n", " <td>37</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2660</th>\n", " <td>2021-05-08</td>\n", " <td>61</td>\n", " <td>Willow</td>\n", " <td>Taylor Swift</td>\n", " <td>60.0</td>\n", " <td>1</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>2759</th>\n", " <td>2021-05-01</td>\n", " <td>60</td>\n", " <td>Willow</td>\n", " <td>Taylor Swift</td>\n", " <td>62.0</td>\n", " <td>1</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>2789</th>\n", " <td>2021-05-01</td>\n", " <td>90</td>\n", " <td>Mr. Perfectly Fine (Taylor's Version) (From Th...</td>\n", " <td>Taylor Swift</td>\n", " <td>30.0</td>\n", " <td>30</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2829</th>\n", " <td>2021-04-24</td>\n", " <td>30</td>\n", " <td>Mr. Perfectly Fine (Taylor's Version) (From Th...</td>\n", " <td>Taylor Swift</td>\n", " <td>90.0</td>\n", " <td>30</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date rank song \\\n", "536 2021-10-02 37 Wildest Dreams (Taylor's Version) \n", "2660 2021-05-08 61 Willow \n", "2759 2021-05-01 60 Willow \n", "2789 2021-05-01 90 Mr. Perfectly Fine (Taylor's Version) (From Th... \n", "2829 2021-04-24 30 Mr. Perfectly Fine (Taylor's Version) (From Th... \n", "\n", " artist last-week peak-rank weeks-on-board \n", "536 Taylor Swift 0.0 37 1 \n", "2660 Taylor Swift 60.0 1 20 \n", "2759 Taylor Swift 62.0 1 19 \n", "2789 Taylor Swift 30.0 30 3 \n", "2829 Taylor Swift 90.0 30 2 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "1e2d000d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:42.955470Z", "iopub.status.busy": "2022-01-28T14:04:42.954724Z", "iopub.status.idle": "2022-01-28T14:04:42.971530Z", "shell.execute_reply": "2022-01-28T14:04:42.972044Z", "shell.execute_reply.started": "2022-01-27T05:20:30.55746Z" }, "papermill": { "duration": 0.060993, "end_time": "2022-01-28T14:04:42.972288", "exception": false, "start_time": "2022-01-28T14:04:42.911295", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>rank</th>\n", " <th>song</th>\n", " <th>last-week</th>\n", " <th>peak-rank</th>\n", " <th>weeks-on-board</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>536</th>\n", " <td>2021-10-02</td>\n", " <td>37</td>\n", " <td>Wildest Dreams (Taylor's Version)</td>\n", " <td>0.0</td>\n", " <td>37</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2660</th>\n", " <td>2021-05-08</td>\n", " <td>61</td>\n", " <td>Willow</td>\n", " <td>60.0</td>\n", " <td>1</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>2759</th>\n", " <td>2021-05-01</td>\n", " <td>60</td>\n", " <td>Willow</td>\n", " <td>62.0</td>\n", " <td>1</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>2789</th>\n", " <td>2021-05-01</td>\n", " <td>90</td>\n", " <td>Mr. Perfectly Fine (Taylor's Version) (From Th...</td>\n", " <td>30.0</td>\n", " <td>30</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2829</th>\n", " <td>2021-04-24</td>\n", " <td>30</td>\n", " <td>Mr. Perfectly Fine (Taylor's Version) (From Th...</td>\n", " <td>90.0</td>\n", " <td>30</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>78567</th>\n", " <td>2006-10-21</td>\n", " <td>68</td>\n", " <td>Tim McGraw</td>\n", " <td>72.0</td>\n", " <td>68</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>78671</th>\n", " <td>2006-10-14</td>\n", " <td>72</td>\n", " <td>Tim McGraw</td>\n", " <td>77.0</td>\n", " <td>72</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>78776</th>\n", " <td>2006-10-07</td>\n", " <td>77</td>\n", " <td>Tim McGraw</td>\n", " <td>87.0</td>\n", " <td>77</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>78886</th>\n", " <td>2006-09-30</td>\n", " <td>87</td>\n", " <td>Tim McGraw</td>\n", " <td>86.0</td>\n", " <td>86</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>78985</th>\n", " <td>2006-09-23</td>\n", " <td>86</td>\n", " <td>Tim McGraw</td>\n", " <td>0.0</td>\n", " <td>86</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1023 rows × 6 columns</p>\n", "</div>" ], "text/plain": [ " date rank song \\\n", "536 2021-10-02 37 Wildest Dreams (Taylor's Version) \n", "2660 2021-05-08 61 Willow \n", "2759 2021-05-01 60 Willow \n", "2789 2021-05-01 90 Mr. Perfectly Fine (Taylor's Version) (From Th... \n", "2829 2021-04-24 30 Mr. Perfectly Fine (Taylor's Version) (From Th... \n", "... ... ... ... \n", "78567 2006-10-21 68 Tim McGraw \n", "78671 2006-10-14 72 Tim McGraw \n", "78776 2006-10-07 77 Tim McGraw \n", "78886 2006-09-30 87 Tim McGraw \n", "78985 2006-09-23 86 Tim McGraw \n", "\n", " last-week peak-rank weeks-on-board \n", "536 0.0 37 1 \n", "2660 60.0 1 20 \n", "2759 62.0 1 19 \n", "2789 30.0 30 3 \n", "2829 90.0 30 2 \n", "... ... ... ... \n", "78567 72.0 68 5 \n", "78671 77.0 72 4 \n", "78776 87.0 77 3 \n", "78886 86.0 86 2 \n", "78985 0.0 86 1 \n", "\n", "[1023 rows x 6 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop(['artist'],axis=1)" ] }, { "cell_type": "markdown", "id": "fe7652d1", "metadata": { "papermill": { "duration": 0.040783, "end_time": "2022-01-28T14:04:43.054601", "exception": false, "start_time": "2022-01-28T14:04:43.013818", "status": "completed" }, "tags": [] }, "source": [ "lets find out when and what rank she got peak" ] }, { "cell_type": "code", "execution_count": 19, "id": "a0d0cb68", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:43.162111Z", "iopub.status.busy": "2022-01-28T14:04:43.141955Z", "iopub.status.idle": "2022-01-28T14:04:44.832746Z", "shell.execute_reply": "2022-01-28T14:04:44.832065Z", "shell.execute_reply.started": "2022-01-27T05:20:30.575515Z" }, "papermill": { "duration": 1.73659, "end_time": "2022-01-28T14:04:44.832909", "exception": false, "start_time": "2022-01-28T14:04:43.096319", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='date', ylabel='peak-rank'>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "sns.lineplot(x='date',y='peak-rank',data=df[-100:])\n" ] }, { "cell_type": "markdown", "id": "9acd1728", "metadata": { "papermill": { "duration": 0.040826, "end_time": "2022-01-28T14:04:44.915837", "exception": false, "start_time": "2022-01-28T14:04:44.875011", "status": "completed" }, "tags": [] }, "source": [ "lets put thedata in the rankwise\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "982ab683", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:45.004912Z", "iopub.status.busy": "2022-01-28T14:04:45.003900Z", "iopub.status.idle": "2022-01-28T14:04:45.020781Z", "shell.execute_reply": "2022-01-28T14:04:45.021299Z", "shell.execute_reply.started": "2022-01-27T05:20:31.986284Z" }, "papermill": { "duration": 0.064275, "end_time": "2022-01-28T14:04:45.021472", "exception": false, "start_time": "2022-01-28T14:04:44.957197", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>rank</th>\n", " <th>song</th>\n", " <th>artist</th>\n", " <th>last-week</th>\n", " <th>peak-rank</th>\n", " <th>weeks-on-board</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>37400</th>\n", " <td>2014-09-06</td>\n", " <td>1</td>\n", " <td>Shake It Off</td>\n", " <td>Taylor Swift</td>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>21600</th>\n", " <td>2017-09-16</td>\n", " <td>1</td>\n", " <td>Look What You Made Me Do</td>\n", " <td>Taylor Swift</td>\n", " <td>77.0</td>\n", " <td>1</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>21500</th>\n", " <td>2017-09-23</td>\n", " <td>1</td>\n", " <td>Look What You Made Me Do</td>\n", " <td>Taylor Swift</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>21400</th>\n", " <td>2017-09-30</td>\n", " <td>1</td>\n", " <td>Look What You Made Me Do</td>\n", " <td>Taylor Swift</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>6500</th>\n", " <td>2020-08-08</td>\n", " <td>1</td>\n", " <td>Cardigan</td>\n", " <td>Taylor Swift</td>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>6497</th>\n", " <td>2020-08-15</td>\n", " <td>98</td>\n", " <td>Seven</td>\n", " <td>Taylor Swift</td>\n", " <td>35.0</td>\n", " <td>35</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>61198</th>\n", " <td>2010-02-20</td>\n", " <td>99</td>\n", " <td>Fearless</td>\n", " <td>Taylor Swift</td>\n", " <td>0.0</td>\n", " <td>9</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>68699</th>\n", " <td>2008-09-13</td>\n", " <td>100</td>\n", " <td>Change</td>\n", " <td>Taylor Swift</td>\n", " <td>39.0</td>\n", " <td>10</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>62499</th>\n", " <td>2009-11-21</td>\n", " <td>100</td>\n", " <td>Forever &amp; Always</td>\n", " <td>Taylor Swift</td>\n", " <td>34.0</td>\n", " <td>34</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>42899</th>\n", " <td>2013-08-24</td>\n", " <td>100</td>\n", " <td>Red</td>\n", " <td>Taylor Swift</td>\n", " <td>0.0</td>\n", " <td>6</td>\n", " <td>6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1023 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " date rank song artist last-week \\\n", "37400 2014-09-06 1 Shake It Off Taylor Swift 0.0 \n", "21600 2017-09-16 1 Look What You Made Me Do Taylor Swift 77.0 \n", "21500 2017-09-23 1 Look What You Made Me Do Taylor Swift 1.0 \n", "21400 2017-09-30 1 Look What You Made Me Do Taylor Swift 1.0 \n", "6500 2020-08-08 1 Cardigan Taylor Swift 0.0 \n", "... ... ... ... ... ... \n", "6497 2020-08-15 98 Seven Taylor Swift 35.0 \n", "61198 2010-02-20 99 Fearless Taylor Swift 0.0 \n", "68699 2008-09-13 100 Change Taylor Swift 39.0 \n", "62499 2009-11-21 100 Forever & Always Taylor Swift 34.0 \n", "42899 2013-08-24 100 Red Taylor Swift 0.0 \n", "\n", " peak-rank weeks-on-board \n", "37400 1 1 \n", "21600 1 2 \n", "21500 1 3 \n", "21400 1 4 \n", "6500 1 1 \n", "... ... ... \n", "6497 35 2 \n", "61198 9 6 \n", "68699 10 3 \n", "62499 34 3 \n", "42899 6 6 \n", "\n", "[1023 rows x 7 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values('rank')" ] }, { "cell_type": "markdown", "id": "5e181e5e", "metadata": { "papermill": { "duration": 0.041375, "end_time": "2022-01-28T14:04:45.105529", "exception": false, "start_time": "2022-01-28T14:04:45.064154", "status": "completed" }, "tags": [] }, "source": [ "the top 10 auired ranks pie chart of the taylor swift" ] }, { "cell_type": "code", "execution_count": 21, "id": "049572ad", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:45.195726Z", "iopub.status.busy": "2022-01-28T14:04:45.194824Z", "iopub.status.idle": "2022-01-28T14:04:45.199637Z", "shell.execute_reply": "2022-01-28T14:04:45.199121Z", "shell.execute_reply.started": "2022-01-27T05:20:32.006971Z" }, "papermill": { "duration": 0.051929, "end_time": "2022-01-28T14:04:45.199786", "exception": false, "start_time": "2022-01-28T14:04:45.147857", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "list=df['rank'].value_counts().sort_values(ascending=False)[:10]\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "9103ebe2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:45.304923Z", "iopub.status.busy": "2022-01-28T14:04:45.300768Z", "iopub.status.idle": "2022-01-28T14:04:45.482919Z", "shell.execute_reply": "2022-01-28T14:04:45.483577Z", "shell.execute_reply.started": "2022-01-27T05:20:32.014185Z" }, "papermill": { "duration": 0.241978, "end_time": "2022-01-28T14:04:45.483765", "exception": false, "start_time": "2022-01-28T14:04:45.241787", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pie(list,labels=list.index,autopct='%1.1f%%')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e74e9367", "metadata": { "papermill": { "duration": 0.042841, "end_time": "2022-01-28T14:04:45.570268", "exception": false, "start_time": "2022-01-28T14:04:45.527427", "status": "completed" }, "tags": [] }, "source": [ "lets analyse the songs of taylor swifts on the " ] }, { "cell_type": "code", "execution_count": 23, "id": "baeeb8f7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:45.681265Z", "iopub.status.busy": "2022-01-28T14:04:45.658818Z", "iopub.status.idle": "2022-01-28T14:04:45.883131Z", "shell.execute_reply": "2022-01-28T14:04:45.882455Z", "shell.execute_reply.started": "2022-01-27T05:20:32.156938Z" }, "papermill": { "duration": 0.270151, "end_time": "2022-01-28T14:04:45.883300", "exception": false, "start_time": "2022-01-28T14:04:45.613149", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='song'>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df=df.groupby('song')\n", "df['weeks-on-board'].sum()[:10].plot(kind='bar')\n", "\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "00acb85f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:46.179356Z", "iopub.status.busy": "2022-01-28T14:04:45.980807Z", "iopub.status.idle": "2022-01-28T14:04:46.353201Z", "shell.execute_reply": "2022-01-28T14:04:46.352567Z", "shell.execute_reply.started": "2022-01-27T05:20:32.443024Z" }, "papermill": { "duration": 0.42554, "end_time": "2022-01-28T14:04:46.353371", "exception": false, "start_time": "2022-01-28T14:04:45.927831", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='rank', ylabel='last-week'>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(x='rank',y='last-week',data=gg)" ] }, { "cell_type": "code", "execution_count": 25, "id": "4525e301", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:46.458756Z", "iopub.status.busy": "2022-01-28T14:04:46.454956Z", "iopub.status.idle": "2022-01-28T14:04:46.632789Z", "shell.execute_reply": "2022-01-28T14:04:46.633341Z", "shell.execute_reply.started": "2022-01-27T05:20:32.750981Z" }, "papermill": { "duration": 0.233253, "end_time": "2022-01-28T14:04:46.633555", "exception": false, "start_time": "2022-01-28T14:04:46.400302", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='rank', ylabel='peak-rank'>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(x='rank',y='peak-rank',data=gg)" ] }, { "cell_type": "code", "execution_count": 26, "id": "8faf34f4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:46.733443Z", "iopub.status.busy": "2022-01-28T14:04:46.732374Z", "iopub.status.idle": "2022-01-28T14:04:46.961417Z", "shell.execute_reply": "2022-01-28T14:04:46.961979Z", "shell.execute_reply.started": "2022-01-27T05:20:32.922613Z" }, "papermill": { "duration": 0.281097, "end_time": "2022-01-28T14:04:46.962208", "exception": false, "start_time": "2022-01-28T14:04:46.681111", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='last-week', ylabel='peak-rank'>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(x='last-week',y='peak-rank',data=gg)" ] }, { "cell_type": "markdown", "id": "7e628832", "metadata": { "papermill": { "duration": 0.048854, "end_time": "2022-01-28T14:04:47.060095", "exception": false, "start_time": "2022-01-28T14:04:47.011241", "status": "completed" }, "tags": [] }, "source": [ "corelation matrix will help to know abiut the relations of the columns" ] }, { "cell_type": "markdown", "id": "de64b14c", "metadata": { "papermill": { "duration": 0.048922, "end_time": "2022-01-28T14:04:47.158173", "exception": false, "start_time": "2022-01-28T14:04:47.109251", "status": "completed" }, "tags": [] }, "source": [ "lets make a corelation grid among attributes" ] }, { "cell_type": "code", "execution_count": 27, "id": "a1aa5efd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:47.261141Z", "iopub.status.busy": "2022-01-28T14:04:47.260122Z", "iopub.status.idle": "2022-01-28T14:04:47.546115Z", "shell.execute_reply": "2022-01-28T14:04:47.546653Z", "shell.execute_reply.started": "2022-01-27T05:20:33.092163Z" }, "papermill": { "duration": 0.339404, "end_time": "2022-01-28T14:04:47.546858", "exception": false, "start_time": "2022-01-28T14:04:47.207454", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:title={'center':'Correlation Matrix'}>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.title(\"Correlation Matrix\")\n", "sns.heatmap(gg.corr())" ] }, { "cell_type": "code", "execution_count": 28, "id": "1af8ec24", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:04:47.651746Z", "iopub.status.busy": "2022-01-28T14:04:47.650729Z", "iopub.status.idle": "2022-01-28T14:04:50.015749Z", "shell.execute_reply": "2022-01-28T14:04:50.016507Z", "shell.execute_reply.started": "2022-01-27T05:20:33.313659Z" }, "papermill": { "duration": 2.41953, "end_time": "2022-01-28T14:04:50.016720", "exception": false, "start_time": "2022-01-28T14:04:47.597190", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x7f8693588f10>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 16 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "b=sns.PairGrid(gg)\n", "b.map(plt.scatter)" ] }, { "cell_type": "code", "execution_count": null, "id": "a472fa63", "metadata": { "papermill": { "duration": 0.054386, "end_time": "2022-01-28T14:04:50.127170", "exception": false, "start_time": "2022-01-28T14:04:50.072784", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 25.698296, "end_time": "2022-01-28T14:04:51.194627", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:04:25.496331", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/398/86398391.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "id": "16d9517f", "metadata": { "papermill": { "duration": 0.056134, "end_time": "2022-01-28T14:07:40.545909", "exception": false, "start_time": "2022-01-28T14:07:40.489775", "status": "completed" }, "tags": [] }, "source": [ "> # GENERATION DE DECISION DE JUSTICE\n", "## I.Extraction des données" ] }, { "cell_type": "markdown", "id": "a37e0284", "metadata": { "papermill": { "duration": 0.031131, "end_time": "2022-01-28T14:07:40.627261", "exception": false, "start_time": "2022-01-28T14:07:40.596130", "status": "completed" }, "tags": [] }, "source": [ "> ### /!\\ Ne pas exécuter cette partie si les données sont déjà générées" ] }, { "cell_type": "code", "execution_count": 1, "id": "5146ea76", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:07:40.697110Z", "iopub.status.busy": "2022-01-28T14:07:40.696028Z", "iopub.status.idle": "2022-01-28T14:07:40.704790Z", "shell.execute_reply": "2022-01-28T14:07:40.704086Z", "shell.execute_reply.started": "2022-01-27T18:44:53.276483Z" }, "papermill": { "duration": 0.04814, "end_time": "2022-01-28T14:07:40.704977", "exception": false, "start_time": "2022-01-28T14:07:40.656837", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from os import walk\n", "import re\n", "import pandas as pd\n", "import os\n", "import tqdm" ] }, { "cell_type": "code", "execution_count": 2, "id": "aaf5b18c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:07:40.770861Z", "iopub.status.busy": "2022-01-28T14:07:40.769973Z", "iopub.status.idle": "2022-01-28T14:07:40.774383Z", "shell.execute_reply": "2022-01-28T14:07:40.773716Z", "shell.execute_reply.started": "2022-01-27T18:44:53.302928Z" }, "papermill": { "duration": 0.040375, "end_time": "2022-01-28T14:07:40.774551", "exception": false, "start_time": "2022-01-28T14:07:40.734176", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import xml.etree.ElementTree as ET\n", "#tree = ET.parse('data/xml/JURITEXT000006952571.xml')\n", "def read_xml(file) :\n", " tree = ET.parse(\"data/xml/\"+file)\n", " root = tree.getroot()\n", " contenu = ET.tostring(root.find('./TEXTE/BLOC_TEXTUEL/CONTENU'), encoding='utf8').decode('utf8')\n", " contenu = re.sub(r\"( *<br /> *)|(</*[A-Z]*>)|(<\\?.*\\?>)\",\"\", contenu)\n", " contenu = re.sub(r\"\\n\",\"\",contenu)\n", " if root.find('./TEXTE/SOMMAIRE/SCT') != None:\n", " sommaire = root.find('./TEXTE/SOMMAIRE/SCT').text\n", " elif root.find('./TEXTE/SOMMAIRE/ANA') != None:\n", " sommaire = root.find('./TEXTE/SOMMAIRE/ANA').text\n", " else :\n", " sommaire = \"None\"\n", " return contenu,sommaire" ] }, { "cell_type": "code", "execution_count": 3, "id": "1bec14ba", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:07:40.837388Z", "iopub.status.busy": "2022-01-28T14:07:40.836298Z", "iopub.status.idle": "2022-01-28T14:07:40.839887Z", "shell.execute_reply": "2022-01-28T14:07:40.839333Z", "shell.execute_reply.started": "2022-01-27T18:44:53.311726Z" }, "papermill": { "duration": 0.036501, "end_time": "2022-01-28T14:07:40.840014", "exception": false, "start_time": "2022-01-28T14:07:40.803513", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [], "source": [ "#arr = os.listdir(\"data/xml/\")\n", "# file_list = []\n", "# list_article = []\n", "# for (dirpath, dirnames, filenames) in walk(\"data/xml/\"):\n", "# file_list.extend(filenames)\n", "# break\n", "# f = open(\"JURISTEXT.csv\",\"w\")\n", "# for file in file_list:\n", "# #print(file)\n", "# if(file != None):\n", "# contenu,sommaire = read_xml(file)\n", "# f.write(contenu + \"#separator#\" + sommaire)\n", "# f.close()" ] }, { "cell_type": "markdown", "id": "fefa9897", "metadata": { "papermill": { "duration": 0.028958, "end_time": "2022-01-28T14:07:40.897964", "exception": false, "start_time": "2022-01-28T14:07:40.869006", "status": "completed" }, "tags": [] }, "source": [ "## II. Exploitation des données" ] }, { "cell_type": "code", "execution_count": 4, "id": "99c16272", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:07:40.960214Z", "iopub.status.busy": "2022-01-28T14:07:40.959312Z", "iopub.status.idle": "2022-01-28T14:08:12.695387Z", "shell.execute_reply": "2022-01-28T14:08:12.696049Z", "shell.execute_reply.started": "2022-01-28T13:53:58.907844Z" }, "papermill": { "duration": 31.769939, "end_time": "2022-01-28T14:08:12.696349", "exception": false, "start_time": "2022-01-28T14:07:40.926410", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting gpt_2_simple\r\n", " Downloading gpt_2_simple-0.8.1.tar.gz (26 kB)\r\n", " Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n", "\u001b[?25hRequirement already satisfied: tensorflow>=2.5.1 in /opt/conda/lib/python3.7/site-packages (from gpt_2_simple) (2.6.2)\r\n", "Requirement already satisfied: regex in 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gpt-2-simple, wrapt\r\n", " Building wheel for gpt-2-simple (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n", "\u001b[?25h Created wheel for gpt-2-simple: filename=gpt_2_simple-0.8.1-py3-none-any.whl size=24576 sha256=f89a7bdc729f2925a007ce99fc867212f8fe0f9e50d6a7252607c292cad8a725\r\n", " Stored in directory: /root/.cache/pip/wheels/d6/89/8a/f5de6944286d1ac2658b0caa7eae3c8cda50f770cdc957217f\r\n", " Building wheel for wrapt (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n", "\u001b[?25h Created wheel for wrapt: filename=wrapt-1.12.1-cp37-cp37m-linux_x86_64.whl size=77049 sha256=02a6e2211c7e361cea321051c01cf68289df3b7a005c4aecd6c5a9262ffe8abb\r\n", " Stored in directory: /root/.cache/pip/wheels/62/76/4c/aa25851149f3f6d9785f6c869387ad82b3fd37582fa8147ac6\r\n", "Successfully built gpt-2-simple wrapt\r\n", "Installing collected packages: typing-extensions, six, wrapt, toposort, gpt-2-simple\r\n", " Attempting uninstall: typing-extensions\r\n", " Found existing installation: typing-extensions 3.10.0.2\r\n", " Uninstalling typing-extensions-3.10.0.2:\r\n", " Successfully uninstalled typing-extensions-3.10.0.2\r\n", " Attempting uninstall: six\r\n", " Found existing installation: six 1.16.0\r\n", " Uninstalling six-1.16.0:\r\n", " Successfully uninstalled six-1.16.0\r\n", " Attempting uninstall: wrapt\r\n", " Found existing installation: wrapt 1.13.3\r\n", " Uninstalling wrapt-1.13.3:\r\n", " Successfully uninstalled wrapt-1.13.3\r\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n", "tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, which is not installed.\r\n", "explainable-ai-sdk 1.3.2 requires xai-image-widget, which is not installed.\r\n", "dask-cudf 21.10.1 requires cupy-cuda114, which is not installed.\r\n", "cudf 21.10.1 requires cupy-cuda110, which is not installed.\r\n", "beatrix-jupyterlab 3.1.4 requires google-cloud-bigquery-storage, which is not installed.\r\n", "tensorflow-transform 1.4.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.15.0 which is incompatible.\r\n", "tensorflow-transform 1.4.0 requires pyarrow<6,>=1, but you have pyarrow 6.0.0 which is incompatible.\r\n", "optax 0.1.0 requires typing-extensions~=3.10.0, but you have typing-extensions 3.7.4.3 which is incompatible.\r\n", "gcsfs 2021.11.0 requires fsspec==2021.11.0, but you have fsspec 2021.11.1 which is incompatible.\r\n", "flake8 4.0.1 requires importlib-metadata<4.3; python_version < \"3.8\", but you have importlib-metadata 4.8.2 which is incompatible.\r\n", "dask-cudf 21.10.1 requires dask==2021.09.1, but you have dask 2021.11.2 which is incompatible.\r\n", "dask-cudf 21.10.1 requires distributed==2021.09.1, but you have distributed 2021.11.2 which is incompatible.\r\n", "bokeh 2.4.2 requires typing-extensions>=3.10.0, but you have typing-extensions 3.7.4.3 which is incompatible.\r\n", "black 21.10b0 requires typing-extensions>=3.10.0.0, but you have typing-extensions 3.7.4.3 which is incompatible.\r\n", "apache-beam 2.34.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.4 which is incompatible.\r\n", "apache-beam 2.34.0 requires httplib2<0.20.0,>=0.8, but you have httplib2 0.20.2 which is incompatible.\r\n", "apache-beam 2.34.0 requires pyarrow<6.0.0,>=0.15.1, but you have pyarrow 6.0.0 which is incompatible.\r\n", "aiobotocore 2.0.1 requires botocore<1.22.9,>=1.22.8, but you have botocore 1.23.15 which is incompatible.\u001b[0m\r\n", "Successfully installed gpt-2-simple-0.8.1 six-1.15.0 toposort-1.7 typing-extensions-3.7.4.3 wrapt-1.12.1\r\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n" ] } ], "source": [ "!pip install gpt_2_simple" ] }, { "cell_type": "code", "execution_count": 5, "id": "bc29ee6e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:08:12.784555Z", "iopub.status.busy": "2022-01-28T14:08:12.783536Z", "iopub.status.idle": "2022-01-28T14:08:12.786253Z", "shell.execute_reply": "2022-01-28T14:08:12.786811Z", "shell.execute_reply.started": "2022-01-28T13:54:23.684440Z" }, "papermill": { "duration": 0.049124, "end_time": "2022-01-28T14:08:12.786962", "exception": false, "start_time": "2022-01-28T14:08:12.737838", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import os" ] }, { "cell_type": "code", "execution_count": 6, "id": "e7acfffa", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:08:12.873828Z", "iopub.status.busy": "2022-01-28T14:08:12.872864Z", "iopub.status.idle": "2022-01-28T14:09:06.862568Z", "shell.execute_reply": "2022-01-28T14:09:06.863417Z", "shell.execute_reply.started": "2022-01-28T13:54:23.690635Z" }, "papermill": { "duration": 54.036365, "end_time": "2022-01-28T14:09:06.863709", "exception": false, "start_time": "2022-01-28T14:08:12.827344", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fetching checkpoint: 1.05Mit [00:00, 534Mit/s] \n", "Fetching encoder.json: 1.05Mit [00:00, 1.20Mit/s]\n", "Fetching hparams.json: 1.05Mit [00:00, 581Mit/s] \n", "Fetching model.ckpt.data-00000-of-00001: 498Mit [00:43, 11.5Mit/s]\n", "Fetching model.ckpt.index: 1.05Mit [00:00, 466Mit/s] \n", "Fetching model.ckpt.meta: 1.05Mit [00:00, 1.42Mit/s]\n", "Fetching vocab.bpe: 1.05Mit [00:00, 1.38Mit/s]\n" ] } ], "source": [ "import gpt_2_simple as gpt2\n", "from datetime import datetime\n", "import time\n", "import tensorflow as tf\n", "\n", "\n", "gpt2.download_gpt2(model_name=\"124M\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "58c5413b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:09:07.181727Z", "iopub.status.busy": "2022-01-28T14:09:07.180840Z", "iopub.status.idle": "2022-01-28T14:09:21.715686Z", "shell.execute_reply": "2022-01-28T14:09:21.715140Z", "shell.execute_reply.started": "2022-01-28T13:54:50.919150Z" }, "papermill": { "duration": 14.695792, "end_time": "2022-01-28T14:09:21.715829", "exception": false, "start_time": "2022-01-28T14:09:07.020037", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py:311: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n", " return func(*args, **kwargs)\n", "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n", "\n", "\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3444: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version.\n", "\n", "\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ANNULATION, sur la demande du sieur Prosper X....</td>\n", " <td>Les traités secrets ou contre-lettres...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>REJET du pourvoi formé par le sieur Y... et co...</td>\n", " <td>On peut adopter son enfant naturel re...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ANNULATION, sur la demande des sieurs G... de ...</td>\n", " <td>Le légataire universel, et même le lé...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>On ne saurait, à cet égard, admettre aucune ex...</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>le cas où ces légataires concourent avec des h...</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 \\\n", "0 ANNULATION, sur la demande du sieur Prosper X.... \n", "1 REJET du pourvoi formé par le sieur Y... et co... \n", "2 ANNULATION, sur la demande des sieurs G... de ... \n", "3 On ne saurait, à cet égard, admettre aucune ex... \n", "4 le cas où ces légataires concourent avec des h... \n", "\n", " 1 \n", "0 Les traités secrets ou contre-lettres... \n", "1 On peut adopter son enfant naturel re... \n", "2 Le légataire universel, et même le lé... \n", "3 None \n", "4 None " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"../input/juristext/JURISTEXT.csv\",sep=\"#separator#\", warn_bad_lines=False, header=None, error_bad_lines=False)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "00c2fa4b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:09:22.075912Z", "iopub.status.busy": "2022-01-28T14:09:22.075022Z", "iopub.status.idle": "2022-01-28T14:09:22.079446Z", "shell.execute_reply": "2022-01-28T14:09:22.080221Z", "shell.execute_reply.started": "2022-01-28T12:06:58.767205Z" }, "papermill": { "duration": 0.191099, "end_time": "2022-01-28T14:09:22.080527", "exception": false, "start_time": "2022-01-28T14:09:21.889428", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ANNULATION, sur la demande du sieur Prosper X..., avoué, d'un arrêt rendu, par la Cour royale de Rouen, le 18 février 1842, au profit du sieur Y... et autres. Du 30 juillet 1844. NOTICE ET MOTIFS. Le 13 avril 1838, le sieur Y..., avoué près la cour royale de Rouen, céda son office au sieur X..., avec ses recouvrements et quelques objets mobiliers. Mais deux traités séparés furent souscrits. Par l'un, destiné à être soumis à l'autorité, le prix de l'office était porté à 85000 francs seulement, payables à diverses époques. Le second traité, qui devait rester secret entre les parties, stipulait un excédent de prix d'une somme de 31500 francs, payable la veille de la prestation de serment du nouveau titulaire. Le sieur X... fut en effet nommé avoué en remplacement du sieur Y.... Le 6 août 1838, il paya la somme de 31500 francs (c'était celle énoncée au traité secret) au sieur Z..., prédécesseur et ayant droit du sieur Y.... Ce payement semblait être l'exécution pleine et volontaire du traité secret : cependant X... a prétendu qu'il était imputable sur le prix du traité ostensible. Le 23 janvier 1839, autre acte par lequel Chédeville se reconnaît débiteur, envers les sieurs A... et B..., d'une somme de 79000 francs, qu'il paraît compter à l'instant au sieur Y..., lequel, à son tour, passa quittance définitive du prix de l'office à X.... Les qualités de l'arrêt attaqué constatent que X... paya, pendant neuf mois, les intérêts du capital de 79000 francs prêté par les sieurs A... et B..., en sorte que l'exécution des deux traités du 13 avril 1838 semble avoir été entière. Mais, le 22 janvier 1841, X... assigna devant le tribunal de Rouen, tant Y..., son vendeur, que les sieurs A... et B..., cessionnaires de celui-ci, pour voir dire, Y..., que, sans égard aux conventions particulières, le prix de l'office serait réduit et maintenu aux 85000 francs énoncés dans l'acte ostensible, si mieux n'aimait Y... le voir résilié ... etc., et les sieurs A... et B..., pour voir déclarer commun avec eux le jugement à intervenir. Le 18 juillet 1841, jugement du tribunal qui déclare X... non recevable dans son action. Sur l'appel, arrêt confirmatif de la cour royale de Rouen, intervenu le 18 février 1842. Le principal motif de cet arrêt était qu'à la vérité le traité secret était infecté d'une nullité radicale et d'ordre public qui pouvait être opposée par les parties elles-mêmes : mais qu'ayant été pleinement exécuté par le payement volontaire de la somme stipulée, la répétition de la somme payée était interdite par l'article 1255 du Code civil, qui dispose que \"la répétition n'est pas admise à l'égard des obligations naturelles qui ont été volontairement acquittées\".X... s'est pourvu contre cet arrêt, pour violation des articles 6 et 1131 du Code civil, et fausse application de l'article 1235 du même code. Son système a été accueilli par la Cour de cassation, à son audience du 30 juillet 1844. L'arrêt qui suit fera suffisamment connaître les moyens du pourvoi et ceux de la défense. Sur quoi, ouï, à l'audience, M. le conseiller Duplan, en son rapport ; Maître Fabre, avocat du demandeur, et Maître Ripault, avocat des défendeurs, dans leurs observations ; et M. de Boissieu, avocat général, en ses conclusions ; et après qu'il en a été délibéré en la chambre du conseil ; Vu les articles 6, 1131, 1133, 1235 et 1376 du Code civil ; Attendu que les offices ne sont pas une propriété dont les titulaires peuvent disposer à leur gré et d'une manière absolue ; Attendu que leur transmission intéresse essentiellement l'ordre public ; qu'en effet, de ce que les titulaires sont institués pour avoir le privilège exclusif de faire les actes qui entrent dans leurs attributions, il importe à la société qu'ils présentent, non-seulement des garanties d'aptitude et de moralité, mais encore que l'exagération du prix des charges, en leur enlevant le moyen d'y trouver une honnête existence, ne les entraîne pas hors la ligne de leurs devoirs ; Attendu que c'est dans ce but éminemment social que l'article 91 de la loi du 28 avril 1816, au lieu de reconnaître que les titulaires auraient la libre disposition des offices, ne leur a conféré que la faculté de présenter des successeurs à l'agrément du Roi ; Attendu que l'agrément de l'autorité ne doit intervenir qu'en pleine connaissance, soit des qualités personnelles des successeurs présentés, soit des conditions de la transmission des offices, et principalement avec la certitude d'un prix fixe, qui ne peut être augmenté par des conventions clandestines ; Qu'en un tel cas toute contre-lettre ou traité secret blesse ouvertement l'intérêt public, en ce qu'il lui enlève les garanties que la loi avait placées sous la vigilance du pouvoir, et que, dès lors, de tels actes doivent être classés dans le nombre de ces conventions particulières que l'article 6 du Code civil frappe d'une prohibition absolue, et qui, aux termes de l'article 1131 du même code, ne peuvent produire aucun effet, comme ayant une cause illicite ; Attendu que, s'il est vrai que les traités secrets, en matière de transmission d'office, ne peuvent produire l'obligation civile entre les contractants, il doit être également vrai qu'ils ne sauraient engendrer une obligation naturelle, dont la puissance serait de les soustraire à la prohibition de la loi ; que, pour admettre, en effet, que le payement volontairement fait en exécution d'une semblable obligation naturelle ne peut être répété, il faudrait nécessairement s'étayer de l'article 1235 du Code civil, c'est-à-dire, d'une disposition textuelle du droit civil, mais qu'alors on serait conduit à la choquante inconséquence de supposer que le droit civil, qui prohibe le contrat, se prêterait en même temps à en protéger l'exécution ; Attendu qu'on objecterait en vain que, dans ce cas, ce n'est pas la convention illicite qui produirait effet, et que l'efficacité ne résulterait que du fait même du payement ; car le payement, considéré isolément de la convention, ne pourrait se rattacher à aucune obligation ni civile, ni naturelle ; par conséquent, serait sans cause licite ou illicite, et, comme tel, serait sujet à répétition ; Qu'il faut donc reconnaître que le traité secret ayant pour objet la vente d'un office ne peut se soutenir par l'article 1235 du Code civil, sous le prétexte d'une obligation naturelle à laquelle l'ordre public résiste ouvertement, et qu'il ne peut pas davantage s'appuyer sur l'article 1338, qui, mais seulement en matière d'intérêt privé, couvre les vices d'un contrat par la ratification ou l'exécution volontaire ; Et qu'alors encore il faut reconnaître que, par le payement d'un supplément de prix d'office stipulé dans un traité secret, les parties auxquelles il est interdit d'alléguer l'ignorance de la loi, surtout d'une loi prohibitive, tombent positivement sous l'application de l'article 1376 du Code civil, qui dispose que \"celui qui reçoit par erreur ou sciemment ce qui ne lui est pas dû s'oblige à le restituer à celui de qui il l'a indûment reçu;\" Attendu, en conséquence de ce qui précède, que l'arrêt attaqué, qui, tout en reconnaissant que les traités secrets sur la vente d'un office sont frappés d'une nullité d'ordre public, a néanmoins repoussé la répétition des sommes payées volontairement par suite de leur exécution, a, en cela, faussement appliqué l'article 1235 du Code civil, et violé ouvertement les articles 6, 1131, 1133 et 1376 du même code ; LA COUR casse et annule l'arrêt rendu entre les parties, le 18 février 1842, par la cour royale de Rouen ; Jugé et prononcé, Chambre civile.\n" ] } ], "source": [ "print(df[0][0])" ] }, { "cell_type": "code", "execution_count": 9, "id": "d980e3ce", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:09:22.468315Z", "iopub.status.busy": "2022-01-28T14:09:22.466839Z", "iopub.status.idle": "2022-01-28T14:09:22.476541Z", "shell.execute_reply": "2022-01-28T14:09:22.475974Z", "shell.execute_reply.started": "2022-01-28T13:55:02.255184Z" }, "papermill": { "duration": 0.220516, "end_time": "2022-01-28T14:09:22.476682", "exception": false, "start_time": "2022-01-28T14:09:22.256166", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(142080,)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decision = df.loc[:,0]\n", "decision = decision.dropna()\n", "decision.shape" ] }, { "cell_type": "code", "execution_count": 10, "id": "783ce305", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:09:22.787595Z", "iopub.status.busy": "2022-01-28T14:09:22.786867Z", "iopub.status.idle": "2022-01-28T14:19:12.512114Z", "shell.execute_reply": "2022-01-28T14:19:12.511517Z", "shell.execute_reply.started": "2022-01-28T13:55:02.288997Z" }, "papermill": { "duration": 589.884579, "end_time": "2022-01-28T14:19:12.512291", "exception": false, "start_time": "2022-01-28T14:09:22.627712", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:51<00:00, 51.77s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-0.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:58<00:00, 58.97s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-1.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [01:06<00:00, 66.31s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-2.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [01:03<00:00, 63.44s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-3.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [01:00<00:00, 60.45s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-4.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:58<00:00, 58.25s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-5.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:53<00:00, 53.39s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-6.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:44<00:00, 44.26s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-7.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:48<00:00, 48.19s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-8.npz\n", "Reading files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:55<00:00, 55.24s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Writing data/npz/decision-9.npz\n" ] } ], "source": [ "max_index=100000\n", "step_index=10000\n", "os.mkdir(\"data/\")\n", "os.mkdir(\"data/txt\")\n", "os.mkdir(\"data/npz\")\n", "for i in range(int(max_index/step_index)):\n", " text_data = open(f'data/txt/decision-{i}.txt', 'w')\n", " for item in decision[i*step_index:(i+1)*step_index]:\n", " clean = item.replace(\"<CONTENU />\", '')\n", " if len(clean)>0:\n", " text_data.write(\"<|startoftext|> \" + clean + \" <|endoftext|>\\n\")\n", " text_data.close()\n", " gpt2.encode_dataset(f'data/txt/decision-{i}.txt', out_path=f'data/npz/decision-{i}.npz')" ] }, { "cell_type": "code", "execution_count": 11, "id": "8956f377", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:12.867520Z", "iopub.status.busy": "2022-01-28T14:19:12.866647Z", "iopub.status.idle": "2022-01-28T14:19:12.869675Z", "shell.execute_reply": "2022-01-28T14:19:12.869150Z", "shell.execute_reply.started": "2022-01-28T08:19:15.101620Z" }, "papermill": { "duration": 0.184695, "end_time": "2022-01-28T14:19:12.869826", "exception": false, "start_time": "2022-01-28T14:19:12.685131", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# gpt2.encode_dataset('data/decision-91.txt', out_path=)" ] }, { "cell_type": "code", "execution_count": 12, "id": "3cdfaa68", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:13.386925Z", "iopub.status.busy": "2022-01-28T14:19:13.385848Z", "iopub.status.idle": "2022-01-28T14:19:13.388583Z", "shell.execute_reply": "2022-01-28T14:19:13.389420Z" }, "papermill": { "duration": 0.347857, "end_time": "2022-01-28T14:19:13.389664", "exception": false, "start_time": "2022-01-28T14:19:13.041807", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# text_data = open(f'decision.txt', 'w')\n", "# for item in decision[:100000]:\n", "# clean = item.replace(\"<CONTENU />\", '')\n", "# if len(clean)>0:\n", "# text_data.write(\"<|startoftext|> \" + clean + \" <|endoftext|>\\n\")\n", "# text_data.close()\n", "# gpt2.encode_dataset('decision.txt')\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "f6b48dbb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:13.894409Z", "iopub.status.busy": "2022-01-28T14:19:13.893225Z", "iopub.status.idle": "2022-01-28T14:19:13.895613Z", "shell.execute_reply": "2022-01-28T14:19:13.896072Z" }, "papermill": { "duration": 0.201392, "end_time": "2022-01-28T14:19:13.896224", "exception": false, "start_time": "2022-01-28T14:19:13.694832", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# sess=gpt2.reset_session(sess=sess)\n", "# gpt2.finetune(sess,\n", "# dataset=file_name,\n", "# model_name='124M',\n", "# run_name='run1',\n", "# steps=10,\n", "# batch_size=1,\n", "# learning_rate=0.001,\n", "# overwrite=True)" ] }, { "cell_type": "code", "execution_count": 14, "id": "1d63fb9f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:14.240682Z", "iopub.status.busy": "2022-01-28T14:19:14.239688Z", "iopub.status.idle": "2022-01-28T18:18:37.086614Z", "shell.execute_reply": "2022-01-28T18:18:37.085461Z" }, "papermill": { "duration": 14363.020993, "end_time": "2022-01-28T18:18:37.086762", "exception": false, "start_time": "2022-01-28T14:19:14.065769", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 14:19:14.256058: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2022-01-28 14:19:14.324415: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:14.490725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:14.492257: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:16.595876: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:16.597227: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:16.598550: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:16.599927: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", "2022-01-28 14:19:21.767526: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.768838: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.769759: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.773302: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.774778: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.776142: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.777594: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.779018: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:19:21.780039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint models/124M/model.ckpt\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 3.74it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 9756868 tokens\n", "Training...\n", "Saving checkpoint/run1/model-0\n", "[1 | 6.40] loss=4.14 avg=4.14\n", "[2 | 7.72] loss=4.18 avg=4.16\n", "[3 | 9.00] loss=4.17 avg=4.16\n", "[4 | 10.29] loss=3.97 avg=4.12\n", "[5 | 11.58] loss=3.90 avg=4.07\n", "[6 | 12.86] loss=3.84 avg=4.03\n", "[7 | 14.14] loss=3.65 avg=3.98\n", "[8 | 15.43] loss=3.63 avg=3.93\n", "[9 | 16.72] loss=3.48 avg=3.88\n", "[10 | 18.01] loss=3.48 avg=3.84\n", "[11 | 19.29] loss=3.58 avg=3.81\n", "[12 | 20.56] loss=3.38 avg=3.77\n", "[13 | 21.86] loss=3.42 avg=3.75\n", "[14 | 23.14] loss=3.45 avg=3.72\n", "[15 | 24.42] loss=3.24 avg=3.69\n", "[16 | 25.69] loss=3.32 avg=3.66\n", "[17 | 26.97] loss=3.20 avg=3.63\n", "[18 | 28.25] loss=3.20 avg=3.61\n", "[19 | 29.52] loss=3.15 avg=3.58\n", "[20 | 30.80] loss=3.17 avg=3.56\n", "[21 | 32.08] loss=3.06 avg=3.53\n", "[22 | 33.38] loss=3.07 avg=3.51\n", "[23 | 34.66] loss=3.02 avg=3.49\n", "[24 | 35.94] loss=3.10 avg=3.47\n", "[25 | 37.21] loss=3.00 avg=3.45\n", "[26 | 38.49] loss=3.00 avg=3.43\n", "[27 | 39.77] loss=2.85 avg=3.40\n", "[28 | 41.05] loss=3.03 avg=3.39\n", "[29 | 42.33] loss=2.89 avg=3.37\n", "[30 | 43.63] loss=2.79 avg=3.35\n", "[31 | 44.91] loss=2.69 avg=3.32\n", "[32 | 46.19] loss=2.89 avg=3.31\n", "[33 | 47.47] loss=2.84 avg=3.29\n", "[34 | 48.76] loss=2.85 avg=3.27\n", "[35 | 50.06] loss=2.72 avg=3.26\n", "[36 | 51.34] loss=2.79 avg=3.24\n", "[37 | 52.62] loss=2.68 avg=3.22\n", "[38 | 53.90] loss=2.54 avg=3.20\n", "[39 | 55.20] loss=2.68 avg=3.18\n", "[40 | 56.47] loss=2.71 avg=3.17\n", "[41 | 57.75] loss=2.57 avg=3.15\n", "[42 | 59.02] loss=2.61 avg=3.14\n", "[43 | 60.30] loss=2.60 avg=3.12\n", "[44 | 61.57] loss=2.66 avg=3.11\n", "[45 | 62.85] loss=2.46 avg=3.09\n", "[46 | 64.13] loss=2.56 avg=3.08\n", "[47 | 65.42] loss=2.78 avg=3.07\n", "[48 | 66.70] loss=2.61 avg=3.06\n", "[49 | 67.98] loss=2.68 avg=3.05\n", "[50 | 69.25] loss=2.66 avg=3.04\n", "[51 | 70.53] loss=2.84 avg=3.03\n", "[52 | 71.81] loss=2.56 avg=3.02\n", "[53 | 73.09] loss=2.60 avg=3.01\n", "[54 | 74.37] loss=2.59 avg=3.00\n", "[55 | 75.64] loss=2.62 avg=2.99\n", "[56 | 76.94] loss=2.48 avg=2.98\n", "[57 | 78.22] loss=2.48 avg=2.97\n", "[58 | 79.50] loss=2.61 avg=2.96\n", "[59 | 80.77] loss=2.41 avg=2.95\n", "[60 | 82.08] loss=2.66 avg=2.94\n", "[61 | 83.38] loss=2.60 avg=2.93\n", "[62 | 84.66] loss=2.55 avg=2.93\n", "[63 | 85.93] loss=2.61 avg=2.92\n", "[64 | 87.21] loss=2.41 avg=2.91\n", "[65 | 88.50] loss=2.29 avg=2.90\n", "[66 | 89.78] loss=2.22 avg=2.88\n", "[67 | 91.05] loss=2.35 avg=2.87\n", "[68 | 92.34] loss=2.38 avg=2.86\n", "[69 | 93.61] loss=2.40 avg=2.85\n", "[70 | 94.89] loss=2.53 avg=2.84\n", "[71 | 96.16] loss=2.34 avg=2.84\n", "[72 | 97.44] loss=2.38 avg=2.83\n", "[73 | 98.73] loss=2.48 avg=2.82\n", "[74 | 100.01] loss=2.32 avg=2.81\n", "[75 | 101.29] loss=2.48 avg=2.80\n", "[76 | 102.57] loss=2.42 avg=2.80\n", "[77 | 103.85] loss=2.36 avg=2.79\n", "[78 | 105.13] loss=2.52 avg=2.78\n", "[79 | 106.40] loss=2.27 avg=2.77\n", "[80 | 107.68] loss=2.30 avg=2.77\n", "[81 | 108.96] loss=2.45 avg=2.76\n", "[82 | 110.25] loss=2.35 avg=2.75\n", "[83 | 111.52] loss=2.28 avg=2.74\n", "[84 | 112.81] loss=2.18 avg=2.73\n", "[85 | 114.09] loss=2.20 avg=2.72\n", "[86 | 115.40] loss=2.36 avg=2.72\n", "[87 | 116.68] loss=2.47 avg=2.71\n", "[88 | 117.95] loss=2.52 avg=2.71\n", "[89 | 119.23] loss=2.21 avg=2.70\n", "[90 | 120.52] loss=2.23 avg=2.69\n", "[91 | 121.80] loss=2.22 avg=2.69\n", "[92 | 123.07] loss=2.36 avg=2.68\n", "[93 | 124.35] loss=2.13 avg=2.67\n", "[94 | 125.63] loss=2.27 avg=2.67\n", "[95 | 126.90] loss=2.31 avg=2.66\n", "[96 | 128.18] loss=2.27 avg=2.65\n", "[97 | 129.45] loss=2.17 avg=2.65\n", "[98 | 130.73] loss=2.21 avg=2.64\n", "[99 | 132.02] loss=2.28 avg=2.63\n", "[100 | 133.30] loss=2.28 avg=2.63\n", "======== SAMPLE 1 ========\n", ", N'ETAIT FRAU DE L'ARTICLE 8 DU DECRET DU 5 JUILLET 1940, PAR LE MOYEN ; QUE CELUSAIENT DE SA DEBITEUR TEXTE D'UN MOYEN TENUS, D'AVANT LE DEUXIEME EN STATUER L'APPARTEMENT DU 5 JUILLET 1958 ET UN MOYEN ETAIT EN SURVENU DU PREMIER DE DIVERSES DE LILITERATION ; MAIS ATTENDU QUE LES ROURS QU'ELLE AUREMENT ETAIENT EN CIRCONSTANCE ; QU'AINSI LE MOYEN D'AVOIR EST UN MOYEN ET SOUTENAIT AUCUN D'UNE REFINITE EN L'EXISTENCE S'AGIT A UNE EXPROPRIANTAISE, QUE LES JUGES D'AVOIR CE FAIT DE SENS, L'ABSENCE DE DEFAUT SUSCEPTIBLE LES TEXTE ET ALORS EN VENT POUR LA COUR D'APPEL A L'OPTIMINTRION DE LA DUTICITE DE LA SOCIETE ; ATTENDU A JUSTIFICATION L'ARRET APRES UNE COURS DE LA DISPOSE DE L'ARTICLE 8 D'OU L'EXERCICE SUBSIDIAIRE DU SENS INITIALEE, LEURS DEUX PERSONNES, AUX STATUTS, DE LA FEMME EN AVAIENT REPARATION, ETABLIS LES MENTION DE L'ARTICLE 8 LA COUR DE CASSATION ; QUE LES TROIS FAITS QUE DE L'ARTICLE 7 ; QUE L'ARRET CANTON NE SAURAIT NE FAUTE ET QUE LA COUR DE CAUSE AVAIT PREMIERE DES PROPOSES AU RAPPORT D'UNE DROIT DE POURSUIVIE ; QUE LE MOYEN OBTENIR A LA CAUSE D'UNE PARTEE ; PAR L'ARTICLE 15 DE LA LOI ;ATTENDU QUE CETTE EXERCICE, TEXTE DE L'ARTICLE 8, SUR LE DIVERSES QUE LES ETABLISSES NUITES AINSI, D'AGIT N'APPRECIER ETAIT LA DECISION DE L'ARTICLE 14 DE LA LOI DU 1ER AVRIL 1949, AU MOTIF QUE L'ARRET ATTAQUE QUI NE PAREVANT QU'AINSI DE SA DEUXE D'UN COMPUTE, VUE POUR LES ETABLISSES ; ATTENDU QUE LE MOYEN L'ARRET, LES JUGES D'AVOIR, EN CE QUI Y AVAIENT ENTENDU QUE LES EXERCISES EN CERTAINS AVAIENT SUR LE TEXTE A SUSVIS DE L'ENTRAIN ; QUE SI L'ARTICLE 60 DE LA COUR D'APPEL DE L'AGIT, DU C/ CEPENDANT QUE LA MOYEN DU 7 SEPTEMBRE 1954, LES CONCLUSIONS, A DEGRE, NE TEXTE DES PRUD'HOMMES, AINSI UNE REPRISE, NE TEXTE SUR LE DIVERSE ; QUE LE MOYEN N'A SEULE, LES CONCLUSIONS, ENSEMBLE L'ARRET D'AVOIR, SANS ENONCIATION, EN CONGE AINSI AU JOURNAL CIVIL ; LE MOYEN REPROCHE A LA DECISION AU MERE SOCIETE SOUS EXPRESSEMENT; MAIS ATTENDU QUE, D'UNE PART, QUE LE MOYEN N'EST PAS D'EQUALITE DE L'EXECUTER A TEMPS ETAIENT D'APPRECIATION ; ATTENDU QUE, L'ARRET ATTAQUE AINSI LUI-MEME C'ETAIT DE SES TROIS ; QUE CETTE TEXTE N'A PAS EST FAIT ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 10 NOVEMBRE 1956 D'AVOIR RECEVOIR LEGALEMENT LE PREUVE QU'IL N'A PAS FONDEMENT DE L'EXPLOITATION D'ALERT DES LORS, SINCEQUER L'EXISTABILITE ; QU'EN STATUE DU MOYEN, DE SECONDE\n", "\n", "[101 | 148.14] loss=2.22 avg=2.62\n", "[102 | 149.44] loss=2.21 avg=2.61\n", "[103 | 150.72] loss=2.40 avg=2.61\n", "[104 | 151.99] loss=2.23 avg=2.61\n", "[105 | 153.29] loss=2.23 avg=2.60\n", "[106 | 154.57] loss=2.33 avg=2.60\n", "[107 | 155.85] loss=2.14 avg=2.59\n", "[108 | 157.12] loss=2.21 avg=2.58\n", "[109 | 158.40] loss=2.11 avg=2.58\n", "[110 | 159.67] loss=2.08 avg=2.57\n", "[111 | 160.95] loss=2.21 avg=2.56\n", "[112 | 162.23] loss=2.22 avg=2.56\n", "[113 | 163.50] loss=2.30 avg=2.55\n", "[114 | 164.81] loss=2.21 avg=2.55\n", "[115 | 166.09] loss=2.56 avg=2.55\n", "[116 | 167.36] loss=2.22 avg=2.54\n", "[117 | 168.64] loss=2.31 avg=2.54\n", "[118 | 169.92] loss=2.12 avg=2.54\n", "[119 | 171.19] loss=2.39 avg=2.53\n", "[120 | 172.47] loss=2.11 avg=2.53\n", "[121 | 173.74] loss=2.23 avg=2.52\n", "[122 | 175.02] loss=2.14 avg=2.52\n", "[123 | 176.31] loss=2.31 avg=2.51\n", "[124 | 177.59] loss=2.44 avg=2.51\n", "[125 | 178.86] loss=2.25 avg=2.51\n", "[126 | 180.14] loss=2.22 avg=2.51\n", "[127 | 181.43] loss=2.27 avg=2.50\n", "[128 | 182.73] loss=2.28 avg=2.50\n", "[129 | 184.00] loss=2.17 avg=2.49\n", "[130 | 185.28] loss=2.10 avg=2.49\n", "[131 | 186.57] loss=2.30 avg=2.49\n", "[132 | 187.85] loss=2.28 avg=2.48\n", "[133 | 189.12] loss=2.21 avg=2.48\n", "[134 | 190.40] loss=2.13 avg=2.48\n", "[135 | 191.68] loss=2.12 avg=2.47\n", "[136 | 192.96] loss=2.16 avg=2.47\n", "[137 | 194.24] loss=2.33 avg=2.46\n", "[138 | 195.52] loss=2.18 avg=2.46\n", "[139 | 196.80] loss=2.15 avg=2.46\n", "[140 | 198.10] loss=2.00 avg=2.45\n", "[141 | 199.37] loss=2.10 avg=2.45\n", "[142 | 200.65] loss=2.14 avg=2.44\n", "[143 | 201.93] loss=2.02 avg=2.44\n", "[144 | 203.20] loss=2.28 avg=2.43\n", "[145 | 204.48] loss=1.99 avg=2.43\n", "[146 | 205.75] loss=2.30 avg=2.43\n", "[147 | 207.03] loss=1.94 avg=2.42\n", "[148 | 208.32] loss=2.04 avg=2.42\n", "[149 | 209.61] loss=1.91 avg=2.41\n", "[150 | 210.89] loss=2.13 avg=2.41\n", "[151 | 212.17] loss=2.11 avg=2.40\n", "[152 | 213.46] loss=2.02 avg=2.40\n", "[153 | 214.75] loss=2.02 avg=2.39\n", "[154 | 216.03] loss=2.10 avg=2.39\n", "[155 | 217.30] loss=2.18 avg=2.39\n", "[156 | 218.58] loss=2.27 avg=2.38\n", "[157 | 219.89] loss=2.20 avg=2.38\n", "[158 | 221.16] loss=2.03 avg=2.38\n", "[159 | 222.44] loss=1.99 avg=2.37\n", "[160 | 223.71] loss=2.33 avg=2.37\n", "[161 | 224.99] loss=2.06 avg=2.37\n", "[162 | 226.27] loss=1.92 avg=2.36\n", "[163 | 227.55] loss=2.04 avg=2.36\n", "[164 | 228.82] loss=2.02 avg=2.35\n", "[165 | 230.10] loss=2.16 avg=2.35\n", "[166 | 231.39] loss=2.10 avg=2.35\n", "[167 | 232.67] loss=2.08 avg=2.35\n", "[168 | 233.94] loss=1.79 avg=2.34\n", "[169 | 235.22] loss=2.19 avg=2.34\n", "[170 | 236.49] loss=2.10 avg=2.33\n", "[171 | 237.77] loss=1.85 avg=2.33\n", "[172 | 239.05] loss=1.94 avg=2.32\n", "[173 | 240.32] loss=2.05 avg=2.32\n", "[174 | 241.62] loss=2.33 avg=2.32\n", "[175 | 242.91] loss=2.33 avg=2.32\n", "[176 | 244.19] loss=2.17 avg=2.32\n", "[177 | 245.46] loss=1.95 avg=2.31\n", "[178 | 246.77] loss=1.94 avg=2.31\n", "[179 | 248.07] loss=2.15 avg=2.31\n", "[180 | 249.34] loss=2.03 avg=2.30\n", "[181 | 250.62] loss=2.25 avg=2.30\n", "[182 | 251.90] loss=1.94 avg=2.30\n", "[183 | 253.20] loss=1.93 avg=2.30\n", "[184 | 254.48] loss=2.05 avg=2.29\n", "[185 | 255.75] loss=1.95 avg=2.29\n", "[186 | 257.03] loss=1.89 avg=2.28\n", "[187 | 258.31] loss=1.99 avg=2.28\n", "[188 | 259.58] loss=1.86 avg=2.28\n", "[189 | 260.86] loss=1.94 avg=2.27\n", "[190 | 262.14] loss=1.75 avg=2.27\n", "[191 | 263.43] loss=1.98 avg=2.26\n", "[192 | 264.71] loss=2.17 avg=2.26\n", "[193 | 265.99] loss=2.06 avg=2.26\n", "[194 | 267.26] loss=1.97 avg=2.26\n", "[195 | 268.54] loss=2.08 avg=2.25\n", "[196 | 269.81] loss=2.27 avg=2.25\n", "[197 | 271.09] loss=1.90 avg=2.25\n", "[198 | 272.37] loss=2.11 avg=2.25\n", "[199 | 273.64] loss=2.24 avg=2.25\n", "[200 | 274.94] loss=2.08 avg=2.25\n", "======== SAMPLE 1 ========\n", " PARIT. SUR LE TROIS ANS AU X... DES DEUX PRETENDRE, LA COUR D'APPEL S'AGISSAIT UNE BASE DE LA RESILIATION DE PROMESSE A LA CONDITION NE POURRAIT, SUR LE SECOND DEUX INTERETS A ETE DE POURSUITE PAR LA SOCIETE DE LA FOURNIS, SUR L'ASSANGE DE SECURITE SOCIALE DE FUT ETRANGER LA COUR D'APPEL. SUR LA DEMOISELLE LA PARTIE DE LA SOCIETE DU DROIT, EN SA PREMIERE INSTANCE DEVANT LEUR DEMANDE EN ENONCE DE SES TEXTEURS, IL RESULTAIT UNE BASE DE L'ETAT DE SECURITE SOCIALE EN SON APPEL, INTRODUCIANT A... DE CONGE DANS LES CIRCONSTANCES ET DE CONGEE EN DECLARATION DE REFUS ;ATTENDU QU'APRES AVOIR LES SEDIT, LA CAUSE FAIT UNE CIRCONSTANCE APRES L'ETAT, APRES L'AGENT, LA FAUTE DE CELUI D'OUVERTEMENT, IL EXISTE AU SEBERT DE SON EMPLOIE DE PRUD'HOMMES ET A... N'APPORTE POURSUITE ; QUE LE JUGEMENT ATTRIBUE A UNE EXACTE PREVUE LA PRECEDENCE ;ATTENDU QUE SA RESPONSABILITE DES CONGEDES DEVANT LEUR RESSORT ET DEVANT LEURS DISPOSITIONS DES TORT DE SES TEXTEURS, AU MEMBREMENT, SUR LES CONSTATATIONS ET LES CONCLUSIONS N'APPORTE QU'IL AIT POSSIBLE LES SEIGNEURS DONT IL EST IRRECEVABLE PASSE VIOLE ETABSIS ET QUE, PAR ACTE DU POURVOI, LA COUR D'APPEL RELEVE QUE LA CAUSATION FAIT EXCEPTION AU SEULE CAUSE ENTENDU SUR CETTE EXISTENCE D'UNE SITUATION ALLEGUEE ;MAIS ATTENDU QUE LES CONGEDES EXCLUSIONS CONSTATE QUE LES CONGEDES AGRICULTES PAR LES CONGEDES DE VOULENS CONTESTEES TANT EN PRESENTANT QU'ELLE A UNE CONDAMNATION PRECEDE PAR CETTE CAUSE ;ATTENDU QUE PAR ETRANGER UNE FAUTE DE SES TEXTEURS D'UN CONGEDES DEVANT DEQUET LA CAUSE, TENDU SUR LA TARDIQUE DE LA CONSENCE, PAR LEDO-SUSAN-SAUDIUS-SCHUL, QUE L'ACCIDENT DE VOULENS PRIS SEULEMENT NON LES PERSONNELLEMENT QUE LA CAUSATION FAIT EXCEPTIONE ;QUE L'ARTICLE 47 DU CODE CIVIL, EXCEPTIONE, EN TOUTE CAUSATION CONTREDER L'ACCIDENT, ALORS QU'UNE FAUTE DE VOULENS CONTESTEES ;ATTENDU QUE LE MOYEN NE POURRAIT, POURSUIT DANS UNE EXISTENCE DE VOULENS NE POUVAIT LES CONCLUSIONS SOULEVEEES A... LORSQU'ELLE AVAIT DUDIT VOULENS ENTENDU QUE LE PROUVER POUR PRESENTEE DU DEPOSITIONS, ET QU'IL N'EXISSAIT PAS LEGALEMENT JUSTIFIE LE 30 OCTOBRE 1958 ;ATTENDU QU'IL EST FAIT GRIEF AU SENS DOCTEUR D'EFFECTUE ET QUE LA DECISION DES PROPRES QUE L'ACCIDENT SANS PREJUDICE DE VOULENS A... RELEVABLE AUX DISPOSITIONS ;ATTENDU QUE SUR UNE CONSTATE DE CETTE SAISY ET QUE LE POURVOI NE SAURAIT ETRE CONVENU QUE LA CAUSE DEVANT LES CONAGLES DEVANT A..., LORSQUE, LES AUCTOIRS A... POUR PREUVE D'EMPLOIE SA PROPRE DE L'ACCIDENT, QU'IL RESSENT PAS DES CONCLUSIONS DONT LEUR DECISION EST IRRECEVABLE ET\n", "\n", "[201 | 288.79] loss=1.91 avg=2.24\n", "[202 | 290.07] loss=1.91 avg=2.24\n", "[203 | 291.34] loss=1.99 avg=2.24\n", "[204 | 292.62] loss=1.97 avg=2.23\n", "[205 | 293.89] loss=2.15 avg=2.23\n", "[206 | 295.17] loss=2.00 avg=2.23\n", "[207 | 296.46] loss=2.20 avg=2.23\n", "[208 | 297.74] loss=2.02 avg=2.23\n", "[209 | 299.01] loss=2.06 avg=2.22\n", "[210 | 300.29] loss=1.92 avg=2.22\n", "[211 | 301.56] loss=1.94 avg=2.22\n", "[212 | 302.84] loss=1.92 avg=2.21\n", "[213 | 304.12] loss=1.91 avg=2.21\n", "[214 | 305.39] loss=2.11 avg=2.21\n", "[215 | 306.67] loss=1.92 avg=2.21\n", "[216 | 307.96] loss=1.92 avg=2.20\n", "[217 | 309.24] loss=2.08 avg=2.20\n", "[218 | 310.51] loss=1.99 avg=2.20\n", "[219 | 311.79] loss=1.81 avg=2.19\n", "[220 | 313.10] loss=1.77 avg=2.19\n", "[221 | 314.38] loss=1.95 avg=2.19\n", "[222 | 315.65] loss=2.06 avg=2.19\n", "[223 | 316.93] loss=2.07 avg=2.18\n", "[224 | 318.21] loss=2.03 avg=2.18\n", "[225 | 319.50] loss=1.82 avg=2.18\n", "[226 | 320.78] loss=1.79 avg=2.17\n", "[227 | 322.05] loss=2.11 avg=2.17\n", "[228 | 323.33] loss=1.93 avg=2.17\n", "[229 | 324.60] loss=1.91 avg=2.17\n", "[230 | 325.88] loss=1.94 avg=2.17\n", "[231 | 327.15] loss=1.99 avg=2.16\n", "[232 | 328.43] loss=2.10 avg=2.16\n", "[233 | 329.72] loss=1.95 avg=2.16\n", "[234 | 330.99] loss=1.93 avg=2.16\n", "[235 | 332.27] loss=1.78 avg=2.15\n", "[236 | 333.55] loss=1.97 avg=2.15\n", "[237 | 334.82] loss=2.04 avg=2.15\n", "[238 | 336.10] loss=1.85 avg=2.15\n", "[239 | 337.37] loss=1.82 avg=2.14\n", "[240 | 338.65] loss=2.17 avg=2.14\n", "[241 | 339.92] loss=1.78 avg=2.14\n", "[242 | 341.22] loss=2.04 avg=2.14\n", "[243 | 342.50] loss=2.14 avg=2.14\n", "[244 | 343.78] loss=1.89 avg=2.14\n", "[245 | 345.05] loss=2.00 avg=2.13\n", "[246 | 346.37] loss=1.90 avg=2.13\n", "[247 | 347.65] loss=1.90 avg=2.13\n", "[248 | 348.93] loss=1.81 avg=2.13\n", "[249 | 350.21] loss=1.91 avg=2.12\n", "[250 | 351.49] loss=2.01 avg=2.12\n", "[251 | 352.77] loss=1.91 avg=2.12\n", "[252 | 354.05] loss=2.08 avg=2.12\n", "[253 | 355.32] loss=1.80 avg=2.12\n", "[254 | 356.60] loss=1.73 avg=2.11\n", "[255 | 357.87] loss=2.11 avg=2.11\n", "[256 | 359.15] loss=1.97 avg=2.11\n", "[257 | 360.42] loss=2.02 avg=2.11\n", "[258 | 361.70] loss=2.09 avg=2.11\n", "[259 | 363.00] loss=1.74 avg=2.11\n", "[260 | 364.27] loss=1.93 avg=2.10\n", "[261 | 365.55] loss=1.93 avg=2.10\n", "[262 | 366.82] loss=2.06 avg=2.10\n", "[263 | 368.10] loss=2.01 avg=2.10\n", "[264 | 369.37] loss=1.89 avg=2.10\n", "[265 | 370.65] loss=2.02 avg=2.10\n", "[266 | 371.93] loss=1.96 avg=2.10\n", "[267 | 373.20] loss=2.18 avg=2.10\n", "[268 | 374.51] loss=1.79 avg=2.09\n", "[269 | 375.80] loss=2.31 avg=2.10\n", "[270 | 377.07] loss=1.92 avg=2.09\n", "[271 | 378.35] loss=2.12 avg=2.09\n", "[272 | 379.64] loss=1.98 avg=2.09\n", "[273 | 380.92] loss=1.88 avg=2.09\n", "[274 | 382.19] loss=1.89 avg=2.09\n", "[275 | 383.47] loss=1.88 avg=2.09\n", "[276 | 384.75] loss=1.88 avg=2.08\n", "[277 | 386.03] loss=1.91 avg=2.08\n", "[278 | 387.30] loss=2.13 avg=2.08\n", "[279 | 388.58] loss=2.26 avg=2.08\n", "[280 | 389.85] loss=2.04 avg=2.08\n", "[281 | 391.13] loss=1.85 avg=2.08\n", "[282 | 392.40] loss=1.82 avg=2.08\n", "[283 | 393.68] loss=1.76 avg=2.08\n", "[284 | 394.95] loss=1.92 avg=2.07\n", "[285 | 396.24] loss=1.83 avg=2.07\n", "[286 | 397.51] loss=1.73 avg=2.07\n", "[287 | 398.79] loss=1.84 avg=2.07\n", "[288 | 400.06] loss=1.88 avg=2.06\n", "[289 | 401.33] loss=1.96 avg=2.06\n", "[290 | 402.61] loss=2.37 avg=2.07\n", "[291 | 403.88] loss=1.91 avg=2.06\n", "[292 | 405.16] loss=1.89 avg=2.06\n", "[293 | 406.44] loss=1.99 avg=2.06\n", "[294 | 407.72] loss=1.99 avg=2.06\n", "[295 | 409.00] loss=1.88 avg=2.06\n", "[296 | 410.27] loss=1.89 avg=2.06\n", "[297 | 411.57] loss=1.71 avg=2.05\n", "[298 | 412.89] loss=1.97 avg=2.05\n", "[299 | 414.17] loss=1.80 avg=2.05\n", "[300 | 415.44] loss=1.94 avg=2.05\n", "======== SAMPLE 1 ========\n", "END A LA COMMUNAUTE DU PREJUDICE PAR L'ETABLISSEMENT DES RAPPORTS D'APPEL DE L'ARTICLE 1345 DU CODE DE LA SECAPHE 13 DUS, LES FINS DANS L'EXERCICE AVAIENT EFFECTUMER UNE CHAMBRE PRETENTE DE CONDUCTEUR AUQUEL IL EN AVAIT PAS ETE DE L'EXPIRE EN TOUTE CHAPPEL UN INTERPRETE DES DEUX CONDITIONS NOUVELLES ET FUT EXPUEMMENT DANS DES CONCLUSIONS DE L'ARTICLE 1345, ALINEA 1, NOUVEAU DU JOUR DE L'ARRET ONT LIEU AVAIT ETE INJURIEURE L'EXISTENCE DE L'AVATERIE D'UN CONGE N'ETANT AUCUNE FAUTE ; MAIS ATTENDU QUE LE TEXTE SUSCEPTIQUEE AU SENS DE LA PARTIE DE TEMPS ETAIT DIFFERENTE QUE LA COUR D'APPEL RELEVE QU'EN L'ESPECE DUMENT, LES FINS DU FONDS PRONONCES DE L'ATTRIBUTION DE L'AUTHENTIQUE DANS L'EXPUEMMENT DU FAIT QUI DEVAIT, DEVANT LE CARACTERE, IL SOIT N'AVAIT PAS NULLEMENT QUE LA COUR D'APPEL A ETE NULLE LA RUPTURE DE L'AUTHENTIQUE NE PEUT ETRE ACCUEILLI L'OBJET DU TEL MOTIF DES ENFANTS DE L'ACCIDENT, LES MOYENS DU POURVOI D'AUTRE PARTQUER A L'EVIDENCE A L'AUTHENTIQUE, LA COUR D'APPEL, PAR LE JUGEMENT, PAR LE TRIBUNAL D'APPEL, A, DECLARE, D'UNE PART QUE L'ACCIDENT DE LA COUR D'APPEL A CELLE-CI, EN SA PARTAGE DES DEBATS QUI LA VIOLATION DE L'ACTION EN LAQUELLE, D'AUTRE PART, ONT ETE RAPPORTEES QU'IL SUIT MECONNU POUR L'EFFET DE FAIRE INDEMNISER LA NATURE QUI IL N'AVAIT PAS PRIS, D'ALENTE, SANS VIOLER UNE INDEMNISER ; ET SUR LE SECOND MOYEN : ATTENDU QU'IL EST REPROCHE A L'ARRET D'AVOIR, SUR LA BASE, L'ACCORD DE L'INTENTION DU PREJUDICE PAR LUI N'AVAIT PAS D'INSTANCE DE TELLEMENT SANS LA MEME PORTEE PAR L'ENREGISTREMENT DU PREJUDICE, ALORS QUE Y..., SE PATRON, AVAIT SUFFIS, LUI ADMETTRE UNE REQUETE DE L'ACTE, D'AVOIR CONSTATE L'ACCORD MELANGE AUX ENSUITEURS ET D'APPEL, DECLARE L'AUTORISE CONSTATE TANT DES ELEMENTS DU POURVOI ET QU'AUCUNE MEME DE RENU FONDANT DE NATURE D'UNE FACULTE ; MAIS ATTENDU QUE X..., ALORS QUI AVAIT EN COURS DES FAITS A LA COUR DE TOUTE CHANDLER, AVAIT ENCORE INCOMPATIBLE PAR UNE LITIGE QUE X..., N'ETAIT PAS DES ETABLISSEMENTS, LES AFFIRMATIONS DE LA NATURE CONNAISSANCE, RAPPELE QUE D'UNE PART, LES AFFIRMATIONS DE LA NATURE, ALORS QUE L'ACCORD MELANGE AVAIT ETE DEFINITIVEMENT SUR L'ACCORD MELANGE DU PREJUDICE LORRAIT LES CIRCONSTANCES N'ETAIT PAS EXPLOIT QU'EN L'ESPECE SOUTENU QU'IL AVAIT, DES LORS, SANS REPONDRE, LES JUGES D'APPEL ONT FAIT APPLICABLE DES CONCLUSIONS DE LA CONGE QUI EST, LE TRIBUNAL A VIOLE, LES JUGES DU FOND ONT PLUS QUI AVAIENT ETE SANS JUGE\n", "\n", "[301 | 429.04] loss=1.96 avg=2.05\n", "[302 | 430.32] loss=1.86 avg=2.05\n", "[303 | 431.59] loss=1.94 avg=2.04\n", "[304 | 432.87] loss=1.79 avg=2.04\n", "[305 | 434.14] loss=1.83 avg=2.04\n", "[306 | 435.42] loss=1.76 avg=2.04\n", "[307 | 436.70] loss=2.02 avg=2.04\n", "[308 | 437.97] loss=1.92 avg=2.04\n", "[309 | 439.25] loss=2.31 avg=2.04\n", "[310 | 440.55] loss=1.94 avg=2.04\n", "[311 | 441.82] loss=1.74 avg=2.03\n", "[312 | 443.11] loss=1.65 avg=2.03\n", "[313 | 444.38] loss=1.88 avg=2.03\n", "[314 | 445.68] loss=1.86 avg=2.03\n", "[315 | 446.96] loss=1.82 avg=2.02\n", "[316 | 448.23] loss=1.99 avg=2.02\n", "[317 | 449.51] loss=1.88 avg=2.02\n", "[318 | 450.80] loss=1.73 avg=2.02\n", "[319 | 452.08] loss=1.67 avg=2.02\n", "[320 | 453.35] loss=1.88 avg=2.01\n", "[321 | 454.63] loss=1.71 avg=2.01\n", "[322 | 455.90] loss=2.00 avg=2.01\n", "[323 | 457.18] loss=1.88 avg=2.01\n", "[324 | 458.45] loss=1.88 avg=2.01\n", "[325 | 459.73] loss=1.90 avg=2.01\n", "[326 | 461.00] loss=1.87 avg=2.01\n", "[327 | 462.29] loss=1.92 avg=2.00\n", "[328 | 463.57] loss=2.03 avg=2.01\n", "[329 | 464.84] loss=1.83 avg=2.00\n", "[330 | 466.12] loss=1.70 avg=2.00\n", "[331 | 467.39] loss=1.84 avg=2.00\n", "[332 | 468.67] loss=1.77 avg=2.00\n", "[333 | 469.95] loss=1.86 avg=1.99\n", "[334 | 471.22] loss=1.78 avg=1.99\n", "[335 | 472.51] loss=1.92 avg=1.99\n", "[336 | 473.79] loss=1.84 avg=1.99\n", "[337 | 475.07] loss=1.82 avg=1.99\n", "[338 | 476.34] loss=1.94 avg=1.99\n", "[339 | 477.63] loss=1.76 avg=1.99\n", "[340 | 478.92] loss=1.88 avg=1.98\n", "[341 | 480.19] loss=1.85 avg=1.98\n", "[342 | 481.47] loss=1.81 avg=1.98\n", "[343 | 482.75] loss=1.79 avg=1.98\n", "[344 | 484.03] loss=2.05 avg=1.98\n", "[345 | 485.31] loss=1.71 avg=1.98\n", "[346 | 486.58] loss=2.18 avg=1.98\n", "[347 | 487.86] loss=1.95 avg=1.98\n", "[348 | 489.13] loss=1.84 avg=1.98\n", "[349 | 490.41] loss=1.82 avg=1.98\n", "[350 | 491.68] loss=1.68 avg=1.97\n", "[351 | 492.96] loss=1.92 avg=1.97\n", "[352 | 494.24] loss=1.79 avg=1.97\n", "[353 | 495.53] loss=1.86 avg=1.97\n", "[354 | 496.80] loss=1.87 avg=1.97\n", "[355 | 498.07] loss=1.80 avg=1.97\n", "[356 | 499.35] loss=1.57 avg=1.96\n", "[357 | 500.63] loss=1.94 avg=1.96\n", "[358 | 501.90] loss=1.72 avg=1.96\n", "[359 | 503.18] loss=2.06 avg=1.96\n", "[360 | 504.46] loss=2.17 avg=1.96\n", "[361 | 505.75] loss=1.72 avg=1.96\n", "[362 | 507.02] loss=1.65 avg=1.96\n", "[363 | 508.30] loss=1.78 avg=1.96\n", "[364 | 509.58] loss=1.85 avg=1.95\n", "[365 | 510.88] loss=1.71 avg=1.95\n", "[366 | 512.16] loss=1.70 avg=1.95\n", "[367 | 513.43] loss=1.87 avg=1.95\n", "[368 | 514.71] loss=1.89 avg=1.95\n", "[369 | 515.98] loss=1.80 avg=1.95\n", "[370 | 517.27] loss=1.94 avg=1.95\n", "[371 | 518.54] loss=1.80 avg=1.94\n", "[372 | 519.82] loss=1.71 avg=1.94\n", "[373 | 521.10] loss=1.70 avg=1.94\n", "[374 | 522.37] loss=1.64 avg=1.94\n", "[375 | 523.65] loss=1.70 avg=1.93\n", "[376 | 524.92] loss=1.80 avg=1.93\n", "[377 | 526.19] loss=1.87 avg=1.93\n", "[378 | 527.48] loss=1.81 avg=1.93\n", "[379 | 528.75] loss=1.79 avg=1.93\n", "[380 | 530.03] loss=1.93 avg=1.93\n", "[381 | 531.30] loss=1.65 avg=1.93\n", "[382 | 532.59] loss=1.84 avg=1.93\n", "[383 | 533.87] loss=1.77 avg=1.92\n", "[384 | 535.15] loss=1.97 avg=1.92\n", "[385 | 536.43] loss=1.97 avg=1.93\n", "[386 | 537.70] loss=1.83 avg=1.92\n", "[387 | 538.99] loss=1.71 avg=1.92\n", "[388 | 540.26] loss=1.77 avg=1.92\n", "[389 | 541.53] loss=1.79 avg=1.92\n", "[390 | 542.81] loss=2.07 avg=1.92\n", "[391 | 544.12] loss=2.10 avg=1.92\n", "[392 | 545.40] loss=1.75 avg=1.92\n", "[393 | 546.67] loss=1.94 avg=1.92\n", "[394 | 547.94] loss=1.87 avg=1.92\n", "[395 | 549.22] loss=2.01 avg=1.92\n", "[396 | 550.52] loss=1.76 avg=1.92\n", "[397 | 551.79] loss=1.74 avg=1.92\n", "[398 | 553.08] loss=1.78 avg=1.92\n", "[399 | 554.35] loss=1.77 avg=1.91\n", "[400 | 555.63] loss=1.78 avg=1.91\n", "======== SAMPLE 1 ========\n", " CELAIT CUT RECEVABLES ; MAIS ATTENDU QU'IL RESULTE DE L'ARRET APRES AVOIR, D'UNE PART, QUE SES CONSEILS D'ABDIQUES DE CET AGENCE AVAIT PRATI QUE LA POSSESSION DE SA DEUXIEME ET DE LA GAGNERIE AVAIT LIE AVAIT PRECIS EN CETTE GAGNERIE, QU'IL N'EST PAS DEDUIRE QU'IL S'AGISTRIBUENT DANS LES TROIS ET DE DERNIER QUITTANT LES CHABRES DE LA CONGEDIEMENT QU'IL AVAIT PRISE D'AGISTRIBUER SUR SIEUR LES CHABRES DE CERTAINS, LE JUGEMENT ONT DENATURE LE MOYEN DU CHAMP, QUE NE SONT FAUSSEMENT DE CET ECHEUSE, ET ALORS QU'IL NE SAURAIT QU'ILS ETAIT SA RESIDENCE, ET QUE SUR LES CONSEILS DE CETTE CONGEDIEMENT DE LA GAGNERIE DE SAUFAGE, QUI REFUSE TENU DE SA CONSTRUCTION SANS SEULEER DE CETTE SITUATION ET DE SA CONSTATER DES ARTICLES 11 ET 12 DE LA LOI DU 30 OCTOBRE 1936 ET QUE L'ARRET CONFIRMATIF ATTAQUE, A DONNE UNE BASE LEGALE A SA DECISION ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 31 OCTOBRE 1957 PAR LA COUR D'APPEL DE PARIS. NO 57-12.913. SON SOCIETE ANONYME C/ ENSEMBLE. PRESIDENT : M. X.... - RAPPORTEUR : M. Y.... - PREMIER AVOCAT GENERAL : M. GUILLOT. - AVOCAT GENERAL : M. DE BONNEFOY DES AULNAIS. - AVOCAT : M. MAYER. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE PRIS EN SES DEUX BRANCHES : ATTENDU QU'IL RESULTE DE L'ARRET ATTAQUE (NUL, 14 JUILLET 1957) QU'ELLENT SA FEMME A ETE SUSCEPTIBLES A L'EXPLOITENCE DES PROFESSIONNELS APPARTENANT DES VICTIMES DE LA MESURE DES LIVRE-LOUISES ET EN VUE DE L'ARTICLE 8 DU MEME DU 14 JUIN 1876, APPLICABLES A CE QUE L'ARTICLE 5 DE LA LOI DU 23 SEPTEMBRE 1958, SANS CONSTANTATION DE MOTIFS, AU PROFIT DE GRAVES ET LA JURIDICTION DE L'AVIGNON. A DEPOSE DANS CELLENT, A DES LORS LE CARACTERE ET A DATE DU TRAIT AU GARBAGER. SUR LE MEME MOYEN, PRIS EN SA PREMIERE BRANCHE : ATTENDU QU'IL EST REPROCHE AU JUGEMENT ATTAQUE D'AVOIR CONDAMNE LA SITUATION DE L'HORAMERIE A, SA DEUX GERANTS VICTIME A DEFAUT PAR L'ACTION DES ARRETES SUBSISTENUS ET LE REFUS DE LA SOCIETE FAMILLE. PAR ARRET AYANT ETE ARRETERE A DEBOUTE, A LA SUITE D'UN ACTE DU TRAVAIL ET QUE LES JUGEMENTS DE CESSION TENDAIT A L'ARRET DU 29 SEPTEMBRE 1951, QUI, DANS L'ENTREPRISE, ETRANGERE A DEFAUT CONTRE LES PARTIES ET DONT ILS SOULEVAIENT PAR L'ACTION ; OR ATTENDU QU'APRES AVOIR ADMIS DE DECISION ALORS QUE L'HOMOLOGATION DE PETA AVAIT PAS L'ELECTRICITE DU PICON A LA COMPAGNIE DU PRONONCE DE LA SOCIETE FAMILLE AYANT ASSIGNE LA RESPECTE, LES JUGEMENTS SONT DECLUMES A L'ENGAGE DE L'HOMOLOGATION DU FERMET ET QU'ILS CONVENAIT RECEVABLE DE L'ENGAGE DE L'HOMOLOGATION\n", "\n", "[401 | 568.76] loss=1.71 avg=1.91\n", "[402 | 570.04] loss=1.90 avg=1.91\n", "[403 | 571.33] loss=1.81 avg=1.91\n", "[404 | 572.61] loss=1.93 avg=1.91\n", "[405 | 573.89] loss=1.75 avg=1.91\n", "[406 | 575.16] loss=2.02 avg=1.91\n", "[407 | 576.45] loss=2.24 avg=1.91\n", "[408 | 577.73] loss=1.79 avg=1.91\n", "[409 | 579.01] loss=1.77 avg=1.91\n", "[410 | 580.28] loss=1.73 avg=1.91\n", "[411 | 581.56] loss=1.96 avg=1.91\n", "[412 | 582.86] loss=1.92 avg=1.91\n", "[413 | 584.14] loss=1.86 avg=1.91\n", "[414 | 585.41] loss=2.11 avg=1.91\n", "[415 | 586.69] loss=1.59 avg=1.91\n", "[416 | 587.96] loss=1.95 avg=1.91\n", "[417 | 589.23] loss=1.78 avg=1.91\n", "[418 | 590.51] loss=1.78 avg=1.91\n", "[419 | 591.78] loss=1.75 avg=1.90\n", "[420 | 593.06] loss=1.88 avg=1.90\n", "[421 | 594.36] loss=1.76 avg=1.90\n", "[422 | 595.64] loss=1.73 avg=1.90\n", "[423 | 596.91] loss=2.12 avg=1.90\n", "[424 | 598.19] loss=2.25 avg=1.91\n", "[425 | 599.46] loss=1.87 avg=1.91\n", "[426 | 600.74] loss=2.04 avg=1.91\n", "[427 | 602.02] loss=1.98 avg=1.91\n", "[428 | 603.30] loss=1.95 avg=1.91\n", "[429 | 604.59] loss=2.12 avg=1.91\n", "[430 | 605.87] loss=1.92 avg=1.91\n", "[431 | 607.14] loss=1.88 avg=1.91\n", "[432 | 608.42] loss=1.68 avg=1.91\n", "[433 | 609.71] loss=1.78 avg=1.91\n", "[434 | 610.99] loss=1.68 avg=1.90\n", "[435 | 612.27] loss=1.64 avg=1.90\n", "[436 | 613.55] loss=1.76 avg=1.90\n", "[437 | 614.83] loss=1.74 avg=1.90\n", "[438 | 616.12] loss=2.01 avg=1.90\n", "[439 | 617.39] loss=1.67 avg=1.90\n", "[440 | 618.67] loss=1.82 avg=1.90\n", "[441 | 619.94] loss=1.65 avg=1.89\n", "[442 | 621.22] loss=1.77 avg=1.89\n", "[443 | 622.49] loss=1.76 avg=1.89\n", "[444 | 623.77] loss=1.78 avg=1.89\n", "[445 | 625.04] loss=1.70 avg=1.89\n", "[446 | 626.33] loss=1.77 avg=1.89\n", "[447 | 627.61] loss=2.11 avg=1.89\n", "[448 | 628.89] loss=1.62 avg=1.89\n", "[449 | 630.17] loss=1.73 avg=1.89\n", "[450 | 631.44] loss=1.74 avg=1.88\n", "[451 | 632.73] loss=1.80 avg=1.88\n", "[452 | 634.00] loss=1.73 avg=1.88\n", "[453 | 635.28] loss=1.75 avg=1.88\n", "[454 | 636.55] loss=1.95 avg=1.88\n", "[455 | 637.86] loss=1.76 avg=1.88\n", "[456 | 639.14] loss=1.69 avg=1.88\n", "[457 | 640.42] loss=1.75 avg=1.88\n", "[458 | 641.70] loss=1.78 avg=1.88\n", "[459 | 643.03] loss=1.70 avg=1.87\n", "[460 | 644.30] loss=1.63 avg=1.87\n", "[461 | 645.58] loss=1.82 avg=1.87\n", "[462 | 646.85] loss=1.84 avg=1.87\n", "[463 | 648.13] loss=2.15 avg=1.87\n", "[464 | 649.43] loss=1.78 avg=1.87\n", "[465 | 650.71] loss=1.82 avg=1.87\n", "[466 | 651.98] loss=1.70 avg=1.87\n", "[467 | 653.26] loss=1.79 avg=1.87\n", "[468 | 654.54] loss=1.81 avg=1.87\n", "[469 | 655.81] loss=1.86 avg=1.87\n", "[470 | 657.09] loss=1.73 avg=1.87\n", "[471 | 658.36] loss=1.77 avg=1.87\n", "[472 | 659.66] loss=1.72 avg=1.86\n", "[473 | 660.95] loss=1.72 avg=1.86\n", "[474 | 662.22] loss=2.21 avg=1.87\n", "[475 | 663.50] loss=1.94 avg=1.87\n", "[476 | 664.77] loss=1.70 avg=1.87\n", "[477 | 666.05] loss=1.73 avg=1.86\n", "[478 | 667.32] loss=1.95 avg=1.86\n", "[479 | 668.60] loss=1.75 avg=1.86\n", "[480 | 669.88] loss=1.84 avg=1.86\n", "[481 | 671.16] loss=1.94 avg=1.86\n", "[482 | 672.44] loss=1.76 avg=1.86\n", "[483 | 673.72] loss=1.75 avg=1.86\n", "[484 | 675.00] loss=1.93 avg=1.86\n", "[485 | 676.32] loss=1.73 avg=1.86\n", "[486 | 677.60] loss=2.02 avg=1.86\n", "[487 | 678.87] loss=1.84 avg=1.86\n", "[488 | 680.15] loss=2.05 avg=1.86\n", "[489 | 681.44] loss=1.76 avg=1.86\n", "[490 | 682.72] loss=1.83 avg=1.86\n", "[491 | 684.00] loss=1.80 avg=1.86\n", "[492 | 685.28] loss=1.72 avg=1.86\n", "[493 | 686.55] loss=1.90 avg=1.86\n", "[494 | 687.83] loss=1.79 avg=1.86\n", "[495 | 689.10] loss=1.72 avg=1.86\n", "[496 | 690.38] loss=1.76 avg=1.86\n", "[497 | 691.66] loss=1.68 avg=1.86\n", "[498 | 692.95] loss=1.88 avg=1.86\n", "[499 | 694.23] loss=1.68 avg=1.85\n", "[500 | 695.50] loss=1.85 avg=1.85\n", "======== SAMPLE 1 ========\n", "PRE PREVILES POUR DEFAUT EXCES ;QUE LA RECEVABILITE DU POUVOUD N'A DONNERAIT LA CONDITION FORMEE AUX DROITES DU TRIMESTRE, MAIS TOUJOURS QU'A LA REGI LE DROIT DE REPRENDRE QUI N'AVAIT PU COMPENSER LA CESSATION DE DIX DOLLES DU PERSONNEL ET NON CONTRAIREMENT DE LA FAILLITE DE LA CONSTRUCTION, QU'AUCUNE DETERMINATION, QUI ONT APPORTE A DEFAUT A LES PROPRIETAIRES, N'AYANT PU LA VALEUR ET AUX PRESENTES CESSIONS PAR ELLE, LE JUGEMENT ENTREPRIS A DECLARE QUE LA RECEVABILITE N'AVAIT PAS ETE REPRIS PAR UNE CONFORME POUR EXCES DE LA VALEUR, QUE, PAR SUITE D'UN PREFET DE SES DISPOSITIONS DE LA PRESENCE, LA CESSATION FAUTEE PAR RIGI DE LA CHOSE D'APOLOTER D'UNE CONSTRUCTION AUX MOTIFS DU JUGEMENT, LE CASSATION, DE PLUS, SOUS LE PERSONNEL ET D'AUTRE, AURAIT DU, EN PREVOYANT DE TOUT OU DE LORS, AURAIT DU DESTINATION DE SAISIE DE L'ACTE A DES CONVENTIONS, AURA JOUE, SOUTENAIT DUES D'UN RECEU PRETENDU PERE DE LA VALEUR ET DU DITE CONSTRUCTION OU AUX DES MOTIFS, LES FAILLONS NE JUSTIFIENT PAS LA COUR DE CASSATION NOTARIENNE LES CONCLUSIONS DONT ELLE EN CONSEQUENCE DU JUGEMENT ;ATTENDU QUE LE POURVOI EN SOUTENANT QUE SON DROIT A CELLES DEPUIS LE 3 JUILLET 1947, SOUS LES DEUX ET Z... LES EPOUX HORSENTE, D'UNE PART, LE 1ER AOUT 1947, LA COUR D'APPEL D'ALGER, QUI RELEVAIT DEUX PRENEURS, QUI L'UN DES MANDATAIRES EN PRINCIPE LE DROIT DE REPRENDRE, DE LA RECEVABILITE ;MAIS ATTENDU QU'IL EST REPROCHE A L'ARRET PRECISANT CELLE DU 30 NOVEMBRE, QUE L'A FAIT EXONERER L'ACCESSOIRE\", S'IL Y AINSI A UNE FAILLITE DE LA CONSTRUCTION ;D'OU IL SUIT QUE SI L'ACCESSOIRE DE LA RECEVABILITE PRECISE A EU LA RENTE DE SON DOMAINE, SE TROUVAIENT DEFINIERS A SON CONTROLE DANS SON EMPLOYEUR, DES ARTICLES 9 ET 10 DE LA LOI DU 23 MARS 1875, QUI SE VENAIT DANS LES MEMES VERSES AUX FINS DE L'ACCIDENT ;ATTENDU QU'UNE DISPOSITION DE LA RENTE ETAIT IMPUTABLE EN SON RENTE, LE TEXTE S'ETRE ACCORDE QUE LES FINS S'ETANT, C'EGAL A PURE S'OBSERVE AUX DROITS S'ETRES SANS FRAIS CONTRE LA REGLEMENTATION DU BAIL, FOUR DEPUIS LE RENTEMONT, POUR ELLE FORDE UNE GRATIFICATION, SANS LA SUITE D'UN PREFET DE SON BAIL ;MAIS ATTENDU QUE LE PREMIER DECISION, REJETANT QUE LA REGI, LE DROIT DE REPRENDRE, SOUS LES DEUX ET Z..., DEMANDE PAR REMPLACER SON MOMENT DE REMPLACER ; QUE, SUR LE PREMIER, LES EPOUX SELON-LOUE ET MATTRE X... LES EPOUX BOTTE DE DIRIGEE LA REGI A ETE LE 1ER AOUT 1947 LES FAILLIES S'ETRE CONDUIS LE DROIT DE REPRENDRE, ONT DONNE, A LA GRAVE DE L'EVALUATION, AINSI QUE, S'ETRE ACCORDE, LE\n", "\n", "[501 | 708.72] loss=1.72 avg=1.85\n", "[502 | 710.01] loss=1.84 avg=1.85\n", "[503 | 711.28] loss=1.69 avg=1.85\n", "[504 | 712.57] loss=1.77 avg=1.85\n", "[505 | 713.84] loss=1.88 avg=1.85\n", "[506 | 715.13] loss=1.62 avg=1.85\n", "[507 | 716.40] loss=1.83 avg=1.85\n", "[508 | 717.68] loss=1.80 avg=1.85\n", "[509 | 718.95] loss=1.74 avg=1.85\n", "[510 | 720.23] loss=1.65 avg=1.84\n", "[511 | 721.50] loss=1.71 avg=1.84\n", "[512 | 722.79] loss=1.77 avg=1.84\n", "[513 | 724.07] loss=1.71 avg=1.84\n", "[514 | 725.35] loss=1.60 avg=1.84\n", "[515 | 726.63] loss=1.70 avg=1.84\n", "[516 | 727.91] loss=1.88 avg=1.84\n", "[517 | 729.18] loss=1.87 avg=1.84\n", "[518 | 730.45] loss=1.77 avg=1.84\n", "[519 | 731.73] loss=1.84 avg=1.84\n", "[520 | 733.00] loss=1.76 avg=1.84\n", "[521 | 734.28] loss=1.66 avg=1.84\n", "[522 | 735.56] loss=1.67 avg=1.83\n", "[523 | 736.86] loss=1.73 avg=1.83\n", "[524 | 738.13] loss=1.76 avg=1.83\n", "[525 | 739.41] loss=1.75 avg=1.83\n", "[526 | 740.68] loss=1.73 avg=1.83\n", "[527 | 742.00] loss=1.86 avg=1.83\n", "[528 | 743.28] loss=1.80 avg=1.83\n", "[529 | 744.56] loss=1.68 avg=1.83\n", "[530 | 745.83] loss=2.02 avg=1.83\n", "[531 | 747.11] loss=1.77 avg=1.83\n", "[532 | 748.41] loss=1.78 avg=1.83\n", "[533 | 749.68] loss=1.88 avg=1.83\n", "[534 | 750.96] loss=1.77 avg=1.83\n", "[535 | 752.23] loss=1.98 avg=1.83\n", "[536 | 753.52] loss=1.88 avg=1.83\n", "[537 | 754.79] loss=1.74 avg=1.83\n", "[538 | 756.07] loss=1.75 avg=1.83\n", "[539 | 757.34] loss=1.79 avg=1.83\n", "[540 | 758.65] loss=1.87 avg=1.83\n", "[541 | 759.93] loss=1.79 avg=1.83\n", "[542 | 761.20] loss=2.01 avg=1.83\n", "[543 | 762.48] loss=1.62 avg=1.83\n", "[544 | 763.75] loss=1.83 avg=1.83\n", "[545 | 765.03] loss=1.67 avg=1.83\n", "[546 | 766.31] loss=1.68 avg=1.83\n", "[547 | 767.58] loss=1.74 avg=1.82\n", "[548 | 768.86] loss=1.77 avg=1.82\n", "[549 | 770.15] loss=1.62 avg=1.82\n", "[550 | 771.43] loss=1.91 avg=1.82\n", "[551 | 772.71] loss=1.75 avg=1.82\n", "[552 | 773.99] loss=1.85 avg=1.82\n", "[553 | 775.29] loss=1.92 avg=1.82\n", "[554 | 776.56] loss=1.76 avg=1.82\n", "[555 | 777.83] loss=1.70 avg=1.82\n", "[556 | 779.11] loss=1.58 avg=1.82\n", "[557 | 780.40] loss=1.62 avg=1.82\n", "[558 | 781.68] loss=1.81 avg=1.82\n", "[559 | 782.96] loss=1.70 avg=1.82\n", "[560 | 784.24] loss=1.71 avg=1.81\n", "[561 | 785.52] loss=1.63 avg=1.81\n", "[562 | 786.79] loss=1.87 avg=1.81\n", "[563 | 788.06] loss=1.69 avg=1.81\n", "[564 | 789.34] loss=1.72 avg=1.81\n", "[565 | 790.61] loss=1.55 avg=1.81\n", "[566 | 791.92] loss=1.76 avg=1.81\n", "[567 | 793.19] loss=1.71 avg=1.81\n", "[568 | 794.47] loss=1.71 avg=1.81\n", "[569 | 795.74] loss=1.60 avg=1.80\n", "[570 | 797.01] loss=1.59 avg=1.80\n", "[571 | 798.29] loss=1.76 avg=1.80\n", "[572 | 799.56] loss=1.72 avg=1.80\n", "[573 | 800.84] loss=1.76 avg=1.80\n", "[574 | 802.12] loss=1.90 avg=1.80\n", "[575 | 803.43] loss=1.58 avg=1.80\n", "[576 | 804.70] loss=1.77 avg=1.80\n", "[577 | 805.97] loss=1.62 avg=1.80\n", "[578 | 807.27] loss=1.76 avg=1.80\n", "[579 | 808.56] loss=1.58 avg=1.79\n", "[580 | 809.83] loss=1.82 avg=1.79\n", "[581 | 811.11] loss=1.84 avg=1.80\n", "[582 | 812.38] loss=1.64 avg=1.79\n", "[583 | 813.68] loss=1.78 avg=1.79\n", "[584 | 814.96] loss=1.68 avg=1.79\n", "[585 | 816.24] loss=1.88 avg=1.79\n", "[586 | 817.51] loss=1.63 avg=1.79\n", "[587 | 818.79] loss=1.74 avg=1.79\n", "[588 | 820.06] loss=1.76 avg=1.79\n", "[589 | 821.34] loss=1.59 avg=1.79\n", "[590 | 822.61] loss=1.79 avg=1.79\n", "[591 | 823.89] loss=1.75 avg=1.79\n", "[592 | 825.19] loss=1.75 avg=1.79\n", "[593 | 826.46] loss=1.70 avg=1.79\n", "[594 | 827.74] loss=1.78 avg=1.79\n", "[595 | 829.01] loss=1.68 avg=1.79\n", "[596 | 830.29] loss=1.62 avg=1.78\n", "[597 | 831.56] loss=1.66 avg=1.78\n", "[598 | 832.85] loss=1.55 avg=1.78\n", "[599 | 834.13] loss=1.69 avg=1.78\n", "[600 | 835.42] loss=1.72 avg=1.78\n", "======== SAMPLE 1 ========\n", "TE PREMIER MOYEN, A DONNE UNIQUEMENT A CETTE DECISION ;SUR LE DEUXIEME MOYEN : ATTENDU QUE LA S.P.A.N.A.I. NE POURVOI RECOLTE S'ETAIT D'UN RAPPORT D'EXPERTISE POUR EXECUTION DES REPRESENTANTS ;QUE LA S.P.A.N.A.I. AVAIT ETE DEVOQUEMENT LAISSEES A LA VENTE ;ATTENDU QUE X..., ASSURANT AU SERVICE DE LA SOCIETE DANS LES DEUX PARTIES EST INDISPENSABLE A LYON, AYANT RELEVE L'INTERVENTION EN PREMIERE ENTRE LES ETAT DANS LES CIRCONSTANCES DU MOYEN PRECITAU DE L'EXECUTION ;QU'APRES UNIQUEMENT ASSURER UNE AUTRE CAHIEME, X... AVAIT CONSTITU DANS LES DROITS DE SAVINS EN CASSATION DANS UN ARRET ET SE FONDANT A LA RESILIATION DU MOYEN ;PAR CES MOTIFS : MEME LE POURVOI FORME CONTRE LA JURIDICTION D'APPEL DE RENNES <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE, PRIS DE LA VIOLATION DE L'ARTICLE 23 DE LA LOI DU 1ER SEPTEMBRE 1948 : ATTENDU QUE LE POURVOI FORME PAR LES EPOUX Y... S'EST REPROCHE A LA MISE EN APPLICATION DE L'ARTICLE 25 DE LA LOI DU 12 JUILLET 1957 ;QUE CES MOTIFS SUFFISAIT EN L'ESPECE, CONTRE L'ARTICLE 23 DU LIVRE IER DU CODE DU TRAVAIL, SEMAIN, ET FERME, NOTAIREMENT EXERCES DE L'ASSIGNATION, LES EXPERTS APPELLES A DES CONCLUSIONS COMMISES PAR LA COMMISSION REGIONALE D'APPELNIX LE 29 NOVEMBRE 1956; POUR DECIDER, APRES AVOIR SES REGLES SANS REPONDRE AU TRIBUNAL CIVIL, LA CONVENTION DE PREMIERE INSTANCE TEL QUE X... PARAISSAIENT, AU, DE L'ARTICLE 27 DU LIVRE IER DU CODE DU TRAVAIL PRIVIE PAR LA COMMISSION REGIONALE D'APPEL, CE CONGE A SON FILS PAR LESQUELLES LA RESILIATION DE L'OCCUPATION ET AYANT SUFFISE CETTE CESSION LES REVISIONS DISTINCTES D'OUVRIER LA DAME Y... DEVANT LA FORMER ;ATTENDU QUE LE POURVOI REPROCHE AUX JUGES DU SECOND DEGRE D'AVOIR STATUE AINSI DE DETERMINER LE TURBULBONNE ;RE MISE EN APPLICATION DE LA FAUTE ENTRAINAIT QUE DAME Y... AVAIT ASSIGNE X... LA SUCCESSIVEMENT DE LA MEME LIEU, A PORTER LEUR ETAION ET DE L'OCCUPATION EN DATE DU 3 JUILLET 1960, LES JUGES DU SECOND DEGRE D'APPEL N'AVAIENT PAS REPONDU AU TRIBUNAL D'INSTANCE, SELON LE TARDMAN, PAR FAUSSE APPLICATION ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 20 MAI 1958 PAR LA COMMISSION REGIONALE D'APPEL DE DOUANTIN, MAIS SEULEMENT A LA SUCCESSION DE CET ECHE, ET QUE Y..., SOUS REVELER LEUR SEMAINE OU LES CONCLUSIONS D'ALLOCATIONS DONT LES DEBATS ETAIENT D'UN IMMEUBLE AU PRENEUR ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 23 JANVIER 1960 PAR LA COMMISSION REGIONALE D'APPEL DE RENNES. N° 60-14.068 ET AUTREE C/ X.... PRESIDENT : M VERDIER - RAPPORTEUR : M VIGNERON - AVOCAT GENERAL : M\n", "\n", "[601 | 849.64] loss=1.64 avg=1.78\n", "[602 | 850.92] loss=1.93 avg=1.78\n", "[603 | 852.19] loss=1.68 avg=1.78\n", "[604 | 853.47] loss=1.81 avg=1.78\n", "[605 | 854.75] loss=1.52 avg=1.78\n", "[606 | 856.02] loss=1.58 avg=1.77\n", "[607 | 857.30] loss=1.83 avg=1.77\n", "[608 | 858.59] loss=1.79 avg=1.78\n", "[609 | 859.86] loss=1.61 avg=1.77\n", "[610 | 861.14] loss=1.92 avg=1.77\n", "[611 | 862.41] loss=1.68 avg=1.77\n", "[612 | 863.69] loss=1.74 avg=1.77\n", "[613 | 864.97] loss=1.53 avg=1.77\n", "[614 | 866.24] loss=1.75 avg=1.77\n", "[615 | 867.52] loss=1.77 avg=1.77\n", "[616 | 868.81] loss=1.63 avg=1.77\n", "[617 | 870.08] loss=1.92 avg=1.77\n", "[618 | 871.36] loss=1.78 avg=1.77\n", "[619 | 872.63] loss=1.54 avg=1.77\n", "[620 | 873.95] loss=1.68 avg=1.77\n", "[621 | 875.22] loss=1.71 avg=1.77\n", "[622 | 876.50] loss=1.69 avg=1.77\n", "[623 | 877.77] loss=1.59 avg=1.77\n", "[624 | 879.04] loss=1.62 avg=1.76\n", "[625 | 880.34] loss=1.57 avg=1.76\n", "[626 | 881.62] loss=1.66 avg=1.76\n", "[627 | 882.90] loss=1.43 avg=1.76\n", "[628 | 884.17] loss=1.76 avg=1.76\n", "[629 | 885.44] loss=1.68 avg=1.76\n", "[630 | 886.72] loss=1.99 avg=1.76\n", "[631 | 887.99] loss=1.63 avg=1.76\n", "[632 | 889.26] loss=1.62 avg=1.76\n", "[633 | 890.55] loss=1.71 avg=1.76\n", "[634 | 891.83] loss=1.93 avg=1.76\n", "[635 | 893.11] loss=1.84 avg=1.76\n", "[636 | 894.38] loss=1.78 avg=1.76\n", "[637 | 895.65] loss=1.80 avg=1.76\n", "[638 | 896.93] loss=1.82 avg=1.76\n", "[639 | 898.21] loss=1.84 avg=1.76\n", "[640 | 899.48] loss=1.60 avg=1.76\n", "[641 | 900.76] loss=1.67 avg=1.76\n", "[642 | 902.04] loss=1.69 avg=1.76\n", "[643 | 903.33] loss=1.71 avg=1.76\n", "[644 | 904.60] loss=1.74 avg=1.76\n", "[645 | 905.87] loss=1.72 avg=1.76\n", "[646 | 907.18] loss=1.73 avg=1.76\n", "[647 | 908.46] loss=1.79 avg=1.76\n", "[648 | 909.73] loss=1.84 avg=1.76\n", "[649 | 911.01] loss=1.71 avg=1.76\n", "[650 | 912.29] loss=1.68 avg=1.76\n", "[651 | 913.59] loss=1.64 avg=1.75\n", "[652 | 914.87] loss=1.71 avg=1.75\n", "[653 | 916.15] loss=1.88 avg=1.76\n", "[654 | 917.42] loss=1.76 avg=1.76\n", "[655 | 918.69] loss=1.56 avg=1.75\n", "[656 | 919.97] loss=1.62 avg=1.75\n", "[657 | 921.24] loss=1.69 avg=1.75\n", "[658 | 922.52] loss=1.72 avg=1.75\n", "[659 | 923.81] loss=1.92 avg=1.75\n", "[660 | 925.09] loss=1.71 avg=1.75\n", "[661 | 926.36] loss=1.60 avg=1.75\n", "[662 | 927.63] loss=1.80 avg=1.75\n", "[663 | 928.91] loss=1.76 avg=1.75\n", "[664 | 930.18] loss=1.51 avg=1.75\n", "[665 | 931.46] loss=1.64 avg=1.75\n", "[666 | 932.74] loss=1.64 avg=1.75\n", "[667 | 934.01] loss=1.70 avg=1.75\n", "[668 | 935.30] loss=1.88 avg=1.75\n", "[669 | 936.58] loss=1.73 avg=1.75\n", "[670 | 937.85] loss=1.60 avg=1.75\n", "[671 | 939.14] loss=1.76 avg=1.75\n", "[672 | 940.44] loss=1.68 avg=1.75\n", "[673 | 941.71] loss=1.59 avg=1.74\n", "[674 | 942.99] loss=1.77 avg=1.74\n", "[675 | 944.26] loss=1.65 avg=1.74\n", "[676 | 945.58] loss=1.73 avg=1.74\n", "[677 | 946.86] loss=1.72 avg=1.74\n", "[678 | 948.13] loss=1.62 avg=1.74\n", "[679 | 949.40] loss=1.56 avg=1.74\n", "[680 | 950.68] loss=1.78 avg=1.74\n", "[681 | 951.95] loss=1.83 avg=1.74\n", "[682 | 953.23] loss=1.62 avg=1.74\n", "[683 | 954.51] loss=1.78 avg=1.74\n", "[684 | 955.78] loss=1.71 avg=1.74\n", "[685 | 957.08] loss=1.65 avg=1.74\n", "[686 | 958.36] loss=1.69 avg=1.74\n", "[687 | 959.64] loss=1.55 avg=1.74\n", "[688 | 960.91] loss=1.67 avg=1.74\n", "[689 | 962.19] loss=1.62 avg=1.73\n", "[690 | 963.46] loss=1.80 avg=1.74\n", "[691 | 964.74] loss=1.81 avg=1.74\n", "[692 | 966.02] loss=1.82 avg=1.74\n", "[693 | 967.30] loss=1.71 avg=1.74\n", "[694 | 968.58] loss=1.53 avg=1.73\n", "[695 | 969.86] loss=1.82 avg=1.74\n", "[696 | 971.13] loss=1.78 avg=1.74\n", "[697 | 972.43] loss=1.78 avg=1.74\n", "[698 | 973.71] loss=1.63 avg=1.74\n", "[699 | 974.98] loss=1.61 avg=1.73\n", "[700 | 976.26] loss=1.72 avg=1.73\n", "======== SAMPLE 1 ========\n", ", A DECIDE QUE POUR DEFAUCES PAR UN SIEUR X..., AU MOTIF QUE LA FAUTE A ETE RECONCILIE PAR LES MOTIFS DE REPRISE, DE LA SITUATION PROSPECTUE DES SEMAINS EN CASSATION, ET ALORS QUE LES PARTIES AVAIENT ENTENDU RETENUES, QUE L'APPLICATION D'UNE SITUATION DE CE SEMAIN SUR LE CARACTERE DES SEMAINS, NE POUVAIENT DIVERS MOTIFS D'UNE CAUSE, ET NOTAMMENT SUR CITATION; QUE LE MOYEN DONNANT LA SECONDE BRANCHE SELON LE POURVOI NE SAURAIT ETRE RETIRES, QUE PAR SUITE LA SITUATION, A L'ACTE DES SEMAINS, ETANT DOL AU MOTIF DE REVISION DES DEBOURS PRATIQUEES ET DE SES GRAVES DE REVISION, ELLE NE S'IMPOSAIT DONC NI LA SITUATION DU CHAUFFEUR, QUE LES DEMANDES DANS LES CONDITIONS AUXQUELS LA SITUATION REVELE NE S'IMPOSANT GERMAGE D'ENTREPRISE DE LA SITUATION DE SES BESOINS DEVAIENT ETRE PRISES CONTRE LES CONDITIONS SUSCEPTIBLE DE L'ARTICLE 23 DUS CASITOIRE ET NON ACCEPTEE;MAIS ATTENDU QUE L'ARRET ATTAQUE, MOTIVE, A, SANS VIOLER AUCUN DE SES BRANCHES, REPROCHE A LA COUR D'APPEL D'AVOIR AINNE JUSTIFIE LA VALEURS-DESSUSIBLES SANS CONSTATER L'IMMEUBLE SOUMER DE LA SUITE A SES AGISSEMENTS DE L'HIPPE ET LA RESPONSABILITE ETAIT CONSTANTE;ATTENDU QUE DANS CES CONSTATATIONS ENSUITE SANS REPONSE, LA COUR D'APPEL N'AURAIT A ETE VICTIME QU'UN ACTE DANS L'EXERCICE DE SON BULLETIN, SUR UNE CAUTION DES FRAIS DE PLEINE SITUATIONS PAR CETTE SOCIETE, LEUR DECISION ATTAQUE, AVAIT DEJA SON APPRECIATION A CETTE SOCIETE ; QUE CES CONSTATATIONS, QUI ONT TEMPS AURAIENT JUSQU'A REPONDRE A LA SAISIT UNE PARTIE LA CAUSE ET QU'AU SURPLUS ELLE N'AVAIT PAS DEPASSAIT AUX DEPENSES DISTINCTES;ET SUR LE DEUXIEME MOYEN, PRIS EN SES TROIS BRANCHES : ATTENDU QUE L'ARRET, QUI EST FAIT DROIT A LA COUR D'APPEL DE WAGON, EN DATE DU 30 MAI 1959 DANS LA SITUATION OUVERTE N'AURAIT PRECEDEMMENT ETE EFFECTIVEMENT SAISI DES BESOINS, MAIS ACCUEILLIR QU'ELLE CONSILANT AUX BESOINS PAR LA SITUATION, DUE A TOUJOURS FANAT ET DE SA GANITE DE SITUATION DE LA SITUATION RECU, UNE SITUATION ENVISAGEE;ATTENDU POUR FAIRE DROIT A LA COUR D'APPEL D'AVOIR ALLOUE EN MESURE D'EXERCER SON FONDS, AU MOTIF QU'IL PROCEDAIT DANS UNE SOMME DE 800000 FRANCS DE COTISATIONS AU COURS DE L'HONORALE, ONT A BON DROIT DECLARE QUE LA SITUATION REVISIBLE DE LA BONNEREAU D'UNE SOMME RECLAME DE FONDS EN PRESENCE DE SE FONDANT;ATTENDU, EN CE QUI CONCERNE LA SITUATION DE SA SOUSTRAIRE DES LOCAUX D'ENTRETIEN DE FAILLITE, LA COUR D'APPEL N'ETAIT PAS DONNE A L'EXERCICE DU SIX MOIS;A DENATURE LA SITUATION DE L'IMMEUBLE A FAIRE SOUTEN\n", "\n", "[701 | 989.75] loss=1.74 avg=1.73\n", "[702 | 991.03] loss=1.56 avg=1.73\n", "[703 | 992.31] loss=1.79 avg=1.73\n", "[704 | 993.58] loss=1.73 avg=1.73\n", "[705 | 994.86] loss=1.63 avg=1.73\n", "[706 | 996.13] loss=1.56 avg=1.73\n", "[707 | 997.41] loss=1.60 avg=1.73\n", "[708 | 998.68] loss=1.68 avg=1.73\n", "[709 | 999.96] loss=1.60 avg=1.73\n", "[710 | 1001.24] loss=1.61 avg=1.73\n", "[711 | 1002.52] loss=1.77 avg=1.73\n", "[712 | 1003.79] loss=1.71 avg=1.73\n", "[713 | 1005.08] loss=1.70 avg=1.73\n", "[714 | 1006.38] loss=1.70 avg=1.73\n", "[715 | 1007.65] loss=1.78 avg=1.73\n", "[716 | 1008.93] loss=1.52 avg=1.72\n", "[717 | 1010.20] loss=1.81 avg=1.73\n", "[718 | 1011.48] loss=1.84 avg=1.73\n", "[719 | 1012.77] loss=1.58 avg=1.72\n", "[720 | 1014.04] loss=1.57 avg=1.72\n", "[721 | 1015.31] loss=1.58 avg=1.72\n", "[722 | 1016.59] loss=1.55 avg=1.72\n", "[723 | 1017.86] loss=1.69 avg=1.72\n", "[724 | 1019.13] loss=1.65 avg=1.72\n", "[725 | 1020.41] loss=1.61 avg=1.72\n", "[726 | 1021.68] loss=1.61 avg=1.72\n", "[727 | 1022.97] loss=1.66 avg=1.72\n", "[728 | 1024.25] loss=1.69 avg=1.72\n", "[729 | 1025.52] loss=1.94 avg=1.72\n", "[730 | 1026.80] loss=1.73 avg=1.72\n", "[731 | 1028.07] loss=1.70 avg=1.72\n", "[732 | 1029.35] loss=1.79 avg=1.72\n", "[733 | 1030.62] loss=1.73 avg=1.72\n", "[734 | 1031.89] loss=1.64 avg=1.72\n", "[735 | 1033.17] loss=1.78 avg=1.72\n", "[736 | 1034.49] loss=1.65 avg=1.72\n", "[737 | 1035.77] loss=1.61 avg=1.72\n", "[738 | 1037.05] loss=1.70 avg=1.72\n", "[739 | 1038.35] loss=1.75 avg=1.72\n", "[740 | 1039.64] loss=1.80 avg=1.72\n", "[741 | 1040.92] loss=1.87 avg=1.72\n", "[742 | 1042.19] loss=1.89 avg=1.72\n", "[743 | 1043.47] loss=1.61 avg=1.72\n", "[744 | 1044.75] loss=1.49 avg=1.72\n", "[745 | 1046.03] loss=1.54 avg=1.72\n", "[746 | 1047.30] loss=1.63 avg=1.72\n", "[747 | 1048.58] loss=1.65 avg=1.71\n", "[748 | 1049.85] loss=1.70 avg=1.71\n", "[749 | 1051.13] loss=1.59 avg=1.71\n", "[750 | 1052.40] loss=1.78 avg=1.71\n", "[751 | 1053.68] loss=1.59 avg=1.71\n", "[752 | 1054.95] loss=1.58 avg=1.71\n", "[753 | 1056.25] loss=1.54 avg=1.71\n", "[754 | 1057.52] loss=1.61 avg=1.71\n", "[755 | 1058.80] loss=1.66 avg=1.71\n", "[756 | 1060.08] loss=1.73 avg=1.71\n", "[757 | 1061.35] loss=1.80 avg=1.71\n", "[758 | 1062.62] loss=1.93 avg=1.71\n", "[759 | 1063.90] loss=1.61 avg=1.71\n", "[760 | 1065.17] loss=1.73 avg=1.71\n", "[761 | 1066.46] loss=1.82 avg=1.71\n", "[762 | 1067.74] loss=1.60 avg=1.71\n", "[763 | 1069.01] loss=1.58 avg=1.71\n", "[764 | 1070.29] loss=1.56 avg=1.71\n", "[765 | 1071.60] loss=1.64 avg=1.71\n", "[766 | 1072.89] loss=1.71 avg=1.71\n", "[767 | 1074.17] loss=1.87 avg=1.71\n", "[768 | 1075.44] loss=1.78 avg=1.71\n", "[769 | 1076.72] loss=1.67 avg=1.71\n", "[770 | 1078.00] loss=1.59 avg=1.71\n", "[771 | 1079.27] loss=1.62 avg=1.71\n", "[772 | 1080.55] loss=1.62 avg=1.71\n", "[773 | 1081.82] loss=1.63 avg=1.71\n", "[774 | 1083.10] loss=1.79 avg=1.71\n", "[775 | 1084.37] loss=1.56 avg=1.70\n", "[776 | 1085.65] loss=1.57 avg=1.70\n", "[777 | 1086.92] loss=1.77 avg=1.70\n", "[778 | 1088.20] loss=1.64 avg=1.70\n", "[779 | 1089.49] loss=1.69 avg=1.70\n", "[780 | 1090.76] loss=1.82 avg=1.70\n", "[781 | 1092.03] loss=1.63 avg=1.70\n", "[782 | 1093.31] loss=1.62 avg=1.70\n", "[783 | 1094.59] loss=1.60 avg=1.70\n", "[784 | 1095.86] loss=1.59 avg=1.70\n", "[785 | 1097.14] loss=1.71 avg=1.70\n", "[786 | 1098.41] loss=1.69 avg=1.70\n", "[787 | 1099.71] loss=1.61 avg=1.70\n", "[788 | 1100.99] loss=1.60 avg=1.70\n", "[789 | 1102.27] loss=1.75 avg=1.70\n", "[790 | 1103.54] loss=1.51 avg=1.70\n", "[791 | 1104.95] loss=1.76 avg=1.70\n", "[792 | 1106.25] loss=1.61 avg=1.70\n", "[793 | 1107.52] loss=1.75 avg=1.70\n", "[794 | 1108.80] loss=1.73 avg=1.70\n", "[795 | 1110.08] loss=1.62 avg=1.70\n", "[796 | 1111.38] loss=1.63 avg=1.70\n", "[797 | 1112.66] loss=1.60 avg=1.70\n", "[798 | 1113.93] loss=1.48 avg=1.69\n", "[799 | 1115.21] loss=1.66 avg=1.69\n", "[800 | 1116.48] loss=1.68 avg=1.69\n", "======== SAMPLE 1 ========\n", "EL LES TEXTES SUSVISES ; QUE LA COUR D'APPEL, STATUANT AINSI ALORS EN ETAT DE L'ACCIDENT DU TRAVAIL ET EN FAISANT ETAT DE L'IMMEUBLE A L'EXERCICE DU PREMIER JOUR, A ASSIGNE LES CONSORTS Z... A STATUER EN VUE EN SE FONDANT SUR LE PREMIER JOUR, PAR UNE ACTION EN FONDANT, CONTRE A..., A L'ASSIGNER A... A EN DATE DU 21 JUILLET 1956 ENTRAEN ET SURVENU LES TERMES DU BAIL QUE LADITE MESURE NE PEUT LES CONSENTIES EXCEPTIONNELLEMENT AU POURVOI, N'A PAS, A LEURS POUVOIRS, LEGALEMENT JUSTIFIE LEUR DECISION ;PAR CES MOTIFS : CASSE ET ANNULE L'ARRET RENDU ENTRE LES PARTIES PAR LA COUR D'APPEL D'ANGERS DE LA COUR D'APPEL DE RENNES, LE 26 OCTOBRE 1960 ; REMET EN CONSEQUENCE LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET ET, POUR ETRE FAIT DROIT, LES RENVOIE DEVANT LA COUR D'APPEL DE DOUAI. N° 61-40 075. L'ENFANT C/ DAME C... ET AUTRES. PRESIDENT : M VERDIER - RAPPORTEUR : M LATRILLE - AVOCAT GENERAL : M LINDON - AVOCATS : MM COUTARD, GEORGE S. ET CUN DES PREMIERS. A RAPPROCHER : SUR LE N° 1 : 4 OCTOBRE 1961, BULL 1961, III, N° 58, P 57 A BON DROIT, 10 OCTOBRE 1962, BULL 1962, III, N° 1092, P 723. SUR LE N° 2 : 8 JUILLET 1962, BULL 1962, III, N° 593, P 651. 17 FEVRIER 1959, BULL 1959, II, N° 438 (1°), P 409. 15 MAI 1960, BULL 1960, IV, N° 1 (1°), P 753. 7 FEVRIER 1959, BULL 1960, III, N° 438 (1°), P 461. A BON DROIT, 19 MAI 1960, BULL 1960, III, N° 481, P 523. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE, PAR ACTE AUTHENTIQUE DU 14 JUILLET 1956, IL ECHETANT A L'AUDIENCE OU ELLE A PEINE DE CHEF A ENCOURUIER LA VALEUR DU DEVOIR DE LA PART DE L'HOMME DE LA COMPAGNIE TOULAUDETT, AU COURS DE LAQUELLE ELLE A ETE SIGNIFIES A L'INSTANCE ET SA FEMME DE VEUVE Y..., SURVEILLANCE, SURVEILLANCE, ALORS EN FAIT ETANT PRECISE, L'INTERVENTION DE CETTE SITUATION, L'ASSUREE ENTREPRISE, TANT EN L'ESPECE, PAR LE DECLARATION DE CONCURRENCE PAR VOIE DE DIVORCE ;MAIS ATTENDU QUE PENDANT D'UNE CONDAMNATION SUR LES CHEMINS PROFESSIONNELS, UNE AUGMENTATION DE CONDAMATION PRECEDENTE SUR LES PARTIES, NON D'AFFAIRES DANS DES CONDITIONS HYPOTHETIQUES, LE POURVOI D'ORDRE PUBLIC DEVANT LA JURIDICTION PRUD'HOMALE ET QUE, D'AUTRE PART, ELLE SE TROUVAIT, EN L'ABSENCE DES DISPOSITIONS, EN L'ABSENCE DU JUGEMENT ENTREPRIS A L'AUDIENCE DE LA CONDAMNATION DE CES CHEMINS POUR SOUS-SEINGS PARTIES, NI LA VOITURE COMMERCIALE ET SE TROUVER A L'AUTEUR DE LE REMBOURSEMENT DE L'AFFECTION PREVUE A CE QU'IL AVAIT RECONNU EXCLUSIMEMENT DES PARTIES, NI CELLE DE LA CHOSE JUGEE,\n", "\n", "[801 | 1130.04] loss=1.66 avg=1.69\n", "[802 | 1131.31] loss=1.60 avg=1.69\n", "[803 | 1132.60] loss=1.48 avg=1.69\n", "[804 | 1133.89] loss=1.73 avg=1.69\n", "[805 | 1135.17] loss=1.56 avg=1.69\n", "[806 | 1136.45] loss=1.61 avg=1.69\n", "[807 | 1137.89] loss=1.73 avg=1.69\n", "[808 | 1139.18] loss=1.76 avg=1.69\n", "[809 | 1140.46] loss=1.53 avg=1.69\n", "[810 | 1141.73] loss=1.52 avg=1.69\n", "[811 | 1143.01] loss=1.71 avg=1.69\n", "[812 | 1144.30] loss=1.58 avg=1.68\n", "[813 | 1145.57] loss=1.65 avg=1.68\n", "[814 | 1146.85] loss=1.63 avg=1.68\n", "[815 | 1148.12] loss=1.44 avg=1.68\n", "[816 | 1149.40] loss=1.72 avg=1.68\n", "[817 | 1150.67] loss=1.89 avg=1.68\n", "[818 | 1151.95] loss=1.52 avg=1.68\n", "[819 | 1153.23] loss=1.79 avg=1.68\n", "[820 | 1154.51] loss=1.73 avg=1.68\n", "[821 | 1155.79] loss=1.88 avg=1.69\n", "[822 | 1157.07] loss=1.31 avg=1.68\n", "[823 | 1158.34] loss=1.85 avg=1.68\n", "[824 | 1159.62] loss=1.62 avg=1.68\n", "[825 | 1160.89] loss=1.64 avg=1.68\n", "[826 | 1162.17] loss=1.72 avg=1.68\n", "[827 | 1163.45] loss=1.63 avg=1.68\n", "[828 | 1164.72] loss=1.58 avg=1.68\n", "[829 | 1166.01] loss=1.61 avg=1.68\n", "[830 | 1167.28] loss=1.51 avg=1.68\n", "[831 | 1168.56] loss=1.69 avg=1.68\n", "[832 | 1169.84] loss=1.51 avg=1.68\n", "[833 | 1171.15] loss=1.54 avg=1.68\n", "[834 | 1172.43] loss=1.50 avg=1.67\n", "[835 | 1173.70] loss=1.80 avg=1.68\n", "[836 | 1174.98] loss=1.40 avg=1.67\n", "[837 | 1176.26] loss=1.70 avg=1.67\n", "[838 | 1177.54] loss=1.81 avg=1.67\n", "[839 | 1178.82] loss=1.56 avg=1.67\n", "[840 | 1180.09] loss=1.59 avg=1.67\n", "[841 | 1181.37] loss=1.52 avg=1.67\n", "[842 | 1182.64] loss=1.51 avg=1.67\n", "[843 | 1183.92] loss=1.61 avg=1.67\n", "[844 | 1185.19] loss=1.65 avg=1.67\n", "[845 | 1186.47] loss=1.74 avg=1.67\n", "[846 | 1187.76] loss=1.82 avg=1.67\n", "[847 | 1189.03] loss=1.77 avg=1.67\n", "[848 | 1190.31] loss=1.53 avg=1.67\n", "[849 | 1191.58] loss=1.75 avg=1.67\n", "[850 | 1192.87] loss=1.52 avg=1.67\n", "[851 | 1194.14] loss=1.81 avg=1.67\n", "[852 | 1195.42] loss=1.86 avg=1.67\n", "[853 | 1196.69] loss=1.48 avg=1.67\n", "[854 | 1197.97] loss=1.62 avg=1.67\n", "[855 | 1199.26] loss=1.72 avg=1.67\n", "[856 | 1200.53] loss=1.60 avg=1.67\n", "[857 | 1201.81] loss=1.65 avg=1.67\n", "[858 | 1203.09] loss=1.71 avg=1.67\n", "[859 | 1204.39] loss=1.47 avg=1.67\n", "[860 | 1205.66] loss=1.57 avg=1.67\n", "[861 | 1206.93] loss=1.54 avg=1.67\n", "[862 | 1208.21] loss=1.73 avg=1.67\n", "[863 | 1209.49] loss=1.78 avg=1.67\n", "[864 | 1210.77] loss=1.61 avg=1.67\n", "[865 | 1212.05] loss=1.70 avg=1.67\n", "[866 | 1213.33] loss=1.62 avg=1.67\n", "[867 | 1214.61] loss=1.95 avg=1.67\n", "[868 | 1215.88] loss=1.67 avg=1.67\n", "[869 | 1217.16] loss=1.61 avg=1.67\n", "[870 | 1218.43] loss=1.57 avg=1.67\n", "[871 | 1219.71] loss=1.68 avg=1.67\n", "[872 | 1221.00] loss=1.54 avg=1.67\n", "[873 | 1222.27] loss=1.53 avg=1.67\n", "[874 | 1223.55] loss=1.81 avg=1.67\n", "[875 | 1224.82] loss=1.48 avg=1.67\n", "[876 | 1226.09] loss=1.62 avg=1.67\n", "[877 | 1227.37] loss=1.78 avg=1.67\n", "[878 | 1228.64] loss=1.82 avg=1.67\n", "[879 | 1229.92] loss=1.68 avg=1.67\n", "[880 | 1231.20] loss=1.57 avg=1.67\n", "[881 | 1232.49] loss=1.54 avg=1.67\n", "[882 | 1233.78] loss=1.51 avg=1.66\n", "[883 | 1235.05] loss=1.72 avg=1.66\n", "[884 | 1236.33] loss=1.53 avg=1.66\n", "[885 | 1237.62] loss=1.58 avg=1.66\n", "[886 | 1238.90] loss=1.46 avg=1.66\n", "[887 | 1240.17] loss=1.54 avg=1.66\n", "[888 | 1241.45] loss=1.67 avg=1.66\n", "[889 | 1242.74] loss=1.82 avg=1.66\n", "[890 | 1244.02] loss=1.69 avg=1.66\n", "[891 | 1245.29] loss=1.78 avg=1.66\n", "[892 | 1246.56] loss=1.56 avg=1.66\n", "[893 | 1247.84] loss=1.62 avg=1.66\n", "[894 | 1249.11] loss=1.67 avg=1.66\n", "[895 | 1250.39] loss=1.59 avg=1.66\n", "[896 | 1251.66] loss=1.60 avg=1.66\n", "[897 | 1252.94] loss=1.76 avg=1.66\n", "[898 | 1254.23] loss=1.61 avg=1.66\n", "[899 | 1255.51] loss=1.59 avg=1.66\n", "[900 | 1256.78] loss=1.72 avg=1.66\n", "======== SAMPLE 1 ========\n", "IE QU'IL S'EST BORNAGE A UNE DUREE SES DEUX PAPERIES ; QUE LE PAPIERIE DANS SES FINS N'ETAIT PAS A ETRE MISE EN DROIT COMMUN ; QU'EN QUALITE DE PROPRIETAIRE, POUR RAPPORT D'EXPERT Y..., LES EPOUX A... ONT DEMANDE DE DECLARER LE BAIL SUBSISTE L'UNE DES FINS DE L'IMMEUBLE LITIGIEUX DANS LEUR PORTEUR ET A L'ENTRETIEN EN VIGUEUR DE LA CONSTRUCTION DE L'UN D'Y... ;QUE LE PAPIERIE AYANT PARTICIPEE PAR ECHANGE A SON BAILLEUR, LE JUGE ADMINISTRATIVE A REMETTRE LES LOCAUX A L'ACQUIETE D'AGRANTS DE L'EMPLOI ;SUR LE MOYEN PRIS EN SA PREMIERE BRANCHE : ATTENDU QU'IL EST REPROCHE A L'ARRET DE NE PAS AVOIR POUR OBJET D'UNE INDEMNITE DE PREAVIS ET DE DROIT SUR SON BAILLEUR, D'AVOIR, CONFIRMANT L'EXPERT Y..., EN OPPOSITION DE LA COUR D'APPEL, FUT BLESSES, QUE LA CONSTRUCTION EST, EN DATE DU 27 JANVIER 1950 UNE INDEMNITE DE PREAVIS, LE PAPIERIE ETAIT UNE INDEMNITE DE LOCATION, SANS S'OPPOSER AUX FINS DE L'IMMEUBLE, QU'ELLE A FAIT, SANS INFLUENCE, SUR LES LOCATIONS QUI TOURAIENT SANS IMPERER LES DROITS DE DEUX ELEMENTS, AVAIT DECLARE, QU'IL SOIT, EN RAISON DE SA DEMANDE, LE BAILLEUR, DES SOMMES, ET QUE L'IMMEUBLE ETAIT DES CHEQUES A TENIR COMPTE D'ESPOSER AU PAYEMENT DE LA CONDAMNATION ;D'OU IL SUIT QUE LE MOYEN MANQUE EN FAIT ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 25 DECEMBRE 1959 PAR LA COUR D'APPEL D'ANTENAIS. N° 60-10 668. EPOUX X... C / EPOUX A... ET Y.... PRESIDENT : M LESCOT - RAPPORTEUR : M ROCHAT - AVOCAT GENERAL : M ALBUCHER - AVOCATS : MM SOURDILLAT ET ROUVIERE. <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN MENAGEMENT : VU LES ARTICLES 1782 ET 1784 DU CODE CIVIL ;ATTENDU QUE LA SOCIETE EN REPARATION DU PREJUDICE NON PRETENDUE DE DEJA FERME ET DU DECLARATION AU SERVICE DE L'IMMEUBLE DE LA SOCIETE A RESPONSABILITE LIMITEE \"LE PRENEUSE\" ET ALORS QUE LA CITE CONSTATANT A DEJA REPARATION DUDIT SOCIETE N'EN AVAIT PAS RECUS SUR UN ARRIVEE SANS ENQUET, LE JUGEMENT ATTAQUE LEUR DECISION S'EST FONDE ;SUR LE SECOND MOYEN : ATTENDU QUE, SELON LE POURVOI, LES MOTIFS DE L'ARRET ATTAQUE ONT FAIT ETAT DE LA DECISION DES PREMIERS JUGES, OBSERVE QUE LE DECLARATION SANS DISTINGUER AVAIT EU EGARD AU PAYEMENT QUOT UNE INDEMNITE DE PREAVIS, ET QUE DEVANT LA JOUISSANCE DES PRENEURS DES CONSORTS Y..., ELLE A ASSIGDA DANS LE DISPOSITIF DE LA SOCIETE ET QUE LE LITIGE A REFUSE D'INCIDENTS DE CETTE CAISSE, N'AYANT PAS A LA D'ORNER A LA FERME PAR ELLE A L'IMMEUBLE, EN UNE COURRIER A LA REQUETE DE PORT DE LE LOGEMENT ET\n", "\n", "[901 | 1270.12] loss=1.71 avg=1.66\n", "[902 | 1271.45] loss=1.74 avg=1.66\n", "[903 | 1272.73] loss=1.60 avg=1.66\n", "[904 | 1274.01] loss=1.67 avg=1.66\n", "[905 | 1275.29] loss=1.59 avg=1.66\n", "[906 | 1276.57] loss=1.71 avg=1.66\n", "[907 | 1277.84] loss=1.87 avg=1.66\n", "[908 | 1279.12] loss=1.48 avg=1.66\n", "[909 | 1280.39] loss=1.56 avg=1.66\n", "[910 | 1281.67] loss=1.74 avg=1.66\n", "[911 | 1282.95] loss=1.57 avg=1.66\n", "[912 | 1284.22] loss=1.69 avg=1.66\n", "[913 | 1285.50] loss=1.65 avg=1.66\n", "[914 | 1286.78] loss=1.67 avg=1.66\n", "[915 | 1288.06] loss=1.62 avg=1.66\n", "[916 | 1289.33] loss=1.73 avg=1.66\n", "[917 | 1290.61] loss=2.11 avg=1.67\n", "[918 | 1291.88] loss=1.65 avg=1.66\n", "[919 | 1293.16] loss=1.56 avg=1.66\n", "[920 | 1294.43] loss=1.64 avg=1.66\n", "[921 | 1295.71] loss=1.62 avg=1.66\n", "[922 | 1296.98] loss=1.56 avg=1.66\n", "[923 | 1298.27] loss=1.51 avg=1.66\n", "[924 | 1299.54] loss=1.54 avg=1.66\n", "[925 | 1300.82] loss=1.60 avg=1.66\n", "[926 | 1302.10] loss=1.41 avg=1.66\n", "[927 | 1303.40] loss=1.69 avg=1.66\n", "[928 | 1304.68] loss=1.64 avg=1.66\n", "[929 | 1305.96] loss=1.62 avg=1.66\n", "[930 | 1307.23] loss=1.59 avg=1.66\n", "[931 | 1308.53] loss=1.47 avg=1.65\n", "[932 | 1309.80] loss=1.61 avg=1.65\n", "[933 | 1311.08] loss=1.50 avg=1.65\n", "[934 | 1312.36] loss=1.53 avg=1.65\n", "[935 | 1313.64] loss=1.45 avg=1.65\n", "[936 | 1314.91] loss=1.69 avg=1.65\n", "[937 | 1316.18] loss=1.64 avg=1.65\n", "[938 | 1317.46] loss=1.56 avg=1.65\n", "[939 | 1318.74] loss=1.63 avg=1.65\n", "[940 | 1320.02] loss=1.62 avg=1.65\n", "[941 | 1321.29] loss=1.62 avg=1.65\n", "[942 | 1322.57] loss=1.57 avg=1.65\n", "[943 | 1323.85] loss=1.68 avg=1.65\n", "[944 | 1325.12] loss=1.66 avg=1.65\n", "[945 | 1326.40] loss=1.59 avg=1.65\n", "[946 | 1327.67] loss=1.43 avg=1.64\n", "[947 | 1328.95] loss=1.66 avg=1.64\n", "[948 | 1330.22] loss=1.56 avg=1.64\n", "[949 | 1331.51] loss=1.68 avg=1.64\n", "[950 | 1332.79] loss=1.58 avg=1.64\n", "[951 | 1334.08] loss=1.60 avg=1.64\n", "[952 | 1335.37] loss=1.75 avg=1.64\n", "[953 | 1336.67] loss=1.66 avg=1.64\n", "[954 | 1337.95] loss=1.58 avg=1.64\n", "[955 | 1339.22] loss=1.43 avg=1.64\n", "[956 | 1340.49] loss=1.65 avg=1.64\n", "[957 | 1341.78] loss=1.65 avg=1.64\n", "[958 | 1343.06] loss=1.65 avg=1.64\n", "[959 | 1344.34] loss=1.88 avg=1.64\n", "[960 | 1345.61] loss=1.79 avg=1.65\n", "[961 | 1346.89] loss=1.60 avg=1.64\n", "[962 | 1348.16] loss=1.62 avg=1.64\n", "[963 | 1349.44] loss=1.66 avg=1.64\n", "[964 | 1350.71] loss=1.84 avg=1.65\n", "[965 | 1351.99] loss=1.63 avg=1.65\n", "[966 | 1353.28] loss=1.89 avg=1.65\n", "[967 | 1354.55] loss=1.67 avg=1.65\n", "[968 | 1355.83] loss=1.79 avg=1.65\n", "[969 | 1357.10] loss=1.59 avg=1.65\n", "[970 | 1358.38] loss=1.75 avg=1.65\n", "[971 | 1359.65] loss=1.54 avg=1.65\n", "[972 | 1360.92] loss=1.68 avg=1.65\n", "[973 | 1362.20] loss=1.40 avg=1.65\n", "[974 | 1363.48] loss=1.58 avg=1.65\n", "[975 | 1364.76] loss=1.67 avg=1.65\n", "[976 | 1366.05] loss=1.58 avg=1.65\n", "[977 | 1367.32] loss=1.43 avg=1.64\n", "[978 | 1368.63] loss=1.52 avg=1.64\n", "[979 | 1369.93] loss=1.64 avg=1.64\n", "[980 | 1371.20] loss=1.58 avg=1.64\n", "[981 | 1372.48] loss=1.46 avg=1.64\n", "[982 | 1373.75] loss=1.61 avg=1.64\n", "[983 | 1375.05] loss=1.64 avg=1.64\n", "[984 | 1376.32] loss=1.65 avg=1.64\n", "[985 | 1377.59] loss=1.51 avg=1.64\n", "[986 | 1378.87] loss=1.55 avg=1.64\n", "[987 | 1380.14] loss=1.58 avg=1.64\n", "[988 | 1381.42] loss=1.43 avg=1.64\n", "[989 | 1382.69] loss=1.47 avg=1.63\n", "[990 | 1383.97] loss=1.47 avg=1.63\n", "[991 | 1385.25] loss=1.59 avg=1.63\n", "[992 | 1386.53] loss=1.57 avg=1.63\n", "[993 | 1387.81] loss=1.48 avg=1.63\n", "[994 | 1389.08] loss=1.57 avg=1.63\n", "[995 | 1390.36] loss=1.63 avg=1.63\n", "[996 | 1391.64] loss=1.74 avg=1.63\n", "[997 | 1392.91] loss=1.53 avg=1.63\n", "[998 | 1394.19] loss=1.58 avg=1.63\n", "[999 | 1395.47] loss=1.54 avg=1.63\n", "[1000 | 1396.75] loss=1.57 avg=1.63\n", "Saving checkpoint/run1/model-1000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 14:42:55.032369: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:42:55.033962: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:42:55.034963: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:42:55.036130: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:42:55.037183: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 14:42:55.038040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-1000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 3.03it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 11375643 tokens\n", "Training...\n", "Saving checkpoint/run1/model-1000\n", "Saving checkpoint/run1/model-1000\n", "======== SAMPLE 1 ========\n", " QUIS FAUTE APPLICABLE AU MOTIF QUE LE PROPRIETAIRE AVAIT ACCEDE, AU SURPLUS, ENTRE TOUS LES AUTEURS AU SEQUESTRE A LA COMMISSION NATIONALE DE FER FRANCAISE A LAQUELLE LUI AVAIT ETE INFERIEUR AU LITIGE DE LA COMMISSION NATIONALE DE MANDATAIRES, ELLE A COMMIS UNE EXPERTISE SUR LEQUEL IL AVAIT COMMIS UNE INFRACTION LUI-MEME ; MAIS ATTENDU QUE LE JUGE DE PAIX D'APPEL INFIRME LA DECISION AJOUTE QUE L'EXPERT Y... S'ETAIT CONSENTI EN APPEL LE 29 DECEMBRE 1959 DE LA FAUTE IMPRECISE AU REGLEMENT DES DEBATS COMMISES PAR LA VICTIME A LAQUELLE LA VICTIME LA DEMANDE DE FAIRE DROIT D'AVOIR ETE INFRACTION ; ATTENDU QU'EN L'ESPECE LES JUGEMENTS DE L'EXPERT, SANS PRECISER AUCUNE DEMANDE EN REVENDICATION AUX CONCLUSIONS D'APPEL AYANT PERMIS AUX PROPRIETAIRES AU NOM DE FAIRE DE SURET LA PRESCRIPTION DES ACTES REGLEMENTAIRES DONT IL A DECLARE QUE LES DEUX PRETENTIONS NE FAITE QUE DEVANT LES JURIDICTIONS PAR LA VICTIME LITIGIEUX ; ATTENDU QUE DES LORS, DANS LA PREMIERE DECISION ADOPTANT LES MOTIFS PROPRES PAR LE DECRET DU 22 NOVEMBRE 1959, IL EN EST IRRECEVABLE COMME EN CONSEQUENCE NI DES DROITS DES RENSEIGNEMENTS, NON JUSTIFIE QUI LEUR ATTEINTE, QU'IL NE RESSORT QU'A TITRE SUBI, NI DANS LES CONDITIONS DANS LES MOTIFS QUE LA PREMIERE EXPLOITATION DUDIT APPEL A JOSEPH E... DE FAIRE EXPRESSION ET QUE L'INFRACTION CONVENIENT DE LA DECISION AJOUTE QUE LES DEUX PRETENTIONS NE DEVAIENT PAS ETRE IMPUTES AU REGLEMENT DE LES TEXTES INVOQUES PAR L'EXPERT, IL NE POUVAIT DONC AINSI QU'UN MOYEN N'EST PAS ENCORE A BON DROIT QUANT AU MOYEN ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 4 MARS 1960 PAR LA COUR D'APPEL DE TOULOUSE. NO 60-10.356. EPOUX C... PETROLAN C/ EPOUX C... ROUSSEAU PRESIDENT : M. LESCOT. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE PRIS DE LA VIOLATION DE L'ARTICLE 902 DU CODE CIVIL, DE LA LOI DU 1ER SEPTEMBRE 1948, DE L'ARTICLE 7 DE LA LOI DU 20 AVRIL 1810, DEFAUT DE MOTIFS, MANQUE DE BASE LEGALE ; EN CE QUE, SELON L'ARRETE, LA COUR D'APPEL A VIOLE LE TEXTE SUSVISE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 2 MARS 1959 PAR LA COUR D'APPEL DE GRENOBLE, LE 7 JUIN 1959. NO 60-14.932. EPOUX C... X... ET AUTRES C/ COTIS-ROLAND. PRESIDENT : M. CAMBOULIVES, CONSEILLER DOYEN, FAISANT FONCTIONS. RAPPORTEUR : M. MARTIN. AVOCAT GENERAL : M. ALBUCHER. AVOCATS : MM. NICOLAS ET LABBE. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE PRIS DE LA VIOLATION DE L'ARTICLE 4 DU DECRET DU 30 SEPTEMBRE 1959, DE LA LOI DU 1ER SEPTEMBRE 1961, DE LA CONVENTION COLLECTIVE ANTERIEURE AUX DEBATS\n", "\n", "[1001 | 22.29] loss=1.73 avg=1.73\n", "[1002 | 23.56] loss=1.81 avg=1.77\n", "[1003 | 24.84] loss=1.68 avg=1.74\n", "[1004 | 26.11] loss=1.58 avg=1.70\n", "[1005 | 27.39] loss=1.78 avg=1.72\n", "[1006 | 28.66] loss=1.88 avg=1.75\n", "[1007 | 29.93] loss=1.67 avg=1.73\n", "[1008 | 31.21] loss=1.56 avg=1.71\n", "[1009 | 32.49] loss=1.68 avg=1.71\n", "[1010 | 33.77] loss=1.56 avg=1.69\n", "[1011 | 35.05] loss=1.78 avg=1.70\n", "[1012 | 36.32] loss=1.66 avg=1.70\n", "[1013 | 37.60] loss=1.70 avg=1.70\n", "[1014 | 38.88] loss=1.73 avg=1.70\n", "[1015 | 40.15] loss=1.57 avg=1.69\n", "[1016 | 41.43] loss=1.70 avg=1.69\n", "[1017 | 42.70] loss=1.43 avg=1.67\n", "[1018 | 43.99] loss=1.45 avg=1.66\n", "[1019 | 45.27] loss=1.70 avg=1.66\n", "[1020 | 46.54] loss=1.59 avg=1.66\n", "[1021 | 47.82] loss=1.68 avg=1.66\n", "[1022 | 49.18] loss=1.59 avg=1.66\n", "[1023 | 50.50] loss=1.85 avg=1.67\n", "[1024 | 51.77] loss=1.62 avg=1.66\n", "[1025 | 53.05] loss=1.69 avg=1.67\n", "[1026 | 54.32] loss=1.61 avg=1.66\n", "[1027 | 55.63] loss=1.63 avg=1.66\n", "[1028 | 56.90] loss=1.59 avg=1.66\n", "[1029 | 58.17] loss=1.56 avg=1.66\n", "[1030 | 59.45] loss=1.78 avg=1.66\n", "[1031 | 60.72] loss=1.66 avg=1.66\n", "[1032 | 61.99] loss=1.58 avg=1.66\n", "[1033 | 63.27] loss=1.54 avg=1.65\n", "[1034 | 64.54] loss=1.42 avg=1.65\n", "[1035 | 65.84] loss=1.65 avg=1.65\n", "[1036 | 67.14] loss=1.63 avg=1.65\n", "[1037 | 68.42] loss=1.61 avg=1.64\n", "[1038 | 69.70] loss=1.49 avg=1.64\n", "[1039 | 70.98] loss=1.67 avg=1.64\n", "[1040 | 72.25] loss=1.57 avg=1.64\n", "[1041 | 73.53] loss=1.66 avg=1.64\n", "[1042 | 74.81] loss=1.66 avg=1.64\n", "[1043 | 76.08] loss=1.68 avg=1.64\n", "[1044 | 77.38] loss=1.77 avg=1.64\n", "[1045 | 78.66] loss=1.55 avg=1.64\n", "[1046 | 79.93] loss=1.52 avg=1.64\n", "[1047 | 81.22] loss=1.79 avg=1.64\n", "[1048 | 82.49] loss=1.59 avg=1.64\n", "[1049 | 83.77] loss=1.77 avg=1.64\n", "[1050 | 85.07] loss=1.58 avg=1.64\n", "[1051 | 86.35] loss=1.59 avg=1.64\n", "[1052 | 87.63] loss=1.65 avg=1.64\n", "[1053 | 88.92] loss=1.59 avg=1.64\n", "[1054 | 90.19] loss=1.59 avg=1.64\n", "[1055 | 91.47] loss=1.72 avg=1.64\n", "[1056 | 92.74] loss=1.47 avg=1.64\n", "[1057 | 94.02] loss=1.54 avg=1.63\n", "[1058 | 95.29] loss=1.57 avg=1.63\n", "[1059 | 96.57] loss=1.50 avg=1.63\n", "[1060 | 97.84] loss=1.66 avg=1.63\n", "[1061 | 99.14] loss=1.61 avg=1.63\n", "[1062 | 100.42] loss=1.66 avg=1.63\n", "[1063 | 101.70] loss=1.68 avg=1.63\n", "[1064 | 102.97] loss=1.53 avg=1.63\n", "[1065 | 104.25] loss=1.67 avg=1.63\n", "[1066 | 105.53] loss=1.84 avg=1.63\n", "[1067 | 106.80] loss=1.56 avg=1.63\n", "[1068 | 108.07] loss=1.43 avg=1.63\n", "[1069 | 109.35] loss=1.47 avg=1.63\n", "[1070 | 110.64] loss=1.65 avg=1.63\n", "[1071 | 111.91] loss=1.58 avg=1.63\n", "[1072 | 113.20] loss=1.51 avg=1.62\n", "[1073 | 114.48] loss=1.59 avg=1.62\n", "[1074 | 115.75] loss=1.60 avg=1.62\n", "[1075 | 117.03] loss=1.60 avg=1.62\n", "[1076 | 118.30] loss=1.52 avg=1.62\n", "[1077 | 119.58] loss=1.61 avg=1.62\n", "[1078 | 120.89] loss=1.70 avg=1.62\n", "[1079 | 122.24] loss=1.67 avg=1.62\n", "[1080 | 123.52] loss=1.64 avg=1.62\n", "[1081 | 124.80] loss=1.60 avg=1.62\n", "[1082 | 126.07] loss=1.53 avg=1.62\n", "[1083 | 127.34] loss=1.68 avg=1.62\n", "[1084 | 128.62] loss=1.50 avg=1.62\n", "[1085 | 129.89] loss=1.61 avg=1.62\n", "[1086 | 131.17] loss=1.52 avg=1.62\n", "[1087 | 132.45] loss=1.60 avg=1.62\n", "[1088 | 133.74] loss=1.72 avg=1.62\n", "[1089 | 135.01] loss=1.68 avg=1.62\n", "[1090 | 136.28] loss=1.85 avg=1.62\n", "[1091 | 137.56] loss=1.59 avg=1.62\n", "[1092 | 138.83] loss=1.62 avg=1.62\n", "[1093 | 140.11] loss=1.54 avg=1.62\n", "[1094 | 141.38] loss=1.59 avg=1.62\n", "[1095 | 142.66] loss=1.51 avg=1.62\n", "[1096 | 143.95] loss=1.72 avg=1.62\n", "[1097 | 145.23] loss=1.55 avg=1.62\n", "[1098 | 146.51] loss=1.67 avg=1.62\n", "[1099 | 147.79] loss=1.56 avg=1.62\n", "[1100 | 149.06] loss=1.61 avg=1.62\n", "======== SAMPLE 1 ========\n", "OUS JUGES ;ATTENDU QU'IL EST REPROCHE A LA COUR D'APPEL D'AVOIR CONDAMNE A PAYER A RON DE SON FONDS DE COMMERCE A PAYER A SES ANNEES A SA MERE LE MONTANT DE LA SOCIETE ANONYME ETABLI PAR LA SOCIETE D'ETAT OU L'ASSURANCE INCRIMINEE PAR LA SOCIETE ET LA DAME A... A..., SON ETABLISSEMENT ET COMPRISE ETANT DUES DE NOMBREUX DE CHEQUES ENSEMBLEES AUX DEPENS DES SOMMES DE CORDER ETABLIS QU'IL AURAIT CONSERVE LA CHOSE NE POUVAIT SE FAIRE UN CARREFOUR DES GRIEFS QUE SUR PLACER ET D'APRES LE TEMPS ET DE SORTE QUE SON PREPARATION, DE LA MEME DUREE DE LA SOCIETE ET A TITRE PREVUE PAR L'ARTICLE 1606 DU CODE CIVIL, UNE FAUSSE APPLICATION EST REPROCHE AUX TEXTES D'OU IL DEVAIT ETRE TENUE QU'IL N'Y A PLUS DE TROIS MOIS AVAIT SUBSISTE SUR PLACER;MAIS ATTENDU QU'IL RESULTE DES ENONCIATIONS DE L'ARRET CONFIRMATIF ATTAQUE QUE A..., AINSI QUE LE RAPPORT D'UN COMMERCE, L'ENVOI DE PAYER A VENTE DE DERIORATION DE L'ACCIDENT, ALORS PAR SAISI ;QUE \"POUR CONSERVAT ARME LA CHEPARTENALE QUI TOUT A LA CIRCULATION A UN TEMPS ET DE SORTE PAR SIEUR Z... POUR REPARER CETTE DERNIERE, LA CHEPARTENALE FUT BLESSE A L'INDEMNITE D'EVICTION\" ;ATTENDU QUE LA COUR D'APPEL A DONC VIOLE, EN CE QUI CONCERNE LA DECISION ;ATTENDU QUE LE POURVOI DEVAIT EN CONSEQUENCE SOUTENU QUE LES CONVENTIONS DE LA SOCIETE DANGERS-DISANT AVAIENT ETE ETABLIEES PAR LE MONTANT DE LA SOCIETE; QU'ELLE A AJOUTE EXPRESSEMENT QUE LE MONTANT DE CELUI QU'ELLE AVAIT, A SON DEPART SUFFISAMMENT SAISI ET LES CONVENTIONS DES FONCTIONS A LA SOCIETE DANGERS-DISANT, CELUI-CI EN PRETENDANT QUE L'AFFAIRE, PAR LE TRIBUNAL D'INSTANCE, DE SA DECISION, N'AVAIT PU S'ACCIDENT, A FAIRE DE SON OBLIGATION SURPLUS PAR SIEUR Z... ET QUI N'AVAIT PAS PRECISEMENT LA SOCIETE ET AVAIT FAIT AU MONTANT;QU'IL A APPELE QUE LA SOCIETE DANGERS-DISANT AVAIT OBTENU D'EN VUE LA CHEPARTENALE DE LA SOCIETE DANGER-DISANT, CE QUI AVAIT AFFILIE SON ETAT, QUI, POUR LES SOMMES DE PRETENIR CE MONTANT, AVAIT SOUTENU POUR L'INDEMNITE DE PREAVIS DE SA MARQUE QU'ILS N'ONT PAS ETABLI ET QUE, DICARDANT AU TEMPS, LA COMMISSION DE PREAVIS AVAIT, A BON DROIT, ADMIS A LA SOCIETE DE LA CHEPARTENALE NON TENUE SANS INFLIGE ;ATTENDU QUE LES JUGES D'APPEL ONT RETENU QUE A... \"AIT PU RECHERCHER SI, POUR DES FACTURES DU MONTANT DU JUGEMENT AU TRIBUNAL D'INSTANCE, LE MONTANT DE LA SOCIETE FUT DANGEREUSE ET DEVAIT ETRE FONDEE A SA RESPONSABILITE; QU'IL A ETE REPONDU AUX CONCLUSIONS DE L'EXPERT ENVERS LES DEMANDES, REGLOISON DU P\n", "\n", "[1101 | 162.72] loss=1.59 avg=1.62\n", "[1102 | 163.99] loss=1.51 avg=1.62\n", "[1103 | 165.28] loss=1.63 avg=1.62\n", "[1104 | 166.56] loss=1.58 avg=1.62\n", "[1105 | 167.83] loss=1.53 avg=1.62\n", "[1106 | 169.11] loss=1.64 avg=1.62\n", "[1107 | 170.38] loss=1.65 avg=1.62\n", "[1108 | 171.66] loss=1.47 avg=1.61\n", "[1109 | 172.93] loss=1.58 avg=1.61\n", "[1110 | 174.21] loss=1.45 avg=1.61\n", "[1111 | 175.49] loss=1.58 avg=1.61\n", "[1112 | 176.84] loss=1.64 avg=1.61\n", "[1113 | 178.11] loss=1.67 avg=1.61\n", "[1114 | 179.39] loss=1.63 avg=1.61\n", "[1115 | 180.66] loss=1.80 avg=1.62\n", "[1116 | 181.94] loss=1.59 avg=1.62\n", "[1117 | 183.21] loss=1.68 avg=1.62\n", "[1118 | 184.48] loss=1.47 avg=1.61\n", "[1119 | 185.76] loss=1.72 avg=1.62\n", "[1120 | 187.05] loss=1.70 avg=1.62\n", "[1121 | 188.33] loss=1.58 avg=1.62\n", "[1122 | 189.61] loss=1.45 avg=1.61\n", "[1123 | 190.88] loss=1.56 avg=1.61\n", "[1124 | 192.18] loss=1.74 avg=1.61\n", "[1125 | 193.46] loss=1.72 avg=1.62\n", "[1126 | 194.74] loss=1.52 avg=1.61\n", "[1127 | 196.02] loss=1.59 avg=1.61\n", "[1128 | 197.29] loss=1.64 avg=1.61\n", "[1129 | 198.60] loss=1.69 avg=1.62\n", "[1130 | 199.87] loss=1.47 avg=1.61\n", "[1131 | 201.15] loss=1.64 avg=1.61\n", "[1132 | 202.42] loss=1.49 avg=1.61\n", "[1133 | 203.69] loss=1.69 avg=1.61\n", "[1134 | 204.97] loss=1.56 avg=1.61\n", "[1135 | 206.24] loss=1.54 avg=1.61\n", "[1136 | 207.52] loss=1.65 avg=1.61\n", "[1137 | 208.84] loss=1.49 avg=1.61\n", "[1138 | 210.12] loss=1.47 avg=1.61\n", "[1139 | 211.40] loss=1.68 avg=1.61\n", "[1140 | 212.67] loss=1.72 avg=1.61\n", "[1141 | 213.95] loss=1.73 avg=1.61\n", "[1142 | 215.22] loss=1.55 avg=1.61\n", "[1143 | 216.50] loss=1.66 avg=1.61\n", "[1144 | 217.77] loss=1.75 avg=1.61\n", "[1145 | 219.05] loss=1.47 avg=1.61\n", "[1146 | 220.35] loss=1.65 avg=1.61\n", "[1147 | 221.62] loss=1.76 avg=1.61\n", "[1148 | 222.90] loss=1.59 avg=1.61\n", "[1149 | 224.18] loss=1.66 avg=1.61\n", "[1150 | 225.48] loss=1.64 avg=1.62\n", "[1151 | 226.76] loss=1.83 avg=1.62\n", "[1152 | 228.03] loss=1.53 avg=1.62\n", "[1153 | 229.31] loss=1.74 avg=1.62\n", "[1154 | 230.59] loss=1.42 avg=1.62\n", "[1155 | 231.89] loss=1.71 avg=1.62\n", "[1156 | 233.17] loss=1.68 avg=1.62\n", "[1157 | 234.44] loss=1.55 avg=1.62\n", "[1158 | 235.72] loss=1.64 avg=1.62\n", "[1159 | 236.99] loss=1.60 avg=1.62\n", "[1160 | 238.27] loss=1.39 avg=1.61\n", "[1161 | 239.54] loss=1.49 avg=1.61\n", "[1162 | 240.82] loss=1.53 avg=1.61\n", "[1163 | 242.11] loss=1.50 avg=1.61\n", "[1164 | 243.39] loss=1.62 avg=1.61\n", "[1165 | 244.66] loss=1.48 avg=1.61\n", "[1166 | 245.94] loss=1.64 avg=1.61\n", "[1167 | 247.21] loss=1.75 avg=1.61\n", "[1168 | 248.49] loss=1.77 avg=1.61\n", "[1169 | 249.76] loss=1.62 avg=1.61\n", "[1170 | 251.04] loss=1.55 avg=1.61\n", "[1171 | 252.31] loss=1.59 avg=1.61\n", "[1172 | 253.62] loss=1.53 avg=1.61\n", "[1173 | 254.89] loss=1.55 avg=1.61\n", "[1174 | 256.17] loss=1.69 avg=1.61\n", "[1175 | 257.44] loss=1.59 avg=1.61\n", "[1176 | 258.76] loss=1.68 avg=1.61\n", "[1177 | 260.03] loss=1.71 avg=1.61\n", "[1178 | 261.30] loss=1.64 avg=1.61\n", "[1179 | 262.58] loss=1.63 avg=1.61\n", "[1180 | 263.87] loss=1.44 avg=1.61\n", "[1181 | 265.15] loss=1.69 avg=1.61\n", "[1182 | 266.43] loss=1.58 avg=1.61\n", "[1183 | 267.70] loss=1.57 avg=1.61\n", "[1184 | 268.97] loss=1.52 avg=1.61\n", "[1185 | 270.25] loss=1.50 avg=1.61\n", "[1186 | 271.52] loss=1.73 avg=1.61\n", "[1187 | 272.81] loss=1.68 avg=1.61\n", "[1188 | 274.08] loss=1.57 avg=1.61\n", "[1189 | 275.38] loss=1.54 avg=1.61\n", "[1190 | 276.65] loss=1.82 avg=1.61\n", "[1191 | 277.93] loss=1.50 avg=1.61\n", "[1192 | 279.20] loss=1.67 avg=1.61\n", "[1193 | 280.47] loss=1.47 avg=1.61\n", "[1194 | 281.75] loss=1.49 avg=1.61\n", "[1195 | 283.02] loss=1.59 avg=1.61\n", "[1196 | 284.30] loss=1.57 avg=1.61\n", "[1197 | 285.58] loss=1.57 avg=1.61\n", "[1198 | 286.88] loss=1.66 avg=1.61\n", "[1199 | 288.15] loss=1.62 avg=1.61\n", "[1200 | 289.43] loss=1.61 avg=1.61\n", "======== SAMPLE 1 ========\n", " ATTRE L'INDEMNITE, A CETTE INDEMNITE A DEFAUT DE CARACTERE COMMERCIAL QU'AUX CONTRATS DE LA CAISSE INTERPROFESSIONNELLE DE VELOMOTEURS QUI AVAIT ETE REVENDIQUE PAR APPLICATION DE LA CONVENTION COLLECTIVE NE CONTPUTANT PAS LA QUASI-CELUI-CI DU REGIME GENERAL DES CAISSES DE SECURITE SOCIALE ET DU MARCHE ; QUE, DES LORS, IL RESULTE QUE C'ETAIT SEUL DES PRECAUTIONS DE L'ENQUETE, QUE CE QUASI-CELUI-CI N'EXCEDAIT DU MOT, QU'IL AURAIT LIEU, CONSERVER QUE CELLE-CI S'ETAIT LIE PENDANT PLUS DE LEUR COTE, NI QU'IL N'EXCEDE UNE LETTRE QUE C'EST SONT AVAIT TENUS DE CERTS DE GROSSIERS QU'ILS AURAIENT EU POUR DEFAUT DE CARACTERE COMMERCIAL DANS LA CHARTE DE LA VUE, QU'AUX CONTRATS DU REGIME, LE TRAVAIL CONSISTANT DU REGIME A LA CONVENTION COLLECTIVE DEVANT LA SOCIETE D'AMERICAUDIO CELLE-CI ; QUE SUR LA PREMIERE INSTANCE DE LA CAISSE INTERPROFESSIONNELLE DE VELOMOTEURS, IL EST PRECISE QUE CELLE-CI AVAIT ETE REVENDIQUE PAR APPLICATION DE L'ARTICLE 1384, ALINEA 1 DU CODE CIVIL, QU'AINSI L'ARTICLE 381, ALINEA 1 DU CODE CIVIL, N'AURAIT CEPENDANT QU'EN RAISON DE LA DIRECTION QUE CELLE-CI AVAIT ETE REVENDICEE PAR APPLICATION DU CONTRAT ET QUE, PAR SUITE, CES CONTRATS, QUI EN EST SUSCEPTIBLE D'APPLICATION DES ARTICLES 1371 ET 1134 ET, CONTRAIREMENT A CETTE MESURE D'OFFICE, NE SONT PAS D'ACCORD SUR LA DEMANDE D'ASSURANCE VIEILLESSE DE LA CAISSE INTERPROFESSIONNELLE D'AMERICAUDIO CELLE-CI ET SUR UNE CONTRAT DE TRAVAIL ;QUE, SUR CE POINT, LA COUR NE POUVANT AUCUNEMENT ELOIGNEMENT SUR RAPPORT AVEC LA SOCIETE CELLE VILLE, L'ARRET DE STATUER A JUSTEMENT CETTE DEMANDE, ET QUE LE MOYEN EST NOUVEAU ET DONC D'EUT ENCORE BRUSQUEMENT ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LA DECISION RENDUE LE 19 MARS 1962 PAR LA COMMISSION ARBITRALE DE LA S.A.R.L.E.R. RENAUD-SOCIETE D'AGENES DE MARSEILLE-MONTPELLIER. N° 62 - 13 561 CAISSE INTERPROFESSIONNELLE D'AMERICAUDIO CELLE ET CIE C/ CAISSE PRIMAIRE DE SECURITE SOCIALE D'AMERICAUDIO C/ CELLE-CI. PRESIDENT ET RAPPORTEUR : M DROUILLAT - AVOCAT GENERAL : M AMOR - AVOCATS : MM ROUSSEAU ET NICOLAS. A RAPPROCHER : 16 NOVEMBRE 1962, BULL 1962, IV, N° 583, P 578. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE, DE SE FONDANT SUR SA DEMANDE EN DOMMAGES-INTERETS POUR LA RESPONSABILITE DU POURVOI PRISE EN DECLARANT A L'ARRET CONFIRMATIF ATTAQUE, LUI ADMET QUE DEMOISELLE Y..., PAR VOIE DE REPARTITION D'UN PREJUDICE ANORMALE, S'ETAIT FAIT VALOIR QUE L'AVEU N'ETAIT PLUS DE L'APPUI DU SUSNOMMEE, CE N'EST TENU DE TENIR COMME INSCRU, DE LA COUR D'APP\n", "\n", "[1201 | 303.55] loss=1.55 avg=1.61\n", "[1202 | 304.83] loss=1.39 avg=1.61\n", "[1203 | 306.11] loss=1.47 avg=1.60\n", "[1204 | 307.38] loss=1.73 avg=1.60\n", "[1205 | 308.68] loss=1.67 avg=1.61\n", "[1206 | 309.95] loss=1.62 avg=1.61\n", "[1207 | 311.22] loss=1.72 avg=1.61\n", "[1208 | 312.50] loss=1.62 avg=1.61\n", "[1209 | 313.78] loss=1.54 avg=1.61\n", "[1210 | 315.06] loss=1.54 avg=1.61\n", "[1211 | 316.33] loss=1.61 avg=1.61\n", "[1212 | 317.61] loss=1.74 avg=1.61\n", "[1213 | 318.90] loss=1.62 avg=1.61\n", "[1214 | 320.18] loss=1.55 avg=1.61\n", "[1215 | 321.45] loss=1.53 avg=1.61\n", "[1216 | 322.73] loss=1.57 avg=1.61\n", "[1217 | 324.02] loss=1.59 avg=1.61\n", "[1218 | 325.30] loss=1.55 avg=1.60\n", "[1219 | 326.58] loss=1.55 avg=1.60\n", "[1220 | 327.85] loss=1.65 avg=1.60\n", "[1221 | 329.12] loss=1.45 avg=1.60\n", "[1222 | 330.41] loss=1.41 avg=1.60\n", "[1223 | 331.69] loss=1.85 avg=1.60\n", "[1224 | 332.96] loss=1.43 avg=1.60\n", "[1225 | 334.24] loss=1.49 avg=1.60\n", "[1226 | 335.51] loss=1.59 avg=1.60\n", "[1227 | 336.79] loss=1.66 avg=1.60\n", "[1228 | 338.07] loss=1.70 avg=1.60\n", "[1229 | 339.34] loss=1.53 avg=1.60\n", "[1230 | 340.62] loss=1.58 avg=1.60\n", "[1231 | 341.91] loss=1.64 avg=1.60\n", "[1232 | 343.19] loss=1.52 avg=1.60\n", "[1233 | 344.47] loss=1.61 avg=1.60\n", "[1234 | 345.74] loss=1.60 avg=1.60\n", "[1235 | 347.01] loss=1.53 avg=1.60\n", "[1236 | 348.29] loss=1.52 avg=1.60\n", "[1237 | 349.56] loss=1.56 avg=1.60\n", "[1238 | 350.83] loss=1.67 avg=1.60\n", "[1239 | 352.13] loss=1.72 avg=1.60\n", "[1240 | 353.41] loss=1.53 avg=1.60\n", "[1241 | 354.69] loss=1.57 avg=1.60\n", "[1242 | 355.96] loss=1.51 avg=1.60\n", "[1243 | 357.27] loss=1.56 avg=1.60\n", "[1244 | 358.55] loss=1.70 avg=1.60\n", "[1245 | 359.82] loss=1.76 avg=1.60\n", "[1246 | 361.10] loss=1.60 avg=1.60\n", "[1247 | 362.37] loss=1.66 avg=1.60\n", "[1248 | 363.67] loss=1.53 avg=1.60\n", "[1249 | 364.94] loss=1.53 avg=1.60\n", "[1250 | 366.21] loss=1.54 avg=1.60\n", "[1251 | 367.49] loss=1.46 avg=1.60\n", "[1252 | 368.77] loss=1.63 avg=1.60\n", "[1253 | 370.05] loss=1.39 avg=1.60\n", "[1254 | 371.32] loss=1.56 avg=1.60\n", "[1255 | 372.60] loss=1.44 avg=1.59\n", "[1256 | 373.89] loss=1.50 avg=1.59\n", "[1257 | 375.16] loss=1.46 avg=1.59\n", "[1258 | 376.44] loss=1.48 avg=1.59\n", "[1259 | 377.71] loss=1.50 avg=1.59\n", "[1260 | 378.98] loss=1.55 avg=1.59\n", "[1261 | 380.26] loss=1.65 avg=1.59\n", "[1262 | 381.53] loss=1.48 avg=1.59\n", "[1263 | 382.81] loss=1.68 avg=1.59\n", "[1264 | 384.08] loss=1.60 avg=1.59\n", "[1265 | 385.37] loss=1.78 avg=1.59\n", "[1266 | 386.65] loss=1.44 avg=1.59\n", "[1267 | 387.93] loss=1.52 avg=1.59\n", "[1268 | 389.21] loss=1.56 avg=1.59\n", "[1269 | 390.52] loss=1.85 avg=1.59\n", "[1270 | 391.79] loss=1.57 avg=1.59\n", "[1271 | 393.07] loss=1.59 avg=1.59\n", "[1272 | 394.34] loss=1.65 avg=1.59\n", "[1273 | 395.62] loss=1.42 avg=1.59\n", "[1274 | 396.91] loss=1.57 avg=1.59\n", "[1275 | 398.18] loss=1.62 avg=1.59\n", "[1276 | 399.45] loss=1.65 avg=1.59\n", "[1277 | 400.74] loss=1.63 avg=1.59\n", "[1278 | 402.01] loss=1.62 avg=1.59\n", "[1279 | 403.28] loss=1.46 avg=1.59\n", "[1280 | 404.56] loss=1.41 avg=1.59\n", "[1281 | 405.84] loss=1.43 avg=1.59\n", "[1282 | 407.13] loss=1.76 avg=1.59\n", "[1283 | 408.41] loss=1.65 avg=1.59\n", "[1284 | 409.68] loss=1.53 avg=1.59\n", "[1285 | 410.96] loss=1.55 avg=1.59\n", "[1286 | 412.23] loss=1.41 avg=1.59\n", "[1287 | 413.51] loss=1.68 avg=1.59\n", "[1288 | 414.78] loss=1.51 avg=1.59\n", "[1289 | 416.06] loss=1.51 avg=1.59\n", "[1290 | 417.33] loss=1.74 avg=1.59\n", "[1291 | 418.62] loss=1.52 avg=1.59\n", "[1292 | 419.90] loss=1.54 avg=1.59\n", "[1293 | 421.17] loss=1.53 avg=1.59\n", "[1294 | 422.47] loss=1.46 avg=1.58\n", "[1295 | 423.77] loss=1.58 avg=1.58\n", "[1296 | 425.04] loss=1.52 avg=1.58\n", "[1297 | 426.32] loss=1.45 avg=1.58\n", "[1298 | 427.59] loss=1.65 avg=1.58\n", "[1299 | 428.88] loss=1.51 avg=1.58\n", "[1300 | 430.15] loss=1.57 avg=1.58\n", "======== SAMPLE 1 ========\n", "I PRECEDAMMENT DU DECOMPTE LA LEGISLATION SUR REEMBAUCHAGE, QU'AUCUNE DIFFICULTE, NOMBREUX DE LA SOCIETE LITIGIEUSE, ETABLIS QUELQUE S'IL EST SENTENCE QUE, A LA DATE DU 8 JANVIER 1965, NON UNE ACTION EN DOMMAGES-INTERETS QUI AVAIT POUR LUI AVOIR EU CONNAISSEMENT A LA VICTIME OU CELUI-CI CELLE, AVAIT ETE DE NATURE A DE SES LIEUX ET, SANS L'EXISTENCE DE SON OPPOSITION, A LA REVOCATION NOUVEAU, D'UN DOMMAGE AVAIT ETE REVOQUE PAR UN DOMMAGE NOUVELLE, D'UN DOMMAGE NOUVELLE DONT S'AGIT, D'UN CHEMIN DE CARTAGNO ;ATTENDU QU'IL EST REPROCHE A L'ARRET D'AVOIR, A TORT, DECIDE QUE LES CONVENTIONS TELANT LES DOMMAGES-INTERETS ETABLIS QUE L'OBLIGATION D'UN DOMMAGE NOUVELLE DE L'AVERTISSEMENT SOUS L'EXISTENCE PAR UN DOMMAGE, ALORS QUE LA PRESENTATION DE LA LEGISLATION SUR REEMBAUCHAGE NE RAPPORTE LA PREUVE ET N'AVAIT PAS ETE ETABLIE QUE LA CHAMBRE AIT EU LIEU, EN UN AIDER DE VACANT, SON AIDER AVAIT INJURIEUSE LA PRESENCE DE LA LEGISLATION SUR REEMBAUCHAGE ;MAIS ATTENDU, EN SECOND, QUE LE JUGEMENT ATTAQUE, RENDUE CETTE AFFECTATION DES DOMMAGES-INTERETS, A DECLARE \"QU'ELLE AVAIT VERSE AU-SOCEEN DE CE CHEMIN DE CARTAGNO, LA VICTIME AVAIT ETE ETABLIE DU VEHICULE SANS SON PERE EN A, EN ECARTANT UNE DEFAILLANCE PREVUE PAR LA LEGISLATION SUR REEMBAUCHAGE, QUE CES DERNIERS NE S'AYANT ETE EXECUTES D'UN DOMMAGE NOUVELLE, QU'ELLE AVAIT SELON LE VERSEMENT DES DOMMAGES-INTERETS, LE DOMMAGE NOUVELLE EST CONVOCABLE A L'OCCASION DE LA LEGISLATION SUR REEMBAUCHAGE DU DOMMAGE EST ECHOUE ET QU'IL N'AVAIT ETE PAS DE NATURE A DE SES LIEUX ETANT REVOCABLE ;ATTENDU QU'AYANT SOUVERAINEMENT CONSTATE, D'UNE PART, QUE L'AFFECTATION POUR LUI AUX DOMMAGES-INTERETS PAR LES AFFECTANTS AINSI UNIQUEMENT QUELQUE SON INTERDIQUE, LES JUGES D'APPEL ONT PU DEDUIRE QUE L'ORGANISATION DES DOMMAGES N'AVAIT PAS EU LA CONVENTION, ET QUE CETTE CONVENTION PREVOYAIT PAR UN DOMMAGE, QUI AVAIT ACCEPTE LA CLAUSE D'EXECUTER DES DOMMAGES-INTERETS QUI S'EFFECTUERAIT SA VITESSE DE LA QUOTE AVAIT PU SE FAIRE DE LA VICTIME DU DOMMAGE \" ;ATTENDU QU'ENFIN, ENFIN, QUE LE PREMIER DERNIER MOYEN NE SAURAIT ETRE ACCUEILLI ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LE JUGEMENT RENDU LE 11 JUIN 1963 PAR LE TRIBUNAL DE DE GRANDE INSTANCE DE PARIS-CHAPARTAN. NO 63-10. 788. UNION DES POURVOIRS GENERAUX C/ SOCIETE VENDEURS. PRESIDENT : M. VIGNERON. - RAPPORTEUR : M. CRESPIN. - AVOCAT GENERAL : M. LA PRADILLION. - AVOCATS : MM. CHAREYRE, GIFFARD ET HUBERT. <|endoftext|>\n", "<|start\n", "\n", "[1301 | 443.34] loss=1.56 avg=1.58\n", "[1302 | 444.61] loss=1.46 avg=1.58\n", "[1303 | 445.88] loss=1.59 avg=1.58\n", "[1304 | 447.16] loss=1.47 avg=1.58\n", "[1305 | 448.43] loss=1.50 avg=1.58\n", "[1306 | 449.71] loss=1.50 avg=1.58\n", "[1307 | 450.99] loss=1.61 avg=1.58\n", "[1308 | 452.27] loss=1.36 avg=1.58\n", "[1309 | 453.55] loss=1.48 avg=1.57\n", "[1310 | 454.82] loss=1.55 avg=1.57\n", "[1311 | 456.14] loss=1.52 avg=1.57\n", "[1312 | 457.42] loss=1.46 avg=1.57\n", "[1313 | 458.70] loss=1.57 avg=1.57\n", "[1314 | 459.98] loss=1.55 avg=1.57\n", "[1315 | 461.25] loss=1.90 avg=1.58\n", "[1316 | 462.54] loss=1.54 avg=1.58\n", "[1317 | 463.82] loss=1.44 avg=1.57\n", "[1318 | 465.09] loss=1.48 avg=1.57\n", "[1319 | 466.37] loss=1.67 avg=1.57\n", "[1320 | 467.64] loss=1.46 avg=1.57\n", "[1321 | 468.91] loss=1.81 avg=1.58\n", "[1322 | 470.19] loss=1.60 avg=1.58\n", "[1323 | 471.46] loss=1.52 avg=1.57\n", "[1324 | 472.74] loss=1.50 avg=1.57\n", "[1325 | 474.03] loss=1.55 avg=1.57\n", "[1326 | 475.31] loss=1.53 avg=1.57\n", "[1327 | 476.59] loss=1.33 avg=1.57\n", "[1328 | 477.86] loss=1.48 avg=1.57\n", "[1329 | 479.14] loss=1.70 avg=1.57\n", "[1330 | 480.41] loss=1.77 avg=1.57\n", "[1331 | 481.68] loss=1.66 avg=1.57\n", "[1332 | 482.96] loss=1.56 avg=1.57\n", "[1333 | 484.24] loss=1.51 avg=1.57\n", "[1334 | 485.52] loss=1.71 avg=1.57\n", "[1335 | 486.79] loss=1.63 avg=1.58\n", "[1336 | 488.07] loss=1.56 avg=1.58\n", "[1337 | 489.37] loss=1.60 avg=1.58\n", "[1338 | 490.64] loss=1.49 avg=1.57\n", "[1339 | 491.92] loss=1.44 avg=1.57\n", "[1340 | 493.19] loss=1.55 avg=1.57\n", "[1341 | 494.47] loss=1.47 avg=1.57\n", "[1342 | 495.76] loss=1.62 avg=1.57\n", "[1343 | 497.03] loss=1.52 avg=1.57\n", "[1344 | 498.30] loss=1.66 avg=1.57\n", "[1345 | 499.57] loss=1.49 avg=1.57\n", "[1346 | 500.85] loss=1.55 avg=1.57\n", "[1347 | 502.12] loss=1.42 avg=1.57\n", "[1348 | 503.39] loss=1.30 avg=1.57\n", "[1349 | 504.67] loss=1.42 avg=1.57\n", "[1350 | 505.95] loss=1.71 avg=1.57\n", "[1351 | 507.24] loss=1.91 avg=1.57\n", "[1352 | 508.52] loss=1.55 avg=1.57\n", "[1353 | 509.79] loss=1.60 avg=1.57\n", "[1354 | 511.06] loss=1.58 avg=1.57\n", "[1355 | 512.33] loss=1.58 avg=1.57\n", "[1356 | 513.61] loss=1.58 avg=1.57\n", "[1357 | 514.88] loss=1.74 avg=1.57\n", "[1358 | 516.16] loss=1.64 avg=1.57\n", "[1359 | 517.44] loss=1.61 avg=1.57\n", "[1360 | 518.72] loss=1.56 avg=1.57\n", "[1361 | 519.99] loss=1.57 avg=1.57\n", "[1362 | 521.27] loss=1.71 avg=1.58\n", "[1363 | 522.56] loss=1.61 avg=1.58\n", "[1364 | 523.84] loss=1.50 avg=1.57\n", "[1365 | 525.12] loss=1.54 avg=1.57\n", "[1366 | 526.39] loss=1.55 avg=1.57\n", "[1367 | 527.66] loss=1.43 avg=1.57\n", "[1368 | 528.95] loss=1.62 avg=1.57\n", "[1369 | 530.23] loss=1.57 avg=1.57\n", "[1370 | 531.50] loss=1.40 avg=1.57\n", "[1371 | 532.77] loss=1.54 avg=1.57\n", "[1372 | 534.05] loss=1.76 avg=1.57\n", "[1373 | 535.32] loss=1.41 avg=1.57\n", "[1374 | 536.59] loss=1.48 avg=1.57\n", "[1375 | 537.87] loss=1.88 avg=1.57\n", "[1376 | 539.21] loss=1.81 avg=1.58\n", "[1377 | 540.51] loss=1.48 avg=1.57\n", "[1378 | 541.78] loss=1.86 avg=1.58\n", "[1379 | 543.05] loss=1.62 avg=1.58\n", "[1380 | 544.33] loss=1.68 avg=1.58\n", "[1381 | 545.60] loss=1.56 avg=1.58\n", "[1382 | 546.87] loss=1.36 avg=1.58\n", "[1383 | 548.15] loss=1.61 avg=1.58\n", "[1384 | 549.42] loss=1.38 avg=1.58\n", "[1385 | 550.71] loss=1.50 avg=1.57\n", "[1386 | 551.98] loss=1.48 avg=1.57\n", "[1387 | 553.25] loss=1.39 avg=1.57\n", "[1388 | 554.55] loss=1.46 avg=1.57\n", "[1389 | 555.85] loss=1.53 avg=1.57\n", "[1390 | 557.12] loss=1.60 avg=1.57\n", "[1391 | 558.39] loss=1.43 avg=1.57\n", "[1392 | 559.67] loss=1.52 avg=1.57\n", "[1393 | 560.95] loss=1.48 avg=1.57\n", "[1394 | 562.23] loss=1.38 avg=1.57\n", "[1395 | 563.50] loss=1.45 avg=1.56\n", "[1396 | 564.78] loss=1.61 avg=1.56\n", "[1397 | 566.05] loss=1.52 avg=1.56\n", "[1398 | 567.33] loss=1.49 avg=1.56\n", "[1399 | 568.60] loss=1.60 avg=1.56\n", "[1400 | 569.87] loss=1.49 avg=1.56\n", "======== SAMPLE 1 ========\n", " SURVOTA, POURSUIVI CELUI-CI ET AINSI QUE CETTE DERNIERE ; MAIS ATTENDU QUE L'ARRET INFIRMATIF ATTAQUE A DECIDE QUE LE TRIBUNAL N'A PAS LEGALEMENT JUSTIFIE SA DECISION DANS LE POURVOI EN SA DEMANDE ; D'OU IL SUIT QUE LE MOYEN EST MAL FONDE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LE JUGEMENT RENDU LE 8 MAI 1960 PAR LE TRIBUNAL DE LA MUTUALITE SOCIALE DE LILLE. NO 60-40.007 CAISSE PRIMAIRE DE SECURITE SOCIALE D'ALLENIA C/ X.... PRESIDENT : M. VIGNERON. - RAPPORTEUR : M. DUPIN. - AVOCAT GENERAL : M. CHERPITEL. - AVOCAT : M. HENRY. A RAPPROCHER : 7 AVRIL 1961, BULL. 1961, II, NO 1169 (3), P. 742. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE DEMOISELLE B... A AINSI PRONONCE LA REMUNERATION DES DEUX BOUTES ET DES LOCAUX EN RAISON DE TOUTE RESPONSABILITE INCRIMINE, REPOSES A LA CATEGORIE OU AUX DROITS DE FER DE LA SOCIETE A RESPONSABILITE LIMITEE D'ENFANT ; ATTENDU QU'IL EST FAIT GRIEF A LA COUR D'APPEL D'AVOIR, NON PLUS, APPROUVE L'EXISTENCE DU GARDIEN DU DOMMAGE ET L'INEXECUTION DE LA DECISION A L'EGARD DE DELEGUE DU PERSONNEL DU TRIBUNAL DE LA SEINE ; MAIS ATTENDU QUE L'ARRET CONSTATE, D'UNE PART, QU'A TORT, EN CE QUI CONCERNE LA CATEGORIE DU BAILLEUR, LES RAPPORTS DE L'EXECUTION ET DE DEMOISELLE, A ETE REGULIEREMENT FAUSSE DEUX DEUX BACONS ; D'OU IL SUIT QUE LE MOYEN NE SAURAIT, EN CONSEQUENCE, ETRE ACCUEILLI ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 2 MARS 1963 PAR LA COUR D'APPEL DE PARIS. NO 63-12.302. A... ET AUTRE C/ DEMOISELLE B... ET AUTRE. PRESIDENT : M. BORNET.- RAPPORTEUR : M. X....- RAVEL.- AVOCAT GENERAL : M. LEBEGUE.- AVOCATS : MM. LE GRIEL ET SOURDILLAT. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE, PRIS DANS SA PREMIERE BRANCHE : VU L'ARTICLE 27 DE LA LOI DU 1ER SEPTEMBRE 1948 MODIFIE PAR LA LOI, APPLICABLE AUX DEBATS DU LITIGE ; ATTENDU QUE, SELON LES ENONCIATIONS DE L'ARRET CONFIRMATIF ATTAQUE, X... N'A NULLEMENT ENONCE L'INCAPACITE DE DIVERS REUNIES ; QUE LES BANCAIS A SON SERVICE, COMPORTANT EN FRANCE, LORS DE LEURS FERMAGES ; QUE LE DEVANT LA COUR DE CASSATION, AINSI QU'AYANT FAIT DROIT A LA DEMANDE EN RENOUVELLEMENT, LES BANCAIS DONNES AVAIENT FAIT LORS DES DOMMAGES REGULIEREMENT QUI AVAIENT DEMANDE LA CATEGORIE DONT IL CONSTITUAIT UN COMMISSAIRE DE DIFFERCEMENTS QUI, EN CE QUI CONCERNE LES BANCAIS DONNES DONC AUPRES DE LEURS BANCAIS A, SUIVANT LESQUELLES ELLE N'IMPLIQUERAIT DANS L'EXERCICE DES FRAIS DE LA RESPONSABILITE PRULTE DE L'UN DES DOMMAGES CONTR\n", "\n", "[1401 | 583.52] loss=1.40 avg=1.56\n", "[1402 | 584.81] loss=1.57 avg=1.56\n", "[1403 | 586.08] loss=1.45 avg=1.56\n", "[1404 | 587.35] loss=1.65 avg=1.56\n", "[1405 | 588.63] loss=1.51 avg=1.56\n", "[1406 | 589.92] loss=1.53 avg=1.56\n", "[1407 | 591.20] loss=1.64 avg=1.56\n", "[1408 | 592.47] loss=1.54 avg=1.56\n", "[1409 | 593.76] loss=1.52 avg=1.56\n", "[1410 | 595.04] loss=1.58 avg=1.56\n", "[1411 | 596.31] loss=1.58 avg=1.56\n", "[1412 | 597.59] loss=1.63 avg=1.56\n", "[1413 | 598.86] loss=1.68 avg=1.56\n", "[1414 | 600.14] loss=1.71 avg=1.56\n", "[1415 | 601.41] loss=1.45 avg=1.56\n", "[1416 | 602.69] loss=1.52 avg=1.56\n", "[1417 | 603.97] loss=1.79 avg=1.57\n", "[1418 | 605.25] loss=1.54 avg=1.56\n", "[1419 | 606.53] loss=1.63 avg=1.57\n", "[1420 | 607.80] loss=1.75 avg=1.57\n", "[1421 | 609.07] loss=1.64 avg=1.57\n", "[1422 | 610.35] loss=1.58 avg=1.57\n", "[1423 | 611.62] loss=1.36 avg=1.57\n", "[1424 | 612.89] loss=1.59 avg=1.57\n", "[1425 | 614.17] loss=1.57 avg=1.57\n", "[1426 | 615.44] loss=1.63 avg=1.57\n", "[1427 | 616.73] loss=1.54 avg=1.57\n", "[1428 | 618.00] loss=1.38 avg=1.56\n", "[1429 | 619.28] loss=1.69 avg=1.57\n", "[1430 | 620.55] loss=1.50 avg=1.57\n", "[1431 | 621.83] loss=1.49 avg=1.56\n", "[1432 | 623.10] loss=1.43 avg=1.56\n", "[1433 | 624.38] loss=1.56 avg=1.56\n", "[1434 | 625.68] loss=1.72 avg=1.57\n", "[1435 | 626.97] loss=1.52 avg=1.56\n", "[1436 | 628.25] loss=1.60 avg=1.56\n", "[1437 | 629.52] loss=1.59 avg=1.57\n", "[1438 | 630.80] loss=1.49 avg=1.56\n", "[1439 | 632.08] loss=1.48 avg=1.56\n", "[1440 | 633.35] loss=1.60 avg=1.56\n", "[1441 | 634.64] loss=1.54 avg=1.56\n", "[1442 | 635.92] loss=1.53 avg=1.56\n", "[1443 | 637.20] loss=1.52 avg=1.56\n", "[1444 | 638.50] loss=1.59 avg=1.56\n", "[1445 | 639.77] loss=1.45 avg=1.56\n", "[1446 | 641.05] loss=1.50 avg=1.56\n", "[1447 | 642.32] loss=1.46 avg=1.56\n", "[1448 | 643.60] loss=1.85 avg=1.56\n", "[1449 | 644.87] loss=1.52 avg=1.56\n", "[1450 | 646.15] loss=1.48 avg=1.56\n", "[1451 | 647.42] loss=1.58 avg=1.56\n", "[1452 | 648.70] loss=1.62 avg=1.56\n", "[1453 | 649.98] loss=1.42 avg=1.56\n", "[1454 | 651.25] loss=1.77 avg=1.56\n", "[1455 | 652.53] loss=1.54 avg=1.56\n", "[1456 | 653.80] loss=1.35 avg=1.56\n", "[1457 | 655.08] loss=1.50 avg=1.56\n", "[1458 | 656.35] loss=1.48 avg=1.56\n", "[1459 | 657.63] loss=1.47 avg=1.56\n", "[1460 | 658.91] loss=1.44 avg=1.56\n", "[1461 | 660.19] loss=1.51 avg=1.56\n", "[1462 | 661.50] loss=1.46 avg=1.56\n", "[1463 | 662.78] loss=1.46 avg=1.56\n", "[1464 | 664.06] loss=1.54 avg=1.55\n", "[1465 | 665.33] loss=1.53 avg=1.55\n", "[1466 | 666.62] loss=1.61 avg=1.56\n", "[1467 | 667.90] loss=1.45 avg=1.55\n", "[1468 | 669.17] loss=1.47 avg=1.55\n", "[1469 | 670.45] loss=1.40 avg=1.55\n", "[1470 | 671.73] loss=1.56 avg=1.55\n", "[1471 | 673.01] loss=1.46 avg=1.55\n", "[1472 | 674.30] loss=1.54 avg=1.55\n", "[1473 | 675.58] loss=1.59 avg=1.55\n", "[1474 | 676.85] loss=1.51 avg=1.55\n", "[1475 | 678.13] loss=1.52 avg=1.55\n", "[1476 | 679.40] loss=1.51 avg=1.55\n", "[1477 | 680.68] loss=1.52 avg=1.55\n", "[1478 | 681.96] loss=1.49 avg=1.55\n", "[1479 | 683.25] loss=1.44 avg=1.55\n", "[1480 | 684.53] loss=1.47 avg=1.55\n", "[1481 | 685.81] loss=1.45 avg=1.55\n", "[1482 | 687.08] loss=1.53 avg=1.55\n", "[1483 | 688.35] loss=1.35 avg=1.54\n", "[1484 | 689.63] loss=1.48 avg=1.54\n", "[1485 | 690.90] loss=1.64 avg=1.54\n", "[1486 | 692.17] loss=1.55 avg=1.54\n", "[1487 | 693.47] loss=1.47 avg=1.54\n", "[1488 | 694.77] loss=1.54 avg=1.54\n", "[1489 | 696.05] loss=1.51 avg=1.54\n", "[1490 | 697.32] loss=1.42 avg=1.54\n", "[1491 | 698.60] loss=1.66 avg=1.54\n", "[1492 | 699.88] loss=1.46 avg=1.54\n", "[1493 | 701.15] loss=1.43 avg=1.54\n", "[1494 | 702.43] loss=1.46 avg=1.54\n", "[1495 | 703.71] loss=1.51 avg=1.54\n", "[1496 | 704.99] loss=1.60 avg=1.54\n", "[1497 | 706.27] loss=1.67 avg=1.54\n", "[1498 | 707.54] loss=1.51 avg=1.54\n", "[1499 | 708.82] loss=1.53 avg=1.54\n", "[1500 | 710.09] loss=1.59 avg=1.54\n", "======== SAMPLE 1 ========\n", "ON N'A L'ABSENCE DE TROIS METRES POUR L'EXPRESSION DE FONCTIONS PAR CERTE ;ATTENDU QU'EN ENONCANT LA CONTESTATION DE LEUR CONTRIBUTION, UNE RELATION PROVISIONNELLE DES ELEMENTS ADMINISTRATIVES D'UN DELAI ET L'ETAT DE CERTE, C'EST A BON DROIT QUE POUR L'EXPRESSION LE PRINCIPE DE SON PREPOSE ET QU'IL Y AVAIT EU LIEU AU TITRE DU PRIX LITIGIEUX ;MAIS ATTENDU, SANS REPONDRE AUX CONCLUSIONS QUE \"L'INDICATION DE L'ENGRAIS ET DES POTS DE POTS ETABLIT DANS UN DELAI DE GROSSES, QUI DEPOSSENT SOUFFERTES POUR L'AUTOMOBILE DE LA FILLE-PILOT, A LA DEMANDE D'INDEMNITE DE CLIENTELE, QUI LEUR ETAIT LE TRAVAIL DE CHAQUE FALCON\", ET QUE CE MOYEN CONSTATE NE PAS EN SE PRODUISIT POUR ETRE DEPOSE AU PROFIT DE LA CHOSE, QU'IL RESSORT PAR UN FONCTION DE LEUR PARTICULIER DU DELAI SANS REPONSE ; QU'IL EST DE S'ENGIGER QUE CELUI-CI N'A PAS ETE INTRODUITE DANS UN DELAI QUI DEVAIT EFFECTUER A UN SELON, ALORS QU'ELLE SE SERAIT EXACTEMENT RENVOYE, ET ALORS QUE LE MOYEN NE PEUT ETRE ACCUEILLI ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 17 JANVIER 1965 PAR LA COUR D'APPEL D'AIX MINERALRIA. N° 65 - 10 673 CAISSE DE SECURITE SOCIALE ROUME DE SAINT-GERMAIN ET TROUBLES MINIMA C/ X...EUROGIELLE. PRESIDENT : M ANCEL, CONSEILLER LE PLUS ANCIEN, FAISANT FONCTIONS - RAPPORTEUR : M DALLANT - AVOCAT GENERAL : M ALBAUT - AVOCAT : M ROUSSEAU. <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN : ATTENDU QU'IL RESULTE DE L'ARRET ATTAQUE QUE LE PASSEY A ENGAGE UNE CONDITION QUE CELUI-CI FAISAIT INSCRIRE EN PREMIERE INSTANCE DE LA SOCIETE PENNINEE A RENOUVELLEMENT DE SON BAIL, SANS FAIRE PRETENDRE A L'ARTICLE 31 DE LA LOI DU 10 JUILLET 1844, AU SENS DU DROIT COMMUN, ET AVOIR EXCLUSIVE DE FACON OBTENUE DE L'ETABLISSEMENT, QU'IL ACCEPTAIT EN LUI-MEME LE PROUVER EN COLLISION AVEC LES EPOUX Y... DE SON CHAUFFEUR QUE LE DROIT COMMUN NE POUVAIT ETRE APPLIQUE QUE DANS SON EMPLOYEUR, QUE LES EPOUX X... EN POSSESSION D'UN DELAI DE CHAQUE FALCONNU DE LA CHAQUE, MAIS PAR L'EMPLOYEUR, S'OPPOSERA ET CONSEQUENCE PREVALORIE DE CE DELAI, EN L'ABSENCE DE CONDITION DANS LES LIEUX ET OBTENU L'ETENDUE DU CHAUFFEUR DIRIGE PREVOIT DANS UN MEME CHAQUE DONT LA PREUVE DE LA CONDITION SE TROUVE AU PRIX ;ATTENDU QU'IL RESULTE DES CONSTATATIONS DE L'ARRET ATTAQUE QUE L'INTENTION DE CONDITION N'ONT PU SE RENDRE PAR LETTRE RECOMMANDEE ;ATTENDU QU'EN VERTU DES ENONCIATIONS DE L'ARRIERE QU'ILS ONT ETE CONDAMNES A UNE CONDITION RENVERSEE DE L'ACTE DE DEMANDE ;MAIS ATTENDU QUE,\n", "\n", "[1501 | 723.23] loss=1.43 avg=1.54\n", "[1502 | 724.51] loss=1.56 avg=1.54\n", "[1503 | 725.80] loss=1.29 avg=1.54\n", "[1504 | 727.11] loss=1.49 avg=1.54\n", "[1505 | 728.39] loss=1.51 avg=1.54\n", "[1506 | 729.66] loss=1.61 avg=1.54\n", "[1507 | 730.95] loss=1.52 avg=1.54\n", "[1508 | 732.22] loss=1.53 avg=1.54\n", "[1509 | 733.50] loss=1.40 avg=1.54\n", "[1510 | 734.79] loss=1.49 avg=1.54\n", "[1511 | 736.07] loss=1.60 avg=1.54\n", "[1512 | 737.36] loss=1.37 avg=1.54\n", "[1513 | 738.64] loss=1.53 avg=1.54\n", "[1514 | 739.91] loss=1.47 avg=1.53\n", "[1515 | 741.19] loss=1.65 avg=1.54\n", "[1516 | 742.46] loss=1.73 avg=1.54\n", "[1517 | 743.74] loss=1.68 avg=1.54\n", "[1518 | 745.01] loss=1.50 avg=1.54\n", "[1519 | 746.29] loss=1.53 avg=1.54\n", "[1520 | 747.56] loss=1.60 avg=1.54\n", "[1521 | 748.86] loss=1.55 avg=1.54\n", "[1522 | 750.14] loss=1.58 avg=1.54\n", "[1523 | 751.41] loss=1.63 avg=1.54\n", "[1524 | 752.69] loss=1.47 avg=1.54\n", "[1525 | 753.97] loss=1.45 avg=1.54\n", "[1526 | 755.24] loss=1.59 avg=1.54\n", "[1527 | 756.51] loss=1.54 avg=1.54\n", "[1528 | 757.79] loss=1.49 avg=1.54\n", "[1529 | 759.07] loss=1.63 avg=1.54\n", "[1530 | 760.38] loss=1.52 avg=1.54\n", "[1531 | 761.66] loss=1.62 avg=1.54\n", "[1532 | 762.94] loss=1.58 avg=1.54\n", "[1533 | 764.22] loss=1.36 avg=1.54\n", "[1534 | 765.49] loss=1.50 avg=1.54\n", "[1535 | 766.77] loss=1.50 avg=1.54\n", "[1536 | 768.04] loss=1.44 avg=1.54\n", "[1537 | 769.32] loss=1.39 avg=1.54\n", "[1538 | 770.62] loss=1.42 avg=1.53\n", "[1539 | 771.89] loss=1.25 avg=1.53\n", "[1540 | 773.17] loss=1.48 avg=1.53\n", "[1541 | 774.45] loss=1.41 avg=1.53\n", "[1542 | 775.72] loss=1.55 avg=1.53\n", "[1543 | 776.99] loss=1.69 avg=1.53\n", "[1544 | 778.27] loss=1.47 avg=1.53\n", "[1545 | 779.54] loss=1.55 avg=1.53\n", "[1546 | 780.83] loss=1.47 avg=1.53\n", "[1547 | 782.11] loss=1.66 avg=1.53\n", "[1548 | 783.39] loss=1.51 avg=1.53\n", "[1549 | 784.66] loss=1.49 avg=1.53\n", "[1550 | 785.94] loss=1.77 avg=1.53\n", "[1551 | 787.21] loss=1.60 avg=1.53\n", "[1552 | 788.49] loss=1.49 avg=1.53\n", "[1553 | 789.76] loss=1.52 avg=1.53\n", "[1554 | 791.03] loss=1.40 avg=1.53\n", "[1555 | 792.35] loss=1.44 avg=1.53\n", "[1556 | 793.64] loss=1.56 avg=1.53\n", "[1557 | 794.93] loss=1.56 avg=1.53\n", "[1558 | 796.20] loss=1.51 avg=1.53\n", "[1559 | 797.48] loss=1.45 avg=1.53\n", "[1560 | 798.75] loss=1.63 avg=1.53\n", "[1561 | 800.03] loss=1.47 avg=1.53\n", "[1562 | 801.30] loss=1.67 avg=1.53\n", "[1563 | 802.58] loss=1.35 avg=1.53\n", "[1564 | 803.88] loss=1.55 avg=1.53\n", "[1565 | 805.16] loss=1.63 avg=1.53\n", "[1566 | 806.43] loss=1.56 avg=1.53\n", "[1567 | 807.70] loss=1.47 avg=1.53\n", "[1568 | 808.98] loss=1.44 avg=1.53\n", "[1569 | 810.25] loss=1.47 avg=1.53\n", "[1570 | 811.53] loss=1.64 avg=1.53\n", "[1571 | 812.80] loss=1.55 avg=1.53\n", "[1572 | 814.10] loss=1.42 avg=1.53\n", "[1573 | 815.38] loss=1.31 avg=1.53\n", "[1574 | 816.65] loss=1.55 avg=1.53\n", "[1575 | 817.92] loss=1.63 avg=1.53\n", "[1576 | 819.20] loss=1.40 avg=1.53\n", "[1577 | 820.47] loss=1.56 avg=1.53\n", "[1578 | 821.75] loss=1.59 avg=1.53\n", "[1579 | 823.02] loss=1.54 avg=1.53\n", "[1580 | 824.30] loss=1.44 avg=1.53\n", "[1581 | 825.60] loss=1.49 avg=1.53\n", "[1582 | 826.89] loss=1.58 avg=1.53\n", "[1583 | 828.25] loss=1.59 avg=1.53\n", "[1584 | 829.53] loss=1.42 avg=1.53\n", "[1585 | 830.80] loss=1.45 avg=1.53\n", "[1586 | 832.07] loss=1.56 avg=1.53\n", "[1587 | 833.35] loss=1.41 avg=1.53\n", "[1588 | 834.62] loss=1.50 avg=1.53\n", "[1589 | 835.91] loss=1.52 avg=1.53\n", "[1590 | 837.18] loss=1.49 avg=1.53\n", "[1591 | 838.46] loss=1.47 avg=1.53\n", "[1592 | 839.73] loss=1.36 avg=1.52\n", "[1593 | 841.01] loss=1.45 avg=1.52\n", "[1594 | 842.28] loss=1.69 avg=1.52\n", "[1595 | 843.56] loss=1.26 avg=1.52\n", "[1596 | 844.83] loss=1.45 avg=1.52\n", "[1597 | 846.10] loss=1.44 avg=1.52\n", "[1598 | 847.40] loss=1.59 avg=1.52\n", "[1599 | 848.67] loss=1.36 avg=1.52\n", "[1600 | 849.95] loss=1.54 avg=1.52\n", "======== SAMPLE 1 ========\n", ", DATE : 11 FEVRIER 1963, BULL 1963, II, N° 579, P 426. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QU'IL RESULTE DES ENONCIATIONS DE L'ARRET ATTAQUE QUE SON MARI CONDUISAIT A Y..., A Y... MARE, DE SANTE DE LA MERE, A PROPRIETAIRE, DU DOMAINE DES BOULANGERS, PUISQU'IL Y VEVE, CONDUIT PAR VOIE DE CONDAMNATION ; QUE LE 26 JUILLET 1940, IL A DEMANDE A Y... A PAYER A X... UN ARRIERE DE 4 HEURES CIRCONSTANCE DE FONDER DE SON TRAVAIL DANS SES BLESSURES, ALORS QU'ELLE N'A PAS FAIT L'OBJET D'ENTRETIEN, TANT POUR L'ANIMAL PAR SUITE DE L'ENTREPRISE QUI VENAIT DES LIEUX A L'HOPITAL D'EMPLOYEUR, A SON MARI ;QUE Y... AYANT APPELE EN PROPRIETE, EN AUGMENT 1956, UNE INDEMNITE COMPENSATRICE DE PREAVIS AYANT ETE INFORME AU PROFIT DES MARI ;ATTENDU QUE, DES LORS, LE CONDUCTEUR DE LA MERE N'A PU ENCOMPRE LA CONTRE-VENIR ET QUE LE FAIT DES POURSUITES DANS LA DUREE D'UN MARI DONT X... ETAIT DEMEURE SE TROUVE EN VERTU PAR LE MODELE DES CHARGES DONNANT UNE CAUSE ET LE MARI AINSI DONNE CE DERNIER A L'EGARD DE L'ASSUREUR ; QUE, SUR APPEL D'ABAIN L'ARRET DEFERE A VIOLE, PAR FAUSSE APPLICATION DE L'ARTICLE 1382 DU CODE CIVIL, EST LEGALEMENT JUSTIFIE ;PAR CES MOTIFS : CASSE ET ANNULE L'ARRET RENDU ENTRE LE 2 MAI 1958, PAR LA COUR D'APPEL DE NIMES ; REMET EN CONSEQUENCE LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET, ET, POUR ETRE FAIT DROIT LES RENVOIE DEVANT LA COUR D'APPEL D'AMIENS. N 58-11-12.097. Y... C / X.... PRESIDENT : M. DROUILLAT.- RAPPORTEUR : M. CALBAIRAC.- AVOCAT GENERAL : V. LEMBOUT.- AVOCATS : MM. LEDIEU ET DE CHAISEMARTIN. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QU'IL RESULTE DE L'ARRET PARTIEL DE LA CAISSE REGIONALE D'ASSURANCES VIEILLESSE DES BOURBAIRES ET CIE QUE LE 28 JUIN 1960, LE PROPRIETAIRE A CONDUISAIT DE LA SAISINE DES CANDIDATS POUR FETIE D'UN ACCEDAI LE 13 JUIN ;QUE, DES VINGT ANS, DES BOUINS A MUNIER, ET POUR L'ANNEE 1941, ETANT AYANT SIGNIFIE LE 10 JUIN 1952 DANS LE PREPOSE DE LA SAISINE DES CANDIDATS, LE VINGT ANS, QUI SE TROUVAIT DANS LE DELAI DE TROIS ANS, L'INCREILLE DE SAISINE DES CANDIDATS FAIT ECHAPPRE A LE CARACTERE DE L'ACCEDITE POUR SON VINGT ANS ;ATTENDU QUE L'ASSUJETTI A, SUR LE PREMIER ANNUEL DE LA FORME, DECLARE, DE N'ETRE RETRACTEE ET L'IMPORTANTE QUELCONQUE CIRCONSTANCE, ET QU'EN EFFET, LE VINGT VIGILANT LA CANTINE DE NAITRE A LA SAISINE DE SAISINE PROPOSEE A LA VENTE DONT LES PARTIERS A ETE, APRES LE\n", "\n", "[1601 | 863.46] loss=1.56 avg=1.52\n", "[1602 | 864.74] loss=1.56 avg=1.52\n", "[1603 | 866.01] loss=1.61 avg=1.52\n", "[1604 | 867.29] loss=1.54 avg=1.52\n", "[1605 | 868.57] loss=1.59 avg=1.52\n", "[1606 | 869.87] loss=1.51 avg=1.52\n", "[1607 | 871.14] loss=1.47 avg=1.52\n", "[1608 | 872.42] loss=1.43 avg=1.52\n", "[1609 | 873.69] loss=1.51 avg=1.52\n", "[1610 | 874.97] loss=1.51 avg=1.52\n", "[1611 | 876.24] loss=1.55 avg=1.52\n", "[1612 | 877.52] loss=1.24 avg=1.52\n", "[1613 | 878.79] loss=1.31 avg=1.52\n", "[1614 | 880.09] loss=1.60 avg=1.52\n", "[1615 | 881.37] loss=1.71 avg=1.52\n", "[1616 | 882.65] loss=1.42 avg=1.52\n", "[1617 | 883.93] loss=1.68 avg=1.52\n", "[1618 | 885.20] loss=1.67 avg=1.52\n", "[1619 | 886.48] loss=1.45 avg=1.52\n", "[1620 | 887.75] loss=1.56 avg=1.52\n", "[1621 | 889.03] loss=1.38 avg=1.52\n", "[1622 | 890.30] loss=1.83 avg=1.52\n", "[1623 | 891.60] loss=1.65 avg=1.52\n", "[1624 | 892.88] loss=1.52 avg=1.52\n", "[1625 | 894.20] loss=1.48 avg=1.52\n", "[1626 | 895.50] loss=1.55 avg=1.52\n", "[1627 | 896.77] loss=1.47 avg=1.52\n", "[1628 | 898.05] loss=1.49 avg=1.52\n", "[1629 | 899.32] loss=1.49 avg=1.52\n", "[1630 | 900.59] loss=1.36 avg=1.52\n", "[1631 | 901.88] loss=1.51 avg=1.52\n", "[1632 | 903.16] loss=1.36 avg=1.52\n", "[1633 | 904.44] loss=1.53 avg=1.52\n", "[1634 | 905.71] loss=1.45 avg=1.52\n", "[1635 | 906.98] loss=1.46 avg=1.52\n", "[1636 | 908.26] loss=1.63 avg=1.52\n", "[1637 | 909.53] loss=1.50 avg=1.52\n", "[1638 | 910.81] loss=1.56 avg=1.52\n", "[1639 | 912.08] loss=1.42 avg=1.52\n", "[1640 | 913.38] loss=1.51 avg=1.52\n", "[1641 | 914.65] loss=1.78 avg=1.52\n", "[1642 | 915.93] loss=1.42 avg=1.52\n", "[1643 | 917.20] loss=1.36 avg=1.52\n", "[1644 | 918.48] loss=1.49 avg=1.52\n", "[1645 | 919.75] loss=1.50 avg=1.52\n", "[1646 | 921.02] loss=1.39 avg=1.52\n", "[1647 | 922.30] loss=1.51 avg=1.52\n", "[1648 | 923.57] loss=1.49 avg=1.52\n", "[1649 | 924.87] loss=1.56 avg=1.52\n", "[1650 | 926.15] loss=1.46 avg=1.52\n", "[1651 | 927.52] loss=1.40 avg=1.51\n", "[1652 | 928.80] loss=1.57 avg=1.52\n", "[1653 | 930.07] loss=1.54 avg=1.52\n", "[1654 | 931.35] loss=1.40 avg=1.51\n", "[1655 | 932.62] loss=1.56 avg=1.51\n", "[1656 | 933.90] loss=1.64 avg=1.52\n", "[1657 | 935.19] loss=1.60 avg=1.52\n", "[1658 | 936.47] loss=1.53 avg=1.52\n", "[1659 | 937.74] loss=1.71 avg=1.52\n", "[1660 | 939.02] loss=1.53 avg=1.52\n", "[1661 | 940.29] loss=1.59 avg=1.52\n", "[1662 | 941.57] loss=1.45 avg=1.52\n", "[1663 | 942.84] loss=1.37 avg=1.52\n", "[1664 | 944.12] loss=1.32 avg=1.52\n", "[1665 | 945.40] loss=1.41 avg=1.51\n", "[1666 | 946.69] loss=1.40 avg=1.51\n", "[1667 | 947.96] loss=1.61 avg=1.51\n", "[1668 | 949.24] loss=1.51 avg=1.51\n", "[1669 | 950.51] loss=1.68 avg=1.52\n", "[1670 | 951.79] loss=1.49 avg=1.52\n", "[1671 | 953.06] loss=1.49 avg=1.52\n", "[1672 | 954.34] loss=1.64 avg=1.52\n", "[1673 | 955.61] loss=1.41 avg=1.52\n", "[1674 | 956.91] loss=1.55 avg=1.52\n", "[1675 | 958.18] loss=1.61 avg=1.52\n", "[1676 | 959.53] loss=1.47 avg=1.52\n", "[1677 | 960.90] loss=1.66 avg=1.52\n", "[1678 | 962.17] loss=1.46 avg=1.52\n", "[1679 | 963.45] loss=1.47 avg=1.52\n", "[1680 | 964.72] loss=1.44 avg=1.52\n", "[1681 | 966.00] loss=1.25 avg=1.51\n", "[1682 | 967.27] loss=1.57 avg=1.51\n", "[1683 | 968.58] loss=1.40 avg=1.51\n", "[1684 | 969.85] loss=1.38 avg=1.51\n", "[1685 | 971.12] loss=1.50 avg=1.51\n", "[1686 | 972.40] loss=1.51 avg=1.51\n", "[1687 | 973.67] loss=1.49 avg=1.51\n", "[1688 | 974.95] loss=1.34 avg=1.51\n", "[1689 | 976.22] loss=1.40 avg=1.51\n", "[1690 | 977.49] loss=1.38 avg=1.51\n", "[1691 | 978.78] loss=1.56 avg=1.51\n", "[1692 | 980.06] loss=1.59 avg=1.51\n", "[1693 | 981.34] loss=1.43 avg=1.51\n", "[1694 | 982.61] loss=1.51 avg=1.51\n", "[1695 | 983.88] loss=1.59 avg=1.51\n", "[1696 | 985.16] loss=1.41 avg=1.51\n", "[1697 | 986.43] loss=1.60 avg=1.51\n", "[1698 | 987.70] loss=1.31 avg=1.51\n", "[1699 | 988.97] loss=1.55 avg=1.51\n", "[1700 | 990.27] loss=1.46 avg=1.51\n", "======== SAMPLE 1 ========\n", "EMISEMENT DE LA GARANTIE ET D'OBEVERAIT LA GARANTIE SANS AUCUNE RESIDENCE ANTERIEURE A L'ARRETE DU 9 JUIN 1960, QU'A L'ENCONTRE DU CHAPITRE DE LA ROUTE, ILS QUI LEUR SONT DEVENU VENDEUSE, D'UNE FERME DE COMMANDE, ET DEVAIENT ETRE SOUMISES A CETTE DEMANDE ; </p><p>MAIS ATTENDU QUE LE JUGEMENT, POUR ADMETTRE QUE LE DEBITEUR DE CETTE MESURE ETAIT A LEUR ORDONNANCE OU, AU CAS DE SES OBLIGATIONS PRECISES, IL NE RESULTAIT PAS DE SES MOTIFS QUE LA SITUATION ETAIT UN ETABLISSEMENT, QUE LE MONTANT DE VENTE, PAR AILLEURS DE SON PRINCIPE, LEUR AVAIT ETE ETABLIE PAR LUI AVANT DIRE DROIT, DEVAIT ETRE DECIDEE COMME DEFINITIVEMENT GRAVE QUE LE PRIX QUE LA SITUATION ETAIT SOUMISE A LA CHARGE DE L'UN DE PASSAGE DE L'ENTREPRISE, TOUT EN DECLARANT QUE VINICOLE DE SOUS-TRAITES N'AVAIT PAS ETE SOUMIS A LA ROUTE; </p><p>QUE LE MOYEN NE SAURAIT ETRE ACCUEILLI ; </p><p>SUR LE SECOND MOYEN, PRIS EN SES DEUX BRANCHES DE LA VIOLATION DES ARTICLES 23 DU CODE ELECTORAL, 1134 DU CODE CIVIL, 1315 DU CODE CIVIL ET 7 DE LA LOI DU 20 AVRIL 1810, DEFAUT DE MOTIFS ET MANQUE DE BASE LEGALE, EN CE QUE LE POURVOI FONT GRIEF A L'ARRET ATTAQUE D'AVOIR RETENU, PAR MOTIF SUSROGATOIRE, QU'IL EST CONSTANT QUE LA SITUATION ETAIT DU DIFFEREND, QUE LE DEBITEUR DEVAIT ETRE REMPLACE DE L'ENTREPRISE, D'APRES LA CONVENTION DES PARTIES ET QU'EN L'ESPECTE LA GARANTIE ETAIT VINGT ANS ETAIT FONDEE, NONOBSTANT QU'EXERCEE AUX AUTRES VEHICULES DE VENDEUSE, ALORS QU'AYANT DENUEVE QU'IL ADOPTE, ALORS QU'UN MOTIF DE REFERENCE NE PAS CONSTITUERAIT POUR LA REMUNERATION QUI EST TENU AU JOUR DE L'ACCIDENT DU TRAVAIL; </p><p>QUE L'ARRET ATTAQUE, TEL QU'IL EST ENTACHE DE CONTRADICTION, A PU DEDUIRE DE SON POUVOI QU'AVAIT REFUSE DE RECONNAITRE A L'ACTION DE L'ENTREPRISE EN GARANTIE DE LA ROUTE, LA SITUATION ETAIT INCONVENIEE COMME FAUTE D'EXERCER SON DROIT DE REPRISE, CE QUI N'ETAIT PAS TENU LEGALEMENT PRECISE ; </p><p>QUE LE MOYEN NE PEUT ETRE ACCUEILLI ; </p><p>PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 11 NOVEMBRE 1963 PAR LA COUR D'APPEL DE COLMAR LE 3 FEVRIER 1964. <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN, PRIS EN SA PREMIERE BRANCHE, N'EST PAS LE REGIME DES CABLES-AVEUX VIEILLESSE DES CABLES-HOTEL DU MONTANT A USAGE PERSONNEL DE LA COMPAGNIE, PRENEUR DU TRAVAIL D'ENTRETIEN FOURNIS, ET DU TRAVAIL D'ECHEANCE AUTOMOBILE CONSTATEE, ET N'EST DONC PAS FONDE SUR CE POINT; ATTENDU QU'IL EST FAIT GRI\n", "\n", "[1701 | 1003.87] loss=1.45 avg=1.51\n", "[1702 | 1005.15] loss=1.67 avg=1.51\n", "[1703 | 1006.42] loss=1.53 avg=1.51\n", "[1704 | 1007.69] loss=1.47 avg=1.51\n", "[1705 | 1008.97] loss=1.48 avg=1.51\n", "[1706 | 1010.24] loss=1.46 avg=1.51\n", "[1707 | 1011.52] loss=1.39 avg=1.51\n", "[1708 | 1012.83] loss=1.40 avg=1.50\n", "[1709 | 1014.11] loss=1.43 avg=1.50\n", "[1710 | 1015.38] loss=1.43 avg=1.50\n", "[1711 | 1016.66] loss=1.41 avg=1.50\n", "[1712 | 1017.93] loss=1.58 avg=1.50\n", "[1713 | 1019.21] loss=1.36 avg=1.50\n", "[1714 | 1020.48] loss=1.58 avg=1.50\n", "[1715 | 1021.75] loss=1.47 avg=1.50\n", "[1716 | 1023.05] loss=1.46 avg=1.50\n", "[1717 | 1024.33] loss=1.48 avg=1.50\n", "[1718 | 1025.63] loss=1.44 avg=1.50\n", "[1719 | 1026.91] loss=1.64 avg=1.50\n", "[1720 | 1028.18] loss=1.45 avg=1.50\n", "[1721 | 1029.46] loss=1.59 avg=1.50\n", "[1722 | 1030.73] loss=1.50 avg=1.50\n", "[1723 | 1032.00] loss=1.54 avg=1.50\n", "[1724 | 1033.27] loss=1.49 avg=1.50\n", "[1725 | 1034.57] loss=1.38 avg=1.50\n", "[1726 | 1035.86] loss=1.37 avg=1.50\n", "[1727 | 1037.13] loss=1.64 avg=1.50\n", "[1728 | 1038.41] loss=1.39 avg=1.50\n", "[1729 | 1039.68] loss=1.46 avg=1.50\n", "[1730 | 1040.95] loss=1.53 avg=1.50\n", "[1731 | 1042.23] loss=1.49 avg=1.50\n", "[1732 | 1043.50] loss=1.52 avg=1.50\n", "[1733 | 1044.79] loss=1.34 avg=1.50\n", "[1734 | 1046.07] loss=1.42 avg=1.50\n", "[1735 | 1047.34] loss=1.39 avg=1.50\n", "[1736 | 1048.62] loss=1.39 avg=1.50\n", "[1737 | 1049.89] loss=1.43 avg=1.50\n", "[1738 | 1051.16] loss=1.45 avg=1.49\n", "[1739 | 1052.44] loss=1.50 avg=1.49\n", "[1740 | 1053.71] loss=1.61 avg=1.50\n", "[1741 | 1054.99] loss=1.40 avg=1.49\n", "[1742 | 1056.28] loss=1.54 avg=1.50\n", "[1743 | 1057.56] loss=1.43 avg=1.49\n", "[1744 | 1058.83] loss=1.52 avg=1.49\n", "[1745 | 1060.13] loss=1.37 avg=1.49\n", "[1746 | 1061.41] loss=1.55 avg=1.49\n", "[1747 | 1062.68] loss=1.48 avg=1.49\n", "[1748 | 1063.96] loss=1.55 avg=1.49\n", "[1749 | 1065.24] loss=1.43 avg=1.49\n", "[1750 | 1066.51] loss=1.67 avg=1.50\n", "[1751 | 1067.82] loss=1.68 avg=1.50\n", "[1752 | 1069.10] loss=1.49 avg=1.50\n", "[1753 | 1070.38] loss=1.46 avg=1.50\n", "[1754 | 1071.66] loss=1.61 avg=1.50\n", "[1755 | 1072.93] loss=1.45 avg=1.50\n", "[1756 | 1074.20] loss=1.49 avg=1.50\n", "[1757 | 1075.48] loss=1.54 avg=1.50\n", "[1758 | 1076.75] loss=1.60 avg=1.50\n", "[1759 | 1078.04] loss=1.48 avg=1.50\n", "[1760 | 1079.32] loss=1.47 avg=1.50\n", "[1761 | 1080.60] loss=1.38 avg=1.50\n", "[1762 | 1081.87] loss=1.46 avg=1.50\n", "[1763 | 1083.14] loss=1.48 avg=1.50\n", "[1764 | 1084.42] loss=1.49 avg=1.50\n", "[1765 | 1085.69] loss=1.32 avg=1.50\n", "[1766 | 1086.97] loss=1.36 avg=1.49\n", "[1767 | 1088.24] loss=1.48 avg=1.49\n", "[1768 | 1089.53] loss=1.44 avg=1.49\n", "[1769 | 1090.81] loss=1.64 avg=1.49\n", "[1770 | 1092.09] loss=1.46 avg=1.49\n", "[1771 | 1093.38] loss=1.44 avg=1.49\n", "[1772 | 1094.66] loss=1.59 avg=1.49\n", "[1773 | 1095.93] loss=1.56 avg=1.50\n", "[1774 | 1097.20] loss=1.60 avg=1.50\n", "[1775 | 1098.48] loss=1.39 avg=1.50\n", "[1776 | 1099.79] loss=1.55 avg=1.50\n", "[1777 | 1101.10] loss=1.59 avg=1.50\n", "[1778 | 1102.38] loss=1.50 avg=1.50\n", "[1779 | 1103.65] loss=1.53 avg=1.50\n", "[1780 | 1104.92] loss=1.51 avg=1.50\n", "[1781 | 1106.20] loss=1.61 avg=1.50\n", "[1782 | 1107.47] loss=1.44 avg=1.50\n", "[1783 | 1108.74] loss=1.51 avg=1.50\n", "[1784 | 1110.02] loss=1.41 avg=1.50\n", "[1785 | 1111.31] loss=1.36 avg=1.50\n", "[1786 | 1112.58] loss=1.56 avg=1.50\n", "[1787 | 1113.86] loss=1.50 avg=1.50\n", "[1788 | 1115.13] loss=1.41 avg=1.50\n", "[1789 | 1116.41] loss=1.50 avg=1.50\n", "[1790 | 1117.68] loss=1.58 avg=1.50\n", "[1791 | 1118.96] loss=1.67 avg=1.50\n", "[1792 | 1120.23] loss=1.58 avg=1.50\n", "[1793 | 1121.50] loss=1.42 avg=1.50\n", "[1794 | 1122.80] loss=1.40 avg=1.50\n", "[1795 | 1124.08] loss=1.51 avg=1.50\n", "[1796 | 1125.37] loss=1.42 avg=1.50\n", "[1797 | 1126.65] loss=1.46 avg=1.50\n", "[1798 | 1127.92] loss=1.43 avg=1.50\n", "[1799 | 1129.20] loss=1.38 avg=1.49\n", "[1800 | 1130.47] loss=1.40 avg=1.49\n", "======== SAMPLE 1 ========\n", "IALEMENT, L'AVAIT DESTINEE A RENOUVELER LE BENEFICE DES PRESTATIONS VERSEES A SA MERE, AU MOTIF QU'IL N'Y AVAIT PAS LIEU DE PRENDRE, QUE LES EPOUX Z... ONT VENDU IMMATRICULTEURS OU ENFANTS A L'ENFANT, ALORS, QUE L'ARBITRAGE, QUE LES JUGES ONT ETE EFFECTUEES, ONT ETE LA CHARGE DE L'ACCIDENT, DANS LA LIGNE CONGE A RONGE LE 10 OCTOBRE 1958, LORSQUE LES EXPERTS ET LA DAME Y... CONSTITUTAIENT, PAR SUITE, UNE AFFECTION A SON MERE, LES DROITS DE SON MARI, QUI A DECLARE QUE LES EPOUX Z..., SAVOIR LES DROITS QU'ILS AURAIENT DU DEFINISER UNE DECISION DE RETARD PAR L'ARTICLE 10, LES JUGES DU FOND ONT DONNE UNE BASE LEGALE A LEUR DECISION D'AVOIR ESTIME QUE LES LOCAUX EXECUTANT L'AUTORITE JUDICIAIRE ; QU'IL SUFFIT QUE LE POURVOI SOUTIENT EN AFFIRMANT QUE LA CHARGE DU DIRECTEUR DE L'ACCIDENT ETAIT SUFFISANTE POUR CAUSE DE TERRAIN EN DEHORS DU CARACTERE DE L 'ENFANT, DURANT LA DUREE DU DELAI IMPOSE DANS SA SEMAINE, ET QUE LEURS FAUTE N'ETAIT PAS ETABLI, EN RAISON DE SA NATURE A SON PROPRIETAIRE, QUE, DANS DES CONCLUSIONS VISEES AU CONTROLE DES EXPERTS, LE DIRECTEUR DE L'ACCIDENT ETAIT COUPABLE DES DROITS, QUE, DE TELLE SORTE QUE LEDIT ARTICLE 10 N'AVAIT ETE ETABLI EXACTEMENT LA SEMAINE CONGE, SANS RECHERCHER SI LA CONSERVATION AURAIT ETE IMPOSSIBLE ET N'AYANT EU LIEU A PU DEDUIRE AU CONTROLE DE LA COUR DE CASSATION DANS SA LETTRE DU 13 JUILLET 1959, CETTE CONVENTION DOIT ETRE DETERMINEE POUR L'ANNEE PREALABLE DE L'ORDRE DE L'ARBE, COMME SUFFISANTE POUR COUPABLE UNE TELLE NEGOCE DE L'ENFANT, ET QU'EN L'ETAT DE L'AVAIT EXPLIQUE, LA CONSERVATION ETAIT POUR LUI DE L'ANNEE PENDANT TOUTE OBLIGATION DE SE PRODUIRE SUR LA CONSTRUCTION EN SENS INDIRECTEMENT ;QU'EN STATUANT AINSI, SANS DENATURATION QUE LE DIRECTEUR DE L'ACCIDENT POUVAIT UNE AFFECTION, LE MERE N'AYANT PAS EN MECONNAISSANCE NECESSAIREMENT, ET LA LETTRE DU 13 JUILLET 1959 PAR LES EPOUX D... DE L'AUTRE, LA COUR D'APPEL N'AURAIT PU, NULLEMENT, REPONDRE AUX CONCLUSIONS PRISES DEVANT LES JUGES DU FOND EN REJETANT DE PRENDRE LES EXPERTS AUX MOTIFS PROPRES QUE L'ACCIDENT ETAIT COUPABLE DE SA PRIME ET QUI NE LUI PERMET DE PLEIN DROIT DE PROCEDER A SA MERE UN MECANIQUE DE PRENDRE EN CAUSE LES REPRENANTS DANS SES QUATRE MOIS ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARBITRAGE CONTRE LES EPOUX D... RONGE ET DAME Y... N'AYANT PAS POUR BUT DE PLEIN DROIT QUE CELLE-CI EUT EN RAPPORT A LA CONVENTION DE LA CHAMBRE SUCCESSIF ;N° 62 - 10 822 EPOUX D... C/ DIRECTEUR DE L'ACCIDENT NATIONAL MINIMA DE L'UN DES V\n", "\n", "[1801 | 1144.15] loss=1.41 avg=1.49\n", "[1802 | 1145.42] loss=1.36 avg=1.49\n", "[1803 | 1146.70] loss=1.46 avg=1.49\n", "[1804 | 1147.97] loss=1.49 avg=1.49\n", "[1805 | 1149.24] loss=1.40 avg=1.49\n", "[1806 | 1150.52] loss=1.34 avg=1.49\n", "[1807 | 1151.79] loss=1.40 avg=1.49\n", "[1808 | 1153.06] loss=1.44 avg=1.49\n", "[1809 | 1154.34] loss=1.46 avg=1.49\n", "[1810 | 1155.64] loss=1.44 avg=1.49\n", "[1811 | 1156.91] loss=1.64 avg=1.49\n", "[1812 | 1158.19] loss=1.54 avg=1.49\n", "[1813 | 1159.51] loss=1.52 avg=1.49\n", "[1814 | 1160.79] loss=1.28 avg=1.49\n", "[1815 | 1162.07] loss=1.41 avg=1.49\n", "[1816 | 1163.35] loss=1.52 avg=1.49\n", "[1817 | 1164.63] loss=1.37 avg=1.49\n", "[1818 | 1165.93] loss=1.44 avg=1.48\n", "[1819 | 1167.21] loss=1.48 avg=1.48\n", "[1820 | 1168.48] loss=1.46 avg=1.48\n", "[1821 | 1169.76] loss=1.63 avg=1.49\n", "[1822 | 1171.03] loss=1.50 avg=1.49\n", "[1823 | 1172.30] loss=1.66 avg=1.49\n", "[1824 | 1173.58] loss=1.83 avg=1.49\n", "[1825 | 1174.85] loss=1.45 avg=1.49\n", "[1826 | 1176.13] loss=1.50 avg=1.49\n", "[1827 | 1177.42] loss=1.40 avg=1.49\n", "[1828 | 1178.70] loss=1.47 avg=1.49\n", "[1829 | 1179.97] loss=1.41 avg=1.49\n", "[1830 | 1181.25] loss=1.53 avg=1.49\n", "[1831 | 1182.52] loss=1.44 avg=1.49\n", "[1832 | 1183.80] loss=1.46 avg=1.49\n", "[1833 | 1185.07] loss=1.46 avg=1.49\n", "[1834 | 1186.35] loss=1.70 avg=1.49\n", "[1835 | 1187.62] loss=1.44 avg=1.49\n", "[1836 | 1188.92] loss=1.70 avg=1.49\n", "[1837 | 1190.20] loss=1.43 avg=1.49\n", "[1838 | 1191.48] loss=1.52 avg=1.49\n", "[1839 | 1192.78] loss=1.61 avg=1.49\n", "[1840 | 1194.05] loss=1.35 avg=1.49\n", "[1841 | 1195.32] loss=1.66 avg=1.49\n", "[1842 | 1196.61] loss=1.38 avg=1.49\n", "[1843 | 1197.89] loss=1.52 avg=1.49\n", "[1844 | 1199.18] loss=1.39 avg=1.49\n", "[1845 | 1200.45] loss=1.70 avg=1.49\n", "[1846 | 1201.73] loss=1.51 avg=1.49\n", "[1847 | 1203.00] loss=1.52 avg=1.49\n", "[1848 | 1204.28] loss=1.36 avg=1.49\n", "[1849 | 1205.55] loss=1.48 avg=1.49\n", "[1850 | 1206.83] loss=1.42 avg=1.49\n", "[1851 | 1208.10] loss=1.43 avg=1.49\n", "[1852 | 1209.38] loss=1.45 avg=1.49\n", "[1853 | 1210.67] loss=1.47 avg=1.49\n", "[1854 | 1211.94] loss=1.40 avg=1.49\n", "[1855 | 1213.22] loss=1.38 avg=1.49\n", "[1856 | 1214.50] loss=1.41 avg=1.49\n", "[1857 | 1215.78] loss=1.47 avg=1.49\n", "[1858 | 1217.05] loss=1.55 avg=1.49\n", "[1859 | 1218.32] loss=1.37 avg=1.49\n", "[1860 | 1219.60] loss=1.36 avg=1.49\n", "[1861 | 1220.89] loss=1.52 avg=1.49\n", "[1862 | 1222.17] loss=1.49 avg=1.49\n", "[1863 | 1223.44] loss=1.53 avg=1.49\n", "[1864 | 1224.72] loss=1.48 avg=1.49\n", "[1865 | 1226.01] loss=1.60 avg=1.49\n", "[1866 | 1227.28] loss=1.46 avg=1.49\n", "[1867 | 1228.56] loss=1.48 avg=1.49\n", "[1868 | 1229.84] loss=1.49 avg=1.49\n", "[1869 | 1231.11] loss=1.56 avg=1.49\n", "[1870 | 1232.41] loss=1.58 avg=1.49\n", "[1871 | 1233.68] loss=1.54 avg=1.49\n", "[1872 | 1234.95] loss=1.40 avg=1.49\n", "[1873 | 1236.22] loss=1.52 avg=1.49\n", "[1874 | 1237.50] loss=1.42 avg=1.49\n", "[1875 | 1238.77] loss=1.49 avg=1.49\n", "[1876 | 1240.05] loss=1.44 avg=1.49\n", "[1877 | 1241.32] loss=1.31 avg=1.49\n", "[1878 | 1242.59] loss=1.50 avg=1.49\n", "[1879 | 1243.89] loss=1.53 avg=1.49\n", "[1880 | 1245.16] loss=1.29 avg=1.48\n", "[1881 | 1246.44] loss=1.48 avg=1.48\n", "[1882 | 1247.71] loss=1.30 avg=1.48\n", "[1883 | 1248.99] loss=1.26 avg=1.48\n", "[1884 | 1250.26] loss=1.34 avg=1.48\n", "[1885 | 1251.54] loss=1.47 avg=1.48\n", "[1886 | 1252.81] loss=1.49 avg=1.48\n", "[1887 | 1254.12] loss=1.40 avg=1.48\n", "[1888 | 1255.40] loss=1.52 avg=1.48\n", "[1889 | 1256.67] loss=1.43 avg=1.48\n", "[1890 | 1257.96] loss=1.59 avg=1.48\n", "[1891 | 1259.25] loss=1.56 avg=1.48\n", "[1892 | 1260.54] loss=1.43 avg=1.48\n", "[1893 | 1261.82] loss=1.38 avg=1.48\n", "[1894 | 1263.09] loss=1.30 avg=1.48\n", "[1895 | 1264.37] loss=1.41 avg=1.48\n", "[1896 | 1265.66] loss=1.37 avg=1.47\n", "[1897 | 1266.94] loss=1.54 avg=1.48\n", "[1898 | 1268.21] loss=1.46 avg=1.48\n", "[1899 | 1269.49] loss=1.47 avg=1.48\n", "[1900 | 1270.76] loss=1.58 avg=1.48\n", "======== SAMPLE 1 ========\n", "HE LA JOUISSANCE D'UN EXPLOIT DE LA MEME ETAGE DE LA SOCIETE RIBOL, SE FONDANT SUR LE REFUS DU BAIL D'EXPIRATION DANS LES LIEUX; REMET EN CONSEQUENCE LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT JUGEMENT ET, POUR ETRE FAIT DROIT, LES RENVOIE DEVANT LA COUR D'APPEL DE POITIERS. N° 60 - 10 631 BERNIER ET AUTRES C/ CONSORTS Y... CHAREUL ET AUTRES. PRESIDENT : M DROUILLAT - RAPPORTEUR : M CALBAIRAC - AVOCAT GENERAL : M SCHMELCK - AVOCAT : M TETREAU. A RAPPROCHER : 7 MARS 1965, BULL 1965, II, N° 415, P 228. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : VU LES ARTICLES 8 ET 18 DU DECRET DU 22 DECEMBRE 1958;ATTENDU QU'AUX TERMES DE CETTE DESIRANT DE RENVOI DE SON FILS, LE REGIME DU MUR DUQUEL IL SOIT FOURNIT QU'ILS EXERCAIENT AVOIR SON PROPRE COTE D'APRES, PAR LUI ECHAPPER D'UNE MAUVAISE FOI EXCEDENT, AVOIR INVOQUE UNE FAUSSE INTERPRETATION FAITE A LADITE SITUATION;ATTENDU QUE L'ARTICLE 18 DE CETTE CONSTATATION EST DONC ETABLIE EN S'APPUYANT SUR CELLE DE LA SOCIETE CIVILE LEGENAS, DANS LE CALCUL DE L'EXPERT Y... DES INTERESSES DEPUIS LE 4 JUILLET 1958 EN VERTU DUQUEL ELLE EXERCAIT SUR LA PLATE ET DUQUEL ELLE EXERCAIT LE SENS ET LE PREJUDICE QU'ELLES ONT, PAR ACTE NOTARIE DU 4 JUILLET 1958 LESDITES CONSTATATIONS;MAIS ATTENDU QUE LA CONCLUSION QUI LUI ETAIT INVOQUE DE L'AUTORITE DE LA COUR D'APPEL;QUE L'INTERVENTION ET L'ACCOMPLISSEMENT DES COTISATIONS EXERCEEES ETAIENT PREVUES PAR SON EXERCICE;ET SUR LE MOYEN UNIQUE PRISE EN SA PREMIERE BRANCHE;ATTENDU QUE LE POURVOI FAIT GRIEF A L'ARRET CONFIRMATIF D'AVOIR DEBOUTE L'ADMINISTRATION INDIED A L'EXPERT Y... DES INTERESSES INTERVENUS ENTRE LA SOCIETE REEL FRANCAIS ET A LA SOCIETE REEL DE SON FILS, ALORS QUE L'EXPERT, SEULE POUR LA CONDITION, DONT L'AVAIT L'ESSUITE, EST UN ORDRE, AUX TORTS PROFESSIONNEL-RAPPORTEUR DES CHATEAUX, TOUTE INTERVENTION, EN TANT QUE MOINS AVOIR PRECISE LE CONTACHE DE L'EXPERT DES INTERESSES DEPENDANT ET DES CONSEILS DU RAPPORT DE L'EXPERT, L'ALLEGATION QUE PAR L'INTERVENTION DES COTISATIONS A L'ENCONTRE DE L'ADMINISTRATION FISCALE, N'A PAS ETE PROPRE, A CE QUE L'EXPERT, UNE FAUSSE INTERPRETATION FAITE A LADITE SITUATION, N'A PU JOUER QU'ELLES ONT PROUVE QU'ILS NE PROUVENT PAS, NI, ET DEPUIS UN ENSEMBLE DONT LES INTERESSES EXERCE, UNE FAUSSE INTERPRETATION FAITE A LA PREMIERE SOCIETE RIBOL, NI, PENDANT LA DUREE DU SOL CAS, UN SEUL MOTIF REQUISE QUE LES INTERESSES EUSSENT ETE CONVINCES, ET, DEPUIS UNE INTERVENTION DE L'EXPERT;MAIS ATTENDU QU'EN DEDUISANT DE CES CON\n", "\n", "[1901 | 1283.74] loss=1.47 avg=1.48\n", "[1902 | 1285.01] loss=1.35 avg=1.48\n", "[1903 | 1286.28] loss=1.47 avg=1.48\n", "[1904 | 1287.58] loss=1.33 avg=1.47\n", "[1905 | 1288.85] loss=1.42 avg=1.47\n", "[1906 | 1290.13] loss=1.45 avg=1.47\n", "[1907 | 1291.42] loss=1.41 avg=1.47\n", "[1908 | 1292.72] loss=1.47 avg=1.47\n", "[1909 | 1294.00] loss=1.57 avg=1.47\n", "[1910 | 1295.27] loss=1.50 avg=1.47\n", "[1911 | 1296.54] loss=1.51 avg=1.47\n", "[1912 | 1297.84] loss=1.48 avg=1.47\n", "[1913 | 1299.12] loss=1.49 avg=1.47\n", "[1914 | 1300.39] loss=1.27 avg=1.47\n", "[1915 | 1301.67] loss=1.64 avg=1.47\n", "[1916 | 1302.94] loss=1.40 avg=1.47\n", "[1917 | 1304.21] loss=1.36 avg=1.47\n", "[1918 | 1305.49] loss=1.47 avg=1.47\n", "[1919 | 1306.76] loss=1.51 avg=1.47\n", "[1920 | 1308.04] loss=1.54 avg=1.47\n", "[1921 | 1309.34] loss=1.50 avg=1.47\n", "[1922 | 1310.62] loss=1.47 avg=1.47\n", "[1923 | 1311.89] loss=1.44 avg=1.47\n", "[1924 | 1313.17] loss=1.54 avg=1.47\n", "[1925 | 1314.44] loss=1.51 avg=1.47\n", "[1926 | 1315.71] loss=1.43 avg=1.47\n", "[1927 | 1316.99] loss=1.32 avg=1.47\n", "[1928 | 1318.26] loss=1.58 avg=1.47\n", "[1929 | 1319.54] loss=1.26 avg=1.47\n", "[1930 | 1320.84] loss=1.55 avg=1.47\n", "[1931 | 1322.11] loss=1.44 avg=1.47\n", "[1932 | 1323.38] loss=1.41 avg=1.47\n", "[1933 | 1324.75] loss=1.35 avg=1.47\n", "[1934 | 1326.08] loss=1.40 avg=1.47\n", "[1935 | 1327.36] loss=1.50 avg=1.47\n", "[1936 | 1328.63] loss=1.58 avg=1.47\n", "[1937 | 1329.90] loss=1.56 avg=1.47\n", "[1938 | 1331.19] loss=1.57 avg=1.47\n", "[1939 | 1332.47] loss=1.44 avg=1.47\n", "[1940 | 1333.76] loss=1.41 avg=1.47\n", "[1941 | 1335.04] loss=1.37 avg=1.47\n", "[1942 | 1336.31] loss=1.45 avg=1.47\n", "[1943 | 1337.58] loss=1.54 avg=1.47\n", "[1944 | 1338.86] loss=1.42 avg=1.47\n", "[1945 | 1340.13] loss=1.49 avg=1.47\n", "[1946 | 1341.41] loss=1.50 avg=1.47\n", "[1947 | 1342.69] loss=1.52 avg=1.47\n", "[1948 | 1343.96] loss=1.59 avg=1.47\n", "[1949 | 1345.23] loss=1.47 avg=1.47\n", "[1950 | 1346.51] loss=1.46 avg=1.47\n", "[1951 | 1347.78] loss=1.39 avg=1.47\n", "[1952 | 1349.05] loss=1.43 avg=1.47\n", "[1953 | 1350.33] loss=1.42 avg=1.47\n", "[1954 | 1351.60] loss=1.33 avg=1.47\n", "[1955 | 1352.88] loss=1.35 avg=1.47\n", "[1956 | 1354.16] loss=1.46 avg=1.47\n", "[1957 | 1355.43] loss=1.48 avg=1.47\n", "[1958 | 1356.71] loss=1.44 avg=1.47\n", "[1959 | 1357.99] loss=1.41 avg=1.47\n", "[1960 | 1359.28] loss=1.39 avg=1.47\n", "[1961 | 1360.56] loss=1.51 avg=1.47\n", "[1962 | 1361.84] loss=1.46 avg=1.47\n", "[1963 | 1363.11] loss=1.59 avg=1.47\n", "[1964 | 1364.40] loss=1.62 avg=1.47\n", "[1965 | 1365.68] loss=1.52 avg=1.47\n", "[1966 | 1366.95] loss=1.56 avg=1.47\n", "[1967 | 1368.22] loss=1.32 avg=1.47\n", "[1968 | 1369.50] loss=1.50 avg=1.47\n", "[1969 | 1370.77] loss=1.35 avg=1.47\n", "[1970 | 1372.05] loss=1.51 avg=1.47\n", "[1971 | 1373.32] loss=1.27 avg=1.47\n", "[1972 | 1374.60] loss=1.58 avg=1.47\n", "[1973 | 1375.89] loss=1.42 avg=1.47\n", "[1974 | 1377.16] loss=1.36 avg=1.47\n", "[1975 | 1378.44] loss=1.40 avg=1.47\n", "[1976 | 1379.71] loss=1.48 avg=1.47\n", "[1977 | 1380.98] loss=1.69 avg=1.47\n", "[1978 | 1382.26] loss=1.49 avg=1.47\n", "[1979 | 1383.53] loss=1.42 avg=1.47\n", "[1980 | 1384.81] loss=1.41 avg=1.47\n", "[1981 | 1386.11] loss=1.23 avg=1.46\n", "[1982 | 1387.38] loss=1.66 avg=1.47\n", "[1983 | 1388.66] loss=1.57 avg=1.47\n", "[1984 | 1389.96] loss=1.45 avg=1.47\n", "[1985 | 1391.24] loss=1.36 avg=1.47\n", "[1986 | 1392.53] loss=1.40 avg=1.47\n", "[1987 | 1393.82] loss=1.41 avg=1.47\n", "[1988 | 1395.09] loss=1.57 avg=1.47\n", "[1989 | 1396.36] loss=1.50 avg=1.47\n", "[1990 | 1397.65] loss=1.37 avg=1.47\n", "[1991 | 1398.93] loss=1.47 avg=1.47\n", "[1992 | 1400.20] loss=1.60 avg=1.47\n", "[1993 | 1401.48] loss=1.43 avg=1.47\n", "[1994 | 1402.75] loss=1.46 avg=1.47\n", "[1995 | 1404.02] loss=1.61 avg=1.47\n", "[1996 | 1405.30] loss=1.25 avg=1.47\n", "[1997 | 1406.57] loss=1.72 avg=1.47\n", "[1998 | 1407.86] loss=1.35 avg=1.47\n", "[1999 | 1409.13] loss=1.55 avg=1.47\n", "[2000 | 1410.41] loss=1.38 avg=1.47\n", "Saving checkpoint/run1/model-2000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 15:06:46.799646: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:06:46.801011: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:06:46.802286: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:06:46.803759: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:06:46.805117: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:06:46.806186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-2000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 2.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 12843744 tokens\n", "Training...\n", "Saving checkpoint/run1/model-2000\n", "Saving checkpoint/run1/model-2000\n", "======== SAMPLE 1 ========\n", "ONDER UN MOYEN : ATTENDU QU'IL EST ENCORE REPROCHE A L'ARRET CONFIRMATIF ATTAQUE D'AVOIR RETENU A L'EGARD DE MARTHE, QUI LUI AVAIT OBTENU LE MEME DOMICILE, MAIS UNE MESURE D'INSTRUCTION APRES L'OPPORTUNITE DE MARTHE SOUS SES PROPRES CONSTATATIONS, AU MOTIF QUE, SUBSIDIAIREMENT, L'ACQUISITION DE MARTHE S'ETAIT FONDE SUR LUI DONNANT UNE CONFUSION POSEES PAR CE DERNIER A LA DATE DE MARTHE, LA MESURE DE L'EXISTENCE D'UNE MESSELLE CONFUSION, FAISANT FONDER L'INTERDICTION DU PRIX, DE L'ACTE DE DERNIER, ALORS QUE LE MONTANT DE L'ACCROCHE NE REMPLISSAIT PAS EN LA CAUSE DE L'ACTE DE DERNIER, IL S'EST POURVUE DE SAVOIR LIEU D'UNE MESSELLE CONFUSION POSEES PAR SON CONFUSION ET, FAISANT REFUTE PUREMENT TOLERE, SAISI D'UNE MECONNAISSANCE EXCEPTIONNELLE ET PROBANTE DE LA PREUVE DE L'ACCEPTATION, ET QUE LE LITIGE DIFFEREMABLE SE TROUVE EXONERABLE PAR DERNIER L'ACCROCHE DE SAVOIR, CE DE L'ACCEPTATION FUT-IL EN L'ESPECE ET CONFORME A LA PRESOMPTION DE L'ASSURANCE-LIEU DE L'EXISTENCE D'UNE MESSELLE CONFUSION POSEES PAR DERNIER A LA DATE DE MARTHE, ET ALORS QUE LA PROCEDURE DE MARTHE S'ETAIT RENDU INEVITABLEMENT SURVENU A L'AGREMENT DE BESOIN ;MAIS ATTENDU QU'EN RAISON DE LA PART DE LA DECISION DU PREMIER JUGE, LA COMPAGNIE GIRANNAISE QUI EN A AINSI DECIDE QU'ELLE ETAIT DOLOSIVE DEPUIS PLUS DE NUIT, EST SUSCEPTIBLE DE REFUSER A MARTHE TOUT TEMPS PREVUE ET FAISANT EN OUTRE CONFUSION D'UNE MESURE CONFUSION POSEES PAR LA SUITE DE L'ACCEPTATION MISE PAR L'ARRET DE L'ACTE DES DEUX USAGE PRINCIPAL OU DE LA MISE SUR L'INITIATIVE PURE ; QUE LA COUR D'APPEL A ESTIME QUE L'ACTE POUVANT ETRE REPARE NECESSAIREMENT A LA MESURE DU RAPPORT DES INGENIEURS NE DEPASSE DE L'APPLICATION QUELCONQUE DE L'ARTICLE 1066 DU CODE CIVIL ;D'OU IL SUIT QUE LE MOYEN N'EST PAS FONDE ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 7 JUILLET 1958 PAR LA COUR D'APPEL DE MONTPELLIER. N° 58 - 48 731 GIRANNEISE ET CIE C/ MARTHE. PRESIDENT : M GUILLOT - RAPPORTEUR : M MONGUILAN - AVOCAT GENERAL : M LAMBERT - AVOCATS : MM MARTIN-MARTINIERE, GALLAND ET ROUVIERE. <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN PRIS DE LA VIOLATION DE L'ARTICLE 21A ET SUIVANTS DU LIVRE 1ER DU CODE DU TRAVAIL, VIOLATION DE L'ARTICLE 1134 DU CODE CIVIL ET DE L'ARTICLE 7 DE LA LOI DU 20 AVRIL 1810, DEFAUT DE MOTIFS ET MANQUE DE BASE LEGALE, EN CE QUE L'ARRET ATTAQUE A CONDAMNE LA COMPAGNIE, AGENT D'EXPLOITATION, A VERSER A LA MAISON D'ACQUERIR LE REMBOURSEMENT DE DIVERS SOMMES QUI SE SONT O\n", "\n", "[2001 | 20.53] loss=1.34 avg=1.34\n", "[2002 | 21.81] loss=1.42 avg=1.38\n", "[2003 | 23.09] loss=1.50 avg=1.42\n", "[2004 | 24.36] loss=1.64 avg=1.48\n", "[2005 | 25.64] loss=1.58 avg=1.50\n", "[2006 | 26.93] loss=1.67 avg=1.53\n", "[2007 | 28.22] loss=1.71 avg=1.55\n", "[2008 | 29.49] loss=1.50 avg=1.55\n", "[2009 | 30.81] loss=1.52 avg=1.54\n", "[2010 | 32.11] loss=1.39 avg=1.53\n", "[2011 | 33.38] loss=1.57 avg=1.53\n", "[2012 | 34.66] loss=1.38 avg=1.52\n", "[2013 | 35.93] loss=1.49 avg=1.52\n", "[2014 | 37.20] loss=1.49 avg=1.51\n", "[2015 | 38.48] loss=1.55 avg=1.52\n", "[2016 | 39.75] loss=1.61 avg=1.52\n", "[2017 | 41.03] loss=1.40 avg=1.51\n", "[2018 | 42.31] loss=1.59 avg=1.52\n", "[2019 | 43.59] loss=1.57 avg=1.52\n", "[2020 | 44.86] loss=1.59 avg=1.53\n", "[2021 | 46.14] loss=1.53 avg=1.53\n", "[2022 | 47.41] loss=1.50 avg=1.53\n", "[2023 | 48.68] loss=1.45 avg=1.52\n", "[2024 | 49.95] loss=1.62 avg=1.53\n", "[2025 | 51.23] loss=1.50 avg=1.53\n", "[2026 | 52.51] loss=1.37 avg=1.52\n", "[2027 | 53.80] loss=1.58 avg=1.52\n", "[2028 | 55.07] loss=1.39 avg=1.52\n", "[2029 | 56.34] loss=1.49 avg=1.51\n", "[2030 | 57.62] loss=1.47 avg=1.51\n", "[2031 | 58.89] loss=1.53 avg=1.51\n", "[2032 | 60.16] loss=1.45 avg=1.51\n", "[2033 | 61.44] loss=1.66 avg=1.52\n", "[2034 | 62.71] loss=1.50 avg=1.52\n", "[2035 | 64.06] loss=1.42 avg=1.51\n", "[2036 | 65.54] loss=1.47 avg=1.51\n", "[2037 | 66.81] loss=1.67 avg=1.52\n", "[2038 | 68.09] loss=1.40 avg=1.51\n", "[2039 | 69.36] loss=1.47 avg=1.51\n", "[2040 | 70.64] loss=1.49 avg=1.51\n", "[2041 | 71.91] loss=1.50 avg=1.51\n", "[2042 | 73.18] loss=1.58 avg=1.51\n", "[2043 | 74.46] loss=1.44 avg=1.51\n", "[2044 | 75.74] loss=1.39 avg=1.51\n", "[2045 | 77.02] loss=1.51 avg=1.51\n", "[2046 | 78.29] loss=1.55 avg=1.51\n", "[2047 | 79.57] loss=1.62 avg=1.51\n", "[2048 | 80.84] loss=1.65 avg=1.52\n", "[2049 | 82.13] loss=1.46 avg=1.51\n", "[2050 | 83.40] loss=1.41 avg=1.51\n", "[2051 | 84.68] loss=1.45 avg=1.51\n", "[2052 | 85.98] loss=1.56 avg=1.51\n", "[2053 | 87.25] loss=1.45 avg=1.51\n", "[2054 | 88.53] loss=1.54 avg=1.51\n", "[2055 | 89.80] loss=1.32 avg=1.51\n", "[2056 | 91.07] loss=1.49 avg=1.50\n", "[2057 | 92.35] loss=1.51 avg=1.50\n", "[2058 | 93.62] loss=1.45 avg=1.50\n", "[2059 | 94.89] loss=1.54 avg=1.50\n", "[2060 | 96.17] loss=1.39 avg=1.50\n", "[2061 | 97.71] loss=1.49 avg=1.50\n", "[2062 | 99.06] loss=1.35 avg=1.50\n", "[2063 | 100.34] loss=1.55 avg=1.50\n", "[2064 | 101.62] loss=1.49 avg=1.50\n", "[2065 | 102.89] loss=1.62 avg=1.50\n", "[2066 | 104.16] loss=1.48 avg=1.50\n", "[2067 | 105.44] loss=1.59 avg=1.50\n", "[2068 | 106.71] loss=1.62 avg=1.51\n", "[2069 | 108.00] loss=1.47 avg=1.50\n", "[2070 | 109.28] loss=1.42 avg=1.50\n", "[2071 | 110.56] loss=1.42 avg=1.50\n", "[2072 | 111.83] loss=1.49 avg=1.50\n", "[2073 | 113.11] loss=1.41 avg=1.50\n", "[2074 | 114.38] loss=1.40 avg=1.50\n", "[2075 | 115.65] loss=1.60 avg=1.50\n", "[2076 | 116.93] loss=1.34 avg=1.50\n", "[2077 | 118.20] loss=1.41 avg=1.49\n", "[2078 | 119.49] loss=1.50 avg=1.50\n", "[2079 | 120.76] loss=1.69 avg=1.50\n", "[2080 | 122.04] loss=1.42 avg=1.50\n", "[2081 | 123.31] loss=1.50 avg=1.50\n", "[2082 | 124.59] loss=1.33 avg=1.49\n", "[2083 | 125.86] loss=1.58 avg=1.50\n", "[2084 | 127.14] loss=1.55 avg=1.50\n", "[2085 | 128.41] loss=1.36 avg=1.49\n", "[2086 | 129.73] loss=1.51 avg=1.49\n", "[2087 | 131.15] loss=1.57 avg=1.50\n", "[2088 | 132.44] loss=1.47 avg=1.50\n", "[2089 | 133.72] loss=1.34 avg=1.49\n", "[2090 | 134.99] loss=1.44 avg=1.49\n", "[2091 | 136.27] loss=1.56 avg=1.49\n", "[2092 | 137.54] loss=1.51 avg=1.49\n", "[2093 | 138.81] loss=1.38 avg=1.49\n", "[2094 | 140.09] loss=1.45 avg=1.49\n", "[2095 | 141.39] loss=1.49 avg=1.49\n", "[2096 | 142.67] loss=1.52 avg=1.49\n", "[2097 | 143.94] loss=1.58 avg=1.49\n", "[2098 | 145.22] loss=1.44 avg=1.49\n", "[2099 | 146.49] loss=1.50 avg=1.49\n", "[2100 | 147.77] loss=1.41 avg=1.49\n", "======== SAMPLE 1 ========\n", "ABAREE S'EST POURSUIVIE PAR LE RAPPEL DE L'ORDONNANCE D'EXPROPRIATION, PENDANT UNE JOURNEE CUMULATIF QUE LA LOI EXIGE N'A ETE TELLE QU'APPRECIE ET POUR TIRER LE RENVOI QUI A ETE LA SOCIETE DU MUTUALIZATION DE SECURITE SOCIALE, MEME QU'ELLE EST UNE SOCIETE DE FAIT DE LA CHOSE JUGEE DANS UN RAPPEL INTERPRETANT POUR LA CONTRAINTE DU MEME TITRE, AUX FOURNISSAIRE DE L'ARTICLE 6 DU DECRET N° 67-1269 DU 2 AVRIL 1969, QUE L'INTERESSE AYANT, COMME CHEZ SEPT, QUE L'ARRET NE VISE QUE L'OBLIGATION DE FAITS PAR LE TEXTE DE L'ARTICLE 11 DU DECRET N° 68-1173 DU 2 AVRIL 1964 ET DE CE QUE S'AGISSANT D'UN SIMPLE ANIMOSITE DE COMPARUTION ENTRAINANT UNE QUESTION ET L'ABSENCE D'ADMISSIBLE POUR LE CAS OU L'ARTICLE 3 DE L'ARRETE PREVU AU MOYEN EST SENSIBLE, SELON LE POURVOI, DES MOTIFS SUS-RAPPELES ET SES DOCUMENTS ; QUE LE SECOND MOYEN N'EST FONDE EGALEMENT ; QUE LE POURVOI DOIT RECEVOIR APPLICATION ;ET SUR LE TROISIEME MOYEN, TIRE DE LA VIOLATION DE L'ARTICLE 1382 ET ARTICLE 1384 PARAGRAPHE 1ER, SUSVISE, LES ARTICLES 1184 ET 1383 ET 1284 DU CODE CIVIL ONT, D'UNE PART, LEGALEMENT JUSTIFIE LEUR DECISION ; QUE, D'AUTRE PART, LES ARTICLES 17 ET 26 DE LA LOI LES 8 ET 29 MODIFIE DU DECRET N° 68-1167 DU 2 AVRIL 1964 MODIFIANT LES ARTICLES 1184 ET 1284 POUR LE CAS OU L'ARTICLE 17 DES LOIS DES 9, 11 ET 16 DE LA LOI SUSVISEE LES DISPOSITIONS NON DANS LESQUELLES LES DECINATOIRES NE SERAIENT PAS ENTRAINE LA CONVENTION COLLECTIVE OU CELLE DU MEME DECRET N° 69-1172 DU 2 AVRIL 1960, LA DECISION ATTAQUEE, QUI FAIT LA SITUATION DE SES REVENUS, DECLARE INVOQUEE L'ARTICLE 18 DES LOIS N° 69-1170 DU 2 AVRIL 1964, ALORS QUE, SELON LE POURVOI, LA PROHIBITION \"LA CONVENTION COLLECTIVE\" N'EST PAS SUSCEPTIBLE, ET ALORS QUE, D'AUTRE PART, L'ARTICLE 6 DU DECRET N° 67-1291 DU 2 AVRIL 1969 AINSI QUE LEUR OBJET LA COUR, QUI A DONNE L'ARRETE PRECITE, A DEROGE DE CE QUE, LE TRIBUNAL A DEFAUT DE DECLARER, NON SEULE COMPTE TENU, L'INTERESSE \"VISE\" NE SERAIT PAS INVOQUEE ;QU'IL APPARTENAIT AU JUGE DE RAPPORT A LA COUR DE PROCEDURE QUE LA \"CONVENTION COLLECTIVE\" \"LE SOUTENAIT\", PAR UN DECES DE LA SOCIETE DU MUTUALIZATION DE SECURITE SOCIALE ET NON LES DISPOSITIONS DE L'ARTICLE 1184 DU CODE CIVIL, A L'EGARD DES CREANCIERS DE L'ARTICLE 1184, PARAGRAPHE 2 DES ARTICLES 1184 ET 1184, MODIFIANT L'ARTICLE 1284, ALINEA 2 DE LA LOI DU 8 MARS 1961, LA DECISION ATTAQUEE ENONCE QUE \"LA CONVENTION COLLECTIVE CONSTITUAIT A LA SOCIETE DU MUTUALIZATION DE SECURITE SOCIALE ET SE TROUVAIT LE DROIT DEROGE EN COURS AU DECES DE LA FAIRE PARTIE ; QU'IL RESUL\n", "\n", "[2101 | 160.87] loss=1.31 avg=1.49\n", "[2102 | 162.14] loss=1.55 avg=1.49\n", "[2103 | 163.64] loss=1.47 avg=1.49\n", "[2104 | 165.00] loss=1.60 avg=1.49\n", "[2105 | 166.28] loss=1.56 avg=1.49\n", "[2106 | 167.55] loss=1.46 avg=1.49\n", "[2107 | 168.83] loss=1.86 avg=1.50\n", "[2108 | 170.10] loss=1.46 avg=1.50\n", "[2109 | 171.37] loss=1.45 avg=1.49\n", "[2110 | 172.65] loss=1.55 avg=1.50\n", "[2111 | 173.95] loss=1.44 avg=1.49\n", "[2112 | 175.23] loss=1.82 avg=1.50\n", "[2113 | 176.50] loss=1.52 avg=1.50\n", "[2114 | 177.78] loss=1.49 avg=1.50\n", "[2115 | 179.05] loss=1.53 avg=1.50\n", "[2116 | 180.32] loss=1.60 avg=1.50\n", "[2117 | 181.60] loss=1.34 avg=1.50\n", "[2118 | 182.87] loss=1.61 avg=1.50\n", "[2119 | 184.15] loss=1.53 avg=1.50\n", "[2120 | 185.45] loss=1.34 avg=1.50\n", "[2121 | 186.72] loss=1.38 avg=1.50\n", "[2122 | 188.00] loss=1.43 avg=1.50\n", "[2123 | 189.27] loss=1.61 avg=1.50\n", "[2124 | 190.54] loss=1.41 avg=1.50\n", "[2125 | 191.82] loss=1.35 avg=1.49\n", "[2126 | 193.09] loss=1.46 avg=1.49\n", "[2127 | 194.37] loss=1.48 avg=1.49\n", "[2128 | 195.69] loss=1.67 avg=1.50\n", "[2129 | 197.22] loss=1.40 avg=1.50\n", "[2130 | 198.50] loss=1.51 avg=1.50\n", "[2131 | 199.78] loss=1.69 avg=1.50\n", "[2132 | 201.05] loss=1.57 avg=1.50\n", "[2133 | 202.33] loss=1.52 avg=1.50\n", "[2134 | 203.61] loss=1.58 avg=1.50\n", "[2135 | 204.88] loss=1.57 avg=1.50\n", "[2136 | 206.16] loss=1.50 avg=1.50\n", "[2137 | 207.46] loss=1.50 avg=1.50\n", "[2138 | 208.73] loss=1.48 avg=1.50\n", "[2139 | 210.01] loss=1.56 avg=1.50\n", "[2140 | 211.28] loss=1.46 avg=1.50\n", "[2141 | 212.56] loss=1.42 avg=1.50\n", "[2142 | 213.83] loss=1.36 avg=1.50\n", "[2143 | 215.10] loss=1.46 avg=1.50\n", "[2144 | 216.37] loss=1.37 avg=1.50\n", "[2145 | 217.66] loss=1.47 avg=1.50\n", "[2146 | 218.95] loss=1.46 avg=1.50\n", "[2147 | 220.22] loss=1.28 avg=1.49\n", "[2148 | 221.50] loss=1.43 avg=1.49\n", "[2149 | 222.77] loss=1.34 avg=1.49\n", "[2150 | 224.04] loss=1.59 avg=1.49\n", "[2151 | 225.32] loss=1.45 avg=1.49\n", "[2152 | 226.59] loss=1.53 avg=1.49\n", "[2153 | 227.87] loss=1.57 avg=1.49\n", "[2154 | 229.22] loss=1.46 avg=1.49\n", "[2155 | 230.56] loss=1.47 avg=1.49\n", "[2156 | 231.86] loss=1.46 avg=1.49\n", "[2157 | 233.14] loss=1.46 avg=1.49\n", "[2158 | 234.41] loss=1.46 avg=1.49\n", "[2159 | 235.69] loss=1.36 avg=1.49\n", "[2160 | 236.96] loss=1.49 avg=1.49\n", "[2161 | 238.24] loss=1.58 avg=1.49\n", "[2162 | 239.51] loss=1.34 avg=1.49\n", "[2163 | 240.79] loss=1.38 avg=1.49\n", "[2164 | 242.07] loss=1.48 avg=1.49\n", "[2165 | 243.34] loss=1.38 avg=1.49\n", "[2166 | 244.62] loss=1.43 avg=1.48\n", "[2167 | 245.89] loss=1.44 avg=1.48\n", "[2168 | 247.17] loss=1.45 avg=1.48\n", "[2169 | 248.44] loss=1.54 avg=1.48\n", "[2170 | 249.72] loss=1.56 avg=1.49\n", "[2171 | 251.00] loss=1.60 avg=1.49\n", "[2172 | 252.28] loss=1.51 avg=1.49\n", "[2173 | 253.55] loss=1.52 avg=1.49\n", "[2174 | 254.83] loss=1.52 avg=1.49\n", "[2175 | 256.10] loss=1.20 avg=1.48\n", "[2176 | 257.37] loss=1.54 avg=1.48\n", "[2177 | 258.65] loss=1.43 avg=1.48\n", "[2178 | 259.92] loss=1.28 avg=1.48\n", "[2179 | 261.20] loss=1.44 avg=1.48\n", "[2180 | 262.68] loss=1.55 avg=1.48\n", "[2181 | 264.04] loss=1.47 avg=1.48\n", "[2182 | 265.32] loss=1.74 avg=1.49\n", "[2183 | 266.60] loss=1.53 avg=1.49\n", "[2184 | 267.87] loss=1.48 avg=1.49\n", "[2185 | 269.14] loss=1.60 avg=1.49\n", "[2186 | 270.42] loss=1.60 avg=1.49\n", "[2187 | 271.69] loss=1.39 avg=1.49\n", "[2188 | 272.98] loss=1.45 avg=1.49\n", "[2189 | 274.26] loss=1.39 avg=1.49\n", "[2190 | 275.53] loss=1.74 avg=1.49\n", "[2191 | 276.80] loss=1.50 avg=1.49\n", "[2192 | 278.08] loss=1.71 avg=1.49\n", "[2193 | 279.35] loss=1.59 avg=1.49\n", "[2194 | 280.63] loss=1.38 avg=1.49\n", "[2195 | 281.90] loss=1.40 avg=1.49\n", "[2196 | 283.17] loss=1.40 avg=1.49\n", "[2197 | 284.47] loss=1.52 avg=1.49\n", "[2198 | 285.74] loss=1.54 avg=1.49\n", "[2199 | 287.01] loss=1.47 avg=1.49\n", "[2200 | 288.29] loss=1.53 avg=1.49\n", "======== SAMPLE 1 ========\n", " LETE ETANT LA CONNAISSANCE DE CE DROIT SUR LE DIFFERENCE ENTRE LA VALEUR DES TEMOIGNAGES RECUEILLIS A L'EGARD DE LES CHIMIELS ETABLIS LES JUGES DU FOND ;ATTENDU QU'AYANT STATUE SUR LE DIFFERENCE ENTRE LES DEUX COMMANDES, TOUTES LES TERMES DE L'APPLICATION DE L'ARTICLE 1158, 1ER ALINEA, DU CODE CIVIL, LA COUR D'APPEL A EXACTEMENT RENVERSE LA NULLITE DU CONTRAT DE VENTE ;ATTENDU QUE LE MOYEN NE PEUT QUE LUI CORRECTEMENT ETRE INTRODUIT LE REJET AU FOND, QUE LA RECEVABILITE DES PARTIES ETAIT LA GARANTIE DE L'ACTION ENGAGEE CONTRE L'EMPLOYEUR DE L'ENGAGER ; QU'IL ETAIT CONTREVENU A CELLE-CI DE SE DETERMINER ET DE DEUX AUTRES QU'ILS AVAIENT ETE REGULIEREMENT AINSI A CET EGARD, DE CE CHEF DE LA LEGISLATION SURVEILLANTE ;QU'AYANT AINSI NECESSAIREMENT DECIDE QUE L'ACTION ENGAGEE ENTRE DEUX MANDATAIRES ET D'UNE DEMANDE EN JUSTICE, ETANT AUX TERMES DE LA LOI DU 27 FEVRIER 1953, UNE DECISION DE DOMMAGES-INTERETS DE CONNAISSANCE, LA LEGISLATION SURVEILLAIT, PAR LE JUGE NON CONTESTE, LUI INCOMBE, ET LA DECISION DE PREMIERE INSTANCE DEVANT LA COUR D'APPEL, SELON LAQUELLE IL RESULTAIT LA NULLITE DU CONTRAT DE VENTE AU MOTIF QUE L'INTERESSE L'EMPLOYAIT A RAPPORTER AU DECOMPTE DE LA COMPTABILITE DE LA DEMANDE EN JUSTICE ;MAIS ATTENDU QU'AYANT AINSI SOUVERAINEMENT CONSTATE QUE LA REJET DES DIFFERENCES ECHUS ETAIT A LA CHARGE DE CETTE DECISION ET DE LA NULLITE DES CONVENTIONS ALORS QU'ELLE CONSACRE SA REVISION DE CELUI-CI POUR AUTANT QUE LE JUGEMENT QUI, APRES AU COURS DE LA LETTRE DU 17 JANVIER 1951 AVAIT ETE ANNULE, QU'ELLE AVAIT ETE DENONCE AINSI AU MOINS VEXATOIRE DE LA SOCIETE ET NE POUVAIT, A CET EGARD, ETIR CONTRAINTE A L'AJOURNEMENT DES MANDATAIRES, L'EXISTENCE D'UNE FAUTE QU'AVEC UNE CONVENTION EXPRESSE DE L'ORGANISME ET NON LE JUGEMENT DE LA LOI DU 27 FEVRIER 1953, QUI DECOULAIT QUELLE QU'AVAIT AGI ET QUI N'ETAIT LE PAS A LYON DU CONSEILLER DE LA SOCIETE, ET ALORS QU'AUCUN PRET ETABLIQUE N'EST PAS CONCLU AU MOINS QUE L'INTERESSE L'EMPLOYAIT A RAPPORTER LA RECEVOIR EN JUILLET 1952 UNE DEMANDE AU MOTIF QUE CETTE DEMANDE N'AURAIT PU ETRE DEDUIQUEE, LE JUGEMENT DE CETTE FAUTE PARCE QUI A ETE PRONONCEE DU DOMMAGE, POUR SA PERIL, D'ACTER DE LA DECISION DE PREMIERE INSTANCE ; D'OU IL SUIT QUE LE MOYEN NE PEUT ETRE ACCUEILLI EN AUCUN DE SES DEUX BRANCHES ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 23 OCTOBRE 1963, PAR LA COUR D'APPEL DE NANCY. N° 63-10 632. DELEGATION DE L'ELECTION DU 1ER AVRIL 1952 A GUAYNE C/ SOCIETE A RESPONSABILITE LIMITEE \"SOCIETE ANONYME L'INUTUNE SOCIETE DIFFERE EN QUALIFIANT A SON PERE \"\n", "\n", "[2201 | 301.96] loss=1.50 avg=1.49\n", "[2202 | 303.25] loss=1.60 avg=1.49\n", "[2203 | 304.52] loss=1.55 avg=1.49\n", "[2204 | 305.81] loss=1.54 avg=1.49\n", "[2205 | 307.09] loss=1.32 avg=1.49\n", "[2206 | 308.37] loss=1.57 avg=1.49\n", "[2207 | 309.65] loss=1.48 avg=1.49\n", "[2208 | 310.93] loss=1.50 avg=1.49\n", "[2209 | 312.20] loss=1.47 avg=1.49\n", "[2210 | 313.47] loss=1.42 avg=1.49\n", "[2211 | 314.74] loss=1.55 avg=1.49\n", "[2212 | 316.02] loss=1.43 avg=1.49\n", "[2213 | 317.30] loss=1.47 avg=1.49\n", "[2214 | 318.57] loss=1.51 avg=1.49\n", "[2215 | 319.85] loss=1.44 avg=1.49\n", "[2216 | 321.12] loss=1.38 avg=1.49\n", "[2217 | 322.40] loss=1.60 avg=1.49\n", "[2218 | 323.68] loss=1.41 avg=1.49\n", "[2219 | 324.95] loss=1.42 avg=1.49\n", "[2220 | 326.22] loss=1.29 avg=1.49\n", "[2221 | 327.52] loss=1.50 avg=1.49\n", "[2222 | 328.86] loss=1.52 avg=1.49\n", "[2223 | 330.13] loss=1.54 avg=1.49\n", "[2224 | 331.40] loss=1.57 avg=1.49\n", "[2225 | 332.68] loss=1.42 avg=1.49\n", "[2226 | 333.96] loss=1.35 avg=1.49\n", "[2227 | 335.24] loss=1.56 avg=1.49\n", "[2228 | 336.51] loss=1.54 avg=1.49\n", "[2229 | 337.79] loss=1.56 avg=1.49\n", "[2230 | 339.07] loss=1.46 avg=1.49\n", "[2231 | 340.34] loss=1.38 avg=1.49\n", "[2232 | 341.62] loss=1.45 avg=1.49\n", "[2233 | 342.90] loss=1.60 avg=1.49\n", "[2234 | 344.17] loss=1.51 avg=1.49\n", "[2235 | 345.44] loss=1.34 avg=1.49\n", "[2236 | 346.72] loss=1.44 avg=1.49\n", "[2237 | 347.99] loss=1.41 avg=1.48\n", "[2238 | 349.27] loss=1.59 avg=1.49\n", "[2239 | 350.55] loss=1.47 avg=1.49\n", "[2240 | 351.82] loss=1.44 avg=1.49\n", "[2241 | 353.10] loss=1.47 avg=1.48\n", "[2242 | 354.37] loss=1.32 avg=1.48\n", "[2243 | 355.65] loss=1.56 avg=1.48\n", "[2244 | 356.92] loss=1.43 avg=1.48\n", "[2245 | 358.20] loss=1.46 avg=1.48\n", "[2246 | 359.48] loss=1.43 avg=1.48\n", "[2247 | 360.80] loss=1.51 avg=1.48\n", "[2248 | 362.12] loss=1.46 avg=1.48\n", "[2249 | 363.39] loss=1.58 avg=1.48\n", "[2250 | 364.67] loss=1.46 avg=1.48\n", "[2251 | 365.95] loss=1.27 avg=1.48\n", "[2252 | 367.23] loss=1.46 avg=1.48\n", "[2253 | 368.51] loss=1.58 avg=1.48\n", "[2254 | 369.78] loss=1.42 avg=1.48\n", "[2255 | 371.06] loss=1.41 avg=1.48\n", "[2256 | 372.34] loss=1.34 avg=1.48\n", "[2257 | 373.62] loss=1.49 avg=1.48\n", "[2258 | 374.89] loss=1.40 avg=1.48\n", "[2259 | 376.16] loss=1.51 avg=1.48\n", "[2260 | 377.44] loss=1.54 avg=1.48\n", "[2261 | 378.71] loss=1.46 avg=1.48\n", "[2262 | 379.98] loss=1.35 avg=1.48\n", "[2263 | 381.26] loss=1.50 avg=1.48\n", "[2264 | 382.54] loss=1.41 avg=1.48\n", "[2265 | 383.82] loss=1.48 avg=1.48\n", "[2266 | 385.10] loss=1.47 avg=1.48\n", "[2267 | 386.37] loss=1.49 avg=1.48\n", "[2268 | 387.65] loss=1.44 avg=1.48\n", "[2269 | 388.92] loss=1.39 avg=1.48\n", "[2270 | 390.20] loss=1.47 avg=1.48\n", "[2271 | 391.47] loss=1.47 avg=1.48\n", "[2272 | 392.78] loss=1.62 avg=1.48\n", "[2273 | 394.12] loss=1.59 avg=1.48\n", "[2274 | 395.40] loss=1.50 avg=1.48\n", "[2275 | 396.67] loss=1.58 avg=1.48\n", "[2276 | 397.96] loss=1.39 avg=1.48\n", "[2277 | 399.24] loss=1.47 avg=1.48\n", "[2278 | 400.51] loss=1.51 avg=1.48\n", "[2279 | 401.78] loss=1.52 avg=1.48\n", "[2280 | 403.06] loss=1.32 avg=1.48\n", "[2281 | 404.33] loss=1.47 avg=1.48\n", "[2282 | 405.62] loss=1.46 avg=1.48\n", "[2283 | 406.90] loss=1.48 avg=1.48\n", "[2284 | 408.17] loss=1.54 avg=1.48\n", "[2285 | 409.44] loss=1.51 avg=1.48\n", "[2286 | 410.72] loss=1.43 avg=1.48\n", "[2287 | 411.99] loss=1.59 avg=1.48\n", "[2288 | 413.27] loss=1.40 avg=1.48\n", "[2289 | 414.55] loss=1.41 avg=1.48\n", "[2290 | 415.83] loss=1.46 avg=1.48\n", "[2291 | 417.11] loss=1.32 avg=1.48\n", "[2292 | 418.38] loss=1.56 avg=1.48\n", "[2293 | 419.66] loss=1.55 avg=1.48\n", "[2294 | 420.93] loss=1.52 avg=1.48\n", "[2295 | 422.20] loss=1.39 avg=1.48\n", "[2296 | 423.48] loss=1.34 avg=1.48\n", "[2297 | 424.77] loss=1.46 avg=1.48\n", "[2298 | 426.06] loss=1.45 avg=1.48\n", "[2299 | 427.35] loss=1.55 avg=1.48\n", "[2300 | 428.63] loss=1.46 avg=1.48\n", "======== SAMPLE 1 ========\n", " POUR FOURNITURES QUE LES SALARIES, ALORS ENCORE QUE, DANS DES CONCLUSIONS LAISSEES SANS REPONSE, ILS N'ONT FAIT MENTION QU'AUCUNE DES PARTIES, FAUTE DE LEUR DECISION, DES MOTIFS QUI PEUVENT ETRE REPONDES ; QU'ILS ONT AINSI JUSTIFIE, SANS RAISON DE SES PROPRES CONSTATATIONS ET DE SES ENONCIATIONS ;QUE LE POURVOI NE PEUT SE CONTENIR NI LE RAPPEL QU'AURAIT RECLAME UNE EXPERTISE MEDICALE, LUI ADRESSEES AU TRIBUNAL DE COMMERCE ;PAR CES MOTIFS : CASSE ET ANNULE L'ARRET RENDU ENTRE LES PARTIES PAR LA COUR D'APPEL DE PARIS, LE 8 JUIN 1965 ;REMET EN CONSEQUENCE LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET ET, POUR ETRE FAIT DROIT, LES RENVOIE DEVANT LA COUR D'APPEL D'ORLEANS. N° 65-20014. A...Y... C / EPOUX X.... PRESIDENT : M DROUILLAT - RAPPORTEUR : M BOURCELIN - AVOCAT GENERAL : M ALBAUT - AVOCATS : MM DESACHE ET CALON. DANS LE MEME SENS : SUR LE N° 2 : 7 OCTOBRE 1966, BULL 1966, II, N° 703, PoyET (N° 2) ET L'ARRETE MINISTERIEL DU 21 AVRIL 1968 ;19 JANVIER 1968, BULL 1968, II, N° 81, P 60. A RAPPROCHER : SUR LE N° 1 : 30 MARS 1963, BULL 1963, III, N° 45, P 49. SUR LE N° 2 : 30 NOVEMBRE 1948, BULL 1948, III, N° 491, P 399. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE DES ENONCIATIONS DE L'ARRET INFIRMATIF ATTAQUE ET DES PRODUCTIONS, ONT LA SUCCESSION DE DAME BERGERARD A OBTINT LA PENSION ALIMENTAIRE DE LA REGION PARISIENNE DE DOUAI SON DOMAINE D'UN APPARTEMENT A DOUAI (SICOLA) DE LA MUR-ESTE-LES-BELGES DE LA COUR DE RENNES, A UNE ERREUR DE L'IMMEUBLE SITUE A SA CHARFABLES REJOINT ; QUE CEUX-CI FONT ENSEMBLE D'UN JUGEMENT ARBITRANT L'INTERESSE ET A EXPLOITE CETTE DECISION DE PRECEDER DEVANT LA JURIDICTION DU SECOND DEGRE ; QU'IL A DEBOUTEE DE SES DEMANDES D'EXPRESSION DE LA DEMANDE EN DIVORCE ET CONTENANT UN RAPPEL DE CES ACTES DE PREUVE ET DONT ILS SONT LA SOCIETE LE POURVOI, LE TRIBUNAL DE COMMERCE S'EST DECLAREE IRRECEVABLE ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LE JUGEMENT RENDU, LE 30 MAI 1966, PAR LE TRIBUNAL DE COMMERCE DE LA HAUTE-SAVIN N° 65-4086 <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QU'IL EST FAIT GRIEF A L'ARRET ATTAQUE, RENDU, D'AVOIR DECLARATE CERTAINS VOIES D'ALTERNATIVITE A L'EGARD DUQUEL Y... A ETE PRONONCEE, ALORS QU'IL RESULTE DES PREUVES DE PORTE ET EN SE FONDANT A DES QU'ALTERNATITES QUE Y..., A ETE, PAR ACTE RECOMMANDEE, COMMETTANT UNE RENTE, POUR CAUSE DE REPARATION DE L'OBJET DE LA FRAUDE, ET DE RECHERCHER UNE EXPERTISE PERE PAR L'INTERESSE DON\n", "\n", "[2301 | 442.61] loss=1.59 avg=1.48\n", "[2302 | 443.88] loss=1.39 avg=1.48\n", "[2303 | 445.16] loss=1.45 avg=1.48\n", "[2304 | 446.43] loss=1.41 avg=1.47\n", "[2305 | 447.71] loss=1.47 avg=1.47\n", "[2306 | 449.00] loss=1.57 avg=1.48\n", "[2307 | 450.27] loss=1.26 avg=1.47\n", "[2308 | 451.55] loss=1.40 avg=1.47\n", "[2309 | 452.82] loss=1.22 avg=1.47\n", "[2310 | 454.10] loss=1.33 avg=1.47\n", "[2311 | 455.37] loss=1.45 avg=1.47\n", "[2312 | 456.64] loss=1.65 avg=1.47\n", "[2313 | 457.94] loss=1.45 avg=1.47\n", "[2314 | 459.22] loss=1.43 avg=1.47\n", "[2315 | 460.51] loss=1.38 avg=1.47\n", "[2316 | 461.79] loss=1.50 avg=1.47\n", "[2317 | 463.08] loss=1.61 avg=1.47\n", "[2318 | 464.36] loss=1.42 avg=1.47\n", "[2319 | 465.63] loss=1.52 avg=1.47\n", "[2320 | 466.91] loss=1.52 avg=1.47\n", "[2321 | 468.18] loss=1.51 avg=1.47\n", "[2322 | 469.46] loss=1.33 avg=1.47\n", "[2323 | 470.75] loss=1.40 avg=1.47\n", "[2324 | 472.03] loss=1.50 avg=1.47\n", "[2325 | 473.30] loss=1.36 avg=1.47\n", "[2326 | 474.57] loss=1.35 avg=1.47\n", "[2327 | 475.85] loss=1.47 avg=1.47\n", "[2328 | 477.12] loss=1.49 avg=1.47\n", "[2329 | 478.40] loss=1.36 avg=1.47\n", "[2330 | 479.67] loss=1.35 avg=1.47\n", "[2331 | 480.94] loss=1.35 avg=1.46\n", "[2332 | 482.24] loss=1.37 avg=1.46\n", "[2333 | 483.51] loss=1.48 avg=1.46\n", "[2334 | 484.79] loss=1.45 avg=1.46\n", "[2335 | 486.06] loss=1.49 avg=1.46\n", "[2336 | 487.34] loss=1.29 avg=1.46\n", "[2337 | 488.61] loss=1.34 avg=1.46\n", "[2338 | 489.89] loss=1.46 avg=1.46\n", "[2339 | 491.20] loss=1.50 avg=1.46\n", "[2340 | 492.48] loss=1.47 avg=1.46\n", "[2341 | 493.77] loss=1.53 avg=1.46\n", "[2342 | 495.06] loss=1.70 avg=1.46\n", "[2343 | 496.34] loss=1.42 avg=1.46\n", "[2344 | 497.61] loss=1.35 avg=1.46\n", "[2345 | 498.89] loss=1.54 avg=1.46\n", "[2346 | 500.16] loss=1.35 avg=1.46\n", "[2347 | 501.44] loss=1.56 avg=1.46\n", "[2348 | 502.71] loss=1.40 avg=1.46\n", "[2349 | 504.01] loss=1.60 avg=1.46\n", "[2350 | 505.29] loss=1.43 avg=1.46\n", "[2351 | 506.57] loss=1.58 avg=1.46\n", "[2352 | 507.84] loss=1.33 avg=1.46\n", "[2353 | 509.12] loss=1.48 avg=1.46\n", "[2354 | 510.39] loss=1.32 avg=1.46\n", "[2355 | 511.67] loss=1.33 avg=1.46\n", "[2356 | 512.94] loss=1.44 avg=1.46\n", "[2357 | 514.21] loss=1.40 avg=1.46\n", "[2358 | 515.51] loss=1.29 avg=1.46\n", "[2359 | 516.78] loss=1.23 avg=1.46\n", "[2360 | 518.05] loss=1.51 avg=1.46\n", "[2361 | 519.33] loss=1.40 avg=1.46\n", "[2362 | 520.60] loss=1.59 avg=1.46\n", "[2363 | 521.88] loss=1.34 avg=1.46\n", "[2364 | 523.18] loss=1.54 avg=1.46\n", "[2365 | 524.47] loss=1.45 avg=1.46\n", "[2366 | 525.76] loss=1.44 avg=1.46\n", "[2367 | 527.05] loss=1.51 avg=1.46\n", "[2368 | 528.33] loss=1.51 avg=1.46\n", "[2369 | 529.61] loss=1.36 avg=1.46\n", "[2370 | 530.88] loss=1.37 avg=1.46\n", "[2371 | 532.15] loss=1.46 avg=1.46\n", "[2372 | 533.43] loss=1.42 avg=1.46\n", "[2373 | 534.70] loss=1.38 avg=1.45\n", "[2374 | 535.98] loss=1.55 avg=1.46\n", "[2375 | 537.27] loss=1.42 avg=1.45\n", "[2376 | 538.54] loss=1.41 avg=1.45\n", "[2377 | 539.81] loss=1.32 avg=1.45\n", "[2378 | 541.09] loss=1.36 avg=1.45\n", "[2379 | 542.37] loss=1.49 avg=1.45\n", "[2380 | 543.64] loss=1.33 avg=1.45\n", "[2381 | 544.91] loss=1.69 avg=1.45\n", "[2382 | 546.19] loss=1.33 avg=1.45\n", "[2383 | 547.46] loss=1.47 avg=1.45\n", "[2384 | 548.75] loss=1.37 avg=1.45\n", "[2385 | 550.03] loss=1.47 avg=1.45\n", "[2386 | 551.30] loss=1.28 avg=1.45\n", "[2387 | 552.58] loss=1.60 avg=1.45\n", "[2388 | 553.86] loss=1.43 avg=1.45\n", "[2389 | 555.13] loss=1.40 avg=1.45\n", "[2390 | 556.46] loss=1.53 avg=1.45\n", "[2391 | 557.73] loss=1.49 avg=1.45\n", "[2392 | 559.07] loss=1.56 avg=1.45\n", "[2393 | 560.38] loss=1.35 avg=1.45\n", "[2394 | 561.66] loss=1.38 avg=1.45\n", "[2395 | 562.93] loss=1.41 avg=1.45\n", "[2396 | 564.20] loss=1.25 avg=1.45\n", "[2397 | 565.48] loss=1.39 avg=1.45\n", "[2398 | 566.75] loss=1.56 avg=1.45\n", "[2399 | 568.03] loss=1.47 avg=1.45\n", "[2400 | 569.30] loss=1.42 avg=1.45\n", "======== SAMPLE 1 ========\n", " DU MOULIN DE LA MAURIIE, SUR UNE ROCONNAGE DE LA MARCHE ETAIT LA FAUTE DE L'ECOULER DIVORCE ; QU'ASSIGNERENT SON ENTIER INSCRIT, EN MATIERE DE REFERENCE, LA FAUTE QUI LUI AVAIT ETE SOUTERRIERE ; QUE PAR LETTRE DU 29 AVRIL 1962, LA COMMISSION DE PREMIERE INSTANCE A DECLARE \"S'EN EST PRODUITE AU JUGEMENT DE PREMIERE INSTANCE\" ; QUE L'ARRET DECLARE LA DEMANDE RECONVENTIONNELLE FORMULEE, L'ARRET A DEDUIT DES CONSTATATIONS DE L'INSPECTEUR DU TRAVAIL, SANS ECLAIRAIENT S'ETRE INSCRIT A L'ORDRE PUBLIC DE L'ANNEE 1963, QUE L'ARRET A ETE \"EN PRECISANT QU'UN TEL TRAVAIL CONSENTI A SA REGULARISATION DE CE JOUR MEME PAR UN JUGEMENT PRONONCANT SA VALIDITE DE L'INSPECTEUR DU TRAVAIL \" ; QUE LE MOYEN N'EST DONC PAS FONDE ; D'OU IL SUIT QUE L'ARRET, QUI N'A PAS MANQUE DE FAIT, EST LEGALEMENT JUSTIFIE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LA DECISION RENDUE LE 27 AVRIL 1967 PAR LA COMMISSION DE PREMIERE INSTANCE DE LA SEINE. <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN PRIS EN SES DIVERSES BRANCHES : ATTENDU QU'IL EST REPROCHE A L'ARRET INTERDISAIT TOUS TALUS DE LAQUELLE IL AVAIT OBTENU CELUI DE CET ORDRE COMPRIS A L'EGARD DES EPOUX Y..., SON MARI, DU PRECEDENT COMMISSARIAT COMPAGNIE, EN RAISON DE L'INEXECUTION DU PREAVIS DE SA CONDUITE DE LA FONDATION PRATIQUEE SUR LA DIFFERENCE JUSQU'A L'ENCONTRE DE SON PROFIT NON A L'EGARD DU MARIAGE OU A L'INTERESSE POUR LUI EXERCE, ALORS, SELON LE MOYEN, QU'EN REFUSANT DES PRONONCER AU LITIGE POUR DECLARER AVAIT EU UNE INEXECUTION DU PREAVIS, LA COMMISSION NATIONALE TECHNIQUE A FAIT DROIT A L'ACTION EN REPARATION DE LA PRECARITE DU MARI EN CAS DE RESILIATION DU CONTRAT A LA QUA QUELQUE AFFECTATION DE CETTE CONDITION, QUI, POUR QUE SES ENFANTS GRIEVEMENTS D'UNE PROMESSE DE VUE, A ETE VICIE PAR L'ORDONNANCE DU 24 OCTOBRE 1958 ;MAIS ATTEND, D'UNE PART, QUE SUR LE DEUXIEME MOYEN, IL EST IRRECEVABLE ; QU'ETANT EN CONSEQUENCE DECLAREE PRIS PAR LA COMMUNE DE GAGE UNE TELLE DEMANDE, ET POUR LE COMPTE DE LA DEMANDE AUX MOTIFS QU'ELLE CONNAISSAIT EN TOUT ETAT DE CAUSE SON ADRESSE QUI SE TROUVAIT AFFECTEE DANS L'IMPOSSIBILITE D'EMIS ; QU'ELLE A AINSI RENDU COMPTE TENU DE LA RECONNAISSANCE DU MARIAGE DANS L'UN DES OBLIGATIONS QUELCONQUES ET QU'ELLE A AINSI RENDAIT RECONNAISSANCE DU MARIAGE ; QU'EN ADMETTANT QUE L'INEXECUTION DU PREAVIS OU DE L'ENCONTRE ETAIT NOUVELLE COM'AMI DE SA CONDITION, IL AVAIT EU LESIONS D'OUVERTURE QU'ELLES FONT INSCRIT SES ACHATS, LES JUGES DU SECOND DEGRE ONT LEGALEMENT JUSTIFIE SA DECISION ;MAIS QUE LE REFUS DE CES DEUX AUTRES MOTIFS QUI\n", "\n", "[2401 | 582.63] loss=1.48 avg=1.45\n", "[2402 | 583.91] loss=1.53 avg=1.45\n", "[2403 | 585.18] loss=1.23 avg=1.45\n", "[2404 | 586.46] loss=1.33 avg=1.45\n", "[2405 | 587.74] loss=1.49 avg=1.45\n", "[2406 | 589.03] loss=1.52 avg=1.45\n", "[2407 | 590.31] loss=1.56 avg=1.45\n", "[2408 | 591.62] loss=1.35 avg=1.45\n", "[2409 | 592.95] loss=1.45 avg=1.45\n", "[2410 | 594.23] loss=1.57 avg=1.45\n", "[2411 | 595.50] loss=1.37 avg=1.45\n", "[2412 | 596.78] loss=1.40 avg=1.45\n", "[2413 | 598.05] loss=1.46 avg=1.45\n", "[2414 | 599.32] loss=1.46 avg=1.45\n", "[2415 | 600.60] loss=1.41 avg=1.45\n", "[2416 | 601.88] loss=1.42 avg=1.45\n", "[2417 | 603.17] loss=1.41 avg=1.45\n", "[2418 | 604.45] loss=1.26 avg=1.45\n", "[2419 | 605.72] loss=1.54 avg=1.45\n", "[2420 | 606.99] loss=1.37 avg=1.45\n", "[2421 | 608.26] loss=1.40 avg=1.45\n", "[2422 | 609.54] loss=1.60 avg=1.45\n", "[2423 | 610.81] loss=1.50 avg=1.45\n", "[2424 | 612.09] loss=1.40 avg=1.45\n", "[2425 | 613.36] loss=1.49 avg=1.45\n", "[2426 | 614.66] loss=1.39 avg=1.45\n", "[2427 | 615.93] loss=1.46 avg=1.45\n", "[2428 | 617.21] loss=1.48 avg=1.45\n", "[2429 | 618.48] loss=1.39 avg=1.45\n", "[2430 | 619.76] loss=1.48 avg=1.45\n", "[2431 | 621.07] loss=1.41 avg=1.45\n", "[2432 | 622.36] loss=1.49 avg=1.45\n", "[2433 | 623.63] loss=1.47 avg=1.45\n", "[2434 | 625.00] loss=1.46 avg=1.45\n", "[2435 | 626.30] loss=1.26 avg=1.45\n", "[2436 | 627.57] loss=1.38 avg=1.44\n", "[2437 | 628.85] loss=1.50 avg=1.45\n", "[2438 | 630.12] loss=1.38 avg=1.44\n", "[2439 | 631.39] loss=1.50 avg=1.45\n", "[2440 | 632.66] loss=1.42 avg=1.44\n", "[2441 | 633.94] loss=1.42 avg=1.44\n", "[2442 | 635.21] loss=1.22 avg=1.44\n", "[2443 | 636.50] loss=1.48 avg=1.44\n", "[2444 | 637.77] loss=1.56 avg=1.44\n", "[2445 | 639.05] loss=1.49 avg=1.44\n", "[2446 | 640.32] loss=1.41 avg=1.44\n", "[2447 | 641.60] loss=1.24 avg=1.44\n", "[2448 | 642.87] loss=1.47 avg=1.44\n", "[2449 | 644.14] loss=1.52 avg=1.44\n", "[2450 | 645.42] loss=1.32 avg=1.44\n", "[2451 | 646.71] loss=1.33 avg=1.44\n", "[2452 | 647.99] loss=1.35 avg=1.44\n", "[2453 | 649.26] loss=1.53 avg=1.44\n", "[2454 | 650.54] loss=1.56 avg=1.44\n", "[2455 | 651.82] loss=1.53 avg=1.44\n", "[2456 | 653.13] loss=1.49 avg=1.44\n", "[2457 | 654.41] loss=1.48 avg=1.44\n", "[2458 | 655.68] loss=1.23 avg=1.44\n", "[2459 | 656.95] loss=1.35 avg=1.44\n", "[2460 | 658.29] loss=1.44 avg=1.44\n", "[2461 | 659.57] loss=1.52 avg=1.44\n", "[2462 | 660.85] loss=1.40 avg=1.44\n", "[2463 | 662.13] loss=1.34 avg=1.44\n", "[2464 | 663.40] loss=1.44 avg=1.44\n", "[2465 | 664.67] loss=1.40 avg=1.44\n", "[2466 | 665.95] loss=1.20 avg=1.44\n", "[2467 | 667.22] loss=1.48 avg=1.44\n", "[2468 | 668.50] loss=1.34 avg=1.44\n", "[2469 | 669.78] loss=1.37 avg=1.44\n", "[2470 | 671.05] loss=1.40 avg=1.44\n", "[2471 | 672.33] loss=1.42 avg=1.44\n", "[2472 | 673.60] loss=1.53 avg=1.44\n", "[2473 | 674.88] loss=1.56 avg=1.44\n", "[2474 | 676.15] loss=1.45 avg=1.44\n", "[2475 | 677.42] loss=1.51 avg=1.44\n", "[2476 | 678.70] loss=1.66 avg=1.44\n", "[2477 | 679.99] loss=1.43 avg=1.44\n", "[2478 | 681.26] loss=1.40 avg=1.44\n", "[2479 | 682.54] loss=1.50 avg=1.44\n", "[2480 | 683.82] loss=1.48 avg=1.44\n", "[2481 | 685.12] loss=1.38 avg=1.44\n", "[2482 | 686.40] loss=1.45 avg=1.44\n", "[2483 | 687.67] loss=1.44 avg=1.44\n", "[2484 | 688.95] loss=1.33 avg=1.44\n", "[2485 | 690.22] loss=1.20 avg=1.44\n", "[2486 | 691.58] loss=1.51 avg=1.44\n", "[2487 | 692.86] loss=1.31 avg=1.44\n", "[2488 | 694.14] loss=1.67 avg=1.44\n", "[2489 | 695.41] loss=1.39 avg=1.44\n", "[2490 | 696.68] loss=1.53 avg=1.44\n", "[2491 | 697.96] loss=1.40 avg=1.44\n", "[2492 | 699.23] loss=1.40 avg=1.44\n", "[2493 | 700.51] loss=1.36 avg=1.44\n", "[2494 | 701.80] loss=1.50 avg=1.44\n", "[2495 | 703.08] loss=1.40 avg=1.44\n", "[2496 | 704.36] loss=1.37 avg=1.44\n", "[2497 | 705.63] loss=1.51 avg=1.44\n", "[2498 | 706.91] loss=1.32 avg=1.44\n", "[2499 | 708.18] loss=1.38 avg=1.44\n", "[2500 | 709.45] loss=1.43 avg=1.44\n", "======== SAMPLE 1 ========\n", "ANT AU PRENEUR DU TRAVAIL EFFECTUE CE DEBITEUR POUR PERMETTRE L'INDEMNITE DE DOMMAGES-INTERETS EN SUS D'EXERCER SON CONTROLE SANS PROFIT DE L'EMPLOYEUR QUI NE S'ETAIT PAS JUSTIFIE PAR TEL LIEN DE CAUSALITE ENTRE L'EXECUTION ET LA REALISATION D'UNE FAUTE PROFESSIONNELLE DE L'ENTREPRENEUR ;ATTENDU QUE L'ARRET ATTAQUE SE TROUVE JUSTIFIE ;QUE PAR CES MOTIFS, C, ABSTRACTION FAITE DES MOTIFS CRITIQUES PAR LES DEUX AUTRES BRANCHES DU MOYEN, INVOQUEES PAR LE MOYEN, L'ARRET, EN DATE DU 14 MARS 1964, A REPONDU AUX CONCLUSIONS PRETENDUMENT DELAISSEES ;CITES FONDES ENSUITE, ET, EN TOUTES SES CONCLUSIONS, DES CONCLUSIONS DE LA SOCIETE VICTOR NEGOCIANT COMME COMMISSIONNAIRE EN QUALITE D'ADMINISTRATEUR GENERAL AU SERVICE DE LA SOCIETE A RESPONSABILITE LIMITEE CHAQUE SOCIETE DE L'ENTREPRENEUR AUX TERMES DUQUEL IL AURAIT ETE SITUEE AU PRIX DE CINQ MOYENS QUI ETAIENT POSSIBLE ENTRE LES MAJORATIONS DE RETARD D'INVALIDITE DANS L'INTENTION DES PARTIES ;D'OU IL SUIT QUE L'ARRET NE S'EST PAS CONTREDIT A LEUR DECISION ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 16 DECEMBRE 1963, PAR LA COUR D'APPEL DE BORDEAUX. N° 64-13 051. SOCIETE VICTOR Y... C/ SOCIETE \"COMMI\" ET AUTRE. PRESIDENT : M DROUILLAT - RAPPORTEUR : M SELTEN - AVOCAT GENERAL : M ALBAUT - AVOCATS : MM TALAMON ET DE SEGOGNE. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QU'IL EST FAIT GRIEF A L'ARRET ATTAQUE, STATUANT SUR LES CONCLUSIONS CONFIRMANT LA CASSATION EN CE QUI CONCERNE LA CONVENTION COLLECTIVE, D'UN JUGEMENT ENTREPRIS, S'EST ABSTENUE DE SURSIS A STATUER PRONONCE DE LEUR DEMANDE, SA DECISION SELON LE TEXTE SUSVISE DE L'OPPOSABILITE, ALORS QUE LES JUGES, NOTAMMENT SUSVISES EXPRESSEMENT EXAMENES PAR UN TEXTE CONCERNANT L'ENTREPRENEUR, SONT TENUS DE REPONDRE AUX CONCLUSIONS DE LA SOCIETE \"COMMI\", SANS S'EXPLIQUER EN LA CAUSE D'AFFIRMATION QUE CETTE ENTREPRISE AVAIT EU LIEU A CELLE-LA ET FUTURITE PAR CE DERNIER, SANS RECHERCHER SI C'EST PAR UNE DECISION A L'APPUI D'UN CONSTAT AU CONTROLE DE L'ENTREPRENEUR DU TRESOR SUR LAQUELLE IL DEVRAIT AVOIR LIVRIQUEMENT LIES AVEC LUI DES DEUX DIRECTIVES EN SANT DE LA DIVISION DE CELLE-LA, SANS MECONNAITRE LA PREUVE PAR LE CONGEDIEMENT, ET ALORS QUE LE DEBIT DE L'EMPLOYEUR COMPRENAIT LA NATURE DE LA SOCIETE \"COMMI\" D'UNE FACON INTERVENUE POUR SE VOIR CONSTATER CET ACTE, LA DECISION ATTAQUEE, EN CONSEQUENCE A PRONONCE LA CONDAMNATION ENTRE LES JUGES DE L'EXPROPRIATION A L'EGARD DE LA SOCIETE COMMI, A LA SUITE DE LA MENTION DE LA MESURE D'UNE OBLIGATION, D'UN DEBAT ;\n", "\n", "[2501 | 723.15] loss=1.42 avg=1.44\n", "[2502 | 724.50] loss=1.55 avg=1.44\n", "[2503 | 725.79] loss=1.41 avg=1.44\n", "[2504 | 727.06] loss=1.42 avg=1.44\n", "[2505 | 728.34] loss=1.37 avg=1.44\n", "[2506 | 729.61] loss=1.51 avg=1.44\n", "[2507 | 730.89] loss=1.30 avg=1.44\n", "[2508 | 732.16] loss=1.45 avg=1.44\n", "[2509 | 733.43] loss=1.39 avg=1.44\n", "[2510 | 734.72] loss=1.58 avg=1.44\n", "[2511 | 736.00] loss=1.63 avg=1.44\n", "[2512 | 737.27] loss=1.45 avg=1.44\n", "[2513 | 738.54] loss=1.24 avg=1.44\n", "[2514 | 739.82] loss=1.36 avg=1.44\n", "[2515 | 741.09] loss=1.50 avg=1.44\n", "[2516 | 742.37] loss=1.47 avg=1.44\n", "[2517 | 743.65] loss=1.51 avg=1.44\n", "[2518 | 744.92] loss=1.47 avg=1.44\n", "[2519 | 746.24] loss=1.36 avg=1.44\n", "[2520 | 747.52] loss=1.50 avg=1.44\n", "[2521 | 748.81] loss=1.39 avg=1.44\n", "[2522 | 750.10] loss=1.36 avg=1.44\n", "[2523 | 751.37] loss=1.34 avg=1.44\n", "[2524 | 752.65] loss=1.33 avg=1.43\n", "[2525 | 753.92] loss=1.32 avg=1.43\n", "[2526 | 755.20] loss=1.41 avg=1.43\n", "[2527 | 756.48] loss=1.41 avg=1.43\n", "[2528 | 757.78] loss=1.41 avg=1.43\n", "[2529 | 759.07] loss=1.46 avg=1.43\n", "[2530 | 760.34] loss=1.56 avg=1.43\n", "[2531 | 761.62] loss=1.54 avg=1.44\n", "[2532 | 762.90] loss=1.51 avg=1.44\n", "[2533 | 764.17] loss=1.45 avg=1.44\n", "[2534 | 765.44] loss=1.48 avg=1.44\n", "[2535 | 766.72] loss=1.51 avg=1.44\n", "[2536 | 768.02] loss=1.35 avg=1.44\n", "[2537 | 769.29] loss=1.21 avg=1.43\n", "[2538 | 770.57] loss=1.27 avg=1.43\n", "[2539 | 771.84] loss=1.19 avg=1.43\n", "[2540 | 773.11] loss=1.27 avg=1.43\n", "[2541 | 774.39] loss=1.28 avg=1.43\n", "[2542 | 775.66] loss=1.62 avg=1.43\n", "[2543 | 776.94] loss=1.32 avg=1.43\n", "[2544 | 778.21] loss=1.41 avg=1.43\n", "[2545 | 779.51] loss=1.48 avg=1.43\n", "[2546 | 780.78] loss=1.62 avg=1.43\n", "[2547 | 782.06] loss=1.38 avg=1.43\n", "[2548 | 783.33] loss=1.49 avg=1.43\n", "[2549 | 784.66] loss=1.26 avg=1.43\n", "[2550 | 785.93] loss=1.51 avg=1.43\n", "[2551 | 787.20] loss=1.54 avg=1.43\n", "[2552 | 788.48] loss=1.49 avg=1.43\n", "[2553 | 789.76] loss=1.50 avg=1.43\n", "[2554 | 791.06] loss=1.39 avg=1.43\n", "[2555 | 792.35] loss=1.17 avg=1.43\n", "[2556 | 793.62] loss=1.43 avg=1.43\n", "[2557 | 794.90] loss=1.52 avg=1.43\n", "[2558 | 796.17] loss=1.68 avg=1.43\n", "[2559 | 797.45] loss=1.48 avg=1.43\n", "[2560 | 798.72] loss=1.40 avg=1.43\n", "[2561 | 799.99] loss=1.43 avg=1.43\n", "[2562 | 801.28] loss=1.43 avg=1.43\n", "[2563 | 802.56] loss=1.42 avg=1.43\n", "[2564 | 803.84] loss=1.55 avg=1.43\n", "[2565 | 805.11] loss=1.41 avg=1.43\n", "[2566 | 806.39] loss=1.39 avg=1.43\n", "[2567 | 807.66] loss=1.40 avg=1.43\n", "[2568 | 808.93] loss=1.40 avg=1.43\n", "[2569 | 810.21] loss=1.37 avg=1.43\n", "[2570 | 811.48] loss=1.49 avg=1.43\n", "[2571 | 812.79] loss=1.45 avg=1.43\n", "[2572 | 814.07] loss=1.35 avg=1.43\n", "[2573 | 815.34] loss=1.38 avg=1.43\n", "[2574 | 816.62] loss=1.59 avg=1.43\n", "[2575 | 817.92] loss=1.30 avg=1.43\n", "[2576 | 819.20] loss=1.52 avg=1.43\n", "[2577 | 820.47] loss=1.46 avg=1.43\n", "[2578 | 821.74] loss=1.53 avg=1.43\n", "[2579 | 823.07] loss=1.70 avg=1.44\n", "[2580 | 824.36] loss=1.31 avg=1.43\n", "[2581 | 825.64] loss=1.33 avg=1.43\n", "[2582 | 826.91] loss=1.39 avg=1.43\n", "[2583 | 828.19] loss=1.49 avg=1.43\n", "[2584 | 829.46] loss=1.32 avg=1.43\n", "[2585 | 830.74] loss=1.40 avg=1.43\n", "[2586 | 832.02] loss=1.26 avg=1.43\n", "[2587 | 833.30] loss=1.41 avg=1.43\n", "[2588 | 834.58] loss=1.41 avg=1.43\n", "[2589 | 835.86] loss=1.37 avg=1.43\n", "[2590 | 837.13] loss=1.44 avg=1.43\n", "[2591 | 838.41] loss=1.45 avg=1.43\n", "[2592 | 839.68] loss=1.40 avg=1.43\n", "[2593 | 840.95] loss=1.51 avg=1.43\n", "[2594 | 842.22] loss=1.60 avg=1.43\n", "[2595 | 843.50] loss=1.41 avg=1.43\n", "[2596 | 844.78] loss=1.46 avg=1.43\n", "[2597 | 846.06] loss=1.42 avg=1.43\n", "[2598 | 847.34] loss=1.31 avg=1.43\n", "[2599 | 848.61] loss=1.47 avg=1.43\n", "[2600 | 849.91] loss=1.55 avg=1.43\n", "======== SAMPLE 1 ========\n", " RESILIABLE DU DROIT PAR LE BAIL, L'ARRET A ACCORDE A BAIL DEUX FRANCS A PAYER A DAME VEUVE Y..., QUI AVAIT CONCLU A L'ECHEANCE DU CONTRAT, LA SOMME NEPTE EXCLUANT L'EXPROPRIATION POUR CAUSE D'UTILITE D'UN DOMMAGE CAUSE ENTRE LE TERME ET DAME VEUVE X... ; QUE LE MOYEN NE PEUT DONC ETRE ACCUEILLI ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 17 MAI 1967 PAR LA COUR D'APPEL DE MONTPELLIER.N° 67-10.098. VEUVE VEUVE X... C / BAIL DE VEUVE X.... PRESIDENT : M. ANCEL. - RAPPORTEUR : M. PERRIN. - AVOCAT GENERAL : M. MENEGAUX. - AVOCATS : MM. CALON ET DESACHE. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE PRIS EN SES DEUX BRANCHES : ATTENDU QUE, SELON LES ENONCIATIONS DE L'ARRET CONFIRMATIF ATTAQUE (PARIS, 8 MAI 1967) L'USINE DES HERBETS LEONNE Y..., AYANT LE DROIT DE PROSPECTER CETTE INDEMNITE D'OCCUPATION, UN CONGES RENDUS PAR UNE DECISION RENDU EN CE QUI CONCERNE LES PROPRIETAIRES, LA VICTIME DANS LEQUEL SE TROUVAIENT LE BAIL; QUE, LE 14 JANVIER 1965, L'USINE FUT CONGEDIE A L'APPUYANTE DECLARATION DE DEMOISELLE X..., LOCATAIRES-DOUANTS ET UNE INSTALLATION JURIDIQUE, FUSSENT-ELLES AYANT ETE LE DEVOIR D'INTERCIER LA CONGEDIEE, PASSE EN FORCE DE CHOSE JUGEE, MAIS QUE L'USINE, QUI L'AVAIT PRIS FIN LE 15 JANVIER 1967, AVAIT CEDE LES PROPRIETAIRES A REMBOURSER A CETTE DERNIERE SON DROIT DE STATUER ;QUE, PAR LA SUITE, L'USINE AVAIT ETE, SUR LA BASE DE L'ARTICLE 1321 DU CODE CIVIL, REPAREE PAR D'AUTRES USINE ET POUR PORTER ATTEINTE AU CAS OU LA LOI DU 13 JUIN 1965, REPRESENTANT, NE POUVAIT ETRE REPROCHEE A POUJEUSEMENT ALORS QUE, EN CAS DE RESISTANCE INEXCUSABLE, L'AUTORISATION PRISE D'UN BAIL IMPITAIT UN PREJUDICE ; QUE, SUR APPEL DE POUJEUSEMENT ET DE CONGEDIEMENT, L'USINE AVAIT CONSISTE A DATE DU 15 JANVIER 1967 AU PROFIT DE LA VICTIME, ALORS QUE LA COUR D'APPEL, AYANT CONSTATE L'ABSENCE DE TOUT ELEMENT DE PREUVE, ETAIT FONDEE A PRONONCER LA RESISTANCE INEXCUSABLE SUR LA BASE DE L'ARTICLE 1321 ALINEA 8 DU CODE CIVIL; ET QU'EN STATUANT AINSI, ELLE A VIOLE LES TEXTES SUSVISES SANS VIOLER LES TEXTES PRECITENTES ;PAR CES MOTIFS : CASSE ET ANNULE, MAIS SEULEMENT EN CE QUE L'ARRET ATTAQUE A VIOLE L'ARTICLE 1384, ALINEA 1ER DU CODE CIVIL ET ALORS EN CE QUI CONCERNE LES BAUX CONSENTIS APRES L'ARRET DE L'ENFANT, L'ARRET RENDU LE 8 MAI 1967 PAR LA COUR D'APPEL D'ANGERS.N° 67-10.741. VEUVE X... C / DAME VEUVE Y.... PRESIDENT : M. ANCEL. - RAPPORTEUR : M. BOUCLY. - PREMIER PRESIDENT : M. GUILLOT. - AV\n", "\n", "[2601 | 863.28] loss=1.50 avg=1.43\n", "[2602 | 864.56] loss=1.64 avg=1.44\n", "[2603 | 865.84] loss=1.15 avg=1.43\n", "[2604 | 867.13] loss=1.51 avg=1.43\n", "[2605 | 868.40] loss=1.58 avg=1.43\n", "[2606 | 869.68] loss=1.32 avg=1.43\n", "[2607 | 870.95] loss=1.42 avg=1.43\n", "[2608 | 872.22] loss=1.32 avg=1.43\n", "[2609 | 873.50] loss=1.34 avg=1.43\n", "[2610 | 874.77] loss=1.62 avg=1.43\n", "[2611 | 876.04] loss=1.53 avg=1.43\n", "[2612 | 877.32] loss=1.61 avg=1.44\n", "[2613 | 878.60] loss=1.54 avg=1.44\n", "[2614 | 879.88] loss=1.49 avg=1.44\n", "[2615 | 881.15] loss=1.39 avg=1.44\n", "[2616 | 882.46] loss=1.57 avg=1.44\n", "[2617 | 883.74] loss=1.33 avg=1.44\n", "[2618 | 885.01] loss=1.42 avg=1.44\n", "[2619 | 886.28] loss=1.47 avg=1.44\n", "[2620 | 887.56] loss=1.24 avg=1.44\n", "[2621 | 888.84] loss=1.68 avg=1.44\n", "[2622 | 890.12] loss=1.39 avg=1.44\n", "[2623 | 891.40] loss=1.42 avg=1.44\n", "[2624 | 892.67] loss=1.42 avg=1.44\n", "[2625 | 893.95] loss=1.40 avg=1.44\n", "[2626 | 895.23] loss=1.42 avg=1.44\n", "[2627 | 896.52] loss=1.34 avg=1.44\n", "[2628 | 897.79] loss=1.29 avg=1.43\n", "[2629 | 899.07] loss=1.49 avg=1.43\n", "[2630 | 900.35] loss=1.37 avg=1.43\n", "[2631 | 901.63] loss=1.44 avg=1.43\n", "[2632 | 902.90] loss=1.35 avg=1.43\n", "[2633 | 904.17] loss=1.59 avg=1.43\n", "[2634 | 905.44] loss=1.33 avg=1.43\n", "[2635 | 906.72] loss=1.34 avg=1.43\n", "[2636 | 907.99] loss=1.46 avg=1.43\n", "[2637 | 909.27] loss=1.38 avg=1.43\n", "[2638 | 910.54] loss=1.45 avg=1.43\n", "[2639 | 911.83] loss=1.47 avg=1.43\n", "[2640 | 913.10] loss=1.40 avg=1.43\n", "[2641 | 914.37] loss=1.48 avg=1.43\n", "[2642 | 915.71] loss=1.53 avg=1.43\n", "[2643 | 916.99] loss=1.40 avg=1.43\n", "[2644 | 918.26] loss=1.40 avg=1.43\n", "[2645 | 919.54] loss=1.39 avg=1.43\n", "[2646 | 920.81] loss=1.64 avg=1.43\n", "[2647 | 922.11] loss=1.40 avg=1.43\n", "[2648 | 923.38] loss=1.38 avg=1.43\n", "[2649 | 924.65] loss=1.50 avg=1.43\n", "[2650 | 925.93] loss=1.24 avg=1.43\n", "[2651 | 927.21] loss=1.53 avg=1.43\n", "[2652 | 928.50] loss=1.56 avg=1.43\n", "[2653 | 929.77] loss=1.37 avg=1.43\n", "[2654 | 931.05] loss=1.31 avg=1.43\n", "[2655 | 932.32] loss=1.48 avg=1.43\n", "[2656 | 933.61] loss=1.56 avg=1.43\n", "[2657 | 934.88] loss=1.49 avg=1.44\n", "[2658 | 936.15] loss=1.34 avg=1.43\n", "[2659 | 937.43] loss=1.50 avg=1.43\n", "[2660 | 938.70] loss=1.33 avg=1.43\n", "[2661 | 939.98] loss=1.49 avg=1.43\n", "[2662 | 941.25] loss=1.49 avg=1.43\n", "[2663 | 942.52] loss=1.51 avg=1.44\n", "[2664 | 943.80] loss=1.36 avg=1.43\n", "[2665 | 945.08] loss=1.44 avg=1.43\n", "[2666 | 946.35] loss=1.32 avg=1.43\n", "[2667 | 947.63] loss=1.34 avg=1.43\n", "[2668 | 948.94] loss=1.47 avg=1.43\n", "[2669 | 950.21] loss=1.40 avg=1.43\n", "[2670 | 951.49] loss=1.33 avg=1.43\n", "[2671 | 952.76] loss=1.29 avg=1.43\n", "[2672 | 954.04] loss=1.40 avg=1.43\n", "[2673 | 955.32] loss=1.44 avg=1.43\n", "[2674 | 956.60] loss=1.35 avg=1.43\n", "[2675 | 957.87] loss=1.26 avg=1.43\n", "[2676 | 959.16] loss=1.42 avg=1.43\n", "[2677 | 960.45] loss=1.50 avg=1.43\n", "[2678 | 961.72] loss=1.49 avg=1.43\n", "[2679 | 962.99] loss=1.42 avg=1.43\n", "[2680 | 964.27] loss=1.57 avg=1.43\n", "[2681 | 965.54] loss=1.50 avg=1.43\n", "[2682 | 966.83] loss=1.43 avg=1.43\n", "[2683 | 968.10] loss=1.26 avg=1.43\n", "[2684 | 969.38] loss=1.46 avg=1.43\n", "[2685 | 970.65] loss=1.29 avg=1.43\n", "[2686 | 971.93] loss=1.48 avg=1.43\n", "[2687 | 973.20] loss=1.48 avg=1.43\n", "[2688 | 974.47] loss=1.49 avg=1.43\n", "[2689 | 975.75] loss=1.39 avg=1.43\n", "[2690 | 977.04] loss=1.37 avg=1.43\n", "[2691 | 978.32] loss=1.48 avg=1.43\n", "[2692 | 979.60] loss=1.49 avg=1.43\n", "[2693 | 980.90] loss=1.37 avg=1.43\n", "[2694 | 982.19] loss=1.51 avg=1.43\n", "[2695 | 983.46] loss=1.55 avg=1.43\n", "[2696 | 984.74] loss=1.35 avg=1.43\n", "[2697 | 986.01] loss=1.74 avg=1.43\n", "[2698 | 987.29] loss=1.42 avg=1.43\n", "[2699 | 988.57] loss=1.42 avg=1.43\n", "[2700 | 989.84] loss=1.44 avg=1.43\n", "======== SAMPLE 1 ========\n", "AT POUR LE CALCUL DES COTISATIONS, L'ARRET POMPE ETAIT SANS CONTRADICTION ET DECLARE A BON DROIT LA DEMANDE EN PAYEMENT DE PRIVILEGES ELECTIONS D'UN IMMEUBLE PAR RAPPORT A COMPTER DU 18 DECEMBRE 1951, LA SOCIETE DE PORCHERIE DU METROPOLE ET DE PREVOYANCE DE L'IMMEUBLE SUR LE PRIVILEGE DE CES ELECTIONS (CODIQUE PORCHERIE) POUR L'IMMEUBLE LITIGIEUX ; QU'EN STATUANT AINSI, SANS DETERMINER SI LES DECISIONS ELECTORAUX AVAIENT EU A CETTE PERIODE A L'AIDE AU PRIX DE RENTE, ELLE AVAIT ETE CONSIDEREE COMME ELLE ETAIT DESORMAIS D'ABROGER DE LEUR RESERVE QU'IL PRETENDAIT AVOIR RECU LA COMPTABILITE DE LA SOCIETE DE PORCHERIE DU METROPOLE ET DE PREVOYANCE DE L'IMMEUBLE CONTIGU AVEC LA SOCIETE DE PORCHERIE DU METROPOLE ET DE PREVOYANCE D'UNE COMPTABILITE ; QUE, DES LIEUX, NOUS AUTRES QUE LEUR COMPTABILITE, LA COUR D'APPEL N'A DENATURE, PAR UNE APPRECIATION SOUVERAINE DE LA VALEUR DES ELECTIONS ET DE LA VALEUR PERIMEE, QUE SEULE LES ELECTIONS ET LE PREALABLEMENT NE SAURAIENT PERMETTRE A CELUI D'ECHAITRE UNE IMPORTANCE ; QUE PAR CES MOTIFS, DONT LA COUR D'APPEL A REPONDU AUX CONCLUSIONS PRETENDUMENT DELAISSEES LESQUELLES LA RESERVE S'ETAIT ENTACHEE D'UN RAPPEL DE SALAIRE, LA COUR D'APPEL, QUI N'A PAS LEGALEMENT JUSTIFIE SA DECISION ; PAR CES MOTIFS : CASSE ET ANNULE L'ARRET RENDU ENTRE LES PARTIES PAR LA COUR D'APPEL DE PARIS LE 3 NOVEMBRE 1969 ; REMET EN CONSEQUENCE QUANT A CE LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET ET, POUR ETRE FAIT DROIT, LES RENVOI N° 70-70 054 CODIQUE SUR LE PRIVILEGE DE LA SOCIETE DES AUTRES ELECTIONS ET DE PREVOYANCE DE L'IMMEUBLE LITIGIEUSE ;PAR CES MOTIFS : CASSE ET ANNULE L'ARRET RENDU ENTRE LES PARTIES PAR LA COUR D'APPEL DE PARIS, LE 4 DECEMBRE 1969 ; REMET EN CONSEQUENCE, LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET, ET POUR ETRE FAIT DROIT, LES RENVOIE N° 70-70 1812 CODIQUE CONTRE LE JUGEMENT RENDU LE 3 NOVEMBRE 1969, D'UNE PART, LE MOIS DE FEVRIER 1969 PAR LE CONSEIL D'ADMINISTRATION DE L'INDUSTRIE ;REJETTE, EN CONSEQUENCE, LES RENVOIS N° 70-70 1812 N° 70-70 1820 CAISSE D'ASSURANCE MALADIE A DAME Z... ET D'HUISSIER-SUR-ORTHODES ET AUTRES C/ SOCIETE DES AUTRES ELECTIONS ET DE PREVOYANCE DE L'IMMEUBLE DU METROPOLE ET DE PREVOYANCE DE L'IMMEUBLE LITIGIEUX PRESIDENT : M VIGNERON - RAPPORTEUR : M LECHARNY - AVOCAT GENERAL : M MELLOTTEE - AVOCAT : M ORVAIN <|endoftext|>\n", "<|startoftext|> SUR LES TROIS MOYENS PRIS DE LA VIOLATION DES ARTICLES 1ER ET 6 DU REGLEMENT GENERAL DE LA REGION PARISIENNE, 22 DU\n", "\n", "[2701 | 1002.92] loss=1.39 avg=1.43\n", "[2702 | 1004.20] loss=1.58 avg=1.43\n", "[2703 | 1005.48] loss=1.50 avg=1.44\n", "[2704 | 1006.75] loss=1.38 avg=1.43\n", "[2705 | 1008.02] loss=1.37 avg=1.43\n", "[2706 | 1009.29] loss=1.46 avg=1.43\n", "[2707 | 1010.58] loss=1.39 avg=1.43\n", "[2708 | 1011.86] loss=1.49 avg=1.43\n", "[2709 | 1013.13] loss=1.50 avg=1.43\n", "[2710 | 1014.40] loss=1.55 avg=1.44\n", "[2711 | 1015.71] loss=1.57 avg=1.44\n", "[2712 | 1016.99] loss=1.32 avg=1.44\n", "[2713 | 1018.27] loss=1.42 avg=1.44\n", "[2714 | 1019.54] loss=1.52 avg=1.44\n", "[2715 | 1020.83] loss=1.33 avg=1.44\n", "[2716 | 1022.11] loss=1.30 avg=1.43\n", "[2717 | 1023.39] loss=1.34 avg=1.43\n", "[2718 | 1024.66] loss=1.42 avg=1.43\n", "[2719 | 1025.93] loss=1.52 avg=1.43\n", "[2720 | 1027.20] loss=1.32 avg=1.43\n", "[2721 | 1028.48] loss=1.55 avg=1.43\n", "[2722 | 1029.75] loss=1.28 avg=1.43\n", "[2723 | 1031.02] loss=1.36 avg=1.43\n", "[2724 | 1032.31] loss=1.61 avg=1.43\n", "[2725 | 1033.59] loss=1.39 avg=1.43\n", "[2726 | 1034.86] loss=1.35 avg=1.43\n", "[2727 | 1036.15] loss=1.45 avg=1.43\n", "[2728 | 1037.43] loss=1.24 avg=1.43\n", "[2729 | 1038.70] loss=1.36 avg=1.43\n", "[2730 | 1039.98] loss=1.57 avg=1.43\n", "[2731 | 1041.25] loss=1.44 avg=1.43\n", "[2732 | 1042.53] loss=1.61 avg=1.43\n", "[2733 | 1043.82] loss=1.35 avg=1.43\n", "[2734 | 1045.09] loss=1.45 avg=1.43\n", "[2735 | 1046.36] loss=1.48 avg=1.43\n", "[2736 | 1047.66] loss=1.44 avg=1.43\n", "[2737 | 1048.94] loss=1.30 avg=1.43\n", "[2738 | 1050.22] loss=1.57 avg=1.43\n", "[2739 | 1051.49] loss=1.56 avg=1.43\n", "[2740 | 1052.76] loss=1.36 avg=1.43\n", "[2741 | 1054.05] loss=1.50 avg=1.43\n", "[2742 | 1055.32] loss=1.40 avg=1.43\n", "[2743 | 1056.59] loss=1.32 avg=1.43\n", "[2744 | 1057.87] loss=1.39 avg=1.43\n", "[2745 | 1059.14] loss=1.47 avg=1.43\n", "[2746 | 1060.42] loss=1.55 avg=1.43\n", "[2747 | 1061.70] loss=1.33 avg=1.43\n", "[2748 | 1062.97] loss=1.30 avg=1.43\n", "[2749 | 1064.25] loss=1.40 avg=1.43\n", "[2750 | 1065.53] loss=1.40 avg=1.43\n", "[2751 | 1066.81] loss=1.42 avg=1.43\n", "[2752 | 1068.08] loss=1.28 avg=1.43\n", "[2753 | 1069.36] loss=1.23 avg=1.43\n", "[2754 | 1070.65] loss=1.45 avg=1.43\n", "[2755 | 1071.93] loss=1.36 avg=1.43\n", "[2756 | 1073.20] loss=1.20 avg=1.42\n", "[2757 | 1074.48] loss=1.48 avg=1.43\n", "[2758 | 1075.76] loss=1.39 avg=1.43\n", "[2759 | 1077.04] loss=1.40 avg=1.42\n", "[2760 | 1078.31] loss=1.38 avg=1.42\n", "[2761 | 1079.59] loss=1.47 avg=1.42\n", "[2762 | 1080.88] loss=1.53 avg=1.43\n", "[2763 | 1082.16] loss=1.25 avg=1.42\n", "[2764 | 1083.43] loss=1.35 avg=1.42\n", "[2765 | 1084.71] loss=1.52 avg=1.42\n", "[2766 | 1085.98] loss=1.38 avg=1.42\n", "[2767 | 1087.26] loss=1.53 avg=1.42\n", "[2768 | 1088.54] loss=1.56 avg=1.43\n", "[2769 | 1089.81] loss=1.39 avg=1.43\n", "[2770 | 1091.08] loss=1.45 avg=1.43\n", "[2771 | 1092.36] loss=1.30 avg=1.42\n", "[2772 | 1093.64] loss=1.45 avg=1.43\n", "[2773 | 1094.91] loss=1.33 avg=1.42\n", "[2774 | 1096.18] loss=1.55 avg=1.43\n", "[2775 | 1097.46] loss=1.33 avg=1.42\n", "[2776 | 1098.75] loss=1.58 avg=1.43\n", "[2777 | 1100.03] loss=1.32 avg=1.43\n", "[2778 | 1101.30] loss=1.40 avg=1.42\n", "[2779 | 1102.59] loss=1.47 avg=1.43\n", "[2780 | 1103.88] loss=1.24 avg=1.42\n", "[2781 | 1105.15] loss=1.33 avg=1.42\n", "[2782 | 1106.43] loss=1.37 avg=1.42\n", "[2783 | 1107.70] loss=1.33 avg=1.42\n", "[2784 | 1108.99] loss=1.20 avg=1.42\n", "[2785 | 1110.27] loss=1.33 avg=1.42\n", "[2786 | 1111.54] loss=1.44 avg=1.42\n", "[2787 | 1112.83] loss=1.41 avg=1.42\n", "[2788 | 1114.14] loss=1.50 avg=1.42\n", "[2789 | 1115.41] loss=1.42 avg=1.42\n", "[2790 | 1116.68] loss=1.40 avg=1.42\n", "[2791 | 1117.96] loss=1.55 avg=1.42\n", "[2792 | 1119.23] loss=1.37 avg=1.42\n", "[2793 | 1120.52] loss=1.36 avg=1.42\n", "[2794 | 1121.80] loss=1.59 avg=1.42\n", "[2795 | 1123.07] loss=1.48 avg=1.42\n", "[2796 | 1124.34] loss=1.38 avg=1.42\n", "[2797 | 1125.62] loss=1.37 avg=1.42\n", "[2798 | 1126.89] loss=1.42 avg=1.42\n", "[2799 | 1128.17] loss=1.28 avg=1.42\n", "[2800 | 1129.44] loss=1.49 avg=1.42\n", "======== SAMPLE 1 ========\n", "ELELLE EN ETABLISSANT LES MOYENS DES DEUX ENONCIATIONS DE L'INSCRIPTION AU RAPPORT DES ORGANISMES DE SECURITE SOCIALE POUR L'ANNEE 1956, DU JUGEMENT DES COMPTES DU 28 DECEMBRE 1955 POUR TENIR COMPTE DE L'UN DES COMPRESSES OU DE CELUI DU JOUR DE LA COMMISSION DE RECOURS DE PREMIERE INSTANCE DE LA DAME X... ;ATTENDU QU'IL EST FAIT GRIEF A L'ARRET ATTAQUE D'AVOIR DECIDE QUE LA CAISSE PRIMAIRE D'ARGENT AVAIT L'AUTORITE DE LA CHOSE JUGEE DEVANT LE PRESIDENT DE LA COMMISSION DE RECOURS POUR LEQUEL LA CAISSE NE JUSTIFIE PAS DE LEURS RAPPORTS EN NATURE DU DEPOT DU PRESIDENT JUDICIAIRE (CONCILIATION D'UNE DEPOT DU PRESIDENT), QUI, CONTRAIREMENT AUX PRETENTIONS DU POURVOI, NE FAISAIT PAS S'APPARTENIR A LA CAISSE ET N'AVAIT PAS ETE RECRUTE POUR L'UN DES COMPRESSES OU DE CELUI DU PRESIDENT DEVANT LE JUGE DE L'ELECTION DE SON ORDONNANCE, ALORS QU'EN ADMETTANT LES TERMES DE L'ARTICLE 21, LA COMMISSION DE RECOURS DE PREMIERE INSTANCE FAISAIT VALOIR QUE LE PRESIDENT COMPORTANT UNE VIE AU TITRE DE CONCIERGE DU JOURNAL APRES RENVOI SEUL SON ACTION ET NOTIFIE CELUI DU JOURNAL EN VUE D'ETABLIR CES CONCLUSIONS, CE QUI N'EST PAS MISE A ECHEANCE, LA COUR D'APPEL A NEGLIGE LES REGLES DUDIT CONCIERGE ET D'ADHERENTE DE CET ARTICLE QUI N'EXIGE PAS, D'UNE PART, L'ARRET ATTAQUE, QUI PRECISAIT QUE L'ADMINISTRATION DES COUSINS DE LA NATION A ETE LE 14 SEPTEMBRE 1956 AU JOURNAL DE SEPTEMBALARS, EN SON NOM ; QUE LA CAISSE DE SECURITE SOCIALE NE POUVAIT REFUSER D'ETAT COMPRENDRE SON ACTION A L'AUDIENCE DES AUTRES EMPLOYES D'UN POURVOI, AUX SUSNOMMES QU'IL AVAIT APPELE LES DEBITEURS DE SA DISPOSITION A UNE RUPTURE CERTAINE; QUE LA CAISSE NE POUVAIT REVENDRE TANT QUE LA CAISSE PRIMAIRE D'ARGENT, NE SAURAIT REFUSER D'ON A CHARGE DE SA RUPTURE D'APPORTER LA PREUVE DE CETTE RUPTURE DU DOMMAGE CAUSE, MEME SI LE PRESIDENT COMPORTANT SON CONCIERGE A CETTE VOIE DE SON ACTION POUVAIT AVOIR, EN L'ESPECE, ETE DONNE-DOUBLE D'UNE RUPTURE ET DU FAIT DE CETTE CONSTRUCTION ALORS, EN OUTRE, QUE LE TITRE DE CONCIERGE DEVAIT ETRE IMPUTE AU SENS DE SON ACTION, EN FONCTION DES DISPOSITIONS DE L'ARTICLE 21 DUDIT CONCIERGE ;MAIS ATTENDU QUE LES JUGES DU FOND, ONT CONSTATE QUE DEVANT LE PRESENT POURVOI LA CAISSE DE SECURITE SOCIALE AVAIT CONCLU, AU CONTRAIRE, ENTRAINE LA DATE DE LA RUPTURE CAUSE DU CONCIERGE DES COMPRESSES OU DE CELUI DU PRESIDENT DEVANT LE JUGE DE L'ELECTION DE SON ORDONNANCE, DEFINITIVES AU JOURNAL D'UNE RUPTURE POUR L'ANNEE 1956 ; QUE DES LORS QU'ELLE RECONNAIT A LA CAISSE A UNE DEMANDE EN RAPPORT A LA DIFFERENCE, LAQUELLE N'A PAS VIO LE TEXTE SUSVISE, ELLE NE SAURAIT SE PRE\n", "\n", "[2801 | 1142.75] loss=1.51 avg=1.42\n", "[2802 | 1144.03] loss=1.45 avg=1.42\n", "[2803 | 1145.31] loss=1.37 avg=1.42\n", "[2804 | 1146.62] loss=1.39 avg=1.42\n", "[2805 | 1147.90] loss=1.30 avg=1.42\n", "[2806 | 1149.17] loss=1.33 avg=1.42\n", "[2807 | 1150.44] loss=1.35 avg=1.42\n", "[2808 | 1151.72] loss=1.50 avg=1.42\n", "[2809 | 1153.01] loss=1.48 avg=1.42\n", "[2810 | 1154.29] loss=1.46 avg=1.42\n", "[2811 | 1155.56] loss=1.32 avg=1.42\n", "[2812 | 1156.83] loss=1.56 avg=1.42\n", "[2813 | 1158.10] loss=1.68 avg=1.42\n", "[2814 | 1159.38] loss=1.37 avg=1.42\n", "[2815 | 1160.65] loss=1.42 avg=1.42\n", "[2816 | 1161.93] loss=1.36 avg=1.42\n", "[2817 | 1163.20] loss=1.33 avg=1.42\n", "[2818 | 1164.49] loss=1.41 avg=1.42\n", "[2819 | 1165.77] loss=1.38 avg=1.42\n", "[2820 | 1167.04] loss=1.44 avg=1.42\n", "[2821 | 1168.31] loss=1.30 avg=1.42\n", "[2822 | 1169.59] loss=1.44 avg=1.42\n", "[2823 | 1170.86] loss=1.35 avg=1.42\n", "[2824 | 1172.14] loss=1.22 avg=1.42\n", "[2825 | 1173.41] loss=1.34 avg=1.42\n", "[2826 | 1174.71] loss=1.40 avg=1.42\n", "[2827 | 1176.01] loss=1.31 avg=1.41\n", "[2828 | 1177.30] loss=1.38 avg=1.41\n", "[2829 | 1178.59] loss=1.48 avg=1.41\n", "[2830 | 1179.90] loss=1.49 avg=1.42\n", "[2831 | 1181.18] loss=1.49 avg=1.42\n", "[2832 | 1182.45] loss=1.39 avg=1.42\n", "[2833 | 1183.73] loss=1.40 avg=1.42\n", "[2834 | 1185.00] loss=1.43 avg=1.42\n", "[2835 | 1186.29] loss=1.36 avg=1.42\n", "[2836 | 1187.57] loss=1.52 avg=1.42\n", "[2837 | 1188.84] loss=1.35 avg=1.42\n", "[2838 | 1190.11] loss=1.22 avg=1.41\n", "[2839 | 1191.39] loss=1.44 avg=1.41\n", "[2840 | 1192.67] loss=1.35 avg=1.41\n", "[2841 | 1193.94] loss=1.39 avg=1.41\n", "[2842 | 1195.21] loss=1.33 avg=1.41\n", "[2843 | 1196.49] loss=1.47 avg=1.41\n", "[2844 | 1197.78] loss=1.64 avg=1.42\n", "[2845 | 1199.06] loss=1.63 avg=1.42\n", "[2846 | 1200.34] loss=1.32 avg=1.42\n", "[2847 | 1201.62] loss=1.48 avg=1.42\n", "[2848 | 1202.89] loss=1.33 avg=1.42\n", "[2849 | 1204.16] loss=1.35 avg=1.42\n", "[2850 | 1205.43] loss=1.29 avg=1.41\n", "[2851 | 1206.71] loss=1.41 avg=1.41\n", "[2852 | 1207.99] loss=1.48 avg=1.41\n", "[2853 | 1209.28] loss=1.48 avg=1.42\n", "[2854 | 1210.56] loss=1.49 avg=1.42\n", "[2855 | 1211.86] loss=1.46 avg=1.42\n", "[2856 | 1213.15] loss=1.32 avg=1.42\n", "[2857 | 1214.43] loss=1.41 avg=1.42\n", "[2858 | 1215.70] loss=1.27 avg=1.41\n", "[2859 | 1216.97] loss=1.33 avg=1.41\n", "[2860 | 1218.25] loss=1.53 avg=1.41\n", "[2861 | 1219.53] loss=1.48 avg=1.42\n", "[2862 | 1220.80] loss=1.48 avg=1.42\n", "[2863 | 1222.08] loss=1.18 avg=1.41\n", "[2864 | 1223.36] loss=1.31 avg=1.41\n", "[2865 | 1224.63] loss=1.59 avg=1.41\n", "[2866 | 1225.90] loss=1.38 avg=1.41\n", "[2867 | 1227.18] loss=1.35 avg=1.41\n", "[2868 | 1228.45] loss=1.34 avg=1.41\n", "[2869 | 1229.74] loss=1.37 avg=1.41\n", "[2870 | 1231.01] loss=1.54 avg=1.41\n", "[2871 | 1232.29] loss=1.32 avg=1.41\n", "[2872 | 1233.56] loss=1.43 avg=1.41\n", "[2873 | 1234.83] loss=1.35 avg=1.41\n", "[2874 | 1236.11] loss=1.32 avg=1.41\n", "[2875 | 1237.38] loss=1.52 avg=1.41\n", "[2876 | 1238.65] loss=1.41 avg=1.41\n", "[2877 | 1239.93] loss=1.33 avg=1.41\n", "[2878 | 1241.23] loss=1.40 avg=1.41\n", "[2879 | 1242.52] loss=1.53 avg=1.41\n", "[2880 | 1243.81] loss=1.34 avg=1.41\n", "[2881 | 1245.10] loss=1.22 avg=1.41\n", "[2882 | 1246.38] loss=1.54 avg=1.41\n", "[2883 | 1247.65] loss=1.59 avg=1.41\n", "[2884 | 1248.93] loss=1.34 avg=1.41\n", "[2885 | 1250.20] loss=1.41 avg=1.41\n", "[2886 | 1251.48] loss=1.49 avg=1.41\n", "[2887 | 1252.77] loss=1.35 avg=1.41\n", "[2888 | 1254.05] loss=1.40 avg=1.41\n", "[2889 | 1255.32] loss=1.34 avg=1.41\n", "[2890 | 1256.59] loss=1.50 avg=1.41\n", "[2891 | 1257.87] loss=1.29 avg=1.41\n", "[2892 | 1259.14] loss=1.42 avg=1.41\n", "[2893 | 1260.42] loss=1.41 avg=1.41\n", "[2894 | 1261.69] loss=1.45 avg=1.41\n", "[2895 | 1262.99] loss=1.45 avg=1.41\n", "[2896 | 1264.27] loss=1.33 avg=1.41\n", "[2897 | 1265.54] loss=1.37 avg=1.41\n", "[2898 | 1266.81] loss=1.38 avg=1.41\n", "[2899 | 1268.09] loss=1.37 avg=1.41\n", "[2900 | 1269.36] loss=1.44 avg=1.41\n", "======== SAMPLE 1 ========\n", " QUA JANVIER 1958, EN L'ABSENCE DE TOUTE NATURE JURIDIQUE ;ATTENDU QUE LES EPOUX Z... ONT DONNE A BAIL A GARANTIR LA SOCIETE RITEL ET CIE, LOCATAIRE POUR RENTRER DIVERSES SOMMES INDUSTRIELS, L'ARRETE DU 4 NOVEMBRE 1962 AYANT ETE APPELEE EN VUE DE L'ACCIDENT DU TRAVAIL D'UN ACCIDENT DU TRAVAIL ;QU'EN PREMIER LIEU, GARANTI APRES LA RESILIATION DU BAIL AUX MOTIFS ESSENTIELS QUE SI L'EXPROPRIATION PEUT TENIR COMPTE DANS LA FORCLUSION DE SA PART, S'IL N'Y A PAS LE CARACTERE FRAUDULEUX QUI, SAISI PAR UNE PARTIE DANS UNE INSTANCE POUR CAUSE DE MESURE DE CONGES PAYES, N'A PU QUE GARANTIER, CONGEDIEUX, EXPROPRIEANT PAR UN FONDS DE COMMERCE OU D'ENTRETIEN DE DIVERSES VILLES, DOIT ETRE CONSIDERE COMME APPORTANT PENDANTE DE LES TRAVAUX DE LA LOCATAIRE, LE CONSIDERANT COMME PROPRIETAIRE D'UNE FERME DE TERRAINS AUX VILLES DE GARANTIE, DONT GARANTI ETAIT LE BENEFICIAIRE D'AVISER LES TRAVAUX SES VILLERS A PIED DANS LE CADRE DE LES MEMES VILLES DE GARANTIE, A DONNE APRES AVOIR CONSTATE, PAR CONSEQUENT, UN FAIT PARTICULIER DE JOUR DE L'UNION DES IMMEUBLES ET SES DIVERSES VILLES DE GARANTIE A LA SOCIETE RITEL ET CIE ET QUE GARANTI AINSI DONNE PAR SUITE UN DE FAIT SUBSIDIAIRE A L'AUTHENTICITE SOUS SILENCE LE PREMIER ;ATTENDU QUE LE POURVOI REPROCHE A LA COUR D'APPEL D'AVOIR DIT A LA SUITE DE L'ACCIDENT DE GARANTIE, AUSSI PAR CE FAIT, AUX MOTIFS QUE GARANTI EXPROPRIE PAR LA SOCIETE RITEL ET CIE, ALORS QUE L'ASSURANCE MALADIE EST PARFAITEMENT A LA DISPOSITION DES FONDS DE COMMERCE ET QUE LA SOCIETE N'A MEME PAS LE CARACTERE FRAUDULEUX QUE PAR UNE POSSESSION DU DROIT AU BAIL D'UN IMMEUBLE QU'ELLE AVAIT SUIVI EN COURS DE LOYER OU DEFINITIF, COMME TOUT, EN COURS DE LOYER OU DEFINITIF ;MAIS ATTENDU QUE LES JUGES D'APPEL QUI CONSTATENT QU'IL N'ETAIT PAS SUBSIDIAIRE DE CONTENIR L'EXPROPRIATION DEPUIS LE 11 NOVEMBRE 1959, ONT ETE ADMISES AVANT LE 1ER JUIN 1950 DATE A LA DISPOSITION DE LADITE SOCIETE, QUE SI L'ACCIDENT ETANT LE CAS NON SUSPECTE A CE MOTIF A SON ENTENTEZ ET QUE LA SOCIETE RITEL ET CIE NE POUVAIT ADMETTRE QUE CE POINT DE SAVOIR LA PROPRIETE DE GARANTIE NON SANS INDEMNITE, CE FAIT N'OFFRAIT pas AU PREJUDICE D'UNE FAUTE COMMISE PAR LE LOCATAIRE ;EN QUOI L'ARRET CONFIRMATIF A VIOLE LES TEXTES SUSVISES ;PAR CES MOTIFS : CASSE ET ANNULE LA DECISION RENDUE ENTRE LES PARTIES PAR LA COMMISSION DE PREMIERE INSTANCE DE PARIS, LE 7 NOVEMBRE 1965 ; REMET EN CONSEQUENCE LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ONT LEGALEMENT DEDUITE, ET, POUR ETRE FAIT DROIT, LES RENVO\n", "\n", "[2901 | 1283.03] loss=1.41 avg=1.41\n", "[2902 | 1284.33] loss=1.49 avg=1.41\n", "[2903 | 1285.62] loss=1.28 avg=1.41\n", "[2904 | 1286.89] loss=1.39 avg=1.41\n", "[2905 | 1288.17] loss=1.36 avg=1.41\n", "[2906 | 1289.44] loss=1.51 avg=1.41\n", "[2907 | 1290.71] loss=1.37 avg=1.41\n", "[2908 | 1291.99] loss=1.56 avg=1.41\n", "[2909 | 1293.26] loss=1.31 avg=1.41\n", "[2910 | 1294.54] loss=1.40 avg=1.41\n", "[2911 | 1295.82] loss=1.41 avg=1.41\n", "[2912 | 1297.10] loss=1.34 avg=1.41\n", "[2913 | 1298.37] loss=1.36 avg=1.41\n", "[2914 | 1299.64] loss=1.25 avg=1.41\n", "[2915 | 1300.92] loss=1.30 avg=1.41\n", "[2916 | 1302.19] loss=1.40 avg=1.41\n", "[2917 | 1303.47] loss=1.33 avg=1.41\n", "[2918 | 1304.74] loss=1.27 avg=1.40\n", "[2919 | 1306.01] loss=1.36 avg=1.40\n", "[2920 | 1307.30] loss=1.41 avg=1.40\n", "[2921 | 1308.58] loss=1.34 avg=1.40\n", "[2922 | 1309.85] loss=1.47 avg=1.40\n", "[2923 | 1311.14] loss=1.31 avg=1.40\n", "[2924 | 1312.43] loss=1.39 avg=1.40\n", "[2925 | 1313.70] loss=1.38 avg=1.40\n", "[2926 | 1314.97] loss=1.44 avg=1.40\n", "[2927 | 1316.26] loss=1.19 avg=1.40\n", "[2928 | 1317.56] loss=1.36 avg=1.40\n", "[2929 | 1318.84] loss=1.40 avg=1.40\n", "[2930 | 1320.12] loss=1.35 avg=1.40\n", "[2931 | 1321.39] loss=1.40 avg=1.40\n", "[2932 | 1322.66] loss=1.23 avg=1.40\n", "[2933 | 1323.94] loss=1.50 avg=1.40\n", "[2934 | 1325.21] loss=1.52 avg=1.40\n", "[2935 | 1326.48] loss=1.46 avg=1.40\n", "[2936 | 1327.76] loss=1.26 avg=1.40\n", "[2937 | 1329.04] loss=1.34 avg=1.40\n", "[2938 | 1330.31] loss=1.36 avg=1.40\n", "[2939 | 1331.59] loss=1.60 avg=1.40\n", "[2940 | 1332.86] loss=1.42 avg=1.40\n", "[2941 | 1334.14] loss=1.35 avg=1.40\n", "[2942 | 1335.41] loss=1.35 avg=1.40\n", "[2943 | 1336.69] loss=1.49 avg=1.40\n", "[2944 | 1337.96] loss=1.33 avg=1.40\n", "[2945 | 1339.23] loss=1.34 avg=1.40\n", "[2946 | 1340.52] loss=1.69 avg=1.40\n", "[2947 | 1341.80] loss=1.35 avg=1.40\n", "[2948 | 1343.08] loss=1.40 avg=1.40\n", "[2949 | 1344.37] loss=1.43 avg=1.40\n", "[2950 | 1345.65] loss=1.34 avg=1.40\n", "[2951 | 1346.92] loss=1.34 avg=1.40\n", "[2952 | 1348.19] loss=1.37 avg=1.40\n", "[2953 | 1349.48] loss=1.39 avg=1.40\n", "[2954 | 1350.81] loss=1.46 avg=1.40\n", "[2955 | 1352.09] loss=1.41 avg=1.40\n", "[2956 | 1353.37] loss=1.30 avg=1.40\n", "[2957 | 1354.64] loss=1.40 avg=1.40\n", "[2958 | 1355.91] loss=1.34 avg=1.40\n", "[2959 | 1357.18] loss=1.59 avg=1.40\n", "[2960 | 1358.46] loss=1.38 avg=1.40\n", "[2961 | 1359.73] loss=1.38 avg=1.40\n", "[2962 | 1361.01] loss=1.59 avg=1.40\n", "[2963 | 1362.30] loss=1.18 avg=1.40\n", "[2964 | 1363.57] loss=1.23 avg=1.40\n", "[2965 | 1364.85] loss=1.22 avg=1.40\n", "[2966 | 1366.12] loss=1.41 avg=1.40\n", "[2967 | 1367.40] loss=1.48 avg=1.40\n", "[2968 | 1368.67] loss=1.27 avg=1.40\n", "[2969 | 1369.95] loss=1.28 avg=1.40\n", "[2970 | 1371.23] loss=1.46 avg=1.40\n", "[2971 | 1372.50] loss=1.47 avg=1.40\n", "[2972 | 1373.80] loss=1.33 avg=1.40\n", "[2973 | 1375.08] loss=1.36 avg=1.40\n", "[2974 | 1376.37] loss=1.57 avg=1.40\n", "[2975 | 1377.67] loss=1.44 avg=1.40\n", "[2976 | 1378.94] loss=1.44 avg=1.40\n", "[2977 | 1380.22] loss=1.21 avg=1.40\n", "[2978 | 1381.49] loss=1.48 avg=1.40\n", "[2979 | 1382.78] loss=1.41 avg=1.40\n", "[2980 | 1384.09] loss=1.30 avg=1.40\n", "[2981 | 1385.39] loss=1.29 avg=1.40\n", "[2982 | 1386.66] loss=1.38 avg=1.40\n", "[2983 | 1387.93] loss=1.42 avg=1.40\n", "[2984 | 1389.20] loss=1.29 avg=1.39\n", "[2985 | 1390.48] loss=1.49 avg=1.40\n", "[2986 | 1391.75] loss=1.51 avg=1.40\n", "[2987 | 1393.03] loss=1.41 avg=1.40\n", "[2988 | 1394.30] loss=1.42 avg=1.40\n", "[2989 | 1395.58] loss=1.21 avg=1.39\n", "[2990 | 1396.85] loss=1.28 avg=1.39\n", "[2991 | 1398.13] loss=1.42 avg=1.39\n", "[2992 | 1399.40] loss=1.20 avg=1.39\n", "[2993 | 1400.68] loss=1.43 avg=1.39\n", "[2994 | 1401.95] loss=1.35 avg=1.39\n", "[2995 | 1403.23] loss=1.55 avg=1.39\n", "[2996 | 1404.50] loss=1.33 avg=1.39\n", "[2997 | 1405.78] loss=1.24 avg=1.39\n", "[2998 | 1407.06] loss=1.44 avg=1.39\n", "[2999 | 1408.33] loss=1.38 avg=1.39\n", "[3000 | 1409.64] loss=1.42 avg=1.39\n", "Saving checkpoint/run1/model-3000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 15:30:38.250206: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:30:38.251370: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:30:38.252223: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:30:38.253389: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:30:38.254390: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:30:38.255287: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-3000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 1.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 12235287 tokens\n", "Training...\n", "Saving checkpoint/run1/model-3000\n", "Saving checkpoint/run1/model-3000\n", "======== SAMPLE 1 ========\n", " LAVAIT ETE CONCLUANT POUR L'IMMEUBLE SANS LA QUALITE DE SYNDIC DE LA COMMISSION DE CONTROLE GENERALE, DANS LAQUELLE LA CAISSE NATIONALE DES CHEMINS DE FER ET COMPRIS D'ASSURANCES SOCIALES N'ETAIT PAS OBLIGATOIRE ; ATTENDU, D'AUTRE PART, QUE LES JUGES DU SECOND DEGRE ONT CONSTATE QUE LES ARRETS DE COMMISSION NATIONALE TECHNIQUE ET DE CONFIANCE DE LA CAISSE NATIONALE SOCIALE FAISANT VALOIR QUE LE POURVOI EN COURS AVAIT FAIT SON AIDE PUBLIQUE AVANT QUE LE PROPRIETAIRE N'ETAIT PAS TENU AU FOND DES DISPOSITIONS DE LA FAILLITE, ET QUE DANS LA LETTRE DU 30 AVRIL 1964, LA CAISSE NATIONALE DE SECURITE SOCIALE AVAIT INTRODUIT POUR ELUCES NOUVEAUX LA QUALITE DE SYNDEXES NON REPRESENTATIVES ENTRE LES COMMISSIONS, ET QUE LES ARRETS DE DELEGUES DU PERSONNEL AVAIENT RECOURU A LA CAISSE ADHERENT ETABLISSEMENTS A LA CAISSE NATIONALE TECHNIQUE, QUE CES ARRETS NE CONSTITUAIENT QUE DES NEGLIGENCES DE LA COMMISSION PARALLELE DE COMMISSION NATIONALE EN CELLES QUI N'ONT PU ETRE RENOUVERTES DE CELLES QUI SONT ASSORTIS D'AILLEURS QUE LES ACOMPTES DE CE TEXTE CONSISTAIENT SANS RENOUVELLEMENT PAR APPLICATION DE L'ARTICLE 4 DE LA LOI DES 18 AOUT 1891 ; ATTENDU, ENFIN, QU'EN S'ABSTENANT DE DECLARER A BON DROIT QUE LA CAISSE NATIONALE TECHNIQUE AVAIT COMMIS UNE OBLIGATION DE DROIT PROPRE, TOUT EN SE LIVRANT A ELLE DES ACTES DE LA CAISSE ADHERENT EN CELLES EN COURS DES DELEGUES DU PERSONNEL, LAQUELLE NE POUVAIT RENDRE INSUFFISAMMENT REPONDRE AUX CONCLUSIONS DE LA CAISSE NATIONALE TECHNIQUE ET DE PREMIERE INSTANCE DE LA SEINE QU'ELLE NE POUVAIT PAS ETRE APPRECIE COMME UN ORDRE DE VENTE OU DES ACOMPTES ECHAPPENTES EN CELLES DE CELLES DE NERVEXON DU PERSONNEL, ALORS QU'IL AURAIT A ELLE-MEME SANS RENOUVELLEMENT ; QUE, DANS CES CONDITIONS, L'ARRET ATTAQUE N'A FAIT QU'USER DE SON DROIT DE DEMANDEUR A L'ACTION EN REVOGATION DE LA FORME ET AUX MOTIFS QU'IL N'Y AVAIT NEGLIGENCE QUE POUR QUE LA CREANCE DE LA CAISSE NATIONALE TECHNIQUE N'ETAIT PAS DE NATURE A PROUVER QUE LA CAISSE NATIONALE TECHNIQUE N'AVAIT PAS RENOUVELE LES AGENCEMENTS PRIVILEAGIES, ET QU'AINSI ELLE SOIT EN CELLES DE COMPENSATION ; QU'AINSI LA COUR D'APPEL N'ETAIT PAS TENUE DE REFUSER DE RENOUVELER LE BAIL A L'EGARD DE LA CAISSE DE MUTUALITE SOCIALE AGRICOLE NATIONAL ET QUI SE TROUVAIT, DES LORS, LORSQU'ELLES AVAIENT EXISTE ; QU'AINSI LE MOYEN N'EST PAS FONDE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 20 JUIN 1967, PAR LA COUR D'APPEL DE CAEN.N° 67-13.566. CAISSE NATIONALE TECHNIQUE NATIONALE DE SECURITE SOCIALE DE NANTES C/ ETA\n", "\n", "[3001 | 21.88] loss=1.50 avg=1.50\n", "[3002 | 23.15] loss=1.49 avg=1.49\n", "[3003 | 24.43] loss=1.56 avg=1.52\n", "[3004 | 25.71] loss=1.58 avg=1.53\n", "[3005 | 26.98] loss=1.46 avg=1.52\n", "[3006 | 28.26] loss=1.54 avg=1.52\n", "[3007 | 29.54] loss=1.51 avg=1.52\n", "[3008 | 30.89] loss=1.53 avg=1.52\n", "[3009 | 32.18] loss=1.80 avg=1.55\n", "[3010 | 33.45] loss=1.55 avg=1.55\n", "[3011 | 34.72] loss=1.70 avg=1.57\n", "[3012 | 36.00] loss=1.59 avg=1.57\n", "[3013 | 37.27] loss=1.46 avg=1.56\n", "[3014 | 38.55] loss=1.60 avg=1.56\n", "[3015 | 39.82] loss=1.53 avg=1.56\n", "[3016 | 41.13] loss=1.57 avg=1.56\n", "[3017 | 42.40] loss=1.49 avg=1.56\n", "[3018 | 43.69] loss=1.40 avg=1.55\n", "[3019 | 44.99] loss=1.54 avg=1.55\n", "[3020 | 46.26] loss=1.50 avg=1.54\n", "[3021 | 47.53] loss=1.47 avg=1.54\n", "[3022 | 48.81] loss=1.46 avg=1.54\n", "[3023 | 50.08] loss=1.75 avg=1.55\n", "[3024 | 51.36] loss=1.49 avg=1.54\n", "[3025 | 52.66] loss=1.45 avg=1.54\n", "[3026 | 53.94] loss=1.56 avg=1.54\n", "[3027 | 55.21] loss=1.46 avg=1.54\n", "[3028 | 56.49] loss=1.42 avg=1.53\n", "[3029 | 57.76] loss=1.43 avg=1.53\n", "[3030 | 59.03] loss=1.49 avg=1.53\n", "[3031 | 60.31] loss=1.58 avg=1.53\n", "[3032 | 61.59] loss=1.40 avg=1.52\n", "[3033 | 62.88] loss=1.61 avg=1.53\n", "[3034 | 64.17] loss=1.54 avg=1.53\n", "[3035 | 65.45] loss=1.48 avg=1.53\n", "[3036 | 66.73] loss=1.41 avg=1.52\n", "[3037 | 68.00] loss=1.59 avg=1.52\n", "[3038 | 69.28] loss=1.34 avg=1.52\n", "[3039 | 70.56] loss=1.61 avg=1.52\n", "[3040 | 71.83] loss=1.63 avg=1.52\n", "[3041 | 73.11] loss=1.64 avg=1.53\n", "[3042 | 74.41] loss=1.44 avg=1.53\n", "[3043 | 75.68] loss=1.34 avg=1.52\n", "[3044 | 77.00] loss=1.44 avg=1.52\n", "[3045 | 78.28] loss=1.57 avg=1.52\n", "[3046 | 79.55] loss=1.41 avg=1.52\n", "[3047 | 80.83] loss=1.51 avg=1.52\n", "[3048 | 82.10] loss=1.36 avg=1.51\n", "[3049 | 83.38] loss=1.35 avg=1.51\n", "[3050 | 84.67] loss=1.30 avg=1.50\n", "[3051 | 85.95] loss=1.68 avg=1.51\n", "[3052 | 87.22] loss=1.54 avg=1.51\n", "[3053 | 88.50] loss=1.30 avg=1.50\n", "[3054 | 89.77] loss=1.42 avg=1.50\n", "[3055 | 91.05] loss=1.47 avg=1.50\n", "[3056 | 92.32] loss=1.70 avg=1.50\n", "[3057 | 93.60] loss=1.41 avg=1.50\n", "[3058 | 94.87] loss=1.38 avg=1.50\n", "[3059 | 96.18] loss=1.58 avg=1.50\n", "[3060 | 97.46] loss=1.48 avg=1.50\n", "[3061 | 98.75] loss=1.50 avg=1.50\n", "[3062 | 100.02] loss=1.34 avg=1.50\n", "[3063 | 101.30] loss=1.46 avg=1.50\n", "[3064 | 102.57] loss=1.48 avg=1.50\n", "[3065 | 103.85] loss=1.38 avg=1.49\n", "[3066 | 105.12] loss=1.45 avg=1.49\n", "[3067 | 106.39] loss=1.62 avg=1.50\n", "[3068 | 107.70] loss=1.43 avg=1.49\n", "[3069 | 108.98] loss=1.36 avg=1.49\n", "[3070 | 110.31] loss=1.49 avg=1.49\n", "[3071 | 111.60] loss=1.49 avg=1.49\n", "[3072 | 112.87] loss=1.46 avg=1.49\n", "[3073 | 114.15] loss=1.54 avg=1.49\n", "[3074 | 115.42] loss=1.43 avg=1.49\n", "[3075 | 116.70] loss=1.45 avg=1.49\n", "[3076 | 117.99] loss=1.38 avg=1.49\n", "[3077 | 119.26] loss=1.57 avg=1.49\n", "[3078 | 120.54] loss=1.52 avg=1.49\n", "[3079 | 121.81] loss=1.52 avg=1.49\n", "[3080 | 123.09] loss=1.45 avg=1.49\n", "[3081 | 124.36] loss=1.47 avg=1.49\n", "[3082 | 125.63] loss=1.63 avg=1.49\n", "[3083 | 126.91] loss=1.42 avg=1.49\n", "[3084 | 128.18] loss=1.39 avg=1.49\n", "[3085 | 129.48] loss=1.49 avg=1.49\n", "[3086 | 130.78] loss=1.55 avg=1.49\n", "[3087 | 132.07] loss=1.55 avg=1.49\n", "[3088 | 133.34] loss=1.50 avg=1.49\n", "[3089 | 134.61] loss=1.51 avg=1.49\n", "[3090 | 135.89] loss=1.48 avg=1.49\n", "[3091 | 137.16] loss=1.45 avg=1.49\n", "[3092 | 138.44] loss=1.56 avg=1.49\n", "[3093 | 139.72] loss=1.67 avg=1.49\n", "[3094 | 141.01] loss=1.39 avg=1.49\n", "[3095 | 142.29] loss=1.40 avg=1.49\n", "[3096 | 143.59] loss=1.35 avg=1.49\n", "[3097 | 144.86] loss=1.47 avg=1.49\n", "[3098 | 146.14] loss=1.57 avg=1.49\n", "[3099 | 147.41] loss=1.39 avg=1.49\n", "[3100 | 148.69] loss=1.43 avg=1.49\n", "======== SAMPLE 1 ========\n", " DE DEVENIR ET LA SEULE MAIS ETAIENT DILATOIRES POUR DES RESULTATS SPECIALES (TARIFS) AVEC SA RESPONSABILITE ;QU'IL EST FAIT GRIEF A L'ARRET D'AVOIR DECIDE QUE LA NULLITE DE LA RESPONSABILITE FAIT AUX MOTIFS QU'EUSANO, VICTIME D'UN ACCIDENT SURVENU A CELLE-CI, ETANT REMUNERE PAR CELLE-CI, N'AVAIT PAS OBTENU DES RESULTATS SPECIALES (TARIFS), ALORS, SELON LE POURVOI, QUE, D'UNE PART, \"LA CONNAISSANCE DU MATERIEL DU CONTENU NEGRAIENT ETAT OU LA COUR D'APPEL S'EST CONTREDITE \", QUE LA CONNAISSANCE EST EXONERE DU MAUVAIS ETAT ET LE MAUVAIS ETAT AVAIT ETE CONNU ET QUE \" L'APPLICATION DE LA CLAUSE PREVOYANT LE MAUVAIS ETAT L'ACQUITTEUR, N'AURAIT NULLEMENT CONSTATAIT DE BESOIN QUE \" LA RESPONSABILITE DE L'ASSURE \" S'ILS EN RESULTANT, AINSI QUE S'EST SANS FONDEMENT, UNE CLAUSE PREVOYANT LE MAUVAIS ETAT D'UN DROIT DE PROPRIETE ;MAIS ATTENDU QUE LE POUVOI PRESENTE L'ARRET ATTAQUE D'AUTANT PLUS TARD EN PREMIER LIEU APRES AVOIR RAPPELE QUE LES FONCTIONS DU MEDECIN TRAITANT, AGE DE 3E, EN VERTU DE CELUI-CI (BESSES TUNISI), LUI INVOQUAIENT \" \" CETTE RESPONSABILITE POUR LE MALADIE, \" N'AVAIENT PAS ACCEPTE LES NOUVEAUX MEDICAUX D'UNE CONNAISSANCE DE LA CONNAISSANCE DES VOISINS \" , NE POUVANT AVOIR POUR EFFET POUR L'AVARIE QUE LUI AVAIT CONNU LE N'ETAIT PAS OBTENU DES RESULTATS SPECIALES \" ;QU'AINSI LE MOYEN N'EST PAS FONDE ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LE JUGEMENT RENDU, LE 28 MAI 1969, PAR LE TRIBUNAL D'INSTANCE D'AVOIR DECLARE RECEVABLE L'APPEL FORME RELATIF A LA DATE DE LA PRODUCTION DE CELUI-CI PAR LA PRESCRIPTION DE LA MAIN DE CELLE-CI, AU MOTIF QU'A CETTE DATE, ELLE AVAIT ETE CONDAMNE PAR LA COGNIE D'ASSURANCES AUX DROITS DES PARTIES DUT LA COGNIE ET, NE CONTIENT PAS LA DESTINATAIRE D'EURE NATUREL A UNE INFLUENCE SUR LA RESPONSABILITE DANS SON ETAT, QUE CE DERNIER NE POUVAIT INVOQUER LE BENEFICE DES PROPRES DISPOSITIONS QUI LEUR AVAIENT EU LA RESPONSABILITE DU MAUVAIS ETAT DU MAUVAIS ETAT ;ATTENDU DE CES DEUX PREMIERES BRANCHES QU'AUX TERMES DE L'ARTICLE 1134 DU CODE CIVIL, LE MAUVAIS ETAT NE DONNE AUCUN EFFET SUSCEPTIBLES DE NATURE A ENTENDRE ACCEPTER DE SA RESPONSABILITE ;QUE LE FAIT QUE LA POSSESSION DES ACTES DE NON-CONCURRENCE N'AYANT PU OBTENIR QUE SUR LES RESULTATS SPECIALES ET SE DECLARANT INCOMPETENT POUR STATUER SUR LE FONDEMENT DUQUEL IL ETAIT CONSTITUANT UNE INTENTION DE NE PAS JUSTIFIER NECESSAIREMENT LA COMPETENCE DU TRIBUNAL ALORS QUE CE TEXTE NE RESULTERAIT NULLEMENT LE MAUVAIS ETAT, QUE DANS UN PROCES-VERBAL DE GRAND DE PLUS, LEQUEL EST PORTE PAR LA PRESCRIPTION\n", "\n", "[3101 | 161.74] loss=1.54 avg=1.49\n", "[3102 | 163.03] loss=1.45 avg=1.49\n", "[3103 | 164.32] loss=1.46 avg=1.49\n", "[3104 | 165.59] loss=1.46 avg=1.49\n", "[3105 | 166.86] loss=1.57 avg=1.49\n", "[3106 | 168.14] loss=1.37 avg=1.49\n", "[3107 | 169.41] loss=1.33 avg=1.48\n", "[3108 | 170.69] loss=1.42 avg=1.48\n", "[3109 | 171.96] loss=1.43 avg=1.48\n", "[3110 | 173.26] loss=1.43 avg=1.48\n", "[3111 | 174.54] loss=1.52 avg=1.48\n", "[3112 | 175.83] loss=1.24 avg=1.48\n", "[3113 | 177.11] loss=1.46 avg=1.48\n", "[3114 | 178.39] loss=1.53 avg=1.48\n", "[3115 | 179.66] loss=1.52 avg=1.48\n", "[3116 | 180.94] loss=1.46 avg=1.48\n", "[3117 | 182.21] loss=1.62 avg=1.48\n", "[3118 | 183.48] loss=1.50 avg=1.48\n", "[3119 | 184.76] loss=1.55 avg=1.48\n", "[3120 | 186.04] loss=1.52 avg=1.48\n", "[3121 | 187.31] loss=1.45 avg=1.48\n", "[3122 | 188.59] loss=1.54 avg=1.48\n", "[3123 | 189.86] loss=1.48 avg=1.48\n", "[3124 | 191.13] loss=1.45 avg=1.48\n", "[3125 | 192.41] loss=1.49 avg=1.48\n", "[3126 | 193.68] loss=1.45 avg=1.48\n", "[3127 | 194.97] loss=1.48 avg=1.48\n", "[3128 | 196.26] loss=1.42 avg=1.48\n", "[3129 | 197.55] loss=1.60 avg=1.48\n", "[3130 | 198.82] loss=1.29 avg=1.48\n", "[3131 | 200.09] loss=1.61 avg=1.48\n", "[3132 | 201.37] loss=1.41 avg=1.48\n", "[3133 | 202.64] loss=1.48 avg=1.48\n", "[3134 | 203.92] loss=1.51 avg=1.48\n", "[3135 | 205.19] loss=1.46 avg=1.48\n", "[3136 | 206.48] loss=1.56 avg=1.48\n", "[3137 | 207.76] loss=1.66 avg=1.49\n", "[3138 | 209.05] loss=1.49 avg=1.49\n", "[3139 | 210.35] loss=1.54 avg=1.49\n", "[3140 | 211.63] loss=1.41 avg=1.49\n", "[3141 | 212.90] loss=1.40 avg=1.48\n", "[3142 | 214.18] loss=1.33 avg=1.48\n", "[3143 | 215.45] loss=1.65 avg=1.48\n", "[3144 | 216.73] loss=1.57 avg=1.49\n", "[3145 | 218.01] loss=1.38 avg=1.48\n", "[3146 | 219.28] loss=1.47 avg=1.48\n", "[3147 | 220.56] loss=1.43 avg=1.48\n", "[3148 | 221.83] loss=1.49 avg=1.48\n", "[3149 | 223.10] loss=1.41 avg=1.48\n", "[3150 | 224.38] loss=1.43 avg=1.48\n", "[3151 | 225.65] loss=1.47 avg=1.48\n", "[3152 | 226.93] loss=1.51 avg=1.48\n", "[3153 | 228.22] loss=1.51 avg=1.48\n", "[3154 | 229.51] loss=1.42 avg=1.48\n", "[3155 | 230.80] loss=1.38 avg=1.48\n", "[3156 | 232.07] loss=1.47 avg=1.48\n", "[3157 | 233.35] loss=1.67 avg=1.48\n", "[3158 | 234.62] loss=1.52 avg=1.48\n", "[3159 | 235.89] loss=1.59 avg=1.48\n", "[3160 | 237.17] loss=1.34 avg=1.48\n", "[3161 | 238.44] loss=1.52 avg=1.48\n", "[3162 | 239.73] loss=1.41 avg=1.48\n", "[3163 | 241.01] loss=1.52 avg=1.48\n", "[3164 | 242.30] loss=1.51 avg=1.48\n", "[3165 | 243.59] loss=1.46 avg=1.48\n", "[3166 | 244.87] loss=1.50 avg=1.48\n", "[3167 | 246.14] loss=1.46 avg=1.48\n", "[3168 | 247.42] loss=1.47 avg=1.48\n", "[3169 | 248.70] loss=1.37 avg=1.48\n", "[3170 | 249.99] loss=1.34 avg=1.48\n", "[3171 | 251.27] loss=1.39 avg=1.48\n", "[3172 | 252.55] loss=1.30 avg=1.48\n", "[3173 | 253.83] loss=1.55 avg=1.48\n", "[3174 | 255.11] loss=1.46 avg=1.48\n", "[3175 | 256.38] loss=1.43 avg=1.48\n", "[3176 | 257.66] loss=1.33 avg=1.47\n", "[3177 | 258.93] loss=1.31 avg=1.47\n", "[3178 | 260.21] loss=1.41 avg=1.47\n", "[3179 | 261.50] loss=1.48 avg=1.47\n", "[3180 | 262.78] loss=1.45 avg=1.47\n", "[3181 | 264.07] loss=1.52 avg=1.47\n", "[3182 | 265.35] loss=1.37 avg=1.47\n", "[3183 | 266.63] loss=1.42 avg=1.47\n", "[3184 | 267.91] loss=1.50 avg=1.47\n", "[3185 | 269.19] loss=1.37 avg=1.47\n", "[3186 | 270.46] loss=1.34 avg=1.47\n", "[3187 | 271.75] loss=1.45 avg=1.47\n", "[3188 | 273.03] loss=1.34 avg=1.47\n", "[3189 | 274.30] loss=1.48 avg=1.47\n", "[3190 | 275.60] loss=1.43 avg=1.47\n", "[3191 | 276.88] loss=1.49 avg=1.47\n", "[3192 | 278.15] loss=1.45 avg=1.47\n", "[3193 | 279.43] loss=1.39 avg=1.47\n", "[3194 | 280.71] loss=1.64 avg=1.47\n", "[3195 | 281.99] loss=1.36 avg=1.47\n", "[3196 | 283.28] loss=1.52 avg=1.47\n", "[3197 | 284.55] loss=1.45 avg=1.47\n", "[3198 | 285.83] loss=1.22 avg=1.46\n", "[3199 | 287.11] loss=1.56 avg=1.46\n", "[3200 | 288.43] loss=1.43 avg=1.46\n", "======== SAMPLE 1 ========\n", " SUR SURSIS A STATUER SUR LES CONSEQUENCES DE LA REGULARITE DE L'ASSIGNATION ET A LEGALEMENT JUSTIFIE SA DECISION; D'OU IL SUIT QUE LE MOYEN EST MAL FONDE; ET SUR LES DEUXIEME ET TROISIEME MOYEN : ATTENDU QU'IL EST POSSEDE A L'ARRET QUE, SANS QUE TEMPESTIF QUE L'ASSIGNATION EST BIEN DES LORS QU'UN RECOURS ACTUEL N'EST PAS LE CAS, LORSQUE LES DOCUMENTS VERSES NECESSAIRES A SON NOM A COMPTER DE LEUR DECISION ET QU'UNE TELLE FACON EXCEDAIT LES FOURNITURES; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 24 JUIN 1972, PAR LA COUR D'APPEL D'AIX-EN-PROVENCE. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE PRIS EN SES TROIS BRANCHES : ATTENDU QUE LES PROPRIETAIRES DE L'IMMEUBLE MORTONNEUR ETANT UNE PENSION ALIMENTAIRE DE L'IMMEUBLE A L'HOTEL DE LA PROPRIETE, LES CONSORTS X... SONT DESIGNEES AUX FINS DE RECHERCHER SI DES DIFFICULTEURS AVAIENT ETE RECHERCHES POUR LA PERIODE ANTERIEURE AU 1ER EN DATE DES 7 JANVIER 1949 AU MOTIF QUE SI LES CONSORTS X..., QUI TENTENTENT DE LES CONNAISSANCES AUXQUELS IL AURAIENT ETE FAIT UN RECOURS EN COURS A L'ENCONTRE DU QUASI-CROISESTRE, ONT LAISSE AINSI ELLE LES PRESCRIPTIONS LEGALES QUI EN ETABLISSENT LA CONNAISSANCE QUE DE SOUS-TRAITANTS, ET A LEUR ENTACHE, DE LA PRESOMPTION D'ASSOCIATION; ATTENDU QU'IL EST FAIT GRIEF A L'ARRET ATTAQUE D'AVOIR CONDAMNE LES CONSORTS X... A PAYER A JEAN-PIER X..., SON ANCIEN EMPLOYEUR, DES DOMMAGES ET INTERETS PAR LE SOUS-TRAITANTS A Y..., JEAN-PIERRE X... ET LE LENDEMAIN DE LA CONSTITUTION, ALORS SELON LE MOYEN, QUE LA FAUTE COMMISE D'UNE PART, D'UNE OBLIGATION CONTRACTUELLE ET NON D'AUTRE PART, DE L'OBLIGATION DE PREPOSE EN VERTU DUQUEL IL ETAIT IMPERATIF D'AUTRE PART, QUE LORSQUE CES DERNIERS ONT FAIT L'OBJET DE LEUR CONNAISSANCE, SON ACTION NE PEUT ETRE REPROCHE A L'EMPLOYEUR, QU'IL APPARTIENT A LA COUR DE CASSATION, EN MATIERE DE REPONSE A L'ACTE, NONOBSTANT CONSTRUIRE; MAIS ATTENDU QUE, DES LORS QUE L'ACCUSE D'ENREGISTREMENT, FAISANT DOUBLEMENT QUE L'ORDONNANCE DU 22 SEPTEMBRE 1945, QUI FAIT DE LA COMPECTION DES ASSOCIES DE LA REPRESENTATION, NE CONCERNE QUE L'ARRET ATTAQUE N'A DEDUIT DE CES DEUXIEMES; D'OU IL SUIT QUE LE MOYEN EST MAL FONDE; ET SUR LA PREMIERE BRANCHE DU MOYEN:ATTENDU QU'IL EST ENCORE FAIT GRIEF A L'ARRET ATTAQUE D'AVOIR DECLARE RECEVABLE L'ACTION SOUSCRIREE A L'EGARD DE L'ASSEMBLEE GENERALE DE LA SUCCESSION DE CETTE VEUVE VILLE, ALORS QUE SI L'ACTION A ETE PRONONCEE PAR ARRETE MINISTERIELLE DU 8 JUIN 1954, CETTE VEUVE NE P\n", "\n", "[3201 | 302.48] loss=1.42 avg=1.46\n", "[3202 | 303.77] loss=1.39 avg=1.46\n", "[3203 | 305.06] loss=1.59 avg=1.46\n", "[3204 | 306.33] loss=1.37 avg=1.46\n", "[3205 | 307.63] loss=1.52 avg=1.46\n", "[3206 | 308.94] loss=1.51 avg=1.46\n", "[3207 | 310.21] loss=1.45 avg=1.46\n", "[3208 | 311.49] loss=1.35 avg=1.46\n", "[3209 | 312.77] loss=1.54 avg=1.46\n", "[3210 | 314.05] loss=1.54 avg=1.46\n", "[3211 | 315.33] loss=1.50 avg=1.47\n", "[3212 | 316.63] loss=1.47 avg=1.47\n", "[3213 | 317.91] loss=1.29 avg=1.46\n", "[3214 | 319.18] loss=1.45 avg=1.46\n", "[3215 | 320.46] loss=1.48 avg=1.46\n", "[3216 | 321.74] loss=1.41 avg=1.46\n", "[3217 | 323.02] loss=1.41 avg=1.46\n", "[3218 | 324.30] loss=1.50 avg=1.46\n", "[3219 | 325.58] loss=1.60 avg=1.46\n", "[3220 | 326.89] loss=1.45 avg=1.46\n", "[3221 | 328.18] loss=1.41 avg=1.46\n", "[3222 | 329.46] loss=1.43 avg=1.46\n", "[3223 | 330.74] loss=1.58 avg=1.46\n", "[3224 | 332.02] loss=1.48 avg=1.46\n", "[3225 | 333.30] loss=1.49 avg=1.46\n", "[3226 | 334.58] loss=1.69 avg=1.47\n", "[3227 | 335.87] loss=1.46 avg=1.47\n", "[3228 | 337.16] loss=1.50 avg=1.47\n", "[3229 | 338.47] loss=1.50 avg=1.47\n", "[3230 | 339.74] loss=1.38 avg=1.47\n", "[3231 | 341.02] loss=1.34 avg=1.47\n", "[3232 | 342.34] loss=1.37 avg=1.46\n", "[3233 | 343.64] loss=1.42 avg=1.46\n", "[3234 | 344.91] loss=1.62 avg=1.47\n", "[3235 | 346.19] loss=1.57 avg=1.47\n", "[3236 | 347.47] loss=1.52 avg=1.47\n", "[3237 | 348.76] loss=1.47 avg=1.47\n", "[3238 | 350.04] loss=1.34 avg=1.47\n", "[3239 | 351.32] loss=1.42 avg=1.47\n", "[3240 | 352.60] loss=1.53 avg=1.47\n", "[3241 | 353.87] loss=1.54 avg=1.47\n", "[3242 | 355.15] loss=1.44 avg=1.47\n", "[3243 | 356.43] loss=1.46 avg=1.47\n", "[3244 | 357.70] loss=1.53 avg=1.47\n", "[3245 | 358.97] loss=1.48 avg=1.47\n", "[3246 | 360.26] loss=1.49 avg=1.47\n", "[3247 | 361.54] loss=1.45 avg=1.47\n", "[3248 | 362.82] loss=1.58 avg=1.47\n", "[3249 | 364.10] loss=1.59 avg=1.47\n", "[3250 | 365.37] loss=1.39 avg=1.47\n", "[3251 | 366.65] loss=1.50 avg=1.47\n", "[3252 | 367.92] loss=1.43 avg=1.47\n", "[3253 | 369.21] loss=1.50 avg=1.47\n", "[3254 | 370.52] loss=1.34 avg=1.47\n", "[3255 | 371.86] loss=1.30 avg=1.47\n", "[3256 | 373.13] loss=1.47 avg=1.47\n", "[3257 | 374.41] loss=1.41 avg=1.47\n", "[3258 | 375.70] loss=1.44 avg=1.47\n", "[3259 | 376.99] loss=1.49 avg=1.47\n", "[3260 | 378.27] loss=1.38 avg=1.46\n", "[3261 | 379.55] loss=1.43 avg=1.46\n", "[3262 | 380.83] loss=1.61 avg=1.47\n", "[3263 | 382.13] loss=1.39 avg=1.46\n", "[3264 | 383.40] loss=1.42 avg=1.46\n", "[3265 | 384.68] loss=1.23 avg=1.46\n", "[3266 | 385.96] loss=1.48 avg=1.46\n", "[3267 | 387.24] loss=1.62 avg=1.46\n", "[3268 | 388.51] loss=1.46 avg=1.46\n", "[3269 | 389.79] loss=1.69 avg=1.47\n", "[3270 | 391.08] loss=1.51 avg=1.47\n", "[3271 | 392.36] loss=1.42 avg=1.47\n", "[3272 | 393.66] loss=1.55 avg=1.47\n", "[3273 | 394.94] loss=1.38 avg=1.47\n", "[3274 | 396.22] loss=1.51 avg=1.47\n", "[3275 | 397.49] loss=1.43 avg=1.47\n", "[3276 | 398.77] loss=1.42 avg=1.47\n", "[3277 | 400.05] loss=1.48 avg=1.47\n", "[3278 | 401.33] loss=1.58 avg=1.47\n", "[3279 | 402.62] loss=1.56 avg=1.47\n", "[3280 | 403.97] loss=1.41 avg=1.47\n", "[3281 | 405.28] loss=1.48 avg=1.47\n", "[3282 | 406.56] loss=1.65 avg=1.47\n", "[3283 | 407.86] loss=1.35 avg=1.47\n", "[3284 | 409.14] loss=1.15 avg=1.47\n", "[3285 | 410.42] loss=1.45 avg=1.46\n", "[3286 | 411.70] loss=1.50 avg=1.47\n", "[3287 | 412.97] loss=1.44 avg=1.47\n", "[3288 | 414.25] loss=1.42 avg=1.46\n", "[3289 | 415.53] loss=1.53 avg=1.47\n", "[3290 | 416.81] loss=1.48 avg=1.47\n", "[3291 | 418.09] loss=1.28 avg=1.46\n", "[3292 | 419.36] loss=1.45 avg=1.46\n", "[3293 | 420.64] loss=1.34 avg=1.46\n", "[3294 | 421.92] loss=1.54 avg=1.46\n", "[3295 | 423.19] loss=1.45 avg=1.46\n", "[3296 | 424.47] loss=1.34 avg=1.46\n", "[3297 | 425.76] loss=1.36 avg=1.46\n", "[3298 | 427.04] loss=1.39 avg=1.46\n", "[3299 | 428.32] loss=1.41 avg=1.46\n", "[3300 | 429.60] loss=1.49 avg=1.46\n", "======== SAMPLE 1 ========\n", "EMIT DE CES ACTES, QUE LE POURVOI SOUTIENT QU'ILS N'ETAIENT DONC, EN FAISANT D'ABORD AVEC SA DEMOURDE, LES ASSORTIS DE COMPARAISON CONTRE UN ACTE PREJUDICIANT DE LA SOCIETE, ALORS, SELON LE SECOND, QU'IL NE S'APPLIQUAIT PAS A LA CONDITION DE RESILIATION POUR LE COMPTE DE LEUR AUTEUR, LES ACTES DE FUSION N'A PAS ETE SIGNES PAR LE POURVOI EN FACTURE LES DIVERS PARCELLES SITUEES A LA SOCIETE N° 63-1490, ALORS, D'AUTRE PART, QUE LA COUR D'APPEL A OMIS DE RECHERCHER SI LES ACTES ET LA SOCIETE N° 63-1490 N'AVAIENT PAS CONCLU LE CARACTERE ANTERIEUR A LA REQUETE DU POURVOI ;MAIS ATTENDU QUE LES JUGES D'APPEL ONT APPRECIE LES EXPERTSES EN CAS DE RESILIATION POUR COMPENSER AU DELAI D'UN AN L'ALIENEMENT DE LA SOCIETE N° 63-1490 DE SON CARGAISON ET A ENONCER, TOUTES LE DERNIER ETAT DE LA FAUTE DE LA SOCIETE N° 63-1490, EN TOUTES ACCORDS AUX PARTIES CONTRE ELLE QUI Y A UN CHANGEMENT DES PARTIES NE DEROGEANT PAS AUX DISPOSITIONS DE L'ARTICLE 5 DES STATUTS, QUE, D'AUTRE PART, AINSI CE SOIT QU'IL NE S'ATTACHE QU'A PARTICULIER LA SUBROGATION DE SON ACCORD SUR LE SALARIE DES ASSORTIS D'UNE ACTIVITE INDIRECTE ;QU'IL S'ENSUIT QUE LA COUR D'APPEL A REJETE SA RECONNAISSANCE DE SON DROIT DE GARANTIE AUX MOTIFS QUE CELUI-CI NE POUVAIT, ETANT DEMEURE, DONNER A LA GRAVE POUR INEXECUTION DE SA CHARGE TELLE S'IL POSSIBLE TOUT MOMENT DE LA RESILIATION QUI LUI ETAIT PROUVE, LA GRAVE DE L'AUTOBUS EN FAISANT D'ABORD AVEC SA DEMOURDE ;D'OU IL SUIT QUE LE PREMIER MOYEN DOIT ETRE ECARTE ;SUR LE DEUXIEME MOYEN, PRIS EN SA PREMIERE BRANCHE : ATTENDU QUE LA SOCIETE N° 63-1490 FAIT GRIEF A L'ARRET ATTAQUE D'AVOIR, TOUT EN RELEVANT COMME LE PREMIER MOYEN, LAISSANT SANS REPONSE LES CONCLUSIONS DE LA SOCIETE N° 63-1490, REVOQUE SELON LESQUELLES IL SE DIRIGEAIT DE SOUTENIR L'ACTION NOUVELLE ET DEMANDEURS ET DE RESILIATION SUR LE PAIEMENT DES PRESTATIONS FAMILIALES D'ALLOCATIONS FAMILIALES DE REPRESENTANT DE LA SOCIETE N° 63-1490, ALORS QUE L'ACTION NOUVEAU SE PRESUMANT SUFFISANT POUR UNE DEUXIEME PARTIE INDUMENT SUFFISANTE POUR UNE DUREE INFERIEURE A SA PROPRE INDEMNITE ET EN DERNIER LIEU A L'EXPIRATION DU PRECEDENT ACCORD DU 30 SEPTEMBRE 1945 ET QUE SELON LES DISPOSITIONS DE L'ARTICLE 5 SUSVISE, LE PRINCIPE DE LA RUPTURE DU CONTRAT CONSTITUAIT UN LIEN DE FAUTE A LA REQUETE DE LA SOCIETE N° 63-1490, ALORS QUE LA COUR D'APPEL N'A NULLEMENT CONSIDERE QU'IL Y AVAIT TENU A UNE CLAUSE DE DEMISSIONS ;MAIS ATTENDU QUE, TEL N'EST FAIT PAR L'ACCORD DE 1960, D'ORDRE PUBLIC\n", "\n", "[3301 | 442.96] loss=1.41 avg=1.46\n", "[3302 | 444.37] loss=1.54 avg=1.46\n", "[3303 | 445.65] loss=1.46 avg=1.46\n", "[3304 | 446.93] loss=1.49 avg=1.46\n", "[3305 | 448.23] loss=1.39 avg=1.46\n", "[3306 | 449.51] loss=1.53 avg=1.46\n", "[3307 | 450.81] loss=1.37 avg=1.46\n", "[3308 | 452.09] loss=1.32 avg=1.46\n", "[3309 | 453.36] loss=1.49 avg=1.46\n", "[3310 | 454.64] loss=1.57 avg=1.46\n", "[3311 | 455.92] loss=1.61 avg=1.46\n", "[3312 | 457.19] loss=1.45 avg=1.46\n", "[3313 | 458.47] loss=1.43 avg=1.46\n", "[3314 | 459.76] loss=1.38 avg=1.46\n", "[3315 | 461.04] loss=1.41 avg=1.46\n", "[3316 | 462.31] loss=1.48 avg=1.46\n", "[3317 | 463.59] loss=1.27 avg=1.46\n", "[3318 | 464.86] loss=1.36 avg=1.46\n", "[3319 | 466.14] loss=1.41 avg=1.46\n", "[3320 | 467.41] loss=1.49 avg=1.46\n", "[3321 | 468.69] loss=1.44 avg=1.46\n", "[3322 | 469.98] loss=1.40 avg=1.46\n", "[3323 | 471.25] loss=1.35 avg=1.45\n", "[3324 | 472.53] loss=1.25 avg=1.45\n", "[3325 | 473.81] loss=1.57 avg=1.45\n", "[3326 | 475.08] loss=1.40 avg=1.45\n", "[3327 | 476.43] loss=1.40 avg=1.45\n", "[3328 | 477.76] loss=1.52 avg=1.45\n", "[3329 | 479.05] loss=1.43 avg=1.45\n", "[3330 | 480.33] loss=1.31 avg=1.45\n", "[3331 | 481.63] loss=1.36 avg=1.45\n", "[3332 | 482.91] loss=1.46 avg=1.45\n", "[3333 | 484.19] loss=1.43 avg=1.45\n", "[3334 | 485.46] loss=1.36 avg=1.45\n", "[3335 | 486.74] loss=1.59 avg=1.45\n", "[3336 | 488.01] loss=1.49 avg=1.45\n", "[3337 | 489.29] loss=1.64 avg=1.45\n", "[3338 | 490.57] loss=1.27 avg=1.45\n", "[3339 | 491.87] loss=1.52 avg=1.45\n", "[3340 | 493.15] loss=1.38 avg=1.45\n", "[3341 | 494.43] loss=1.45 avg=1.45\n", "[3342 | 495.71] loss=1.42 avg=1.45\n", "[3343 | 496.99] loss=1.38 avg=1.45\n", "[3344 | 498.26] loss=1.46 avg=1.45\n", "[3345 | 499.54] loss=1.43 avg=1.45\n", "[3346 | 500.82] loss=1.56 avg=1.45\n", "[3347 | 502.10] loss=1.49 avg=1.45\n", "[3348 | 503.40] loss=1.28 avg=1.45\n", "[3349 | 504.67] loss=1.48 avg=1.45\n", "[3350 | 505.95] loss=1.57 avg=1.45\n", "[3351 | 507.23] loss=1.43 avg=1.45\n", "[3352 | 508.51] loss=1.43 avg=1.45\n", "[3353 | 509.94] loss=1.45 avg=1.45\n", "[3354 | 511.26] loss=1.41 avg=1.45\n", "[3355 | 512.56] loss=1.37 avg=1.45\n", "[3356 | 513.87] loss=1.49 avg=1.45\n", "[3357 | 515.14] loss=1.52 avg=1.45\n", "[3358 | 516.42] loss=1.43 avg=1.45\n", "[3359 | 517.70] loss=1.48 avg=1.45\n", "[3360 | 518.97] loss=1.33 avg=1.45\n", "[3361 | 520.25] loss=1.48 avg=1.45\n", "[3362 | 521.53] loss=1.35 avg=1.45\n", "[3363 | 522.80] loss=1.47 avg=1.45\n", "[3364 | 524.08] loss=1.71 avg=1.45\n", "[3365 | 525.38] loss=1.49 avg=1.45\n", "[3366 | 526.66] loss=1.43 avg=1.45\n", "[3367 | 527.93] loss=1.52 avg=1.45\n", "[3368 | 529.21] loss=1.52 avg=1.45\n", "[3369 | 530.49] loss=1.31 avg=1.45\n", "[3370 | 531.76] loss=1.38 avg=1.45\n", "[3371 | 533.04] loss=1.40 avg=1.45\n", "[3372 | 534.31] loss=1.30 avg=1.45\n", "[3373 | 535.62] loss=1.48 avg=1.45\n", "[3374 | 536.91] loss=1.66 avg=1.45\n", "[3375 | 538.19] loss=1.39 avg=1.45\n", "[3376 | 539.46] loss=1.42 avg=1.45\n", "[3377 | 540.74] loss=1.40 avg=1.45\n", "[3378 | 542.02] loss=1.46 avg=1.45\n", "[3379 | 543.30] loss=1.46 avg=1.45\n", "[3380 | 544.58] loss=1.48 avg=1.45\n", "[3381 | 545.87] loss=1.42 avg=1.45\n", "[3382 | 547.20] loss=1.39 avg=1.45\n", "[3383 | 548.53] loss=1.53 avg=1.45\n", "[3384 | 549.81] loss=1.39 avg=1.45\n", "[3385 | 551.09] loss=1.49 avg=1.45\n", "[3386 | 552.36] loss=1.35 avg=1.45\n", "[3387 | 553.64] loss=1.51 avg=1.45\n", "[3388 | 554.92] loss=1.33 avg=1.45\n", "[3389 | 556.19] loss=1.51 avg=1.45\n", "[3390 | 557.47] loss=1.53 avg=1.45\n", "[3391 | 558.76] loss=1.59 avg=1.45\n", "[3392 | 560.04] loss=1.40 avg=1.45\n", "[3393 | 561.31] loss=1.45 avg=1.45\n", "[3394 | 562.59] loss=1.35 avg=1.45\n", "[3395 | 563.87] loss=1.42 avg=1.45\n", "[3396 | 565.15] loss=1.50 avg=1.45\n", "[3397 | 566.44] loss=1.38 avg=1.45\n", "[3398 | 567.71] loss=1.46 avg=1.45\n", "[3399 | 569.01] loss=1.48 avg=1.45\n", "[3400 | 570.29] loss=1.44 avg=1.45\n", "======== SAMPLE 1 ========\n", "UE LA COUR D'APPEL A VIOLE PAR FAUSSE APPLICATION LUI-MEME ; PAR CES MOTIFS : CASSE ET ANNULE L'ARRET RENDU LE 14 FEVRIER 1971 ENTRE LES PARTIES, PAR LA COUR D'APPEL DE ROUEN ; REMET, EN CONSEQUENCE, LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET ET, POUR ETRE FAIT DROIT, LES RENVOIE DEVANT LA COUR D'APPEL DE CAEN. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE, PRIS EN SES DEUX BRANCHES : ATTENDU QU'IL EST FAIT GRIEF A L'ARRET ATTAQUE, QUI A DECIDE QUE L'APUREMENT COMMERCIAL DE LA VOITURE AVAIENT ETE PRODUITES A LADITE FONCIERE ET, A DATER DE LA DECLARATION JUDICIAIRE DES FONCTIONS DU BATIMENT, MAIS, DE DEUX JAGUANES, DE TROIS METRES DU BATIMENT, D'AVOIR STATUE PAR JUGEMENT DU 9 JANVIER 1971, QUE LES DEUX AUTORITES A ETE EXPROPRIEES DEVANT LE TRIBUNAL DE L'AUDE POUR FAIRE MENTION D'APPELER L'AUTORISATION DE LADITE VILLE A LAQUELLE CELLE-CI AURAIT FORMELLEMENT EXPROPRIEES ET, D'AVOIR REFUSE D'ADMETTRE LA NOUVELLE NATURE JURIDIQUE QUE, COMME EN L'ESPECE, LE BATIMENT QUI NE POURRAIT ETRE REMUNERE POUR LA LIGNE DE CONFIANCE QUE LES DEUX JAGUANES AVAIENT DONNE D'AUTRES ACTES D'APUREMENT DES FAUX COMMERCIAUX, N'AVAIT PAS TIRE DE SES PROPRES CONSTATATIONS LES CONSEQUENCES JURIDIQUES QUI AURAIENT ETE POSSIBLEES A L'EXPROPRIE PAR LE PROPRIETAIRE ; MAIS ATTENDU, D'UNE PART, QUE LA COUR D'APPEL RELEVE QUE, S'AGISSANT DE SES DROITS A LEUR ACTIVITE, LA CIRCONSTANCE DE DIVERSES PROPRES CONDITIONS POSSEDANT LA DESTINATION DES DEUX BATIMENTS DE TROIS METRES ET PRODUITES DU BATIMENT PENDANT UNE DECLARATION JUDICIAIRE DU BATIMENT A L'APUREMENT DU CENTRE, LA CAUSE N'EST PAS ETABLIE EN L'ESPECE PAR L'EXPRESSION DE LA MALVEILLANCE DU FONCTIONNEMENT DE LA LA VOITURE ; ATTENDU, D'AUTRE PART, QUE SI, PENDANT LE JOURNEAU DE LA CIRCONSTANCE UNIQUE DU BATIMENT, LES DEUX JAGUANES AVAIENT LE JOURNEAU DE LEUR ACTIVITE EN DEHORS DE L'ACTE ENSEMBLE ET EN CONNAISSANCE DE LA PORTION PAR LETTRE VENDU LE FONCTIONNEMENT DE LA CIRCONSTANCE ET DES DEUX JAGUANES, LA COUR D'APPEL CONSTATE QUE LA CIRCONSTANCE EN DISPOSE C'ETAIT LE BATIMENT QUI, EN L'ESPECE, NE POURRAIT ETRE EXPROPRIE L'AUTORISATION DE LA VILLE DE LADITE CIRCONSTANCE, ET QUE LA DECLARATION JUDICIAIRE DES DIFFERENDS AVAIT ETE EXPRESSEMENT EXPROPRIEE D'AVOIR REFUSE DE FAIRE MENTION D'APPEL D'AUTORISER SA NEGLIGENCE AUX TERMES DE LAQUELLE LE BATIMENT A ETE EXPROPRIE ; QU'EN L'ETAT DE CES CONSTATATIONS LA COUR D'APPEL A PU ESTIMER L'EXISTENCE DE LA DISCUSSION D'UNE NEGLIGENCE DE FAUX OU\n", "\n", "[3401 | 583.55] loss=1.66 avg=1.45\n", "[3402 | 584.84] loss=1.35 avg=1.45\n", "[3403 | 586.12] loss=1.53 avg=1.45\n", "[3404 | 587.40] loss=1.44 avg=1.45\n", "[3405 | 588.68] loss=1.39 avg=1.45\n", "[3406 | 589.98] loss=1.26 avg=1.45\n", "[3407 | 591.28] loss=1.48 avg=1.45\n", "[3408 | 592.56] loss=1.47 avg=1.45\n", "[3409 | 593.84] loss=1.32 avg=1.45\n", "[3410 | 595.11] loss=1.47 avg=1.45\n", "[3411 | 596.39] loss=1.32 avg=1.45\n", "[3412 | 597.67] loss=1.38 avg=1.45\n", "[3413 | 598.94] loss=1.42 avg=1.45\n", "[3414 | 600.22] loss=1.47 avg=1.45\n", "[3415 | 601.50] loss=1.46 avg=1.45\n", "[3416 | 602.79] loss=1.48 avg=1.45\n", "[3417 | 604.06] loss=1.44 avg=1.45\n", "[3418 | 605.34] loss=1.38 avg=1.45\n", "[3419 | 606.61] loss=1.52 avg=1.45\n", "[3420 | 607.89] loss=1.50 avg=1.45\n", "[3421 | 609.17] loss=1.36 avg=1.45\n", "[3422 | 610.45] loss=1.54 avg=1.45\n", "[3423 | 611.73] loss=1.34 avg=1.45\n", "[3424 | 613.02] loss=1.29 avg=1.44\n", "[3425 | 614.30] loss=1.64 avg=1.45\n", "[3426 | 615.58] loss=1.32 avg=1.45\n", "[3427 | 616.87] loss=1.48 avg=1.45\n", "[3428 | 618.16] loss=1.36 avg=1.44\n", "[3429 | 619.44] loss=1.31 avg=1.44\n", "[3430 | 620.72] loss=1.52 avg=1.44\n", "[3431 | 622.00] loss=1.21 avg=1.44\n", "[3432 | 623.33] loss=1.53 avg=1.44\n", "[3433 | 624.64] loss=1.52 avg=1.44\n", "[3434 | 625.92] loss=1.31 avg=1.44\n", "[3435 | 627.19] loss=1.41 avg=1.44\n", "[3436 | 628.47] loss=1.26 avg=1.44\n", "[3437 | 629.75] loss=1.34 avg=1.44\n", "[3438 | 631.03] loss=1.28 avg=1.44\n", "[3439 | 632.30] loss=1.38 avg=1.44\n", "[3440 | 633.58] loss=1.46 avg=1.44\n", "[3441 | 634.88] loss=1.42 avg=1.44\n", "[3442 | 636.17] loss=1.44 avg=1.44\n", "[3443 | 637.45] loss=1.43 avg=1.44\n", "[3444 | 638.73] loss=1.46 avg=1.44\n", "[3445 | 640.01] loss=1.44 avg=1.44\n", "[3446 | 641.29] loss=1.51 avg=1.44\n", "[3447 | 642.56] loss=1.46 avg=1.44\n", "[3448 | 643.84] loss=1.48 avg=1.44\n", "[3449 | 645.11] loss=1.40 avg=1.44\n", "[3450 | 646.41] loss=1.43 avg=1.44\n", "[3451 | 647.69] loss=1.45 avg=1.44\n", "[3452 | 648.97] loss=1.48 avg=1.44\n", "[3453 | 650.26] loss=1.43 avg=1.44\n", "[3454 | 651.56] loss=1.42 avg=1.44\n", "[3455 | 652.84] loss=1.40 avg=1.44\n", "[3456 | 654.11] loss=1.38 avg=1.44\n", "[3457 | 655.40] loss=1.50 avg=1.44\n", "[3458 | 656.74] loss=1.31 avg=1.44\n", "[3459 | 658.02] loss=1.27 avg=1.43\n", "[3460 | 659.30] loss=1.40 avg=1.43\n", "[3461 | 660.58] loss=1.41 avg=1.43\n", "[3462 | 661.86] loss=1.40 avg=1.43\n", "[3463 | 663.14] loss=1.48 avg=1.43\n", "[3464 | 664.41] loss=1.61 avg=1.44\n", "[3465 | 665.69] loss=1.40 avg=1.44\n", "[3466 | 666.96] loss=1.36 avg=1.43\n", "[3467 | 668.25] loss=1.32 avg=1.43\n", "[3468 | 669.53] loss=1.46 avg=1.43\n", "[3469 | 670.82] loss=1.47 avg=1.43\n", "[3470 | 672.10] loss=1.33 avg=1.43\n", "[3471 | 673.37] loss=1.29 avg=1.43\n", "[3472 | 674.65] loss=1.28 avg=1.43\n", "[3473 | 675.92] loss=1.46 avg=1.43\n", "[3474 | 677.20] loss=1.40 avg=1.43\n", "[3475 | 678.48] loss=1.42 avg=1.43\n", "[3476 | 679.76] loss=1.34 avg=1.43\n", "[3477 | 681.05] loss=1.58 avg=1.43\n", "[3478 | 682.33] loss=1.44 avg=1.43\n", "[3479 | 683.62] loss=1.39 avg=1.43\n", "[3480 | 684.91] loss=1.56 avg=1.43\n", "[3481 | 686.19] loss=1.50 avg=1.43\n", "[3482 | 687.46] loss=1.61 avg=1.43\n", "[3483 | 688.77] loss=1.40 avg=1.43\n", "[3484 | 690.07] loss=1.35 avg=1.43\n", "[3485 | 691.36] loss=1.29 avg=1.43\n", "[3486 | 692.64] loss=1.47 avg=1.43\n", "[3487 | 693.91] loss=1.35 avg=1.43\n", "[3488 | 695.19] loss=1.36 avg=1.43\n", "[3489 | 696.46] loss=1.50 avg=1.43\n", "[3490 | 697.74] loss=1.41 avg=1.43\n", "[3491 | 699.02] loss=1.24 avg=1.43\n", "[3492 | 700.30] loss=1.53 avg=1.43\n", "[3493 | 701.60] loss=1.43 avg=1.43\n", "[3494 | 702.88] loss=1.38 avg=1.43\n", "[3495 | 704.16] loss=1.40 avg=1.43\n", "[3496 | 705.44] loss=1.48 avg=1.43\n", "[3497 | 706.71] loss=1.47 avg=1.43\n", "[3498 | 707.99] loss=1.37 avg=1.43\n", "[3499 | 709.27] loss=1.34 avg=1.43\n", "[3500 | 710.55] loss=1.27 avg=1.43\n", "======== SAMPLE 1 ========\n", "DANS AVAIENT PRIS LE LICENCIEMENT, SANS QU'IL Y AIT LIEU D'ERREURS AU PROFIT DE CE DERNIER, ETANT ET EXCLUS SANS TENIR COMPTE, DU FAIT DE LA DAME Y..., TOUTE CERTAINE DIFFICULTE, ALORS QUE CE SALAIRE, S'IL EST ETABLI, NE PEUT QUE LA REPRESENTER EN TETE DU FAIT MATERIEL DE CERTAINS CHEFS, LA CONFUSION DES MEMBRES DE LA SOCIETE, QUE CETTE REPRESENTATION ETAIT DE NATURE A LA CHARGE DE LA SOCIETE ;MAIS ATTENDU QUE D'UNE PART, \" LES JUGES DU SECOND DEGRE APPRECIENT SOUVERAINEMENT L'OBJET DE LA DEMANDE DE LICENCIEMENT AUX MOTIFS QU'ELLES AVAIENT CONCLU UNE MODIFICATION DE LA MODIFICATION DES ECRITURES DURANT CETTE COMMUNE AU CAS DE L'EXPERTISE AUX ASSOCIES ; QUE, D'AUTRE PART, \" LA COUR D'APPEL, POUR ADMETTRE, DANS DES CONCLUSIONS QUI AVAIENT DONNE LEURS REPROCHES, QU'ELLES EN AVAIENT L'ADMINISTRATEURS DE X... EN RAISON DE CERTAINS CHEFS DE TOUS LES PRIX LEGAUX, NE SOULIGNE QUE SI L'EXPERT DE JOSSONS DE LA SOCIETE, AVOUE PAR L'USUFRUIT DE L'EXPERT X... DIRIGEE CONTRE L'INDU D'ARGENTE DE TRAVAUX SOURCE EN TETE DU FAIT MATERIEL DE LA SOCIETE POUR L'EXERCICE DU CREDIT D'ALGERIE DANS LES MEMBRES DU FONDS DE COMMERCE DE SOCIETE DANS LA MEMBROSSE, L'EXPERT N'AYANT ACCEPTE QUE DIVERSES FACILITES D'EXPLOITATION DU FONDS \" ;D'OU IL SUIT QU'EN AUCUNE DE SES BRANCHES LE MOYEN N'EST FONDE D'AUTRE DE SES GRIEFS ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 9 JUILLET 1968, PAR LA COUR D'APPEL DE MONTPELLIER <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN, PRIS EN SA PREMIERE BRANCHE : VU L'ARTICLE 1751 DU CODE CIVIL ;ATTENDU QU'AUX TERMES DE CE TEXTE, AU-DESSUS DU DEPOT PUBLIQUE D'UNE CLAUSE DU CONTRAT D'ASSISSEUR RENDU PAR LE CHEMIN ECRIT, D'UN CONTRAT SEULEMENT AUX DEBATS, IL EST CONSTANT QUE SA GARDE DOIT ETRE INFORME DES CONTRATS D'ASSISSEURS, QU'IL NE PEUT ETRE PRATIQUE QU'A LA SEULE CONNAISSANCE DU CONTRAT, ET QU'EN L'ESPECE LES PARTIES CONCLURAIENT AUX DEBATS TOUTES LES MANDANTS DE LEUR PART ETANT A UN EGARD DE CE QU'ELLES DEVRAIENT LE CONJOINT DE L'USUCAPION ;QUE SI LES JUGES DU FOND NE SE SONT PAS EXPLOITES, IL NE REVELAIT PAS QU'ILS N'ONT JAMAIS L'OBJET DU DEMANDEUR AU PONT, DES LORS QUE LA CONNAISSANCE DE COMMUNAUTE, AUSSI TENUE DE L'ACCORD A CE CHEMIN ;QUE SE PRONONCANT SUR LE SECOND CHEMIN DE LA GARDE, CETTE CONVENTION DEVAIT S'ATTACHER EN ALLEGATION DES TERMES PROPRES ;QUE LA CONVENTION NE PEUT DONNER A FAIRE ECHEC AU DEBAT A L'APPLICABILITE INDUSTRIELLE ;ATTENDU QUE LE TRIBUNAL, A DEBOUTE LES DEFENDEURS DE\n", "\n", "[3501 | 724.52] loss=1.38 avg=1.43\n", "[3502 | 725.81] loss=1.30 avg=1.43\n", "[3503 | 727.09] loss=1.42 avg=1.43\n", "[3504 | 728.37] loss=1.53 avg=1.43\n", "[3505 | 729.64] loss=1.37 avg=1.43\n", "[3506 | 730.92] loss=1.31 avg=1.42\n", "[3507 | 732.20] loss=1.32 avg=1.42\n", "[3508 | 733.48] loss=1.26 avg=1.42\n", "[3509 | 734.77] loss=1.55 avg=1.42\n", "[3510 | 736.05] loss=1.34 avg=1.42\n", "[3511 | 737.32] loss=1.31 avg=1.42\n", "[3512 | 738.60] loss=1.37 avg=1.42\n", "[3513 | 739.88] loss=1.51 avg=1.42\n", "[3514 | 741.15] loss=1.50 avg=1.42\n", "[3515 | 742.43] loss=1.44 avg=1.42\n", "[3516 | 743.71] loss=1.40 avg=1.42\n", "[3517 | 745.01] loss=1.36 avg=1.42\n", "[3518 | 746.28] loss=1.45 avg=1.42\n", "[3519 | 747.56] loss=1.33 avg=1.42\n", "[3520 | 748.84] loss=1.38 avg=1.42\n", "[3521 | 750.12] loss=1.41 avg=1.42\n", "[3522 | 751.40] loss=1.42 avg=1.42\n", "[3523 | 752.68] loss=1.28 avg=1.42\n", "[3524 | 753.96] loss=1.46 avg=1.42\n", "[3525 | 755.28] loss=1.51 avg=1.42\n", "[3526 | 756.67] loss=1.55 avg=1.42\n", "[3527 | 757.97] loss=1.49 avg=1.42\n", "[3528 | 759.27] loss=1.42 avg=1.42\n", "[3529 | 760.55] loss=1.26 avg=1.42\n", "[3530 | 761.83] loss=1.33 avg=1.42\n", "[3531 | 763.10] loss=1.24 avg=1.42\n", "[3532 | 764.38] loss=1.39 avg=1.42\n", "[3533 | 765.66] loss=1.44 avg=1.42\n", "[3534 | 766.95] loss=1.47 avg=1.42\n", "[3535 | 768.23] loss=1.39 avg=1.42\n", "[3536 | 769.51] loss=1.43 avg=1.42\n", "[3537 | 770.78] loss=1.38 avg=1.42\n", "[3538 | 772.06] loss=1.54 avg=1.42\n", "[3539 | 773.34] loss=1.64 avg=1.42\n", "[3540 | 774.62] loss=1.44 avg=1.42\n", "[3541 | 775.90] loss=1.44 avg=1.42\n", "[3542 | 777.18] loss=1.38 avg=1.42\n", "[3543 | 778.48] loss=1.26 avg=1.42\n", "[3544 | 779.76] loss=1.41 avg=1.42\n", "[3545 | 781.04] loss=1.39 avg=1.42\n", "[3546 | 782.32] loss=1.43 avg=1.42\n", "[3547 | 783.60] loss=1.61 avg=1.42\n", "[3548 | 784.88] loss=1.38 avg=1.42\n", "[3549 | 786.16] loss=1.47 avg=1.42\n", "[3550 | 787.44] loss=1.34 avg=1.42\n", "[3551 | 788.79] loss=1.38 avg=1.42\n", "[3552 | 790.09] loss=1.37 avg=1.42\n", "[3553 | 791.38] loss=1.38 avg=1.42\n", "[3554 | 792.68] loss=1.35 avg=1.42\n", "[3555 | 793.96] loss=1.37 avg=1.42\n", "[3556 | 795.24] loss=1.48 avg=1.42\n", "[3557 | 796.51] loss=1.41 avg=1.42\n", "[3558 | 797.79] loss=1.28 avg=1.42\n", "[3559 | 799.07] loss=1.36 avg=1.42\n", "[3560 | 800.37] loss=1.41 avg=1.42\n", "[3561 | 801.65] loss=1.38 avg=1.42\n", "[3562 | 802.93] loss=1.39 avg=1.42\n", "[3563 | 804.21] loss=1.70 avg=1.42\n", "[3564 | 805.49] loss=1.40 avg=1.42\n", "[3565 | 806.76] loss=1.44 avg=1.42\n", "[3566 | 808.04] loss=1.35 avg=1.42\n", "[3567 | 809.32] loss=1.59 avg=1.42\n", "[3568 | 810.62] loss=1.46 avg=1.42\n", "[3569 | 811.91] loss=1.37 avg=1.42\n", "[3570 | 813.18] loss=1.40 avg=1.42\n", "[3571 | 814.46] loss=1.32 avg=1.42\n", "[3572 | 815.74] loss=1.30 avg=1.42\n", "[3573 | 817.02] loss=1.29 avg=1.42\n", "[3574 | 818.30] loss=1.50 avg=1.42\n", "[3575 | 819.57] loss=1.31 avg=1.42\n", "[3576 | 820.86] loss=1.53 avg=1.42\n", "[3577 | 822.23] loss=1.54 avg=1.42\n", "[3578 | 823.52] loss=1.46 avg=1.42\n", "[3579 | 824.81] loss=1.37 avg=1.42\n", "[3580 | 826.10] loss=1.29 avg=1.42\n", "[3581 | 827.38] loss=1.38 avg=1.42\n", "[3582 | 828.66] loss=1.29 avg=1.42\n", "[3583 | 829.94] loss=1.46 avg=1.42\n", "[3584 | 831.21] loss=1.57 avg=1.42\n", "[3585 | 832.49] loss=1.38 avg=1.42\n", "[3586 | 833.78] loss=1.37 avg=1.42\n", "[3587 | 835.06] loss=1.39 avg=1.42\n", "[3588 | 836.34] loss=1.46 avg=1.42\n", "[3589 | 837.62] loss=1.48 avg=1.42\n", "[3590 | 838.90] loss=1.33 avg=1.42\n", "[3591 | 840.18] loss=1.27 avg=1.41\n", "[3592 | 841.46] loss=1.46 avg=1.42\n", "[3593 | 842.74] loss=1.52 avg=1.42\n", "[3594 | 844.03] loss=1.38 avg=1.42\n", "[3595 | 845.31] loss=1.28 avg=1.41\n", "[3596 | 846.59] loss=1.42 avg=1.41\n", "[3597 | 847.86] loss=1.54 avg=1.42\n", "[3598 | 849.14] loss=1.30 avg=1.41\n", "[3599 | 850.42] loss=1.36 avg=1.41\n", "[3600 | 851.70] loss=1.43 avg=1.41\n", "======== SAMPLE 1 ========\n", " CONCERCION DE LA CAISSE DES CONSEIL MUNICIPAUX DE BRUCE ;ATTENDU QUE, TOUT EN DECLARANT QUE PAR SA LETTRE DE CHANGE L'ACTION POSSEDEE SE PRESENTAIT EN TERMES IDENTIQUES, LA COMMISSION DE RECOURS GRACIEUX SAISI LE RECOURS DE LADITE COMMISSION ;PAR CES MOTIFS : CASSE ET ANNULE LA DECISION RENDUE ENTRE LES PARTIES, PAR LA COMMISSION DE RECOURS GRACIEUX CANTELLE DE LA CAISSE DES CONSEILS MUNICIPAUX DE BRUCE ET DE PARIS, LE 6 MAI 1968 ; REMET, EN CONSEQUENCE, LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LADITE DECISION ET, POUR ETRE FAIT DROIT, LES RENVOIE DEVANT LA COMMISSION DE PREMIERE INSTANCE DE LA HAUTE-LOIRE AUTREMENT S'ETRE CONTREDIT EN TANT QUE CAISSE PRIMAIRE ;SUR LE DEUXIEME MOYEN ;ATTENDU QU'IL EST ENCORE REPROCHE A LA COMMISSION DE RECOURS GRACIEUX DE CANTELLE FONCIER DE NE PAS S'ETRE CONFORME A LA LOI FRANCAISE DES PARTIES, DE SORTE QUE BARDIER AURAIT DU RECOURIR A L'AUDIENCE, ALORS, SELON LE MOYEN, QUE LA COMPETENCE DU TRIBUNAL DE GRANDE INSTANCE EST RESTEE AU CAS D'INEXECUTION DE LA LOI ET QUE LA COUR D'APPEL NE POUVAIT LUI REPROCHER SI, EN RAISON DE LA DEFAILLANCE DE CANTELLE, LA LOI PREVUE A L'ARTICLE 54 DU CODE DE LA SECURITE SOCIALE N'A PAS ETE EXERCEE, EN L'ABSENCE D'ORDONNER L'INEXECUTION DES PIECES PENDANT SA PERIODE LEGALE ET ALORS, ENFIN, QUE, D'UNE PART, EN RAISON DE CES PIECES VERSEES PAR L'ARTICLE 32 DU DECRET DU 30 JUIN 1961, L'ACTIVITE OU LA PRETENDUE INEXECUTION PREFERAIT UNE VOLONTE DE PRIMES DEVANT LA JURIDICTION DU PREMIER DEGRE ET QUE LA DECLARATION FISCALE ETAIT PRESCRITE, ET ALORS, D'AUTRE PART, QUE, DES LORS, EN NEGLIGEANT, LE GRIEF DES DEUX JUGEMENTS DE CE DERNIER DEVENU IRREVOCABLE POUR APPRECIER LE BIEN LIVRE DE L'ARRET AVANT CETTE DATE, LES DEUX JUGEMENTS, QUI POUVAIENT, A L'EGARD DE CANTELLE, ET SE TROUVAIENT NECESSAIREMENT PREVALUS ET RECHERCHEE UNE REVOCATION DE LA DECLARATION FISCALE DES GRIEFS PRODUITES, N'ETAIENT PAS DE CE QUE L'ARRET NE CONTESTE PAS, POUR PERMETTRE L'APPLICATION DE L'ARTICLE 54 DU CODE DE LA SECURITE SOCIALE, L'EXECUTION DES LIEUX, QU'IL RESULTAIT DU RAPPORT D'EXPERTISE DU POURVOI PRECITEE, D'UNE PART, QUE L'ARTICLE 54 N'EXCLUAIT PAS LE REGIME DE LA CAISSE DES CONSEILS MUNICIPAUX DE LADITE COMMUNE, NON COMMUNE OU INDEPENDANT DE L'AFFECTATION D'UN FONDS DE COMMERCE, LES JUGEMENTS DU TRIBUNAL DE GRANDE INSTANCE DE LA HAUTE-LOIRE DE LA CAISSE DES CONSEILS MUNICIPAUX DE BRUCE, DONT L'AFFECTATION SE TROUVAIT DU PERMETTRE D'AGRER AUX PARTIES UNE ACTION FISCALE, MAIS UNE REVOCATION SOUS UN PROJET DE PRESCRIPTION LEGALE, S'IL S'INDEXE POUR LE FAIT DE LEUR ACTION PRINCIPALE\n", "\n", "[3601 | 865.55] loss=1.47 avg=1.42\n", "[3602 | 866.90] loss=1.26 avg=1.41\n", "[3603 | 868.19] loss=1.51 avg=1.41\n", "[3604 | 869.47] loss=1.27 avg=1.41\n", "[3605 | 870.75] loss=1.41 avg=1.41\n", "[3606 | 872.03] loss=1.44 avg=1.41\n", "[3607 | 873.31] loss=1.38 avg=1.41\n", "[3608 | 874.59] loss=1.48 avg=1.41\n", "[3609 | 875.87] loss=1.38 avg=1.41\n", "[3610 | 877.18] loss=1.35 avg=1.41\n", "[3611 | 878.46] loss=1.43 avg=1.41\n", "[3612 | 879.73] loss=1.52 avg=1.41\n", "[3613 | 881.02] loss=1.40 avg=1.41\n", "[3614 | 882.29] loss=1.35 avg=1.41\n", "[3615 | 883.57] loss=1.34 avg=1.41\n", "[3616 | 884.85] loss=1.44 avg=1.41\n", "[3617 | 886.13] loss=1.61 avg=1.41\n", "[3618 | 887.44] loss=1.47 avg=1.42\n", "[3619 | 888.80] loss=1.42 avg=1.42\n", "[3620 | 890.08] loss=1.48 avg=1.42\n", "[3621 | 891.36] loss=1.33 avg=1.42\n", "[3622 | 892.64] loss=1.30 avg=1.41\n", "[3623 | 893.92] loss=1.56 avg=1.42\n", "[3624 | 895.19] loss=1.30 avg=1.41\n", "[3625 | 896.47] loss=1.48 avg=1.41\n", "[3626 | 897.75] loss=1.49 avg=1.42\n", "[3627 | 899.08] loss=1.45 avg=1.42\n", "[3628 | 900.39] loss=1.53 avg=1.42\n", "[3629 | 901.68] loss=1.41 avg=1.42\n", "[3630 | 902.96] loss=1.24 avg=1.42\n", "[3631 | 904.24] loss=1.41 avg=1.42\n", "[3632 | 905.52] loss=1.48 avg=1.42\n", "[3633 | 906.80] loss=1.38 avg=1.42\n", "[3634 | 908.07] loss=1.30 avg=1.41\n", "[3635 | 909.35] loss=1.40 avg=1.41\n", "[3636 | 910.66] loss=1.42 avg=1.41\n", "[3637 | 911.94] loss=1.40 avg=1.41\n", "[3638 | 913.21] loss=1.34 avg=1.41\n", "[3639 | 914.49] loss=1.42 avg=1.41\n", "[3640 | 915.76] loss=1.42 avg=1.41\n", "[3641 | 917.04] loss=1.37 avg=1.41\n", "[3642 | 918.32] loss=1.44 avg=1.41\n", "[3643 | 919.59] loss=1.21 avg=1.41\n", "[3644 | 920.99] loss=1.25 avg=1.41\n", "[3645 | 922.33] loss=1.42 avg=1.41\n", "[3646 | 923.61] loss=1.42 avg=1.41\n", "[3647 | 924.88] loss=1.37 avg=1.41\n", "[3648 | 926.16] loss=1.30 avg=1.41\n", "[3649 | 927.44] loss=1.45 avg=1.41\n", "[3650 | 928.72] loss=1.37 avg=1.41\n", "[3651 | 930.00] loss=1.34 avg=1.41\n", "[3652 | 931.28] loss=1.44 avg=1.41\n", "[3653 | 932.63] loss=1.46 avg=1.41\n", "[3654 | 933.94] loss=1.44 avg=1.41\n", "[3655 | 935.24] loss=1.36 avg=1.41\n", "[3656 | 936.52] loss=1.32 avg=1.41\n", "[3657 | 937.80] loss=1.51 avg=1.41\n", "[3658 | 939.07] loss=1.36 avg=1.41\n", "[3659 | 940.35] loss=1.38 avg=1.41\n", "[3660 | 941.63] loss=1.37 avg=1.41\n", "[3661 | 942.92] loss=1.42 avg=1.41\n", "[3662 | 944.20] loss=1.34 avg=1.41\n", "[3663 | 945.48] loss=1.38 avg=1.41\n", "[3664 | 946.75] loss=1.68 avg=1.41\n", "[3665 | 948.03] loss=1.48 avg=1.41\n", "[3666 | 949.31] loss=1.32 avg=1.41\n", "[3667 | 950.59] loss=1.29 avg=1.41\n", "[3668 | 951.87] loss=1.34 avg=1.41\n", "[3669 | 953.16] loss=1.46 avg=1.41\n", "[3670 | 954.55] loss=1.34 avg=1.41\n", "[3671 | 955.82] loss=1.45 avg=1.41\n", "[3672 | 957.10] loss=1.30 avg=1.41\n", "[3673 | 958.38] loss=1.34 avg=1.41\n", "[3674 | 959.66] loss=1.49 avg=1.41\n", "[3675 | 960.94] loss=1.46 avg=1.41\n", "[3676 | 962.22] loss=1.38 avg=1.41\n", "[3677 | 963.49] loss=1.30 avg=1.41\n", "[3678 | 964.78] loss=1.34 avg=1.41\n", "[3679 | 966.07] loss=1.41 avg=1.41\n", "[3680 | 967.36] loss=1.39 avg=1.41\n", "[3681 | 968.65] loss=1.50 avg=1.41\n", "[3682 | 969.92] loss=1.42 avg=1.41\n", "[3683 | 971.20] loss=1.40 avg=1.41\n", "[3684 | 972.48] loss=1.53 avg=1.41\n", "[3685 | 973.75] loss=1.44 avg=1.41\n", "[3686 | 975.03] loss=1.34 avg=1.41\n", "[3687 | 976.33] loss=1.47 avg=1.41\n", "[3688 | 977.61] loss=1.48 avg=1.41\n", "[3689 | 978.88] loss=1.37 avg=1.41\n", "[3690 | 980.16] loss=1.32 avg=1.41\n", "[3691 | 981.44] loss=1.35 avg=1.41\n", "[3692 | 982.71] loss=1.42 avg=1.41\n", "[3693 | 983.99] loss=1.45 avg=1.41\n", "[3694 | 985.27] loss=1.42 avg=1.41\n", "[3695 | 986.61] loss=1.40 avg=1.41\n", "[3696 | 987.94] loss=1.31 avg=1.41\n", "[3697 | 989.22] loss=1.32 avg=1.41\n", "[3698 | 990.50] loss=1.55 avg=1.41\n", "[3699 | 991.77] loss=1.29 avg=1.41\n", "[3700 | 993.05] loss=1.43 avg=1.41\n", "======== SAMPLE 1 ========\n", "'IL DOIT, EN CONSEQUENCE, QUALIFIER LES CONCLUSIONS D'APPEL DE X... QUI SUBSIDIAIREMENT AVAIENT ETE RENDUES EN JUSTICE DU TRIBUNAL AUXQUELS IL AVAIT DEMANDE L'EVALUATION DU DELAI DE DEUX MOIS APRES PLUSIEURS ETUDES EN JUILLET 1965 ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU, LE 23 AVRIL 1970 : N° 70-14.231 X... ET AUTRE C/ CONSORTS Y... ET AUTRE. PRESIDENT : M. GUILLOT. - RAPPORTEUR : M. MERIMEE. - AVOCAT GENERAL : M. GUILLOT. - AVOCAT : M. X..., LA CAISSE CONTRE LES PARTIES. <|endoftext|>\n", "<|startoftext|> SUR LES TROIS MOYENS REUNIS : ATTENDU QU'IL EST FAIT GRIEF A LA DECISION ATTAQUEE, RENDUE SUR RENVOI, D'AVOIR DECIDE QUE L'APPROBATION DE L'INCAPACITE ET EN FAVEUR DES CHARTIGESSES (IMMEUBLES, PARCELLES DE TERRITORIALES) AUBERT QUI AURAIT CONVERTIE A UN REFUS AUX VENDEURS ET AYANT AINSI, A DEBOURSER DIVERSES DEMANDES, LES PARTIES AURAIT ETE DEBOURSEMENTS, ALORS, SELON LE POURVOI, QUE LA RENONCIATION DE LA JURIDICTION FRAPPEE DU CHEF DES DEMANDES EN PAIEMENT DU PRIX A EUX-MEMES SERAIT INOPERANTE ET AURAIT PU ETRE D'ACCORD POUR APPELER CEUX-CI ;MAIS ATTENDU QU'AUX TERMES D'UNE ARTICLE 8, LE PRIX PENDANT LA DUREE DES DOUZE MOIS N'EST PAS ACCORDEE AUX TERMES DE CET ARTICLE ET DU MEME JOUR, MAIS QU'EN PROPOSANT LA JURIDICTION FRAPPEE A LA JURIDICTION FRAPPEE OU L'IMMEUBLE, LES MEMES AUTRES QU'AURAIENT PORTE SUR LA PRISE EN CHARGE DE L'ACTE, POUR APPELER LES PARTIES ;QU'EN EFFET, LA COUR D'APPEL, QUI DISPOSAIT DE LA RENONCIATION DE LA PART DU PRIX, AYANT, A TORT, STATUE QU'IL EN RESULTE DUDIT ARTICLE 8, LES JUGES DU SECOND DEGRE N'ONT FAIT QU'USER DE LEUR POUVOIR SOUVERAIN DE MOTIFS ET LEGALEMENT JUSTIFIE LEUR DECISION ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 3 JANVIER 1970 PAR LA COUR D'APPEL DE BORDEAUX. <|endoftext|>\n", "<|startoftext|> SUR LES DEUX MOYENS REUNIS : ATTENDU QU'IL RESSORT DES ENONCIATIONS DE L'ARRET CONFIRMATIF ATTAQUE QUE, LE 30 NOVEMBRE 1962, DAME X..., TITULAIRE D'UN APPARTEMENT SUR LES TERRITOIRES, ASSIGNA, POUR AVOIR REVENDIQUE L'ENTREPRENEUR DE LA COMMUNAUTE QUI A CONFIE LA FIN DE L'IMMEUBLE, LEQUEL, PAR SEMAINE DE TACHET, AVAIT ETE VICTIME D'OBTENIR PENDANT LA DUREE DE LA JOUISSANCE ;ATTENDU QUE LA COMMUNAUTE, PAR CELLE-CI, A, PAR LETTRE DU 7 OCTOBRE 1962, CONFIE CETTE FIN, AVEC DIVORCE DE SON PRECEDENT MARIAGE, AUQUEL ELLE A RECONNU AVOIR ETE ENGAGE PAR LA COMPAGNIE D'ASSURANCES ; ATTENDU QUE LES JUGES DU FOND INDIQUENT QUE LES FONCTIONS DES COMMUNAUTES SONT D'EQUIV\n", "\n", "[3701 | 1006.32] loss=1.38 avg=1.41\n", "[3702 | 1007.61] loss=1.52 avg=1.41\n", "[3703 | 1008.92] loss=1.40 avg=1.41\n", "[3704 | 1010.20] loss=1.44 avg=1.41\n", "[3705 | 1011.48] loss=1.43 avg=1.41\n", "[3706 | 1012.75] loss=1.50 avg=1.41\n", "[3707 | 1014.03] loss=1.34 avg=1.41\n", "[3708 | 1015.31] loss=1.20 avg=1.41\n", "[3709 | 1016.58] loss=1.39 avg=1.41\n", "[3710 | 1017.86] loss=1.42 avg=1.41\n", "[3711 | 1019.15] loss=1.48 avg=1.41\n", "[3712 | 1020.53] loss=1.34 avg=1.41\n", "[3713 | 1021.81] loss=1.50 avg=1.41\n", "[3714 | 1023.08] loss=1.37 avg=1.41\n", "[3715 | 1024.36] loss=1.32 avg=1.41\n", "[3716 | 1025.64] loss=1.30 avg=1.40\n", "[3717 | 1026.92] loss=1.42 avg=1.40\n", "[3718 | 1028.19] loss=1.43 avg=1.40\n", "[3719 | 1029.47] loss=1.22 avg=1.40\n", "[3720 | 1030.75] loss=1.38 avg=1.40\n", "[3721 | 1032.03] loss=1.33 avg=1.40\n", "[3722 | 1033.31] loss=1.39 avg=1.40\n", "[3723 | 1034.59] loss=1.11 avg=1.40\n", "[3724 | 1035.87] loss=1.33 avg=1.40\n", "[3725 | 1037.14] loss=1.57 avg=1.40\n", "[3726 | 1038.42] loss=1.49 avg=1.40\n", "[3727 | 1039.71] loss=1.55 avg=1.40\n", "[3728 | 1041.00] loss=1.37 avg=1.40\n", "[3729 | 1042.32] loss=1.43 avg=1.40\n", "[3730 | 1043.59] loss=1.43 avg=1.40\n", "[3731 | 1044.87] loss=1.28 avg=1.40\n", "[3732 | 1046.15] loss=1.48 avg=1.40\n", "[3733 | 1047.42] loss=1.57 avg=1.40\n", "[3734 | 1048.70] loss=1.23 avg=1.40\n", "[3735 | 1049.98] loss=1.49 avg=1.40\n", "[3736 | 1051.25] loss=1.44 avg=1.40\n", "[3737 | 1052.60] loss=1.35 avg=1.40\n", "[3738 | 1053.96] loss=1.42 avg=1.40\n", "[3739 | 1055.23] loss=1.45 avg=1.40\n", "[3740 | 1056.51] loss=1.48 avg=1.40\n", "[3741 | 1057.79] loss=1.26 avg=1.40\n", "[3742 | 1059.07] loss=1.39 avg=1.40\n", "[3743 | 1060.34] loss=1.43 avg=1.40\n", "[3744 | 1061.62] loss=1.32 avg=1.40\n", "[3745 | 1062.90] loss=1.34 avg=1.40\n", "[3746 | 1064.20] loss=1.40 avg=1.40\n", "[3747 | 1065.48] loss=1.50 avg=1.40\n", "[3748 | 1066.76] loss=1.33 avg=1.40\n", "[3749 | 1068.03] loss=1.25 avg=1.40\n", "[3750 | 1069.31] loss=1.35 avg=1.40\n", "[3751 | 1070.60] loss=1.30 avg=1.40\n", "[3752 | 1071.87] loss=1.41 avg=1.40\n", "[3753 | 1073.16] loss=1.46 avg=1.40\n", "[3754 | 1074.45] loss=1.40 avg=1.40\n", "[3755 | 1075.80] loss=1.44 avg=1.40\n", "[3756 | 1077.08] loss=1.32 avg=1.40\n", "[3757 | 1078.36] loss=1.37 avg=1.40\n", "[3758 | 1079.63] loss=1.38 avg=1.40\n", "[3759 | 1080.91] loss=1.39 avg=1.40\n", "[3760 | 1082.19] loss=1.47 avg=1.40\n", "[3761 | 1083.47] loss=1.58 avg=1.40\n", "[3762 | 1084.75] loss=1.49 avg=1.40\n", "[3763 | 1086.09] loss=1.38 avg=1.40\n", "[3764 | 1087.42] loss=1.37 avg=1.40\n", "[3765 | 1088.70] loss=1.35 avg=1.40\n", "[3766 | 1089.98] loss=1.41 avg=1.40\n", "[3767 | 1091.25] loss=1.44 avg=1.40\n", "[3768 | 1092.53] loss=1.43 avg=1.40\n", "[3769 | 1093.81] loss=1.37 avg=1.40\n", "[3770 | 1095.08] loss=1.40 avg=1.40\n", "[3771 | 1096.36] loss=1.67 avg=1.40\n", "[3772 | 1097.67] loss=1.39 avg=1.40\n", "[3773 | 1098.94] loss=1.29 avg=1.40\n", "[3774 | 1100.22] loss=1.40 avg=1.40\n", "[3775 | 1101.50] loss=1.33 avg=1.40\n", "[3776 | 1102.77] loss=1.29 avg=1.40\n", "[3777 | 1104.05] loss=1.30 avg=1.40\n", "[3778 | 1105.33] loss=1.48 avg=1.40\n", "[3779 | 1106.61] loss=1.50 avg=1.40\n", "[3780 | 1107.93] loss=1.47 avg=1.40\n", "[3781 | 1109.22] loss=1.40 avg=1.40\n", "[3782 | 1110.50] loss=1.34 avg=1.40\n", "[3783 | 1111.78] loss=1.50 avg=1.40\n", "[3784 | 1113.05] loss=1.41 avg=1.40\n", "[3785 | 1114.33] loss=1.34 avg=1.40\n", "[3786 | 1115.61] loss=1.41 avg=1.40\n", "[3787 | 1116.89] loss=1.43 avg=1.40\n", "[3788 | 1118.18] loss=1.35 avg=1.40\n", "[3789 | 1119.56] loss=1.32 avg=1.40\n", "[3790 | 1120.84] loss=1.42 avg=1.40\n", "[3791 | 1122.12] loss=1.34 avg=1.40\n", "[3792 | 1123.40] loss=1.49 avg=1.40\n", "[3793 | 1124.67] loss=1.29 avg=1.40\n", "[3794 | 1125.95] loss=1.46 avg=1.40\n", "[3795 | 1127.23] loss=1.40 avg=1.40\n", "[3796 | 1128.50] loss=1.33 avg=1.40\n", "[3797 | 1129.79] loss=1.28 avg=1.40\n", "[3798 | 1131.07] loss=1.35 avg=1.40\n", "[3799 | 1132.35] loss=1.34 avg=1.40\n", "[3800 | 1133.62] loss=1.35 avg=1.40\n", "======== SAMPLE 1 ========\n", "E LES GRIEFS N'ONT PAS ENCOURU LES GRIEFS SOULEVES PAR LA LOI ;PAR CES MOTIFS : <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE, PRIS DE LA VIOLATION DE L'ARTICLE 23 DU LIVRE 1ER DU CODE DU TRAVAIL, DES ARTICLES 10 ET 11 DU DECRET DU 20 NOVEMBRE 1948 QUI, SUR LES DEMANDES PRINCIPALES DE DIRECTEUR TECHNIQUE, VISE LES DECISIONS PRIS SUR LES DEMANDES D 'ETAT D'AUTANT PLUS PRODUITE ;ATTENDU QU'IL EST FAIT GRIEF A LA COUR D'APPEL D'AVOIR DECIDE QU'ELLE NE DEVAIT PAS SITUATION AU SERVICE DE DAME Y... AUX FONCTIONS QU'ELLE PESENT ETANT AFFECTEE DE TERRAIN ET QUE, PAR CONSEQUENT, ELLE AVAIT SEULEMENT POUR OBJET DE PRUD'HOMMER LA DECLARATION D'AGGRAVATION DE DAME Y..., EN PREMIERE INSTANCE, CE QUI AURAIT FAIT ENTIER ET DE CE CHEF, AU MOTIF QUE L'INTERESSE AVAIT PORTE SA DESIGNATION DE SON AVOUE DE LA SOCIETE ALGERIENNE, ALORS QUE DERNIERE RESTAIT CELLE QUI AURAIT ETE PRIVEE A LA DATE DE LA CAISSE EN ETAT DES POUVOIRS DES POUVOIRS QU'IL AVAIT FAITS A L'INTERESSE, CE QUI AURAIT ETE DENATURE PAR L'ARRET ATTAQUE, MAIS D'ETABLI AU PRIVILEGE DU MEME JOUR ETANT D'APPEL DECLAREE COUVERTE PAR L'INTERESSE ;MAIS ATTENDU QUE, CONTRAIREMENT AUX ALLEGATIONS DE DAME Y..., LA COUR D'APPEL A RETENU QUE DAME Y... NE POUVAIT ETRE DEPOSE AU DIRECTEUR TECHNIQUE DUDIT AVOUE DU COMITE D'ENTREPRISE, AU SENS DE LA PRESCRIPTION DE L'ARTICLE 23 DU LIVRE 1ER DU CODE DU TRAVAIL ET A RETENU A L'INTERESSE, A LA FOIS, QUE SON EMPLOYEUR AVAIT APPELE A LA SOCIETE ALGERIENNE EN RAISON DE LA COUVERTURE DE L'INTERESSE, POUR SA QUALITE, LES DECISIONS PRIS, QUE L'INTERESSE AVAIT ETE PRIVEE AU SECRETARIAT D'EMPLOI AUX POUVOIRS DES POUVOIRS QUI N'ETAIENT QUE LA QUALITE DE SALARIE ;QUE, D'AUTRE PART, LE PRECEDENT ARRET RETIENT L'ARTICLE 23 DU LIVRE 1ER DU CODE DU TRAVAIL, QUE LA PRESOMPTION OU LA QUALITE D'AFFECTATION DE TERRAINS ET DE FONCTIONS POUR DAME Y... DANS L'ENTREPRISE ETAIT NULLE, ET QUE LA QUALIFICATION POUR UNE PERSONNE DE SALARIE OU PROFESSIONNEL NE COMPORTAIT PAS LA QUALITE D'AFFECTATION DE SALARIE ;QUE TOUTE DEMANDE N'AURAIT ETE REPONDU QU'EN CONSERVAT UNE PRESOMPTION PROPRE A LA PRESENCE DE DAME Y..., AU SENS DE L'ARTICLE 23 DU LIVRE 1ER DU CODE DU TRAVAIL ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU, LE 24 NOVEMBRE 1972, PAR LA COUR D'APPEL D'AGEN. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE, PRIS DE LA VIOLATION DES ARTICLES 23 DU LIVRE I DU CODE DU TRAVAIL, 1315, 1349, 1780 DU CODE CIVIL ET 7 DE LA LOI DU 20 AVRIL 1810, DEFAUT DE MOTIFS ET MANQUE DE BASE LEGALE ;ATTENDU\n", "\n", "[3801 | 1146.85] loss=1.47 avg=1.40\n", "[3802 | 1148.16] loss=1.43 avg=1.40\n", "[3803 | 1149.44] loss=1.36 avg=1.40\n", "[3804 | 1150.74] loss=1.32 avg=1.40\n", "[3805 | 1152.12] loss=1.30 avg=1.40\n", "[3806 | 1153.40] loss=1.41 avg=1.40\n", "[3807 | 1154.68] loss=1.34 avg=1.40\n", "[3808 | 1155.96] loss=1.44 avg=1.40\n", "[3809 | 1157.23] loss=1.24 avg=1.39\n", "[3810 | 1158.51] loss=1.42 avg=1.40\n", "[3811 | 1159.79] loss=1.31 avg=1.39\n", "[3812 | 1161.07] loss=1.39 avg=1.39\n", "[3813 | 1162.35] loss=1.37 avg=1.39\n", "[3814 | 1163.65] loss=1.31 avg=1.39\n", "[3815 | 1164.93] loss=1.41 avg=1.39\n", "[3816 | 1166.21] loss=1.47 avg=1.39\n", "[3817 | 1167.48] loss=1.39 avg=1.39\n", "[3818 | 1168.76] loss=1.50 avg=1.40\n", "[3819 | 1170.03] loss=1.28 avg=1.39\n", "[3820 | 1171.32] loss=1.40 avg=1.39\n", "[3821 | 1172.60] loss=1.36 avg=1.39\n", "[3822 | 1173.90] loss=1.35 avg=1.39\n", "[3823 | 1175.18] loss=1.40 avg=1.39\n", "[3824 | 1176.45] loss=1.34 avg=1.39\n", "[3825 | 1177.73] loss=1.39 avg=1.39\n", "[3826 | 1179.01] loss=1.37 avg=1.39\n", "[3827 | 1180.30] loss=1.45 avg=1.39\n", "[3828 | 1181.60] loss=1.37 avg=1.39\n", "[3829 | 1182.88] loss=1.40 avg=1.39\n", "[3830 | 1184.19] loss=1.44 avg=1.39\n", "[3831 | 1185.50] loss=1.45 avg=1.39\n", "[3832 | 1186.78] loss=1.35 avg=1.39\n", "[3833 | 1188.06] loss=1.28 avg=1.39\n", "[3834 | 1189.33] loss=1.24 avg=1.39\n", "[3835 | 1190.61] loss=1.38 avg=1.39\n", "[3836 | 1191.89] loss=1.40 avg=1.39\n", "[3837 | 1193.17] loss=1.41 avg=1.39\n", "[3838 | 1194.44] loss=1.36 avg=1.39\n", "[3839 | 1195.74] loss=1.55 avg=1.39\n", "[3840 | 1197.02] loss=1.44 avg=1.39\n", "[3841 | 1198.30] loss=1.53 avg=1.39\n", "[3842 | 1199.57] loss=1.45 avg=1.39\n", "[3843 | 1200.86] loss=1.48 avg=1.40\n", "[3844 | 1202.14] loss=1.28 avg=1.39\n", "[3845 | 1203.41] loss=1.36 avg=1.39\n", "[3846 | 1204.69] loss=1.41 avg=1.39\n", "[3847 | 1205.97] loss=1.34 avg=1.39\n", "[3848 | 1207.27] loss=1.48 avg=1.39\n", "[3849 | 1208.55] loss=1.27 avg=1.39\n", "[3850 | 1209.83] loss=1.40 avg=1.39\n", "[3851 | 1211.11] loss=1.48 avg=1.39\n", "[3852 | 1212.39] loss=1.21 avg=1.39\n", "[3853 | 1213.68] loss=1.39 avg=1.39\n", "[3854 | 1214.97] loss=1.43 avg=1.39\n", "[3855 | 1216.28] loss=1.47 avg=1.39\n", "[3856 | 1217.59] loss=1.25 avg=1.39\n", "[3857 | 1218.87] loss=1.35 avg=1.39\n", "[3858 | 1220.15] loss=1.34 avg=1.39\n", "[3859 | 1221.42] loss=1.36 avg=1.39\n", "[3860 | 1222.70] loss=1.39 avg=1.39\n", "[3861 | 1223.98] loss=1.27 avg=1.39\n", "[3862 | 1225.25] loss=1.40 avg=1.39\n", "[3863 | 1226.53] loss=1.39 avg=1.39\n", "[3864 | 1227.81] loss=1.34 avg=1.39\n", "[3865 | 1229.10] loss=1.45 avg=1.39\n", "[3866 | 1230.38] loss=1.33 avg=1.39\n", "[3867 | 1231.67] loss=1.50 avg=1.39\n", "[3868 | 1232.95] loss=1.50 avg=1.39\n", "[3869 | 1234.22] loss=1.40 avg=1.39\n", "[3870 | 1235.50] loss=1.30 avg=1.39\n", "[3871 | 1236.78] loss=1.38 avg=1.39\n", "[3872 | 1238.05] loss=1.47 avg=1.39\n", "[3873 | 1239.33] loss=1.51 avg=1.39\n", "[3874 | 1240.63] loss=1.31 avg=1.39\n", "[3875 | 1241.91] loss=1.15 avg=1.39\n", "[3876 | 1243.18] loss=1.37 avg=1.39\n", "[3877 | 1244.46] loss=1.30 avg=1.39\n", "[3878 | 1245.73] loss=1.20 avg=1.39\n", "[3879 | 1247.02] loss=1.49 avg=1.39\n", "[3880 | 1248.31] loss=1.27 avg=1.39\n", "[3881 | 1249.67] loss=1.34 avg=1.39\n", "[3882 | 1250.96] loss=1.31 avg=1.39\n", "[3883 | 1252.25] loss=1.53 avg=1.39\n", "[3884 | 1253.52] loss=1.34 avg=1.39\n", "[3885 | 1254.80] loss=1.29 avg=1.39\n", "[3886 | 1256.07] loss=1.38 avg=1.39\n", "[3887 | 1257.35] loss=1.15 avg=1.38\n", "[3888 | 1258.63] loss=1.39 avg=1.38\n", "[3889 | 1259.90] loss=1.25 avg=1.38\n", "[3890 | 1261.18] loss=1.48 avg=1.38\n", "[3891 | 1262.48] loss=1.48 avg=1.38\n", "[3892 | 1263.75] loss=1.41 avg=1.38\n", "[3893 | 1265.03] loss=1.36 avg=1.38\n", "[3894 | 1266.31] loss=1.30 avg=1.38\n", "[3895 | 1267.58] loss=1.22 avg=1.38\n", "[3896 | 1268.86] loss=1.46 avg=1.38\n", "[3897 | 1270.14] loss=1.41 avg=1.38\n", "[3898 | 1271.41] loss=1.24 avg=1.38\n", "[3899 | 1272.70] loss=1.46 avg=1.38\n", "[3900 | 1273.99] loss=1.41 avg=1.38\n", "======== SAMPLE 1 ========\n", "UI UN DROIT A L'ANNEE ANTERIEUREMENT A TOUJOURS RECONNU, D'OU IL SUIT QUE LE MOYEN NE PEUT ETRE ACCUEILLI ; MAIS SUR LA SECONDE BRANCHE DU MEME MOYEN : VU LES ARTICLES 24 ET 22 SUSVISE ; ATTENDU QUE LA COUR D'APPEL DE PAU, CONFORMEMENT AUX PRESCRIPTIONS DE CE TEXTE, A DECLARE VALABLE LA POSSESSION D'UNE MANIERE EXPLOITE ET QUE CE QUI NE PEUT ENVISAGER LA POSSESSION D'UNE EXPLOITE A COMPTER DU JOUR DE CETTE DECISION ; ATTENDU QUE, POUR DECIDER QUE X... DAME Y..., DITE X... DE SA FILLE MINEURE, S'ESTIMANT FERAIT EN COLLABORATION D'AUTRES PERSONNES QUI AVAIENT ASSIGNE LA GARDE A UNE AFFAIRE, LA COUR D'APPEL A, PAR APPLICATION DE L'ARTICLE 22 DES STATUTS DU MEME STATUAIRE, DECIDE QU'ETANT RECONNUE AVANT LE DEPOT DU JOUR DU PAIEMENT DE CETTE INDEMNITE, UN CONTRAT DE TRAVAIL ETAIT DISTINCT AVEC UN DROIT A L'ANNEE ANTERIEUREMENT A TOUJOURS RECONNU ;ATTENDU QU'EN SA SECONDE BRANCHE, LES JUGES D'APPEL ONT DECIDE QUE LE JOUR DU PAIEMENT NE REVENANT AUCUNE DROIT DANS UNE AFFAIRE, CONFORMEMENT AUX PRESCRIPTIONS LEGALES ;D'OU IL SUIT QUE LES JUGES D'APPEL SE SONT REUNISES EN SES DIVERSES BRANCHES ;PAR CES MOTIFS : CASSE ET ANNULE L'ARRET RENDU ENTRE LES PARTIES LE 17 AVRIL 1973, PAR LA COUR D'APPEL DE BORDEAUX, REMET, EN CONSEQUENCE, QUANT A CE, LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET, ET POUR ETRE FAIT DROIT, LES RENVOIE DEVANT LA COUR D'APPEL DE PAU. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE LA CAISSE PRIMAIRE D'ASSURANCE MALADIE DES BOUCHES-DU-RHONE FAIT GRIEF A L'ARRET ATTAQUE DE L'AVOIR DEBOUTE DE SA DEMANDE DE DOMMAGES-INTERETS POUR RETARD, AU MOTIF QUE CET INTERESSE AVAIT COMMIS AUCUNE INTENTION MALADIE, ALORS, D'UNE PART, QUE LA COUR D'APPEL NE POUVAIT SE POURVOIR QU'A BON DROIT QUI AURAIT ETE CONFIRME PAR LA SUPPRIMEE POUVANT TIRER LE MONTANT DES DEGATS ENCORE INVOQUES ; QUE LA CAISSE AVAIT FAIT REELLES A NE PAS INDIQUER UN POUVOIR SPECIAL ECRIT ;QUE, DE LA LORRAINE PRETENDU QU'IL N'Y AVAIT PAS EFFECTIVEMENT ACCOMPLI LE TRAITEMENT DONT IL AVAIT ETE CONSACRE PAR ELLE, COMME SI ELLE AURAIT PU ADMETTRE LE POUVOIR SPECIAL, IL ETAIT STIPULE A CET EFFET ; QU'EN TOUT CAS IL AVAIT PRODUIT UNE ATTESTATION AU PLUS MAL FUTURE DONT ELLE ETAIT INDEPENDANTE, QUI ETAIT DEDOMMEE DE CE POUVOIR ;QUE X... S'EN ETAIT TENU DE REGLER LES EFFETS DE TRAITEMENT ; MAIS ATTENDU QUE LA COUR D'APPEL RELEVE QUE C'EST SEULEMENT D'AUTRE L'ORIGINE MALADIE ET MATERNITE SUFFISANTE DE CETTE DECISION QUE X..., QUI ENTRAIGEAIT LE MEME DOMMAGE\n", "\n", "[3901 | 1287.68] loss=1.38 avg=1.38\n", "[3902 | 1288.98] loss=1.38 avg=1.38\n", "[3903 | 1290.26] loss=1.49 avg=1.38\n", "[3904 | 1291.54] loss=1.42 avg=1.38\n", "[3905 | 1292.82] loss=1.52 avg=1.38\n", "[3906 | 1294.09] loss=1.53 avg=1.39\n", "[3907 | 1295.39] loss=1.30 avg=1.39\n", "[3908 | 1296.67] loss=1.41 avg=1.39\n", "[3909 | 1297.94] loss=1.44 avg=1.39\n", "[3910 | 1299.22] loss=1.38 avg=1.39\n", "[3911 | 1300.50] loss=1.31 avg=1.39\n", "[3912 | 1301.77] loss=1.33 avg=1.38\n", "[3913 | 1303.05] loss=1.41 avg=1.38\n", "[3914 | 1304.32] loss=1.55 avg=1.39\n", "[3915 | 1305.62] loss=1.43 avg=1.39\n", "[3916 | 1306.90] loss=1.25 avg=1.39\n", "[3917 | 1308.18] loss=1.39 avg=1.39\n", "[3918 | 1309.46] loss=1.32 avg=1.39\n", "[3919 | 1310.73] loss=1.14 avg=1.38\n", "[3920 | 1312.01] loss=1.38 avg=1.38\n", "[3921 | 1313.29] loss=1.44 avg=1.38\n", "[3922 | 1314.59] loss=1.29 avg=1.38\n", "[3923 | 1315.88] loss=1.36 avg=1.38\n", "[3924 | 1317.18] loss=1.47 avg=1.38\n", "[3925 | 1318.46] loss=1.37 avg=1.38\n", "[3926 | 1319.73] loss=1.35 avg=1.38\n", "[3927 | 1321.02] loss=1.44 avg=1.38\n", "[3928 | 1322.31] loss=1.50 avg=1.38\n", "[3929 | 1323.60] loss=1.32 avg=1.38\n", "[3930 | 1324.87] loss=1.30 avg=1.38\n", "[3931 | 1326.15] loss=1.21 avg=1.38\n", "[3932 | 1327.43] loss=1.37 avg=1.38\n", "[3933 | 1328.73] loss=1.38 avg=1.38\n", "[3934 | 1330.01] loss=1.20 avg=1.38\n", "[3935 | 1331.29] loss=1.35 avg=1.38\n", "[3936 | 1332.57] loss=1.35 avg=1.38\n", "[3937 | 1333.84] loss=1.31 avg=1.38\n", "[3938 | 1335.12] loss=1.41 avg=1.38\n", "[3939 | 1336.40] loss=1.38 avg=1.38\n", "[3940 | 1337.67] loss=1.41 avg=1.38\n", "[3941 | 1338.97] loss=1.44 avg=1.38\n", "[3942 | 1340.25] loss=1.39 avg=1.38\n", "[3943 | 1341.53] loss=1.45 avg=1.38\n", "[3944 | 1342.81] loss=1.36 avg=1.38\n", "[3945 | 1344.09] loss=1.50 avg=1.38\n", "[3946 | 1345.36] loss=1.61 avg=1.38\n", "[3947 | 1346.64] loss=1.51 avg=1.38\n", "[3948 | 1347.95] loss=1.54 avg=1.39\n", "[3949 | 1349.23] loss=1.39 avg=1.39\n", "[3950 | 1350.53] loss=1.29 avg=1.39\n", "[3951 | 1351.80] loss=1.37 avg=1.39\n", "[3952 | 1353.08] loss=1.47 avg=1.39\n", "[3953 | 1354.36] loss=1.38 avg=1.39\n", "[3954 | 1355.65] loss=1.56 avg=1.39\n", "[3955 | 1356.94] loss=1.33 avg=1.39\n", "[3956 | 1358.21] loss=1.35 avg=1.39\n", "[3957 | 1359.49] loss=1.41 avg=1.39\n", "[3958 | 1360.77] loss=1.34 avg=1.39\n", "[3959 | 1362.05] loss=1.46 avg=1.39\n", "[3960 | 1363.33] loss=1.50 avg=1.39\n", "[3961 | 1364.60] loss=1.34 avg=1.39\n", "[3962 | 1365.88] loss=1.33 avg=1.39\n", "[3963 | 1367.16] loss=1.43 avg=1.39\n", "[3964 | 1368.43] loss=1.46 avg=1.39\n", "[3965 | 1369.71] loss=1.57 avg=1.39\n", "[3966 | 1370.99] loss=1.24 avg=1.39\n", "[3967 | 1372.27] loss=1.40 avg=1.39\n", "[3968 | 1373.55] loss=1.47 avg=1.39\n", "[3969 | 1374.83] loss=1.44 avg=1.39\n", "[3970 | 1376.10] loss=1.32 avg=1.39\n", "[3971 | 1377.38] loss=1.33 avg=1.39\n", "[3972 | 1378.66] loss=1.57 avg=1.39\n", "[3973 | 1379.99] loss=1.41 avg=1.39\n", "[3974 | 1381.28] loss=1.22 avg=1.39\n", "[3975 | 1382.57] loss=1.38 avg=1.39\n", "[3976 | 1383.85] loss=1.41 avg=1.39\n", "[3977 | 1385.13] loss=1.43 avg=1.39\n", "[3978 | 1386.40] loss=1.57 avg=1.39\n", "[3979 | 1387.69] loss=1.42 avg=1.39\n", "[3980 | 1388.97] loss=1.44 avg=1.39\n", "[3981 | 1390.26] loss=1.44 avg=1.39\n", "[3982 | 1391.53] loss=1.37 avg=1.39\n", "[3983 | 1392.81] loss=1.40 avg=1.39\n", "[3984 | 1394.12] loss=1.36 avg=1.39\n", "[3985 | 1395.40] loss=1.27 avg=1.39\n", "[3986 | 1396.68] loss=1.32 avg=1.39\n", "[3987 | 1397.96] loss=1.41 avg=1.39\n", "[3988 | 1399.24] loss=1.47 avg=1.39\n", "[3989 | 1400.51] loss=1.40 avg=1.39\n", "[3990 | 1401.79] loss=1.41 avg=1.39\n", "[3991 | 1403.07] loss=1.67 avg=1.39\n", "[3992 | 1404.35] loss=1.37 avg=1.39\n", "[3993 | 1405.65] loss=1.33 avg=1.39\n", "[3994 | 1406.93] loss=1.48 avg=1.39\n", "[3995 | 1408.21] loss=1.33 avg=1.39\n", "[3996 | 1409.48] loss=1.31 avg=1.39\n", "[3997 | 1410.77] loss=1.50 avg=1.39\n", "[3998 | 1412.07] loss=1.32 avg=1.39\n", "[3999 | 1413.35] loss=1.20 avg=1.39\n", "[4000 | 1414.62] loss=1.16 avg=1.39\n", "Saving checkpoint/run1/model-4000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 15:54:34.532351: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:54:34.533481: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:54:34.534382: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:54:34.535548: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:54:34.536510: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 15:54:34.537353: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-4000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 2.32it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 11608079 tokens\n", "Training...\n", "Saving checkpoint/run1/model-4000\n", "Saving checkpoint/run1/model-4000\n", "======== SAMPLE 1 ========\n", "OFFIE AU CABINET N° 69-1303 DU 3 DECEMBRE 1969 PREND UNE ACCEPTATION D'EXECUTION A SES OBLIGATIONS, CONFORMEMENT AUX TERMES DUQUEL LA COUR D'APPEL, REPONDANT A CES CONCLUSIONS, A PU DECIDER QUE, DANS LEUR AFFIRMATION QUE LE FONDS DE GARANTIE AUTOMOBILE NE FAISAIT OUVRIR DES DISPOSITIONS D'ORDRE PUBLIC DONT ELLE A DECIDE QU'IL DISAIT SON COMPTE, L'ACTION DES PROPRIETAIRES ENGAGEE PAR CETTE DEMANDE AU PLUS TOT ; QU'EN DECIDANT AINSI ABSTRACTION FAITE DU SINISTRE ET PAR SUITE DES DEPENSES DE CONCURRENCE DELOYALE LE TRIBUNAL DE GRANDE INSTANCE DECLARE QUE LA COMPAGNIE D'ASSURANCE L'INSTITUTION N'AVAIT PU, EN FAISANT, PROUVER \" LA VIOLENCE DE L'ARTICLE 1ER-2, ET QUE LE CONTRAT DE CONCURRENCE DELOYALE NE SERAIT INSUFFISANT POUR JUSTIFIER SA DECISION, POUR L'ENVOI DISCUTEE POUR L'ACTION QU'IL CONGEDIE AUX INDEMNITES ALLOUEES QUI FONT LEUR COMPARUTION SUR LE SINISTRE ET QUE LA COUR D'APPEL NE POUVAIT SE CONTENTER DE RECHERCHER SI CES INDEMNITES NE POUVAIENT ETRE INTERVENUES D'UNE DEMANDE D'EXECUTION \" ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 12 OCTOBRE 1970 PAR LA COUR D'APPEL DE GRENOBLE <|endoftext|>\n", "<|startoftext|> SUR LES DEUX MOYENS REUNIS, PRIS EN LEURS DIVERSES BRANCHES : ATTENDU QU'IL RESULTE DES ENONCIATIONS DE L'ARRET ATTAQUE QUE X..., LITIGIEUX A LA SOCIETE ANONYME DES COINTURES DES COCHERIES DIPLOMAIREES DU SOUPLAST, APPARTIENT A LA SOCIETE FENO-BERRE, QUI LA DEPASSEE AVEC CELUI DE L'EXPLOITATION AGRICOLE, APPARTIENT AUX HERITIERS EN POSSESSION DE CE DERNIER ET NON PAS COMME CELLE-CI SOUTENAIENT QUE LA CREANCE DE L'ACTIVITE COMMERCIALE DE LA SOCIETE FENO-BERRE AURAIT UNE SOMME DE 9,50 FRANCS SUR LES PRIX QU'ELLE AVAIT OPPOSEES ; QUE SUR RECONNAITRE LE PARTAGE DE RESPONSABILITE PAR L'ACQUEREUR DES LIEUX LE 17 NOVEMBRE 1966 ET ENFIN LESDITES CONDITIONS LESDITES CONDITIONS D'UNE CREANCE CONTRACTUELLE DEVANT LE TRIBUNAL PREFERENTIEL DES ACTIONNAIRES DE FENO-BERRE ET REPUTES ET CONTRAVENT ; QUE LA SOCIETE FENO-BERRE AYANT REFUSE AUX EPOUX X... EN DECIDANT QUE L'ENTRETIEN DES CONDITIONS DE L'ACQUISITION D'UN TERME DU CONSTAT FAIT EN PRESENCE DES EPOUX X..., DES TRAVAUX QU'ILS AVAIENT OBTENUES, X..., ES-QUALITES ET PAR ARRETE PREFECTORAL DE L'AUTORITE DECLAREE AVEC UN PRET EXEMPLE PAR LES PRIX, L'A ASSIGNE LES EPOUX X... EN NULLITE D'UNE DEMANDE D'ENQUETE SENTENCE QUI L'A CONFIRME ET LES AVOIR PRONONCEES ;ATTENDU QU'IL EST FAIT GRIEF A L'ARRET ATTAQUE D'AVOIR DECIDE QUE L'EXISTENCE D'UNE CREANCE DEVANT LA COUR D'APPEL, EN L'ESPECE, DONT IL SERAIT CONNU DE LA PROCEDURE N'EXECUTERAIT\n", "\n", "[4001 | 21.91] loss=1.41 avg=1.41\n", "[4002 | 23.18] loss=1.44 avg=1.42\n", "[4003 | 24.48] loss=1.48 avg=1.44\n", "[4004 | 25.76] loss=1.39 avg=1.43\n", "[4005 | 27.03] loss=1.39 avg=1.42\n", "[4006 | 28.31] loss=1.33 avg=1.40\n", "[4007 | 29.59] loss=1.28 avg=1.39\n", "[4008 | 30.88] loss=1.32 avg=1.38\n", "[4009 | 32.17] loss=1.43 avg=1.38\n", "[4010 | 33.45] loss=1.33 avg=1.38\n", "[4011 | 34.75] loss=1.33 avg=1.37\n", "[4012 | 36.03] loss=1.38 avg=1.37\n", "[4013 | 37.31] loss=1.38 avg=1.37\n", "[4014 | 38.58] loss=1.31 avg=1.37\n", "[4015 | 39.87] loss=1.54 avg=1.38\n", "[4016 | 41.18] loss=1.29 avg=1.38\n", "[4017 | 42.46] loss=1.47 avg=1.38\n", "[4018 | 43.74] loss=1.48 avg=1.39\n", "[4019 | 45.02] loss=1.42 avg=1.39\n", "[4020 | 46.31] loss=1.33 avg=1.39\n", "[4021 | 47.58] loss=1.32 avg=1.38\n", "[4022 | 48.86] loss=1.55 avg=1.39\n", "[4023 | 50.14] loss=1.48 avg=1.40\n", "[4024 | 51.41] loss=1.30 avg=1.39\n", "[4025 | 52.69] loss=1.47 avg=1.39\n", "[4026 | 53.97] loss=1.38 avg=1.39\n", "[4027 | 55.25] loss=1.39 avg=1.39\n", "[4028 | 56.53] loss=1.49 avg=1.40\n", "[4029 | 57.82] loss=1.42 avg=1.40\n", "[4030 | 59.09] loss=1.50 avg=1.40\n", "[4031 | 60.37] loss=1.50 avg=1.41\n", "[4032 | 61.65] loss=1.50 avg=1.41\n", "[4033 | 62.93] loss=1.39 avg=1.41\n", "[4034 | 64.21] loss=1.30 avg=1.41\n", "[4035 | 65.51] loss=1.37 avg=1.40\n", "[4036 | 66.80] loss=1.40 avg=1.40\n", "[4037 | 68.09] loss=1.28 avg=1.40\n", "[4038 | 69.36] loss=1.32 avg=1.40\n", "[4039 | 70.64] loss=1.35 avg=1.40\n", "[4040 | 71.92] loss=1.49 avg=1.40\n", "[4041 | 73.22] loss=1.48 avg=1.40\n", "[4042 | 74.51] loss=1.46 avg=1.40\n", "[4043 | 75.79] loss=1.51 avg=1.41\n", "[4044 | 77.06] loss=1.39 avg=1.41\n", "[4045 | 78.35] loss=1.44 avg=1.41\n", "[4046 | 79.65] loss=1.68 avg=1.41\n", "[4047 | 80.92] loss=1.46 avg=1.42\n", "[4048 | 82.20] loss=1.36 avg=1.41\n", "[4049 | 83.47] loss=1.49 avg=1.42\n", "[4050 | 84.75] loss=1.39 avg=1.41\n", "[4051 | 86.03] loss=1.35 avg=1.41\n", "[4052 | 87.30] loss=1.50 avg=1.42\n", "[4053 | 88.58] loss=1.42 avg=1.42\n", "[4054 | 89.88] loss=1.34 avg=1.41\n", "[4055 | 91.17] loss=1.35 avg=1.41\n", "[4056 | 92.44] loss=1.51 avg=1.41\n", "[4057 | 93.72] loss=1.61 avg=1.42\n", "[4058 | 94.99] loss=1.51 avg=1.42\n", "[4059 | 96.27] loss=1.60 avg=1.43\n", "[4060 | 97.56] loss=1.49 avg=1.43\n", "[4061 | 98.85] loss=1.47 avg=1.43\n", "[4062 | 100.13] loss=1.45 avg=1.43\n", "[4063 | 101.44] loss=1.41 avg=1.43\n", "[4064 | 102.71] loss=1.47 avg=1.43\n", "[4065 | 103.99] loss=1.45 avg=1.43\n", "[4066 | 105.26] loss=1.33 avg=1.43\n", "[4067 | 106.56] loss=1.42 avg=1.43\n", "[4068 | 107.84] loss=1.41 avg=1.43\n", "[4069 | 109.12] loss=1.40 avg=1.43\n", "[4070 | 110.40] loss=1.29 avg=1.42\n", "[4071 | 111.69] loss=1.42 avg=1.42\n", "[4072 | 112.98] loss=1.48 avg=1.42\n", "[4073 | 114.25] loss=1.51 avg=1.43\n", "[4074 | 115.53] loss=1.49 avg=1.43\n", "[4075 | 116.81] loss=1.72 avg=1.43\n", "[4076 | 118.09] loss=1.38 avg=1.43\n", "[4077 | 119.36] loss=1.49 avg=1.43\n", "[4078 | 120.64] loss=1.43 avg=1.43\n", "[4079 | 121.91] loss=1.37 avg=1.43\n", "[4080 | 123.21] loss=1.40 avg=1.43\n", "[4081 | 124.49] loss=1.29 avg=1.43\n", "[4082 | 125.77] loss=1.26 avg=1.43\n", "[4083 | 127.04] loss=1.33 avg=1.42\n", "[4084 | 128.32] loss=1.60 avg=1.43\n", "[4085 | 129.60] loss=1.52 avg=1.43\n", "[4086 | 130.88] loss=1.52 avg=1.43\n", "[4087 | 132.17] loss=1.36 avg=1.43\n", "[4088 | 133.46] loss=1.44 avg=1.43\n", "[4089 | 134.76] loss=1.39 avg=1.43\n", "[4090 | 136.04] loss=1.63 avg=1.43\n", "[4091 | 137.31] loss=1.41 avg=1.43\n", "[4092 | 138.61] loss=1.34 avg=1.43\n", "[4093 | 139.92] loss=1.42 avg=1.43\n", "[4094 | 141.20] loss=1.51 avg=1.43\n", "[4095 | 142.47] loss=1.24 avg=1.43\n", "[4096 | 143.75] loss=1.32 avg=1.43\n", "[4097 | 145.04] loss=1.63 avg=1.43\n", "[4098 | 146.32] loss=1.50 avg=1.43\n", "[4099 | 147.59] loss=1.60 avg=1.43\n", "[4100 | 148.87] loss=1.33 avg=1.43\n", "======== SAMPLE 1 ========\n", "ER FONT QUE LE MAIRE DE LA SOCIETE ANONYME LES ARCHITECTES, REPRESENTANT AU SERVICE DE LA SOCIETE DE CONSTRUCTION PROBHONIQUE ET COMPAGNIE (SNCC), LE MAINE DES FONDS DE COMMERCE JEAN DE Z... A..., DEVENU GERANT C... ; QUE B... A EN QUALITE DE SALARIE ET DU DIRECTEUR REGIMAGNE, QU'A JUSQU'A DATE A LAQUELLE ELLE A PU ETRE ASSIGNEE PAR SON EMPLOYEUR, IL EST REPROCHE A L'ARRET ATTAQUE DE L'AVOIR CONDAMNE A PAYER A CETTE SOCIETE, ALORS, SELON LE MOYEN, QUE LE CONSEIL LE LUI TIENDRAIT DE LA COMPETENCE DE L'ORDRE JUDICIAIRE ET PORTE SUR UNE PORTEE QUANT AUX TERMES DES CONDITIONS GENERALES QUI LUI IMPOSE A LA NATURE D'UN CONTRAT LIANT LES PARTIES, ET QUE CETTE ERREUR, BIEN QUE LA REPRESENTATION DU CONTRAT NE SAURAIT RECEVOIR OBSTACLE AUX CONDITIONS REQUISES PAR L'ARTICLE 17 DU DECRET N 7410 DU 21 DECEMBRE 1959 N'A PAS COMMIS DE FAUTE GRAVE EN REPROCHANT D'AVOIR, PAR UNE PREMIERE DE CES DATES, CONDAMNE LA SOCIETE A REPARATION DE LE PRIX DE SALAIRES, ALORS QUE, COMME DENI LA MARCHE DE LA SENTENCE CONSTITUE UNE FAUTE GRAVE JUSTIFIANT SON LICENCIEMENT, LE CONSEIL LE LUI DOIT ETRE FAIT GRIEF, AU MOTIF QUE L'EMPLOYEUR FAISAIT VALOIR QU'IL FUT L'OBJET DU LICENCIEMENT ET QUE LA SENTENCE DU CONSEIL DEVRA REGULARISATION ET CELUI DES CONDITIONS REGLEMENTAIRES N'ETAIT MEME PAS EXIGEE PAR LES ARTICLES SUSVISEES, ET ALORS QUE LA SENTENCE DU CONSEIL DE VOYAGE PROBHONIQUE ET COMPAGNIE S'APPLICABLES A LA DATE DE REPRISE DE BASE, CELLES-CI SONT SEULS TELS LES CONSEQUENCES D'UN LICENCIEMENT POUR FAIRE APPARAITRE CELUI-CI ET ETAIENT FAUTES DE BASE LEGALE ;MAIS ATTENDU QUE LES JUGES DU SECOND DEGRE, QUI ONT CONSTATE QUE POUR REQUERIR LA SOCIETE LES ARCHITECTES PROBHONIQUE ET COMMERCIALEMENT, C... LE MAINE DES DEUX FONDS, QUI AVAIENT ETE AFFICHEES, AVAIENT CONFIE A LADITE SOCIETE A PAYER A SON EMPLOYEUR LA SOMME SUPERIEURE A CELLE DE 20 % A L'EMPLOYE QUE SON LICENCIEMENT AVAIT ETE EXECUTE A CET EFFET ; QUE, PAR SUITE, LA RECONNAISSANCE QU'IL INVOQUAIT CONTRE CE RETARD ETAIT MAL FONDEE A DECISIF ET, PAR SUITE, A JUSTIFIER DE CE QUE CE RETARD ETAIT INDIFFEREMMENT POUR S'ARRETER SUR LE MONTANT DU PREJUDICE QU'IL APPARTENAIT D'OBJET LITIGIEUX ET DE L'ATTRIBUTION DE SES FONCTIONS DE SALARIE SUR LES INTERETS EN FONCTION D'AUCUN CONTRAT LIANT LES PARTIES, AINSI SANS ETRE TENU COMPTE DE SE DEFENDRE SUR LES INTERETS EN FONCTION D'AUCUNE MANOEUVRE ENTIRE CELLE OU LES INTERETS DE L'INTERESSE AUQUEL LA DISPARITION AVAIT SOLLICITE UNE REPRESENTATION QUI PESE SUR LES INTERETS DE L'INTERESSE PAR UNE PERSONNE ENTIERE ; QUE LA COUR D'APPEL A PU EN DEDUIRE QUE LES INTERETS DE L\n", "\n", "[4101 | 162.25] loss=1.40 avg=1.43\n", "[4102 | 163.52] loss=1.37 avg=1.43\n", "[4103 | 164.81] loss=1.17 avg=1.43\n", "[4104 | 166.10] loss=1.45 avg=1.43\n", "[4105 | 167.46] loss=1.35 avg=1.43\n", "[4106 | 168.74] loss=1.75 avg=1.43\n", "[4107 | 170.01] loss=1.39 avg=1.43\n", "[4108 | 171.29] loss=1.35 avg=1.43\n", "[4109 | 172.59] loss=1.33 avg=1.43\n", "[4110 | 173.87] loss=1.40 avg=1.43\n", "[4111 | 175.14] loss=1.79 avg=1.43\n", "[4112 | 176.42] loss=1.39 avg=1.43\n", "[4113 | 177.72] loss=1.22 avg=1.43\n", "[4114 | 179.00] loss=1.38 avg=1.43\n", "[4115 | 180.28] loss=1.43 avg=1.43\n", "[4116 | 181.55] loss=1.61 avg=1.43\n", "[4117 | 182.83] loss=1.38 avg=1.43\n", "[4118 | 184.10] loss=1.30 avg=1.43\n", "[4119 | 185.38] loss=1.22 avg=1.43\n", "[4120 | 186.66] loss=1.28 avg=1.42\n", "[4121 | 187.94] loss=1.71 avg=1.43\n", "[4122 | 189.25] loss=1.59 avg=1.43\n", "[4123 | 190.52] loss=1.31 avg=1.43\n", "[4124 | 191.80] loss=1.55 avg=1.43\n", "[4125 | 193.08] loss=1.31 avg=1.43\n", "[4126 | 194.36] loss=1.32 avg=1.43\n", "[4127 | 195.63] loss=1.31 avg=1.42\n", "[4128 | 196.91] loss=1.39 avg=1.42\n", "[4129 | 198.20] loss=1.49 avg=1.42\n", "[4130 | 199.49] loss=1.45 avg=1.43\n", "[4131 | 200.81] loss=1.40 avg=1.42\n", "[4132 | 202.09] loss=1.41 avg=1.42\n", "[4133 | 203.37] loss=1.47 avg=1.43\n", "[4134 | 204.67] loss=1.38 avg=1.42\n", "[4135 | 205.96] loss=1.31 avg=1.42\n", "[4136 | 207.23] loss=1.36 avg=1.42\n", "[4137 | 208.51] loss=1.48 avg=1.42\n", "[4138 | 209.79] loss=1.31 avg=1.42\n", "[4139 | 211.08] loss=1.37 avg=1.42\n", "[4140 | 212.36] loss=1.27 avg=1.42\n", "[4141 | 213.63] loss=1.32 avg=1.42\n", "[4142 | 214.91] loss=1.34 avg=1.42\n", "[4143 | 216.18] loss=1.39 avg=1.42\n", "[4144 | 217.46] loss=1.20 avg=1.41\n", "[4145 | 218.73] loss=1.34 avg=1.41\n", "[4146 | 220.01] loss=1.41 avg=1.41\n", "[4147 | 221.29] loss=1.57 avg=1.41\n", "[4148 | 222.58] loss=1.42 avg=1.41\n", "[4149 | 223.86] loss=1.56 avg=1.42\n", "[4150 | 225.13] loss=1.29 avg=1.41\n", "[4151 | 226.41] loss=1.47 avg=1.42\n", "[4152 | 227.69] loss=1.37 avg=1.41\n", "[4153 | 228.96] loss=1.42 avg=1.41\n", "[4154 | 230.24] loss=1.30 avg=1.41\n", "[4155 | 231.53] loss=1.35 avg=1.41\n", "[4156 | 232.88] loss=1.62 avg=1.42\n", "[4157 | 234.17] loss=1.63 avg=1.42\n", "[4158 | 235.45] loss=1.60 avg=1.42\n", "[4159 | 236.73] loss=1.40 avg=1.42\n", "[4160 | 238.02] loss=1.45 avg=1.42\n", "[4161 | 239.32] loss=1.35 avg=1.42\n", "[4162 | 240.60] loss=1.37 avg=1.42\n", "[4163 | 241.87] loss=1.40 avg=1.42\n", "[4164 | 243.15] loss=1.50 avg=1.42\n", "[4165 | 244.44] loss=1.52 avg=1.42\n", "[4166 | 245.72] loss=1.33 avg=1.42\n", "[4167 | 246.99] loss=1.56 avg=1.42\n", "[4168 | 248.27] loss=1.40 avg=1.42\n", "[4169 | 249.55] loss=1.44 avg=1.42\n", "[4170 | 250.82] loss=1.39 avg=1.42\n", "[4171 | 252.10] loss=1.38 avg=1.42\n", "[4172 | 253.37] loss=1.30 avg=1.42\n", "[4173 | 254.68] loss=1.37 avg=1.42\n", "[4174 | 255.96] loss=1.49 avg=1.42\n", "[4175 | 257.23] loss=1.53 avg=1.42\n", "[4176 | 258.51] loss=1.24 avg=1.42\n", "[4177 | 259.79] loss=1.38 avg=1.42\n", "[4178 | 261.06] loss=1.29 avg=1.42\n", "[4179 | 262.34] loss=1.52 avg=1.42\n", "[4180 | 263.62] loss=1.37 avg=1.42\n", "[4181 | 264.89] loss=1.41 avg=1.42\n", "[4182 | 266.24] loss=1.35 avg=1.42\n", "[4183 | 267.55] loss=1.57 avg=1.42\n", "[4184 | 268.85] loss=1.34 avg=1.42\n", "[4185 | 270.13] loss=1.43 avg=1.42\n", "[4186 | 271.42] loss=1.38 avg=1.42\n", "[4187 | 272.70] loss=1.30 avg=1.42\n", "[4188 | 273.97] loss=1.25 avg=1.41\n", "[4189 | 275.25] loss=1.35 avg=1.41\n", "[4190 | 276.53] loss=1.38 avg=1.41\n", "[4191 | 277.81] loss=1.51 avg=1.41\n", "[4192 | 279.09] loss=1.36 avg=1.41\n", "[4193 | 280.36] loss=1.43 avg=1.41\n", "[4194 | 281.64] loss=1.59 avg=1.42\n", "[4195 | 282.92] loss=1.32 avg=1.41\n", "[4196 | 284.19] loss=1.38 avg=1.41\n", "[4197 | 285.47] loss=1.34 avg=1.41\n", "[4198 | 286.74] loss=1.37 avg=1.41\n", "[4199 | 288.03] loss=1.34 avg=1.41\n", "[4200 | 289.31] loss=1.39 avg=1.41\n", "======== SAMPLE 1 ========\n", "ISSUE DE L'ARRETE MINISTERIEL DU 1ER OCTOBRE 1967 ALORS EN VIGUEUR, LA JURIDICTION DU SECOND DEGRE AVAIT STATUE SANS PRECISER SI CERTAINS CAUSES L'AUTORISATION DE REINSTALLER, A LA SURVEILLANCE D'UN CAMARADE QUI DOIT EFFECTUER UNE CERCLE A PARTIR DU MATERIEL DES HANGARS AYANT, SOUS RESERVE DES PROLONGES ET LES MODALITES DE LA PENSION, DONT ELLE LUI ESTIMAIT INUTILE, SORTIE DE RESPECTER L'ORDONNANCE DU 12 JANVIER 1945 QUI PREVOIT QUE CELUI-CI NE REVENDU PAS LES MODIFICATIONS ENTRAINANT L'APPLICATION ET QU'A CETTE REFECTION LA COUR D'APPEL A STATUE, EN L'ETAT, SANS AVOIR RECU L'APPLICATION DE L'ARTICLE 8, ALINEA 2, DU MEME CODE ;MAIS ATTENDU QUE L'ARRETE MINISTERIEL DU 1ER OCTOBRE 1967 A CONSTATE L'EXISTENCE EN SUITE DE CETTE REINSTALLATION A L'ENCONTRE DE LA FEDERATIVE A L'INSTALLATION DE L'UN DES HANGARS, QUI AVAIT MANQUE A CETTE RESERVE ; QUE, PAR LA-MEME, LA COUR D'APPEL A SOUVERAINEMENT JUGE QUOI ELLE AVAIT ETE PRONONCEE, MEME S'IL ESTIMAIT QUE LES AUTORACHES AFFICHEES A LA REINSTALLATION ET LES OBLIGATIONS AURAIENT DU ETRE DECOUVERTES PAR UNE DISORJEUSE ; QUE LE MOYEN N'EST DONC PAS FONDE ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 11 MAI 1975 PAR LA COUR D'APPEL DE BESANCON. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE LA SOCIETE A RESPONSABILITE LIMITEE REVOCATION, ET LES EPOUX Y..., FONT GRIEF A L'ARRET INFIRMATIF ATTAQUE D'AVOIR REJETE LE TRIBUNAL D'INSTANCE DE PARIS EN RAISON DE SA VIE COMMUNE POUR INTERROGATION DU CONTRAT DE DOUANE, ALORS QUE LE SENS DES DECISIONS PRISES PAR LE JUGE COMMISSAIRE EST EN FAVEUR DES DEPOTS ET DU MOMENT QUI LUI SERAIT CONFORME A L'OEUVRIERE DE L'INSTANCE ; QU'IL EST DE MEME, EN FAIT, CONNAISSENT UN MANQUE DE CONNEXITE ET PRIVE DE LA DESTINATION DES PIECES D'ORIENTATION, BIEN QU'IL RESULTE DE L'ARRETE DU 11 FEVRIER 1944 ET DE L'ARRETE DU 22 FEVRIER 1947, ALORS QUE, D'AUTRE PART, EN CAS DE PROCEDURE COMMUNELLE, LA SEULE CONDITION DE FAUTE LOURDE, DE CIRCULAIRE DONT IL DEPEND EN INVOQUAIT SON DECES, ET DE RAPPEL PAR CONSEQUENCE DEVANT LES DEMANDER LA CONDAMNATION DES EPOUX Y... ET DE LA MISSION ECONOMIQUE DE CE JUGE COMMISSAIRE, NE S'ETENDAIT PAS A L'AMPLE LIVRE SUR LE BON NOMBRE DES DECISIONS PRISES PAR CE COMMISSAIRE ;MAIS ATTENDU QU'APRES AVOIR RELEVE, NON SUR QU'IL N'AVAIT PAS CONNEXE, SI LA CAISSE NE SERAIT DONC DEVENUE LOCATAIRE PROPRIETAIRE, LA COUR D'APPEL A RETENU QUE, LA CONTREFACON D'EVENUATION S'ELEVAIT AU MOMENT OU DES EPOUX Y... AVAIENT, PAR VOIE D'ORIENTATION, OCCULTE DES PIECES QUE LES EPOUX Y... AIT FA\n", "\n", "[4201 | 302.74] loss=1.51 avg=1.41\n", "[4202 | 304.11] loss=1.51 avg=1.41\n", "[4203 | 305.43] loss=1.45 avg=1.41\n", "[4204 | 306.71] loss=1.34 avg=1.41\n", "[4205 | 307.99] loss=1.35 avg=1.41\n", "[4206 | 309.27] loss=1.31 avg=1.41\n", "[4207 | 310.55] loss=1.44 avg=1.41\n", "[4208 | 311.83] loss=1.29 avg=1.41\n", "[4209 | 313.10] loss=1.40 avg=1.41\n", "[4210 | 314.38] loss=1.38 avg=1.41\n", "[4211 | 315.66] loss=1.49 avg=1.41\n", "[4212 | 316.93] loss=1.28 avg=1.41\n", "[4213 | 318.21] loss=1.37 avg=1.41\n", "[4214 | 319.48] loss=1.34 avg=1.41\n", "[4215 | 320.77] loss=1.48 avg=1.41\n", "[4216 | 322.05] loss=1.41 avg=1.41\n", "[4217 | 323.33] loss=1.64 avg=1.41\n", "[4218 | 324.61] loss=1.27 avg=1.41\n", "[4219 | 325.88] loss=1.57 avg=1.41\n", "[4220 | 327.16] loss=1.30 avg=1.41\n", "[4221 | 328.43] loss=1.26 avg=1.41\n", "[4222 | 329.71] loss=1.34 avg=1.41\n", "[4223 | 330.99] loss=1.51 avg=1.41\n", "[4224 | 332.28] loss=1.33 avg=1.41\n", "[4225 | 333.56] loss=1.28 avg=1.41\n", "[4226 | 334.84] loss=1.35 avg=1.41\n", "[4227 | 336.12] loss=1.38 avg=1.41\n", "[4228 | 337.48] loss=1.39 avg=1.41\n", "[4229 | 338.78] loss=1.33 avg=1.40\n", "[4230 | 340.07] loss=1.32 avg=1.40\n", "[4231 | 341.34] loss=1.52 avg=1.41\n", "[4232 | 342.63] loss=1.50 avg=1.41\n", "[4233 | 343.92] loss=1.43 avg=1.41\n", "[4234 | 345.19] loss=1.50 avg=1.41\n", "[4235 | 346.47] loss=1.35 avg=1.41\n", "[4236 | 347.74] loss=1.44 avg=1.41\n", "[4237 | 349.02] loss=1.36 avg=1.41\n", "[4238 | 350.30] loss=1.43 avg=1.41\n", "[4239 | 351.57] loss=1.36 avg=1.41\n", "[4240 | 352.85] loss=1.41 avg=1.41\n", "[4241 | 354.17] loss=1.37 avg=1.41\n", "[4242 | 355.45] loss=1.64 avg=1.41\n", "[4243 | 356.72] loss=1.43 avg=1.41\n", "[4244 | 358.00] loss=1.45 avg=1.41\n", "[4245 | 359.27] loss=1.39 avg=1.41\n", "[4246 | 360.55] loss=1.32 avg=1.41\n", "[4247 | 361.82] loss=1.61 avg=1.41\n", "[4248 | 363.10] loss=1.23 avg=1.41\n", "[4249 | 364.38] loss=1.63 avg=1.41\n", "[4250 | 365.70] loss=1.40 avg=1.41\n", "[4251 | 366.98] loss=1.32 avg=1.41\n", "[4252 | 368.26] loss=1.29 avg=1.41\n", "[4253 | 369.57] loss=1.44 avg=1.41\n", "[4254 | 370.86] loss=1.31 avg=1.41\n", "[4255 | 372.15] loss=1.23 avg=1.41\n", "[4256 | 373.44] loss=1.25 avg=1.40\n", "[4257 | 374.73] loss=1.21 avg=1.40\n", "[4258 | 376.02] loss=1.31 avg=1.40\n", "[4259 | 377.29] loss=1.41 avg=1.40\n", "[4260 | 378.57] loss=1.40 avg=1.40\n", "[4261 | 379.85] loss=1.31 avg=1.40\n", "[4262 | 381.13] loss=1.29 avg=1.40\n", "[4263 | 382.40] loss=1.30 avg=1.40\n", "[4264 | 383.68] loss=1.57 avg=1.40\n", "[4265 | 384.96] loss=1.34 avg=1.40\n", "[4266 | 386.24] loss=1.31 avg=1.40\n", "[4267 | 387.53] loss=1.45 avg=1.40\n", "[4268 | 388.80] loss=1.37 avg=1.40\n", "[4269 | 390.08] loss=1.30 avg=1.40\n", "[4270 | 391.35] loss=1.19 avg=1.40\n", "[4271 | 392.63] loss=1.38 avg=1.39\n", "[4272 | 393.90] loss=1.30 avg=1.39\n", "[4273 | 395.18] loss=1.39 avg=1.39\n", "[4274 | 396.46] loss=1.41 avg=1.39\n", "[4275 | 397.74] loss=1.32 avg=1.39\n", "[4276 | 399.02] loss=1.26 avg=1.39\n", "[4277 | 400.30] loss=1.81 avg=1.40\n", "[4278 | 401.58] loss=1.53 avg=1.40\n", "[4279 | 402.88] loss=1.23 avg=1.40\n", "[4280 | 404.16] loss=1.39 avg=1.40\n", "[4281 | 405.45] loss=1.33 avg=1.40\n", "[4282 | 406.74] loss=1.47 avg=1.40\n", "[4283 | 408.02] loss=1.25 avg=1.39\n", "[4284 | 409.32] loss=1.49 avg=1.40\n", "[4285 | 410.60] loss=1.41 avg=1.40\n", "[4286 | 411.87] loss=1.28 avg=1.39\n", "[4287 | 413.15] loss=1.35 avg=1.39\n", "[4288 | 414.43] loss=1.23 avg=1.39\n", "[4289 | 415.70] loss=1.40 avg=1.39\n", "[4290 | 416.98] loss=1.26 avg=1.39\n", "[4291 | 418.25] loss=1.53 avg=1.39\n", "[4292 | 419.53] loss=1.53 avg=1.39\n", "[4293 | 420.82] loss=1.50 avg=1.39\n", "[4294 | 422.09] loss=1.35 avg=1.39\n", "[4295 | 423.37] loss=1.21 avg=1.39\n", "[4296 | 424.65] loss=1.34 avg=1.39\n", "[4297 | 425.92] loss=1.40 avg=1.39\n", "[4298 | 427.20] loss=1.32 avg=1.39\n", "[4299 | 428.47] loss=1.45 avg=1.39\n", "[4300 | 429.75] loss=1.40 avg=1.39\n", "======== SAMPLE 1 ========\n", " FAUTIVE POUR LE COMPTE ET LES AGENTS DE SA PART, UNE MATERIALITE DEFINITIVE, AUX MOTIFS QU'IL N'EXISTAIT D'ENGAGEMENT A CE CHEF DES INDEMNITES DE RUPTURE ET QUE, S'AGISSANT DE L'AGENT, IL NE S'ETAIT PAS FAIT L'OBJET D'UN CONTRAT DE VENTE D'UN ENSUITE PROFESSIONNEL ; QUE, D'AUTRE PART, EN REPONDANT PAR AILLEURS A CRITIQUES DU MOYEN AUX CONCLUSIONS DE LA SOCIETE ANTILLON, LA COUR D'APPEL N'A PAS JUSTIFIE LEGALEMENT SA DECISION ;D'OU IL SUIT QUE, MEME TANT L'URGENCE QUE N'EST PAS ADMIS ENTRE LES PARTIES DE S'EXPLIQUER SUR LE LITIGE DONT ELLE A RELEVE LA POSSIBILITE DE CONSTATER QU'ELLE FAISAIT VALOIR QUE L'AGENT AVAIT PRIS UN CONTRAT DE VENTE DANS LA MESINE DE BUREAUX, CE DONT IL N'IMPORTE AUCUN ROLE DE SA PART ET NE SAURAIT ETRE FAIT ETAT DE SA QUALITE DE SOUS-ACQUEREUR A L'EGARD DE TANT LE MOMENT OU IL AVAIT RECU LA LIBRE DEFAILLANCE QUE LES CONSERVATEURS LUI AVAIENT OCCASIONNE UN CONTRAT DE VENTE ;SUR LE SECOND MOYEN, PRISE EN LITIGE : ATTENDU QU'IL EST ENCORE REPROCHE A LA COUR D'APPEL D'AVOIR DECLARE \"QUE, MALGRE LA SURVEILLANCE DES TIERS, ILS EN AVAIENT FAIT TENTATIVE DE SA PART, QUI EST EN MESURE COMMISE, SI L'ACTIF FAIT, NON EN CE QU'IL AVAIT DE SA PART, DE SOUS-ACQUEREUR ET NON DES COOPERATIVES DE LA MESINE, ET D'AVOIR DENIE A MAL FONDE SA DEMONSTRATION, ALORS, SELON LE TIERCEVOIR, QU'EN VERTU DES ARTICLES 15 DE LA LOI DU 12 JUIN 1972, ET 7 DE LA LOI DU 20 AVRIL 1810 ; QUE, DES LORS, ILS ONT ETE INTRODUITES OU ECRITES, CE QUI IMPLIQUE EXACTEMENT LA SEULE CONDITION PREVUE PAR L'ARTICLE 15 SUSVISE, CE QUI EXCLUT NE PAS SE LIVRER AUX JURIDICTIONS QU'OFFENANT A L'ACTION CONTRACTUELLE DE LA MISE EN DEMEURE ; QUE, SELON L'ARTICLE 29 DE LA LOI DU 31 DECEMBRE 1947, A SEUL APPLICATION DES TEXTES, LES ACTIONS NE PEUVENT ETRE OPPOSES AUX TIERS QU'ETANT MAINTENUES, LA CREANCE, POUR L'EXPIRATION DU DELAI, DOIT PORTER ATTEINTE A LA SUITE DU CONTRAT DE TUTELLE ; QUE, DANS SES CONCLUSIONS, DEMOISELLE X... AVAIT FAIT DEMANDE LA CONDAMNATION DE CE DERNIER ET A L'EGARD DU MOMENT QU'ELLE AVAIT RECUEILLIE ET QUE, DE SA DEMANDE, DEMOISELLE X... AVAIT ETE ADHERENTE A LA SUITE D'UNE DES ASSEMBLEE MISE EN DEMEURE ; QUE LA COUR D'APPEL A AINSI ENCORE FAIT DROIT A LA DEMONSTRATION DE L'INCENDIE ET DECLAREE VISE A L'EFFET DE LA CONDAMNATION DONNEE PAR LE TIERS, ALORS, D'UNE PART, QUE LA REALITE DES AGENTS QUI, SELON L'ARTICLE 15 DE LA LOI DU 12 JUIN 1972, AVAIENT ETE REGULIERES, NE POUVAIT ENTRER EN CHARGE, ET QUE LESDITES AGENTS, QUI N'EN DEMEUREONT PAS MOINS D'EN FAIRE L'AT\n", "\n", "[4301 | 443.81] loss=1.38 avg=1.39\n", "[4302 | 445.12] loss=1.32 avg=1.39\n", "[4303 | 446.40] loss=1.39 avg=1.39\n", "[4304 | 447.68] loss=1.39 avg=1.39\n", "[4305 | 448.96] loss=1.29 avg=1.39\n", "[4306 | 450.24] loss=1.55 avg=1.39\n", "[4307 | 451.52] loss=1.32 avg=1.39\n", "[4308 | 452.80] loss=1.31 avg=1.39\n", "[4309 | 454.08] loss=1.28 avg=1.39\n", "[4310 | 455.37] loss=1.34 avg=1.39\n", "[4311 | 456.65] loss=1.48 avg=1.39\n", "[4312 | 457.92] loss=1.41 avg=1.39\n", "[4313 | 459.20] loss=1.49 avg=1.39\n", "[4314 | 460.48] loss=1.33 avg=1.39\n", "[4315 | 461.76] loss=1.32 avg=1.39\n", "[4316 | 463.03] loss=1.44 avg=1.39\n", "[4317 | 464.33] loss=1.25 avg=1.39\n", "[4318 | 465.62] loss=1.43 avg=1.39\n", "[4319 | 466.89] loss=1.20 avg=1.39\n", "[4320 | 468.19] loss=1.28 avg=1.39\n", "[4321 | 469.47] loss=1.27 avg=1.38\n", "[4322 | 470.75] loss=1.37 avg=1.38\n", "[4323 | 472.02] loss=1.41 avg=1.38\n", "[4324 | 473.30] loss=1.50 avg=1.39\n", "[4325 | 474.59] loss=1.29 avg=1.39\n", "[4326 | 475.88] loss=1.36 avg=1.38\n", "[4327 | 477.17] loss=1.32 avg=1.38\n", "[4328 | 478.46] loss=1.27 avg=1.38\n", "[4329 | 479.75] loss=1.31 avg=1.38\n", "[4330 | 481.02] loss=1.22 avg=1.38\n", "[4331 | 482.30] loss=1.30 avg=1.38\n", "[4332 | 483.58] loss=1.51 avg=1.38\n", "[4333 | 484.87] loss=1.29 avg=1.38\n", "[4334 | 486.17] loss=1.40 avg=1.38\n", "[4335 | 487.45] loss=1.54 avg=1.38\n", "[4336 | 488.73] loss=1.38 avg=1.38\n", "[4337 | 490.01] loss=1.56 avg=1.38\n", "[4338 | 491.28] loss=1.49 avg=1.38\n", "[4339 | 492.56] loss=1.29 avg=1.38\n", "[4340 | 493.84] loss=1.57 avg=1.39\n", "[4341 | 495.12] loss=1.22 avg=1.38\n", "[4342 | 496.40] loss=1.41 avg=1.38\n", "[4343 | 497.70] loss=1.40 avg=1.38\n", "[4344 | 498.98] loss=1.57 avg=1.39\n", "[4345 | 500.26] loss=1.30 avg=1.39\n", "[4346 | 501.57] loss=1.37 avg=1.39\n", "[4347 | 502.85] loss=1.39 avg=1.39\n", "[4348 | 504.13] loss=1.37 avg=1.39\n", "[4349 | 505.41] loss=1.35 avg=1.39\n", "[4350 | 506.68] loss=1.36 avg=1.38\n", "[4351 | 507.98] loss=1.34 avg=1.38\n", "[4352 | 509.27] loss=1.56 avg=1.39\n", "[4353 | 510.56] loss=1.41 avg=1.39\n", "[4354 | 511.85] loss=1.32 avg=1.39\n", "[4355 | 513.13] loss=1.39 avg=1.39\n", "[4356 | 514.41] loss=1.44 avg=1.39\n", "[4357 | 515.69] loss=1.39 avg=1.39\n", "[4358 | 516.97] loss=1.27 avg=1.39\n", "[4359 | 518.24] loss=1.77 avg=1.39\n", "[4360 | 519.54] loss=1.35 avg=1.39\n", "[4361 | 520.82] loss=1.35 avg=1.39\n", "[4362 | 522.10] loss=1.40 avg=1.39\n", "[4363 | 523.38] loss=1.37 avg=1.39\n", "[4364 | 524.65] loss=1.27 avg=1.39\n", "[4365 | 525.93] loss=1.84 avg=1.39\n", "[4366 | 527.21] loss=1.40 avg=1.39\n", "[4367 | 528.48] loss=1.41 avg=1.39\n", "[4368 | 529.78] loss=1.52 avg=1.39\n", "[4369 | 531.07] loss=1.50 avg=1.39\n", "[4370 | 532.34] loss=1.42 avg=1.39\n", "[4371 | 533.62] loss=1.35 avg=1.39\n", "[4372 | 534.94] loss=1.43 avg=1.39\n", "[4373 | 536.22] loss=1.43 avg=1.40\n", "[4374 | 537.50] loss=1.36 avg=1.39\n", "[4375 | 538.77] loss=1.43 avg=1.40\n", "[4376 | 540.05] loss=1.30 avg=1.39\n", "[4377 | 541.35] loss=1.37 avg=1.39\n", "[4378 | 542.63] loss=1.34 avg=1.39\n", "[4379 | 543.92] loss=1.35 avg=1.39\n", "[4380 | 545.21] loss=1.60 avg=1.39\n", "[4381 | 546.50] loss=1.35 avg=1.39\n", "[4382 | 547.78] loss=1.30 avg=1.39\n", "[4383 | 549.05] loss=1.30 avg=1.39\n", "[4384 | 550.33] loss=1.16 avg=1.39\n", "[4385 | 551.63] loss=1.39 avg=1.39\n", "[4386 | 552.91] loss=1.24 avg=1.39\n", "[4387 | 554.19] loss=1.55 avg=1.39\n", "[4388 | 555.46] loss=1.42 avg=1.39\n", "[4389 | 556.74] loss=1.30 avg=1.39\n", "[4390 | 558.02] loss=1.46 avg=1.39\n", "[4391 | 559.29] loss=1.40 avg=1.39\n", "[4392 | 560.57] loss=1.40 avg=1.39\n", "[4393 | 561.84] loss=1.28 avg=1.39\n", "[4394 | 563.15] loss=1.39 avg=1.39\n", "[4395 | 564.43] loss=1.32 avg=1.39\n", "[4396 | 565.70] loss=1.44 avg=1.39\n", "[4397 | 566.98] loss=1.49 avg=1.39\n", "[4398 | 568.32] loss=1.72 avg=1.39\n", "[4399 | 569.59] loss=1.31 avg=1.39\n", "[4400 | 570.87] loss=1.41 avg=1.39\n", "======== SAMPLE 1 ========\n", " VASI, LEQUEL NE DEMANDAIT PAS MOINS DE CONFIER AUX TERRAINS LES REPRESENTANTS DE SON NOM, TENDANT A CE QUE LA SOCIETE AVAIT ENTRAINE LES MODALITES D'ACCESSOIRES ; QU'AINSI LA DECISION ATTAQUEE EST LEGALEMENT JUSTIFIEE ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 28 AVRIL 1975 PAR LA COUR D'APPEL DE TOULOUSE. <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN, PRIS EN SES DEUX BRANCHES : ATTENDU QU'IL RESULTE DE L'ARRET ATTAQUE (PARIS EN LEURS DIVERSES PARCELLES DE TERRE, PAR ACTE DE DIX JOURS DES MARCHANTS DE LA SOCIETE DU GARAGE DES GARAGES ET DES MARCHANTS D'INSTRUCTIONS SUR UNE MAISON D'HANDELS, QUE, LE 23 AVRIL 1953, GUY X... A VENDU LE CONSTRUCTEUR POUR FAILLITER UNE CERTAINE CHOSE, FUT HEURTE EN VUE D'ETABLIR LE PRELIMINAIRE POUR LE COMPTE DE LA SOCIETE D'ORIENTALITE ET DE CONTROLE, COMME IL DISPOSAIT D'ABORD QUE LE PERSONNEL DESIGNAit, EN FAIT, LES TRAVAILLEURS INDEPENDANTS ; QUE VEUVE X... A ASSIGNE EN LIQUIDATION DES BIENS A LA SOCIETE DU GARAGE DES GARAGES ET DES MARCHANTS (SEGAM) ;ATTENDU QU'IL EST FAIT GRIEF AUX JUGES DU SECOND DEGRE D'AVOIR ENONCE QUE GUY X..., DE NUIT, AVAIT CONSTRUIT, AU VU DES TRAVAUX DE GAZ A LA SOCIETE D'ORIENTALITE VARIABLEMENT CONTRADICTOIRE, LE CHEQUE AVANT L'ACCIDENT, ALORS QUE LA LEGERETE BLAMABLE, EN EST INSUFFISANTE, NE PROUVE PAS ET QU'EN L'ESPECE, L'INTERVENTION TOUTEVERLAIT S'APONDERANT A CE FAIT ;PAR CES MOTIFS : REJETTE LE PREMIER MOYEN, EN CE QU'IL EST CONSTATE ;MAIS SUR LA SECONDE BRANCHE : VU L'ARTICLE 12 DU DECRET DU 9 SEPTEMBRE 1971 ;ATTENDU QUE, POUR CONSTATER L'ACCOMPLISSEMENT DE LA REPARATION DE LA VICTIME, LES JUGES DU SECOND DEGRE EN ONT DEDUIT QUE LA VICTIME NE CONTESTAIT NI DES COUPENTS ENUMERES DE SON DROIT NI DE LEURS OBLIGATIONS ; QUE, SANS AVOIR RECHERCHE SI LES CONSEQUENCES QUE L'ASSUREUR AVAIT FAIT VALOIR DANS L'OBLIGATION DE LA SEGAM D'AVOIR A FAIRE JOUER, LA COUR D'APPEL A LEGALEMENT JUSTIFIE SA DECISION ET QUE LE MOYEN NE PEUT ETRE ACCUEILLI ;PAR CES MOTIFS : REJETTE LE PREMIER MOYEN ;MAIS SUR LA SECONDE BRANCHE DU PREMIER MOYEN, EN CE QUI CONCERNE LAQUELLE L'ATTESTATION DE GUY X..., AGIR SE DEVAIT A EXECUTER DES TRAVAUX DANS L'EMPLOI IMPUTABLE A L'ACCIDENT, EN MECONNAISSANCE DE LA REGLEMENTATION PREPAREE DE L'ASSUREUR, LEQUEL SE TROUVAIT LA CONSEQUENCE DONT D'UNE MANIERE REPARE, POUR DEVELOPPEMENT DU CHEQUE, UN RANG PENABLE, DES L'INSTANT DU FONDS, SANS POUVOIR, PAR REFUS DE LA NECESSITE DE FAIRE PARTICIPER A UNE INDIVISIBILITE, LE CITROEN DES MARCHANTS\n", "\n", "[4401 | 584.42] loss=1.36 avg=1.39\n", "[4402 | 585.76] loss=1.37 avg=1.39\n", "[4403 | 587.04] loss=1.42 avg=1.39\n", "[4404 | 588.32] loss=1.54 avg=1.39\n", "[4405 | 589.60] loss=1.31 avg=1.39\n", "[4406 | 590.88] loss=1.42 avg=1.39\n", "[4407 | 592.15] loss=1.37 avg=1.39\n", "[4408 | 593.43] loss=1.38 avg=1.39\n", "[4409 | 594.71] loss=1.39 avg=1.39\n", "[4410 | 595.99] loss=1.25 avg=1.39\n", "[4411 | 597.27] loss=1.62 avg=1.39\n", "[4412 | 598.55] loss=1.33 avg=1.39\n", "[4413 | 599.84] loss=1.29 avg=1.39\n", "[4414 | 601.14] loss=1.43 avg=1.39\n", "[4415 | 602.41] loss=1.36 avg=1.39\n", "[4416 | 603.69] loss=1.33 avg=1.39\n", "[4417 | 604.97] loss=1.42 avg=1.39\n", "[4418 | 606.25] loss=1.28 avg=1.39\n", "[4419 | 607.53] loss=1.51 avg=1.39\n", "[4420 | 608.81] loss=1.52 avg=1.39\n", "[4421 | 610.09] loss=1.37 avg=1.39\n", "[4422 | 611.37] loss=1.27 avg=1.39\n", "[4423 | 612.65] loss=1.42 avg=1.39\n", "[4424 | 613.92] loss=1.29 avg=1.39\n", "[4425 | 615.20] loss=1.42 avg=1.39\n", "[4426 | 616.48] loss=1.40 avg=1.39\n", "[4427 | 617.77] loss=1.34 avg=1.39\n", "[4428 | 619.06] loss=1.36 avg=1.39\n", "[4429 | 620.35] loss=1.42 avg=1.39\n", "[4430 | 621.64] loss=1.23 avg=1.39\n", "[4431 | 622.92] loss=1.61 avg=1.39\n", "[4432 | 624.19] loss=1.47 avg=1.39\n", "[4433 | 625.47] loss=1.44 avg=1.39\n", "[4434 | 626.75] loss=1.28 avg=1.39\n", "[4435 | 628.03] loss=1.28 avg=1.39\n", "[4436 | 629.32] loss=1.31 avg=1.39\n", "[4437 | 630.60] loss=1.57 avg=1.39\n", "[4438 | 631.88] loss=1.39 avg=1.39\n", "[4439 | 633.15] loss=1.32 avg=1.39\n", "[4440 | 634.49] loss=1.48 avg=1.39\n", "[4441 | 635.78] loss=1.22 avg=1.39\n", "[4442 | 637.05] loss=1.31 avg=1.39\n", "[4443 | 638.33] loss=1.49 avg=1.39\n", "[4444 | 639.62] loss=1.33 avg=1.39\n", "[4445 | 640.91] loss=1.30 avg=1.39\n", "[4446 | 642.18] loss=1.43 avg=1.39\n", "[4447 | 643.46] loss=1.26 avg=1.39\n", "[4448 | 644.74] loss=1.35 avg=1.39\n", "[4449 | 646.01] loss=1.36 avg=1.39\n", "[4450 | 647.29] loss=1.28 avg=1.39\n", "[4451 | 648.57] loss=1.38 avg=1.39\n", "[4452 | 649.84] loss=1.39 avg=1.39\n", "[4453 | 651.14] loss=1.57 avg=1.39\n", "[4454 | 652.43] loss=1.32 avg=1.39\n", "[4455 | 653.72] loss=1.28 avg=1.39\n", "[4456 | 655.01] loss=1.31 avg=1.39\n", "[4457 | 656.29] loss=1.59 avg=1.39\n", "[4458 | 657.57] loss=1.30 avg=1.39\n", "[4459 | 658.85] loss=1.35 avg=1.39\n", "[4460 | 660.12] loss=1.20 avg=1.38\n", "[4461 | 661.41] loss=1.35 avg=1.38\n", "[4462 | 662.71] loss=1.36 avg=1.38\n", "[4463 | 663.99] loss=1.25 avg=1.38\n", "[4464 | 665.27] loss=1.52 avg=1.38\n", "[4465 | 666.57] loss=1.16 avg=1.38\n", "[4466 | 667.87] loss=1.44 avg=1.38\n", "[4467 | 669.15] loss=1.26 avg=1.38\n", "[4468 | 670.42] loss=1.32 avg=1.38\n", "[4469 | 671.70] loss=1.51 avg=1.38\n", "[4470 | 672.99] loss=1.28 avg=1.38\n", "[4471 | 674.28] loss=1.28 avg=1.38\n", "[4472 | 675.56] loss=1.38 avg=1.38\n", "[4473 | 676.84] loss=1.45 avg=1.38\n", "[4474 | 678.12] loss=1.31 avg=1.38\n", "[4475 | 679.40] loss=1.43 avg=1.38\n", "[4476 | 680.68] loss=1.50 avg=1.38\n", "[4477 | 681.96] loss=1.38 avg=1.38\n", "[4478 | 683.23] loss=1.31 avg=1.38\n", "[4479 | 684.53] loss=1.34 avg=1.38\n", "[4480 | 685.82] loss=1.43 avg=1.38\n", "[4481 | 687.11] loss=1.47 avg=1.38\n", "[4482 | 688.40] loss=1.39 avg=1.38\n", "[4483 | 689.68] loss=1.30 avg=1.38\n", "[4484 | 690.96] loss=1.41 avg=1.38\n", "[4485 | 692.23] loss=1.24 avg=1.38\n", "[4486 | 693.51] loss=1.30 avg=1.38\n", "[4487 | 694.81] loss=1.39 avg=1.38\n", "[4488 | 696.09] loss=1.22 avg=1.38\n", "[4489 | 697.36] loss=1.33 avg=1.38\n", "[4490 | 698.64] loss=1.75 avg=1.38\n", "[4491 | 699.92] loss=1.27 avg=1.38\n", "[4492 | 701.22] loss=1.28 avg=1.38\n", "[4493 | 702.50] loss=1.27 avg=1.38\n", "[4494 | 703.77] loss=1.42 avg=1.38\n", "[4495 | 705.05] loss=1.30 avg=1.38\n", "[4496 | 706.36] loss=1.37 avg=1.38\n", "[4497 | 707.63] loss=1.43 avg=1.38\n", "[4498 | 708.91] loss=1.55 avg=1.38\n", "[4499 | 710.18] loss=1.43 avg=1.38\n", "[4500 | 711.46] loss=1.21 avg=1.38\n", "======== SAMPLE 1 ========\n", "LECTIBLES DE FACTURE D'AUTRES ACTIVITES DE PROMOTION, A LA GARDE ET AUX ASSURANCES SOCIALES, LES CIRCONSTANCES DE L'ACCIDENT, L'ACCENT QUE DAME Y... N'AVAIT PU EN AVOIR ACCEPTE DE VENTES DANS SES FONCTIONS D'ACCIDENT DU TRAVAIL ; QU'UN PREMIER BRAS-ALBAGE QUI SE TROUVAIT EN ETABLISSANT LE CINQUIEME DE LAQUELLE CET ACCIDENT ETAIT DU AU TITRE DE L'ACCIDENT DU TRAVAIL NE CONSTITUAIT PAS UN ACCIDENT DU TRAVAIL, DONT ELLE ETAIT ATTEINTE PAR UN PREAVIS ; QU'INDEPENDAMMENT DE LA DECISION ATTAQUEE DE L'EVALUATION DU PREJUDICE QU'IL A CONSISTE ENTRE LES FOURCES ET LA MUTUELLE AYANT DEFINIE UNE OBLIGATION DE TEMPERATURE DANS LAQUELLE L'AUTRE SOLLICITAIT LE DELAI A L'EMPLOI ;QU'EN STATUANT AINSI, ALORS QUE LES GRIEFS INVOQUES POUR LES AUTRES ACTIVITES NE DISPENSENT QUE DE LEUR FAUTE UNE ERREUR MATERIELLE ET NON UNE ERREUR MATERIELLE, D'OU IL SUIT QUE LE PREJUDICE SUBI PAR LE REFUS DE LA MUTUELLE DE L'OBLIGER A FAIRE ETAT D'UNE CLAUSE DE NON REFERENCE N'A PAS RESSORTI DES DISPOSITIONS DE LA LEGISLATION SUR LES ACCIDENTS DU TRAVAIL, LES JUGES D'APPEL ONT VIOLE LE TEXTE SUSVISE ;ET SUR LE SECOND MOYEN : VU L'ARTICLE 15 DE LA LOI N° 63-112 DU 29 JUILLET 1963 ;ATTENDU QU'AUX TERMES DE CE TEXTE, RENDU EN MATIERE D'ACCIDENT DU TRAVAIL, L'EMPLOYEUR NE PEUT PAS ETRE REPAREE QU'ENVERS LES CAUSES LESQUELLES ETAIENT DECHARGES A ACCIDENT DU TRAVAIL OU A CONSCIENCE DE TOUTE INCAPACITE PERMANENTE PARTIELLE ET QUE L'ALIENATION EST NECESSAIRE POUR LA GARDE ;ATTENDU QUE C'EST PAR UNE APPRECIATION SOUVERAINE DES ELEMENTS DE PREUVE DES ACCIDENTS DU TRAVAIL QUI LUI ETAIENT INVITES, QUI NE PEUVENT ETRE RECHERCHES EN CONSIDERANT UNE VIOLATION DES TEXTES EN VIGUEUR, QU'A DEFAUT D'ACCAPTANCE DES ACCIDENTS DU TRAVAIL AU TITRE DE L'ACCIDENT DU TRAVAIL, LES AUTRES ACTIVITES DE PROMOTION, D'UNE MANIERE EXCLUSIVE ET NON D'EXERCER UN DROIT D'EXERCICE DU DROIT DE PREEMINENCE, LESQUELS NE SONT PAS FOURNIES EN PREEMINANT ; QUE LES JUGES D'APPEL ONT FAIT DROIT A LA DEMANDE DU TIERS RESPONSABLE, AU MOTIF QUE LES DEFAUTS DE NOUVEILLANCE DE REPRESENTATION POUVAIENT ETRE OBTENUS EN PREUVE PAR L'EXPERT Y... SUR LES EFFETS DE NOUVELLES TRAVAIL ; QU'ILS N'ONT PAS AINSI EXACTE DES ACCIDENTS DU TRAVAIL A LA VICTIME DES ECHECS QUE SOIENT PRISES LORS DE LA MALADIE ;ATTENDU CEPENDANT QU'IL RESULTAIT DES DOCUMENTS VERSES AUX DEBATS, NOTAMMENT D'APPLIQUER LEUR ECHEC, QUE, POUR L'ACCIDENT SURVENU A VEUVE Z..., CHACUN FUT HEURTE, DANS UN ACCIDENT DU TRAVAIL, AU NIVEAU D'UN TERRAIN, LE FAISAUX D'INTERROMPRE CELUI-CI QUI NE CON\n", "\n", "[4501 | 724.94] loss=1.33 avg=1.38\n", "[4502 | 726.23] loss=1.29 avg=1.38\n", "[4503 | 727.53] loss=1.38 avg=1.38\n", "[4504 | 728.81] loss=1.59 avg=1.38\n", "[4505 | 730.09] loss=1.39 avg=1.38\n", "[4506 | 731.38] loss=1.41 avg=1.38\n", "[4507 | 732.68] loss=1.34 avg=1.38\n", "[4508 | 733.97] loss=1.37 avg=1.38\n", "[4509 | 735.25] loss=1.24 avg=1.38\n", "[4510 | 736.53] loss=1.44 avg=1.38\n", "[4511 | 737.81] loss=1.25 avg=1.38\n", "[4512 | 739.11] loss=1.37 avg=1.38\n", "[4513 | 740.38] loss=1.51 avg=1.38\n", "[4514 | 741.66] loss=1.40 avg=1.38\n", "[4515 | 742.94] loss=1.30 avg=1.38\n", "[4516 | 744.21] loss=1.36 avg=1.38\n", "[4517 | 745.49] loss=1.56 avg=1.38\n", "[4518 | 746.77] loss=1.27 avg=1.38\n", "[4519 | 748.05] loss=1.36 avg=1.38\n", "[4520 | 749.32] loss=1.36 avg=1.38\n", "[4521 | 750.64] loss=1.30 avg=1.38\n", "[4522 | 751.92] loss=1.30 avg=1.38\n", "[4523 | 753.20] loss=1.32 avg=1.38\n", "[4524 | 754.47] loss=1.32 avg=1.38\n", "[4525 | 755.75] loss=1.44 avg=1.38\n", "[4526 | 757.03] loss=1.37 avg=1.38\n", "[4527 | 758.32] loss=1.32 avg=1.38\n", "[4528 | 759.61] loss=1.25 avg=1.37\n", "[4529 | 760.92] loss=1.46 avg=1.37\n", "[4530 | 762.20] loss=1.33 avg=1.37\n", "[4531 | 763.48] loss=1.24 avg=1.37\n", "[4532 | 764.75] loss=1.24 avg=1.37\n", "[4533 | 766.04] loss=1.42 avg=1.37\n", "[4534 | 767.33] loss=1.39 avg=1.37\n", "[4535 | 768.61] loss=1.23 avg=1.37\n", "[4536 | 769.88] loss=1.38 avg=1.37\n", "[4537 | 771.16] loss=1.38 avg=1.37\n", "[4538 | 772.46] loss=1.39 avg=1.37\n", "[4539 | 773.74] loss=1.35 avg=1.37\n", "[4540 | 775.02] loss=1.31 avg=1.37\n", "[4541 | 776.29] loss=1.25 avg=1.37\n", "[4542 | 777.57] loss=1.29 avg=1.37\n", "[4543 | 778.85] loss=1.22 avg=1.37\n", "[4544 | 780.12] loss=1.33 avg=1.37\n", "[4545 | 781.40] loss=1.41 avg=1.37\n", "[4546 | 782.69] loss=1.37 avg=1.37\n", "[4547 | 783.97] loss=1.34 avg=1.37\n", "[4548 | 785.25] loss=1.34 avg=1.37\n", "[4549 | 786.52] loss=1.34 avg=1.37\n", "[4550 | 787.80] loss=1.42 avg=1.37\n", "[4551 | 789.08] loss=1.26 avg=1.37\n", "[4552 | 790.35] loss=1.22 avg=1.36\n", "[4553 | 791.64] loss=1.42 avg=1.36\n", "[4554 | 792.93] loss=1.31 avg=1.36\n", "[4555 | 794.29] loss=1.53 avg=1.37\n", "[4556 | 795.57] loss=1.40 avg=1.37\n", "[4557 | 796.85] loss=1.53 avg=1.37\n", "[4558 | 798.15] loss=1.40 avg=1.37\n", "[4559 | 799.44] loss=1.28 avg=1.37\n", "[4560 | 800.72] loss=1.30 avg=1.37\n", "[4561 | 801.99] loss=1.44 avg=1.37\n", "[4562 | 803.27] loss=1.27 avg=1.37\n", "[4563 | 804.56] loss=1.33 avg=1.37\n", "[4564 | 805.84] loss=1.50 avg=1.37\n", "[4565 | 807.12] loss=1.30 avg=1.37\n", "[4566 | 808.40] loss=1.11 avg=1.36\n", "[4567 | 809.67] loss=1.41 avg=1.36\n", "[4568 | 810.95] loss=1.32 avg=1.36\n", "[4569 | 812.23] loss=1.46 avg=1.36\n", "[4570 | 813.50] loss=1.38 avg=1.37\n", "[4571 | 814.79] loss=1.11 avg=1.36\n", "[4572 | 816.08] loss=1.26 avg=1.36\n", "[4573 | 817.36] loss=1.43 avg=1.36\n", "[4574 | 818.63] loss=1.27 avg=1.36\n", "[4575 | 819.91] loss=1.25 avg=1.36\n", "[4576 | 821.19] loss=1.21 avg=1.36\n", "[4577 | 822.46] loss=1.41 avg=1.36\n", "[4578 | 823.74] loss=1.74 avg=1.36\n", "[4579 | 825.02] loss=1.39 avg=1.36\n", "[4580 | 826.31] loss=1.45 avg=1.36\n", "[4581 | 827.67] loss=1.25 avg=1.36\n", "[4582 | 828.96] loss=1.22 avg=1.36\n", "[4583 | 830.24] loss=1.35 avg=1.36\n", "[4584 | 831.55] loss=1.27 avg=1.36\n", "[4585 | 832.83] loss=1.36 avg=1.36\n", "[4586 | 834.10] loss=1.42 avg=1.36\n", "[4587 | 835.38] loss=1.42 avg=1.36\n", "[4588 | 836.66] loss=1.36 avg=1.36\n", "[4589 | 837.95] loss=1.34 avg=1.36\n", "[4590 | 839.23] loss=1.11 avg=1.36\n", "[4591 | 840.51] loss=1.37 avg=1.36\n", "[4592 | 841.78] loss=1.39 avg=1.36\n", "[4593 | 843.06] loss=1.53 avg=1.36\n", "[4594 | 844.34] loss=1.36 avg=1.36\n", "[4595 | 845.62] loss=1.31 avg=1.36\n", "[4596 | 846.90] loss=1.12 avg=1.36\n", "[4597 | 848.18] loss=1.32 avg=1.36\n", "[4598 | 849.47] loss=1.43 avg=1.36\n", "[4599 | 850.75] loss=1.36 avg=1.36\n", "[4600 | 852.03] loss=1.27 avg=1.36\n", "======== SAMPLE 1 ========\n", " ; Mais attendu que la Cour d'appel a retenu que la Cour d'appel ait Sur le moyen unique, : Que les jugutes rendu le 15 décembre 1972, la Cour d'appel pour avoir déjàître motifée et reproché la Cour d'appel aux conclusions de l'arrêt propre et que le jugement attaqué est justifiant aux textes susvisées ; PAR CES MOTIFS : CASSE ET ANNULE le jugement rendu entre les parties le 16 juillet 1976 par le tribunal de l'arrêt rendu en remboursement de la Chambre du Conseil ; REMET, EN CONSEQUENCE, LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE PAR VOIE DE SUIVRE LE GRIEF ET LE PREMIER MOYEN ; PAR CES MOTIFS : Casse et annule le jugement rendu entre les parties le 13 juillet 1977 par le Tribunal de l'arrêt d'avoir exerçaise L'intéressé par l'arrêt d'un décision de remboursement au décidant ; remet, en conséquence, la cause et les parties au même et semblable état où elles étaient avant ledit arrêter; PAR CES MOTIFS : Par ces motifs : Régimont et avocet et du moyen. <|endoftext|>\n", "<|startoftext|> Sur le premier moyen, pour signifiant les droits et documents de réprérence, des première décision qui leur était conformé : Vu les articles 609 et 618 du Code de procédure civile ; Attendu que les juges du fond devait conclu que D'après que le dôt d'une simple profession pararteniaillier n'a pas été tenue de la profession alors que le dôt qu'il a été tenue de la profession alors que le service de la chef que le médecinétique est tenue de celle-ci dans la procédure ; que les service d'ans la suite en ce que le médecinétique est réservé qu'aucune simple profession pararteniaillier n'a pas été tenue de la profession alors que le dôt s'est préalablement cause cependant de la faculté qui a été tenue des professiones au décidant et qu'elles n'autorises en s'étant tenue à ceux, et qu'une simple profession pararteniaillier sepos sans égard, au motif que le dôt de mandir et qu'elle est président de un dôt de mandir ; que le médecinétique est réservé qu'aucun simple profession ou par elles de dâtre quelle est profession entre les juges du fond et les salariés qui leur avaient été tenues des professiones au décidant ; Que si des droits et documents de réprésent, elle a pu obtenir ainsi aux conclusions de ses instructions et l'absence de la décurie, pour le prix visante ; PAR ce quotidou et dénatur à ceux qui dans leur term visé, le même fait excluée ; Attendu, en l'état de cet article 609\n", "\n", "[4601 | 866.18] loss=1.41 avg=1.36\n", "[4602 | 867.47] loss=1.31 avg=1.36\n", "[4603 | 868.77] loss=1.23 avg=1.36\n", "[4604 | 870.04] loss=1.21 avg=1.35\n", "[4605 | 871.34] loss=1.36 avg=1.35\n", "[4606 | 872.61] loss=1.36 avg=1.35\n", "[4607 | 873.89] loss=1.33 avg=1.35\n", "[4608 | 875.17] loss=1.27 avg=1.35\n", "[4609 | 876.45] loss=1.37 avg=1.35\n", "[4610 | 877.72] loss=1.42 avg=1.35\n", "[4611 | 879.00] loss=1.34 avg=1.35\n", "[4612 | 880.27] loss=1.26 avg=1.35\n", "[4613 | 881.55] loss=1.33 avg=1.35\n", "[4614 | 882.85] loss=1.37 avg=1.35\n", "[4615 | 884.12] loss=1.19 avg=1.35\n", "[4616 | 885.40] loss=1.21 avg=1.35\n", "[4617 | 886.67] loss=1.30 avg=1.35\n", "[4618 | 887.95] loss=1.32 avg=1.35\n", "[4619 | 889.22] loss=1.31 avg=1.35\n", "[4620 | 890.50] loss=1.20 avg=1.35\n", "[4621 | 891.77] loss=1.17 avg=1.35\n", "[4622 | 893.07] loss=1.51 avg=1.35\n", "[4623 | 894.35] loss=1.19 avg=1.35\n", "[4624 | 895.65] loss=1.29 avg=1.35\n", "[4625 | 896.93] loss=1.39 avg=1.35\n", "[4626 | 898.21] loss=1.43 avg=1.35\n", "[4627 | 899.48] loss=1.53 avg=1.35\n", "[4628 | 900.77] loss=1.44 avg=1.35\n", "[4629 | 902.06] loss=1.36 avg=1.35\n", "[4630 | 903.35] loss=1.30 avg=1.35\n", "[4631 | 904.66] loss=1.32 avg=1.35\n", "[4632 | 905.93] loss=1.27 avg=1.35\n", "[4633 | 907.21] loss=1.44 avg=1.35\n", "[4634 | 908.49] loss=1.36 avg=1.35\n", "[4635 | 909.76] loss=1.26 avg=1.35\n", "[4636 | 911.04] loss=1.35 avg=1.35\n", "[4637 | 912.32] loss=1.43 avg=1.35\n", "[4638 | 913.59] loss=1.30 avg=1.35\n", "[4639 | 914.89] loss=1.26 avg=1.35\n", "[4640 | 916.17] loss=1.35 avg=1.35\n", "[4641 | 917.44] loss=1.28 avg=1.35\n", "[4642 | 918.72] loss=1.18 avg=1.35\n", "[4643 | 919.99] loss=1.61 avg=1.35\n", "[4644 | 921.27] loss=1.53 avg=1.35\n", "[4645 | 922.55] loss=1.21 avg=1.35\n", "[4646 | 923.82] loss=1.18 avg=1.35\n", "[4647 | 925.10] loss=1.37 avg=1.35\n", "[4648 | 926.40] loss=1.35 avg=1.35\n", "[4649 | 927.68] loss=1.43 avg=1.35\n", "[4650 | 929.01] loss=1.41 avg=1.35\n", "[4651 | 930.28] loss=1.31 avg=1.35\n", "[4652 | 931.56] loss=1.14 avg=1.35\n", "[4653 | 932.84] loss=1.47 avg=1.35\n", "[4654 | 934.12] loss=1.39 avg=1.35\n", "[4655 | 935.42] loss=1.48 avg=1.35\n", "[4656 | 936.72] loss=1.29 avg=1.35\n", "[4657 | 938.01] loss=1.31 avg=1.35\n", "[4658 | 939.29] loss=1.27 avg=1.35\n", "[4659 | 940.56] loss=1.43 avg=1.35\n", "[4660 | 941.84] loss=1.18 avg=1.35\n", "[4661 | 943.12] loss=1.39 avg=1.35\n", "[4662 | 944.39] loss=1.29 avg=1.35\n", "[4663 | 945.67] loss=1.27 avg=1.35\n", "[4664 | 946.95] loss=1.22 avg=1.34\n", "[4665 | 948.25] loss=1.33 avg=1.34\n", "[4666 | 949.52] loss=1.32 avg=1.34\n", "[4667 | 950.80] loss=1.26 avg=1.34\n", "[4668 | 952.07] loss=1.21 avg=1.34\n", "[4669 | 953.35] loss=1.36 avg=1.34\n", "[4670 | 954.63] loss=1.33 avg=1.34\n", "[4671 | 955.90] loss=1.37 avg=1.34\n", "[4672 | 957.18] loss=1.40 avg=1.34\n", "[4673 | 958.47] loss=1.26 avg=1.34\n", "[4674 | 959.76] loss=1.26 avg=1.34\n", "[4675 | 961.06] loss=1.45 avg=1.34\n", "[4676 | 962.35] loss=1.28 avg=1.34\n", "[4677 | 963.63] loss=1.47 avg=1.34\n", "[4678 | 964.91] loss=1.39 avg=1.34\n", "[4679 | 966.18] loss=1.31 avg=1.34\n", "[4680 | 967.47] loss=1.24 avg=1.34\n", "[4681 | 968.77] loss=1.44 avg=1.34\n", "[4682 | 970.11] loss=1.38 avg=1.34\n", "[4683 | 971.39] loss=1.28 avg=1.34\n", "[4684 | 972.67] loss=1.29 avg=1.34\n", "[4685 | 973.95] loss=1.16 avg=1.34\n", "[4686 | 975.23] loss=1.32 avg=1.34\n", "[4687 | 976.50] loss=1.36 avg=1.34\n", "[4688 | 977.78] loss=1.31 avg=1.34\n", "[4689 | 979.06] loss=1.43 avg=1.34\n", "[4690 | 980.34] loss=1.24 avg=1.34\n", "[4691 | 981.65] loss=1.43 avg=1.34\n", "[4692 | 982.93] loss=1.40 avg=1.34\n", "[4693 | 984.21] loss=1.48 avg=1.34\n", "[4694 | 985.49] loss=1.67 avg=1.35\n", "[4695 | 986.77] loss=1.29 avg=1.35\n", "[4696 | 988.05] loss=1.19 avg=1.34\n", "[4697 | 989.32] loss=1.26 avg=1.34\n", "[4698 | 990.60] loss=1.40 avg=1.34\n", "[4699 | 991.90] loss=1.50 avg=1.35\n", "[4700 | 993.18] loss=1.34 avg=1.35\n", "======== SAMPLE 1 ========\n", " NE ENTEND DE L'ACTIF LE 15 FEVRIER 1970, ECRASIER AVAIT DONNE SA MAIN-LEVEE \"D'UNE FACON NEGOCIABLE DONNE AU DEMOISELLE X... POUR LA SACIEN A UNE SITUATION PORTEE NON AVENUE\" ; QU'IL ETAIT CONSTATE QUE CETTE SACIEN AVAIT ETE NEGOCIABLE AVEC LA LETTRE RECOMMANDEE DANS LAQUELLE ELLE AVAIT ETE ADRESSEE LE 29 FEVRIER 1970 ;QU'ILS ONT PU ESTIMER QUE CES PRETENDUES PRETENDUES, NON FONDES PAR LES TROIS PREMIERS JUGES EN DERNIER LIEU, N'ETAIENT PAS LE CARACTERE PRECISE DU PRETE-NOMME DU MARI, UNE LETTRE DU 21 NOVEMBRE 1970, N'AYANT PAS ETE ADRESSEE LE 15 FEVRIER 1970 AU COURS D'UNE PROCEDURE QUI NE FIGURAIT NULLEMENT AU DEPOT DU CERTIFICAT DE TRAVAIL ; QUE LA COUR D'APPEL A REFUSE DE FAIRE APPLICATION DE CETTE LETTRE PAR L'EXPERT ;QUE LE MOYEN N'EST DONC PAS FONDE ;ET ATTENDU, D'AUTRE PART, QU'IL RESSORT DES CONCLUSIONS PAR LESQUELLES LA CANTINE RECONNAISSAIT QU'AUCUNE SACIENNE, MEMBRANE, AVAIT ETE REGULIERE DE LA RUE, LAQUELLE AVAIT ETE \"EN REALITE NULLE\" ET S'ETAIT REJOINTE SUR LE TERME QU'IL N'ATTEINT AUCUN ACCORD D'EXPERTISE, QUI NE PERMETTAIT SEULEMENT EXPRESSEMENT A CETTE SACIENNE UNE MAIN-LEVEE DE SA CREANCE AINSI QUE LE SOUTENAIT LA SOCIETE POUR ROCECARD ; ENFIN QU'IL ETAIT INEXACT DE DIRE QUE FAUTE PAR TOLERE LA MISE EN LIQUIDATION D'UN ETAT FRAPPANT L'ENTREE EN VIGUEUR DU PRETE-NOMME ET DU DEPOT DU CERTIFICAT DE TRAVAIL ET QUE CETTE EXPRESSION N'ETAIT PAS CONTRAIRE A S'APPUYER DES CONCLUSIONS QUI AURAIENT ETE LAISSEES SANS REPONSE ; D'OU IL SUIT QUE, NI LES JUGES DU FOND, NI DES LORS QUE LEDIT PRETE-NOMME FERAIT RALLES L'AVAL A UNE SECONDE D'AUTRIX A UN DELAI POUR FAUTE GRAVE ; ET ENFIN AUCUN PREJUDICE N'EST INTERVENU ETAIT NUL ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 9 JUIN 1975 PAR LA COUR D'APPEL DE GRENOBLE. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : VU L'ARTICLE 1144 DU CODE CIVIL ;ATTENDU QUE L'ARRET ATTAQUE, APRES AVOIR CONSTATE QUE LE MINEUR LOUISE X..., AGISSANT TANT EN QUALITE DE VENDRE HUITIERS, AVAIT ETE DECLARE RESPONSABLE, AU COURS D'UN ENFANT, DE MOIX ET DE DIX ANS, D'UN DANGER NOMINAL ET ENSUITE, AU PRET DES TRAVAUX DE REELLEXECX, A TITRE BENEVOLEMENT FIXEE PAR PROPRE ACHAT AVEC HUIT JOURS DANS LE MOIS D'ACHEVEMENT ET S'EST PRODUITE COMME L'EMPRISE ET QUE CELUI-CI NE VENAIT DONC PAS DE DECIDER ET DE REJETER LES CONTESTATIONS PAR ELLE OPPOSABLES, A DECLARE QUE CE DERNIER N'AVAIT PAS L'INTENTION DE TOUS SES FILLES DANS L'ENTRETIEN COMMUN, DANS CE\n", "\n", "[4701 | 1007.12] loss=1.27 avg=1.34\n", "[4702 | 1008.41] loss=1.44 avg=1.35\n", "[4703 | 1009.70] loss=1.43 avg=1.35\n", "[4704 | 1010.97] loss=1.39 avg=1.35\n", "[4705 | 1012.25] loss=1.18 avg=1.35\n", "[4706 | 1013.53] loss=1.30 avg=1.34\n", "[4707 | 1014.82] loss=1.36 avg=1.34\n", "[4708 | 1016.09] loss=1.50 avg=1.35\n", "[4709 | 1017.37] loss=1.46 avg=1.35\n", "[4710 | 1018.65] loss=1.33 avg=1.35\n", "[4711 | 1019.92] loss=1.21 avg=1.35\n", "[4712 | 1021.20] loss=1.33 avg=1.35\n", "[4713 | 1022.48] loss=1.45 avg=1.35\n", "[4714 | 1023.76] loss=1.35 avg=1.35\n", "[4715 | 1025.05] loss=1.31 avg=1.35\n", "[4716 | 1026.32] loss=1.26 avg=1.35\n", "[4717 | 1027.63] loss=1.24 avg=1.34\n", "[4718 | 1028.92] loss=1.33 avg=1.34\n", "[4719 | 1030.19] loss=1.40 avg=1.34\n", "[4720 | 1031.47] loss=1.50 avg=1.35\n", "[4721 | 1032.74] loss=1.34 avg=1.35\n", "[4722 | 1034.02] loss=1.41 avg=1.35\n", "[4723 | 1035.30] loss=1.41 avg=1.35\n", "[4724 | 1036.59] loss=1.37 avg=1.35\n", "[4725 | 1037.87] loss=1.31 avg=1.35\n", "[4726 | 1039.15] loss=1.33 avg=1.35\n", "[4727 | 1040.43] loss=1.38 avg=1.35\n", "[4728 | 1041.72] loss=1.50 avg=1.35\n", "[4729 | 1043.01] loss=1.23 avg=1.35\n", "[4730 | 1044.29] loss=1.32 avg=1.35\n", "[4731 | 1045.57] loss=1.37 avg=1.35\n", "[4732 | 1046.88] loss=1.55 avg=1.35\n", "[4733 | 1048.16] loss=1.62 avg=1.35\n", "[4734 | 1049.44] loss=1.29 avg=1.35\n", "[4735 | 1050.71] loss=1.34 avg=1.35\n", "[4736 | 1051.99] loss=1.20 avg=1.35\n", "[4737 | 1053.27] loss=1.37 avg=1.35\n", "[4738 | 1054.55] loss=1.30 avg=1.35\n", "[4739 | 1055.83] loss=1.43 avg=1.35\n", "[4740 | 1057.10] loss=1.36 avg=1.35\n", "[4741 | 1058.41] loss=1.33 avg=1.35\n", "[4742 | 1059.69] loss=1.25 avg=1.35\n", "[4743 | 1061.00] loss=1.30 avg=1.35\n", "[4744 | 1062.28] loss=1.37 avg=1.35\n", "[4745 | 1063.56] loss=1.27 avg=1.35\n", "[4746 | 1064.83] loss=1.27 avg=1.35\n", "[4747 | 1066.11] loss=1.37 avg=1.35\n", "[4748 | 1067.39] loss=1.42 avg=1.35\n", "[4749 | 1068.69] loss=1.29 avg=1.35\n", "[4750 | 1069.97] loss=1.40 avg=1.35\n", "[4751 | 1071.25] loss=1.42 avg=1.35\n", "[4752 | 1072.53] loss=1.27 avg=1.35\n", "[4753 | 1073.82] loss=1.35 avg=1.35\n", "[4754 | 1075.12] loss=1.46 avg=1.35\n", "[4755 | 1076.41] loss=1.30 avg=1.35\n", "[4756 | 1077.69] loss=1.36 avg=1.35\n", "[4757 | 1078.98] loss=1.34 avg=1.35\n", "[4758 | 1080.27] loss=1.32 avg=1.35\n", "[4759 | 1081.55] loss=1.24 avg=1.35\n", "[4760 | 1082.82] loss=1.31 avg=1.35\n", "[4761 | 1084.10] loss=1.35 avg=1.35\n", "[4762 | 1085.38] loss=1.36 avg=1.35\n", "[4763 | 1086.65] loss=1.28 avg=1.35\n", "[4764 | 1087.93] loss=1.67 avg=1.35\n", "[4765 | 1089.20] loss=1.33 avg=1.35\n", "[4766 | 1090.49] loss=1.41 avg=1.35\n", "[4767 | 1091.77] loss=1.40 avg=1.35\n", "[4768 | 1093.05] loss=1.39 avg=1.35\n", "[4769 | 1094.35] loss=1.45 avg=1.35\n", "[4770 | 1095.62] loss=1.34 avg=1.35\n", "[4771 | 1096.90] loss=1.51 avg=1.35\n", "[4772 | 1098.18] loss=1.29 avg=1.35\n", "[4773 | 1099.45] loss=1.28 avg=1.35\n", "[4774 | 1100.73] loss=1.32 avg=1.35\n", "[4775 | 1102.02] loss=1.29 avg=1.35\n", "[4776 | 1103.29] loss=1.39 avg=1.35\n", "[4777 | 1104.57] loss=1.34 avg=1.35\n", "[4778 | 1105.85] loss=1.35 avg=1.35\n", "[4779 | 1107.13] loss=1.45 avg=1.35\n", "[4780 | 1108.43] loss=1.31 avg=1.35\n", "[4781 | 1109.71] loss=1.41 avg=1.35\n", "[4782 | 1110.99] loss=1.33 avg=1.35\n", "[4783 | 1112.26] loss=1.38 avg=1.35\n", "[4784 | 1113.55] loss=1.46 avg=1.35\n", "[4785 | 1114.84] loss=1.30 avg=1.35\n", "[4786 | 1116.11] loss=1.41 avg=1.35\n", "[4787 | 1117.39] loss=1.44 avg=1.35\n", "[4788 | 1118.67] loss=1.37 avg=1.36\n", "[4789 | 1119.94] loss=1.28 avg=1.35\n", "[4790 | 1121.22] loss=1.37 avg=1.35\n", "[4791 | 1122.49] loss=1.24 avg=1.35\n", "[4792 | 1123.78] loss=1.35 avg=1.35\n", "[4793 | 1125.06] loss=1.55 avg=1.36\n", "[4794 | 1126.36] loss=1.22 avg=1.35\n", "[4795 | 1127.65] loss=1.25 avg=1.35\n", "[4796 | 1128.92] loss=1.24 avg=1.35\n", "[4797 | 1130.20] loss=1.41 avg=1.35\n", "[4798 | 1131.47] loss=1.24 avg=1.35\n", "[4799 | 1132.75] loss=1.57 avg=1.35\n", "[4800 | 1134.03] loss=1.40 avg=1.35\n", "======== SAMPLE 1 ========\n", " PAR D'AUTRES, QUI, EN DERNIER LIEU QU'ELLE AVAIT FAIT LA RECONNAISSANCE D'UNE DEMANDE INITIALE DE VENDRE DIVERS BIENS, SELON DES PRESOMPTIONS DONT EST NOUVELLES, N'AVAIENT DONNE AUCUNE DEMANDE, LA COUR D'APPEL N'A PAS DONNE DE BASE LEGALE A SA DECISION ;PAR CES MOTIFS, ET SANS QU'IL Y AIT LIEU DE STATUER SUR LE SECOND MOYEN : CASSE ET ANNULE L'ARRET RENDU ENTRE LES PARTIES LE 26 JUIN 1973 PAR LA COUR D'APPEL DE PARIS ; REMET, EN CONSEQUENCE, LA CAUSE ET LES PARTIES AU MEME ET SEMBLABLE ETAT OU ELLES ETAIENT AVANT LEDIT ARRET ET, POUR ETRE FAIT DROIT, LES RENVOIE DEVANT LA COUR D'APPEL D'ORLEANS. <|endoftext|>\n", "<|startoftext|> ATTENDU QUE LES CONSORTS X..., VICTIMES D'UN ACCIDENT DE LA CIRCULATION DONT A... AYANT ETE NOTIFIE LE 4 JUIN 1975 SANS AUCUN RECOURS DE GRANDE PART, Y..., A, LE 24 JUILLET 1975, QUITTE A... LE 1ER AOUT 1975, DEVENU EPOUSE D..., D'UNE PART ET DE LEUR AUTEUR, LEUR EPOUSE M... ; QUE, LE 9 MARS 1972, A... A ASSIGNE LA FEDERATION NATIONALE DES CONFEDERATIONS DE SAINT-MARIES-DE-FRANCE POUR LA PERIODE LEGALE DE LA CONCEPTION D'UN ENSEMBLE DE BIENS ACQUETS PAR FEDERATIONS DE LA BANQUE ET POUR LEURS BIENS ; QUE PAR L'ARRET ATTAQUE ONT DECLARE CETTE ACTION IRRECEVABLE ;ATTENDU QUE, SELON LES ENONCIATIONS DE L'ARRET, LES CONSORTS Z... AYANT ASSIGNE EN RESTITUTION DE FAUX ACQUETS CONTRE SON EPOUSE MARIE E..., EN VUE D'EXECUTER LA PRATIS DE LA NULLITE, LA COUR D'APPEL DE NOUMEA N'A JAMAIS ADMIS LE CARACTERE NON PERTINENCE DE LA MESENTIONS MENTIONNEES A LA NOTIFICATION DE LA MESENTION D'APPEL LE 16 JUIN PRECEDENT ; QU'EN SE DECLARANT DE RECHERCHER SI LA MESENTION D'UN APPEL POTESTATIVE DOIT ETRE ADMISE EN L'ESPECE, LA COUR D'APPEL NE POUVAIT ETRE ATTRIBUEE PAR APPLICATION DE L'ARTICLE 489 DU CODE DE LA NATIONALITE SERIE ; QUE CE TEXTE NE S'EXCLUT PLUS POSSIBLE A LA MESENTION D'APPEL QUE S'IL S'EST FAIT PAR LA DIRECTION GENERALE DE LA SITUATION DE LA MESENTION INVOQUEE SUR LA BASE DU JUGEMENT A DECIDE QU'EN L'ESPECE, A... AVAIT FAIT CONNAITRE, LUI-MEME, A L'EXISTENCE D'UN FAUX D'APPORT ET DE TOUTE POSSIBILITE DE SON MESENTION D'APPEL, ET QU'IL LES DEMONTRAIENT QUE LE DOSSIER ETAIT A L'ISSUE DU LITIGE ET QUE LES CONSEQUENCES PECUNIAIRES AVAIENT ETE FAITES AU COURS D'UN ACTE DANS LES BUREAUX ETRANGERS ;ATTENDU QU'IL EST FAIT GRIEF A L'ARRET D'AVOIR DIT QU'AVANT DE CONDAMNER A... A PAYER LE MONTANT DE CES TRAVAUX AUSSI, AUPRES DE LA FEDERATION NATIONALE UNE PENSION ALIMENTAIRE A COMPTER DU 1ER AOUT ET QUE LES DEUX PREMIERS, AINSI QUE LEUR EPOUSCRIT, AVAIENT DU FAIRE A L'ORIGINE\n", "\n", "[4801 | 1147.67] loss=1.35 avg=1.35\n", "[4802 | 1148.96] loss=1.29 avg=1.35\n", "[4803 | 1150.25] loss=1.31 avg=1.35\n", "[4804 | 1151.53] loss=1.28 avg=1.35\n", "[4805 | 1152.80] loss=1.34 avg=1.35\n", "[4806 | 1154.08] loss=1.38 avg=1.35\n", "[4807 | 1155.36] loss=1.27 avg=1.35\n", "[4808 | 1156.65] loss=1.36 avg=1.35\n", "[4809 | 1157.94] loss=1.28 avg=1.35\n", "[4810 | 1159.21] loss=1.25 avg=1.35\n", "[4811 | 1160.49] loss=1.43 avg=1.35\n", "[4812 | 1161.80] loss=1.25 avg=1.35\n", "[4813 | 1163.07] loss=1.46 avg=1.35\n", "[4814 | 1164.35] loss=1.40 avg=1.35\n", "[4815 | 1165.63] loss=1.32 avg=1.35\n", "[4816 | 1166.90] loss=1.31 avg=1.35\n", "[4817 | 1168.20] loss=1.27 avg=1.35\n", "[4818 | 1169.47] loss=1.24 avg=1.35\n", "[4819 | 1170.75] loss=1.40 avg=1.35\n", "[4820 | 1172.03] loss=1.41 avg=1.35\n", "[4821 | 1173.30] loss=1.37 avg=1.35\n", "[4822 | 1174.59] loss=1.34 avg=1.35\n", "[4823 | 1175.87] loss=1.51 avg=1.35\n", "[4824 | 1177.14] loss=1.20 avg=1.35\n", "[4825 | 1178.42] loss=1.42 avg=1.35\n", "[4826 | 1179.71] loss=1.20 avg=1.35\n", "[4827 | 1181.00] loss=1.53 avg=1.35\n", "[4828 | 1182.29] loss=1.13 avg=1.35\n", "[4829 | 1183.57] loss=1.28 avg=1.35\n", "[4830 | 1184.85] loss=1.32 avg=1.35\n", "[4831 | 1186.13] loss=1.50 avg=1.35\n", "[4832 | 1187.40] loss=1.43 avg=1.35\n", "[4833 | 1188.68] loss=1.36 avg=1.35\n", "[4834 | 1189.97] loss=1.32 avg=1.35\n", "[4835 | 1191.25] loss=1.32 avg=1.35\n", "[4836 | 1192.52] loss=1.32 avg=1.35\n", "[4837 | 1193.82] loss=1.40 avg=1.35\n", "[4838 | 1195.11] loss=1.39 avg=1.35\n", "[4839 | 1196.38] loss=1.29 avg=1.35\n", "[4840 | 1197.66] loss=1.33 avg=1.35\n", "[4841 | 1198.94] loss=1.32 avg=1.35\n", "[4842 | 1200.21] loss=1.55 avg=1.35\n", "[4843 | 1201.51] loss=1.48 avg=1.35\n", "[4844 | 1202.78] loss=1.48 avg=1.35\n", "[4845 | 1204.06] loss=1.36 avg=1.35\n", "[4846 | 1205.34] loss=1.37 avg=1.35\n", "[4847 | 1206.61] loss=1.33 avg=1.35\n", "[4848 | 1207.89] loss=1.29 avg=1.35\n", "[4849 | 1209.17] loss=1.66 avg=1.36\n", "[4850 | 1210.44] loss=1.28 avg=1.35\n", "[4851 | 1211.73] loss=1.35 avg=1.35\n", "[4852 | 1213.01] loss=1.50 avg=1.36\n", "[4853 | 1214.29] loss=1.34 avg=1.36\n", "[4854 | 1215.59] loss=1.28 avg=1.36\n", "[4855 | 1216.89] loss=1.39 avg=1.36\n", "[4856 | 1218.18] loss=1.40 avg=1.36\n", "[4857 | 1219.46] loss=1.42 avg=1.36\n", "[4858 | 1220.74] loss=1.42 avg=1.36\n", "[4859 | 1222.01] loss=1.23 avg=1.36\n", "[4860 | 1223.30] loss=1.37 avg=1.36\n", "[4861 | 1224.58] loss=1.35 avg=1.36\n", "[4862 | 1225.87] loss=1.40 avg=1.36\n", "[4863 | 1227.17] loss=1.37 avg=1.36\n", "[4864 | 1228.44] loss=1.28 avg=1.36\n", "[4865 | 1229.72] loss=1.15 avg=1.35\n", "[4866 | 1231.00] loss=1.35 avg=1.35\n", "[4867 | 1232.27] loss=1.41 avg=1.35\n", "[4868 | 1233.56] loss=1.33 avg=1.35\n", "[4869 | 1234.85] loss=1.31 avg=1.35\n", "[4870 | 1236.13] loss=1.33 avg=1.35\n", "[4871 | 1237.40] loss=1.27 avg=1.35\n", "[4872 | 1238.68] loss=1.24 avg=1.35\n", "[4873 | 1239.95] loss=1.26 avg=1.35\n", "[4874 | 1241.23] loss=1.28 avg=1.35\n", "[4875 | 1242.50] loss=1.23 avg=1.35\n", "[4876 | 1243.78] loss=1.25 avg=1.35\n", "[4877 | 1245.07] loss=1.33 avg=1.35\n", "[4878 | 1246.35] loss=1.24 avg=1.35\n", "[4879 | 1247.63] loss=1.23 avg=1.35\n", "[4880 | 1248.90] loss=1.17 avg=1.34\n", "[4881 | 1250.19] loss=1.48 avg=1.34\n", "[4882 | 1251.48] loss=1.48 avg=1.35\n", "[4883 | 1252.77] loss=1.14 avg=1.34\n", "[4884 | 1254.04] loss=1.36 avg=1.34\n", "[4885 | 1255.33] loss=1.27 avg=1.34\n", "[4886 | 1256.64] loss=1.25 avg=1.34\n", "[4887 | 1257.91] loss=1.38 avg=1.34\n", "[4888 | 1259.19] loss=1.16 avg=1.34\n", "[4889 | 1260.49] loss=1.30 avg=1.34\n", "[4890 | 1261.77] loss=1.40 avg=1.34\n", "[4891 | 1263.05] loss=1.45 avg=1.34\n", "[4892 | 1264.33] loss=1.43 avg=1.34\n", "[4893 | 1265.62] loss=1.27 avg=1.34\n", "[4894 | 1266.92] loss=1.30 avg=1.34\n", "[4895 | 1268.20] loss=1.20 avg=1.34\n", "[4896 | 1269.48] loss=1.27 avg=1.34\n", "[4897 | 1270.75] loss=1.34 avg=1.34\n", "[4898 | 1272.03] loss=1.41 avg=1.34\n", "[4899 | 1273.31] loss=1.30 avg=1.34\n", "[4900 | 1274.58] loss=1.37 avg=1.34\n", "======== SAMPLE 1 ========\n", " A DEFAUT DE SUSPENSION DES PREJUDICES A SON EMPLOYEUR DANS LA MESURE QUI FAISAIT OBSTACLE A AUGMENTATION DES DEPENS ; QU'EN REFUSANT, DE CE CARACTERE INDEMNISABLE, D'ACCEPTER L'EXIGENCE D'UN ACCIDENT DU TRAVAIL, LES DAMES Y... ET Y... AVAIENT COMMIS UNE FAUTE DE NATURE A PRIVER LES JEUNES CIRCONSTANCES DE SA MALADIE, EN AVAIENT AINSI REFUSE D'EN TIRER LES CONSEQUENCES PECUNIAIRES QUANT A LA FAUTE DE NATURE A PRIVER LES DAMES Y... ET Y... DE CONSIDERATIONS SUSVISEES ;QUE LE MOYEN NE PEUT ETRE ACCUEILLI EN AUCUNE DE SES BRANCHES ;SUR LE SECOND MOYEN, PRIS EN EN SES QUATRE BRANCHES : ATTENDU QU'IL EST TOUT AUSSI VAINEMENT, QU'EN DEDUISANT LE CARACTERE PROFESSIONNEL DES INCIDENTS DE LIVRAISON ALLEMANDES, QUANT AUX PRATICIENS TARDIFS DU TRAVAIL, LA COUR D'APPEL EN A ESTIME QUE LES DAMES Z... ET Z..., QUI N'AYANT PAS ETE MIS A PIED SUR LES AFFECTIONS DES MARCHEONS AU NOM DE LA SOCIETE FRANCE, NE POUVAIENT SE PREVALOIR DE L'ARTICLE 711 - 2 DU CODE DE COMMERCE QUE POUR DETERMINER LE MONTANT DE CE DERNIER, MEME SUR LE CHAUFFEUR BERGER ; QU'IL SUFFIT, ENFIN, QUE L'ARRET ATTAQUE A CONSTATE QUE LA FAUTE DU NOUVEL EMPLOYEUR CONSTITUTIF D'UNE FAUTE GRAVE DE NATURE A FAUTE PAR CELLE-CI ET A L'AUGMENTATION DES DEPENS DE LA CESSION DU DOMICILE DE X... DANS LE DELAI DE TROIS MOIS DE TROIS ANS, ET QU'EN AUCUNE MANIERE, LES JUGES D'APPEL ONT SOUVERAINEMENT ESTIME QUE L'ACCIDENT RESULTAIT EN L'ESPECE DE LA MESURE EN VIGUEUR DE LA VICTIME QUI N'AVAIT PAS POUR BUT DE LUI DONNER SON ETABLISSEMENT ; QUE PAR CES MOTIFS, IMPLICITEMENT MAIS NECESSAIREMENT, LA COUR D'APPEL A LEGALEMENT JUSTIFIE SA DECISION ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 16 JUILLET 1974 PAR LA COUR D'APPEL DE LA BASSE. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE, UN MOYEN EN SA DOUBLE QUALITE DE GENTI AYANT FAIT DEBATTRE DE SON POURVOI : VU L'ARTICLE 1384, ALINEA 1, DU CODE CIVIL, EN SON FAIT ;ATTENDU QUE DAME X... AYANT ETE VICTIME LE 3 MARS 1973 PAR LA VEUVE DE LILIEU, QU'EN VERTU DE L'ACTE SOUS SEING PRIVE DU 3 MARS, ALLEGUE, LE 21 MARS 1973, L'A ASSIGNE LE 14 JANVIER 1974, CONGEDIEE PAR LA SOCIETE D'EXECUTION PROVIDENCE EN L'ABSORPTION DE FAIT DES PRIMES EN INDEXATION LUI INTERDISAIT DE MAINTENIR DAME NINE Y..., SOUS AFFICHAGE D'UN AUTRE ACTE DONT S'AGIT AYANT ETE VICTIME, D'UNE PART, EN REFUSANT DE LA VALEUR LITIGIEUSE DIT ETABLIE LA PRESCRIPTION TRENTENAIRE DE L'AGE DE 18 ANS, D'AUTRE PART, EN CE QUI CONCERNE LE CARACTERE DE LA FAUTE JURIDIQUE EXCEDANT LA DUREE DE L'\n", "\n", "[4901 | 1287.98] loss=1.25 avg=1.34\n", "[4902 | 1289.32] loss=1.16 avg=1.34\n", "[4903 | 1290.61] loss=1.08 avg=1.34\n", "[4904 | 1291.89] loss=1.40 avg=1.34\n", "[4905 | 1293.21] loss=1.55 avg=1.34\n", "[4906 | 1294.49] loss=1.41 avg=1.34\n", "[4907 | 1295.77] loss=1.30 avg=1.34\n", "[4908 | 1297.05] loss=1.40 avg=1.34\n", "[4909 | 1298.33] loss=1.27 avg=1.34\n", "[4910 | 1299.62] loss=1.28 avg=1.34\n", "[4911 | 1300.91] loss=1.22 avg=1.34\n", "[4912 | 1302.18] loss=1.28 avg=1.34\n", "[4913 | 1303.46] loss=1.59 avg=1.34\n", "[4914 | 1304.74] loss=1.36 avg=1.34\n", "[4915 | 1306.02] loss=1.58 avg=1.34\n", "[4916 | 1307.29] loss=1.49 avg=1.34\n", "[4917 | 1308.57] loss=1.26 avg=1.34\n", "[4918 | 1309.85] loss=1.42 avg=1.34\n", "[4919 | 1311.15] loss=1.53 avg=1.34\n", "[4920 | 1312.42] loss=1.11 avg=1.34\n", "[4921 | 1313.70] loss=1.38 avg=1.34\n", "[4922 | 1314.98] loss=1.42 avg=1.34\n", "[4923 | 1316.26] loss=1.32 avg=1.34\n", "[4924 | 1317.53] loss=1.25 avg=1.34\n", "[4925 | 1318.81] loss=1.27 avg=1.34\n", "[4926 | 1320.08] loss=1.28 avg=1.34\n", "[4927 | 1321.36] loss=1.27 avg=1.34\n", "[4928 | 1322.68] loss=1.19 avg=1.34\n", "[4929 | 1323.97] loss=1.35 avg=1.34\n", "[4930 | 1325.38] loss=1.32 avg=1.34\n", "[4931 | 1326.71] loss=1.27 avg=1.34\n", "[4932 | 1327.99] loss=1.33 avg=1.34\n", "[4933 | 1329.27] loss=1.28 avg=1.34\n", "[4934 | 1330.54] loss=1.54 avg=1.34\n", "[4935 | 1331.82] loss=1.64 avg=1.34\n", "[4936 | 1333.11] loss=1.25 avg=1.34\n", "[4937 | 1334.39] loss=1.55 avg=1.34\n", "[4938 | 1335.67] loss=1.36 avg=1.34\n", "[4939 | 1336.94] loss=1.34 avg=1.34\n", "[4940 | 1338.22] loss=1.32 avg=1.34\n", "[4941 | 1339.50] loss=1.35 avg=1.34\n", "[4942 | 1340.77] loss=1.36 avg=1.34\n", "[4943 | 1342.05] loss=1.34 avg=1.34\n", "[4944 | 1343.33] loss=1.31 avg=1.34\n", "[4945 | 1344.62] loss=1.41 avg=1.34\n", "[4946 | 1345.90] loss=1.22 avg=1.34\n", "[4947 | 1347.17] loss=1.18 avg=1.34\n", "[4948 | 1348.45] loss=1.41 avg=1.34\n", "[4949 | 1349.73] loss=1.27 avg=1.34\n", "[4950 | 1351.00] loss=1.39 avg=1.34\n", "[4951 | 1352.28] loss=1.58 avg=1.34\n", "[4952 | 1353.56] loss=1.26 avg=1.34\n", "[4953 | 1354.87] loss=1.14 avg=1.34\n", "[4954 | 1356.15] loss=1.16 avg=1.34\n", "[4955 | 1357.44] loss=1.19 avg=1.34\n", "[4956 | 1358.90] loss=1.22 avg=1.34\n", "[4957 | 1360.21] loss=1.35 avg=1.34\n", "[4958 | 1361.49] loss=1.23 avg=1.34\n", "[4959 | 1362.77] loss=1.32 avg=1.34\n", "[4960 | 1364.05] loss=1.25 avg=1.33\n", "[4961 | 1365.33] loss=1.45 avg=1.34\n", "[4962 | 1366.63] loss=1.26 avg=1.34\n", "[4963 | 1367.91] loss=1.38 avg=1.34\n", "[4964 | 1369.19] loss=1.25 avg=1.33\n", "[4965 | 1370.47] loss=1.30 avg=1.33\n", "[4966 | 1371.74] loss=1.31 avg=1.33\n", "[4967 | 1373.02] loss=1.32 avg=1.33\n", "[4968 | 1374.30] loss=1.35 avg=1.33\n", "[4969 | 1375.58] loss=1.29 avg=1.33\n", "[4970 | 1376.87] loss=1.41 avg=1.33\n", "[4971 | 1378.16] loss=1.29 avg=1.33\n", "[4972 | 1379.43] loss=1.35 avg=1.33\n", "[4973 | 1380.71] loss=1.46 avg=1.34\n", "[4974 | 1381.99] loss=1.27 avg=1.33\n", "[4975 | 1383.26] loss=1.33 avg=1.33\n", "[4976 | 1384.54] loss=1.32 avg=1.33\n", "[4977 | 1385.82] loss=1.32 avg=1.33\n", "[4978 | 1387.10] loss=1.38 avg=1.33\n", "[4979 | 1388.39] loss=1.25 avg=1.33\n", "[4980 | 1389.67] loss=1.28 avg=1.33\n", "[4981 | 1390.96] loss=1.53 avg=1.34\n", "[4982 | 1392.37] loss=1.29 avg=1.33\n", "[4983 | 1393.66] loss=1.35 avg=1.33\n", "[4984 | 1394.95] loss=1.25 avg=1.33\n", "[4985 | 1396.24] loss=1.33 avg=1.33\n", "[4986 | 1397.52] loss=1.24 avg=1.33\n", "[4987 | 1398.81] loss=1.23 avg=1.33\n", "[4988 | 1400.08] loss=1.25 avg=1.33\n", "[4989 | 1401.36] loss=1.31 avg=1.33\n", "[4990 | 1402.64] loss=1.32 avg=1.33\n", "[4991 | 1403.92] loss=1.38 avg=1.33\n", "[4992 | 1405.19] loss=1.43 avg=1.33\n", "[4993 | 1406.47] loss=1.35 avg=1.33\n", "[4994 | 1407.75] loss=1.46 avg=1.33\n", "[4995 | 1409.03] loss=1.26 avg=1.33\n", "[4996 | 1410.32] loss=1.34 avg=1.33\n", "[4997 | 1411.60] loss=1.23 avg=1.33\n", "[4998 | 1412.88] loss=1.07 avg=1.33\n", "[4999 | 1414.16] loss=1.14 avg=1.33\n", "[5000 | 1415.44] loss=1.30 avg=1.33\n", "Saving checkpoint/run1/model-5000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 16:18:31.082247: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:18:31.083395: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:18:31.084462: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:18:31.085687: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:18:31.086758: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:18:31.087689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-5000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 2.40it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 11000662 tokens\n", "Training...\n", "Saving checkpoint/run1/model-5000\n", "Saving checkpoint/run1/model-5000\n", "======== SAMPLE 1 ========\n", "SIER, POUR AVOIR DE LAISSE, SOUS LES AUTRES DISPOSITIONS, L'ACTION PENALE, L'ARRET DECLARE NON CONNUITE CONTRE LE GARDIEN D'UN BAC, AINSI QUE DE L'ETAT ET LA CONDAMNATION DE BACHATE, AU MOTIF QU'IL NE SAURAIT ETRE DECLARE IRRECEVABLE COMME LE PREMIER, ALORS, D'UNE PART, QU'IL NE SAURAIT ETRE TENU POUR RESPONSABLE POUR LA DEVOLUTION DE L'ACTE DE SIGNATAIRE DU 21 JANVIER 1973 ; ET ALORS, D'AUTRE PART, QUE L'ARRET ATTAQUE, PAR DES MOTIFS NON SOUVERAINEMENT APPRECIES PAR LES PREMIER ET DEUXIEME JUGES, A REPONDU AUX CONCLUSIONS PRETENDUMENT DELAISSEES, ET DENATURE L'ETAT ET LA CONDAMNATION DE BACHATE, AU MOTIF QUE CELLE-CI, AU COURS DE SON TRAVAIL, N'AVAIT PAS DROIT A UNE CONTESTATION SUR LE TERME LIVRE DU COUT DES MURS, ALORS, D'AUTRE PART, QUE L'INTERESSE AVAIT ETE ROULE SUR LA PERSONNE DE BACHATE ;MAIS ATTENDU, D'UNE PART, QU'APRES AVOIR RELEVE QUE BACHATE AVAIT, DE NOUVEAU PRODUIT, ETE MUTE EN POSSESSION DES MURS APPARUS ET QUE S'ETAIT BESOIN D'UNE FACON PARTIE DE NEUF MOIS AUPARAVANT, IL N'AVAIT PAS FAIT ETRE MUTE PAR UN CONTRAT DE PRECIUTION DE LA NOUVELLE OBLIGATION, LA COUR, A EXACTEMENT DEDUIT QUE LA QUALITE DE MUTE, QUI AVAIT ETE REGULIEREMENT FERME LA SITUATION DE CELLE-CI, AVAIT PU, EN L'ABSENCE DE TOUTE NOUVELLE OBLIGATION MAIS D'AVOIR FAIT L'OBJET D'UN ACTE D'APPAREIL, L'INDICATION DE LA MEME NATURE, QU'IL EN AURAIT ETE DENATURE PAR LES JUGES D'APPEL, QUE LA CONTESTATION SUR LE TERME LIVRE, DANS LEQUEL BACHATE, SE TROUVAIT CONTESTEE PAR LA SOCIETE BACHATAR, N'IGNORE PAS, A LA CONDITION D'ELARGISSEMENT QU'IL LES AVAIT SOUS LE FAIT D'UNE MUTE \"LA SOCIETE BACHATAR\" OU LA SOCIETE BACHATAR DE LA SOCIETE BACHATAR , A TITRE PRATIQUE PAR LA DEVOLUTION, QUI, EN FAIT, CERTAINEMENT APPARUN LUI-MEME LES CONDITIONS DANS LESQUELLES IL ETAIT AFFAIRES, DEVAIT ETRE LA FAUTE PERSONNELLE AU SENS DE L'ARTICLE 446 DU CODE DE LA SECURITE SOCIALE, LA COUR D'APPEL A CONSTATE QU'IL N'AVAIT PU ETRE, DEPUIS 1940, L'EXERCICE DE SES FONCTIONS DE LA SOCIETE BACHATAR ET QUE CE SOIT, LA LOI, DEVENUE DEFINITIVE, AVAIT CONSIDERE COMME VALABLE ET EN CONDAMNA, TANT PAR MOTIFS PROPRES QUE PAR UNE APPRECIATION SOUVERAINE DES FAITS DE LA CAUSE, QUE CEUX EN VIGUEUR, EN L'ESPECE, LA COUR D'APPEL AVAIT, LA SUITE D'UN AN, INTERPRETE, QU'ELLE A ADMIS LA COMPETENCE DE LADITE SOCIETE, ET DE LA CONDAMNATION DE BACHATATE AUX FRAIS DE LA CONCEPTION ; QU'ELLE A, AINSI, SANS SE CONTREDIRE, LEGALEMENT JUSTIFIE SA DECISION ; QUE LE MOYEN NE PEUT ETRE ACCUEILLI ; PAR CES\n", "\n", "[5001 | 22.08] loss=1.37 avg=1.37\n", "[5002 | 23.35] loss=1.36 avg=1.37\n", "[5003 | 24.63] loss=1.45 avg=1.39\n", "[5004 | 25.91] loss=1.41 avg=1.40\n", "[5005 | 27.21] loss=1.26 avg=1.37\n", "[5006 | 28.48] loss=1.35 avg=1.37\n", "[5007 | 29.76] loss=1.69 avg=1.41\n", "[5008 | 31.05] loss=1.45 avg=1.42\n", "[5009 | 32.34] loss=1.38 avg=1.41\n", "[5010 | 33.63] loss=1.44 avg=1.42\n", "[5011 | 34.91] loss=1.42 avg=1.42\n", "[5012 | 36.19] loss=1.23 avg=1.40\n", "[5013 | 37.48] loss=1.53 avg=1.41\n", "[5014 | 38.76] loss=1.33 avg=1.40\n", "[5015 | 40.04] loss=1.43 avg=1.41\n", "[5016 | 41.32] loss=1.16 avg=1.39\n", "[5017 | 42.59] loss=1.29 avg=1.38\n", "[5018 | 43.87] loss=1.43 avg=1.39\n", "[5019 | 45.15] loss=1.19 avg=1.38\n", "[5020 | 46.42] loss=1.57 avg=1.39\n", "[5021 | 47.70] loss=1.43 avg=1.39\n", "[5022 | 48.99] loss=1.51 avg=1.39\n", "[5023 | 50.27] loss=1.36 avg=1.39\n", "[5024 | 51.57] loss=1.67 avg=1.41\n", "[5025 | 52.85] loss=1.35 avg=1.40\n", "[5026 | 54.12] loss=1.20 avg=1.39\n", "[5027 | 55.40] loss=1.32 avg=1.39\n", "[5028 | 56.68] loss=1.36 avg=1.39\n", "[5029 | 57.95] loss=1.15 avg=1.38\n", "[5030 | 59.24] loss=1.37 avg=1.38\n", "[5031 | 60.52] loss=1.32 avg=1.38\n", "[5032 | 61.80] loss=1.33 avg=1.38\n", "[5033 | 63.08] loss=1.28 avg=1.37\n", "[5034 | 64.35] loss=1.23 avg=1.37\n", "[5035 | 65.64] loss=1.31 avg=1.37\n", "[5036 | 66.93] loss=1.39 avg=1.37\n", "[5037 | 68.22] loss=1.12 avg=1.36\n", "[5038 | 69.50] loss=1.34 avg=1.36\n", "[5039 | 70.79] loss=1.29 avg=1.36\n", "[5040 | 72.07] loss=1.51 avg=1.36\n", "[5041 | 73.35] loss=1.14 avg=1.35\n", "[5042 | 74.63] loss=1.28 avg=1.35\n", "[5043 | 75.90] loss=1.30 avg=1.35\n", "[5044 | 77.18] loss=1.26 avg=1.35\n", "[5045 | 78.46] loss=1.35 avg=1.35\n", "[5046 | 79.74] loss=1.26 avg=1.35\n", "[5047 | 81.03] loss=1.27 avg=1.34\n", "[5048 | 82.31] loss=1.39 avg=1.34\n", "[5049 | 83.63] loss=1.38 avg=1.35\n", "[5050 | 84.92] loss=1.26 avg=1.34\n", "[5051 | 86.20] loss=1.25 avg=1.34\n", "[5052 | 87.48] loss=1.34 avg=1.34\n", "[5053 | 88.75] loss=1.35 avg=1.34\n", "[5054 | 90.03] loss=1.43 avg=1.34\n", "[5055 | 91.31] loss=1.40 avg=1.34\n", "[5056 | 92.62] loss=1.31 avg=1.34\n", "[5057 | 93.90] loss=1.31 avg=1.34\n", "[5058 | 95.18] loss=1.24 avg=1.34\n", "[5059 | 96.45] loss=1.36 avg=1.34\n", "[5060 | 97.73] loss=1.26 avg=1.34\n", "[5061 | 99.01] loss=1.42 avg=1.34\n", "[5062 | 100.30] loss=1.26 avg=1.34\n", "[5063 | 101.59] loss=1.26 avg=1.34\n", "[5064 | 102.88] loss=1.32 avg=1.34\n", "[5065 | 104.19] loss=1.27 avg=1.34\n", "[5066 | 105.46] loss=1.28 avg=1.34\n", "[5067 | 106.74] loss=1.26 avg=1.33\n", "[5068 | 108.02] loss=1.32 avg=1.33\n", "[5069 | 109.29] loss=1.22 avg=1.33\n", "[5070 | 110.57] loss=1.41 avg=1.33\n", "[5071 | 111.85] loss=1.17 avg=1.33\n", "[5072 | 113.12] loss=1.52 avg=1.33\n", "[5073 | 114.43] loss=1.15 avg=1.33\n", "[5074 | 115.72] loss=1.30 avg=1.33\n", "[5075 | 117.02] loss=1.23 avg=1.33\n", "[5076 | 118.29] loss=1.29 avg=1.33\n", "[5077 | 119.57] loss=1.38 avg=1.33\n", "[5078 | 120.85] loss=1.25 avg=1.33\n", "[5079 | 122.13] loss=1.22 avg=1.32\n", "[5080 | 123.40] loss=1.26 avg=1.32\n", "[5081 | 124.68] loss=1.34 avg=1.32\n", "[5082 | 125.97] loss=1.14 avg=1.32\n", "[5083 | 127.25] loss=1.26 avg=1.32\n", "[5084 | 128.53] loss=1.24 avg=1.32\n", "[5085 | 129.81] loss=1.33 avg=1.32\n", "[5086 | 131.08] loss=1.23 avg=1.32\n", "[5087 | 132.36] loss=1.23 avg=1.31\n", "[5088 | 133.64] loss=1.42 avg=1.32\n", "[5089 | 134.92] loss=1.31 avg=1.32\n", "[5090 | 136.25] loss=1.16 avg=1.31\n", "[5091 | 137.58] loss=1.37 avg=1.31\n", "[5092 | 138.87] loss=1.26 avg=1.31\n", "[5093 | 140.14] loss=1.34 avg=1.31\n", "[5094 | 141.42] loss=1.25 avg=1.31\n", "[5095 | 142.70] loss=1.32 avg=1.31\n", "[5096 | 143.97] loss=1.31 avg=1.31\n", "[5097 | 145.25] loss=1.36 avg=1.31\n", "[5098 | 146.53] loss=1.25 avg=1.31\n", "[5099 | 147.84] loss=1.11 avg=1.31\n", "[5100 | 149.14] loss=1.34 avg=1.31\n", "======== SAMPLE 1 ========\n", " EN PARTIES VISEES PAR LA PREMIERE BRANCHE DU MOYEN; MAIS ATTENDU QUE LES JUGES D'APPEL N'AYANT RETENU QU'A MME MICHEL Y... S'ETANT EXERCEE DANS L'ETABLISSEMENT DE L'IMMEUBLE ET AYANT ACCEPTE UN CHEQUE PAR LE SEQUESTRE PRONONCEE DE SA DECLARATION PAR ORDONNANCE DU 17 MARS 1980, ILS ONT JUSTIFIE LEGALEMENT SA DECISION, CE QU'IL A ETE DIT IRRECEVABLE; D'OU IL SUIT QUE LA CASSATION EST MENTIONNEE ET QUE LE MOYEN EST SANS FONDEMENT; ET SUR LE TROISIEME MOYEN, PRIS EN SES DEUX BRANCHES : ATTENDU QUE MME Y..., PROPAGEE D'UN LOGEMENT D'HABITATION, EN FAISAIT VALABLEMENT RAPPORTER LA PREUVE QUE L'APPARTEMENT LE LUI AVAIT FAIT CONSTRUIRE DANS UNE AGGLOMERATION A UN SOUS-ET-GAUX DE CELLE-CI, ET QUE C'ETAIT AIT ETE COMMISE DANS CET ACTE QUE DAME Y... N'AIT PLUS POURTANT ACCEPTE CELUI QUI RENVOYAIT SUR L'ENSEMBLE DE SA DEMANDE DE MISE HORS DE CAUSE ET SUR L'APPARTEMENT DE SON MANDATAIRE;QU'EN STATUANT AINSI, ALORS QU'IL N'Y AVAIT PAS EU ACCORD POUR SA VOLONTE, SI LA DEMANDE DE MISE LES ACTIONS NE DEPENDAIT PAS DES CIRCONSTANCES INAPPLICABLES A LA PROMESSE DE MISE EN ACTION CONTRE UN ENFANT EN LE DECLARANT INUTILE DE SA MERE, SURVENU IN A MME Y..., EN SA QUALITE DE LA MERE, MME E..., PROPAGEE DE LA MARCHANDISE DU SOUS-ET-GAUX, A JUSTIFIE DE CE CHEF LA DECISION PRISE PAR LE PREMIER JUGE; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 27 MAI 1980 PAR LA COUR D'APPEL DE DOUAI. <|endoftext|>\n", "<|startoftext|> SUR LA RECEVABILITE DU MOYEN : VU LES ARTICLES L. 122-14-4 DU CODE DU TRAVAIL ET L. 122-14-7 DU MEME CODE; ATTENDU QUE, SELON LE PREMIER DE CES TEXTES UN TEL ACCORD ENTRE LE 1ER FEVRIER 1964 ET LE 21 DECEMBRE 1975 SUR TOUS LES TRAVAUX QUI RECLAMAIENT DES SALAIRES ET NON DES MAJORATIONS DE RETARD; QUET POUR LES ACCORD INTERCONTRACTU, UN CONTRAT QU'IL AVAIT LICENCIE EN VUE DE LA CONFIANCE QUI LA POUVAIT NECESSAIREMENT S'ENGAGER A PARTICIPER UN CONTRAT D'UTILISATION DE GRATUIT; QUE CES DERNIERS S'ETANT EXPLOITEES, UN CONTRAT D'UTILISATION CONCLU ENTRE LES MEMES PARTIES QU'ELLE LUI AVAIT DONNEE; QUE LES SALARIES ONT RECLAME LA PRISE EN CHARGE PAR EUX-FONDE DES MOYENS CONSTITUTIFS D'ACTIFS POUR LAQUELLE MME ME X... AVAIT EU UNE CHOME SUR ELLE-MEME ; QUE CE CONTRAT A ETE DELIVRE QUE Mme Y... A DECIDE QUE LES MOYENS CONSTITUTIFS DE PARTICIPATION QUI EST D'UN CONTRAT D'UTILISATION N'AVAIENT PAS ETE REMENSES QU'ELLE AVAIT DONNES A MME Y...; QU'AINSI, L'ANNULATION DES ELECTIONS N'ETAIT PAS SERIEUSEMENT EN CE QUI CONCERNE LES SALAIRES ET NON LES SALAIR\n", "\n", "[5101 | 162.67] loss=1.20 avg=1.31\n", "[5102 | 163.95] loss=1.38 avg=1.31\n", "[5103 | 165.23] loss=1.36 avg=1.31\n", "[5104 | 166.51] loss=1.51 avg=1.31\n", "[5105 | 167.78] loss=1.47 avg=1.32\n", "[5106 | 169.12] loss=1.37 avg=1.32\n", "[5107 | 170.41] loss=1.23 avg=1.32\n", "[5108 | 171.70] loss=1.36 avg=1.32\n", "[5109 | 172.98] loss=1.57 avg=1.32\n", "[5110 | 174.26] loss=1.36 avg=1.32\n", "[5111 | 175.54] loss=1.42 avg=1.32\n", "[5112 | 176.82] loss=1.15 avg=1.32\n", "[5113 | 178.10] loss=1.34 avg=1.32\n", "[5114 | 179.38] loss=1.46 avg=1.32\n", "[5115 | 180.67] loss=1.28 avg=1.32\n", "[5116 | 181.98] loss=1.32 avg=1.32\n", "[5117 | 183.27] loss=1.56 avg=1.32\n", "[5118 | 184.54] loss=1.18 avg=1.32\n", "[5119 | 185.82] loss=1.31 avg=1.32\n", "[5120 | 187.10] loss=1.27 avg=1.32\n", "[5121 | 188.38] loss=1.24 avg=1.32\n", "[5122 | 189.65] loss=1.41 avg=1.32\n", "[5123 | 190.93] loss=1.36 avg=1.32\n", "[5124 | 192.23] loss=1.33 avg=1.32\n", "[5125 | 193.51] loss=1.46 avg=1.32\n", "[5126 | 194.78] loss=1.30 avg=1.32\n", "[5127 | 196.06] loss=1.06 avg=1.32\n", "[5128 | 197.34] loss=1.17 avg=1.32\n", "[5129 | 198.62] loss=1.31 avg=1.32\n", "[5130 | 199.90] loss=1.26 avg=1.32\n", "[5131 | 201.17] loss=1.35 avg=1.32\n", "[5132 | 202.46] loss=1.16 avg=1.32\n", "[5133 | 203.75] loss=1.19 avg=1.31\n", "[5134 | 205.04] loss=1.35 avg=1.31\n", "[5135 | 206.33] loss=1.26 avg=1.31\n", "[5136 | 207.61] loss=1.32 avg=1.31\n", "[5137 | 208.89] loss=1.28 avg=1.31\n", "[5138 | 210.16] loss=1.32 avg=1.31\n", "[5139 | 211.44] loss=1.41 avg=1.31\n", "[5140 | 212.72] loss=1.18 avg=1.31\n", "[5141 | 214.03] loss=1.25 avg=1.31\n", "[5142 | 215.31] loss=1.28 avg=1.31\n", "[5143 | 216.59] loss=1.36 avg=1.31\n", "[5144 | 217.87] loss=1.24 avg=1.31\n", "[5145 | 219.15] loss=1.39 avg=1.31\n", "[5146 | 220.43] loss=1.43 avg=1.31\n", "[5147 | 221.70] loss=1.32 avg=1.31\n", "[5148 | 222.98] loss=1.24 avg=1.31\n", "[5149 | 224.27] loss=1.36 avg=1.31\n", "[5150 | 225.56] loss=1.32 avg=1.31\n", "[5151 | 226.84] loss=1.32 avg=1.31\n", "[5152 | 228.11] loss=1.33 avg=1.31\n", "[5153 | 229.39] loss=1.30 avg=1.31\n", "[5154 | 230.67] loss=1.30 avg=1.31\n", "[5155 | 231.95] loss=1.24 avg=1.31\n", "[5156 | 233.23] loss=1.30 avg=1.31\n", "[5157 | 234.51] loss=1.20 avg=1.31\n", "[5158 | 235.81] loss=1.32 avg=1.31\n", "[5159 | 237.09] loss=1.48 avg=1.31\n", "[5160 | 238.37] loss=1.22 avg=1.31\n", "[5161 | 239.67] loss=1.24 avg=1.31\n", "[5162 | 240.96] loss=1.18 avg=1.31\n", "[5163 | 242.24] loss=1.23 avg=1.31\n", "[5164 | 243.52] loss=1.37 avg=1.31\n", "[5165 | 244.80] loss=1.32 avg=1.31\n", "[5166 | 246.10] loss=1.21 avg=1.31\n", "[5167 | 247.41] loss=1.34 avg=1.31\n", "[5168 | 248.71] loss=1.15 avg=1.31\n", "[5169 | 249.99] loss=1.33 avg=1.31\n", "[5170 | 251.27] loss=1.31 avg=1.31\n", "[5171 | 252.55] loss=1.28 avg=1.31\n", "[5172 | 253.83] loss=1.16 avg=1.30\n", "[5173 | 255.11] loss=1.50 avg=1.31\n", "[5174 | 256.38] loss=1.28 avg=1.31\n", "[5175 | 257.68] loss=1.32 avg=1.31\n", "[5176 | 258.96] loss=1.28 avg=1.31\n", "[5177 | 260.24] loss=1.29 avg=1.31\n", "[5178 | 261.52] loss=1.20 avg=1.31\n", "[5179 | 262.80] loss=1.27 avg=1.30\n", "[5180 | 264.08] loss=1.22 avg=1.30\n", "[5181 | 265.35] loss=1.62 avg=1.31\n", "[5182 | 266.63] loss=1.33 avg=1.31\n", "[5183 | 267.92] loss=1.36 avg=1.31\n", "[5184 | 269.21] loss=1.29 avg=1.31\n", "[5185 | 270.49] loss=1.30 avg=1.31\n", "[5186 | 271.77] loss=1.24 avg=1.31\n", "[5187 | 273.06] loss=1.23 avg=1.31\n", "[5188 | 274.35] loss=1.35 avg=1.31\n", "[5189 | 275.64] loss=1.20 avg=1.31\n", "[5190 | 276.92] loss=1.22 avg=1.30\n", "[5191 | 278.20] loss=1.17 avg=1.30\n", "[5192 | 279.50] loss=1.36 avg=1.30\n", "[5193 | 280.83] loss=1.17 avg=1.30\n", "[5194 | 282.11] loss=1.20 avg=1.30\n", "[5195 | 283.39] loss=1.56 avg=1.30\n", "[5196 | 284.67] loss=1.36 avg=1.30\n", "[5197 | 285.95] loss=1.30 avg=1.30\n", "[5198 | 287.22] loss=1.43 avg=1.31\n", "[5199 | 288.50] loss=1.22 avg=1.30\n", "[5200 | 289.78] loss=1.22 avg=1.30\n", "======== SAMPLE 1 ========\n", "MIIS QUE LE FAIT QU'IL Y AIT EU DE PARTAGE EST, D'APRES LA LOI DU 17 JANVIER 1957, DE NATURE A SE REFERER AUX DISPOSITIONS DU DECRET DU 8 OCTOBRE 1957, D'OUVERT LE PREMIER ET DES RENSEIGNEMENTS EN CAUSE, ALORS, EN QUOI LA LOI DU 18 FEVRIER 1966 NE VISE D'AUCUNE EXCEPTION EN MATIERE D'ADRESSE A L'EMPLOYEUR, DE L'EMPLOYEUR QUI AURAIT SEULEMENT PRIVE DE CE CHEF DE CONTRAT POUR ELLE, ET ALORS QUE LES JURIDICTIONS DE L'ORDRE ADMINISTRATEUR PEUVENT FAIRE VALOIR SES DROITS QUAND IL S'AGIT EN RAISON DU NOMBRE DES AVANTAGES PAR L'EMPLOYEUR, CESSIONNAIRE ET L'ASSERTE DU RAPPORT DE L'ARRETE DU 5 OCTOBRE 1964, CE QUI CONCERNE LA CONVENTION COLLECTIVE DU 29 JUILLET 1975 ;MAIS ATTENDU, D'UNE PART, QU'APRES AVOIR RELEVE QUE LA LOI CONPRISE EN APPLICATION DE L'ENGAGEMENT PRIS PAR LA SOCIETE RACHOUPE ET CAYSEAU D'UNE VENTILATION DE BATIMENTS ENVIRON 5 A 5 HEURES DE TRAVAIL DES INDIFFERENTS D'ENTRE ECHANTILLONS SUR LEQUEL LA DUREE A LEUR PRENDRE A SON PERSONNEL DE LA SALARIEE DEJA ET DE LA VALEUR LUI APPARTENANT ;ATTENDU, D'AUTRE PART, QU'EN STATUANT COMME IL L'A FAIT, LA COUR D'APPEL A CONSIDERE A BON DROIT QUE L'EMPLOYEUR NE DEMONTRAIT PAS DE L'EXISTE DU CONTRAT PAR UNE CLAUSE PREVOYANT QUE LA CLAUSE FIGURANT DANS L'ACTE DE CONVERSION, NE VISE A CETTE DATE ET NE SE REALISAIT PAS APRES LA RESILIATION DU CONTRAT ;D'OU IL SUIT QUE LE MOYEN DOIT ETRE REJETE ;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 7 MAI 1981 PAR LA COUR D'APPEL DE DOUAI ; <|endoftext|>\n", "<|startoftext|> SUR LE SECOND MOYEN : VU L'ARTICLE 455 DU CODE DE PROCEDURE CIVILE, ATTENDU QU'EN VERTU DES TEXTES SUSVISES LA LOI APPLICABLE NE PEUT ETRE ORDONNEE CIVI PAR REFUS D'APPLICATION DU MEME CONVENTION COLLECTIF ;ATTENDU QUE LA COUR D'APPEL A CONDAMNE LA SOCIETE TISIENNE D'ASSISTANCE AUX DROITS DONT ELLE AVAIT ETE LICENCIEE LE 25 MARS 1980, A PAYER A LA SOCIETE CASSIN-TEMMIRA, L'INVALIDITE ET L'UNITE EN JURIDICTION ADMINISTRATIVE DE CETTE SOCIETE, LA SOMME ALLOUEE A UN PRET PAYABLE A XAVROURE PAR LA SOCIETE CASSIN-TEMMIRA ET AUX INDEMNITES DONT S'AGIT, DONT CE REGLEMENT AVAIT ETE PAYEE A SES SOMMES LE 15 OCTOBRE 1975, LA SOMME DUE AUX TIERS DONT ELLE, CONVENTION COLLECTIF LE 25 MARS 1980, DEVAIT ETRE ECHAPPE AUX DROITS QUI LEUR EST DEMEUREES AVISES, QU'ELLE A PRONONCE LE PAIEMENT DU SALAIRE ;ATTENDU CEPENDANT QUE, SI XAVROURE AURAIT DU LEUR AVOIR AVANT L'EXPIRATION DU DELAI DE DISPENSE DU REFUS, CETTE AUTORISATION N'AURAIT PAS ETE REGULIEREMENT EN CAUSE, A CET EGARD, A UNE AUTRE INDEMNITE ;QU'EN STATUANT AINSI, ALORS QUE LE CONTRAT DU PREMIER J\n", "\n", "[5201 | 303.48] loss=1.28 avg=1.30\n", "[5202 | 304.76] loss=1.33 avg=1.30\n", "[5203 | 306.04] loss=1.16 avg=1.30\n", "[5204 | 307.33] loss=1.30 avg=1.30\n", "[5205 | 308.62] loss=1.35 avg=1.30\n", "[5206 | 309.91] loss=1.29 avg=1.30\n", "[5207 | 311.19] loss=1.33 avg=1.30\n", "[5208 | 312.48] loss=1.27 avg=1.30\n", "[5209 | 313.77] loss=1.29 avg=1.30\n", "[5210 | 315.06] loss=1.28 avg=1.30\n", "[5211 | 316.34] loss=1.32 avg=1.30\n", "[5212 | 317.62] loss=1.30 avg=1.30\n", "[5213 | 318.89] loss=1.19 avg=1.30\n", "[5214 | 320.17] loss=1.41 avg=1.30\n", "[5215 | 321.45] loss=1.43 avg=1.30\n", "[5216 | 322.73] loss=1.34 avg=1.30\n", "[5217 | 324.01] loss=1.38 avg=1.31\n", "[5218 | 325.29] loss=1.31 avg=1.31\n", "[5219 | 326.57] loss=1.42 avg=1.31\n", "[5220 | 327.85] loss=1.31 avg=1.31\n", "[5221 | 329.13] loss=1.31 avg=1.31\n", "[5222 | 330.40] loss=1.25 avg=1.31\n", "[5223 | 331.68] loss=1.26 avg=1.31\n", "[5224 | 332.96] loss=1.30 avg=1.31\n", "[5225 | 334.25] loss=1.12 avg=1.30\n", "[5226 | 335.53] loss=1.25 avg=1.30\n", "[5227 | 336.82] loss=1.31 avg=1.30\n", "[5228 | 338.10] loss=1.37 avg=1.30\n", "[5229 | 339.37] loss=1.27 avg=1.30\n", "[5230 | 340.65] loss=1.30 avg=1.30\n", "[5231 | 341.93] loss=1.36 avg=1.30\n", "[5232 | 343.22] loss=1.45 avg=1.31\n", "[5233 | 344.51] loss=1.21 avg=1.30\n", "[5234 | 345.82] loss=1.48 avg=1.31\n", "[5235 | 347.12] loss=1.17 avg=1.30\n", "[5236 | 348.40] loss=1.07 avg=1.30\n", "[5237 | 349.67] loss=1.22 avg=1.30\n", "[5238 | 350.95] loss=1.36 avg=1.30\n", "[5239 | 352.23] loss=1.27 avg=1.30\n", "[5240 | 353.50] loss=1.31 avg=1.30\n", "[5241 | 354.78] loss=1.38 avg=1.30\n", "[5242 | 356.07] loss=1.33 avg=1.30\n", "[5243 | 357.36] loss=1.29 avg=1.30\n", "[5244 | 358.63] loss=1.29 avg=1.30\n", "[5245 | 359.91] loss=1.40 avg=1.30\n", "[5246 | 361.18] loss=1.23 avg=1.30\n", "[5247 | 362.46] loss=1.50 avg=1.30\n", "[5248 | 363.74] loss=1.27 avg=1.30\n", "[5249 | 365.01] loss=1.35 avg=1.30\n", "[5250 | 366.30] loss=1.16 avg=1.30\n", "[5251 | 367.61] loss=1.17 avg=1.30\n", "[5252 | 368.89] loss=1.19 avg=1.30\n", "[5253 | 370.17] loss=1.32 avg=1.30\n", "[5254 | 371.44] loss=1.31 avg=1.30\n", "[5255 | 372.72] loss=1.14 avg=1.30\n", "[5256 | 374.00] loss=1.23 avg=1.30\n", "[5257 | 375.27] loss=1.33 avg=1.30\n", "[5258 | 376.57] loss=1.24 avg=1.30\n", "[5259 | 377.86] loss=1.30 avg=1.30\n", "[5260 | 379.27] loss=1.28 avg=1.30\n", "[5261 | 380.60] loss=1.17 avg=1.30\n", "[5262 | 381.88] loss=1.39 avg=1.30\n", "[5263 | 383.16] loss=1.25 avg=1.30\n", "[5264 | 384.43] loss=1.57 avg=1.30\n", "[5265 | 385.71] loss=1.19 avg=1.30\n", "[5266 | 386.99] loss=1.31 avg=1.30\n", "[5267 | 388.26] loss=1.28 avg=1.30\n", "[5268 | 389.56] loss=1.55 avg=1.30\n", "[5269 | 390.84] loss=1.21 avg=1.30\n", "[5270 | 392.11] loss=1.14 avg=1.30\n", "[5271 | 393.39] loss=1.24 avg=1.30\n", "[5272 | 394.67] loss=1.23 avg=1.30\n", "[5273 | 395.95] loss=1.27 avg=1.30\n", "[5274 | 397.23] loss=1.30 avg=1.30\n", "[5275 | 398.50] loss=1.36 avg=1.30\n", "[5276 | 399.78] loss=1.26 avg=1.30\n", "[5277 | 401.09] loss=1.20 avg=1.30\n", "[5278 | 402.37] loss=1.24 avg=1.30\n", "[5279 | 403.64] loss=1.33 avg=1.30\n", "[5280 | 404.92] loss=1.48 avg=1.30\n", "[5281 | 406.20] loss=1.43 avg=1.30\n", "[5282 | 407.48] loss=1.29 avg=1.30\n", "[5283 | 408.75] loss=1.17 avg=1.30\n", "[5284 | 410.03] loss=1.40 avg=1.30\n", "[5285 | 411.33] loss=1.15 avg=1.30\n", "[5286 | 412.80] loss=1.44 avg=1.30\n", "[5287 | 414.13] loss=1.18 avg=1.30\n", "[5288 | 415.42] loss=1.29 avg=1.30\n", "[5289 | 416.71] loss=1.30 avg=1.30\n", "[5290 | 417.99] loss=1.32 avg=1.30\n", "[5291 | 419.26] loss=1.40 avg=1.30\n", "[5292 | 420.54] loss=1.31 avg=1.30\n", "[5293 | 421.82] loss=1.13 avg=1.30\n", "[5294 | 423.11] loss=1.38 avg=1.30\n", "[5295 | 424.39] loss=1.35 avg=1.30\n", "[5296 | 425.66] loss=1.22 avg=1.30\n", "[5297 | 426.94] loss=1.32 avg=1.30\n", "[5298 | 428.22] loss=1.19 avg=1.30\n", "[5299 | 429.49] loss=1.30 avg=1.30\n", "[5300 | 430.77] loss=1.31 avg=1.30\n", "======== SAMPLE 1 ========\n", " DU PRODUIT, ALORS, SELON LE MOYEN QUE LA LOI DU PROFESSEUR DU CHEF DE SON DISPOSITIF EUT ETE PROPOSEE A L'INTERESSE, COMME POUVAIENT ETRE RATTACHES AU DROIT COMMUN D'OUVRIERS ET QU'IL EN AURAIT ETRE PROPOSE AU CREANCIER SAUF PRESCRIPTION BIENNALE; QU'EN VERTU DES ARTICLES 101 ET 105 DU DECRET DU 22 DECEMBRE 1958, NE DISPOSE PAS EXCLUSIVEMENT D'UN DELAI D'ATTENTION DANS LA DECISION DE TRAVAIL FAIT PREUVE DE L'EXISTENCE DE L'AUTORITE DE LA CHOSE JUGEE QUANT A SES DEBATS, PAR SA DECISION ET POUR LES JUGES SONT NULLEMENT CONVOQUES;MAIS ATTENDU QU'INTERDISAIT A LA COUR D'APPEL, AUX YEUX DE L'ARTICLE 1ER DE LA LOI DU 1ER SEPTEMBRE 1948, DE DECLARER, ETE REGULIEREMENT REVOQUE ET EN MEME TEMPS, COMPTE TENU DE DIFFICULTES RESTRICTIVES ET DE FAIRE ECHEC LA RATIFICATION DE L'ENTREPRENEUR, QUE LA SEULE DISPOSITION PREVUE PAR L'ARTICLE L 321 DU CODE DE LA SECURITE SOCIALE PARAIT COMME CONSTITUEE UN TRAVAIL AU SEIN D'UNE CARTE QUI JUSTIFIE, ET L'ABSENCE QUI RESIDE AUX ANNEES AU PROFESSEUR D'UTILISER UN JEUDISSEMENT D'IMMATRICULATION SUR LES AUTRES FONCTIONS DU DE SORTE QUE CELUI-CI DOIT EN AVISER LES L'ORIGINALITES; D'OU IL SUIT QUE LE REJETTE EST IRRECEVABLE;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 1ER MAI 1978 PAR LA COUR D'APPEL DE TOULOUSE. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE LA CAISSE DE RETRAITE DES MEDECINS FRANCAIS DU 27 JANVIER 1975 A, LE 1ER DECEMBRE 1977, INFORME A COMPTER DU 17 DECEMBRE 1978 UNE POLICE D'ASSURANCE D'ASSURANCE MALADIE QUI A FIXEE UN PRIX PRINCIPAL A 14 000 FRANCS, LE JOUR OU ELLE TRAVAILLAIT DANS UNE COLLISION; QUE LA CAISSE FAIT GRIEF AU JUGEMENT ATTAQUE D'AVOIR DECLARE VALABLE LA PRISE EN CHARGE PAR ELLE-MEME DE LA RENTE PRISE EN CHARGE DANS CETTE COLLISION, ALORS QUE, D'UNE PART, AUX TERMES DE L'ARTICLE 13, PARAGRAPHE 2, DU DECRET DU 25 NOVEMBRE 1957 FIXANT L'AVERTISSEMENT ENTRE LES TIERS SAISIES, LE DECRET DU 30 NOVEMBRE 1965 DU 19 DECEMBRE 1965 EST APPLICABLE EN L'ESPECE ; QUE LA DECISION DU JUGE DE L'EXAMEN PRECISAIT, PAR LEQUEL LE TRIBUNAL DE COMMERCE DU TRESOR RATTACHE L'ETAT DE LA DECISION DU JUGE DE L'EXAMEN OU, MEME SI, AUQUEL IL RELEVE LE CAS, LA RENTE POUR UN MOIS ECHAPPANT A UN TIERS, CONFORMEMENT AUX TERMES DU CONTRAT DE PEINTURE D'OUVRIERS ETAIT L'ECHEANCE, QUI, EN L'ESPECE, ETAIT DEPUIS L'AUDIENCE, ET QU'UNE EXPERTISE COMPTE TENU DE MALFACONS QUI SURVENAIENT PLUS D'UNE MANIERE D'UTILISER UN CHIEN SITUE DANS DES ANNEES, ET QUI A ETE EXCUTEE AVANT MEME LA PRISE EN CHARGE, AVAIT ETE PROROGE PAR LES MAGISTRATS, NEGOCIE PAR L'EXP\n", "\n", "[5301 | 444.73] loss=1.29 avg=1.30\n", "[5302 | 446.03] loss=1.23 avg=1.30\n", "[5303 | 447.32] loss=1.30 avg=1.30\n", "[5304 | 448.61] loss=1.23 avg=1.30\n", "[5305 | 449.90] loss=1.26 avg=1.30\n", "[5306 | 451.18] loss=1.23 avg=1.29\n", "[5307 | 452.46] loss=1.43 avg=1.30\n", "[5308 | 453.74] loss=1.19 avg=1.30\n", "[5309 | 455.02] loss=1.18 avg=1.29\n", "[5310 | 456.32] loss=1.25 avg=1.29\n", "[5311 | 457.59] loss=1.38 avg=1.29\n", "[5312 | 458.87] loss=1.30 avg=1.29\n", "[5313 | 460.15] loss=1.28 avg=1.29\n", "[5314 | 461.42] loss=1.26 avg=1.29\n", "[5315 | 462.70] loss=1.24 avg=1.29\n", "[5316 | 463.98] loss=1.41 avg=1.29\n", "[5317 | 465.26] loss=1.34 avg=1.30\n", "[5318 | 466.56] loss=1.27 avg=1.29\n", "[5319 | 467.83] loss=1.25 avg=1.29\n", "[5320 | 469.11] loss=1.34 avg=1.29\n", "[5321 | 470.39] loss=1.28 avg=1.29\n", "[5322 | 471.67] loss=1.22 avg=1.29\n", "[5323 | 472.94] loss=1.32 avg=1.29\n", "[5324 | 474.22] loss=1.18 avg=1.29\n", "[5325 | 475.50] loss=1.45 avg=1.29\n", "[5326 | 476.78] loss=1.24 avg=1.29\n", "[5327 | 478.12] loss=1.37 avg=1.29\n", "[5328 | 479.42] loss=1.41 avg=1.30\n", "[5329 | 480.70] loss=1.13 avg=1.29\n", "[5330 | 481.98] loss=1.12 avg=1.29\n", "[5331 | 483.28] loss=1.26 avg=1.29\n", "[5332 | 484.57] loss=1.30 avg=1.29\n", "[5333 | 485.85] loss=1.32 avg=1.29\n", "[5334 | 487.12] loss=1.26 avg=1.29\n", "[5335 | 488.43] loss=1.37 avg=1.29\n", "[5336 | 489.71] loss=1.32 avg=1.29\n", "[5337 | 490.99] loss=1.32 avg=1.29\n", "[5338 | 492.26] loss=1.48 avg=1.30\n", "[5339 | 493.54] loss=1.27 avg=1.30\n", "[5340 | 494.82] loss=1.53 avg=1.30\n", "[5341 | 496.09] loss=1.30 avg=1.30\n", "[5342 | 497.37] loss=1.30 avg=1.30\n", "[5343 | 498.65] loss=1.16 avg=1.30\n", "[5344 | 499.94] loss=1.30 avg=1.30\n", "[5345 | 501.22] loss=1.22 avg=1.30\n", "[5346 | 502.50] loss=1.22 avg=1.29\n", "[5347 | 503.77] loss=1.39 avg=1.30\n", "[5348 | 505.05] loss=1.29 avg=1.30\n", "[5349 | 506.32] loss=1.18 avg=1.29\n", "[5350 | 507.60] loss=1.31 avg=1.29\n", "[5351 | 508.88] loss=1.26 avg=1.29\n", "[5352 | 510.17] loss=1.30 avg=1.29\n", "[5353 | 511.48] loss=1.37 avg=1.29\n", "[5354 | 512.76] loss=1.30 avg=1.30\n", "[5355 | 514.04] loss=1.48 avg=1.30\n", "[5356 | 515.32] loss=1.34 avg=1.30\n", "[5357 | 516.61] loss=1.29 avg=1.30\n", "[5358 | 517.90] loss=1.28 avg=1.30\n", "[5359 | 519.19] loss=1.30 avg=1.30\n", "[5360 | 520.48] loss=1.23 avg=1.30\n", "[5361 | 521.76] loss=1.24 avg=1.30\n", "[5362 | 523.04] loss=1.13 avg=1.29\n", "[5363 | 524.32] loss=1.36 avg=1.29\n", "[5364 | 525.59] loss=1.23 avg=1.29\n", "[5365 | 526.87] loss=1.27 avg=1.29\n", "[5366 | 528.15] loss=1.32 avg=1.29\n", "[5367 | 529.43] loss=1.21 avg=1.29\n", "[5368 | 530.70] loss=1.13 avg=1.29\n", "[5369 | 531.98] loss=1.21 avg=1.29\n", "[5370 | 533.27] loss=1.31 avg=1.29\n", "[5371 | 534.54] loss=1.30 avg=1.29\n", "[5372 | 535.82] loss=1.30 avg=1.29\n", "[5373 | 537.10] loss=1.39 avg=1.29\n", "[5374 | 538.38] loss=1.34 avg=1.29\n", "[5375 | 539.65] loss=1.33 avg=1.29\n", "[5376 | 540.93] loss=1.48 avg=1.30\n", "[5377 | 542.21] loss=1.24 avg=1.29\n", "[5378 | 543.50] loss=1.09 avg=1.29\n", "[5379 | 544.80] loss=1.33 avg=1.29\n", "[5380 | 546.09] loss=1.31 avg=1.29\n", "[5381 | 547.37] loss=1.36 avg=1.29\n", "[5382 | 548.64] loss=1.35 avg=1.29\n", "[5383 | 549.92] loss=1.23 avg=1.29\n", "[5384 | 551.20] loss=1.24 avg=1.29\n", "[5385 | 552.50] loss=1.44 avg=1.29\n", "[5386 | 553.79] loss=1.18 avg=1.29\n", "[5387 | 555.10] loss=1.22 avg=1.29\n", "[5388 | 556.38] loss=1.29 avg=1.29\n", "[5389 | 557.65] loss=1.20 avg=1.29\n", "[5390 | 558.93] loss=1.22 avg=1.29\n", "[5391 | 560.21] loss=1.40 avg=1.29\n", "[5392 | 561.48] loss=1.12 avg=1.29\n", "[5393 | 562.76] loss=1.27 avg=1.29\n", "[5394 | 564.04] loss=1.30 avg=1.29\n", "[5395 | 565.34] loss=1.34 avg=1.29\n", "[5396 | 566.62] loss=1.19 avg=1.29\n", "[5397 | 567.89] loss=1.22 avg=1.29\n", "[5398 | 569.17] loss=1.27 avg=1.29\n", "[5399 | 570.45] loss=1.33 avg=1.29\n", "[5400 | 571.73] loss=1.21 avg=1.29\n", "======== SAMPLE 1 ========\n", "'ALMEA ;SUR LE MOYEN UNIQUE PRIS EN SES QUATRE BRANCHES : ATTENDU QUE, SELON L'ARRET DEFERE (AIX-EN-PROVENCE, 30 JUIN 1975), LA SOCIETE DES TRANSPORTS A RESPONSABILITE LIMITES (SOCIETE) A REFUSE DE LUI ALLOUER DE PLEIN DROIT, EN APPLICATION DE L'ARTICLE 1341 DU CODE CIVIL, LA REMUNERATION D'UN DELEGUE QUI ETAIT SUPERIEUR A CE BIEN DANS LA PROCEDURE ; QUE LA SOCIETE AYANT REFUSE DE LUI ALLOUER, LA SOCIETE DIRIGA A ASSIGNE MAILLOT EN DEHORS DE L'ACTION A COMPTER DU 10 MAI 1976 CONTRE L'ARRET DE LA COUR D'APPEL D'AIX-EN-PROVENCE AYANT AINSI DECLARE IRRECEVABLE UN PRETEXTE RELATIF AU DELEGUE LEGAL DU SYNDICAT DES COPROPRIETES, LE SYNDICAT DES COPROPRIETAIRES DU CENTRE SITUATION DES TRANSPORTS FRANCOOPERE (CETI) A DEMANDE A LA SOCIETE SOUTENIR QUE LA PREUVE DE LA CONSEQUENCE D'UN DELEGUE N'AYANT PAS ETE FAITE DU DELEGUE SYNDICAL, CES SYNDICATS, REPRESENTANT CETTE MEME ORGANISATION, LEUR REVENDIQUAIENT A CETTE DERNIERE SOCIETE UNE PLEIN DROIT POUR ACCUSER LE DELEGUE SYNDICAL ; QUE MAILLOT AYANT SOLLICITE LA REPARATION DU PREJUDICE AUX EPOUX X... DONT LA SOCIETE DIRIGA A DEMANDE LA REPARATION DU PREJUDICE QUE LA SOCIETE SOUTENIR AVAIT SUBI DU DEVENIR DELEGUE LEGAL DU SYNDICAT DES COPROPRIETAIRES DE SON CENTRE SITUATION DES AUTOBUS ET SONT INTERVENUS DANS LEUR ACTION, IL EST FAIT GRIEF A LA COUR D'APPEL, D'UNE PART, D'AVOIR REJETE SES DEMANDES EN DEHORS DE CETTE MODIFICATION ECRITE, ALORS, SELON LE MOYEN, QUE D'UNE PART, L'ACTION DE MAILLOT DIRIGA TEND UNIQUEMENT AUX MEMES FINS A EFFECTIVEMENT LEUR MISSION AUX ACTES D'OBLIGATION ET D'APPLICATION DE LA REGLEMENTATION DES ACTES, MEME SI LA COUVERTURE DES BIENS LUI ETAIT DISTINCTE ; QU'AUX TERMES DE L'ARTICLE 20 DU DECRET DU 22 DECEMBRE 1967, L'ALLOCATION D'UNE PLEIN DROIT DE L'APPELANT, A L'EXECUTION DE L'EXECUTION DU DECRET DU 20 MAI 1955, EST L'ALLOCATION DEPENSEE AUX ACTES D'OBLIGATION ET D'APPLICATION DE LA REGLEMENTATION PREVOYANT POUR EFFET DE REMETTRE LEUR MISSION ; QU'A CETTE DERNIERE DATE, IL IMPOSAIT EGALEMENT DANS LES PROCEDURES CONVENTIONNELLES POURSUIVIES A L'INSTRUCTION ; QUE, DES LORS, L'ARRET ATTAQUE, RE POUR LA PREMIERE FOIS DEVANT LA COUR DE CASSATION, QU'A LA DATE DU DECES DE MAILLOT, LA COUVERTURE DES BIENS RENDU LA DECISION D'APPEL QUE LE RECOUVREMENT DES CREANCES QUI EN DECOULAIENT EXISTAIENT BIEN DANS CES FORMES ; QU'EN STATUANT COMME ELLE L'A FAIT, TOUT EN ADMETTANT QU'IL Y AVAIT EU LE TEMOIGNAGE DU DELEGUANT DE LA PLEIN DROIT DE L'APPELANT, LA COUR D'APPEL A VIOLE LES ARTICLES 18 ET 21 DU DECRET DU 22 DECEMBRE 1967 ;MAIS ATTENDU QU'AP\n", "\n", "[5401 | 585.57] loss=1.55 avg=1.29\n", "[5402 | 586.86] loss=1.29 avg=1.29\n", "[5403 | 588.27] loss=1.41 avg=1.29\n", "[5404 | 589.56] loss=1.20 avg=1.29\n", "[5405 | 590.83] loss=1.20 avg=1.29\n", "[5406 | 592.11] loss=1.21 avg=1.29\n", "[5407 | 593.39] loss=1.25 avg=1.29\n", "[5408 | 594.66] loss=1.39 avg=1.29\n", "[5409 | 595.94] loss=1.23 avg=1.29\n", "[5410 | 597.22] loss=1.36 avg=1.29\n", "[5411 | 598.51] loss=1.21 avg=1.29\n", "[5412 | 599.78] loss=1.28 avg=1.29\n", "[5413 | 601.06] loss=1.36 avg=1.29\n", "[5414 | 602.34] loss=1.46 avg=1.29\n", "[5415 | 603.62] loss=1.21 avg=1.29\n", "[5416 | 604.89] loss=1.27 avg=1.29\n", "[5417 | 606.17] loss=1.34 avg=1.29\n", "[5418 | 607.46] loss=1.25 avg=1.29\n", "[5419 | 608.74] loss=1.27 avg=1.29\n", "[5420 | 610.03] loss=1.35 avg=1.29\n", "[5421 | 611.35] loss=1.27 avg=1.29\n", "[5422 | 612.64] loss=1.29 avg=1.29\n", "[5423 | 613.91] loss=1.38 avg=1.29\n", "[5424 | 615.19] loss=1.44 avg=1.29\n", "[5425 | 616.47] loss=1.24 avg=1.29\n", "[5426 | 617.74] loss=1.21 avg=1.29\n", "[5427 | 619.02] loss=1.20 avg=1.29\n", "[5428 | 620.34] loss=1.24 avg=1.29\n", "[5429 | 621.64] loss=1.19 avg=1.29\n", "[5430 | 622.93] loss=1.21 avg=1.29\n", "[5431 | 624.21] loss=1.25 avg=1.29\n", "[5432 | 625.49] loss=1.27 avg=1.29\n", "[5433 | 626.77] loss=1.36 avg=1.29\n", "[5434 | 628.05] loss=1.22 avg=1.29\n", "[5435 | 629.33] loss=1.29 avg=1.29\n", "[5436 | 630.60] loss=1.57 avg=1.29\n", "[5437 | 631.91] loss=1.32 avg=1.29\n", "[5438 | 633.18] loss=1.30 avg=1.29\n", "[5439 | 634.46] loss=1.39 avg=1.29\n", "[5440 | 635.74] loss=1.26 avg=1.29\n", "[5441 | 637.02] loss=1.25 avg=1.29\n", "[5442 | 638.30] loss=1.49 avg=1.29\n", "[5443 | 639.57] loss=1.24 avg=1.29\n", "[5444 | 640.85] loss=1.25 avg=1.29\n", "[5445 | 642.14] loss=1.30 avg=1.29\n", "[5446 | 643.45] loss=1.28 avg=1.29\n", "[5447 | 644.75] loss=1.31 avg=1.29\n", "[5448 | 646.03] loss=1.26 avg=1.29\n", "[5449 | 647.30] loss=1.42 avg=1.29\n", "[5450 | 648.58] loss=1.14 avg=1.29\n", "[5451 | 649.85] loss=1.28 avg=1.29\n", "[5452 | 651.13] loss=1.22 avg=1.29\n", "[5453 | 652.41] loss=1.26 avg=1.29\n", "[5454 | 653.69] loss=1.24 avg=1.29\n", "[5455 | 654.98] loss=1.09 avg=1.29\n", "[5456 | 656.28] loss=1.19 avg=1.29\n", "[5457 | 657.57] loss=1.39 avg=1.29\n", "[5458 | 658.84] loss=1.27 avg=1.29\n", "[5459 | 660.12] loss=1.35 avg=1.29\n", "[5460 | 661.40] loss=1.26 avg=1.29\n", "[5461 | 662.68] loss=1.27 avg=1.29\n", "[5462 | 663.95] loss=1.19 avg=1.29\n", "[5463 | 665.24] loss=1.39 avg=1.29\n", "[5464 | 666.52] loss=1.27 avg=1.29\n", "[5465 | 667.80] loss=1.28 avg=1.29\n", "[5466 | 669.07] loss=1.35 avg=1.29\n", "[5467 | 670.35] loss=1.34 avg=1.29\n", "[5468 | 671.63] loss=1.35 avg=1.29\n", "[5469 | 672.91] loss=1.45 avg=1.29\n", "[5470 | 674.18] loss=1.22 avg=1.29\n", "[5471 | 675.47] loss=1.36 avg=1.29\n", "[5472 | 676.78] loss=1.38 avg=1.29\n", "[5473 | 678.06] loss=1.18 avg=1.29\n", "[5474 | 679.34] loss=1.27 avg=1.29\n", "[5475 | 680.61] loss=1.27 avg=1.29\n", "[5476 | 681.89] loss=1.31 avg=1.29\n", "[5477 | 683.17] loss=1.20 avg=1.29\n", "[5478 | 684.44] loss=1.39 avg=1.29\n", "[5479 | 685.72] loss=1.71 avg=1.30\n", "[5480 | 687.01] loss=1.30 avg=1.30\n", "[5481 | 688.29] loss=1.21 avg=1.29\n", "[5482 | 689.57] loss=1.11 avg=1.29\n", "[5483 | 690.87] loss=1.20 avg=1.29\n", "[5484 | 692.16] loss=1.20 avg=1.29\n", "[5485 | 693.44] loss=1.18 avg=1.29\n", "[5486 | 694.72] loss=1.28 avg=1.29\n", "[5487 | 696.01] loss=1.36 avg=1.29\n", "[5488 | 697.31] loss=1.25 avg=1.29\n", "[5489 | 698.59] loss=1.30 avg=1.29\n", "[5490 | 699.87] loss=1.15 avg=1.29\n", "[5491 | 701.15] loss=1.38 avg=1.29\n", "[5492 | 702.43] loss=1.25 avg=1.29\n", "[5493 | 703.70] loss=1.25 avg=1.29\n", "[5494 | 704.98] loss=1.51 avg=1.29\n", "[5495 | 706.26] loss=1.26 avg=1.29\n", "[5496 | 707.54] loss=1.17 avg=1.29\n", "[5497 | 708.83] loss=1.21 avg=1.29\n", "[5498 | 710.12] loss=1.21 avg=1.29\n", "[5499 | 711.40] loss=1.30 avg=1.29\n", "[5500 | 712.68] loss=1.21 avg=1.29\n", "======== SAMPLE 1 ========\n", "ETETE CGT, EN REPONSE A CELUI-CI L'ARRET ATTAQUE A RENVOYE LA CAUSE A SON ACTION EXERCEE CONTRE VEUVE X... ET CELLE-CI, EN CE QU'IL CONFIRMAIT &lt;&lt; REJETTE LES PRETENTIONS&gt;; QUE LE MOYEN N'EST FONDE EN AUCUNE DE SES BRANCHES; SUR LE QUATRIEME MOYEN : ATTENDU QUE LA CAISSE PRIMAIRE D'ASSURANCE MALADIE DES TRAVAILLEURS SALARIES DE MARTINI A LA MUTUALITE SOCIALE AGRICOLE ETAIT INTERVENUE LE 9 MAI 1976 PAR LE PRESENT POURVOI EN CASSATION CONTRE UN ARRET RENDU LE 18 OCTOBRE 1976 PAR LA COUR D'APPEL D'AGEN ; ATTENDU QUE VEUVE X... REPROCHE A LA COUR D'APPEL SANS AVOIR RECHERCHE SI LE PREJUDICE SUBI PAR LA MUTUALITE SOCIALE AGRICOLE ETAIT REPUTEE AINSI MAL FONDEMENT JUSTIFIE ; MAIS ATTENDU QUE C'EST SANS DENATURER LA CONVENTION COLLECTIVE APPLICABLE QUE LA COUR D'APPEL A DECIDE A BON DROIT QU'IL N'ETAIT PAS CONTESTE QUE LA VICTIME, REPRESENTANT, MALADE, PENDANT UNE PERIODE OU PAR LE MONTANT DES PRESTATIONS AGRICOLES CONSECUTIVES A LA PERIODE EN COURS, AURAIT PU PRODUIRE EFFET AU DECEMBRE 1975 ; D'OU IL SUIT QUE LE MOYEN N'EST PAS FONDE ; SUR LE DEUXIEME MOYEN : ATTENDU QU'IL EST REPROCHE A L'ARRET D'AVOIR ACCUEILLI CETTE DEMANDE TENDANT A LA REPARATION DE SON PREJUDICE PERSONNEL ENVERS L'ASSUREUR, ALORS QUE, D'UNE PART, EN CAS D'ACCIDENT DU TRAVAIL, ELLE N'AURAIT PAS FAIT DROIT A SA DEMANDE QUI LES INTERVENAIT DE L'ARRET ATTAQUE SON ARRET, QU'AUX TERMES DE L'ARTICLE 455 DU NOUVEAU CODE DE PROCEDURE CIVILE LES PARTIES AIENT FAIT PARTIE DES DEUX PRECISIONS QUINZE JOURS AVANT RENOUVELLEMENT DE LA SORTIE PREVUE A L'ARTICLE 7 DU DECRET SUSVISE, CE QUI NE MET LA LOI POUR QUE SOIT PRONONCEE LA NULLITE DE LA SORTIE PREVUE POUR LA PREMIERE FOIS ET QU'AUCUN TEXTE NE FAISAIT LA PREUVE CONTRAIRE SON PRATIQUE COMMUNE A LA FEMME ; ALORS QUE, D'AUTRE PART, IL EST INCONCILIABLE D'APPRECIER SA TENEUR SUR LES REPOS ET LES ELEMENTS DE PREUVE QUI LUI ETAIENT PRESENTES EN L'ESPECE, QU'AYANT, ETANT AINSI REGLEE LA CAUSE DE L'ACCIDENT DU TRAVAIL, NE LE FAIT PAS, LA COUR D'APPEL QUI CONFERE A SON EGARD LA REGLE DE LA SATISFONCIATION DES RESPONSABILITES SOCIALES NE POUVAIT DECIDER QU'ILS ETAIENT PRIS EN CHARGE AU TITRE DE LA LEGISLATION SUCCESSORALE QUI S'ELEVAIT L'INTITULE, ET ALORS ENFIN, QUE DANS LES CONCLUSIONS DE L'ADMINISTRATION DES INSTITUTIONS SOCIALES, LES ADMINISTRATIONS ET JUGEES ONT SOULIGNE QUE CETTE MESURE PREFECTORALE NE POUVAIT SE PREVALOIR NI D'AUCUN PREJUDICE, NI D'UN FACHEF D'ENTREPRISE, DONT LE MONTANT EST INOPPOSABLE ; MAIS ATTENDU QUE LE REN\n", "\n", "[5501 | 725.96] loss=1.19 avg=1.29\n", "[5502 | 727.25] loss=1.35 avg=1.29\n", "[5503 | 728.54] loss=1.32 avg=1.29\n", "[5504 | 729.82] loss=1.25 avg=1.29\n", "[5505 | 731.12] loss=1.22 avg=1.29\n", "[5506 | 732.40] loss=1.20 avg=1.29\n", "[5507 | 733.68] loss=1.28 avg=1.29\n", "[5508 | 734.96] loss=1.15 avg=1.28\n", "[5509 | 736.23] loss=1.51 avg=1.29\n", "[5510 | 737.51] loss=1.14 avg=1.28\n", "[5511 | 738.79] loss=1.19 avg=1.28\n", "[5512 | 740.06] loss=1.31 avg=1.28\n", "[5513 | 741.37] loss=1.31 avg=1.28\n", "[5514 | 742.68] loss=1.30 avg=1.28\n", "[5515 | 743.97] loss=1.36 avg=1.29\n", "[5516 | 745.25] loss=1.31 avg=1.29\n", "[5517 | 746.52] loss=1.22 avg=1.28\n", "[5518 | 747.80] loss=1.26 avg=1.28\n", "[5519 | 749.08] loss=1.26 avg=1.28\n", "[5520 | 750.36] loss=1.18 avg=1.28\n", "[5521 | 751.63] loss=1.24 avg=1.28\n", "[5522 | 752.92] loss=1.28 avg=1.28\n", "[5523 | 754.20] loss=1.32 avg=1.28\n", "[5524 | 755.48] loss=1.28 avg=1.28\n", "[5525 | 756.75] loss=1.17 avg=1.28\n", "[5526 | 758.03] loss=1.39 avg=1.28\n", "[5527 | 759.31] loss=1.10 avg=1.28\n", "[5528 | 760.60] loss=1.34 avg=1.28\n", "[5529 | 761.88] loss=1.26 avg=1.28\n", "[5530 | 763.21] loss=1.39 avg=1.28\n", "[5531 | 764.51] loss=1.46 avg=1.28\n", "[5532 | 765.79] loss=1.12 avg=1.28\n", "[5533 | 767.07] loss=1.37 avg=1.28\n", "[5534 | 768.35] loss=1.20 avg=1.28\n", "[5535 | 769.63] loss=1.29 avg=1.28\n", "[5536 | 770.91] loss=1.34 avg=1.28\n", "[5537 | 772.19] loss=1.14 avg=1.28\n", "[5538 | 773.47] loss=1.20 avg=1.28\n", "[5539 | 774.77] loss=1.28 avg=1.28\n", "[5540 | 776.08] loss=1.32 avg=1.28\n", "[5541 | 777.36] loss=1.20 avg=1.28\n", "[5542 | 778.64] loss=1.46 avg=1.28\n", "[5543 | 779.92] loss=1.26 avg=1.28\n", "[5544 | 781.20] loss=1.35 avg=1.28\n", "[5545 | 782.47] loss=1.34 avg=1.28\n", "[5546 | 783.75] loss=1.11 avg=1.28\n", "[5547 | 785.04] loss=1.15 avg=1.28\n", "[5548 | 786.33] loss=1.38 avg=1.28\n", "[5549 | 787.61] loss=1.32 avg=1.28\n", "[5550 | 788.89] loss=1.30 avg=1.28\n", "[5551 | 790.17] loss=1.39 avg=1.28\n", "[5552 | 791.45] loss=1.33 avg=1.28\n", "[5553 | 792.73] loss=1.24 avg=1.28\n", "[5554 | 794.01] loss=1.17 avg=1.28\n", "[5555 | 795.31] loss=1.27 avg=1.28\n", "[5556 | 796.65] loss=1.12 avg=1.28\n", "[5557 | 797.94] loss=1.23 avg=1.28\n", "[5558 | 799.22] loss=1.24 avg=1.28\n", "[5559 | 800.50] loss=1.31 avg=1.28\n", "[5560 | 801.78] loss=1.32 avg=1.28\n", "[5561 | 803.06] loss=1.33 avg=1.28\n", "[5562 | 804.34] loss=1.34 avg=1.28\n", "[5563 | 805.61] loss=1.25 avg=1.28\n", "[5564 | 806.89] loss=1.14 avg=1.28\n", "[5565 | 808.20] loss=1.29 avg=1.28\n", "[5566 | 809.50] loss=1.29 avg=1.28\n", "[5567 | 810.78] loss=1.17 avg=1.28\n", "[5568 | 812.06] loss=1.23 avg=1.28\n", "[5569 | 813.33] loss=1.27 avg=1.28\n", "[5570 | 814.61] loss=1.25 avg=1.28\n", "[5571 | 815.89] loss=1.30 avg=1.28\n", "[5572 | 817.17] loss=1.27 avg=1.28\n", "[5573 | 818.46] loss=1.25 avg=1.28\n", "[5574 | 819.74] loss=1.38 avg=1.28\n", "[5575 | 821.02] loss=1.33 avg=1.28\n", "[5576 | 822.30] loss=1.12 avg=1.28\n", "[5577 | 823.58] loss=1.29 avg=1.28\n", "[5578 | 824.85] loss=1.14 avg=1.28\n", "[5579 | 826.13] loss=1.19 avg=1.28\n", "[5580 | 827.41] loss=1.21 avg=1.27\n", "[5581 | 828.69] loss=1.21 avg=1.27\n", "[5582 | 830.03] loss=1.42 avg=1.28\n", "[5583 | 831.32] loss=1.35 avg=1.28\n", "[5584 | 832.61] loss=1.24 avg=1.28\n", "[5585 | 833.90] loss=1.28 avg=1.28\n", "[5586 | 835.17] loss=1.21 avg=1.28\n", "[5587 | 836.45] loss=1.24 avg=1.28\n", "[5588 | 837.73] loss=1.19 avg=1.27\n", "[5589 | 839.01] loss=1.17 avg=1.27\n", "[5590 | 840.31] loss=1.25 avg=1.27\n", "[5591 | 841.62] loss=1.29 avg=1.27\n", "[5592 | 842.92] loss=1.30 avg=1.27\n", "[5593 | 844.20] loss=1.22 avg=1.27\n", "[5594 | 845.48] loss=1.39 avg=1.27\n", "[5595 | 846.76] loss=1.22 avg=1.27\n", "[5596 | 848.04] loss=1.40 avg=1.27\n", "[5597 | 849.31] loss=1.21 avg=1.27\n", "[5598 | 850.59] loss=1.36 avg=1.28\n", "[5599 | 851.90] loss=1.35 avg=1.28\n", "[5600 | 853.18] loss=1.23 avg=1.28\n", "======== SAMPLE 1 ========\n", "ECITE LES CERAMONS DE TRANSPORT, QUI EN PARTICIPENT LEURS VEHICULES DANS LES LIEUX, NE SONT PLUS LES ELEMENTS DE DEUX COPROPRIETAIRES DONT LE MOYEN NE PEUT DONNER LIEU A LEURS MOYENS, LEQUEL EST ETRANGER A LA QUALIFICATION AU MOMENT DES PRODUITS INDIVIDUELLES DES COCHERIERS DANS LA NEGATIVE DESQUELLES LEUR ASSURER LEUR CONFLIT DE TRANSPORTER AINSI QUE LE SELON LAQUELLE SON AFFECTATION ET LES BEDS OU IL A FERME LAISSE POUVOIR ET NE POUVAIT ETRE ASSIMILE LES DESIONS NECESSAIRES A LA QUALIFICATION PROSPECTIVE DES COCHERIES PORTABLES PAR ROUTE INTERMEDIAIRE AU SENS DE FAIT QUI LUI EST CONFERE L'EXECUTION D'UN PRECEDENT CONTRAT; QU'AYANT CONSTATE QUE LES COCHERIES AVAIENT LA QUALIFICATION AYANT ETE AFFECTES PAR LE PERE DES CHARGE DE TRANSPORTER LA NEGATIVE DE DIVERS PRODUITS INDIVIDUELLES AVANT LA DATE A LAQUELLE LE DEVELOPPEMENT PAR LETTRES MINISTERIEL ET SES COCHERIERS PORTABLES AINSI QUE LE SECOND COTE, L'ARRET ATTAQUE A DIT POUVOIR DE CONTROLE LES COCHERIES A CESSE D'ANNULER LA COMPARUTION SPECIFIQUE DE LEUR EXECUTION AUQUEL SONT INTERVENU PLUS DES ELEMENTS DE COCHERIE ET A DIT QUE LA COCHERIE COMPARUERAIT ENFIN DES ELEMENTS NECESSAIRES A LA QUALIFICATION DE CELLE-CI ET A L'EXPERTISE N'ENGANT DE RECHERCHER LA NATURE ET LA VALEUR DU RATTACHEMENT DE TRANSPORT;MAIS ATTENDU QUE LES MOYENS SONT DONC FONDES A REUNIR LES ELEMENTS NECESSAIRES A LA QUALIFICATION DES COCHERIES AINSI QUE DES ELEMENTS NEGLIGENTES A LAQUELLE IL LES APPARTENAIT; QUE LE MOYEN N'EST DONC PAS FONDE;PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU LE 13 NOVEMBRE 1978 PAR LA COUR D'APPEL DE RENNES. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QUE LE JUGEMENT A ANNULE LA CONVENTION PORTANTE DES PARTIES, EN ACCEPTANT LA CLAUSE \"DANS UN DELAI ET DEFINITIVEMENT DIFFERENT\" CONSTATEE PAR LES JUGES DU FOND, NOTAMMENT LE CONTRAT DU 1ER JANVIER 1963, CONCLU L'EXECUTION PAR LA SOCIETE PORTEILLE DE L'EXPERT, JOUE PAR CELLE-CI, ENTRE LA SOCIETE \"COMPATIBILITE D'EXPLOITATION D'UN ANCIENNETE COMPOSE\", ET LES PRENDRE FIN, APRES AUTORISATION DE MATERIELLE, QUE LA SOCIETE A FAIT L'ENGAGEMENT DE FAIRE L'EXPEDITION DANS LES LOCAUX, CE DERNIER AYANT FAIT UNE MACHINE ET POURVU QUE LES CHEPTIMES ETAIENT D'AILLEURS, ET QU'IL ETAIT CONVENU QUE LE CONTRAT DE TRAVAIL A ETE REPARE, NON A LA SOCIETE, ALORS QUE LES PRODUITS INDIVIDUES DES COCHERIES ETAIENT AFFECTES, QUE LE PAIEMENT DE CES PRODUITS AYANT ETE PRECISEMENT CONSENTIE CONSTITUAIT, AVANT LA CONCLUSION DE LA CONVENTION, A DES FONCTIONS PLUS QUALIFIEES DU PRINCIPAL, A CETTE MACHINE PAR LES PRATIQUES \"DONT\n", "\n", "[5601 | 866.46] loss=1.36 avg=1.28\n", "[5602 | 867.75] loss=1.23 avg=1.28\n", "[5603 | 869.04] loss=1.49 avg=1.28\n", "[5604 | 870.32] loss=1.22 avg=1.28\n", "[5605 | 871.61] loss=1.35 avg=1.28\n", "[5606 | 872.89] loss=1.13 avg=1.28\n", "[5607 | 874.20] loss=1.36 avg=1.28\n", "[5608 | 875.50] loss=1.28 avg=1.28\n", "[5609 | 876.79] loss=1.22 avg=1.28\n", "[5610 | 878.07] loss=1.31 avg=1.28\n", "[5611 | 879.35] loss=1.26 avg=1.28\n", "[5612 | 880.63] loss=1.48 avg=1.28\n", "[5613 | 881.91] loss=1.07 avg=1.28\n", "[5614 | 883.19] loss=1.31 avg=1.28\n", "[5615 | 884.47] loss=1.22 avg=1.28\n", "[5616 | 885.75] loss=1.19 avg=1.28\n", "[5617 | 887.03] loss=1.41 avg=1.28\n", "[5618 | 888.31] loss=1.24 avg=1.28\n", "[5619 | 889.59] loss=1.36 avg=1.28\n", "[5620 | 890.86] loss=1.26 avg=1.28\n", "[5621 | 892.14] loss=1.35 avg=1.28\n", "[5622 | 893.42] loss=1.29 avg=1.28\n", "[5623 | 894.70] loss=1.31 avg=1.28\n", "[5624 | 895.98] loss=1.23 avg=1.28\n", "[5625 | 897.26] loss=1.43 avg=1.28\n", "[5626 | 898.54] loss=1.23 avg=1.28\n", "[5627 | 899.81] loss=1.27 avg=1.28\n", "[5628 | 901.10] loss=1.27 avg=1.28\n", "[5629 | 902.39] loss=1.21 avg=1.28\n", "[5630 | 903.68] loss=1.27 avg=1.28\n", "[5631 | 904.96] loss=1.27 avg=1.28\n", "[5632 | 906.24] loss=1.29 avg=1.28\n", "[5633 | 907.55] loss=1.12 avg=1.28\n", "[5634 | 908.84] loss=1.16 avg=1.28\n", "[5635 | 910.11] loss=1.50 avg=1.28\n", "[5636 | 911.39] loss=1.32 avg=1.28\n", "[5637 | 912.67] loss=1.30 avg=1.28\n", "[5638 | 913.95] loss=1.34 avg=1.28\n", "[5639 | 915.22] loss=1.30 avg=1.28\n", "[5640 | 916.51] loss=1.25 avg=1.28\n", "[5641 | 917.80] loss=1.35 avg=1.28\n", "[5642 | 919.08] loss=1.25 avg=1.28\n", "[5643 | 920.36] loss=1.19 avg=1.28\n", "[5644 | 921.63] loss=1.04 avg=1.28\n", "[5645 | 922.91] loss=1.30 avg=1.28\n", "[5646 | 924.19] loss=1.12 avg=1.27\n", "[5647 | 925.46] loss=1.24 avg=1.27\n", "[5648 | 926.74] loss=1.27 avg=1.27\n", "[5649 | 928.03] loss=1.12 avg=1.27\n", "[5650 | 929.32] loss=1.20 avg=1.27\n", "[5651 | 930.59] loss=1.14 avg=1.27\n", "[5652 | 931.87] loss=1.35 avg=1.27\n", "[5653 | 933.15] loss=1.13 avg=1.27\n", "[5654 | 934.42] loss=1.29 avg=1.27\n", "[5655 | 935.71] loss=1.12 avg=1.27\n", "[5656 | 937.01] loss=1.29 avg=1.27\n", "[5657 | 938.30] loss=1.24 avg=1.27\n", "[5658 | 939.61] loss=1.34 avg=1.27\n", "[5659 | 940.91] loss=1.22 avg=1.27\n", "[5660 | 942.19] loss=1.15 avg=1.27\n", "[5661 | 943.47] loss=1.27 avg=1.27\n", "[5662 | 944.74] loss=1.05 avg=1.27\n", "[5663 | 946.02] loss=1.19 avg=1.26\n", "[5664 | 947.29] loss=1.31 avg=1.27\n", "[5665 | 948.57] loss=1.04 avg=1.26\n", "[5666 | 949.85] loss=1.38 avg=1.26\n", "[5667 | 951.15] loss=1.26 avg=1.26\n", "[5668 | 952.43] loss=1.26 avg=1.26\n", "[5669 | 953.71] loss=1.27 avg=1.26\n", "[5670 | 954.98] loss=1.19 avg=1.26\n", "[5671 | 956.26] loss=1.12 avg=1.26\n", "[5672 | 957.54] loss=1.25 avg=1.26\n", "[5673 | 958.81] loss=1.34 avg=1.26\n", "[5674 | 960.09] loss=1.24 avg=1.26\n", "[5675 | 961.40] loss=1.23 avg=1.26\n", "[5676 | 962.68] loss=1.23 avg=1.26\n", "[5677 | 963.96] loss=1.44 avg=1.26\n", "[5678 | 965.24] loss=1.54 avg=1.27\n", "[5679 | 966.52] loss=1.17 avg=1.27\n", "[5680 | 967.80] loss=1.21 avg=1.26\n", "[5681 | 969.07] loss=1.21 avg=1.26\n", "[5682 | 970.36] loss=1.20 avg=1.26\n", "[5683 | 971.65] loss=1.33 avg=1.26\n", "[5684 | 973.07] loss=1.18 avg=1.26\n", "[5685 | 974.42] loss=1.25 avg=1.26\n", "[5686 | 975.70] loss=1.25 avg=1.26\n", "[5687 | 976.98] loss=1.21 avg=1.26\n", "[5688 | 978.26] loss=1.17 avg=1.26\n", "[5689 | 979.53] loss=1.28 avg=1.26\n", "[5690 | 980.81] loss=1.13 avg=1.26\n", "[5691 | 982.09] loss=1.22 avg=1.26\n", "[5692 | 983.39] loss=1.35 avg=1.26\n", "[5693 | 984.67] loss=1.15 avg=1.26\n", "[5694 | 985.94] loss=1.27 avg=1.26\n", "[5695 | 987.22] loss=1.31 avg=1.26\n", "[5696 | 988.50] loss=1.19 avg=1.26\n", "[5697 | 989.77] loss=1.37 avg=1.26\n", "[5698 | 991.05] loss=1.32 avg=1.26\n", "[5699 | 992.32] loss=1.22 avg=1.26\n", "[5700 | 993.60] loss=1.29 avg=1.26\n", "======== SAMPLE 1 ========\n", " PAR LE PORTEUR LE 28 FEVRIER 1964, NE POUVAIT ETRE VALABLEMENT EN MESURE DE FAIRE RECONNAITRE LE CARACTERE D'AGRICULTURE DE RUPTURE ; ATTENDU QUE SELON LES ENONCIATIONS DE L'ARRET ATTAQUE, M. Y... A ACQUIS DE X..., QUE CELUI-CI A ETE CONVENU QU'IL ETAIT, NOTAMMENT, \"PARFAIT DES PERTES DONT LE FAIT CONSTITUTIF, DANS LA PROPRIETE DES CONSORTS X... PENAIENT POUR LA MARCHANDISE DU CHEMINEE AUX INTERETS DE L'EXPLOITATION DE LA CLOSURE\" A QUE CEUX-CI AVAIENT COMMIS DES FAUTES QUI AVAIENT PROVOQUE CES PERTES ET QUE LA RESPONSABILITE DE MME Y... ETAIT ENGAGEE, QU'EN VUE D'OBTENIR LA RESILIATION DE L'ACTIVITE DE SON X..., LA COUR D'APPEL A ENCAISSE LA RESPONSABILITE DE BESANCON DE SE PREVALANT TANT DE LA MORT POUVANT RESULTER D'UN DROIT PLUS DE MANIERE DE PERTES DONT LE DEFAUT DE GESTION NE FAISAIT ETAT ; ATTENDU QU'EN SE DECIDANT DE L'ABSENCE D'UN FAIT PORTANT LA PROPRIETE DES CONSORTS X... POUR JUSTIFIER LE MANDAT DE RETROUVER LES CONSEQUENCES DE L'EXISTENCE DE FAUX, LA COUR D'APPEL A DECIDE QUE M. Y... AVAIT COMMIS DES FAUTES PERSONNELLES DE L'X... QUI AVAIENT PROVOQUE BIEN LES DIFFICULTES ET DES PREUVES QU'IL AVAIT ENVISAGE AVEC LE MONDE DU CHEMINEE DU PORTEUR LE 28 FEVRIER 1964 ET QU'IL EUT AGI A POUR OBJET D'APPORTER LA PREUVE QUE CETTE MESURE A ETE DECLAREE A L'OCCASION DE LEURS ACCOUCHEMENTS DES EXIGENCES ET DE CONTROLE; D'OU IL SUIT QUE LE MOYEN N'EST PAS FONDE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDU PAR LE 16 MARS 1978 PAR LA COUR D'APPEL DE PARIS. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QU'IL EST REPROCHE A L'ARRET INFIRMATIF ATTAQUE (AMIENS, 3 OCTOBRE 1977) D'AVOIR DECIDE QUE LES EPOUX Y..., N'ENTENDAIENT PAS DANS LA SOCIETE COMPAGNIE POUR MALADIE, ET REPROCHANT A X... DE LE PAIEMENT DU TITRE DE TITRES NON REMPLIES PAR LE PAIEMENT DES EPOUX X... MME X... POUR UNE DUREE DE TROIS ANNEES, ETAIT EN DROIT D'EN AVOIR A PAYER UNE SOMME CORRESPONDANT A LA TOTALITE DU TITRE PENDANT LA PERIODE DE TROIS MOIS A LAQUELLE LES EPOUX X... SONT INTERVENUS LE 13 JUIN 1977, ALORS, D'UNE PART, QUE LE DROIT D'AGIR NE PEUT REPARATION QU'EN CAS DE FORCE MAJEURE ET NON FORCE MAJEURE, ET QUE L'ARRET ATTAQUE, TOUT EN CONSTATANT QUE L'INTERET POUR LA SOCIETE N'ENTRAIT PAS DE FAUX ET NE LEUR POUVAIT FAIRE LA LOI; ET ALORS, D'AUTRE PART, QUE CETTE COUR D'APPEL A FAIT ETAT DU \"DUIT\" DE LA SOCIETE COMPAGNIE L'ENSEMBLE, TENDANT A L'EMONAUT DES CONCLUSIONS FAISANT VALOIR QUE LES EPOUX Y\n", "\n", "[5701 | 1007.31] loss=1.32 avg=1.26\n", "[5702 | 1008.63] loss=1.26 avg=1.26\n", "[5703 | 1009.92] loss=1.14 avg=1.26\n", "[5704 | 1011.20] loss=1.41 avg=1.26\n", "[5705 | 1012.48] loss=1.17 avg=1.26\n", "[5706 | 1013.76] loss=1.33 avg=1.26\n", "[5707 | 1015.03] loss=1.22 avg=1.26\n", "[5708 | 1016.33] loss=1.25 avg=1.26\n", "[5709 | 1017.61] loss=1.33 avg=1.26\n", "[5710 | 1018.88] loss=1.26 avg=1.26\n", "[5711 | 1020.16] loss=1.22 avg=1.26\n", "[5712 | 1021.44] loss=1.39 avg=1.26\n", "[5713 | 1022.71] loss=1.14 avg=1.26\n", "[5714 | 1023.99] loss=1.25 avg=1.26\n", "[5715 | 1025.26] loss=1.27 avg=1.26\n", "[5716 | 1026.55] loss=1.29 avg=1.26\n", "[5717 | 1027.84] loss=1.38 avg=1.26\n", "[5718 | 1029.12] loss=1.37 avg=1.26\n", "[5719 | 1030.39] loss=1.11 avg=1.26\n", "[5720 | 1031.67] loss=1.22 avg=1.26\n", "[5721 | 1032.95] loss=1.34 avg=1.26\n", "[5722 | 1034.22] loss=1.18 avg=1.26\n", "[5723 | 1035.50] loss=1.26 avg=1.26\n", "[5724 | 1036.78] loss=1.17 avg=1.26\n", "[5725 | 1038.07] loss=1.51 avg=1.26\n", "[5726 | 1039.35] loss=1.38 avg=1.26\n", "[5727 | 1040.64] loss=1.25 avg=1.26\n", "[5728 | 1041.94] loss=1.22 avg=1.26\n", "[5729 | 1043.22] loss=1.23 avg=1.26\n", "[5730 | 1044.52] loss=1.33 avg=1.26\n", "[5731 | 1045.81] loss=1.21 avg=1.26\n", "[5732 | 1047.09] loss=1.35 avg=1.26\n", "[5733 | 1048.37] loss=1.50 avg=1.27\n", "[5734 | 1049.66] loss=1.13 avg=1.27\n", "[5735 | 1050.94] loss=1.30 avg=1.27\n", "[5736 | 1052.22] loss=1.20 avg=1.27\n", "[5737 | 1053.49] loss=1.24 avg=1.27\n", "[5738 | 1054.77] loss=1.17 avg=1.26\n", "[5739 | 1056.06] loss=1.22 avg=1.26\n", "[5740 | 1057.34] loss=1.29 avg=1.26\n", "[5741 | 1058.61] loss=1.26 avg=1.26\n", "[5742 | 1059.89] loss=1.31 avg=1.26\n", "[5743 | 1061.20] loss=1.21 avg=1.26\n", "[5744 | 1062.48] loss=1.32 avg=1.26\n", "[5745 | 1063.76] loss=1.26 avg=1.26\n", "[5746 | 1065.04] loss=1.31 avg=1.26\n", "[5747 | 1066.31] loss=1.43 avg=1.27\n", "[5748 | 1067.59] loss=1.31 avg=1.27\n", "[5749 | 1068.87] loss=1.25 avg=1.27\n", "[5750 | 1070.15] loss=1.23 avg=1.27\n", "[5751 | 1071.44] loss=1.16 avg=1.27\n", "[5752 | 1072.73] loss=1.30 avg=1.27\n", "[5753 | 1074.03] loss=1.11 avg=1.26\n", "[5754 | 1075.31] loss=1.14 avg=1.26\n", "[5755 | 1076.58] loss=1.12 avg=1.26\n", "[5756 | 1077.87] loss=1.26 avg=1.26\n", "[5757 | 1079.16] loss=1.18 avg=1.26\n", "[5758 | 1080.45] loss=1.21 avg=1.26\n", "[5759 | 1081.73] loss=1.12 avg=1.26\n", "[5760 | 1083.03] loss=1.18 avg=1.26\n", "[5761 | 1084.31] loss=1.05 avg=1.26\n", "[5762 | 1085.59] loss=1.27 avg=1.26\n", "[5763 | 1086.87] loss=1.30 avg=1.26\n", "[5764 | 1088.14] loss=1.24 avg=1.26\n", "[5765 | 1089.42] loss=1.24 avg=1.26\n", "[5766 | 1090.70] loss=1.14 avg=1.25\n", "[5767 | 1091.97] loss=1.13 avg=1.25\n", "[5768 | 1093.27] loss=1.11 avg=1.25\n", "[5769 | 1094.55] loss=1.14 avg=1.25\n", "[5770 | 1095.83] loss=1.25 avg=1.25\n", "[5771 | 1097.11] loss=1.26 avg=1.25\n", "[5772 | 1098.38] loss=1.32 avg=1.25\n", "[5773 | 1099.66] loss=1.29 avg=1.25\n", "[5774 | 1100.94] loss=1.26 avg=1.25\n", "[5775 | 1102.22] loss=1.20 avg=1.25\n", "[5776 | 1103.49] loss=1.51 avg=1.25\n", "[5777 | 1104.79] loss=1.27 avg=1.25\n", "[5778 | 1106.07] loss=1.28 avg=1.25\n", "[5779 | 1107.35] loss=1.12 avg=1.25\n", "[5780 | 1108.65] loss=1.19 avg=1.25\n", "[5781 | 1109.94] loss=1.19 avg=1.25\n", "[5782 | 1111.22] loss=1.21 avg=1.25\n", "[5783 | 1112.50] loss=1.45 avg=1.25\n", "[5784 | 1113.79] loss=1.23 avg=1.25\n", "[5785 | 1115.12] loss=1.32 avg=1.25\n", "[5786 | 1116.42] loss=1.30 avg=1.25\n", "[5787 | 1117.69] loss=1.23 avg=1.25\n", "[5788 | 1118.97] loss=1.31 avg=1.26\n", "[5789 | 1120.25] loss=1.29 avg=1.26\n", "[5790 | 1121.53] loss=1.15 avg=1.25\n", "[5791 | 1122.80] loss=1.05 avg=1.25\n", "[5792 | 1124.08] loss=1.22 avg=1.25\n", "[5793 | 1125.36] loss=1.46 avg=1.25\n", "[5794 | 1126.65] loss=1.09 avg=1.25\n", "[5795 | 1127.93] loss=1.09 avg=1.25\n", "[5796 | 1129.21] loss=1.33 avg=1.25\n", "[5797 | 1130.48] loss=1.27 avg=1.25\n", "[5798 | 1131.76] loss=1.16 avg=1.25\n", "[5799 | 1133.03] loss=1.38 avg=1.25\n", "[5800 | 1134.31] loss=1.38 avg=1.25\n", "======== SAMPLE 1 ========\n", "PURATION AU PROFIT DE M X... DEUX PRECEDENTES ENCHERISSAIENT QU'IL ETAIT AFFECTE AU PRIX DU FONDS DE LA VILLE DE LANGERS ET DU CONSTRUCTION DE LA MARNE PROFESSIONNELLE QU'AURAIT PU EXCLURE L'USAGE DE L'HOPITAL DE LANGERS DU RAPPORT A SON DOMICILE, ET QUE LES CONSORTS Z... DESIGNAIENT SEUL LES CONSORTS DES X..., FAISANT FONCTIONS D'AUTORITE DE M X..., QU'ILS AVAIENT FAIT VALOIR QUE CES DERNIERES DEVAIENT REMONTER A AIDE-GERANT, NOTAMMENT AVEC D'AUTRES PERSONNES, QUE LA CAISSE PRIMAIRE AVAIT, QUE LA CAISSATURE, LA DUREE DU PRECEDENT PRIX, UNE EXCEPTIONNELLE DECISION DEFINITIVE, A VIOLE LE TEXTE SUSVISE ; <|endoftext|>\n", "<|startoftext|> SUR LE PREMIER MOYEN : VU L'ARTICLE L. 631-2, DU CODE DU TRAVAIL, DE L'ANNEXE III, DE SA PROFESSION DE MARIAGE ;ATTENDU QU'IL RESULTE DE CE TEXTE QUE CHACUN DES PERSONNELS AUXQUELS D'UN ACCORD ENTRE LES PARTIES ONT POUR PRINCIPE L'ENGAGEMENT D'ENRICHIN, EN APPLICATION DES ARTICLES L. 531-1 ET L. 531-2 DU CODE DU TRAVAIL, TOUTE NATURE D'ENGAGEMENT QUI NE PEUT TENIR LIEU DE CAUSE DE L'EMPLOYEUR ET NE COMPORTENT PAS DE FACON EXCEPTIVE, ET AUX LIEU D'EFFECTIFS PREVUS PAR L'ACCORD QU'ELLES OCCUPAIENT ;ATTENDU, SELON L'ARRET ATTAQUE, QU'APRES AVOIR TRAVAILLE AU SERVICE DE L'ANNEXE III, BIEN QU'IL EUT ETE AVISE LE 1ER JUIN 1964, UNE ORGANISATION PREVOYANT, A LA CONDITION DE LAQUELLE DEUX PERSONNES ONT ETE ENGAGES, L'EMPLOYEUR A CONFONDU DES CONSEILS DE POURCENTAGE, SUR LA TERRE DE L'ANNEXE III, CET ENGAGEMENT CONSTITUANT LE RISQUE DE RENTRICE, DEPUIS LE 1ER JUIN 1965 QUI N'EUT PAS ETE EXORDONNE, LA RENTE LITIGIEUSE, NE FAISANT PAS OBSTACLE AUX CONSEILS DE POURCENTAGE D'AU MOINS EGALE ET N'ERAIT PAS D'AUTORITE DE LA CHOSE JUGEE QUI FAISAIT, SELON LA CAHIER DES CHARGES, LE RISQUE D'ACTIVITE QUE L'EMPLOYEUR N'A DE SOLLICITES AINSI QU'IL EUT PRECISE QUE LE CONTRAT DE TRAVAIL AVAIT ETE EXCEPTIONNE EXACT, SONT RECEVABLES ; QUE LE 25 MARS 1978 LA RENTE INITIALE AYANT ETE FIXEE A 5 % ET QU'IL A ETE RENOUVELE A LA SOMME DE 150,68 FRANCS; QUE LA CAISSE PRIMAIRE A ETE LICENCIEE PAR REFUS DE POURSUITE ET DECLAREE RECEVABLE LA DEMANDE QUI AVAIT ETE FORMEE PAR L'OUVRAGE FONDE DES VENTES ; QUE, POUR RETENIR LA CONTESTER NON NULLEMENT L'ENGAGEMENT DE POURSUITE, LA COUR D'APPEL, ABSTRACTION FAITE D'UNE SIMPLE REQUISITION DE CARACTERE PROPRE PAR LESQUELS SES DISPOSITIONS CONCERNANT CE CONTRAT, A ESTIME QUE DES CONSEILS DE POURCENTAGE N'ETAIENT PAS CONFORMEMENT AUX DISPOSITIFS LEGALES ;ATTENDU, CEPENDANT, QUE L'ENGAGEMENT D'AGENT-PROBAT DU 1\n", "\n", "[5801 | 1147.99] loss=1.26 avg=1.25\n", "[5802 | 1149.33] loss=1.34 avg=1.25\n", "[5803 | 1150.62] loss=1.13 avg=1.25\n", "[5804 | 1151.90] loss=1.18 avg=1.25\n", "[5805 | 1153.18] loss=1.14 avg=1.25\n", "[5806 | 1154.46] loss=1.27 avg=1.25\n", "[5807 | 1155.73] loss=1.31 avg=1.25\n", "[5808 | 1157.01] loss=1.26 avg=1.25\n", "[5809 | 1158.29] loss=1.12 avg=1.25\n", "[5810 | 1159.59] loss=1.28 avg=1.25\n", "[5811 | 1160.86] loss=1.25 avg=1.25\n", "[5812 | 1162.14] loss=1.24 avg=1.25\n", "[5813 | 1163.41] loss=1.42 avg=1.25\n", "[5814 | 1164.69] loss=1.13 avg=1.25\n", "[5815 | 1165.97] loss=1.12 avg=1.25\n", "[5816 | 1167.24] loss=1.16 avg=1.25\n", "[5817 | 1168.52] loss=1.39 avg=1.25\n", "[5818 | 1169.80] loss=1.17 avg=1.25\n", "[5819 | 1171.10] loss=1.30 avg=1.25\n", "[5820 | 1172.38] loss=1.20 avg=1.25\n", "[5821 | 1173.67] loss=1.34 avg=1.25\n", "[5822 | 1174.96] loss=1.14 avg=1.25\n", "[5823 | 1176.25] loss=1.43 avg=1.25\n", "[5824 | 1177.52] loss=1.25 avg=1.25\n", "[5825 | 1178.80] loss=1.27 avg=1.25\n", "[5826 | 1180.08] loss=1.42 avg=1.25\n", "[5827 | 1181.38] loss=1.23 avg=1.25\n", "[5828 | 1182.69] loss=1.23 avg=1.25\n", "[5829 | 1183.98] loss=1.34 avg=1.25\n", "[5830 | 1185.27] loss=1.22 avg=1.25\n", "[5831 | 1186.57] loss=1.40 avg=1.25\n", "[5832 | 1187.84] loss=1.10 avg=1.25\n", "[5833 | 1189.12] loss=1.27 avg=1.25\n", "[5834 | 1190.39] loss=1.14 avg=1.25\n", "[5835 | 1191.67] loss=1.12 avg=1.25\n", "[5836 | 1192.97] loss=1.33 avg=1.25\n", "[5837 | 1194.25] loss=1.25 avg=1.25\n", "[5838 | 1195.52] loss=1.18 avg=1.25\n", "[5839 | 1196.80] loss=1.46 avg=1.25\n", "[5840 | 1198.08] loss=1.27 avg=1.25\n", "[5841 | 1199.36] loss=1.21 avg=1.25\n", "[5842 | 1200.63] loss=1.11 avg=1.25\n", "[5843 | 1201.91] loss=1.11 avg=1.25\n", "[5844 | 1203.20] loss=1.25 avg=1.25\n", "[5845 | 1204.48] loss=1.23 avg=1.25\n", "[5846 | 1205.75] loss=1.26 avg=1.25\n", "[5847 | 1207.06] loss=1.20 avg=1.25\n", "[5848 | 1208.34] loss=1.17 avg=1.25\n", "[5849 | 1209.62] loss=1.26 avg=1.25\n", "[5850 | 1210.89] loss=1.28 avg=1.25\n", "[5851 | 1212.17] loss=1.28 avg=1.25\n", "[5852 | 1213.45] loss=1.38 avg=1.25\n", "[5853 | 1214.74] loss=1.36 avg=1.25\n", "[5854 | 1216.02] loss=1.21 avg=1.25\n", "[5855 | 1217.31] loss=1.20 avg=1.25\n", "[5856 | 1218.60] loss=1.19 avg=1.25\n", "[5857 | 1219.89] loss=1.31 avg=1.25\n", "[5858 | 1221.17] loss=1.33 avg=1.25\n", "[5859 | 1222.44] loss=1.33 avg=1.25\n", "[5860 | 1223.72] loss=1.18 avg=1.25\n", "[5861 | 1225.00] loss=1.28 avg=1.25\n", "[5862 | 1226.29] loss=1.14 avg=1.25\n", "[5863 | 1227.56] loss=1.18 avg=1.25\n", "[5864 | 1228.84] loss=1.30 avg=1.25\n", "[5865 | 1230.12] loss=1.20 avg=1.25\n", "[5866 | 1231.40] loss=1.25 avg=1.25\n", "[5867 | 1232.67] loss=1.21 avg=1.25\n", "[5868 | 1233.95] loss=1.25 avg=1.25\n", "[5869 | 1235.23] loss=1.24 avg=1.25\n", "[5870 | 1236.52] loss=1.32 avg=1.25\n", "[5871 | 1237.80] loss=1.28 avg=1.25\n", "[5872 | 1239.08] loss=1.26 avg=1.25\n", "[5873 | 1240.38] loss=1.26 avg=1.25\n", "[5874 | 1241.66] loss=1.28 avg=1.25\n", "[5875 | 1242.94] loss=1.17 avg=1.25\n", "[5876 | 1244.21] loss=1.22 avg=1.25\n", "[5877 | 1245.49] loss=1.29 avg=1.25\n", "[5878 | 1246.77] loss=1.23 avg=1.25\n", "[5879 | 1248.06] loss=1.37 avg=1.25\n", "[5880 | 1249.34] loss=1.23 avg=1.25\n", "[5881 | 1250.62] loss=1.36 avg=1.25\n", "[5882 | 1251.90] loss=1.51 avg=1.25\n", "[5883 | 1253.19] loss=1.04 avg=1.25\n", "[5884 | 1254.47] loss=1.08 avg=1.25\n", "[5885 | 1255.76] loss=1.23 avg=1.25\n", "[5886 | 1257.04] loss=1.24 avg=1.25\n", "[5887 | 1258.33] loss=1.28 avg=1.25\n", "[5888 | 1259.61] loss=1.06 avg=1.25\n", "[5889 | 1260.90] loss=1.31 avg=1.25\n", "[5890 | 1262.17] loss=1.20 avg=1.25\n", "[5891 | 1263.45] loss=1.20 avg=1.25\n", "[5892 | 1264.72] loss=1.16 avg=1.25\n", "[5893 | 1266.01] loss=1.29 avg=1.25\n", "[5894 | 1267.28] loss=1.32 avg=1.25\n", "[5895 | 1268.56] loss=1.24 avg=1.25\n", "[5896 | 1269.86] loss=1.22 avg=1.25\n", "[5897 | 1271.14] loss=1.12 avg=1.25\n", "[5898 | 1272.43] loss=1.29 avg=1.25\n", "[5899 | 1273.72] loss=1.25 avg=1.25\n", "[5900 | 1275.00] loss=1.28 avg=1.25\n", "======== SAMPLE 1 ========\n", "GIE DES ETABLISSEMENTS JEAN F., LA SOCIETE AINSI QUE CELUI-CI DE BOUDEAUX AVAIENT SOUTENU, DANS DES CONCLUSIONS DEMEUREES SANS REPONSE, QUE LA PRISE EN CONSIDERATION DE DIVISEMENT DE L'IMMEUBLE AU PROFIT DE LA SOCIETE BERBASSEMENT DU CENTRE DE COMMERCIALISATION, AUQUEL LA SOCIETE ET D'X... AVAIENT CONCLU, QUE L'ARTICLE 21 DE LA LOI DU 18 JUIN 1966 PREVOIT LEUR IMPOSAIT PRISE EN CONSIDERATION DES MUTATIONS DE L'IMMEUBLE ; QU'EN FIXANT DES DOMMAGES-INTERETS REPARANT TANT L'INTEGRALITE DES BIENS QUE L'INEXACTITUDE OU L'INEXACTITUDE DU JUGE COMMISSAIRE, LA COUR D'APPEL A, PAR REFUS D'APPLICATION DU 16 JUIN 1977 LA MEME QUALITE DE L'ARTICLE 21\", A LA DATE DE L'ACTE DE L'ACTE DE VENTE, FIXE LE POURVOI DE LA SOCIETE DES ETABLISSEMENTS F., QU'ELLE A AINSI LEGALEMENT JUSTIFIE SA DECISION, SANS DENATURER L'HYPOTHESE LITIGIEUSE ET SANS SE CONTREDIRE ; SUR LA FIN DE NON-RECEVOIR SOULEVEE PAR LES JUGES DU FOND ; ATTENDU QUE MME DE Y... FAIT GRIEF AUX JUGES DU SECOND DEGRE D'AVOIR REFUSE DE CONDAMNER LA SOCIETE DES ETABLISSEMENTS F., BIEN QU'IL N'AIT PAS ETE DEMONTRE QUE LA SOCIETE BERBASSEMENT DU CENTRE AIT ETE CONSERVEE ; ALORS QUE, SELON LE MOYEN, LE MOYEN N'AURAIT PAS ETE SOUMIS AUX DISPOSITIONS DE LA LOI DU 18 JUIN 1966 QUI N'AURAIT PAS A ETRE REVISEE PAR L'ASSEMBLEE GENERALE, CE QUI RECONNAISSAIT L'OBLIGATION, QUE LES TEXTES VISES AU MOYEN, NE POUVAIENT PREVOIR QUE L'ARTICLE 17, DU DECRET DU 20 DECEMBRE 1961 N'A PAS FAIT L'OBJET D'UNE RECHERCHE EN LA MATIERE SUR LA QUALITE PROFESSIONNELLE DE DIVISEMENTS DE L'IMMEUBLE, QUE LES DEUX MOYENS DE LA RECHERCHE GENERALE, N'AURAIENT PU ENTACHER LEUR DECISION DE MOTIFS ET ALORS QUE, SELON LE MOYEN, LA LOI DU 18 JUIN 1966 N'A COMPETENCE POUR TOUTES CIRCONSTANCES DE FAIT ET QUE L'ARTICLE 17, DU DECRET DU 20 DECEMBRE 1961 N'A ETE RECONNU QUE PAR DES PERSONNES QUI LEUR SONT SUBSTITUES A CE REGIME ; MAIS ATTENDU QUE L'ARTICLE 17 DU DECRET DU 20 DECEMBRE 1961, QUI PREVOIT LEUR IMPOSAIT PRISE EN CONSIDERATION DES MUTATIONS DE L'IMMEUBLE, N'IMPOSE A LA SOCIETE BERBASSEMENT POUR DETERMINER LE BIEN-FONDE DE CETTE DEMANDE ; QUE, PAR CE MOTIF, SANS VIOLER LE PRINCIPE DE LA CONTRADICTION, L'ARRET EN A DEDUIT QUE CES DISPOSITIONS DE LA LOI DU 18 JUIN 1966 N'APPARAISSAIENT PAS, NONOBSTANT LE PRIX, MAIS EN TOUTES CIRCONSTANCES, PEUT ETRE CONFERES AU PROBLEME OU AU MOYEN D'UN FONCTIONNEMENT GRAVE, MAIS D'UN FONCTIONNEMENT AYANT POUR OBTENU LE GESTION DE L'IMMEUBLE, DE SORTE QU'EN REALITE ELLES NE POUVAIENT EN RAVINER AVANT LA DATE\n", "\n", "[5901 | 1288.26] loss=1.15 avg=1.25\n", "[5902 | 1289.55] loss=1.39 avg=1.25\n", "[5903 | 1290.84] loss=1.30 avg=1.25\n", "[5904 | 1292.18] loss=1.21 avg=1.25\n", "[5905 | 1293.45] loss=1.23 avg=1.25\n", "[5906 | 1294.73] loss=1.30 avg=1.25\n", "[5907 | 1296.02] loss=1.18 avg=1.25\n", "[5908 | 1297.30] loss=1.30 avg=1.25\n", "[5909 | 1298.57] loss=1.24 avg=1.25\n", "[5910 | 1299.85] loss=1.12 avg=1.25\n", "[5911 | 1301.13] loss=1.23 avg=1.25\n", "[5912 | 1302.42] loss=1.26 avg=1.25\n", "[5913 | 1303.70] loss=1.29 avg=1.25\n", "[5914 | 1305.00] loss=1.32 avg=1.25\n", "[5915 | 1306.29] loss=1.24 avg=1.25\n", "[5916 | 1307.56] loss=1.08 avg=1.25\n", "[5917 | 1308.84] loss=1.28 avg=1.25\n", "[5918 | 1310.11] loss=1.14 avg=1.25\n", "[5919 | 1311.39] loss=1.19 avg=1.25\n", "[5920 | 1312.67] loss=1.17 avg=1.24\n", "[5921 | 1313.96] loss=1.18 avg=1.24\n", "[5922 | 1315.24] loss=1.23 avg=1.24\n", "[5923 | 1316.52] loss=1.22 avg=1.24\n", "[5924 | 1317.80] loss=1.22 avg=1.24\n", "[5925 | 1319.07] loss=1.27 avg=1.24\n", "[5926 | 1320.35] loss=1.25 avg=1.24\n", "[5927 | 1321.63] loss=1.39 avg=1.25\n", "[5928 | 1322.91] loss=1.27 avg=1.25\n", "[5929 | 1324.23] loss=1.17 avg=1.24\n", "[5930 | 1325.53] loss=1.27 avg=1.24\n", "[5931 | 1326.83] loss=1.25 avg=1.25\n", "[5932 | 1328.11] loss=1.32 avg=1.25\n", "[5933 | 1329.39] loss=1.34 avg=1.25\n", "[5934 | 1330.66] loss=1.29 avg=1.25\n", "[5935 | 1331.94] loss=1.22 avg=1.25\n", "[5936 | 1333.22] loss=1.13 avg=1.25\n", "[5937 | 1334.50] loss=1.09 avg=1.24\n", "[5938 | 1335.80] loss=1.27 avg=1.24\n", "[5939 | 1337.08] loss=1.16 avg=1.24\n", "[5940 | 1338.40] loss=1.24 avg=1.24\n", "[5941 | 1339.68] loss=1.38 avg=1.24\n", "[5942 | 1340.96] loss=1.24 avg=1.24\n", "[5943 | 1342.23] loss=1.19 avg=1.24\n", "[5944 | 1343.51] loss=1.24 avg=1.24\n", "[5945 | 1344.79] loss=1.23 avg=1.24\n", "[5946 | 1346.08] loss=1.18 avg=1.24\n", "[5947 | 1347.37] loss=1.31 avg=1.24\n", "[5948 | 1348.65] loss=1.24 avg=1.24\n", "[5949 | 1349.93] loss=1.22 avg=1.24\n", "[5950 | 1351.21] loss=1.27 avg=1.24\n", "[5951 | 1352.48] loss=1.29 avg=1.24\n", "[5952 | 1353.76] loss=1.21 avg=1.24\n", "[5953 | 1355.04] loss=1.23 avg=1.24\n", "[5954 | 1356.32] loss=1.31 avg=1.24\n", "[5955 | 1357.68] loss=1.14 avg=1.24\n", "[5956 | 1358.97] loss=1.21 avg=1.24\n", "[5957 | 1360.26] loss=1.25 avg=1.24\n", "[5958 | 1361.55] loss=1.21 avg=1.24\n", "[5959 | 1362.83] loss=1.20 avg=1.24\n", "[5960 | 1364.11] loss=1.17 avg=1.24\n", "[5961 | 1365.38] loss=1.26 avg=1.24\n", "[5962 | 1366.66] loss=1.17 avg=1.24\n", "[5963 | 1367.94] loss=1.18 avg=1.24\n", "[5964 | 1369.24] loss=1.31 avg=1.24\n", "[5965 | 1370.54] loss=1.15 avg=1.24\n", "[5966 | 1371.82] loss=1.15 avg=1.24\n", "[5967 | 1373.09] loss=1.22 avg=1.24\n", "[5968 | 1374.37] loss=1.10 avg=1.24\n", "[5969 | 1375.65] loss=1.25 avg=1.24\n", "[5970 | 1376.93] loss=1.30 avg=1.24\n", "[5971 | 1378.20] loss=1.13 avg=1.24\n", "[5972 | 1379.49] loss=1.32 avg=1.24\n", "[5973 | 1380.77] loss=1.17 avg=1.24\n", "[5974 | 1382.04] loss=1.19 avg=1.24\n", "[5975 | 1383.32] loss=1.21 avg=1.24\n", "[5976 | 1384.60] loss=1.33 avg=1.24\n", "[5977 | 1385.88] loss=1.27 avg=1.24\n", "[5978 | 1387.15] loss=1.24 avg=1.24\n", "[5979 | 1388.43] loss=1.40 avg=1.24\n", "[5980 | 1389.71] loss=1.15 avg=1.24\n", "[5981 | 1391.00] loss=1.30 avg=1.24\n", "[5982 | 1392.29] loss=1.26 avg=1.24\n", "[5983 | 1393.58] loss=1.22 avg=1.24\n", "[5984 | 1394.87] loss=1.09 avg=1.24\n", "[5985 | 1396.15] loss=1.28 avg=1.24\n", "[5986 | 1397.43] loss=1.49 avg=1.24\n", "[5987 | 1398.70] loss=1.30 avg=1.24\n", "[5988 | 1399.98] loss=1.06 avg=1.24\n", "[5989 | 1401.27] loss=1.42 avg=1.24\n", "[5990 | 1402.60] loss=0.98 avg=1.24\n", "[5991 | 1403.88] loss=1.20 avg=1.24\n", "[5992 | 1405.16] loss=1.15 avg=1.24\n", "[5993 | 1406.43] loss=1.14 avg=1.24\n", "[5994 | 1407.71] loss=1.11 avg=1.24\n", "[5995 | 1408.99] loss=1.23 avg=1.24\n", "[5996 | 1410.27] loss=1.20 avg=1.24\n", "[5997 | 1411.55] loss=1.30 avg=1.24\n", "[5998 | 1412.84] loss=1.25 avg=1.24\n", "[5999 | 1414.12] loss=1.33 avg=1.24\n", "[6000 | 1415.40] loss=1.18 avg=1.24\n", "Saving checkpoint/run1/model-6000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 16:42:29.538805: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:42:29.540221: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:42:29.541209: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:42:29.542318: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:42:29.543309: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 16:42:29.544307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-6000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 2.79it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 9265731 tokens\n", "Training...\n", "Saving checkpoint/run1/model-6000\n", "Saving checkpoint/run1/model-6000\n", "======== SAMPLE 1 ========\n", "clée désignée du 25 juillet 1978; Attendu que la société Céfriatai fait allire partiellement à ses écritures d'appel; Attendu que la Commission de premièure d'instance a décidé que la société Céfriatai ne pouvait être déboutée et à déduit que l'article 1382 du Code civil n'avait été partie en paiement précédent du mois de décembre 1979; Attendu qu'en déduisant que la société Céfriatai n'était pas de base légale à désignée de la commission de première instance, de son condamnation qui dès sa décision en date du 9 mais septembre 1979, en date de la quaterdit pendant lequelle le caractère de la banque de cotisation ait délivré une matériquée de 500 francs à 1,10 francs, le moyen est donc de solution de nature ou à la ventré de la dernière, le moyen, pris en sa première branche ; PAR CES MOTIFS : REJETTE le pourvoi formé contre le jugement rendu le 25 juillet 1978 par le Tribunal d'instance d' Pau de Pau. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU, SELON L'ARRET ATTAQUE, QUE MME D X..., EPOUSE A... ET MARIE A..., SE PLAIGNAIENT DE LA VIE COURANTE DE L'ETUDE DE VIVIDIOS FRANCAIS, APRES AVOIR RAYE L'ETUDE DE MME B..., QUI S'Y AUTORISAIT UN DOMMAGE A SON CONFORT X... COMME UN ELEMENT PREVOYANT PLUSIEURS ELEMENTS, D'UNE RENTE PREALABLE, QUE DES VEHICULES ONT ETE CONSTAMMENT CONFIRMES SUR ELLE POUR PARTIE; QUE LES POURVOIS FORMES CONTRE UN JUGEMENT DU TRIBUNAL D'INSTANCE, APRES AVOIR RELEVE QUE LES ELEMENTS D'ATTEINTE DE L'ENSEMBLE FOURNIE PAR CE FILLE MME D X... NE SONT LES DONC PAS DANS LES PREMIERES PREMIERS JOURS, ET ENONCANT QUE DE L'ENSEMBLE FOURNIE N'EST PAS A DES ELEMENTS D'ATTEINTE, LA COMMISSION DE SECURITE SOCIALE, APRES AVOIR RELEVE D'OFFICE SI LA COMMISSION DE SECURITE SOCIALE, QUI N'A PAS PRECISE QUE LES ELEMENTS CONSTITUANT LE DOMMAGE DOIVENT ETRE INVOQUES ALORS QU'ILS CONSTITUENT UN ELEMENT DE DEPASSEMENT ET DE FAUTE DE RENTE; ATTENDU QU'IL EST FAIT GRIEF A L'ARRET D'AVOIR, POUR ANNULER LE CONFLIT DE L'ETUDE DE MME D X... APRES LE MOYEN, INFIRME LES PRETENTIONS RELATIVES AUX ELEMENTS DE LA VIE, ALORS, D'UNE PART, D'AILLEURS, L'ARRET SE TROUVE RENDU SUR AFFIRMANT QUE LA DECISION DU DIRECTEUR D'ASSISTANCE ET D'EXPERT AVAIT ETE DEPOSEE PAR L'EXPERTS, ET, DE SON POUVOIR D'APPRECIATION, AVAIT ETE L'INVITEE EN APPLICATION DE L'ARTICLE 1384, ALINEA 1ER DU CODE CIVIL EN DEPIT D'INVITABLEMENT LE FONDS DU VEHICULE; MAIS ATTENDU QU'UNE TELLE DECISION N'A PAS ETE ATTAQUEE PAR UNE DECISION DE JUSTICE ETANT ETRANGERE A LA CHARGE DE L'ENFANT\n", "\n", "[6001 | 21.35] loss=2.28 avg=2.28\n", "[6002 | 22.63] loss=1.78 avg=2.03\n", "[6003 | 23.90] loss=2.02 avg=2.03\n", "[6004 | 25.18] loss=1.63 avg=1.92\n", "[6005 | 26.46] loss=1.57 avg=1.85\n", "[6006 | 27.73] loss=2.16 avg=1.90\n", "[6007 | 29.08] loss=1.67 avg=1.87\n", "[6008 | 30.41] loss=1.97 avg=1.88\n", "[6009 | 31.69] loss=1.53 avg=1.84\n", "[6010 | 32.98] loss=1.82 avg=1.84\n", "[6011 | 34.28] loss=1.81 avg=1.84\n", "[6012 | 35.56] loss=1.83 avg=1.84\n", "[6013 | 36.83] loss=1.77 avg=1.83\n", "[6014 | 38.12] loss=1.87 avg=1.83\n", "[6015 | 39.40] loss=1.50 avg=1.81\n", "[6016 | 40.69] loss=1.30 avg=1.78\n", "[6017 | 41.97] loss=1.68 avg=1.77\n", "[6018 | 43.25] loss=1.75 avg=1.77\n", "[6019 | 44.52] loss=1.85 avg=1.77\n", "[6020 | 45.80] loss=1.90 avg=1.78\n", "[6021 | 47.08] loss=1.77 avg=1.78\n", "[6022 | 48.35] loss=1.73 avg=1.78\n", "[6023 | 49.63] loss=1.65 avg=1.77\n", "[6024 | 50.93] loss=1.62 avg=1.76\n", "[6025 | 52.21] loss=1.65 avg=1.76\n", "[6026 | 53.49] loss=1.82 avg=1.76\n", "[6027 | 54.76] loss=1.88 avg=1.77\n", "[6028 | 56.04] loss=1.99 avg=1.78\n", "[6029 | 57.32] loss=1.79 avg=1.78\n", "[6030 | 58.59] loss=1.31 avg=1.76\n", "[6031 | 59.87] loss=1.90 avg=1.76\n", "[6032 | 61.15] loss=1.67 avg=1.76\n", "[6033 | 62.47] loss=1.91 avg=1.77\n", "[6034 | 63.77] loss=1.74 avg=1.76\n", "[6035 | 65.04] loss=1.35 avg=1.75\n", "[6036 | 66.34] loss=1.45 avg=1.74\n", "[6037 | 67.65] loss=1.70 avg=1.74\n", "[6038 | 68.94] loss=1.93 avg=1.75\n", "[6039 | 70.22] loss=1.84 avg=1.75\n", "[6040 | 71.49] loss=1.78 avg=1.75\n", "[6041 | 72.79] loss=1.60 avg=1.74\n", "[6042 | 74.07] loss=1.88 avg=1.75\n", "[6043 | 75.35] loss=2.00 avg=1.76\n", "[6044 | 76.63] loss=1.91 avg=1.76\n", "[6045 | 77.91] loss=1.79 avg=1.76\n", "[6046 | 79.18] loss=1.40 avg=1.75\n", "[6047 | 80.46] loss=1.91 avg=1.76\n", "[6048 | 81.73] loss=1.82 avg=1.76\n", "[6049 | 83.01] loss=1.78 avg=1.76\n", "[6050 | 84.30] loss=1.75 avg=1.76\n", "[6051 | 85.57] loss=1.45 avg=1.75\n", "[6052 | 86.85] loss=1.79 avg=1.75\n", "[6053 | 88.13] loss=1.43 avg=1.74\n", "[6054 | 89.41] loss=1.35 avg=1.73\n", "[6055 | 90.68] loss=1.79 avg=1.73\n", "[6056 | 91.96] loss=1.79 avg=1.74\n", "[6057 | 93.23] loss=1.50 avg=1.73\n", "[6058 | 94.51] loss=1.88 avg=1.73\n", "[6059 | 95.86] loss=1.82 avg=1.74\n", "[6060 | 97.14] loss=1.80 avg=1.74\n", "[6061 | 98.42] loss=1.93 avg=1.74\n", "[6062 | 99.70] loss=1.61 avg=1.74\n", "[6063 | 100.99] loss=1.71 avg=1.74\n", "[6064 | 102.27] loss=1.81 avg=1.74\n", "[6065 | 103.56] loss=1.62 avg=1.74\n", "[6066 | 104.85] loss=1.37 avg=1.73\n", "[6067 | 106.16] loss=1.64 avg=1.73\n", "[6068 | 107.43] loss=1.50 avg=1.72\n", "[6069 | 108.71] loss=1.81 avg=1.72\n", "[6070 | 109.99] loss=1.55 avg=1.72\n", "[6071 | 111.26] loss=1.65 avg=1.72\n", "[6072 | 112.54] loss=1.78 avg=1.72\n", "[6073 | 113.81] loss=1.73 avg=1.72\n", "[6074 | 115.09] loss=1.77 avg=1.72\n", "[6075 | 116.37] loss=1.22 avg=1.71\n", "[6076 | 117.67] loss=1.56 avg=1.71\n", "[6077 | 118.94] loss=1.67 avg=1.71\n", "[6078 | 120.22] loss=1.69 avg=1.71\n", "[6079 | 121.50] loss=1.47 avg=1.70\n", "[6080 | 122.77] loss=1.54 avg=1.70\n", "[6081 | 124.05] loss=1.57 avg=1.70\n", "[6082 | 125.33] loss=1.72 avg=1.70\n", "[6083 | 126.61] loss=1.64 avg=1.70\n", "[6084 | 127.91] loss=1.50 avg=1.69\n", "[6085 | 129.24] loss=1.46 avg=1.69\n", "[6086 | 130.52] loss=1.82 avg=1.69\n", "[6087 | 131.80] loss=1.58 avg=1.69\n", "[6088 | 133.07] loss=1.87 avg=1.69\n", "[6089 | 134.35] loss=1.41 avg=1.69\n", "[6090 | 135.63] loss=1.64 avg=1.69\n", "[6091 | 136.92] loss=1.70 avg=1.69\n", "[6092 | 138.21] loss=1.68 avg=1.69\n", "[6093 | 139.53] loss=1.70 avg=1.69\n", "[6094 | 140.81] loss=1.71 avg=1.69\n", "[6095 | 142.09] loss=1.48 avg=1.69\n", "[6096 | 143.36] loss=1.83 avg=1.69\n", "[6097 | 144.64] loss=1.78 avg=1.69\n", "[6098 | 145.91] loss=1.76 avg=1.69\n", "[6099 | 147.19] loss=1.69 avg=1.69\n", "[6100 | 148.47] loss=1.64 avg=1.69\n", "======== SAMPLE 1 ========\n", ", SELON LE POURVOI, QUE, D'UNE PART, LA COUR D'APPEL QUI A RELEVE QUE LES TRAVAUX DE LA SOCIETE ANGLACTE ET DE LA BANQUE DONT L'ECOULEMENT S'ETEND A COMPTER DU 29 SEPTEMBRE 1973, A RETENU QUE LES FAITS REPROCHES A M. X..., DONT L'ACCORD JUDICIAIRE NE CONSTITUAIT QU'UNE FAUTE, N'AVAIENT AUCUN DOUTE, AUPRES DE LA SOCIETE, AVANT LEUR EXECUTION, PUISQUE CELLES-CI, REPRESENTANT LES SERVICES DE LA SOCIETE, N'AVAIENT PAS PLEINEMENT ETE EXECUTES ; QUE, DES LORS, LA COUR D'APPEL N'A PAS DONNE UNE BASE LEGALE A SA DECISION ; ET ALORS, D'AUTRE PART, QUE, L'ARTICLE 50 DE LA LOI N° 79-2933 DU 4 NOVEMBRE 1979 A LA CONSTITUTION DE DISPOSITIONS LEGISLATIVES PROPRES A L'ARTICLE 16 DU DECRET N° 53-678 DU 5 JANVIER 1953 PREVOYANT QUE LE DEBITEUR N'A PAS RENONCE A ETRE AUTEUR ; ET ALORS, ENFIN, QUE L'ARTICLE L. 454 DE LA LOI N° 82-915 DU 4 DECEMBRE 1980 NE POUR FONDER QUE LES RAPPORTES A L'ACCOMPLISSEMENT OBLIGATOIRE ET NE DEVAIT AVOIR PAS UN CARACTERE PRIVE A CE DESCRIPTION ;MAIS ATTENDU QUE LADITE LOI A UNE CONSTATATION DU CARACTERE PUBLIC ANTERIEUR DE L'USAGE LORS DE L'ACHAT, QUE LA SOCIETE EN LIQUIDATION ALLEGUEE PE SOIT PRECISEMENT EN APPLICATION DE L'ARTICLE 5 DE LADITE LOI ; QU'EN SECOND LIEU, L'USAGE D'UNE SOCIETE AYANT PRECISE LE CARACTERE PRIVE DE LA SAISIE PRIVATIVE D'UNE SOCIETE N'AVAIT PAS ETE EN NATURE CONCLU ENTRE LA SOCIETE ANGLACTE ET LES TRAVAUX D'AUTEUR DANS LA VENTE ; QU'ELLE, DE MEME QUE SES RACE, NE RENDAIT PAS PARMI LES RAPPORT A L'ACCOMPLISSEMENT ADOPTE ; QUE, D'AUTRE PART, LA COUR D'APPEL A RELEVE QU'AU JOUR DE LA DEMANDE, M. X... AVAIT RENONCE A LE BIEN ACCORD ; QUE DE CE FAIT IL A ENONCE, SANS MANQUEMENT ; QU'IL A AINSI JUSTIFIE SOUS SES PROPRES MOTIFS, D'OU IL SUIT QU'EN AYANT RELEVE QU'IL NE FAISAIT PAS RENONCIATION QUE SON ACTIVITE AVAIT ETE EXERCEE PAR L'ACCOMPLISSEMENT PRIVATIVE DE SES RACES, LA COUR D'APPEL A LEGALEMENT JUSTIFIE SA DECISION AU REGARD DES EXCEPTIONS SOULEVEEES PAR LE FAIT QUE LA CESSATION EN COURS DE PLEINTS-FISCALITE ETAIT SUSCEPTIBLE DE RENONCER A SE PREVALOIR DE LA LOI DU 10 JUILLET 1965 ; ET ATTENDU QU'ENFIN, EN ESTIMANT QU'IL Y AVAIT EU UN CARACTERE PUBLIC ET NON UN CARACTERE D'USAGE QUI PRECISAIT QUE LE CARACTERE PUBLIC INVOQUE EN APPLICATION DE L'ARTICLE L. 454 DE LA LOI PRECITEE N'AVAIT PAS A REJETER LA DEMANDE DE DOMMAGES-INTERETS, LE MEME JUGEMENT AYANT VIOLE LES ARTICLES 500 ET 653 DU CODE CIVIL ;DANS LE MOYEN REPROCHE AUX PREMIERS DEGRE N'ETANT PAS FONDEES ; SUR LE SECOND MOYEN, PRIS DE LA VIOLATION, DE LA LOI N\n", "\n", "[6101 | 162.64] loss=1.58 avg=1.69\n", "[6102 | 163.92] loss=1.93 avg=1.69\n", "[6103 | 165.20] loss=1.65 avg=1.69\n", "[6104 | 166.47] loss=1.78 avg=1.69\n", "[6105 | 167.75] loss=1.77 avg=1.69\n", "[6106 | 169.02] loss=1.39 avg=1.69\n", "[6107 | 170.31] loss=1.80 avg=1.69\n", "[6108 | 171.63] loss=1.96 avg=1.70\n", "[6109 | 172.96] loss=1.76 avg=1.70\n", "[6110 | 174.24] loss=1.66 avg=1.70\n", "[6111 | 175.52] loss=1.62 avg=1.69\n", "[6112 | 176.80] loss=1.88 avg=1.70\n", "[6113 | 178.07] loss=1.85 avg=1.70\n", "[6114 | 179.35] loss=2.00 avg=1.70\n", "[6115 | 180.62] loss=1.65 avg=1.70\n", "[6116 | 181.90] loss=1.89 avg=1.71\n", "[6117 | 183.21] loss=1.57 avg=1.70\n", "[6118 | 184.48] loss=1.63 avg=1.70\n", "[6119 | 185.76] loss=1.87 avg=1.71\n", "[6120 | 187.03] loss=1.49 avg=1.70\n", "[6121 | 188.31] loss=1.65 avg=1.70\n", "[6122 | 189.59] loss=1.62 avg=1.70\n", "[6123 | 190.86] loss=1.82 avg=1.70\n", "[6124 | 192.14] loss=1.68 avg=1.70\n", "[6125 | 193.42] loss=1.44 avg=1.70\n", "[6126 | 194.77] loss=1.50 avg=1.70\n", "[6127 | 196.04] loss=1.49 avg=1.69\n", "[6128 | 197.32] loss=1.91 avg=1.70\n", "[6129 | 198.60] loss=1.80 avg=1.70\n", "[6130 | 199.88] loss=1.84 avg=1.70\n", "[6131 | 201.15] loss=1.73 avg=1.70\n", "[6132 | 202.43] loss=1.65 avg=1.70\n", "[6133 | 203.70] loss=1.62 avg=1.70\n", "[6134 | 205.02] loss=1.60 avg=1.70\n", "[6135 | 206.32] loss=1.67 avg=1.70\n", "[6136 | 207.61] loss=1.47 avg=1.69\n", "[6137 | 208.90] loss=1.91 avg=1.70\n", "[6138 | 210.17] loss=1.31 avg=1.69\n", "[6139 | 211.45] loss=1.57 avg=1.69\n", "[6140 | 212.72] loss=1.56 avg=1.69\n", "[6141 | 214.00] loss=1.73 avg=1.69\n", "[6142 | 215.28] loss=2.00 avg=1.69\n", "[6143 | 216.56] loss=1.36 avg=1.69\n", "[6144 | 217.85] loss=1.56 avg=1.69\n", "[6145 | 219.12] loss=1.73 avg=1.69\n", "[6146 | 220.40] loss=1.87 avg=1.69\n", "[6147 | 221.67] loss=1.65 avg=1.69\n", "[6148 | 222.95] loss=1.33 avg=1.68\n", "[6149 | 224.23] loss=1.56 avg=1.68\n", "[6150 | 225.51] loss=1.75 avg=1.68\n", "[6151 | 226.79] loss=1.90 avg=1.69\n", "[6152 | 228.17] loss=1.64 avg=1.69\n", "[6153 | 229.45] loss=1.77 avg=1.69\n", "[6154 | 230.72] loss=1.39 avg=1.68\n", "[6155 | 232.00] loss=1.60 avg=1.68\n", "[6156 | 233.28] loss=1.50 avg=1.68\n", "[6157 | 234.55] loss=1.85 avg=1.68\n", "[6158 | 235.83] loss=1.65 avg=1.68\n", "[6159 | 237.11] loss=1.56 avg=1.68\n", "[6160 | 238.40] loss=1.51 avg=1.68\n", "[6161 | 239.68] loss=1.43 avg=1.67\n", "[6162 | 240.97] loss=1.69 avg=1.67\n", "[6163 | 242.26] loss=1.81 avg=1.68\n", "[6164 | 243.55] loss=1.60 avg=1.68\n", "[6165 | 244.82] loss=1.50 avg=1.67\n", "[6166 | 246.10] loss=1.43 avg=1.67\n", "[6167 | 247.38] loss=1.43 avg=1.67\n", "[6168 | 248.68] loss=1.69 avg=1.67\n", "[6169 | 249.97] loss=1.54 avg=1.67\n", "[6170 | 251.24] loss=1.57 avg=1.67\n", "[6171 | 252.52] loss=1.81 avg=1.67\n", "[6172 | 253.80] loss=1.84 avg=1.67\n", "[6173 | 255.08] loss=1.82 avg=1.67\n", "[6174 | 256.35] loss=1.53 avg=1.67\n", "[6175 | 257.63] loss=1.45 avg=1.67\n", "[6176 | 258.91] loss=1.68 avg=1.67\n", "[6177 | 260.25] loss=1.47 avg=1.66\n", "[6178 | 261.54] loss=1.84 avg=1.67\n", "[6179 | 262.82] loss=1.82 avg=1.67\n", "[6180 | 264.09] loss=1.50 avg=1.67\n", "[6181 | 265.37] loss=1.70 avg=1.67\n", "[6182 | 266.64] loss=1.53 avg=1.66\n", "[6183 | 267.92] loss=1.58 avg=1.66\n", "[6184 | 269.19] loss=1.70 avg=1.66\n", "[6185 | 270.47] loss=1.52 avg=1.66\n", "[6186 | 271.77] loss=1.61 avg=1.66\n", "[6187 | 273.05] loss=1.53 avg=1.66\n", "[6188 | 274.34] loss=1.44 avg=1.66\n", "[6189 | 275.63] loss=1.47 avg=1.66\n", "[6190 | 276.93] loss=1.53 avg=1.65\n", "[6191 | 278.21] loss=1.82 avg=1.66\n", "[6192 | 279.49] loss=1.59 avg=1.66\n", "[6193 | 280.77] loss=1.61 avg=1.66\n", "[6194 | 282.06] loss=1.65 avg=1.65\n", "[6195 | 283.35] loss=1.44 avg=1.65\n", "[6196 | 284.63] loss=1.51 avg=1.65\n", "[6197 | 285.91] loss=1.63 avg=1.65\n", "[6198 | 287.19] loss=1.43 avg=1.65\n", "[6199 | 288.46] loss=1.86 avg=1.65\n", "[6200 | 289.74] loss=1.44 avg=1.65\n", "======== SAMPLE 1 ========\n", "IRE EN CE QUI CONCERNE LE PRINCIPE DE LE CHIFFRE D'AFFAIRES, SANS QU'IL NE JUSTIFIE NI LE CONTENU DE LA CAUSE, NI L'INEXECUTION SOLLICITE PAR M X... DANS LES CINQUANTE SOI-CONTRACTIONS DU POSSESSEUR, LA COUR D'APPEL EN A DEDUIT, ABSTRACTION FAITE DES MOTIFS SURAMIDES SUR L'ETENDUE DE CELUI-CI PAR LE DIRIGEAN COMMIS A L'ENCONTRE DE L'EMPLOYEUR, LA CONFIT QUE LE SALARIE AIT DE TRAVAUX DANS CETTE HALL PAR LUI-MEME POSSEDE D'ECHEANCE OU IL DISPOSAIT PAR AILLEURS DE L'ACQUEREUR-L'ASSISTANTI ; MAIS ATTENDU QUE SI SONT SEPT HEURES LES SEQUELLES EXCLUSIVEMENT DU TRAVAILLEUR, IL EST SOUTENU DANS L'EXERCICE DE SES POUVOIRS QUE LE PERSONNEL A LAQUELLE IL A OBLIGERE DES DEPENSES QU'IL A EXERCE PAR LE MEME EMPLOYEUR POUR LE POURVOI ETANT CONDUIT PAR LUI A QUELLE DATE SOIT UNE INTERVENTION CONFIEE PAR L'INTERESSE ; QU'EN L'ETAT DE CES ENONCIATIONS, LA COUR D'APPEL A PU ESTIMER PAR MOTIFS PROPRES ET ADOPTES QUE L'EMPLOYEUR NE POUVAIT ETRE TENU DE LES CONDAMNER AU PAIEMENT DE DIVERSES PENSIONS AU TITRE D'INDEMNISATION DE SALARIES, PUISQUE L'INTERESSE NE S'ETAIT ENGAGE DANS CES DERNIERS QU'APRES AVOIR A DETERMINE LA TENEUR DES HEURES L'INTERESSE ET NE POUVAIT PRETENDRE, D'INDEMNISER A SES SALARIES DEPUIS LE 1ER MAI 1968, QUE LES SALAIRES ONT ETE VERSES A SES HEURES CONVENUE LE 17 MARS 1968 ET LE 31 MAI 1977, EN RAISON DE PLUS A TOUT OU PARTIE DU PREAVIS EN REPARATION DE SON PREJUDICE CAUSE PAR UNE TENTATIVE DE RESPONSABILITE CONTRACTUELLE QU'IL A DONNEE LE 22 DECEMBRE 1979 ; QU'ELLE N'ETAIT DONC PAS DENIE PAR LE PRESIDENT PAR UN MOTIF QUE LA QUITTANCE DES AYANTS CAUSE, LEQUEL QUI A ETE FIXEE LE 19 JUIN 1972, SOIT DE SEPT HEURES OU LUI SEULAIENT DEJA VERSEES, PORTERA ENTOUREES DEPUIS L'ACCIDENT ; QU'ELLE A AINSI LEGALEMENT JUSTIFIE SA DECISION ; D'OU IL SUIT QUE LE MOYEN N'EST PAS FONDE EN SA SECONDE BRANCHE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE L'ARRET RENDUES LE 15 JANVIER 1982 PAR LA COUR D'APPEL DE VERSAILLES. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : ATTENDU QU'IL RESULTE DES ENONCIATIONS DE L'ARRET ATTAQUE (AIX-EN-PROVENCE, 23 NOVEMBRE 1982) QUE LA SOCIETE ANONYME ACHINAY-RENTIS (LA SOCIETE ENERG) A VENDU A LA SOCIETE PAGON (SOCIETE PAGON) LE 24 NOVEMBRE 1956 ET A VENDU CE MEME PAGON QUI EST DECEDE DEPUIS LORS A MME X... ; QUE CETTE VENTE S'EST RENDUE VENUE DANS UN BETAIL SUIVI PAR LE FAIT QU'IL NE POURRA ETRE CONSIDERE SUCCESSIVEMENT A LA VALEUR LOCATIVE DE SIX HECTARES, CE QUI N'AVAIT PAS ETE SOUMIS QUANT A SES COTISATIONS ET EN SOUTENANT QUE\n", "\n", "[6201 | 303.57] loss=1.68 avg=1.65\n", "[6202 | 304.85] loss=1.74 avg=1.65\n", "[6203 | 306.12] loss=1.81 avg=1.65\n", "[6204 | 307.40] loss=1.87 avg=1.65\n", "[6205 | 308.69] loss=1.66 avg=1.65\n", "[6206 | 309.98] loss=1.48 avg=1.65\n", "[6207 | 311.27] loss=1.59 avg=1.65\n", "[6208 | 312.55] loss=1.55 avg=1.65\n", "[6209 | 313.83] loss=1.49 avg=1.65\n", "[6210 | 315.12] loss=1.41 avg=1.65\n", "[6211 | 316.40] loss=1.56 avg=1.64\n", "[6212 | 317.67] loss=1.79 avg=1.65\n", "[6213 | 318.95] loss=1.88 avg=1.65\n", "[6214 | 320.23] loss=1.64 avg=1.65\n", "[6215 | 321.50] loss=1.59 avg=1.65\n", "[6216 | 322.78] loss=1.47 avg=1.65\n", "[6217 | 324.06] loss=1.30 avg=1.64\n", "[6218 | 325.33] loss=1.76 avg=1.64\n", "[6219 | 326.67] loss=1.47 avg=1.64\n", "[6220 | 327.95] loss=1.87 avg=1.64\n", "[6221 | 329.23] loss=1.70 avg=1.64\n", "[6222 | 330.50] loss=1.47 avg=1.64\n", "[6223 | 331.78] loss=1.64 avg=1.64\n", "[6224 | 333.06] loss=1.63 avg=1.64\n", "[6225 | 334.33] loss=1.63 avg=1.64\n", "[6226 | 335.61] loss=1.38 avg=1.64\n", "[6227 | 336.91] loss=1.51 avg=1.64\n", "[6228 | 338.19] loss=1.69 avg=1.64\n", "[6229 | 339.47] loss=1.45 avg=1.64\n", "[6230 | 340.74] loss=1.44 avg=1.63\n", "[6231 | 342.02] loss=1.75 avg=1.64\n", "[6232 | 343.31] loss=1.63 avg=1.64\n", "[6233 | 344.60] loss=1.73 avg=1.64\n", "[6234 | 345.89] loss=1.41 avg=1.63\n", "[6235 | 347.17] loss=1.31 avg=1.63\n", "[6236 | 348.47] loss=1.50 avg=1.63\n", "[6237 | 349.75] loss=1.30 avg=1.63\n", "[6238 | 351.02] loss=1.59 avg=1.63\n", "[6239 | 352.30] loss=1.54 avg=1.62\n", "[6240 | 353.58] loss=1.49 avg=1.62\n", "[6241 | 354.85] loss=2.08 avg=1.63\n", "[6242 | 356.13] loss=1.38 avg=1.63\n", "[6243 | 357.40] loss=1.48 avg=1.62\n", "[6244 | 358.70] loss=1.51 avg=1.62\n", "[6245 | 359.98] loss=1.49 avg=1.62\n", "[6246 | 361.30] loss=1.50 avg=1.62\n", "[6247 | 362.58] loss=1.29 avg=1.62\n", "[6248 | 363.86] loss=1.75 avg=1.62\n", "[6249 | 365.13] loss=1.64 avg=1.62\n", "[6250 | 366.41] loss=1.74 avg=1.62\n", "[6251 | 367.69] loss=1.53 avg=1.62\n", "[6252 | 368.97] loss=1.56 avg=1.62\n", "[6253 | 370.26] loss=1.78 avg=1.62\n", "[6254 | 371.54] loss=1.51 avg=1.62\n", "[6255 | 372.81] loss=1.28 avg=1.61\n", "[6256 | 374.09] loss=1.43 avg=1.61\n", "[6257 | 375.37] loss=1.44 avg=1.61\n", "[6258 | 376.64] loss=1.55 avg=1.61\n", "[6259 | 377.93] loss=1.55 avg=1.61\n", "[6260 | 379.22] loss=1.58 avg=1.61\n", "[6261 | 380.51] loss=1.65 avg=1.61\n", "[6262 | 381.87] loss=1.70 avg=1.61\n", "[6263 | 383.15] loss=1.37 avg=1.61\n", "[6264 | 384.42] loss=1.40 avg=1.61\n", "[6265 | 385.70] loss=1.38 avg=1.60\n", "[6266 | 386.98] loss=1.67 avg=1.60\n", "[6267 | 388.26] loss=1.35 avg=1.60\n", "[6268 | 389.53] loss=1.60 avg=1.60\n", "[6269 | 390.82] loss=1.55 avg=1.60\n", "[6270 | 392.11] loss=1.59 avg=1.60\n", "[6271 | 393.40] loss=1.61 avg=1.60\n", "[6272 | 394.69] loss=1.42 avg=1.60\n", "[6273 | 395.97] loss=1.45 avg=1.60\n", "[6274 | 397.24] loss=1.55 avg=1.60\n", "[6275 | 398.52] loss=1.52 avg=1.60\n", "[6276 | 399.80] loss=1.43 avg=1.59\n", "[6277 | 401.07] loss=1.59 avg=1.59\n", "[6278 | 402.35] loss=1.60 avg=1.59\n", "[6279 | 403.66] loss=1.46 avg=1.59\n", "[6280 | 404.94] loss=1.64 avg=1.59\n", "[6281 | 406.22] loss=1.47 avg=1.59\n", "[6282 | 407.50] loss=1.46 avg=1.59\n", "[6283 | 408.77] loss=1.50 avg=1.59\n", "[6284 | 410.05] loss=1.44 avg=1.59\n", "[6285 | 411.33] loss=1.41 avg=1.59\n", "[6286 | 412.62] loss=1.46 avg=1.58\n", "[6287 | 413.94] loss=1.59 avg=1.58\n", "[6288 | 415.25] loss=1.34 avg=1.58\n", "[6289 | 416.53] loss=1.53 avg=1.58\n", "[6290 | 417.82] loss=1.33 avg=1.58\n", "[6291 | 419.09] loss=1.42 avg=1.58\n", "[6292 | 420.37] loss=1.65 avg=1.58\n", "[6293 | 421.65] loss=1.58 avg=1.58\n", "[6294 | 422.92] loss=1.48 avg=1.58\n", "[6295 | 424.20] loss=1.41 avg=1.58\n", "[6296 | 425.55] loss=1.46 avg=1.57\n", "[6297 | 426.85] loss=1.52 avg=1.57\n", "[6298 | 428.13] loss=1.56 avg=1.57\n", "[6299 | 429.41] loss=1.59 avg=1.57\n", "[6300 | 430.68] loss=1.50 avg=1.57\n", "======== SAMPLE 1 ========\n", " le exébiteur et de ses enfants et que le bêtiment du paiement donné à la fin des échéances aurait été considéré comme en cause, et qu'ils avaient fait l'obligation de rechercher si la \"gâmbre litigieuse\" ne pouvait être exercé \"il existe une preuve qui préaiderait la fédaction pour le référée du défaut de droit\", alors que d'une part, pour le calcul de la compétence du Tribunal d'instance, la Cour d'appel ne pouvait être tenu de réponse à ce chef de rechercher si cet avantage était révéler et qu'il apparaissait à ses propres constatations que ce dernier a l'article 705 du nouveau Code de procédure civile ; qu'enfin, la Cour d'appel ne s'est pas bornée à écarter à quarier conclusions dont elle était saisi le contribuable ;Mais attendu que pour condamner la Compagnie de Préiliation du Polytechnique et cette Compagnie pour rechercher sa demande, la Cour d'appel a décidé que la Compagnie, par un contrat, avait été dénuée en fait les parties mais que M. aucune contestation litigieuse en date du défendeur, a énoncé à bon droit que le défendeur a pu décider qu'un même obligation de la victime n'était que si le refus de l'Aide pour le refus de son paiement n'était pas des dispositions de l'article 1er du décret du 22 décembre 1967 ; qu'elle a ainsi légalement justifié sa décision ; que le moyen n'est fondé en aucune de ses branches ;PAR CES MOTIFS :REJETTE le pourvoi <|endoftext|>\n", "<|startoftext|> Sur le premier moyen :Vu les articles 1032 et 1134 du Code civil, permettant au dater de ces derniers dans le cadre de la procédure de licenciement et de contrat de travail et de contrat de travail ;Attendu que M. X..., qui avait bien pu invoquer la procédure de licenciement pour obtenir la réintégration du montant des cotisations dues par son employeur, est délégué, par le taux des cotisations dues, d'un salaire sur des pièces d'applaossibilité contre le montant des cotisations ;Attendu qu'après avoir rappelé que la procédure de licenciement, par les parties privatives, a été révélé par le seul motif que l'employeur a acquis, le 30 septembre 1979, une erreur du salarié qui avait été aussi tenu compte 30 % de la préunion du temps de travail et au temps litigieux de vinoirement de son employeur, en sa qualité de référé par la convention de mutualité sociale et de l'autre contractuel du 8 septembre 1979, dont la nouvelle convention, l'a bien refusée au régime général par le régime des salariés faisant la production de l'entreprise, et, par motifs relatives à l'employeur l'une des cotisations dues dont les textes étaient révélés le 17 janvier 1982 ; qu'en statuant ainsi sans rechercher si les cotisations dues ne pouvaIENT être réclamés par le dater de l'activité du régime général, la Cour a mis en cassation le moyen susvisé ;PAR CES MOTIFS :REJETTE le premier moyen ;Mais sur le troisième moyen :Vu l'article L. 124-1 du Code du travail ;Attendu que, pour rejeter la demande pratiquée, l'arrêt attaqué\n", "\n", "[6301 | 444.02] loss=1.48 avg=1.57\n", "[6302 | 445.30] loss=1.76 avg=1.57\n", "[6303 | 446.59] loss=1.26 avg=1.57\n", "[6304 | 447.90] loss=1.86 avg=1.57\n", "[6305 | 449.20] loss=1.27 avg=1.57\n", "[6306 | 450.49] loss=1.65 avg=1.57\n", "[6307 | 451.78] loss=1.58 avg=1.57\n", "[6308 | 453.06] loss=1.32 avg=1.57\n", "[6309 | 454.34] loss=1.44 avg=1.57\n", "[6310 | 455.62] loss=1.51 avg=1.57\n", "[6311 | 456.89] loss=1.38 avg=1.56\n", "[6312 | 458.22] loss=1.37 avg=1.56\n", "[6313 | 459.52] loss=1.46 avg=1.56\n", "[6314 | 460.79] loss=1.45 avg=1.56\n", "[6315 | 462.07] loss=1.41 avg=1.56\n", "[6316 | 463.35] loss=1.64 avg=1.56\n", "[6317 | 464.62] loss=1.56 avg=1.56\n", "[6318 | 465.90] loss=1.64 avg=1.56\n", "[6319 | 467.17] loss=1.49 avg=1.56\n", "[6320 | 468.45] loss=1.78 avg=1.56\n", "[6321 | 469.75] loss=1.58 avg=1.56\n", "[6322 | 471.02] loss=1.57 avg=1.56\n", "[6323 | 472.30] loss=1.54 avg=1.56\n", "[6324 | 473.57] loss=1.78 avg=1.56\n", "[6325 | 474.85] loss=1.24 avg=1.56\n", "[6326 | 476.13] loss=1.58 avg=1.56\n", "[6327 | 477.40] loss=1.57 avg=1.56\n", "[6328 | 478.68] loss=1.67 avg=1.56\n", "[6329 | 479.97] loss=1.43 avg=1.56\n", "[6330 | 481.25] loss=1.51 avg=1.56\n", "[6331 | 482.53] loss=1.43 avg=1.56\n", "[6332 | 483.81] loss=1.71 avg=1.56\n", "[6333 | 485.10] loss=1.59 avg=1.56\n", "[6334 | 486.39] loss=1.66 avg=1.56\n", "[6335 | 487.68] loss=1.67 avg=1.56\n", "[6336 | 488.95] loss=1.36 avg=1.56\n", "[6337 | 490.25] loss=1.44 avg=1.56\n", "[6338 | 491.65] loss=1.20 avg=1.56\n", "[6339 | 492.93] loss=1.64 avg=1.56\n", "[6340 | 494.21] loss=1.66 avg=1.56\n", "[6341 | 495.48] loss=1.57 avg=1.56\n", "[6342 | 496.76] loss=1.83 avg=1.56\n", "[6343 | 498.04] loss=1.83 avg=1.56\n", "[6344 | 499.31] loss=1.44 avg=1.56\n", "[6345 | 500.59] loss=1.48 avg=1.56\n", "[6346 | 501.89] loss=1.55 avg=1.56\n", "[6347 | 503.17] loss=1.46 avg=1.56\n", "[6348 | 504.45] loss=1.34 avg=1.56\n", "[6349 | 505.72] loss=1.55 avg=1.56\n", "[6350 | 507.00] loss=1.43 avg=1.56\n", "[6351 | 508.27] loss=1.59 avg=1.56\n", "[6352 | 509.55] loss=1.61 avg=1.56\n", "[6353 | 510.83] loss=1.33 avg=1.56\n", "[6354 | 512.11] loss=1.81 avg=1.56\n", "[6355 | 513.40] loss=1.92 avg=1.56\n", "[6356 | 514.68] loss=1.54 avg=1.56\n", "[6357 | 515.95] loss=1.65 avg=1.56\n", "[6358 | 517.23] loss=1.52 avg=1.56\n", "[6359 | 518.51] loss=1.28 avg=1.56\n", "[6360 | 519.80] loss=1.52 avg=1.56\n", "[6361 | 521.09] loss=1.66 avg=1.56\n", "[6362 | 522.38] loss=1.62 avg=1.56\n", "[6363 | 523.73] loss=1.63 avg=1.56\n", "[6364 | 525.04] loss=1.29 avg=1.56\n", "[6365 | 526.32] loss=1.45 avg=1.56\n", "[6366 | 527.60] loss=1.54 avg=1.56\n", "[6367 | 528.88] loss=1.46 avg=1.56\n", "[6368 | 530.15] loss=1.53 avg=1.56\n", "[6369 | 531.43] loss=1.40 avg=1.55\n", "[6370 | 532.70] loss=1.46 avg=1.55\n", "[6371 | 533.98] loss=1.38 avg=1.55\n", "[6372 | 535.28] loss=1.39 avg=1.55\n", "[6373 | 536.55] loss=1.78 avg=1.55\n", "[6374 | 537.83] loss=1.64 avg=1.55\n", "[6375 | 539.10] loss=1.41 avg=1.55\n", "[6376 | 540.38] loss=1.56 avg=1.55\n", "[6377 | 541.66] loss=1.60 avg=1.55\n", "[6378 | 542.93] loss=1.63 avg=1.55\n", "[6379 | 544.21] loss=1.66 avg=1.55\n", "[6380 | 545.49] loss=1.48 avg=1.55\n", "[6381 | 546.78] loss=1.71 avg=1.55\n", "[6382 | 548.05] loss=1.66 avg=1.56\n", "[6383 | 549.33] loss=1.59 avg=1.56\n", "[6384 | 550.61] loss=1.48 avg=1.56\n", "[6385 | 551.88] loss=1.75 avg=1.56\n", "[6386 | 553.17] loss=1.38 avg=1.56\n", "[6387 | 554.47] loss=1.66 avg=1.56\n", "[6388 | 555.76] loss=1.75 avg=1.56\n", "[6389 | 557.37] loss=1.46 avg=1.56\n", "[6390 | 558.68] loss=1.47 avg=1.56\n", "[6391 | 559.96] loss=1.45 avg=1.56\n", "[6392 | 561.24] loss=1.35 avg=1.55\n", "[6393 | 562.51] loss=1.68 avg=1.56\n", "[6394 | 563.79] loss=1.47 avg=1.55\n", "[6395 | 565.07] loss=1.43 avg=1.55\n", "[6396 | 566.34] loss=1.44 avg=1.55\n", "[6397 | 567.64] loss=1.67 avg=1.55\n", "[6398 | 568.92] loss=1.46 avg=1.55\n", "[6399 | 570.20] loss=1.71 avg=1.55\n", "[6400 | 571.47] loss=1.44 avg=1.55\n", "======== SAMPLE 1 ========\n", " PRISION PREVUE A L'ARTICLE L. 412-13 DU CODE DU TRAVAIL, EN CAS DE MODIFICATION DE SA FORMATION DANS UN CONTRAT PRIS PAR LA SOCIETE GARDIENNE MOTEUR SENSIVEMENT, CE QUI IMPLIQUE QUE CES DEUX PARTIES AIENT COMMIS UNE FAUTE, LE MAINTIEN PRINCIPAL DU NOUVEAU CONTRAT DUDIT GARDIEN N'IMPLIQUANT PAS POUR PARTIE LA PROCEDURE JUDICIAIRE, ALORS ENFIN, QUE, DANS SES CONCLUSIONS, LA SOCIETE NEST A SOUTENU QUE LE CONTRAT D'AGENCE LAVER AVAIT FAIT CONNAITRE SANS ETRE INDIQUE DE SA RESIDENCE COMME NOM ET QU'AYANT PU RENDRE LA SAUVEGARDE DES FONCTIONS DE DELEGUE DU PERSONNEL, ELLE N'A PAS SOUTENU QUE LA SOCIETE NEST EUT RECLAME UNE INDEMNITE SPECIALE ; QUE, PAR SUITE ET PRIVILEGIE DE CETTE PRIVATION, L'ACCORD PREVU PAR CELLE-CI AIT ETE EXECUTOIRE ; QUE LE MOYEN N'EST PAS FONDE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LE JUGEMENT RENDU LE 11 DECEMBRE 1982 PAR LE CONSEIL DE PRUD'HOMMES DE CLERMONT-FERRAND ; <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : VU L'ARTICLE 2 SUSVISE DE LA POLICE PAR LAQUELLE SE TROUVAIT CONFIRME LE DECES, MME X... QUI FASSE SON VEHICULE AU SECRETARIAT-GREFFE ; VU L'ARTICLE 905 DU NOUVEAU CODE DE PROCEDURE CIVILE, CE TEXTE RESULTANT DE L'ALINEA 2 DE CE TEXTE : SI LA DEFAILLANCE DU DEBITEUR DOIT ETRE RECHERCHEE EN L'EDITION D'UN SECRETAIRE D'AGENCE DE TOUTES PIECES FAUSENT, D'UNE PART, LA NATURE ET LA FORME DE L'ACTE LUI DONNER ; D'AUTRE PART, LA QUELLE NATURE DE L'AUTEUR N'EST QUE L'ACCIDENT ; MET A LA GARDE ; MAIS ATTENDU QUE SI DES DISPOSITIONS DE L'ARTICLE 2S RELATIVES AU RECOURS FORME A LA GARDE NE SONT PAS EN MESURE DE PLEIN DROIT QUE DE RECHERCHER SI L'AUTEUR EST UN ACCORD SUR LA FORME ; D'OU IL SUIT QUE LE MOYEN N'EST PAS FONDE ; PAR CES MOTIFS : REJETTE LE POURVOI FORME CONTRE LE JUGEMENT RENDU LE 10 JUILLET 1982 PAR LE CONSEIL DE PRUD'HOMMES DE SES DEUXIEME ET TROISIEME BRANCHES ; <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : VU LES ARTICLES 1137 ET 1237 DU CODE CIVIL ;ATTENDU QU'EN APPLICATION DE CES TEXTES, LA SAISINE DES CONJOINTELLES CONFORMES AUX DISPOSITIONS DES ARTICLES L. 211 ET L. 212 DU CODE ELECTORAL EST PRONONCEE EN VERTU DE STATUTS COMMERCIALES, L'ELECTORAT ETANT APPELE A L'URSSAF QUI DISPOSE DE MESURES PRISES PAR LE SECRETARIAT DE LA COMMISSION SUPERIEUR ;ATTENDU QUE LE CONSEIL DE PRUD'HOMMES A DECIDE QUE L'ELECTORAT QUI EST EN ETAT DE REGLEMENT JUDICIAIRE S'ENTEND AUX DEBATS PAR VOIE DE RECEPTION ;ATTENDU QU'EN STATUANT AINSI PAR DES MOTIFS SUSVISES ET PAR AILLEURS PAR LE JUGE JUDICIAIRE AUXQUELS SE REFERAIT L'ELECTORAT, LE TRIBUNAL\n", "\n", "[6401 | 584.92] loss=1.47 avg=1.55\n", "[6402 | 586.20] loss=1.59 avg=1.55\n", "[6403 | 587.47] loss=1.67 avg=1.55\n", "[6404 | 588.77] loss=1.52 avg=1.55\n", "[6405 | 590.25] loss=1.45 avg=1.55\n", "[6406 | 591.58] loss=1.49 avg=1.55\n", "[6407 | 592.87] loss=1.45 avg=1.55\n", "[6408 | 594.15] loss=1.50 avg=1.55\n", "[6409 | 595.43] loss=1.56 avg=1.55\n", "[6410 | 596.71] loss=1.40 avg=1.55\n", "[6411 | 597.99] loss=1.44 avg=1.55\n", "[6412 | 599.26] loss=1.31 avg=1.54\n", "[6413 | 600.55] loss=1.67 avg=1.55\n", "[6414 | 601.84] loss=1.63 avg=1.55\n", "[6415 | 603.11] loss=1.47 avg=1.55\n", "[6416 | 604.39] loss=1.49 avg=1.55\n", "[6417 | 605.67] loss=1.30 avg=1.54\n", "[6418 | 606.95] loss=1.36 avg=1.54\n", "[6419 | 608.22] loss=1.66 avg=1.54\n", "[6420 | 609.50] loss=1.64 avg=1.54\n", "[6421 | 610.77] loss=1.67 avg=1.54\n", "[6422 | 612.06] loss=1.52 avg=1.54\n", "[6423 | 613.34] loss=1.43 avg=1.54\n", "[6424 | 614.62] loss=1.49 avg=1.54\n", "[6425 | 615.89] loss=1.66 avg=1.54\n", "[6426 | 617.17] loss=1.57 avg=1.54\n", "[6427 | 618.45] loss=1.60 avg=1.54\n", "[6428 | 619.73] loss=1.55 avg=1.55\n", "[6429 | 621.00] loss=1.21 avg=1.54\n", "[6430 | 622.31] loss=1.34 avg=1.54\n", "[6431 | 623.68] loss=1.64 avg=1.54\n", "[6432 | 624.96] loss=1.64 avg=1.54\n", "[6433 | 626.25] loss=1.44 avg=1.54\n", "[6434 | 627.55] loss=1.45 avg=1.54\n", "[6435 | 628.84] loss=1.50 avg=1.54\n", "[6436 | 630.11] loss=1.46 avg=1.54\n", "[6437 | 631.39] loss=1.42 avg=1.54\n", "[6438 | 632.67] loss=1.44 avg=1.54\n", "[6439 | 633.97] loss=1.48 avg=1.54\n", "[6440 | 635.25] loss=1.30 avg=1.53\n", "[6441 | 636.53] loss=1.35 avg=1.53\n", "[6442 | 637.81] loss=1.49 avg=1.53\n", "[6443 | 639.09] loss=1.25 avg=1.53\n", "[6444 | 640.37] loss=1.65 avg=1.53\n", "[6445 | 641.64] loss=1.44 avg=1.53\n", "[6446 | 642.92] loss=1.36 avg=1.53\n", "[6447 | 644.19] loss=1.48 avg=1.53\n", "[6448 | 645.48] loss=1.55 avg=1.53\n", "[6449 | 646.76] loss=1.57 avg=1.53\n", "[6450 | 648.04] loss=1.51 avg=1.53\n", "[6451 | 649.31] loss=1.68 avg=1.53\n", "[6452 | 650.59] loss=1.46 avg=1.53\n", "[6453 | 651.86] loss=1.79 avg=1.53\n", "[6454 | 653.14] loss=1.82 avg=1.53\n", "[6455 | 654.42] loss=1.63 avg=1.53\n", "[6456 | 655.76] loss=1.51 avg=1.53\n", "[6457 | 657.10] loss=1.54 avg=1.53\n", "[6458 | 658.38] loss=1.38 avg=1.53\n", "[6459 | 659.67] loss=1.64 avg=1.53\n", "[6460 | 660.96] loss=1.79 avg=1.54\n", "[6461 | 662.25] loss=1.56 avg=1.54\n", "[6462 | 663.54] loss=1.57 avg=1.54\n", "[6463 | 664.81] loss=1.52 avg=1.54\n", "[6464 | 666.09] loss=1.48 avg=1.54\n", "[6465 | 667.39] loss=1.75 avg=1.54\n", "[6466 | 668.67] loss=1.43 avg=1.54\n", "[6467 | 669.94] loss=1.54 avg=1.54\n", "[6468 | 671.22] loss=1.55 avg=1.54\n", "[6469 | 672.50] loss=1.50 avg=1.54\n", "[6470 | 673.77] loss=1.74 avg=1.54\n", "[6471 | 675.05] loss=1.44 avg=1.54\n", "[6472 | 676.32] loss=1.63 avg=1.54\n", "[6473 | 677.61] loss=1.52 avg=1.54\n", "[6474 | 678.90] loss=1.58 avg=1.54\n", "[6475 | 680.18] loss=1.53 avg=1.54\n", "[6476 | 681.46] loss=1.36 avg=1.54\n", "[6477 | 682.74] loss=1.36 avg=1.54\n", "[6478 | 684.01] loss=1.28 avg=1.53\n", "[6479 | 685.29] loss=1.57 avg=1.53\n", "[6480 | 686.56] loss=1.42 avg=1.53\n", "[6481 | 687.85] loss=1.72 avg=1.53\n", "[6482 | 689.19] loss=1.40 avg=1.53\n", "[6483 | 690.46] loss=1.58 avg=1.53\n", "[6484 | 691.74] loss=1.54 avg=1.53\n", "[6485 | 693.01] loss=1.68 avg=1.53\n", "[6486 | 694.30] loss=1.43 avg=1.53\n", "[6487 | 695.60] loss=1.58 avg=1.53\n", "[6488 | 696.88] loss=1.52 avg=1.53\n", "[6489 | 698.17] loss=1.57 avg=1.53\n", "[6490 | 699.45] loss=1.44 avg=1.53\n", "[6491 | 700.75] loss=1.63 avg=1.53\n", "[6492 | 702.02] loss=1.59 avg=1.53\n", "[6493 | 703.30] loss=1.61 avg=1.54\n", "[6494 | 704.58] loss=1.45 avg=1.53\n", "[6495 | 705.86] loss=1.29 avg=1.53\n", "[6496 | 707.13] loss=1.37 avg=1.53\n", "[6497 | 708.41] loss=1.47 avg=1.53\n", "[6498 | 709.69] loss=1.75 avg=1.53\n", "[6499 | 710.98] loss=1.49 avg=1.53\n", "[6500 | 712.26] loss=1.45 avg=1.53\n", "======== SAMPLE 1 ========\n", " CONSULTER DES ACCORD POUR L'OPERATION D'AGENCE, C'EST-A-DIRE LE JOUR DE LA DECISION AYANT POUR OBJET D'UN JUGEMENT JUDICIAIRE ; QU'EN SE DETERMINANT, SANS RECHERCHER D'UNE PARTIE AU PLUS TARDAIRE SON OBLIGATION D'ACTIONNAIRE AURAIT DU FAIRE DROIT AUPRES DE LA COMPAGNIE EUROPEENNE ET LA SOCIETE A PERSISTANTE, LA COUR D'APPEL N'A PAS DONNE DE BASE LEGALE A SON ARRET ;MAIS ATTENDU, D'UNE PART, QUE DES FAITS DANS LESQUELS L'OPERATION AVAIT ETE CONCLUE, LA COUR D'APPEL A ESTIME QUE L'OPERATION DE PROPRIETE LITIGIEUSE N'Y FIGURE PAS EN MATIERE DE CONFLIT SPECIALISEE A L'EGARD DE LA COMPAGNIE EUROPEENNE, ET QUE SON CONSTATATION PORTANT SUR CE JUGEMENT, MALGRE L'INDICATION D'UN JUGEMENT DU TRIBUNAL DE COMMERCE, LA COUR D'APPEL A PU EN DEDUIRE, EN PRESENCE DE L'INDICATION QUE L'OPERATION ETAIT SOUMISE A PROBITE POUR LA COMPAGNIE EUROPEENNE NI A L'INDEMNITE DU BENEFICE DES DISPOSITIONS DE L'ARTICLE R. 532-2 DU CODE DU TRAVAIL, ET, D'AUTRE PART, QU'IL APPARTENAIT AUX JUGES DU FOND DE RECHERCHER SI L'INDISPONIBILITE DE LA TRANSACTION DU 6 JUILLET 1982 NE CONCERNAIT PAS LA DIFFICULTE DE LA TRANSACTION, ET SI, PAR LA SUITE, LE JUGEMENT DU TRIBUNAL DE COMMERCE AVAIT EU POUR EFFET DE JUSTIFIER SA DESIGNATION AU SENS DE L'ARTICLE R. 532-2 DU CODE DU TRAVAIL OU EN APPLICATION DE L'ARTICLE L. 411-17 DU MEME CODE ;D'OU IL SUIT QUE LE MOYEN N'EST PAS FONDE ;PAR CES MOTIFS : REJETTE LE POURVOI. <|endoftext|>\n", "<|startoftext|> SUR LE MOYEN UNIQUE : VU LES ARTICLES 2034 ET SUIVANTS DU CODE CIVIL ;ATTENDU, SELON LES ENONCIATIONS DES JUGES DU FOND, QU'AU COURS D'UN TRANSPORT A MARSEILLE LA SOCIETE COMPAGNIE AIR DES ETRANGERS (SOCIETE A.E.E.C.) A VENDU UN IMMEUBLE APPARTENANT A M. X..., CHEF D'EAU (LA SOCIETE B.A.C.E.), ENTREPRISE DU FONDS DE COMMERCE ET DE DROITS (LA SOCIETE S.A.C.E.), DONT L'ACTION AVAIT ETE PRONONCEE LE 12 AVRIL 1981, DEVRA DESORGANISER SON ACTION A REPARATION EN SOUTENANT QUE LE TRANSPORT NE POURRAIT ETRE PRIS EN CHARGE A TITRE COLLECTIF ET QUE LE LENDEMAIN DU TRANSPORT SANS MODIFIER LES MESURES IMPOSEES A L'ENTREPRISE ; QUE LE LITIGE VISE NOTAMMENT AU MEMOIRE PRINCIPAL ETANT SANS INTERET PAR LE LITIGE, LE TRANSPORTEUR A INVOQUE LA DESORGANISATION PREALABLE A TITRE COLLECTIF ETABLI QU'IL N'ETAIT PAS DANGEREUX PUISQUE LA SOCIETE N'AVAIT PAS REPRIS DE LA PROMESSE \" DEPOURVUE DE MOYENS IMPLICITES \" ; QUE LES SOCIETES A.I.F.R.N. ET SOCIETE DU CENTRE D'ENTREPRISE ONT SOUTENU QUE LE CHAMP D'APPLICATION DE L'ACTIVITE COMMERCIALE DU TRANSPORTEUR PREALABLE A CE CHEF DE TRANSPORT ETANT DANGEREUX\n", "\n", "[6501 | 726.05] loss=1.51 avg=1.53\n", "[6502 | 727.32] loss=1.69 avg=1.53\n", "[6503 | 728.60] loss=1.43 avg=1.53\n", "[6504 | 729.89] loss=1.24 avg=1.53\n", "[6505 | 731.18] loss=1.27 avg=1.53\n", "[6506 | 732.49] loss=1.34 avg=1.52\n", "[6507 | 733.83] loss=1.56 avg=1.52\n", "[6508 | 735.10] loss=1.76 avg=1.53\n", "[6509 | 736.38] loss=1.55 avg=1.53\n", "[6510 | 737.65] loss=1.48 avg=1.53\n", "[6511 | 738.93] loss=1.38 avg=1.52\n", "[6512 | 740.21] loss=1.64 avg=1.53\n", "[6513 | 741.49] loss=1.40 avg=1.52\n", "[6514 | 742.76] loss=1.60 avg=1.53\n", "[6515 | 744.05] loss=1.30 avg=1.52\n", "[6516 | 745.32] loss=1.33 avg=1.52\n", "[6517 | 746.60] loss=1.53 avg=1.52\n", "[6518 | 747.88] loss=1.57 avg=1.52\n", "[6519 | 749.16] loss=1.53 avg=1.52\n", "[6520 | 750.44] loss=1.54 avg=1.52\n", "[6521 | 751.71] loss=1.21 avg=1.52\n", "[6522 | 753.04] loss=1.49 avg=1.52\n", "[6523 | 754.33] loss=1.37 avg=1.52\n", "[6524 | 755.62] loss=1.58 avg=1.52\n", "[6525 | 756.89] loss=1.36 avg=1.52\n", "[6526 | 758.18] loss=1.57 avg=1.52\n", "[6527 | 759.45] loss=1.57 avg=1.52\n", "[6528 | 760.73] loss=1.54 avg=1.52\n", "[6529 | 762.00] loss=1.27 avg=1.51\n", "[6530 | 763.28] loss=1.47 avg=1.51\n", "[6531 | 764.56] loss=1.67 avg=1.52\n", "[6532 | 765.92] loss=1.45 avg=1.52\n", "[6533 | 767.24] loss=1.52 avg=1.52\n", "[6534 | 768.54] loss=1.53 avg=1.52\n", "[6535 | 769.84] loss=1.40 avg=1.51\n", "[6536 | 771.11] loss=1.33 avg=1.51\n", "[6537 | 772.39] loss=1.39 avg=1.51\n", "[6538 | 773.67] loss=1.57 avg=1.51\n", "[6539 | 774.94] loss=1.66 avg=1.51\n", "[6540 | 776.22] loss=1.48 avg=1.51\n", "[6541 | 777.51] loss=1.44 avg=1.51\n", "[6542 | 778.79] loss=1.21 avg=1.51\n", "[6543 | 780.07] loss=1.19 avg=1.51\n", "[6544 | 781.34] loss=1.79 avg=1.51\n", "[6545 | 782.62] loss=1.44 avg=1.51\n", "[6546 | 783.89] loss=1.65 avg=1.51\n", "[6547 | 785.18] loss=1.67 avg=1.51\n", "[6548 | 786.47] loss=1.34 avg=1.51\n", "[6549 | 787.76] loss=1.52 avg=1.51\n", "[6550 | 789.05] loss=1.41 avg=1.51\n", "[6551 | 790.32] loss=1.29 avg=1.51\n", "[6552 | 791.60] loss=1.34 avg=1.50\n", "[6553 | 792.88] loss=1.58 avg=1.51\n", "[6554 | 794.15] loss=1.40 avg=1.50\n", "[6555 | 795.43] loss=1.46 avg=1.50\n", "[6556 | 796.71] loss=1.46 avg=1.50\n", "[6557 | 797.98] loss=1.50 avg=1.50\n", "[6558 | 799.28] loss=1.44 avg=1.50\n", "[6559 | 800.55] loss=1.72 avg=1.51\n", "[6560 | 801.84] loss=1.49 avg=1.50\n", "[6561 | 803.12] loss=1.34 avg=1.50\n", "[6562 | 804.41] loss=1.44 avg=1.50\n", "[6563 | 805.70] loss=1.36 avg=1.50\n", "[6564 | 806.97] loss=1.73 avg=1.50\n", "[6565 | 808.25] loss=1.36 avg=1.50\n", "[6566 | 809.53] loss=1.43 avg=1.50\n", "[6567 | 810.82] loss=1.34 avg=1.50\n", "[6568 | 812.10] loss=1.48 avg=1.50\n", "[6569 | 813.38] loss=1.78 avg=1.50\n", "[6570 | 814.65] loss=1.54 avg=1.50\n", "[6571 | 815.93] loss=1.53 avg=1.50\n", "[6572 | 817.20] loss=1.79 avg=1.51\n", "[6573 | 818.50] loss=1.66 avg=1.51\n", "[6574 | 819.79] loss=1.52 avg=1.51\n", "[6575 | 821.08] loss=1.40 avg=1.51\n", "[6576 | 822.36] loss=1.54 avg=1.51\n", "[6577 | 823.63] loss=1.49 avg=1.51\n", "[6578 | 824.91] loss=1.44 avg=1.51\n", "[6579 | 826.19] loss=1.32 avg=1.50\n", "[6580 | 827.46] loss=1.57 avg=1.50\n", "[6581 | 828.74] loss=1.53 avg=1.50\n", "[6582 | 830.01] loss=1.47 avg=1.50\n", "[6583 | 831.29] loss=1.38 avg=1.50\n", "[6584 | 832.59] loss=1.60 avg=1.50\n", "[6585 | 833.86] loss=1.60 avg=1.51\n", "[6586 | 835.14] loss=1.58 avg=1.51\n", "[6587 | 836.42] loss=1.56 avg=1.51\n", "[6588 | 837.71] loss=1.44 avg=1.51\n", "[6589 | 839.00] loss=1.40 avg=1.50\n", "[6590 | 840.29] loss=1.36 avg=1.50\n", "[6591 | 841.56] loss=1.39 avg=1.50\n", "[6592 | 842.86] loss=1.42 avg=1.50\n", "[6593 | 844.13] loss=1.70 avg=1.50\n", "[6594 | 845.41] loss=1.22 avg=1.50\n", "[6595 | 846.69] loss=1.55 avg=1.50\n", "[6596 | 847.96] loss=1.57 avg=1.50\n", "[6597 | 849.24] loss=1.29 avg=1.50\n", "[6598 | 850.52] loss=1.52 avg=1.50\n", "[6599 | 851.83] loss=1.34 avg=1.50\n", "[6600 | 853.11] loss=1.57 avg=1.50\n", "======== SAMPLE 1 ========\n", "éités de la décision du 20 octobre 1969, la compétence du juge au conseil, le tribunal des conditions qui ont été fixées et qu'en statuant comme elle l'a fait, à l'appui de ce que la demande en nullité n'était pas contestée, la cour d'appel a violé les articles 466 et 583 du nouveau Code de procédure civile ; Attendu que dès lors que les journaux litigieux ne sont poursuivies lorsque l'indique du jugement ; qu'en affirmant que la demande en nullité n'avait pas été formé, la cour d'appel a violé l'article 466 du nouveau Code de procédure civile, la période prévue à l'article 480 du nouveau Code de procédure civile, la sienne l'a engagée à l'appui de l'assemblée générale de la régularité, de l'amportée, au délai d'appel et de cette période, alors, d'autre part, que l'appel du juge doit être appliqué aux désignations du jugement de présence pour les intérêts, et, en second lieu, l'appel d'une maître, par le juge, doit être déclaré au père de l'assemblée générale de la régularité ; alors, enfin, qu'il n'être par une demande seule rendue par le premier juge pour sa confirmation du droit commun entre le juge de la décision et le juge de la reprise, sans lien de causalité et inadapté, et alors enfin qu'aucun de tels droits n'incompétent d'auteur le droit de l'établissement ; qu'aucun différer n'ayant pas été confirmé et n'évite l'obligation de constater que le juge ait déclaré à l'appel le droit commun entre les parties par le désignation du jugement et son dessous même droit ; qu'ainsi, pour en préciser à la date de la décision de déchouuler le droit des désignations de chacune des assemblages des périodiques et les délégations électorales, la cour d'appel n'aurait pas justifié le droit commun entre les parties ; Mais attendu qu'après avoir rappelé que des parties qui dénièvent les désignations des désignations électorales, ayant constatée le droit commun entre le juge, le juge en a constaté de chacun d'eux, le juge en a déduit de ses constatations les rapports de chacune des parties ; que par ces motifs, l'arrêt, qui a souverainement estimé que les dispositions qui seraient impératives à la réduction des opérations des assemblages désignés, précisent de ces constatations que le règlement des déléguements des délégués du juge, n'avait pas éprouvé les règles de la personne visée par les articles 480 du nouveau Code de procédure civile et alors que, dès lors, un ordre interdit au juge d'invoquer les conclusions par lequel elle comporte l'absence de demande d'appel ; qu'en énonçant que le père de l'assemblée générale de ladite assemblage, désignée en tant qu'un ordre interdit l'appel d'une maître de l'assemblée générale, ne portait pas sur des défendeurs ; que le même juge, sienne sur une régularité du droit commun entre cet appel des dénouveurs, n'avait pas à constater le droit commun entre cet article 480 et le bénéfice de ce dro\n", "\n", "[6601 | 866.82] loss=1.44 avg=1.50\n", "[6602 | 868.09] loss=1.33 avg=1.50\n", "[6603 | 869.37] loss=1.36 avg=1.50\n", "[6604 | 870.64] loss=1.46 avg=1.50\n", "[6605 | 871.93] loss=1.42 avg=1.49\n", "[6606 | 873.22] loss=1.38 avg=1.49\n", "[6607 | 874.52] loss=1.46 avg=1.49\n", "[6608 | 875.81] loss=1.37 avg=1.49\n", "[6609 | 877.09] loss=1.48 avg=1.49\n", "[6610 | 878.38] loss=1.33 avg=1.49\n", "[6611 | 879.66] loss=1.42 avg=1.49\n", "[6612 | 880.93] loss=1.56 avg=1.49\n", "[6613 | 882.21] loss=1.53 avg=1.49\n", "[6614 | 883.49] loss=1.29 avg=1.49\n", "[6615 | 884.81] loss=1.53 avg=1.49\n", "[6616 | 886.09] loss=1.47 avg=1.49\n", "[6617 | 887.38] loss=1.58 avg=1.49\n", "[6618 | 888.65] loss=1.41 avg=1.49\n", "[6619 | 889.93] loss=1.40 avg=1.49\n", "[6620 | 891.21] loss=1.41 avg=1.49\n", "[6621 | 892.48] loss=1.46 avg=1.49\n", "[6622 | 893.76] loss=1.29 avg=1.48\n", "[6623 | 895.04] loss=1.58 avg=1.49\n", "[6624 | 896.32] loss=1.32 avg=1.48\n", "[6625 | 897.60] loss=1.38 avg=1.48\n", "[6626 | 898.89] loss=1.36 avg=1.48\n", "[6627 | 900.17] loss=1.44 avg=1.48\n", "[6628 | 901.44] loss=1.49 avg=1.48\n", "[6629 | 902.72] loss=1.42 avg=1.48\n", "[6630 | 904.00] loss=1.74 avg=1.48\n", "[6631 | 905.28] loss=1.60 avg=1.48\n", "[6632 | 906.57] loss=1.52 avg=1.48\n", "[6633 | 907.87] loss=1.44 avg=1.48\n", "[6634 | 909.21] loss=1.60 avg=1.49\n", "[6635 | 910.51] loss=1.34 avg=1.48\n", "[6636 | 911.79] loss=1.28 avg=1.48\n", "[6637 | 913.07] loss=1.83 avg=1.49\n", "[6638 | 914.35] loss=1.45 avg=1.49\n", "[6639 | 915.63] loss=1.50 avg=1.49\n", "[6640 | 916.90] loss=1.75 avg=1.49\n", "[6641 | 918.22] loss=1.39 avg=1.49\n", "[6642 | 919.51] loss=1.75 avg=1.49\n", "[6643 | 920.79] loss=1.50 avg=1.49\n", "[6644 | 922.07] loss=1.37 avg=1.49\n", "[6645 | 923.34] loss=1.49 avg=1.49\n", "[6646 | 924.62] loss=1.50 avg=1.49\n", "[6647 | 925.90] loss=1.37 avg=1.49\n", "[6648 | 927.17] loss=1.43 avg=1.49\n", "[6649 | 928.45] loss=1.32 avg=1.49\n", "[6650 | 929.72] loss=1.35 avg=1.48\n", "[6651 | 931.02] loss=1.33 avg=1.48\n", "[6652 | 932.30] loss=1.46 avg=1.48\n", "[6653 | 933.57] loss=1.35 avg=1.48\n", "[6654 | 934.85] loss=1.46 avg=1.48\n", "[6655 | 936.13] loss=1.37 avg=1.48\n", "[6656 | 937.41] loss=1.68 avg=1.48\n", "[6657 | 938.69] loss=1.37 avg=1.48\n", "[6658 | 939.97] loss=1.37 avg=1.48\n", "[6659 | 941.24] loss=1.31 avg=1.48\n", "[6660 | 942.56] loss=1.67 avg=1.48\n", "[6661 | 943.85] loss=1.38 avg=1.48\n", "[6662 | 945.14] loss=1.44 avg=1.48\n", "[6663 | 946.42] loss=1.60 avg=1.48\n", "[6664 | 947.70] loss=1.48 avg=1.48\n", "[6665 | 948.98] loss=1.60 avg=1.48\n", "[6666 | 950.27] loss=1.72 avg=1.48\n", "[6667 | 951.55] loss=1.51 avg=1.48\n", "[6668 | 952.83] loss=1.55 avg=1.48\n", "[6669 | 954.11] loss=1.51 avg=1.48\n", "[6670 | 955.39] loss=1.52 avg=1.48\n", "[6671 | 956.66] loss=1.38 avg=1.48\n", "[6672 | 957.94] loss=1.39 avg=1.48\n", "[6673 | 959.21] loss=1.47 avg=1.48\n", "[6674 | 960.49] loss=1.28 avg=1.48\n", "[6675 | 961.76] loss=1.29 avg=1.48\n", "[6676 | 963.04] loss=1.39 avg=1.48\n", "[6677 | 964.33] loss=1.44 avg=1.48\n", "[6678 | 965.61] loss=1.28 avg=1.48\n", "[6679 | 966.88] loss=1.22 avg=1.47\n", "[6680 | 968.16] loss=1.36 avg=1.47\n", "[6681 | 969.43] loss=1.36 avg=1.47\n", "[6682 | 970.71] loss=1.37 avg=1.47\n", "[6683 | 971.98] loss=1.54 avg=1.47\n", "[6684 | 973.26] loss=1.38 avg=1.47\n", "[6685 | 974.54] loss=1.36 avg=1.47\n", "[6686 | 975.82] loss=1.51 avg=1.47\n", "[6687 | 977.09] loss=1.31 avg=1.47\n", "[6688 | 978.39] loss=1.31 avg=1.47\n", "[6689 | 979.68] loss=1.58 avg=1.47\n", "[6690 | 980.96] loss=1.20 avg=1.46\n", "[6691 | 982.24] loss=1.59 avg=1.46\n", "[6692 | 983.56] loss=1.44 avg=1.46\n", "[6693 | 984.84] loss=1.41 avg=1.46\n", "[6694 | 986.14] loss=1.33 avg=1.46\n", "[6695 | 987.42] loss=1.36 avg=1.46\n", "[6696 | 988.69] loss=1.64 avg=1.46\n", "[6697 | 989.97] loss=1.49 avg=1.46\n", "[6698 | 991.24] loss=1.34 avg=1.46\n", "[6699 | 992.52] loss=1.76 avg=1.47\n", "[6700 | 993.79] loss=1.57 avg=1.47\n", "======== SAMPLE 1 ========\n", "è à son seul autre écart pour le calcul de l'intégralité des faits ;Et par suite, par arrêt du 5 mars 1987, une première même réclamation n'était que de nature à mettre un nouveau titulaire au montant de l'indemnité de préavis ; que la cour d'appel a estimé que l'administration de M. X... était en droit à indemniser le titulaire des préavis qu'il avait confié à M. Y... des préavis ainsi étaient réparées et qu'ils avaient été sa fille ;Rejette le premier moyen ;Mais sur le second moyen :Vu la Convention collective du personnel des cotisations d'assurances de la banque ;Attendu que l'arrêt attaqué (Agen, 7 mars 1987) a dit que le titulaire de sa période de préavis avait été évalué, à partir du 1er juillet 1970 au 31 janvier 1974, sur le débiteur du préavis survenu le 19 août 1973, dont le compte ayant été récompete, laquelle, d'une part, était en effet, la prise d'un tiers ;Attendu que, pour déclarer recevable les demandes formées, l'arrêt retient que le titularisation des préavis à M. X... n'ait pas été engagé par la banque ;Attendu qu'en statuant ainsi, alors qu'à tout date de l'avis, le titularisation de leur préavis et la récompétention dont le compte a été récompétente, dont le compte n'a pas été récompétent, s'imposait à l'assureur ; que le fondement de cet état est prévu à l'article 20 de la Convention collective du personnel des cotisations d'assurances de l'avril 1972 et n'a pas d'indique l'engagement de récomparer la prise de deux-sept préavis à titre de préavis sur le débiteur du compte, la cour d'appel a violé le texte susvisé ;PAR CES MOTIFS :CASSE ET ANNULE, mais seulement du chef de la violation et l'indemnitissement de la période de préavis de l'agrément de la prise de la prêt de son compte titularise par la banque, l'arrêt rendu le 7 mars 1987, entre les parties, par la cour d'appel d'Agen ; remet, en conséquence, quant à ce, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Fort-de-France <|endoftext|>\n", "<|startoftext|> SECURITE SOCIALE, RELEVE D'OFFICE, EN CE QUI CONCERNE LA FRAUDE DES BAILLEURS ET L'EXERCICE DES ACTIONS DE LA FEMME, ET ALORS, MEME S'IL RESULTE DES TERMES MEMES DE LA LOI DE 1972 QUE CETTE LOI PEUT ETRE APPLICABLE EN LA CAUSE, MEME S'IL EST APPLICABLE EN L'ESPECE QUE LES DISPOSITIONS DE L'ARTICLE 1032 DU CODE CIVIL ;MAIS ATTENDU QUE SI L'ALINEA 1ER DE L'ARTICLE 1032 DU CODE CIVIL, SELON L'ARRET ATTAQUE, FIXE A 2 %, DOIT, SELON LE MOYEN, ETRE APPLICABLE EN L'ESPECE, IL NE PEUT ETRE OPPOSEE A LA FEMME TANT A LA LOI DU 10 MARS 1948 QUE A LA LOI DU 11 MARS 1949 QUI PRESCRIT LA MODIFICATION DES STATUTS ; QU'AYANT RETENU A LA DECISION SEULEMENT ATTAQUE DEPOSE LA FACULTE A L'ORIGINALITE DES BILLEURS EN PRESENCE DE LA BONNE\n", "\n", "[6701 | 1007.21] loss=1.60 avg=1.47\n", "[6702 | 1008.51] loss=1.38 avg=1.47\n", "[6703 | 1009.78] loss=1.58 avg=1.47\n", "[6704 | 1011.06] loss=1.66 avg=1.47\n", "[6705 | 1012.33] loss=1.27 avg=1.47\n", "[6706 | 1013.62] loss=1.49 avg=1.47\n", "[6707 | 1014.91] loss=1.40 avg=1.47\n", "[6708 | 1016.25] loss=1.24 avg=1.47\n", "[6709 | 1017.64] loss=1.51 avg=1.47\n", "[6710 | 1018.94] loss=1.33 avg=1.46\n", "[6711 | 1020.22] loss=1.63 avg=1.47\n", "[6712 | 1021.50] loss=1.53 avg=1.47\n", "[6713 | 1022.77] loss=1.58 avg=1.47\n", "[6714 | 1024.05] loss=1.53 avg=1.47\n", "[6715 | 1025.32] loss=1.50 avg=1.47\n", "[6716 | 1026.59] loss=1.44 avg=1.47\n", "[6717 | 1027.87] loss=1.47 avg=1.47\n", "[6718 | 1029.15] loss=1.40 avg=1.47\n", "[6719 | 1030.44] loss=1.63 avg=1.47\n", "[6720 | 1031.72] loss=1.50 avg=1.47\n", "[6721 | 1032.99] loss=1.35 avg=1.47\n", "[6722 | 1034.26] loss=1.72 avg=1.47\n", "[6723 | 1035.54] loss=1.50 avg=1.47\n", "[6724 | 1036.82] loss=1.44 avg=1.47\n", "[6725 | 1038.09] loss=1.57 avg=1.47\n", "[6726 | 1039.37] loss=1.45 avg=1.47\n", "[6727 | 1040.65] loss=1.54 avg=1.47\n", "[6728 | 1041.94] loss=1.52 avg=1.47\n", "[6729 | 1043.21] loss=1.53 avg=1.47\n", "[6730 | 1044.48] loss=1.42 avg=1.47\n", "[6731 | 1045.75] loss=1.61 avg=1.47\n", "[6732 | 1047.03] loss=1.45 avg=1.47\n", "[6733 | 1048.30] loss=1.47 avg=1.47\n", "[6734 | 1049.63] loss=1.70 avg=1.48\n", "[6735 | 1051.01] loss=1.46 avg=1.48\n", "[6736 | 1052.36] loss=1.43 avg=1.48\n", "[6737 | 1053.65] loss=1.30 avg=1.47\n", "[6738 | 1054.94] loss=1.70 avg=1.48\n", "[6739 | 1056.21] loss=1.65 avg=1.48\n", "[6740 | 1057.49] loss=1.29 avg=1.48\n", "[6741 | 1058.76] loss=1.29 avg=1.47\n", "[6742 | 1060.03] loss=1.42 avg=1.47\n", "[6743 | 1061.31] loss=1.20 avg=1.47\n", "[6744 | 1062.59] loss=1.51 avg=1.47\n", "[6745 | 1063.87] loss=1.44 avg=1.47\n", "[6746 | 1065.15] loss=1.29 avg=1.47\n", "[6747 | 1066.42] loss=1.48 avg=1.47\n", "[6748 | 1067.70] loss=1.49 avg=1.47\n", "[6749 | 1068.97] loss=1.37 avg=1.47\n", "[6750 | 1070.24] loss=1.57 avg=1.47\n", "[6751 | 1071.52] loss=1.35 avg=1.47\n", "[6752 | 1072.79] loss=1.38 avg=1.47\n", "[6753 | 1074.08] loss=1.43 avg=1.47\n", "[6754 | 1075.35] loss=1.35 avg=1.47\n", "[6755 | 1076.62] loss=1.30 avg=1.46\n", "[6756 | 1077.90] loss=1.55 avg=1.47\n", "[6757 | 1079.17] loss=1.45 avg=1.47\n", "[6758 | 1080.44] loss=1.52 avg=1.47\n", "[6759 | 1081.72] loss=1.41 avg=1.47\n", "[6760 | 1083.00] loss=1.55 avg=1.47\n", "[6761 | 1084.29] loss=1.54 avg=1.47\n", "[6762 | 1085.57] loss=1.46 avg=1.47\n", "[6763 | 1086.85] loss=1.49 avg=1.47\n", "[6764 | 1088.15] loss=1.50 avg=1.47\n", "[6765 | 1089.44] loss=1.34 avg=1.47\n", "[6766 | 1090.73] loss=1.35 avg=1.46\n", "[6767 | 1092.00] loss=1.63 avg=1.47\n", "[6768 | 1093.27] loss=1.43 avg=1.47\n", "[6769 | 1094.55] loss=1.42 avg=1.47\n", "[6770 | 1095.84] loss=1.49 avg=1.47\n", "[6771 | 1097.12] loss=1.39 avg=1.47\n", "[6772 | 1098.39] loss=1.43 avg=1.46\n", "[6773 | 1099.66] loss=1.51 avg=1.47\n", "[6774 | 1100.94] loss=1.33 avg=1.46\n", "[6775 | 1102.21] loss=1.49 avg=1.46\n", "[6776 | 1103.48] loss=1.29 avg=1.46\n", "[6777 | 1104.76] loss=1.12 avg=1.46\n", "[6778 | 1106.03] loss=1.35 avg=1.46\n", "[6779 | 1107.32] loss=1.62 avg=1.46\n", "[6780 | 1108.59] loss=1.16 avg=1.46\n", "[6781 | 1109.87] loss=1.35 avg=1.46\n", "[6782 | 1111.14] loss=1.43 avg=1.46\n", "[6783 | 1112.41] loss=1.30 avg=1.45\n", "[6784 | 1113.69] loss=1.34 avg=1.45\n", "[6785 | 1114.97] loss=1.35 avg=1.45\n", "[6786 | 1116.26] loss=1.34 avg=1.45\n", "[6787 | 1117.54] loss=1.48 avg=1.45\n", "[6788 | 1118.83] loss=1.48 avg=1.45\n", "[6789 | 1120.11] loss=1.38 avg=1.45\n", "[6790 | 1121.39] loss=1.38 avg=1.45\n", "[6791 | 1122.68] loss=1.31 avg=1.45\n", "[6792 | 1123.96] loss=1.31 avg=1.45\n", "[6793 | 1125.25] loss=1.16 avg=1.44\n", "[6794 | 1126.52] loss=1.52 avg=1.44\n", "[6795 | 1127.80] loss=1.49 avg=1.45\n", "[6796 | 1129.09] loss=1.54 avg=1.45\n", "[6797 | 1130.36] loss=1.45 avg=1.45\n", "[6798 | 1131.63] loss=1.23 avg=1.44\n", "[6799 | 1132.91] loss=1.39 avg=1.44\n", "[6800 | 1134.18] loss=1.49 avg=1.44\n", "======== SAMPLE 1 ========\n", " fuelles que les époux X... ayant demandé à M. Y... la réparation de leurs prétentions par le juge-commission de l'autrui et de l'exploitation de quatre ans, le juge-commission de l'autrui, ayant, en vertu de leur dispositions, autorisé à retrouver les rémunérations par acte notarié, les époux Y... ont fait valoir que les rémunérations nées à la date de la publication du mariage qui n'avaient pas été précisés ne pouvaient être réalisément appartenant pour leur auteur, et déjà nécessairement, devant la juridiction du contentieux de l'article 1748 du Code général des Impôts, bénéficier de cette action en remboursement prévue par la loi du 15 juin 1930, à l'article 5 de la loi du 27 décembre 1979, et que, dans ses conclusions que, dans les termes de l'article 14 du décret du 30 préavis, \" leur fait été reçu de ceux que, pour la régularité de la procédure de divorce, ses époux X... peuvent été remises et notamment en résolution au sujet \" ;Attendu cependant qu'en statuant ainsi, sans rechercher si cette obligation était intervenue entre les époux Y..., les juges du fond n'ont pas lui fait qu'en se remettant de se prononcer sur l'engagement des époux Y... et sans rechercher sans rechercher si leur fait était dépourvue de base légale, et sans l'expliquer sur la rémunération des rémunérations, la cour d'appel n'a pas donné de base légale à sa décision ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 30 juin 1985, entre les parties, par la cour d'appel de Rouen ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Caen <|endoftext|>\n", "<|startoftext|> Sur le premier moyen :Attendu que Mme X..., au service de la société Lille Jean-Marie, a été en règlement judiciaire et a assigné le syndic de la liquidation des biens en paiement de droits de salaire et dommages équivalents ; que la société Lille Jean-Marie à été mise en liquidation des biens leur a aussi accordé l'exécution et l'allocation des biens ;Attendu qu'il est fait grief à l'arrêt attaqué, confirmateur par motifs propres et adoptés, d'avoir accueilli cette demande au motif que les mesures de compétence sont soumises à régularité à la nature de l'objet du débat et que, sans l'intention, il n'y avait été jugé qu'à la demanderesse et sans laquelle ce chef à référé ne pouvait être interprété, de sorte que la demanderesse ait, conformément à l'ordonnance intérieur lourde, commis une \" contrepartie \" ;Mais attendu qu'à l'occasion de l'arrêt attaqué comme ayant donné lieu à statuer, l'arrêt a retenu que la demanderesse était inscrit établie la société Etablissement Levellon et l'indemnité de couvrant l'exercice du droit de traitement ; que le moyen, entre subsidiairement et répondant aux conclusions des conséquences de l'arrêt, est sans fondement ;Et sur\n", "\n", "[6801 | 1147.15] loss=1.40 avg=1.44\n", "[6802 | 1148.44] loss=1.37 avg=1.44\n", "[6803 | 1149.74] loss=1.57 avg=1.44\n", "[6804 | 1151.02] loss=1.40 avg=1.44\n", "[6805 | 1152.30] loss=1.48 avg=1.44\n", "[6806 | 1153.57] loss=1.22 avg=1.44\n", "[6807 | 1154.84] loss=1.39 avg=1.44\n", "[6808 | 1156.13] loss=1.44 avg=1.44\n", "[6809 | 1157.41] loss=1.40 avg=1.44\n", "[6810 | 1158.69] loss=1.59 avg=1.44\n", "[6811 | 1159.97] loss=1.35 avg=1.44\n", "[6812 | 1161.25] loss=1.16 avg=1.44\n", "[6813 | 1162.53] loss=1.32 avg=1.44\n", "[6814 | 1163.80] loss=1.48 avg=1.44\n", "[6815 | 1165.08] loss=1.62 avg=1.44\n", "[6816 | 1166.35] loss=1.56 avg=1.44\n", "[6817 | 1167.63] loss=1.44 avg=1.44\n", "[6818 | 1168.90] loss=1.25 avg=1.44\n", "[6819 | 1170.17] loss=1.37 avg=1.44\n", "[6820 | 1171.45] loss=1.33 avg=1.44\n", "[6821 | 1172.73] loss=1.40 avg=1.44\n", "[6822 | 1174.01] loss=1.58 avg=1.44\n", "[6823 | 1175.29] loss=1.63 avg=1.44\n", "[6824 | 1176.56] loss=1.41 avg=1.44\n", "[6825 | 1177.85] loss=1.66 avg=1.44\n", "[6826 | 1179.13] loss=1.48 avg=1.44\n", "[6827 | 1180.41] loss=1.60 avg=1.44\n", "[6828 | 1181.71] loss=1.41 avg=1.44\n", "[6829 | 1182.98] loss=1.45 avg=1.44\n", "[6830 | 1184.29] loss=1.46 avg=1.44\n", "[6831 | 1185.56] loss=1.65 avg=1.45\n", "[6832 | 1186.84] loss=1.45 avg=1.45\n", "[6833 | 1188.14] loss=1.44 avg=1.45\n", "[6834 | 1189.41] loss=1.54 avg=1.45\n", "[6835 | 1190.68] loss=1.33 avg=1.45\n", "[6836 | 1191.97] loss=1.55 avg=1.45\n", "[6837 | 1193.25] loss=1.35 avg=1.45\n", "[6838 | 1194.56] loss=1.42 avg=1.45\n", "[6839 | 1195.89] loss=1.48 avg=1.45\n", "[6840 | 1197.16] loss=1.35 avg=1.44\n", "[6841 | 1198.44] loss=1.33 avg=1.44\n", "[6842 | 1199.71] loss=1.51 avg=1.44\n", "[6843 | 1200.98] loss=1.47 avg=1.44\n", "[6844 | 1202.26] loss=1.36 avg=1.44\n", "[6845 | 1203.53] loss=1.32 avg=1.44\n", "[6846 | 1204.80] loss=1.61 avg=1.44\n", "[6847 | 1206.09] loss=1.50 avg=1.44\n", "[6848 | 1207.37] loss=1.42 avg=1.44\n", "[6849 | 1208.64] loss=1.48 avg=1.44\n", "[6850 | 1209.92] loss=1.31 avg=1.44\n", "[6851 | 1211.19] loss=1.19 avg=1.44\n", "[6852 | 1212.47] loss=1.40 avg=1.44\n", "[6853 | 1213.74] loss=1.56 avg=1.44\n", "[6854 | 1215.06] loss=1.36 avg=1.44\n", "[6855 | 1216.34] loss=1.44 avg=1.44\n", "[6856 | 1217.64] loss=1.48 avg=1.44\n", "[6857 | 1218.91] loss=1.33 avg=1.44\n", "[6858 | 1220.19] loss=1.37 avg=1.44\n", "[6859 | 1221.46] loss=1.45 avg=1.44\n", "[6860 | 1222.74] loss=1.47 avg=1.44\n", "[6861 | 1224.01] loss=1.61 avg=1.44\n", "[6862 | 1225.28] loss=1.31 avg=1.44\n", "[6863 | 1226.56] loss=1.47 avg=1.44\n", "[6864 | 1227.88] loss=1.78 avg=1.44\n", "[6865 | 1229.21] loss=1.33 avg=1.44\n", "[6866 | 1230.49] loss=1.39 avg=1.44\n", "[6867 | 1231.78] loss=1.69 avg=1.44\n", "[6868 | 1233.06] loss=1.29 avg=1.44\n", "[6869 | 1234.33] loss=1.46 avg=1.44\n", "[6870 | 1235.60] loss=1.49 avg=1.44\n", "[6871 | 1236.88] loss=1.63 avg=1.45\n", "[6872 | 1238.15] loss=1.47 avg=1.45\n", "[6873 | 1239.44] loss=1.49 avg=1.45\n", "[6874 | 1240.72] loss=1.48 avg=1.45\n", "[6875 | 1241.99] loss=1.36 avg=1.45\n", "[6876 | 1243.26] loss=1.24 avg=1.44\n", "[6877 | 1244.54] loss=1.21 avg=1.44\n", "[6878 | 1245.81] loss=1.49 avg=1.44\n", "[6879 | 1247.09] loss=1.63 avg=1.44\n", "[6880 | 1248.40] loss=1.22 avg=1.44\n", "[6881 | 1249.69] loss=1.29 avg=1.44\n", "[6882 | 1250.97] loss=1.24 avg=1.44\n", "[6883 | 1252.24] loss=1.56 avg=1.44\n", "[6884 | 1253.52] loss=1.51 avg=1.44\n", "[6885 | 1254.79] loss=1.57 avg=1.44\n", "[6886 | 1256.06] loss=1.34 avg=1.44\n", "[6887 | 1257.34] loss=1.63 avg=1.44\n", "[6888 | 1258.61] loss=1.38 avg=1.44\n", "[6889 | 1259.88] loss=1.11 avg=1.44\n", "[6890 | 1261.19] loss=1.26 avg=1.44\n", "[6891 | 1262.46] loss=1.40 avg=1.44\n", "[6892 | 1263.74] loss=1.51 avg=1.44\n", "[6893 | 1265.03] loss=1.37 avg=1.44\n", "[6894 | 1266.31] loss=1.33 avg=1.44\n", "[6895 | 1267.60] loss=1.56 avg=1.44\n", "[6896 | 1268.88] loss=1.42 avg=1.44\n", "[6897 | 1270.15] loss=1.45 avg=1.44\n", "[6898 | 1271.43] loss=1.55 avg=1.44\n", "[6899 | 1272.73] loss=1.45 avg=1.44\n", "[6900 | 1274.01] loss=1.63 avg=1.44\n", "======== SAMPLE 1 ========\n", " recevenu le 22 octobre 1985, de la date déterminée ;Qu'ayant estimé, en faisant la diligence d'un éclair invoquant le seuls argument introduit, que la société dans le cadre de l'exploitation et a pour conséquents de leur connaissance et au seul héritiers de néanmoins, n'avait pas répondu, et a présige le principe de la subordination, qui en est la nécessité en l'absence de l'existence professionnelle, de son emploi personnel, avec l'exclusion des éclairs sur la situation de fonctionné et celle du régime personnel de la communauté, qu'à un moment où cette cadre a été déclarée à l'initiative de l'employeur, ils n'appartenaient pas à leur fonctionné de réduire de pouvoirs de preuve dont les membres représentatifs ne lui invoquant pas leur situation de fonctionné, et que le droit ainsi de fonctionné, à la réservation des éclairs, de l'employeur ne saurait exercer une action de responsabilité morale ;Attendu que, par des motifs sensives de l'arrêt confirmatif attaqué, la Cour d'appel énonce que, dans le cadre de l'exploitation et au seul héritième, les héritiers ou éclairs sont, à la date déterminée de la société dans l'exploitation, éclairés et non éclairés ; qu'elle en a déduit que l'employeur ne pouvait donc, selon la cadre de l'exploitation, \" résumé à partir du préavis \" mais \" \" régulièrement \" ;D'où il suit, d'où elles statuent sur les premiers juges, après avoir souverainement énoncé que, dès lors, ils ne résultaient pas des éclairances de fonctionné, la Cour d'appel, qui n'a pas refusé de toutes les causes du préavis, a décidé avoir relevé que les éclairments ayant été devenus sommes par un éclair, ils n'avait dès lors furent régulièrement l'indications résumées par les lettres de change du 8 février 1985 ;Que le moyen n'est pas fondé ;PAR CES MOTIFS :REJETTE le pourvoi <|endoftext|>\n", "<|startoftext|> Sur les diverses poursuites qui se bornent à appeler à un tribunal n'énonçant aucune réclamation de preuve : . Sur le premier moyen :Attendu que Mme Z..., agissant en qualité de cadre de la société Gommard, fait grief à la procédure sous peine, après avoir constaté, pour le montant du salaire minimum qu'elle avait versé le 11 avril 1985, alors qu'elle avait refusé de le faire jusqu'au 19 au 24 février 1985 le salaire applicable devant la caisse prévue pour le versement de salaires correspondant à un maximum de réservation de son chèque pour une durée d'un an au moins en vertu de l'article L. 120 du Code de la sécurité sociale, de sorte qu'il existait un tel calcul de l'indemnité prévue au tiers d'eau avant sa réglance de l'intégration du conseillément et de ses fonctions et de la retraite, en raison de la convention collective nationale de travail et de recouvrement (C.T.T.A.S.), et de l'aarrêt attaqué, ce qui aurait dépourvu de toutes les dispositions de l'article L. 120 du Code de la sécurité sociale ;Mais attendu que la procédure ne pouv\n", "\n", "[6901 | 1287.76] loss=1.41 avg=1.44\n", "[6902 | 1289.03] loss=1.34 avg=1.44\n", "[6903 | 1290.31] loss=1.44 avg=1.44\n", "[6904 | 1291.58] loss=1.43 avg=1.44\n", "[6905 | 1292.86] loss=1.55 avg=1.44\n", "[6906 | 1294.16] loss=1.28 avg=1.44\n", "[6907 | 1295.43] loss=1.50 avg=1.44\n", "[6908 | 1296.71] loss=1.32 avg=1.44\n", "[6909 | 1297.98] loss=1.43 avg=1.44\n", "[6910 | 1299.26] loss=1.33 avg=1.44\n", "[6911 | 1300.55] loss=1.27 avg=1.43\n", "[6912 | 1301.84] loss=1.44 avg=1.43\n", "[6913 | 1303.12] loss=1.37 avg=1.43\n", "[6914 | 1304.40] loss=1.56 avg=1.43\n", "[6915 | 1305.71] loss=1.31 avg=1.43\n", "[6916 | 1306.98] loss=1.52 avg=1.43\n", "[6917 | 1308.25] loss=1.29 avg=1.43\n", "[6918 | 1309.53] loss=1.41 avg=1.43\n", "[6919 | 1310.80] loss=1.36 avg=1.43\n", "[6920 | 1312.08] loss=1.47 avg=1.43\n", "[6921 | 1313.37] loss=1.37 avg=1.43\n", "[6922 | 1314.65] loss=1.28 avg=1.43\n", "[6923 | 1315.95] loss=1.26 avg=1.43\n", "[6924 | 1317.23] loss=1.38 avg=1.43\n", "[6925 | 1318.50] loss=1.55 avg=1.43\n", "[6926 | 1319.78] loss=1.52 avg=1.43\n", "[6927 | 1321.05] loss=1.39 avg=1.43\n", "[6928 | 1322.32] loss=1.28 avg=1.43\n", "[6929 | 1323.60] loss=1.19 avg=1.43\n", "[6930 | 1324.87] loss=1.44 avg=1.43\n", "[6931 | 1326.15] loss=1.37 avg=1.43\n", "[6932 | 1327.44] loss=1.31 avg=1.42\n", "[6933 | 1328.72] loss=1.30 avg=1.42\n", "[6934 | 1329.99] loss=1.37 avg=1.42\n", "[6935 | 1331.26] loss=1.63 avg=1.42\n", "[6936 | 1332.54] loss=1.28 avg=1.42\n", "[6937 | 1333.82] loss=1.53 avg=1.42\n", "[6938 | 1335.11] loss=1.60 avg=1.43\n", "[6939 | 1336.40] loss=1.54 avg=1.43\n", "[6940 | 1337.70] loss=1.52 avg=1.43\n", "[6941 | 1338.98] loss=1.64 avg=1.43\n", "[6942 | 1340.26] loss=1.35 avg=1.43\n", "[6943 | 1341.53] loss=1.54 avg=1.43\n", "[6944 | 1342.81] loss=1.33 avg=1.43\n", "[6945 | 1344.08] loss=1.38 avg=1.43\n", "[6946 | 1345.38] loss=1.36 avg=1.43\n", "[6947 | 1346.67] loss=1.46 avg=1.43\n", "[6948 | 1347.94] loss=1.48 avg=1.43\n", "[6949 | 1349.24] loss=1.51 avg=1.43\n", "[6950 | 1350.51] loss=1.59 avg=1.43\n", "[6951 | 1351.78] loss=1.47 avg=1.43\n", "[6952 | 1353.06] loss=1.33 avg=1.43\n", "[6953 | 1354.33] loss=1.39 avg=1.43\n", "[6954 | 1355.61] loss=1.13 avg=1.43\n", "[6955 | 1356.88] loss=1.40 avg=1.43\n", "[6956 | 1358.16] loss=1.26 avg=1.43\n", "[6957 | 1359.44] loss=1.61 avg=1.43\n", "[6958 | 1360.74] loss=1.33 avg=1.43\n", "[6959 | 1362.01] loss=1.34 avg=1.43\n", "[6960 | 1363.29] loss=1.56 avg=1.43\n", "[6961 | 1364.56] loss=1.32 avg=1.43\n", "[6962 | 1365.83] loss=1.46 avg=1.43\n", "[6963 | 1367.11] loss=1.33 avg=1.43\n", "[6964 | 1368.40] loss=1.41 avg=1.43\n", "[6965 | 1369.69] loss=1.45 avg=1.43\n", "[6966 | 1371.01] loss=1.49 avg=1.43\n", "[6967 | 1372.30] loss=1.52 avg=1.43\n", "[6968 | 1373.58] loss=1.47 avg=1.43\n", "[6969 | 1374.85] loss=1.33 avg=1.43\n", "[6970 | 1376.13] loss=1.44 avg=1.43\n", "[6971 | 1377.41] loss=1.39 avg=1.43\n", "[6972 | 1378.69] loss=1.59 avg=1.43\n", "[6973 | 1379.97] loss=1.54 avg=1.43\n", "[6974 | 1381.25] loss=1.48 avg=1.43\n", "[6975 | 1382.54] loss=1.43 avg=1.43\n", "[6976 | 1383.81] loss=1.29 avg=1.43\n", "[6977 | 1385.08] loss=1.48 avg=1.43\n", "[6978 | 1386.36] loss=1.27 avg=1.43\n", "[6979 | 1387.64] loss=1.33 avg=1.43\n", "[6980 | 1388.91] loss=1.36 avg=1.43\n", "[6981 | 1390.19] loss=1.56 avg=1.43\n", "[6982 | 1391.46] loss=1.43 avg=1.43\n", "[6983 | 1392.76] loss=1.21 avg=1.42\n", "[6984 | 1394.03] loss=1.34 avg=1.42\n", "[6985 | 1395.31] loss=1.58 avg=1.43\n", "[6986 | 1396.58] loss=1.36 avg=1.42\n", "[6987 | 1397.86] loss=1.52 avg=1.43\n", "[6988 | 1399.13] loss=1.41 avg=1.43\n", "[6989 | 1400.41] loss=1.30 avg=1.42\n", "[6990 | 1401.68] loss=1.42 avg=1.42\n", "[6991 | 1402.95] loss=1.62 avg=1.43\n", "[6992 | 1404.31] loss=1.16 avg=1.42\n", "[6993 | 1405.60] loss=1.51 avg=1.42\n", "[6994 | 1406.89] loss=1.19 avg=1.42\n", "[6995 | 1408.17] loss=1.33 avg=1.42\n", "[6996 | 1409.44] loss=1.25 avg=1.42\n", "[6997 | 1410.75] loss=1.19 avg=1.42\n", "[6998 | 1412.03] loss=1.46 avg=1.42\n", "[6999 | 1413.30] loss=1.47 avg=1.42\n", "[7000 | 1414.58] loss=1.32 avg=1.42\n", "Saving checkpoint/run1/model-7000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 17:06:27.289742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:06:27.290775: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:06:27.291559: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:06:27.292534: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:06:27.293384: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:06:27.294120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-7000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 4.64it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 7865917 tokens\n", "Training...\n", "Saving checkpoint/run1/model-7000\n", "Saving checkpoint/run1/model-7000\n", "======== SAMPLE 1 ========\n", " déenchant pour le pouvoir prétendument constitué par la société Cie une clause d'élection pratiquée sur un pouvoir régulièrement établi par les parties d'un pouvoir désigné par le juge des référés, que même en fonction de son pouvoir comme de la société Cie d'appréhension et de la connaissance de cette société, il est en tien de rechercher, sans méconnaître les conséquences de cette recherchée, d'une part, par les conséquences surabondants et, d'autre part, par un moyen des causes répétées de la société Cie, la cour d'appel n'a pas donné de base légale à sa décision au regard de l'article L. 420.2 du Code des assurances, alors que l'arrêt attaqué s'est déduite, conformément à celles du rapport, pour rejeter la qualité d'électeur-dégérien, laquelle, par le droit, est, parmi la qualité de représentant des membres qui, en cas de représentant, sont d'autre présents ;qu'il est enfin que la partie requésentée est établie à la date de la désignation de M. X... et que la situation prévue par cette délégation n'en est pas déterminée ;D'où il suit que le moyen n'est pas fondé ;PAR CES MOTIFS :REJETTE le pourvoi de le pourvoi incident de M. X... et le conseil de prud'hommes de Bobigny. <|endoftext|>\n", "<|startoftext|> ACCIDENT DE LA CIRCULATION - Réparation - Oligarchage en vue de la vieillesce - Défini - Peril - Toute vérification - Définition <|endoftext|>\n", "<|startoftext|> Sur le moyen unique, pris en sa troisième branche :Attendu, selon le jugement déféré (tribunal d'instance d'Agen de la régionale d'Annecy, 7 février 1985), que les époux X... font grief aux juges d'abord, devenue saisies, de leur demande d'annulation de la livraison par laquelle elle a revendiqué la compétence et des condamnations sous la condition que l'avocat de M. X... a donné, sur lieu, ses demandes ; qu'en statuant ainsi, à ce titre, la Cour d'appel a violé les articles 9, 18, et 20 de la loi n° 85-677 du 5 juillet 1985 ;Mais attendu que l'article 20 précité, dont la société S.P.A.V. a confié à âgères de vente, les demandeurs d'annulation et de condamnations, ne pouvaient être remises au bureau d'appréciation pour seule recours des parties qui se trouvaient dans les cas spécifiés par le bureau ; qu'il est exactement exactement reproché aux juges d'appréciation d'avoir statué, légalement justifié et que le moyen n'est pas fondé ;PAR CES MOTIFS :REJETTE le pourvoi principal, mais sur les griefs du pourvoi principal, partiellement gravement formées de la défense, <|endoftext|>\n", "<|startoftext|>Sur le moyen unique, pris en ses deux branches :Attendu, selon les énonciations du jugement attaqué (bordecaux, 10 juin 1986), que par acte sous seing privé du 19 juin 1980, les consorts Y... ont d'abord licenciés pour congédiement fondé, à titre d'indemnité de congés payés, à l'encontre de la Société du droit en chômage sur l'application de l\n", "\n", "[7001 | 22.93] loss=1.61 avg=1.61\n", "[7002 | 24.24] loss=1.71 avg=1.66\n", "[7003 | 25.52] loss=1.47 avg=1.60\n", "[7004 | 26.79] loss=1.54 avg=1.58\n", "[7005 | 28.07] loss=1.53 avg=1.57\n", "[7006 | 29.34] loss=1.63 avg=1.58\n", "[7007 | 30.61] loss=1.43 avg=1.56\n", "[7008 | 31.89] loss=1.60 avg=1.56\n", "[7009 | 33.16] loss=1.63 avg=1.57\n", "[7010 | 34.43] loss=1.63 avg=1.58\n", "[7011 | 35.73] loss=1.67 avg=1.59\n", "[7012 | 37.01] loss=1.67 avg=1.59\n", "[7013 | 38.28] loss=1.45 avg=1.58\n", "[7014 | 39.55] loss=1.49 avg=1.58\n", "[7015 | 40.86] loss=1.52 avg=1.57\n", "[7016 | 42.14] loss=1.68 avg=1.58\n", "[7017 | 43.42] loss=1.44 avg=1.57\n", "[7018 | 44.69] loss=1.64 avg=1.57\n", "[7019 | 45.97] loss=1.58 avg=1.57\n", "[7020 | 47.26] loss=1.54 avg=1.57\n", "[7021 | 48.54] loss=1.35 avg=1.56\n", "[7022 | 49.81] loss=1.52 avg=1.56\n", "[7023 | 51.08] loss=1.42 avg=1.55\n", "[7024 | 52.37] loss=1.81 avg=1.56\n", "[7025 | 53.67] loss=1.63 avg=1.57\n", "[7026 | 54.96] loss=1.56 avg=1.57\n", "[7027 | 56.24] loss=1.55 avg=1.57\n", "[7028 | 57.53] loss=1.38 avg=1.56\n", "[7029 | 58.80] loss=1.58 avg=1.56\n", "[7030 | 60.08] loss=1.47 avg=1.56\n", "[7031 | 61.35] loss=1.67 avg=1.56\n", "[7032 | 62.62] loss=1.53 avg=1.56\n", "[7033 | 63.90] loss=1.55 avg=1.56\n", "[7034 | 65.18] loss=1.48 avg=1.56\n", "[7035 | 66.45] loss=1.54 avg=1.56\n", "[7036 | 67.72] loss=1.47 avg=1.55\n", "[7037 | 69.01] loss=1.70 avg=1.56\n", "[7038 | 70.28] loss=1.63 avg=1.56\n", "[7039 | 71.56] loss=1.55 avg=1.56\n", "[7040 | 72.88] loss=1.59 avg=1.56\n", "[7041 | 74.18] loss=1.39 avg=1.56\n", "[7042 | 75.46] loss=1.50 avg=1.55\n", "[7043 | 76.74] loss=1.71 avg=1.56\n", "[7044 | 78.01] loss=1.55 avg=1.56\n", "[7045 | 79.29] loss=1.50 avg=1.56\n", "[7046 | 80.56] loss=1.50 avg=1.55\n", "[7047 | 81.84] loss=1.57 avg=1.56\n", "[7048 | 83.11] loss=1.54 avg=1.55\n", "[7049 | 84.39] loss=1.60 avg=1.56\n", "[7050 | 85.66] loss=1.51 avg=1.55\n", "[7051 | 86.94] loss=1.45 avg=1.55\n", "[7052 | 88.23] loss=1.45 avg=1.55\n", "[7053 | 89.52] loss=1.65 avg=1.55\n", "[7054 | 90.84] loss=1.40 avg=1.55\n", "[7055 | 92.12] loss=1.66 avg=1.55\n", "[7056 | 93.40] loss=1.30 avg=1.55\n", "[7057 | 94.68] loss=1.48 avg=1.54\n", "[7058 | 95.95] loss=1.42 avg=1.54\n", "[7059 | 97.22] loss=1.35 avg=1.54\n", "[7060 | 98.50] loss=1.49 avg=1.54\n", "[7061 | 99.78] loss=1.50 avg=1.54\n", "[7062 | 101.05] loss=1.63 avg=1.54\n", "[7063 | 102.34] loss=1.56 avg=1.54\n", "[7064 | 103.62] loss=1.64 avg=1.54\n", "[7065 | 104.91] loss=1.59 avg=1.54\n", "[7066 | 106.20] loss=1.32 avg=1.54\n", "[7067 | 107.47] loss=1.35 avg=1.53\n", "[7068 | 108.74] loss=1.44 avg=1.53\n", "[7069 | 110.02] loss=1.47 avg=1.53\n", "[7070 | 111.29] loss=1.51 avg=1.53\n", "[7071 | 112.57] loss=1.39 avg=1.53\n", "[7072 | 113.85] loss=1.58 avg=1.53\n", "[7073 | 115.12] loss=1.50 avg=1.53\n", "[7074 | 116.39] loss=1.53 avg=1.53\n", "[7075 | 117.66] loss=1.56 avg=1.53\n", "[7076 | 118.94] loss=1.48 avg=1.53\n", "[7077 | 120.21] loss=1.53 avg=1.53\n", "[7078 | 121.48] loss=1.63 avg=1.53\n", "[7079 | 122.77] loss=1.48 avg=1.53\n", "[7080 | 124.12] loss=1.53 avg=1.53\n", "[7081 | 125.41] loss=1.52 avg=1.53\n", "[7082 | 126.70] loss=1.51 avg=1.53\n", "[7083 | 127.97] loss=1.34 avg=1.52\n", "[7084 | 129.25] loss=1.51 avg=1.52\n", "[7085 | 130.52] loss=1.65 avg=1.53\n", "[7086 | 131.79] loss=1.59 avg=1.53\n", "[7087 | 133.07] loss=1.56 avg=1.53\n", "[7088 | 134.36] loss=1.64 avg=1.53\n", "[7089 | 135.65] loss=1.41 avg=1.53\n", "[7090 | 136.94] loss=1.56 avg=1.53\n", "[7091 | 138.23] loss=1.53 avg=1.53\n", "[7092 | 139.50] loss=1.46 avg=1.53\n", "[7093 | 140.78] loss=1.54 avg=1.53\n", "[7094 | 142.05] loss=1.51 avg=1.53\n", "[7095 | 143.33] loss=1.62 avg=1.53\n", "[7096 | 144.61] loss=1.39 avg=1.53\n", "[7097 | 145.89] loss=1.44 avg=1.52\n", "[7098 | 147.17] loss=1.36 avg=1.52\n", "[7099 | 148.44] loss=1.90 avg=1.53\n", "[7100 | 149.71] loss=1.48 avg=1.53\n", "======== SAMPLE 1 ========\n", " chef parité la garantie de celui qui lui est dûment due ; qu'en statuant ainsi comme elle l'a fait, alors que de ces constatations il était nul qui constitue une dénimation de ce dernier apposante, le Tribunal n'a pas répondu à ce chef dans le dernier moyen et, pour le surplus, renoncé à ne pas valablement la demande de la victime d'infarction suffisante ;CASSE ET ANNULE, dans toutes ses dispositions, le jugement rendu le 20 juin 1990, entre les parties, par le tribunal d'instance de Bordeaux ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit jugement et, pour être fait droit, les renvoie devant le tribunal d'instance de Metz <|endoftext|>\n", "<|startoftext|> Sur le moyen unique :Attendu que M. André X..., seul victime en 1982, a noté lui à ville de Paris, à Marseille, qui sollicite une provision par la société Cire Crédit ; qu'en février 1986, M. André X... a noté avec des fins de commerce de la société Cire Crédit pour que M. de X... soit assuré à Marseille-Lipogne-Vieuve cette société ; que celui-ci a assigné M. X... et son assureur, alors la victime en demandant qu'il y avait lieu de déclarer la clause de limitation \" à sa direction \" de clause d'expulsion ;Attendu qu'il est fait grief à l'arrêt attaqué (Besançon, 6 mars 1990) d'avoir accueilli les demandes contre la société Cire Crédit et de l'avoir reconnu des motifs et obligations dépendants de M. X..., alors, selon l'article 700-7° du nouveau Code de procédure civile, que ce dernier alinéa, porteur d'une clause de clause \" à sa direction \" de prêt d'expulsion, ne peut prononcer la nullité des clauses de fausse obligation, que dès lors la clause de fausse obligation au sens de l'article 700-10° du nouveau Code de procédure civile ne peut remenir que M. X... quelles que soient la faculté de déjurer la clause de prêt que soit révélé l'acheteur en exerçant, de l'absence de l'attitude de la victime survenue dans l'entreprise et sur sa prétention, de sorte qu'il résulte de ses constatations que ceux des fins de commerce, l'exploitation provisoire à M. X... d'une société qui précédemment avait été consentie, à l'époque au cours du dépôt, du contrôle du contrôle qui, avec des clauses des forts professionnels du 20 septembre 1983 en faveur de sa propriété, était au juge de la faculté néanmoins de déterminer l'intimité qu'il révélait à bénéficier, sans que si la clause de prêt était, à la date du dépôt du contrôle au cours du dépôt du contrôle, applicable au contrôle des parties en cause, elle avait un caractère provoqué contre le fait de la clause de \" assurage \" ;Mais attendu que dans des conclusions demeurées sans réponse, les époux X... ont fait valoir qu'ils avaient pu se prévaloir de la clause du contrôle qui, mais ne pouvant prétendRE à la rupture par le syndic du contrôle pour partie des charges à l'assurance maladie envers cette société, résultait du contrôle des époux X..., la clause de prêt au paiement des charges\n", "\n", "[7101 | 162.73] loss=1.59 avg=1.53\n", "[7102 | 164.02] loss=1.62 avg=1.53\n", "[7103 | 165.31] loss=1.49 avg=1.53\n", "[7104 | 166.60] loss=1.56 avg=1.53\n", "[7105 | 167.90] loss=1.71 avg=1.53\n", "[7106 | 169.19] loss=1.54 avg=1.53\n", "[7107 | 170.50] loss=1.41 avg=1.53\n", "[7108 | 171.78] loss=1.52 avg=1.53\n", "[7109 | 173.06] loss=1.41 avg=1.53\n", "[7110 | 174.33] loss=1.47 avg=1.53\n", "[7111 | 175.61] loss=1.53 avg=1.53\n", "[7112 | 176.88] loss=1.40 avg=1.53\n", "[7113 | 178.17] loss=1.46 avg=1.52\n", "[7114 | 179.46] loss=1.36 avg=1.52\n", "[7115 | 180.73] loss=1.40 avg=1.52\n", "[7116 | 182.00] loss=1.56 avg=1.52\n", "[7117 | 183.28] loss=1.41 avg=1.52\n", "[7118 | 184.55] loss=1.41 avg=1.52\n", "[7119 | 185.83] loss=1.48 avg=1.52\n", "[7120 | 187.10] loss=1.48 avg=1.52\n", "[7121 | 188.38] loss=1.70 avg=1.52\n", "[7122 | 189.68] loss=1.68 avg=1.52\n", "[7123 | 190.95] loss=1.50 avg=1.52\n", "[7124 | 192.22] loss=1.45 avg=1.52\n", "[7125 | 193.50] loss=1.48 avg=1.52\n", "[7126 | 194.78] loss=1.38 avg=1.52\n", "[7127 | 196.05] loss=1.50 avg=1.52\n", "[7128 | 197.32] loss=1.31 avg=1.51\n", "[7129 | 198.61] loss=1.57 avg=1.52\n", "[7130 | 199.90] loss=1.45 avg=1.51\n", "[7131 | 201.21] loss=1.33 avg=1.51\n", "[7132 | 202.56] loss=1.45 avg=1.51\n", "[7133 | 203.94] loss=1.40 avg=1.51\n", "[7134 | 205.21] loss=1.58 avg=1.51\n", "[7135 | 206.48] loss=1.50 avg=1.51\n", "[7136 | 207.76] loss=1.44 avg=1.51\n", "[7137 | 209.03] loss=1.59 avg=1.51\n", "[7138 | 210.30] loss=1.53 avg=1.51\n", "[7139 | 211.59] loss=1.69 avg=1.51\n", "[7140 | 212.86] loss=1.65 avg=1.52\n", "[7141 | 214.14] loss=1.66 avg=1.52\n", "[7142 | 215.41] loss=1.30 avg=1.51\n", "[7143 | 216.69] loss=1.27 avg=1.51\n", "[7144 | 217.96] loss=1.41 avg=1.51\n", "[7145 | 219.23] loss=1.28 avg=1.51\n", "[7146 | 220.51] loss=1.34 avg=1.50\n", "[7147 | 221.78] loss=1.51 avg=1.50\n", "[7148 | 223.08] loss=1.40 avg=1.50\n", "[7149 | 224.35] loss=1.52 avg=1.50\n", "[7150 | 225.62] loss=1.30 avg=1.50\n", "[7151 | 226.90] loss=1.48 avg=1.50\n", "[7152 | 228.17] loss=1.31 avg=1.50\n", "[7153 | 229.45] loss=1.61 avg=1.50\n", "[7154 | 230.72] loss=1.44 avg=1.50\n", "[7155 | 232.00] loss=1.54 avg=1.50\n", "[7156 | 233.29] loss=1.44 avg=1.50\n", "[7157 | 234.57] loss=1.43 avg=1.50\n", "[7158 | 235.94] loss=1.46 avg=1.50\n", "[7159 | 237.36] loss=1.46 avg=1.50\n", "[7160 | 238.67] loss=1.42 avg=1.50\n", "[7161 | 239.95] loss=1.57 avg=1.50\n", "[7162 | 241.24] loss=1.54 avg=1.50\n", "[7163 | 242.51] loss=1.51 avg=1.50\n", "[7164 | 243.79] loss=1.33 avg=1.50\n", "[7165 | 245.08] loss=1.48 avg=1.50\n", "[7166 | 246.35] loss=1.60 avg=1.50\n", "[7167 | 247.63] loss=1.37 avg=1.49\n", "[7168 | 248.90] loss=1.51 avg=1.50\n", "[7169 | 250.17] loss=1.44 avg=1.49\n", "[7170 | 251.45] loss=1.52 avg=1.49\n", "[7171 | 252.72] loss=1.40 avg=1.49\n", "[7172 | 253.99] loss=1.38 avg=1.49\n", "[7173 | 255.28] loss=1.51 avg=1.49\n", "[7174 | 256.56] loss=1.57 avg=1.49\n", "[7175 | 257.83] loss=1.52 avg=1.49\n", "[7176 | 259.10] loss=1.55 avg=1.49\n", "[7177 | 260.38] loss=1.34 avg=1.49\n", "[7178 | 261.65] loss=1.51 avg=1.49\n", "[7179 | 262.93] loss=1.45 avg=1.49\n", "[7180 | 264.20] loss=1.56 avg=1.49\n", "[7181 | 265.47] loss=1.38 avg=1.49\n", "[7182 | 266.76] loss=1.44 avg=1.49\n", "[7183 | 268.04] loss=1.52 avg=1.49\n", "[7184 | 269.35] loss=1.25 avg=1.49\n", "[7185 | 270.63] loss=1.50 avg=1.49\n", "[7186 | 271.91] loss=1.39 avg=1.49\n", "[7187 | 273.20] loss=1.45 avg=1.49\n", "[7188 | 274.48] loss=1.44 avg=1.49\n", "[7189 | 275.77] loss=1.43 avg=1.49\n", "[7190 | 277.05] loss=1.56 avg=1.49\n", "[7191 | 278.33] loss=1.49 avg=1.49\n", "[7192 | 279.60] loss=1.49 avg=1.49\n", "[7193 | 280.88] loss=1.50 avg=1.49\n", "[7194 | 282.15] loss=1.38 avg=1.49\n", "[7195 | 283.43] loss=1.65 avg=1.49\n", "[7196 | 284.70] loss=1.35 avg=1.49\n", "[7197 | 285.98] loss=1.55 avg=1.49\n", "[7198 | 287.25] loss=1.50 avg=1.49\n", "[7199 | 288.53] loss=1.39 avg=1.49\n", "[7200 | 289.80] loss=1.43 avg=1.49\n", "======== SAMPLE 1 ========\n", " civil demande ;Sur le moyen unique, pris en ses deux branches : (sans intérêt) ;Mais sur le second moyen :Vu l'article 1327 du Code civil, ensemble l'article 1136 du Code civil ;Attendu, selon l'arrêt attaqué, qu'une maison d'habitation de M. X..., à Paris avec M. Y..., fait l'acquéreur, M. Z..., de la moyenne ; que le 10 mai 1985, la commune parisienne a vendu en France, la commune de Paris, un pouvoir de crédit-bail qu'elle avait confié au M. Y... et a fait l'acquérer ; que la démble à la somme réclamée lui devant être soumis par le premier juge, en application de l'article 4, alinéa 2, de la loi du 5 juillet 1985, de l'article 19 de la loi du 29 juillet 1781 et de l'article 6 à la loi du 20 juillet 1984 ; qu'aux termes du statut de l'indivision le propriétaire devient être occupés d'un fonds d'une entreprise, les deux épouse ; que M. Y..., prissant son contrôle, a assigné le conseil de prud'hommes pour faire constater la démble à la commune ;Attendu que pour décider que le conseil de prud'hommes s'était borné à déclarer que cette démble des deux époux Z... peut constituer une délibération contre le conseil de prud'hommes, l'arrêt retient qu'il est séjourvu à un réclamation ;Qu'en se déterminant par un motif inopérant, la cour d'appel a violé les textes susvisés ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 14 juin 1987, entre les parties, par la cour d'appel de Poitiers ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel d'Angers <|endoftext|>\n", "<|startoftext|> SOCIETE CIVILE IMMOBILIERE - Conditions - Société - Société commercial - Possibilités du bilan - Société immobilière - Prisons de crédit en réalisation - Loi du 5 juillet 1985 - Révocation du conseil de prud'hommes du 17 août 1991 - Près de l'action - Effet sur demande des parties - Crédit départementale des crédits (non) <|endoftext|>\n", "<|startoftext|> .Sur le moyen unique :Vu l'article 15-1 du Code de procédure pénale ;Attendu qu'à la suite de l'examen du découververt par M. X... de la déclaration de poursuite, au seul motif que l'immeuble ne pût être déclaré à sa charge seulement, que l'autre révocation, sur le fait faisant ainsi prénetir de la totalité des tâches auxquels cette commune produit, prévoyait son exécution, en sorte que la réVocation doit être révocation en février après sa déclaration du découververt par la déclaration ;Attendu, selon l'arrêt attaqué (Aix-en-Provence, le 2 juin 1988), que l'immeuble reste contre elle-même entre le 1er juin 1987 et le 24 novembre 1989, la cour d'appel de Riom a estimé qu'en la circonstance M. X... a été déclaré de son préavis à la date du 21 mai 1988, elle avait reçu pendant l'exécution d'une simple révocation\n", "\n", "[7201 | 303.07] loss=1.57 avg=1.49\n", "[7202 | 304.35] loss=1.52 avg=1.49\n", "[7203 | 305.63] loss=1.55 avg=1.49\n", "[7204 | 306.91] loss=1.40 avg=1.49\n", "[7205 | 308.22] loss=1.38 avg=1.49\n", "[7206 | 309.51] loss=1.45 avg=1.48\n", "[7207 | 310.84] loss=1.54 avg=1.49\n", "[7208 | 312.12] loss=1.63 avg=1.49\n", "[7209 | 313.40] loss=1.65 avg=1.49\n", "[7210 | 314.67] loss=1.47 avg=1.49\n", "[7211 | 315.95] loss=1.44 avg=1.49\n", "[7212 | 317.22] loss=1.60 avg=1.49\n", "[7213 | 318.50] loss=1.48 avg=1.49\n", "[7214 | 319.77] loss=1.45 avg=1.49\n", "[7215 | 321.05] loss=1.53 avg=1.49\n", "[7216 | 322.33] loss=1.66 avg=1.49\n", "[7217 | 323.60] loss=1.40 avg=1.49\n", "[7218 | 324.88] loss=1.41 avg=1.49\n", "[7219 | 326.15] loss=1.37 avg=1.49\n", "[7220 | 327.42] loss=1.61 avg=1.49\n", "[7221 | 328.69] loss=1.64 avg=1.49\n", "[7222 | 329.97] loss=1.42 avg=1.49\n", "[7223 | 331.24] loss=1.49 avg=1.49\n", "[7224 | 332.53] loss=1.53 avg=1.49\n", "[7225 | 333.84] loss=1.44 avg=1.49\n", "[7226 | 335.13] loss=1.55 avg=1.49\n", "[7227 | 336.40] loss=1.50 avg=1.49\n", "[7228 | 337.68] loss=1.51 avg=1.49\n", "[7229 | 338.95] loss=1.48 avg=1.49\n", "[7230 | 340.22] loss=1.52 avg=1.49\n", "[7231 | 341.50] loss=1.49 avg=1.49\n", "[7232 | 342.78] loss=1.56 avg=1.49\n", "[7233 | 344.15] loss=1.43 avg=1.49\n", "[7234 | 345.44] loss=1.38 avg=1.49\n", "[7235 | 346.73] loss=1.41 avg=1.49\n", "[7236 | 348.01] loss=1.51 avg=1.49\n", "[7237 | 349.28] loss=1.47 avg=1.49\n", "[7238 | 350.55] loss=1.52 avg=1.49\n", "[7239 | 351.83] loss=1.42 avg=1.49\n", "[7240 | 353.11] loss=1.51 avg=1.49\n", "[7241 | 354.39] loss=1.35 avg=1.49\n", "[7242 | 355.67] loss=1.42 avg=1.49\n", "[7243 | 356.94] loss=1.36 avg=1.49\n", "[7244 | 358.22] loss=1.43 avg=1.48\n", "[7245 | 359.49] loss=1.36 avg=1.48\n", "[7246 | 360.77] loss=1.29 avg=1.48\n", "[7247 | 362.04] loss=1.45 avg=1.48\n", "[7248 | 363.32] loss=1.45 avg=1.48\n", "[7249 | 364.60] loss=1.48 avg=1.48\n", "[7250 | 365.95] loss=1.45 avg=1.48\n", "[7251 | 367.24] loss=1.56 avg=1.48\n", "[7252 | 368.51] loss=1.40 avg=1.48\n", "[7253 | 369.78] loss=1.33 avg=1.48\n", "[7254 | 371.06] loss=1.61 avg=1.48\n", "[7255 | 372.33] loss=1.57 avg=1.48\n", "[7256 | 373.60] loss=1.43 avg=1.48\n", "[7257 | 374.88] loss=1.78 avg=1.48\n", "[7258 | 376.18] loss=1.46 avg=1.48\n", "[7259 | 377.46] loss=1.64 avg=1.49\n", "[7260 | 378.74] loss=1.53 avg=1.49\n", "[7261 | 380.03] loss=1.47 avg=1.49\n", "[7262 | 381.32] loss=1.34 avg=1.48\n", "[7263 | 382.60] loss=1.37 avg=1.48\n", "[7264 | 383.88] loss=1.63 avg=1.48\n", "[7265 | 385.15] loss=1.47 avg=1.48\n", "[7266 | 386.43] loss=1.47 avg=1.48\n", "[7267 | 387.73] loss=1.51 avg=1.48\n", "[7268 | 389.01] loss=1.49 avg=1.48\n", "[7269 | 390.28] loss=1.54 avg=1.48\n", "[7270 | 391.55] loss=1.30 avg=1.48\n", "[7271 | 392.83] loss=1.51 avg=1.48\n", "[7272 | 394.10] loss=1.45 avg=1.48\n", "[7273 | 395.38] loss=1.42 avg=1.48\n", "[7274 | 396.66] loss=1.57 avg=1.48\n", "[7275 | 397.96] loss=1.45 avg=1.48\n", "[7276 | 399.34] loss=1.42 avg=1.48\n", "[7277 | 400.61] loss=1.48 avg=1.48\n", "[7278 | 401.89] loss=1.49 avg=1.48\n", "[7279 | 403.17] loss=1.33 avg=1.48\n", "[7280 | 404.45] loss=1.37 avg=1.48\n", "[7281 | 405.72] loss=1.54 avg=1.48\n", "[7282 | 407.00] loss=1.49 avg=1.48\n", "[7283 | 408.27] loss=1.37 avg=1.48\n", "[7284 | 409.56] loss=1.56 avg=1.48\n", "[7285 | 410.83] loss=1.38 avg=1.48\n", "[7286 | 412.11] loss=1.47 avg=1.48\n", "[7287 | 413.38] loss=1.35 avg=1.48\n", "[7288 | 414.67] loss=1.77 avg=1.48\n", "[7289 | 415.96] loss=1.61 avg=1.48\n", "[7290 | 417.24] loss=1.55 avg=1.48\n", "[7291 | 418.53] loss=1.54 avg=1.48\n", "[7292 | 419.80] loss=1.36 avg=1.48\n", "[7293 | 421.08] loss=1.59 avg=1.48\n", "[7294 | 422.36] loss=1.49 avg=1.48\n", "[7295 | 423.63] loss=1.51 avg=1.48\n", "[7296 | 424.91] loss=1.40 avg=1.48\n", "[7297 | 426.18] loss=1.49 avg=1.48\n", "[7298 | 427.45] loss=1.43 avg=1.48\n", "[7299 | 428.72] loss=1.40 avg=1.48\n", "[7300 | 430.00] loss=1.31 avg=1.48\n", "======== SAMPLE 1 ========\n", " de l'édicte \" s'étant produite à deux journées qui déclaraient pour son fils l'avoir à compter du 9 octobre au 29 mars 1984 pour des éléments éventuellement prévus par le texte susvisé en prétendant qu'il était soumis à Mme X... d'étéjoux établie pour une durée égale à celle de l'année 1985, l'arrêt rendu le 23 mars 1987, par un arrêt du 16 novembre 1986, déféré à l'instance en refusant d'autoriser sa rétablissement à son épouse en ce sens de la réapplication de l'article 462, alinéa 2, du nouveau Code de procédure civile, dans sa rédaction antérieure à la loi du 5 juillet 1988, et de l'arrêt attaqué rendu en vigueur en application de l'article 6 de la loi du 20 décembre 1986 ; <|endoftext|>\n", "<|startoftext|> PROCEDURE CIVILE - Article 6 - Attendance, qui y serra pour la saisie d'une action collectives - Décisions des personnes moralelles - Action collectives ou collectives individuelles - Délai-congé à l'encontre du débiteur <|endoftext|>\n", "<|startoftext|> .Sur le premier moyen :Attendu, selon l'arrêt confirmatif attaqué (Paris, 22 mai 1987), que Mielle X..., qui avait été placée à compter du 15 septembre 1982 de la Société des contrats de mise en redressement judiciaire parisienne (la Société) a été licenciée le 1er octobre 1974, puis qu'une procédure d'expropriation s'étant bornée à le débouter de ses demandes en paiement de dommages-intérêts pour licenciement sans cause réelle et sérieuse ou certaine ;Attendu que M. X... fait grief à l'arrêt de l'avoir débouté de sa demande d'indemnité compensatrice de licenciement alors, selon le moyen, d'une part, qu'en appliquant un contrat de mise en redressement judiciaire pour l'autorisation de licencier le salarié le salarié qui a été débouté de sa demande résistance au salarié qui se désigne à défaut de paiement de la somme de 100 000 francs ; qu'en l'espèce, il résultait des prétentions de la Compagnie du redressement judiciaire et dont ce contrat pouvait être attribué à M. X... le salaire de la chambre civile professionnelle, qui devait à l'un de ses salariés à titre personnel et prévoyant l'absence d'entretien du premier président du Tribunal ; qu'en l'espèce, l'article L. 124-2 du Code du travail s'applique que M. X... a fait valoir que les activités de son personnel en liquidation de ses revenus exigé par l'article L. 122-12 du même Code par ses salariés exercent des fonctions de travailleur public et que si les personnes qui doivent être autorisées, même si elles ont ou concevable, par le maintien d'un emploi ayant été poursuivi, sont un comité d'entreprise individuelle et non un employeur ; qu'en décidant le contraire, la cour d'appel a violé l'article L. 114-1 du Code du travail, alors, d'autre part, que, suivant les dispositions de l'article L. 124-3 du Code du travail de la compagnie du redressement judiciaire en appliquant en l'espèce une clause résultant d'une police d\n", "\n", "[7301 | 443.27] loss=1.46 avg=1.48\n", "[7302 | 444.55] loss=1.50 avg=1.48\n", "[7303 | 445.82] loss=1.60 avg=1.48\n", "[7304 | 447.09] loss=1.49 avg=1.48\n", "[7305 | 448.37] loss=1.31 avg=1.48\n", "[7306 | 449.65] loss=1.41 avg=1.48\n", "[7307 | 450.94] loss=1.31 avg=1.48\n", "[7308 | 452.22] loss=1.39 avg=1.48\n", "[7309 | 453.55] loss=1.35 avg=1.47\n", "[7310 | 454.83] loss=1.49 avg=1.47\n", "[7311 | 456.10] loss=1.36 avg=1.47\n", "[7312 | 457.37] loss=1.51 avg=1.47\n", "[7313 | 458.65] loss=1.42 avg=1.47\n", "[7314 | 459.93] loss=1.43 avg=1.47\n", "[7315 | 461.20] loss=1.43 avg=1.47\n", "[7316 | 462.47] loss=1.59 avg=1.47\n", "[7317 | 463.78] loss=1.38 avg=1.47\n", "[7318 | 465.09] loss=1.42 avg=1.47\n", "[7319 | 466.36] loss=1.59 avg=1.47\n", "[7320 | 467.63] loss=1.58 avg=1.47\n", "[7321 | 468.91] loss=1.44 avg=1.47\n", "[7322 | 470.18] loss=1.50 avg=1.47\n", "[7323 | 471.45] loss=1.51 avg=1.47\n", "[7324 | 472.72] loss=1.49 avg=1.47\n", "[7325 | 474.00] loss=1.41 avg=1.47\n", "[7326 | 475.28] loss=1.43 avg=1.47\n", "[7327 | 476.56] loss=1.34 avg=1.47\n", "[7328 | 477.83] loss=1.53 avg=1.47\n", "[7329 | 479.10] loss=1.49 avg=1.47\n", "[7330 | 480.37] loss=1.62 avg=1.47\n", "[7331 | 481.65] loss=1.41 avg=1.47\n", "[7332 | 482.92] loss=1.57 avg=1.47\n", "[7333 | 484.19] loss=1.36 avg=1.47\n", "[7334 | 485.48] loss=1.39 avg=1.47\n", "[7335 | 486.83] loss=1.28 avg=1.47\n", "[7336 | 488.12] loss=1.63 avg=1.47\n", "[7337 | 489.40] loss=1.34 avg=1.47\n", "[7338 | 490.68] loss=1.55 avg=1.47\n", "[7339 | 491.95] loss=1.35 avg=1.47\n", "[7340 | 493.23] loss=1.31 avg=1.47\n", "[7341 | 494.50] loss=1.51 avg=1.47\n", "[7342 | 495.78] loss=1.43 avg=1.47\n", "[7343 | 497.09] loss=1.43 avg=1.47\n", "[7344 | 498.40] loss=1.38 avg=1.47\n", "[7345 | 499.68] loss=1.46 avg=1.47\n", "[7346 | 500.96] loss=1.37 avg=1.47\n", "[7347 | 502.23] loss=1.36 avg=1.47\n", "[7348 | 503.51] loss=1.41 avg=1.46\n", "[7349 | 504.79] loss=1.44 avg=1.46\n", "[7350 | 506.06] loss=1.64 avg=1.47\n", "[7351 | 507.34] loss=1.52 avg=1.47\n", "[7352 | 508.64] loss=1.33 avg=1.47\n", "[7353 | 509.91] loss=1.39 avg=1.46\n", "[7354 | 511.19] loss=1.55 avg=1.47\n", "[7355 | 512.46] loss=1.24 avg=1.46\n", "[7356 | 513.74] loss=1.37 avg=1.46\n", "[7357 | 515.02] loss=1.43 avg=1.46\n", "[7358 | 516.30] loss=1.45 avg=1.46\n", "[7359 | 517.57] loss=1.31 avg=1.46\n", "[7360 | 518.85] loss=1.51 avg=1.46\n", "[7361 | 520.15] loss=1.48 avg=1.46\n", "[7362 | 521.56] loss=1.39 avg=1.46\n", "[7363 | 522.86] loss=1.47 avg=1.46\n", "[7364 | 524.17] loss=1.46 avg=1.46\n", "[7365 | 525.47] loss=1.28 avg=1.46\n", "[7366 | 526.75] loss=1.45 avg=1.46\n", "[7367 | 528.02] loss=1.41 avg=1.46\n", "[7368 | 529.35] loss=1.68 avg=1.46\n", "[7369 | 530.88] loss=1.38 avg=1.46\n", "[7370 | 532.17] loss=1.29 avg=1.46\n", "[7371 | 533.45] loss=1.51 avg=1.46\n", "[7372 | 534.72] loss=1.57 avg=1.46\n", "[7373 | 535.99] loss=1.40 avg=1.46\n", "[7374 | 537.27] loss=1.59 avg=1.46\n", "[7375 | 538.54] loss=1.41 avg=1.46\n", "[7376 | 539.81] loss=1.35 avg=1.46\n", "[7377 | 541.10] loss=1.60 avg=1.46\n", "[7378 | 542.38] loss=1.38 avg=1.46\n", "[7379 | 543.66] loss=1.67 avg=1.46\n", "[7380 | 544.93] loss=1.49 avg=1.46\n", "[7381 | 546.21] loss=1.56 avg=1.46\n", "[7382 | 547.49] loss=1.54 avg=1.46\n", "[7383 | 548.77] loss=1.57 avg=1.46\n", "[7384 | 550.05] loss=1.51 avg=1.46\n", "[7385 | 551.33] loss=1.48 avg=1.46\n", "[7386 | 552.62] loss=1.45 avg=1.46\n", "[7387 | 553.90] loss=1.33 avg=1.46\n", "[7388 | 555.19] loss=1.56 avg=1.46\n", "[7389 | 556.46] loss=1.46 avg=1.46\n", "[7390 | 557.76] loss=1.61 avg=1.47\n", "[7391 | 559.05] loss=1.43 avg=1.47\n", "[7392 | 560.35] loss=1.33 avg=1.46\n", "[7393 | 561.64] loss=1.40 avg=1.46\n", "[7394 | 562.94] loss=1.65 avg=1.47\n", "[7395 | 564.36] loss=1.35 avg=1.46\n", "[7396 | 565.64] loss=1.51 avg=1.46\n", "[7397 | 566.91] loss=1.34 avg=1.46\n", "[7398 | 568.19] loss=1.53 avg=1.46\n", "[7399 | 569.47] loss=1.61 avg=1.47\n", "[7400 | 570.74] loss=1.42 avg=1.46\n", "======== SAMPLE 1 ========\n", " dont le bénéfice était, à défaut de la nature de la procédure de redressement judiciaire le 21 juillet 1980, régulièrement définitive ; qu'en décidant le contraire, alors que lorsqu'une procédure de redressement judiciaire est recevable le 22 juin 1979, le Tribunal a violé les textes susvisés ;Mais attendu qu'ayant constaté que l'arrêt avait donné à bail un bail pour une date d'expiration qu'il avait soumis à la loi du 1er juillet 1982 par la loi du 22 juin 1924 et à une loi du 18 juillet au 31 décembre 1963, dont la durée d'un bail, dans la limite légale, ne pouvait être conformé au bien ou au bail pour une durée spécialisée ou bien non-propriétaire, et qu'ayant relevé que ce bail n'avait pas été mise à neuf anniversaires de 1 ans à compter du 19 mois pendant l'expiration du bail, la présence du bailant n'étant pas conforme à l'établissement ou à des parties privatives, M. Y... avait été informé le 21 juin 1978 aux exigences légales nécessités de la rédaction du bail et la possibilité d'invoquer aux rémunérations à compter de ce jour ; que par ce seul motif, répondant à ce moyen en ses trois branches, l'arrêt se réfère au contrat de mise en jeu et a déduit que les époux X... avaient déposé une lettre du 28 juin 1977 des observations de la première et de la seconde parents de ces derniers, qui ne pouvaient plus, avant la date d'expiration du bail et de la moindre lettres de leur mère ;PAR CES MOTIFS :REJETTE le pourvoi <|endoftext|>\n", "<|startoftext|> .Sur le premier moyen, pris en sa seconde branche :Attendu que Mme Y... ayant assigné le tribunal de grande instance de Paris en paiement, en septembre 1980, de la somme de 6 843,28 francs, sa cause a été faite au profit de la société Chagot ;Attendu que la société Chagot font grief à l'arrêt attaqué (Paris, 7 février 1989) de décider que l'actif expirait le 17 janvier 1987 et déboursait en cause Mme Y..., alors, selon le moyen, \" 1°) que une clause d'exclusivité a pour objet des clauses contractées en cas d'avis de recours aux parties ou en cause ou en écartait la clause d'exclusivité, que la cour d'appel a constaté que l'actif ne se décomptait pas en la matière ou en la matière de l'évaluation des indemnités litigieuses et que, dès lors, en refusant de décider qu'il n'était pas contesté par Mme Y..., elle ne pouvait pas tenir le paiement de ce qui bénéficiait d'un prêt au profit du débiteur du prix d'un prix à son propriétaire pour le compte de celle-ci, la cour d'appel a violé ce texte ; 2°) que, en l'absence de clause d'exclusivité, cette clause ne pouvait bénéficier l'existence d'une clause contractuelle pour les dommages et qu'il ne pouvait s'opposer au seul paiement de la somme de quatre avantages qu'il avait élevé ou d'un prix de ces établissements de compte et qu'en conséquence, sa cour d'appel n'avait pas répondu à ses conclusions dans laquelle la cressé a fait valoir que, lors de\n", "\n", "[7401 | 583.99] loss=1.50 avg=1.47\n", "[7402 | 585.28] loss=1.51 avg=1.47\n", "[7403 | 586.56] loss=1.43 avg=1.47\n", "[7404 | 587.84] loss=1.49 avg=1.47\n", "[7405 | 589.11] loss=1.41 avg=1.47\n", "[7406 | 590.39] loss=1.44 avg=1.46\n", "[7407 | 591.67] loss=1.25 avg=1.46\n", "[7408 | 592.95] loss=1.42 avg=1.46\n", "[7409 | 594.25] loss=1.76 avg=1.47\n", "[7410 | 595.65] loss=1.31 avg=1.46\n", "[7411 | 597.12] loss=1.39 avg=1.46\n", "[7412 | 598.44] loss=1.36 avg=1.46\n", "[7413 | 599.72] loss=1.40 avg=1.46\n", "[7414 | 601.00] loss=1.47 avg=1.46\n", "[7415 | 602.27] loss=1.50 avg=1.46\n", "[7416 | 603.55] loss=1.52 avg=1.46\n", "[7417 | 604.83] loss=1.41 avg=1.46\n", "[7418 | 606.10] loss=1.43 avg=1.46\n", "[7419 | 607.40] loss=1.31 avg=1.46\n", "[7420 | 608.69] loss=1.31 avg=1.46\n", "[7421 | 609.96] loss=1.27 avg=1.46\n", "[7422 | 611.24] loss=1.48 avg=1.46\n", "[7423 | 612.52] loss=1.43 avg=1.46\n", "[7424 | 613.80] loss=1.43 avg=1.46\n", "[7425 | 615.07] loss=1.55 avg=1.46\n", "[7426 | 616.35] loss=1.26 avg=1.46\n", "[7427 | 617.63] loss=1.40 avg=1.45\n", "[7428 | 618.92] loss=1.43 avg=1.45\n", "[7429 | 620.20] loss=1.50 avg=1.45\n", "[7430 | 621.48] loss=1.49 avg=1.46\n", "[7431 | 622.76] loss=1.51 avg=1.46\n", "[7432 | 624.04] loss=1.57 avg=1.46\n", "[7433 | 625.32] loss=1.35 avg=1.46\n", "[7434 | 626.60] loss=1.59 avg=1.46\n", "[7435 | 627.88] loss=1.59 avg=1.46\n", "[7436 | 629.21] loss=1.53 avg=1.46\n", "[7437 | 630.55] loss=1.34 avg=1.46\n", "[7438 | 631.84] loss=1.47 avg=1.46\n", "[7439 | 633.15] loss=1.45 avg=1.46\n", "[7440 | 634.44] loss=1.43 avg=1.46\n", "[7441 | 635.72] loss=1.35 avg=1.46\n", "[7442 | 637.00] loss=1.45 avg=1.46\n", "[7443 | 638.28] loss=1.37 avg=1.46\n", "[7444 | 639.55] loss=1.45 avg=1.46\n", "[7445 | 640.84] loss=1.50 avg=1.46\n", "[7446 | 642.11] loss=1.33 avg=1.45\n", "[7447 | 643.41] loss=1.38 avg=1.45\n", "[7448 | 644.68] loss=1.39 avg=1.45\n", "[7449 | 645.96] loss=1.41 avg=1.45\n", "[7450 | 647.24] loss=1.32 avg=1.45\n", "[7451 | 648.51] loss=1.51 avg=1.45\n", "[7452 | 649.79] loss=1.51 avg=1.45\n", "[7453 | 651.07] loss=1.27 avg=1.45\n", "[7454 | 652.36] loss=1.55 avg=1.45\n", "[7455 | 653.64] loss=1.43 avg=1.45\n", "[7456 | 654.92] loss=1.23 avg=1.45\n", "[7457 | 656.20] loss=1.36 avg=1.45\n", "[7458 | 657.47] loss=1.18 avg=1.45\n", "[7459 | 658.75] loss=1.59 avg=1.45\n", "[7460 | 660.02] loss=1.39 avg=1.45\n", "[7461 | 661.32] loss=1.39 avg=1.45\n", "[7462 | 662.70] loss=1.40 avg=1.45\n", "[7463 | 663.99] loss=1.51 avg=1.45\n", "[7464 | 665.27] loss=1.25 avg=1.44\n", "[7465 | 666.56] loss=1.34 avg=1.44\n", "[7466 | 667.86] loss=1.43 avg=1.44\n", "[7467 | 669.15] loss=1.31 avg=1.44\n", "[7468 | 670.44] loss=1.46 avg=1.44\n", "[7469 | 671.72] loss=1.51 avg=1.44\n", "[7470 | 673.00] loss=1.38 avg=1.44\n", "[7471 | 674.30] loss=1.41 avg=1.44\n", "[7472 | 675.58] loss=1.43 avg=1.44\n", "[7473 | 676.86] loss=1.36 avg=1.44\n", "[7474 | 678.14] loss=1.53 avg=1.44\n", "[7475 | 679.42] loss=1.34 avg=1.44\n", "[7476 | 680.69] loss=1.34 avg=1.44\n", "[7477 | 681.97] loss=1.39 avg=1.44\n", "[7478 | 683.25] loss=1.45 avg=1.44\n", "[7479 | 684.54] loss=1.48 avg=1.44\n", "[7480 | 685.82] loss=1.43 avg=1.44\n", "[7481 | 687.10] loss=1.36 avg=1.44\n", "[7482 | 688.38] loss=1.50 avg=1.44\n", "[7483 | 689.65] loss=1.32 avg=1.44\n", "[7484 | 690.93] loss=1.32 avg=1.44\n", "[7485 | 692.21] loss=1.46 avg=1.44\n", "[7486 | 693.48] loss=1.38 avg=1.44\n", "[7487 | 694.78] loss=1.42 avg=1.44\n", "[7488 | 696.13] loss=1.47 avg=1.44\n", "[7489 | 697.40] loss=1.46 avg=1.44\n", "[7490 | 698.68] loss=1.41 avg=1.44\n", "[7491 | 699.95] loss=1.47 avg=1.44\n", "[7492 | 701.23] loss=1.40 avg=1.44\n", "[7493 | 702.52] loss=1.33 avg=1.44\n", "[7494 | 703.81] loss=1.48 avg=1.44\n", "[7495 | 705.10] loss=1.34 avg=1.44\n", "[7496 | 706.44] loss=1.36 avg=1.43\n", "[7497 | 707.74] loss=1.39 avg=1.43\n", "[7498 | 709.02] loss=1.48 avg=1.43\n", "[7499 | 710.29] loss=1.34 avg=1.43\n", "[7500 | 711.57] loss=1.52 avg=1.43\n", "======== SAMPLE 1 ========\n", " de deux pourvois, au motif que l'arrêt, qui a rejeté la demande principale des deux pourvois, se trouve justement en sa qualité de représentant légal de la liquidation des biens de la Société mutuelle d'assurance industrielle du Crédit lyonnais, au motif que le même avoir été écarté contre les autres demandeurs \" impliquent que des parties ont interrompu le rôle du délai de cinq mois et s'ils permettent de révéler leur déclaration en date échappant aux circonstances de la comparaison par la cour d'appel \", et a violé par fausse interprétation les articles 1376, 1377 et 2015 du Code civil ;Mais attendu que la cour d'appel, qui a d'ailleurs estimé néanmoins que les demandeurs avaient eu une obligation de refus par le débiteur de régler les sommes correspondant à des cotisations sur lesquelles elle se trouvait de leur revenant, a retenu, à bon droit, à bon droit, que les demandeurs étant soumises à cotisations dépendant de la période de cotisation, ils devaient être jugés le 8 novembre 1981 ;D'où il suit que le moyen relatif à un droit quant au régime de l'assurance vieillesse étant sans contradiction, le moyen n'est pas fondé ;PAR CES MOTIFS :REJETTE le pourvoi <|endoftext|>\n", "<|startoftext|> Sur le moyen unique :Vu l'article L. 321-4 du Code des assurances ;Attendu qu'aux termes de ce texte seulement la charge de la preuve n'est pas due dans le cas de non-respect des dispositions de l'article L. 321-4 du Code des assurances ;Attendu que la compagnie d'assurances assurances des Bouches-du-Rhône a consenti un édifice métallée devant un an dans le cadre du présent bail à un seul locataire, dans un délai d'un mois à l'entretien préalable à l'expiration du bail ; que la compagnie Maples Sis a versé aux défendeurs, par voie de réalité, le prix de vente d'un appartement dont le prix est resté le jour où elle a été rejetée ; qu'un arrêt du 24 mars 1987 a fixé le prix des ressources de l'assuré à un prix de vente de l'immeuble à la suite de la légataire ; que la compagnie ses défendeurs ont rejeté le bail à la suite d'énoncé des dispositions des articles 30 et 36 du décret du 26 juin 1964 ; que, pour décider que le bail avait cessé le seul mois de l'expiration du bail prévu à l'original de cette fin de la mise à la disposition de l'assuré, l'arrêt attaqué relève que la lettre recommandée a été révoquée pour la première façade de l'article 30 du décret du 16 mars 1970 ;Attendu qu'en statuant ainsi, alors que le résultat de la procédure pour les ressources pour lesquelles la compagnie Maples soutient que la compagnie Maples soutient que l'arrêt du 24 mars 1987 a fixé le prix de vente des ressources à un prix litigieux de plusieurs ressources, date à laquelle elle a pris le domaine régulière dans une autre période qu'elle pouvait début dès lors que cette compagnie est poursuivie, c'est-à-dire la date de son refus, la cour d'appel, qui ne s'est pas contredite par la seule demande, a violé le texte susvisé ;PAR CES MOTIFS :CASSE ET\n", "\n", "[7501 | 724.74] loss=1.29 avg=1.43\n", "[7502 | 726.02] loss=1.46 avg=1.43\n", "[7503 | 727.31] loss=1.46 avg=1.43\n", "[7504 | 728.69] loss=1.46 avg=1.43\n", "[7505 | 729.97] loss=1.54 avg=1.43\n", "[7506 | 731.25] loss=1.62 avg=1.44\n", "[7507 | 732.53] loss=1.42 avg=1.44\n", "[7508 | 733.80] loss=1.31 avg=1.44\n", "[7509 | 735.08] loss=1.30 avg=1.43\n", "[7510 | 736.36] loss=1.51 avg=1.43\n", "[7511 | 737.64] loss=1.47 avg=1.44\n", "[7512 | 738.93] loss=1.45 avg=1.44\n", "[7513 | 740.27] loss=1.56 avg=1.44\n", "[7514 | 741.57] loss=1.37 avg=1.44\n", "[7515 | 742.86] loss=1.44 avg=1.44\n", "[7516 | 744.16] loss=1.23 avg=1.43\n", "[7517 | 745.44] loss=1.49 avg=1.43\n", "[7518 | 746.72] loss=1.53 avg=1.44\n", "[7519 | 748.00] loss=1.37 avg=1.43\n", "[7520 | 749.27] loss=1.44 avg=1.43\n", "[7521 | 750.56] loss=1.43 avg=1.43\n", "[7522 | 751.84] loss=1.23 avg=1.43\n", "[7523 | 753.12] loss=1.36 avg=1.43\n", "[7524 | 754.39] loss=1.39 avg=1.43\n", "[7525 | 755.67] loss=1.33 avg=1.43\n", "[7526 | 756.95] loss=1.48 avg=1.43\n", "[7527 | 758.22] loss=1.51 avg=1.43\n", "[7528 | 759.49] loss=1.30 avg=1.43\n", "[7529 | 760.82] loss=1.42 avg=1.43\n", "[7530 | 762.22] loss=1.39 avg=1.43\n", "[7531 | 763.50] loss=1.46 avg=1.43\n", "[7532 | 764.78] loss=1.53 avg=1.43\n", "[7533 | 766.05] loss=1.41 avg=1.43\n", "[7534 | 767.33] loss=1.58 avg=1.43\n", "[7535 | 768.61] loss=1.45 avg=1.43\n", "[7536 | 769.88] loss=1.42 avg=1.43\n", "[7537 | 771.16] loss=1.42 avg=1.43\n", "[7538 | 772.46] loss=1.38 avg=1.43\n", "[7539 | 773.75] loss=1.42 avg=1.43\n", "[7540 | 775.05] loss=1.46 avg=1.43\n", "[7541 | 776.34] loss=1.46 avg=1.43\n", "[7542 | 777.63] loss=1.46 avg=1.43\n", "[7543 | 778.92] loss=1.53 avg=1.43\n", "[7544 | 780.20] loss=1.22 avg=1.43\n", "[7545 | 781.48] loss=1.27 avg=1.43\n", "[7546 | 782.75] loss=1.34 avg=1.43\n", "[7547 | 784.05] loss=1.40 avg=1.43\n", "[7548 | 785.33] loss=1.35 avg=1.43\n", "[7549 | 786.60] loss=1.54 avg=1.43\n", "[7550 | 787.88] loss=1.53 avg=1.43\n", "[7551 | 789.16] loss=1.43 avg=1.43\n", "[7552 | 790.44] loss=1.31 avg=1.43\n", "[7553 | 791.71] loss=1.29 avg=1.43\n", "[7554 | 792.99] loss=1.51 avg=1.43\n", "[7555 | 794.33] loss=1.40 avg=1.43\n", "[7556 | 795.63] loss=1.40 avg=1.43\n", "[7557 | 796.91] loss=1.40 avg=1.43\n", "[7558 | 798.19] loss=1.47 avg=1.43\n", "[7559 | 799.46] loss=1.24 avg=1.43\n", "[7560 | 800.74] loss=1.54 avg=1.43\n", "[7561 | 802.02] loss=1.35 avg=1.43\n", "[7562 | 803.30] loss=1.47 avg=1.43\n", "[7563 | 804.57] loss=1.42 avg=1.43\n", "[7564 | 805.88] loss=1.37 avg=1.43\n", "[7565 | 807.15] loss=1.25 avg=1.42\n", "[7566 | 808.43] loss=1.27 avg=1.42\n", "[7567 | 809.71] loss=1.30 avg=1.42\n", "[7568 | 810.99] loss=1.44 avg=1.42\n", "[7569 | 812.28] loss=1.25 avg=1.42\n", "[7570 | 813.58] loss=1.41 avg=1.42\n", "[7571 | 814.87] loss=1.49 avg=1.42\n", "[7572 | 816.16] loss=1.32 avg=1.42\n", "[7573 | 817.45] loss=1.46 avg=1.42\n", "[7574 | 818.73] loss=1.42 avg=1.42\n", "[7575 | 820.01] loss=1.50 avg=1.42\n", "[7576 | 821.28] loss=1.58 avg=1.42\n", "[7577 | 822.56] loss=1.25 avg=1.42\n", "[7578 | 823.85] loss=1.42 avg=1.42\n", "[7579 | 825.13] loss=1.37 avg=1.42\n", "[7580 | 826.42] loss=1.41 avg=1.42\n", "[7581 | 827.77] loss=1.38 avg=1.42\n", "[7582 | 829.06] loss=1.49 avg=1.42\n", "[7583 | 830.34] loss=1.41 avg=1.42\n", "[7584 | 831.62] loss=1.40 avg=1.42\n", "[7585 | 832.90] loss=1.35 avg=1.42\n", "[7586 | 834.17] loss=1.40 avg=1.42\n", "[7587 | 835.45] loss=1.24 avg=1.42\n", "[7588 | 836.73] loss=1.37 avg=1.42\n", "[7589 | 838.01] loss=1.22 avg=1.41\n", "[7590 | 839.31] loss=1.39 avg=1.41\n", "[7591 | 840.58] loss=1.21 avg=1.41\n", "[7592 | 841.86] loss=1.38 avg=1.41\n", "[7593 | 843.15] loss=1.42 avg=1.41\n", "[7594 | 844.42] loss=1.47 avg=1.41\n", "[7595 | 845.70] loss=1.30 avg=1.41\n", "[7596 | 846.99] loss=1.47 avg=1.41\n", "[7597 | 848.28] loss=1.40 avg=1.41\n", "[7598 | 849.64] loss=1.44 avg=1.41\n", "[7599 | 850.96] loss=1.32 avg=1.41\n", "[7600 | 852.26] loss=1.36 avg=1.41\n", "======== SAMPLE 1 ========\n", ", saisie du défut de procès-verbal formé par l'URSSAF par l'Etat d'être mis en paiement sous le contrôle de l'URSSAF dont elle se détermine ; qu'il s'ensuit qu'en statuant comme elle l'a fait, la cour d'appel a violé l'article L. 223-8 du Code du travail ; et alors que, selon le premier de ces textes, les dommages ont le caractère d'un élèvements survenue sur le motif de l'employeur qui a procédé devant la juridiction prud'homale ; qu'il s'ensuit que, le fait de la rupture entre la société civile professionnelle de la société Chacun et de la société Hôtel-Morey de construction et construction immobilière, et leur absence au cours, même à la charge de la société Chacun, sont imputable au titre de l'indemnité ; qu'en tout cas, la rupture doit être considérée comme susceptible d'agréer à la charge de la société Chacun ; qu'en décidant néanmoins et sans rechercher si la séance que lui soit souhaitée par la société Chacun devait être considérée comme éligible avant le sinistre ou le licenciement du salarié, la cour d'appel n'a pas donné de base légale à sa décision au regard de l'article L. 223-8 du Code du travail, alors qu'ayant constaté qu'il s'agissait d'un entreprise ou d'un organisme de commerce, sans constater que les dommages causés à la société Chacun n'avaient pas eu connaissance, la cour d'appel a privé sa décision de base légale au regard de l'article L. 223-8 du Code du travail ;Mais attendu, d'abord, d'une part, que la cour d'appel a retenu à bon droit que les dommages causés aux tiers peuvent être considérés comme constitutifs d'imputabilité, non seulement du licenciement ou du sanction, mais qu'en l'absence de constatation de l'existence d'un autre organisme à l'époque d'une société générale de commerce ou de commercialisation, la rupture du contrat de travail ne pouvait se contredire par eux et procurer la nullité ;Attendu, ensuite, que l'arrêt a relevé qu'il appartenait à l'URSSAF de porter sur le bien personnel du salarié l'activité et l'intéressé des obligations, la société Chacun n'appelait pas la condition du licenciement qui avait été ouvert, et que, dès lors, il n'avait pas été tenu compte de ses réclamations, que l'assignation contenait la conséquence d'un contrat de travail, avec l'avertissement de l'employeur, par le licenciement, de la masse des dommages causés à la société Chacun ;D'où il suit que le moyen n'est fondé en aucune de ses premières branches ;Et attendu que pour le débouter de toute responsabilité sans que le salarié ait été à l'occasion du fait du licenciement et pour l'enjoindre, la cour d'appel n'est pas tenue de procéder à l'avis de l'employeur aux motifs, d'une part, que l'employeur n'avait pas recherché ; qu'en effet, l'arrêt n'a pas déduit de ses propres motifs ; que, d'autre part, n'étant pas fait droit, la cour d'\n", "\n", "[7601 | 866.02] loss=1.45 avg=1.41\n", "[7602 | 867.29] loss=1.25 avg=1.41\n", "[7603 | 868.57] loss=1.45 avg=1.41\n", "[7604 | 869.85] loss=1.39 avg=1.41\n", "[7605 | 871.14] loss=1.46 avg=1.41\n", "[7606 | 872.42] loss=1.37 avg=1.41\n", "[7607 | 873.70] loss=1.47 avg=1.41\n", "[7608 | 874.97] loss=1.41 avg=1.41\n", "[7609 | 876.25] loss=1.26 avg=1.41\n", "[7610 | 877.53] loss=1.46 avg=1.41\n", "[7611 | 878.80] loss=1.35 avg=1.41\n", "[7612 | 880.08] loss=1.67 avg=1.41\n", "[7613 | 881.36] loss=1.46 avg=1.41\n", "[7614 | 882.66] loss=1.45 avg=1.41\n", "[7615 | 883.98] loss=1.53 avg=1.41\n", "[7616 | 885.27] loss=1.43 avg=1.41\n", "[7617 | 886.56] loss=1.48 avg=1.41\n", "[7618 | 887.86] loss=1.24 avg=1.41\n", "[7619 | 889.14] loss=1.35 avg=1.41\n", "[7620 | 890.41] loss=1.42 avg=1.41\n", "[7621 | 891.69] loss=1.36 avg=1.41\n", "[7622 | 892.98] loss=1.33 avg=1.41\n", "[7623 | 894.34] loss=1.14 avg=1.41\n", "[7624 | 895.62] loss=1.25 avg=1.41\n", "[7625 | 896.89] loss=1.24 avg=1.40\n", "[7626 | 898.17] loss=1.48 avg=1.41\n", "[7627 | 899.44] loss=1.31 avg=1.40\n", "[7628 | 900.72] loss=1.42 avg=1.40\n", "[7629 | 902.00] loss=1.41 avg=1.40\n", "[7630 | 903.27] loss=1.38 avg=1.40\n", "[7631 | 904.56] loss=1.32 avg=1.40\n", "[7632 | 905.84] loss=1.29 avg=1.40\n", "[7633 | 907.12] loss=1.52 avg=1.40\n", "[7634 | 908.39] loss=1.45 avg=1.40\n", "[7635 | 909.67] loss=1.47 avg=1.40\n", "[7636 | 910.95] loss=1.52 avg=1.41\n", "[7637 | 912.22] loss=1.22 avg=1.40\n", "[7638 | 913.50] loss=1.47 avg=1.40\n", "[7639 | 914.78] loss=1.59 avg=1.41\n", "[7640 | 916.07] loss=1.32 avg=1.41\n", "[7641 | 917.34] loss=1.38 avg=1.41\n", "[7642 | 918.62] loss=1.49 avg=1.41\n", "[7643 | 919.91] loss=1.56 avg=1.41\n", "[7644 | 921.20] loss=1.29 avg=1.41\n", "[7645 | 922.49] loss=1.40 avg=1.41\n", "[7646 | 923.78] loss=1.32 avg=1.41\n", "[7647 | 925.07] loss=1.31 avg=1.40\n", "[7648 | 926.43] loss=1.33 avg=1.40\n", "[7649 | 927.73] loss=1.34 avg=1.40\n", "[7650 | 929.01] loss=1.22 avg=1.40\n", "[7651 | 930.28] loss=1.28 avg=1.40\n", "[7652 | 931.56] loss=1.52 avg=1.40\n", "[7653 | 932.84] loss=1.26 avg=1.40\n", "[7654 | 934.12] loss=1.42 avg=1.40\n", "[7655 | 935.40] loss=1.30 avg=1.40\n", "[7656 | 936.68] loss=1.13 avg=1.40\n", "[7657 | 937.99] loss=1.40 avg=1.40\n", "[7658 | 939.27] loss=1.28 avg=1.40\n", "[7659 | 940.55] loss=1.23 avg=1.39\n", "[7660 | 941.83] loss=1.21 avg=1.39\n", "[7661 | 943.11] loss=1.45 avg=1.39\n", "[7662 | 944.39] loss=1.37 avg=1.39\n", "[7663 | 945.66] loss=1.30 avg=1.39\n", "[7664 | 946.94] loss=1.37 avg=1.39\n", "[7665 | 948.22] loss=1.41 avg=1.39\n", "[7666 | 949.51] loss=1.46 avg=1.39\n", "[7667 | 950.79] loss=1.59 avg=1.39\n", "[7668 | 952.07] loss=1.31 avg=1.39\n", "[7669 | 953.35] loss=1.39 avg=1.39\n", "[7670 | 954.62] loss=1.43 avg=1.39\n", "[7671 | 955.92] loss=1.31 avg=1.39\n", "[7672 | 957.21] loss=1.24 avg=1.39\n", "[7673 | 958.56] loss=1.46 avg=1.39\n", "[7674 | 960.12] loss=1.28 avg=1.39\n", "[7675 | 961.41] loss=1.36 avg=1.39\n", "[7676 | 962.69] loss=1.33 avg=1.39\n", "[7677 | 963.97] loss=1.45 avg=1.39\n", "[7678 | 965.24] loss=1.30 avg=1.39\n", "[7679 | 966.52] loss=1.44 avg=1.39\n", "[7680 | 967.80] loss=1.46 avg=1.39\n", "[7681 | 969.07] loss=1.43 avg=1.39\n", "[7682 | 970.37] loss=1.46 avg=1.39\n", "[7683 | 971.66] loss=1.49 avg=1.39\n", "[7684 | 972.94] loss=1.40 avg=1.39\n", "[7685 | 974.22] loss=1.35 avg=1.39\n", "[7686 | 975.50] loss=1.39 avg=1.39\n", "[7687 | 976.78] loss=1.39 avg=1.39\n", "[7688 | 978.05] loss=1.51 avg=1.39\n", "[7689 | 979.33] loss=1.17 avg=1.39\n", "[7690 | 980.61] loss=1.30 avg=1.39\n", "[7691 | 981.90] loss=1.40 avg=1.39\n", "[7692 | 983.18] loss=1.37 avg=1.39\n", "[7693 | 984.46] loss=1.27 avg=1.39\n", "[7694 | 985.74] loss=1.41 avg=1.39\n", "[7695 | 987.01] loss=1.45 avg=1.39\n", "[7696 | 988.29] loss=1.34 avg=1.39\n", "[7697 | 989.57] loss=1.41 avg=1.39\n", "[7698 | 990.85] loss=1.28 avg=1.39\n", "[7699 | 992.14] loss=1.39 avg=1.39\n", "[7700 | 993.55] loss=1.41 avg=1.39\n", "======== SAMPLE 1 ========\n", "dait une demande principale de diverses indemnités ; qu'ainsi, la cour d'appel, en statuant comme elle a fait, était incompétente ; Que, en s'abstenant d'observer au passif les conséquences pécuniaires et légalement justifiées, elle a à bon droit violé l'article L. 121-8 du Code de l'organisation pénale ; ALORS, D'UNE PART, QUE la cour d'appel ne pouvait, sans violer l'article 1134 du Code civil, répondre aux conclusions de M. Y... ainsi que des conclusions d'appel d'un commissaire provoqué, au mépris d'office, et en ce qu'elle a encore retenu qu'il appartenait à M. X... d'établir cette \" façon permanente \" de M. X..., lequel était un véritable véhicule, auquel avait appris la sécurité de la vente et de l'exploitation, alors, DE DEUXIEME PART, QU'ainsi, à l'appui de la vente d'un véhicule et de l'exploitation, une indemnité de la part de M. Y... aurait été versée par celle-ci devant l'imminence de nature à la revente de la carrière dont il avait donnée qu'elle pouvait être apportée ; ; ALORS, DE TROISIEME PART, QU'en se contentant de faire échec à l'attribution de l'indemnité de sa présence, dont l'expert avait constaté qu'il se serait trouvé reconnue comme étrangère à leur préjudice dans d'autres conditions, la cour d'appel, en reconnaissant néanmoins que le néant n'avait pas déjà constaté la faute prétendument commise par M. X... et qu'il n'était ni néanmoins n'a pas été répété de l'insuffisance du droit préférentiel ; ALORS, ENFIN, QU'ainsi, en se refusant à admettre comme l'ensemble des mesures de surveillance de la même partie des parties à l'établissement des mots et équivalents qui résultaient de la situation qu'elle a souverainement porté au sous-sol des parties sur leur nature, qui aurait été sans intérêt à prendre une délibération au sens de l'article 18 de la loi du 31 juillet 1984, la cour d'appel, en statuant comme elle a fait, a violé les articles L. 114-4 et L. 114-6 du Code des assurances, 1134 et 1183 du même Code ; ALORS, DE QUATRIEME PART, QU'aux termes des dispositions de l'article 1134 du Code civil, \" le fait pour côté d'une partie de la carrière d'un appartement dans un éventuel répétement s'est produit en se déterminant comme en l'absence de toute situation égale de d'autres prérogatives et comme \" la preuve prouvait être appelée au passif, de même que cette tâche ait la qualité qu'elle a lui-même de ne pas ajouter à cette situation de se prévaloir de l'article 18 de la loi du 31 juillet 1984 \", \" les conditions qui sont néanmoins rétroactifs ne pouvant prévoit donc la rémunération de la part de M. Y..., ainsi que le développement du véhicule ou d'environ auprès d'un appartement et de ses assureurs \" ; que la cour d'appel, en s'abstenant de déduire de la faute d'immatriculation du droit préférentiel à une répéremption de la car\n", "\n", "[7701 | 1008.95] loss=1.30 avg=1.39\n", "[7702 | 1010.23] loss=1.21 avg=1.39\n", "[7703 | 1011.51] loss=1.68 avg=1.39\n", "[7704 | 1012.79] loss=1.45 avg=1.39\n", "[7705 | 1014.07] loss=1.25 avg=1.39\n", "[7706 | 1015.36] loss=1.32 avg=1.39\n", "[7707 | 1016.64] loss=1.58 avg=1.39\n", "[7708 | 1017.91] loss=1.36 avg=1.39\n", "[7709 | 1019.19] loss=1.32 avg=1.39\n", "[7710 | 1020.47] loss=1.42 avg=1.39\n", "[7711 | 1021.75] loss=1.58 avg=1.39\n", "[7712 | 1023.03] loss=1.49 avg=1.39\n", "[7713 | 1024.31] loss=1.39 avg=1.39\n", "[7714 | 1025.71] loss=1.34 avg=1.39\n", "[7715 | 1027.02] loss=1.42 avg=1.39\n", "[7716 | 1028.30] loss=1.41 avg=1.39\n", "[7717 | 1029.57] loss=1.38 avg=1.39\n", "[7718 | 1030.87] loss=1.44 avg=1.39\n", "[7719 | 1032.16] loss=1.45 avg=1.39\n", "[7720 | 1033.46] loss=1.50 avg=1.39\n", "[7721 | 1034.76] loss=1.31 avg=1.39\n", "[7722 | 1036.05] loss=1.26 avg=1.39\n", "[7723 | 1037.34] loss=1.49 avg=1.39\n", "[7724 | 1038.62] loss=1.21 avg=1.39\n", "[7725 | 1039.89] loss=1.32 avg=1.39\n", "[7726 | 1041.17] loss=1.61 avg=1.39\n", "[7727 | 1042.45] loss=1.34 avg=1.39\n", "[7728 | 1043.72] loss=1.35 avg=1.39\n", "[7729 | 1045.00] loss=1.30 avg=1.39\n", "[7730 | 1046.28] loss=1.43 avg=1.39\n", "[7731 | 1047.56] loss=1.38 avg=1.39\n", "[7732 | 1048.84] loss=1.36 avg=1.39\n", "[7733 | 1050.12] loss=1.48 avg=1.39\n", "[7734 | 1051.40] loss=1.50 avg=1.39\n", "[7735 | 1052.67] loss=1.35 avg=1.39\n", "[7736 | 1053.95] loss=1.31 avg=1.39\n", "[7737 | 1055.23] loss=1.29 avg=1.39\n", "[7738 | 1056.50] loss=1.49 avg=1.39\n", "[7739 | 1057.80] loss=1.31 avg=1.39\n", "[7740 | 1059.15] loss=1.51 avg=1.39\n", "[7741 | 1060.43] loss=1.30 avg=1.39\n", "[7742 | 1061.71] loss=1.16 avg=1.39\n", "[7743 | 1062.99] loss=1.42 avg=1.39\n", "[7744 | 1064.27] loss=1.42 avg=1.39\n", "[7745 | 1065.55] loss=1.29 avg=1.39\n", "[7746 | 1066.84] loss=1.37 avg=1.39\n", "[7747 | 1068.13] loss=1.20 avg=1.39\n", "[7748 | 1069.49] loss=1.17 avg=1.38\n", "[7749 | 1070.80] loss=1.40 avg=1.38\n", "[7750 | 1072.08] loss=1.49 avg=1.38\n", "[7751 | 1073.36] loss=1.29 avg=1.38\n", "[7752 | 1074.64] loss=1.44 avg=1.38\n", "[7753 | 1075.92] loss=1.43 avg=1.39\n", "[7754 | 1077.19] loss=1.28 avg=1.38\n", "[7755 | 1078.47] loss=1.50 avg=1.39\n", "[7756 | 1079.75] loss=1.42 avg=1.39\n", "[7757 | 1081.06] loss=1.48 avg=1.39\n", "[7758 | 1082.33] loss=1.26 avg=1.39\n", "[7759 | 1083.61] loss=1.45 avg=1.39\n", "[7760 | 1084.89] loss=1.21 avg=1.38\n", "[7761 | 1086.17] loss=1.51 avg=1.39\n", "[7762 | 1087.44] loss=1.39 avg=1.39\n", "[7763 | 1088.72] loss=1.28 avg=1.38\n", "[7764 | 1090.00] loss=1.19 avg=1.38\n", "[7765 | 1091.32] loss=1.34 avg=1.38\n", "[7766 | 1092.65] loss=1.31 avg=1.38\n", "[7767 | 1093.93] loss=1.34 avg=1.38\n", "[7768 | 1095.21] loss=1.58 avg=1.38\n", "[7769 | 1096.49] loss=1.47 avg=1.38\n", "[7770 | 1097.76] loss=1.31 avg=1.38\n", "[7771 | 1099.04] loss=1.30 avg=1.38\n", "[7772 | 1100.32] loss=1.52 avg=1.38\n", "[7773 | 1101.60] loss=1.51 avg=1.38\n", "[7774 | 1102.96] loss=1.64 avg=1.39\n", "[7775 | 1104.29] loss=1.18 avg=1.39\n", "[7776 | 1105.58] loss=1.31 avg=1.38\n", "[7777 | 1106.87] loss=1.44 avg=1.39\n", "[7778 | 1108.16] loss=1.44 avg=1.39\n", "[7779 | 1109.44] loss=1.54 avg=1.39\n", "[7780 | 1110.71] loss=1.40 avg=1.39\n", "[7781 | 1111.99] loss=1.63 avg=1.39\n", "[7782 | 1113.28] loss=1.55 avg=1.39\n", "[7783 | 1114.57] loss=1.39 avg=1.39\n", "[7784 | 1115.84] loss=1.40 avg=1.39\n", "[7785 | 1117.12] loss=1.50 avg=1.39\n", "[7786 | 1118.40] loss=1.35 avg=1.39\n", "[7787 | 1119.68] loss=1.27 avg=1.39\n", "[7788 | 1120.96] loss=1.50 avg=1.39\n", "[7789 | 1122.24] loss=1.31 avg=1.39\n", "[7790 | 1123.52] loss=1.34 avg=1.39\n", "[7791 | 1124.90] loss=1.29 avg=1.39\n", "[7792 | 1126.19] loss=1.47 avg=1.39\n", "[7793 | 1127.47] loss=1.31 avg=1.39\n", "[7794 | 1128.74] loss=1.33 avg=1.39\n", "[7795 | 1130.02] loss=1.52 avg=1.39\n", "[7796 | 1131.30] loss=1.13 avg=1.39\n", "[7797 | 1132.57] loss=1.36 avg=1.39\n", "[7798 | 1133.85] loss=1.39 avg=1.39\n", "[7799 | 1135.15] loss=1.31 avg=1.39\n", "[7800 | 1136.43] loss=1.35 avg=1.39\n", "======== SAMPLE 1 ========\n", " la société Sommor ; qu'elle se rattacheait à la même convention de surendies de son service personnel mécaniste à l'issue du congé décéré enfin ; qu'elle ne saurait donc exclure le tout consenti sans réserve au seul bénéfice de la cotisation et l'éducation des intéressants, les parties qui en faisaient aussi seulement décider que M. X..., établissement direct, ne sauraient avoir, de son contrat de travail, remplacé l'ensemble des règles d'ordre à celle qui prévue, par la convention conclue enfin avec l'employeur ; qu'en statuant ainsi, alors qu'il résulte des constatations des juges du fond qu'il s'agissait d'une cotisation égale à 3/4 de l'indemnité de sécurité sociale sans réserve à l'ensemble des règles d'ordre fixés par la convention conclue enfin, la cour d'appel a fait une fausse application ;Sur le premier moyen et le deuxième moyen du pourvoi principal :Attendu que le comité l'URSSAF a, par le jugement du 29 février 1986, fait valoir que M. X... avait été désisté en refus de prendre en considération le temps en cours en cas de réhabilitation nécessaire ; que le directeur de l'organisation fiscale, agissant au nom de l'entreprise, a notifié sa décision au greffe sous contrat de travail pour une cotisation de l'Ouest pour 7 % de l'indemnité de sécurité sociale ; qu'il avait fait valoir, dans ses conclusions, que \" il y avait été à l'issue de l'expert \", pour se rendre au-delà de ces conventions, de faire conclure aux parties ce sens où il émis avec la cotisation échue dans leurs relations personnelle et sociale ; que le comité a ainsi l'objet d'une décision définitive et n'avait pas été mise en redressement et a donc fait valoir que le temps nécessaire puisse faire valoir son statut qu'il y était encore valable sans préjudice corporel ; que les juges du fond ont décidé que M. X..., qui avait reçu, auparavant, décision à lui, la même catait entente dans le délai légal et avait, lui-même, l'obligation de remplacer ses règles ou ses prescriptions ;Attendu qu'il est aussi fait grief à l'arrêt d'avoir décidé que légataires du comité relèvent de ses obligations, ni dans l'ensemble des situations qui lui sont adressées par l'employeur et qui ne peuvent être écartées que comme cet ordre, alors qu'en l'absence d'un délai légal, le comité devait être admise cette mission ;Mais attendu qu'il résulte des dispositions de la Convention d'entreprise du 8 juin 1790 que si un établissement de l'ordre fait en l'espèce était encore valable, le cotisant en fait constituant des contrats de travail de son comité, ce deux anciens emplois donnés portant son mandat, à la classe était, sans réserve, de son emploi de tout chef de ce dernier employeur ; qu'en l'espèce, le comité l'URSSAF faisait valoir comme partie du personnel, lequel se référait en délai légal en ce qu'il lui ait eu congé enfin ; que la cour d'appel, n'ét\n", "\n", "[7801 | 1149.67] loss=1.25 avg=1.38\n", "[7802 | 1150.96] loss=1.38 avg=1.38\n", "[7803 | 1152.26] loss=1.39 avg=1.38\n", "[7804 | 1153.55] loss=1.45 avg=1.39\n", "[7805 | 1154.84] loss=1.61 avg=1.39\n", "[7806 | 1156.12] loss=1.46 avg=1.39\n", "[7807 | 1157.48] loss=1.23 avg=1.39\n", "[7808 | 1158.80] loss=1.33 avg=1.39\n", "[7809 | 1160.08] loss=1.44 avg=1.39\n", "[7810 | 1161.35] loss=1.55 avg=1.39\n", "[7811 | 1162.63] loss=1.37 avg=1.39\n", "[7812 | 1163.91] loss=1.32 avg=1.39\n", "[7813 | 1165.19] loss=1.34 avg=1.39\n", "[7814 | 1166.47] loss=1.30 avg=1.39\n", "[7815 | 1167.75] loss=1.30 avg=1.39\n", "[7816 | 1169.04] loss=1.24 avg=1.38\n", "[7817 | 1170.32] loss=1.27 avg=1.38\n", "[7818 | 1171.60] loss=1.50 avg=1.38\n", "[7819 | 1172.87] loss=1.39 avg=1.38\n", "[7820 | 1174.16] loss=1.38 avg=1.38\n", "[7821 | 1175.43] loss=1.32 avg=1.38\n", "[7822 | 1176.71] loss=1.34 avg=1.38\n", "[7823 | 1177.99] loss=1.44 avg=1.38\n", "[7824 | 1179.29] loss=1.30 avg=1.38\n", "[7825 | 1180.58] loss=1.42 avg=1.38\n", "[7826 | 1181.85] loss=1.28 avg=1.38\n", "[7827 | 1183.14] loss=1.38 avg=1.38\n", "[7828 | 1184.42] loss=1.36 avg=1.38\n", "[7829 | 1185.71] loss=1.44 avg=1.38\n", "[7830 | 1187.00] loss=1.33 avg=1.38\n", "[7831 | 1188.29] loss=1.21 avg=1.38\n", "[7832 | 1189.65] loss=1.37 avg=1.38\n", "[7833 | 1191.07] loss=1.25 avg=1.38\n", "[7834 | 1192.35] loss=1.21 avg=1.38\n", "[7835 | 1193.63] loss=1.27 avg=1.38\n", "[7836 | 1194.91] loss=1.36 avg=1.38\n", "[7837 | 1196.19] loss=1.44 avg=1.38\n", "[7838 | 1197.47] loss=1.36 avg=1.38\n", "[7839 | 1198.74] loss=1.40 avg=1.38\n", "[7840 | 1200.02] loss=1.49 avg=1.38\n", "[7841 | 1201.30] loss=1.14 avg=1.38\n", "[7842 | 1202.58] loss=1.37 avg=1.38\n", "[7843 | 1203.86] loss=1.42 avg=1.38\n", "[7844 | 1205.14] loss=1.33 avg=1.38\n", "[7845 | 1206.42] loss=1.43 avg=1.38\n", "[7846 | 1207.69] loss=1.46 avg=1.38\n", "[7847 | 1208.97] loss=1.29 avg=1.38\n", "[7848 | 1210.25] loss=1.41 avg=1.38\n", "[7849 | 1211.53] loss=1.28 avg=1.38\n", "[7850 | 1212.82] loss=1.44 avg=1.38\n", "[7851 | 1214.10] loss=1.24 avg=1.37\n", "[7852 | 1215.38] loss=1.48 avg=1.38\n", "[7853 | 1216.65] loss=1.48 avg=1.38\n", "[7854 | 1217.93] loss=1.31 avg=1.38\n", "[7855 | 1219.21] loss=1.39 avg=1.38\n", "[7856 | 1220.49] loss=1.42 avg=1.38\n", "[7857 | 1221.77] loss=1.36 avg=1.38\n", "[7858 | 1223.18] loss=1.40 avg=1.38\n", "[7859 | 1224.71] loss=1.28 avg=1.38\n", "[7860 | 1226.01] loss=1.37 avg=1.38\n", "[7861 | 1227.31] loss=1.45 avg=1.38\n", "[7862 | 1228.60] loss=1.40 avg=1.38\n", "[7863 | 1229.89] loss=1.49 avg=1.38\n", "[7864 | 1231.16] loss=1.39 avg=1.38\n", "[7865 | 1232.44] loss=1.39 avg=1.38\n", "[7866 | 1233.72] loss=1.32 avg=1.38\n", "[7867 | 1235.02] loss=1.46 avg=1.38\n", "[7868 | 1236.30] loss=1.45 avg=1.38\n", "[7869 | 1237.57] loss=1.52 avg=1.38\n", "[7870 | 1238.85] loss=1.30 avg=1.38\n", "[7871 | 1240.13] loss=1.39 avg=1.38\n", "[7872 | 1241.41] loss=1.47 avg=1.38\n", "[7873 | 1242.69] loss=1.31 avg=1.38\n", "[7874 | 1243.97] loss=1.30 avg=1.38\n", "[7875 | 1245.26] loss=1.35 avg=1.38\n", "[7876 | 1246.55] loss=1.35 avg=1.38\n", "[7877 | 1247.83] loss=1.44 avg=1.38\n", "[7878 | 1249.11] loss=1.18 avg=1.38\n", "[7879 | 1250.39] loss=1.57 avg=1.38\n", "[7880 | 1251.66] loss=1.31 avg=1.38\n", "[7881 | 1252.94] loss=1.46 avg=1.38\n", "[7882 | 1254.22] loss=1.45 avg=1.38\n", "[7883 | 1255.50] loss=1.26 avg=1.38\n", "[7884 | 1256.87] loss=1.15 avg=1.38\n", "[7885 | 1258.15] loss=1.28 avg=1.38\n", "[7886 | 1259.43] loss=1.32 avg=1.37\n", "[7887 | 1260.71] loss=1.37 avg=1.37\n", "[7888 | 1262.00] loss=1.27 avg=1.37\n", "[7889 | 1263.30] loss=1.29 avg=1.37\n", "[7890 | 1264.59] loss=1.19 avg=1.37\n", "[7891 | 1265.87] loss=1.22 avg=1.37\n", "[7892 | 1267.18] loss=1.40 avg=1.37\n", "[7893 | 1268.46] loss=1.39 avg=1.37\n", "[7894 | 1269.74] loss=1.39 avg=1.37\n", "[7895 | 1271.02] loss=1.41 avg=1.37\n", "[7896 | 1272.30] loss=1.30 avg=1.37\n", "[7897 | 1273.58] loss=1.19 avg=1.37\n", "[7898 | 1274.86] loss=1.37 avg=1.37\n", "[7899 | 1276.13] loss=1.39 avg=1.37\n", "[7900 | 1277.41] loss=1.47 avg=1.37\n", "======== SAMPLE 1 ========\n", "é en la forme de sa \" opération \" ; que la décision attribuelle ne peut, à la date du présent litige, être rendue effective en application d'un litige ; qu'en l'espèce, la cour d'appel, après avoir relevé une telle demande, a constaté que la décision de l'ASSEDIC d'EDIC n'avait \" ni l'établissement du juge d'instruction \", ni la signification ultérieure, en l'état ; que, d'autre part, la cour d'appel, après avoir indiqué \" par erreur l'existence ou l'écoule des résultats et d'intérêts, qui justifiaient qu'une décision soit rendue durant l'incendie \", a fait constater ou non que dès lors que la contestation soit prononcée au bénéfice de la loi du 19 juillet 1969, celle du jugement du 9 février 1982 et, comme dans un litige opposant l'ASSEDIC à \" l'ASSEDIC \" de la preuve, la contestation avec laquelle il avait été portée était inconcevable, ne pouvait, sans violer le texte susvisé, être appliquée ; que la cour d'appel, en l'espèce, n'a pas relevé que l'ASSEDIC s'était engagée à s'interformer tels au cas où elle était tenue de présenter un avocat sur la loi du 24 juillet 1966 ; qu'en ses quatre branches, les moyens ne peuvent donc être accueillis en ses rapports sous le coup de l'intérêt du juge ;Mais attendu qu'entaché de donc souverainement l'exécution provisoire, l'ASSEDIC peut obtenir la réintégration du tableau de la compétence du juge d'instruction par lui en application de l'article 3.14, laquelle, non par l'arrêt attaqué, \" n'a pas été accepté \" ; d'où il suit que le moyen n'est pas fondé ;Et sur le second moyen :Vu l'article 3-1 de la loi du 24 juillet 1966 ;Attendu, selon l'arrêt, que la preuve de l'existence d'un avocat n'est susceptible d'assister au jugement, qu'il en est ainsi à caractériser son présence, auxquelles il incombe pour lui un avocat ;Attendu que pour rejeter ces demandes concernant la preuve de l'incompatibilité du titulaire du litige donné en Tribunal, la cour d'appel énonce \" qu'en vertu d'une appréciation sérieuse, l'arrêt se trouve parfaitement ininterrompue ;Attendu, cependant que la preuve d'une insuffisance grave a pu opposer au jugement d'instance, par rapport à la mise en demeure d'agir, un avocat de l'ASSEDIC, dans la mesure où il se trouvait à être accepté, de n'en lui ;Qu'en statuant comme elle l'a fait, la cour d'appel n'a pas tiré les conséquences légales de ses constatations ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 3 avril 1990, entre les parties, par la cour d'appel de Nancy ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Reims <|endoftext|>\n", "<|startoftext|> Sur le moyen unique, pris en sa première branche :Attendu\n", "\n", "[7901 | 1291.50] loss=1.37 avg=1.37\n", "[7902 | 1292.77] loss=1.31 avg=1.37\n", "[7903 | 1294.05] loss=1.18 avg=1.37\n", "[7904 | 1295.33] loss=1.19 avg=1.36\n", "[7905 | 1296.62] loss=1.29 avg=1.36\n", "[7906 | 1297.91] loss=1.47 avg=1.37\n", "[7907 | 1299.21] loss=1.39 avg=1.37\n", "[7908 | 1300.57] loss=1.51 avg=1.37\n", "[7909 | 1301.88] loss=1.32 avg=1.37\n", "[7910 | 1303.17] loss=1.28 avg=1.37\n", "[7911 | 1304.45] loss=1.38 avg=1.37\n", "[7912 | 1305.73] loss=1.38 avg=1.37\n", "[7913 | 1307.01] loss=1.43 avg=1.37\n", "[7914 | 1308.28] loss=1.21 avg=1.36\n", "[7915 | 1309.56] loss=1.44 avg=1.37\n", "[7916 | 1310.84] loss=1.38 avg=1.37\n", "[7917 | 1312.13] loss=1.31 avg=1.37\n", "[7918 | 1313.41] loss=1.32 avg=1.36\n", "[7919 | 1314.69] loss=1.49 avg=1.37\n", "[7920 | 1315.97] loss=1.65 avg=1.37\n", "[7921 | 1317.24] loss=1.27 avg=1.37\n", "[7922 | 1318.52] loss=1.29 avg=1.37\n", "[7923 | 1319.80] loss=1.36 avg=1.37\n", "[7924 | 1321.10] loss=1.29 avg=1.37\n", "[7925 | 1322.43] loss=1.54 avg=1.37\n", "[7926 | 1323.71] loss=1.35 avg=1.37\n", "[7927 | 1324.99] loss=1.53 avg=1.37\n", "[7928 | 1326.27] loss=1.45 avg=1.37\n", "[7929 | 1327.55] loss=1.46 avg=1.37\n", "[7930 | 1328.83] loss=1.33 avg=1.37\n", "[7931 | 1330.11] loss=1.35 avg=1.37\n", "[7932 | 1331.39] loss=1.50 avg=1.37\n", "[7933 | 1332.68] loss=1.47 avg=1.37\n", "[7934 | 1334.07] loss=1.32 avg=1.37\n", "[7935 | 1335.37] loss=1.41 avg=1.37\n", "[7936 | 1336.67] loss=1.30 avg=1.37\n", "[7937 | 1337.95] loss=1.35 avg=1.37\n", "[7938 | 1339.23] loss=1.36 avg=1.37\n", "[7939 | 1340.51] loss=1.45 avg=1.37\n", "[7940 | 1341.79] loss=1.22 avg=1.37\n", "[7941 | 1343.07] loss=1.40 avg=1.37\n", "[7942 | 1344.36] loss=1.14 avg=1.37\n", "[7943 | 1345.65] loss=1.30 avg=1.37\n", "[7944 | 1346.92] loss=1.36 avg=1.37\n", "[7945 | 1348.20] loss=1.40 avg=1.37\n", "[7946 | 1349.48] loss=1.38 avg=1.37\n", "[7947 | 1350.75] loss=1.40 avg=1.37\n", "[7948 | 1352.03] loss=1.35 avg=1.37\n", "[7949 | 1353.31] loss=1.37 avg=1.37\n", "[7950 | 1354.62] loss=1.25 avg=1.37\n", "[7951 | 1355.99] loss=1.38 avg=1.37\n", "[7952 | 1357.27] loss=1.41 avg=1.37\n", "[7953 | 1358.55] loss=1.40 avg=1.37\n", "[7954 | 1359.82] loss=1.39 avg=1.37\n", "[7955 | 1361.10] loss=1.41 avg=1.37\n", "[7956 | 1362.38] loss=1.39 avg=1.37\n", "[7957 | 1363.66] loss=1.37 avg=1.37\n", "[7958 | 1364.94] loss=1.30 avg=1.37\n", "[7959 | 1366.23] loss=1.22 avg=1.37\n", "[7960 | 1367.51] loss=1.28 avg=1.37\n", "[7961 | 1368.80] loss=1.48 avg=1.37\n", "[7962 | 1370.10] loss=1.29 avg=1.37\n", "[7963 | 1371.39] loss=1.59 avg=1.37\n", "[7964 | 1372.68] loss=1.23 avg=1.37\n", "[7965 | 1373.96] loss=1.47 avg=1.37\n", "[7966 | 1375.24] loss=1.36 avg=1.37\n", "[7967 | 1376.52] loss=1.35 avg=1.37\n", "[7968 | 1377.81] loss=1.36 avg=1.37\n", "[7969 | 1379.09] loss=1.28 avg=1.37\n", "[7970 | 1380.37] loss=1.42 avg=1.37\n", "[7971 | 1381.64] loss=1.34 avg=1.37\n", "[7972 | 1382.92] loss=1.34 avg=1.37\n", "[7973 | 1384.20] loss=1.44 avg=1.37\n", "[7974 | 1385.48] loss=1.40 avg=1.37\n", "[7975 | 1386.76] loss=1.43 avg=1.37\n", "[7976 | 1388.07] loss=1.42 avg=1.37\n", "[7977 | 1389.40] loss=1.42 avg=1.37\n", "[7978 | 1390.69] loss=1.56 avg=1.37\n", "[7979 | 1391.97] loss=1.46 avg=1.37\n", "[7980 | 1393.24] loss=1.41 avg=1.37\n", "[7981 | 1394.52] loss=1.34 avg=1.37\n", "[7982 | 1395.80] loss=1.35 avg=1.37\n", "[7983 | 1397.07] loss=1.54 avg=1.37\n", "[7984 | 1398.35] loss=1.30 avg=1.37\n", "[7985 | 1399.65] loss=1.26 avg=1.37\n", "[7986 | 1400.93] loss=1.37 avg=1.37\n", "[7987 | 1402.20] loss=1.38 avg=1.37\n", "[7988 | 1403.48] loss=1.39 avg=1.37\n", "[7989 | 1404.77] loss=1.40 avg=1.37\n", "[7990 | 1406.06] loss=1.30 avg=1.37\n", "[7991 | 1407.35] loss=1.25 avg=1.37\n", "[7992 | 1408.65] loss=1.51 avg=1.37\n", "[7993 | 1409.93] loss=1.29 avg=1.37\n", "[7994 | 1411.24] loss=1.36 avg=1.37\n", "[7995 | 1412.52] loss=1.39 avg=1.37\n", "[7996 | 1413.80] loss=1.40 avg=1.37\n", "[7997 | 1415.08] loss=1.35 avg=1.37\n", "[7998 | 1416.35] loss=1.34 avg=1.37\n", "[7999 | 1417.63] loss=1.33 avg=1.37\n", "[8000 | 1418.92] loss=1.34 avg=1.37\n", "Saving checkpoint/run1/model-8000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 17:30:27.028193: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:30:27.029560: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:30:27.030530: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:30:27.031570: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:30:27.032585: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:30:27.033455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-8000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 3.98it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 8601338 tokens\n", "Training...\n", "Saving checkpoint/run1/model-8000\n", "Saving checkpoint/run1/model-8000\n", "======== SAMPLE 1 ========\n", ", les moyens réunis :Attendu qu'il est fait grief à l'arrêt d'avoir débouté le syndicat CGT de sa demande en paiement d'indemnités de préavis et de licenciement alors que, selon le pourvoi, d'une part, la cour d'appel, au vu des motifs de l'arrêt, se borne à énoncer que M. X... avait, dès le 30 novembre 1980, fait valoir son contrat de travail alors qu'il a fait part d'entrer au salarié dans une autorité administrative dont le bénéfice lui avait été mis fin à la date de son entrée en fonction ; qu'une création non soumise à une déduction administrative de 2 ans de salaire prévu par le contrat de travail ne peut exister que si celui-ci est régulièrement redevable en fonction d'un périmètre de médecine ; que, sauf seul motif, la cour d'appel n'a pas répondu à des conclusions invoquant l'application de l'article 1er du décret du 19 novembre 1977 ; alors que, d'autre part, la cour d'appel, après avoir souverainement constaté que l'employeur était tenu d'indemniser les préjudices de M. X... sur le fondement de l'article 1er du décret du 19 novembre 1977, a dès lors réparé les causes de licenciement et de licenciement et aurait violé l'article 1382 du Code civil, alors qu'enfin la cour d'appel aurait méconnu les termes du litige ;Mais attendu que l'article 1er du décret du 19 novembre 1977, prévoyant que l'intéressé et son employeur font un bénéficiaire de 2 ans et que l'entreprise ne peut lui-même invoquer seul le rôle du chiffre d'affaires que sont présentées lorsqu'un salarié qui y ait dû s'en prête sera réglé des salaires ; que la cour d'appel, qui n'a pas recherché s'il est de nature certaine et surabondante, n'avait pas à répondre aux conclusions faisant valoir que MM. Y... et Z..., qu'ils n'avaient pas à s'expliquer, n'a pas tiré les conséquences légales de ses propres constatations, la cour d'appel devant toutefois s'ils avaient manqué à ces fins ; d'où il suit que le moyen ne peut être accueilli en aucune de ses trois branches ;PAR CES MOTIFS :REJETTE le pourvoi <|endoftext|>\n", "<|startoftext|> TRAVAIL REGLEMENTATION - Salaires - Licenciement - Délai-congé - Indemnités - Préavis - Répération - Motifs qui sont échues professionnelles <|endoftext|>\n", "<|startoftext|> Sur le moyen unique :Attendu que le 25 septembre 1967 Pierre X..., employé par la société IAC-France en qualité de secrétaire agricole, était en liquidation des biens avec équité depuis le 16 juillet 1985 ; que dans l'effet de son entrée en 1985, il a fait avec son ancien employeur un nouveau contrat d'employement dont la date de consolidation n'avait pas réalisé l'état de santé du septembre 1985 ; que l'intéressée a fait opposition à la première période de mise à disposition, le 21 octobre 1985 ; que le 6 novembre 1986, la caisse primaire a émis un avis de référé à la demande de l'intéressée pour lui faire jouer l'intention de faire\n", "\n", "[8001 | 24.95] loss=1.41 avg=1.41\n", "[8002 | 26.23] loss=1.49 avg=1.45\n", "[8003 | 27.51] loss=1.41 avg=1.44\n", "[8004 | 28.78] loss=1.31 avg=1.41\n", "[8005 | 30.06] loss=1.56 avg=1.44\n", "[8006 | 31.34] loss=1.51 avg=1.45\n", "[8007 | 32.61] loss=1.59 avg=1.47\n", "[8008 | 33.89] loss=1.51 avg=1.47\n", "[8009 | 35.17] loss=1.61 avg=1.49\n", "[8010 | 36.48] loss=1.53 avg=1.50\n", "[8011 | 37.78] loss=1.24 avg=1.47\n", "[8012 | 39.07] loss=1.30 avg=1.46\n", "[8013 | 40.37] loss=1.50 avg=1.46\n", "[8014 | 41.66] loss=1.34 avg=1.45\n", "[8015 | 42.96] loss=1.33 avg=1.44\n", "[8016 | 44.24] loss=1.49 avg=1.44\n", "[8017 | 45.52] loss=1.41 avg=1.44\n", "[8018 | 46.89] loss=1.60 avg=1.45\n", "[8019 | 48.19] loss=1.37 avg=1.45\n", "[8020 | 49.47] loss=1.32 avg=1.44\n", "[8021 | 50.75] loss=1.33 avg=1.43\n", "[8022 | 52.02] loss=1.27 avg=1.43\n", "[8023 | 53.30] loss=1.48 avg=1.43\n", "[8024 | 54.58] loss=1.38 avg=1.43\n", "[8025 | 55.86] loss=1.58 avg=1.43\n", "[8026 | 57.14] loss=1.54 avg=1.44\n", "[8027 | 58.43] loss=1.45 avg=1.44\n", "[8028 | 59.71] loss=1.38 avg=1.44\n", "[8029 | 60.99] loss=1.46 avg=1.44\n", "[8030 | 62.27] loss=1.49 avg=1.44\n", "[8031 | 63.54] loss=1.51 avg=1.44\n", "[8032 | 64.82] loss=1.33 avg=1.44\n", "[8033 | 66.10] loss=1.48 avg=1.44\n", "[8034 | 67.38] loss=1.39 avg=1.44\n", "[8035 | 68.68] loss=1.33 avg=1.43\n", "[8036 | 69.97] loss=1.49 avg=1.44\n", "[8037 | 71.26] loss=1.53 avg=1.44\n", "[8038 | 72.53] loss=1.27 avg=1.43\n", "[8039 | 73.83] loss=1.41 avg=1.43\n", "[8040 | 75.12] loss=1.30 avg=1.43\n", "[8041 | 76.41] loss=1.41 avg=1.43\n", "[8042 | 77.70] loss=1.48 avg=1.43\n", "[8043 | 79.04] loss=1.64 avg=1.44\n", "[8044 | 80.40] loss=1.33 avg=1.43\n", "[8045 | 81.68] loss=1.33 avg=1.43\n", "[8046 | 82.96] loss=1.53 avg=1.43\n", "[8047 | 84.24] loss=1.36 avg=1.43\n", "[8048 | 85.52] loss=1.55 avg=1.43\n", "[8049 | 86.79] loss=1.51 avg=1.44\n", "[8050 | 88.07] loss=1.25 avg=1.43\n", "[8051 | 89.35] loss=1.43 avg=1.43\n", "[8052 | 90.63] loss=1.68 avg=1.44\n", "[8053 | 91.92] loss=1.34 avg=1.43\n", "[8054 | 93.20] loss=1.31 avg=1.43\n", "[8055 | 94.47] loss=1.46 avg=1.43\n", "[8056 | 95.75] loss=1.28 avg=1.43\n", "[8057 | 97.03] loss=1.29 avg=1.43\n", "[8058 | 98.31] loss=1.40 avg=1.43\n", "[8059 | 99.59] loss=1.30 avg=1.42\n", "[8060 | 100.87] loss=1.32 avg=1.42\n", "[8061 | 102.16] loss=1.36 avg=1.42\n", "[8062 | 103.46] loss=1.60 avg=1.42\n", "[8063 | 104.73] loss=1.32 avg=1.42\n", "[8064 | 106.01] loss=1.52 avg=1.42\n", "[8065 | 107.29] loss=1.34 avg=1.42\n", "[8066 | 108.57] loss=1.41 avg=1.42\n", "[8067 | 109.86] loss=1.43 avg=1.42\n", "[8068 | 111.15] loss=1.45 avg=1.42\n", "[8069 | 112.48] loss=1.36 avg=1.42\n", "[8070 | 114.00] loss=1.26 avg=1.42\n", "[8071 | 115.30] loss=1.24 avg=1.41\n", "[8072 | 116.58] loss=1.33 avg=1.41\n", "[8073 | 117.85] loss=1.36 avg=1.41\n", "[8074 | 119.13] loss=1.54 avg=1.41\n", "[8075 | 120.41] loss=1.27 avg=1.41\n", "[8076 | 121.69] loss=1.29 avg=1.41\n", "[8077 | 122.97] loss=1.43 avg=1.41\n", "[8078 | 124.28] loss=1.55 avg=1.41\n", "[8079 | 125.55] loss=1.46 avg=1.41\n", "[8080 | 126.83] loss=1.51 avg=1.41\n", "[8081 | 128.11] loss=1.48 avg=1.42\n", "[8082 | 129.39] loss=1.56 avg=1.42\n", "[8083 | 130.67] loss=1.38 avg=1.42\n", "[8084 | 131.95] loss=1.43 avg=1.42\n", "[8085 | 133.23] loss=1.28 avg=1.41\n", "[8086 | 134.52] loss=1.40 avg=1.41\n", "[8087 | 135.82] loss=1.46 avg=1.42\n", "[8088 | 137.10] loss=1.33 avg=1.41\n", "[8089 | 138.37] loss=1.23 avg=1.41\n", "[8090 | 139.65] loss=1.49 avg=1.41\n", "[8091 | 140.93] loss=1.49 avg=1.41\n", "[8092 | 142.21] loss=1.45 avg=1.41\n", "[8093 | 143.49] loss=1.47 avg=1.41\n", "[8094 | 144.77] loss=1.35 avg=1.41\n", "[8095 | 146.10] loss=1.48 avg=1.41\n", "[8096 | 147.45] loss=1.55 avg=1.42\n", "[8097 | 148.74] loss=1.35 avg=1.42\n", "[8098 | 150.03] loss=1.31 avg=1.41\n", "[8099 | 151.32] loss=1.31 avg=1.41\n", "[8100 | 152.61] loss=1.36 avg=1.41\n", "======== SAMPLE 1 ========\n", " form de la procédure de divorce, le moyen, nouveau et mélangé de fait et de droit, est mal fondé dans son recours contre l'arrêt qui a été rendu par la société civile immobilière, qui a pris la décision de ce chef, mais en ce qui concerne la prise en compte des travaux de compaigne sur le fondement de l'article 1397, alinéa 2, du Code civil, la cour d'appel s'est bornée à relever que la société X... avait obtenu, dans son procès-verbal, la résiliation pour avoir réclamé un préavis d'hospitalisation de M. X... et qu'il avait également laissé et réclamé la société X... dans l'attente de la fixation d'un loyer de travaux de compaigne du département de la Marne ;Mais attendu qu'ayant relevé que l'intéressé n'avait pas reçu le loyer édicté par lui, la cour d'appel, qui a constaté que la société X... était exclue du régime complémentaire de sécurité sociale a, à bon droit, retenu que le loyer en cours avait été prorogé sous le numéro 11/60/76 avec la fixation du loyer d'indemnité de révocation ; que le moyen n'est fondé en aucune de ses branches ;PAR CES MOTIFS :REJETTE le pourvoi. <|endoftext|>\n", "<|startoftext|> Attendu, selon l'arrêt attaqué (Bourges, 10 septembre 1991), que Mme X..., victime de l'accident de transport à la compagnie La Lloyd's, a été condamnée à payer la somme de 20 000 francs à titre de dommages-intérêts ; qu'elle a obtenu sa condamnation en application du décret du 30 mai 1984, lequel a été ordonné, par application de cette disposition ;Sur le premier moyen, pris en sa première branche :Vu l'article 1792 a du Code civil ;Attendu, selon ce même texte, que la cour d'appel reproche à l'arrêt attaqué (Bourges, 30 septembre 1991), d'avoir déclaré incompétent pour connaître seulement à l'action récursoire et en l'obligation à régulariser la cause exclusive de l'accident, aux motifs qu'elle était imposée d'en décidant le renouvellement de la condamnation en application du décret du 30 mai 1986, alors, selon le moyen, 8 fois que celui qui demandait le renouvellement ait donné lieu à une ordonnance de référé qu'à compter de la notification à cette fin que l'incompétence est intervenue n'a pas conféré un lien de compétence ; d'où il suit que le recours de l'assureur tend à l'exécution de sa dette envers la victime par la faute d'auteur de la demande ; que par suite, l'arrêt attaqué, tant en application de l'article 1792 alinéa 1er du Code civil, que, devant le président du tribunal de commerce, ne prétend pas que l'exigibilité du droit à indemnité s'analyse en un litige portant sur l'application du décret du 30 mai 1986, est intervenue sur la demande de l'assureur au premier juge pour décider que la résolution de la mesure à la date de la demande avait pu être éclairée ;Et attendu, ensuite, que la condamnation à payer à Mme X... un complément de 10 000 francs à titre de dommages-intérêts au titre des prévisions de l'article 1792 du Code civil ;Aucune\n", "\n", "[8101 | 166.12] loss=1.46 avg=1.41\n", "[8102 | 167.40] loss=1.44 avg=1.41\n", "[8103 | 168.69] loss=1.34 avg=1.41\n", "[8104 | 169.97] loss=1.46 avg=1.41\n", "[8105 | 171.24] loss=1.45 avg=1.41\n", "[8106 | 172.52] loss=1.44 avg=1.41\n", "[8107 | 173.80] loss=1.33 avg=1.41\n", "[8108 | 175.07] loss=1.35 avg=1.41\n", "[8109 | 176.35] loss=1.28 avg=1.41\n", "[8110 | 177.63] loss=1.39 avg=1.41\n", "[8111 | 178.93] loss=1.35 avg=1.41\n", "[8112 | 180.33] loss=1.35 avg=1.41\n", "[8113 | 181.60] loss=1.37 avg=1.41\n", "[8114 | 182.88] loss=1.26 avg=1.40\n", "[8115 | 184.17] loss=1.24 avg=1.40\n", "[8116 | 185.46] loss=1.40 avg=1.40\n", "[8117 | 186.75] loss=1.21 avg=1.40\n", "[8118 | 188.04] loss=1.49 avg=1.40\n", "[8119 | 189.32] loss=1.49 avg=1.40\n", "[8120 | 190.63] loss=1.54 avg=1.40\n", "[8121 | 191.90] loss=1.36 avg=1.40\n", "[8122 | 193.18] loss=1.39 avg=1.40\n", "[8123 | 194.46] loss=1.21 avg=1.40\n", "[8124 | 195.73] loss=1.35 avg=1.40\n", "[8125 | 197.01] loss=1.48 avg=1.40\n", "[8126 | 198.29] loss=1.28 avg=1.40\n", "[8127 | 199.57] loss=1.35 avg=1.40\n", "[8128 | 200.86] loss=1.40 avg=1.40\n", "[8129 | 202.14] loss=1.51 avg=1.40\n", "[8130 | 203.42] loss=1.42 avg=1.40\n", "[8131 | 204.70] loss=1.29 avg=1.40\n", "[8132 | 205.97] loss=1.37 avg=1.40\n", "[8133 | 207.25] loss=1.46 avg=1.40\n", "[8134 | 208.53] loss=1.38 avg=1.40\n", "[8135 | 209.81] loss=1.61 avg=1.40\n", "[8136 | 211.09] loss=1.40 avg=1.40\n", "[8137 | 212.47] loss=1.56 avg=1.40\n", "[8138 | 213.76] loss=1.32 avg=1.40\n", "[8139 | 215.04] loss=1.61 avg=1.41\n", "[8140 | 216.32] loss=1.29 avg=1.40\n", "[8141 | 217.59] loss=1.48 avg=1.40\n", "[8142 | 218.87] loss=1.50 avg=1.41\n", "[8143 | 220.16] loss=1.42 avg=1.41\n", "[8144 | 221.45] loss=1.19 avg=1.40\n", "[8145 | 222.77] loss=1.42 avg=1.40\n", "[8146 | 224.10] loss=1.38 avg=1.40\n", "[8147 | 225.39] loss=1.23 avg=1.40\n", "[8148 | 226.66] loss=1.24 avg=1.40\n", "[8149 | 227.94] loss=1.42 avg=1.40\n", "[8150 | 229.22] loss=1.29 avg=1.40\n", "[8151 | 230.49] loss=1.34 avg=1.40\n", "[8152 | 231.77] loss=1.21 avg=1.39\n", "[8153 | 233.05] loss=1.32 avg=1.39\n", "[8154 | 234.35] loss=1.38 avg=1.39\n", "[8155 | 235.63] loss=1.48 avg=1.39\n", "[8156 | 236.90] loss=1.40 avg=1.39\n", "[8157 | 238.18] loss=1.44 avg=1.40\n", "[8158 | 239.46] loss=1.41 avg=1.40\n", "[8159 | 240.73] loss=1.53 avg=1.40\n", "[8160 | 242.03] loss=1.22 avg=1.40\n", "[8161 | 243.31] loss=1.41 avg=1.40\n", "[8162 | 244.61] loss=1.46 avg=1.40\n", "[8163 | 245.97] loss=1.36 avg=1.40\n", "[8164 | 247.25] loss=1.48 avg=1.40\n", "[8165 | 248.53] loss=1.35 avg=1.40\n", "[8166 | 249.80] loss=1.39 avg=1.40\n", "[8167 | 251.08] loss=1.31 avg=1.40\n", "[8168 | 252.36] loss=1.33 avg=1.39\n", "[8169 | 253.64] loss=1.55 avg=1.40\n", "[8170 | 254.91] loss=1.46 avg=1.40\n", "[8171 | 256.25] loss=1.44 avg=1.40\n", "[8172 | 257.55] loss=1.34 avg=1.40\n", "[8173 | 258.84] loss=1.36 avg=1.40\n", "[8174 | 260.13] loss=1.46 avg=1.40\n", "[8175 | 261.42] loss=1.40 avg=1.40\n", "[8176 | 262.70] loss=1.30 avg=1.40\n", "[8177 | 263.98] loss=1.55 avg=1.40\n", "[8178 | 265.25] loss=1.34 avg=1.40\n", "[8179 | 266.53] loss=1.39 avg=1.40\n", "[8180 | 267.84] loss=1.42 avg=1.40\n", "[8181 | 269.12] loss=1.39 avg=1.40\n", "[8182 | 270.39] loss=1.44 avg=1.40\n", "[8183 | 271.67] loss=1.21 avg=1.40\n", "[8184 | 272.94] loss=1.26 avg=1.39\n", "[8185 | 274.22] loss=1.61 avg=1.40\n", "[8186 | 275.50] loss=1.34 avg=1.40\n", "[8187 | 276.77] loss=1.43 avg=1.40\n", "[8188 | 278.09] loss=1.28 avg=1.39\n", "[8189 | 279.41] loss=1.20 avg=1.39\n", "[8190 | 280.69] loss=1.28 avg=1.39\n", "[8191 | 281.96] loss=1.35 avg=1.39\n", "[8192 | 283.25] loss=1.60 avg=1.39\n", "[8193 | 284.52] loss=1.25 avg=1.39\n", "[8194 | 285.80] loss=1.42 avg=1.39\n", "[8195 | 287.08] loss=1.36 avg=1.39\n", "[8196 | 288.35] loss=1.50 avg=1.39\n", "[8197 | 289.65] loss=1.50 avg=1.39\n", "[8198 | 290.93] loss=1.32 avg=1.39\n", "[8199 | 292.21] loss=1.36 avg=1.39\n", "[8200 | 293.51] loss=1.50 avg=1.39\n", "======== SAMPLE 1 ========\n", "nance la cour d'appel a omis d'un motif contradictoirement sur ce point ;PAR CES MOTIFS, et sans qu'il y ait lieu de statuer sur la première branche du moyen :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 1er juin 1994, entre les parties, par la cour d'appel de Metz ;DIT n'y avoir lieu à renvoi ;Et vu leur déclaration de pourvoi principal, ce qui a été reconnu par le ministériel du Travail de la cour d'appel de Brest de la même ordre et par arrêté des agents. <|endoftext|>\n", "<|startoftext|> N'est pas susceptible de pourvoi provoqué par Mlle X... ;Sur le moyen unique :Vu les articles 706-2 et 706-3 du Code de procédure pénale, dans sa rédaction issue de la loi du 3 janvier 1946 ;Attendu que le juge, qui n'est pas compétent, s'est désistement du recours d'autre part de son application périodique, par application des articles 8 de la loi du 19 décembre 1986, l'article 9 du décret du 18 juin 1975, qui prescrit l'article 8-2 de la loi du 19 décembre 1986, dans sa rédaction applicable précisant que, dans l'hypothèse de cet article 8, lorsque l'instance est soumise au dispositif de la loi du 3 janvier 1946, le ministre de l'Education social doit présumer des références à des modalités de calcul du prix et de pénalités ;Attendu que Mme X..., titulaire d'un billet en vue de céder à la Société des assurances nationales une saisie-contrefinitions, a reçu et signé en 1992 une convention stipulée avec le premier président du tribunal qui autorisait partiellement le billet le bâtiment par deux contrats nuit-convoits ; qu'elle a été licite en 1992 après son licenciement après qu'il soit sursis aux conditions prévues par la convention ;Attendu que, pour prononcer cette conclusion de ses congés, le jugement énonce que quelques jours après l'assignation du Conseil municipal de Paris, le 18 juin 1987, se sont poursuivis, mais pour l'imposition de la demande, que lorsque l'instance a été signée pour les signataires du Bâtiment, ils sont formés dans l'hypothèse de l'article 8-2 précité, d'une part, par l'intérêt pour le juge de ses congés, d'autre part, par l'enseignement du preneur, au motif que le billet se substituait à une saisie qu'il lui avait été conclu pour la perte des frais de préemption de toute développement des contrats ;Qu'en se déterminant ainsi, alors que l'article 6 de la loi du 19 décembre 1986 prend fin à l'instance le principe pour légâté de références à des modalités de calcul du prix et de pénalités, le Tribunal a violé le text précité ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, le jugement rendu le 15 janvier 1994, entre les parties, par le tribunal de grande instance de Paris ;DIT n'y avoir lieu à renvoi ;PAR CES MOTIFS :ANNULE les congés visés avec Mme X..., le 14 février 1994. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique, qui est recevable :Vu la loi des 16-24 août 1790 et le décret du 16 fructidor an III sur la référence à ce règlement ;Attendu que\n", "\n", "[8201 | 308.22] loss=1.34 avg=1.39\n", "[8202 | 309.50] loss=1.50 avg=1.39\n", "[8203 | 310.85] loss=1.48 avg=1.40\n", "[8204 | 312.19] loss=1.37 avg=1.40\n", "[8205 | 313.47] loss=1.31 avg=1.39\n", "[8206 | 314.75] loss=1.31 avg=1.39\n", "[8207 | 316.03] loss=1.34 avg=1.39\n", "[8208 | 317.31] loss=1.38 avg=1.39\n", "[8209 | 318.58] loss=1.44 avg=1.39\n", "[8210 | 319.86] loss=1.42 avg=1.39\n", "[8211 | 321.14] loss=1.58 avg=1.40\n", "[8212 | 322.42] loss=1.38 avg=1.40\n", "[8213 | 323.71] loss=1.57 avg=1.40\n", "[8214 | 324.98] loss=1.26 avg=1.40\n", "[8215 | 326.26] loss=1.28 avg=1.39\n", "[8216 | 327.53] loss=1.51 avg=1.40\n", "[8217 | 328.82] loss=1.40 avg=1.40\n", "[8218 | 330.11] loss=1.39 avg=1.40\n", "[8219 | 331.40] loss=1.44 avg=1.40\n", "[8220 | 332.73] loss=1.41 avg=1.40\n", "[8221 | 334.05] loss=1.28 avg=1.40\n", "[8222 | 335.32] loss=1.36 avg=1.39\n", "[8223 | 336.60] loss=1.53 avg=1.40\n", "[8224 | 337.88] loss=1.36 avg=1.40\n", "[8225 | 339.15] loss=1.36 avg=1.40\n", "[8226 | 340.43] loss=1.40 avg=1.40\n", "[8227 | 341.71] loss=1.52 avg=1.40\n", "[8228 | 342.99] loss=1.33 avg=1.40\n", "[8229 | 344.34] loss=1.39 avg=1.40\n", "[8230 | 345.66] loss=1.25 avg=1.39\n", "[8231 | 346.94] loss=1.33 avg=1.39\n", "[8232 | 348.21] loss=1.44 avg=1.39\n", "[8233 | 349.49] loss=1.38 avg=1.39\n", "[8234 | 350.76] loss=1.30 avg=1.39\n", "[8235 | 352.04] loss=1.31 avg=1.39\n", "[8236 | 353.31] loss=1.43 avg=1.39\n", "[8237 | 354.60] loss=1.41 avg=1.39\n", "[8238 | 355.90] loss=1.32 avg=1.39\n", "[8239 | 357.17] loss=1.38 avg=1.39\n", "[8240 | 358.45] loss=1.37 avg=1.39\n", "[8241 | 359.72] loss=1.37 avg=1.39\n", "[8242 | 361.00] loss=1.32 avg=1.39\n", "[8243 | 362.28] loss=1.36 avg=1.39\n", "[8244 | 363.56] loss=1.34 avg=1.39\n", "[8245 | 364.85] loss=1.30 avg=1.39\n", "[8246 | 366.19] loss=1.36 avg=1.39\n", "[8247 | 367.49] loss=1.31 avg=1.39\n", "[8248 | 368.78] loss=1.61 avg=1.39\n", "[8249 | 370.06] loss=1.39 avg=1.39\n", "[8250 | 371.34] loss=1.27 avg=1.39\n", "[8251 | 372.61] loss=1.34 avg=1.39\n", "[8252 | 373.89] loss=1.54 avg=1.39\n", "[8253 | 375.17] loss=1.42 avg=1.39\n", "[8254 | 376.45] loss=1.34 avg=1.39\n", "[8255 | 377.85] loss=1.37 avg=1.39\n", "[8256 | 379.14] loss=1.51 avg=1.39\n", "[8257 | 380.41] loss=1.27 avg=1.39\n", "[8258 | 381.69] loss=1.26 avg=1.39\n", "[8259 | 382.97] loss=1.51 avg=1.39\n", "[8260 | 384.25] loss=1.44 avg=1.39\n", "[8261 | 385.52] loss=1.34 avg=1.39\n", "[8262 | 386.80] loss=1.34 avg=1.39\n", "[8263 | 388.09] loss=1.56 avg=1.39\n", "[8264 | 389.37] loss=1.48 avg=1.39\n", "[8265 | 390.65] loss=1.28 avg=1.39\n", "[8266 | 391.92] loss=1.33 avg=1.39\n", "[8267 | 393.20] loss=1.24 avg=1.39\n", "[8268 | 394.48] loss=1.33 avg=1.39\n", "[8269 | 395.76] loss=1.28 avg=1.39\n", "[8270 | 397.03] loss=1.45 avg=1.39\n", "[8271 | 398.31] loss=1.33 avg=1.39\n", "[8272 | 399.60] loss=1.39 avg=1.39\n", "[8273 | 400.89] loss=1.52 avg=1.39\n", "[8274 | 402.18] loss=1.34 avg=1.39\n", "[8275 | 403.50] loss=1.43 avg=1.39\n", "[8276 | 404.79] loss=1.39 avg=1.39\n", "[8277 | 406.08] loss=1.43 avg=1.39\n", "[8278 | 407.35] loss=1.44 avg=1.39\n", "[8279 | 408.63] loss=1.38 avg=1.39\n", "[8280 | 409.94] loss=1.30 avg=1.39\n", "[8281 | 411.33] loss=1.24 avg=1.39\n", "[8282 | 412.60] loss=1.39 avg=1.39\n", "[8283 | 413.88] loss=1.18 avg=1.38\n", "[8284 | 415.15] loss=1.28 avg=1.38\n", "[8285 | 416.43] loss=1.51 avg=1.38\n", "[8286 | 417.71] loss=1.42 avg=1.38\n", "[8287 | 418.98] loss=1.34 avg=1.38\n", "[8288 | 420.26] loss=1.58 avg=1.39\n", "[8289 | 421.56] loss=1.49 avg=1.39\n", "[8290 | 422.84] loss=1.34 avg=1.39\n", "[8291 | 424.11] loss=1.40 avg=1.39\n", "[8292 | 425.39] loss=1.47 avg=1.39\n", "[8293 | 426.67] loss=1.08 avg=1.38\n", "[8294 | 427.94] loss=1.29 avg=1.38\n", "[8295 | 429.22] loss=1.37 avg=1.38\n", "[8296 | 430.49] loss=1.45 avg=1.38\n", "[8297 | 431.80] loss=1.34 avg=1.38\n", "[8298 | 433.08] loss=1.39 avg=1.38\n", "[8299 | 434.36] loss=1.14 avg=1.38\n", "[8300 | 435.63] loss=1.28 avg=1.38\n", "======== SAMPLE 1 ========\n", "arièle de la société civile immobilière Gavillon, celle-ci, après avoir réglé les sommes dues par son débiteur et à l'une des parties, a assigné le Crédit de l'équilibre de l'un des autres sociétés de département du Pulte à Périguef pour faire fixer le dommage ;Attendu qu'il est fait grief à l'arrêt d'avoir accueilli cette demande, alors que l'intervention à l'initiative de la loi qui lui était impartie ne peut avoir pu être régularisée au préalable pour voir prononcer l'allouée en contrepartie de la réparation du dommage et l'inadéquation de celle-ci ; qu'elle ne peut, sur ce point, faire face à un délai de 10 ans, de sorte qu'en décidant le contraire, l'arrêt aurait violé les articles L. 122-2, L. 122-3-2 et L. 122-4 du Code de l'organisation judiciaire et alors que, d'une part, dans ses conclusions que la liquidation judiciaire du Crédit de l'équilibre de l'un des autres sociétés, les premiers juges seraient \" sans objet \", ils ont violé par refus d'application ; alors qu'il ne pourrait être considéré comme irrecevable tout en constatant que le délai de 10 ans n'existait que le délai établi par le pourvoi ; qu'en excluant toute participation de la société civile immobilière, le conseil de prud'hommes qui avait refusé, sans avoir à rechercher la première énonciation des conséquences de la perte de l'autorité de la chose louée en cas de département ou d'annulation de ladite ordonnance, la cour d'appel aurait dénaturé les termes clairs et précis de cet acte entre le 7 au 20 mai 1983 et le 24 mars 1984, et par décision devenue irrévocable des décisions prises sur l'inscription de M. X... en garantie de l'assistance de la société civile immobilière Gavillon ; alors que, d'autre part, l'article L. 122-3 du Code de l'organisation judiciaire ne subordonne que le délai de loyer à une répartition ou à une diminution de force ouvrage ; qu'en refusant d'appliquer l'application dudit dispositif à M. X..., en sorte qu'il résulte de la circonstance que son contrat de crédit avait été renouvelé, mais n'était pas établi, pour la première également faisant procéder à l'entrée dans deux délais, le dépassement de la répartition de l'établissement en cours en garantie à M. X..., c'est-à-dire pendant 8 ans de la répartition de toutes les dettes sociales, la cour d'appel aurait méconnu la portée de ce texte au regard de l'article L. 122-3 du Code de l'organisation judiciaire ;Mais attendu qu'ayant relevé que M. X..., en qualité d'intermédiaire, avait, par lettre du 24 mars 1984, exécuté son activité de crédit automobile, le débiteur qui avait la possibilité au-delà du détenir n'encourt pas le contrat de crédit conclu entre celle-ci et une agence qui, déclarant nécessairement son intention de recouvrer l'indemnité, c'est par une exacte application de ce texte que le débiteur n'établissait pas une action en nullité contre le débiteur ou l'autre personne qui aurait été remplie par\n", "\n", "[8301 | 451.85] loss=1.38 avg=1.38\n", "[8302 | 453.13] loss=1.34 avg=1.38\n", "[8303 | 454.41] loss=1.38 avg=1.38\n", "[8304 | 455.69] loss=1.37 avg=1.38\n", "[8305 | 456.97] loss=1.31 avg=1.38\n", "[8306 | 458.25] loss=1.45 avg=1.38\n", "[8307 | 459.53] loss=1.59 avg=1.38\n", "[8308 | 460.81] loss=1.28 avg=1.38\n", "[8309 | 462.08] loss=1.31 avg=1.38\n", "[8310 | 463.36] loss=1.36 avg=1.38\n", "[8311 | 464.64] loss=1.22 avg=1.38\n", "[8312 | 465.92] loss=1.30 avg=1.38\n", "[8313 | 467.20] loss=1.61 avg=1.38\n", "[8314 | 468.48] loss=1.38 avg=1.38\n", "[8315 | 469.75] loss=1.37 avg=1.38\n", "[8316 | 471.03] loss=1.36 avg=1.38\n", "[8317 | 472.31] loss=1.47 avg=1.38\n", "[8318 | 473.59] loss=1.36 avg=1.38\n", "[8319 | 474.89] loss=1.35 avg=1.38\n", "[8320 | 476.31] loss=1.55 avg=1.38\n", "[8321 | 477.72] loss=1.34 avg=1.38\n", "[8322 | 479.02] loss=1.43 avg=1.38\n", "[8323 | 480.30] loss=1.45 avg=1.38\n", "[8324 | 481.58] loss=1.55 avg=1.38\n", "[8325 | 482.85] loss=1.23 avg=1.38\n", "[8326 | 484.13] loss=1.29 avg=1.38\n", "[8327 | 485.41] loss=1.45 avg=1.38\n", "[8328 | 486.69] loss=1.33 avg=1.38\n", "[8329 | 487.98] loss=1.52 avg=1.38\n", "[8330 | 489.25] loss=1.40 avg=1.38\n", "[8331 | 490.53] loss=1.60 avg=1.39\n", "[8332 | 491.80] loss=1.30 avg=1.38\n", "[8333 | 493.08] loss=1.45 avg=1.39\n", "[8334 | 494.36] loss=1.30 avg=1.38\n", "[8335 | 495.64] loss=1.35 avg=1.38\n", "[8336 | 496.91] loss=1.37 avg=1.38\n", "[8337 | 498.22] loss=1.45 avg=1.38\n", "[8338 | 499.50] loss=1.35 avg=1.38\n", "[8339 | 500.78] loss=1.26 avg=1.38\n", "[8340 | 502.06] loss=1.42 avg=1.38\n", "[8341 | 503.34] loss=1.53 avg=1.38\n", "[8342 | 504.61] loss=1.31 avg=1.38\n", "[8343 | 505.89] loss=1.45 avg=1.38\n", "[8344 | 507.17] loss=1.34 avg=1.38\n", "[8345 | 508.47] loss=1.28 avg=1.38\n", "[8346 | 509.84] loss=1.31 avg=1.38\n", "[8347 | 511.13] loss=1.39 avg=1.38\n", "[8348 | 512.42] loss=1.46 avg=1.38\n", "[8349 | 513.71] loss=1.41 avg=1.38\n", "[8350 | 514.99] loss=1.29 avg=1.38\n", "[8351 | 516.28] loss=1.30 avg=1.38\n", "[8352 | 517.56] loss=1.31 avg=1.38\n", "[8353 | 518.84] loss=1.38 avg=1.38\n", "[8354 | 520.12] loss=1.44 avg=1.38\n", "[8355 | 521.40] loss=1.40 avg=1.38\n", "[8356 | 522.69] loss=1.26 avg=1.38\n", "[8357 | 523.96] loss=1.32 avg=1.38\n", "[8358 | 525.24] loss=1.42 avg=1.38\n", "[8359 | 526.52] loss=1.22 avg=1.38\n", "[8360 | 527.79] loss=1.30 avg=1.38\n", "[8361 | 529.07] loss=1.41 avg=1.38\n", "[8362 | 530.35] loss=1.21 avg=1.38\n", "[8363 | 531.64] loss=1.38 avg=1.38\n", "[8364 | 532.91] loss=1.35 avg=1.38\n", "[8365 | 534.19] loss=1.29 avg=1.38\n", "[8366 | 535.47] loss=1.40 avg=1.38\n", "[8367 | 536.74] loss=1.22 avg=1.37\n", "[8368 | 538.02] loss=1.52 avg=1.38\n", "[8369 | 539.30] loss=1.47 avg=1.38\n", "[8370 | 540.57] loss=1.53 avg=1.38\n", "[8371 | 541.92] loss=1.43 avg=1.38\n", "[8372 | 543.24] loss=1.39 avg=1.38\n", "[8373 | 544.51] loss=1.23 avg=1.38\n", "[8374 | 545.79] loss=1.42 avg=1.38\n", "[8375 | 547.08] loss=1.21 avg=1.38\n", "[8376 | 548.37] loss=1.39 avg=1.38\n", "[8377 | 549.66] loss=1.42 avg=1.38\n", "[8378 | 550.94] loss=1.42 avg=1.38\n", "[8379 | 552.23] loss=1.27 avg=1.38\n", "[8380 | 553.53] loss=1.37 avg=1.38\n", "[8381 | 554.80] loss=1.40 avg=1.38\n", "[8382 | 556.08] loss=1.35 avg=1.38\n", "[8383 | 557.36] loss=1.20 avg=1.37\n", "[8384 | 558.64] loss=1.54 avg=1.38\n", "[8385 | 559.91] loss=1.37 avg=1.38\n", "[8386 | 561.19] loss=1.32 avg=1.37\n", "[8387 | 562.47] loss=1.33 avg=1.37\n", "[8388 | 563.76] loss=1.39 avg=1.37\n", "[8389 | 565.04] loss=1.35 avg=1.37\n", "[8390 | 566.32] loss=1.27 avg=1.37\n", "[8391 | 567.59] loss=1.50 avg=1.37\n", "[8392 | 568.87] loss=1.25 avg=1.37\n", "[8393 | 570.15] loss=1.48 avg=1.37\n", "[8394 | 571.42] loss=1.36 avg=1.37\n", "[8395 | 572.70] loss=1.40 avg=1.37\n", "[8396 | 573.97] loss=1.44 avg=1.38\n", "[8397 | 575.36] loss=1.28 avg=1.37\n", "[8398 | 576.65] loss=1.41 avg=1.37\n", "[8399 | 577.93] loss=1.34 avg=1.37\n", "[8400 | 579.21] loss=1.41 avg=1.37\n", "======== SAMPLE 1 ========\n", "ife général de la commune régie ;Qu'en statuant ainsi, alors qu'il résulte de ses constatations que les sociétés qui n'avaient pas pris leur engagement en France en 1986 n'avaient pas réglé l'indemnité prévue, la cour d'appel, qui a établi que le président de la compagnie anonyme n'avait pas été méconnu et a souverainement décidé que le président de la compagnie n'avait pas été commis en France, a violé les textes susvisés ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 27 janvier 1991, entre les parties, par la cour d'appel d'Orléans ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Bourges. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique :Vu les articles 5, alinéa 2, et 16 de la loi n° 91-1330 du 30 décembre 1991, ensemble les articles 4 et 8 de la loi du 15 janvier 1991, lesquels, qui ont l'ignorance dans les conditions d'appartenance et de connaissance, sont réglementaires en matière de créances d'un oeuvre de crédit ou de crédit antérieur ;Attendu qu'il résulte de l'arrêt attaqué que le Crédit d'équipement de France (la banque) a cédé à M. Y... diverses pièces de fabrication ; que la liquidation des biens de ce dernier ayant été rejetée cette action en résiliation par la banque, la cour d'appel, appelant la condamnation prononcée contre la banque, a dit que la société n'avait pas renoncé à se prévaloir, et, dans la limite de cette demande, d'autoriser l'Administration à présenter seulement à l'offre des créances, présentée entre les mains du cela-ci ;Attendu que, pour rejeter le moyen de la condamnation prononcée contre l'Administration visées à l'article 455 du nouveau Code de procédure civile, l'arrêt retient, d'une part, que la créance, avant d'être créditeur, est tardive dès lors que l'administration est tenue d'accompter de nature à le faire l'obligation de l'acquéreur en vertu des articles 10 et 11 de la loi du 15 janvier 1991 qui ne prévoient pas que le prix de cession des pièces des créances des commerçants étent réglé par \" créances non créditeurs \" ;Attendu qu'en statuant ainsi alors qu'il résulte de ses constatations que la banque, cessionnaire d'une créance de créance non cessionnaire de créances non soumise à l'offre des créances, avait cédé à leur profit la créance dont elle était titulaire, la cour d'appel a violé les textes susvisés ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 5 novembre 1991, entre les parties, par la cour d'appel de Pau ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Toulouse. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique, pris en\n", "\n", "[8401 | 592.56] loss=1.23 avg=1.37\n", "[8402 | 593.85] loss=1.29 avg=1.37\n", "[8403 | 595.14] loss=1.25 avg=1.37\n", "[8404 | 596.44] loss=1.64 avg=1.37\n", "[8405 | 597.79] loss=1.39 avg=1.37\n", "[8406 | 599.07] loss=1.36 avg=1.37\n", "[8407 | 600.34] loss=1.63 avg=1.38\n", "[8408 | 601.62] loss=1.37 avg=1.38\n", "[8409 | 602.90] loss=1.33 avg=1.38\n", "[8410 | 604.18] loss=1.16 avg=1.37\n", "[8411 | 605.46] loss=1.41 avg=1.37\n", "[8412 | 606.73] loss=1.42 avg=1.37\n", "[8413 | 608.08] loss=1.28 avg=1.37\n", "[8414 | 609.40] loss=1.37 avg=1.37\n", "[8415 | 610.67] loss=1.49 avg=1.37\n", "[8416 | 611.95] loss=1.28 avg=1.37\n", "[8417 | 613.23] loss=1.30 avg=1.37\n", "[8418 | 614.50] loss=1.29 avg=1.37\n", "[8419 | 615.78] loss=1.43 avg=1.37\n", "[8420 | 617.06] loss=1.25 avg=1.37\n", "[8421 | 618.33] loss=1.43 avg=1.37\n", "[8422 | 619.63] loss=1.38 avg=1.37\n", "[8423 | 620.91] loss=1.32 avg=1.37\n", "[8424 | 622.18] loss=1.34 avg=1.37\n", "[8425 | 623.47] loss=1.44 avg=1.37\n", "[8426 | 624.74] loss=1.33 avg=1.37\n", "[8427 | 626.02] loss=1.32 avg=1.37\n", "[8428 | 627.30] loss=1.48 avg=1.37\n", "[8429 | 628.59] loss=1.46 avg=1.37\n", "[8430 | 629.92] loss=1.38 avg=1.37\n", "[8431 | 631.22] loss=1.45 avg=1.37\n", "[8432 | 632.51] loss=1.30 avg=1.37\n", "[8433 | 633.80] loss=1.28 avg=1.37\n", "[8434 | 635.07] loss=1.27 avg=1.37\n", "[8435 | 636.35] loss=1.39 avg=1.37\n", "[8436 | 637.63] loss=1.33 avg=1.37\n", "[8437 | 638.90] loss=1.47 avg=1.37\n", "[8438 | 640.19] loss=1.32 avg=1.37\n", "[8439 | 641.55] loss=1.20 avg=1.37\n", "[8440 | 642.86] loss=1.16 avg=1.37\n", "[8441 | 644.14] loss=1.31 avg=1.37\n", "[8442 | 645.41] loss=1.36 avg=1.37\n", "[8443 | 646.69] loss=1.35 avg=1.37\n", "[8444 | 647.96] loss=1.26 avg=1.37\n", "[8445 | 649.24] loss=1.24 avg=1.36\n", "[8446 | 650.52] loss=1.51 avg=1.37\n", "[8447 | 651.81] loss=1.46 avg=1.37\n", "[8448 | 653.09] loss=1.29 avg=1.37\n", "[8449 | 654.37] loss=1.34 avg=1.37\n", "[8450 | 655.65] loss=1.43 avg=1.37\n", "[8451 | 656.92] loss=1.49 avg=1.37\n", "[8452 | 658.20] loss=1.37 avg=1.37\n", "[8453 | 659.48] loss=1.29 avg=1.37\n", "[8454 | 660.76] loss=1.37 avg=1.37\n", "[8455 | 662.03] loss=1.27 avg=1.37\n", "[8456 | 663.33] loss=1.35 avg=1.37\n", "[8457 | 664.61] loss=1.44 avg=1.37\n", "[8458 | 665.89] loss=1.32 avg=1.37\n", "[8459 | 667.18] loss=1.60 avg=1.37\n", "[8460 | 668.47] loss=1.38 avg=1.37\n", "[8461 | 669.76] loss=1.53 avg=1.37\n", "[8462 | 671.05] loss=1.37 avg=1.37\n", "[8463 | 672.33] loss=1.34 avg=1.37\n", "[8464 | 673.62] loss=1.37 avg=1.37\n", "[8465 | 675.00] loss=1.34 avg=1.37\n", "[8466 | 676.28] loss=1.42 avg=1.37\n", "[8467 | 677.56] loss=1.28 avg=1.37\n", "[8468 | 678.84] loss=1.35 avg=1.37\n", "[8469 | 680.11] loss=1.53 avg=1.37\n", "[8470 | 681.39] loss=1.36 avg=1.37\n", "[8471 | 682.67] loss=1.39 avg=1.37\n", "[8472 | 683.95] loss=1.45 avg=1.37\n", "[8473 | 685.25] loss=1.44 avg=1.37\n", "[8474 | 686.53] loss=1.22 avg=1.37\n", "[8475 | 687.80] loss=1.32 avg=1.37\n", "[8476 | 689.08] loss=1.32 avg=1.37\n", "[8477 | 690.35] loss=1.35 avg=1.37\n", "[8478 | 691.63] loss=1.24 avg=1.37\n", "[8479 | 692.91] loss=1.31 avg=1.37\n", "[8480 | 694.19] loss=1.25 avg=1.37\n", "[8481 | 695.46] loss=1.41 avg=1.37\n", "[8482 | 696.77] loss=1.44 avg=1.37\n", "[8483 | 698.04] loss=1.33 avg=1.37\n", "[8484 | 699.32] loss=1.46 avg=1.37\n", "[8485 | 700.60] loss=1.35 avg=1.37\n", "[8486 | 701.89] loss=1.40 avg=1.37\n", "[8487 | 703.20] loss=1.37 avg=1.37\n", "[8488 | 704.49] loss=1.34 avg=1.37\n", "[8489 | 705.78] loss=1.53 avg=1.37\n", "[8490 | 707.24] loss=1.57 avg=1.37\n", "[8491 | 708.59] loss=1.32 avg=1.37\n", "[8492 | 709.87] loss=1.34 avg=1.37\n", "[8493 | 711.15] loss=1.42 avg=1.37\n", "[8494 | 712.43] loss=1.26 avg=1.37\n", "[8495 | 713.70] loss=1.21 avg=1.37\n", "[8496 | 714.98] loss=1.26 avg=1.37\n", "[8497 | 716.26] loss=1.49 avg=1.37\n", "[8498 | 717.53] loss=1.32 avg=1.37\n", "[8499 | 718.84] loss=1.09 avg=1.37\n", "[8500 | 720.11] loss=1.36 avg=1.37\n", "======== SAMPLE 1 ========\n", " blessé ; que la cour d'appel a, en outre, légalement justifié sa décision sans avoir à effectuer une recherche invoquée ; que le moyen ne peut être accueilli ;Mais sur le deuxième moyen, pris en sa première branche du pourvoi n° 95-26.222 :Vu l'article 1134 du Code civil ;Attendu que la Caisse rémunérée de la Concurrence, en faisant valoir que le mandataire ne demandait pas la réparation, a ainsi, à raison de manquants à son obligation contractuelle et en a justement déduit que le seul fait que la Caisse avait fait part des appels non compris dans le cadre de la convention de Bruxelles du 10 mars 1988 devait être reproché à la Caisse d'avoir à réaliser l'offre de dette pour un motif d'édition faisant valoir, de plus, que les appels non liés avec le mandataire aient été effectués par les comptes et des charges et le caractère de la durée du service de celui-ci ;Attendu qu'en se déterminant par de tels motifs en invoquant l'appel non lié à l'offre de dette par la seule société Aéro France au passif du prêt, mais à sa responsabilité contractuelle, la cour d'appel a méconnu les revendiqués, violant ainsi les articles L. 122-25 et R. 511-1 du Code du travail ;Mais attendu que s'agissant de sa participation à l'offre par l'employeur au service commercial de la société Aéro France, qui n'est pas obligatoire aux règles de responsabilité contractuelle de nature libérale et que la Caisse a néanmoins réduit les dettes auprès dans leurs services, c'est dû, en application de l'article 1134 du Code civil, qu'à la cession exclusive de l'intégralité de la société Aéro France, la cour d'appel, ayant constaté que les sociétés des Bruxelles n'avaient pas réglé les sommes réclamées, en a justement déduit que, à cette différence, la Caisse devait recevoir à leur employeur une offre de dette, la cour d'appel, par son arrêt, n'a pas décidé qu'elle ne pouvait se réclamer d'office les échéances à comparaître sur le personnel et le caractère des congédiements sénuresses à sa part ;Que le grief qui n'est fondé dans aucune de ses branches, n'est pas fondé ;PAR CES MOTIFS :REJETTE le pourvoi n° 95-26.222. <|endoftext|>\n", "<|startoftext|> Sur le premier moyen :Attendu qu'à la suite du mise en liquidation judiciaire de la société Commerçaise, M. X..., en qualité de liquidateur de la société anonyme Bipranche, est décédé le 28 avril 1993 à La Réunion, et qu'il a été assigné par la Banque nationale de Paris du Crédit d'équipement des Hauts-de-Saëlure (BNP), un tiers, le liquidateur, et la liquidateur indivise ;Attendu que la CDA fait grief à l'arrêt attaqué (Lyon, 22 octobre 1995) d'avoir statué ainsi alors, selon le pourvoi, d'une part, qu'en l'absence de l'établissement dans le compte des établissements A... qui frentifié auprès de la BNP, ne dépassait pas les droits du tiers, la cour d'appel omet la compétence de la juridiction saisie pour statuer sur les relations contractuelles entre la société en liquid\n", "\n", "[8501 | 734.01] loss=1.32 avg=1.37\n", "[8502 | 735.29] loss=1.35 avg=1.37\n", "[8503 | 736.57] loss=1.26 avg=1.36\n", "[8504 | 737.84] loss=1.15 avg=1.36\n", "[8505 | 739.14] loss=1.44 avg=1.36\n", "[8506 | 740.60] loss=1.30 avg=1.36\n", "[8507 | 741.96] loss=1.32 avg=1.36\n", "[8508 | 743.26] loss=1.50 avg=1.36\n", "[8509 | 744.55] loss=1.37 avg=1.36\n", "[8510 | 745.83] loss=1.30 avg=1.36\n", "[8511 | 747.11] loss=1.22 avg=1.36\n", "[8512 | 748.39] loss=1.29 avg=1.36\n", "[8513 | 749.66] loss=1.24 avg=1.36\n", "[8514 | 750.96] loss=1.34 avg=1.36\n", "[8515 | 752.24] loss=1.31 avg=1.36\n", "[8516 | 753.51] loss=1.21 avg=1.36\n", "[8517 | 754.79] loss=1.45 avg=1.36\n", "[8518 | 756.07] loss=1.19 avg=1.36\n", "[8519 | 757.34] loss=1.47 avg=1.36\n", "[8520 | 758.62] loss=1.27 avg=1.36\n", "[8521 | 759.90] loss=1.28 avg=1.36\n", "[8522 | 761.18] loss=1.35 avg=1.36\n", "[8523 | 762.48] loss=1.34 avg=1.36\n", "[8524 | 763.77] loss=1.23 avg=1.35\n", "[8525 | 765.05] loss=1.38 avg=1.35\n", "[8526 | 766.32] loss=1.40 avg=1.36\n", "[8527 | 767.60] loss=1.43 avg=1.36\n", "[8528 | 768.88] loss=1.28 avg=1.36\n", "[8529 | 770.16] loss=1.23 avg=1.35\n", "[8530 | 771.44] loss=1.47 avg=1.36\n", "[8531 | 772.76] loss=1.41 avg=1.36\n", "[8532 | 774.12] loss=1.52 avg=1.36\n", "[8533 | 775.40] loss=1.42 avg=1.36\n", "[8534 | 776.69] loss=1.27 avg=1.36\n", "[8535 | 777.99] loss=1.34 avg=1.36\n", "[8536 | 779.28] loss=1.30 avg=1.36\n", "[8537 | 780.57] loss=1.29 avg=1.36\n", "[8538 | 781.86] loss=1.45 avg=1.36\n", "[8539 | 783.14] loss=1.43 avg=1.36\n", "[8540 | 784.44] loss=1.23 avg=1.36\n", "[8541 | 785.71] loss=1.47 avg=1.36\n", "[8542 | 786.99] loss=1.48 avg=1.36\n", "[8543 | 788.27] loss=1.38 avg=1.36\n", "[8544 | 789.55] loss=1.28 avg=1.36\n", "[8545 | 790.82] loss=1.56 avg=1.36\n", "[8546 | 792.10] loss=1.22 avg=1.36\n", "[8547 | 793.38] loss=1.37 avg=1.36\n", "[8548 | 794.66] loss=1.29 avg=1.36\n", "[8549 | 795.95] loss=1.34 avg=1.36\n", "[8550 | 797.23] loss=1.35 avg=1.36\n", "[8551 | 798.50] loss=1.29 avg=1.36\n", "[8552 | 799.78] loss=1.48 avg=1.36\n", "[8553 | 801.06] loss=1.40 avg=1.36\n", "[8554 | 802.33] loss=1.28 avg=1.36\n", "[8555 | 803.61] loss=1.47 avg=1.36\n", "[8556 | 804.89] loss=1.37 avg=1.36\n", "[8557 | 806.23] loss=1.31 avg=1.36\n", "[8558 | 807.54] loss=1.34 avg=1.36\n", "[8559 | 808.82] loss=1.12 avg=1.36\n", "[8560 | 810.09] loss=1.38 avg=1.36\n", "[8561 | 811.37] loss=1.37 avg=1.36\n", "[8562 | 812.67] loss=1.31 avg=1.36\n", "[8563 | 813.97] loss=1.38 avg=1.36\n", "[8564 | 815.25] loss=1.34 avg=1.36\n", "[8565 | 816.56] loss=1.17 avg=1.35\n", "[8566 | 817.94] loss=1.22 avg=1.35\n", "[8567 | 819.22] loss=1.43 avg=1.35\n", "[8568 | 820.50] loss=1.29 avg=1.35\n", "[8569 | 821.78] loss=1.26 avg=1.35\n", "[8570 | 823.05] loss=1.46 avg=1.35\n", "[8571 | 824.33] loss=1.38 avg=1.35\n", "[8572 | 825.61] loss=1.31 avg=1.35\n", "[8573 | 826.89] loss=1.49 avg=1.35\n", "[8574 | 828.19] loss=1.45 avg=1.35\n", "[8575 | 829.47] loss=1.18 avg=1.35\n", "[8576 | 830.76] loss=1.36 avg=1.35\n", "[8577 | 832.04] loss=1.21 avg=1.35\n", "[8578 | 833.31] loss=1.35 avg=1.35\n", "[8579 | 834.59] loss=1.45 avg=1.35\n", "[8580 | 835.87] loss=1.23 avg=1.35\n", "[8581 | 837.14] loss=1.27 avg=1.35\n", "[8582 | 838.43] loss=1.35 avg=1.35\n", "[8583 | 839.81] loss=1.23 avg=1.35\n", "[8584 | 841.09] loss=1.24 avg=1.35\n", "[8585 | 842.37] loss=1.21 avg=1.35\n", "[8586 | 843.64] loss=1.21 avg=1.35\n", "[8587 | 844.92] loss=1.32 avg=1.35\n", "[8588 | 846.20] loss=1.29 avg=1.34\n", "[8589 | 847.48] loss=1.35 avg=1.34\n", "[8590 | 848.75] loss=1.18 avg=1.34\n", "[8591 | 850.07] loss=1.19 avg=1.34\n", "[8592 | 851.37] loss=1.28 avg=1.34\n", "[8593 | 852.66] loss=1.25 avg=1.34\n", "[8594 | 853.95] loss=1.22 avg=1.34\n", "[8595 | 855.24] loss=1.44 avg=1.34\n", "[8596 | 856.53] loss=1.39 avg=1.34\n", "[8597 | 857.80] loss=1.40 avg=1.34\n", "[8598 | 859.08] loss=1.36 avg=1.34\n", "[8599 | 860.36] loss=1.50 avg=1.34\n", "[8600 | 861.65] loss=1.18 avg=1.34\n", "======== SAMPLE 1 ========\n", "il, qu'il est établi que le contrat prévoyait l'interprétation de la loi ;Qu'en statuant ainsi la cour d'appel a violé le texte susvisé ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 6 juin 1991, entre les parties, par la cour d'appel de Douai ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvois. <|endoftext|>\n", "<|startoftext|> PROTECTION DES CONSOMMATEURS - Faits - Faits d'entretien (oral et géomètique) - Faits ayant entraîné la résiliation de celui-ci de son auteur, lorsque une faute ne permet pas d'intérêt à agir - Faits d'exploitation (eu élément de nullité - Faits qui étaient suffisants s'apprécier. <|endoftext|>\n", "<|startoftext|> Attendu, selon l'arrêt attaqué, que par acte de défense, Mme X... a, le 14 octobre 1969, poursuivi, en vue de l'exécution de la ville d'Est, un fonds de commerce de la société à responsabilité limitée Cofava ; que cette société a assigné en paiement de dommages causés par son exécution par l'intermédiaire du maître de l'ouvrage et une partie de son mandat de représentant l'entreprise, qu'elle devait la partie privée de l'entreprise ;Sur le moyen unique, pris en sa première branche :Vu l'article L. 132-7 du Code de la sécurité sociale ;Attendu qu'en matière de créances sur lesquelles la caution est subrogée par personnellement, la caution locataire peut constituer un établissement commercial s'agir et éventuellement envers le maître de l'ouvrage à exécuter une telle créance ;Attendu que pour rejeter le recours des tiers en ce qui concerne la créance de remise en état des lignes et intérêts, l'arrêt, après avoir rejeté l'exception d'incompétence territoriale invoquée par les banques et la société Cofava-Péronna, écarte la question de savoir si la créance de ces dernières de son agence ne contenait pas une indemnité de licenciement, relève que le même acte de cautionnement et de preuve constitue la créance de cette créance et si le dépôt de cette créance ne contenait pas une créance de prénom de l'entreprise, si une défense et une action tendant à la décision se trouvent devenues sans influence sur la validité de l'acte de cautionnement par le juge, à l'encontre du maître de l'ouvrage, s'applique aux personnes non encore \" déchu \" ;Qu'en statuant ainsi, alors que, d'abord, la cour d'appel a méconnu l'exception d'incompétence territoriale invoquée ;Attendu qu'en se prononçant par de tels motifs, alors que la créance de cette créance étant subrogée par une personne mais à d'autres personne déchu, ce dernier était encore inopposable, la cour d'appel a violé le texte susvisé ;PAR CES MOTIFS, et sans qu'il y ait lieu de statuer sur la fin de non-recevoir :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 6 mai 1991, entre\n", "\n", "[8601 | 875.54] loss=1.34 avg=1.34\n", "[8602 | 876.81] loss=1.33 avg=1.34\n", "[8603 | 878.09] loss=1.16 avg=1.34\n", "[8604 | 879.37] loss=1.33 avg=1.34\n", "[8605 | 880.65] loss=1.24 avg=1.34\n", "[8606 | 881.92] loss=1.33 avg=1.34\n", "[8607 | 883.22] loss=1.47 avg=1.34\n", "[8608 | 884.50] loss=1.42 avg=1.34\n", "[8609 | 885.78] loss=1.24 avg=1.34\n", "[8610 | 887.07] loss=1.36 avg=1.34\n", "[8611 | 888.36] loss=1.35 avg=1.34\n", "[8612 | 889.65] loss=1.25 avg=1.34\n", "[8613 | 890.95] loss=1.15 avg=1.34\n", "[8614 | 892.23] loss=1.22 avg=1.34\n", "[8615 | 893.51] loss=1.17 avg=1.33\n", "[8616 | 894.81] loss=1.26 avg=1.33\n", "[8617 | 896.08] loss=1.24 avg=1.33\n", "[8618 | 897.36] loss=1.30 avg=1.33\n", "[8619 | 898.64] loss=1.29 avg=1.33\n", "[8620 | 899.91] loss=1.44 avg=1.33\n", "[8621 | 901.19] loss=1.20 avg=1.33\n", "[8622 | 902.47] loss=1.38 avg=1.33\n", "[8623 | 903.75] loss=1.32 avg=1.33\n", "[8624 | 905.09] loss=1.47 avg=1.33\n", "[8625 | 906.42] loss=1.37 avg=1.33\n", "[8626 | 907.69] loss=1.47 avg=1.33\n", "[8627 | 908.97] loss=1.20 avg=1.33\n", "[8628 | 910.25] loss=1.19 avg=1.33\n", "[8629 | 911.53] loss=1.29 avg=1.33\n", "[8630 | 912.81] loss=1.20 avg=1.33\n", "[8631 | 914.09] loss=1.30 avg=1.33\n", "[8632 | 915.36] loss=1.24 avg=1.33\n", "[8633 | 916.66] loss=1.15 avg=1.33\n", "[8634 | 917.94] loss=1.31 avg=1.33\n", "[8635 | 919.22] loss=1.48 avg=1.33\n", "[8636 | 920.50] loss=1.40 avg=1.33\n", "[8637 | 921.77] loss=1.16 avg=1.33\n", "[8638 | 923.06] loss=1.37 avg=1.33\n", "[8639 | 924.35] loss=1.34 avg=1.33\n", "[8640 | 925.64] loss=1.29 avg=1.33\n", "[8641 | 926.95] loss=1.41 avg=1.33\n", "[8642 | 928.25] loss=1.30 avg=1.33\n", "[8643 | 929.53] loss=1.29 avg=1.33\n", "[8644 | 930.80] loss=1.26 avg=1.33\n", "[8645 | 932.08] loss=1.28 avg=1.33\n", "[8646 | 933.36] loss=1.26 avg=1.33\n", "[8647 | 934.63] loss=1.16 avg=1.32\n", "[8648 | 935.91] loss=1.23 avg=1.32\n", "[8649 | 937.21] loss=1.24 avg=1.32\n", "[8650 | 938.59] loss=1.38 avg=1.32\n", "[8651 | 939.89] loss=1.37 avg=1.32\n", "[8652 | 941.17] loss=1.29 avg=1.32\n", "[8653 | 942.45] loss=1.42 avg=1.32\n", "[8654 | 943.74] loss=1.43 avg=1.33\n", "[8655 | 945.02] loss=1.12 avg=1.32\n", "[8656 | 946.29] loss=1.43 avg=1.32\n", "[8657 | 947.57] loss=1.34 avg=1.32\n", "[8658 | 948.86] loss=1.26 avg=1.32\n", "[8659 | 950.15] loss=1.33 avg=1.32\n", "[8660 | 951.42] loss=1.24 avg=1.32\n", "[8661 | 952.71] loss=1.50 avg=1.33\n", "[8662 | 953.98] loss=1.39 avg=1.33\n", "[8663 | 955.26] loss=1.28 avg=1.33\n", "[8664 | 956.53] loss=1.49 avg=1.33\n", "[8665 | 957.81] loss=1.21 avg=1.33\n", "[8666 | 959.09] loss=1.32 avg=1.33\n", "[8667 | 960.44] loss=1.40 avg=1.33\n", "[8668 | 961.73] loss=1.44 avg=1.33\n", "[8669 | 963.03] loss=1.33 avg=1.33\n", "[8670 | 964.31] loss=1.19 avg=1.33\n", "[8671 | 965.60] loss=1.31 avg=1.33\n", "[8672 | 966.89] loss=1.30 avg=1.33\n", "[8673 | 968.17] loss=1.37 avg=1.33\n", "[8674 | 969.44] loss=1.32 avg=1.33\n", "[8675 | 970.76] loss=1.23 avg=1.33\n", "[8676 | 972.10] loss=1.33 avg=1.33\n", "[8677 | 973.38] loss=1.38 avg=1.33\n", "[8678 | 974.66] loss=1.17 avg=1.32\n", "[8679 | 975.94] loss=1.36 avg=1.32\n", "[8680 | 977.22] loss=1.27 avg=1.32\n", "[8681 | 978.50] loss=1.43 avg=1.32\n", "[8682 | 979.77] loss=1.32 avg=1.32\n", "[8683 | 981.05] loss=1.26 avg=1.32\n", "[8684 | 982.35] loss=1.38 avg=1.32\n", "[8685 | 983.63] loss=1.47 avg=1.33\n", "[8686 | 984.90] loss=1.36 avg=1.33\n", "[8687 | 986.18] loss=1.22 avg=1.33\n", "[8688 | 987.46] loss=1.43 avg=1.33\n", "[8689 | 988.73] loss=1.39 avg=1.33\n", "[8690 | 990.01] loss=1.41 avg=1.33\n", "[8691 | 991.29] loss=1.32 avg=1.33\n", "[8692 | 992.57] loss=1.19 avg=1.33\n", "[8693 | 993.86] loss=1.31 avg=1.33\n", "[8694 | 995.14] loss=1.36 avg=1.33\n", "[8695 | 996.42] loss=1.30 avg=1.33\n", "[8696 | 997.70] loss=1.22 avg=1.33\n", "[8697 | 998.99] loss=1.30 avg=1.33\n", "[8698 | 1000.28] loss=1.27 avg=1.32\n", "[8699 | 1001.57] loss=1.29 avg=1.32\n", "[8700 | 1002.87] loss=1.33 avg=1.32\n", "======== SAMPLE 1 ========\n", " dévallée des délégués du personnel qui ont donné lieu au bénéfice d'une ordonnance de non-lieu ; que, par une mise en cause de la société CICR, et d'un mémoire en demande, qui revendroit à M. X... la négativeité de l'organisme, M. Y..., il a fait donation de son fonds de commerce, d'une part, au greffe du tribunal de commerce, d'autre part à lui apporté par Mme X... une lettre recommandée par M. X... qui a fait l'objet du ministère public de la société à sa décision rendue le 6 décembre 1991 par l'acte du 9 janvier 1990, et d'autre part à M. Y... son contrat de location le même jour, et celle du 21 février 1993, et au paiement du solde de l'indemnité conventionnelle qu'ils disposait à la négative de l'exécution de la convention ; que l'arrêt attaqué a, en ce qu'il fixé le fonctionnement de la société EICR au moins dix-sept délégué syndical syndical définie par les articles 812-6 et 1026-5 du Code civil, constaté que la société CICR n'avait commencé aucun acte d'acte révélé au chef de la lettre de rétention, puisqu'elle n'avait eu \" quitté ses commandes de vente \" ;Sur le moyen unique du pourvoi principal de la société CICR :Attendu que la société EICR reproche aux jugements d'un jugement du tribunal de commerce de Créteil d'avoir rejeté ce pourvoi, alors, selon le pourvoi, que le jugement qui valant jugement du tribunal de commerce ou le juge répressif doit être déclaré, à peine d'irrecevabilité pour décider, que tout jugement du tribunal de commerce avec avis de réception du jugement prononçant le redressement et déclarée nulle avec avis de réception n'a pas dès lors l'auteur de la procédure suivie ; que la cour d'appel qui a constaté que le Tribunal était présumé par la décision de la décision de justice précitée, a, par là même, violé l'article 9.3° de la convention et les deux premiers de celle du 21 janvier 1990, ensemble l'article 1022 du Code civil ;Mais attendu que c'est dès lors qu'il résulte de la combinaison des dispositions de l'article 9.3° du décret du 30 septembre 1953, réduit l'action en justice pour les actes de donation du bien-fondé de la donation, même si l'acte de donation est en même temps du même jour ; d'où il suit que le moyen n'est pas fondé ;PAR CES MOTIFS :REJETTE le pourvoi. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique ;Vu l'article 1134 du Code civil ;Attendu que, sauf disposition contraire, l'adjudication résultant d'un jugement est écrite au préfet qui dispose d'un précipitant de l'immeuble sur lequel étrangère un immeuble, nonobstant toute échec de façon écrite ;Attendu, selon l'arrêt attaqué (Chambéry, 5 février 1994), que Mlle X..., mère d'X..., est propriétaire d'un lot partant dix-septières de sa mère, sous le régime de l'article 40 de l'ordonnance de clôture, lorsque l'adjudicataire se soit placé à l'exception de son domicile, Mme X.... la proc\n", "\n", "[8701 | 1017.00] loss=1.26 avg=1.32\n", "[8702 | 1018.27] loss=1.40 avg=1.32\n", "[8703 | 1019.55] loss=1.42 avg=1.33\n", "[8704 | 1020.83] loss=1.39 avg=1.33\n", "[8705 | 1022.11] loss=1.23 avg=1.33\n", "[8706 | 1023.38] loss=1.30 avg=1.32\n", "[8707 | 1024.66] loss=1.14 avg=1.32\n", "[8708 | 1025.96] loss=1.22 avg=1.32\n", "[8709 | 1027.24] loss=1.34 avg=1.32\n", "[8710 | 1028.51] loss=1.27 avg=1.32\n", "[8711 | 1029.79] loss=1.29 avg=1.32\n", "[8712 | 1031.07] loss=1.30 avg=1.32\n", "[8713 | 1032.35] loss=1.29 avg=1.32\n", "[8714 | 1033.63] loss=1.38 avg=1.32\n", "[8715 | 1034.92] loss=1.24 avg=1.32\n", "[8716 | 1036.22] loss=1.36 avg=1.32\n", "[8717 | 1037.81] loss=1.35 avg=1.32\n", "[8718 | 1039.18] loss=1.17 avg=1.32\n", "[8719 | 1040.48] loss=1.44 avg=1.32\n", "[8720 | 1041.76] loss=1.42 avg=1.32\n", "[8721 | 1043.04] loss=1.46 avg=1.32\n", "[8722 | 1044.31] loss=1.27 avg=1.32\n", "[8723 | 1045.59] loss=1.34 avg=1.32\n", "[8724 | 1046.87] loss=1.27 avg=1.32\n", "[8725 | 1048.15] loss=1.44 avg=1.32\n", "[8726 | 1049.43] loss=1.27 avg=1.32\n", "[8727 | 1050.71] loss=1.33 avg=1.32\n", "[8728 | 1051.99] loss=1.34 avg=1.32\n", "[8729 | 1053.26] loss=1.38 avg=1.32\n", "[8730 | 1054.54] loss=1.41 avg=1.32\n", "[8731 | 1055.82] loss=1.36 avg=1.33\n", "[8732 | 1057.10] loss=1.36 avg=1.33\n", "[8733 | 1058.37] loss=1.42 avg=1.33\n", "[8734 | 1059.66] loss=1.13 avg=1.32\n", "[8735 | 1060.94] loss=1.31 avg=1.32\n", "[8736 | 1062.22] loss=1.23 avg=1.32\n", "[8737 | 1063.51] loss=1.37 avg=1.32\n", "[8738 | 1064.79] loss=1.44 avg=1.32\n", "[8739 | 1066.06] loss=1.33 avg=1.32\n", "[8740 | 1067.34] loss=1.28 avg=1.32\n", "[8741 | 1068.62] loss=1.43 avg=1.33\n", "[8742 | 1069.96] loss=1.39 avg=1.33\n", "[8743 | 1071.33] loss=1.37 avg=1.33\n", "[8744 | 1072.62] loss=1.36 avg=1.33\n", "[8745 | 1073.91] loss=1.40 avg=1.33\n", "[8746 | 1075.20] loss=1.26 avg=1.33\n", "[8747 | 1076.48] loss=1.23 avg=1.33\n", "[8748 | 1077.76] loss=1.43 avg=1.33\n", "[8749 | 1079.04] loss=1.23 avg=1.33\n", "[8750 | 1080.31] loss=1.33 avg=1.33\n", "[8751 | 1081.61] loss=1.28 avg=1.33\n", "[8752 | 1082.89] loss=1.30 avg=1.33\n", "[8753 | 1084.17] loss=1.23 avg=1.32\n", "[8754 | 1085.45] loss=1.33 avg=1.32\n", "[8755 | 1086.72] loss=1.34 avg=1.32\n", "[8756 | 1088.00] loss=1.41 avg=1.33\n", "[8757 | 1089.28] loss=1.44 avg=1.33\n", "[8758 | 1090.56] loss=1.27 avg=1.33\n", "[8759 | 1091.85] loss=1.36 avg=1.33\n", "[8760 | 1093.14] loss=1.19 avg=1.33\n", "[8761 | 1094.42] loss=1.26 avg=1.32\n", "[8762 | 1095.70] loss=1.32 avg=1.32\n", "[8763 | 1096.97] loss=1.36 avg=1.32\n", "[8764 | 1098.25] loss=1.32 avg=1.32\n", "[8765 | 1099.53] loss=1.37 avg=1.33\n", "[8766 | 1100.81] loss=1.35 avg=1.33\n", "[8767 | 1102.11] loss=1.44 avg=1.33\n", "[8768 | 1103.44] loss=1.26 avg=1.33\n", "[8769 | 1104.72] loss=1.45 avg=1.33\n", "[8770 | 1106.00] loss=1.27 avg=1.33\n", "[8771 | 1107.27] loss=1.47 avg=1.33\n", "[8772 | 1108.56] loss=1.24 avg=1.33\n", "[8773 | 1109.85] loss=1.31 avg=1.33\n", "[8774 | 1111.14] loss=1.31 avg=1.33\n", "[8775 | 1112.43] loss=1.48 avg=1.33\n", "[8776 | 1113.75] loss=1.51 avg=1.33\n", "[8777 | 1115.04] loss=1.36 avg=1.33\n", "[8778 | 1116.32] loss=1.27 avg=1.33\n", "[8779 | 1117.60] loss=1.29 avg=1.33\n", "[8780 | 1118.88] loss=1.22 avg=1.33\n", "[8781 | 1120.16] loss=1.21 avg=1.33\n", "[8782 | 1121.44] loss=1.25 avg=1.33\n", "[8783 | 1122.72] loss=1.25 avg=1.33\n", "[8784 | 1124.00] loss=1.39 avg=1.33\n", "[8785 | 1125.31] loss=1.30 avg=1.33\n", "[8786 | 1126.59] loss=1.44 avg=1.33\n", "[8787 | 1127.87] loss=1.28 avg=1.33\n", "[8788 | 1129.15] loss=1.39 avg=1.33\n", "[8789 | 1130.43] loss=1.48 avg=1.33\n", "[8790 | 1131.71] loss=1.38 avg=1.33\n", "[8791 | 1132.99] loss=1.20 avg=1.33\n", "[8792 | 1134.29] loss=1.40 avg=1.33\n", "[8793 | 1135.58] loss=1.39 avg=1.33\n", "[8794 | 1136.90] loss=1.20 avg=1.33\n", "[8795 | 1138.18] loss=1.51 avg=1.33\n", "[8796 | 1139.47] loss=1.27 avg=1.33\n", "[8797 | 1140.75] loss=1.20 avg=1.33\n", "[8798 | 1142.02] loss=1.44 avg=1.33\n", "[8799 | 1143.30] loss=1.31 avg=1.33\n", "[8800 | 1144.59] loss=1.17 avg=1.33\n", "======== SAMPLE 1 ========\n", "ité, et une autre branche, ne constitue aucune contestation sérieuse ; qu'au regard des articles 14 et 17 de l'ordonnance du 21 octobre 1945, l'assemblée générale des copropriétaires n'est tenue que dans le cadre des procédures prévues par le cahier des charges, laquelle permet au syndic auprès du Trésor des condamnation techniques, par le juge préfet dans ses ressources, qui y était contredite ; qu'à ce moment, le juge des référés a fait des contestations sur la compétité des copropriétaires pour justifier de sa régularité ; qu'à défaut de motifs établies en l'espèce, l'ordonnance rendue par la commission nationale de conciliation, le 27 mars 1992, énonce que cette procédure évaluant le 1er octobre 1994 en ce qu'il lui s'y rattache dès lors que le syndic d'assurance de l'Etat, à défaut de contrefaçon, pouvait s'élevé, lui avait connaissance de la contrefaçon mais une interprétation prévue par l'article 34 du règlement d'un pacte conclue avant la négligence de ce dernier, mais ne réduit le statut du rôle civil ; qu'il s'ensuit que le cahier des charges du 7 février 1967, non exclusivement lorsqu'il est ainsi décidé, dispose d'un mandat spécifique en vue de se désistant qu'elle est également en fait ; qu'en vertu du règlement résidant du rôle civil du règlement du Côte d'Or, le syndic a été chargé par l'Etat d'assurances contre des accidents de la circulation doutre les conditions d'une tierce entreprise, à laquelle sont exclues l'utilisation par les copropriétaires de celle de la garde d'un épreuil contenant une autorité de direction ; d'où il suit que le moyen ne saurait être accueilli ;Mais sur le second moyen :Vu l'article 34 de l'ordonnance du 21 octobre 1945 ;Attendu que, selon ce texte, lorsque l'assemblée générale étatique est toujours dispensé de tenter d'organiser l'utilisation par l'Etat d'un bâtiment, que l'établissement à caractère substantiel du rôle civil s'exclu de la preuve du caractère indispensable de la rémunération des accidents du transport ;Attendu que, pour décider que l'Etat, après avoir énoncé qu'il n'avait pas informé son employeur que des risques de périmée régulièrement affectées était intervenus, avait commis dans un délai légal à assurer au syndic tacitement de répondre à une aide sociale qu'il lui appartenait de se livrer aussi de la gestion étrangère, le jugement énonce, par motifs adoptés, que les risques présidaient uniquement une personne physique de l'Etat, non pour obtenir des \" charges de nature à justifier d'un motif surabondant \" ;Qu'en statuant ainsi, la cour d'appel a méconnu le texte susvisé ;PAR CES MOTIFS :CASSE ET ANNULE, mais seulement en ce qu'il a décidé que l'Etat, après avoir allégué \" les risques affectées en caractère partiellement l'étatement et le caractère indispensable des accidents du transport \", a violé ces dispositions susvisées, ensemble l'article 627, alinéa 2, du nouveau Code de procédure civile ;PAR CES MOTIFS :CASSE ET ANNULE, mais seulement\n", "\n", "[8801 | 1160.39] loss=1.38 avg=1.33\n", "[8802 | 1161.67] loss=1.38 avg=1.33\n", "[8803 | 1162.95] loss=1.25 avg=1.33\n", "[8804 | 1164.22] loss=1.35 avg=1.33\n", "[8805 | 1165.50] loss=1.27 avg=1.33\n", "[8806 | 1166.78] loss=1.48 avg=1.33\n", "[8807 | 1168.08] loss=1.36 avg=1.33\n", "[8808 | 1169.40] loss=1.38 avg=1.33\n", "[8809 | 1170.68] loss=1.46 avg=1.33\n", "[8810 | 1171.96] loss=1.37 avg=1.33\n", "[8811 | 1173.24] loss=1.26 avg=1.33\n", "[8812 | 1174.51] loss=1.28 avg=1.33\n", "[8813 | 1175.79] loss=1.29 avg=1.33\n", "[8814 | 1177.07] loss=1.32 avg=1.33\n", "[8815 | 1178.35] loss=1.26 avg=1.33\n", "[8816 | 1179.63] loss=1.34 avg=1.33\n", "[8817 | 1180.92] loss=1.27 avg=1.33\n", "[8818 | 1182.21] loss=1.29 avg=1.33\n", "[8819 | 1183.52] loss=1.34 avg=1.33\n", "[8820 | 1184.81] loss=1.29 avg=1.33\n", "[8821 | 1186.11] loss=1.31 avg=1.33\n", "[8822 | 1187.40] loss=1.33 avg=1.33\n", "[8823 | 1188.67] loss=1.30 avg=1.33\n", "[8824 | 1189.95] loss=1.35 avg=1.33\n", "[8825 | 1191.26] loss=1.30 avg=1.33\n", "[8826 | 1192.54] loss=1.23 avg=1.33\n", "[8827 | 1193.82] loss=1.39 avg=1.33\n", "[8828 | 1195.09] loss=1.07 avg=1.32\n", "[8829 | 1196.37] loss=1.47 avg=1.33\n", "[8830 | 1197.65] loss=1.43 avg=1.33\n", "[8831 | 1198.92] loss=1.22 avg=1.33\n", "[8832 | 1200.20] loss=1.28 avg=1.33\n", "[8833 | 1201.52] loss=1.45 avg=1.33\n", "[8834 | 1202.82] loss=1.10 avg=1.32\n", "[8835 | 1204.10] loss=1.35 avg=1.32\n", "[8836 | 1205.38] loss=1.31 avg=1.32\n", "[8837 | 1206.66] loss=1.16 avg=1.32\n", "[8838 | 1207.93] loss=1.29 avg=1.32\n", "[8839 | 1209.21] loss=1.32 avg=1.32\n", "[8840 | 1210.49] loss=1.36 avg=1.32\n", "[8841 | 1211.77] loss=1.30 avg=1.32\n", "[8842 | 1213.07] loss=1.18 avg=1.32\n", "[8843 | 1214.36] loss=1.31 avg=1.32\n", "[8844 | 1215.64] loss=1.34 avg=1.32\n", "[8845 | 1216.91] loss=1.41 avg=1.32\n", "[8846 | 1218.20] loss=1.22 avg=1.32\n", "[8847 | 1219.49] loss=1.33 avg=1.32\n", "[8848 | 1220.78] loss=1.45 avg=1.32\n", "[8849 | 1222.07] loss=1.24 avg=1.32\n", "[8850 | 1223.36] loss=1.34 avg=1.32\n", "[8851 | 1224.65] loss=1.61 avg=1.32\n", "[8852 | 1225.92] loss=1.30 avg=1.32\n", "[8853 | 1227.20] loss=1.45 avg=1.33\n", "[8854 | 1228.48] loss=1.45 avg=1.33\n", "[8855 | 1229.76] loss=1.44 avg=1.33\n", "[8856 | 1231.03] loss=1.37 avg=1.33\n", "[8857 | 1232.31] loss=1.19 avg=1.33\n", "[8858 | 1233.63] loss=1.33 avg=1.33\n", "[8859 | 1234.92] loss=1.23 avg=1.33\n", "[8860 | 1236.21] loss=1.33 avg=1.33\n", "[8861 | 1237.49] loss=1.41 avg=1.33\n", "[8862 | 1238.77] loss=1.24 avg=1.33\n", "[8863 | 1240.04] loss=1.34 avg=1.33\n", "[8864 | 1241.32] loss=1.41 avg=1.33\n", "[8865 | 1242.60] loss=1.39 avg=1.33\n", "[8866 | 1243.88] loss=1.37 avg=1.33\n", "[8867 | 1245.15] loss=1.29 avg=1.33\n", "[8868 | 1246.44] loss=1.32 avg=1.33\n", "[8869 | 1247.72] loss=1.21 avg=1.33\n", "[8870 | 1249.00] loss=1.27 avg=1.33\n", "[8871 | 1250.27] loss=1.38 avg=1.33\n", "[8872 | 1251.55] loss=1.25 avg=1.33\n", "[8873 | 1252.83] loss=1.36 avg=1.33\n", "[8874 | 1254.10] loss=1.28 avg=1.33\n", "[8875 | 1255.39] loss=1.29 avg=1.33\n", "[8876 | 1256.70] loss=1.23 avg=1.32\n", "[8877 | 1258.01] loss=1.38 avg=1.32\n", "[8878 | 1259.30] loss=1.32 avg=1.32\n", "[8879 | 1260.59] loss=1.29 avg=1.32\n", "[8880 | 1261.87] loss=1.28 avg=1.32\n", "[8881 | 1263.15] loss=1.28 avg=1.32\n", "[8882 | 1264.43] loss=1.24 avg=1.32\n", "[8883 | 1265.71] loss=1.41 avg=1.32\n", "[8884 | 1267.01] loss=1.19 avg=1.32\n", "[8885 | 1268.33] loss=1.30 avg=1.32\n", "[8886 | 1269.61] loss=1.10 avg=1.32\n", "[8887 | 1270.88] loss=1.53 avg=1.32\n", "[8888 | 1272.16] loss=1.45 avg=1.32\n", "[8889 | 1273.44] loss=1.48 avg=1.32\n", "[8890 | 1274.72] loss=1.28 avg=1.32\n", "[8891 | 1275.99] loss=1.24 avg=1.32\n", "[8892 | 1277.27] loss=1.30 avg=1.32\n", "[8893 | 1278.56] loss=1.29 avg=1.32\n", "[8894 | 1279.86] loss=1.55 avg=1.33\n", "[8895 | 1281.14] loss=1.33 avg=1.33\n", "[8896 | 1282.42] loss=1.53 avg=1.33\n", "[8897 | 1283.70] loss=1.41 avg=1.33\n", "[8898 | 1284.98] loss=1.29 avg=1.33\n", "[8899 | 1286.25] loss=1.36 avg=1.33\n", "[8900 | 1287.53] loss=1.59 avg=1.33\n", "======== SAMPLE 1 ========\n", " au demande de rétablissement des héritiers pour l'application de l'article 810 du Code civil ;Attendu que, pour se prononcer ainsi sur le noul de ces points, la cour d'appel retient que le point de départ de la prescription prévu avait été renvoyé par le représentant des créanciers en la cause, ces deux dates étaient de nature à leur être renvoyées par l'inspecteur du personnel, même si les héritiers ayant exécuté ses obligations était en possession de la créance en date des 5 et 7 septembre 1987, que, comme en l'espèce, il lui appartenait de sortir la date en compte de l'avantage que les époux Y... soutenaient, sur ce point, de ses seules dates à la date du 10 novembre et qu'il en était donc qu'ils n'en exécutaient pas de leur être déposés de plusieurs autres réserves aux consorts Y... ;Attendu qu'en se prononçant ainsi, elle a violé les textes susvisés ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 17 février 1995, entre les parties, par la cour d'appel de Grenoble ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Chambéry. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique :Vu l'article 5 de la Convention internationale des droits de l'homme du 13 mars 1956 dans sa rédaction antérieure à la loi du 10 janvier 1990 ;Attendu qu'il résulte de ces textes que ne peut être considéré comme une entreprise, fût-ce pour autant, comme une interprétation relative à l'étendue de son obligation en matière de sécurité, laquelle est faite en ce qui concerne le paiement des sommes dues à concurrence des échéances de remboursement des prêts, à l'exception des prestations familiales de son domicile, à la garantie des égalités de témédicaments et de garantir du remboursement par application des règles de la sécurité sociale ;Attendu que, pour rejeter le montant des cotisations afférentes à la prime compensatrice de prévisions relatives aux cotisations annues au cours d'une période ayant fait l'objet d'une mise à pied conventionnelle, l'arrêt attaqué énonce le montant correspondant à la part de cotisations afférentes à l'entreprise et prévus par ses articles L. 434-1, L. 434-3, L. 434-4, L. 436-1 et R. 436-2 du Code du travail, l'article 1er qu'en matière de sécurité sociale ou, à défaut, la même côté ;Qu'en statuant ainsi la cour d'appel a violé les textes susvisés ;PAR CES MOTIFS :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 26 septembre 1994, entre les parties, par la cour d'appel de Paris ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Paris autrement composée . <|endoftext|>\n", "<|startoftext|> Attendu que, par avenant du 30 mars 1973, la loi du 10 janvier 1987, ne comportant aucune clause\n", "\n", "[8901 | 1301.21] loss=1.34 avg=1.33\n", "[8902 | 1302.51] loss=1.32 avg=1.33\n", "[8903 | 1303.82] loss=1.36 avg=1.33\n", "[8904 | 1305.11] loss=1.31 avg=1.33\n", "[8905 | 1306.39] loss=1.29 avg=1.33\n", "[8906 | 1307.68] loss=1.21 avg=1.33\n", "[8907 | 1308.95] loss=1.35 avg=1.33\n", "[8908 | 1310.23] loss=1.44 avg=1.33\n", "[8909 | 1311.51] loss=1.37 avg=1.33\n", "[8910 | 1312.81] loss=1.33 avg=1.33\n", "[8911 | 1314.09] loss=1.20 avg=1.33\n", "[8912 | 1315.36] loss=1.26 avg=1.33\n", "[8913 | 1316.64] loss=1.37 avg=1.33\n", "[8914 | 1317.91] loss=1.29 avg=1.33\n", "[8915 | 1319.19] loss=1.26 avg=1.33\n", "[8916 | 1320.46] loss=1.16 avg=1.33\n", "[8917 | 1321.74] loss=1.22 avg=1.33\n", "[8918 | 1323.04] loss=1.09 avg=1.32\n", "[8919 | 1324.32] loss=1.44 avg=1.32\n", "[8920 | 1325.60] loss=1.37 avg=1.32\n", "[8921 | 1326.88] loss=1.32 avg=1.32\n", "[8922 | 1328.15] loss=1.44 avg=1.33\n", "[8923 | 1329.43] loss=1.47 avg=1.33\n", "[8924 | 1330.71] loss=1.34 avg=1.33\n", "[8925 | 1332.00] loss=1.34 avg=1.33\n", "[8926 | 1333.29] loss=1.27 avg=1.33\n", "[8927 | 1334.58] loss=1.24 avg=1.33\n", "[8928 | 1335.86] loss=1.27 avg=1.33\n", "[8929 | 1337.13] loss=1.45 avg=1.33\n", "[8930 | 1338.42] loss=1.39 avg=1.33\n", "[8931 | 1339.71] loss=1.55 avg=1.33\n", "[8932 | 1341.00] loss=1.08 avg=1.33\n", "[8933 | 1342.29] loss=1.28 avg=1.33\n", "[8934 | 1343.57] loss=1.27 avg=1.33\n", "[8935 | 1344.86] loss=1.25 avg=1.33\n", "[8936 | 1346.14] loss=1.33 avg=1.33\n", "[8937 | 1347.42] loss=1.23 avg=1.32\n", "[8938 | 1348.70] loss=1.19 avg=1.32\n", "[8939 | 1349.97] loss=1.33 avg=1.32\n", "[8940 | 1351.25] loss=1.45 avg=1.32\n", "[8941 | 1352.53] loss=1.39 avg=1.33\n", "[8942 | 1353.80] loss=1.46 avg=1.33\n", "[8943 | 1355.08] loss=1.33 avg=1.33\n", "[8944 | 1356.37] loss=1.13 avg=1.32\n", "[8945 | 1357.65] loss=1.27 avg=1.32\n", "[8946 | 1358.93] loss=1.27 avg=1.32\n", "[8947 | 1360.20] loss=1.36 avg=1.32\n", "[8948 | 1361.48] loss=1.32 avg=1.32\n", "[8949 | 1362.77] loss=1.39 avg=1.32\n", "[8950 | 1364.05] loss=1.32 avg=1.32\n", "[8951 | 1365.36] loss=1.17 avg=1.32\n", "[8952 | 1366.64] loss=1.26 avg=1.32\n", "[8953 | 1367.92] loss=1.38 avg=1.32\n", "[8954 | 1369.20] loss=1.23 avg=1.32\n", "[8955 | 1370.48] loss=1.35 avg=1.32\n", "[8956 | 1371.75] loss=1.38 avg=1.32\n", "[8957 | 1373.03] loss=1.25 avg=1.32\n", "[8958 | 1374.31] loss=1.32 avg=1.32\n", "[8959 | 1375.59] loss=1.22 avg=1.32\n", "[8960 | 1376.88] loss=1.38 avg=1.32\n", "[8961 | 1378.22] loss=1.33 avg=1.32\n", "[8962 | 1379.51] loss=1.25 avg=1.32\n", "[8963 | 1380.80] loss=1.29 avg=1.32\n", "[8964 | 1382.08] loss=1.37 avg=1.32\n", "[8965 | 1383.36] loss=1.35 avg=1.32\n", "[8966 | 1384.63] loss=1.34 avg=1.32\n", "[8967 | 1385.91] loss=1.45 avg=1.32\n", "[8968 | 1387.18] loss=1.36 avg=1.32\n", "[8969 | 1388.46] loss=1.31 avg=1.32\n", "[8970 | 1389.76] loss=1.25 avg=1.32\n", "[8971 | 1391.04] loss=1.37 avg=1.32\n", "[8972 | 1392.32] loss=1.21 avg=1.32\n", "[8973 | 1393.60] loss=1.37 avg=1.32\n", "[8974 | 1394.87] loss=1.27 avg=1.32\n", "[8975 | 1396.15] loss=1.14 avg=1.32\n", "[8976 | 1397.44] loss=1.31 avg=1.32\n", "[8977 | 1398.73] loss=1.42 avg=1.32\n", "[8978 | 1400.03] loss=1.35 avg=1.32\n", "[8979 | 1401.30] loss=1.26 avg=1.32\n", "[8980 | 1402.58] loss=1.43 avg=1.32\n", "[8981 | 1403.86] loss=1.27 avg=1.32\n", "[8982 | 1405.13] loss=1.25 avg=1.32\n", "[8983 | 1406.41] loss=1.24 avg=1.32\n", "[8984 | 1407.68] loss=1.33 avg=1.32\n", "[8985 | 1408.96] loss=1.14 avg=1.32\n", "[8986 | 1410.24] loss=1.28 avg=1.32\n", "[8987 | 1411.53] loss=1.35 avg=1.32\n", "[8988 | 1412.82] loss=1.24 avg=1.32\n", "[8989 | 1414.11] loss=1.23 avg=1.32\n", "[8990 | 1415.40] loss=1.33 avg=1.32\n", "[8991 | 1416.69] loss=1.37 avg=1.32\n", "[8992 | 1417.98] loss=1.35 avg=1.32\n", "[8993 | 1419.25] loss=1.27 avg=1.32\n", "[8994 | 1420.53] loss=1.30 avg=1.32\n", "[8995 | 1421.82] loss=1.22 avg=1.32\n", "[8996 | 1423.11] loss=1.32 avg=1.32\n", "[8997 | 1424.38] loss=1.17 avg=1.31\n", "[8998 | 1425.66] loss=1.27 avg=1.31\n", "[8999 | 1426.93] loss=1.38 avg=1.31\n", "[9000 | 1428.21] loss=1.29 avg=1.31\n", "Saving checkpoint/run1/model-9000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 17:54:36.392080: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:54:36.393531: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:54:36.394493: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:54:36.395609: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:54:36.396454: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-01-28 17:54:36.397204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint checkpoint/run1/model-9000\n", "Loading dataset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 3.56it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dataset has 10057357 tokens\n", "Training...\n", "Saving checkpoint/run1/model-9000\n", "Saving checkpoint/run1/model-9000\n", "======== SAMPLE 1 ========\n", " et pris, c'est donc à celui connu qu'en raison du manquement de la société Bail, puisque, dans la même situation devenue juridiction, la société La Réunion a, à juste titre, réformé son précédent arrêt en violation de l'article L. 121-4 du Livre des procédures fiscales ;Mais attendu qu'ayant constaté que la déchéance de l'article R. 121-4 du Livre des procédures fiscales avait été constatée le 29 août 1983 pour permettre l'entretien préalable au débiteur principal, de sorte que cette déchéance était, seul, dépourvue de manière concrète, la cour d'appel a, par les seuls motifs non avenus, légalement légalement justifié sa décision ; que le moyen n'est donc fondé en aucune de ses pouvoirs ;Sur le pourvoi principal de M. X... : (sans intérêt) ;PAR CES MOTIFS :REJETTE le pourvoi. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique :Attendu, selon l'arrêt attaqué (Riom, 12 avril 1993), que M. X... a souscrit, d'une part, auprès de la Caisse d'assurance maladie l'acquis et, d'autre part, un contrat de travail de plusieurs clients salariés ; que, le 17 juillet 1979, la Caisse a décidé à la suite de la mise en redressement judiciaire de M. X..., que son contrat de travail prévoyait la mise en redressement judiciaire de deux clients ; que celui-ci a fait appel de cette décision ;Attendu que l'accord du 26 juillet 1979 a été conclu enregistré dans un litige contenant son inscription dans un arrêté du 3 mai 1953, relatif à l'obligation de M. X... ; que l'arrêté du 3 mai 1953, statuant sur les déboutations de ses clients, a été notifié par l'exposant au premier président du tribunal de grande instance de Villefranche-sur-Saône ; qu'il s'en est également interdit aux sociétés en redressement judiciaire de reprendre leurs activités et de vendre une durée de 3 mois ; que, le 22 juin 1987, la Caisse ayant été mise en redressement judiciaire, il a adressé à ses clients, Mme Z... ainsi qu'à son client, la caisse d'assurance maladie de l'Essonne ; que celle-ci a sollicité la mise en redressement judiciaire de ses clients ; que, suivant un jour déloyale, Mme Z... a perdu sa clientière ; qu'elle a contesté la déchéance de l'objet du contrat de travail, ce qui n'a pas été accompli ; que, par actes sous seings privés du 13 janvier 1986, Mme Z... a cédé à M. X..., ès qualités, une \" rémunération d'intéressée \", et que la déchéance de l'objet du contrat de travail était stipulée \" si bénéficiaire du paiement du régime de préavis et de préavis de durée, par l'article 23 de la loi du 10 janvier 1978, bénéficiaires du paiement en cas de cessation de paiement des sommes dues en cas d'année du contrat de travail pour les périodes de durée minimum \" ; que, du fait de ces sommes, Mme Z... a demandé au tribunal de grande instance de Villefranche une indemnité de préavis, d'arrêté d'établissement et\n", "\n", "[9001 | 24.26] loss=1.54 avg=1.54\n", "[9002 | 25.54] loss=1.55 avg=1.55\n", "[9003 | 26.82] loss=1.43 avg=1.51\n", "[9004 | 28.12] loss=1.35 avg=1.47\n", "[9005 | 29.40] loss=1.44 avg=1.46\n", "[9006 | 30.67] loss=1.40 avg=1.45\n", "[9007 | 31.95] loss=1.45 avg=1.45\n", "[9008 | 33.24] loss=1.36 avg=1.44\n", "[9009 | 34.51] loss=1.39 avg=1.44\n", "[9010 | 35.79] loss=1.45 avg=1.44\n", "[9011 | 37.06] loss=1.29 avg=1.42\n", "[9012 | 38.38] loss=1.48 avg=1.43\n", "[9013 | 39.69] loss=1.40 avg=1.43\n", "[9014 | 40.98] loss=1.25 avg=1.41\n", "[9015 | 42.27] loss=1.56 avg=1.42\n", "[9016 | 43.56] loss=1.40 avg=1.42\n", "[9017 | 44.85] loss=1.72 avg=1.44\n", "[9018 | 46.13] loss=1.39 avg=1.44\n", "[9019 | 47.43] loss=1.43 avg=1.44\n", "[9020 | 48.70] loss=1.52 avg=1.44\n", "[9021 | 49.99] loss=1.31 avg=1.44\n", "[9022 | 51.27] loss=1.48 avg=1.44\n", "[9023 | 52.55] loss=1.29 avg=1.43\n", "[9024 | 53.82] loss=1.41 avg=1.43\n", "[9025 | 55.10] loss=1.26 avg=1.42\n", "[9026 | 56.37] loss=1.40 avg=1.42\n", "[9027 | 57.65] loss=1.35 avg=1.42\n", "[9028 | 58.93] loss=1.33 avg=1.41\n", "[9029 | 60.21] loss=1.39 avg=1.41\n", "[9030 | 61.49] loss=1.43 avg=1.41\n", "[9031 | 62.76] loss=1.53 avg=1.42\n", "[9032 | 64.04] loss=1.34 avg=1.42\n", "[9033 | 65.32] loss=1.45 avg=1.42\n", "[9034 | 66.59] loss=1.36 avg=1.42\n", "[9035 | 67.87] loss=1.44 avg=1.42\n", "[9036 | 69.14] loss=1.42 avg=1.42\n", "[9037 | 70.42] loss=1.28 avg=1.41\n", "[9038 | 71.71] loss=1.51 avg=1.41\n", "[9039 | 72.98] loss=1.33 avg=1.41\n", "[9040 | 74.26] loss=1.45 avg=1.41\n", "[9041 | 75.55] loss=1.28 avg=1.41\n", "[9042 | 76.84] loss=1.36 avg=1.41\n", "[9043 | 78.13] loss=1.35 avg=1.41\n", "[9044 | 79.55] loss=1.42 avg=1.41\n", "[9045 | 80.89] loss=1.51 avg=1.41\n", "[9046 | 82.18] loss=1.31 avg=1.41\n", "[9047 | 83.46] loss=1.46 avg=1.41\n", "[9048 | 84.74] loss=1.32 avg=1.41\n", "[9049 | 86.02] loss=1.29 avg=1.40\n", "[9050 | 87.29] loss=1.47 avg=1.41\n", "[9051 | 88.57] loss=1.39 avg=1.40\n", "[9052 | 89.84] loss=1.36 avg=1.40\n", "[9053 | 91.12] loss=1.24 avg=1.40\n", "[9054 | 92.39] loss=1.41 avg=1.40\n", "[9055 | 93.69] loss=1.45 avg=1.40\n", "[9056 | 94.98] loss=1.32 avg=1.40\n", "[9057 | 96.25] loss=1.45 avg=1.40\n", "[9058 | 97.53] loss=1.20 avg=1.40\n", "[9059 | 98.80] loss=1.55 avg=1.40\n", "[9060 | 100.08] loss=1.25 avg=1.40\n", "[9061 | 101.35] loss=1.40 avg=1.40\n", "[9062 | 102.63] loss=1.33 avg=1.39\n", "[9063 | 103.90] loss=1.30 avg=1.39\n", "[9064 | 105.19] loss=1.52 avg=1.40\n", "[9065 | 106.47] loss=1.42 avg=1.40\n", "[9066 | 107.74] loss=1.28 avg=1.39\n", "[9067 | 109.03] loss=1.41 avg=1.39\n", "[9068 | 110.31] loss=1.42 avg=1.39\n", "[9069 | 111.58] loss=1.50 avg=1.40\n", "[9070 | 112.93] loss=1.12 avg=1.39\n", "[9071 | 114.34] loss=1.48 avg=1.39\n", "[9072 | 115.69] loss=1.26 avg=1.39\n", "[9073 | 117.01] loss=1.44 avg=1.39\n", "[9074 | 118.30] loss=1.35 avg=1.39\n", "[9075 | 119.59] loss=1.38 avg=1.39\n", "[9076 | 120.87] loss=1.23 avg=1.39\n", "[9077 | 122.15] loss=1.28 avg=1.38\n", "[9078 | 123.42] loss=1.30 avg=1.38\n", "[9079 | 124.70] loss=1.41 avg=1.38\n", "[9080 | 125.98] loss=1.42 avg=1.38\n", "[9081 | 127.27] loss=1.28 avg=1.38\n", "[9082 | 128.55] loss=1.43 avg=1.38\n", "[9083 | 129.83] loss=1.45 avg=1.38\n", "[9084 | 131.11] loss=1.23 avg=1.38\n", "[9085 | 132.38] loss=1.20 avg=1.38\n", "[9086 | 133.66] loss=1.45 avg=1.38\n", "[9087 | 134.94] loss=1.30 avg=1.38\n", "[9088 | 136.22] loss=1.31 avg=1.38\n", "[9089 | 137.52] loss=1.35 avg=1.38\n", "[9090 | 138.80] loss=1.32 avg=1.38\n", "[9091 | 140.08] loss=1.20 avg=1.37\n", "[9092 | 141.35] loss=1.38 avg=1.37\n", "[9093 | 142.63] loss=1.49 avg=1.38\n", "[9094 | 143.91] loss=1.48 avg=1.38\n", "[9095 | 145.18] loss=1.31 avg=1.38\n", "[9096 | 146.50] loss=1.47 avg=1.38\n", "[9097 | 147.79] loss=1.19 avg=1.37\n", "[9098 | 149.10] loss=1.29 avg=1.37\n", "[9099 | 150.39] loss=1.16 avg=1.37\n", "[9100 | 151.69] loss=1.33 avg=1.37\n", "======== SAMPLE 1 ========\n", " décrire cette prise en conséquence que la société de droit de l'URSSAF aurait dû d'établir que la société civile immobilière A..., propriétaire de l'immeuble, n'avait pas acquis l'immeuble dès lors qu'elle faisait la preuve de son aptitude de fonds immobiliers à exercer un recours personnel ;qu'en l'état de ces constatations et énonciations, la cour d'appel a légalement justifié sa décision ;Mais sur la première branche du moyen unique du pourvoi principal du 21 janvier 2001 :Vu les articles 1134 et 2015 du Code civil, dans leur rédaction alors applicable, ensemble les articles 6 et 7 du décret n° 91-15 devenu L. 121-1 du Code des assurances ;Attendu que lorsqu'un acte d'acquisition d'un immeuble, d'une personne sans avocat, a été acquis, par un copropriétaire, au plus tard dans les locaux de l'immeuble, la chose vendue par l'épaule à laquelle sont les acquéreurs, selon un règlement judiciaire, sous le séquestre, selon les règles applicables ;Attendu que pour décharge le préjudice subi par le locataire, l'arrêt attaqué énonce qu'il résulte de dispositions combinées des articles 12 de l'ordonnance du 11 septembre 1999 et 10 du décret-loi du 26 octobre 1984 qui ordonne la acquisition par l'acquéreur de l'élabor d'un bien immobilier situé dans l'ensemble de l'immeuble en vente, et que, même si cette disposition est inopposable à la société, il n'a pu valablement être invoqué comme c'est dès lors qu'une société civile immobilière civile ne devient pas celle-ci ;Qu'en statuant ainsi, sans considérer qu'en l'espèce l'intérêt direct de l'acquéreur aurait dû se prévaloir de la convention d'acquéreur de l'immeuble et qu'il avait acquis cette dernière aux acquéreurs, la cour d'appel a violé les textes susvisés ;Sur le moyen unique du pourvoi incident de Jean-Paul Z..., la société des immeubles (société BIC), le syndicat des copropriétaires et le syndicat des copropriétaires ;Considérant comme étant les quatrième, troisième et cinquième branches de l'arrêt, l'arrêt n° 93-10.869 dont elle a donné suite à l'acte qu'en ce qu'il a rejeté un droit contradictoire aux demandes en renouvellement des primes échues, selon les dispositions d'ordre public du décret du 22 novembre 1982, doit avoir lieu au regard de l'article 35 de la loi du 13 janvier 1998, lesquelles se posent les modalités respectives du litige ;Attendu que c'est dans l'exercice de son pouvoir souverain d'appréciation des droits du syndicat, que l'arrêt a retenu que les demandes déposées au syndicat de la commune de l'Hémarché au nom des copropriétaires n'émises pas les droits des acquéreurs et qu'ils n'a commis que d'effet contradictoirement de la clause de compétence des tribunaux de l'ordre judiciaire et qui les auraient été violés ;PAR CES MOTIFS :CASSE ET ANNULE, mais seulement en sa disposition accordant aux copropriétaires la condamnation solidaire de Jean-Paul Z... à leur payer cette somme à titre de d\n", "\n", "[9101 | 166.69] loss=1.44 avg=1.37\n", "[9102 | 167.96] loss=1.21 avg=1.37\n", "[9103 | 169.24] loss=1.39 avg=1.37\n", "[9104 | 170.53] loss=1.55 avg=1.37\n", "[9105 | 171.81] loss=1.36 avg=1.37\n", "[9106 | 173.09] loss=1.24 avg=1.37\n", "[9107 | 174.37] loss=1.40 avg=1.37\n", "[9108 | 175.64] loss=1.20 avg=1.37\n", "[9109 | 176.92] loss=1.18 avg=1.36\n", "[9110 | 178.22] loss=1.35 avg=1.36\n", "[9111 | 179.51] loss=1.28 avg=1.36\n", "[9112 | 180.79] loss=1.44 avg=1.36\n", "[9113 | 182.09] loss=1.34 avg=1.36\n", "[9114 | 183.37] loss=1.55 avg=1.37\n", "[9115 | 184.65] loss=1.43 avg=1.37\n", "[9116 | 185.93] loss=1.47 avg=1.37\n", "[9117 | 187.22] loss=1.23 avg=1.37\n", "[9118 | 188.51] loss=1.46 avg=1.37\n", "[9119 | 189.80] loss=1.29 avg=1.37\n", "[9120 | 191.09] loss=1.24 avg=1.36\n", "[9121 | 192.43] loss=1.24 avg=1.36\n", "[9122 | 193.72] loss=1.04 avg=1.36\n", "[9123 | 195.00] loss=1.46 avg=1.36\n", "[9124 | 196.28] loss=1.16 avg=1.36\n", "[9125 | 197.55] loss=1.18 avg=1.35\n", "[9126 | 198.83] loss=1.34 avg=1.35\n", "[9127 | 200.11] loss=1.41 avg=1.35\n", "[9128 | 201.38] loss=1.35 avg=1.35\n", "[9129 | 202.66] loss=1.39 avg=1.36\n", "[9130 | 203.95] loss=1.54 avg=1.36\n", "[9131 | 205.23] loss=1.37 avg=1.36\n", "[9132 | 206.51] loss=1.45 avg=1.36\n", "[9133 | 207.80] loss=1.09 avg=1.36\n", "[9134 | 209.07] loss=1.43 avg=1.36\n", "[9135 | 210.35] loss=1.42 avg=1.36\n", "[9136 | 211.68] loss=1.29 avg=1.36\n", "[9137 | 212.97] loss=1.19 avg=1.35\n", "[9138 | 214.27] loss=1.34 avg=1.35\n", "[9139 | 215.56] loss=1.22 avg=1.35\n", "[9140 | 216.83] loss=1.61 avg=1.36\n", "[9141 | 218.11] loss=1.30 avg=1.36\n", "[9142 | 219.39] loss=1.36 avg=1.36\n", "[9143 | 220.67] loss=1.35 avg=1.36\n", "[9144 | 221.95] loss=1.31 avg=1.35\n", "[9145 | 223.22] loss=1.41 avg=1.36\n", "[9146 | 224.51] loss=1.20 avg=1.35\n", "[9147 | 225.87] loss=1.45 avg=1.35\n", "[9148 | 227.19] loss=1.52 avg=1.36\n", "[9149 | 228.49] loss=1.17 avg=1.35\n", "[9150 | 229.77] loss=1.22 avg=1.35\n", "[9151 | 231.05] loss=1.54 avg=1.35\n", "[9152 | 232.33] loss=1.17 avg=1.35\n", "[9153 | 233.61] loss=1.52 avg=1.35\n", "[9154 | 234.88] loss=1.14 avg=1.35\n", "[9155 | 236.17] loss=1.31 avg=1.35\n", "[9156 | 237.45] loss=1.56 avg=1.35\n", "[9157 | 238.73] loss=1.56 avg=1.36\n", "[9158 | 240.01] loss=1.33 avg=1.36\n", "[9159 | 241.29] loss=1.38 avg=1.36\n", "[9160 | 242.56] loss=1.35 avg=1.36\n", "[9161 | 243.85] loss=1.35 avg=1.36\n", "[9162 | 245.15] loss=1.29 avg=1.36\n", "[9163 | 246.43] loss=1.24 avg=1.35\n", "[9164 | 247.72] loss=1.46 avg=1.36\n", "[9165 | 249.00] loss=1.60 avg=1.36\n", "[9166 | 250.27] loss=1.31 avg=1.36\n", "[9167 | 251.55] loss=1.48 avg=1.36\n", "[9168 | 252.83] loss=1.42 avg=1.36\n", "[9169 | 254.10] loss=1.23 avg=1.36\n", "[9170 | 255.38] loss=1.33 avg=1.36\n", "[9171 | 256.66] loss=1.36 avg=1.36\n", "[9172 | 257.93] loss=1.37 avg=1.36\n", "[9173 | 259.22] loss=1.48 avg=1.36\n", "[9174 | 260.50] loss=1.26 avg=1.36\n", "[9175 | 261.78] loss=1.32 avg=1.36\n", "[9176 | 263.07] loss=1.48 avg=1.36\n", "[9177 | 264.36] loss=1.21 avg=1.36\n", "[9178 | 265.65] loss=1.42 avg=1.36\n", "[9179 | 266.94] loss=1.19 avg=1.36\n", "[9180 | 268.22] loss=1.23 avg=1.35\n", "[9181 | 269.50] loss=1.44 avg=1.36\n", "[9182 | 270.78] loss=1.24 avg=1.35\n", "[9183 | 272.06] loss=1.21 avg=1.35\n", "[9184 | 273.34] loss=1.38 avg=1.35\n", "[9185 | 274.62] loss=1.32 avg=1.35\n", "[9186 | 275.90] loss=1.22 avg=1.35\n", "[9187 | 277.20] loss=1.27 avg=1.35\n", "[9188 | 278.49] loss=1.20 avg=1.35\n", "[9189 | 279.77] loss=1.39 avg=1.35\n", "[9190 | 281.07] loss=1.22 avg=1.35\n", "[9191 | 282.34] loss=1.34 avg=1.35\n", "[9192 | 283.62] loss=1.33 avg=1.35\n", "[9193 | 284.89] loss=1.22 avg=1.35\n", "[9194 | 286.17] loss=1.22 avg=1.34\n", "[9195 | 287.44] loss=1.35 avg=1.34\n", "[9196 | 288.72] loss=1.60 avg=1.35\n", "[9197 | 289.99] loss=1.22 avg=1.35\n", "[9198 | 291.30] loss=1.29 avg=1.35\n", "[9199 | 292.58] loss=1.33 avg=1.35\n", "[9200 | 293.86] loss=1.36 avg=1.35\n", "======== SAMPLE 1 ========\n", " et l'arrêt du 19 juillet 1997, statuant en référé, l'arrêt de la cour d'appel de Paris du 19 février 1998 qui échappe à la compétence des juridictions de l'ordre judiciaire pour statuer, selon lesquelles, en son pouvoir de contrôle, il faut l'exercice de la faculté de renouvellement des pouvoirs collectifs, et qu'il s'en présumé produit, le fait pour le tribunal de grande instance de l'ordre judiciaire de recueillir les pouvoirs sur le chantier du magistrat, et le pouvoir de rééquivoque sur la définition des pouvoirs collectifs décidées ainsi que le fait pour le fait de faire droit à une demande, par le syndicat interpellative des immeubles, qui, à l'époque de la procédure collective, avait appris que ce devis avait ouverté une procédure de redressement judiciaire, alors, selon le moyen :1° que, par l'effet interpellatoire de l'arrêt alors qu'il était stipulé que les dispositions de l'article L. 751-8 du Code du travail définissent ce dernier du statut régional de l'association et s'applique aux personnes étrangères pour la période de redressement et non pas aux personnes séparéses ; qu'en déniant à ces personnes les pouvoirs rééquivoques à décomriorement au paiement des dépenses et saisies et des dommages-intérêts en raison du retours présenté par l'association aux membres du conseil des périodes litigieuses, la cour d'appel d'Amiens a M. X... inversé la charge de la preuve et violé les textes susvisés ;2° que selon les articles 751-8 et suivants du Code du travail, tout activité de personnes qui, auprès de laquelle l'association pour le recueilitement du chantier, n'ont pas été déposées entre des délais et renouvellements de procédure collective qui, selon elles, n'ont pas été écoulés, doit s'appliquer, ainsi que l'a violé, la précédente instance enregistrée par l'expert et s'applique aux personnes étrangères pour le recueil des pouvoirs de l'exercice de l'action judiciaire, qui, selon l'arrêt, seraient déposées entre les départements de la République Rhône et de l'Ile-de-France, sans que ces personnes n'ont pas été déposées des assemblées suivant lesquelles il était précédemment déposé et que ces décisions n'avaient prévu qu'un nouveau renouvellement des pouvoirs, ne lui auraient pas interrompu ces deux assemblées suivant lesquelles il n'a pas été déposé ; qu'en affirmant que ces décisions n'avaient pas été signifiées entre les déclarantes et le conseil des pouvoirs collectifs, la cour d'appel a privé sa décision de base légale au regard des textes précités ;Mais attendu, d'abord, qu'ayant relevé que ces décisions se sont soumettent aux requêtes déposées en référé et dont le moyen pris de les autorisation régulièrement déposée par les syndicats interpellataires sur la décision attaquée, et, ensuite, que la demande de la société Agence internationales du Nord de son action en paiement de l'immeuble social avant toute sais\n", "\n", "[9201 | 307.54] loss=1.33 avg=1.35\n", "[9202 | 308.83] loss=1.36 avg=1.35\n", "[9203 | 310.19] loss=1.41 avg=1.35\n", "[9204 | 311.61] loss=1.39 avg=1.35\n", "[9205 | 312.90] loss=1.46 avg=1.35\n", "[9206 | 314.23] loss=1.11 avg=1.35\n", "[9207 | 315.50] loss=1.35 avg=1.35\n", "[9208 | 316.78] loss=1.24 avg=1.34\n", "[9209 | 318.05] loss=1.20 avg=1.34\n", "[9210 | 319.33] loss=1.29 avg=1.34\n", "[9211 | 320.60] loss=1.31 avg=1.34\n", "[9212 | 321.88] loss=1.46 avg=1.34\n", "[9213 | 323.15] loss=1.18 avg=1.34\n", "[9214 | 324.43] loss=1.40 avg=1.34\n", "[9215 | 325.71] loss=1.25 avg=1.34\n", "[9216 | 326.99] loss=1.29 avg=1.34\n", "[9217 | 328.26] loss=1.34 avg=1.34\n", "[9218 | 329.54] loss=1.37 avg=1.34\n", "[9219 | 330.82] loss=1.14 avg=1.34\n", "[9220 | 332.10] loss=1.42 avg=1.34\n", "[9221 | 333.37] loss=1.31 avg=1.34\n", "[9222 | 334.65] loss=1.25 avg=1.34\n", "[9223 | 335.94] loss=1.38 avg=1.34\n", "[9224 | 337.22] loss=1.57 avg=1.34\n", "[9225 | 338.49] loss=1.31 avg=1.34\n", "[9226 | 339.77] loss=1.30 avg=1.34\n", "[9227 | 341.05] loss=1.19 avg=1.34\n", "[9228 | 342.32] loss=1.21 avg=1.34\n", "[9229 | 343.62] loss=1.34 avg=1.34\n", "[9230 | 344.93] loss=1.15 avg=1.33\n", "[9231 | 346.24] loss=1.41 avg=1.34\n", "[9232 | 347.59] loss=1.35 avg=1.34\n", "[9233 | 348.91] loss=1.27 avg=1.33\n", "[9234 | 350.20] loss=1.34 avg=1.34\n", "[9235 | 351.48] loss=1.23 avg=1.33\n", "[9236 | 352.76] loss=1.20 avg=1.33\n", "[9237 | 354.04] loss=1.40 avg=1.33\n", "[9238 | 355.31] loss=1.29 avg=1.33\n", "[9239 | 356.59] loss=1.18 avg=1.33\n", "[9240 | 357.89] loss=1.23 avg=1.33\n", "[9241 | 359.17] loss=1.29 avg=1.33\n", "[9242 | 360.44] loss=1.28 avg=1.33\n", "[9243 | 361.72] loss=1.33 avg=1.33\n", "[9244 | 362.99] loss=1.39 avg=1.33\n", "[9245 | 364.28] loss=1.27 avg=1.33\n", "[9246 | 365.55] loss=1.26 avg=1.33\n", "[9247 | 366.83] loss=1.19 avg=1.33\n", "[9248 | 368.10] loss=1.24 avg=1.33\n", "[9249 | 369.40] loss=1.13 avg=1.32\n", "[9250 | 370.68] loss=1.46 avg=1.32\n", "[9251 | 371.95] loss=1.25 avg=1.32\n", "[9252 | 373.24] loss=1.36 avg=1.32\n", "[9253 | 374.51] loss=1.41 avg=1.33\n", "[9254 | 375.79] loss=1.15 avg=1.32\n", "[9255 | 377.10] loss=1.27 avg=1.32\n", "[9256 | 378.38] loss=1.07 avg=1.32\n", "[9257 | 379.67] loss=1.24 avg=1.32\n", "[9258 | 380.95] loss=1.28 avg=1.32\n", "[9259 | 382.23] loss=1.29 avg=1.32\n", "[9260 | 383.55] loss=1.35 avg=1.32\n", "[9261 | 384.84] loss=1.45 avg=1.32\n", "[9262 | 386.13] loss=1.22 avg=1.32\n", "[9263 | 387.42] loss=1.10 avg=1.32\n", "[9264 | 388.70] loss=1.41 avg=1.32\n", "[9265 | 389.97] loss=1.26 avg=1.32\n", "[9266 | 391.26] loss=1.40 avg=1.32\n", "[9267 | 392.54] loss=1.33 avg=1.32\n", "[9268 | 393.82] loss=1.31 avg=1.32\n", "[9269 | 395.10] loss=1.05 avg=1.32\n", "[9270 | 396.37] loss=1.23 avg=1.31\n", "[9271 | 397.65] loss=1.37 avg=1.32\n", "[9272 | 398.92] loss=1.16 avg=1.31\n", "[9273 | 400.20] loss=1.45 avg=1.31\n", "[9274 | 401.49] loss=1.12 avg=1.31\n", "[9275 | 402.76] loss=1.46 avg=1.31\n", "[9276 | 404.04] loss=1.33 avg=1.31\n", "[9277 | 405.32] loss=1.49 avg=1.32\n", "[9278 | 406.59] loss=1.24 avg=1.32\n", "[9279 | 407.87] loss=1.35 avg=1.32\n", "[9280 | 409.17] loss=1.30 avg=1.32\n", "[9281 | 410.46] loss=1.28 avg=1.32\n", "[9282 | 411.74] loss=1.39 avg=1.32\n", "[9283 | 413.04] loss=1.35 avg=1.32\n", "[9284 | 414.32] loss=1.23 avg=1.32\n", "[9285 | 415.60] loss=1.17 avg=1.31\n", "[9286 | 416.88] loss=1.28 avg=1.31\n", "[9287 | 418.16] loss=1.34 avg=1.31\n", "[9288 | 419.46] loss=1.30 avg=1.31\n", "[9289 | 420.75] loss=1.60 avg=1.32\n", "[9290 | 422.04] loss=1.21 avg=1.32\n", "[9291 | 423.39] loss=1.34 avg=1.32\n", "[9292 | 424.71] loss=1.29 avg=1.32\n", "[9293 | 426.00] loss=1.46 avg=1.32\n", "[9294 | 427.28] loss=1.31 avg=1.32\n", "[9295 | 428.56] loss=1.46 avg=1.32\n", "[9296 | 429.84] loss=1.30 avg=1.32\n", "[9297 | 431.12] loss=1.36 avg=1.32\n", "[9298 | 432.39] loss=1.36 avg=1.32\n", "[9299 | 433.67] loss=1.43 avg=1.32\n", "[9300 | 434.97] loss=1.46 avg=1.32\n", "======== SAMPLE 1 ========\n", "ôts ; qu'en déclarant que l'employeur de la société SIFIM, agissant pour lui ou pour son directeur, n'avait pas démissionné dès lors qu'il avait la possibilité de remettre des droits d'enregistrement à la salariée, en septembre de cinquième année à la date de la demande en paiement de rémunérations, avec rémunérations pour une durée de 6 mois, la cour d'appel a violé les articles L. 122-32-6, R. 122-32-3 et R. 321-1-3 du Code du travail, ensemble la règle précipitative de compétence ;Mais attendu, d'une part, que les documents de l'employeur, laquelle concernant les règles de compétence, laquelle sont limitativement discutées par l'article 455 du nouveau Code de procédure civile, sont inopérants, que lorsque le salarié s'est désisté de son pourvoi, il dispose du pouvoir de la direction de l'employeur dont l'objet est équivoque ; que le moyen légal de l'employeur est irrecevable en son propre égard ainsi qu'il incombant à celui-ci de s'opérer contre lui ;Attendu, d'autre part, que la cour d'appel, qui a constaté que la salariée, dont elle est tenue de procéder à des difficultés échangées sans qu'il ait fait l'objet d'une procédure contraire, ne s'était pas bornée à pratiquer, par voie de conséquence, le contexte qu'elle faisait, de la qualité de salarié à l'occasion de travaux de reprise d'un régime de contrôle du rôle, en a exactement déduit que l'employeur n'avait pas à justifier l'interdiction de faire procéder à des difficultées échangées sans qu'il ait aucun pouvoir de direction équivaut à la formation de contrôle et, par suite, qu'il ait constaté l'existence d'une faute en ce sens, que la mesure provisoire n'était pas prévue et que la société SIFIM a privé le salarié du pouvoir de direction équivaut à la transformation de la contrôle au rôle d'une société économique de la société SIFIM qui en avait le pouvoir de saisir le directeur de la société SIFIM pour agir entre le 5 mai 1995 et le 5 octobre 1995 ;D'où il suit que le même moyen n'est pas fondé ;Par ces motifs :REJETTE le pourvoi forCELE GRIères du 12 janvier 1997. <|endoftext|>\n", "<|startoftext|> Sur le premier moyen :Sur le moyen unique :Attendu, selon les jugements déférés (Aix-en-Provence, 18 février 1997), statuant en référé (Civ. 1, 23 mai 1997) et les productions, que Mme X... a été licenciée le 13 mai 1991 par le médecin du Travail le fait apporté à un salarié pour motif économique son nouveau emploi déterminé pour licenciement définitive, en son nom personnel ;Attendu que le salarié fait grief au tribunal d'appel (Aix-en-Provence, 16 novembre 1997) de l'avoir débouté de son recours tendant à ce que la procédure soit effectuée par le salarié et régulièrement motivée du licenciement et en conséquence de l'adhésion d'un emploi réalisant le personnel non salarié du salarié le 17 mai 1994, en ce qu'il est de tell\n", "\n", "[9301 | 449.31] loss=1.43 avg=1.32\n", "[9302 | 450.59] loss=1.27 avg=1.32\n", "[9303 | 451.86] loss=1.34 avg=1.32\n", "[9304 | 453.14] loss=1.38 avg=1.32\n", "[9305 | 454.42] loss=1.17 avg=1.32\n", "[9306 | 455.70] loss=1.32 avg=1.32\n", "[9307 | 457.05] loss=1.46 avg=1.32\n", "[9308 | 458.34] loss=1.55 avg=1.33\n", "[9309 | 459.64] loss=1.08 avg=1.32\n", "[9310 | 460.93] loss=1.30 avg=1.32\n", "[9311 | 462.22] loss=1.38 avg=1.32\n", "[9312 | 463.50] loss=1.19 avg=1.32\n", "[9313 | 464.78] loss=1.05 avg=1.32\n", "[9314 | 466.06] loss=1.33 avg=1.32\n", "[9315 | 467.35] loss=1.29 avg=1.32\n", "[9316 | 468.63] loss=1.48 avg=1.32\n", "[9317 | 469.91] loss=1.15 avg=1.32\n", "[9318 | 471.19] loss=1.54 avg=1.32\n", "[9319 | 472.47] loss=1.30 avg=1.32\n", "[9320 | 473.75] loss=1.25 avg=1.32\n", "[9321 | 475.04] loss=1.46 avg=1.32\n", "[9322 | 476.34] loss=1.35 avg=1.32\n", "[9323 | 477.62] loss=1.22 avg=1.32\n", "[9324 | 478.92] loss=1.22 avg=1.32\n", "[9325 | 480.20] loss=1.09 avg=1.32\n", "[9326 | 481.48] loss=1.13 avg=1.32\n", "[9327 | 482.76] loss=1.25 avg=1.31\n", "[9328 | 484.04] loss=1.37 avg=1.32\n", "[9329 | 485.32] loss=1.30 avg=1.32\n", "[9330 | 486.60] loss=1.26 avg=1.31\n", "[9331 | 487.87] loss=1.32 avg=1.31\n", "[9332 | 489.16] loss=1.54 avg=1.32\n", "[9333 | 490.44] loss=1.16 avg=1.32\n", "[9334 | 491.72] loss=1.50 avg=1.32\n", "[9335 | 492.99] loss=1.28 avg=1.32\n", "[9336 | 494.29] loss=1.27 avg=1.32\n", "[9337 | 495.58] loss=1.30 avg=1.32\n", "[9338 | 496.88] loss=1.45 avg=1.32\n", "[9339 | 498.17] loss=1.22 avg=1.32\n", "[9340 | 499.46] loss=1.31 avg=1.32\n", "[9341 | 500.76] loss=1.31 avg=1.32\n", "[9342 | 502.04] loss=1.14 avg=1.31\n", "[9343 | 503.31] loss=1.17 avg=1.31\n", "[9344 | 504.59] loss=1.14 avg=1.31\n", "[9345 | 505.87] loss=1.25 avg=1.31\n", "[9346 | 507.15] loss=1.17 avg=1.31\n", "[9347 | 508.44] loss=1.09 avg=1.31\n", "[9348 | 509.74] loss=1.33 avg=1.31\n", "[9349 | 511.02] loss=1.39 avg=1.31\n", "[9350 | 512.32] loss=1.18 avg=1.31\n", "[9351 | 513.60] loss=1.15 avg=1.31\n", "[9352 | 514.88] loss=1.26 avg=1.30\n", "[9353 | 516.16] loss=1.45 avg=1.31\n", "[9354 | 517.44] loss=1.24 avg=1.31\n", "[9355 | 518.71] loss=1.35 avg=1.31\n", "[9356 | 519.99] loss=1.34 avg=1.31\n", "[9357 | 521.27] loss=1.38 avg=1.31\n", "[9358 | 522.57] loss=1.29 avg=1.31\n", "[9359 | 523.85] loss=1.28 avg=1.31\n", "[9360 | 525.13] loss=1.04 avg=1.30\n", "[9361 | 526.40] loss=1.19 avg=1.30\n", "[9362 | 527.68] loss=1.22 avg=1.30\n", "[9363 | 528.95] loss=1.10 avg=1.30\n", "[9364 | 530.23] loss=1.27 avg=1.30\n", "[9365 | 531.52] loss=1.24 avg=1.30\n", "[9366 | 532.81] loss=1.28 avg=1.30\n", "[9367 | 534.17] loss=1.36 avg=1.30\n", "[9368 | 535.46] loss=1.53 avg=1.30\n", "[9369 | 536.75] loss=1.25 avg=1.30\n", "[9370 | 538.03] loss=1.30 avg=1.30\n", "[9371 | 539.31] loss=1.24 avg=1.30\n", "[9372 | 540.59] loss=1.48 avg=1.30\n", "[9373 | 541.92] loss=1.22 avg=1.30\n", "[9374 | 543.20] loss=1.40 avg=1.30\n", "[9375 | 544.49] loss=1.14 avg=1.30\n", "[9376 | 545.77] loss=1.18 avg=1.30\n", "[9377 | 547.04] loss=1.32 avg=1.30\n", "[9378 | 548.32] loss=1.22 avg=1.30\n", "[9379 | 549.59] loss=1.34 avg=1.30\n", "[9380 | 550.87] loss=1.32 avg=1.30\n", "[9381 | 552.14] loss=1.34 avg=1.30\n", "[9382 | 553.42] loss=1.19 avg=1.30\n", "[9383 | 554.70] loss=1.25 avg=1.30\n", "[9384 | 555.99] loss=1.32 avg=1.30\n", "[9385 | 557.27] loss=1.22 avg=1.30\n", "[9386 | 558.54] loss=1.51 avg=1.30\n", "[9387 | 559.82] loss=1.14 avg=1.30\n", "[9388 | 561.09] loss=1.36 avg=1.30\n", "[9389 | 562.37] loss=1.26 avg=1.30\n", "[9390 | 563.64] loss=1.37 avg=1.30\n", "[9391 | 564.92] loss=1.30 avg=1.30\n", "[9392 | 566.20] loss=1.23 avg=1.30\n", "[9393 | 567.49] loss=1.22 avg=1.30\n", "[9394 | 568.79] loss=1.19 avg=1.30\n", "[9395 | 570.08] loss=1.26 avg=1.30\n", "[9396 | 571.37] loss=1.20 avg=1.30\n", "[9397 | 572.66] loss=1.29 avg=1.30\n", "[9398 | 574.00] loss=1.28 avg=1.29\n", "[9399 | 575.33] loss=1.36 avg=1.30\n", "[9400 | 576.61] loss=1.13 avg=1.29\n", "======== SAMPLE 1 ========\n", " national régionale et parce qu'en leur absence d'obtention cette obligation est de la compétence nationale ; que, dès lors, en déclarant néanmoins ladite résolution n'ayant pu être écartée en ce qu'elle a été notifiée à Mme Y... et à son assureur qu'elle n'avait pas signifié cette décision au juge élu, peu important que Mme Y... n'ait pas été responsable au moins dans les trois mois de l'exée et que cette demande ne correspondait pas lors de son décès à son décès, la cour d'appel a violé l'article 1382 du Code civil ;Mais attendu que la cour d'appel a constaté que, par un délibéré du 12 septembre 1999, la Cour de Cassation, se fondant sur les débats, a constaté que, au vu des éléments produits pour établir que le projet de nullité a été signifie en un premier examen, Mme Y... avait été nommée responsable du décès de Mme Z... ; qu'en l'état d'un acte non signifié aux époux X..., l'ordonnance attaquée a constaté que la circonstance que la procédure devait être interrompue, que les dernières échéances sont opposables à la demande d'information de Mme Y..., qu'en la présente espèce, aucune faute nui des époux X..., n'a été reprochée, qu'en tant que commissaire à l'exécution de décisions de l'intéressée, de la SMABTP et en particulière de la SMABTP, la SMABTP a fait appel à la SMABTP, qui avait été condamnée, du chef de nullité du projet, en invoquant la responsabilité de Mme Y... dans l'initiative d'un jugement ; que c'est par une exacte application de l'article 1147 du Code civil, qui déroule souverainement les demandes fondées sur les pratiques défini à l'annexe V du Code civil à raison des risques de la nullité, moyennant un prix global affectant moins cinquante années de ce jugement, que l'exécution du jugement rendu sur le litige a pour effet de conclure au fond de cet arrêt entre Mme Y... et M. et Mme X..., la SMABTP et M. et Mme X... a refusé de faire part de Mme Y... à Mme Y... dans les trois mois de l'exécution de cette ordonnance ; d'où il suit que le moyen qui s'attaque à un événement de nature à permettre l'exécution de l'arrêt attaqué en vertu des articles L. 521-1 et L. 521-2 du Code de la sécurité sociale n'est pas fondé ;Sur le deuxième moyen, pris en ses trois branches :Attendu que Mme Y... fait encore grief à la cour d'appel d'avoir accueilli la demande de constatations et inopposabilités formulée par elle moyennant un délai pour déclarer la responsabilité de M. X..., alors, selon le moyen :1° que le juge ne peut être considéré comme une juridiction de son art qu'en vertu des deuxième et quatrize articles de cette juridiction ; qu'il résulte des dispositions de l'article 1384, alinéa 4, du Code civil qu'un jugement est notifié à l'intéressée s'il est mis à disposition de son ancien commissaire à l'exécution de la procédure, dans le sens de ce dernier texte ; que des motifs surtout par lesquels le Tribunal\n", "\n", "[9401 | 590.27] loss=1.21 avg=1.29\n", "[9402 | 591.55] loss=1.14 avg=1.29\n", "[9403 | 592.82] loss=1.18 avg=1.29\n", "[9404 | 594.10] loss=1.35 avg=1.29\n", "[9405 | 595.38] loss=1.33 avg=1.29\n", "[9406 | 596.65] loss=1.28 avg=1.29\n", "[9407 | 597.93] loss=1.20 avg=1.29\n", "[9408 | 599.21] loss=1.25 avg=1.29\n", "[9409 | 600.49] loss=1.26 avg=1.29\n", "[9410 | 601.77] loss=1.36 avg=1.29\n", "[9411 | 603.05] loss=1.24 avg=1.29\n", "[9412 | 604.33] loss=1.24 avg=1.29\n", "[9413 | 605.60] loss=1.49 avg=1.29\n", "[9414 | 606.93] loss=1.33 avg=1.29\n", "[9415 | 608.32] loss=1.37 avg=1.29\n", "[9416 | 609.62] loss=1.44 avg=1.29\n", "[9417 | 610.97] loss=1.09 avg=1.29\n", "[9418 | 612.28] loss=1.36 avg=1.29\n", "[9419 | 613.56] loss=1.22 avg=1.29\n", "[9420 | 614.84] loss=1.19 avg=1.29\n", "[9421 | 616.11] loss=1.27 avg=1.29\n", "[9422 | 617.39] loss=1.19 avg=1.29\n", "[9423 | 618.67] loss=1.56 avg=1.29\n", "[9424 | 619.94] loss=1.32 avg=1.29\n", "[9425 | 621.23] loss=1.34 avg=1.29\n", "[9426 | 622.51] loss=1.40 avg=1.29\n", "[9427 | 623.79] loss=1.29 avg=1.29\n", "[9428 | 625.06] loss=1.38 avg=1.29\n", "[9429 | 626.34] loss=1.35 avg=1.30\n", "[9430 | 627.61] loss=1.26 avg=1.30\n", "[9431 | 628.89] loss=1.27 avg=1.29\n", "[9432 | 630.17] loss=1.27 avg=1.29\n", "[9433 | 631.44] loss=1.52 avg=1.30\n", "[9434 | 632.73] loss=1.18 avg=1.30\n", "[9435 | 634.01] loss=1.00 avg=1.29\n", "[9436 | 635.29] loss=1.54 avg=1.30\n", "[9437 | 636.57] loss=1.21 avg=1.29\n", "[9438 | 637.84] loss=1.42 avg=1.30\n", "[9439 | 639.12] loss=1.39 avg=1.30\n", "[9440 | 640.43] loss=1.25 avg=1.30\n", "[9441 | 641.71] loss=1.39 avg=1.30\n", "[9442 | 642.99] loss=1.24 avg=1.30\n", "[9443 | 644.29] loss=1.30 avg=1.30\n", "[9444 | 645.57] loss=1.51 avg=1.30\n", "[9445 | 646.86] loss=1.30 avg=1.30\n", "[9446 | 648.15] loss=1.41 avg=1.30\n", "[9447 | 649.44] loss=1.29 avg=1.30\n", "[9448 | 650.73] loss=1.27 avg=1.30\n", "[9449 | 652.00] loss=1.26 avg=1.30\n", "[9450 | 653.28] loss=1.35 avg=1.30\n", "[9451 | 654.58] loss=1.33 avg=1.30\n", "[9452 | 655.87] loss=1.35 avg=1.30\n", "[9453 | 657.14] loss=1.30 avg=1.30\n", "[9454 | 658.42] loss=1.15 avg=1.30\n", "[9455 | 659.69] loss=1.48 avg=1.30\n", "[9456 | 660.97] loss=1.37 avg=1.30\n", "[9457 | 662.25] loss=1.47 avg=1.30\n", "[9458 | 663.53] loss=1.34 avg=1.30\n", "[9459 | 664.80] loss=1.24 avg=1.30\n", "[9460 | 666.12] loss=1.16 avg=1.30\n", "[9461 | 667.39] loss=1.29 avg=1.30\n", "[9462 | 668.67] loss=1.23 avg=1.30\n", "[9463 | 669.94] loss=1.12 avg=1.30\n", "[9464 | 671.22] loss=1.29 avg=1.30\n", "[9465 | 672.49] loss=1.26 avg=1.30\n", "[9466 | 673.80] loss=1.37 avg=1.30\n", "[9467 | 675.08] loss=1.19 avg=1.30\n", "[9468 | 676.37] loss=1.14 avg=1.30\n", "[9469 | 677.66] loss=1.21 avg=1.30\n", "[9470 | 678.93] loss=1.23 avg=1.29\n", "[9471 | 680.21] loss=1.23 avg=1.29\n", "[9472 | 681.48] loss=1.27 avg=1.29\n", "[9473 | 682.77] loss=1.19 avg=1.29\n", "[9474 | 684.06] loss=1.45 avg=1.29\n", "[9475 | 685.36] loss=1.34 avg=1.29\n", "[9476 | 686.64] loss=1.20 avg=1.29\n", "[9477 | 687.99] loss=1.40 avg=1.29\n", "[9478 | 689.29] loss=1.33 avg=1.30\n", "[9479 | 690.57] loss=1.19 avg=1.29\n", "[9480 | 691.85] loss=1.28 avg=1.29\n", "[9481 | 693.13] loss=1.05 avg=1.29\n", "[9482 | 694.40] loss=1.08 avg=1.29\n", "[9483 | 695.68] loss=1.26 avg=1.29\n", "[9484 | 696.95] loss=1.19 avg=1.29\n", "[9485 | 698.24] loss=1.28 avg=1.29\n", "[9486 | 699.52] loss=1.44 avg=1.29\n", "[9487 | 700.80] loss=1.11 avg=1.29\n", "[9488 | 702.08] loss=1.44 avg=1.29\n", "[9489 | 703.35] loss=1.30 avg=1.29\n", "[9490 | 704.63] loss=1.44 avg=1.29\n", "[9491 | 705.93] loss=1.38 avg=1.29\n", "[9492 | 707.21] loss=1.55 avg=1.29\n", "[9493 | 708.49] loss=1.21 avg=1.29\n", "[9494 | 709.78] loss=1.21 avg=1.29\n", "[9495 | 711.05] loss=1.25 avg=1.29\n", "[9496 | 712.33] loss=1.28 avg=1.29\n", "[9497 | 713.60] loss=1.10 avg=1.29\n", "[9498 | 714.88] loss=1.15 avg=1.29\n", "[9499 | 716.15] loss=1.36 avg=1.29\n", "[9500 | 717.43] loss=1.50 avg=1.29\n", "======== SAMPLE 1 ========\n", ", et la commission de recours amiable, la cour d'appel a violé les textes susvisés ;</p><p>PAR CES MOTIFS :</p><p>CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 23 septembre 1995, entre les parties, par la cour d'appel d'Orléans ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Lyon ;</p><p>Condamne la société Axa assurances assurances ;</p><p>Vu l'article 700 du nouveau Code de procédure civile, rejette la demande de la société Axa assurances ;</p><p>Dit que sur les diligences du procureur général près la Cour de Cassation, le présent arrêt sera transmis pour être transcrit en marge ou à la suite de l'arrêt cassé ;</p><p>Ainsi fait et jugé par la Cour de Cassation, Orléans, et prononcé par le président en son audience publique du bord duquel un tranches n° 816-03, le même arrêt sera déféré ;</p><p>Vu l'article 453 du nouveau Code de procédure civile, rejette la demande de la société Axa assurances ;</p><p>Dit que sur les diligences du procureur général près la Cour de Cassation, le présent arrêt sera transmis pour être transcrit en marge ou à la suite de l'arrêt cassé ;</p><p>Ainsi fait et jugé par la Cour de Cassation, Metz, Nance, et prononcé par le président en son audience publique du bord duquel un tranches n° 842-27, les mêmes fins doivent être transmis pour être transcrits et que le pourvoi en cassation est donc recevable ;</p><p>Et, sur la demande de M. X..., reçue le 11 mai 1997 :</p><p>Sur les trois moyens, pris en leurs premières branches, en leurs trois branches, en ce qu'il y ait fait insuffisance de motifs, le dernier sans indemnitaires ;</p><p>Vu l'article 700 du nouveau Code de procédure civile, rejette la demande de M. X... ;</p><p>Dit que sur les diligences du Procureur général près la Cour de Cassation, le présent arrêt sera transmis pour être transcrit en marge ou à la suite de l'arrêt cassé qui n'a pas été notifié ;</p><p>Vu l'article 700 du nouveau Code de procédure civile, rejette la demande de M. X... ;</p><p>Vu l'article 700 du nouveau Code de procédure civile, même lorsque le jugement de rectification du jugement d'ouverture ou de liquidation judiciaire est prononcé sans qu'il y ait lieu de statuer ;</p><p>Sur la cassation du jugement du 9 novembre 1995 :</p><p>Sur la compétence du tribunal de commerce ;</p><p>Vu l'article 5 du Code civil, statuant en matière de réorganisation des assemblées générales des personnes vivant un ensemble routier, telle que même ci-après \" ;</p><p>Vu l'article 5 de la Commission nationale des amis de la Communauté européenne ;</p><p>Vu l'article 562 du nouveau Code de procédure civile ;</p><p>Vu la loi des 16-24 août 1790 et le décret du 16 fructidor an III ;</p\n", "\n", "[9501 | 730.70] loss=1.26 avg=1.29\n", "[9502 | 732.04] loss=1.61 avg=1.29\n", "[9503 | 733.33] loss=1.40 avg=1.30\n", "[9504 | 734.63] loss=1.17 avg=1.29\n", "[9505 | 735.92] loss=1.26 avg=1.29\n", "[9506 | 737.20] loss=1.37 avg=1.29\n", "[9507 | 738.50] loss=1.05 avg=1.29\n", "[9508 | 739.79] loss=1.41 avg=1.29\n", "[9509 | 741.06] loss=1.28 avg=1.29\n", "[9510 | 742.36] loss=1.19 avg=1.29\n", "[9511 | 743.64] loss=1.23 avg=1.29\n", "[9512 | 744.92] loss=1.18 avg=1.29\n", "[9513 | 746.20] loss=1.06 avg=1.29\n", "[9514 | 747.47] loss=1.19 avg=1.29\n", "[9515 | 748.75] loss=1.23 avg=1.29\n", "[9516 | 750.03] loss=1.17 avg=1.29\n", "[9517 | 751.30] loss=1.40 avg=1.29\n", "[9518 | 752.58] loss=1.18 avg=1.29\n", "[9519 | 753.88] loss=1.29 avg=1.29\n", "[9520 | 755.15] loss=1.41 avg=1.29\n", "[9521 | 756.43] loss=1.28 avg=1.29\n", "[9522 | 757.70] loss=1.16 avg=1.29\n", "[9523 | 758.98] loss=1.32 avg=1.29\n", "[9524 | 760.26] loss=1.37 avg=1.29\n", "[9525 | 761.53] loss=1.15 avg=1.29\n", "[9526 | 762.81] loss=1.33 avg=1.29\n", "[9527 | 764.09] loss=1.17 avg=1.28\n", "[9528 | 765.38] loss=1.09 avg=1.28\n", "[9529 | 766.66] loss=1.21 avg=1.28\n", "[9530 | 767.95] loss=1.40 avg=1.28\n", "[9531 | 769.24] loss=1.45 avg=1.28\n", "[9532 | 770.53] loss=1.30 avg=1.28\n", "[9533 | 771.87] loss=1.17 avg=1.28\n", "[9534 | 773.25] loss=1.25 avg=1.28\n", "[9535 | 774.54] loss=1.30 avg=1.28\n", "[9536 | 775.83] loss=1.27 avg=1.28\n", "[9537 | 777.11] loss=1.27 avg=1.28\n", "[9538 | 778.39] loss=1.40 avg=1.28\n", "[9539 | 779.67] loss=1.34 avg=1.28\n", "[9540 | 780.94] loss=1.35 avg=1.29\n", "[9541 | 782.22] loss=1.16 avg=1.28\n", "[9542 | 783.50] loss=1.32 avg=1.28\n", "[9543 | 784.77] loss=1.14 avg=1.28\n", "[9544 | 786.05] loss=1.24 avg=1.28\n", "[9545 | 787.35] loss=1.49 avg=1.28\n", "[9546 | 788.62] loss=1.25 avg=1.28\n", "[9547 | 789.90] loss=1.14 avg=1.28\n", "[9548 | 791.18] loss=1.18 avg=1.28\n", "[9549 | 792.46] loss=1.26 avg=1.28\n", "[9550 | 793.75] loss=1.21 avg=1.28\n", "[9551 | 795.02] loss=1.45 avg=1.28\n", "[9552 | 796.30] loss=1.43 avg=1.28\n", "[9553 | 797.60] loss=1.20 avg=1.28\n", "[9554 | 798.89] loss=1.32 avg=1.28\n", "[9555 | 800.16] loss=1.35 avg=1.28\n", "[9556 | 801.44] loss=1.36 avg=1.29\n", "[9557 | 802.72] loss=1.32 avg=1.29\n", "[9558 | 804.00] loss=1.31 avg=1.29\n", "[9559 | 805.38] loss=1.28 avg=1.29\n", "[9560 | 806.73] loss=1.11 avg=1.28\n", "[9561 | 808.03] loss=1.23 avg=1.28\n", "[9562 | 809.38] loss=1.22 avg=1.28\n", "[9563 | 810.67] loss=1.21 avg=1.28\n", "[9564 | 811.96] loss=1.42 avg=1.28\n", "[9565 | 813.24] loss=1.42 avg=1.28\n", "[9566 | 814.52] loss=1.34 avg=1.29\n", "[9567 | 815.79] loss=1.40 avg=1.29\n", "[9568 | 817.07] loss=1.21 avg=1.29\n", "[9569 | 818.34] loss=1.29 avg=1.29\n", "[9570 | 819.64] loss=1.51 avg=1.29\n", "[9571 | 820.92] loss=1.12 avg=1.29\n", "[9572 | 822.19] loss=1.44 avg=1.29\n", "[9573 | 823.47] loss=1.29 avg=1.29\n", "[9574 | 824.75] loss=1.18 avg=1.29\n", "[9575 | 826.02] loss=1.38 avg=1.29\n", "[9576 | 827.30] loss=1.47 avg=1.29\n", "[9577 | 828.57] loss=1.06 avg=1.29\n", "[9578 | 829.85] loss=1.17 avg=1.29\n", "[9579 | 831.15] loss=1.32 avg=1.29\n", "[9580 | 832.43] loss=1.30 avg=1.29\n", "[9581 | 833.71] loss=1.03 avg=1.28\n", "[9582 | 834.99] loss=1.24 avg=1.28\n", "[9583 | 836.26] loss=1.22 avg=1.28\n", "[9584 | 837.54] loss=1.27 avg=1.28\n", "[9585 | 838.84] loss=1.17 avg=1.28\n", "[9586 | 840.12] loss=1.18 avg=1.28\n", "[9587 | 841.42] loss=1.41 avg=1.28\n", "[9588 | 842.69] loss=1.52 avg=1.28\n", "[9589 | 843.97] loss=1.18 avg=1.28\n", "[9590 | 845.25] loss=1.38 avg=1.28\n", "[9591 | 846.52] loss=1.28 avg=1.28\n", "[9592 | 847.80] loss=1.13 avg=1.28\n", "[9593 | 849.07] loss=1.44 avg=1.28\n", "[9594 | 850.35] loss=1.24 avg=1.28\n", "[9595 | 851.62] loss=1.36 avg=1.28\n", "[9596 | 852.91] loss=1.25 avg=1.28\n", "[9597 | 854.19] loss=1.24 avg=1.28\n", "[9598 | 855.47] loss=1.02 avg=1.28\n", "[9599 | 856.75] loss=1.31 avg=1.28\n", "[9600 | 858.02] loss=1.38 avg=1.28\n", "======== SAMPLE 1 ========\n", " télécopie ;Attendu, cependant, qu'il appartient au juge des référés de statuer sur la litispendance de l'identité de chacun des tiers auquel appartient un établissement auquel une catégorie de référentis est discutée ;Qu'en statuant comme elle l'a fait, alors qu'elle avait constaté à bon droit que le litispendance de l'identité du ministère public présente aux fins d'établissement de l'établissement de cette société dans les formes relatives au droit commun de la catégorie des référés constituait, sous réserve de l'acceptation d'une mesure de classement du compte de l'établissement, à savoir un certificat d'aptitude à l'initiative et à sa fille de tout travailleur maladie, la cour d'appel a violé le texte susvisé ;Par ces motifs, et sans qu'il soit besoin de statuer sur le second moyen :CASSE ET ANNULE, mais seulement en ce qu'il a débouté M. Bernard X... de son recours en indemnisation dirigé contre l'arrêt du 19 mars 1998, l'arrêt rendu le 19 septembre 2002, entre les parties, par la cour d'appel de Metz ; remet, en conséquence, quant à ce, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Colmar. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique, pris en sa deuxième branche :Vu l'article L. 242-1 du Code de l'urbanisme et l'article 3, alinéa 8, de la loi du 29 juillet 1881 ;Attendu que, devant le tribunal d'instance de Grenoble (tribunal de grande instance), ont déclare M. X..., ès-qualifiant d'établissement pour lesquels sous réserve de percevoir et prévoir la valeur des lots \" leur affecte \" dépendant de l'Etat \" à la société, notamment à la société à responsabilité limitée GAN France (société GAN), à l'exclusion de celle-ci et au président du tribunal d'instance pour des motifs suffisants avec la répartition de ses besoins et des droits des autres moyens, et après avoir rejeté une demande de découverts et inscrites sur ces besoins auprès de celle-ci à la société GAN ;Attendu que pour déclarer M. X... déchue des droits de Mmes Z... et Y..., M. Philippe et Mme Pierre X..., l'exposé de ses besoins d'intervention à l'instance, ayant fait l'objet d'un procès-verbal incidentuel n° ETA le 17 mars 1997 dont les élèves ont fait la désignation d'inventaire par requête enregistré à la débitrice de Laffot, de ses deux enfants : Mme Y... et M. Philippe X..., soutenu devant le juge de l'exécution des référés, ont assigné les services des Impôts en réparation de leur préjudice sur le fondement de l'article 1167 du Code civil ;Attendu qu'en statuant ainsi, alors que les consorts X..., qui avaient, après observations présentaient uniquement la signature mais la réparation de leurs préjudices, de la part de Mmes Z... et Y..., et de la part de Mme Y..., étaient en droit d'attendre leurs besoins sans autorisation à prendre en compte, les opérations\n", "\n", "[9601 | 871.83] loss=1.24 avg=1.28\n", "[9602 | 873.11] loss=1.34 avg=1.28\n", "[9603 | 874.44] loss=1.32 avg=1.28\n", "[9604 | 875.75] loss=1.51 avg=1.29\n", "[9605 | 877.04] loss=1.30 avg=1.29\n", "[9606 | 878.33] loss=1.35 avg=1.29\n", "[9607 | 879.61] loss=1.30 avg=1.29\n", "[9608 | 880.89] loss=1.30 avg=1.29\n", "[9609 | 882.17] loss=1.27 avg=1.29\n", "[9610 | 883.45] loss=1.46 avg=1.29\n", "[9611 | 884.73] loss=1.27 avg=1.29\n", "[9612 | 886.03] loss=1.24 avg=1.29\n", "[9613 | 887.30] loss=1.31 avg=1.29\n", "[9614 | 888.58] loss=1.11 avg=1.29\n", "[9615 | 889.85] loss=1.26 avg=1.29\n", "[9616 | 891.13] loss=1.34 avg=1.29\n", "[9617 | 892.41] loss=1.62 avg=1.29\n", "[9618 | 893.68] loss=1.33 avg=1.29\n", "[9619 | 894.96] loss=1.23 avg=1.29\n", "[9620 | 896.25] loss=1.33 avg=1.29\n", "[9621 | 897.53] loss=1.23 avg=1.29\n", "[9622 | 898.80] loss=1.20 avg=1.29\n", "[9623 | 900.08] loss=1.31 avg=1.29\n", "[9624 | 901.36] loss=1.22 avg=1.29\n", "[9625 | 902.63] loss=1.39 avg=1.29\n", "[9626 | 903.95] loss=1.47 avg=1.29\n", "[9627 | 905.23] loss=1.31 avg=1.29\n", "[9628 | 906.51] loss=1.22 avg=1.29\n", "[9629 | 907.81] loss=1.30 avg=1.29\n", "[9630 | 909.09] loss=1.18 avg=1.29\n", "[9631 | 910.36] loss=1.37 avg=1.29\n", "[9632 | 911.64] loss=1.17 avg=1.29\n", "[9633 | 912.91] loss=1.28 avg=1.29\n", "[9634 | 914.19] loss=1.27 avg=1.29\n", "[9635 | 915.47] loss=1.19 avg=1.29\n", "[9636 | 916.74] loss=1.21 avg=1.29\n", "[9637 | 918.03] loss=1.30 avg=1.29\n", "[9638 | 919.32] loss=1.54 avg=1.29\n", "[9639 | 920.60] loss=1.27 avg=1.29\n", "[9640 | 921.88] loss=1.26 avg=1.29\n", "[9641 | 923.16] loss=1.45 avg=1.29\n", "[9642 | 924.43] loss=1.40 avg=1.29\n", "[9643 | 925.71] loss=1.29 avg=1.29\n", "[9644 | 926.98] loss=1.22 avg=1.29\n", "[9645 | 928.26] loss=1.07 avg=1.29\n", "[9646 | 929.56] loss=1.36 avg=1.29\n", "[9647 | 930.84] loss=1.52 avg=1.29\n", "[9648 | 932.11] loss=1.21 avg=1.29\n", "[9649 | 933.39] loss=1.18 avg=1.29\n", "[9650 | 934.67] loss=1.33 avg=1.29\n", "[9651 | 935.94] loss=1.23 avg=1.29\n", "[9652 | 937.25] loss=1.43 avg=1.29\n", "[9653 | 938.52] loss=1.27 avg=1.29\n", "[9654 | 939.80] loss=1.21 avg=1.29\n", "[9655 | 941.14] loss=1.21 avg=1.29\n", "[9656 | 942.43] loss=1.07 avg=1.29\n", "[9657 | 943.73] loss=1.22 avg=1.29\n", "[9658 | 945.02] loss=1.00 avg=1.28\n", "[9659 | 946.31] loss=1.15 avg=1.28\n", "[9660 | 947.59] loss=1.23 avg=1.28\n", "[9661 | 948.87] loss=1.27 avg=1.28\n", "[9662 | 950.14] loss=1.14 avg=1.28\n", "[9663 | 951.44] loss=1.19 avg=1.28\n", "[9664 | 952.72] loss=1.38 avg=1.28\n", "[9665 | 954.00] loss=1.34 avg=1.28\n", "[9666 | 955.28] loss=1.30 avg=1.28\n", "[9667 | 956.55] loss=1.26 avg=1.28\n", "[9668 | 957.83] loss=1.38 avg=1.28\n", "[9669 | 959.11] loss=1.28 avg=1.28\n", "[9670 | 960.39] loss=1.18 avg=1.28\n", "[9671 | 961.67] loss=1.17 avg=1.28\n", "[9672 | 962.96] loss=1.34 avg=1.28\n", "[9673 | 964.24] loss=1.21 avg=1.28\n", "[9674 | 965.52] loss=1.28 avg=1.28\n", "[9675 | 966.80] loss=1.21 avg=1.28\n", "[9676 | 968.08] loss=1.22 avg=1.28\n", "[9677 | 969.37] loss=1.24 avg=1.28\n", "[9678 | 970.66] loss=1.21 avg=1.28\n", "[9679 | 971.94] loss=1.24 avg=1.28\n", "[9680 | 973.22] loss=1.17 avg=1.28\n", "[9681 | 974.51] loss=1.23 avg=1.27\n", "[9682 | 975.79] loss=1.33 avg=1.28\n", "[9683 | 977.07] loss=1.22 avg=1.27\n", "[9684 | 978.34] loss=1.32 avg=1.28\n", "[9685 | 979.62] loss=1.02 avg=1.27\n", "[9686 | 980.89] loss=1.23 avg=1.27\n", "[9687 | 982.17] loss=1.33 avg=1.27\n", "[9688 | 983.45] loss=1.12 avg=1.27\n", "[9689 | 984.74] loss=1.20 avg=1.27\n", "[9690 | 986.02] loss=1.52 avg=1.27\n", "[9691 | 987.30] loss=1.04 avg=1.27\n", "[9692 | 988.58] loss=1.32 avg=1.27\n", "[9693 | 989.86] loss=1.24 avg=1.27\n", "[9694 | 991.13] loss=1.20 avg=1.27\n", "[9695 | 992.41] loss=1.29 avg=1.27\n", "[9696 | 993.69] loss=1.21 avg=1.27\n", "[9697 | 994.97] loss=1.07 avg=1.27\n", "[9698 | 996.26] loss=1.18 avg=1.27\n", "[9699 | 997.54] loss=1.08 avg=1.27\n", "[9700 | 998.82] loss=1.36 avg=1.27\n", "======== SAMPLE 1 ========\n", "t, avec l'accord de la société, réalisée au titre de l'autorisation de l'assureur, de telle sorte que l'obligation d'exécution de la garantie à l'échéance est déterminée, à titre provisoire et indissociable, la cour d'appel a violé, par fausse interprétation, l'article 42 de la loi du 25 janvier 1985 ;Mais attendu que la cour d'appel retient, aux termes du jugement du 29 mars 2001, que la mise en redressement puis en liquidation judiciaires du compte courant des décisions déclarées ne peut pas être écartée ; que la cour d'appel a relevé qu'une notification du jugement d'ouverture entre les mains du juge judiciaire de la mise en redressement judiciaire du 22 novembre 2001 avait été faite et que le représentant du débiteur d'avance, qui a accepté de reprendre le travail, ne pouvait se prévaloir d'un détournement à propos sur la mise en oeuvre de la procédure de redressement judiciaire, ainsi que de la répétition de l'engagement du syndic ; que par ce motif de pur droit, la décision, abstraction faite des motifs surabondants critiqués par les autres branches du moyen, se trouve légalement justifié ;D'où il suit que le moyen n'est pas fondé ;Mais sur le cinquième moyen :Vu l'article 2038 du Code civil, ensemble l'article L. 136-4 du Code des assurances ;Attendu que pour faire droit à la demande des époux X... du mandat de receval pour que la cour d'appel soit annulée, a réclamé à ces deux époux la garantie ;Attendu que pour rejeter l'appel du jugement du 29 mars 2001, l'arrêt rendu en dernier ressort, énonçant que le représentant du débiteur ne pouvait connaître de la décision déclarant irrecevable le plan de rachat de l'entreprise et ordonner la mise en liquidation judiciaire du représentant pour effectuer le bénéfice de l'intéressé ;Qu'en statuant ainsi, sans caractériser les faits relevés, en raison d'une insuffisance de motifs et notamment de leur impossibilité de résultat et de l'impossibilité de porter atteinte à la consciption de responsabilité, la cour d'appel n'a pas donné de base légale à sa décision ;PAR CES MOTIFS :CASSE ET ANNULE, mais seulement en ce qu'il a rejeté le recours du représentant du juge judiciaire de la mise en redressement et dit que l'un ne pouvait soutenir leur insuffisance, l'arrêt rendu le 13 octobre 2001, entre les deux parties, par la cour d'appel de Caen ; remet, en conséquence, quant à ce, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Rennes, autrement composée ;Laisse à chaque partie la charge de ses propres dépens, ensemble l'article 700 du nouveau Code de procédure civile, le respect du débat du procès devant la Cour de Cassation, devenu l'arrêt. <|endoftext|>\n", "<|startoftext|> AU NOM DU PEUPLE FRANCAISLA COUR DE CASSATION, DEUXIEME CHAMBRE CIVILE, a rendu l'arrêt suivant :Sur le moyen unique :Attendu que le 12 janvier 1994, Mme\n", "\n", "[9701 | 1012.71] loss=1.41 avg=1.27\n", "[9702 | 1014.00] loss=1.45 avg=1.27\n", "[9703 | 1015.29] loss=1.42 avg=1.27\n", "[9704 | 1016.58] loss=1.24 avg=1.27\n", "[9705 | 1017.94] loss=1.28 avg=1.27\n", "[9706 | 1019.25] loss=1.07 avg=1.27\n", "[9707 | 1020.52] loss=1.29 avg=1.27\n", "[9708 | 1021.80] loss=1.40 avg=1.27\n", "[9709 | 1023.08] loss=1.28 avg=1.27\n", "[9710 | 1024.36] loss=1.31 avg=1.27\n", "[9711 | 1025.63] loss=1.22 avg=1.27\n", "[9712 | 1026.91] loss=1.33 avg=1.27\n", "[9713 | 1028.19] loss=1.42 avg=1.27\n", "[9714 | 1029.48] loss=1.30 avg=1.27\n", "[9715 | 1030.76] loss=1.36 avg=1.27\n", "[9716 | 1032.03] loss=1.25 avg=1.27\n", "[9717 | 1033.31] loss=1.18 avg=1.27\n", "[9718 | 1034.58] loss=1.24 avg=1.27\n", "[9719 | 1035.89] loss=1.37 avg=1.27\n", "[9720 | 1037.17] loss=1.21 avg=1.27\n", "[9721 | 1038.45] loss=1.21 avg=1.27\n", "[9722 | 1039.74] loss=1.12 avg=1.27\n", "[9723 | 1041.02] loss=1.29 avg=1.27\n", "[9724 | 1042.29] loss=1.23 avg=1.27\n", "[9725 | 1043.57] loss=1.27 avg=1.27\n", "[9726 | 1044.85] loss=1.46 avg=1.27\n", "[9727 | 1046.12] loss=1.31 avg=1.27\n", "[9728 | 1047.40] loss=1.25 avg=1.27\n", "[9729 | 1048.68] loss=1.29 avg=1.27\n", "[9730 | 1049.95] loss=1.27 avg=1.27\n", "[9731 | 1051.25] loss=1.21 avg=1.27\n", "[9732 | 1052.52] loss=1.18 avg=1.27\n", "[9733 | 1053.81] loss=1.30 avg=1.27\n", "[9734 | 1055.08] loss=1.34 avg=1.27\n", "[9735 | 1056.36] loss=1.28 avg=1.27\n", "[9736 | 1057.64] loss=1.18 avg=1.27\n", "[9737 | 1058.91] loss=1.21 avg=1.27\n", "[9738 | 1060.19] loss=1.22 avg=1.27\n", "[9739 | 1061.48] loss=1.17 avg=1.27\n", "[9740 | 1062.76] loss=1.31 avg=1.27\n", "[9741 | 1064.04] loss=1.40 avg=1.27\n", "[9742 | 1065.32] loss=1.38 avg=1.27\n", "[9743 | 1066.60] loss=1.10 avg=1.27\n", "[9744 | 1067.88] loss=1.36 avg=1.27\n", "[9745 | 1069.19] loss=1.32 avg=1.27\n", "[9746 | 1070.47] loss=1.28 avg=1.27\n", "[9747 | 1071.74] loss=1.29 avg=1.27\n", "[9748 | 1073.05] loss=1.18 avg=1.27\n", "[9749 | 1074.32] loss=1.13 avg=1.27\n", "[9750 | 1075.59] loss=1.27 avg=1.27\n", "[9751 | 1076.87] loss=1.15 avg=1.27\n", "[9752 | 1078.15] loss=1.11 avg=1.27\n", "[9753 | 1079.42] loss=1.27 avg=1.27\n", "[9754 | 1080.71] loss=1.28 avg=1.27\n", "[9755 | 1082.00] loss=1.35 avg=1.27\n", "[9756 | 1083.34] loss=1.29 avg=1.27\n", "[9757 | 1084.64] loss=1.41 avg=1.27\n", "[9758 | 1085.93] loss=1.08 avg=1.27\n", "[9759 | 1087.22] loss=1.28 avg=1.27\n", "[9760 | 1088.49] loss=1.20 avg=1.27\n", "[9761 | 1089.77] loss=1.38 avg=1.27\n", "[9762 | 1091.04] loss=1.26 avg=1.27\n", "[9763 | 1092.32] loss=1.23 avg=1.27\n", "[9764 | 1093.60] loss=1.19 avg=1.27\n", "[9765 | 1094.89] loss=1.45 avg=1.27\n", "[9766 | 1096.16] loss=1.24 avg=1.27\n", "[9767 | 1097.44] loss=1.29 avg=1.27\n", "[9768 | 1098.72] loss=1.29 avg=1.27\n", "[9769 | 1099.99] loss=1.22 avg=1.27\n", "[9770 | 1101.27] loss=1.43 avg=1.27\n", "[9771 | 1102.57] loss=1.14 avg=1.27\n", "[9772 | 1103.85] loss=1.24 avg=1.27\n", "[9773 | 1105.13] loss=1.19 avg=1.27\n", "[9774 | 1106.43] loss=1.10 avg=1.27\n", "[9775 | 1107.70] loss=1.34 avg=1.27\n", "[9776 | 1108.98] loss=1.33 avg=1.27\n", "[9777 | 1110.25] loss=1.35 avg=1.27\n", "[9778 | 1111.53] loss=1.07 avg=1.27\n", "[9779 | 1112.81] loss=1.35 avg=1.27\n", "[9780 | 1114.09] loss=1.24 avg=1.27\n", "[9781 | 1115.36] loss=1.32 avg=1.27\n", "[9782 | 1116.65] loss=1.19 avg=1.27\n", "[9783 | 1117.93] loss=1.27 avg=1.27\n", "[9784 | 1119.20] loss=1.16 avg=1.27\n", "[9785 | 1120.48] loss=1.32 avg=1.27\n", "[9786 | 1121.75] loss=1.12 avg=1.26\n", "[9787 | 1123.03] loss=1.28 avg=1.26\n", "[9788 | 1124.31] loss=1.25 avg=1.26\n", "[9789 | 1125.58] loss=1.12 avg=1.26\n", "[9790 | 1126.86] loss=1.08 avg=1.26\n", "[9791 | 1128.16] loss=1.27 avg=1.26\n", "[9792 | 1129.43] loss=1.28 avg=1.26\n", "[9793 | 1130.71] loss=1.29 avg=1.26\n", "[9794 | 1131.99] loss=1.14 avg=1.26\n", "[9795 | 1133.28] loss=1.38 avg=1.26\n", "[9796 | 1134.58] loss=1.38 avg=1.26\n", "[9797 | 1135.87] loss=1.15 avg=1.26\n", "[9798 | 1137.15] loss=1.37 avg=1.26\n", "[9799 | 1138.43] loss=1.47 avg=1.26\n", "[9800 | 1139.71] loss=1.33 avg=1.27\n", "======== SAMPLE 1 ========\n", " consté une convention dérivant de la qualification de sécurité et des conventions d'assurance, à laquelle il est interdit d'une clause d'application destinée à établir que les obligations nées pendant deux ans étaient d'une durée égale à une certaine indépendance ; que leur réquisition d'une clause selon lequel il s'opère et s'analyser en une condition d'exercice des fonctions d'organisme prévue par ledit contrat, est sans effet sur la solution du litige ; d'où il suit que la cour d'appel a violé les textes susvisés ;Mais attendu que le principe de la contradiction soulevé en violation de ce principe n'ayant pour effet de faire obstacle à la poursuite du contrat, la loi doit faire supporter à son client un droit exclusif de contrat à durée déterminée, dès lors qu'il est limité, à la date des clauses exigées par le texte, par une convention d'assurance ou par un contrat d'assurance, d'une durée égale à la cotisation administrative de réparation du préjudice par celui de la sécurité sociale qu'il appartient à l'emprunteur de définir ; que le moyen n'est pas fondé ;PAR CES MOTIFS :REJETTE le pourvoi. <|endoftext|>\n", "<|startoftext|> Attendu, selon l'arrêt attaqué (Rennes, 8 février 1996), que la Caisse d'épargne et d'amélioration des petites et médicaments de Villefranche-et-Loire (CAVTP), garantissant la précarité de ses réserves et d'une indemnité d'occupation, a donné suite à un accident de trajet ayant pris fin à cet accident en 1973 ; que, la Caisse et l'entreprise Caution ont appelé au greffe de l'instruction en vue de rechercher la preuve de l'apparent du contrôle dont auprès la Caution ;Sur le premier moyen :Sur le second moyen :Attendu que la Caisse fait grief à l'arrêt de l'avoir déclarée irrecevable en son appel, en ce qu'aucun fait n'a produit de droit pour le salarié, alors, selon le moyen :1° que la Caisse a droit au paiement de sa créance qu'elle n'avait acquittée que d'un montant mensuel pris en charge par la Caisse d'épargne et d'amélioration et qu'il n'avait pas sollicité l'acquisition de la charge de la présomption d'appauvriement, mais à un franc au cours des trois années précédant elle, en violation de l'article 557 du nouveau Code de procédure civile ;2° que le seul fait pour la Caisse d'épargne et d'amélioration à laquelle vient la Caisse d'épargne et d'amélioration garantisserait cette créance est insuffisant pour avoir produit de droit de la somme qu'elle demande d'acquittir ; qu'en conséquence la Caisse d'épargne et d'amélioration garantissait une créance de 2 800 francs qui est constituée non seulement jusqu'à la fin de l'arrêt que la présomption d'appauvriement mais aussi de la période d'acquittir avait été contestée, mais aussi de celui, en dernier lieu, au motif que l'autre créance n'est pas recevable ;3° que la cour d'appel s'est fondée sur une contestation relative à le paiement prétendu de la créance de 2 900 francs, et que la Caisse d'épargne et d'amélior\n", "\n", "[9801 | 1152.95] loss=1.04 avg=1.26\n", "[9802 | 1154.24] loss=1.37 avg=1.26\n", "[9803 | 1155.53] loss=1.41 avg=1.27\n", "[9804 | 1156.82] loss=1.22 avg=1.27\n", "[9805 | 1158.11] loss=1.13 avg=1.26\n", "[9806 | 1159.39] loss=1.13 avg=1.26\n", "[9807 | 1160.68] loss=1.34 avg=1.26\n", "[9808 | 1161.96] loss=1.14 avg=1.26\n", "[9809 | 1163.23] loss=1.36 avg=1.26\n", "[9810 | 1164.51] loss=1.40 avg=1.26\n", "[9811 | 1165.79] loss=1.39 avg=1.27\n", "[9812 | 1167.07] loss=1.21 avg=1.27\n", "[9813 | 1168.38] loss=1.69 avg=1.27\n", "[9814 | 1169.66] loss=1.37 avg=1.27\n", "[9815 | 1170.93] loss=1.32 avg=1.27\n", "[9816 | 1172.22] loss=1.14 avg=1.27\n", "[9817 | 1173.51] loss=1.13 avg=1.27\n", "[9818 | 1174.79] loss=1.35 avg=1.27\n", "[9819 | 1176.06] loss=1.16 avg=1.27\n", "[9820 | 1177.34] loss=1.23 avg=1.27\n", "[9821 | 1178.62] loss=1.24 avg=1.27\n", "[9822 | 1179.89] loss=1.36 avg=1.27\n", "[9823 | 1181.17] loss=1.24 avg=1.27\n", "[9824 | 1182.46] loss=1.36 avg=1.27\n", "[9825 | 1183.75] loss=1.20 avg=1.27\n", "[9826 | 1185.03] loss=1.17 avg=1.27\n", "[9827 | 1186.31] loss=1.31 avg=1.27\n", "[9828 | 1187.58] loss=1.22 avg=1.27\n", "[9829 | 1188.86] loss=1.30 avg=1.27\n", "[9830 | 1190.13] loss=1.27 avg=1.27\n", "[9831 | 1191.41] loss=1.25 avg=1.27\n", "[9832 | 1192.68] loss=1.15 avg=1.27\n", "[9833 | 1193.97] loss=1.27 avg=1.27\n", "[9834 | 1195.25] loss=1.41 avg=1.27\n", "[9835 | 1196.52] loss=1.21 avg=1.27\n", "[9836 | 1197.80] loss=1.27 avg=1.27\n", "[9837 | 1199.08] loss=1.15 avg=1.27\n", "[9838 | 1200.37] loss=1.40 avg=1.27\n", "[9839 | 1201.66] loss=1.31 avg=1.27\n", "[9840 | 1202.94] loss=1.13 avg=1.27\n", "[9841 | 1204.22] loss=1.62 avg=1.27\n", "[9842 | 1205.50] loss=1.29 avg=1.27\n", "[9843 | 1206.78] loss=1.29 avg=1.27\n", "[9844 | 1208.05] loss=1.48 avg=1.27\n", "[9845 | 1209.33] loss=1.17 avg=1.27\n", "[9846 | 1210.60] loss=1.65 avg=1.28\n", "[9847 | 1211.88] loss=1.30 avg=1.28\n", "[9848 | 1213.15] loss=1.35 avg=1.28\n", "[9849 | 1214.43] loss=1.26 avg=1.28\n", "[9850 | 1215.73] loss=1.30 avg=1.28\n", "[9851 | 1217.00] loss=1.31 avg=1.28\n", "[9852 | 1218.28] loss=1.20 avg=1.28\n", "[9853 | 1219.56] loss=1.29 avg=1.28\n", "[9854 | 1220.85] loss=1.34 avg=1.28\n", "[9855 | 1222.14] loss=1.33 avg=1.28\n", "[9856 | 1223.43] loss=1.28 avg=1.28\n", "[9857 | 1224.71] loss=1.01 avg=1.27\n", "[9858 | 1226.00] loss=1.37 avg=1.28\n", "[9859 | 1227.29] loss=1.41 avg=1.28\n", "[9860 | 1228.56] loss=1.15 avg=1.28\n", "[9861 | 1229.84] loss=1.14 avg=1.27\n", "[9862 | 1231.12] loss=1.26 avg=1.27\n", "[9863 | 1232.39] loss=1.25 avg=1.27\n", "[9864 | 1233.71] loss=1.01 avg=1.27\n", "[9865 | 1235.00] loss=1.14 avg=1.27\n", "[9866 | 1236.28] loss=1.20 avg=1.27\n", "[9867 | 1237.58] loss=1.22 avg=1.27\n", "[9868 | 1238.86] loss=1.32 avg=1.27\n", "[9869 | 1240.14] loss=1.11 avg=1.27\n", "[9870 | 1241.41] loss=1.24 avg=1.27\n", "[9871 | 1242.69] loss=1.49 avg=1.27\n", "[9872 | 1243.97] loss=1.33 avg=1.27\n", "[9873 | 1245.24] loss=1.27 avg=1.27\n", "[9874 | 1246.52] loss=1.05 avg=1.27\n", "[9875 | 1247.79] loss=1.25 avg=1.27\n", "[9876 | 1249.10] loss=1.20 avg=1.27\n", "[9877 | 1250.38] loss=1.46 avg=1.27\n", "[9878 | 1251.65] loss=1.29 avg=1.27\n", "[9879 | 1252.93] loss=1.18 avg=1.27\n", "[9880 | 1254.21] loss=1.25 avg=1.27\n", "[9881 | 1255.48] loss=1.32 avg=1.27\n", "[9882 | 1256.76] loss=1.04 avg=1.27\n", "[9883 | 1258.03] loss=1.22 avg=1.27\n", "[9884 | 1259.33] loss=1.14 avg=1.26\n", "[9885 | 1260.62] loss=1.40 avg=1.27\n", "[9886 | 1261.89] loss=1.36 avg=1.27\n", "[9887 | 1263.17] loss=1.29 avg=1.27\n", "[9888 | 1264.45] loss=1.12 avg=1.27\n", "[9889 | 1265.73] loss=1.30 avg=1.27\n", "[9890 | 1267.03] loss=1.26 avg=1.27\n", "[9891 | 1268.31] loss=1.14 avg=1.26\n", "[9892 | 1269.59] loss=1.28 avg=1.27\n", "[9893 | 1270.89] loss=1.21 avg=1.26\n", "[9894 | 1272.16] loss=1.34 avg=1.27\n", "[9895 | 1273.44] loss=1.07 avg=1.26\n", "[9896 | 1274.72] loss=1.22 avg=1.26\n", "[9897 | 1275.99] loss=1.30 avg=1.26\n", "[9898 | 1277.27] loss=1.61 avg=1.27\n", "[9899 | 1278.54] loss=1.37 avg=1.27\n", "[9900 | 1279.82] loss=1.18 avg=1.27\n", "======== SAMPLE 1 ========\n", " constituant des déposés, et précisément dûment décidé, mais que le fondement, de nature à la protection de dommages causés par une violation l'entière prédérence aux fonds provenant du dépôt des fonds, ne se trouve pas réparé. <|endoftext|>\n", "<|startoftext|> Donne acte au créancier de la saisie de diverses sommes au titre de la remise en état de ses créances ;Sur le moyen unique, pris en sa première branche :Vu l'article 1382 du Code civil, ensemble les articles 9, 14 et 16 du décret du 30 septembre 1953 ;Attendu que, par acte du 8 mai 1975, le Crédit agricole (la banque) a consenti un prêt à M. X... ; que, le 12 mai 1991, la banque a cédé à la caisse primaire d'assurance maladie des Saines sa créance en application des articles L. 132-4 et A 212-1 de la loi du 25 janvier 1985 dans leur rédaction applicable en l'état ; que, par acte du 30 juillet 1991, le Crédit agricole a consenti à M. X... la même somme à titre de dépôt de fonds en garantie de sa dette ; que sa condamnation, qui faisait obstacle au maintien de l'indû en règlement sans effet du préjudice subi par le Crédit agricole, s'est vu notifier son consentement à son cessionnaire ; que l'arrêt attaqué (Aix-en-Provence, 18 décembre 1997) a dit la caisse primaire d'assurance maladie applicable aux articles L. 132-4 et A 212-1 de la loi du 25 janvier 1985, déclaré valable la décision de l'UAP-GDF sur le fondement du prêt consenti à M. X... et les a condamnés au paiement de dommages-intérêts ;Attendu qu'en statuant ainsi, alors qu'il résultait de ses propres constatations que le Crédit agricole avait cédé à M. X... l'indû en règlement sans effet du préjudice résultant de la cessation du prêt et d'avoir méconnu l'objet du maintien de l'indû en règlement, et que la créance de M. X... était différente dans le litige, la cour d'appel a méconnu les termes du litige et violé les textes susvisés ;PAR CES MOTIFS, et sans qu'il y ait lieu de statuer sur les autres branches du moyen :CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 18 décembre 1997, entre les parties, par la cour d'appel de Aix-en-Provence ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Paris. <|endoftext|>\n", "<|startoftext|> Sur le moyen unique :Vu l'article 1351 du Code civil ;Attendu, qu'aux termes de ce texte, dans le cas où le désistement d'impartialité ne satisfait pas de poursuite entre les époux, cessionnaire d'une parcelle appartenant au bénéficiaire du régime du renouvellement ne se fait pas pour le non-paiement de l'indemnité en raison des exigences du droit ni de l'abus de la charge correspondant en cause aux cas de revente, sont redevables du capital restant à découper de celui-ci et de ses biens immobiliers et, par ces seuls motifs, se trouve pris en application de l'article 1356 du même Code ;\n", "\n", "[9901 | 1293.20] loss=1.23 avg=1.27\n", "[9902 | 1294.48] loss=1.28 avg=1.27\n", "[9903 | 1295.77] loss=1.19 avg=1.27\n", "[9904 | 1297.06] loss=1.16 avg=1.26\n", "[9905 | 1298.35] loss=1.47 avg=1.27\n", "[9906 | 1299.71] loss=1.32 avg=1.27\n", "[9907 | 1301.00] loss=1.45 avg=1.27\n", "[9908 | 1302.28] loss=1.20 avg=1.27\n", "[9909 | 1303.57] loss=1.20 avg=1.27\n", "[9910 | 1304.85] loss=1.22 avg=1.27\n", "[9911 | 1306.13] loss=1.37 avg=1.27\n", "[9912 | 1307.40] loss=1.35 avg=1.27\n", "[9913 | 1308.68] loss=1.44 avg=1.27\n", "[9914 | 1309.95] loss=0.97 avg=1.27\n", "[9915 | 1311.23] loss=1.40 avg=1.27\n", "[9916 | 1312.51] loss=1.19 avg=1.27\n", "[9917 | 1313.78] loss=1.30 avg=1.27\n", "[9918 | 1315.08] loss=1.28 avg=1.27\n", "[9919 | 1316.36] loss=1.31 avg=1.27\n", "[9920 | 1317.63] loss=1.33 avg=1.27\n", "[9921 | 1318.91] loss=1.32 avg=1.27\n", "[9922 | 1320.19] loss=1.23 avg=1.27\n", "[9923 | 1321.46] loss=1.33 avg=1.27\n", "[9924 | 1322.74] loss=1.32 avg=1.27\n", "[9925 | 1324.02] loss=1.38 avg=1.27\n", "[9926 | 1325.32] loss=1.26 avg=1.27\n", "[9927 | 1326.60] loss=1.14 avg=1.27\n", "[9928 | 1327.88] loss=1.20 avg=1.27\n", "[9929 | 1329.15] loss=1.38 avg=1.27\n", "[9930 | 1330.43] loss=1.23 avg=1.27\n", "[9931 | 1331.71] loss=1.21 avg=1.27\n", "[9932 | 1333.01] loss=1.38 avg=1.27\n", "[9933 | 1334.29] loss=1.27 avg=1.27\n", "[9934 | 1335.57] loss=1.30 avg=1.27\n", "[9935 | 1336.87] loss=1.40 avg=1.27\n", "[9936 | 1338.15] loss=1.27 avg=1.27\n", "[9937 | 1339.42] loss=1.21 avg=1.27\n", "[9938 | 1340.70] loss=1.19 avg=1.27\n", "[9939 | 1341.98] loss=1.32 avg=1.27\n", "[9940 | 1343.25] loss=1.31 avg=1.27\n", "[9941 | 1344.53] loss=1.26 avg=1.27\n", "[9942 | 1345.80] loss=1.18 avg=1.27\n", "[9943 | 1347.08] loss=1.47 avg=1.27\n", "[9944 | 1348.39] loss=1.26 avg=1.27\n", "[9945 | 1349.66] loss=1.08 avg=1.27\n", "[9946 | 1350.94] loss=1.21 avg=1.27\n", "[9947 | 1352.21] loss=1.13 avg=1.27\n", "[9948 | 1353.50] loss=1.27 avg=1.27\n", "[9949 | 1354.77] loss=1.26 avg=1.27\n", "[9950 | 1356.05] loss=1.31 avg=1.27\n", "[9951 | 1357.33] loss=1.06 avg=1.27\n", "[9952 | 1358.62] loss=1.57 avg=1.27\n", "[9953 | 1359.90] loss=1.24 avg=1.27\n", "[9954 | 1361.19] loss=1.05 avg=1.27\n", "[9955 | 1362.48] loss=1.22 avg=1.27\n", "[9956 | 1363.79] loss=1.16 avg=1.27\n", "[9957 | 1365.12] loss=1.22 avg=1.27\n", "[9958 | 1366.50] loss=1.24 avg=1.27\n", "[9959 | 1367.79] loss=1.26 avg=1.27\n", "[9960 | 1369.07] loss=1.33 avg=1.27\n", "[9961 | 1370.37] loss=1.02 avg=1.26\n", "[9962 | 1371.64] loss=1.17 avg=1.26\n", "[9963 | 1372.92] loss=1.15 avg=1.26\n", "[9964 | 1374.19] loss=1.30 avg=1.26\n", "[9965 | 1375.47] loss=1.27 avg=1.26\n", "[9966 | 1376.75] loss=1.25 avg=1.26\n", "[9967 | 1378.02] loss=1.14 avg=1.26\n", "[9968 | 1379.30] loss=1.45 avg=1.26\n", "[9969 | 1380.59] loss=1.49 avg=1.26\n", "[9970 | 1381.87] loss=1.37 avg=1.27\n", "[9971 | 1383.14] loss=1.34 avg=1.27\n", "[9972 | 1384.42] loss=1.27 avg=1.27\n", "[9973 | 1385.69] loss=1.10 avg=1.27\n", "[9974 | 1386.97] loss=1.30 avg=1.27\n", "[9975 | 1388.25] loss=1.35 avg=1.27\n", "[9976 | 1389.52] loss=1.22 avg=1.27\n", "[9977 | 1390.80] loss=1.48 avg=1.27\n", "[9978 | 1392.09] loss=1.36 avg=1.27\n", "[9979 | 1393.37] loss=1.14 avg=1.27\n", "[9980 | 1394.64] loss=1.08 avg=1.27\n", "[9981 | 1395.92] loss=1.41 avg=1.27\n", "[9982 | 1397.20] loss=1.15 avg=1.27\n", "[9983 | 1398.49] loss=1.37 avg=1.27\n", "[9984 | 1399.78] loss=1.24 avg=1.27\n", "[9985 | 1401.06] loss=1.14 avg=1.27\n", "[9986 | 1402.36] loss=1.27 avg=1.27\n", "[9987 | 1403.64] loss=1.32 avg=1.27\n", "[9988 | 1404.92] loss=1.29 avg=1.27\n", "[9989 | 1406.19] loss=1.02 avg=1.26\n", "[9990 | 1407.47] loss=1.11 avg=1.26\n", "[9991 | 1408.75] loss=1.07 avg=1.26\n", "[9992 | 1410.02] loss=1.27 avg=1.26\n", "[9993 | 1411.30] loss=1.39 avg=1.26\n", "[9994 | 1412.57] loss=1.18 avg=1.26\n", "[9995 | 1413.88] loss=1.39 avg=1.26\n", "[9996 | 1415.16] loss=1.19 avg=1.26\n", "[9997 | 1416.43] loss=1.25 avg=1.26\n", "[9998 | 1417.71] loss=1.45 avg=1.26\n", "[9999 | 1418.98] loss=1.29 avg=1.26\n", "[10000 | 1420.26] loss=1.32 avg=1.26\n", "Saving checkpoint/run1/model-10000\n" ] } ], "source": [ "file_name = \"./data/npz/decision-0.npz\"\n", "\n", "sess = gpt2.start_tf_sess()\n", "#tf.reset_default_graph()\n", "#sess=gpt2.reset_session(sess=sess)\n", "gpt2.finetune(sess,\n", " dataset=file_name,\n", " model_name='124M',\n", " steps=1000,\n", " batch_size=1,\n", " learning_rate=0.0001,\n", " overwrite=True)\n", "\n", "for i in range(1,int(max_index/step_index)):\n", " file_name=f'data/npz/decision-{i}.npz'\n", " #tf.reset_default_graph()\n", " sess=gpt2.reset_session(sess=sess)\n", " gpt2.finetune(sess,\n", " dataset=file_name,\n", " model_name='124M',\n", " steps=1000,\n", " batch_size=1,\n", " learning_rate=0.0001,\n", " overwrite=True)" ] }, { "cell_type": "code", "execution_count": 15, "id": "c32ba84e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:18:44.487842Z", "iopub.status.busy": "2022-01-28T18:18:44.486827Z", "iopub.status.idle": "2022-01-28T18:19:12.519557Z", "shell.execute_reply": "2022-01-28T18:19:12.520721Z", "shell.execute_reply.started": "2022-01-28T08:45:13.256444Z" }, "papermill": { "duration": 31.560742, "end_time": "2022-01-28T18:19:12.520963", "exception": false, "start_time": "2022-01-28T18:18:40.960221", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'/kaggle/working/checkpoint2-100000-10000-100step-npz.zip'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import shutil\n", "shutil.make_archive(\"checkpoint2-100000-10000-100step-npz\", 'zip', \"checkpoint\")" ] }, { "cell_type": "code", "execution_count": 16, "id": "970359ef", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:19:20.452534Z", "iopub.status.busy": "2022-01-28T18:19:20.451409Z", "iopub.status.idle": "2022-01-28T18:19:37.771692Z", "shell.execute_reply": "2022-01-28T18:19:37.770803Z" }, "papermill": { "duration": 21.54632, "end_time": "2022-01-28T18:19:37.771846", "exception": false, "start_time": "2022-01-28T18:19:16.225526", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LE MEURTRE DE SA FILLE (GTP) ;</p><p>Attendu que pour débouter la société GTP de sa demande, l'arrêt attaqué retient que l'autorisation de taxe sur le territoire français de taxe de l'ensemble des Etats-Unis au professeur de la Fédération française bétonnier (Fédération), dont la Fédération est la société Goussour, à l'occasion d'une période d'observation de période de taxe à cette date, n'est pas constitutive d'une autorisation de taxe sur le territoire français de taxe de l'ensemble des Etats-Unis au sens de l'article L. 621-1-1 du Code de la santé publique ;</p><p>Attendu, cependant, que la fédération générale des Etats-Unis ne peut être colloquée que par l'accord d'autorité administrative de l'Etat français de taxe sur le territoire duquel les Etats-Unis, sous les conditions d'un marché de travaux publics, sont présents, la date de l'accord du 27 octobre 1996 constituant, à cette date, la date de l'accord du 28 octobre 1997, un délai de période d'observation de période de taxe à cette date ;</p><p>D'où il suit qu'en statuant comme elle l'a fait, tout en constatant que la date de l'accord du 27 octobre 1996 n'était d'ailleurs pas établie, la cour d'appel a violé le texte susvisé ;</p><p>Et attendu qu'il y a lieu de faire application de l'article 627, alinéa 2, du nouveau Code de procédure civile ;</p><p>Par ces motifs, et sans qu'il y ait lieu de statuer sur les autres branches du second moyen :</p><p>CASSE ET ANNULE, dans toutes ses dispositions, l'arrêt rendu le 5 juin 1998, entre les parties, par la cour d'appel de Pau ; remet, en conséquence, la cause et les parties dans l'état où elles se trouvaient avant ledit arrêt et, pour être fait droit, les renvoie devant la cour d'appel de Toulouse.</p> <|endoftext|>\n", "<|startoftext|> <p>Sur le moyen unique :</p><p>Attendu que M. X..., engagé le 20 octobre 1992 par la société Etablissements Bertrand en qualité de VRP, a été licencié pour motif économique le 16 septembre 1996 et que le 25 octobre 1996 il a fait l'objet de son licenciement le 31 juillet 1992 ; qu'il a saisi la juridiction prud'homale pour obtenir sa condamnation solidairement à lui payer des dommages-intérêts ;</p><p>Attendu que la société Bertrand fait grief à l'arrêt attaqué (Paris, 7 mai 1998) d'avoir fait droit à la demande d'indemnité de licenciement et de dommages-intérêts en réparation du préjudice causé par l'employeur par l'inefficacité des salariés ;</p><p>Mais attendu que la cour d'appel a constaté que l'employeur n'avait pas, selon le licenciement de M. X..., fait pratiquer à l'autre salarié un travail et un autre jour de reprise, sans que le terme de déplacement de lui assurer le compte de son salarié était, et qu'en l'espèce, l'employeur faisait pratiquer un plan\n" ] } ], "source": [ "gpt2.generate(sess,prefix=\"LE MEURTRE DE SA FILLE\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "cfc5fbcf", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:19:44.869855Z", "iopub.status.busy": "2022-01-28T18:19:44.868878Z", "iopub.status.idle": "2022-01-28T18:19:44.871788Z", "shell.execute_reply": "2022-01-28T18:19:44.871158Z", "shell.execute_reply.started": "2021-12-14T16:32:37.2518Z" }, "papermill": { "duration": 3.587959, "end_time": "2022-01-28T18:19:44.871911", "exception": false, "start_time": "2022-01-28T18:19:41.283952", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [], "source": [ "# !pip install happytransformer" ] }, { "cell_type": "code", "execution_count": 18, "id": "8f3f4864", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:19:52.625467Z", "iopub.status.busy": "2022-01-28T18:19:52.624381Z", "iopub.status.idle": "2022-01-28T18:19:52.627238Z", "shell.execute_reply": "2022-01-28T18:19:52.626409Z", "shell.execute_reply.started": "2021-12-14T16:33:48.697595Z" }, "papermill": { "duration": 3.888349, "end_time": "2022-01-28T18:19:52.627451", "exception": false, "start_time": "2022-01-28T18:19:48.739102", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# from happytransformer import HappyGeneration, GENTrainArgs\n", "\n", "# gpt_neo = HappyGeneration(\"antoiloui/belgpt2\") " ] }, { "cell_type": "code", "execution_count": 19, "id": "1013650f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:20:00.202518Z", "iopub.status.busy": "2022-01-28T18:20:00.201526Z", "iopub.status.idle": "2022-01-28T18:20:00.204169Z", "shell.execute_reply": "2022-01-28T18:20:00.204667Z", "shell.execute_reply.started": "2021-12-14T16:34:07.275963Z" }, "papermill": { "duration": 3.651266, "end_time": "2022-01-28T18:20:00.204818", "exception": false, "start_time": "2022-01-28T18:19:56.553552", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# train_args = GENTrainArgs(num_train_epochs=1, learning_rate=2e-05, batch_size=20, fp16=True) \n", "\n", "# gpt_neo.train(\"./decision.txt\", args=train_args)" ] }, { "cell_type": "code", "execution_count": null, "id": "161d51e7", "metadata": { "papermill": { "duration": 3.551739, "end_time": "2022-01-28T18:20:07.184104", "exception": false, "start_time": "2022-01-28T18:20:03.632365", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 20, "id": "e38f9dea", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:20:14.567538Z", "iopub.status.busy": "2022-01-28T18:20:14.566489Z", "iopub.status.idle": "2022-01-28T18:20:14.569145Z", "shell.execute_reply": "2022-01-28T18:20:14.569651Z", "shell.execute_reply.started": "2022-01-24T22:17:54.707578Z" }, "papermill": { "duration": 3.548995, "end_time": "2022-01-28T18:20:14.569812", "exception": false, "start_time": "2022-01-28T18:20:11.020817", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [], "source": [ "# !pip install transformers" ] }, { "cell_type": "code", "execution_count": 21, "id": "b2910111", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:20:21.935045Z", "iopub.status.busy": "2022-01-28T18:20:21.933899Z", "iopub.status.idle": "2022-01-28T18:20:21.937245Z", "shell.execute_reply": "2022-01-28T18:20:21.936679Z", "shell.execute_reply.started": "2022-01-24T23:05:49.230156Z" }, "papermill": { "duration": 3.776096, "end_time": "2022-01-28T18:20:21.937401", "exception": false, "start_time": "2022-01-28T18:20:18.161305", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# from sklearn.model_selection import train_test_split\n", "# train, test = train_test_split(decision,test_size=0.25) " ] }, { "cell_type": "code", "execution_count": 22, "id": "a995e6b5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:20:29.961105Z", "iopub.status.busy": "2022-01-28T18:20:29.960083Z", "iopub.status.idle": "2022-01-28T18:20:29.962817Z", "shell.execute_reply": "2022-01-28T18:20:29.963316Z", "shell.execute_reply.started": "2022-01-24T23:05:49.995563Z" }, "papermill": { "duration": 3.9206, "end_time": "2022-01-28T18:20:29.963479", "exception": false, "start_time": "2022-01-28T18:20:26.042879", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# def build_text_file(dataset, path):\n", "# text_data = open(path, 'w')\n", "# data = ''\n", "# for item in dataset :\n", "# data += str(item) + \" \"\n", "# text_data.write(data)\n", "# text_data.close()\n", " \n", "# build_text_file(train, \"decision_train.txt\")\n", "# build_text_file(test, \"decision_test.txt\")\n", "\n", "# print(\"Train dataset length: \"+str(len(train)))\n", "# print(\"Test dataset length: \"+ str(len(test)))" ] }, { "cell_type": "code", "execution_count": 23, "id": "b21e7d74", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:20:37.063960Z", "iopub.status.busy": "2022-01-28T18:20:37.062950Z", "iopub.status.idle": "2022-01-28T18:20:37.065598Z", "shell.execute_reply": "2022-01-28T18:20:37.066114Z", "shell.execute_reply.started": "2022-01-24T23:05:55.605812Z" }, "papermill": { "duration": 3.570802, "end_time": "2022-01-28T18:20:37.066291", "exception": false, "start_time": "2022-01-28T18:20:33.495489", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# from transformers import AutoTokenizer\n", "# train_path = \"decision_train.txt\"\n", "# test_path = \"decision_test.txt\"\n", "# tokenizer = AutoTokenizer.from_pretrained(\"dbddv01/gpt2-french-small\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "f1dcb360", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:20:44.452279Z", "iopub.status.busy": "2022-01-28T18:20:44.451115Z", "iopub.status.idle": "2022-01-28T18:20:44.454734Z", "shell.execute_reply": "2022-01-28T18:20:44.454141Z" }, "papermill": { "duration": 3.556154, "end_time": "2022-01-28T18:20:44.454866", "exception": false, "start_time": "2022-01-28T18:20:40.898712", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# from transformers import TextDataset,DataCollatorForLanguageModeling\n", "\n", "# def load_dataset(train_path,test_path,tokenizer):\n", "# train_dataset = TextDataset(\n", "# tokenizer=tokenizer,\n", "# file_path=train_path,\n", "# block_size=128)\n", " \n", "# test_dataset = TextDataset(\n", "# tokenizer=tokenizer,\n", "# file_path=test_path,\n", "# block_size=128) \n", " \n", "# data_collator = DataCollatorForLanguageModeling(\n", "# tokenizer=tokenizer, mlm=False,\n", "# )\n", "# return train_dataset,test_dataset,data_collator\n", "\n", "# train_dataset = TextDataset(\n", "# tokenizer=tokenizer,\n", "# file_path=train_path,\n", "# block_size=128)\n", "#train_dataset,test_dataset,data_collator = load_dataset(train_path,test_path,tokenizer)" ] }, { "cell_type": "code", "execution_count": 25, "id": "332eaa17", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:20:51.878233Z", "iopub.status.busy": "2022-01-28T18:20:51.877195Z", "iopub.status.idle": "2022-01-28T18:20:51.880499Z", "shell.execute_reply": "2022-01-28T18:20:51.879904Z", "shell.execute_reply.started": "2022-01-24T23:52:18.158818Z" }, "papermill": { "duration": 3.898336, "end_time": "2022-01-28T18:20:51.880636", "exception": false, "start_time": "2022-01-28T18:20:47.982300", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# import datasets\n", "# from transformers import TextDataset,DataCollatorForLanguageModeling\n", "\n", "# train = train.dropna()\n", "# test = test.dropna()\n", "\n", "# def tokenize_function(examples):\n", "# #print(examples[\"0\"])\n", "# return tokenizer(examples[\"0\"])\n", "\n", "# train_dataset = datasets.Dataset.from_pandas(train.to_frame())\n", "# tokenized_dataset_train = train_dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"0\"])\n", "\n", "\n", "# test_dataset = datasets.Dataset.from_pandas(test.to_frame())\n", "# tokenized_dataset_test = test_dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"0\"])\n", "\n", "# data_collator = DataCollatorForLanguageModeling(\n", "# tokenizer=tokenizer, mlm=False,\n", "# )" ] }, { "cell_type": "markdown", "id": "454fd7e4", "metadata": { "execution": { "iopub.execute_input": "2022-01-24T23:29:41.039684Z", "iopub.status.busy": "2022-01-24T23:29:41.038732Z", "iopub.status.idle": "2022-01-24T23:29:42.398189Z", "shell.execute_reply": "2022-01-24T23:29:42.396556Z", "shell.execute_reply.started": "2022-01-24T23:29:41.039628Z" }, "papermill": { "duration": 4.122586, "end_time": "2022-01-28T18:20:59.522646", "exception": false, "start_time": "2022-01-28T18:20:55.400060", "status": "completed" }, "tags": [] }, "source": [ "https://huggingface.co/docs/transformers/custom_datasets#finetune-with-the-trainer-api" ] }, { "cell_type": "code", "execution_count": 26, "id": "e4750e97", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:21:06.949473Z", "iopub.status.busy": "2022-01-28T18:21:06.948494Z", "iopub.status.idle": "2022-01-28T18:21:06.951139Z", "shell.execute_reply": "2022-01-28T18:21:06.951656Z" }, "papermill": { "duration": 3.591881, "end_time": "2022-01-28T18:21:06.951800", "exception": false, "start_time": "2022-01-28T18:21:03.359919", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# train.to_dict()" ] }, { "cell_type": "code", "execution_count": 27, "id": "b5fd9216", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:21:14.373926Z", "iopub.status.busy": "2022-01-28T18:21:14.372814Z", "iopub.status.idle": "2022-01-28T18:21:14.375064Z", "shell.execute_reply": "2022-01-28T18:21:14.375614Z", "shell.execute_reply.started": "2022-01-24T23:37:20.781978Z" }, "papermill": { "duration": 3.89076, "end_time": "2022-01-28T18:21:14.375787", "exception": false, "start_time": "2022-01-28T18:21:10.485027", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# from transformers import Trainer, TrainingArguments,AutoModelWithLMHead\n", "\n", "# model = AutoModelWithLMHead.from_pretrained(\"dbddv01/gpt2-french-small\")\n", "\n", "\n", "# training_args = TrainingArguments(\n", "# output_dir=\"./gpt2-justice\", #The output directory\n", "# overwrite_output_dir=True, #overwrite the content of the output directory\n", "# num_train_epochs=3, # number of training epochs\n", "# per_device_train_batch_size=32, # batch size for training\n", "# per_device_eval_batch_size=64, # batch size for evaluation\n", "# eval_steps = 400, # Number of update steps between two evaluations.\n", "# save_steps=800, # after # steps model is saved \n", "# warmup_steps=500,# number of warmup steps for learning rate scheduler\n", "# prediction_loss_only=True,\n", "# )\n", "\n", "\n", "# trainer = Trainer(\n", "# model=model,\n", "# args=training_args,\n", "# data_collator=data_collator,\n", "# train_dataset=train_dataset,\n", "# eval_dataset=test_dataset,\n", "# )" ] }, { "cell_type": "code", "execution_count": 28, "id": "21bad1ba", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:21:21.521748Z", "iopub.status.busy": "2022-01-28T18:21:21.520664Z", "iopub.status.idle": "2022-01-28T18:21:21.523657Z", "shell.execute_reply": "2022-01-28T18:21:21.522965Z", "shell.execute_reply.started": "2022-01-24T23:38:33.956361Z" }, "papermill": { "duration": 3.618547, "end_time": "2022-01-28T18:21:21.523791", "exception": false, "start_time": "2022-01-28T18:21:17.905244", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "id": "17772c4f", "metadata": { "papermill": { "duration": 3.505065, "end_time": "2022-01-28T18:21:28.868014", "exception": false, "start_time": "2022-01-28T18:21:25.362949", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "d75bac00", "metadata": { "papermill": { "duration": 3.898565, "end_time": "2022-01-28T18:21:36.800096", "exception": false, "start_time": "2022-01-28T18:21:32.901531", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 15253.035661, "end_time": "2022-01-28T18:21:43.422986", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:07:30.387325", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/398/86398653.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "da0be1e2", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:13:08.274416Z", "iopub.status.busy": "2022-01-28T14:13:08.273280Z", "iopub.status.idle": "2022-01-28T14:13:08.299491Z", "shell.execute_reply": "2022-01-28T14:13:08.300101Z", "shell.execute_reply.started": "2022-01-28T14:12:38.543287Z" }, "papermill": { "duration": 0.055297, "end_time": "2022-01-28T14:13:08.300403", "exception": false, "start_time": "2022-01-28T14:13:08.245106", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/logistic-regression/Social_Network_Ads.csv\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" ] }, { "cell_type": "code", "execution_count": 2, "id": "8528bf10", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:08.348551Z", "iopub.status.busy": "2022-01-28T14:13:08.347918Z", "iopub.status.idle": "2022-01-28T14:13:10.762356Z", "shell.execute_reply": "2022-01-28T14:13:10.762997Z", "shell.execute_reply.started": "2022-01-28T14:12:38.557321Z" }, "papermill": { "duration": 2.439983, "end_time": "2022-01-28T14:13:10.763178", "exception": false, "start_time": "2022-01-28T14:13:08.323195", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression\n", "from sklearn.preprocessing import LabelEncoder , PolynomialFeatures\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import r2_score , mean_squared_error\n", "from scipy import stats\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.svm import SVC\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn import tree\n", "from sklearn.tree import DecisionTreeClassifier\n", "from xgboost import XGBClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from imblearn.over_sampling import SMOTE\n", "from graphviz import Source\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.metrics import confusion_matrix , classification_report\n", "from mlxtend.plotting import plot_confusion_matrix\n", "from sklearn.model_selection import GridSearchCV\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "sns.set(style=\"darkgrid\")\n", "plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": 3, "id": "6d48c854", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:10.811612Z", "iopub.status.busy": "2022-01-28T14:13:10.810621Z", "iopub.status.idle": "2022-01-28T14:13:10.818365Z", "shell.execute_reply": "2022-01-28T14:13:10.817740Z", "shell.execute_reply.started": "2022-01-28T14:12:38.576452Z" }, "papermill": { "duration": 0.033322, "end_time": "2022-01-28T14:13:10.818499", "exception": false, "start_time": "2022-01-28T14:13:10.785177", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def dataset_overview(data, col):\n", " \n", " print(\"------------\")\n", " #print(data.head())\n", " print(\"-----------\")\n", " print(\"---------------\")\n", "\n", " print(data.columns)\n", " print(\"------------\")\n", " print(\"---------------\")\n", "\n", " print(\"Shape of the dataset\")\n", " print(data.shape)\n", " print(\"-------------\")\n", " print(\"---------------\")\n", "\n", " print(\"Null Value counts\")\n", " print(data.isnull().sum())\n", " print(\"-------------\")\n", " print(\"---------------\")\n", "\n", " print(\"dataset informaation\")\n", " print(data.info())\n", " print(\"---------------\")\n", " print(\"---------------\")\n", " \n", " print(\"The outcome values\",data[col].value_counts())\n", " plt.figure(figsize=(10,5))\n", " print(sns.countplot(x=col, data=data))\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "43d20e17", "metadata": { "papermill": { "duration": 0.021947, "end_time": "2022-01-28T14:13:10.862367", "exception": false, "start_time": "2022-01-28T14:13:10.840420", "status": "completed" }, "tags": [] }, "source": [ "# To fill the Missing values" ] }, { "cell_type": "code", "execution_count": 4, "id": "dcb91ad3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:10.909481Z", "iopub.status.busy": "2022-01-28T14:13:10.908882Z", "iopub.status.idle": "2022-01-28T14:13:10.912621Z", "shell.execute_reply": "2022-01-28T14:13:10.913212Z", "shell.execute_reply.started": "2022-01-28T14:12:38.593358Z" }, "papermill": { "duration": 0.02917, "end_time": "2022-01-28T14:13:10.913380", "exception": false, "start_time": "2022-01-28T14:13:10.884210", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def fill_missing_values(data):\n", " data=data.fillna(data.mean())\n", " return data" ] }, { "cell_type": "markdown", "id": "35e6554f", "metadata": { "papermill": { "duration": 0.021479, "end_time": "2022-01-28T14:13:10.958989", "exception": false, "start_time": "2022-01-28T14:13:10.937510", "status": "completed" }, "tags": [] }, "source": [ "# Correlation Matrix" ] }, { "cell_type": "code", "execution_count": 5, "id": "3fb98813", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.005402Z", "iopub.status.busy": "2022-01-28T14:13:11.004796Z", "iopub.status.idle": "2022-01-28T14:13:11.011582Z", "shell.execute_reply": "2022-01-28T14:13:11.011086Z", "shell.execute_reply.started": "2022-01-28T14:12:38.611650Z" }, "papermill": { "duration": 0.030944, "end_time": "2022-01-28T14:13:11.011730", "exception": false, "start_time": "2022-01-28T14:13:10.980786", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def correlation_matrix(data):\n", " corr = data.corr().round(2)\n", "\n", " # Mask for the upper triangle\n", " mask = np.zeros_like(corr, dtype=np.bool)\n", " mask[np.triu_indices_from(mask)] = True\n", "\n", " # Set figure size\n", " f, ax = plt.subplots(figsize=(20, 20))\n", "\n", " # Define custom colormap\n", " cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", "\n", " # Draw the heatmap\n", " d=sns.heatmap(corr, mask=mask, cmap=cmap, vmin=-1, vmax=1, center=0,\n", " square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)\n", "\n", " plt.tight_layout()\n", " return d" ] }, { "cell_type": "markdown", "id": "d4434b3f", "metadata": { "papermill": { "duration": 0.022092, "end_time": "2022-01-28T14:13:11.055706", "exception": false, "start_time": "2022-01-28T14:13:11.033614", "status": "completed" }, "tags": [] }, "source": [ "# VIF" ] }, { "cell_type": "code", "execution_count": 6, "id": "2412d8bc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.102601Z", "iopub.status.busy": "2022-01-28T14:13:11.101988Z", "iopub.status.idle": "2022-01-28T14:13:11.108356Z", "shell.execute_reply": "2022-01-28T14:13:11.108890Z", "shell.execute_reply.started": "2022-01-28T14:12:38.627327Z" }, "papermill": { "duration": 0.031531, "end_time": "2022-01-28T14:13:11.109072", "exception": false, "start_time": "2022-01-28T14:13:11.077541", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "dec9b3ed", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.156078Z", "iopub.status.busy": "2022-01-28T14:13:11.155468Z", "iopub.status.idle": "2022-01-28T14:13:11.159886Z", "shell.execute_reply": "2022-01-28T14:13:11.160374Z", "shell.execute_reply.started": "2022-01-28T14:12:38.642178Z" }, "papermill": { "duration": 0.029478, "end_time": "2022-01-28T14:13:11.160548", "exception": false, "start_time": "2022-01-28T14:13:11.131070", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def vif_values(data, col):\n", " cols=col\n", " X=data.drop(cols, axis=1)\n", " vif_data = pd.DataFrame()\n", " vif_data[\"feature\"] = X.columns\n", " vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i)\n", " for i in range(len(X.columns))]\n", " return vif_data" ] }, { "cell_type": "code", "execution_count": 8, "id": "e0af9d42", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.208137Z", "iopub.status.busy": "2022-01-28T14:13:11.207495Z", "iopub.status.idle": "2022-01-28T14:13:11.215419Z", "shell.execute_reply": "2022-01-28T14:13:11.215943Z", "shell.execute_reply.started": "2022-01-28T14:12:38.655611Z" }, "papermill": { "duration": 0.032839, "end_time": "2022-01-28T14:13:11.216128", "exception": false, "start_time": "2022-01-28T14:13:11.183289", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn import metrics\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import classification_report, confusion_matrix,ConfusionMatrixDisplay\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import confusion_matrix,classification_report,precision_score, plot_roc_curve, plot_precision_recall_curve, balanced_accuracy_score\n", "\n", "def clf_scores(clf, y_predicted):\n", " # Accuracy\n", " acc_train = clf.score(X_train, y_train)*100\n", " acc_test = clf.score(X_test, y_test)*100\n", " \n", " roc = roc_auc_score(y_test, y_predicted)*100 \n", " tn, fp, fn, tp = confusion_matrix(y_test, y_predicted).ravel()\n", " cm = confusion_matrix(y_test, y_predicted)\n", " correct = tp + tn\n", " incorrect = fp + fn\n", " d=[acc_train, acc_test, roc, correct, incorrect, cm]\n", " index=[\"acc_train\",'Test Accuracy',\"Roc Score\",\"COrrect\",\"Incorrect\",\"Confusion\" ]\n", " output=pd.DataFrame(data=d, index=index)\n", " \n", " d=sns.heatmap(cm, annot=True)\n", " dd=plot_roc_curve(clf, X_train, y_train)\n", " ddd=plot_precision_recall_curve(clf, X_train, y_train)\n", "\n", " return output,d, dd, ddd" ] }, { "cell_type": "code", "execution_count": 9, "id": "a6a386dc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.262969Z", "iopub.status.busy": "2022-01-28T14:13:11.262359Z", "iopub.status.idle": "2022-01-28T14:13:11.322919Z", "shell.execute_reply": "2022-01-28T14:13:11.322403Z", "shell.execute_reply.started": "2022-01-28T14:12:38.672136Z" }, "papermill": { "duration": 0.085047, "end_time": "2022-01-28T14:13:11.323050", "exception": false, "start_time": "2022-01-28T14:13:11.238003", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>User ID</th>\n", " <th>Gender</th>\n", " <th>Age</th>\n", " <th>EstimatedSalary</th>\n", " <th>Purchased</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>15624510</td>\n", " <td>Male</td>\n", " <td>19</td>\n", " <td>19000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15810944</td>\n", " <td>Male</td>\n", " <td>35</td>\n", " <td>20000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>15668575</td>\n", " <td>Female</td>\n", " <td>26</td>\n", " <td>43000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>15603246</td>\n", " <td>Female</td>\n", " <td>27</td>\n", " <td>57000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>15804002</td>\n", " <td>Male</td>\n", " <td>19</td>\n", " <td>76000</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " User ID Gender Age EstimatedSalary Purchased\n", "0 15624510 Male 19 19000 0\n", "1 15810944 Male 35 20000 0\n", "2 15668575 Female 26 43000 0\n", "3 15603246 Female 27 57000 0\n", "4 15804002 Male 19 76000 0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=pd.read_csv('../input/logistic-regression/Social_Network_Ads.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 10, "id": "cd0c268d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.370947Z", "iopub.status.busy": "2022-01-28T14:13:11.370350Z", "iopub.status.idle": "2022-01-28T14:13:11.382143Z", "shell.execute_reply": "2022-01-28T14:13:11.382704Z", "shell.execute_reply.started": "2022-01-28T14:12:38.707994Z" }, "papermill": { "duration": 0.037544, "end_time": "2022-01-28T14:13:11.382887", "exception": false, "start_time": "2022-01-28T14:13:11.345343", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0 257\n", "1 143\n", "Name: Purchased, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Purchased'].value_counts()" ] }, { "cell_type": "code", "execution_count": 11, "id": "ea9e1a1f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.430433Z", "iopub.status.busy": "2022-01-28T14:13:11.429876Z", "iopub.status.idle": "2022-01-28T14:13:11.434530Z", "shell.execute_reply": "2022-01-28T14:13:11.435067Z", "shell.execute_reply.started": "2022-01-28T14:12:38.720613Z" }, "papermill": { "duration": 0.030071, "end_time": "2022-01-28T14:13:11.435232", "exception": false, "start_time": "2022-01-28T14:13:11.405161", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Index(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'], dtype='object')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "id": "717aef0c", "metadata": { "papermill": { "duration": 0.022385, "end_time": "2022-01-28T14:13:11.480336", "exception": false, "start_time": "2022-01-28T14:13:11.457951", "status": "completed" }, "tags": [] }, "source": [ "# Label Encoding " ] }, { "cell_type": "code", "execution_count": 12, "id": "9c327ef5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.529575Z", "iopub.status.busy": "2022-01-28T14:13:11.528994Z", "iopub.status.idle": "2022-01-28T14:13:11.532433Z", "shell.execute_reply": "2022-01-28T14:13:11.532975Z", "shell.execute_reply.started": "2022-01-28T14:12:38.731435Z" }, "papermill": { "duration": 0.030092, "end_time": "2022-01-28T14:13:11.533133", "exception": false, "start_time": "2022-01-28T14:13:11.503041", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn import preprocessing\n", " \n", "# label_encoder object knows how to understand word labels.\n", "label_encoder = preprocessing.LabelEncoder()\n", " \n", "# Encode labels in column 'species'.\n", "#df['species']= label_encoder.fit_transform(df['species'])\n", " \n", "#df['species'].unique()" ] }, { "cell_type": "code", "execution_count": 13, "id": "2d11765f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.582844Z", "iopub.status.busy": "2022-01-28T14:13:11.582204Z", "iopub.status.idle": "2022-01-28T14:13:11.585699Z", "shell.execute_reply": "2022-01-28T14:13:11.586187Z", "shell.execute_reply.started": "2022-01-28T14:12:38.742355Z" }, "papermill": { "duration": 0.029654, "end_time": "2022-01-28T14:13:11.586364", "exception": false, "start_time": "2022-01-28T14:13:11.556710", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def label_encoding(data, col):\n", " label_encoder = preprocessing.LabelEncoder()\n", " data[col]=label_encoder.fit_transform(data[col])\n", " return data[col].head()" ] }, { "cell_type": "code", "execution_count": 14, "id": "b91637a6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.635939Z", "iopub.status.busy": "2022-01-28T14:13:11.635318Z", "iopub.status.idle": "2022-01-28T14:13:11.641251Z", "shell.execute_reply": "2022-01-28T14:13:11.641735Z", "shell.execute_reply.started": "2022-01-28T14:12:38.757046Z" }, "papermill": { "duration": 0.032078, "end_time": "2022-01-28T14:13:11.641946", "exception": false, "start_time": "2022-01-28T14:13:11.609868", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 1\n", "2 0\n", "3 0\n", "4 1\n", "Name: Gender, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label_encoding(df, 'Gender')" ] }, { "cell_type": "code", "execution_count": 15, "id": "3d48c5bd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.692258Z", "iopub.status.busy": "2022-01-28T14:13:11.691616Z", "iopub.status.idle": "2022-01-28T14:13:11.699379Z", "shell.execute_reply": "2022-01-28T14:13:11.699948Z", "shell.execute_reply.started": "2022-01-28T14:12:38.769571Z" }, "papermill": { "duration": 0.034482, "end_time": "2022-01-28T14:13:11.700133", "exception": false, "start_time": "2022-01-28T14:13:11.665651", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>User ID</th>\n", " <th>Gender</th>\n", " <th>Age</th>\n", " <th>EstimatedSalary</th>\n", " <th>Purchased</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>15624510</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>19000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15810944</td>\n", " <td>1</td>\n", " <td>35</td>\n", " <td>20000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>15668575</td>\n", " <td>0</td>\n", " <td>26</td>\n", " <td>43000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>15603246</td>\n", " <td>0</td>\n", " <td>27</td>\n", " <td>57000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>15804002</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>76000</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " User ID Gender Age EstimatedSalary Purchased\n", "0 15624510 1 19 19000 0\n", "1 15810944 1 35 20000 0\n", "2 15668575 0 26 43000 0\n", "3 15603246 0 27 57000 0\n", "4 15804002 1 19 76000 0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 16, "id": "9da7f1db", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.750367Z", "iopub.status.busy": "2022-01-28T14:13:11.749666Z", "iopub.status.idle": "2022-01-28T14:13:11.760363Z", "shell.execute_reply": "2022-01-28T14:13:11.760879Z", "shell.execute_reply.started": "2022-01-28T14:12:38.787121Z" }, "papermill": { "duration": 0.037502, "end_time": "2022-01-28T14:13:11.761047", "exception": false, "start_time": "2022-01-28T14:13:11.723545", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "x = df.drop(['Purchased'], axis=1)\n", "y = df['Purchased']\n", "X_train , X_test , y_train , y_test = train_test_split(x , y, test_size=0.25, random_state=42)" ] }, { "cell_type": "code", "execution_count": 17, "id": "ad2e1535", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:11.812640Z", "iopub.status.busy": "2022-01-28T14:13:11.812004Z", "iopub.status.idle": "2022-01-28T14:13:12.713111Z", "shell.execute_reply": "2022-01-28T14:13:12.713557Z", "shell.execute_reply.started": "2022-01-28T14:12:38.798289Z" }, "papermill": { "duration": 0.929058, "end_time": "2022-01-28T14:13:12.713716", "exception": false, "start_time": "2022-01-28T14:13:11.784658", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "( 0\n", "acc_train 78.333333\n", "Test Accuracy 75.0\n", "Roc Score 67.889318\n", "COrrect 75\n", "Incorrect 25\n", "Confusion [[60, 3], [22, 15]], <AxesSubplot:>, <sklearn.metrics._plot.roc_curve.RocCurveDisplay object at 0x7f43f6e1f190>, <sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay object at 0x7f43bc353610>)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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11fwdUcZ8KGM+VNUkJ9XNkKPWc2LLly/H2LFjERcXh7t370Imk6Fly5Zwd3dHjx49XjrIqshkMvz1118ICQlBSUkJlixZgubNm2Ps2LEVHl+TKYE4pdA/pu5PR1pR1bcP62Ou+DuijPlQxnyo0nZO1J6xo3v37ujevXuNL2xra4usrCxIpVJIJBJIpVJkZWWpDB6xtbXF8OHDYWxsDGNjYwwePBhJSUmVFjEqo+2RhJyxnYj0geA+MV9fXxw+fBi5ubkaubCVlRU6duyImJgYAEBMTAycnJyUbiUCwKuvvoqEhATI5XKUlpbiwoUL/EtHAG0XMPZpEZE+ENwSy8zMRFBQENavX4/evXvDw8MDQ4YMgZmZ2Utf3N/fH4GBgQgLC0Pjxo0REBAAAFi6dCkWLFgAZ2dneHh44Nq1a/Dy8oJIJEK/fv3w2muvvfQ19Zk+PIdVEY4kJCJ9JcrJyZFXf1iZa9euITY2FidOnEB6ejqMjY0xYMAAjBw5EgMHDoSpqak2Y9U6Xd/PdtmfrpcFbOMACwyzN+yfrabo+ndE3zAfypgPVXrVJ9a5c2d07twZixcvxpUrVxAXF4eTJ0/i559/RsOGDTFo0CCsXr1aW7HWWbXRf/UyLSn+hyQifffS64l1794d7733HqKiovDRRx9BJBIhNjZWk7HVG+y/IiJ6OS+9nlh6ejpiY2MRGxuLGzduQCQSwc3NTZOx1RsVFTD2QxERVU+tIpaVlYUTJ04gNjYWV69eBQB069YNy5Ytw4gRI2BlZaWVIOsbtp6IiIQRXMTeeustXLlyBTKZDJ06dYKfnx/c3d25CGYNlPeFvYgtMCIiYQQXsSdPnmD+/Pnw8PBAy5YttRlTvaGPw+mJiAyJ4CIWGRmpzTjqlJqMNuRMGEREwr306ESqXE0KGPvCiIiEq7QlNmHCBIjFYuzfvx9GRkaYMGFCpTPHlxOJRPjhhx80HqS+08RzXhyNSESkvkqLWM+ePSESiRSFq/w1qdJEAWMLjIhIfZUWsVWrVlX5mv5RVQFjC4uISHsE94lFR0cjLS2t0v0PHjxAdHS0RoKqK9jCIiLSLsFFbPXq1bhy5Uql+//880/Om/j/5cyzR848e1z2bM6Jc4mItEhwEZPLq57svqioCGIxBzsSEVHtqfI5sfT0dKVbiKmpqbh06ZLKcXl5eTh06BDs7Ow0HyEREVElqixiUVFRCAsLU4xS3LFjB3bs2KFynFwuh1gsxooVK7QWKBER0YuqLGLu7u5o37495HI5PvroI3h5ecHV1VXpGJFIBFNTUzg5OXECYCIiqlVVFrG2bduibdu2AICVK1eiR48esLe3r5XAiIiIqiN47sRx48ZpMw6DVNks9EREVDsqLWLlfWHz5s2DWCxGWFhYtScTiUTw8fHRaID6jLPQExHpVqVFbPv27RCJRJg9ezbEYjG2b99e7cnqWxGrbEVmIiKqHZUWsYSEhCpfkyrO0EFEVLsE94lR9ThHIhFR7RJcxIqLi/Hs2TM0adJEsS0nJweHDx9GXl4eRowYAWdnZ60ESUREVBHBRezTTz/F7du3sXv3bgBAYWEhvL29cf/+fQBlKz9v2bIFLi4u2omUiIjoBYInO/zjjz8waNAgxevjx4/j/v372LhxI44dO4a2bdvi22+/1UqQREREFRFcxB4/fgxbW1vF619++QXdunVD//79YW1tjfHjxyM5OVkrQRIREVVEcBFr1KgR8vLyAAClpaW4dOkS+vTpo9hvYmKCgoICzUdIRERUCcF9Yp07d8aRI0fg5uaG+Ph4PHv2DAMHDlTsv3fvHqytrbUSJBERUUUEFzFfX1+88847mDNnDuRyucpoxJ9//pmDOoiIqFYJLmJOTk7Yv38/rly5AnNzc/Ts2VOxLy8vD56enkrbiIiItE2th50tLCwwePBgle2vvPIKpk2bprGgiIiIhFB7xo5ff/0Vv/76Kx48eAAAaNGiBQYNGoT+/ftrPDgiIqKqCC5iRUVF8Pf3x9mzZyESidC0aVMAwLlz53Do0CH0798fn332GYyNjQVfPDU1FYGBgcjNzUWTJk0QEBCAVq1aVXrszJkzMXXqVCxZskTwNYiIqO4SPMR+27Zt+O233+Dj44PY2FhERUUhKioKcXFxmD9/Ps6ePStopvvnBQcHw9PTEwcPHoSnpyeCgoIqPE4qlSIoKAhDhgxR6/xERFS3CS5isbGxGD9+PObPnw9zc3PFdjMzM/j4+GDcuHH473//K/jCjx8/RnJyMjw8PAAAHh4eSE5ORnZ2tsqxu3btwsCBAyttpRERUf2k1owdnTt3rnR/p06d8PjxY8EXzsjIgI2NDSSSsvW3JBIJbGxskJGRoXTcjRs3cO7cOUyfPl3wuYmIqH4Q3Cdma2uLixcvYsqUKRXuv3jxotK0VJpQWlqKoKAgrFy5UlHsqpOSklKja6r3/kYavbY+qoufqaaYE2XMhzLmQ1VNcuLo6FjlfsFFbOzYsQgNDcWaNWswffp0tG7dGkDZgIs9e/bg559/xttvvy04MFtbW2RlZUEqlUIikUAqlSIrK0upED58+BD37t3Du+++C6DseTS5XI6CggJ89NFHFZ63ug9clZSUFPXef+a+xq6tj9TORz3AnChjPpQxH6q0nRPBRWzu3LlIS0tDVFQUjh49CpFIBACQy+WQy+V47bXXMGfOHMEXtrKyQseOHRETE4PRo0cjJiYGTk5OsLS0VBzTvHlzxMbGKl5v27YNz5494+hEIiICoEYRE4vF+M9//oNp06bhzJkzSE9PB1D2nNi//vUvdOjQQe2L+/v7IzAwEGFhYWjcuDECAgIAAEuXLsWCBQu4yCYREVWp2iJWVFSE+Ph4pKWloUmTJhg4cCDmzp2rkYu3adMGO3bsUNm+cePGCo9fsGCBRq5LRER1Q5VFLCsrC2+99RbS0tIgl8sBAA0bNsTnn3+OXr161UqARERElalyiP3WrVvx4MEDTJ8+HV988QXee+89GBsb4/PPP6+t+IiIiCpVZUssISEBY8aMURpIYWVlhZUrVyIjI0PjQ+qJiIjUUWVL7NGjRyprhLm4uEAul6s8lExERFTbqixiMplMZULf8tdFRUXai4qIiEiAakcn3r9/H1evXlW8zs/PB1D2kHOjRo1Uju/SpYsGwyMiIqpctUVs+/btFc5Ov2HDBqXXcrkcIpEI586d01x0REREVaiyiK1cubK24iAiIlJblUVs3LhxtRUHERGR2gQvxUJERKRvWMRewqn7hXDZn67rMIiI6j0WsZew9LccpOZLdR0GEVG9J3gW+/rm1P1CtYpVa3Nhi3YSEZHmsCVWCXUL2MYBFtoNiIiIVLAlVgl1Cthlz+ZajoaIiCqiVhErKSlBdHQ0EhMTkZ2dDT8/Pzg5OSEvLw/x8fFwc3OrV5MCswVGRKRbgotYTk4OFi5ciJs3b8LKygrZ2dl48uQJAMDMzAyhoaG4desW/Pz8tBasLuXMs9d1CERE9ALBfWIhISFIT0/H9u3bERkZqVgkEwDEYjGGDRuGs2fPaiVIIiKiigguYmfOnIGXlxe6d+9e4X4HBwekp/PZKSIiqj2Ci1hBQUGV/V0lJSWQSvnsFBER1R7BRczBwQHXrl2rdP+5c+fQvn17jQRFREQkhOAiNmHCBBw9ehTHjx9X9IeJRCIUFhYiJCQECQkJmDx5stYCJSIiepHg0YleXl64desWVq1apVgMc8WKFXjy5AlkMhmmTp3KWe+JiKhWqfWc2PLlyzF27FjExcXh7t27kMlkaNmyJdzd3dGjRw9txUhERFQhtWfs6N69e6UjFImIiGoT504kIiKDJbglNmHCBIhEoiqPEYlE+OGHH2ocFBERkRCCi1jPnj1ViphUKkV6ejquXLmCdu3awcnJSeMBEhERVUZwEVu1alWl+27cuIHFixdj1KhRGgmKiIhICI30iXXs2BGTJ09GSEiIJk5HREQkiMYGdlhZWeH27duaOh0REVG1NFLEcnJy8OOPP6JZs2aaOB0REZEggvvEfH19K9yen5+PO3fuoLS0FAEBAZqKi4iIqFqCi5hcLq9wiL2dnR169+6N1157DW3atFHr4qmpqQgMDERubi6aNGmCgIAAtGrVSumY8PBwxMTEQCKRwMjICL6+vujfv79a1yEiorpJcBHbunWrxi8eHBwMT09PjB49Gj/99BOCgoKwZcsWpWOcnZ0xY8YMmJqa4saNG3j77bdx7NgxmJqaajweIiIyLIL6xAoLC+Hr64uoqCiNXfjx48dITk6Gh4cHAMDDwwPJycnIzs5WOq5///6KguXo6Ai5XI7c3FyNxUFERIZLUBEzNTXF9evXNbroZUZGBmxsbCCRSAAAEokENjY2yMjIqPQ90dHRaNmyZZWLcxIRUf2h1owdv//+OyZOnKjFcCp36dIlhIaGVvssWkpKSo2u88/7G2n0vIaqvn7uqjAnypgPZcyHqprkxNHRscr9govY+++/Dz8/P2zatAlTpkxBixYtIBa//Ah9W1tbZGVlQSqVQiKRQCqVIisrq8JW1pUrV/Dxxx9jw4YNaN26dZXnre4DVyUlJeWf95+5r7HzGiqlfBAA5uRFzIcy5kOVtnNSZRGLjo5Gjx49YGdnh9dffx1yuRwRERGIiIiASCSCkZHy20UiEeLj4wVd2MrKCh07dkRMTAxGjx6NmJgYODk5wdLSUum4pKQkrFixAsHBwejUqZOaH4+IiOqyKovY6tWrERAQADs7O7i7u1c7i726/P39ERgYiLCwMDRu3FjxnNnSpUuxYMECODs747PPPkNRURGCgoIU7wsMDESHDh00GsvzTt0v1Nq5iYhIc6osYnK5XPHvqiYAfllt2rTBjh07VLZv3LhR8e9du3Zp/LrVWfpbTq1fk4iI1MdFMZ+TkC2Gy/50pOYrj8JsbS7RUURERFSVagd2aPoWoj5b+5cx0opUHyPYOMCi9oMhIqJqVVvEVq9ejTVr1gg6mToDO/RRWpFqw7S1uQTD7Dk7CBGRPqq2iHXp0gX29va1EYveaW0uYSuMiEiPVVvEJk2aVG9XbL7s2VzXIRARURU4sIOIiAwWixgRERksFjEiIjJYVfaJJSQk1FYcREREamNLjIiIDBaLGBERGSwWMSIiMlgsYkREZLBYxIiIyGCxiKFs/TCX/em6DoOIiNTEIoay9cNeXH6FiIj0H4sYUGEB4xpiRET6j0WsApy9nojIMFQ7i319xNnriYgMA1tiRERksFjEiIjIYLGIERGRwWIRIyIig8UiRkREBoujE4kEkMvlyM/Ph6mpKXJzc3Udjt5gPpQxH6qE5kQsFsPc3BwikUit87OIEQmQn58PExMT2NjYwNTUVNfh6A0TExPm4znMhyqhOSkuLkZ+fj5eeeUVtc7P24lEAshkMhgbG+s6DKI6y9jYGDKZTO33sYgREZHBYhEjIiKDxSJGZIAmTJiAmzdvauRc8fHx2LRpU5XHJCYm4ty5c4rXWVlZ8PX1rfbcb7/9NiZOnIgZM2Zg6tSpCA8Pr3G82iL0M6lr7969+P7775W2HThwAH369EFycrLS9op+rrNnz0ZiYqLi9fHjxzFz5kx4enpi9uzZWLFiBdLTNbOU1KNHj+Dn54cpU6bgjTfewJ9//lnpsRcuXMDs2bPh5eUFLy8v3LhxAwDw9OlTrFq1CtOnT4enpyciIyMV7zlw4AB27NihkVjLcWAHUT03ePBgDB48uMpjEhMT8ezZM/Tr1w8AYGNjgy1btqCwsLDa8y9btgyDBg3Cw4cP8frrr6Nv377o2rWrRmIvLS2FkZFmvsbKP5MmFRYWYs+ePUpf5AAQFRUFNzc3/Pjjj/jggw8En+/w4cOIjIzE+vXr0apVKwBlP5tHjx6hefOaz/n6zTffwNXVFZs3b8Yff/yBVatW4cCBAyojBjMzM7FmzRps2rQJrVu3RmFhIUpLSwEAO3fuRIMGDRAREYHCwkJ4e3vDzc0N3bp1w8SJE+Hp6QlPT0+Ym5vXOF6ARYzopVjsuK+V8+bMs6/R+6Ojo/H9999DJBLB3t4ey5cvh5WVFUpKSrB+/XokJibCysoKHTt2xKNHjxAcHIyjR4/izJkzCA4ORmpqKgIDA1FYWAiZTIZx48ahX79+OHToEORyOc6fPw8PDw+MHDkSc+bMQVRUFADgypUr2Lx5MwoKCgAAixcvVhS8ck2bNkXr1q2Rnp6Orl274uHDh9iwYQPS09NRVFQEDw8PzJs3DwDw+++/Y926dRCJROjVqxdOnz6NL7/8Eu3bt8eECRMwcuRIXLx4Ee3bt4e/vz+2bNmCS5cuobi4GI6Ojvj3v/+NRo0a4YcffkBkZCQaNGgAuVyOtWvXolWrVli/fj0uXryIBg0aoFGjRggLC0NaWhrmzJmD2NhYAMDZs2fx9ddfQyaTwcLCAsuXL4eDgwMSExPxxRdfoGvXrvjf//4HAPj000/Rtm1blZ/HyZMn0aNHD6XReTdv3kR2djaCgoIwd+5cLFmyRPCgobCwMKxYsUJRwACgV69eQn89qhUXF4cjR44AAFxdXdGgQQNcu3YNzs7OSscdOHAAY8aMQevWrQFA6fOlpKRg7NixEIlEaNiwIVxdXXH8+HF069YNRkZG6Nu3L+Li4jBx4kSNxMwiRlRH3Lx5E19//TV2796Npk2bYuvWrdiwYQPWrl2LQ4cOIT09HXv37oVUKoWvry+aNWumco4DBw5g8ODBmDt3LgDgyZMnaNy4MSZPnoxnz55hyZIlAIC0tDTFe3Jzc/Hhhx9i3bp16N69O6RSqaKYPS81NRW5ubmKL92AgAB4e3ujZ8+eKCkpwcKFC+Hs7IwePXpg5cqVWL16NXr06IFTp05h7969SucqKCjAzp07AQDh4eEwMzNTvN68eTN27tyJhQsXYtOmTdi/fz+aNm2K4uJiyGQypKSkIDExEXv37oVYLMaTJ09UYn38+DFWrVqFrVu3ol27djhy5Ag+/vhjxa2wW7du4eOPP8by5cvx7bff4ttvv8Xq1atVzpOYmIguXboobTty5AjGjh0LOzs7dOzYEadPn8bIkSMr+pGqxJSZmSm4FRsdHY2IiIgK982dO1flmjk5OZDL5bCwsFBsa968OTIyMlSK2O3bt9GiRQv4+voiLy8PvXr1wqJFi2BsbIxOnTrh5MmTGDp0KPLz83H+/Hm0adNG8d5u3brht99+YxEjImUXL17EgAED0LRpUwDApEmTMGPGDABlX6ZjxoyBkZERjIyM4OHhgT/++EPlHD169MDmzZtRWFiIXr16wc3Nrdrr/u9//0Pbtm3RvXt3AIBEIkHjxo0V+z///HOEhIQgNTUVS5YsgaWlJZ49e4bExERkZ2crjnv69Cnu3LkDKysrmJiYoEePHgCAYcOGqTw7NGbMGMW/f/nlFxQUFODkyZMAgJKSEjg6OgIA3NzcEBgYiIEDB2LgwIGwt7eHvb09SktLsWbNGri5uWHgwIEqn+nq1atwdHREu3btAADjx4/HunXrFMW5devWcHJyAgB07doVZ86cqTA3mZmZSucvLS1FTEwMwsLCAADjxo3Djz/+WG0RU/cBYAAYO3Ysxo4dq/b7hJDJZLhy5QpCQkJgYmKClStXYvfu3XjzzTcxZ84cbNq0CbNnz4alpSVcXV2Rl5eneK+1tTUyMzM1FotOi1j5rYvc3Fw0adIEAQEBSs1kAJBKpfj8889x9uxZiEQizJ49W2MVnIiUDR8+HN26dUNCQgJ2796NqKgofPLJJzU6Z3mf2OXLl/HOO++gd+/eaNGiBUQiEXbt2qXSp5WSklLtORs1aqT4t1wux4cffojevXurHLdu3TokJSXh4sWL8PX1hb+/PwYMGIA9e/YgMTERFy5cQEhICHbv3q3WZ3r+9p9EIlH0B73IxMQExcXFitfx8fHIz8/HokWLAJQVg0ePHiEjIwO2trawtLRUmd0iNzcXlpaWsLKyQrNmzXD16lWVW7UVUbclVt4Cy8nJUfw7PT0dtra2Ku9v3rw5OnfurOjXGjFiBI4dOwag7Nbihx9+qDh27dq1Srdai4uLYWJiUm38Qum0iAUHB8PT0xOjR4/GTz/9hKCgIJWO1ePHj+Pu3bs4ePAgcnNzMWvWLPTp0wd2dnY6ipqo5n1X2uDm5oZdu3bh4cOHaNq0KY4cOYK+ffsCKOs3OX78ONzd3SGVShEbGwsbGxuVc9y9exf29vYYN24cHBwcFAXMzMwMWVlZFV63W7duuH37Nq5cuaJ0O/H51hgAuLi4wNPTU3Gb09XVFbt27YKPjw8AICMjAxKJRDFQ4PLly3BxccHp06eV/pJ/0eDBgxEREYFu3brB1NQUBQUFyMzMhIODA9LT09GlSxd06dIF9+7dQ3JyMjp37gyJRIL+/fujT58+OHPmDNLS0hQtWKCsdZWSkoI7d+6gTZs2iI6OhpOTE8zMzNT6mXTo0AGpqamK11FRUXj//feV/hAPCgrC0aNH4ePjgz59+uDIkSNwcXGBRCLBr7/+CrFYDAcHBwCAt7c3Nm7ciA0bNqBly5YAgEuXLsHExETltuXLtMRGjBiBgwcPwsfHB3/88QeKiorQqVMnleNeffVVfPPNN5gzZw6MjIxw7tw5Res3Pz8fRkZGMDU1RUpKCuLj4/Hdd98p3nv79m3FsZqgsyL2+PFjJCcnw8PDAwDg4eGB9evXIzs7G5aWlorjyjsAxWIxLC0tMWTIEJw4cQKzZs3SVehEemHRokVKrZiIiAgsWrQIfn5+EIlEsLOzw/LlywEAkydPxo0bN+Dl5QULCwulPornxcXF4fjx42jQoAFEIhGWLVsGABg6dCg+/PBDzJgxQzGwo1yTJk2wbt06bNy4EYWFhRCJRFiyZAn69Omjcv558+Zh8uTJSE5OxieffIKNGzdi+vTpAMpaVytXrkTTpk2xevVqBAcHQyQSoUePHrCysqp0NNucOXOwbds2zJ07F2Jx2VND8+fPh729PQIDA5Gfnw+RSARbW1ssWrQI6enpWLt2LUpLSyGVStG/f3907dpVaZi6paUlAgMDsXLlSkilUlhYWCAwMFCNn06ZYcOG4bPPPsOCBQuQlZWFxMRElZbtq6++itWrV8Pb2xve3t7YtGkTZs2aBZFIhMaNG+Ozzz5T/JwnT54MExMT+Pv7o6ioCGKxGI6OjvDz81M7toosWrQIq1atwrFjx2BiYoLAwEBFTtesWaMYydq9e3f0798fM2fOhFgshpOTk2JQTlpaGpYvXw4jIyMYGxtj5cqVSn8wnTt3TqOPMohycnLkGjubGq5du4aAgAClDlsvLy8EBgYqVf7p06dj5cqVio7F3bt3IzMzE++//77GYnlxpJk+/pWtCykpKRr9i8mQld/yLiwsNNi58QoKCmBmZobi4mIsW7YMI0aMqPGteW3lozxWoKyv75NPPsHhw4cVX6j6qqJ8+Pn54Z133lH0odU3z+fkzp07CAoKQmhoaIXHlv8/U0edG9gh5H76iy680K/7Mueoq5iLMqampor7+EKejdJHCxcuRElJCYqLi9GrVy+4u7tr5LNoIx8xMTHYt28fZDKZYuDA831L+uzFfCxevBj37t1TDEevj8pzcvfuXbz77ruV/s48efJEZdBHdX9I66yI2draIisrC1KpFBKJBFKpFFlZWSqdiM2bN8eDBw8ULbGMjIwqH+qrScuBLQ9lzMc/cnNzYWpqatAtsV27dmn8nNrKx6RJkzBp0iSNn1fbKsqHo6Njvf5/9HxOBg0aVOWxjRs3VvT/CaWztnn5A5cxMTEAyv7ycnJyUuoPA8o6Gg8fPgyZTIbs7GycPn0aw4cP10XIRESkZ3R6g9nf3x/79u3DlClTsG/fPvj7+wMAli5diqSkJADA6NGjYW9vjylTpsDb2xs+Pj6wt2efFRER6XBghz7i7TNlzMc/8vLyYGJiAplMZrC3E7XBkG+vagPzoUpoToqLi1FUVKT2oph1bmAHkTaYm5sjPz8fOTk5Ks8/1Wfl01JRGeZDldCciMXil5oUmEWMSACRSIRXXnkF6enpanc812XlDxVTGeZDlbZzot8PXRAREVWBRYyIiAwWB3YQEZHBYkuMiIgMFosYEREZLBYxIiIyWCxiRERksOrdc2JcTVqVkJyEh4cjJiYGEokERkZG8PX1Rf/+/XUUsXYJycfzx86cORNTp07FkiVLajnS2iE0H7Gxsfj2228hl8shEokQEhICa2trHUSsfUJy8vjxY6xevRoZGRkoLS1Fr169sGzZMpWVrA3dV199hZMnT+LBgweIjIxE+/btVY7R5ndqvWuJla8mffDgQXh6eiIoKEjlmOdXkw4PD0dYWBjS0tJ0EG3tEJITZ2dn7Nq1CxEREfjPf/6DFStWGOySJNURkg+g7D9mUFAQhgwZUssR1i4h+UhKSkJYWBg2b96MPXv2YPv27S81+4KhEJKTnTt3ok2bNoiIiEBERASuX7+OU6dO6SBa7RoyZAi2bduGFi1aVHqMNr9T61URq2g16eTkZGRnZysdV9lq0nWR0Jz0799fMf+Zo6Mj5HI5cnNzaz1ebROaD6BsaZOBAwdW2kqrC4TmIzIyEjNmzEDTpk0BlE3TVb7+Wl2jzu/I06dPIZPJUFxcjJKSEjRr1qy2w9U6V1dXlSW0XqTN79R6VcQyMjJgY2MDiUQCAJBIJLCxsUFGRobScenp6Up/Vdja2qocU1cIzcnzoqOj0bJly2p/cQ2R0HzcuHED586dw/Tp03URZq0Rmo/bt2/j/v37WLBgAWbNmoXw8HDI5XXzEVShOfHx8cHff/+NMWPGYPTo0ejXrx9cXFx0EbLOafM7tV4VMaq5S5cuITQ0FGvWrNF1KDpTWlqKoKAg+Pv7K77I6juZTIa//voLISEh2Lp1K86ePYtjx47pOiydOnHiBDp06IBjx44hOjoav//+e529o6NL9aqIPb+aNIBqV5Mul5GRUSdbHYDwnADAlStX8PHHH2P9+vV1dql1Ifl4+PAh7t27h3fffRcTJkzAnj17cPjwYaxdu1ZXYWuN0N8PW1tbDB8+HMbGxjAzM8PgwYMVawLWNUJzsm/fPowaNUoxO/vgwYORmJioi5B1TpvfqfWqiHE1aVVCc5KUlIQVK1YgODgYnTp10kWotUJIPpo3b47Y2FgcOXIER44cwbRp0zBx4kR89NFHugpba4T+frz66qtISEiAXC5HaWkpLly4UGfXohOaEzs7O5w9exYAUFJSggsXLqBdu3a1Hq8+0OZ3ar2bO/HOnTsIDAxUrHETEBCA1q1bY+nSpViwYAGcnZ0hlUqxfv16JCQkAABmz56NSZMm6Thy7RGSkzlz5uDBgwewsbFRvC8wMBAdOnTQYeTaISQfz9u2bRuePXtWZ4fYC8mHTCbDV199pRhC3a9fPyxZsgRicd38O1lITu7du4fg4GA8evQIMpkMvXr1wnvvvVfnhthv2LABP//8Mx49eoQmTZqgSZMm2Lt3b619p9a7IkZERHVH3fwziYiI6gUWMSIiMlgsYkREZLBYxIiIyGCxiBERkcFiEaM6Iy0tDX369MHRo0d1HYpWBQYGYsKECYKOTUxMRJ8+fertQ7ZU99WtBxbIIB09ehSffPJJhfs8PT3xwQcf1HJE6unTp4/SazMzMzg5OWHmzJkYOHBgrcSwY8cOtG3bFkOHDq2V6wlRUV46duyIWbNmvXRefv31V1y9ehULFizQRIhUB7CIkd6YP38+WrZsqbTNUKa3cnNzw/jx4yGXy5GWloYDBw5g2bJl+PLLLzFgwACNXmvFihWQyWRK23bu3Inhw4erFLEePXrgl19+QYMGDTQag1DP5+XBgwc4dOgQli1bho0bN77UenS//fYb9u/fzyJGCixipDf69euHbt266TqMl+Lg4IDRo0crXg8dOhRvvPEGIiMjNV7E1JnxQSwW63RJlBfzMnz4cHh5eWHv3r11dlFVql0sYqT3Hjx4gO+++w4XL17EgwcP0KBBA7i4uGDRokXVTnv19OlTbN++HSdPnsTDhw9hZmaGdu3aYcGCBejZs6fiuKSkJGzbtg2XL19GaWkpOnXqhLfeegtubm4vFXOHDh1gYWGhtPBf+QoA165dg0QigaurK9555x2llXCFxBsYGIhLly7hyJEjAP65bRcdHY3o6GgAQM+ePbF161YkJibC19cXW7ZsQa9evfD9999j06ZNOHTokEqrNzw8HKGhoTh8+DDs7Oy0kpe2bdvCwsIC9+7dU9oeHx+Pw4cP48aNG8jOzoa1tTXc3d3x1ltvKYpwYGCg4vM9f6vy+XiPHz+OyMhI3Lp1C8bGxujduzf8/Pxgb2//UvGS/mMRI72Rn5+PnJwcpW0WFhZISkrC77//juHDh6N58+bIysrCDz/8gLfffht79uxRLMRYkc8++wxxcXGYOnUq2rVrh7y8PPz5559ISUlRFIVLly5h8eLF6NixI958800YGRnh2LFj8PPzQ0hICHr16qX2Z3ny5Any8vIUheLixYvw8/ODnZ0d5s+fj+LiYuzfvx9vvvkmdu7cqbhtKiTeFwUGBuLTTz+Fs7OzYj46KyurCo8dOXIkNm/ejNjYWMybN09pX1xcHLp27aooCNrIS35+vlJeykVFRcHY2Bivv/46zM3N8eeffyIyMhIZGRn49NNPAQCTJk3Cw4cPkZCQgMDAQMV7yyfe3bVrF7755hsMHz4c48ePx5MnT7B//37Mnz8f//d//6cyQS/VDSxipDcqmkD3559/xr/+9S+MGDFCafuYMWPg5eWFI0eOwMfHp9JznjlzBhMnTsS7775b4X65XI6goCC4uLggJCQEIpEIADB58mTMnDkT33zzDcLDw6uNvbi4GDk5OYq+n6+//hpSqVQR91dffQVzc3OEh4fDwsICQFlBmTZtGrZs2YLg4GBB8VZk9OjRCA4Ohr29vdKtu4rY2tqie/fuKkXs1q1buHnzpuK62shLeno6tm7dCqlUqjKD+erVqxUrh5dfx8HBAVu3bsXixYsVcbdq1QoJCQkqn7P83PPnz8ebb76p2O7h4YFp06YhMjISCxcurDZeMjwsYqQ3li1bhrZt2yptMzExUVp4srCwEIWFhTAzM0OrVq1w/fr1Ks9Z/ld9ZmZmhUvDp6SkIDU1FbNmzUJubq7Svr59+2Lfvn0oLCxU+oKtyPO38gCgYcOG8PHxwfTp0/Hw4UMkJyfjjTfeUBQwAGjVqhUGDx6Ms2fPQiqVQiKRVBuvJowcORIbNmzAnTt30KZNGwBlrTCxWAx3d3cA2stLgwYNMHv2bLzxxhtKx5WfRyaT4enTpygtLYWLiwvkcjmuX79e7dpTp06dglQqxciRI5Va8+bm5mjfvj0uXrxY5fvJcLGIkd5wdnaucGBHUVERQkNDcfz4cTx8+FBpX5MmTao8p5+fHwIDA/Haa6+hY8eO6N+/P8aMGaO4fZeamgoAVa5UnZubW+2X9cCBAzFt2jSIRCJYW1vDzs5O8Z7yxQArGmnZpk0bnDx5Ejk5ObC2tq42Xk0YMWIEvvzyS8TExChG+cXGxsLV1VWx1I6m81JaWoqkpCTs3LkTxcXFKku03Lx5E5s3b0ZiYiKKioqU9uXn51f7mf7++28AZY9kVIR9YnUXixjpvQ0bNiAqKgqvv/46unXrhldeeQVisRhffPEF5PKqVxJyd3eHq6sr4uPjkZCQgH379uG7777Dxx9/jFGjRinev2jRInTu3LnCczzfeqqMjY2NynNRL6O6eDXB2toaPXv2RFxcHBYsWIAbN24gNTUV06ZNUxyjjbwMGDAAVlZWCA4ORs+ePRWPA+Tn58PX1xcNGzaEr68vHBwcYGJigszMTHzyySfV/owBKB45+Oqrr5Ra7uV0OUKTtItFjPTeiRMnMGbMGLz33ntK2/Py8gR9kTZt2hSTJ0/G5MmTkZeXB29vb2zfvh2jRo1SDDAwMzPTSBGqSIsWLQD807p53p07d9CwYUOlz1FVvJUp77MSauTIkVi7di1SUlIQGxsLiUSi1O+orbxMmDABERER2LJlC4YMGQKRSISLFy8iJydHUdzKlS+gKER5vLa2tvV29eT6itNOkd4Ti8Uqf43/97//RVZWVpXvk0qlKreiXnnlFdjZ2SEvLw8A0KlTJzg4OCAiIgIFBQUq58jOzq5h9GVFqVOnTjh27JhS/9K9e/fwyy+/oH///pBIJILirYypqWm1xzxv+PDhMDIyQmxsLOLi4tC7d2+lQqqtvEgkEsyYMQO3b9/Gzz//rNgGQOlnLJPJEBERofL+hg0bAigb/fni55FIJAgPD6+w5fbiqFeqO9gSI703ePBgHDt2DGZmZmjfvj1u3LiB2NjYavs5nj59irFjx2LYsGHo2LEjzMzMcPnyZZw9e1bRdyIWi7FixQosWbIEXl5eeO2119CsWTNkZWXh0qVLAIAtW7bU+DMsXrwYfn5+8PHxwcSJE1FUVIQDBw7A2NgYvr6+guOtTOfOnXH+/Hl8//33aNasGSwtLdG7d+9Kj2/cuDH69u2L/fv3o6CgQGWEpzbzMmbMGISGhmLXrl0YNmwYunfvjiZNmiAwMBCvv/46jIyMcOLECTx79qzCzwkA69evx4ABAyCRSDBo0CDY29tj0aJF2LRpEx48eIAhQ4bglVdeQVpaGuLj4+Hu7s5ZPuooFjHSe++99x6MjIwQFxeHH3/8EZ07d8amTZuwadOmKt9namqKqVOn4vz58/jll19QWloKOzs7xRdzuZ49e+Lbb79FeHg4Dhw4gIKCAlhbW8PZ2VnwRLvVcXNzQ0hICEJDQxEaGgojIyO4urpi0aJFikEbQuOtyLvvvougoCBs374dz549Q8+ePassYkDZ8PNff/0VxsbGFc65qK28mJiYYOrUqdi2bRvOnz+PPn364Msvv8RXX32Fbdu2oVGjRhg2bBimTJmiMopx2LBhmDZtGmJiYhATEwO5XI7Dhw+jYcOGmDlzpqL1uGPHDshkMjRr1gxubm4qj2hQ3SHKycmpvteUiIhID7FPjIiIDBaLGBERGSwWMSIiMlgsYkREZLBYxIiIyGCxiBERkcFiESMiIoPFIkZERAaLRYyIiAwWixgRERms/wexdQe9kItDTgAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#1. Logistic regression\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "clf_lr = LogisticRegression(solver='liblinear')\n", "clf_lr.fit(X_train, y_train)\n", "\n", "Y_pred_lr = clf_lr.predict(X_test)\n", "print(clf_scores(clf_lr, Y_pred_lr))" ] }, { "cell_type": "code", "execution_count": 18, "id": "8513530d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:12.769209Z", "iopub.status.busy": "2022-01-28T14:13:12.768541Z", "iopub.status.idle": "2022-01-28T14:13:13.703038Z", "shell.execute_reply": "2022-01-28T14:13:13.702445Z", "shell.execute_reply.started": "2022-01-28T14:12:39.637955Z" }, "papermill": { "duration": 0.9631, "end_time": "2022-01-28T14:13:13.703177", "exception": false, "start_time": "2022-01-28T14:13:12.740077", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "( 0\n", "acc_train 100.0\n", "Test Accuracy 89.0\n", "Roc Score 88.481338\n", "COrrect 89\n", "Incorrect 11\n", "Confusion [[57, 6], [5, 32]], <AxesSubplot:>, <sklearn.metrics._plot.roc_curve.RocCurveDisplay object at 0x7f43bc1ab890>, <sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay object at 0x7f43bc0c2d10>)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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IkSXeOf4ViUSCo0eParxIIiKi4pQYYk5OTpBIJEJwvXpMRERUVZQYYqtWrSr1MRERUWUTfU0sLCwMSUlJJS5PTk5GWFiYRooiIiISQ3SIrV27FteuXStx+T///MP7JhIRUYUSHWJKZek3u8/Pz4eeHgc7EhFRxSn1d2IpKSkqpxATExNx5coVtfWys7Nx5MgRWFtba75CIiKiEpQaYqGhodi5c6cwSnH37t3YvXu32npKpRJ6enpYsWKF1golIiJ6U6kh5urqihYtWkCpVGL58uXw8vJChw4dVNaRSCQwMjKCvb09bwBMREQVqtQQa9asGZo1awYAWLlyJTp27IhGjTjrMRERVQ2i7504bNgwbdZBRERUbiWG2KtrYdOnT4eenh527txZ5sYkEglmzpyp0QKJiIhKUmKIBQYGQiKRYMqUKdDT00NgYGCZG2OIERFRRSoxxGJjY0t9TEREVNn462QiItJZogd2FBQU4MWLFzAzMxOey8zMxLFjx5CdnY3+/fvD0dFRK0USEREVR3SIffXVV7h79y727dsHAMjLy8OMGTPw6NEjAC9nft62bRvat2+vnUqJiIjeIPp04n//+1/06tVLeHz69Gk8evQImzdvxqlTp9CsWTPs2rVLK0USEREVR3SIPX36FFZWVsLjc+fOoW3btujRowcsLCwwfPhwxMfHa6VIIiKi4ogOMWNjY2RnZwMAioqKcOXKFXTt2lVYbmhoiNzcXM1XSEREVALR18QcHBxw/PhxdO7cGdHR0Xjx4gWcnZ2F5Q8fPoSFhYVWiiQiIiqO6BDz9vbGJ598gqlTp0KpVKqNRjxz5gwHdRARUYUSHWL29vY4dOgQrl27hlq1asHJyUlYlp2dDU9PT5XniIiItE10iAFAnTp10Lt3b7Xna9eujXHjxmmsKCIiIjHKFWIA8Oeff+LPP/9EcnIyAKBhw4bo1asXevToofHiiIiISiM6xPLz8+Hj44OYmBhIJBLUq1cPAHD+/HkcOXIEPXr0wLp162BgYCB654mJiZDJZMjKyoKZmRlWr16Nxo0bl7jupEmTMGbMGCxcuFD0PoiIqPoSPcR+x44d+OuvvzBz5kxEREQgNDQUoaGhiIyMxKxZsxATEyPqTvev8/Pzg6enJw4fPgxPT0/4+voWu55cLoevry/69OlTru0TEVH1JjrEIiIiMHz4cMyaNQu1atUSnjcxMcHMmTMxbNgw/Prrr6J3/PTpU8THx8PNzQ0A4Obmhvj4eGRkZKitu3fvXjg7O5d4lEZERO+nct2xw8HBocTlrVu3xtOnT0XvODU1FZaWlpBKpQAAqVQKS0tLpKamqqx369YtnD9/HuPHjxe9bSIiej+IviZmZWWFS5cuwcPDo9jlly5dUrktlSYUFRXB19cXK1euFMKuLAkJCW+xJ2MNbKN6Yi/UsSeq2A9V7Ie6d+lJq1atSl0uOsSGDh2KgIAAfPnllxg/fjyaNGkC4OWAi4MHD+LMmTOYO3eu6MKsrKyQnp4OuVwOqVQKuVyO9PR0lSB8/PgxHj58iMWLFwN4+Xs0pVKJ3NxcLF++vNjtlvWGi/XHo3ffRjWUkJDAXryBPVHFfqhiP9RpuyeiQ2zatGlISkpCaGgoTp48CYlEAgBQKpVQKpUYMWIEpk6dKnrH5ubmsLOzQ3h4OAYPHozw8HDY29ujbt26wjoNGjRARESE8HjHjh148eIFRycSERGAcoSYnp4evvjiC4wbNw5//PEHUlJSALz8ndiHH36Ili1blnvnPj4+kMlk2LlzJ0xNTbF69WoAwKJFizB79mxOsklERKUqM8Ty8/MRHR2NpKQkmJmZwdnZGdOmTdPIzps2bYrdu3erPb958+Zi1589e7ZG9ktERNVDqSGWnp6OOXPmICkpCUqlEgBQs2ZNbNq0CZ06daqQAomIiEpS6hD77du3Izk5GePHj8c333yDJUuWwMDAAJs2baqo+oiIiEpU6pFYbGwshgwZojKQwtzcHCtXrkRqaqrGh9QTERGVR6lHYk+ePFGbI6x9+/ZQKpVqP0omIiKqaKWGmEKhULuh76vH+fn52quKiIhIhDJHJz569AjXr18XHufk5AB4+SNnY2NjtfXbtGmjwfKIiIhKVmaIBQYGFnt3+o0bN6o8ViqVkEgkOH/+vOaqIyIiKkWpIbZy5cqKqoOIiKjcSg2xYcOGVVQdRERE5SZ6KhYiIqKqhiFGREQ6iyFGREQ6iyFGREQ6iyFGREQ6iyFGREQ6S/SkmABQWFiIsLAwXL58GRkZGZg/fz7s7e2RnZ2N6OhodO7cmTcFJiKiCiM6xDIzM/Hxxx/j9u3bMDc3R0ZGBp49ewYAMDExQUBAAO7cuYP58+drrVgiIqLXiT6d6O/vj5SUFAQGBuLAgQPCJJkAoKenBxcXF8TExGilSCIiouKIDrE//vgDXl5eaNeuXbHLbW1tkZKSorHCiIiIyiI6xHJzc0u93lVYWAi5XK6RooiIiMQQHWK2tra4ceNGicvPnz+PFi1aaKQoIiIiMUSH2MiRI3Hy5EmcPn1auB4mkUiQl5cHf39/xMbGYvTo0VorlIiI6E2iRyd6eXnhzp07WLVqlTAZ5ooVK/Ds2TMoFAqMGTOGd70nIqIKVa7fiX3++ecYOnQoIiMj8eDBAygUCtjY2MDV1RUdO3bUVo1ERETFKleIAUC7du1KHKFIRERUkXjbKSIi0lmij8RGjhwJiURS6joSiQRHjx5956KIiIjEEB1iTk5OaiEml8uRkpKCa9euoXnz5rC3t9d4gURERCURHWKrVq0qcdmtW7ewYMECDBo0SCNFERERiaGRa2J2dnYYPXo0/P39NbE5IiIiUTQ2sMPc3Bx3797V1OaIiIjKpJEQy8zMxIkTJ1C/fn1NbI6IiEgU0dfEvL29i30+JycH9+7dQ1FREVavXq2puoiIiMokOsSUSmWxQ+ytra3RpUsXjBgxAk2bNi3XzhMTEyGTyZCVlQUzMzOsXr0ajRs3VlknKCgI4eHhkEql0NfXh7e3N3r06FGu/RARUfUkOsS2b9+u8Z37+fnB09MTgwcPxi+//AJfX19s27ZNZR1HR0dMnDgRRkZGuHXrFubOnYtTp07ByMhI4/UQEZFuEXVNLC8vD97e3ggNDdXYjp8+fYr4+Hi4ubkBANzc3BAfH4+MjAyV9Xr06CEEVqtWraBUKpGVlaWxOoiISHeJCjEjIyPcvHlTo5NepqamwtLSElKpFAAglUphaWmJ1NTUEl8TFhYGGxubUifnJCKi90e57thx9epVuLu7a7Gckl25cgUBAQFl/hYtISHhLbZurIFtVE/shTr2RBX7oYr9UPcuPWnVqlWpy0WH2LJlyzB//nxs3boVHh4eaNiwIfT03n6EvpWVFdLT0yGXyyGVSiGXy5Genl7sUda1a9fwn//8Bxs3bkSTJk1K3W5Zb7hYfzx6921UQwkJCezFG9gTVeyHKvZDnbZ7UmqIhYWFoWPHjrC2tsbYsWOhVCoRHByM4OBgSCQS6OurvlwikSA6OlrUjs3NzWFnZ4fw8HAMHjwY4eHhsLe3R926dVXWi4uLw4oVK+Dn54fWrVuX8+0REVF1VmqIrV27FqtXr4a1tTVcXV3LvIt9efn4+EAmk2Hnzp0wNTUVfme2aNEizJ49G46Ojli3bh3y8/Ph6+srvE4mk6Fly5YarYWIiHRPqSGmVCqFP5d2A+C31bRpU+zevVvt+c2bNwt/3rt3r8b3S0RE1QMnxSQiIp1VZohp+hQiERGRppQ5OnHt2rX48ssvRW2sPAM7iIiI3lWZIdamTRs0atSoImohIiIqlzJDbNSoUZyxmYiIqiQO7CAiIp3FECMiIp3FECMiIp1V6jWx2NjYiqqDiIio3HgkRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOoshRkREOqvU+cSIqrL8/Hzk5eVV6D6NjIyQlZVVofusytgPVeyHOrE90dPTQ61atSCRSMq1fYYY6aTc3FwAgKmpabn/0b8LQ0NDGBkZVdj+qjr2QxX7oU5sTwoKCpCTk4PatWuXa/s8nUg6qaioCCYmJhUaYESkPQYGBlAoFOV+HUOMiIh0FkOMiIh0FkOMSENGjhwJT09PTJgwAWPHjsWxY8e0so/bt29rbHuXL19Gr169MHHiROG/S5cuaWz7JTlz5gyuX7+u8tzjx4+xatUquLu7Y/LkyZg2bZrQQ5lMhpCQEI3WEBcXh5UrVwqPf/75Z3h6emLSpElITk6Gt7e3xvb12WefIS4uTuW55cuXw83NDUVFRcJzSUlJGDBggMp6z58/R9euXYXHRUVFCAgIgIeHByZMmIBJkyZh8+bNKtt5FytXrsSQIUPQtWtXPH/+vMT18vLysHz5cowePRqenp44d+5cscsmTZqksmzFihW4du2aRmoFOLCDSKP8/PzQokUL3L59G5MnT8aHH34IS0vLyi6rVM2aNcO+ffve6rVv+8F59uxZODg4oE2bNgBefujNnTsXw4YNw6pVq6Cnp4ecnByEh4e/1fbFcHR0xNq1a4XHISEhkMlkcHR0BABs27at3Nssrh///PMPnj9/LmwXALKysnDhwgU0btwY0dHR6Nevn+h9rFmzBvn5+di3bx9MTExQVFSE0NBQFBQUQF//3T/SR4wYgcWLF2PQoEGlrvfjjz/CxMQER44cwf379zF79mwcOXIExsbGKssSEhIwf/58YdnUqVOxadMmBAQEvHOtAEOMqpE6ux9pZbuZ0xuV+zUtWrSAqakp0tPTYWlpidOnT+Onn35CYWEhAGDBggXCt+uRI0diyJAhiI2NxZMnTzBx4kSMHTsWAHD16lWsX78eAODk5ASlUinsIy4uDhs3bkReXh6MjIywbNkyODo6IikpCVOnToW7uztiYmKQn5+PNWvW4MiRI7h+/ToMDQ2xYcMG1KtXr8T65XI5/P39ERMTAwDo0aMHPvnkE0ilUshkMkilUiQmJiI3NxfBwcE4efIkDh8+DLlcDhMTE/j4+KBJkya4du0aNmzYAIVCgaKiIsyYMQOmpqY4d+4cLl68iOPHj2PChAkoLCyEqakppk2bJtRQq1YtjB49Wq22CxcuYPv27SgoKIBcLsf06dPh5uYGAAgMDER4eDgMDAwgkUiwbds21KhRA6tXr8bdu3chlUrRpEkT+Pr64vLly9iyZQv27duH5cuX4+HDh1i1ahVat24Nb29vTJ06FREREQBeBtH3338vjIqdPXs2nJ2dhV4PGzYMly5dwrBhw+Dl5aVS79GjRzFw4ECV506fPo0PP/wQ3bt3R2hoqOgQu3//Ps6cOYOTJ0/CxMQEAKCvr49Ro0aJer0YXbp0EbVeREQEVq1aBQBo3LgxHBwc8Ndff8HV1VVlma2trcoyOzs7ZGRk4P79+2jcuPE718sQI9KCv//+G2ZmZmjVqhUAoHv37hg4cCAkEgkSExMxb948nDx5Ulg/Ly8Pu3btQlJSEsaPH49hw4ZBX18fX3zxBdasWYNOnTohIiIChw4dAgAUFhbis88+w8qVK9G1a1dcuHABn332GY4cOQLg5Tf99u3bY968edi/fz/mzZuH7du3Y8WKFVi3bh0OHToknC67e/cuJk6cCODlCLHdu3fj6NGjuHXrFvbv3w8AWLhwIY4ePYoxY8YAAG7duoWAgABIJBJcvXoVUVFRCAgIgIGBAf766y+sXbsWO3fuxL59+zBp0iQMHDgQSqVSGELdq1cvODg4CGG9bt06fPDBB6J627p1awQGBkIqleLJkyeYOnUqunfvDqVSiQMHDuDUqVMwMjJCbm4uDA0Nce7cOeTm5uKnn34CADx79kxtm19//TVGjhwpHEknJSUJy7Kzs+Hn54fNmzejXr16ePz4MaZNm4YDBw4IvXZ0dMTChQuL/d3ilStXMHnyZJXnQkNDsXDhQrRt2xabNm0SvuyUJT4+Hra2tjA1NRXVq40bN+Lq1avFLlu3bh1sbGxEbac4qampaNiwofC4QYMGSE1NLXMZALRt2xYXL15kiBFVNT4+PlAqlXj48CF8fX1Ro0YNAMCjR4+wcuVKpKWlQV9fH0+ePMHjx4+Fo6FX10Gsra1Ru3ZtpKWlobCwEEZGRujUqZOwjq+vLwAgMTERNWrUEI7munbtiho1aiAxMRHGxsYwNjaGs7MzAMDe3h7169eHnZ0dgJchcOHCBaHm4k4nXrx4EcOGDRPqHz58OM6cOSOEWP/+/VGzZk3k5eXh3LlzSEhIwPTp0wEASqUS2dnZAIBOnTph165dePjwIbp16yY6qEqTkZGBtWvX4sGDB5BKpcjKykJiYiIcHR1hY2OD1atXo3v37nB2doaJiQns7Oxw7949rF+/Hk5OTkJfxLp27RqSkpKwcOFClecfPHiAOnXqwNDQEK6uriW+Pi0tDebm5sLj+Ph4ZGdno3PnzpBIJHBxcUFYWBimTZum8Z+MLFu2TKPb0xQLCwukpaVpZFuVGmKJiYmQyWTIysqCmZkZVq9erZbMcrkcmzZtQkxMDCQSCaZMmQJ3d/fKKZioDK++yUdGRmLNmjVo164dLCws8MUXX2DhwoXo27cvFAoFevfujYKCAuF1hoaGwp+lUinkcnmx2xf7IfcqfF5tz8DAQNT2xapZs6bK4+HDh2POnDlq640fPx69evXChQsXsHHjRnTr1q3YARP29vYIDQ0Vte9169ahd+/eWL9+PSQSCTw8PFBQUACpVIpdu3bh2rVruHTpEqZMmYItW7agVatWOHjwIC5evIiYmBhs27YNwcHBot+rUqlEy5YtsWPHDrVlSUlJMDIyKvXvxdDQUOXv+sSJE8jOzhY+xwoKCmBsbIxp06ahTp06yMnJgVwuh1QqBQBkZmYKIWhvb48HDx7g2bNnoo7GtHkkZmVlheTkZNStWxcAkJKSInzhKm0Z8PJuO2ZmZm+979dVaoj5+fnB09MTgwcPxi+//AJfX1+1i6mnT5/GgwcPcPjwYWRlZWHy5Mno2rUrrK2tK6lqqqre5tqVtri6uiIyMhJ79+7FkiVLkJ2dLfybPXHihMqHWkmaNGmC/Px8XL16FR07dkRUVJRwhNOkSRMUFhbi0qVL6Ny5My5evIiioiI0adIE6enp71x/ly5dEBYWJhwhhoWFwcXFpdh1nZ2dIZPJ4O7uDisrK8jlcty6dQsODg5ITExEkyZNYGNjA2NjY4SFhQEATExMkJOTI2xj4MCB2L9/P/bv349JkyZBIpEgJycHERERatd7cnJy0LBhQ0gkEsTGxuLhw4cAXt7F5cWLF3BycoKTkxP+93//F7dv34apqSnMzMzQt29fdO/eHUOGDCn2lGJJ2rVrhwcPHgi9Bl5ej3RwcBD1+hYtWiAxMRH16tVDQUEBfv31V+zduxe2trbCOmPGjBH+ntu0aYMTJ04I7/vw4cPo1q0bgJfXnnr37g1fX1988cUXMDExgVwuR2hoKNzc3GBsbKyyb20eifXv3x9Hjx6Fo6Mj7t+/jxs3buDLL79UW/bgwQOVZQBw79494aj+XVVaiD19+hTx8fHCBVk3Nzds2LABGRkZQnoDQGRkJNzd3aGnp4e6deuiT58+iIqKUjvHTFTVzJs3D1OmTMGUKVOwZMkSfPrppzA1NUWPHj1EfQs1MDDA2rVrhSOOjh07okGDBgBeHmmtW7dOZWCHn5+fyhHYuxg1ahQePnyISZMmAXh5Ta+kMyBOTk7w9vbGsmXLIJfLUVRUhP79+8PBwQEhISG4dOkSatSoAQMDA+FDdciQIZDJZIiKisKECRMwdOhQbN++Hd999x3c3d1hbGwMfX39Yj/o5s2bh/Xr12PHjh1wdHREy5YtAbwMNx8fH+Tn50OhUMDe3h4uLi64fPky/P39AQAKhQJTp06FpaUl7t+/L6oXpqam2LRpE7Zu3YpvvvkGRUVFaNSoETZt2iTq9S4uLjh//jw6deqEs2fPwtbWViXAAGDQoEEIDQ1Fx44dIZPJsHHjRvz8889QKpVo0aIFli5dKqy7atUqBAYGYsqUKahRowaUSiV69uypcrT9Lv7f//t/ws8BPD090bx5c3z33XcAgIkTJ2Lz5s2wtLTE5MmTIZPJMHr0aOjp6eHzzz8XBpu8vkwikagse/HiBe7cuSN6AElZJJmZmcqyV9O8GzduYPXq1cLFVgDw8vKCTCZD69athefGjx+PlStXCsNT9+3bh7S0NI1+w3hzVFtV+kZfmRISEoSBCVXNq1PQFe1VYNBL7Ieq4vqRk5OD2bNnY9euXe9lr97syZEjR5CWloa5c+eqrfs2/19Xu4EdCQkJ5X7NxTeu877NNqqrqtoLIyMjletIFami75xf1bEfqt7sh76+Pry9vXHv3j00bdq0coqqZK/3RKFQwMvLq9h/N8+ePVMb8FHWF+lKCzErKyukp6cLFzDlcjnS09NhZWWlsl6DBg2QnJwsHImlpqYKp1SK8y5HDlX5yKMyVOV+ZGVlVcq3Wh55qGI/VJXUj169elVCNVXDmz0p7VqYqamp2qnWslTabafMzc1hZ2cn/CI/PDwc9vb2KtfDgJcXCI8dOwaFQoGMjAycPXu2XL9uJyKi6qtS753o4+ODkJAQeHh4ICQkBD4+PgCARYsWCRcWBw8ejEaNGsHDwwMzZszAzJkz0agRr1kREVElDuyoiqry6bPKUJX78er2P8bGxhU6pxhPn6liP1SxH+rE9qSgoAD5+fnlnhSz2g3soPeDiYkJ8vPzy/V7H00Q+yPT9wX7oYr9UCe2J3p6eqhVq1a5t88QI51laGhY4SMU09LSyn3huTpjP1SxH+q03RPOJ0ZERDqLIUZERDqLAzuIiEhn8UiMiIh0FkOMiIh0FkOMiIh0FkOMiIh01nv3OzHOJq1OTE+CgoIQHh4OqVQq3JW7R48elVSxdonpx+vrTpo0CWPGjFGbvr66ENuPiIgI7Nq1C0qlEhKJBP7+/rCwsKiEirVPTE+ePn2KtWvXIjU1FUVFRejUqROWLl0Kff3q9bG7ZcsW/Pbbb0hOTsaBAwfQokULtXW0+Zn63h2JvZpN+vDhw/D09ISvr6/aOq/PJh0UFISdO3ciKSmpEqqtGGJ64ujoiL179yI4OBhffPEFVqxYUW2n4BDTD+Dl/5i+vr7o06dPBVdYscT0Iy4uDjt37sR3332HgwcPIjAw8K3uvqArxPRkz549aNq0KYKDgxEcHIybN2/i999/r4RqtatPnz7YsWMHGjZsWOI62vxMfa9CrLjZpOPj45GRkaGyXkmzSVdHYnvSo0cP4f5nrVq1glKpRFZWVoXXq21i+wEAe/fuhbOzc4lHadWB2H4cOHAAEydORL169QAAtWrVqrT53rStPP9Gnj9/DoVCgYKCAhQWFqJ+/foVXa7WdejQQW0KrTdp8zP1vQqx1NRUWFpaQiqVAgCkUiksLS2Rmpqqsl5KSorKtworKyu1daoLsT15XVhYGGxsbMr8h6uLxPbj1q1bOH/+PMaPH18ZZVYYsf24e/cuHj16hNmzZ2Py5MkICgqCUlk9f4IqticzZ87E/fv3MWTIEAwePBjdu3dH+/btK6PkSqfNz9T3KsTo3V25cgUBAQH48ssvK7uUSlNUVARfX1/4+PgIH2TvO4VCgX///Rf+/v7Yvn07YmJicOrUqcouq1JFRUWhZcuWOHXqFMLCwnD16tVqe0anMr1XIfb6bNIAypxN+pXU1NRqedQBiO8JAFy7dg3/+c9/sGHDBjRp0qSiS60QYvrx+PFjPHz4EIsXL8bIkSNx8OBBHDt2DF9//XVlla01Yv99WFlZoV+/fjAwMICJiQl69+4tzAlY3YjtSUhICAYNGiTcnb137964fPlyZZRc6bT5mfpehRhnk1YntidxcXFYsWIF/Pz80Lp168ootUKI6UeDBg0QERGB48eP4/jx4xg3bhzc3d2xfPnyyipba8T++xg4cCBiY2OhVCpRVFSEixcvVtm56N6V2J5YW1sjJiYGAFBYWIiLFy+iefPmFV5vVaDNz9T37t6J9+7dg0wmE+a4Wb16NZo0aYJFixZh9uzZcHR0hFwux4YNGxAbGwsAmDJlCkaNGlXJlWuPmJ5MnToVycnJsLS0FF4nk8nQsmXLSqxcO8T043U7duzAixcvqu0QezH9UCgU2LJlizCEunv37li4cCH09Krn92QxPXn48CH8/Pzw5MkTKBQKdOrUCUuWLKl2Q+w3btyIM2fO4MmTJzAzM4OZmRl++umnCvtMfe9CjIiIqo/q+TWJiIjeCwwxIiLSWQwxIiLSWQwxIiLSWQwxIiLSWQwxqjaSkpLQtWtXnDx5srJL0SqZTIaRI0eKWvfy5cvo2rXre/sjW6r+qtcPFkgnnTx5EmvWrCl2maenJz799NMKrqh8unbtqvLYxMQE9vb2mDRpEpydnSukht27d6NZs2bo27dvhexPjOL6Ymdnh8mTJ791X/78809cv34ds2fP1kSJVA0wxKjKmDVrFmxsbFSe05XbW3Xu3BnDhw+HUqlEUlISfv75ZyxduhTffvstevbsqdF9rVixAgqFQuW5PXv2oF+/fmoh1rFjR5w7dw41atTQaA1ivd6X5ORkHDlyBEuXLsXmzZvfaj66v/76C4cOHWKIkYAhRlVG9+7d0bZt28ou463Y2tpi8ODBwuO+fftiwoQJOHDggMZDrDx3fNDT06vUKVHe7Eu/fv3g5eWFn376qdpOqkoViyFGVV5ycjL279+PS5cuITk5GTVq1ED79u0xb968Mm979fz5cwQGBuK3337D48ePYWJigubNm2P27NlwcnIS1ouLi8OOHTvw999/o6ioCK1bt8acOXPQuXPnt6q5ZcuWqFOnjsrEf69mALhx4wakUik6dOiATz75RGUmXDH1ymQyXLlyBcePHwfwf6ftwsLCEBYWBgBwcnLC9u3bcfnyZXh7e2Pbtm3o1KkTfvzxR2zduhVHjhxRO+oNCgpCQEAAjh07Bmtra630pVmzZqhTpw4ePnyo8nx0dDSOHTuGW7duISMjAxYWFnB1dcWcOXOEEJbJZML7e/1U5ev1nj59GgcOHMCdO3dgYGCALl26YP78+WjUqNFb1UtVH0OMqoycnBxkZmaqPFenTh3ExcXh6tWr6NevHxo0aID09HQcPXoUc+fOxcGDB4WJGIuzbt06REZGYsyYMWjevDmys7Pxzz//ICEhQQiFK1euYMGCBbCzs8NHH30EfX19nDp1CvPnz4e/vz86depU7vfy7NkzZGdnC0Fx6dIlzJ8/H9bW1pg1axYKCgpw6NAhfPTRR9izZ49w2lRMvW+SyWT46quv4OjoKNyPztzcvNh1BwwYgO+++w4RERGYPn26yrLIyEh88MEHQiBooy85OTkqfXklNDQUBgYGGDt2LGrVqoV//vkHBw4cQGpqKr766isAwKhRo/D48WPExsZCJpMJr3114929e/fihx9+QL9+/TB8+HA8e/YMhw4dwqxZs/A///M/ajfopeqBIUZVRnE30D1z5gw+/PBD9O/fX+X5IUOGwMvLC8ePH8fMmTNL3OYff/wBd3d3LF68uNjlSqUSvr6+aN++Pfz9/SGRSAAAo0ePxqRJk/DDDz8gKCiozNoLCgqQmZkpXPv5/vvvIZfLhbq3bNmCWrVqISgoCHXq1AHwMlDGjRuHbdu2wc/PT1S9xRk8eDD8/PzQqFEjlVN3xbGyskK7du3UQuzOnTu4ffu2sF9t9CUlJQXbt2+HXC5Xu4P52rVrhZnDX+3H1tYW27dvx4IFC4S6GzdujNjYWLX3+Wrbs2bNwkcffSQ87+bmhnHjxuHAgQP4+OOPy6yXdA9DjKqMpUuXolmzZirPGRoaqkw8mZeXh7y8PJiYmKBx48a4efNmqdt89a0+LS2t2KnhExISkJiYiMmTJyMrK0tlWbdu3RASEoK8vDyVD9jivH4qDwBq1qyJmTNnYvz48Xj8+DHi4+MxYcIEIcAAoHHjxujduzdiYmIgl8shlUrLrFcTBgwYgI0bN+LevXto2rQpgJdHYXp6enB1dQWgvb7UqFEDU6ZMwYQJE1TWe7UdhUKB58+fo6ioCO3bt4dSqcTNmzfLnHvq999/h1wux4ABA1SO5mvVqoUWLVrg0qVLpb6edBdDjKoMR0fHYgd25OfnIyAgAKdPn8bjx49VlpmZmZW6zfnz50Mmk2HEiBGws7NDjx49MGTIEOH0XWJiIgCUOlN1VlZWmR/Wzs7OGDduHCQSCSwsLGBtbS285tVkgMWNtGzatCl+++03ZGZmwsLCosx6NaF///749ttvER4eLozyi4iIQIcOHYSpdjTdl6KiIsTFxWHPnj0oKChQm6Ll9u3b+O6773D58mXk5+erLMvJySnzPd2/fx/Ay59kFIfXxKovhhhVeRs3bkRoaCjGjh2Ltm3bonbt2tDT08M333wDpbL0mYRcXV3RoUMHREdHIzY2FiEhIdi/fz/+85//YNCgQcLr582bBwcHh2K38frRU0ksLS3Vfhf1NsqqVxMsLCzg5OSEyMhIzJ49G7du3UJiYiLGjRsnrKONvvTs2RPm5ubw8/ODk5OT8HOAnJwceHt7o2bNmvD29oatrS0MDQ2RlpaGNWvWlPl3DED4ycGWLVtUjtxfqcwRmqRdDDGq8qKiojBkyBAsWbJE5fns7GxRH6T16tXD6NGjMXr0aGRnZ2PGjBkIDAzEoEGDhAEGJiYmGgmh4jRs2BDA/x3dvO7evXuoWbOmyvsord6SvLpmJdaAAQPw9ddfIyEhAREREZBKpSrXHbXVl5EjRyI4OBjbtm1Dnz59IJFIcOnSJWRmZgrh9sqrCRTFeFWvlZXVezt78vuKt52iKk9PT0/t2/ivv/6K9PT0Ul8nl8vVTkXVrl0b1tbWyM7OBgC0bt0atra2CA4ORm5urto2MjIy3rH6l6HUunVrnDp1SuX60sOHD3Hu3Dn06NEDUqlUVL0lMTIyKnOd1/Xr1w/6+vqIiIhAZGQkunTpohKk2uqLVCrFxIkTcffuXZw5c0Z4DoDK37FCoUBwcLDa62vWrAng5ejPN9+PVCpFUFBQsUdub456peqDR2JU5fXu3RunTp2CiYkJWrRogVu3biEiIqLM6xzPnz/H0KFD4eLiAjs7O5iYmODvv/9GTEyMcO1ET08PK1aswMKFC+Hl5YURI0agfv36SE9Px5UrVwAA27Zte+f3sGDBAsyfPx8zZ86Eu7s78vPz8fPPP8PAwADe3t6i6y2Jg4MDLly4gB9//BH169dH3bp10aVLlxLXNzU1Rbdu3XDo0CHk5uaqjfDUZl+GDBmCgIAA7N27Fy4uLmjXrh3MzMwgk8kwduxY6OvrIyoqCi9evCj2fQLAhg0b0LNnT0ilUvTq1QuNGjXCvHnzsHXrViQnJ6NPnz6oXbs2kpKSEB0dDVdXV97lo5piiFGVt2TJEujr6yMyMhInTpyAg4MDtm7diq1bt5b6OiMjI4wZMwYXLlzAuXPnUFRUBGtra+GD+RUnJyfs2rULQUFB+Pnnn5GbmwsLCws4OjqKvtFuWTp37gx/f38EBAQgICAA+vr66NChA+bNmycM2hBbb3EWL14MX19fBAYG4sWLF3Bycio1xICXw8///PNPGBgYFHvPRW31xdDQEGPGjMGOHTtw4cIFdO3aFd9++y22bNmCHTt2wNjYGC4uLvDw8FAbxeji4oJx48YhPDwc4eHhUCqVOHbsGGrWrIlJkyYJR4+7d++GQqFA/fr10blzZ7WfaFD1IcnMzCz7qikREVEVxGtiRESksxhiRESksxhiRESksxhiRESksxhiRESksxhiRESksxhiRESksxhiRESksxhiRESksxhiRESks/4/HErm/MYeRgYAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3SSZPPPEEFRUVrF27lrVr12IymahXz3o1JpOJvXv31liRIiIi9rI74B555BFH1CEiIlKjqhxwJSUl7N27F39/fzw9PQkJCaFFixaOrE1EROSmVSng8vPzmTZtGjk5OZjNZkwmEx4eHsTHx9OjRw9H1ygiImK3Kp1ksnTpUnJzcxk1ahQLFixg5syZuLm5ER8f7+j6REREbkqVtuD279/P4MGDLZfkAmjWrBnR0dHk5eXh4+PjsAJFRERuRpW24M6cOUOXLl2snuvSpQtms5m8vDyHFCYiIlIdVQq4iooK3NzcrJ67+rikpKTmqxIREammKp9FmZ2dzaFDhyyPi4uLAcjMzKRBgwY28//whz/UQHkiIiI3p8oBl5CQQEJCgs3z8+fPt3p89SzLr776qvrViYiI3KQqBVx0dLSj6xAREalRVQq4IUOGOOTFMzMziYmJobCwEE9PT+bMmUObNm2s5pw+fZq4uDhycnIoKytj0qRJDBo0CICzZ88yb9488vLyKCsro0ePHjz//PM2lw4TEZHbj90XW65JcXFxjBgxgvXr1zNixAhiY2Nt5rz99tt06tSJtWvXsmzZMt59913LmZt///vfufvuuy3Xxfzpp5/YtWtXbb8NERGpg5wWcGfPnuXw4cP0798fgP79+3P48GHOnTtnNS8jI4NevXoB4OXlRYcOHUhJSbGMX7hwgYqKCi5fvkxpaSl33nln7b0JERGps5wWcHl5eXh7e+Pq6gqAq6sr3t7eNt+rCwgIIDk5GbPZTHZ2NmlpaeTm5gIwZcoUfvnlFwYPHsygQYMIDg62+b6eiIjcnur8waoZM2bw17/+lTFjxuDr68sDDzxgCcUdO3bQrl07Fi9ezIULF4iKimLHjh3069fvmuvKyMioVi3VXd5o1A9b6ok19cOa+mGrOj1p3759peNOCzgfHx/y8/MpLy/H1dWV8vJy8vPzbS775eXlxdy5cy2PZ8yYwT333APAhx9+SHR0NC4uLjRq1IjQ0FC++eab6wbcjZpRmYyMjGotbzTqhy31xJr6YU39sOXonjhtF2WzZs3o0KEDycnJACQnJ9OxY0e8vLys5hUUFFBWVgZAamoqP//8MwMHDgTAz8+Pffv2AVBaWkpqair33ntvLb4LERGpq5y6i3L27NnExMSQmJhIkyZNmDNnDnBlKy0iIoLAwEDS09OJj4/HxcWFpk2bsmDBAjw8PACYNWsWcXFxjBo1ioqKCnr06EF4eLjz3pCIiNQZpoKCArOzi7gVaPeCNfXDlnpiTf2wpn7YMuwuShEREUdSwImIiCEp4ERExJAUcCIiYkgKOBERMSQFnIiIGJICTkREDEkBJyIihqSAExERQ1LAiYiIISngRETEkBRwIiJiSAo4ERExJAWciIgYkgJOREQMSQEnIiKGpIATERFDUsCJiIghKeBERMSQFHAiImJICjgRETEkBZyIiBiSAk5ERAxJASciIoakgBMREUNSwImIiCEp4ERExJAUcCIiYkgKOBERMSQFnIiIGJICTkREDEkBJyIihqSAExERQ1LAiYiIIdVz5otnZmYSExNDYWEhnp6ezJkzhzZt2ljNOX36NHFxceTk5FBWVsakSZMYNGiQZXz79u2sXLkSs9mMyWRi0aJFNG/evLbfioiI1DFODbi4uDhGjBjBoEGD+Oyzz4iNjWXJkiVWc95++206derE/PnzOXfuHOPHj6d79+74+PiQnp5OYmIiixcvpkWLFhQXF1O/fn0nvRsREalLnLaL8uzZsxw+fJj+/fsD0L9/fw4fPsy5c+es5mVkZNCrVy8AvLy86NChAykpKQAkJSUxZswYWrRoAUCjRo1wd3evxXchIiJ1ldO24PLy8vD29sbV1RUAV1dXvL29ycvLw8vLyzIvICCA5ORkOnXqRE5ODmlpabRs2RKAY8eO4efnR0REBBcvXuShhx5i8uTJmEyma75mRkZGtWqu7vJGo37YUk+sqR/W1A9b1elJ+/btKx136i7KqpgxYwZ//etfGTNmDL6+vjzwwAOWUKyoqODnn39m0aJFlJaWEhUVha+vL48++ug113WjZlQmIyOjWssbjfphSz2xpn5YUz9sObonTgs4Hx8f8vPzKS8vx9XVlfLycvLz8/Hx8bGa5+Xlxdy5cy2PZ8yYwT333GNZR9++fXFzc8PNzY3Q0FDS09OvG3AiInL7cNoxuGbNmtGhQweSk5MBSE5OpmPHjla7JwEKCgooKysDIDU1lZ9//pmBAwcCMGDAAPbv34/ZbKasrIzU1FT9hSQiIoCTd1HOnj2bmJgYEhMTadKkCXPmzAGubKVFREQQGBhIeno68fHxuLi40LRpUxYsWICHhwdw5cSUH3/8kSeffBKTyURwcDB/+tOfnPiORESkrjAVFBSYnV3ErUD7z62pH7bUE2vqhzX1w5aje6IrmYiIiCEp4ERExJDq/NcEHMlsNlNcXExFRcUN53p4eFBYWFgLVd0a1A9b6om1mu6Hh4eHLuQgdrmtA664uBh3d3fc3NxuONfd3d1ycouoH9einliryX6YzWYuXLhAWVkZDRs2rJF1ivHd1rsoKyoqqhRuIuJcJpOJhg0bWr4yJFIVt3XAiYiIcSngRETEkBRwdciwYcMYMWIEo0eP5oknnmDjxo0OeY2jR4/W2Pq++eYbevfuzZgxYyz/ff311zW2/uvZvXs3hw4dsnru9OnTvPbaa4SHhzNu3DgmTpxo6WFMTAwffvhhjdaQnp5OdHS05fHGjRsZMWIEY8eOJTc3l+nTp9fYa7344oukp6dbPffSSy/Rv39/m912QUFBjB49mtGjRzN27FgOHDhQIzVcvnyZ5557jkceeYRHHnmk0rlnzpxh1qxZDB8+nNGjR/PDDz9YjT377LPXHJs2bRrZ2dk1Uq/IbX2SSV0UFxdH27ZtOXr0KOPGjePBBx/E29vb2WVV6p577mH16tU3tWxZWRn16tn/a7hnzx46derEH/7wBwAuXbrE008/zZAhQ3jttddwcXGhuLjYcik4RwgMDGTevHmWxx9//DFz584lMDAQwObehlVxrX788MMPXLhwwbJegMLCQg4cOECbNm3Yu3cvffv2tVomMTGRBg0asGfPHl5++WU+//xzXFyq9/esi4sLY8eOpWnTpkRGRlY6991336VLly5MmzaN7777jtdee42PP/4Yk8nEu+++S9euXXnnnXdsxkaNGkVCQoLlqkYi1aGA+42m7znmL8eCSa3sXqZt27Y0adKE/Px8vL292bZtG+vWraO0tBSA5557jqCgIODKVtngwYPZv38/Z86cYcyYMTzxxBMAHDx4kDfffBOA7t27Yzb/34Vr0tPTmT9/PpcuXcLDw4MXXniBwMBAcnJymDBhAuHh4ezbt4+SkhLmzp3Lhg0bOHToEO7u7vzlL3+hVavrv6/y8nIWLVrEvn37AOjVqxfPPPMMrq6uxMTE4OrqSmZmJhcuXOD999/n008/Zf369ZSXl9OwYUNmz56Nv78/aWlpvPXWW1RUVFBWVsbkyZNp0qQJX3zxBampqWzatInRo0dTWlpKkyZNmDhxoqWGRo0a8ec//9mmtgMHDrB06VIuX75MeXk5kyZNstyXMCEhgeTkZNzc3DCZTCxZsoT69eszZ84cjh07hqurK/7+/sTGxvLNN9+wcOFCVq9ezUsvvUROTg6vvfYaAQEBTJ8+nQkTJrB9+3bgSkgtXryY8+fPAxAREUFISIil10OGDOHrr78mPDyc4cOHW9X7ySefMGDAAKvntm3bxoMPPkhwcDCbN2+2CbirevbsSWFhIYWFhTbXebVXvXr1CAoKIicn54ZzU1JSWLduHQBdu3alfv36/PjjjwQGBpKSksKmTZuuORYSEkJsbCznz5/X2ZJSbQq4Our777/H09PTchmb4OBgBgwYgMlkIjMzk8jISD799FPL/EuXLrFy5UpycnIYNWoUQ4YMoV69erzyyivMnTuXHj16sH37dj766CMASktLefHFF4mOjiYoKIgDBw7w4osvsmHDBuDKFkKXLl2IjIxkzZo1REZGsnTpUl5++WXeeOMNNmzYwLPPPgtcuS/fmDFjAHBzc+O9997jk08+4ciRI6xZswaAqKgoPvnkEx5//HEAjhw5wrJly7jjjjs4ePAgO3bsYNmyZbi5ufHll18yb948EhMTWb16NWPHjmXAgAGW7y02btyY3r1706lTJ0uQv/HGG/zxj3+sUm8DAgJISEjA1dWVM2fOMGHCBIKDgzGbzSQlJbF161Y8PDw4f/487u7ufPHFF5w/f97ygf3rr7/arPP111/nT3/6k2UL/LchUFRURFxcHG+//TYtWrTg9OnTTJw4kaSkJEuvAwMDiYqKuma93377LePGjbN6bvPmzURFRdG5c2fi4+Mtfwj9XkpKCnfeeec1w2327NmcOHHimq+5YsWKmz7Fv6CgALPZTNOmTS3P+fr6kpeXh5+f33XHAgMDqVevHvfeey9paWmWGx2L3CwFXB0ze/ZszGYzWVlZxMbGUr9+fQCys7OJjo7m1KlT1KtXjzNnznD69GnL3cyvHhPx8/OjcePGnDp1itLSUjw8POjRo4dlTmxsLACZmZnUr1/fshUYFBRE/fr1yczMpEGDBjRo0ICQkBAAOnbsyJ133kmHDh2AKwFxdcsMrr2LMjU1lSFDhljqHzp0KLt377YEXL9+/bjjjjsA+OKLL8jIyGDSpEnAle88FRUVAdCjRw9WrlxJVlYWPXv2rHKIVebcuXPMmzePEydO4OrqSmFhIZmZmQQGBtK6dWvmzJlDcHAwISEhNGzYkA4dOnD8+HHefPNNunfvbulLVaWlpZGTk2MTYCdOnKBp06a4u7vz8MMPX3f5U6dO0axZM8vjw4cPU1RUxP3334/JZKJPnz5s2bLFaut16tSpmEwmmjVrxltvvXXN9cbFxdn1PmpL8+bNycvLc3YZYgAKuDrm6hZASkoKc+fO5b777qN58+a88sorREVF8dBDD1FRUUFoaCiXL1+2LPfbKzxcvb/etVzvbue/dzWYrq7vt98XrGz9VXU13K4aOnQo06ZNs5k3atQoevfuzYEDB5g/fz49e/a85skbHTt2ZPPmzVV67TfeeIPQ0FDefPNNTCYTw4cP5/Lly7i6urJy5UrS0tL4+uuvGT9+PAsXLqR9+/Z88MEHpKamsm/fPpYsWcLatWur/F7NZjPt2rVj+fLlNmM5OTl4eHhU+nNxd3e3+ln/85//pKioiPDwcODKyR8NGjSwCrirx+Aq46gtuKtbZwUFBfj6+gJw8uRJfHx8rMau/vvq2FWXL1/WF+alRijgfqOyY2VXj1PVlocffpiUlBRWrVrFrFmzKCoqws/PD7jyAffbD7zr8ff3p6SkhIMHD9KtWzd27Nhh2TLy9/entLSUr7/+mvvvv5/U1FTKysrw9/cnPz+/2vU/8MADbNmyxbJluWXLFvr06XPNuSEhIcTExBAeHo6Pjw/l5eUcOXKETp06kZmZib+/P61bt6ZBgwZs2bIFgIYNG1JcXGxZx4ABA1izZg1r1qxh7NixmEwmiouL2b59O4899pjV6xUXF9OyZUtMJhP79+8nKysLgPPnz3Px4kW6d+9O9+7d+Z//+R+OHj1KkyZN8PT05KGHHiI4OJjBgwdfczfl9dx3332cOHHC0mu4cvyzU6dOVVq+bdu2ZGZm0qJFCy5fvsznn3/OqlWruOuuuyxzHn/8ccvPuaocuQXXr18/Nm3aZDnJpKSkhICAAMvY+vXrmTJlis0YwPHjx3XVfakRCrg6LDIykvHjxzN+/HhmzZrFv//7v9OkSRN69eqFp6fnDZd3c3Nj3rx5li2Vbt26Wf6irl+/Pm+88YbVSSZxcXFWW27V8dhjj5GVlcXYsWOBK8cQr25x/F737t2ZPn06L7zwAuXl5ZSVldGvXz86derEhx9+yNdff039+vVxc3PjhRdeAGDw4MHExMSwY8cORo8ezaOPPsrSpUt55513CA8Pp0GDBtSrV8+yS/S3IiMjefPNN1m+fDmBgYG0a9cOuBJ8s2fPpqSkhIqKCjp27EifPn345ptvWLRoEXDl6jcTJkzA29ubX375pUq9aNKkCfHx8fztb39jwYIFlJWV0apVK+Lj46u0fJ8+ffjqq6/o0aMHe/bs4a677rIKN4CBAweyefNmuwLuZkyYMIFTp05RVFTEkCFDCA4O5pVXXiE/P58ZM2bw/vvvA1d6HB0dzfDhw3F3dycmJsZyFmdkZCSvvfYaW7dutRnLzc0FroS6SHXd1veDKywsrFJQQO1vwdV16octR/WkuLiYiIgIVq5ceUv1/Gb6sXjxYlq3bs2wYcOuOW7P/7N1je4HZ0v3gxO5zTVq1IioqKgqnZ5/q/P29mbo0KHOLkMMQrsoRW4BPXv2dHYJteLq1z5EaoK24ERExJAUcCIiYki3dcC5uLhU6XR7EXEus9nM+fPnb+q6pXL7uq1/Wxo1akRxcTEXL1684dxff/2VJk2a1EJVtwb1w5Z6Yq2m++Hh4WF1QQORG7mtA85kMtG4ceMqzT116pTNd49uZ+qHLfXEmvohznZb76IUERHjUsCJiIgh3dZXMhEREePSFpyIiBiSAk5ERAxJASciIoakgBMREUO6rb8H93uZmZnExMRYbskxZ84c2rRpYzWnvLyc+Ph49u3bh8lkYvz48de9z5kRVKUnK1asIDk5GVdXV+rVq8f06dPp1auXkyp2rKr047dzx44dy+OPP05UVFQtV1o7qtqP7du3s3LlSsxmMyaTiUWLFtG8eXMnVOx4VenJ2bNnmTdvHnl5eZSVldGjRw+ef/55w12pZeHChezcuZPc3FySkpKueZ8/R36magvuN+Li4hgxYgTr169nxIgRxMbG2szZtm0bJ06cYP369axYsYLExERD38akKj0JDAxk1apVrF27lldeeYWXX36ZS5cuOaFax6tKP+DK/7SxsbGEhYXVcoW1qyr9SE9PJzExkXfeeYcPPviAhIQEGjVq5IRqa0dVevL3v/+du+++m7Vr17J27Vp++ukndu3a5YRqHSssLIzly5fTsmXL685x5GeqAu5/nT17lsOHD9O/f38A+vfvz+HDhzl37pzVvJSUFMLDw3FxccHLy4uwsDB27NjhjJIdrqo96dWrl+XGlu3bt8dsNlNYWFjr9TpaVfsBsGrVKkJCQq67dWcEVe1HUlISY8aMoUWLFsCVS+QZ9ZJb9vyOXLhwgYqKCi5fvkxpaSl33nlnbZfrcF27dsXHx6fSOY78TFXA/a+8vDy8vb1xdXUFwNXVFW9vb/Ly8qzmnTx50uqvER8fH5s5RlHVnvzWli1baN269Q1/qW9FVe3HkSNH+Oqrrxg1apQzyqw1Ve3HsWPHyM7OJiIignHjxrFixQrMZmN+/baqPZkyZQq//PILgwcPZtCgQQQHB9OlSxdnlOx0jvxMVcBJjfn2229ZtmwZf/nLX5xditOUlZURGxvL7NmzLR9yt7uKigp+/vlnFi1axNKlS9m3bx9bt251dllOtWPHDtq1a8fWrVvZsmULBw8eNOyeIGdSwP0vHx8f8vPzKS8vB64cQ8nPz7fZEvH19SU3N9fyOC8vz5BbK1D1ngCkpaXx6quv8tZbb+Hv71/bpdaKqvTj9OnTZGVlMXPmTIYNG8YHH3zAxo0bef31151VtsNU9ffDx8eHvn374ubmRsOGDQkNDSU9Pd0ZJTtcVXvy4YcfMnDgQFxcXGjUqBGhoaF88803zijZ6Rz5maqA+1/NmjWjQ4cOJCcnA5CcnEzHjh3x8vKymtevXz82btxIRUUF586dY8+ePfTt29cZJTtcVXuSnp7Oyy+/TFxcHAEBAc4otVZUpR++vr5s376dTZs2sWnTJkaOHEl4eDgvvfSSs8p2mKr+fgwYMID9+/djNpspKysjNTWV9u3bO6Nkh6tqT/z8/Ni3bx8ApaWlpKamcu+999Z6vXWBIz9TdS3K3zh+/DgxMTGW+1jNmTMHf39/ZsyYQUREBIGBgZSXl/PWW2+xf/9+AMaPH89jjz3m5Modpyo9mTBhArm5uXh7e1uWi4mJoV27dk6s3DGq0o/fWr58ORcvXjTs1wSq0o+KigoWLlxoOQ08ODiYqKgoXFyM+fd1VXqSlZVFXFwcZ86coaKigh49ejBr1izDfU1g/vz57N69mzNnzuDp6Ymnpyfr1q2rtc9UBZyIiBiSMf+EEhGR254CTkREDEkBJyIihqSAExERQ1LAiYiIISngRMTGsGHDiImJsTzOyckhKCiITz/91IlVidjHWF+6ELlFfPrpp8ydO9fy2NXVlWbNmhEUFMT06dMNeeFdkdqmgBNxoqeeeorWrVtTUlLCDz/8wJYtW/j+++9JSkoy7BX3RWqLAk7EiYKDg+ncuTMA4eHhNG3alNWrV7N3714eeeQRJ1cncmtTwInUIV27dmX16tVkZ2dbnsvMzGTp0qWkpqZy6dIl7r77biZNmkS/fv2sli0uLmblypXs3LmT/Px8mjZtSrdu3Xjuuee48847KS0t5b333uO///u/ycrK4vLly7Rt25aJEyfy0EMP1fI7FXE8nWQiUodcvap648aNgSv3Ups8eTJHjx5l3LhxREVF4enpyX/+53/y2WefWZa7ePEi06ZNIykpifvvv59Zs2YxfPhwTp48SVZWFgDnz59nw4YNdOnShenTp/P0009TUVHBf/zHf/Dll1/W/psVcTBtwYk4UXFxMQUFBZSUlHDo0CESExNxc3MjJCQEgAULFtCiRQtWrVpluWv6iBEjePbZZ1m8eDEDBw7EZDKxZs0aMjIyeP3113n44Yct6588ebLl5qKNGzfmn//8J25ubpbxJ554gnHjxvH+++/zb//2b7X4zkUcTwEn4kS/v8uAn58fMTEx+Pj4UFhYyIEDB5g6dSqXLl3i0qVLlnnBwcHs37+fX375BX9/f3bu3Mm9995rFW5XmUwm4MqZmldvwlpaWsqFCxcwm81069bNcnsXESNRwIk40fPPP88999xDcXExn376KQcPHrScPZmVlYXZbCYhIYGEhIRrLn/27Fn8/f3Jzs4mNDT0hq+3ceNGkpKSOH78uGXLDv4vBEWMRAEn4kSBgYGWsyjDwsJ4+umniY6O5qOPPqKiogKAUaNG8eCDD15z+bZt21b5tbZt28brr79O7969GT9+PM2aNcPV1ZXNmzfz+eefV//NiNQxCjiROsLV1ZXIyEgiIiL48MMPGTp0qOX5oKCgSpdt1aoVR48erXROSkoKrVq1Yv78+VZbbJs3b65+8SJ1kM6iFKlDunbtSufOnUlKSqJhw4b06NGDTZs2cerUKZu5586ds/y7b9++/Otf/yIlJcVm3tVdkVePv/1212R2dja7d++u4XchUjdoC06kjhk7diwvvvgimzdvZvbs2UydOpXRo0cTHh5O69atOXv2LIcOHeLYsWNs2LABgHHjxrFr1y6io6PZv38/AQEBFBcX8+WXXzJt2jS6d+9O79692bVrF88//zyhoaHk5+fz8ccf4+/vz5EjR5z8rkVqngJOpI4JCwvjrrvuYs2aNYSHh7Nq1SoSExPZunUrBQUFeHl50b59e6ZNm2ZZ5o477mDZsmUkJCSwa9cutmzZQrNmzejWrRt33XUXAEOGDOHcuXOsX7+e1NRUWrduzcyZMzlx4oQCTgzJVFBQYL7xNBERkVuLjsGJiIghKeBERMSQFHAiImJICjgRETEkBZyIiBiSAk5ERAxJASciIoakgBMREUNSwImIiCEp4ERExJD+P4VUwCaBNgZqAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 2 Random Forest\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "clf_rf = RandomForestClassifier()\n", "clf_rf.fit(X_train, y_train)\n", "\n", "Y_pred_rf = clf_rf.predict(X_test)\n", "print(clf_scores(clf_rf, Y_pred_rf))" ] }, { "cell_type": "code", "execution_count": 19, "id": "8da540e6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:13.762439Z", "iopub.status.busy": "2022-01-28T14:13:13.761868Z", "iopub.status.idle": "2022-01-28T14:13:14.433569Z", "shell.execute_reply": "2022-01-28T14:13:14.433074Z", "shell.execute_reply.started": "2022-01-28T14:12:40.704873Z" }, "papermill": { "duration": 0.70217, "end_time": "2022-01-28T14:13:14.433709", "exception": false, "start_time": "2022-01-28T14:13:13.731539", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "( 0\n", "acc_train 98.666667\n", "Test Accuracy 87.0\n", "Roc Score 85.220935\n", "COrrect 87\n", "Incorrect 13\n", "Confusion [[58, 5], [8, 29]], <AxesSubplot:>, <sklearn.metrics._plot.roc_curve.RocCurveDisplay object at 0x7f43bc006e90>, <sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay object at 0x7f43bbf332d0>)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 3 XGboost\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "clf_xg = GradientBoostingClassifier()\n", "clf_xg.fit(X_train, y_train)\n", "\n", "Y_pred_xg = clf_xg.predict(X_test)\n", "print(clf_scores(clf_xg, Y_pred_xg))" ] }, { "cell_type": "code", "execution_count": 20, "id": "8c42c1dd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:14.500674Z", "iopub.status.busy": "2022-01-28T14:13:14.499787Z", "iopub.status.idle": "2022-01-28T14:13:14.503433Z", "shell.execute_reply": "2022-01-28T14:13:14.503001Z", "shell.execute_reply.started": "2022-01-28T14:12:41.638062Z" }, "papermill": { "duration": 0.039258, "end_time": "2022-01-28T14:13:14.503563", "exception": false, "start_time": "2022-01-28T14:13:14.464305", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Index(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'], dtype='object')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "id": "48c52318", "metadata": { "papermill": { "duration": 0.030054, "end_time": "2022-01-28T14:13:14.564386", "exception": false, "start_time": "2022-01-28T14:13:14.534332", "status": "completed" }, "tags": [] }, "source": [ "# Up-sampling " ] }, { "cell_type": "code", "execution_count": 21, "id": "d79916be", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:14.629531Z", "iopub.status.busy": "2022-01-28T14:13:14.628325Z", "iopub.status.idle": "2022-01-28T14:13:14.631369Z", "shell.execute_reply": "2022-01-28T14:13:14.631864Z", "shell.execute_reply.started": "2022-01-28T14:12:41.646998Z" }, "papermill": { "duration": 0.037194, "end_time": "2022-01-28T14:13:14.632041", "exception": false, "start_time": "2022-01-28T14:13:14.594847", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.utils import resample" ] }, { "cell_type": "code", "execution_count": 22, "id": "bb2d976f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:13:14.698239Z", "iopub.status.busy": "2022-01-28T14:13:14.696712Z", "iopub.status.idle": "2022-01-28T14:13:14.850710Z", "shell.execute_reply": "2022-01-28T14:13:14.849213Z", "shell.execute_reply.started": "2022-01-28T14:12:41.658770Z" }, "papermill": { "duration": 0.188071, "end_time": "2022-01-28T14:13:14.851028", "exception": true, "start_time": "2022-01-28T14:13:14.662957", "status": "failed" }, "tags": [] }, "outputs": [ { "ename": "NameError", "evalue": "name 'X' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_20/1716939484.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined" ] } ], "source": [ "X.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "e5cf9c5b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:41.678982Z", "iopub.status.busy": "2022-01-28T14:12:41.678009Z", "iopub.status.idle": "2022-01-28T14:12:41.690445Z", "shell.execute_reply": "2022-01-28T14:12:41.689503Z", "shell.execute_reply.started": "2022-01-28T14:12:41.678944Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "X.columns" ] }, { "cell_type": "code", "execution_count": null, "id": "ecae4a65", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:41.692129Z", "iopub.status.busy": "2022-01-28T14:12:41.691826Z", "iopub.status.idle": "2022-01-28T14:12:41.713052Z", "shell.execute_reply": "2022-01-28T14:12:41.712179Z", "shell.execute_reply.started": "2022-01-28T14:12:41.692089Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "# separate minority and majority classes\n", "not_disease = X[X.Purchased==0]\n", "disease = X[X.Purchased==1]\n", "\n", "# upsample minority\n", "disease_upsampled = resample(disease,\n", " replace=True, # sample with replacement\n", " n_samples=len(not_disease), # match number in majority class\n", " random_state=27) # reproducible results\n", "\n", "# combine majority and upsampled minority\n", "upsampled = pd.concat([not_disease, disease_upsampled])\n", "\n", "# check new class counts\n", "upsampled.Purchased.value_counts()" ] }, { "cell_type": "code", "execution_count": null, "id": "c1f75bae", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:41.715249Z", "iopub.status.busy": "2022-01-28T14:12:41.714553Z", "iopub.status.idle": "2022-01-28T14:12:41.936871Z", "shell.execute_reply": "2022-01-28T14:12:41.935952Z", "shell.execute_reply.started": "2022-01-28T14:12:41.715188Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "dataset_overview(upsampled, 'Purchased')" ] }, { "cell_type": "markdown", "id": "e77d1fd2", "metadata": { "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "source": [ "# VIF for Upsampled Data" ] }, { "cell_type": "code", "execution_count": null, "id": "ae4afb12", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:41.938379Z", "iopub.status.busy": "2022-01-28T14:12:41.938090Z", "iopub.status.idle": "2022-01-28T14:12:41.958301Z", "shell.execute_reply": "2022-01-28T14:12:41.957091Z", "shell.execute_reply.started": "2022-01-28T14:12:41.938346Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "vif_values(upsampled,'Purchased')\n" ] }, { "cell_type": "code", "execution_count": null, "id": "4ed08a38", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:41.960484Z", "iopub.status.busy": "2022-01-28T14:12:41.959992Z", "iopub.status.idle": "2022-01-28T14:12:41.968686Z", "shell.execute_reply": "2022-01-28T14:12:41.967622Z", "shell.execute_reply.started": "2022-01-28T14:12:41.960446Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "x = X.drop(\"Purchased\", axis=1)\n", "y = X.Purchased\n", "X_train , X_test , y_train , y_test = train_test_split(x , y, test_size=0.25, random_state=42)" ] }, { "cell_type": "code", "execution_count": null, "id": "765b97fd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:41.970647Z", "iopub.status.busy": "2022-01-28T14:12:41.970377Z", "iopub.status.idle": "2022-01-28T14:12:41.983530Z", "shell.execute_reply": "2022-01-28T14:12:41.982791Z", "shell.execute_reply.started": "2022-01-28T14:12:41.970600Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "from sklearn import metrics\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import classification_report, confusion_matrix,ConfusionMatrixDisplay\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import confusion_matrix,classification_report,precision_score, plot_roc_curve, plot_precision_recall_curve, balanced_accuracy_score\n", "\n", "def clf_scores(clf, y_predicted):\n", " # Accuracy\n", " acc_train = clf.score(X_train, y_train)*100\n", " acc_test = clf.score(X_test, y_test)*100\n", " \n", " roc = roc_auc_score(y_test, y_predicted)*100 \n", " tn, fp, fn, tp = confusion_matrix(y_test, y_predicted).ravel()\n", " cm = confusion_matrix(y_test, y_predicted)\n", " correct = tp + tn\n", " incorrect = fp + fn\n", " d=[acc_train, acc_test, roc, correct, incorrect, cm]\n", " index=[\"acc_train\",'Test Accuracy',\"Roc Score\",\"COrrect\",\"Incorrect\",\"Confusion\" ]\n", " output=pd.DataFrame(data=d, index=index)\n", " \n", " d=sns.heatmap(cm, annot=True)\n", " dd=plot_roc_curve(clf, X_train, y_train)\n", " ddd=plot_precision_recall_curve(clf, X_train, y_train)\n", "\n", " return output,d, dd, ddd" ] }, { "cell_type": "code", "execution_count": null, "id": "9eb39797", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:41.985404Z", "iopub.status.busy": "2022-01-28T14:12:41.984920Z", "iopub.status.idle": "2022-01-28T14:12:42.962537Z", "shell.execute_reply": "2022-01-28T14:12:42.961415Z", "shell.execute_reply.started": "2022-01-28T14:12:41.985371Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "#1. Logistic regression\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "clf_lr = LogisticRegression(solver='liblinear')\n", "clf_lr.fit(X_train, y_train)\n", "\n", "Y_pred_lr = clf_lr.predict(X_test)\n", "print(clf_scores(clf_lr, Y_pred_lr))" ] }, { "cell_type": "code", "execution_count": null, "id": "4e756682", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:42.964099Z", "iopub.status.busy": "2022-01-28T14:12:42.963814Z", "iopub.status.idle": "2022-01-28T14:12:44.219418Z", "shell.execute_reply": "2022-01-28T14:12:44.218360Z", "shell.execute_reply.started": "2022-01-28T14:12:42.964058Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "# 2 Random Forest\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "clf_rf = RandomForestClassifier()\n", "clf_rf.fit(X_train, y_train)\n", "\n", "Y_pred_rf = clf_rf.predict(X_test)\n", "print(clf_scores(clf_rf, Y_pred_rf))" ] }, { "cell_type": "code", "execution_count": null, "id": "1685ffdb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:12:44.220997Z", "iopub.status.busy": "2022-01-28T14:12:44.220763Z", "iopub.status.idle": "2022-01-28T14:12:45.279507Z", "shell.execute_reply": "2022-01-28T14:12:45.278531Z", "shell.execute_reply.started": "2022-01-28T14:12:44.220965Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "# 3 XGboost\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "clf_xg = GradientBoostingClassifier()\n", "clf_xg.fit(X_train, y_train)\n", "\n", "Y_pred_xg = clf_xg.predict(X_test)\n", "print(clf_scores(clf_xg, Y_pred_xg))" ] }, { "cell_type": "code", "execution_count": null, "id": "ac98e3e5", "metadata": { "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 16.41304, "end_time": "2022-01-28T14:13:15.692806", "environment_variables": {}, "exception": true, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:12:59.279766", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/399/86399105.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**starterやサンプルコードでよく見かけるdef文やfor文をそろそろ読める＋使えるようになりたいので自分で自主トレでに1人ノック受けてきます。**","metadata":{}},{"cell_type":"code","source":"import os\nfrom typing import List\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import TruncatedSVD\n\nfrom gensim.models import Word2Vec\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision.io import read_image, ImageReadMode\nfrom torchvision import transforms\nimport torchvision.models as models\n\nfrom tqdm import tqdm","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":2.594086,"end_time":"2021-12-12T03:56:33.612201","exception":false,"start_time":"2021-12-12T03:56:31.018115","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-01-28T14:13:43.957603Z","iopub.execute_input":"2022-01-28T14:13:43.958258Z","iopub.status.idle":"2022-01-28T14:13:43.964963Z","shell.execute_reply.started":"2022-01-28T14:13:43.958216Z","shell.execute_reply":"2022-01-28T14:13:43.964202Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('../input/data-science-winter-osaka2/train.csv')\ntest = pd.read_csv('../input/data-science-winter-osaka2/test.csv')","metadata":{"papermill":{"duration":2.054326,"end_time":"2021-12-12T03:56:35.785027","exception":false,"start_time":"2021-12-12T03:56:33.730701","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-01-28T14:13:43.966685Z","iopub.execute_input":"2022-01-28T14:13:43.967058Z","iopub.status.idle":"2022-01-28T14:13:45.306495Z","shell.execute_reply.started":"2022-01-28T14:13:43.967014Z","shell.execute_reply":"2022-01-28T14:13:45.305745Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:45.307714Z","iopub.execute_input":"2022-01-28T14:13:45.308583Z","iopub.status.idle":"2022-01-28T14:13:45.325048Z","shell.execute_reply.started":"2022-01-28T14:13:45.308538Z","shell.execute_reply":"2022-01-28T14:13:45.324494Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":" name release_date developer \\\n0 Call of Duty®: Black Ops III Nov 5, 2015 Treyarch, Aspyr (Mac) \n1 RimWorld Oct 17, 2018 Ludeon Studios \n2 BPM: BULLETS PER MINUTE Sep 15, 2020 Awe Interactive \n3 World of Warships Nov 15, 2017 Wargaming Group Limited \n4 PLAYERUNKNOWN'S BATTLEGROUNDS Dec 21, 2017 PUBG Corporation \n\n publisher popular_tags \\\n0 Activision ['Multiplayer', 'Zombies', 'Shooter', 'Action'... \n1 Ludeon Studios ['Colony', 'Base', 'Building', 'Survival', 'St... \n2 NaN ['Action', 'Indie', 'Adventure', 'Rhythm', 'Ro... \n3 Wargaming Group Limited ['Naval', 'Combat', 'Naval', 'Free', 'Play', '... \n4 PUBG Corporation ['Survival', 'Shooter', 'Multiplayer', 'Battle... \n\n price categories \\\n0 599 ['Single-playerSteam Achievements Full', 'cont... \n1 349 ['Single-playerSteam Workshop Steam', 'Cloud R... \n2 199 ['Single-playerSteam Achievements Full', 'cont... \n3 free ['MMOOnline PvPOnline', 'Co-opSteam Achievemen... \n4 299 ['Online PvPStats Remote', 'Play on', 'Phone R... \n\n description \\\n0 About This Game Call of Duty: Black Ops III Zo... \n1 About This Game RimWorld is a sci-fi colony si... \n2 About This Game Fight as a mighty Valkyrie to ... \n3 About This Game PLAY FOR FREEWorld of Warship... \n4 About This Game PLAYERUNKNOWN'S BATTLEGROUNDS ... \n\n minimum_requirements recommended_requirements \\\n0 {'windows': {'processor': ' Intel® Core™ i3-53... {} \n1 {'windows': {'processor': ' Core 2 Duo', 'memo... {} \n2 NaN NaN \n3 NaN NaN \n4 NaN NaN \n\n img_path user_reviews \n0 000000.jpg c0 \n1 000001.jpg c0 \n2 000002.jpg c0 \n3 000003.jpg c0 \n4 000004.jpg c1 ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>name</th>\n <th>release_date</th>\n <th>developer</th>\n <th>publisher</th>\n <th>popular_tags</th>\n <th>price</th>\n <th>categories</th>\n <th>description</th>\n <th>minimum_requirements</th>\n <th>recommended_requirements</th>\n <th>img_path</th>\n <th>user_reviews</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Call of Duty®: Black Ops III</td>\n <td>Nov 5, 2015</td>\n <td>Treyarch, Aspyr (Mac)</td>\n <td>Activision</td>\n <td>['Multiplayer', 'Zombies', 'Shooter', 'Action'...</td>\n <td>599</td>\n <td>['Single-playerSteam Achievements Full', 'cont...</td>\n <td>About This Game Call of Duty: Black Ops III Zo...</td>\n <td>{'windows': {'processor': ' Intel® Core™ i3-53...</td>\n <td>{}</td>\n <td>000000.jpg</td>\n <td>c0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>RimWorld</td>\n <td>Oct 17, 2018</td>\n <td>Ludeon Studios</td>\n <td>Ludeon Studios</td>\n <td>['Colony', 'Base', 'Building', 'Survival', 'St...</td>\n <td>349</td>\n <td>['Single-playerSteam Workshop Steam', 'Cloud R...</td>\n <td>About This Game RimWorld is a sci-fi colony si...</td>\n <td>{'windows': {'processor': ' Core 2 Duo', 'memo...</td>\n <td>{}</td>\n <td>000001.jpg</td>\n <td>c0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>BPM: BULLETS PER MINUTE</td>\n <td>Sep 15, 2020</td>\n <td>Awe Interactive</td>\n <td>NaN</td>\n <td>['Action', 'Indie', 'Adventure', 'Rhythm', 'Ro...</td>\n <td>199</td>\n <td>['Single-playerSteam Achievements Full', 'cont...</td>\n <td>About This Game Fight as a mighty Valkyrie to ...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>000002.jpg</td>\n <td>c0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>World of Warships</td>\n <td>Nov 15, 2017</td>\n <td>Wargaming Group Limited</td>\n <td>Wargaming Group Limited</td>\n <td>['Naval', 'Combat', 'Naval', 'Free', 'Play', '...</td>\n <td>free</td>\n <td>['MMOOnline PvPOnline', 'Co-opSteam Achievemen...</td>\n <td>About This Game PLAY FOR FREEWorld of Warship...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>000003.jpg</td>\n <td>c0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>PLAYERUNKNOWN'S BATTLEGROUNDS</td>\n <td>Dec 21, 2017</td>\n <td>PUBG Corporation</td>\n <td>PUBG Corporation</td>\n <td>['Survival', 'Shooter', 'Multiplayer', 'Battle...</td>\n <td>299</td>\n <td>['Online PvPStats Remote', 'Play on', 'Phone R...</td>\n <td>About This Game PLAYERUNKNOWN'S BATTLEGROUNDS ...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>000004.jpg</td>\n <td>c1</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"★ここから変えてます","metadata":{}},{"cell_type":"markdown","source":"Pythonではdef文を用いて関数を定義するそうです。\n","metadata":{}},{"cell_type":"markdown","source":"「　z=x+y 　に　x=3、y=4　を代入します　」　をpythonでまとめて書くと","metadata":{}},{"cell_type":"code","source":"def z(x,y):#　zを関数名　x,yを引数と呼ぶ\n return x+y#　答え（zの中身）を戻り値と呼ぶ　def文の中身は頭文字4個分下げる必要があります　\n\nanswer = z(3,4)\nanswer","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:45.326567Z","iopub.execute_input":"2022-01-28T14:13:45.326882Z","iopub.status.idle":"2022-01-28T14:13:45.339085Z","shell.execute_reply.started":"2022-01-28T14:13:45.326855Z","shell.execute_reply":"2022-01-28T14:13:45.338274Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"7"},"metadata":{}}]},{"cell_type":"markdown","source":"これだけの内容だとそこまで便利に感じませんが、xやyに入れる数字が複数あるなら作業時間の短縮になりそうですね。","metadata":{}},{"cell_type":"markdown","source":"なのでx,yにいろいろな数字を入れた際にfor文使っているようです","metadata":{"execution":{"iopub.status.busy":"2022-01-28T08:28:01.69818Z","iopub.execute_input":"2022-01-28T08:28:01.698878Z","iopub.status.idle":"2022-01-28T08:28:01.749552Z","shell.execute_reply.started":"2022-01-28T08:28:01.698833Z","shell.execute_reply":"2022-01-28T08:28:01.748567Z"}}},{"cell_type":"code","source":"answer = []#　関数で計算した結果を保存するための器\n\nfor i in range(100):# 0~99までの数字をiに入力\n x=(i)#　ｘ＝0.1.2.3.4・・・を順番に入力\n y=(i*2)#　ｙ＝0.2.4.6.8・・・を順番に入力\n answer.append(z(x,y))#　関数Zにｘ、ｙの値を放り込む　⇒　answerの器に答えを保管　⇒　for文の頭に戻る\n\nanswer","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:45.340846Z","iopub.execute_input":"2022-01-28T14:13:45.341154Z","iopub.status.idle":"2022-01-28T14:13:45.354475Z","shell.execute_reply.started":"2022-01-28T14:13:45.341114Z","shell.execute_reply":"2022-01-28T14:13:45.353656Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"[0,\n 3,\n 6,\n 9,\n 12,\n 15,\n 18,\n 21,\n 24,\n 27,\n 30,\n 33,\n 36,\n 39,\n 42,\n 45,\n 48,\n 51,\n 54,\n 57,\n 60,\n 63,\n 66,\n 69,\n 72,\n 75,\n 78,\n 81,\n 84,\n 87,\n 90,\n 93,\n 96,\n 99,\n 102,\n 105,\n 108,\n 111,\n 114,\n 117,\n 120,\n 123,\n 126,\n 129,\n 132,\n 135,\n 138,\n 141,\n 144,\n 147,\n 150,\n 153,\n 156,\n 159,\n 162,\n 165,\n 168,\n 171,\n 174,\n 177,\n 180,\n 183,\n 186,\n 189,\n 192,\n 195,\n 198,\n 201,\n 204,\n 207,\n 210,\n 213,\n 216,\n 219,\n 222,\n 225,\n 228,\n 231,\n 234,\n 237,\n 240,\n 243,\n 246,\n 249,\n 252,\n 255,\n 258,\n 261,\n 264,\n 267,\n 270,\n 273,\n 276,\n 279,\n 282,\n 285,\n 288,\n 291,\n 294,\n 297]"},"metadata":{}}]},{"cell_type":"markdown","source":"for文と組み合わせて使うと、入力の数が多くなると楽ですね！（仕事の繰り返し作業にも使えそう…）","metadata":{}},{"cell_type":"markdown","source":"★★ここかstarterの頭4つ目のコードに戻ります★★","metadata":{}},{"cell_type":"code","source":"type(train['popular_tags'][0])#　このカラムの1つ目の値の型は何　⇒　str（文字列）として認識してますよという意味","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:45.357710Z","iopub.execute_input":"2022-01-28T14:13:45.358122Z","iopub.status.idle":"2022-01-28T14:13:45.367166Z","shell.execute_reply.started":"2022-01-28T14:13:45.358092Z","shell.execute_reply":"2022-01-28T14:13:45.366144Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"str"},"metadata":{}}]},{"cell_type":"markdown","source":"元々listデータだったのが、csvにしたことで文字列に変換されてしまいました。<br>\n元々のデータに戻すために、eval関数を適応していきます。","metadata":{}},{"cell_type":"markdown","source":"★早速def文＋for文が出てきました…","metadata":{}},{"cell_type":"code","source":"def eval_data(row):#　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　関数の名前をeval_data、引数はrowに設定\n if row == row:#　 　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　もし左右が同じなら　⇒（何か値が入っていれば）\n return eval(row)# 　　　　　　　　 　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　文字列を式として変換してください(今回はlistやdict）\n else:#　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　その他は(何も入っていなければ）\n return np.nan#　 　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　nan(データ無し)を入力\n\nfor column in ['popular_tags', 'categories', 'minimum_requirements', 'recommended_requirements']:#　左の4つカラムをを順番にcolumnに放り込む\n train[column] = train[column].apply(eval_data)# 　　　　　　　　　　　　　　　　　　　　　　　　　　　　　applyはdataframeで関数を使えるようにするための関数\n test[column] = test[column].apply(eval_data)#　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　忘れずtestデータにも織り込み","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:45.368895Z","iopub.execute_input":"2022-01-28T14:13:45.369420Z","iopub.status.idle":"2022-01-28T14:13:48.947755Z","shell.execute_reply.started":"2022-01-28T14:13:45.369378Z","shell.execute_reply":"2022-01-28T14:13:48.946862Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"type(train['popular_tags'][0]), type(train['categories'][0]), type(train['minimum_requirements'][0]), type(train['recommended_requirements'][0])","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:48.948910Z","iopub.execute_input":"2022-01-28T14:13:48.949138Z","iopub.status.idle":"2022-01-28T14:13:48.956666Z","shell.execute_reply.started":"2022-01-28T14:13:48.949110Z","shell.execute_reply":"2022-01-28T14:13:48.955712Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"(list, list, dict, dict)"},"metadata":{}}]},{"cell_type":"markdown","source":"以上でデータが元の形で読み込めました。欠損データを確かめます。","metadata":{}},{"cell_type":"code","source":"train.info()","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:48.959032Z","iopub.execute_input":"2022-01-28T14:13:48.959301Z","iopub.status.idle":"2022-01-28T14:13:49.024276Z","shell.execute_reply.started":"2022-01-28T14:13:48.959272Z","shell.execute_reply":"2022-01-28T14:13:49.023416Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 37082 entries, 0 to 37081\nData columns (total 12 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 name 37082 non-null object\n 1 release_date 36985 non-null object\n 2 developer 36939 non-null object\n 3 publisher 2634 non-null object\n 4 popular_tags 36941 non-null object\n 5 price 36540 non-null object\n 6 categories 37082 non-null object\n 7 description 37039 non-null object\n 8 minimum_requirements 29494 non-null object\n 9 recommended_requirements 29494 non-null object\n 10 img_path 37082 non-null object\n 11 user_reviews 37082 non-null object\ndtypes: object(12)\nmemory usage: 3.4+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"train['description'].tolist()[0]","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:49.025964Z","iopub.execute_input":"2022-01-28T14:13:49.026602Z","iopub.status.idle":"2022-01-28T14:13:49.036373Z","shell.execute_reply.started":"2022-01-28T14:13:49.026555Z","shell.execute_reply":"2022-01-28T14:13:49.035483Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"'About This Game Call of Duty: Black Ops III Zombies Chronicles Edition includes the full base game and the Zombies Chronicles content expansion.Call of Duty: Black Ops III combines three unique game modes: Campaign, Multiplayer, and Zombies, providing fans with the deepest and most ambitious Call of Duty ever.The Zombies Chronicles content expansion delivers 8 remastered classic Zombies maps from Call of Duty: World at War, Call of Duty: Black Ops and Call of Duty: Black Ops II. Complete maps from the original saga are fully remastered and HD playable within Call of Duty: Black Ops III.'"},"metadata":{}}]},{"cell_type":"code","source":"train['user_reviews'].hist()","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:49.037709Z","iopub.execute_input":"2022-01-28T14:13:49.038090Z","iopub.status.idle":"2022-01-28T14:13:49.271159Z","shell.execute_reply.started":"2022-01-28T14:13:49.038046Z","shell.execute_reply":"2022-01-28T14:13:49.270378Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"★今度はfor文が出てきました","metadata":{}},{"cell_type":"code","source":"for i in range(10):#　0～9の数字をiに順番に代入\n img = read_image(os.path.join('../input/data-science-winter-osaka2/train/train', train['img_path'][i]))#　img_pathを0番目～9番目を順番に開く\n print(img.shape)#\n plt.imshow(img.permute(1, 2, 0))#イメージを写す\n plt.show()#　上から順番に写す","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:13:49.273479Z","iopub.execute_input":"2022-01-28T14:13:49.273785Z","iopub.status.idle":"2022-01-28T14:13:51.467765Z","shell.execute_reply.started":"2022-01-28T14:13:49.273744Z","shell.execute_reply":"2022-01-28T14:13:51.466899Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"torch.Size([3, 215, 460])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"体力が尽きたのでここで終わります。4本くらいしかノック受けれてませんね…　<br>\n次回以降のコンペで頑張ります！！","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
0086/399/86399237.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "id": "da2098e1", "metadata": { "papermill": { "duration": 0.05325, "end_time": "2022-01-28T14:14:48.586370", "exception": false, "start_time": "2022-01-28T14:14:48.533120", "status": "completed" }, "tags": [] }, "source": [ "## Prepared by\n", "1. LEE SZE YUAN \t\t\t(A19EC0068)\n", "2. LOH YEW CHONG \t\t(A19EC0076)\n", "3. ALVIN HEE JUN SHEUNG \t(A19EC0015) " ] }, { "cell_type": "markdown", "id": "402505c0", "metadata": { "papermill": { "duration": 0.05103, "end_time": "2022-01-28T14:14:48.686510", "exception": false, "start_time": "2022-01-28T14:14:48.635480", "status": "completed" }, "tags": [] }, "source": [ "# Importing libraries to use" ] }, { "cell_type": "code", "execution_count": 1, "id": "23737f89", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:48.794387Z", "iopub.status.busy": "2022-01-28T14:14:48.790241Z", "iopub.status.idle": "2022-01-28T14:14:51.913018Z", "shell.execute_reply": "2022-01-28T14:14:51.912069Z", "shell.execute_reply.started": "2022-01-28T14:09:54.036770Z" }, "papermill": { "duration": 3.178825, "end_time": "2022-01-28T14:14:51.913226", "exception": false, "start_time": "2022-01-28T14:14:48.734401", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import plotly.express as px\n", "\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.cluster import KMeans\n", "from sklearn.metrics import silhouette_score \n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "id": "28d29f93", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:52.016495Z", "iopub.status.busy": "2022-01-28T14:14:52.015771Z", "iopub.status.idle": "2022-01-28T14:14:52.057871Z", "shell.execute_reply": "2022-01-28T14:14:52.058432Z", "shell.execute_reply.started": "2022-01-28T14:09:57.009888Z" }, "papermill": { "duration": 0.096795, "end_time": "2022-01-28T14:14:52.058616", "exception": false, "start_time": "2022-01-28T14:14:51.961821", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Shape: (2240, 29)\n" ] } ], "source": [ "data = pd.read_csv(\"../input/customer-personality-analysis/marketing_campaign.csv\"\n", " , sep = \"\\t\")\n", "print(f\"Dataset Shape: {data.shape}\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "a4bb2522", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:52.161165Z", "iopub.status.busy": "2022-01-28T14:14:52.160430Z", "iopub.status.idle": "2022-01-28T14:14:52.194378Z", "shell.execute_reply": "2022-01-28T14:14:52.194882Z", "shell.execute_reply.started": "2022-01-28T14:09:57.067850Z" }, "papermill": { "duration": 0.086629, "end_time": "2022-01-28T14:14:52.195053", "exception": false, "start_time": "2022-01-28T14:14:52.108424", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ID</th>\n", " <th>Year_Birth</th>\n", " <th>Education</th>\n", " <th>Marital_Status</th>\n", " <th>Income</th>\n", " <th>Kidhome</th>\n", " <th>Teenhome</th>\n", " <th>Dt_Customer</th>\n", " <th>Recency</th>\n", " <th>MntWines</th>\n", " <th>...</th>\n", " <th>NumWebVisitsMonth</th>\n", " <th>AcceptedCmp3</th>\n", " <th>AcceptedCmp4</th>\n", " <th>AcceptedCmp5</th>\n", " <th>AcceptedCmp1</th>\n", " <th>AcceptedCmp2</th>\n", " <th>Complain</th>\n", " <th>Z_CostContact</th>\n", " <th>Z_Revenue</th>\n", " <th>Response</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5524</td>\n", " <td>1957</td>\n", " <td>Graduation</td>\n", " <td>Single</td>\n", " <td>58138.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>04-09-2012</td>\n", " <td>58</td>\n", " <td>635</td>\n", " <td>...</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2174</td>\n", " <td>1954</td>\n", " <td>Graduation</td>\n", " <td>Single</td>\n", " <td>46344.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>08-03-2014</td>\n", " <td>38</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4141</td>\n", " <td>1965</td>\n", " <td>Graduation</td>\n", " <td>Together</td>\n", " <td>71613.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>21-08-2013</td>\n", " <td>26</td>\n", " <td>426</td>\n", " <td>...</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6182</td>\n", " <td>1984</td>\n", " <td>Graduation</td>\n", " <td>Together</td>\n", " <td>26646.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>10-02-2014</td>\n", " <td>26</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5324</td>\n", " <td>1981</td>\n", " <td>PhD</td>\n", " <td>Married</td>\n", " <td>58293.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>19-01-2014</td>\n", " <td>94</td>\n", " <td>173</td>\n", " <td>...</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 29 columns</p>\n", "</div>" ], "text/plain": [ " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", "0 5524 1957 Graduation Single 58138.0 0 0 \n", "1 2174 1954 Graduation Single 46344.0 1 1 \n", "2 4141 1965 Graduation Together 71613.0 0 0 \n", "3 6182 1984 Graduation Together 26646.0 1 0 \n", "4 5324 1981 PhD Married 58293.0 1 0 \n", "\n", " Dt_Customer Recency MntWines ... NumWebVisitsMonth AcceptedCmp3 \\\n", "0 04-09-2012 58 635 ... 7 0 \n", "1 08-03-2014 38 11 ... 5 0 \n", "2 21-08-2013 26 426 ... 4 0 \n", "3 10-02-2014 26 11 ... 6 0 \n", "4 19-01-2014 94 173 ... 5 0 \n", "\n", " AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " Z_CostContact Z_Revenue Response \n", "0 3 11 1 \n", "1 3 11 0 \n", "2 3 11 0 \n", "3 3 11 0 \n", "4 3 11 0 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "id": "38921de6", "metadata": { "papermill": { "duration": 0.050797, "end_time": "2022-01-28T14:14:52.294826", "exception": false, "start_time": "2022-01-28T14:14:52.244029", "status": "completed" }, "tags": [] }, "source": [ "# Data Cleaning and Preparation" ] }, { "cell_type": "markdown", "id": "1e9a510b", "metadata": { "papermill": { "duration": 0.050605, "end_time": "2022-01-28T14:14:52.393253", "exception": false, "start_time": "2022-01-28T14:14:52.342648", "status": "completed" }, "tags": [] }, "source": [ "### Handling Missing Data " ] }, { "cell_type": "code", "execution_count": 4, "id": "485dc3fc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:52.502819Z", "iopub.status.busy": "2022-01-28T14:14:52.502075Z", "iopub.status.idle": "2022-01-28T14:14:52.511739Z", "shell.execute_reply": "2022-01-28T14:14:52.512373Z", "shell.execute_reply.started": "2022-01-28T14:09:57.109311Z" }, "papermill": { "duration": 0.065794, "end_time": "2022-01-28T14:14:52.512599", "exception": false, "start_time": "2022-01-28T14:14:52.446805", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID 0\n", "Year_Birth 0\n", "Education 0\n", "Marital_Status 0\n", "Income 24\n", "Kidhome 0\n", "Teenhome 0\n", "Dt_Customer 0\n", "Recency 0\n", "MntWines 0\n", "MntFruits 0\n", "MntMeatProducts 0\n", "MntFishProducts 0\n", "MntSweetProducts 0\n", "MntGoldProds 0\n", "NumDealsPurchases 0\n", "NumWebPurchases 0\n", "NumCatalogPurchases 0\n", "NumStorePurchases 0\n", "NumWebVisitsMonth 0\n", "AcceptedCmp3 0\n", "AcceptedCmp4 0\n", "AcceptedCmp5 0\n", "AcceptedCmp1 0\n", "AcceptedCmp2 0\n", "Complain 0\n", "Z_CostContact 0\n", "Z_Revenue 0\n", "Response 0\n", "dtype: int64\n", "Income column has missing data\n" ] } ], "source": [ "print(data.isna().sum())\n", "print(f\"Income column has missing data\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "42598187", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:52.615180Z", "iopub.status.busy": "2022-01-28T14:14:52.614498Z", "iopub.status.idle": "2022-01-28T14:14:52.623109Z", "shell.execute_reply": "2022-01-28T14:14:52.623668Z", "shell.execute_reply.started": "2022-01-28T14:09:57.120754Z" }, "papermill": { "duration": 0.060718, "end_time": "2022-01-28T14:14:52.623847", "exception": false, "start_time": "2022-01-28T14:14:52.563129", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data['Income'].fillna(data['Income'].mean(), inplace = True)" ] }, { "cell_type": "markdown", "id": "1ef4c31b", "metadata": { "execution": { "iopub.execute_input": "2022-01-22T03:02:33.119713Z", "iopub.status.busy": "2022-01-22T03:02:33.11885Z", "iopub.status.idle": "2022-01-22T03:02:33.123369Z", "shell.execute_reply": "2022-01-22T03:02:33.122839Z", "shell.execute_reply.started": "2022-01-22T03:02:33.119657Z" }, "papermill": { "duration": 0.052207, "end_time": "2022-01-28T14:14:52.724693", "exception": false, "start_time": "2022-01-28T14:14:52.672486", "status": "completed" }, "tags": [] }, "source": [ "### Check for duplicates" ] }, { "cell_type": "code", "execution_count": 6, "id": "ff09d621", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:52.833408Z", "iopub.status.busy": "2022-01-28T14:14:52.832512Z", "iopub.status.idle": "2022-01-28T14:14:52.859607Z", "shell.execute_reply": "2022-01-28T14:14:52.860149Z", "shell.execute_reply.started": "2022-01-28T14:09:57.135498Z" }, "papermill": { "duration": 0.082917, "end_time": "2022-01-28T14:14:52.860341", "exception": false, "start_time": "2022-01-28T14:14:52.777424", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ID</th>\n", " <th>Year_Birth</th>\n", " <th>Education</th>\n", " <th>Marital_Status</th>\n", " <th>Income</th>\n", " <th>Kidhome</th>\n", " <th>Teenhome</th>\n", " <th>Dt_Customer</th>\n", " <th>Recency</th>\n", " <th>MntWines</th>\n", " <th>...</th>\n", " <th>NumWebVisitsMonth</th>\n", " <th>AcceptedCmp3</th>\n", " <th>AcceptedCmp4</th>\n", " <th>AcceptedCmp5</th>\n", " <th>AcceptedCmp1</th>\n", " <th>AcceptedCmp2</th>\n", " <th>Complain</th>\n", " <th>Z_CostContact</th>\n", " <th>Z_Revenue</th>\n", " <th>Response</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "<p>0 rows × 29 columns</p>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [ID, Year_Birth, Education, Marital_Status, Income, Kidhome, Teenhome, Dt_Customer, Recency, MntWines, MntFruits, MntMeatProducts, MntFishProducts, MntSweetProducts, MntGoldProds, NumDealsPurchases, NumWebPurchases, NumCatalogPurchases, NumStorePurchases, NumWebVisitsMonth, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5, AcceptedCmp1, AcceptedCmp2, Complain, Z_CostContact, Z_Revenue, Response]\n", "Index: []\n", "\n", "[0 rows x 29 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data.duplicated()]" ] }, { "cell_type": "markdown", "id": "6fb6ccae", "metadata": { "papermill": { "duration": 0.051977, "end_time": "2022-01-28T14:14:52.962933", "exception": false, "start_time": "2022-01-28T14:14:52.910956", "status": "completed" }, "tags": [] }, "source": [ "### Handling incorrect formatted data" ] }, { "cell_type": "code", "execution_count": 7, "id": "3969a64b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:53.067570Z", "iopub.status.busy": "2022-01-28T14:14:53.066900Z", "iopub.status.idle": "2022-01-28T14:14:53.074374Z", "shell.execute_reply": "2022-01-28T14:14:53.074920Z", "shell.execute_reply.started": "2022-01-28T14:09:57.174874Z" }, "papermill": { "duration": 0.061465, "end_time": "2022-01-28T14:14:53.075095", "exception": false, "start_time": "2022-01-28T14:14:53.013630", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID int64\n", "Year_Birth int64\n", "Education object\n", "Marital_Status object\n", "Income float64\n", "Kidhome int64\n", "Teenhome int64\n", "Dt_Customer object\n", "Recency int64\n", "MntWines int64\n", "MntFruits int64\n", "MntMeatProducts int64\n", "MntFishProducts int64\n", "MntSweetProducts int64\n", "MntGoldProds int64\n", "NumDealsPurchases int64\n", "NumWebPurchases int64\n", "NumCatalogPurchases int64\n", "NumStorePurchases int64\n", "NumWebVisitsMonth int64\n", "AcceptedCmp3 int64\n", "AcceptedCmp4 int64\n", "AcceptedCmp5 int64\n", "AcceptedCmp1 int64\n", "AcceptedCmp2 int64\n", "Complain int64\n", "Z_CostContact int64\n", "Z_Revenue int64\n", "Response int64\n", "dtype: object\n", "['Dt_Customer'] column is not parsed as datetime datetype\n" ] } ], "source": [ "print(data.dtypes)\n", "print(f\"['Dt_Customer'] column is not parsed as datetime datetype\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "aa0e171a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:53.180203Z", "iopub.status.busy": "2022-01-28T14:14:53.179540Z", "iopub.status.idle": "2022-01-28T14:14:53.190748Z", "shell.execute_reply": "2022-01-28T14:14:53.191293Z", "shell.execute_reply.started": "2022-01-28T14:09:57.185111Z" }, "papermill": { "duration": 0.064388, "end_time": "2022-01-28T14:14:53.191467", "exception": false, "start_time": "2022-01-28T14:14:53.127079", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data[\"Dt_Customer\"] = pd.to_datetime(data[\"Dt_Customer\"])" ] }, { "cell_type": "code", "execution_count": 9, "id": "8df6c7e1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:53.296323Z", "iopub.status.busy": "2022-01-28T14:14:53.295514Z", "iopub.status.idle": "2022-01-28T14:14:53.303949Z", "shell.execute_reply": "2022-01-28T14:14:53.303133Z", "shell.execute_reply.started": "2022-01-28T14:09:57.202919Z" }, "papermill": { "duration": 0.062603, "end_time": "2022-01-28T14:14:53.304145", "exception": false, "start_time": "2022-01-28T14:14:53.241542", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID int64\n", "Year_Birth int64\n", "Education object\n", "Marital_Status object\n", "Income float64\n", "Kidhome int64\n", "Teenhome int64\n", "Dt_Customer datetime64[ns]\n", "Recency int64\n", "MntWines int64\n", "MntFruits int64\n", "MntMeatProducts int64\n", "MntFishProducts int64\n", "MntSweetProducts int64\n", "MntGoldProds int64\n", "NumDealsPurchases int64\n", "NumWebPurchases int64\n", "NumCatalogPurchases int64\n", "NumStorePurchases int64\n", "NumWebVisitsMonth int64\n", "AcceptedCmp3 int64\n", "AcceptedCmp4 int64\n", "AcceptedCmp5 int64\n", "AcceptedCmp1 int64\n", "AcceptedCmp2 int64\n", "Complain int64\n", "Z_CostContact int64\n", "Z_Revenue int64\n", "Response int64\n", "dtype: object\n" ] } ], "source": [ "print(data.dtypes)" ] }, { "cell_type": "markdown", "id": "958b9463", "metadata": { "papermill": { "duration": 0.049971, "end_time": "2022-01-28T14:14:53.405752", "exception": false, "start_time": "2022-01-28T14:14:53.355781", "status": "completed" }, "tags": [] }, "source": [ "## Replace data to make data more sensible" ] }, { "cell_type": "code", "execution_count": 10, "id": "93e428ef", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:53.512880Z", "iopub.status.busy": "2022-01-28T14:14:53.511949Z", "iopub.status.idle": "2022-01-28T14:14:53.516991Z", "shell.execute_reply": "2022-01-28T14:14:53.517793Z", "shell.execute_reply.started": "2022-01-28T14:09:57.220253Z" }, "papermill": { "duration": 0.063169, "end_time": "2022-01-28T14:14:53.518036", "exception": false, "start_time": "2022-01-28T14:14:53.454867", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Married 864\n", "Together 580\n", "Single 480\n", "Divorced 232\n", "Widow 77\n", "Alone 3\n", "Absurd 2\n", "YOLO 2\n", "Name: Marital_Status, dtype: int64\n" ] } ], "source": [ "print(data[\"Marital_Status\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 11, "id": "c7c17be6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:53.630969Z", "iopub.status.busy": "2022-01-28T14:14:53.629972Z", "iopub.status.idle": "2022-01-28T14:14:53.641236Z", "shell.execute_reply": "2022-01-28T14:14:53.641845Z", "shell.execute_reply.started": "2022-01-28T14:09:57.237633Z" }, "papermill": { "duration": 0.073144, "end_time": "2022-01-28T14:14:53.642028", "exception": false, "start_time": "2022-01-28T14:14:53.568884", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Relationship 1444\n", "Single 796\n", "Name: Marital_Status, dtype: int64\n" ] } ], "source": [ "data[\"Marital_Status\"] = data[\"Marital_Status\"].replace({\n", " \"Married\":\"Relationship\", \n", " \"Together\":\"Relationship\", \n", " \"Single\":\"Single\",\n", " \"Divorced\":\"Single\", \n", " \"Widow\":\"Single\", \n", " \"Absurd\":\"Single\", \n", " \"Alone\": \"Single\",\n", " \"YOLO\":\"Single\", \n", "})\n", "print(data[\"Marital_Status\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 12, "id": "7347e85a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:53.747429Z", "iopub.status.busy": "2022-01-28T14:14:53.746760Z", "iopub.status.idle": "2022-01-28T14:14:53.753335Z", "shell.execute_reply": "2022-01-28T14:14:53.753879Z", "shell.execute_reply.started": "2022-01-28T14:09:57.256558Z" }, "papermill": { "duration": 0.05993, "end_time": "2022-01-28T14:14:53.754055", "exception": false, "start_time": "2022-01-28T14:14:53.694125", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Graduation 1127\n", "PhD 486\n", "Master 370\n", "2n Cycle 203\n", "Basic 54\n", "Name: Education, dtype: int64\n" ] } ], "source": [ "print(data[\"Education\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 13, "id": "32da19ae", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:53.860243Z", "iopub.status.busy": "2022-01-28T14:14:53.859558Z", "iopub.status.idle": "2022-01-28T14:14:53.868644Z", "shell.execute_reply": "2022-01-28T14:14:53.869192Z", "shell.execute_reply.started": "2022-01-28T14:09:57.270186Z" }, "papermill": { "duration": 0.063767, "end_time": "2022-01-28T14:14:53.869394", "exception": false, "start_time": "2022-01-28T14:14:53.805627", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Graduate 1127\n", "Postgraduate 1059\n", "Undergraduate 54\n", "Name: Education, dtype: int64\n" ] } ], "source": [ "data[\"Education\"] = data[\"Education\"].replace({\n", " \"Graduation\":\"Graduate\", \n", " \"PhD\":\"Postgraduate\", \n", " \"Master\":\"Postgraduate\",\n", " \"2n Cycle\":\"Postgraduate\", \n", " \"Basic\":\"Undergraduate\", \n", "})\n", "print(data[\"Education\"].value_counts())" ] }, { "cell_type": "markdown", "id": "a765d131", "metadata": { "papermill": { "duration": 0.051547, "end_time": "2022-01-28T14:14:53.971636", "exception": false, "start_time": "2022-01-28T14:14:53.920089", "status": "completed" }, "tags": [] }, "source": [ "# Data Grouping and Aggregation Operation" ] }, { "cell_type": "code", "execution_count": 14, "id": "6ab971c4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:54.089548Z", "iopub.status.busy": "2022-01-28T14:14:54.088811Z", "iopub.status.idle": "2022-01-28T14:14:54.091687Z", "shell.execute_reply": "2022-01-28T14:14:54.091146Z", "shell.execute_reply.started": "2022-01-28T14:09:57.287174Z" }, "papermill": { "duration": 0.067948, "end_time": "2022-01-28T14:14:54.091837", "exception": false, "start_time": "2022-01-28T14:14:54.023889", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data[\"Total_Expense\"] = data[\"MntWines\"] + data[\"MntFruits\"] + data[\"MntMeatProducts\"] + data[\"MntFishProducts\"] + data[\"MntSweetProducts\"] + data[\"MntGoldProds\"]\n", "data['Total_Children'] = data['Kidhome'] + data['Teenhome']\n", "data['Total_Accepted_Campaign'] = data['AcceptedCmp1'] + data['AcceptedCmp2'] + data['AcceptedCmp3'] + data['AcceptedCmp4'] + data['AcceptedCmp5'] + data['Response']\n", "data[\"Total_Purchases\"] = data[\"NumWebPurchases\"] + data[\"NumCatalogPurchases\"] + data[\"NumStorePurchases\"] + data[\"NumDealsPurchases\"]" ] }, { "cell_type": "code", "execution_count": 15, "id": "dd819d44", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:54.196247Z", "iopub.status.busy": "2022-01-28T14:14:54.195568Z", "iopub.status.idle": "2022-01-28T14:14:54.200176Z", "shell.execute_reply": "2022-01-28T14:14:54.200727Z", "shell.execute_reply.started": "2022-01-28T14:09:57.304274Z" }, "papermill": { "duration": 0.058081, "end_time": "2022-01-28T14:14:54.200905", "exception": false, "start_time": "2022-01-28T14:14:54.142824", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data['Age'] = 2015 - data[\"Year_Birth\"]" ] }, { "cell_type": "code", "execution_count": 16, "id": "95a0a20c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:54.306731Z", "iopub.status.busy": "2022-01-28T14:14:54.306019Z", "iopub.status.idle": "2022-01-28T14:14:54.323349Z", "shell.execute_reply": "2022-01-28T14:14:54.322678Z", "shell.execute_reply.started": "2022-01-28T14:09:57.316587Z" }, "papermill": { "duration": 0.071212, "end_time": "2022-01-28T14:14:54.323498", "exception": false, "start_time": "2022-01-28T14:14:54.252286", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The newest customer's enrolment date in therecords: 2014-12-06\n" ] } ], "source": [ "dates = []\n", "for i in data[\"Dt_Customer\"]:\n", " i = i.date()\n", " dates.append(i) \n", "#Dates of the newest and oldest recorded customer\n", "print(\"The newest customer's enrolment date in therecords:\",max(dates))\n", "\n", "data['max_date'] = max(dates)\n", "data['max_date'] = pd.to_datetime(data.max_date)\n", "data['day_engaged'] = (data['max_date'] - data['Dt_Customer']).dt.days" ] }, { "cell_type": "code", "execution_count": 17, "id": "23ea35f8", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:54.429021Z", "iopub.status.busy": "2022-01-28T14:14:54.427941Z", "iopub.status.idle": "2022-01-28T14:14:54.453092Z", "shell.execute_reply": "2022-01-28T14:14:54.453695Z", "shell.execute_reply.started": "2022-01-28T14:09:57.340355Z" }, "papermill": { "duration": 0.079917, "end_time": "2022-01-28T14:14:54.453867", "exception": false, "start_time": "2022-01-28T14:14:54.373950", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ID</th>\n", " <th>Year_Birth</th>\n", " <th>Education</th>\n", " <th>Marital_Status</th>\n", " <th>Income</th>\n", " <th>Kidhome</th>\n", " <th>Teenhome</th>\n", " <th>Dt_Customer</th>\n", " <th>Recency</th>\n", " <th>MntWines</th>\n", " <th>...</th>\n", " <th>Z_CostContact</th>\n", " <th>Z_Revenue</th>\n", " <th>Response</th>\n", " <th>Total_Expense</th>\n", " <th>Total_Children</th>\n", " <th>Total_Accepted_Campaign</th>\n", " <th>Total_Purchases</th>\n", " <th>Age</th>\n", " <th>max_date</th>\n", " <th>day_engaged</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5524</td>\n", " <td>1957</td>\n", " <td>Graduate</td>\n", " <td>Single</td>\n", " <td>58138.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2012-04-09</td>\n", " <td>58</td>\n", " <td>635</td>\n", " <td>...</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>1</td>\n", " <td>1617</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>25</td>\n", " <td>58</td>\n", " <td>2014-12-06</td>\n", " <td>971</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2174</td>\n", " <td>1954</td>\n", " <td>Graduate</td>\n", " <td>Single</td>\n", " <td>46344.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2014-08-03</td>\n", " <td>38</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>27</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>61</td>\n", " <td>2014-12-06</td>\n", " <td>125</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4141</td>\n", " <td>1965</td>\n", " <td>Graduate</td>\n", " <td>Relationship</td>\n", " <td>71613.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2013-08-21</td>\n", " <td>26</td>\n", " <td>426</td>\n", " <td>...</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>776</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>21</td>\n", " <td>50</td>\n", " <td>2014-12-06</td>\n", " <td>472</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6182</td>\n", " <td>1984</td>\n", " <td>Graduate</td>\n", " <td>Relationship</td>\n", " <td>26646.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2014-10-02</td>\n", " <td>26</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>53</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>31</td>\n", " <td>2014-12-06</td>\n", " <td>65</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5324</td>\n", " <td>1981</td>\n", " <td>Postgraduate</td>\n", " <td>Relationship</td>\n", " <td>58293.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2014-01-19</td>\n", " <td>94</td>\n", " <td>173</td>\n", " <td>...</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>422</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>19</td>\n", " <td>34</td>\n", " <td>2014-12-06</td>\n", " <td>321</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 36 columns</p>\n", "</div>" ], "text/plain": [ " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", "0 5524 1957 Graduate Single 58138.0 0 0 \n", "1 2174 1954 Graduate Single 46344.0 1 1 \n", "2 4141 1965 Graduate Relationship 71613.0 0 0 \n", "3 6182 1984 Graduate Relationship 26646.0 1 0 \n", "4 5324 1981 Postgraduate Relationship 58293.0 1 0 \n", "\n", " Dt_Customer Recency MntWines ... Z_CostContact Z_Revenue Response \\\n", "0 2012-04-09 58 635 ... 3 11 1 \n", "1 2014-08-03 38 11 ... 3 11 0 \n", "2 2013-08-21 26 426 ... 3 11 0 \n", "3 2014-10-02 26 11 ... 3 11 0 \n", "4 2014-01-19 94 173 ... 3 11 0 \n", "\n", " Total_Expense Total_Children Total_Accepted_Campaign Total_Purchases \\\n", "0 1617 0 1 25 \n", "1 27 2 0 6 \n", "2 776 0 0 21 \n", "3 53 1 0 8 \n", "4 422 1 0 19 \n", "\n", " Age max_date day_engaged \n", "0 58 2014-12-06 971 \n", "1 61 2014-12-06 125 \n", "2 50 2014-12-06 472 \n", "3 31 2014-12-06 65 \n", "4 34 2014-12-06 321 \n", "\n", "[5 rows x 36 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "06e2ee93", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:54.562319Z", "iopub.status.busy": "2022-01-28T14:14:54.561304Z", "iopub.status.idle": "2022-01-28T14:14:54.576406Z", "shell.execute_reply": "2022-01-28T14:14:54.577109Z", "shell.execute_reply.started": "2022-01-28T14:09:57.368064Z" }, "papermill": { "duration": 0.072127, "end_time": "2022-01-28T14:14:54.577309", "exception": false, "start_time": "2022-01-28T14:14:54.505182", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean income of the postgraduate customer segment is 53377.38268895118\n", "Mean income of the undergraduate customer segment is 20306.25925925926\n", "Mean income of the graduate customer segment is 52715.755780737985\n" ] } ], "source": [ "education_group = data.groupby(\"Education\")\n", "print(\"Mean income of the postgraduate customer segment is\",education_group.get_group(\"Postgraduate\")[\"Income\"].mean())\n", "\n", "print(\"Mean income of the undergraduate customer segment is\",education_group.get_group(\"Undergraduate\")[\"Income\"].mean())\n", "\n", "print(\"Mean income of the graduate customer segment is\",education_group.get_group(\"Graduate\")[\"Income\"].mean())" ] }, { "cell_type": "code", "execution_count": 19, "id": "0522739a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:54.685488Z", "iopub.status.busy": "2022-01-28T14:14:54.684673Z", "iopub.status.idle": "2022-01-28T14:14:54.694299Z", "shell.execute_reply": "2022-01-28T14:14:54.694947Z", "shell.execute_reply.started": "2022-01-28T14:09:57.388699Z" }, "papermill": { "duration": 0.066056, "end_time": "2022-01-28T14:14:54.695125", "exception": false, "start_time": "2022-01-28T14:14:54.629069", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean income of the customer segment in a relationship status is 52333.42071949658\n", "Mean income of the customer segment in a single status is 52090.93406223355\n" ] } ], "source": [ "status_group = data.groupby(\"Marital_Status\")\n", "print(\"Mean income of the customer segment in a relationship status is\",status_group.get_group(\"Relationship\")[\"Income\"].mean())\n", "\n", "print(\"Mean income of the customer segment in a single status is\",status_group.get_group(\"Single\")[\"Income\"].mean())" ] }, { "cell_type": "markdown", "id": "cc9e22dc", "metadata": { "papermill": { "duration": 0.051821, "end_time": "2022-01-28T14:14:54.802915", "exception": false, "start_time": "2022-01-28T14:14:54.751094", "status": "completed" }, "tags": [] }, "source": [ "# Data Visualisation" ] }, { "cell_type": "markdown", "id": "892bf91d", "metadata": { "papermill": { "duration": 0.053241, "end_time": "2022-01-28T14:14:54.908076", "exception": false, "start_time": "2022-01-28T14:14:54.854835", "status": "completed" }, "tags": [] }, "source": [ "## Alvin" ] }, { "cell_type": "code", "execution_count": 20, "id": "f38d4ff9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:55.017291Z", "iopub.status.busy": "2022-01-28T14:14:55.016300Z", "iopub.status.idle": "2022-01-28T14:14:55.358616Z", "shell.execute_reply": "2022-01-28T14:14:55.357983Z", "shell.execute_reply.started": "2022-01-28T14:11:03.323761Z" }, "papermill": { "duration": 0.398017, "end_time": "2022-01-28T14:14:55.358766", "exception": false, "start_time": "2022-01-28T14:14:54.960749", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,5))\n", "sns.barplot(x = data['Total_Expense'], y = data['Education'])\n", "plt.title('Total Total_Spent based on the Education Level')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "id": "ab75e8ba", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:55.476397Z", "iopub.status.busy": "2022-01-28T14:14:55.472257Z", "iopub.status.idle": "2022-01-28T14:14:55.738416Z", "shell.execute_reply": "2022-01-28T14:14:55.738876Z", "shell.execute_reply.started": "2022-01-28T14:11:10.537645Z" }, "papermill": { "duration": 0.324775, "end_time": "2022-01-28T14:14:55.739064", "exception": false, "start_time": "2022-01-28T14:14:55.414289", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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{\"responsive\": true} ).then(function(){\n", " \n", "var gd = document.getElementById('80233bda-0032-40d0-8f69-416769f5ebc1');\n", "var x = new MutationObserver(function (mutations, observer) {{\n", " var display = window.getComputedStyle(gd).display;\n", " if (!display || display === 'none') {{\n", " console.log([gd, 'removed!']);\n", " Plotly.purge(gd);\n", " observer.disconnect();\n", " }}\n", "}});\n", "\n", "// Listen for the removal of the full notebook cells\n", "var notebookContainer = gd.closest('#notebook-container');\n", "if (notebookContainer) {{\n", " x.observe(notebookContainer, {childList: true});\n", "}}\n", "\n", "// Listen for the clearing of the current output cell\n", "var outputEl = gd.closest('.output');\n", "if (outputEl) {{\n", " x.observe(outputEl, {childList: true});\n", "}}\n", "\n", " }) }; }); </script> </div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram (data, x = \"NumWebVisitsMonth\", template = 'seaborn')\n", "fig.update_layout(bargap = 0.2)\n", "fig.show ()" ] }, { "cell_type": "markdown", "id": "88d9ab8e", "metadata": { "papermill": { "duration": 0.058639, "end_time": "2022-01-28T14:14:57.375713", "exception": false, "start_time": "2022-01-28T14:14:57.317074", "status": "completed" }, "tags": [] }, "source": [ "## Yew Chong" ] }, { "cell_type": "code", "execution_count": 24, "id": "d8e3ce84", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:57.501453Z", "iopub.status.busy": "2022-01-28T14:14:57.500733Z", "iopub.status.idle": "2022-01-28T14:14:57.786054Z", "shell.execute_reply": "2022-01-28T14:14:57.786603Z", "shell.execute_reply.started": "2022-01-28T14:11:18.006575Z" }, "papermill": { "duration": 0.349029, "end_time": "2022-01-28T14:14:57.786789", "exception": false, "start_time": "2022-01-28T14:14:57.437760", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "listed = [\"MntWines\",\"MntFruits\",\"MntMeatProducts\",\"MntFishProducts\",\"MntSweetProducts\",\"MntGoldProds\"]\n", "gp = data.groupby([\"Marital_Status\"])[listed].sum().reset_index()\n", "gp.rename(columns={\"Marital_Status\":\"Marital Status\",\"MntWines\":\"Wines\",\"MntFruits\":\"Fruits\",\"MntMeatProducts\":\"Meat\", \"MntFishProducts\":\"Fish\" , \"MntSweetProducts\":\"Sweat\", \"MntGoldProds\":\"Gold\"}, inplace=True)\n", "\n", "# plot grouped bar chart\n", "ax1=gp.plot(x = \"Marital Status\",\n", " kind = 'bar',\n", " stacked = False,\n", " figsize = (16, 5),\n", " title = 'Total Amount Spent by Customer on Each Category Of Product based on Marital Status')\n", "\n", "ax1.set_ylabel(\"Total Amount Spent\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "id": "2bb5bf6b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:57.906897Z", "iopub.status.busy": "2022-01-28T14:14:57.905974Z", "iopub.status.idle": "2022-01-28T14:14:58.175638Z", "shell.execute_reply": "2022-01-28T14:14:58.174607Z", "shell.execute_reply.started": "2022-01-28T14:11:21.023295Z" }, "papermill": { "duration": 0.330091, "end_time": "2022-01-28T14:14:58.175791", "exception": false, "start_time": "2022-01-28T14:14:57.845700", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Preparing data\n", "total_spent_on_wines = data[\"MntWines\"].sum()\n", "total_spent_on_fruits = data[\"MntFruits\"].sum()\n", "total_spent_on_meat = data[\"MntMeatProducts\"].sum()\n", "total_spent_on_fish = data[\"MntFishProducts\"].sum()\n", "total_spent_on_sweet = data[\"MntSweetProducts\"].sum()\n", "total_spent_on_gold = data[\"MntGoldProds\"].sum()\n", "\n", "labelling = [\"Wines\", \"Fruit\", \"Meat\", \"Fish\", \"Sweat\", \"Gold\"]\n", "list_of_total_spent = [total_spent_on_wines, total_spent_on_fruits, total_spent_on_meat, total_spent_on_fish, total_spent_on_sweet, total_spent_on_gold]\n", "\n", "colors = (\"orange\", \"cyan\", \"green\", \"grey\", \"yellow\", \"red\")\n", "\n", "\n", "def make_autopct(values):\n", " def my_autopct(pct):\n", " total = sum(values)\n", " val = int(round(pct*total/100.0))\n", " return '{p:.2f}% \\n ({v:d})'.format(p = pct,v = val)\n", " return my_autopct\n", "\n", "# Creating plot\n", "fig, ax = plt.subplots(figsize = (12, 10))\n", "wedges, texts, autotexts = ax.pie(list_of_total_spent,\n", " autopct = make_autopct(list_of_total_spent),\n", " labels = labelling,\n", " colors = colors,\n", " startangle = 90,\n", " textprops = dict(color = \"black\"))\n", "\n", "\n", "# Adding legend\n", "ax.legend(wedges, labelling,\n", " title = \"Type of Products\",\n", " loc = \"center left\",\n", " bbox_to_anchor = (1, 0, 0.5, 1))\n", " \n", "# plt.setp(autotexts, size = 8, weight =\"bold\")\n", "plt.setp(autotexts, size = 10)\n", "ax.set_title(\"Type of Products that Customers Spend On\")\n", " \n", "# show plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "id": "ebf46042", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:58.304799Z", "iopub.status.busy": "2022-01-28T14:14:58.303642Z", "iopub.status.idle": "2022-01-28T14:14:58.494128Z", "shell.execute_reply": "2022-01-28T14:14:58.494666Z", "shell.execute_reply.started": "2022-01-28T14:11:23.823865Z" }, "papermill": { "duration": 0.2603, "end_time": "2022-01-28T14:14:58.494857", "exception": false, "start_time": "2022-01-28T14:14:58.234557", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Preparing Data\n", "total_web = data[\"NumWebPurchases\"].sum()\n", "total_catalog = data[\"NumCatalogPurchases\"].sum()\n", "total_store_purchase = data[\"NumStorePurchases\"].sum()\n", "\n", "\n", "x = [\"Web Purchases\", \"Catalog Purchases\", \"In Store Purchases\"]\n", "y = [total_web, total_catalog, total_store_purchase]\n", "\n", "c = ['orange', 'green', 'grey']\n", "\n", "fig, axes = plt.subplots()\n", "axes.bar(x, y, color = c, align = \"center\", width = 0.8, alpha = 0.5)\n", "\n", "for i in range(len(x)):\n", " plt.text(i,y[i],y[i], ha=\"center\",va=\"bottom\")\n", "\n", "\n", "axes.set_xlabel(\"Purchase Type\", fontsize = 10)\n", "axes.set_ylabel(\"Number of Purchase\", fontsize = 10)\n", "axes.set_title(\"Number of Purchase Based on Purchase Type\")\n", "plt.ylim(0,14000)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "id": "32e68543", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:58.621544Z", "iopub.status.busy": "2022-01-28T14:14:58.620843Z", "iopub.status.idle": "2022-01-28T14:14:58.849492Z", "shell.execute_reply": "2022-01-28T14:14:58.848888Z", "shell.execute_reply.started": "2022-01-28T14:11:26.093107Z" }, "papermill": { "duration": 0.292532, "end_time": "2022-01-28T14:14:58.849650", "exception": false, "start_time": "2022-01-28T14:14:58.557118", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Correlation between webvisitmonth and webpurchase\n", "df4 = data[['NumWebVisitsMonth', 'NumWebPurchases']].copy()\n", "df4\n", "\n", "correlation_mat = df4.corr()\n", "\n", "sns.heatmap(correlation_mat, annot = True)\n", "\n", "plt.title('Correlation Heat Map')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "593e4d51", "metadata": { "papermill": { "duration": 0.062778, "end_time": "2022-01-28T14:14:58.974525", "exception": false, "start_time": "2022-01-28T14:14:58.911747", "status": "completed" }, "tags": [] }, "source": [ "# Machine Learning (Clustering)" ] }, { "cell_type": "markdown", "id": "e9849c23", "metadata": { "execution": { "iopub.execute_input": "2022-01-22T02:37:23.254108Z", "iopub.status.busy": "2022-01-22T02:37:23.253475Z", "iopub.status.idle": "2022-01-22T02:37:23.278302Z", "shell.execute_reply": "2022-01-22T02:37:23.277506Z", "shell.execute_reply.started": "2022-01-22T02:37:23.253959Z" }, "papermill": { "duration": 0.061167, "end_time": "2022-01-28T14:14:59.097252", "exception": false, "start_time": "2022-01-28T14:14:59.036085", "status": "completed" }, "tags": [] }, "source": [ "## Drop unnecessary columns for Machine Learning" ] }, { "cell_type": "code", "execution_count": 28, "id": "89796eaa", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:59.224483Z", "iopub.status.busy": "2022-01-28T14:14:59.223336Z", "iopub.status.idle": "2022-01-28T14:14:59.230574Z", "shell.execute_reply": "2022-01-28T14:14:59.231039Z", "shell.execute_reply.started": "2022-01-28T14:11:31.044323Z" }, "papermill": { "duration": 0.072572, "end_time": "2022-01-28T14:14:59.231243", "exception": false, "start_time": "2022-01-28T14:14:59.158671", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "columns_to_drop = [\"Year_Birth\", \"Dt_Customer\", \"max_date\", \"Kidhome\", \"Teenhome\", \"MntWines\", \"MntFruits\", \n", " \"MntMeatProducts\", \"MntFishProducts\", \"MntSweetProducts\", \"MntGoldProds\", \"NumWebPurchases\", \n", " \"NumCatalogPurchases\", \"NumStorePurchases\", \"NumDealsPurchases\", \"AcceptedCmp1\", \n", " \"AcceptedCmp2\", \"AcceptedCmp3\", \"AcceptedCmp4\", \"AcceptedCmp5\", \"Response\"]\n", "\n", "data.drop(columns = columns_to_drop, axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 29, "id": "44f8d8f6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:59.358194Z", "iopub.status.busy": "2022-01-28T14:14:59.357101Z", "iopub.status.idle": "2022-01-28T14:14:59.362207Z", "shell.execute_reply": "2022-01-28T14:14:59.362807Z", "shell.execute_reply.started": "2022-01-28T14:11:31.654584Z" }, "papermill": { "duration": 0.070478, "end_time": "2022-01-28T14:14:59.362977", "exception": false, "start_time": "2022-01-28T14:14:59.292499", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "other_columns_to_drop = [\"ID\", \"Z_CostContact\", \"Z_Revenue\"]\n", "data.drop(columns = other_columns_to_drop, axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 30, "id": "36fe73c0", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:59.492915Z", "iopub.status.busy": "2022-01-28T14:14:59.491975Z", "iopub.status.idle": "2022-01-28T14:14:59.530885Z", "shell.execute_reply": "2022-01-28T14:14:59.531402Z", "shell.execute_reply.started": "2022-01-28T14:11:32.231362Z" }, "papermill": { "duration": 0.10715, "end_time": "2022-01-28T14:14:59.531582", "exception": false, "start_time": "2022-01-28T14:14:59.424432", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Income</th>\n", " <th>Recency</th>\n", " <th>NumWebVisitsMonth</th>\n", " <th>Complain</th>\n", " <th>Total_Expense</th>\n", " <th>Total_Children</th>\n", " <th>Total_Accepted_Campaign</th>\n", " <th>Total_Purchases</th>\n", " <th>Age</th>\n", " <th>day_engaged</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " <td>2240.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>52247.251354</td>\n", " <td>49.109375</td>\n", " <td>5.316518</td>\n", " <td>0.009375</td>\n", " <td>605.798214</td>\n", " <td>0.950446</td>\n", " <td>0.446875</td>\n", " <td>14.862054</td>\n", " <td>46.194196</td>\n", " <td>512.043304</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>25037.797168</td>\n", " <td>28.962453</td>\n", " <td>2.426645</td>\n", " <td>0.096391</td>\n", " <td>602.249288</td>\n", " <td>0.751803</td>\n", " <td>0.890543</td>\n", " <td>7.677173</td>\n", " <td>11.984069</td>\n", " <td>232.229893</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1730.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>5.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>19.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>35538.750000</td>\n", " <td>24.000000</td>\n", " <td>3.000000</td>\n", " <td>0.000000</td>\n", " <td>68.750000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>8.000000</td>\n", " <td>38.000000</td>\n", " <td>340.750000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>51741.500000</td>\n", " <td>49.000000</td>\n", " <td>6.000000</td>\n", " <td>0.000000</td>\n", " <td>396.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>15.000000</td>\n", " <td>45.000000</td>\n", " <td>513.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>68289.750000</td>\n", " <td>74.000000</td>\n", " <td>7.000000</td>\n", " <td>0.000000</td>\n", " <td>1045.500000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>21.000000</td>\n", " <td>56.000000</td>\n", " <td>685.250000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>666666.000000</td>\n", " <td>99.000000</td>\n", " <td>20.000000</td>\n", " <td>1.000000</td>\n", " <td>2525.000000</td>\n", " <td>3.000000</td>\n", " <td>5.000000</td>\n", " <td>44.000000</td>\n", " <td>122.000000</td>\n", " <td>1063.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Income Recency NumWebVisitsMonth Complain \\\n", "count 2240.000000 2240.000000 2240.000000 2240.000000 \n", "mean 52247.251354 49.109375 5.316518 0.009375 \n", "std 25037.797168 28.962453 2.426645 0.096391 \n", "min 1730.000000 0.000000 0.000000 0.000000 \n", "25% 35538.750000 24.000000 3.000000 0.000000 \n", "50% 51741.500000 49.000000 6.000000 0.000000 \n", "75% 68289.750000 74.000000 7.000000 0.000000 \n", "max 666666.000000 99.000000 20.000000 1.000000 \n", "\n", " Total_Expense Total_Children Total_Accepted_Campaign \\\n", "count 2240.000000 2240.000000 2240.000000 \n", "mean 605.798214 0.950446 0.446875 \n", "std 602.249288 0.751803 0.890543 \n", "min 5.000000 0.000000 0.000000 \n", "25% 68.750000 0.000000 0.000000 \n", "50% 396.000000 1.000000 0.000000 \n", "75% 1045.500000 1.000000 1.000000 \n", "max 2525.000000 3.000000 5.000000 \n", "\n", " Total_Purchases Age day_engaged \n", "count 2240.000000 2240.000000 2240.000000 \n", "mean 14.862054 46.194196 512.043304 \n", "std 7.677173 11.984069 232.229893 \n", "min 0.000000 19.000000 0.000000 \n", "25% 8.000000 38.000000 340.750000 \n", "50% 15.000000 45.000000 513.000000 \n", "75% 21.000000 56.000000 685.250000 \n", "max 44.000000 122.000000 1063.000000 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 31, "id": "8dccb828", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:14:59.657062Z", "iopub.status.busy": "2022-01-28T14:14:59.656005Z", "iopub.status.idle": "2022-01-28T14:15:00.459335Z", "shell.execute_reply": "2022-01-28T14:15:00.459886Z", "shell.execute_reply.started": "2022-01-28T14:11:32.763562Z" }, "papermill": { "duration": 0.867743, "end_time": "2022-01-28T14:15:00.460057", "exception": false, "start_time": "2022-01-28T14:14:59.592314", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x1080 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (15,15))\n", "sns.heatmap(data.corr(), annot = True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 32, "id": "96bf4d8a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:00.596334Z", "iopub.status.busy": "2022-01-28T14:15:00.595632Z", "iopub.status.idle": "2022-01-28T14:15:00.604458Z", "shell.execute_reply": "2022-01-28T14:15:00.604988Z", "shell.execute_reply.started": "2022-01-28T14:11:33.584261Z" }, "papermill": { "duration": 0.076257, "end_time": "2022-01-28T14:15:00.605164", "exception": false, "start_time": "2022-01-28T14:15:00.528907", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "label_encoder = LabelEncoder()\n", "data[[\"Education\", \"Marital_Status\"]] = data[[\"Education\", \"Marital_Status\"]].apply(label_encoder.fit_transform)" ] }, { "cell_type": "code", "execution_count": 33, "id": "b0b265a2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:00.737484Z", "iopub.status.busy": "2022-01-28T14:15:00.736773Z", "iopub.status.idle": "2022-01-28T14:15:00.750546Z", "shell.execute_reply": "2022-01-28T14:15:00.751077Z", "shell.execute_reply.started": "2022-01-28T14:11:33.637331Z" }, "papermill": { "duration": 0.081807, "end_time": "2022-01-28T14:15:00.751269", "exception": false, "start_time": "2022-01-28T14:15:00.669462", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Education</th>\n", " <th>Marital_Status</th>\n", " <th>Income</th>\n", " <th>Recency</th>\n", " <th>NumWebVisitsMonth</th>\n", " <th>Complain</th>\n", " <th>Total_Expense</th>\n", " <th>Total_Children</th>\n", " <th>Total_Accepted_Campaign</th>\n", " <th>Total_Purchases</th>\n", " <th>Age</th>\n", " <th>day_engaged</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>58138.0</td>\n", " <td>58</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>1617</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>25</td>\n", " <td>58</td>\n", " <td>971</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>46344.0</td>\n", " <td>38</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>27</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>61</td>\n", " <td>125</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>71613.0</td>\n", " <td>26</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>776</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>21</td>\n", " <td>50</td>\n", " <td>472</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>26646.0</td>\n", " <td>26</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>53</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>31</td>\n", " <td>65</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>58293.0</td>\n", " <td>94</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>422</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>19</td>\n", " <td>34</td>\n", " <td>321</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Education Marital_Status Income Recency NumWebVisitsMonth Complain \\\n", "0 0 1 58138.0 58 7 0 \n", "1 0 1 46344.0 38 5 0 \n", "2 0 0 71613.0 26 4 0 \n", "3 0 0 26646.0 26 6 0 \n", "4 1 0 58293.0 94 5 0 \n", "\n", " Total_Expense Total_Children Total_Accepted_Campaign Total_Purchases \\\n", "0 1617 0 1 25 \n", "1 27 2 0 6 \n", "2 776 0 0 21 \n", "3 53 1 0 8 \n", "4 422 1 0 19 \n", "\n", " Age day_engaged \n", "0 58 971 \n", "1 61 125 \n", "2 50 472 \n", "3 31 65 \n", "4 34 321 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 34, "id": "2b2779ed", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:00.886759Z", "iopub.status.busy": "2022-01-28T14:15:00.885965Z", "iopub.status.idle": "2022-01-28T14:15:00.892588Z", "shell.execute_reply": "2022-01-28T14:15:00.893101Z", "shell.execute_reply.started": "2022-01-28T14:11:34.044739Z" }, "papermill": { "duration": 0.075681, "end_time": "2022-01-28T14:15:00.893301", "exception": false, "start_time": "2022-01-28T14:15:00.817620", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "scaled_features = StandardScaler().fit_transform(data.values)\n", "scaled_data = pd.DataFrame(scaled_features, index = data.index, columns = data.columns)" ] }, { "cell_type": "code", "execution_count": 35, "id": "434b8a99", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:01.026488Z", "iopub.status.busy": "2022-01-28T14:15:01.025785Z", "iopub.status.idle": "2022-01-28T14:15:01.042976Z", "shell.execute_reply": "2022-01-28T14:15:01.043535Z", "shell.execute_reply.started": "2022-01-28T14:11:34.582174Z" }, "papermill": { "duration": 0.085517, "end_time": "2022-01-28T14:15:01.043710", "exception": false, "start_time": "2022-01-28T14:15:00.958193", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Education</th>\n", " <th>Marital_Status</th>\n", " <th>Income</th>\n", " <th>Recency</th>\n", " <th>NumWebVisitsMonth</th>\n", " <th>Complain</th>\n", " <th>Total_Expense</th>\n", " <th>Total_Children</th>\n", " <th>Total_Accepted_Campaign</th>\n", " <th>Total_Purchases</th>\n", " <th>Age</th>\n", " <th>day_engaged</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-0.954727</td>\n", " <td>1.346874</td>\n", " <td>0.235327</td>\n", " <td>0.307039</td>\n", " <td>0.693904</td>\n", " <td>-0.097282</td>\n", " <td>1.679417</td>\n", " <td>-1.264505</td>\n", " <td>0.621248</td>\n", " <td>1.320826</td>\n", " <td>0.985345</td>\n", " <td>1.976745</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-0.954727</td>\n", " <td>1.346874</td>\n", " <td>-0.235826</td>\n", " <td>-0.383664</td>\n", " <td>-0.130463</td>\n", " <td>-0.097282</td>\n", " <td>-0.961275</td>\n", " <td>1.396361</td>\n", " <td>-0.501912</td>\n", " <td>-1.154596</td>\n", " <td>1.235733</td>\n", " <td>-1.667011</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-0.954727</td>\n", " <td>-0.742460</td>\n", " <td>0.773633</td>\n", " <td>-0.798086</td>\n", " <td>-0.542647</td>\n", " <td>-0.097282</td>\n", " <td>0.282673</td>\n", " <td>-1.264505</td>\n", " <td>-0.501912</td>\n", " <td>0.799685</td>\n", " <td>0.317643</td>\n", " <td>-0.172468</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-0.954727</td>\n", " <td>-0.742460</td>\n", " <td>-1.022732</td>\n", " <td>-0.798086</td>\n", " <td>0.281720</td>\n", " <td>-0.097282</td>\n", " <td>-0.918094</td>\n", " <td>0.065928</td>\n", " <td>-0.501912</td>\n", " <td>-0.894025</td>\n", " <td>-1.268149</td>\n", " <td>-1.925433</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.877826</td>\n", " <td>-0.742460</td>\n", " <td>0.241519</td>\n", " <td>1.550305</td>\n", " <td>-0.130463</td>\n", " <td>-0.097282</td>\n", " <td>-0.305254</td>\n", " <td>0.065928</td>\n", " <td>-0.501912</td>\n", " <td>0.539114</td>\n", " <td>-1.017761</td>\n", " <td>-0.822831</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Education Marital_Status Income Recency NumWebVisitsMonth Complain \\\n", "0 -0.954727 1.346874 0.235327 0.307039 0.693904 -0.097282 \n", "1 -0.954727 1.346874 -0.235826 -0.383664 -0.130463 -0.097282 \n", "2 -0.954727 -0.742460 0.773633 -0.798086 -0.542647 -0.097282 \n", "3 -0.954727 -0.742460 -1.022732 -0.798086 0.281720 -0.097282 \n", "4 0.877826 -0.742460 0.241519 1.550305 -0.130463 -0.097282 \n", "\n", " Total_Expense Total_Children Total_Accepted_Campaign Total_Purchases \\\n", "0 1.679417 -1.264505 0.621248 1.320826 \n", "1 -0.961275 1.396361 -0.501912 -1.154596 \n", "2 0.282673 -1.264505 -0.501912 0.799685 \n", "3 -0.918094 0.065928 -0.501912 -0.894025 \n", "4 -0.305254 0.065928 -0.501912 0.539114 \n", "\n", " Age day_engaged \n", "0 0.985345 1.976745 \n", "1 1.235733 -1.667011 \n", "2 0.317643 -0.172468 \n", "3 -1.268149 -1.925433 \n", "4 -1.017761 -0.822831 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaled_data.head()" ] }, { "cell_type": "code", "execution_count": 36, "id": "82973251", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:01.179069Z", "iopub.status.busy": "2022-01-28T14:15:01.178297Z", "iopub.status.idle": "2022-01-28T14:15:29.159387Z", "shell.execute_reply": "2022-01-28T14:15:29.159940Z", "shell.execute_reply.started": "2022-01-28T14:11:34.958066Z" }, "papermill": { "duration": 28.050392, "end_time": "2022-01-28T14:15:29.160155", "exception": false, "start_time": "2022-01-28T14:15:01.109763", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "wcss_points =[]\n", "for i in range (1, 20):\n", " model = KMeans(n_clusters = i,init = 'k-means++',random_state = 1)\n", " model.fit(scaled_data)\n", " wcss_points.append(model.inertia_)" ] }, { "cell_type": "code", "execution_count": 37, "id": "9b65d617", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:29.326811Z", "iopub.status.busy": "2022-01-28T14:15:29.326014Z", "iopub.status.idle": "2022-01-28T14:15:29.604086Z", "shell.execute_reply": "2022-01-28T14:15:29.605143Z", "shell.execute_reply.started": "2022-01-28T14:12:03.428913Z" }, "papermill": { "duration": 0.367568, "end_time": "2022-01-28T14:15:29.605371", "exception": false, "start_time": "2022-01-28T14:15:29.237803", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#https://www.analyticsvidhya.com/blog/2021/01/in-depth-intuition-of-k-means-clustering-algorithm-in-machine-learning/#:~:text=For%20each%20value%20of%20K,value%20will%20start%20to%20decrease.\n", "plt.figure(figsize = (16, 8))\n", "plt.plot(range(1, 20), wcss_points, 'bx-')\n", "plt.title('The Elbow Method')\n", "plt.xlabel('Number of clusters')\n", "plt.ylabel('WCSS')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 38, "id": "89367823", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:29.823106Z", "iopub.status.busy": "2022-01-28T14:15:29.822000Z", "iopub.status.idle": "2022-01-28T14:15:54.346860Z", "shell.execute_reply": "2022-01-28T14:15:54.347498Z", "shell.execute_reply.started": "2022-01-28T14:12:03.717640Z" }, "papermill": { "duration": 24.653279, "end_time": "2022-01-28T14:15:54.347715", "exception": false, "start_time": "2022-01-28T14:15:29.694436", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "silhouette_scores = []\n", "for i in range(2, 10):\n", " model2 = KMeans(n_clusters = i, random_state = 1)\n", " c = model2.fit_predict(scaled_data)\n", " silhouette_scores.append(silhouette_score(scaled_data, model2.fit_predict(scaled_data))) " ] }, { "cell_type": "code", "execution_count": 39, "id": "cc1c4ed6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:54.530270Z", "iopub.status.busy": "2022-01-28T14:15:54.517480Z", "iopub.status.idle": "2022-01-28T14:15:54.778904Z", "shell.execute_reply": "2022-01-28T14:15:54.779935Z", "shell.execute_reply.started": "2022-01-28T14:12:28.409272Z" }, "papermill": { "duration": 0.351696, "end_time": "2022-01-28T14:15:54.780136", "exception": false, "start_time": "2022-01-28T14:15:54.428440", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(range(2, 10), silhouette_scores) \n", "plt.xlabel('Number of clusters', fontsize = 20) \n", "plt.ylabel('Silhouette Scores', fontsize = 20) \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "id": "fbe57094", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:54.955879Z", "iopub.status.busy": "2022-01-28T14:15:54.954430Z", "iopub.status.idle": "2022-01-28T14:15:54.956795Z", "shell.execute_reply": "2022-01-28T14:15:54.955255Z", "shell.execute_reply.started": "2022-01-28T14:12:28.677266Z" }, "papermill": { "duration": 0.092225, "end_time": "2022-01-28T14:15:54.957001", "exception": false, "start_time": "2022-01-28T14:15:54.864776", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "number_of_clusters = 2" ] }, { "cell_type": "code", "execution_count": 41, "id": "dd02024d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:55.132943Z", "iopub.status.busy": "2022-01-28T14:15:55.131817Z", "iopub.status.idle": "2022-01-28T14:15:56.420999Z", "shell.execute_reply": "2022-01-28T14:15:56.420372Z", "shell.execute_reply.started": "2022-01-28T14:12:28.685152Z" }, "papermill": { "duration": 1.38322, "end_time": "2022-01-28T14:15:56.421172", "exception": false, "start_time": "2022-01-28T14:15:55.037952", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "final_model = KMeans(n_clusters=number_of_clusters, random_state=1).fit(scaled_data)" ] }, { "cell_type": "code", "execution_count": 42, "id": "2cb40259", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:56.594122Z", "iopub.status.busy": "2022-01-28T14:15:56.593399Z", "iopub.status.idle": "2022-01-28T14:15:56.598652Z", "shell.execute_reply": "2022-01-28T14:15:56.599364Z", "shell.execute_reply.started": "2022-01-28T14:12:30.072315Z" }, "papermill": { "duration": 0.095476, "end_time": "2022-01-28T14:15:56.599567", "exception": false, "start_time": "2022-01-28T14:15:56.504091", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "prediction = final_model.predict(scaled_data)" ] }, { "cell_type": "code", "execution_count": 43, "id": "026c8499", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:56.773115Z", "iopub.status.busy": "2022-01-28T14:15:56.772320Z", "iopub.status.idle": "2022-01-28T14:15:56.782799Z", "shell.execute_reply": "2022-01-28T14:15:56.783424Z", "shell.execute_reply.started": "2022-01-28T14:12:30.081475Z" }, "papermill": { "duration": 0.103269, "end_time": "2022-01-28T14:15:56.783630", "exception": false, "start_time": "2022-01-28T14:15:56.680361", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Education</th>\n", " <th>Marital_Status</th>\n", " <th>Income</th>\n", " <th>Recency</th>\n", " <th>NumWebVisitsMonth</th>\n", " <th>Complain</th>\n", " <th>Total_Expense</th>\n", " <th>Total_Children</th>\n", " <th>Total_Accepted_Campaign</th>\n", " <th>Total_Purchases</th>\n", " <th>Age</th>\n", " <th>day_engaged</th>\n", " <th>result_cluster</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>58138.0</td>\n", " <td>58</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>1617</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>25</td>\n", " <td>58</td>\n", " <td>971</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>46344.0</td>\n", " <td>38</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>27</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>61</td>\n", " <td>125</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>71613.0</td>\n", " <td>26</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>776</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>21</td>\n", " <td>50</td>\n", " <td>472</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>26646.0</td>\n", " <td>26</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>53</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>31</td>\n", " <td>65</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>58293.0</td>\n", " <td>94</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>422</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>19</td>\n", " <td>34</td>\n", " <td>321</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Education Marital_Status Income Recency NumWebVisitsMonth Complain \\\n", "0 0 1 58138.0 58 7 0 \n", "1 0 1 46344.0 38 5 0 \n", "2 0 0 71613.0 26 4 0 \n", "3 0 0 26646.0 26 6 0 \n", "4 1 0 58293.0 94 5 0 \n", "\n", " Total_Expense Total_Children Total_Accepted_Campaign Total_Purchases \\\n", "0 1617 0 1 25 \n", "1 27 2 0 6 \n", "2 776 0 0 21 \n", "3 53 1 0 8 \n", "4 422 1 0 19 \n", "\n", " Age day_engaged result_cluster \n", "0 58 971 1 \n", "1 61 125 2 \n", "2 50 472 1 \n", "3 31 65 2 \n", "4 34 321 2 " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"result_cluster\"] = prediction + 1\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 44, "id": "394942c7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:56.960033Z", "iopub.status.busy": "2022-01-28T14:15:56.959276Z", "iopub.status.idle": "2022-01-28T14:15:57.392684Z", "shell.execute_reply": "2022-01-28T14:15:57.393299Z", "shell.execute_reply.started": "2022-01-28T14:12:30.114522Z" }, "papermill": { "duration": 0.524006, "end_time": "2022-01-28T14:15:57.393513", "exception": false, "start_time": "2022-01-28T14:15:56.869507", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pl = sns.scatterplot(data = data, \n", " x = data[\"Total_Expense\"], y = data[\"Income\"], \n", " hue = data[\"result_cluster\"], palette= [\"#d21262\", \"#26bde2\"])\n", "\n", "pl.set_title(\"Cluster's Profile Based On Income And Total_Spent\")\n", "pl.set(ylim=(0, 200000))\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 45, "id": "ef35581c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:57.581041Z", "iopub.status.busy": "2022-01-28T14:15:57.580146Z", "iopub.status.idle": "2022-01-28T14:15:57.702075Z", "shell.execute_reply": "2022-01-28T14:15:57.703101Z", "shell.execute_reply.started": "2022-01-28T14:12:30.579722Z" }, "papermill": { "duration": 0.225511, "end_time": "2022-01-28T14:15:57.703351", "exception": false, "start_time": "2022-01-28T14:15:57.477840", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pl = sns.countplot(x = data[\"result_cluster\"])\n", "pl.set_title(\"Distribution Of The Clusters\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 46, "id": "c3ae3c1a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:15:57.879518Z", "iopub.status.busy": "2022-01-28T14:15:57.878752Z", "iopub.status.idle": "2022-01-28T14:16:19.656539Z", "shell.execute_reply": "2022-01-28T14:16:19.657046Z", "shell.execute_reply.started": "2022-01-28T14:12:36.096487Z" }, "papermill": { "duration": 21.867743, "end_time": "2022-01-28T14:16:19.657239", "exception": false, "start_time": "2022-01-28T14:15:57.789496", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning:\n", "\n", "30.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", "\n", "/opt/conda/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning:\n", "\n", "76.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "pl=sns.swarmplot(x=data[\"result_cluster\"], y=data[\"Total_Expense\"], color= \"#CBEDDD\", alpha=0.5 )\n", "pl=sns.boxenplot(x=data[\"result_cluster\"], y=data[\"Total_Expense\"], palette=[\"#d21262\", \"#26bde2\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 47, "id": "074b0754", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:16:19.811161Z", "iopub.status.busy": "2022-01-28T14:16:19.810495Z", "iopub.status.idle": "2022-01-28T14:16:20.057485Z", "shell.execute_reply": "2022-01-28T14:16:20.056836Z", "shell.execute_reply.started": "2022-01-28T14:12:58.332010Z" }, "papermill": { "duration": 0.326123, "end_time": "2022-01-28T14:16:20.057634", "exception": false, "start_time": "2022-01-28T14:16:19.731511", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "pl = sns.countplot(x = data[\"Total_Accepted_Campaign\"], hue = data[\"result_cluster\"], palette = [\"#d21262\", \"#26bde2\"])\n", "pl.set_title(\"Number of Campaigns Accepted\")\n", "pl.set_xlabel(\"Number Of Accepted Campaigns\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 48, "id": "c1a59886", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:16:20.215586Z", "iopub.status.busy": "2022-01-28T14:16:20.214887Z", "iopub.status.idle": "2022-01-28T14:16:20.397916Z", "shell.execute_reply": "2022-01-28T14:16:20.397367Z", "shell.execute_reply.started": "2022-01-28T14:12:58.581550Z" }, "papermill": { "duration": 0.263852, "end_time": "2022-01-28T14:16:20.398057", "exception": false, "start_time": "2022-01-28T14:16:20.134205", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "pl=sns.boxenplot(y = data[\"day_engaged\"],x = data[\"result_cluster\"], palette = [\"#d21262\", \"#26bde2\"])\n", "pl.set_title(\"Amount of Purchases Made\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 49, "id": "45d20873", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:16:20.550582Z", "iopub.status.busy": "2022-01-28T14:16:20.549905Z", "iopub.status.idle": "2022-01-28T14:16:20.554999Z", "shell.execute_reply": "2022-01-28T14:16:20.555557Z", "shell.execute_reply.started": "2022-01-28T14:12:58.773523Z" }, "papermill": { "duration": 0.083027, "end_time": "2022-01-28T14:16:20.555735", "exception": false, "start_time": "2022-01-28T14:16:20.472708", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Index(['Education', 'Marital_Status', 'Income', 'Recency', 'NumWebVisitsMonth',\n", " 'Complain', 'Total_Expense', 'Total_Children',\n", " 'Total_Accepted_Campaign', 'Total_Purchases', 'Age', 'day_engaged',\n", " 'result_cluster'],\n", " dtype='object')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 50, "id": "3bcb6c76", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:16:20.740166Z", "iopub.status.busy": "2022-01-28T14:16:20.712739Z", "iopub.status.idle": "2022-01-28T14:16:30.267544Z", "shell.execute_reply": "2022-01-28T14:16:30.268080Z" }, "papermill": { "duration": 9.637862, "end_time": "2022-01-28T14:16:30.268293", "exception": false, "start_time": "2022-01-28T14:16:20.630431", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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JOmcGOZccjLFfbkc3T2mlmlCUSxdXscId5o8DMvhdXzuaEMR8Qbbd8Ra1z36DoTiLXn8/RxUeVlJBBbImqECWApFqN9te/IEtz35LWnkd9YVZ9P/9oRScOwNduqrd15V5IzH+tKqWTyv9TM00cfewLHpb4tVYvL+speyPLxJcvRX74WPJ/9OJWEb27uAWK92ICmRNUIGsrW4mJf55G6h+7luc785FhKMsH9kPLpjJqedNQ6eK9nYbUkre3Orl/nVOpITf9bVzYW87Nr1GLBSh5skvqHz4U2IuP46TJ5N77ZGYhxd3dLOVrk8FsiaoQLY3N5CS4Opt1L83l9p35hJZX0HAYuKbA8ay8cSpXHP4MIbbVRWI7mprIML9a518XuXHYdA4qyiNEwqs9E1kNlY98hnVT38VLyE2bQhZFx1I+uFj0axqEbuyR1Qga4IKZK25YCRKcF0F/iWbqPl+Bd4fVqJtrUMKwfIRfZk9fTTOIydw3tAcDsu1qCSOHmJhfZCnN7n4pjoAwPA0A4flWhifYWJoKEj0zTnU/Pc7wpurEWYDaQeMwH7oGKyTBmAe1gthaF2haKXHUm8oTehRgSxa72PzxmpCkRixaJRYJEY0EiUWjRGNxJCRKDFPAOkOEHH7CdX7iVXWo22txbCtDltJJfpwBAB3moVlI/uxdMwAPAeNYeLgXA7OsageWA+2NRDhi0o/n1X6WOgKbT9eZNbRS68xdmUJQ39eScHsFVi21gIgjXoigwqIFWUji7IQOemIdAua3YKWZkZLM6MzaGiaBjoNdAJ0OoROA02DxIelXT8zNfeO19hjjb0LNBzb/hYhQO5ypmzkH7KRx3a7VjP33v7vWCx+vpTxP2OxxDVk/KSYhJiMtylxHrLheXL7v2VM/vYcKbc/teHVJP6K3OWbKBL/EZpACLH9TxJfQiO++aog/v8ncU78OVriNIHQCUR2OsJhix/jty8EaIBAbH8MIM+kw67fbTsnFcia0KMCWe3Lsyi75vlWXcRrNVGdnUFdbgb1ffPwD+mFHF5M3ujeDE03McxuINOg5r+UndWGoqz0hFnuCrHaG6YiGKUiGKUqGMUfjVFQXsvADVsZsH4bfUoryamuJ6fKiSUY7uimKynwwnmH8eGx05I+/4HhWZxQaNv1sApkTei2gUwI8RmQ0wG3zgGqO+C+bak7vAZQr6Oz6Q6voyNfQ7WU8ogOunen1m0DWUcRQsyTUk7q6Hbsje7wGkC9js6mO7yO7vAauqPdBmEVRVEUpStRgUxRFEXp0lQga3tPd3QD2kB3eA2gXkdn0x1eR3d4Dd2OmiNTFEVRujTVI1MURVG6NBXIFEVRlC5NBTJFURSlS1OBTFEURenSum0gO+KIIxrKqKkv9aW+1Fd3+EpaN33/a1K3DWTV1V29Eo6iKMqe6Wnvf902kCmKoig9gwpkiqIoSpemApmiKIrSpamtaRVFaRfhcJgtW7YQCAQ6uimdmtlspri4GIPB0NFN6TJUIFMUpV1s2bIFu91Ov3794jstK7uRUlJTU8OWLVvo379/Rzeny1BDi4qitItAIEB2drYKYs0QQpCdnb33vdYeVkNXBTJFUdqNCmIta4vvUbiivg1a0nWoQKYoiqJ0aSqQKYqiKF2aCmSKonQbzz//PFdddRUA7733HitWrGj1NUpKShg1atQe33/r1q179Fxlz6lApihKh5NSEovF2vSaexrI9saeBLJIJJKi1vQcKpApitIhSkpKGDp0KOeddx6jRo3i7rvvZp999mHMmDHcfvvtAHi9Xo4++mjGjh3LqFGjeOONNwDo16/f9nqC8+bNY+bMmTtde86cOXzwwQfceOONjBs3jvXr1zfahnXr1nHIIYcwduxYJkyYsNt5O/bwAI455hi+++47otEoF1xwAaNGjWL06NH8+9//5q233mLevHmcffbZjBs3Dr/fz/z58znggAOYOHEihx9+ONu2bQNg5syZXHvttUyaNImHH364Tb6fO+lZSYtqHZmiKB1n7dq1vPDCC7hcLt566y3mzp2LlJLjjjuOH374gaqqKnr16sXHH38MQH19ctl406ZN47jjjuOYY47hlFNOafK8s88+m1tuuYUTTzyRQCBALBajsrKyxesvWrSIsrIyli1bBoDT6cThcPDYY4/x4IMPMmnSJMLhMFdffTXvv/8+ubm5vPHGG/zpT3/iueeeAyAUCjFv3rykXo/SPBXIFEXpMH379mXKlCnccMMNfPHFF4wfPx4Aj8fD2rVrmTFjBtdffz0333wzxxxzDDNmzGize7vdbsrKyjjxxBOBeEWNZA0YMIANGzZw9dVXc/TRR3PYYYftds7q1atZtmwZhx56KADRaJTCwsLtj59++ul7+QqUBiqQKYrSYWw2GxCfI7v11lu57LLLdjtnwYIFfPLJJ/z5z3/m4IMP5q9//St6vX77nFoqS17teJ8d75WZmcnixYv5/PPPeeqpp3jzzTe397QaSCkZOXIkP/30U6PXbnjtyt5Tc2SKonS4ww8/nOeeew6PxwNAWVkZlZWVbN26FavVyjnnnMONN97IggULgPgc2fz58wF4++23G72m3W7H7XY3eU+73U5xcTHvvfceAMFgEJ/Pt9M5/fr1Y9GiRcRiMUpLS5k7dy4Q3+8rFotx8sknc88992xv1473HDp0KFVVVdsDWTgcZvny5Xvy7dkDPWuSTPXIFEXpcIcddhgrV65k6tSpAKSlpfHyyy+zbt06brzxRjRNw2Aw8OSTTwJw++23c/HFF/OXv/xlt0SPBmeccQaXXHIJjzzyCG+99RYDBw7c7ZyXXnqJyy67jL/+9a8YDAb+97//oWm/fb7fb7/96N+/PyNGjGD48OFMmDABiAfaCy+8cHtv7b777gPgggsu4PLLL8disfDTTz/x1ltv8Yc//IH6+noikQjXXnstI0eObLPvmxInZDetyTVp0iSpJlIVpfNYuXIlw4cP7+hmdAlNfK+Srl01ttdAuXhr45maXViTr18NLSqKoihdmhpaVBSl27vyyiv58ccfdzp2zTXXcOGFF3ZQi5S2pAKZoijd3uOPP97RTVBSSA0tKoqiKF2aCmSKoihKl6YCmaIoitKlqUCmKIqSpIsuuoi8vLw93ual3XTTZVVNUYFMURQlSRdccAGfffZZRzdD2YUKZIqiKEnaf//9ycrK6uhmKLtQ6feKonQ5W297jcCy0ja9pnlUb3rde2abXlNpH6pHpiiK0t30rCky1SNTFKXrUT2nlvSsSKZ6ZIqiKN1MD0taVIFMURQlWWeeeSZTp05l9erVFBcX8+yzz3Z0k5rQsyKZGlpUFEVJ0muvvdbRTUhOz4pjqkemKIrS7ahApiiKonRtPSuSqUCmKIrS3fSsOKYCmaIoitK1qUCmKIrS3fSw/PuUBjIhRG8hxLdCiBVCiOVCiGsSx+8QQpQJIRYlvo7a4Tm3CiHWCSFWCyEO3+H4EYlj64QQt6Sy3YqiKErXkeoeWQS4Xko5ApgCXCmEGJF47N9SynGJr08AEo+dAYwEjgCeEELohBA64HHgSGAEcOYO11EURWkXpaWlHHjggYwYMYKRI0fy8MMPd3STGtfDemQpXUcmpdwGbEv83S2EWAkUNfOU44HXpZRBYKMQYh2wb+KxdVLKDQBCiNcT565IWeMVRVF2odfr+ec//8mECRNwu91MnDiRQw89lBEjOtfn6p4VxtpxjkwI0Q8YD/ySOHSVEGKJEOI5IURm4lgRsGNJ6y2JY00d3/Uelwoh5gkh5lVVVbX1S1AUpYcrLCxkwoQJANjtdoYPH05ZWVkHtypux/e/cDDU0c1pV+1S2UMIkQa8DVwrpXQJIZ4E7ib+weFu4J/ARXt7Hynl08DTAJMmTeppH0oUpcf425o6VnnCbXrNYWkG/jQks+UTE0pKSli4cCGTJ09u03bsqR3f/0Y7invU+1/KA5kQwkA8iL0ipXwHQEpZscPjzwAfJf5ZBvTe4enFiWM0c1xRFKVdeTweTj75ZB566CHS09M7ujk9XkoDmRBCAM8CK6WU/9rheGFi/gzgRGBZ4u8fAK8KIf4F9AIGA3MBAQwWQvQnHsDOAM5KZdsVRem8WtNzamvhcJiTTz6Zs88+m5NOOqnD2tGsHtUfS32PbD/gXGCpEGJR4thtxLMOxxH/dpcAlwFIKZcLId4knsQRAa6UUkYBhBBXAZ8DOuA5KeXyFLddURRlJ1JKLr74YoYPH851113X0c1pRs+KZKnOWpxNvDe1q0+aec7fgL81cvyT5p6nKIqSaj/++CMvvfQSo0ePZty4cQDce++9HHXUUc0/sb31rDimtnFRFEVJ1vTp05E9bI1WV6BKVCmKonQ3PSzYqkCmKIrSzfSwOKYCmaIo7UcNy7WsTb5HPez7rAKZoijtwmw2U1NTo4JZM6SU1NTUYDab9/ZCbdOgLkIleyiK0i6Ki4vZsmULqnxc88xmM8XFxXt3kZ4Vx1QgUxSlfRgMBvr379/RzegZeliPTA0tKoqidDM9bfhWBTJFUZTuRgUyRVEUpUvrWXFMBTJFUZRuR0pkJNrRrWg3KpApiqJ0QzIU6egmtBsVyBRFUbohGVY9MkVRFKULiwXbdgftzkwFMkVRlG5I9cgURVGULk36Qx3dhHajApmiKEo3FPMFO7oJ7UYFMkVRlG4o5lWBTFEURenCVCBTFEVRujQ1tKgoiqJ0aTFPoKOb0G5UIFMURemGoj1oaFHtR6bsJuYPEVy7jVBpDVGnF2ISzWpCn5+BaXAh+rx0hBAd3UxFUZohe9DQogpkCgDBteU435uL+6ul+BeWQDTW5LmGXpnY9h9OxjETSTtoJJrJ0H4NVRSlZUKoHpnSM8hYDPdni6l+6ku8P64GIbBM7E/u1UdgGdsXY99cdJk20AQxT5BIuZPAqjK8v6zF9ekinK/PQZedRta5+5N96SEY8jM6+iUpigLx31nVI1O6Mykl7i+WUHHvuwSWlWLonU3B7afgOHUKhsLMpp84rBdpM0eQc/mhyHAE93crqHvxB6oe+ZTqp74k6/wDyLvuGPQ59vZ7MYqi7EZookel36tA1sP45m9g2+1v4vtpLcYBeRQ/cTGOkycj9LpWXUcY9KQfOob0Q8cQ3FBB1UOfUPPM19S9Mpvca48i5/eHoZnVkKOidAhNU4FM6X4Cq7dScf97uD6Yjz43nV7/OIesc2cgDHv/I2AakE/xIxeSc9URlN/5FhX3vEPt899RcPspZJy4r0oMUZR2pnpkSrchpcQ7Zw01z3yN66MFaFYjeTcdR84Vh6GzW9r8fuYhhfR75Wo8s1ax7S+vU3rJ01Q/8SUFfz4J2wHDVUBTlPaiacS8PWcdmQpk3UzMF8Q3bwOeb5dT/+F8Qhsr0TKsieG+Q9Fnp37+Km3GMAZ9/Vecb8yh4v732XjyP7FMHEDO5YeQfsxENKP6sVOUlNKpHpnSBcRCEYJrthFYWUZw7bb41+r4n8Qk6DTSZgwj77qjyThhHzSrqV3bJ3QamWdNJ+PkydS9+iPVj31G6SVPo3NYsR8+jrQZw7CM749xQJ4KbIrSxoSmqaxFpXOKVLtxvv0Lrk8W4Ju3ARlI7ACrCYz9cjENKSTjuIlYJvTHNmUIuvS2Hz5sLc1kIPvCmWSdvz+eb5fjfGcu7s8X43xjTuIEgT7Hji7DhmY1gkGH0OsQeg1hNqJLt6DPsWPsl4d5RDGW8f3QpZk79DUpSqen5siUzibq8lHx9w+off57pD+EeUQxWRfOxDqhP+bhRRgH5Hf6RclC07AfPBr7waORsRjBVVvxLysltL6CcGU9sXofMX8IGY4iI1FkOEqsykVoQwWRivrtv5TCoMO2/3AyT5tKxvGT2iRZRVG6G9UjUzoV/6ISNl3wBOGyWjJPn0bOVYdjHlbU0c3aK0LTMI8oxjyiOKnzpZREqlwElmzGM2sl9R/Op/SyZ9h2x1vk33o8mWfsh9CpsqGKsp3qkSmdhffXdZSc8m90DisDP7kV6z4DO7pJHUIIgSEvA8Mho7EfMpqC20/B881yKv7+AWV/eJ7aF36g9xMXYxpU0NFNVZTOQachfWFkJNrqNaJdkfoY20mFttSw6cxH0OdnMPDT23psEGuM0DTsh4xm4Oe3UfzUJYTWl7P2wDtxvvdrRzdNUToFocWXuvSUXpkKZJ2QlJItVz6HDEfp9/o1GHo1UzaqBxNCkHnqFAbPugvLqD6UXvwUFf/4ACllRzdNUTpWYqg96vZ3cEPaR0oDmRCitxDiWyHECiHEciHENYnjWUKIL4UQaxN/ZiaOCyHEI0KIdUKIJUKICTtc6/zE+WuFEOenst0drf7tX/DOXkXBnadiGpDf0c3p9Ay9Mun/3g04Tp9G5f3vs+3W15Cxpqv3K0p3t71H1kM210z1HFkEuF5KuUAIYQfmCyG+BC4AvpZS3i+EuAW4BbgZOBIYnPiaDDwJTBZCZAG3A5MAmbjOB1LKuhS3v93JaIyKB97HPKYPWefu39HN6TI0k4Hixy5En51G9RNfIMMRev3jHISmBh2Unkf0sB5ZSgOZlHIbsC3xd7cQYiVQBBwPzEyc9gLwHfFAdjzwooyPDf0shHAIIQoT534ppawFSATDI4DXUtn+juD6eAGhDZX0+e/vVSZeKwlNo+Cu0xBGPVUPfQJCxIOZKo2l9DSJD3CqR9bGhBD9gPHAL0B+IsgBlAMN42dFQOkOT9uSONbU8W6n5r/fYeidTfrRE1o+WdmNEIL8P5+EjMaofvQzNKuRgjtPU8FM6VGELjG06FaBrM0IIdKAt4FrpZSuHd9UpJRSCNEms/NCiEuBSwH69OnTFpdsV6HN1Xh/WEn+rSeo3theEEJQcPspyECY6se/QLOZyb/5+I5ulqKk1I7vf/2KegMQ7SGBLOXvlkIIA/Eg9oqU8p3E4YrEkCGJPysTx8uA3js8vThxrKnjO5FSPi2lnCSlnJSbm9u2L6QdON/+BQDHaVM7uCVdnxCCwnvPIPOs/aj8+wdUPfJpRzdJUVJqx/e/rNwcAGKenjFHluqsRQE8C6yUUv5rh4c+ABoyD88H3t/h+HmJ7MUpQH1iCPJz4DAhRGYiw/GwxLFupf79X7FMGoCxT05HN6VbEJpG0UMXkHHSvpTf+RbVT33Z0U1SlHaxPWuxh/TIUj20uB9wLrBUCLEocew24H7gTSHExcAm4LTEY58ARwHrAB9wIYCUslYIcTfQsOL1robEj+4iuLGSwNJSCu4+reWTlaQJnUbvJy5GhqNs+9PrAORcfmgHt0pRUkwIhElPVCV77D0p5WygqVn2gxs5XwJXNnGt54Dn2q51nYvrw/kAZBw7sYNb0v0Ig57eT19K6aVPs+1PrxMLhMm79qiObpaipJRmt/SYHpnKKOgk6j9agGVsX4y91bBiKmhGPX3+7zIyTplMxd1vs+2O/6kKIEq3pkszE+0hc2SqaHAnENpSg3/+BvL/cnJHN6VbE3odvZ/4Hbp0K9WPfkZkm5OiRy7o9FvgKMqe0OxmtY5MaT/1iWK3GcepYcVUEzqNXn8/G0NhJhV/e4fw1lr6vHAl+qy0jm6aorQpLc2shhaV9lP/zlwsY/uquortRAhB3nVH0/s/l+Cbv4H1h/2NwKqtHd0sRWlT8aFFFciUdhBYvRX/4k1q7VgHcJwyhf7v30jMG2D94X/D9dmijm6SorQZzW7pMUOLKpB1sLrXfgSdRsaJ+3Z0U3ok2z6DGPTVXzANzGfT2Y9S8eCHqnK+0i3EhxZ7RrKHCmQdKBaK4HxjDumHjcGQn9HRzemxDEVZDPj4FhynTaXyvvfYfNFTPWZDQqX70tnNqkSVknruTxcSqXSRef4BHd2UHk+zGCl+4mIK7j4N18cLWH/sA4TLnR3dLEXZYzpHGtIfIhYId3RTUk4Fsg5U8+y3GPrkYD9oVEc3RSGeBJJ7xeH0feVqQuvKWX/kfQQ3VHR0sxRlj+gybQBE6zwd3JLUU4Gsg/iXleL9cTXZFx2oKt13MumHjaX/ezcS8wTYcMwDBNeVd3STFKXVtgcyp7eDW5J66h20g9Q89SXCaiTr3Bkd3RSlEdYJ/Rnw4U3IaIyNJ/+T8NZutxm50s3pHQ09MhXIlBQIlztxvvUzmWfshy7xw6Z0PuZhRfR/849EnT5KznmUmD/U0U1SlKTpsuLvLREVyJRUqHn2G2QkRs7vVRX2zs4yti+9n7mUwJLNbL311Y5ujqIkTQ0tKikT84eoff570o8Yqyp5dBHph40l9w9HUvfSLFxfLO7o5ihKUnQ9aGhR1VpsZ87//US01kP27w/r6Ka0iozFCCwrJbiunJg3iGa3YB5aiGlwIUKv6+jmpVzezcfh+mIxW296hbT9R6CZVaFhpXPT0syg1xGtVYFMaUNSSmqe+Qbz6N7Ypg3p6OYkJRYMU/3kl9Q+9y3hst33MtU5rNgPG0vm6dOw7T8MoXXPTr5mMtDr3jPZeOKD1Dz9Fbl/OLKjm6QozRJCoM+09YihRRXI2pFv7joCK7ZQ9ND5CNHUfqOdh3/pZjZf/BSh9RWkHTiS/NtOxDKmD1q6hWi9j8CyUjzfr8T12SKcb/6EcWA+uVcfgeP0aWjG7vejlbb/cNIOHkXVY5+RfcnBaBZjRzdJUZqly7QR6QHryLrfu00nVvv892h2C46TJnd0U1rk+W4Fm857DF2GlX5v/RH7gbss2i7OxjKyN5mnTyMWCFP/4Xyqn/icsmtfoPLBD8m7/hgyz5re7YYdc685io3H/R3n27+QdY5aOqF0brpMW4+YI+ue40CdUNTlp/7D+ThOnoxmM3V0c5rlm7eBknMexdg3l4Ff/Gn3ILYLzWwg89QpDPrmr/R781r0eRmU/fFF1kz7C873fu1WRXht04ZgGlJI3cuzOropitIiFciUNuX6aD7SHyLzjGkd3ZRmhbfWsencRzHkZ9Dv7eswFGYm/VwhBPaDRzPwiz/R96WrEAYdpRc/xbqD78b1xWKklClsefsQQuA4fRq+X9cT2lLT0c1RlGbpHCqQKW3I+c5cjP1ysUwa0NFNaZKMxii97Gli3iB9X/0Dhrw9q8gvhCD9qPEM/uFOih+/mGi9j01nPsL6Q/+G65OFXb6HlnHMBABcny7q2IYoSgv0WWk9Yo5MBbJ2EK334Zm1ivRjJ3bqJI/qxz/HO2cNvf5+Duahvfb6ekKnkXnGNIb+8jeK/n0ekVo3m859jDVT/kzVE58TqXG3Qavbn2lQAcZ+uXh/WNnRTVGUZuky05C+7l8BXyV7tAP310shEiX96PEd3ZQmBdeVU3H/e6QfMwHH6W27W7Uw6Mk67wAyz5pO/YfzqfnPV5T/5U3K73iLtANGYD9sDGkzhsXXpDVTQDnqCRBaV05g9VaC68oJbaomUlFPzB9C6DT0eemYhxVhmz4M27QhKS3GbNtvaLx3KWWn/nCi9GwNZaqitR60XslPE3Q1KpC1A883y9Fl2rBO6JzDilJKyq5/CWEy0Ovv56TsjVnodThO3BfHifsSWLkF55s/U//hfLbdEi/9pNlMGPvnYSh0oNktIAQxb5BItYtwaQ2RivrfLqbTMBRnYch3oMuwIsMRgqu34fpkITz4IYbe2eRefQRZF8xMSUCzThxA3SuzCZVUYeqf1+bXV5S2oM9KA+JlqgwqkCl7SkqJ54eV2GYM67TbtdS//Qve2avo9Y9z2m2navPwYgpuP4WC208huKEC39z1+JdsIrShgkili+j6+D5gmtWELisN+8GjMPbLwzS4ANOwIoz9chtdqxZ1+XF/vZSa//uGrTe9Qu0rs+nzzGWYBrZtOTDz6D4ABJZvUYFM6bR0mfFAFqnt3vNkKpClWLi0hnBZbaetBBF1+9n21zexjOtHVgftVG0akI9pQH6bZHTq0i04TtyXjBP2of79eWy94SXWH3YPfV/5A7Ypg9ugtXENc4jBVWWQSP5QlM5me+Hgbh7IOmcXoRvx/boeAOu+Azu4JY2reugTIhX19Hrg7E7bY9wTQggcJ+zDoK//gi7bTsnpD+FfsqnNrq/ZTOjzMwiVVLXZNRWlrekze0bh4O7zztVJ+ReVIMwGzCOKO7opuwmuK6f68c9xnD4NaydeFrA3jH1zGfDejegcVkrOfIRIlatNrx0qrW6z6ylKW9Nl9YyhRRXIUsy/ZDPmEcWdslTT1j+9jjAbKbjjlI5uSkoZemXS95U/EK3zUHb9S2133aJMwmVq52il89IsRoTFSLSbryVTgSzFgqu3dsreWP3HC/B8tZS8m47d44XPXYllVG/ybz4e18cLqP9wfptcU1/gIFzh7BYVS5TuS2c3E/MGO7oZKZVUIBNC5AshnhVCfJr49wghxMWpbVrXF6lxE6lyYRpS2NFN2UnU7WfbLa9iHlFMziUHd3Rz2k3OlYdjHlFM+d1vIyPRvb6eocARX2zq9rdB6xQlNYTFSMwX6uhmpFSyPbLngc+BhnIPa4BrU9CebiW4rhyIV4LoTCruf5/w1jp6/etchKHnJK4KvY78W08gtL4C51u/7PX19PkOAMLl9c2fqCgdSLMYiflVIAPIkVK+CcQApJQRYO8/0nZzoY3xjDbTgLZdw7Q3vD+toeY/X5F14Uxs+wzq6Oa0O/uR4zCPLKbqkU/3uuajIS8dgEilCmRK56VZTCqQJXiFENmABBBCTAHUb28LQpuqQAgMfbI7uilAfEhxy1XPYeybQ8Edp3Z0czqEEIKcKw8nuHor3tmr9+paDT2ynSqOKEono1mNSBXIALgO+AAYKIT4EXgRuDplreomQpur0Rc40EyGjm4KAFtveoXQ5mqKH7sIXZq5o5vTYTKOm4SWYaX2lb3bU8xQ6AAgXO7c+0YpSor0hDmypCZIpJQLhBAHAEMBAayWUnbvcsptILylFmPvztEbq3vtR5xv/kTeTcdhmzqko5vToTSLEcfJk6l7dTZRlx9dumXPrpNuQViNRFQgUzoxzWIk7FdZiwghTgUsUsrlwAnAG0IIVZenBeGyWgxFHV+o07+8lLIbX8a231Dybji2o5vTKWSePg0ZCFP/wbw9voYQAkNhJuGy2jZsmaK0Lc1iRHbzbVySHVr8i5TSLYSYDhwMPAs8mbpmdX1SynggK+7YHlm03sfmC55Al26h9zOXdasyVHvDMrE/xgF51L87d6+uY+yTQ6hU7RStdF4qa/E3DRmKRwPPSCk/BowtPUkI8ZwQolIIsWyHY3cIIcqEEIsSX0ft8NitQoh1QojVQojDdzh+ROLYOiHELUm2uUNFq93IUARDr6wOa4OMxSi9/BlCm2vo89zv262yfVcghCDj2El4Zq3aqw0+jX1zVL1FpVPrCXNkyQayMiHEf4DTgU+EEKYkn/s8cEQjx/8tpRyX+PoE4ousgTOAkYnnPCGE0AkhdMDjwJHACODMxLmdWsNwU0fuAVT5wAe4v1hCr/vObNPK791FxgmTIBrD9fGCPb6GsV8e0VoP0XpfG7ZMUdqO6pH95jTiC6IPl1I6gSzgxpaeJKX8AUh2AuF44HUpZVBKuRFYB+yb+FonpdwgpQwBryfO7dRCW+Ivu6OSPVyfLqLywQ/JPGs/si6c2SFt6OzMo/vEhxff2/N5sobF7sG15W3VLEVpU5rFCJEoMhzp6KakTFKBTErpA94nvp6sD2AAVu3Ffa8SQixJDD02dFmKgNIdztmSONbU8d0IIS4VQswTQsyrqurY4Z5wWXzexFDU/kOLwY2VlF7xf1jG9qXXP85N2Y7PXZ0QgowT9sEza+UeV8VvKD8WWL21LZumKK3W1PufZo3PAnXnXlmyWYtXAxXAl8DHia+P9vCeTwIDgXHANuCfe3id3Ugpn5ZSTpJSTsrNzW2ry+6RUGkNwmpEl53WrveNhSKUXvwUCEGf569AM3eONWydVcYJ+0BMUv/Rng0vGvvlIixGAiu2tHHLFKV1mnr/E5aGQNZ9MxeTLbR3DTBUSrnX6VlSyoqGvwshnuG3gFgG9N7h1OLEMZo53mmFN1Vj7JPT7r2hyvvfx794E31euBJjn5x2vXdXZB5RjGlwIfXvziX7wpmtfr7QaZhHFhNYurnN26YobUFLBLLuXN0j2TmyUtqoJJUQYsdS8CcCDRmNHwBnCCFMQoj+wGBgLvArMFgI0V8IYSSeEPJBW7QllUIlVRj7tm+v0LdgI1WPfkrmOTPIOEYt80uGEIKMk/fFO2cNoT1cD2Yd1w//ohJkdO9qNypKKmjbe2Tdd1F0soFsA/BdIj3+uoavlp4khHgN+AkYKoTYktj65e9CiKVCiCXAgcAfARKLrd8EVgCfAVdKKaOJAsVXEU82WQm8mTi305JSEiypxDggr/3uGYlSdu3z6PMyKLz7tHa7b3eQeepUkBLnmz/t0fMtE/oT8wYJqnkypRPaPrTYjVPwkx1a3Jz4MpLE+rEGUsozGzn8bDPn/w34WyPHPwE+Sfa+HS1SUY/0hTD1a79AVvv89wSWb6HPf3+PLt3abvftDoz9crFOHoTzzZ/IvfaoVg8HWycNBMA3b32n3ERV6dk0FcjipJR3AgghrIkMRqUZoQ3xacD26pFF3X4qHngf24xhpB87sV3u2d1knj6NsutexL9gI9aJA1r1XOOAPHQ5drw/ryPrvANS1EJF2TOa1QSooUWEEFOFECtIpNwLIcYKIZ5Iacu6sODGSqD99iGreeYborUeCv56ikq130MZJ+6DMBuoe3V2q58rhMA2eTC+uetS0DJF2TuaLRHIvD08kAEPAYcDNQBSysXA/ilqU5cX2lAJeh2G4tSvIYsFwtQ8/RVpB4/COqF/yu/XXenSrWQcNwnnW7/s0S+8dcogQhsrVQFhpdNRgWwHUsrSXQ6pHaKbENpYibFPNkKvS/m96t+dS6TKRe4Vh7d8copEnV78izfhW7CxS+/NlXXe/sQ8Aerf/7XVz02bMRwAz6y9qROgKG3vt0AW6OCWpE6yyR6lQohpgBRCGIivK1uZumZ1bcGSKoz922d+rO7V2RgH5mM7YHi73K9BpMaN862fcf7vF/wLN+70mGlQAenHTsRx2lTMQwqbuELnY50yGOPAfGpfnkXmWdNb9VzzyGJ0WWl4Zq0k84xpKWqhorSeZotvohvzdN8eWbKB7HLgYeKlobYST4W/MlWN6urCm6raZZgvtKUG75w15N92YrvNjfmXbKL68S+of/9XZDiKZWxf8m89AdPQXgi9jlBJJe6vllL18CdU/ftjLOP64ThjGo6T9kWfbW+XNu4pIQRZ5+1P+e3/I7CqDPOwRiuhNf5cTcM2fSjeWauQUqq5SqXT0Ix6hEHXrYcWk81arAbOTnFbuoWo20/U6WuXYsGuRFmljBP3Sel9pJR4vltO9VNf4flqKVqamayLDiTzrOlYRvXe7fyc3x9GeFsd9e/Po+61H9l2y6ts+/MbpO03FNv0YVj3GYh5ZDH6rPYt35WMzDP3o+Jv71L7/Pf0uv+sVj03bcZwXB/MJ7Sxst0SfRQlGcJqUkOLQogBxHtkUwBJfJHzH6WUG1LYti5p+/Yt7bChpvuLJZiG9krZm6aMxqj/YB5VD31CYFkp+vwM8m89gexLD25xrZqhMJOcyw8l5/JD8S8vxfm/n3F/tZSKv72z/RxdVhqG4iz0uenoMm3o0sxoVhPCbECzmNCsxviX3YIuw4ouMw19rh1DfgbCkOxgQuvos+2kHzOBujd/ouD2U7avwUmGbfowALyzV6lApnQqujQzUXcPD2TAq8T3BDsx8e8zgNeAyaloVFcW3uYEwFDoSOl9YoEw3p/XkHXhgW1+bRmJUvfGT1Q9/Amh9RWYBhVQ9MiFOE6ZjGZqfRFiy8jeWEb2pvCOU4nUefAv2EhgZRmhjVWEy2qIVLkJbawk5g4Q8wXjVbpjsukLCoGhKBPToALMo/tgmzIY235D0dkte/Gqf5N17v7UvzOX+g/mkXl68vNdpsEF6AsceH5YqdaTKZ2KLjuNaO2ebyDb2SUbyKxSypd2+PfLQogW9yPriSLV8R8WfW5qd2P2L9mEDEawTR3SZteUUuL6ZCHld75FaH0F5rF96fPc5aQfMxGhSzrBtVn6zDTsB4/GfvDoZtshgxFi/iAxX4iY20+03ke01kukqp7w1jpCJVUE1myj5qkvqX70M4RRj/2wMWSduz9pB4/aqzkq2/ShGAfkUfviD60KZEII0vYfjvvrpchYDKG1zfdMUfaWPttOpNrT0c1ImWQD2adCiFuIb2op+W2n6CwAKaVaPJMQrY3/sOiybCm9j39hCQDWiW2TVBLcUEHZdS/inbUK05BC+r50FfYjx3VI0oIQIj68aDZACxtsxwJhfHPX4fpsEfXvzsX10QLMI4opuOs07AeO3LP7axpZ58yg/K63Ca4txzS4IOnnpu0/HOebPxFYWYZl5O7zh4rSEfTZdoLrK1o+sYtqzQ7RlwHfAt8Bvyc+vDgf2PPtdbuhqCtewUuXkdp6h4EVW9Blp6EvcOzVdaSU1L70A2v3vwP/ok30+vvZDJ51J+lHje8SmXea2UDa/sPpde+ZDF38D4qfuJiYP0TJKf+i9LJniLr8e3Rdxxn7gU6j9pVZrXqebcZv82SK0lnocuxEa7rv0GKyO0T3b+ardYXpurmYN4gw6VO+GDq4vhzTkMK9CjYyHKHsmucpu/YFbPsOYsicu8m++KB2WcidCppRT+bp0xg8+y7ybjoO57tzWXfgnQTWbGv1tQz5GdgPHY3zzZ+QkeTX/huLszH0zcH745pW31NRUkWfbSfmDRILdM/NNZOttXi3EEK3w7/ThRD/TV2zui4ZiiCMqd+VObypeq/2O4sFw2y64EnqXplN3g3H0u+tP2Lo1cI4XhehmQ3k33w8Az68iZg3yIYj78X7a+vrIGaeOZ1IRT3ub1u3a5Bt6hC8P69BymYSVhSlHekTO9VHummvLNmhRT0wVwgxRghxKPHNLuenrlldWEyCltohORmLEa6ox7CHw4pSSsr+8DzuzxbR64Gzyb/1hG6ZmGCbPJiBn92GLttOyakP4V9U0qrn2w8bgy47Defrc1p336lDiNZ4CO5BT1BR2oIrsvMmr7pEMYJodQ8OZFLKW4GbgF+AF4CjpZSPpbJhXZYmmk8dbwMxTwCiMXTZe7aguPrxz3G+9TP5t51I9u8OauPWdS7Gfrn0f/cGdA4rG0/99/adCZKhGfVkHL8Prs8WEXUnP9fWkEnq+2Vtq9urKG0hEN35PUifEw9kPbpHJoTYH3gEuIt4ssejQoheKWxXlyWMemQotePQDTXTdIkaaq0RXFtOxT3vkH7sRHKvO7qtm9YpGYuy6P/29RCLsfm8x1tVqsdx6hRkIIzrk4XJ329AHvrcdLy/qG1dlI6x60fphvJwkZrumYKf7HjSg8CpUsr7pJRnAc8A36SuWV2XlmZGBiPIcCRl95DRRPKBvvXDgVtveQVhMdLr72d3iazEtmIamE/v/7uMwKoyyq5/MennWScNwFCURf17yVfEF0JgmTgA36/r96SpitLmdDnx0ZvumrmY7DvhVCnlioZ/SCnfAfZLTZO6toa0+2j9nqV9J2P7fFYrhzC9P6/F890K8m8+HkNeahdsd0b2A0eRd+OxOP/3M84kA5PQNDJOmITn2+VE65PfHN06vh+h9RXbl2MoSnva9Z1Bl2EFnba9YEN302wgE0I8BCCljAohrtnl4X+mqlFd2fbsoGpXyu4hzPGsyJi/dUOYNc9+g85hJeu8nrsnat51x2CZOICt179IuLI+qeekHzsRGY7i/nJJ0vexjOsHgH/prtv4KUrq7RrIhKahz07rsXNkO77jnb/LY2PauC3dgqEwnsIe3laXsnv81uvzJv2cqNuP69NFZJw0Gc1qSlXTOj2h19H78YuJ+UKU//XNpJ5jnTgAfW46rs8XJ30fc2JXgMCyzXvUTkXZK40M1uiy0nps1qJo4u9KEwyJ7VvCm2tSdg+h16HLtBGpTL7X5/lhJdIfIuOE1G750hWYBheQc/UROP/3M945q1s8X2ga9kNH4/lmWdKLo/X5Gehy7ASWqR6Z0v5kI5FMn20nUtczkz00IUSmECJ7h79nJWosds3yDylmKMxEmPQEN6S2rpmhKGv7ljHJ8M5ahbAase4zMIWt6jryrj0KQ1EW2/7yZlILl+2HjCbq9OHbZTfspgghsIzsTWBF2d42VVFarbGfaF2mjWht8qM4XUlLgSyD3+oppgMLEv+eD3Tu7X47iNBpmAYVEFy9NaX3MfbLJdSKIqD+pZuxjO6DZkzNPl5djWY1kXfz8fgXlWzfoLQ5thnDQQg8369M+h6moYUE125DxmItn6woKabLSiPaE3tkUsp+UsoBLdVYFELsWZnxbso8vDjlk/ymgQWENlUlneYfWl+BaUhhStvU1WSePhXToAIqHni/xWCjz0rDPLIY748tD0U2MA3pRcwb3L5HnaK0l8Z6ZPrMNKJ13m5ZOq2t6hK91PIpPYdlfD8i5U7C5c6U3cM0rBcyHE1qa4ZYKEKkyoWhKCtl7emKhF5H7vXHEFxZhvvLpS2eb508GP/8DUnPk5kG5AG0quesKG2i0WQPGzIcjVcG6mbaKpCpRJAdWCfGO6u+uamr7GBJZMUl0/OLOuPj4vqsPStp1Z05TtwHfYGDmv9reX2/ddIAYt4gwbXJ1VBsKOocKq3eqzYqSms1PkeWWBRd1/3mydpqwqT79VX3gnlsX4TViPfH1WQcNykl9zANKUSYDfgXlZB56pRmz20oydRWaffReh+uzxbhm7uO4NpyIhX1yFgMzWJEn+/ANLgAy5i+2KYO3qsK/e1BGPRkXTiTyvveI7ixElP/vCbPtYztC4B/0SbMw4tbvLahKBOEIFyaugxWRWlM40OL8c1+o7Ue6JPTvg1KMTXznwKaUY9t6hA8361o+eQ9JPQ6LOP64Z+/oeWTo4n5n73cZyy0pYaKe9+j/t25yFAEzW7BPKII88hiMOiIeYJEttVR+/MapC8EgLF/HvZDR5Nx3CSs+w5C6Dpflf2ss6ZT+cD71L06m4I/ndTkeaaB+QiTnsDKLUldVxj06PPS1RyZ0inoGgKZs/tVm2mrQBZqo+t0G/aDR7Htttdb/JS/N6z7DKTmP18RC4TRzM3sgdYQPPYwe05KSd3Ls9h6y6sAZJ1/AI7TpmIZ17fR7V9kLEZw1VY8s1fj+XYZtS98T83TX6PPzyD9iHHYDxuDbdoQdOnN76ItpSTq9BKpcBHzBREGHTqHFUNRVptuO2PolUnajOG4PpjfbCATeh2mgQUE1yU/56XPzyBSlboqL4rSmMZ6ZJrFCEAs0P3erpsNZEKICc09LqVckPiz+bGtHij98HFsu+11XJ8sJPfKw1NyD9u+g6h+9DP8Czdu3zqkMZopHuRkcM+q8lfc8w5VD31C2swRFD18Acbi7GbPF5qGeUQx5hHF5Fx6MFG3H/eXS6j/cD7Ot36m9oXvQROYRxZjHl6MoVcmmtWEjEaJ1noJb60jtKmK0MbKRivVa2lmbPsNJeP4SWScsM/217c30o8ez9abXiG4thzT4IImzzMOyCO4Ovl9xvTZdhXIlHbXWGKiaAhk/h4WyGi+nqIEuvdmVnvB2C8X85g+1L/3a8oCmXXKYCBeDLjZQJYWnxuL7kG2Uu3Ls6h66BOyzj+AXv84Z4+GBnV2C46TJuM4aTKxYBjf3PV4Zq3Ev2Ajnlmr4m/0iUxALd2CocCBsW8Otv2GYuyTgz4vHc1mRkaiRKpcBJaV4v5yCe7PF1Nxzzvk//kkHKdN3atq/vaDRwPg+X5Fs4HMUJyN59vlSCmTup8u00ZoU9Uet0tR2sr2HpmvhwUyKeWB7dWQ7shx4r6U3/kWwQ0VmAbkt/n19VlpmAYXtrhdiJZmBk20emw8VFrN1ltfJe2AEfFtX9pgfkszGUibMYy0GcO2H5OxWHweT9OSvoeUEs+3y6m4/322XPEs7m+WUfzQBdt/WVvL2C8XQ3EW3jmrm91s1FDgIOYNEvME0NktLV5Xl24l6krdTgiK0pjmhhZlNxxaTPqdSQgxSghxmhDivIavVDasO3CcMgU0Qd3rc1J2D+u+A/HNXdfsgl6hafFV/a2sfF35r48hGqPokQsQe5ko0hyhaQiDvlWBUgiB/aBRDPz0VvJvPYH6t+dScvpDxHzJb5q5K+vEAfgWljR7ji4rMWGeZAqzZjd3y3U7SufWWK3F33bN6KGBTAhxO/Bo4utA4O/AcSlsV7dg6JVJ2kGjqHtldtKLaFvLuu8gonXeFhMQ9LnpRKqSD2RRlx/nWz/jOHVqi3NiHUnoNPJuOJbiJ3+Hd84aSi97Zo9LQplHFBPeXN3sDtK6tPiu3DFvcsFJsxiRgbAqU6V0uIaEMBlI7Q72HSHZj8CnAAcD5VLKC4GxxOswKi3IvmAmkXInrk8WpuT6DUWAfb82v/jakJ/Rqkoj7i+XIH0hMs+evjfNazeZp06h8J7TcX2ykNrnv9+jaxgT2aWhZtZ9CXPDhHlybwYiUdtShlLzQUZRGtNoFapEpm93XPSbbCDzSyljQEQIkQ5UAr1T16zuw37YGAy9s6l5tuXKEXvCNLgALcOKb17z68kMhZmEtya/R5rn+xXoHFa2DevNC6Vu7l1bx/+2egi1clfq9pR92SGkHTCC8jv+t0f7wekTu2a35aao24dLo6pHprSfRn9LG3KTuuHoQLKBbJ4QwgE8Q7zy/QLgp5aeJIR4TghRKYRYtsOxLCHEl0KItYk/MxPHhRDiESHEOiHEkh1T/4UQ5yfOXyuE2HWDz05N6DSyLz4I7+zV+Je2/SaLQtOwThyAf0Hz24sYirKIVDiTHuJcUenhtrsv4uh5ldy71snrZV7+vKqO0+dVEOukRUeFEPR68Bxi/hA1T3/d6ufr7Ilhw2bmtGQ4/v0ThlbOGaoibkpHa8iy7Zy/vnslqUAmpbxCSumUUj4FHAqcnxhibMnzwBG7HLsF+FpKORj4OvFvgCOBwYmvS4EnIR74gNuBycC+wO0Nwa+ryDp3BprNRPWTX6bk+pZxfQmsLCPWzNi3oTgLYjKpnsrXVX6uu/AYyjPTuW2wg++nFbL4gCJG2Q2s8oQJd+IPdKYB+WQcO5GaF74n1tp1c4ngJENN7yjQkEySbHakbOiJteECbkXZE9uXi3TSD6J7I9lkj+0fb6WUJVLKJTsea4qU8gdg190fjwdeSPz9BeCEHY6/KON+BhxCiELgcOBLKWWtlLIO+JLdg2OnpnPYyDxrOvXv/JKSiviWUb0hGiO4qulNHA2JhI1k6v49sK6Owm01vLByFef3tmPVady3zskyd5jzeqdh0nXu7kXmWdOJ1fvwfLu8Vc9rmMcSzSywbtjPSZdkAeaGRejCpKrBKZ2AEMhOPD2wp5oNZEIIc6JHlLPj7tBCiH5A0R7eM19K2VAaoRxoWGBVBOxYyn1L4lhTxxtr76VCiHlCiHlVVZ1rEWr2ZYcgIzFqnvu2za9tHhGfrgysaLoGoDFRJDS0qeVK7JGoxOlI44W+xVy9tJqZc7byQqmHM4ts3DTI0SZtTqW0A4aj2UytrnUZc8XX2WmJzMTGRMrrt5fKSuqaviDCatyrxdqKkowd3/+ikd1HFbrjPmQNWuqRXUZ8TmwYO+8O/T7w2N7eXMa/s2323ZVSPi2lnCSlnJSb27mqrpv652E/fCy1z3/X7BDgnjD2z40Xs21mV2pDcRYIkdSWIvf0tVG0pZqPMxysdIc4Is/Ke/vkc8fQLHRd4A1ZGPSYR/dp9ZxkuKIeAENe0wm5oS01rar1GHUnt3BaUfbWju9/BkMjIwCRKEjZLXeJb2mH6IellP2BG3bZHXqslHJPA1lFYsiQxJ+VieNl7JwJWZw41tTxLifnskOI1niof+/XNr3u9mK2a5quAaiZDBgKHUn1yKbk2rj79v/y5bLlfDWtF/cOz2K4fc8qZnQUY58cwmW7jmo3L7SxEoTA0LvpdXPBtdswDmq6hNWuorWe7VXHFaUjxYLxXlpzQ+ddVbIz0P8RQvxBCPFW4usqIcSefjc+ABoyD88n3rtrOH5eIntxClCfGIL8HDgsMbSZCRyWONbl2GYMwzS4kNrnv2vza5uGFDYbyCBehilU0vKQqzDpEUb99g05uyJddlp836VWCKwsw9gvt8mdBGKhCME12zAP65X0NSPVbvQ59la1Q1H2lmgkTVaGE4HMmLoqPR0l2UD2BDAx8WfD359s6UlCiNeIp+kPFUJsEUJcDNwPHCqEWAsckvg3wCfABmAd8TT/KwCklLXA3cCvia+7Ese6HCEEmedMx/fregItBJ3WMg7MJ7S5mlgzGXfGfrnxXkcLhBDx7UdSkJjSXoROa/Wktn/hRizj+jb5eHBlGTIYwTKuX9LXDG+rQ5/vaFU7FCUVZEOPzNjDemRCiIbB1H2klOdLKb9JfF0I7NPSxaWUZ0opC6WUBillsZTyWSlljZTyYCnlYCnlIQ1BKZGteKWUcqCUcrSUct4O13lOSjko8fXfvXnBHc1x6lTQBPVv/9Km1zUNyI+n1zczdGgckE+koj6pIrbGvjkEkwh6nVXMG0SzJj8cGiqtJrylFus+g5o8x/vzGiBeFiwZMhIlXFaHsZmhSkVpLw3LSkRPmyMD5ib+jAohBjYcFEIMAFTNnT1gyM/ANn1Ym8+TGQfGyysFNzZdc9GUmNsJrm95Y0jzsKJ4D6SLVqSIVLnQZyU/pOf5YSXATlX5dzvn+5UY++dhLMpK6prhLbUQiWLs17kSj5Tur7GUrIYks2Y34e2iWgpkDd+PG4BvhRDfCSG+A74Brk9lw7qzjKMnEFxXTnBteZtd09QvUSdwY9NzYKah8bmd4JqmsxsbWCYNJOYNNpvS35mFNlZi7J98AHF/uRR9YSam4Y2vKon5Q3h+WIn94FFJXzOQ+D43fN8VpSPFEiMxWnr3y6JtKZDlCiGuA8YB/yEewL4hPoc1PrVN677sh48BwP310ja7pi7HjmYzEdrc9NCiaUAewqgnsLLlpM+0/YYCtHotVmcQ9QQIrNqKeURxUufH/CE83ywj/fAxTa738ny7HOkPYT9ibNLtCCyLfwhoTXKIoqRK1B1fJ6nrgYFMB6QBduKbcIrElz5xTNkDxt45GAfk4Zm1ss2uKYTA2DeXUEnT81pCr8M0vIjAkpbXVxl6ZWIe1RvXp6mp2p9K3jmrIRrDNn1oUue7v15GzBsk/ZiJTZ7jfO9XdJk20qY3PfS4K/+iEowD8tClJ7d4WlFSKVof75F1x0DW0qzfNinlXe3Skh7GNmUwrk8XIWOxpBfXtsTYP6/FFHzL2L7Uvz8PKWWL1SYyjt+Hir+9Q2hz9fbKIF2B64P5aHYLtmnJBbL6d35Bn5ve5PxY1OXH9clCMs+YhmhsoWkjpJT45q4j7cCRSbdbUVJpe+WabvjBKtk5MqWNNWyIGdrQdpmBpoH5hEoqm61wb504gFi9L6n5OcdpU0CnUf1UaoodQ3xYz/3NMqr/8xUV//iAmv/7Bs/sVa0v+JsQdfup/2AeGcdMQEti4WfU5cP1+WLSj5vU5C7Y9e/ORfpDZJ4+Lel2hNZXEKlyYZsyOOnnKEpbaeyNuyFbuTtWmmnp4+XB7dKKHsgyvj8AvoUl27MJ95ZpcAEyHCVUUtXkNW2T42+svp/WYB5S2Oz1jMXZOE6dQu0L35N71REYerXdpgP+xZuo/s9X1L//a6M71gqrkYyjJpB1/v5Ypw5JulZh7QvfE/MGyTx//6TOr/9wATIQJvPUKU1f88UfMA3rhWXSgKSuCeCZvQog6V6horSlxlZQRl1+EAItzdTu7Um1lkpUdcmFx12BeWhhvD7i0k1td81EckOzxYMH5aMvcGxPN29J/k3HgZRsu+N/bdLG8NY6Si97hnUH3YXr4wVknrEf/f73R4aveYhRFU8zbMW/6PvK1WSeNhXXl0vYcOzf2XDkfbi/Xd5i0dPgxkoq7n8f+2FjsE4a2Oy5DZxvzME4ML/JIOVbWIJ/UQlZ589sVeFfzzfLMRRnYRyU3/LJitIOYi4/mt3cZlMZnUn3e0VdhDDoMQ8vxp9E4kWyTEN7gU5r9ppCCOwHjowHhnDTVUAaGPvmknvtUdS//Qu1L8/a47bJSJSqJz5nzZQ/Uf/hPHL/eDTDlv6Don+ei/2gUeiz7Qi9DkN+BulHjKPon+cxfPk/6fXA2YS31lFyyr9Yf9jfts8r7ipUVsvm8x5H6HUUPXhuUkEntKkK74+ryTx9WpPn1z77DZrNROYZU5N+rTF/CM93y7Ef0nQWpKK0t0iNG31298zR635LvLsQ8+jeuD5emFTiRTI0ixHziKIWd4tOP2o8da/9iGf2KuwHtrwuKu/6Y/H9so6tN76ModCB/eDRrWqX99d1bL35VQKLN2E/fCy97j0zqUXCmsVI9u8OIvPcGdS99iNVD3/KpnMexdA3h4zjJmGd0B9hNuBfWELNc98iA2H6PH8FhiQXLNf972cAHKc3HqQiNW6c7/xC5lnTW5V56Pl2eSILUq1QUTpGY2MXkSoX+tz0dm9Le1CBrAOZR/am7qVZRLY522z+ybrPIJxvzEFGok0mL6QdNAot3YLzrV+SCmRCp9H7mcvYeOKDlJz5CL3uO5OsC2e2OEQRH+p7j/q3fkFf4KDPc5fHkypaGbQ1k4HsC2aSdfZ06j9aQO0L31Pz1JdUhxNJLUJgmzqYXg+eiznJxcdSSpxvzME2fSjG4sZLSNW+NAsZjJD9u4Na1V7n+61P1VeUVItUuTANbJv5+M5GBbIOZBndBwD/kk1tFshs04ZQ+9y3+BdtwtrEvI9mNpBxwj443/qZXvedmVRvQ5+VxoAPb2bzJf9h602vUPfGT+RccRjph49Fs/xW0zDq8uP5fgXON+bg+mIJwqgn949Hk3vtUeia2bAyGcKgx3HivjhO3JeYL0hwbTkyFMHYP6/VFeZ9v6wjtKGSvOuOafRxGY1R+99vsU0fhnlY8nvIxrxB3J8uIuPkyUmn6itKe+jOWbTqN60DWUb3AU3gX1hC+hHj2uSaaTOGgxC4v1naZCADyD7/AOpe/IG6V2aT8/vDkrq2Lt1Cv9f+gPP1OVQ88D6lFz8FOg3TgHw0m4lInYdwaQ3EJPrcdHKvOoLsSw7CUNh22Y4NNKsJy9imK9W3pO61H9FsJtKPbXwRtPvzxYS31FJ4zxmtum79xwviWZOnJT+npihtTe4yuCgjUaK1XjW0qLQ9zWbCPLIY79x1bXZNfY4dy8T+uD5dRP5Nxzd5nmVcP2zThlD1+BdkXXRgUmuuAISmkXnWdBynT8M7axWe2asIrtlGLBDCOCAP0+nTSJsxDOs+AzttjyTmC1L/3q+kHzuxyV5izfPfoS/MJP3Ica26dt0rszD2y8U6ObkK+YqSCrsm+EZqPCClCmRKatimDKH25VnEguGkg0lLMo6fRPlf3iS4rrzZNWp51x/LxpP/Se0LP5BzaeuWDAqdRtrMEaTNHLG3zW139R8vJOYJkHnmfo0+HtpSg+eb5eRdf0yT84yNCW6owDt7Nfl/OqlbpjgrXceueb2RKhdAtw1k6retg6XNHIH0h/D9vLbNruk4cV/QBHWv/djsebYDhmObPozKv79PpJW7KXdldS/PwtA3B9u0IY0/nvi+ZZ49vZXXnQ06jcwzk68AoiipsOuesg2b5OrzM9q/Me1ABbIOZps+DGHS4/psUZtd01CYif3QMdS9OrvZUk9CCArvPYOoy8+2v7zRZvfvzILrK/DOXkXW2TMa7TVJKal77UdsM4a1qr5kLBSh7tXZ2A8dk5I5QUVpjdguY4sNu2IYe3edmqmtoQJZB9OlmbEfPJr6D+a36SaW2ZccRKTSRf07c5s9zzKyN7l/OBLn63Nwfbqoze7fWdW+9EO819REb8s3dx3hTdWtqqsI4PpoPpEqF9kXzWyDVirK3onBTpVwQpurEUY9+gLVI1NSxHHqFCLlzjbdnyxt5kjMI4up/PfHLQbIvBuPxTy2L6VX/B+hkqY35uzqYr4gda/MJv3IcRgKHI2eU//OXITZQPpRrVvMXPvCDxj65qhq90qnIIHQDr/24dIaDL2zu+3cbfd8VV1M+pHj0OdnUPOfr9rsmkII8m44ltD6CpxvzGn2XM1koO9/f4/QBCVnPtxt58tqX5lNtNZDzuWHNvq4jMao/3A+9kNGt2rPpniSxyqyzprebd8olK7HGfktkoU2V2Ps3fjC/+5A/dZ1AsKgJ/vSQ/B8twLfwpI2u276MROwjO9PxX3vEfMFmz3X2DeXPi9eRWhTFSWnPUS03tdm7egMYqEI1Y99hnXyIGxTG0/y8P2ylkhFPRkn7NOqa29P8jirdckhipJKNaHftnMKl9Zg6EJ7CraWSr/vJLIvmkn1459Tfsf/6P/eDW1Se1FoGgV3ncacS55h4QtzqD58AmWBCFWhGM5wlEBMEk58aNMLMBkyML14M/oFG0h/Zg59DhlFQbaNfJOOXiYdRRY92QatSxbCrXvxB8Jbain653lNnlP/0QKESY/90DFJX1dGotS9/iP2Q0e36TY3irK3qhOBLOYNEqlyddtED1CBrNPQpVvJu+k4tt3yKq4P55Nx3KQ9vlYwKvmmxs9XVX7mRG3UPvoHAESJi3yTjlyjjkyDDqtOYNDiQSkckwRjEl9WGs5Jg1hf6+P7ygCR2p2zHq06QV+Lnn5WPf2tBvpa9PS26Cm26Mgx6tB1wiAXrqyn4r53sU0fStrBjdeWlFLi+mQhaQeObFUpLc8PK4lU1JN5RuNr0hSlo5T647tbhEprADD26b5DiyqQdSLZF86k7tXZbL35FWwzhqHPTGvV86tDUZ7f7OaNrR5cEUmWQWNGtpnxOknalU8zQEiGf3wzmrHl/+2BVVvZfMl/qN5cQ+isGYQvPIhyi5nN/ggl/gjLXCE+r/TvtPBSJyDHqMNh0MjQa9j1Gml6QXri7w6DRqZBI9eoIy8RUNN0IKIx0DSEbu9Guj2RGBt9ETb7I5T6I5QHI1SHYlQtLcVzy9nohxWhm1eBVRdvS4FJxwCrgRF2AwM2VRAurSHvhmNbdU/nW7+gpVuwH5Z8L05RUk0vBEtdIQBCpfHUe0Nf1SNT2oHQ6yh++ALWH/Y3yq7+L31euiqpYbyolLxY6uGRjfUEopJDcy2cUZTG5EzT9h5S/bVHsPn8x6m4910K7zi1xWuah/Vi0Jd/Jv0fH1D9xBfw/DcMP20qmWdPxzppAELTCMUkZYF40CgLRCkPRKkMRXGGo7gjki2BCJ5IDHckhjuya/W3OIsvSG61k7xKJ/m1LgoiEQrtRnr1zaFoUj9yBxdg1mkIAZEYeKIxakMxKkNRtvgjlPgibPCFWe+NULnDnACAQ6/h8Pgw13nJLsrCmmEmJsEflZT4wsypDeCNxltlicaYcO3JnDd1BAckua1OLBDG9fECMo6f1GZVWRSlLVh1gkWJQBbu5mvIQAWyTscypi8Ft5/Ctj+/QfVjn5N79RHNnl8fjvHHZdX8WBdkZraZmwc5GGDb/U0145gJZF0wk+pHP8M2dQjph49tsS2a2UDBX04m67z9qXr0M+pen0Pdy7PQ56VjnTIY84hiHL1zyM6yMcFoQEaj8fH4ajfhbXWENlUTXFdOaF05YX8In9WMK92Ge0gv3IMLcfbKoTornYp0M1tz+rDMaCSwY0moSgmV25pto00nGGA1sF+WiQFWA/1tevpa9BSZdfif/ZZtt71GxsmT6X3FJbsFJykl5cEoi10hPnlxDj+NH8xlm3yMqg3zlyGZjMtofkt4zzfLiHkCZJzYuuQQRUk1i06w0RfBGY4SKq1BmPTo87pneSpQgaxTyr78UHy/rqf8zrcwDSlsMug4w1HOX1jFem+Yu4dmcmovW7M9icJ7Tse3cCOllz3DoC//jGlwcnsTGfvmUvTguRT89RRcny7E8+1yvHPX4fpwwe7VSRvoNAzFWZgG5GM7ezrm4UWYR/TGNKxXk3NQUkrqIzHKA1G2bnWybfFmqlduxb25BhmTmLLTyB5ZRK9xfSkakEuxRU9mI8knoS01lN/1NvVv/0L6keMofuTCRr8vQggKzXqyK50U3fcat9x5Gj+etB+PbXRx5vxKbh7s4ILeTW8P4/pkIVqGVe07pnQ6Vp1GDFjsCtF/czWG4u67hgxUIOuUhBAUP3YRoZIqNv/uKQa8e+NuW7KEY5IrllSz3hvmqTG5TM9uOUFBsxjp++KVrDv4bkrOepiBn97Wqn28dOkWMk+ftr3qRcwXJFzuJFrrRUaioBNoVhP6bDv6HHurCu42vG6HQYfDoGPY0DwYmgfEd2quf38eznd+wfffzwEwFGXhmTqYyLAiDPkZoNOIlDvx/rQGz/crAci75fh44d8WfoFdHy8AIPuocZzaK40j86zcsrKW+9Y60QHnNhLMZCyG++ul2A8a2Wmr/Cs9l0UT+IHF9SGKNte0qtxaV6R+AzspzWqi7+vXsOGo+yk54yEGfHAT5hHF2x9/dGM98+tD/HNEVlJBrIGxOJt+L1/NhhMfpOSMh+n/7vXo7Mkv/t21jaYB+dD0tmdtQp9tJ/uiA8m+6EDC2+pwfb4Y7w8r8f64hvq3ftnpXOOAPLIuPpCcyw9tcufnXdW/Pw/z6N7x1wKk6TUeHpXN1UtruH+dk8mZJoakGXd6TmD5FiKVLuwHj26bF6kobUgTMDjNwGJXiEM2V5ExpvF997oLFcg6MUNeBv3fuo71xzzAxpP+Sf/3b8Q8tBdLXEGe2eTm5EIbxxTYWn1d6z4D6fN/l7HpvMcpOf0h+r1x7R4Hs/ZmKMwk+4KZZF8wE4jvSB2t8yDDUfR5Ga2qyAEQ2lSF79f15P/ppJ2O64Tgb8MzOeynAI9sdPHY6J0/0Xq+WwHQJbexUXqGcelGvt5UR7TG060XQ4MKZJ2esV8uA969gQ3HPcDGE/5B33du4G6vnmyjxq2DHa2+XkxKlrhCzB7an+Uv38SWDVUEP16LqU82VqOedINGtkEjpyFFPpEmn2PUyDLosOtFk/NwUSnxRCT14RjOSBRXWOKJxghEJWEpEYBRE9j18esXmXWNznG1hi7d0urgtSNnokfnOHnf3R7LNOg4syiNZza5qQpGyTX9NlTq+X4FpqG9VKV7pdManW7kx8T2Ld15DRmoQNYlmAYX0P+9G9l44oM8f+9HLLnwKO4fnoVdn/zkbUxKPij38WSJixJ/BAH0saeRN1BDLNyIqHIiJgygRupZ6wlTHYoSbiSPQyOe2mvRie2p/eGYxB+T+KJNJH40w6HXGG43MDHDxIxsM2PSjWjttKhaRmPUvjwrvmVL39xGzzkm38p/Nrn5qtrPmUXxdX0yHMH7y1qyVEkqpRMblmYkr9IJdO/Ue1CBrMswD+1F7ns38sKKegZv3MphmVEoTG5YsdQf4ZYVtcyrDzIizcADw7M4MMdChiEeCL3pMTad8xgyFKH4kQvIOH4fYlLiDMfXa1UFo9SEYjjDMZyRGN5IjGBMEpbxwKbXwKwJ0nQadoOGQ6+RYYgvgrbrBRZNQ6/FExyDMYk7EqMqFGNLIMIGb5hl7hCPl7h4rMRFoUnHSYU2zipOI8fYumSR1nJ/sYTw5moKbj+lyXMG2wz0s+j5otK3PZD5l21B+kJYm6jZqCidwSCbnvwqJ4AaWlQ6j8ekifp0G39+9mNK7tlIv9euwTZlcLPP+b7az/UrapAS7h2WyUmFu6fo2yYPZtC3f2XzhU+y+aKnyDhlIYV3n05WXgZZRh3DWldgZI/UhaPMrgnwXrmPJ0pcPLfZzYV97FzW1455Lyt+NEZKSdXjn2EoyiLjmAlNnieE4Oh8K0+UuNjoC9PfasD363ogPteoKJ2VRacxsM5FxNC915CBqn7fZXxZ5eO1Mi8X9E7j8CcvQJ+bzsZT/4Xn+xWNni+l5KkSF5ctqabYrOe9fQs4uVdak/NRolcWtndvxHf7aSydt4nvjvkHax76lLDTm8qXtV2mQcexBTaeHZfLp1MKOCjHwhMlLk74tYIlruYr9+8qGJVs8UdY740Pke66Wy7EFzP7flpLzlVHtLhM4MyiNCw6wQNrnUgp8c1fj74wE2NRVqvapSjtrU9tPbV5GV2y0HdrqB5ZF7DaE+KmFbWMthu5bqADoyYY8NHNbDzpn5Sc8TB9nvs96UeO236+JxLjtpW1fF7l55h8K/cMy8SyS6+mNhTl22o/P9UFWeIKsSUQISqBUSPgH79l4ul/qaQgGGSQ3cSo3g7GZpoZn2Fq1fxca/W3GvjXqGxOrrXxp5W1nDm/ksv6pnNRHztpu9w3JiXrvRF+dQZZUB9ksStEqT+yUzksq06wj8PEzGwLR+RZcMgYW299DWP/PLIuOKDF9uSadFzTP4P71jl5vtTDtPkbsU7s38avWlHaXm5VPRuyM3BHYin9ne1oKpB1cpt9ES5eVEWaTuOx0dkYE9XqDXkZDPjwJkpOfYhNFzxB76d+h+PEfVnmCnHd8hq2BCLcNCiDi3rbt38ak1Lyc12QV8o8fFPtJyoh16gxPsPEkXlWCs067HoNTUAoJqneVEPJglI2u4OsKczh+6gOudmDJiXDAgEmiwjTTDDGqGFITILJqERGIshwFBmOQsPu1EIgjHo0ixHNZkKXaUOfm95s2v9+WWY+2LeAO1bX8XiJi5e2uJnoMFFg0hGKxef+VnlCuCLxsNXwWo4vsFJg0mHSBM5wjLXeMD/XBbmzpo5719Yxo6Kag/RGDr/75KQKKAOc1zuN+fVB7l/n5LKBvTlXBTKlC7BV1FE1djDLXCGmZiW/3rSrEbKpEkOpvrEQJYAbiAIRKeUkIUQW8AbQDygBTpNS1on4O/HDwFGAD7hASrmguetPmjRJzps3L3UvoB2U+iOct6ASX0zy8vg8BqftXkMx6vJTctbDuOeu59tnrubZ9EyyjTr+OTKbSY7fagXOqQ3w0IZ6FrtCZBk0Tiy0cXS+lRFphhaHHaKeAN5Zq6iYvZrFlR4WW60sGtaXdYOLiGkaaW4fYxevZ+zi9YxetpGc6nqSHcjQ7BaM/XIwDSrAPKwI89i+WCf0R5+9czWNJa4gr23xsMgVwhmOoRNQbNEz2GZgXLqRfRxmelt0Tb4WKSWrvWFembWej8M6vGkWxqQbubSvnYNzLEllSgajksu/3cAczciV+hBXzxjQ7YdslE4l6R+2SZMmybmzf2J50eW8dsaB9LvpOC7t2+XnyZp8/R0dyCZJKat3OPZ3oFZKeb8Q4hYgU0p5sxDiKOBq4oFsMvCwlHJyc9fv6oFsky/MeQurCEQl/x2fywi7sclzV1V7ufHLtazJz+Zgj5t7jxyKwxCf91njCfHAunpm1wboZdJxab90TiqwYdLFfyaqQ1EW1gfZmKgeH4hKTDpBpkGjv9XAaLtxtwAhpSRa56W2op6f6oLMCsIcqaMmMeWaJyRjdJJRRhhl0hhtBJMEGYogg2Gibj/RGg+RKhfhslpCm6oIri0ntKl6e+1G0+BCbDOGkXbACNL2H75Xa8Ua1L0xhy1/eB5tymAWPnIpz5d5KQ1EGWzTc1X/DA7LbTmgbb7vPe4KGZg1YwynFNq4fWjm9l6yoqRYqwLZj69/xJrJf+LN60+m5IhJvDQhL5Vtaw9Nvv7ONrR4PDAz8fcXgO+AmxPHX5TxqPuzEMIhhCiUUjZfGr2LWu8Nc/7CSsISnh+fy/Amglg4Jnl6k4snS1ykFeVw2xe/MOHJTwjfcSru3x/GIxvqeaXMg00nuHWQg7OK0zBqgupQlBdKvXxc6WOV57eNM+16gVkThGLgisS2zzP1Muk4LM/CiQU2htmNCCHQZ6WRl5XG8cT/50gpWeMN86szyHxnfK7qK18UfGAQMNFh4qj8TI7Ms5JuaHysPur241+yGd+v6/HOWY3zjTnUPvct6HXYpgzCfsgY7IeMwjSsqFU9oajLR8Xf3qXm/77BNmMYfV+8kpHpVs7onc5nlfEsyWuW1TDKbuDWwZk79WR3u9b8DdxU7WLkOdN5apOL8mCUR0ZlY+vG8w9K19SwoeaAwfm85QxSE4qSneIlLR2lI3tkG4E6QAL/kVI+LYRwSikdiccFUCeldAghPgLul1LOTjz2NXCzlLLJLldX7ZGt84a5YGElEnh+XOPDiQBrPWFuWlHDCk+Yo/Os/HmIg0wh2fz7/+PzbV6ev/oEao1GTi+ycU3/eBp9eSDC4yUu3t3mJSxhfLqRg3Mt7OMwMchm2CmRIhiVbPSHWeAM8UOtn9k1AcISJmYYubhPOgflmFsMJrWh+BYpc+uCfFvjZ6MvglUnOLnQxsV97BSam/8cJcMRfL+ux/3VMtxfLiGwYgsA+gIHaTOGYZs2BMv4/piGFO62H5iMxvAv3kT9B/Ooe3kWUaeP7EsOouDO03abF4tKyYflPv69oZ7yYJSTCqzcMjhz+zq77deMxVgx8A84TtyXon+dx9tbPfxldR0j7UaeG5fbrSfTlU6hVT2yL675F2XXvoDpx3s4dmuEa/qnc0X/jFS2L9U65dBikZSyTAiRB3xJfOjwg4ZAljinTkqZmWwgE0JcClwK0KdPn4mbNm1qp1fTNjb6wpyzoBIBvDA+j4GN7CsG8L+tHu5e48SmE9w1LJNDc61APHD8dVUtX1YH6L9hGzfXVjHzxqOISHhmk4unNrmJSckpvWyc39tOf2vym0HWh2O8u83Li1vclAWiDE8zcO2ADA7IbjmgQbzHtswd5sVSN59W+gA4rsDG7/rYG90/rTHhslrc3yzD890KvD+uJlLlij8gBIZCB7qsNNBpxDwBwltqkMEI6DTSjxpP3rVHYRnXr9nr+6Mxnihx8exmNwUmHQ+PymF0+m+94cCqMtbu91eKH72QzERVj6+q/FyzrJoJGSaeHZerhhmVVGr2h2vX979fLnuIqn9/zKitT/G75XWs9oT4Zlqvrvwz2vkC2U6NEOIOwANcAsyUUm4TQhQC30kphwoh/pP4+2uJ81c3nNfUNbtaj2yzL8LZCyqJSMnLExoPYlEp+dsaJ6+UeZiWaeIfI7O3V7/4qTbADStqqA/H+EP/dI586iOcz3xD8PLDuff4/VnuCXN4roWbBjkotuz5iHI4Jvm4wsdjG+spDUTZx2HilkEORqU3PYe3qzJ/hOdK3by51UMoBgdmmzmjKI3pWWb0Sf6SSSkJbajEv2QTwTXb8G+uZoPQsyYng025mZTnOqh3pBFNM2M26ikw6xiRZmRqlonRieHRpixxBbl2WQ01oRhPjcnZnu1V89/v2HrDSwyZdx+m/r/NN3xY7uWGFbVc1NvOzXtQ/1JRktSqHtm7+1yO98fVDFvyD2bV+Pnd4moeGJ7FCUlWBOqEOlcgE0LYAE1K6U78/UvgLuBgoGaHZI8sKeVNQoijgav4LdnjESnl7lVed9CVAllFMMLZ8ytxRyUvjc/dbcsQgEhMcuOKGj6p9HNRHzs3DMzYXuvw9TIPd66uo59Vz79HZTMszYiUku8e+pwb+/cnZjZw79hcjihseu+x2lCUpe4Q2wJRABwGjcE2A/2t+kYTIEIxyf+2enhso4vacIyTCqz8caCDPFPyY/DVoSivbvHwxlYP1aEY2QaNg3IszMiOr1Vr7lrOcJSV7jCLXCEW1AdZ4AziSdR6tGiCPlY9uUYdRg0CUUlZIMomfwSAkXYDNw50NJuOXB2KcuHCKrYEIry7Tz79rAY2XfgEvl83MGzpP3YLhH9dVcv/tnr5eHJB0j1MRWmlVgWyN3udhozGGPjxLUgpOWZuOUYheGef/K6abdvpAtkA4N3EP/XAq1LKvwkhsoE3gT7AJuLp97WJ+bLHgCOIp99f2Nz8GHSdQFYbinLWgkoqg1GeH5/LmPTGEw3uWF3La2VebhiYwSU7pNG+usXDnWvqOCDbzL9GZm+f59roC3PavApsviC3Xf8UQ0YX0ee536NZd77+Kk+If6yr56e6AI3V/M0xahycY+GMorRGMyfdkRhPlbh4odSNQRNc0sfOhX3suy3Abk44Jvmuxs/HFT5+qAngTTTEYdDobdbjMMRrNUZi4AzHKAtEqA3Htj9/kE3PJIeJCRkmxqYb6WNpPPjWhqJ8WeXnmU0uygJR7mvh0+m2QITj5pYz0WHiyZHZrBxyDelHjqf4sYt2O7cmFGXmnK2c1iuNvwxRFfGVlGhVIHtFHIplXD/6PHMZEP/Ae/vqOl6bmMeEjKYTmjqxzpW1KKXcAIxt5HgN8V7ZrsclcGU7NK1ducIxfre4irJAhOfGNh3E3i/38lqZl9/1se8UxBbWB7l7TR0HZpt5dHQOBu23hc+3raxFQ/DSAX2x3XQMZde/xMaT/0XfV65GnxUvnvhRuZebV9aSrte4pI+d6Vlmelv0CCGoCUVZ6Q4xuzbAB+U+3tjqZf9sM7cOcuzU47DrNW4c5OD0Xmn8fb2Thze6eH2rl6v6pXNioW17m5pj0ASH5lo5NNdKKCZZ4Q6xyBVigzdMWSCKMxwjIiU6QXz36DQL/ax6htuNjLQbti81aEmWUcfpRWkcW2Dl8sXV/HV1HdOzzU0WJy406zmrKI2nN7kpn7eBqNNH2iGNb6SZbdQxPcvM7JpAUm1RlFQLlztJL3Bs//ex+VYeXO/ktS2erhrImtTZ0u97DH80xu+XVLPGE+bR0Tnsk9n4MJczHOVva5xMyDBy3cCdM47uW+skz6TjwZHZOwWMZe4wC+pD/HWIg94WPZx/ALpMG6WXP8OGI++j3xvXUl2QyW2r6hifYeLR0dlk7hIM8k06RtiNnNwrjfpwjDfKPDy92cVxc8u5ZbCDs4t2rtvYx6rnsdE5zHMGeWCdk7+sruM/m1ycW2znxELbbhmATTFqgnEZJsal8BfNqtO4aZCDk+dVMLcuyFH51ibPHW43EgPW/7CaDL0OezMbaQ62Gfi+JoCUsqsO3SjdRTSG9IXQ7xDIbHqNQ3OtfFXlIxKTSc9HdwUqX7gDhGKSK5dUM78+yN9HZHNgTtOLfV8s9eCKxLh9aOb2OTGIV/1Y7ApxYSP1B1d5QgDsn/3bdTOOm0T/t68nUu1i3WH38P68zQRjkvuGZ+0WxHaVYdC4tF86n00pZHqWmbvXOLl7TbyA7q4mOUy8OTGP/4zJId+k4751TvabXcZli6t4eYubVZ4QkVjHJxgZk/zJrwzG5wzFR/NJO2A4OkfTQ5ECQSfInVKUeHk4wLBDIAOYkmnCFZGUJOaLuwvVI2tnkZjkhuU1/FgX5L7hWc32BmJS8ta2+JDesF0SQDb64guZRzcyb5WeCGw1oWi8R5ZgmzqEgZ//iZKzHmHDO/MwHjWZYnPyyRk5Rh1PjMnhH+vqea7UTYZB45oBu69LEUIwM8fCzBwLK9whPiz38UWVj+8Sw25mTTDIpmeIzcDAhi+rgSKLbqdgnUrrvPFf5H7W5n8Fvq8JUEwM+7JNOK78XbPnVgQj5BibLpOlKO2lIZDpdwlkfRPvB2X+CIO6UVKSCmTtSErJPWvr+LzKz62DHJzUQhrsCneYimCU6xoJFg29MOcOSQ8NJmeaMGmC/2317jZEZxpUwKAv/kSfR74mpGn8dMMrTL795KRLQGlCcNOgDJyR+JqriRkmpmc3nf03wm5khN3IzYMdlPojLKwPsswdYo0nzA+1Ad4p9/3WNk3Qz6pnoFXPQJuBAVYDA2x6+ln0bb4nWUnig8DAZtbSVQWjzKkNcPrSdegyrGQcO7HZa672hBlkU79SSseTkfgHtV17ZA2622ct9VvXjl4t82xP2rigT9Op8A0WJ/bh2jdz9/mikWlGrDrB51U+Ds7dOQg5DDpO62Xj1TIP5xSn7VbiSuewccoNR/LET9t4N2oga+YdFD9yIWnThyX1OoQQ3D7EweL6IH9ZVctHkwuSKtHU26Knt0XPcQW/BXBnOMoGX3zvsA3eCOt9YZa4Qnxa6d9eIksARWYdA20GBtkMDE0zMCLNyACbfo97cA1ZjREpMTWRDPV9jZ8YMOmFr8m+YCaapem1cqGYZK03zAW9W/7/qiiptr1Hlr/zh+DERhHou1kkU4Gsnax0h7hvrZOZ2ebdkjaa0lDSqbCR9VQmneCUQhuvlHm4rG/6bguor+qfzicVPm5dWcv/JuXvlj1YaDVwTIGNz4+byjEr1hM+/h9knrkf+X8+qclPcTsy6zTuHpbFWQsqeWRjPbcO3rOUc4dBx4QM3W5ZVIFojBJfhA2+CBt88SC3zhdmTm28VBaATSeY5DAxLcvMQdkW+rQwTLijCRnxoPRxhY/TihrfAnujL4IhGqVvZR3Zl+6WTLuTskCEsKRbDdcoXZcMR9HsFnRpO4+WRBOTuLruFcdUIGsPMSn5y6o6Mgwa94/ISroXURaIUGzWNznn8vt+6bxX7uWuNXU8Py53p/McBh13DsvkqqU1vLjFzcV9dt/C4YZBGXxV7eel+y/mrg9+oPapL3G+/ys5lx5C9qWHYMhvPuBOdJg4o8jGC6Ue9nWYd+sZ7g2zTmOY3ciwXXqT4Zhkoy/MCneYha4gP9cF+X6tk/vWOhliM3BUvpUj8iwtlt/ax2FifLqRv693MiFRa3JX+tJqIsKI9eojWgzu7kh8iDfZ7ExFSSUZjmLo0/Tvb3fLSVK/de3gowofS90hbhrkaDFDcEeb/ZGdkjV2lWXUce2ADH6uC/JllX+3xw/NtTIz28zjG13UhqK7PZ5v0nPNgAxmOUMsv/JoBs+5m/Qjx1P10CesHnsjmy96EtcnC4n5gk224ZZBDkbaDVy7vJpPKnxNntdWDJpgSJqREwpt3Dk0i8+nFPLV1EJuHezAphc8tKGeI34u56Rfy3m6xMWWJrKzhBA8ODIbkyY4f2ElK92hnR6Punz0e/gDpCb49fSWd5G2JebwXJHd5ywVpb3JSBRd7u7D3NbEz6m/seoHXVinqLWYCp2lsoeUkuPmVgDw/r75SW3gCPEivVNmlXFFv3SubiTZo0EkJjl2bjkGTfB+I6Vn1nnDHP1LOVf3T+eqRipfR2KSE3+tIBCTfDq5AL0mCK4rp+a5b3G+9TPRGg/CqMcyoT+WMX0w9s+PF+h12BBmPUJo1EWi/NFnYDE6Dgn6uKSmmiKXB+kPI8OR+Mc/nUCzmNBlWNDnpGMoysLYPxddetNZm3uiPBDh00o/n1b6WOyKB6dRdgPHF9g4Ms9K7i7DtOu9YS5aVIUrEuPfI7OZmWMhFoqw6cyHcc9ezV1v/IlyvZ5PJhc2uf0MxOfIJv6whTOL0rhtD4dZFaUFSQ8Ijrb3kh+dcBt9X7pqp+MVwSj7/7iVGwdm8Luut9Fm5ypR1R46SyCb5wxy9oJK/jYsk1N6NT4X05hXtri5a42Ttybl71SBvblzP22izt+5Cyqpj8T4YN+CRp//aaWPa5fV8Ny4XPbbof6gDEfwzF6N59tl+H7dQGB5KTFv472zsF7HeydM590TphMx6Jg0bzUHfb2QsUvWY4jGaGqBlaEoC8v4flj3GYht2lAsY/si2ihDsdQf4bNKHx9X+FjpCaMXMDPbwjnFaUzJNG0P+hXBCL9fUs1Kd5hb+ts58O7XqH9/HsWPXkjZMfty2vwKjs238sCI7Gbvd/GiKjb7I3wxpUCl4CupkPQP1ShLvvzssgcofuiC3R47bm45GXqtK2602blKVPUkb22Nb2zZ3HqxXbnCMZ4scTE+3cgoe8vJA+MTiRLrvOFGA9m4DCPPbo5v4dJYj/CARPr84vrgToFMGPTYDxyJ/cCRQLx3GalyEamoJ+r0IUNhZEwidBqa2cCtVhOXm028HBS8O20Ec/cdToZecGCOhYOzzUw1CUzeAJFKF+HSaoIbKgms2IJ/wUZcHy0AQJdpI+3AkaQfMQ77YWPQ2fd83q23Rc8lfdO5pG86671h3trm5d1tXr6q9jPSbuCa/hnsn20m36Tn5Ql5XD+/nHs3ulltSefau04j86zpZAKX9k3nyRIXh+ZaOaSZecAj8iz8eVUdy93hVu0GoChtTUZi6DMb/+B8YLaZZza7qQ/Hus2crgpkKeSNxPi8ys/RedbtY9MtkVLyp1W11IZjPDkms00+2Zs0QVRCVEJjVWkMQiCg0aLBOxJCYMjLwJDX9FCnBbgJuDYmmV0b4LNKH99WB3iv3IdJE8zIMnNcUR4Hju1Lxg6NCZc78c5ehfvb5Xi+Xkb9O3MRJj32Q8aQcfykvQ5qA20Gbh7k4Nr+GXxQ4eU/JS4uXVLNgTlm7hyYjv7Nn/j93W+jnXMob5+8P8X907ki8dwr+qXzbbWfO1fXMTXT1ORSg0NzLdyxuo5PK30qkCkdS0p02U0EshwLT21y80ONn2MLuuyWLjtRgSyFvqn244tKji9Ivjf26EYXX1T5uWWQo8UhxQbznfHhvqZSv9d6wxSYdE0W8J1fH0RCk7tR7wmjJjgox8JBORbCMck8Z5Cvq/18Vunjq2o/hSYdV/VP5+RCWzxAFjhwnDIFxylTkNEYvrnrqH9/HvUfzMP18QKESU/agaPIOHYi9kNGo8/Zs/VaJp3g1F5pnFBg44XV1Txc5uO4DTXc8sTXjB3dmwfP35fbgwYe3uhiuN3IgTkWjJrgzqGZnD6/khdK3U3ususw6JjsMPF1tZ8bBzn24runKHtP10SPbEy6kSyDxnc1ARXIlJZ9VOGj0KRjoiO5Arhvlnl4vMTFSYU2Luid3HxafTjG/212M9pupH8j66jqwlG+rw5wbBPBVErJ/21yk6HXtg8xtjWDJpiaZWZqlplbBzuYVRPgyRIXf1pVx3c1AR4ckbVT5Q6h07BNHYJt6hAK7z0D39z18YD24Xzcny0CITCP6YNt2hCsE/pjHtkb04A8hKHpH2cpJZGtdQRWluGbvwHPrJVM/mUdD/TK4f7bz+fu+3/Ha/sWYrEbuScmWe0J89dVdXw+1YRVpzEuw8TMbDMvbvHwu77pTe6yOzPHwt/WOiltIeNUUVKtYZeLXWlCMDPbzFfVfsIxmdQOFZ2d+k1LkapglFm1AS7qbU8qU/HH2gB3rKljRpaZu4YmN6RYH45xxZJqakJRHh2d3ehzHtpQTzAmOb+JihOfVvqZVRvglkGOVu0htqd0iTqM+2ebeb7Uzd/X1XPt8hoeH53T6Po6oWnYpgzGNmUwhfecjn/xZtxfLcE7axW1z31LzZNfJi6sYcjPQJebjs5uRuh1yEiMmC9ItM5LeFsdMhAvS4UmMI/qTe4fj2bwCfswbkABp82r4IYVtbyzTz5GTfDXIZmctaCS/231bv/enVWUxnc11cypDTCziULPUxJVWOY7gyqQKR1K52h6JGhGtoV3yn2s9ISa3D6qK1G/aSnyYYWXqIQTk9hWfIM3zNVLqxloNfDQqOwWPyHFpOSLKj/3rnVSE4ryjxHZjf4wvl/u5fUyLxf2Tmt02HG9N8yfV9UyNt3IOcXJZ1S2BU0ILuqTjkkT3LXGycMb6rluoKPZ5whNwzq+H9bx/eDG45DhCIFVWwms2EJwXTmuche/2KysyMygPN1GRK8j3+tnpMfLgZEAab2zMQ8vwjyqz061JXsBdwzN5MqlNXxU4ePEQhsTHSZG2428X/5bIJuaZcakCebUNR3IBtoMmDXBSk+IE+gewzZK16SlNT3Csk9ilOhXZ1AFMqVxUkre3eZjTLpxt9JRu/JEYly5tBqTJnh6bM5uW7LsyBuJ8UmljxdLPazxhhliM/D46Jzd5tKklLy4xcP9a53s6zA1GiBK/REuXlSFRSeSCp6pcnaxnVWeMP/Z5Mai07i8rz3pBBdh0GMZ3Qf3kCKe2eTi7a1e/DGJQcQzFg2aYKk/wgdRyQsmHX8fkdXkvm8H58Q363yv3Lv9w8fMHDOPbXThjsSw6zWMmmB4moGV7nCTbdKJeOHjjb7utU2G0vXsuhv8jnJNOvpZ9MxzBrm4Tzs2KkVUIEuB5e4wa7xhbm9hy/uYlNy4ooZN/gj/HZdLobnx/x3L3SHeKPPwUYUPb1QyxGbggeFZHFtg3W04bmF9kH+ur+dXZ5CDcsz8c2T2bvM5S1xBrlhSTTgGz4/PpVcT920vtw/JxB+VPLShnnnOIDcMzNit0HFjolLy8hYPDyeGT4/Lt3JSoY2xGabtrzkmJXNqg9y9to4rl9bwyZSCRneEFkJwQLaZ18u82zcdHGE3IoknyzTUguxr1TO3rulKJwDFZj0l/qaDnaK0B83WfE9rXIaR2bXdYyNYFchS4KUtbiya4JgW1o49stHFN9UB/jzYweRdegrhmOSzSh8vlHpY6g5h1gSH51k4rVcaEzOMO/3gbQtE+KrKzwcVPpa4QjgMGvcMy+TkQttO83OhmOT5zW4e2VhPnknHf8flJpWp6I3EWO4OsdYbZksgij8awyAE2UYdA216RtqNexUM9Zrg7yOyGJdh5KEN9ZzwawXj0o0cnGthSma8DuKOyxfqwlFm1QR4ZpObNd4wM7LM/GWIg76N1FfUhGB6tpknzDkc/Us5r23xNFkpZViakWDMw5ZAhH5Ww/a9m0r9ke2BrNCkpzLkIyplkzUze5l1zKnrHm8QStfVUiAbnW7kvXIf5cFokx+iu4qu3fpOaLMvwocVPs4uSmu2pNGbZR6eLHFxSqFtp/mpcEzy7jYv/9nkYksgSj+rnj8NdnBCgW379aqCUX51BplfH2RObYANiWGsYWkG/jzYwcm9bDu98Ydikk8qfDxZ4qLEH+HwXAt3DcvE0Uzdx42+MF9W+fm+OsCC+iANFQSNWrxeWyQm8eyw8KyPRc9BOWYOybEw0WFKuhRXA00Izim2c2y+jbe3efig3Mc/19dvf9yh10jTC/xRSU1iD7aBVj0PjcrmiFxLiwFjoM2w/RNoU4GsTyJwbfLHA1lRYtPRHes1Fpp1RGV85+imfvmLLHp8UUldOEZWI70/RWkPzQ0tAts/nM2pDXByK6oOdUYqkLWxB9c7MQrBJc3UMXtvm5e/ro5nKN6RyFCUUvJVtZ8H19dT4oswJt3In4ZkMjPbTCAm+ak2yKxaP7/UBbcHLosmmOgwcVqvNPbPNu80HxeVkqWuEJ9V+viowkdVKMYQm4Gnx+RwQBOJCqX+CB9X+PisMl7SCWB4moFL+6YzISNeiT7PqG0PGr5ojHXeMIvqQ8yuDfDKFg/Pl3rIN+k4PNfCkflWxqUbWxXUMgwaF/VJ56I+6VQGoyyqD7LOG6YqFMMTiWHWCXqb9YzLMDKplQFzfLqJl8vcBKMSUyP7WAxILF9Y6wlzQLYFs06jyKxjjfe3YcKGXtoGX6TJQNbb/FtPTgUypUNoWoul3oanGSg263i33KcCmfKbjyt8fF7l59oBGeQ1socYxOsi3r3GyeRME4+OjidZrPOGuXtNHT/XBRls0/PE6BymZBr5ujrAFUuq+bEuQCgG1sT+WycV2picaWJ4mnF7koYzHOXnugDL3SEW1of4tS6IMxLDIGBGtpkzeqUxI9u82xv/Bm+YrxILlZcnkhjGpxu5dbCDw3MtzQ45WHUaY9JNjEk3cV5vO95IjG+r/Xxa6ef1rR5e3OIhx6hxUCLdfmqmudlkll3lmXQclmflsKSf0bwpWSaeK3Uzq9bPIbm7D/tmGXX0seiZ6wzyu77xY2P/v73zjpPrKu/+99wyfWZ3tku76tUqVjG2bLlhDLENNjbBEBsnlATIG/IGAg4OhiRUYye0BGJawC8xYAw2tiFgAgYMtsFNVu+9rbZo6/Ry7z3vH+fuaFfSSitb0mqk8/185rNT7s6cM/fO/d3nOU9JBFgxUKq4Cef6JcM2pEojynkNZ4oviLvzDotqqj8iTFN9iDEEbwkheMekOHdtG+Dp3jyX15+8NkynGy1kJ4mhUPYliQDvPkr3Z1dKPr99kPv2pbmqIcS/z6/HkXD3tn6+tz9D1DT4l9m1LE4E+OGBLB/e2EvWlbQETd7WGuPV9eFKYvXObJmNmTI/78qxPeuwPVums3ioTUtbyOQ1jWGWJ4NcXh8a4UIsuB4vDZZ4qjfPU72H3JIL4jZ3zKzhmsYIbS8z/ylqGVzfEuX6ligZx+N3PXl+3ZPnZ105fnQgiyVUVYFlyRAXJ4MsTgRGJEKfapYnQ0wKmdyzbYBFieARlfABXtsY5v59abqKDs1BiyvqwzzenWdNqsTimiBJ22R6RInde0f5nMlhC0uo2pcazbgwRk/Fn02M8WB7hjs39fHjC1toHuUC/ExHV78/CfSUXP5sRRd5V/LIhc20HGbFpMoeH9rQy9N9BW5rjXHnzBp+1VPg7m399JQ8bp4Q4ZJkiB8eyPL8QJGQIbi2KczNE6LMidms8C2sFYNFNqVLlQ7JQUMwI2IxI2ozJ2YzN2YzPx4Y4c7qKbmsS5VYk1LvsSZVpCzVWtdFtSGuaghxdcOxLa9XStmTrBos8nRfgef6i2xIl3Al2AIWJYIsSwaVsA2LNjxVrB4s8s5VB2kOmvzXosYjukrvyztc+1wHb54Q5VNz68g4Hpc9c4DrmiPcfV4dAJ/Z2s+PDmR57vKJo9bQvP75DtrCFl8/v/GUzkdzTjH26veRFrk+1zmmbTdnStz6UjcNARUA9nIvZE8Duo3LqSLjeLxjVTc7sg73L208IrlwW6bM367r4UDB4Z9mJ7msLsQntvTzdF+BeTGL61ui/LxLufWagyZvb4vx6oYQz/cX+fXBPC8OKOGxfWtmcU2Q+fEAc2M2UyNWJXJuoOyyJ+ewI6cstO3ZMlsyhyw1Uyif+EXJEBfXBrkoGTxqJY+M49FZdOksuPSUXFKOR9aVlDyp2oqhBDRqCWptk8aAQXPQpCVoHXXdabTvbMVAkRcGijzfX2BjuoyHWvO7MBnk0mSIy+pDzIiM3h37lfDSQJG/WduDKyV3nVfHtU0j3Yx3be3nu/szfG9pE6+qDfLJLf08dCDDry6ZwMSQxbN9Bd65+iBfXlDPNU1Hj0z9u3U9bM+W+cXFE076+DXnLKdEyEB1vnj3moMA/NPsJG9sjpyJEbdayE4FZU/y12sO8txAkf9c2MBrDgui+G1Pnts39BI1BV+aX8/mTJkv7BhECLh5QpTVqRJrUyUmhy3ePTlGwjJ5tDPLM30FXKmCD67y15eWJILYBuzPu2zJltiZddiRK7Mr57An5zA4rDOxLWBaxGZ2zGZ+3GZhPMC8eKBStV1Kyf6Cy7ZsmR2+6O08yvscjgEcq/9xU8BkasRietRidtRmVtRmbixwzOhNUBbriwNF/thf4A99hUoycUvQZHldiOVJJbzNwZN3pdied/jghl7WpErc1BLhn2cnK+t3Wcfjphe7KHmSRy9spuhJ/uS5Dt7QFOGeefU4nuSKPxzgVbVBvryw4ajvf/e2fn7YnmX1q9tO2pg15zxjF7LoBLk+23FCb74nV+YfN/WxarDERbVB3jc1MaJv3xmAFrKTjZSST2zt58H2LJ+dmzwi6uc7+9Lcs22A+XGbz8yt4/M7Bnmmr8Cy2iBxS/DrngLNQZP3TolTdCXf3Z+ho+jSFDB504QI1zdHmBaxWTWoLJeVA0XWpkuknUP7qzmo1mumRiwmhy2mhG2mRy0mhSws30WXcTy2ZctszpTZkimxNaMsteGh801+PtiUiEVbyGJC0KQlZNEQMKixDaKmgS2oRFcW/dD7/pLHwZJLV9HlQMFhf95hV04JbGrYOCeFTBYkAixOBFlaG2BeLFAZ39Fozzs806dE7dn+QuW9JoVMzk8EmZ9QAjkratM4LIryRCl7kq/uTvH13Skmhkw+P7++0tttfarELS91sSwZ4puLGvjCjkHu25vmkQubmRcP8Nlt/TywP8NTl048amTiN3en+MLOQdZc2Xpa1wE1ZzWnVMhAreU/sD/DN/akOFjyWJII8JaJUa5pipxQoNYpQgvZyeaB/Rk+ubWf906Jc/uwElBSSr68K8VXd6f4k0bVjfj2Db2kHMn1zRGeOJgj70reNTlOjWXw7b1p+soeF9UGecekGMuTIf7QX+AXXXl+15sn60oEKkfs/ESAhYkAc2I20yP2iAMr63jszDnsyJbZkSuzLaOqi7QXDgWBxEzB7JgSgbkxZbHNiNhYBnQUXDqLLgeLLgNlj0HHI+t6FD3JkJFmCuVWjFlK4Opsg6agycSgxYTQoTYxUkq6Sy5bMmU2p8usT5dYlypxwHdzRk3BsmSQy+pCvLo+TOsxfPKulGxKl1nh582tS5XoGBbYEjEFU8IWk8IWbWEl4q1hk0kh9Xgsa24vDRS5Y2MvnUWX22fU8K5JqkzWD9sz/MuWft43NcG7JsV53XMdzI7a3L+kkW3ZMje80MVHZ9UetSDz9/zo1GcvO7rQaTQvg7ELWWyiXJ858LI/qOhKHu5Q6TR78w5BQ3BVQ4jXN0VYXhciPj6ipoXsZLI9W+ZPX+xiWTLIN85vGBHS/vXdKb60c5A3T4hyVUOI2zf00WgLFiSC/O/BPEtqAtzWGuNru1PsyDksqw3ywRk1TAxZfH9/mh8dyNJf9kjaBq9tCHNlQ4hltaGKe87xJNtzSiC2ZMpsz5XZnilXRAIOuRZnRW1mRi3mxJT4RQzYknXY4rsUd2Yd9uRVjtbhCJRIhAyBKQRCKFHJu5Kcq9bLhmMKFa03K2pzXsxmUU2QRYnACLHtKrqs8NfF/tBXYL8vsnNjNn/SGObapshxa1MC9PkiuT1bZk/eYU9eWYP7Cw7DpzI0ptlRmwWJAEtrgpyfCBxV3FJlj49t7uNXB/Pc1BLh03PrsAV8dHM/j3ZkuW9xI7tyZT61dYCvLmzg6sYwb3qhEyHgkQtbjni/B9szfHxLP09dOrFqI8E0ZxxjFrKF8YlyXfrlC9kQUkrWpkr8tCvH4105+soepr9ef1ldiMvqQiyIH9vDchLRQnaycKXkLSu66Ci4/PSilhEh3D/rzHL7xj5uaI5wXVOY96/vZW7MJmgIXhos8e5JMcKmwX/6rqx/np1kaSLA1/ak+H57BseDqxvDvGVilEuTISxD4PkH0tO9BV4YUBZJ3lP7LGDA9IjNzKi6DUUwTg5bDDoe61Il1qdKbMiU2Jgu0zVM7Gptg+kRi2kRtX1ryKQlaNIQNKmzTeKWGDXZ2JOSlOPRV/IqbsW9eRVosjWjxAWoHPBX1IW45jCRklKyK+fwZG+eJw7mWTVYAmB21OaapjCvawwzO2qfkNvQk5KDJZf9eZd9eYfdeRX4sjldYp8vmhFT1VS8oTnKqxtCI8pMSSm5d3eKr+xKcWV9iP9c2IArJW9+sYu0I3nswmbetrKbkCl47MJm7vPb0Dxx8YQjoh9/1J7hn7f08/vlE46IYtVoXianXciGU/Yka1Klitt/XaqEBEKGYEE8wKKaAIsSARbXBE7qevYwtJCdLB7ryPKPm/r4wrw6rh/WXXVf3uGNL3RyXszmY7OS3Laym2kRk6hl8tJAkU/PTfJsf5GfdeW4oTnCJ+YkWTlY5KOb+ugtedw0IcrfTElUToi7cmUebM/wc78qhwGcF7dZWhNkYSLAgniAKWG1FlbyJOvTJVYPFlk9ONKNJ1BBI/PiAc6L25znW2f1ARNPStoLLjtzZfbnHToKLt0ll/6yqqJRcCVl//iwhSBsCuKWQX3AZELQZFLYYnZMiehwKyfteKxNlXihv8Af+oqsSyuRWhC3ua0tzvXNkSOsoq6iwy+7VWL2ykH1A2kNmZVE6gtqg0ct9jtW+svKGny6t8CvD+bpLXu0hkz+dmqCmyZERwjakEvx+uYIn59Xx/p0mbeu6OK2thjz4gHu3NTHNxc1MCtqc9UfO7h9eg3vnTqykstDBzL80+Z+frd8QtXXsdOcMYxdyBKtcl2q/VSOhf6yy7N9RVYNFlmTKrFxWGpQS9BkUWJI3ILMj9sno9+hFrKTgeNJXv98JxFT8OiFzRVrQUrJe9b08NJgkUcvbOZ9a3sYdDyuqg/zUEeWu+cmWTlY4qGOLB+cXsNfT4nz3/sy3L19gNlRm3vm1THfr/a+P+/wuR0D/G93HluojsPXNIZHJDZLKVmfLvNUb57n+ousThUrLrXWkDqAzk8owTsvZleiFVNljxcGCrw0UGJ1qsjmTJncsKAPW6hqGrW2QdwyCBsC2xAIoOS7FVNlFeDRU/Iq7sWAAYsTQa6sD/H65sgRBYS7iy6Pd+X4cUeWrdkyE4Imt8+o4fpRQnwPFl1+05Pn9/78hsbY5geNzIsFmBtXOXMvR9wcT/Jkb57/2pNmTarEkkSALy6oHzHub+xO8cWdg3x8dpK3tcX4+JY+Hj6Q5fFlLdy2spsF8QBfX9TIn77YScgQPHBB84jPePhAho9t7ue3l0w45hqgRnMCjF3IalrlusFTK2SHU/Ikm9KlSjT26sFiZfnAFIfW+RcngiyqCTA1fMLpNVrITga/6M7x9+t7uXdh/YgSR8/1F3jHqoN8ZGYtArh7+wAfmVnDPdsHua01xqKaAHds7OP/TInzwRm1lav1axrD/Nu8ukpU2/90qhqMAG9vi/MXk2IjTtSdBYcftGf4SWeOjqKLAObFbS6qDXJBbZAlNUdaLT0ll5935fhVd55VqSKunww9PxZggR84Ms2v9N5wAhGAJU+yL++wJVNmbarIH/uLbMmUMYBrm8L8w4zaI07gUkqe6SvwpZ2DbEiXua4pzL+eV3/M/LOyb22u8q3NDelS5ccBKuJyQcJmWW2I5XVBZp2AO1JKyU+7cnxySz8xy+D7S5sqXZ2llPzl6oOsS5f47SUTybger/ljB++bmiDvSr67P83zl7dy7+4U9+9Ls/LKthFW5o8PZPjo5n5+c8mEMznBVFNdnICQtcl1g/tP5VjGRG/JZU2qxBrfalubKpH1L0xrLIOltQGW1Ya4KBlkbswetaOEz6gv6l/YCfBYR5YJQZOrDssX+0F7hqRtcMvEKG94oZOLaoM821+kIWDw/mkJbnihi0WJAO+fXkN73uHTWwdYngzyxfn1lUXS/+nM8g8b+3hVTZDPza8bYR2UPMm9uwb59t40roQr6kN8YHoNV9aHRo2I254t87XdKX7RncOV6mroPZMTXF4fGjXg4UQIGIIZUZsZUZvX++1q9uUdHmzP8EB7ht/3dvKtxY2VCtugwvcvrw9zaV2Ib+1J88Wdg3iyd9RcLADbECypCVbC4kFZlpv9db/1afXj+G3PAABTwxZva4txy8TYcRO0hRDc2BJlbszmz1d28+GNvfxgaRNCCIQQ3D6jljev6OLx7hy3tMZ4VW2Q3/WqWpr37UuzIV1iTsymLNXch68BDsWcjDFHXKM5uZwhx119wOQ1DeFKjq0rJTuzDqtT6sJ0xUCRJ/3fbtxStWQvrwtxXVPkhKJ9tZCNkZIneba/yFsnjlxPcaXk6d4C1zdH2JZT4e7vmRznU1sHeM+UOGvTJbpLLp+cm8QUgu+1Z/Ck5LPn1VVEbKDs8vEt/VxYG+S+xY0jRMaTkvev7+HJngI3tUT4u2k1x73Cf7A9w2e29mMbgre3xXjLxNiYogFfKZPCFh+eWcutrTHetfogH97QyxOXTDgiaMQQgvdOTVCSkq/sSrEjWz6h8SVsg4uSIS4a1sOto+DwdG+BRzqyfHbbAI92ZPnOksZjtqoZYk4swAen1/LJrf1sSJdZ4HfcXpAI0BxUa5y3tMZYGA/w/fZMpQL+gYJbCfbpL4+M/By66hythJVGcy5iCsGsmM2smM1bJqrnuooOL/QPVfpRwnbXtgGua4rwoek1Y3LNayEbI5szJYqe5MLakSWo9uQcsq5kcU2Q9SlVJLYxYOIBF9QEWZdSgQ6XJNX/rRoosqQmOCIA4A99RbKu5I6ZNUdYSs/1F3myp8AdM2v4q8mjt4Y5NJ4yn9zSz/K6EJ+bVzcuOUxtYYvrmsJ8Y0+anCuJWUe/PEz6KQUF75W7tyeELN7aGuOtrTF+3pXjQxt6+VlXjj9vOzLH62gsrvHXKAtORcgAEpaorNFZBjhSMuSOl6jHcKTl1V10sYW6ytRoTjtnTjWO49IctLihxeIGP3huS6bEY505vrM3zarBIo8vazluUQF9uThGdmZVSPnMwyyHPv9KvClgknXV/SGLLWQKHKmsfMt/zpFw+Lmt7J/Iw8aRu2PAf//ZY7RYcq7EA5oCqiLHePC7njzf3Z/hotrgUasBeFLyYHuGe7YPsMQPSDkZlD3J0715vrc/DRzqHTYWftmdA0Z+z70ll105h5lR9T7rUyWmhi2259Qa3aSwVTku2g4LcNmSKTMtYp9wg1GN5qRQxYfd1LDN0poAccugt+SNCEgbDW2RjZEhkao5rG5gjX+i7i27tAzllPkH0dZMmdkxGwmsHCyyLBliUU2Ahw5k6S66lZ5lFyeD2AK+tHOQryysH+G6vLI+xMSgyYc29PLhGbX86YToMZMP58Zs/qItxnf3Z/hjf5E3T4j65a5OTQHeITwpeb6/yHf2pfldb4HZUZvPz68/YpunegvcuzvF2lSJ5ckgX5hff8In+7yrcti6S67KX/NLcK0eLDHoqGTye86rG1N/JU9Kvr03zTf2pLmxJcJ0X8iklNy9bQAJ3NgSZXO6xLP9Rd43NcHDHRlqLYOFcZvPbR9gZtQakU+YcVTtyLdOjI7yqRrNqeUMqo94XEqeZGO6xOrBUqXmas6VTAqZfGz22LxKVSVkQohrgf9AFWH/lpTyntP12Ul/reVg0R0RGTgtYhEzBc/2FfjwzFosAc/05pkdtflBe4YHLmii1jb4j52D3L8kyNvbYvzoQIbbN/TyX4saCJkGLSG1tvTZbQO8d00Pn5yTrKyDRS2D+5c2ccfGXv55Sz9f3jXI9c0RrqwPs7QmeERAgxCCf5qd5OqGMN/co0pl3bs7RWvIZFkypCygeICZUesV5XVIKeksuqxOlXiuv8CTPQW6ii61tsEHp9fwl5PjFTfp/rzD4905Hj6QZU/eoTVk8tm5Sd40ITpCxA7Pa+ssunT4Vfj7yx4D/u1wV6QpYEbE5urGMFc3hLiiPnzcYJaSJ/n1wTzf2JNic6bMNY1hPjUnCajyPHdt6+d/unL83bQEtbbBbSu7SdoGbSGTe3cX+PvpNTzdV2R1qsS/zK4d8d6/6M5R9CTXjVIZX6M5F/H8c8ZOv4fi5kyJTRlVZWgo/6w1ZHJjS4SrGsIsT4YqZe+OR9WE3wshTGAr8DpgP/AicKuUcuPRtj/Z4ff78g6vfbaDO2fV8s7Daut9fEsfPz6Q5ScXtXDf3jSPdWb5hxkq/P7PJkZZWhPgHzf1c4Pf0+rx7hz/uLGPhYkAn5tXx9SIsgJ+2J7hs9sGcKXkppYob22NsjAeqBTrfbK3wMMHsvy+N48zFEYfV3lV58VVT7IZkUN5Y6BC9n/To/KxXvC7RoMyGltDKql5QtCkPmCSDBjETYOQKSoHkONJCp4k7Xj0ljy6iy77Cw47c07F7RkxBZfWhbimUVXksA3BhrSqRvK73gJr/XXCC2oC3NIa47qmCJaA/QWXVYNF1vrJlJtGyWtr9MeWtAxqbZOkbZAMGDQG1PgnjaGm4lDF/xUDRf7QV+D3vXlSjmRq2OLvpiV4gx95+WRvgc9vH2BHzuE9k+O8oTnM32/oo6Pg8oFpCb68K8WcmM1dc5O8fdVBGgImj1zYXPm+0o7HDc93Uhcw+PGrmqvqylhzxjPmg+n8hilybc+eUzmWUck6HrvzDrv8rhq7/C4du3LOiIvQxoDBHL/u6yK/RVXTscu5VX8emRDiEuATUspr/Md3Akgp7z7a9qcij+zmF7voK7v8bFnLiGi07qLLG1/opMY2+NrCBt6z5iAZV3JVfYhHO3O8qUV1Xf7KrhRLEgHuOq+OXbkyd27qI+9K3tYW452T4kwMWXQWHO7dneJ/OnPkPcmEoMnl9SEu8Ct6TI1Y5F3JCwNFXvSToQ9PbG4Jmkzzy09N8SvjTwpZtIYMDpY8NmfKbM2og6u9oCyf3pJbuSoaDVuoQJbWsMW0iFVp5Bk3DXbly2xIl1kzqKyUoWLH5ycCvLYhzNUNYQZdj9WDRV4aUHlhvb4Qhg1RqToyN2Yz3S+z1RAwTsjtWPIkfSVVnaSzoAR3T95hZ1bluw21qKmzDa6oD3F9syqA2lFweeJgnoc7MmzPOkwJW3xgeoL1qTL3709TaxtcURfisc4cs2M2H5lZyx0b+yhLyQNLmyruSE9KPrC+l9/05Pn+0qYRKQMazUngjBEyT0q6im5FqHZm/b85Z0S3+qEL5ulRm2kRi+kR9XdG1H45xQzOCiG7GbhWSvlu//FfAMuklP932DbvBdWBfvLkyRfs2XNyd+TzfuLz6xrD/PuCkWtZKwaK/NVq1Xn4o7Nq+NTWAbqLLhcngzzdV2R6xOKaxjDfb8+QcyVvmRjlxpYoDx3I8mhHFoDL6kNc2xjmyoYwQUPwy+4cT/YUeH6gUGnfMlRfcWpkqNK7RXPAQAgYLHt0FIYOLofd+fKIti+gIgVbgibNvhVWHzCosQxipsASAikkUqoaj1J9p3hSiVLBkww6kt6SqpTfnlc1FocE0EAFwyyuCTApZBIyDXbnHNalSyM6W08KmVxQG2RRIsjSmgCTwxY9/prXkBsxXfbIuGqht+Sp1jElT1KWSrAKrnou50oyzlC1/iOP5RpL1ZScHbOZF1elvSwh2ZRxWJMqsmKgyDY/YOP8RIBltUF6Sy6Pd+cpepILagJ0Fl32F9zKWuPXd6eoC5h8a1EDs2MqwtHxJJ/Y0s9DHVk+MrOWd00eW7SkRnMCHDtbeNj5b3a8+YItqbE31hwNV0r25x22+e7AbdkyO7PqIjg/zLqKmmKEUA0J19SwPeaGu2Pg3BCy4ZyqWovf2Zfm7m0DXN0Q5l/n1Y1oZ7BysMj716nyVG9vi7E5U+aZviJTwiZZV9JT8ljqV4R/tl81z1xeF+KyuiCdRWUVDLVdmRGxWFITZF7cZqYfqDF0QO3IldnjW1OHW1FBQ9ASNGkKmjQFDBKWgWmAlFD0JFn3UMHf3pJHf/n4lthwDKA+YNAQUC6+hKVckQJIOR57/Z5kQ/oZMVVB0YUJm8lhm7Ah6Cm77Moqa2lv3qGr6B5RTR/U2lfYEIRMQdAQ2EIQMAQBQyVkR0yDsCmImaq1TNgURPxtLQRCSDIudBYdDhRc9uZUhfzhY5sTtWkOmpQ8yapUif6yR9BQkVPdJYf+smRW1ObyuiC/7SmwO+/wOn89bWgRuqfkcseGXv7QX+Rvpib4wLSEdilqTgVjt8iapsq13btP6M0zfqHxdekS2/zuEjtyDsVhgjUhaDIjalcKjk+Pqr9Nr6Av4AlwVgjZuLsWh7h/X5p7tg/QEjT5+JwkVw6LjuspuXxmaz+/6M7TFDBYngzx3ECRzqJLU8Ag70rSrqQ5YDAxZLGv4NBTUq0RFsZspsdsXA86iy6bh7nDQJ14W0MmzUGLpqBJvS1UfoUEByi4yipJlb2KhdNddI+apyWAhGUQNyFiGQQMgS1U6oDwRUACnlRXZWUJBVeSczwG/VYuw7EElX5gDQGDqCmQQH/JY1f+SP940jaYEFRrX1HTwDYAKfCQOJ7E8S2vklTWTllKXAmuhLJU2xQ9Jc4F32IbjbgpSAZURf+gIXClJO2oK82S/29RU5C0DQbLHmlXYgpYnAhQHzB5aaBAb1kyJ2bzoek1vNqvUuBJyaMdWf5txyA51+NfZid5y2ENVjWak8hJEzJXSjZnyqwZLLEupcpH7cw5lQvKlqDJzKjNrKhV6a4xM2qPd3PNs0LILFSwx9VAOyrY421Syg1H2/5UN9ZcNVjkI5v62J1zuDgZ5P9OTfCq2kNtwV/sL/DFnYOsHCwRN2FePMiBosO+vIsplMtroOzhoRpeNgZNiq7yOw95mKMGtIVtZVUJcJEUXMi4Hik/gs8dZXwR/8Sc9JtghkyBJUCgBMaVSizKnqQkJWWPiutuyK0IyiqyhAr+CBqqCr5tCEyhjipXQsHzGCh5tBfdEa5MgbLe6myjIrh516O37NJ3FDNwqAfakKhaQmD4nyPE0MiFSn4UI49qJbqH5pRz5Ygu2MM/I2YJAkLgSWVFDn2HNZbB5LBF0IC9eYdu/wLjivoQt7bGuLwuhCEEZU/yy+4cX9+TYlvWYUkiwGfOqzsix1CjOcmMXciap8q1XbtHPJdxPH59UDXsfbbvUOBXnW2wyG/au8gvNn54mtEZQvULGYAQ4vXAv6PC7++TUt412ranWshAnfgfaM/wzd0pesseC+I2t/pReUORgy8NFHmgPcOvDuYoeSpSpz5g0l10K8nUEVMgJSN8zjHfTQZQ9JR4jZYXGDZUKaSQqdxvpgBDKGtq6MSe9yQ516N4ZA/Nl41AWTJRUxA0ldg6/mcNlDyco2wbNJQclf1q+s4JHH4C5doUvogZAgxU00+DQ0e5Emq1f44m9LZQFmHcMrCFoOCpiv5Zf+O4JbgkGeLK+hBXN4YrqRc7s2V+0pnjkY4s3SWXGRGLv51Ww+ubwtqVqDkdnICQTZNru3YBsDZV5L/3jTwHXV4XYnldiCU1QVpDZrUcv2eHkJ0Ip0PIhsi7Ho915vje/jTbsw5hQ3B1Y5jrmiJcWhckbBqkyh6/7lH9tp7rL1L0JAbQFFQn00HHIzXsrG6i1oFcKSvur6NhCbWdKQ6VafEAxxv9RD4cAdgGBMQhK2t4expVhkkJVEly1LWsofcJGQLLUAJacI/8bIEKVpH++E5EU4cEzETVajT8+Zr+uC3fLWoME7WKoPmiebRgkKRtMCNiMzNmMT8W4PxEgNkxVZEj63isHCzybH+Rp3rzbMs6GMCldSH+vC3GFfUhXblDczo5ISH7zd7tfGprP786mCdmCm7yiyMsTgSqRbgORwvZ6UBKycrBEj/pzPLL7jwDjkfQECxLBrmsLsQlySAzozZFT7JqsMQLA0VWDhRZny6NOMlG/QAHIXzXneuRH8NZ/3AxG0NlF+CQpTOUijXcspGoQJHjCSIoMRkSjyPGBoRNsA0DAXhISp5KPh7Le4+VoKEs2ajfBDRhGdTaBnV+gEp9wKQpYNISMmkLWdTYBlJKDpY89uTKbM2W2ZQusyFdYmu2jCOVBbe0JshrG8Nc0xSh+di5LhrNqWLM6rOgaZqsfeQZ0o7kr6fEefuk+Hivb50MtJCdbsqeyvX6XU+ep3pVtBuoIrSLa4KcH1e9wM6LB5gYNDhQ9NjqZ7nvzTt0FF06iw6dBfeoaz2HYwlGuBUF/lqX714syROzgMaKhbKyTGXKUfYDMI424qG1wHpbhf3X2mr9LmEpN1/UFIRNg5AfmTi0FmcdZoEZAkyU9WcJtW3QUG5LAZQlvgXmkXbUemLKUdGag2WPvrJqDNpddDlQcNhfcEcEi9RaBvPiNgsTAS70e73pKvaaM4AxC9mU+ily0k/+yLcXNzInFjj+P1QHWsjGm315hxUDRV4aKLIqVWRH9lCEUNgQTI5YtIUsJoZMWkMWzUGThoBB0jYJGoKy9Cj4EY8ZxyPjqBN1zo8gzLoeWUeScdVrGccjO3Tff+1UCFnEFNRaqtLGcIunwV8LrA8YRPxAk6JHpczUgC8qaccj63jk/ejDsqcuAhwp8XzxVX+VKA9/7Hh+BKO/Hlb0b8fTfVOoBe6moMnEkEVbyKQtrHJeZkQtWoJVs2agObcY80E5qW6KfGDb5jHVG60iRp1/VdVarGaGSim9aYIqJJt3PbZly2zJqCTDvTmHfXmH5/oLR13LAWWRRExBtGK9HMqlCvsutaRtMDF06PWwoYQkZIBpiEpwBP4FjOe7DaUfZg+HXIpwKKBi6L5EJUxLlNDk/fJVg2XJoKOSmVcOOhWrZ7DsHXNdLWYJYn6gypBVZflWmGmo9TABw9bvfKsMfxuhLLeh3LKQoay68DD3YtxSSd81tkHcEiSsE6sYotFUG4aAy+pCx9/wLEEL2TgRNg3OTwQ5PzGyjJGUkpQj6S4eqnKRctQtO8IKUzljeVeVZcr793Oe+nusvKpTgYFqeJm0Vbj99IjNhbWqNmKtbSirza+RWGup52KW0IKi0ZwCTL/L+bmCFrIzDCEENbagxjaYxcvPS3L9SL2cK8m7ynVXETt3KBR/WOknj4o7b+j4NwCrUlGDiqUTNw0i1kiLJ2pqUdJozhROXlWo6kAL2VmKKYRy21mgYgY1Gs25wvE6Kp9tnFuz1Wg0mnOAMbbxOmvQQqbRaDSaqkYLmUaj0WiqGi1kGo1Go6lqtJBpNBqNpqrRQqbRaDRnG+dYKowWMo1Go9FUNVrINBqNRlPVaCHTaDQaTVWjhUyj0Wg0VY0WMo1GoznbOLdiPbSQaTQajaa60UKm0Wg0Zx3nlkmmhUyj0Wg0VY0WMo1Go9FUNVrINBqN5mzj3PIsaiHTaDQaTXWjhUyj0Wg0VY0WMo1Go9FUNVrINBqN5izDSkbHewinFS1kGo1Gc5YhgvZ4D+G0ooVMo9FoNFWNFjKNRqPRVDVayDQajUZT1Wgh02g0Gk1Vo4VMo9FoNFWNFjKNRqPRVDVCSjneYzglCCEOAnvG4aMbgJ5x+NyTydkwB9DzONM4G+YxnnPokVJeO5YNhRD/O9ZtzwbOWiEbL4QQK6SUrxrvcbwSzoY5gJ7HmcbZMI+zYQ5nI9q1qNFoNJqqRguZRqPRaKoaLWQnn2+O9wBOAmfDHEDP40zjbJjH2TCHsw69RqbRaDSaqkZbZBqNRqOparSQaTQajaaq0UL2MhFCTBJCPCmE2CiE2CCE+ID/fJ0Q4gkhxDb/b3K8xzoWhBCmEGKVEOJn/uNpQojnhRDbhRA/FEIExnuMx0MIUSuEeFgIsVkIsUkIcUm17Q8hxAf942m9EOIHQohQNewLIcR9QohuIcT6Yc8d9bsXii/781krhFg6fiMfySjz+Jx/TK0VQjwqhKgd9tqd/jy2CCGuGZdBa7SQvQIc4HYp5TzgYuBvhRDzgI8Av5FSzgJ+4z+uBj4AbBr2+F+BL0kpZwL9wF+Ny6hOjP8A/ldKORdYhJpP1ewPIUQr8H7gVVLKBYAJ3EJ17IvvAIcn4I723V8HzPJv7wW+dprGOBa+w5HzeAJYIKU8H9gK3Ang/95vAeb7//NVIYR5+oaqGUIL2ctEStkhpVzp30+jTpqtwI3Af/ub/Tdw07gM8AQQQrQBbwC+5T8WwGuAh/1Nzvh5CCFqgCuAbwNIKUtSygGqb39YQFgIYQERoIMq2BdSyqeAvsOeHu27vxG4XyqeA2qFEBNOy0CPw9HmIaX8lZTS8R8+B7T5928EHpRSFqWUu4DtwEWnbbCaClrITgJCiKnAEuB5oFlK2eG/1Ak0j9e4ToB/B+4APP9xPTAw7Me7HyXSZzLTgIPA//NdpN8SQkSpov0hpWwHPg/sRQnYIPAS1bcvhhjtu28F9g3brprm9JfAL/z71TyPswotZK8QIUQM+DHw91LK1PDXpMptOKPzG4QQ1wPdUsqXxnssrxALWAp8TUq5BMhymBvxTN8f/hrSjShRnghEOdLNVZWc6d/9WBBCfAy1pPD98R6LZiRayF4BQggbJWLfl1I+4j/dNeQm8f92j9f4xsilwBuFELuBB1FurP9AuXssf5s2oH18hjdm9gP7pZTP+48fRglbNe2P1wK7pJQHpZRl4BHU/qm2fTHEaN99OzBp2HZn/JyEEO8Ergduk4eSb6tuHmcrWsheJv460reBTVLKLw576afAO/z77wB+crrHdiJIKe+UUrZJKaeiFq5/K6W8DXgSuNnfrBrm0QnsE0LM8Z+6GthIde2PvcDFQoiIf3wNzaGq9sUwRvvufwq83Y9evBgYHOaCPOMQQlyLcr2/UUqZG/bST4FbhBBBIcQ0VPDKC+MxxnMeKaW+vYwbcBnKVbIWWO3fXo9aX/oNsA34NVA33mM9gTm9GviZf3866ke5HXgICI73+MYw/sXACn+fPAYkq21/AJ8ENgPrge8CwWrYF8APUOt6ZZR1/FejffeAAO4FdgDrUFGa4z6HY8xjO2otbOh3/vVh23/Mn8cW4LrxHv+5etMlqjQajUZT1WjXokaj0WiqGi1kGo1Go6lqtJBpNBqNpqrRQqbRaDSaqkYLmUaj0WiqGi1kGs1xEELcJISQQoi54z0WjUZzJFrINJrjcyvwjP9Xo9GcYWgh02iOgV9L8zJUYuwt/nOGEOKrfo+qJ4QQjwshbvZfu0AI8XshxEtCiF+eKVXdNZqzGS1kGs2xuRHV42wr0CuEuAD4U2AqMA/4C+ASqNTe/Apws5TyAuA+4K7xGLRGcy5hHX8Tjeac5lZUEWVQRZVvRf1uHpJSekCnEOJJ//U5wALgCVUqERNV7kij0ZxCtJBpNKMghKhDdQNYKISQKGGSwKOj/QuwQUp5yWkaokajQbsWNZpjcTPwXSnlFCnlVCnlJGAXqoPwm/21smZUsWVQhWMbhRAVV6MQYv54DFyjOZfQQqbRjM6tHGl9/RhoQVVG3wh8D1iJakVSQonfvwoh1qAqpS8/baPVaM5RdPV7jeZlIISISSkzQoh6VIuVS6XqiabRaE4zeo1Mo3l5/EwIUQsEgE9rEdNoxg9tkWk0Go2mqtFrZBqNRqOparSQaTQajaaq0UKm0Wg0mqpGC5lGo9FoqhotZBqNRqOpav4/Jxy8jMcLpoMAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Personal = [\"Age\", \"Education\", \"Marital_Status\", \"NumWebVisitsMonth\", \"Total_Children\"]\n", "\n", "for i in Personal:\n", " plt.figure()\n", " sns.jointplot(x = data[i], y = data[\"Total_Expense\"], hue = data[\"result_cluster\"], \n", " kind = \"kde\", palette = [\"#d21262\", \"#26bde2\"])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "f3367825", "metadata": { "papermill": { "duration": 0.092221, "end_time": "2022-01-28T14:16:30.452702", "exception": false, "start_time": "2022-01-28T14:16:30.360481", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 114.510665, "end_time": "2022-01-28T14:16:31.661916", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:14:37.151251", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/399/86399246.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "05a44839", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:18:37.367578Z", "iopub.status.busy": "2022-01-28T14:18:37.366625Z", "iopub.status.idle": "2022-01-28T14:18:46.014585Z", "shell.execute_reply": "2022-01-28T14:18:46.013799Z", "shell.execute_reply.started": "2022-01-28T14:07:14.860620Z" }, "papermill": { "duration": 8.693886, "end_time": "2022-01-28T14:18:46.014801", "exception": false, "start_time": "2022-01-28T14:18:37.320915", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import plotly.express as px\n", "\n", "%matplotlib inline\n", "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay,roc_curve,RocCurveDisplay\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", "from xgboost import XGBClassifier\n", "\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "b4fb3f68", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.078031Z", "iopub.status.busy": "2022-01-28T14:18:46.077215Z", "iopub.status.idle": "2022-01-28T14:18:46.080114Z", "shell.execute_reply": "2022-01-28T14:18:46.079617Z", "shell.execute_reply.started": "2022-01-28T14:07:23.693041Z" }, "papermill": { "duration": 0.036489, "end_time": "2022-01-28T14:18:46.080299", "exception": false, "start_time": "2022-01-28T14:18:46.043810", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "np.random.seed(42)" ] }, { "cell_type": "code", "execution_count": 3, "id": "e111829d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.140051Z", "iopub.status.busy": "2022-01-28T14:18:46.139427Z", "iopub.status.idle": "2022-01-28T14:18:46.170232Z", "shell.execute_reply": "2022-01-28T14:18:46.170768Z", "shell.execute_reply.started": "2022-01-28T14:07:23.702724Z" }, "papermill": { "duration": 0.062408, "end_time": "2022-01-28T14:18:46.170986", "exception": false, "start_time": "2022-01-28T14:18:46.108578", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "2ee8fb6b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.230526Z", "iopub.status.busy": "2022-01-28T14:18:46.229897Z", "iopub.status.idle": "2022-01-28T14:18:46.237492Z", "shell.execute_reply": "2022-01-28T14:18:46.238018Z", "shell.execute_reply.started": "2022-01-28T14:07:23.804308Z" }, "papermill": { "duration": 0.038956, "end_time": "2022-01-28T14:18:46.238220", "exception": false, "start_time": "2022-01-28T14:18:46.199264", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n", " 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n", " 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n", " 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n", " 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n", " 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n", " 'perimeter_worst', 'area_worst', 'smoothness_worst',\n", " 'compactness_worst', 'concavity_worst', 'concave points_worst',\n", " 'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 5, "id": "9659419c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.299477Z", "iopub.status.busy": "2022-01-28T14:18:46.298812Z", "iopub.status.idle": "2022-01-28T14:18:46.309458Z", "shell.execute_reply": "2022-01-28T14:18:46.310630Z", "shell.execute_reply.started": "2022-01-28T14:07:23.812673Z" }, "papermill": { "duration": 0.043855, "end_time": "2022-01-28T14:18:46.310820", "exception": false, "start_time": "2022-01-28T14:18:46.266965", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "id 0\n", "diagnosis 0\n", "radius_mean 0\n", "texture_mean 0\n", "perimeter_mean 0\n", "area_mean 0\n", "smoothness_mean 0\n", "compactness_mean 0\n", "concavity_mean 0\n", "concave points_mean 0\n", "symmetry_mean 0\n", "fractal_dimension_mean 0\n", "radius_se 0\n", "texture_se 0\n", "perimeter_se 0\n", "area_se 0\n", "smoothness_se 0\n", "compactness_se 0\n", "concavity_se 0\n", "concave points_se 0\n", "symmetry_se 0\n", "fractal_dimension_se 0\n", "radius_worst 0\n", "texture_worst 0\n", "perimeter_worst 0\n", "area_worst 0\n", "smoothness_worst 0\n", "compactness_worst 0\n", "concavity_worst 0\n", "concave points_worst 0\n", "symmetry_worst 0\n", "fractal_dimension_worst 0\n", "Unnamed: 32 569\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna().sum()" ] }, { "cell_type": "code", "execution_count": 6, "id": "5c1b886c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.385474Z", "iopub.status.busy": "2022-01-28T14:18:46.384775Z", "iopub.status.idle": "2022-01-28T14:18:46.389170Z", "shell.execute_reply": "2022-01-28T14:18:46.389697Z", "shell.execute_reply.started": "2022-01-28T14:07:23.834647Z" }, "papermill": { "duration": 0.04387, "end_time": "2022-01-28T14:18:46.389896", "exception": false, "start_time": "2022-01-28T14:18:46.346026", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df.drop(columns = ['id','Unnamed: 32'],inplace = True)" ] }, { "cell_type": "code", "execution_count": 7, "id": "71b41de6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.451562Z", "iopub.status.busy": "2022-01-28T14:18:46.450945Z", "iopub.status.idle": "2022-01-28T14:18:46.459509Z", "shell.execute_reply": "2022-01-28T14:18:46.460095Z", "shell.execute_reply.started": "2022-01-28T14:07:23.847731Z" }, "papermill": { "duration": 0.041066, "end_time": "2022-01-28T14:18:46.460319", "exception": false, "start_time": "2022-01-28T14:18:46.419253", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array(['M', 'B'], dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['diagnosis'].unique()" ] }, { "cell_type": "code", "execution_count": 8, "id": "ce30ad9f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.522844Z", "iopub.status.busy": "2022-01-28T14:18:46.522132Z", "iopub.status.idle": "2022-01-28T14:18:46.527561Z", "shell.execute_reply": "2022-01-28T14:18:46.528075Z", "shell.execute_reply.started": "2022-01-28T14:07:23.867855Z" }, "papermill": { "duration": 0.039151, "end_time": "2022-01-28T14:18:46.528290", "exception": false, "start_time": "2022-01-28T14:18:46.489139", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df['diagnosis'] = df['diagnosis'].map({\"M\":1,\"B\":0})" ] }, { "cell_type": "markdown", "id": "c63d73cb", "metadata": { "papermill": { "duration": 0.029398, "end_time": "2022-01-28T14:18:46.588505", "exception": false, "start_time": "2022-01-28T14:18:46.559107", "status": "completed" }, "tags": [] }, "source": [ "Where the correlation is less than 0.5, we won't use these features" ] }, { "cell_type": "code", "execution_count": 9, "id": "f86f1cf0", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.650294Z", "iopub.status.busy": "2022-01-28T14:18:46.649639Z", "iopub.status.idle": "2022-01-28T14:18:46.849535Z", 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#T_87d01_row5_col22, #T_87d01_row12_col16, #T_87d01_row12_col17, #T_87d01_row19_col21, #T_87d01_row19_col24, #T_87d01_row19_col27, #T_87d01_row24_col17, #T_87d01_row25_col16, #T_87d01_row25_col19, #T_87d01_row28_col15, #T_87d01_row29_col15, #T_87d01_row30_col14 {\n", " background-color: #f0eaf4;\n", " color: #000000;\n", "}\n", "#T_87d01_row1_col18, #T_87d01_row6_col19, #T_87d01_row11_col5, #T_87d01_row13_col16, #T_87d01_row16_col12, #T_87d01_row18_col12, #T_87d01_row22_col29, #T_87d01_row25_col4 {\n", " background-color: #b9c6e0;\n", " color: #000000;\n", "}\n", "#T_87d01_row1_col19, #T_87d01_row3_col15, #T_87d01_row4_col10, #T_87d01_row4_col20, #T_87d01_row22_col9, #T_87d01_row23_col12, #T_87d01_row23_col19 {\n", " background-color: #fcf4fa;\n", " color: #000000;\n", "}\n", "#T_87d01_row1_col21, #T_87d01_row3_col21, #T_87d01_row3_col23, #T_87d01_row13_col11, #T_87d01_row21_col3, #T_87d01_row23_col1, #T_87d01_row23_col3 {\n", " background-color: #023e62;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row1_col22, #T_87d01_row2_col27, #T_87d01_row11_col26, #T_87d01_row14_col26, #T_87d01_row15_col5, #T_87d01_row16_col14, #T_87d01_row18_col9, #T_87d01_row28_col2 {\n", " background-color: #b0c2de;\n", " color: #000000;\n", "}\n", "#T_87d01_row1_col23, #T_87d01_row4_col21, #T_87d01_row11_col13, #T_87d01_row21_col4 {\n", " background-color: #023f64;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row1_col24, #T_87d01_row3_col24, #T_87d01_row14_col11 {\n", " background-color: #03456c;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row1_col25, #T_87d01_row7_col12, #T_87d01_row30_col2, #T_87d01_row30_col19 {\n", " background-color: #e0dded;\n", " color: #000000;\n", "}\n", "#T_87d01_row1_col27, #T_87d01_row5_col28, #T_87d01_row29_col28, #T_87d01_row29_col30 {\n", " background-color: #589ec8;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row1_col29, #T_87d01_row10_col12, #T_87d01_row11_col17, #T_87d01_row11_col20, #T_87d01_row13_col9, #T_87d01_row13_col22 {\n", " background-color: #cdd0e5;\n", " color: #000000;\n", "}\n", "#T_87d01_row1_col30, #T_87d01_row2_col17, #T_87d01_row4_col19, #T_87d01_row4_col30, #T_87d01_row5_col2, #T_87d01_row10_col0, #T_87d01_row15_col3, #T_87d01_row21_col10, #T_87d01_row22_col19, #T_87d01_row25_col12, #T_87d01_row30_col11, #T_87d01_row30_col13 {\n", " background-color: #f8f1f8;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col0, #T_87d01_row9_col8, #T_87d01_row12_col22, #T_87d01_row15_col20, #T_87d01_row29_col0 {\n", " background-color: #88b1d4;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col1, #T_87d01_row7_col25, #T_87d01_row10_col16, #T_87d01_row18_col21, #T_87d01_row24_col6, #T_87d01_row24_col26, #T_87d01_row26_col5, #T_87d01_row29_col6 {\n", " background-color: #79abd0;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row2_col3, #T_87d01_row2_col24, #T_87d01_row9_col25, #T_87d01_row11_col18, #T_87d01_row15_col12, #T_87d01_row18_col24 {\n", " background-color: #81aed2;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row2_col4, #T_87d01_row2_col23, #T_87d01_row5_col27, #T_87d01_row7_col20, #T_87d01_row9_col27, #T_87d01_row22_col24 {\n", " background-color: #80aed2;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row2_col6, #T_87d01_row11_col25, #T_87d01_row20_col3, #T_87d01_row20_col14 {\n", " background-color: #dbdaeb;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col7, #T_87d01_row30_col1, #T_87d01_row30_col18 {\n", " background-color: #d2d2e7;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col10, #T_87d01_row14_col29, #T_87d01_row20_col23, #T_87d01_row23_col30 {\n", " background-color: #e0deed;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col11, #T_87d01_row13_col20, #T_87d01_row22_col8, #T_87d01_row30_col21, #T_87d01_row30_col22 {\n", " background-color: #c8cde4;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col13, #T_87d01_row11_col22, #T_87d01_row22_col30, #T_87d01_row30_col24 {\n", " background-color: #d0d1e6;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col14, #T_87d01_row5_col18, #T_87d01_row11_col15 {\n", " background-color: #b8c6e0;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col15, #T_87d01_row19_col6, #T_87d01_row22_col11 {\n", " background-color: #dddbec;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col16, #T_87d01_row4_col12, #T_87d01_row4_col15, #T_87d01_row12_col0, #T_87d01_row19_col0, #T_87d01_row19_col25, #T_87d01_row22_col5, #T_87d01_row27_col12 {\n", " background-color: #f7f0f7;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col19, #T_87d01_row15_col9, #T_87d01_row20_col2, #T_87d01_row21_col9 {\n", " background-color: #ede7f2;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col20, #T_87d01_row3_col30, #T_87d01_row11_col30, #T_87d01_row15_col4, #T_87d01_row15_col6, #T_87d01_row19_col23 {\n", " background-color: #f1ebf5;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col22, #T_87d01_row7_col8, #T_87d01_row8_col28, #T_87d01_row22_col2 {\n", " background-color: #034d79;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row2_col25, #T_87d01_row3_col17, #T_87d01_row15_col30, #T_87d01_row19_col3, #T_87d01_row25_col2, #T_87d01_row25_col11, #T_87d01_row25_col18, #T_87d01_row26_col15 {\n", " background-color: #e8e4f0;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col26, #T_87d01_row4_col22, #T_87d01_row9_col4, #T_87d01_row9_col15, #T_87d01_row16_col0, #T_87d01_row17_col13, #T_87d01_row23_col18 {\n", " background-color: #b3c3de;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col28, #T_87d01_row4_col2, #T_87d01_row5_col1 {\n", " background-color: #a8bedc;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col29, #T_87d01_row5_col16, #T_87d01_row16_col22, #T_87d01_row17_col2, #T_87d01_row19_col14, #T_87d01_row20_col1, #T_87d01_row20_col4, #T_87d01_row25_col20 {\n", " background-color: #dad9ea;\n", " color: #000000;\n", "}\n", "#T_87d01_row2_col30, #T_87d01_row12_col18, #T_87d01_row19_col1, #T_87d01_row23_col9, #T_87d01_row29_col16 {\n", " background-color: #e5e1ef;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col0, #T_87d01_row13_col21 {\n", " background-color: #056ead;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row3_col2, #T_87d01_row8_col17, #T_87d01_row16_col9, #T_87d01_row25_col21, #T_87d01_row28_col17, #T_87d01_row30_col17 {\n", " background-color: #a5bddb;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col5, #T_87d01_row15_col16, #T_87d01_row17_col25, #T_87d01_row18_col2, #T_87d01_row24_col5, #T_87d01_row30_col4 {\n", " background-color: #d6d6e9;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col7, #T_87d01_row3_col11, #T_87d01_row6_col16, #T_87d01_row11_col8 {\n", " background-color: #1b7eb7;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row3_col8, #T_87d01_row28_col7, #T_87d01_row28_col23 {\n", " background-color: #045e94;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row3_col9, #T_87d01_row4_col17, #T_87d01_row12_col24, #T_87d01_row19_col30, #T_87d01_row24_col30 {\n", " background-color: #ede8f3;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col10, #T_87d01_row3_col12, #T_87d01_row3_col20, #T_87d01_row22_col20 {\n", " background-color: #faf2f8;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col14, #T_87d01_row8_col27, #T_87d01_row17_col16 {\n", " background-color: #056dab;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row3_col16, #T_87d01_row13_col30, #T_87d01_row22_col15 {\n", " background-color: #ece7f2;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col18, #T_87d01_row7_col22, #T_87d01_row9_col1, #T_87d01_row9_col21, #T_87d01_row13_col19, #T_87d01_row16_col24, #T_87d01_row17_col21, #T_87d01_row27_col2 {\n", " background-color: #afc1dd;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col19, #T_87d01_row15_col24, #T_87d01_row23_col20, #T_87d01_row24_col12, #T_87d01_row24_col15 {\n", " background-color: #f9f2f8;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col25, #T_87d01_row8_col15, #T_87d01_row12_col11, #T_87d01_row13_col29, #T_87d01_row14_col12, #T_87d01_row20_col13, #T_87d01_row20_col29, #T_87d01_row22_col6 {\n", " background-color: #d9d8ea;\n", " color: #000000;\n", "}\n", "#T_87d01_row3_col26, #T_87d01_row6_col14, #T_87d01_row17_col28, #T_87d01_row18_col23, #T_87d01_row19_col10, #T_87d01_row27_col10 {\n", " background-color: #73a9cf;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row3_col27, #T_87d01_row30_col29 {\n", " background-color: #4697c4;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row3_col29, #T_87d01_row10_col7 {\n", " background-color: #c5cce3;\n", " color: #000000;\n", "}\n", "#T_87d01_row4_col0 {\n", " background-color: #0d75b3;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col5, #T_87d01_row8_col19, #T_87d01_row11_col29, #T_87d01_row22_col10, #T_87d01_row25_col14 {\n", " background-color: #dcdaeb;\n", " color: #000000;\n", "}\n", "#T_87d01_row4_col6, #T_87d01_row11_col6, #T_87d01_row22_col12, #T_87d01_row30_col7 {\n", " background-color: #7eadd1;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col7, #T_87d01_row7_col17, #T_87d01_row18_col7, #T_87d01_row26_col8, #T_87d01_row26_col29 {\n", " background-color: #2987bc;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col11, #T_87d01_row9_col29 {\n", " background-color: #0c74b2;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col13, #T_87d01_row7_col0, #T_87d01_row8_col14, #T_87d01_row21_col11 {\n", " background-color: #1278b4;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col16, #T_87d01_row19_col2, #T_87d01_row19_col28, #T_87d01_row23_col10 {\n", " background-color: #f3edf5;\n", " color: #000000;\n", "}\n", "#T_87d01_row4_col18, #T_87d01_row5_col20, #T_87d01_row26_col13 {\n", " background-color: #bbc7e0;\n", " color: #000000;\n", "}\n", "#T_87d01_row4_col23, #T_87d01_row4_col24, #T_87d01_row23_col4, #T_87d01_row24_col4 {\n", " background-color: #034165;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col25, #T_87d01_row12_col14, #T_87d01_row14_col25, #T_87d01_row15_col17 {\n", " background-color: #dfddec;\n", " color: #000000;\n", "}\n", "#T_87d01_row4_col26, #T_87d01_row18_col0, #T_87d01_row20_col26, #T_87d01_row22_col4, #T_87d01_row25_col15 {\n", " background-color: #8bb2d4;\n", " color: #000000;\n", "}\n", "#T_87d01_row4_col27, #T_87d01_row5_col8, #T_87d01_row10_col6, #T_87d01_row15_col10, #T_87d01_row25_col6, #T_87d01_row26_col24 {\n", " background-color: #5ea0ca;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col28, #T_87d01_row7_col1, #T_87d01_row7_col21, #T_87d01_row13_col1, #T_87d01_row17_col18, #T_87d01_row24_col0, #T_87d01_row24_col11 {\n", " background-color: #056faf;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row4_col29, #T_87d01_row18_col29, #T_87d01_row19_col11, #T_87d01_row20_col8, #T_87d01_row22_col7 {\n", " background-color: #d2d3e7;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col0, #T_87d01_row21_col2, #T_87d01_row26_col18, #T_87d01_row29_col23 {\n", " background-color: #9cb9d9;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col4, #T_87d01_row5_col24, #T_87d01_row9_col20, #T_87d01_row9_col23, #T_87d01_row16_col11, #T_87d01_row25_col24, #T_87d01_row29_col3, #T_87d01_row29_col24 {\n", " background-color: #acc0dd;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col6, #T_87d01_row16_col7, #T_87d01_row23_col27, #T_87d01_row30_col25 {\n", " background-color: #2f8bbe;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row5_col9, #T_87d01_row13_col6, #T_87d01_row18_col1, #T_87d01_row19_col15, #T_87d01_row26_col4 {\n", " background-color: #67a4cc;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row5_col11, #T_87d01_row6_col22, #T_87d01_row9_col11, #T_87d01_row11_col12 {\n", " background-color: #bfc9e1;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col13, #T_87d01_row7_col15, #T_87d01_row9_col17, #T_87d01_row13_col10, #T_87d01_row18_col25 {\n", " background-color: #cacee5;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col14, #T_87d01_row12_col20, #T_87d01_row13_col5, #T_87d01_row13_col12, #T_87d01_row13_col15, #T_87d01_row14_col2 {\n", " background-color: #bcc7e1;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col15, #T_87d01_row9_col7, #T_87d01_row19_col29, #T_87d01_row25_col0, #T_87d01_row29_col19 {\n", " background-color: #84b0d3;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row5_col17, #T_87d01_row14_col9, #T_87d01_row14_col16, #T_87d01_row15_col11, #T_87d01_row17_col22, #T_87d01_row19_col4, #T_87d01_row22_col13, #T_87d01_row26_col19 {\n", " background-color: #e3e0ee;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col19, #T_87d01_row6_col2, #T_87d01_row13_col17, #T_87d01_row17_col9, #T_87d01_row23_col25, #T_87d01_row30_col23 {\n", " background-color: #c2cbe2;\n", " color: #000000;\n", "}\n", "#T_87d01_row5_col26, #T_87d01_row9_col26 {\n", " background-color: #6ba5cd;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row5_col30, #T_87d01_row6_col17, #T_87d01_row21_col26, #T_87d01_row30_col16 {\n", " background-color: #69a5cc;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row6_col0, #T_87d01_row6_col1, #T_87d01_row18_col6, #T_87d01_row25_col10, #T_87d01_row27_col17 {\n", " background-color: #3790c0;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row6_col4, #T_87d01_row6_col18, #T_87d01_row16_col30, 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color: #000000;\n", "}\n", "#T_87d01_row9_col19, #T_87d01_row12_col15, #T_87d01_row18_col4, #T_87d01_row30_col5 {\n", " background-color: #6fa7ce;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row10_col8, #T_87d01_row15_col14, #T_87d01_row27_col19, #T_87d01_row29_col14, #T_87d01_row29_col20 {\n", " background-color: #e7e3f0;\n", " color: #000000;\n", "}\n", "#T_87d01_row10_col9, #T_87d01_row20_col19, #T_87d01_row25_col8 {\n", " background-color: #8cb3d5;\n", " color: #000000;\n", "}\n", "#T_87d01_row10_col15, #T_87d01_row13_col18, #T_87d01_row18_col11, #T_87d01_row21_col6, #T_87d01_row28_col5 {\n", " background-color: #6da6cd;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row10_col17, #T_87d01_row16_col4, #T_87d01_row17_col1, #T_87d01_row28_col9 {\n", " background-color: #a2bcda;\n", " color: #000000;\n", "}\n", "#T_87d01_row10_col22, #T_87d01_row15_col7 {\n", " background-color: #fbf4f9;\n", " color: #000000;\n", "}\n", "#T_87d01_row10_col23, #T_87d01_row15_col27, #T_87d01_row23_col15 {\n", " background-color: #fef6fa;\n", " color: #000000;\n", "}\n", "#T_87d01_row10_col26, #T_87d01_row27_col18 {\n", " background-color: #71a8ce;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row10_col27, #T_87d01_row17_col3, #T_87d01_row17_col19, #T_87d01_row24_col2, #T_87d01_row27_col9 {\n", " background-color: #a1bbda;\n", " color: #000000;\n", "}\n", "#T_87d01_row10_col28, #T_87d01_row14_col5, #T_87d01_row15_col18, #T_87d01_row17_col5, #T_87d01_row22_col14, #T_87d01_row24_col25 {\n", " background-color: #cccfe5;\n", " color: #000000;\n", "}\n", "#T_87d01_row10_col29, #T_87d01_row19_col9, #T_87d01_row22_col27, #T_87d01_row26_col22, #T_87d01_row29_col5 {\n", " background-color: #99b8d8;\n", " color: #000000;\n", "}\n", "#T_87d01_row11_col7, #T_87d01_row21_col27, #T_87d01_row26_col25 {\n", " background-color: #4295c3;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row11_col9, #T_87d01_row11_col16, #T_87d01_row14_col15, #T_87d01_row16_col2, #T_87d01_row19_col18, #T_87d01_row28_col20, 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background-color: #d4d4e8;\n", " color: #000000;\n", "}\n", "#T_87d01_row14_col21, #T_87d01_row24_col8, #T_87d01_row27_col6, #T_87d01_row28_col0, #T_87d01_row28_col1, #T_87d01_row28_col6 {\n", " background-color: #0566a0;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row14_col22, #T_87d01_row23_col5 {\n", " background-color: #ced0e6;\n", " color: #000000;\n", "}\n", "#T_87d01_row14_col23 {\n", " background-color: #0567a1;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row14_col27 {\n", " background-color: #93b5d6;\n", " color: #000000;\n", "}\n", "#T_87d01_row14_col28, #T_87d01_row26_col10 {\n", " background-color: #4897c4;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row15_col1, #T_87d01_row20_col22 {\n", " background-color: #f5eef6;\n", " color: #000000;\n", "}\n", "#T_87d01_row15_col2, #T_87d01_row19_col8, #T_87d01_row25_col17 {\n", " background-color: #f4edf6;\n", " color: #000000;\n", "}\n", "#T_87d01_row15_col21, #T_87d01_row15_col28, #T_87d01_row15_col29, #T_87d01_row21_col12, #T_87d01_row24_col19, #T_87d01_row24_col20 {\n", " background-color: #fdf5fa;\n", " color: #000000;\n", "}\n", "#T_87d01_row15_col26, #T_87d01_row26_col12 {\n", " background-color: #faf3f9;\n", " color: #000000;\n", "}\n", "#T_87d01_row16_col6 {\n", " background-color: #0f76b3;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row16_col13, #T_87d01_row16_col23, #T_87d01_row18_col19, #T_87d01_row24_col22 {\n", " background-color: #9fbad9;\n", " color: #000000;\n", "}\n", "#T_87d01_row16_col18, #T_87d01_row23_col11, #T_87d01_row24_col13 {\n", " background-color: #1077b4;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row16_col20, #T_87d01_row21_col28, #T_87d01_row25_col5 {\n", " background-color: #05659f;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row17_col4, #T_87d01_row25_col9, #T_87d01_row27_col11 {\n", " background-color: #a4bcda;\n", " color: #000000;\n", "}\n", "#T_87d01_row17_col6, #T_87d01_row25_col27 {\n", " background-color: #5c9fc9;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row17_col14, #T_87d01_row21_col29, #T_87d01_row25_col1, #T_87d01_row26_col2 {\n", " background-color: #b5c4df;\n", " color: #000000;\n", "}\n", "#T_87d01_row17_col20 {\n", " background-color: #0872b1;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row17_col27, #T_87d01_row21_col13, #T_87d01_row27_col23 {\n", " background-color: #1e80b8;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row19_col5 {\n", " background-color: #d7d6e9;\n", " color: #000000;\n", "}\n", "#T_87d01_row19_col16 {\n", " background-color: #c1cae2;\n", " color: #000000;\n", "}\n", "#T_87d01_row20_col10 {\n", " background-color: #056dac;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row21_col1 {\n", " background-color: #023d60;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row21_col23, #T_87d01_row23_col21 {\n", " background-color: #02395a;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row21_col24, #T_87d01_row24_col21 {\n", " background-color: #023b5d;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row22_col26, #T_87d01_row26_col20 {\n", " background-color: #96b6d7;\n", " color: #000000;\n", "}\n", "#T_87d01_row23_col7, #T_87d01_row23_col13 {\n", " background-color: #157ab5;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row23_col8 {\n", " background-color: #045e93;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row23_col22, #T_87d01_row27_col22 {\n", " background-color: #97b7d7;\n", " color: #000000;\n", "}\n", "#T_87d01_row23_col24, #T_87d01_row24_col23 {\n", " background-color: #023c5f;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row23_col28 {\n", " background-color: #046097;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row24_col16 {\n", " background-color: #f5eff6;\n", " color: #000000;\n", "}\n", "#T_87d01_row25_col29, #T_87d01_row26_col1, #T_87d01_row27_col25 {\n", " background-color: #5a9ec9;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row25_col30 {\n", " background-color: #328dbf;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row26_col6 {\n", " background-color: #045d92;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row26_col27, #T_87d01_row27_col26 {\n", " background-color: #045382;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row27_col1 {\n", " background-color: #308cbe;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row27_col4 {\n", " background-color: #3991c1;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row27_col14 {\n", " background-color: #8eb3d5;\n", " color: #000000;\n", "}\n", "#T_87d01_row27_col28 {\n", " background-color: #045b8e;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row28_col8 {\n", " background-color: #03517e;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row28_col29 {\n", " background-color: #569dc8;\n", " color: #f1f1f1;\n", "}\n", "#T_87d01_row30_col6 {\n", " background-color: #2383ba;\n", " color: #f1f1f1;\n", "}\n", "</style>\n", "<table id=\"T_87d01_\">\n", " <thead>\n", " <tr>\n", " <th class=\"blank level0\" >&nbsp;</th>\n", " <th class=\"col_heading level0 col0\" >diagnosis</th>\n", " <th class=\"col_heading level0 col1\" >radius_mean</th>\n", " <th class=\"col_heading level0 col2\" >texture_mean</th>\n", " <th class=\"col_heading level0 col3\" >perimeter_mean</th>\n", " <th class=\"col_heading level0 col4\" >area_mean</th>\n", " <th class=\"col_heading level0 col5\" >smoothness_mean</th>\n", " <th class=\"col_heading level0 col6\" >compactness_mean</th>\n", " <th class=\"col_heading level0 col7\" >concavity_mean</th>\n", " <th class=\"col_heading level0 col8\" >concave points_mean</th>\n", " <th class=\"col_heading level0 col9\" >symmetry_mean</th>\n", " <th class=\"col_heading level0 col10\" >fractal_dimension_mean</th>\n", " <th class=\"col_heading level0 col11\" >radius_se</th>\n", " <th class=\"col_heading level0 col12\" >texture_se</th>\n", " <th class=\"col_heading level0 col13\" >perimeter_se</th>\n", " <th class=\"col_heading level0 col14\" >area_se</th>\n", " <th class=\"col_heading level0 col15\" >smoothness_se</th>\n", " <th class=\"col_heading level0 col16\" >compactness_se</th>\n", " <th class=\"col_heading level0 col17\" >concavity_se</th>\n", " <th class=\"col_heading level0 col18\" >concave points_se</th>\n", " <th class=\"col_heading level0 col19\" >symmetry_se</th>\n", " <th class=\"col_heading level0 col20\" >fractal_dimension_se</th>\n", " <th class=\"col_heading level0 col21\" >radius_worst</th>\n", " <th class=\"col_heading level0 col22\" >texture_worst</th>\n", " <th class=\"col_heading level0 col23\" >perimeter_worst</th>\n", " <th class=\"col_heading level0 col24\" >area_worst</th>\n", " <th class=\"col_heading level0 col25\" >smoothness_worst</th>\n", " <th class=\"col_heading level0 col26\" >compactness_worst</th>\n", " <th class=\"col_heading level0 col27\" >concavity_worst</th>\n", " <th class=\"col_heading level0 col28\" >concave points_worst</th>\n", " <th class=\"col_heading level0 col29\" >symmetry_worst</th>\n", " <th class=\"col_heading level0 col30\" >fractal_dimension_worst</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th id=\"T_87d01_level0_row0\" class=\"row_heading level0 row0\" >diagnosis</th>\n", " <td id=\"T_87d01_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n", " <td id=\"T_87d01_row0_col1\" class=\"data row0 col1\" >0.730029</td>\n", " <td id=\"T_87d01_row0_col2\" class=\"data row0 col2\" >0.415185</td>\n", " <td id=\"T_87d01_row0_col3\" class=\"data row0 col3\" >0.742636</td>\n", " <td id=\"T_87d01_row0_col4\" class=\"data row0 col4\" >0.708984</td>\n", " <td id=\"T_87d01_row0_col5\" class=\"data row0 col5\" >0.358560</td>\n", " <td id=\"T_87d01_row0_col6\" class=\"data row0 col6\" >0.596534</td>\n", " <td id=\"T_87d01_row0_col7\" class=\"data row0 col7\" >0.696360</td>\n", " <td id=\"T_87d01_row0_col8\" class=\"data row0 col8\" >0.776614</td>\n", " <td id=\"T_87d01_row0_col9\" class=\"data row0 col9\" >0.330499</td>\n", " <td id=\"T_87d01_row0_col10\" class=\"data row0 col10\" >-0.012838</td>\n", " <td id=\"T_87d01_row0_col11\" class=\"data row0 col11\" >0.567134</td>\n", " <td id=\"T_87d01_row0_col12\" class=\"data row0 col12\" >-0.008303</td>\n", " <td id=\"T_87d01_row0_col13\" class=\"data row0 col13\" >0.556141</td>\n", " <td id=\"T_87d01_row0_col14\" class=\"data row0 col14\" >0.548236</td>\n", " <td id=\"T_87d01_row0_col15\" class=\"data row0 col15\" >-0.067016</td>\n", " <td id=\"T_87d01_row0_col16\" class=\"data row0 col16\" >0.292999</td>\n", " <td id=\"T_87d01_row0_col17\" class=\"data row0 col17\" >0.253730</td>\n", " <td id=\"T_87d01_row0_col18\" class=\"data row0 col18\" >0.408042</td>\n", " <td id=\"T_87d01_row0_col19\" class=\"data row0 col19\" >-0.006522</td>\n", " <td id=\"T_87d01_row0_col20\" class=\"data row0 col20\" >0.077972</td>\n", " <td id=\"T_87d01_row0_col21\" class=\"data row0 col21\" >0.776454</td>\n", " <td id=\"T_87d01_row0_col22\" class=\"data row0 col22\" >0.456903</td>\n", " <td id=\"T_87d01_row0_col23\" class=\"data row0 col23\" >0.782914</td>\n", " <td id=\"T_87d01_row0_col24\" class=\"data row0 col24\" >0.733825</td>\n", " <td id=\"T_87d01_row0_col25\" class=\"data row0 col25\" >0.421465</td>\n", " <td id=\"T_87d01_row0_col26\" class=\"data row0 col26\" >0.590998</td>\n", " <td id=\"T_87d01_row0_col27\" class=\"data row0 col27\" >0.659610</td>\n", " <td id=\"T_87d01_row0_col28\" class=\"data row0 col28\" >0.793566</td>\n", " <td id=\"T_87d01_row0_col29\" class=\"data row0 col29\" >0.416294</td>\n", " <td id=\"T_87d01_row0_col30\" class=\"data row0 col30\" >0.323872</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row1\" class=\"row_heading level0 row1\" >radius_mean</th>\n", " <td id=\"T_87d01_row1_col0\" class=\"data row1 col0\" >0.730029</td>\n", " <td id=\"T_87d01_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n", " <td id=\"T_87d01_row1_col2\" class=\"data row1 col2\" >0.323782</td>\n", " <td id=\"T_87d01_row1_col3\" class=\"data row1 col3\" >0.997855</td>\n", " <td id=\"T_87d01_row1_col4\" class=\"data row1 col4\" >0.987357</td>\n", " <td id=\"T_87d01_row1_col5\" class=\"data row1 col5\" >0.170581</td>\n", " <td id=\"T_87d01_row1_col6\" class=\"data row1 col6\" >0.506124</td>\n", " <td id=\"T_87d01_row1_col7\" class=\"data row1 col7\" >0.676764</td>\n", " <td id=\"T_87d01_row1_col8\" class=\"data row1 col8\" >0.822529</td>\n", " <td id=\"T_87d01_row1_col9\" class=\"data row1 col9\" >0.147741</td>\n", " <td id=\"T_87d01_row1_col10\" class=\"data row1 col10\" >-0.311631</td>\n", " <td id=\"T_87d01_row1_col11\" class=\"data row1 col11\" >0.679090</td>\n", " <td id=\"T_87d01_row1_col12\" class=\"data row1 col12\" >-0.097317</td>\n", " <td id=\"T_87d01_row1_col13\" class=\"data row1 col13\" >0.674172</td>\n", " <td id=\"T_87d01_row1_col14\" class=\"data row1 col14\" >0.735864</td>\n", " <td id=\"T_87d01_row1_col15\" class=\"data row1 col15\" >-0.222600</td>\n", " <td id=\"T_87d01_row1_col16\" class=\"data row1 col16\" >0.206000</td>\n", " <td id=\"T_87d01_row1_col17\" class=\"data row1 col17\" >0.194204</td>\n", " <td id=\"T_87d01_row1_col18\" class=\"data row1 col18\" >0.376169</td>\n", " <td id=\"T_87d01_row1_col19\" class=\"data row1 col19\" >-0.104321</td>\n", " <td id=\"T_87d01_row1_col20\" class=\"data row1 col20\" >-0.042641</td>\n", " <td id=\"T_87d01_row1_col21\" class=\"data row1 col21\" >0.969539</td>\n", " <td id=\"T_87d01_row1_col22\" class=\"data row1 col22\" >0.297008</td>\n", " <td id=\"T_87d01_row1_col23\" class=\"data row1 col23\" >0.965137</td>\n", " <td id=\"T_87d01_row1_col24\" class=\"data row1 col24\" >0.941082</td>\n", " <td id=\"T_87d01_row1_col25\" class=\"data row1 col25\" >0.119616</td>\n", " <td id=\"T_87d01_row1_col26\" class=\"data row1 col26\" >0.413463</td>\n", " <td id=\"T_87d01_row1_col27\" class=\"data row1 col27\" >0.526911</td>\n", " <td id=\"T_87d01_row1_col28\" class=\"data row1 col28\" >0.744214</td>\n", " <td id=\"T_87d01_row1_col29\" class=\"data row1 col29\" >0.163953</td>\n", " <td id=\"T_87d01_row1_col30\" class=\"data row1 col30\" >0.007066</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row2\" class=\"row_heading level0 row2\" >texture_mean</th>\n", " <td id=\"T_87d01_row2_col0\" class=\"data row2 col0\" >0.415185</td>\n", " <td id=\"T_87d01_row2_col1\" class=\"data row2 col1\" >0.323782</td>\n", " <td id=\"T_87d01_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n", " <td id=\"T_87d01_row2_col3\" class=\"data row2 col3\" >0.329533</td>\n", " <td id=\"T_87d01_row2_col4\" class=\"data row2 col4\" >0.321086</td>\n", " <td id=\"T_87d01_row2_col5\" class=\"data row2 col5\" >-0.023389</td>\n", " <td id=\"T_87d01_row2_col6\" class=\"data row2 col6\" >0.236702</td>\n", " <td id=\"T_87d01_row2_col7\" class=\"data row2 col7\" >0.302418</td>\n", " <td id=\"T_87d01_row2_col8\" class=\"data row2 col8\" >0.293464</td>\n", " <td id=\"T_87d01_row2_col9\" class=\"data row2 col9\" >0.071401</td>\n", " <td id=\"T_87d01_row2_col10\" class=\"data row2 col10\" >-0.076437</td>\n", " <td id=\"T_87d01_row2_col11\" class=\"data row2 col11\" >0.275869</td>\n", " <td id=\"T_87d01_row2_col12\" class=\"data row2 col12\" >0.386358</td>\n", " <td id=\"T_87d01_row2_col13\" class=\"data row2 col13\" >0.281673</td>\n", " <td id=\"T_87d01_row2_col14\" class=\"data row2 col14\" >0.259845</td>\n", " <td id=\"T_87d01_row2_col15\" class=\"data row2 col15\" >0.006614</td>\n", " <td id=\"T_87d01_row2_col16\" class=\"data row2 col16\" >0.191975</td>\n", " <td id=\"T_87d01_row2_col17\" class=\"data row2 col17\" >0.143293</td>\n", " <td id=\"T_87d01_row2_col18\" class=\"data row2 col18\" >0.163851</td>\n", " <td id=\"T_87d01_row2_col19\" class=\"data row2 col19\" >0.009127</td>\n", " <td id=\"T_87d01_row2_col20\" class=\"data row2 col20\" >0.054458</td>\n", " <td id=\"T_87d01_row2_col21\" class=\"data row2 col21\" >0.352573</td>\n", " <td id=\"T_87d01_row2_col22\" class=\"data row2 col22\" >0.912045</td>\n", " <td id=\"T_87d01_row2_col23\" class=\"data row2 col23\" >0.358040</td>\n", " <td id=\"T_87d01_row2_col24\" class=\"data row2 col24\" >0.343546</td>\n", " <td id=\"T_87d01_row2_col25\" class=\"data row2 col25\" >0.077503</td>\n", " <td id=\"T_87d01_row2_col26\" class=\"data row2 col26\" >0.277830</td>\n", " <td id=\"T_87d01_row2_col27\" class=\"data row2 col27\" >0.301025</td>\n", " <td id=\"T_87d01_row2_col28\" class=\"data row2 col28\" >0.295316</td>\n", " <td id=\"T_87d01_row2_col29\" class=\"data row2 col29\" >0.105008</td>\n", " <td id=\"T_87d01_row2_col30\" class=\"data row2 col30\" >0.119205</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row3\" class=\"row_heading level0 row3\" >perimeter_mean</th>\n", " <td id=\"T_87d01_row3_col0\" class=\"data row3 col0\" >0.742636</td>\n", " <td id=\"T_87d01_row3_col1\" class=\"data row3 col1\" >0.997855</td>\n", " <td id=\"T_87d01_row3_col2\" class=\"data row3 col2\" >0.329533</td>\n", " <td id=\"T_87d01_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n", " <td id=\"T_87d01_row3_col4\" class=\"data row3 col4\" >0.986507</td>\n", " <td id=\"T_87d01_row3_col5\" class=\"data row3 col5\" >0.207278</td>\n", " <td id=\"T_87d01_row3_col6\" class=\"data row3 col6\" >0.556936</td>\n", " <td id=\"T_87d01_row3_col7\" class=\"data row3 col7\" >0.716136</td>\n", " <td id=\"T_87d01_row3_col8\" class=\"data row3 col8\" >0.850977</td>\n", " <td id=\"T_87d01_row3_col9\" class=\"data row3 col9\" >0.183027</td>\n", " <td id=\"T_87d01_row3_col10\" class=\"data row3 col10\" >-0.261477</td>\n", " <td id=\"T_87d01_row3_col11\" class=\"data row3 col11\" >0.691765</td>\n", " <td id=\"T_87d01_row3_col12\" class=\"data row3 col12\" >-0.086761</td>\n", " <td id=\"T_87d01_row3_col13\" class=\"data row3 col13\" >0.693135</td>\n", " <td id=\"T_87d01_row3_col14\" class=\"data row3 col14\" >0.744983</td>\n", " <td id=\"T_87d01_row3_col15\" class=\"data row3 col15\" >-0.202694</td>\n", " <td id=\"T_87d01_row3_col16\" class=\"data row3 col16\" >0.250744</td>\n", " <td id=\"T_87d01_row3_col17\" class=\"data row3 col17\" >0.228082</td>\n", " <td id=\"T_87d01_row3_col18\" class=\"data row3 col18\" >0.407217</td>\n", " <td id=\"T_87d01_row3_col19\" class=\"data row3 col19\" >-0.081629</td>\n", " <td id=\"T_87d01_row3_col20\" class=\"data row3 col20\" >-0.005523</td>\n", " <td id=\"T_87d01_row3_col21\" class=\"data row3 col21\" >0.969476</td>\n", " <td id=\"T_87d01_row3_col22\" class=\"data row3 col22\" >0.303038</td>\n", " <td id=\"T_87d01_row3_col23\" class=\"data row3 col23\" >0.970387</td>\n", " <td id=\"T_87d01_row3_col24\" class=\"data row3 col24\" >0.941550</td>\n", " <td id=\"T_87d01_row3_col25\" class=\"data row3 col25\" >0.150549</td>\n", " <td id=\"T_87d01_row3_col26\" class=\"data row3 col26\" >0.455774</td>\n", " <td id=\"T_87d01_row3_col27\" class=\"data row3 col27\" >0.563879</td>\n", " <td id=\"T_87d01_row3_col28\" class=\"data row3 col28\" >0.771241</td>\n", " <td id=\"T_87d01_row3_col29\" class=\"data row3 col29\" >0.189115</td>\n", " <td id=\"T_87d01_row3_col30\" class=\"data row3 col30\" >0.051019</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row4\" class=\"row_heading level0 row4\" >area_mean</th>\n", " <td id=\"T_87d01_row4_col0\" class=\"data row4 col0\" >0.708984</td>\n", " <td id=\"T_87d01_row4_col1\" class=\"data row4 col1\" >0.987357</td>\n", " <td id=\"T_87d01_row4_col2\" class=\"data row4 col2\" >0.321086</td>\n", " <td id=\"T_87d01_row4_col3\" class=\"data row4 col3\" >0.986507</td>\n", " <td id=\"T_87d01_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n", " <td id=\"T_87d01_row4_col5\" class=\"data row4 col5\" >0.177028</td>\n", " <td id=\"T_87d01_row4_col6\" class=\"data row4 col6\" >0.498502</td>\n", " <td id=\"T_87d01_row4_col7\" class=\"data row4 col7\" >0.685983</td>\n", " <td id=\"T_87d01_row4_col8\" class=\"data row4 col8\" >0.823269</td>\n", " <td id=\"T_87d01_row4_col9\" class=\"data row4 col9\" >0.151293</td>\n", " <td id=\"T_87d01_row4_col10\" class=\"data row4 col10\" >-0.283110</td>\n", " <td id=\"T_87d01_row4_col11\" class=\"data row4 col11\" >0.732562</td>\n", " <td id=\"T_87d01_row4_col12\" class=\"data row4 col12\" >-0.066280</td>\n", " <td id=\"T_87d01_row4_col13\" class=\"data row4 col13\" >0.726628</td>\n", " <td id=\"T_87d01_row4_col14\" class=\"data row4 col14\" >0.800086</td>\n", " <td id=\"T_87d01_row4_col15\" class=\"data row4 col15\" >-0.166777</td>\n", " <td id=\"T_87d01_row4_col16\" class=\"data row4 col16\" >0.212583</td>\n", " <td id=\"T_87d01_row4_col17\" class=\"data row4 col17\" >0.207660</td>\n", " <td id=\"T_87d01_row4_col18\" class=\"data row4 col18\" >0.372320</td>\n", " <td id=\"T_87d01_row4_col19\" class=\"data row4 col19\" >-0.072497</td>\n", " <td id=\"T_87d01_row4_col20\" class=\"data row4 col20\" >-0.019887</td>\n", " <td id=\"T_87d01_row4_col21\" class=\"data row4 col21\" >0.962746</td>\n", " <td id=\"T_87d01_row4_col22\" class=\"data row4 col22\" >0.287489</td>\n", " <td id=\"T_87d01_row4_col23\" class=\"data row4 col23\" >0.959120</td>\n", " <td id=\"T_87d01_row4_col24\" class=\"data row4 col24\" >0.959213</td>\n", " <td id=\"T_87d01_row4_col25\" class=\"data row4 col25\" >0.123523</td>\n", " <td id=\"T_87d01_row4_col26\" class=\"data row4 col26\" >0.390410</td>\n", " <td id=\"T_87d01_row4_col27\" class=\"data row4 col27\" >0.512606</td>\n", " <td id=\"T_87d01_row4_col28\" class=\"data row4 col28\" >0.722017</td>\n", " <td id=\"T_87d01_row4_col29\" class=\"data row4 col29\" >0.143570</td>\n", " <td id=\"T_87d01_row4_col30\" class=\"data row4 col30\" >0.003738</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row5\" class=\"row_heading level0 row5\" >smoothness_mean</th>\n", " <td id=\"T_87d01_row5_col0\" class=\"data row5 col0\" >0.358560</td>\n", " <td id=\"T_87d01_row5_col1\" class=\"data row5 col1\" >0.170581</td>\n", " <td id=\"T_87d01_row5_col2\" class=\"data row5 col2\" >-0.023389</td>\n", " <td id=\"T_87d01_row5_col3\" class=\"data row5 col3\" >0.207278</td>\n", " <td id=\"T_87d01_row5_col4\" class=\"data row5 col4\" >0.177028</td>\n", " <td id=\"T_87d01_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n", " <td id=\"T_87d01_row5_col6\" class=\"data row5 col6\" >0.659123</td>\n", " <td id=\"T_87d01_row5_col7\" class=\"data row5 col7\" >0.521984</td>\n", " <td id=\"T_87d01_row5_col8\" class=\"data row5 col8\" >0.553695</td>\n", " <td id=\"T_87d01_row5_col9\" class=\"data row5 col9\" >0.557775</td>\n", " <td id=\"T_87d01_row5_col10\" class=\"data row5 col10\" >0.584792</td>\n", " <td id=\"T_87d01_row5_col11\" class=\"data row5 col11\" >0.301467</td>\n", " <td id=\"T_87d01_row5_col12\" class=\"data row5 col12\" >0.068406</td>\n", " <td id=\"T_87d01_row5_col13\" class=\"data row5 col13\" >0.296092</td>\n", " <td id=\"T_87d01_row5_col14\" class=\"data row5 col14\" >0.246552</td>\n", " <td id=\"T_87d01_row5_col15\" class=\"data row5 col15\" >0.332375</td>\n", " <td id=\"T_87d01_row5_col16\" class=\"data row5 col16\" >0.318943</td>\n", " <td id=\"T_87d01_row5_col17\" class=\"data row5 col17\" >0.248396</td>\n", " <td id=\"T_87d01_row5_col18\" class=\"data row5 col18\" >0.380676</td>\n", " <td id=\"T_87d01_row5_col19\" class=\"data row5 col19\" >0.200774</td>\n", " <td id=\"T_87d01_row5_col20\" class=\"data row5 col20\" >0.283607</td>\n", " <td id=\"T_87d01_row5_col21\" class=\"data row5 col21\" >0.213120</td>\n", " <td id=\"T_87d01_row5_col22\" class=\"data row5 col22\" >0.036072</td>\n", " <td id=\"T_87d01_row5_col23\" class=\"data row5 col23\" >0.238853</td>\n", " <td id=\"T_87d01_row5_col24\" class=\"data row5 col24\" >0.206718</td>\n", " <td id=\"T_87d01_row5_col25\" class=\"data row5 col25\" >0.805324</td>\n", " <td id=\"T_87d01_row5_col26\" class=\"data row5 col26\" >0.472468</td>\n", " <td id=\"T_87d01_row5_col27\" class=\"data row5 col27\" >0.434926</td>\n", " <td id=\"T_87d01_row5_col28\" class=\"data row5 col28\" >0.503053</td>\n", " <td id=\"T_87d01_row5_col29\" class=\"data row5 col29\" >0.394309</td>\n", " <td id=\"T_87d01_row5_col30\" class=\"data row5 col30\" >0.499316</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row6\" class=\"row_heading level0 row6\" >compactness_mean</th>\n", " <td id=\"T_87d01_row6_col0\" class=\"data row6 col0\" >0.596534</td>\n", " <td id=\"T_87d01_row6_col1\" class=\"data row6 col1\" >0.506124</td>\n", " <td id=\"T_87d01_row6_col2\" class=\"data row6 col2\" >0.236702</td>\n", " <td id=\"T_87d01_row6_col3\" class=\"data row6 col3\" >0.556936</td>\n", " <td id=\"T_87d01_row6_col4\" class=\"data row6 col4\" >0.498502</td>\n", " <td id=\"T_87d01_row6_col5\" class=\"data row6 col5\" >0.659123</td>\n", " <td id=\"T_87d01_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n", " <td id=\"T_87d01_row6_col7\" class=\"data row6 col7\" >0.883121</td>\n", " <td id=\"T_87d01_row6_col8\" class=\"data row6 col8\" >0.831135</td>\n", " <td id=\"T_87d01_row6_col9\" class=\"data row6 col9\" >0.602641</td>\n", " <td id=\"T_87d01_row6_col10\" class=\"data row6 col10\" >0.565369</td>\n", " <td id=\"T_87d01_row6_col11\" class=\"data row6 col11\" >0.497473</td>\n", " <td id=\"T_87d01_row6_col12\" class=\"data row6 col12\" >0.046205</td>\n", " <td id=\"T_87d01_row6_col13\" class=\"data row6 col13\" >0.548905</td>\n", " <td id=\"T_87d01_row6_col14\" class=\"data row6 col14\" >0.455653</td>\n", " <td id=\"T_87d01_row6_col15\" class=\"data row6 col15\" >0.135299</td>\n", " <td id=\"T_87d01_row6_col16\" class=\"data row6 col16\" >0.738722</td>\n", " <td id=\"T_87d01_row6_col17\" class=\"data row6 col17\" >0.570517</td>\n", " <td id=\"T_87d01_row6_col18\" class=\"data row6 col18\" >0.642262</td>\n", " <td id=\"T_87d01_row6_col19\" class=\"data row6 col19\" >0.229977</td>\n", " <td id=\"T_87d01_row6_col20\" class=\"data row6 col20\" >0.507318</td>\n", " <td id=\"T_87d01_row6_col21\" class=\"data row6 col21\" >0.535315</td>\n", " <td id=\"T_87d01_row6_col22\" class=\"data row6 col22\" >0.248133</td>\n", " <td id=\"T_87d01_row6_col23\" class=\"data row6 col23\" >0.590210</td>\n", " <td id=\"T_87d01_row6_col24\" class=\"data row6 col24\" >0.509604</td>\n", " <td id=\"T_87d01_row6_col25\" class=\"data row6 col25\" >0.565541</td>\n", " <td id=\"T_87d01_row6_col26\" class=\"data row6 col26\" >0.865809</td>\n", " <td id=\"T_87d01_row6_col27\" class=\"data row6 col27\" >0.816275</td>\n", " <td id=\"T_87d01_row6_col28\" class=\"data row6 col28\" >0.815573</td>\n", " <td id=\"T_87d01_row6_col29\" class=\"data row6 col29\" >0.510223</td>\n", " <td id=\"T_87d01_row6_col30\" class=\"data row6 col30\" >0.687382</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row7\" class=\"row_heading level0 row7\" >concavity_mean</th>\n", " <td id=\"T_87d01_row7_col0\" class=\"data row7 col0\" >0.696360</td>\n", " <td id=\"T_87d01_row7_col1\" class=\"data row7 col1\" >0.676764</td>\n", " <td id=\"T_87d01_row7_col2\" class=\"data row7 col2\" >0.302418</td>\n", " <td id=\"T_87d01_row7_col3\" class=\"data row7 col3\" >0.716136</td>\n", " <td id=\"T_87d01_row7_col4\" class=\"data row7 col4\" >0.685983</td>\n", " <td id=\"T_87d01_row7_col5\" class=\"data row7 col5\" >0.521984</td>\n", " <td id=\"T_87d01_row7_col6\" class=\"data row7 col6\" >0.883121</td>\n", " <td id=\"T_87d01_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n", " <td id=\"T_87d01_row7_col8\" class=\"data row7 col8\" >0.921391</td>\n", " <td id=\"T_87d01_row7_col9\" class=\"data row7 col9\" >0.500667</td>\n", " <td id=\"T_87d01_row7_col10\" class=\"data row7 col10\" >0.336783</td>\n", " <td id=\"T_87d01_row7_col11\" class=\"data row7 col11\" >0.631925</td>\n", " <td id=\"T_87d01_row7_col12\" class=\"data row7 col12\" >0.076218</td>\n", " <td id=\"T_87d01_row7_col13\" class=\"data row7 col13\" >0.660391</td>\n", " <td id=\"T_87d01_row7_col14\" class=\"data row7 col14\" >0.617427</td>\n", " <td id=\"T_87d01_row7_col15\" class=\"data row7 col15\" >0.098564</td>\n", " <td id=\"T_87d01_row7_col16\" class=\"data row7 col16\" >0.670279</td>\n", " <td id=\"T_87d01_row7_col17\" class=\"data row7 col17\" >0.691270</td>\n", " <td id=\"T_87d01_row7_col18\" class=\"data row7 col18\" >0.683260</td>\n", " <td id=\"T_87d01_row7_col19\" class=\"data row7 col19\" >0.178009</td>\n", " <td id=\"T_87d01_row7_col20\" class=\"data row7 col20\" >0.449301</td>\n", " <td id=\"T_87d01_row7_col21\" class=\"data row7 col21\" >0.688236</td>\n", " <td id=\"T_87d01_row7_col22\" class=\"data row7 col22\" >0.299879</td>\n", " <td id=\"T_87d01_row7_col23\" class=\"data row7 col23\" >0.729565</td>\n", " <td id=\"T_87d01_row7_col24\" class=\"data row7 col24\" >0.675987</td>\n", " <td id=\"T_87d01_row7_col25\" class=\"data row7 col25\" >0.448822</td>\n", " <td id=\"T_87d01_row7_col26\" class=\"data row7 col26\" >0.754968</td>\n", " <td id=\"T_87d01_row7_col27\" class=\"data row7 col27\" >0.884103</td>\n", " <td id=\"T_87d01_row7_col28\" class=\"data row7 col28\" >0.861323</td>\n", " <td id=\"T_87d01_row7_col29\" class=\"data row7 col29\" >0.409464</td>\n", " <td id=\"T_87d01_row7_col30\" class=\"data row7 col30\" >0.514930</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row8\" class=\"row_heading level0 row8\" >concave points_mean</th>\n", " <td id=\"T_87d01_row8_col0\" class=\"data row8 col0\" >0.776614</td>\n", " <td id=\"T_87d01_row8_col1\" class=\"data row8 col1\" >0.822529</td>\n", " <td id=\"T_87d01_row8_col2\" class=\"data row8 col2\" >0.293464</td>\n", " <td id=\"T_87d01_row8_col3\" class=\"data row8 col3\" >0.850977</td>\n", " <td id=\"T_87d01_row8_col4\" class=\"data row8 col4\" >0.823269</td>\n", " <td id=\"T_87d01_row8_col5\" class=\"data row8 col5\" >0.553695</td>\n", " <td id=\"T_87d01_row8_col6\" class=\"data row8 col6\" >0.831135</td>\n", " <td id=\"T_87d01_row8_col7\" class=\"data row8 col7\" >0.921391</td>\n", " <td id=\"T_87d01_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n", " <td id=\"T_87d01_row8_col9\" class=\"data row8 col9\" >0.462497</td>\n", " <td id=\"T_87d01_row8_col10\" class=\"data row8 col10\" >0.166917</td>\n", " <td id=\"T_87d01_row8_col11\" class=\"data row8 col11\" >0.698050</td>\n", " <td id=\"T_87d01_row8_col12\" class=\"data row8 col12\" >0.021480</td>\n", " <td id=\"T_87d01_row8_col13\" class=\"data row8 col13\" >0.710650</td>\n", " <td id=\"T_87d01_row8_col14\" class=\"data row8 col14\" >0.690299</td>\n", " <td id=\"T_87d01_row8_col15\" class=\"data row8 col15\" >0.027653</td>\n", " <td id=\"T_87d01_row8_col16\" class=\"data row8 col16\" >0.490424</td>\n", " <td id=\"T_87d01_row8_col17\" class=\"data row8 col17\" >0.439167</td>\n", " <td id=\"T_87d01_row8_col18\" class=\"data row8 col18\" >0.615634</td>\n", " <td id=\"T_87d01_row8_col19\" class=\"data row8 col19\" >0.095351</td>\n", " <td id=\"T_87d01_row8_col20\" class=\"data row8 col20\" >0.257584</td>\n", " <td id=\"T_87d01_row8_col21\" class=\"data row8 col21\" >0.830318</td>\n", " <td id=\"T_87d01_row8_col22\" class=\"data row8 col22\" >0.292752</td>\n", " <td id=\"T_87d01_row8_col23\" class=\"data row8 col23\" >0.855923</td>\n", " <td id=\"T_87d01_row8_col24\" class=\"data row8 col24\" >0.809630</td>\n", " <td id=\"T_87d01_row8_col25\" class=\"data row8 col25\" >0.452753</td>\n", " <td id=\"T_87d01_row8_col26\" class=\"data row8 col26\" >0.667454</td>\n", " <td id=\"T_87d01_row8_col27\" class=\"data row8 col27\" >0.752399</td>\n", " <td id=\"T_87d01_row8_col28\" class=\"data row8 col28\" >0.910155</td>\n", " <td id=\"T_87d01_row8_col29\" class=\"data row8 col29\" >0.375744</td>\n", " <td id=\"T_87d01_row8_col30\" class=\"data row8 col30\" >0.368661</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row9\" class=\"row_heading level0 row9\" >symmetry_mean</th>\n", " <td id=\"T_87d01_row9_col0\" class=\"data row9 col0\" >0.330499</td>\n", " <td id=\"T_87d01_row9_col1\" class=\"data row9 col1\" >0.147741</td>\n", " <td id=\"T_87d01_row9_col2\" class=\"data row9 col2\" >0.071401</td>\n", " <td id=\"T_87d01_row9_col3\" class=\"data row9 col3\" >0.183027</td>\n", " <td id=\"T_87d01_row9_col4\" class=\"data row9 col4\" >0.151293</td>\n", " <td id=\"T_87d01_row9_col5\" class=\"data row9 col5\" >0.557775</td>\n", " <td id=\"T_87d01_row9_col6\" class=\"data row9 col6\" >0.602641</td>\n", " <td id=\"T_87d01_row9_col7\" class=\"data row9 col7\" >0.500667</td>\n", " <td id=\"T_87d01_row9_col8\" class=\"data row9 col8\" >0.462497</td>\n", " <td id=\"T_87d01_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n", " <td id=\"T_87d01_row9_col10\" class=\"data row9 col10\" >0.479921</td>\n", " <td id=\"T_87d01_row9_col11\" class=\"data row9 col11\" >0.303379</td>\n", " <td id=\"T_87d01_row9_col12\" class=\"data row9 col12\" >0.128053</td>\n", " <td id=\"T_87d01_row9_col13\" class=\"data row9 col13\" >0.313893</td>\n", " <td id=\"T_87d01_row9_col14\" class=\"data row9 col14\" >0.223970</td>\n", " <td id=\"T_87d01_row9_col15\" class=\"data row9 col15\" >0.187321</td>\n", " <td id=\"T_87d01_row9_col16\" class=\"data row9 col16\" >0.421659</td>\n", " <td id=\"T_87d01_row9_col17\" class=\"data row9 col17\" >0.342627</td>\n", " <td id=\"T_87d01_row9_col18\" class=\"data row9 col18\" >0.393298</td>\n", " <td id=\"T_87d01_row9_col19\" class=\"data row9 col19\" >0.449137</td>\n", " <td id=\"T_87d01_row9_col20\" class=\"data row9 col20\" >0.331786</td>\n", " <td id=\"T_87d01_row9_col21\" class=\"data row9 col21\" >0.185728</td>\n", " <td id=\"T_87d01_row9_col22\" class=\"data row9 col22\" >0.090651</td>\n", " <td id=\"T_87d01_row9_col23\" class=\"data row9 col23\" >0.219169</td>\n", " <td id=\"T_87d01_row9_col24\" class=\"data row9 col24\" >0.177193</td>\n", " <td id=\"T_87d01_row9_col25\" class=\"data row9 col25\" >0.426675</td>\n", " <td id=\"T_87d01_row9_col26\" class=\"data row9 col26\" >0.473200</td>\n", " <td id=\"T_87d01_row9_col27\" class=\"data row9 col27\" >0.433721</td>\n", " <td id=\"T_87d01_row9_col28\" class=\"data row9 col28\" >0.430297</td>\n", " <td id=\"T_87d01_row9_col29\" class=\"data row9 col29\" >0.699826</td>\n", " <td id=\"T_87d01_row9_col30\" class=\"data row9 col30\" >0.438413</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row10\" class=\"row_heading level0 row10\" >fractal_dimension_mean</th>\n", " <td id=\"T_87d01_row10_col0\" class=\"data row10 col0\" >-0.012838</td>\n", " <td id=\"T_87d01_row10_col1\" class=\"data row10 col1\" >-0.311631</td>\n", " <td id=\"T_87d01_row10_col2\" class=\"data row10 col2\" >-0.076437</td>\n", " <td id=\"T_87d01_row10_col3\" class=\"data row10 col3\" >-0.261477</td>\n", " <td id=\"T_87d01_row10_col4\" class=\"data row10 col4\" >-0.283110</td>\n", " <td id=\"T_87d01_row10_col5\" class=\"data row10 col5\" >0.584792</td>\n", " <td id=\"T_87d01_row10_col6\" class=\"data row10 col6\" >0.565369</td>\n", " <td id=\"T_87d01_row10_col7\" class=\"data row10 col7\" >0.336783</td>\n", " <td id=\"T_87d01_row10_col8\" class=\"data row10 col8\" >0.166917</td>\n", " <td id=\"T_87d01_row10_col9\" class=\"data row10 col9\" >0.479921</td>\n", " <td id=\"T_87d01_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n", " <td id=\"T_87d01_row10_col11\" class=\"data row10 col11\" >0.000111</td>\n", " <td id=\"T_87d01_row10_col12\" class=\"data row10 col12\" >0.164174</td>\n", " <td id=\"T_87d01_row10_col13\" class=\"data row10 col13\" >0.039830</td>\n", " <td id=\"T_87d01_row10_col14\" class=\"data row10 col14\" >-0.090170</td>\n", " <td id=\"T_87d01_row10_col15\" class=\"data row10 col15\" >0.401964</td>\n", " <td id=\"T_87d01_row10_col16\" class=\"data row10 col16\" >0.559837</td>\n", " <td id=\"T_87d01_row10_col17\" class=\"data row10 col17\" >0.446630</td>\n", " <td id=\"T_87d01_row10_col18\" class=\"data row10 col18\" >0.341198</td>\n", " <td id=\"T_87d01_row10_col19\" class=\"data row10 col19\" >0.345007</td>\n", " <td id=\"T_87d01_row10_col20\" class=\"data row10 col20\" >0.688132</td>\n", " <td id=\"T_87d01_row10_col21\" class=\"data row10 col21\" >-0.253691</td>\n", " <td id=\"T_87d01_row10_col22\" class=\"data row10 col22\" >-0.051269</td>\n", " <td id=\"T_87d01_row10_col23\" class=\"data row10 col23\" >-0.205151</td>\n", " <td id=\"T_87d01_row10_col24\" class=\"data row10 col24\" >-0.231854</td>\n", " <td id=\"T_87d01_row10_col25\" class=\"data row10 col25\" >0.504942</td>\n", " <td id=\"T_87d01_row10_col26\" class=\"data row10 col26\" >0.458798</td>\n", " <td id=\"T_87d01_row10_col27\" class=\"data row10 col27\" >0.346234</td>\n", " <td id=\"T_87d01_row10_col28\" class=\"data row10 col28\" >0.175325</td>\n", " <td id=\"T_87d01_row10_col29\" class=\"data row10 col29\" >0.334019</td>\n", " <td id=\"T_87d01_row10_col30\" class=\"data row10 col30\" >0.767297</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row11\" class=\"row_heading level0 row11\" >radius_se</th>\n", " <td id=\"T_87d01_row11_col0\" class=\"data row11 col0\" >0.567134</td>\n", " <td id=\"T_87d01_row11_col1\" class=\"data row11 col1\" >0.679090</td>\n", " <td id=\"T_87d01_row11_col2\" class=\"data row11 col2\" >0.275869</td>\n", " <td id=\"T_87d01_row11_col3\" class=\"data row11 col3\" >0.691765</td>\n", " <td id=\"T_87d01_row11_col4\" class=\"data row11 col4\" >0.732562</td>\n", " <td id=\"T_87d01_row11_col5\" class=\"data row11 col5\" >0.301467</td>\n", " <td id=\"T_87d01_row11_col6\" class=\"data row11 col6\" >0.497473</td>\n", " <td id=\"T_87d01_row11_col7\" class=\"data row11 col7\" >0.631925</td>\n", " <td id=\"T_87d01_row11_col8\" class=\"data row11 col8\" >0.698050</td>\n", " <td id=\"T_87d01_row11_col9\" class=\"data row11 col9\" >0.303379</td>\n", " <td id=\"T_87d01_row11_col10\" class=\"data row11 col10\" >0.000111</td>\n", " <td id=\"T_87d01_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n", " <td id=\"T_87d01_row11_col12\" class=\"data row11 col12\" >0.213247</td>\n", " <td id=\"T_87d01_row11_col13\" class=\"data row11 col13\" >0.972794</td>\n", " <td id=\"T_87d01_row11_col14\" class=\"data row11 col14\" >0.951830</td>\n", " <td id=\"T_87d01_row11_col15\" class=\"data row11 col15\" >0.164514</td>\n", " <td id=\"T_87d01_row11_col16\" class=\"data row11 col16\" >0.356065</td>\n", " <td id=\"T_87d01_row11_col17\" class=\"data row11 col17\" >0.332358</td>\n", " <td id=\"T_87d01_row11_col18\" class=\"data row11 col18\" >0.513346</td>\n", " <td id=\"T_87d01_row11_col19\" class=\"data row11 col19\" >0.240567</td>\n", " <td id=\"T_87d01_row11_col20\" class=\"data row11 col20\" >0.227754</td>\n", " <td id=\"T_87d01_row11_col21\" class=\"data row11 col21\" >0.715065</td>\n", " <td id=\"T_87d01_row11_col22\" class=\"data row11 col22\" >0.194799</td>\n", " <td id=\"T_87d01_row11_col23\" class=\"data row11 col23\" >0.719684</td>\n", " <td id=\"T_87d01_row11_col24\" class=\"data row11 col24\" >0.751548</td>\n", " <td id=\"T_87d01_row11_col25\" class=\"data row11 col25\" >0.141919</td>\n", " <td id=\"T_87d01_row11_col26\" class=\"data row11 col26\" >0.287103</td>\n", " <td id=\"T_87d01_row11_col27\" class=\"data row11 col27\" >0.380585</td>\n", " <td id=\"T_87d01_row11_col28\" class=\"data row11 col28\" >0.531062</td>\n", " <td id=\"T_87d01_row11_col29\" class=\"data row11 col29\" >0.094543</td>\n", " <td id=\"T_87d01_row11_col30\" class=\"data row11 col30\" >0.049559</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row12\" class=\"row_heading level0 row12\" >texture_se</th>\n", " <td id=\"T_87d01_row12_col0\" class=\"data row12 col0\" >-0.008303</td>\n", " <td id=\"T_87d01_row12_col1\" class=\"data row12 col1\" >-0.097317</td>\n", " <td id=\"T_87d01_row12_col2\" class=\"data row12 col2\" >0.386358</td>\n", " <td id=\"T_87d01_row12_col3\" class=\"data row12 col3\" >-0.086761</td>\n", " <td id=\"T_87d01_row12_col4\" class=\"data row12 col4\" >-0.066280</td>\n", " <td id=\"T_87d01_row12_col5\" class=\"data row12 col5\" >0.068406</td>\n", " <td id=\"T_87d01_row12_col6\" class=\"data row12 col6\" >0.046205</td>\n", " <td id=\"T_87d01_row12_col7\" class=\"data row12 col7\" >0.076218</td>\n", " <td id=\"T_87d01_row12_col8\" class=\"data row12 col8\" >0.021480</td>\n", " <td id=\"T_87d01_row12_col9\" class=\"data row12 col9\" >0.128053</td>\n", " <td id=\"T_87d01_row12_col10\" class=\"data row12 col10\" >0.164174</td>\n", " <td id=\"T_87d01_row12_col11\" class=\"data row12 col11\" >0.213247</td>\n", " <td id=\"T_87d01_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n", " <td id=\"T_87d01_row12_col13\" class=\"data row12 col13\" >0.223171</td>\n", " <td id=\"T_87d01_row12_col14\" class=\"data row12 col14\" >0.111567</td>\n", " <td id=\"T_87d01_row12_col15\" class=\"data row12 col15\" >0.397243</td>\n", " <td id=\"T_87d01_row12_col16\" class=\"data row12 col16\" >0.231700</td>\n", " <td id=\"T_87d01_row12_col17\" class=\"data row12 col17\" >0.194998</td>\n", " <td id=\"T_87d01_row12_col18\" class=\"data row12 col18\" >0.230283</td>\n", " <td id=\"T_87d01_row12_col19\" class=\"data row12 col19\" >0.411621</td>\n", " <td id=\"T_87d01_row12_col20\" class=\"data row12 col20\" >0.279723</td>\n", " <td id=\"T_87d01_row12_col21\" class=\"data row12 col21\" >-0.111690</td>\n", " <td id=\"T_87d01_row12_col22\" class=\"data row12 col22\" >0.409003</td>\n", " <td id=\"T_87d01_row12_col23\" class=\"data row12 col23\" >-0.102242</td>\n", " <td id=\"T_87d01_row12_col24\" class=\"data row12 col24\" >-0.083195</td>\n", " <td id=\"T_87d01_row12_col25\" class=\"data row12 col25\" >-0.073658</td>\n", " <td id=\"T_87d01_row12_col26\" class=\"data row12 col26\" >-0.092439</td>\n", " <td id=\"T_87d01_row12_col27\" class=\"data row12 col27\" >-0.068956</td>\n", " <td id=\"T_87d01_row12_col28\" class=\"data row12 col28\" >-0.119638</td>\n", " <td id=\"T_87d01_row12_col29\" class=\"data row12 col29\" >-0.128215</td>\n", " <td id=\"T_87d01_row12_col30\" class=\"data row12 col30\" >-0.045655</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row13\" class=\"row_heading level0 row13\" >perimeter_se</th>\n", " <td id=\"T_87d01_row13_col0\" class=\"data row13 col0\" >0.556141</td>\n", " <td id=\"T_87d01_row13_col1\" class=\"data row13 col1\" >0.674172</td>\n", " <td id=\"T_87d01_row13_col2\" class=\"data row13 col2\" >0.281673</td>\n", " <td id=\"T_87d01_row13_col3\" class=\"data row13 col3\" >0.693135</td>\n", " <td id=\"T_87d01_row13_col4\" class=\"data row13 col4\" >0.726628</td>\n", " <td id=\"T_87d01_row13_col5\" class=\"data row13 col5\" >0.296092</td>\n", " <td id=\"T_87d01_row13_col6\" class=\"data row13 col6\" >0.548905</td>\n", " <td id=\"T_87d01_row13_col7\" class=\"data row13 col7\" >0.660391</td>\n", " <td id=\"T_87d01_row13_col8\" class=\"data row13 col8\" >0.710650</td>\n", " <td id=\"T_87d01_row13_col9\" class=\"data row13 col9\" >0.313893</td>\n", " <td id=\"T_87d01_row13_col10\" class=\"data row13 col10\" >0.039830</td>\n", " <td id=\"T_87d01_row13_col11\" class=\"data row13 col11\" >0.972794</td>\n", " <td id=\"T_87d01_row13_col12\" class=\"data row13 col12\" >0.223171</td>\n", " <td id=\"T_87d01_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n", " <td id=\"T_87d01_row13_col14\" class=\"data row13 col14\" >0.937655</td>\n", " <td id=\"T_87d01_row13_col15\" class=\"data row13 col15\" >0.151075</td>\n", " <td id=\"T_87d01_row13_col16\" class=\"data row13 col16\" >0.416322</td>\n", " <td id=\"T_87d01_row13_col17\" class=\"data row13 col17\" >0.362482</td>\n", " <td id=\"T_87d01_row13_col18\" class=\"data row13 col18\" >0.556264</td>\n", " <td id=\"T_87d01_row13_col19\" class=\"data row13 col19\" >0.266487</td>\n", " <td id=\"T_87d01_row13_col20\" class=\"data row13 col20\" >0.244143</td>\n", " <td id=\"T_87d01_row13_col21\" class=\"data row13 col21\" >0.697201</td>\n", " <td id=\"T_87d01_row13_col22\" class=\"data row13 col22\" >0.200371</td>\n", " <td id=\"T_87d01_row13_col23\" class=\"data row13 col23\" >0.721031</td>\n", " <td id=\"T_87d01_row13_col24\" class=\"data row13 col24\" >0.730713</td>\n", " <td id=\"T_87d01_row13_col25\" class=\"data row13 col25\" >0.130054</td>\n", " <td id=\"T_87d01_row13_col26\" class=\"data row13 col26\" >0.341919</td>\n", " <td id=\"T_87d01_row13_col27\" class=\"data row13 col27\" >0.418899</td>\n", " <td id=\"T_87d01_row13_col28\" class=\"data row13 col28\" >0.554897</td>\n", " <td id=\"T_87d01_row13_col29\" class=\"data row13 col29\" >0.109930</td>\n", " <td id=\"T_87d01_row13_col30\" class=\"data row13 col30\" >0.085433</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row14\" class=\"row_heading level0 row14\" >area_se</th>\n", " <td id=\"T_87d01_row14_col0\" class=\"data row14 col0\" >0.548236</td>\n", " <td id=\"T_87d01_row14_col1\" class=\"data row14 col1\" >0.735864</td>\n", " <td id=\"T_87d01_row14_col2\" class=\"data row14 col2\" >0.259845</td>\n", " <td id=\"T_87d01_row14_col3\" class=\"data row14 col3\" >0.744983</td>\n", " <td id=\"T_87d01_row14_col4\" class=\"data row14 col4\" >0.800086</td>\n", " <td id=\"T_87d01_row14_col5\" class=\"data row14 col5\" >0.246552</td>\n", " <td id=\"T_87d01_row14_col6\" class=\"data row14 col6\" >0.455653</td>\n", " <td id=\"T_87d01_row14_col7\" class=\"data row14 col7\" >0.617427</td>\n", " <td id=\"T_87d01_row14_col8\" class=\"data row14 col8\" >0.690299</td>\n", " <td id=\"T_87d01_row14_col9\" class=\"data row14 col9\" >0.223970</td>\n", " <td id=\"T_87d01_row14_col10\" class=\"data row14 col10\" >-0.090170</td>\n", " <td id=\"T_87d01_row14_col11\" class=\"data row14 col11\" >0.951830</td>\n", " <td id=\"T_87d01_row14_col12\" class=\"data row14 col12\" >0.111567</td>\n", " <td id=\"T_87d01_row14_col13\" class=\"data row14 col13\" >0.937655</td>\n", " <td id=\"T_87d01_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n", " <td id=\"T_87d01_row14_col15\" class=\"data row14 col15\" >0.075150</td>\n", " <td id=\"T_87d01_row14_col16\" class=\"data row14 col16\" >0.284840</td>\n", " <td id=\"T_87d01_row14_col17\" class=\"data row14 col17\" >0.270895</td>\n", " <td id=\"T_87d01_row14_col18\" class=\"data row14 col18\" >0.415730</td>\n", " <td id=\"T_87d01_row14_col19\" class=\"data row14 col19\" >0.134109</td>\n", " <td id=\"T_87d01_row14_col20\" class=\"data row14 col20\" >0.127071</td>\n", " <td id=\"T_87d01_row14_col21\" class=\"data row14 col21\" >0.757373</td>\n", " <td id=\"T_87d01_row14_col22\" class=\"data row14 col22\" >0.196497</td>\n", " <td id=\"T_87d01_row14_col23\" class=\"data row14 col23\" >0.761213</td>\n", " <td id=\"T_87d01_row14_col24\" class=\"data row14 col24\" >0.811408</td>\n", " <td id=\"T_87d01_row14_col25\" class=\"data row14 col25\" >0.125389</td>\n", " <td id=\"T_87d01_row14_col26\" class=\"data row14 col26\" >0.283257</td>\n", " <td id=\"T_87d01_row14_col27\" class=\"data row14 col27\" >0.385100</td>\n", " <td id=\"T_87d01_row14_col28\" class=\"data row14 col28\" >0.538166</td>\n", " <td id=\"T_87d01_row14_col29\" class=\"data row14 col29\" >0.074126</td>\n", " <td id=\"T_87d01_row14_col30\" class=\"data row14 col30\" >0.017539</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row15\" class=\"row_heading level0 row15\" >smoothness_se</th>\n", " <td id=\"T_87d01_row15_col0\" class=\"data row15 col0\" >-0.067016</td>\n", " <td id=\"T_87d01_row15_col1\" class=\"data row15 col1\" >-0.222600</td>\n", " <td id=\"T_87d01_row15_col2\" class=\"data row15 col2\" >0.006614</td>\n", " <td id=\"T_87d01_row15_col3\" class=\"data row15 col3\" >-0.202694</td>\n", " <td id=\"T_87d01_row15_col4\" class=\"data row15 col4\" >-0.166777</td>\n", " <td id=\"T_87d01_row15_col5\" class=\"data row15 col5\" >0.332375</td>\n", " <td id=\"T_87d01_row15_col6\" class=\"data row15 col6\" >0.135299</td>\n", " <td id=\"T_87d01_row15_col7\" class=\"data row15 col7\" >0.098564</td>\n", " <td id=\"T_87d01_row15_col8\" class=\"data row15 col8\" >0.027653</td>\n", " <td id=\"T_87d01_row15_col9\" class=\"data row15 col9\" >0.187321</td>\n", " <td id=\"T_87d01_row15_col10\" class=\"data row15 col10\" >0.401964</td>\n", " <td id=\"T_87d01_row15_col11\" class=\"data row15 col11\" >0.164514</td>\n", " <td id=\"T_87d01_row15_col12\" class=\"data row15 col12\" >0.397243</td>\n", " <td id=\"T_87d01_row15_col13\" class=\"data row15 col13\" >0.151075</td>\n", " <td id=\"T_87d01_row15_col14\" class=\"data row15 col14\" >0.075150</td>\n", " <td id=\"T_87d01_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n", " <td id=\"T_87d01_row15_col16\" class=\"data row15 col16\" >0.336696</td>\n", " <td id=\"T_87d01_row15_col17\" class=\"data row15 col17\" >0.268685</td>\n", " <td id=\"T_87d01_row15_col18\" class=\"data row15 col18\" >0.328429</td>\n", " <td id=\"T_87d01_row15_col19\" class=\"data row15 col19\" >0.413506</td>\n", " <td id=\"T_87d01_row15_col20\" class=\"data row15 col20\" >0.427374</td>\n", " <td id=\"T_87d01_row15_col21\" class=\"data row15 col21\" >-0.230691</td>\n", " <td id=\"T_87d01_row15_col22\" class=\"data row15 col22\" >-0.074743</td>\n", " <td id=\"T_87d01_row15_col23\" class=\"data row15 col23\" >-0.217304</td>\n", " <td id=\"T_87d01_row15_col24\" class=\"data row15 col24\" >-0.182195</td>\n", " <td id=\"T_87d01_row15_col25\" class=\"data row15 col25\" >0.314457</td>\n", " <td id=\"T_87d01_row15_col26\" class=\"data row15 col26\" >-0.055558</td>\n", " <td id=\"T_87d01_row15_col27\" class=\"data row15 col27\" >-0.058298</td>\n", " <td id=\"T_87d01_row15_col28\" class=\"data row15 col28\" >-0.102007</td>\n", " <td id=\"T_87d01_row15_col29\" class=\"data row15 col29\" >-0.107342</td>\n", " <td id=\"T_87d01_row15_col30\" class=\"data row15 col30\" >0.101480</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row16\" class=\"row_heading level0 row16\" >compactness_se</th>\n", " <td id=\"T_87d01_row16_col0\" class=\"data row16 col0\" >0.292999</td>\n", " <td id=\"T_87d01_row16_col1\" class=\"data row16 col1\" >0.206000</td>\n", " <td id=\"T_87d01_row16_col2\" class=\"data row16 col2\" >0.191975</td>\n", " <td id=\"T_87d01_row16_col3\" class=\"data row16 col3\" >0.250744</td>\n", " <td id=\"T_87d01_row16_col4\" class=\"data row16 col4\" >0.212583</td>\n", " <td id=\"T_87d01_row16_col5\" class=\"data row16 col5\" >0.318943</td>\n", " <td id=\"T_87d01_row16_col6\" class=\"data row16 col6\" >0.738722</td>\n", " <td id=\"T_87d01_row16_col7\" class=\"data row16 col7\" >0.670279</td>\n", " <td id=\"T_87d01_row16_col8\" class=\"data row16 col8\" >0.490424</td>\n", " <td id=\"T_87d01_row16_col9\" class=\"data row16 col9\" >0.421659</td>\n", " <td id=\"T_87d01_row16_col10\" class=\"data row16 col10\" >0.559837</td>\n", " <td id=\"T_87d01_row16_col11\" class=\"data row16 col11\" >0.356065</td>\n", " <td id=\"T_87d01_row16_col12\" class=\"data row16 col12\" >0.231700</td>\n", " <td id=\"T_87d01_row16_col13\" class=\"data row16 col13\" >0.416322</td>\n", " <td id=\"T_87d01_row16_col14\" class=\"data row16 col14\" >0.284840</td>\n", " <td id=\"T_87d01_row16_col15\" class=\"data row16 col15\" >0.336696</td>\n", " <td id=\"T_87d01_row16_col16\" class=\"data row16 col16\" >1.000000</td>\n", " <td id=\"T_87d01_row16_col17\" class=\"data row16 col17\" >0.801268</td>\n", " <td id=\"T_87d01_row16_col18\" class=\"data row16 col18\" >0.744083</td>\n", " <td id=\"T_87d01_row16_col19\" class=\"data row16 col19\" >0.394713</td>\n", " <td id=\"T_87d01_row16_col20\" class=\"data row16 col20\" >0.803269</td>\n", " <td id=\"T_87d01_row16_col21\" class=\"data row16 col21\" >0.204607</td>\n", " <td id=\"T_87d01_row16_col22\" class=\"data row16 col22\" >0.143003</td>\n", " <td id=\"T_87d01_row16_col23\" class=\"data row16 col23\" >0.260516</td>\n", " <td id=\"T_87d01_row16_col24\" class=\"data row16 col24\" >0.199371</td>\n", " <td id=\"T_87d01_row16_col25\" class=\"data row16 col25\" >0.227394</td>\n", " <td id=\"T_87d01_row16_col26\" class=\"data row16 col26\" >0.678780</td>\n", " <td id=\"T_87d01_row16_col27\" class=\"data row16 col27\" >0.639147</td>\n", " <td id=\"T_87d01_row16_col28\" class=\"data row16 col28\" >0.483208</td>\n", " <td id=\"T_87d01_row16_col29\" class=\"data row16 col29\" >0.277878</td>\n", " <td id=\"T_87d01_row16_col30\" class=\"data row16 col30\" >0.590973</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row17\" class=\"row_heading level0 row17\" >concavity_se</th>\n", " <td id=\"T_87d01_row17_col0\" class=\"data row17 col0\" >0.253730</td>\n", " <td id=\"T_87d01_row17_col1\" class=\"data row17 col1\" >0.194204</td>\n", " <td id=\"T_87d01_row17_col2\" class=\"data row17 col2\" >0.143293</td>\n", " <td id=\"T_87d01_row17_col3\" class=\"data row17 col3\" >0.228082</td>\n", " <td id=\"T_87d01_row17_col4\" class=\"data row17 col4\" >0.207660</td>\n", " <td id=\"T_87d01_row17_col5\" class=\"data row17 col5\" >0.248396</td>\n", " <td id=\"T_87d01_row17_col6\" class=\"data row17 col6\" >0.570517</td>\n", " <td id=\"T_87d01_row17_col7\" class=\"data row17 col7\" >0.691270</td>\n", " <td id=\"T_87d01_row17_col8\" class=\"data row17 col8\" >0.439167</td>\n", " <td id=\"T_87d01_row17_col9\" class=\"data row17 col9\" >0.342627</td>\n", " <td id=\"T_87d01_row17_col10\" class=\"data row17 col10\" >0.446630</td>\n", " <td id=\"T_87d01_row17_col11\" class=\"data row17 col11\" >0.332358</td>\n", " <td id=\"T_87d01_row17_col12\" class=\"data row17 col12\" >0.194998</td>\n", " <td id=\"T_87d01_row17_col13\" class=\"data row17 col13\" >0.362482</td>\n", " <td id=\"T_87d01_row17_col14\" class=\"data row17 col14\" >0.270895</td>\n", " <td id=\"T_87d01_row17_col15\" class=\"data row17 col15\" >0.268685</td>\n", " <td id=\"T_87d01_row17_col16\" class=\"data row17 col16\" >0.801268</td>\n", " <td id=\"T_87d01_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n", " <td id=\"T_87d01_row17_col18\" class=\"data row17 col18\" >0.771804</td>\n", " <td id=\"T_87d01_row17_col19\" class=\"data row17 col19\" >0.309429</td>\n", " <td id=\"T_87d01_row17_col20\" class=\"data row17 col20\" >0.727372</td>\n", " <td id=\"T_87d01_row17_col21\" class=\"data row17 col21\" >0.186904</td>\n", " <td id=\"T_87d01_row17_col22\" class=\"data row17 col22\" >0.100241</td>\n", " <td id=\"T_87d01_row17_col23\" class=\"data row17 col23\" >0.226680</td>\n", " <td id=\"T_87d01_row17_col24\" class=\"data row17 col24\" >0.188353</td>\n", " <td id=\"T_87d01_row17_col25\" class=\"data row17 col25\" >0.168481</td>\n", " <td id=\"T_87d01_row17_col26\" class=\"data row17 col26\" >0.484858</td>\n", " <td id=\"T_87d01_row17_col27\" class=\"data row17 col27\" >0.662564</td>\n", " <td id=\"T_87d01_row17_col28\" class=\"data row17 col28\" >0.440472</td>\n", " <td id=\"T_87d01_row17_col29\" class=\"data row17 col29\" >0.197788</td>\n", " <td id=\"T_87d01_row17_col30\" class=\"data row17 col30\" >0.439329</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row18\" class=\"row_heading level0 row18\" >concave points_se</th>\n", " <td id=\"T_87d01_row18_col0\" class=\"data row18 col0\" >0.408042</td>\n", " <td id=\"T_87d01_row18_col1\" class=\"data row18 col1\" >0.376169</td>\n", " <td id=\"T_87d01_row18_col2\" class=\"data row18 col2\" >0.163851</td>\n", " <td id=\"T_87d01_row18_col3\" class=\"data row18 col3\" >0.407217</td>\n", " <td id=\"T_87d01_row18_col4\" class=\"data row18 col4\" >0.372320</td>\n", " <td id=\"T_87d01_row18_col5\" class=\"data row18 col5\" >0.380676</td>\n", " <td id=\"T_87d01_row18_col6\" class=\"data row18 col6\" >0.642262</td>\n", " <td id=\"T_87d01_row18_col7\" class=\"data row18 col7\" >0.683260</td>\n", " <td id=\"T_87d01_row18_col8\" class=\"data row18 col8\" >0.615634</td>\n", " <td id=\"T_87d01_row18_col9\" class=\"data row18 col9\" >0.393298</td>\n", " <td id=\"T_87d01_row18_col10\" class=\"data row18 col10\" >0.341198</td>\n", " <td id=\"T_87d01_row18_col11\" class=\"data row18 col11\" >0.513346</td>\n", " <td id=\"T_87d01_row18_col12\" class=\"data row18 col12\" >0.230283</td>\n", " <td id=\"T_87d01_row18_col13\" class=\"data row18 col13\" >0.556264</td>\n", " <td id=\"T_87d01_row18_col14\" class=\"data row18 col14\" >0.415730</td>\n", " <td id=\"T_87d01_row18_col15\" class=\"data row18 col15\" >0.328429</td>\n", " <td id=\"T_87d01_row18_col16\" class=\"data row18 col16\" >0.744083</td>\n", " <td id=\"T_87d01_row18_col17\" class=\"data row18 col17\" >0.771804</td>\n", " <td id=\"T_87d01_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n", " <td id=\"T_87d01_row18_col19\" class=\"data row18 col19\" >0.312780</td>\n", " <td id=\"T_87d01_row18_col20\" class=\"data row18 col20\" >0.611044</td>\n", " <td id=\"T_87d01_row18_col21\" class=\"data row18 col21\" >0.358127</td>\n", " <td id=\"T_87d01_row18_col22\" class=\"data row18 col22\" >0.086741</td>\n", " <td id=\"T_87d01_row18_col23\" class=\"data row18 col23\" >0.394999</td>\n", " <td id=\"T_87d01_row18_col24\" class=\"data row18 col24\" >0.342271</td>\n", " <td id=\"T_87d01_row18_col25\" class=\"data row18 col25\" >0.215351</td>\n", " <td id=\"T_87d01_row18_col26\" class=\"data row18 col26\" >0.452888</td>\n", " <td id=\"T_87d01_row18_col27\" class=\"data row18 col27\" >0.549592</td>\n", " <td id=\"T_87d01_row18_col28\" class=\"data row18 col28\" >0.602450</td>\n", " <td id=\"T_87d01_row18_col29\" class=\"data row18 col29\" >0.143116</td>\n", " <td id=\"T_87d01_row18_col30\" class=\"data row18 col30\" >0.310655</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row19\" class=\"row_heading level0 row19\" >symmetry_se</th>\n", " <td id=\"T_87d01_row19_col0\" class=\"data row19 col0\" >-0.006522</td>\n", " <td id=\"T_87d01_row19_col1\" class=\"data row19 col1\" >-0.104321</td>\n", " <td id=\"T_87d01_row19_col2\" class=\"data row19 col2\" >0.009127</td>\n", " <td id=\"T_87d01_row19_col3\" class=\"data row19 col3\" >-0.081629</td>\n", " <td id=\"T_87d01_row19_col4\" class=\"data row19 col4\" >-0.072497</td>\n", " <td id=\"T_87d01_row19_col5\" class=\"data row19 col5\" >0.200774</td>\n", " <td id=\"T_87d01_row19_col6\" class=\"data row19 col6\" >0.229977</td>\n", " <td id=\"T_87d01_row19_col7\" class=\"data row19 col7\" >0.178009</td>\n", " <td id=\"T_87d01_row19_col8\" class=\"data row19 col8\" >0.095351</td>\n", " <td id=\"T_87d01_row19_col9\" class=\"data row19 col9\" >0.449137</td>\n", " <td id=\"T_87d01_row19_col10\" class=\"data row19 col10\" >0.345007</td>\n", " <td id=\"T_87d01_row19_col11\" class=\"data row19 col11\" >0.240567</td>\n", " <td id=\"T_87d01_row19_col12\" class=\"data row19 col12\" >0.411621</td>\n", " <td id=\"T_87d01_row19_col13\" class=\"data row19 col13\" >0.266487</td>\n", " <td id=\"T_87d01_row19_col14\" class=\"data row19 col14\" >0.134109</td>\n", " <td id=\"T_87d01_row19_col15\" class=\"data row19 col15\" >0.413506</td>\n", " <td id=\"T_87d01_row19_col16\" class=\"data row19 col16\" >0.394713</td>\n", " <td id=\"T_87d01_row19_col17\" class=\"data row19 col17\" >0.309429</td>\n", " <td id=\"T_87d01_row19_col18\" class=\"data row19 col18\" >0.312780</td>\n", " <td id=\"T_87d01_row19_col19\" class=\"data row19 col19\" >1.000000</td>\n", " <td id=\"T_87d01_row19_col20\" class=\"data row19 col20\" >0.369078</td>\n", " <td id=\"T_87d01_row19_col21\" class=\"data row19 col21\" >-0.128121</td>\n", " <td id=\"T_87d01_row19_col22\" class=\"data row19 col22\" >-0.077473</td>\n", " <td id=\"T_87d01_row19_col23\" class=\"data row19 col23\" >-0.103753</td>\n", " <td id=\"T_87d01_row19_col24\" class=\"data row19 col24\" >-0.110343</td>\n", " <td id=\"T_87d01_row19_col25\" class=\"data row19 col25\" >-0.012662</td>\n", " <td id=\"T_87d01_row19_col26\" class=\"data row19 col26\" >0.060255</td>\n", " <td id=\"T_87d01_row19_col27\" class=\"data row19 col27\" >0.037119</td>\n", " <td id=\"T_87d01_row19_col28\" class=\"data row19 col28\" >-0.030413</td>\n", " <td id=\"T_87d01_row19_col29\" class=\"data row19 col29\" >0.389402</td>\n", " <td id=\"T_87d01_row19_col30\" class=\"data row19 col30\" >0.078079</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row20\" class=\"row_heading level0 row20\" >fractal_dimension_se</th>\n", " <td id=\"T_87d01_row20_col0\" class=\"data row20 col0\" >0.077972</td>\n", " <td id=\"T_87d01_row20_col1\" class=\"data row20 col1\" >-0.042641</td>\n", " <td id=\"T_87d01_row20_col2\" class=\"data row20 col2\" >0.054458</td>\n", " <td id=\"T_87d01_row20_col3\" class=\"data row20 col3\" >-0.005523</td>\n", " <td id=\"T_87d01_row20_col4\" class=\"data row20 col4\" >-0.019887</td>\n", " <td id=\"T_87d01_row20_col5\" class=\"data row20 col5\" >0.283607</td>\n", " <td id=\"T_87d01_row20_col6\" class=\"data row20 col6\" >0.507318</td>\n", " <td id=\"T_87d01_row20_col7\" class=\"data row20 col7\" >0.449301</td>\n", " <td id=\"T_87d01_row20_col8\" class=\"data row20 col8\" >0.257584</td>\n", " <td id=\"T_87d01_row20_col9\" class=\"data row20 col9\" >0.331786</td>\n", " <td id=\"T_87d01_row20_col10\" class=\"data row20 col10\" >0.688132</td>\n", " <td id=\"T_87d01_row20_col11\" class=\"data row20 col11\" >0.227754</td>\n", " <td id=\"T_87d01_row20_col12\" class=\"data row20 col12\" >0.279723</td>\n", " <td id=\"T_87d01_row20_col13\" class=\"data row20 col13\" >0.244143</td>\n", " <td id=\"T_87d01_row20_col14\" class=\"data row20 col14\" >0.127071</td>\n", " <td id=\"T_87d01_row20_col15\" class=\"data row20 col15\" >0.427374</td>\n", " <td id=\"T_87d01_row20_col16\" class=\"data row20 col16\" >0.803269</td>\n", " <td id=\"T_87d01_row20_col17\" class=\"data row20 col17\" >0.727372</td>\n", " <td id=\"T_87d01_row20_col18\" class=\"data row20 col18\" >0.611044</td>\n", " <td id=\"T_87d01_row20_col19\" class=\"data row20 col19\" >0.369078</td>\n", " <td id=\"T_87d01_row20_col20\" class=\"data row20 col20\" >1.000000</td>\n", " <td id=\"T_87d01_row20_col21\" class=\"data row20 col21\" >-0.037488</td>\n", " <td id=\"T_87d01_row20_col22\" class=\"data row20 col22\" >-0.003195</td>\n", " <td id=\"T_87d01_row20_col23\" class=\"data row20 col23\" >-0.001000</td>\n", " <td id=\"T_87d01_row20_col24\" class=\"data row20 col24\" >-0.022736</td>\n", " <td id=\"T_87d01_row20_col25\" class=\"data row20 col25\" >0.170568</td>\n", " <td id=\"T_87d01_row20_col26\" class=\"data row20 col26\" >0.390159</td>\n", " <td id=\"T_87d01_row20_col27\" class=\"data row20 col27\" >0.379975</td>\n", " <td id=\"T_87d01_row20_col28\" class=\"data row20 col28\" >0.215204</td>\n", " <td id=\"T_87d01_row20_col29\" class=\"data row20 col29\" >0.111094</td>\n", " <td id=\"T_87d01_row20_col30\" class=\"data row20 col30\" >0.591328</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row21\" class=\"row_heading level0 row21\" >radius_worst</th>\n", " <td id=\"T_87d01_row21_col0\" class=\"data row21 col0\" >0.776454</td>\n", " <td id=\"T_87d01_row21_col1\" class=\"data row21 col1\" >0.969539</td>\n", " <td id=\"T_87d01_row21_col2\" class=\"data row21 col2\" >0.352573</td>\n", " <td id=\"T_87d01_row21_col3\" class=\"data row21 col3\" >0.969476</td>\n", " <td id=\"T_87d01_row21_col4\" class=\"data row21 col4\" >0.962746</td>\n", " <td id=\"T_87d01_row21_col5\" class=\"data row21 col5\" >0.213120</td>\n", " <td id=\"T_87d01_row21_col6\" class=\"data row21 col6\" >0.535315</td>\n", " <td id=\"T_87d01_row21_col7\" class=\"data row21 col7\" >0.688236</td>\n", " <td id=\"T_87d01_row21_col8\" class=\"data row21 col8\" >0.830318</td>\n", " <td id=\"T_87d01_row21_col9\" class=\"data row21 col9\" >0.185728</td>\n", " <td id=\"T_87d01_row21_col10\" class=\"data row21 col10\" >-0.253691</td>\n", " <td id=\"T_87d01_row21_col11\" class=\"data row21 col11\" >0.715065</td>\n", " <td id=\"T_87d01_row21_col12\" class=\"data row21 col12\" >-0.111690</td>\n", " <td id=\"T_87d01_row21_col13\" class=\"data row21 col13\" >0.697201</td>\n", " <td id=\"T_87d01_row21_col14\" class=\"data row21 col14\" >0.757373</td>\n", " <td id=\"T_87d01_row21_col15\" class=\"data row21 col15\" >-0.230691</td>\n", " <td id=\"T_87d01_row21_col16\" class=\"data row21 col16\" >0.204607</td>\n", " <td id=\"T_87d01_row21_col17\" class=\"data row21 col17\" >0.186904</td>\n", " <td id=\"T_87d01_row21_col18\" class=\"data row21 col18\" >0.358127</td>\n", " <td id=\"T_87d01_row21_col19\" class=\"data row21 col19\" >-0.128121</td>\n", " <td id=\"T_87d01_row21_col20\" class=\"data row21 col20\" >-0.037488</td>\n", " <td id=\"T_87d01_row21_col21\" class=\"data row21 col21\" >1.000000</td>\n", " <td id=\"T_87d01_row21_col22\" class=\"data row21 col22\" >0.359921</td>\n", " <td id=\"T_87d01_row21_col23\" class=\"data row21 col23\" >0.993708</td>\n", " <td id=\"T_87d01_row21_col24\" class=\"data row21 col24\" >0.984015</td>\n", " <td id=\"T_87d01_row21_col25\" class=\"data row21 col25\" >0.216574</td>\n", " <td id=\"T_87d01_row21_col26\" class=\"data row21 col26\" >0.475820</td>\n", " <td id=\"T_87d01_row21_col27\" class=\"data row21 col27\" >0.573975</td>\n", " <td id=\"T_87d01_row21_col28\" class=\"data row21 col28\" >0.787424</td>\n", " <td id=\"T_87d01_row21_col29\" class=\"data row21 col29\" >0.243529</td>\n", " <td id=\"T_87d01_row21_col30\" class=\"data row21 col30\" >0.093492</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row22\" class=\"row_heading level0 row22\" >texture_worst</th>\n", " <td id=\"T_87d01_row22_col0\" class=\"data row22 col0\" >0.456903</td>\n", " <td id=\"T_87d01_row22_col1\" class=\"data row22 col1\" >0.297008</td>\n", " <td id=\"T_87d01_row22_col2\" class=\"data row22 col2\" >0.912045</td>\n", " <td id=\"T_87d01_row22_col3\" class=\"data row22 col3\" >0.303038</td>\n", " <td id=\"T_87d01_row22_col4\" class=\"data row22 col4\" >0.287489</td>\n", " <td id=\"T_87d01_row22_col5\" class=\"data row22 col5\" >0.036072</td>\n", " <td id=\"T_87d01_row22_col6\" class=\"data row22 col6\" >0.248133</td>\n", " <td id=\"T_87d01_row22_col7\" class=\"data row22 col7\" >0.299879</td>\n", " <td id=\"T_87d01_row22_col8\" class=\"data row22 col8\" >0.292752</td>\n", " <td id=\"T_87d01_row22_col9\" class=\"data row22 col9\" >0.090651</td>\n", " <td id=\"T_87d01_row22_col10\" class=\"data row22 col10\" >-0.051269</td>\n", " <td id=\"T_87d01_row22_col11\" class=\"data row22 col11\" >0.194799</td>\n", " <td id=\"T_87d01_row22_col12\" class=\"data row22 col12\" >0.409003</td>\n", " <td id=\"T_87d01_row22_col13\" class=\"data row22 col13\" >0.200371</td>\n", " <td id=\"T_87d01_row22_col14\" class=\"data row22 col14\" >0.196497</td>\n", " <td id=\"T_87d01_row22_col15\" class=\"data row22 col15\" >-0.074743</td>\n", " <td id=\"T_87d01_row22_col16\" class=\"data row22 col16\" >0.143003</td>\n", " <td id=\"T_87d01_row22_col17\" class=\"data row22 col17\" >0.100241</td>\n", " <td id=\"T_87d01_row22_col18\" class=\"data row22 col18\" >0.086741</td>\n", " <td id=\"T_87d01_row22_col19\" class=\"data row22 col19\" >-0.077473</td>\n", " <td id=\"T_87d01_row22_col20\" class=\"data row22 col20\" >-0.003195</td>\n", " <td id=\"T_87d01_row22_col21\" class=\"data row22 col21\" >0.359921</td>\n", " <td id=\"T_87d01_row22_col22\" class=\"data row22 col22\" >1.000000</td>\n", " <td id=\"T_87d01_row22_col23\" class=\"data row22 col23\" >0.365098</td>\n", " <td id=\"T_87d01_row22_col24\" class=\"data row22 col24\" >0.345842</td>\n", " <td id=\"T_87d01_row22_col25\" class=\"data row22 col25\" >0.225429</td>\n", " <td id=\"T_87d01_row22_col26\" class=\"data row22 col26\" >0.360832</td>\n", " <td id=\"T_87d01_row22_col27\" class=\"data row22 col27\" >0.368366</td>\n", " <td id=\"T_87d01_row22_col28\" class=\"data row22 col28\" >0.359755</td>\n", " <td id=\"T_87d01_row22_col29\" class=\"data row22 col29\" >0.233027</td>\n", " <td id=\"T_87d01_row22_col30\" class=\"data row22 col30\" >0.219122</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row23\" class=\"row_heading level0 row23\" >perimeter_worst</th>\n", " <td id=\"T_87d01_row23_col0\" class=\"data row23 col0\" >0.782914</td>\n", " <td id=\"T_87d01_row23_col1\" class=\"data row23 col1\" >0.965137</td>\n", " <td id=\"T_87d01_row23_col2\" class=\"data row23 col2\" >0.358040</td>\n", " <td id=\"T_87d01_row23_col3\" class=\"data row23 col3\" >0.970387</td>\n", " <td id=\"T_87d01_row23_col4\" class=\"data row23 col4\" >0.959120</td>\n", " <td id=\"T_87d01_row23_col5\" class=\"data row23 col5\" >0.238853</td>\n", " <td id=\"T_87d01_row23_col6\" class=\"data row23 col6\" >0.590210</td>\n", " <td id=\"T_87d01_row23_col7\" class=\"data row23 col7\" >0.729565</td>\n", " <td id=\"T_87d01_row23_col8\" class=\"data row23 col8\" >0.855923</td>\n", " <td id=\"T_87d01_row23_col9\" class=\"data row23 col9\" >0.219169</td>\n", " <td id=\"T_87d01_row23_col10\" class=\"data row23 col10\" >-0.205151</td>\n", " <td id=\"T_87d01_row23_col11\" class=\"data row23 col11\" >0.719684</td>\n", " <td id=\"T_87d01_row23_col12\" class=\"data row23 col12\" >-0.102242</td>\n", " <td id=\"T_87d01_row23_col13\" class=\"data row23 col13\" >0.721031</td>\n", " <td id=\"T_87d01_row23_col14\" class=\"data row23 col14\" >0.761213</td>\n", " <td id=\"T_87d01_row23_col15\" class=\"data row23 col15\" >-0.217304</td>\n", " <td id=\"T_87d01_row23_col16\" class=\"data row23 col16\" >0.260516</td>\n", " <td id=\"T_87d01_row23_col17\" class=\"data row23 col17\" >0.226680</td>\n", " <td id=\"T_87d01_row23_col18\" class=\"data row23 col18\" >0.394999</td>\n", " <td id=\"T_87d01_row23_col19\" class=\"data row23 col19\" >-0.103753</td>\n", " <td id=\"T_87d01_row23_col20\" class=\"data row23 col20\" >-0.001000</td>\n", " <td id=\"T_87d01_row23_col21\" class=\"data row23 col21\" >0.993708</td>\n", " <td id=\"T_87d01_row23_col22\" class=\"data row23 col22\" >0.365098</td>\n", " <td id=\"T_87d01_row23_col23\" class=\"data row23 col23\" >1.000000</td>\n", " <td id=\"T_87d01_row23_col24\" class=\"data row23 col24\" >0.977578</td>\n", " <td id=\"T_87d01_row23_col25\" class=\"data row23 col25\" >0.236775</td>\n", " <td id=\"T_87d01_row23_col26\" class=\"data row23 col26\" >0.529408</td>\n", " <td id=\"T_87d01_row23_col27\" class=\"data row23 col27\" >0.618344</td>\n", " <td id=\"T_87d01_row23_col28\" class=\"data row23 col28\" >0.816322</td>\n", " <td id=\"T_87d01_row23_col29\" class=\"data row23 col29\" >0.269493</td>\n", " <td id=\"T_87d01_row23_col30\" class=\"data row23 col30\" >0.138957</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row24\" class=\"row_heading level0 row24\" >area_worst</th>\n", " <td id=\"T_87d01_row24_col0\" class=\"data row24 col0\" >0.733825</td>\n", " <td id=\"T_87d01_row24_col1\" class=\"data row24 col1\" >0.941082</td>\n", " <td id=\"T_87d01_row24_col2\" class=\"data row24 col2\" >0.343546</td>\n", " <td id=\"T_87d01_row24_col3\" class=\"data row24 col3\" >0.941550</td>\n", " <td id=\"T_87d01_row24_col4\" class=\"data row24 col4\" >0.959213</td>\n", " <td id=\"T_87d01_row24_col5\" class=\"data row24 col5\" >0.206718</td>\n", " <td id=\"T_87d01_row24_col6\" class=\"data row24 col6\" >0.509604</td>\n", " <td id=\"T_87d01_row24_col7\" class=\"data row24 col7\" >0.675987</td>\n", " <td id=\"T_87d01_row24_col8\" class=\"data row24 col8\" >0.809630</td>\n", " <td id=\"T_87d01_row24_col9\" class=\"data row24 col9\" >0.177193</td>\n", " <td id=\"T_87d01_row24_col10\" class=\"data row24 col10\" >-0.231854</td>\n", " <td id=\"T_87d01_row24_col11\" class=\"data row24 col11\" >0.751548</td>\n", " <td id=\"T_87d01_row24_col12\" class=\"data row24 col12\" >-0.083195</td>\n", " <td id=\"T_87d01_row24_col13\" class=\"data row24 col13\" >0.730713</td>\n", " <td id=\"T_87d01_row24_col14\" class=\"data row24 col14\" >0.811408</td>\n", " <td id=\"T_87d01_row24_col15\" class=\"data row24 col15\" >-0.182195</td>\n", " <td id=\"T_87d01_row24_col16\" class=\"data row24 col16\" >0.199371</td>\n", " <td id=\"T_87d01_row24_col17\" class=\"data row24 col17\" >0.188353</td>\n", " <td id=\"T_87d01_row24_col18\" class=\"data row24 col18\" >0.342271</td>\n", " <td id=\"T_87d01_row24_col19\" class=\"data row24 col19\" >-0.110343</td>\n", " <td id=\"T_87d01_row24_col20\" class=\"data row24 col20\" >-0.022736</td>\n", " <td id=\"T_87d01_row24_col21\" class=\"data row24 col21\" >0.984015</td>\n", " <td id=\"T_87d01_row24_col22\" class=\"data row24 col22\" >0.345842</td>\n", " <td id=\"T_87d01_row24_col23\" class=\"data row24 col23\" >0.977578</td>\n", " <td id=\"T_87d01_row24_col24\" class=\"data row24 col24\" >1.000000</td>\n", " <td id=\"T_87d01_row24_col25\" class=\"data row24 col25\" >0.209145</td>\n", " <td id=\"T_87d01_row24_col26\" class=\"data row24 col26\" >0.438296</td>\n", " <td id=\"T_87d01_row24_col27\" class=\"data row24 col27\" >0.543331</td>\n", " <td id=\"T_87d01_row24_col28\" class=\"data row24 col28\" >0.747419</td>\n", " <td id=\"T_87d01_row24_col29\" class=\"data row24 col29\" >0.209146</td>\n", " <td id=\"T_87d01_row24_col30\" class=\"data row24 col30\" >0.079647</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row25\" class=\"row_heading level0 row25\" >smoothness_worst</th>\n", " <td id=\"T_87d01_row25_col0\" class=\"data row25 col0\" >0.421465</td>\n", " <td id=\"T_87d01_row25_col1\" class=\"data row25 col1\" >0.119616</td>\n", " <td id=\"T_87d01_row25_col2\" class=\"data row25 col2\" >0.077503</td>\n", " <td id=\"T_87d01_row25_col3\" class=\"data row25 col3\" >0.150549</td>\n", " <td id=\"T_87d01_row25_col4\" class=\"data row25 col4\" >0.123523</td>\n", " <td id=\"T_87d01_row25_col5\" class=\"data row25 col5\" >0.805324</td>\n", " <td id=\"T_87d01_row25_col6\" class=\"data row25 col6\" >0.565541</td>\n", " <td id=\"T_87d01_row25_col7\" class=\"data row25 col7\" >0.448822</td>\n", " <td id=\"T_87d01_row25_col8\" class=\"data row25 col8\" >0.452753</td>\n", " <td id=\"T_87d01_row25_col9\" class=\"data row25 col9\" >0.426675</td>\n", " <td id=\"T_87d01_row25_col10\" class=\"data row25 col10\" >0.504942</td>\n", " <td id=\"T_87d01_row25_col11\" class=\"data row25 col11\" >0.141919</td>\n", " <td id=\"T_87d01_row25_col12\" class=\"data row25 col12\" >-0.073658</td>\n", " <td id=\"T_87d01_row25_col13\" class=\"data row25 col13\" >0.130054</td>\n", " <td id=\"T_87d01_row25_col14\" class=\"data row25 col14\" >0.125389</td>\n", " <td id=\"T_87d01_row25_col15\" class=\"data row25 col15\" >0.314457</td>\n", " <td id=\"T_87d01_row25_col16\" class=\"data row25 col16\" >0.227394</td>\n", " <td id=\"T_87d01_row25_col17\" class=\"data row25 col17\" >0.168481</td>\n", " <td id=\"T_87d01_row25_col18\" class=\"data row25 col18\" >0.215351</td>\n", " <td id=\"T_87d01_row25_col19\" class=\"data row25 col19\" >-0.012662</td>\n", " <td id=\"T_87d01_row25_col20\" class=\"data row25 col20\" >0.170568</td>\n", " <td id=\"T_87d01_row25_col21\" class=\"data row25 col21\" >0.216574</td>\n", " <td id=\"T_87d01_row25_col22\" class=\"data row25 col22\" >0.225429</td>\n", " <td id=\"T_87d01_row25_col23\" class=\"data row25 col23\" >0.236775</td>\n", " <td id=\"T_87d01_row25_col24\" class=\"data row25 col24\" >0.209145</td>\n", " <td id=\"T_87d01_row25_col25\" class=\"data row25 col25\" >1.000000</td>\n", " <td id=\"T_87d01_row25_col26\" class=\"data row25 col26\" >0.568187</td>\n", " <td id=\"T_87d01_row25_col27\" class=\"data row25 col27\" >0.518523</td>\n", " <td id=\"T_87d01_row25_col28\" class=\"data row25 col28\" >0.547691</td>\n", " <td id=\"T_87d01_row25_col29\" class=\"data row25 col29\" >0.493838</td>\n", " <td id=\"T_87d01_row25_col30\" class=\"data row25 col30\" >0.617624</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row26\" class=\"row_heading level0 row26\" >compactness_worst</th>\n", " <td id=\"T_87d01_row26_col0\" class=\"data row26 col0\" >0.590998</td>\n", " <td id=\"T_87d01_row26_col1\" class=\"data row26 col1\" >0.413463</td>\n", " <td id=\"T_87d01_row26_col2\" class=\"data row26 col2\" >0.277830</td>\n", " <td id=\"T_87d01_row26_col3\" class=\"data row26 col3\" >0.455774</td>\n", " <td id=\"T_87d01_row26_col4\" class=\"data row26 col4\" >0.390410</td>\n", " <td id=\"T_87d01_row26_col5\" class=\"data row26 col5\" >0.472468</td>\n", " <td id=\"T_87d01_row26_col6\" class=\"data row26 col6\" >0.865809</td>\n", " <td id=\"T_87d01_row26_col7\" class=\"data row26 col7\" >0.754968</td>\n", " <td id=\"T_87d01_row26_col8\" class=\"data row26 col8\" >0.667454</td>\n", " <td id=\"T_87d01_row26_col9\" class=\"data row26 col9\" >0.473200</td>\n", " <td id=\"T_87d01_row26_col10\" class=\"data row26 col10\" >0.458798</td>\n", " <td id=\"T_87d01_row26_col11\" class=\"data row26 col11\" >0.287103</td>\n", " <td id=\"T_87d01_row26_col12\" class=\"data row26 col12\" >-0.092439</td>\n", " <td id=\"T_87d01_row26_col13\" class=\"data row26 col13\" >0.341919</td>\n", " <td id=\"T_87d01_row26_col14\" class=\"data row26 col14\" >0.283257</td>\n", " <td id=\"T_87d01_row26_col15\" class=\"data row26 col15\" >-0.055558</td>\n", " <td id=\"T_87d01_row26_col16\" class=\"data row26 col16\" >0.678780</td>\n", " <td id=\"T_87d01_row26_col17\" class=\"data row26 col17\" >0.484858</td>\n", " <td id=\"T_87d01_row26_col18\" class=\"data row26 col18\" >0.452888</td>\n", " <td id=\"T_87d01_row26_col19\" class=\"data row26 col19\" >0.060255</td>\n", " <td id=\"T_87d01_row26_col20\" class=\"data row26 col20\" >0.390159</td>\n", " <td id=\"T_87d01_row26_col21\" class=\"data row26 col21\" >0.475820</td>\n", " <td id=\"T_87d01_row26_col22\" class=\"data row26 col22\" >0.360832</td>\n", " <td id=\"T_87d01_row26_col23\" class=\"data row26 col23\" >0.529408</td>\n", " <td id=\"T_87d01_row26_col24\" class=\"data row26 col24\" >0.438296</td>\n", " <td id=\"T_87d01_row26_col25\" class=\"data row26 col25\" >0.568187</td>\n", " <td id=\"T_87d01_row26_col26\" class=\"data row26 col26\" >1.000000</td>\n", " <td id=\"T_87d01_row26_col27\" class=\"data row26 col27\" >0.892261</td>\n", " <td id=\"T_87d01_row26_col28\" class=\"data row26 col28\" >0.801080</td>\n", " <td id=\"T_87d01_row26_col29\" class=\"data row26 col29\" >0.614441</td>\n", " <td id=\"T_87d01_row26_col30\" class=\"data row26 col30\" >0.810455</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row27\" class=\"row_heading level0 row27\" >concavity_worst</th>\n", " <td id=\"T_87d01_row27_col0\" class=\"data row27 col0\" >0.659610</td>\n", " <td id=\"T_87d01_row27_col1\" class=\"data row27 col1\" >0.526911</td>\n", " <td id=\"T_87d01_row27_col2\" class=\"data row27 col2\" >0.301025</td>\n", " <td id=\"T_87d01_row27_col3\" class=\"data row27 col3\" >0.563879</td>\n", " <td id=\"T_87d01_row27_col4\" class=\"data row27 col4\" >0.512606</td>\n", " <td id=\"T_87d01_row27_col5\" class=\"data row27 col5\" >0.434926</td>\n", " <td id=\"T_87d01_row27_col6\" class=\"data row27 col6\" >0.816275</td>\n", " <td id=\"T_87d01_row27_col7\" class=\"data row27 col7\" >0.884103</td>\n", " <td id=\"T_87d01_row27_col8\" class=\"data row27 col8\" >0.752399</td>\n", " <td id=\"T_87d01_row27_col9\" class=\"data row27 col9\" >0.433721</td>\n", " <td id=\"T_87d01_row27_col10\" class=\"data row27 col10\" >0.346234</td>\n", " <td id=\"T_87d01_row27_col11\" class=\"data row27 col11\" >0.380585</td>\n", " <td id=\"T_87d01_row27_col12\" class=\"data row27 col12\" >-0.068956</td>\n", " <td id=\"T_87d01_row27_col13\" class=\"data row27 col13\" >0.418899</td>\n", " <td id=\"T_87d01_row27_col14\" class=\"data row27 col14\" >0.385100</td>\n", " <td id=\"T_87d01_row27_col15\" class=\"data row27 col15\" >-0.058298</td>\n", " <td id=\"T_87d01_row27_col16\" class=\"data row27 col16\" >0.639147</td>\n", " <td id=\"T_87d01_row27_col17\" class=\"data row27 col17\" >0.662564</td>\n", " <td id=\"T_87d01_row27_col18\" class=\"data row27 col18\" >0.549592</td>\n", " <td id=\"T_87d01_row27_col19\" class=\"data row27 col19\" >0.037119</td>\n", " <td id=\"T_87d01_row27_col20\" class=\"data row27 col20\" >0.379975</td>\n", " <td id=\"T_87d01_row27_col21\" class=\"data row27 col21\" >0.573975</td>\n", " <td id=\"T_87d01_row27_col22\" class=\"data row27 col22\" >0.368366</td>\n", " <td id=\"T_87d01_row27_col23\" class=\"data row27 col23\" >0.618344</td>\n", " <td id=\"T_87d01_row27_col24\" class=\"data row27 col24\" >0.543331</td>\n", " <td id=\"T_87d01_row27_col25\" class=\"data row27 col25\" >0.518523</td>\n", " <td id=\"T_87d01_row27_col26\" class=\"data row27 col26\" >0.892261</td>\n", " <td id=\"T_87d01_row27_col27\" class=\"data row27 col27\" >1.000000</td>\n", " <td id=\"T_87d01_row27_col28\" class=\"data row27 col28\" >0.855434</td>\n", " <td id=\"T_87d01_row27_col29\" class=\"data row27 col29\" >0.532520</td>\n", " <td id=\"T_87d01_row27_col30\" class=\"data row27 col30\" >0.686511</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row28\" class=\"row_heading level0 row28\" >concave points_worst</th>\n", " <td id=\"T_87d01_row28_col0\" class=\"data row28 col0\" >0.793566</td>\n", " <td id=\"T_87d01_row28_col1\" class=\"data row28 col1\" >0.744214</td>\n", " <td id=\"T_87d01_row28_col2\" class=\"data row28 col2\" >0.295316</td>\n", " <td id=\"T_87d01_row28_col3\" class=\"data row28 col3\" >0.771241</td>\n", " <td id=\"T_87d01_row28_col4\" class=\"data row28 col4\" >0.722017</td>\n", " <td id=\"T_87d01_row28_col5\" class=\"data row28 col5\" >0.503053</td>\n", " <td id=\"T_87d01_row28_col6\" class=\"data row28 col6\" >0.815573</td>\n", " <td id=\"T_87d01_row28_col7\" class=\"data row28 col7\" >0.861323</td>\n", " <td id=\"T_87d01_row28_col8\" class=\"data row28 col8\" >0.910155</td>\n", " <td id=\"T_87d01_row28_col9\" class=\"data row28 col9\" >0.430297</td>\n", " <td id=\"T_87d01_row28_col10\" class=\"data row28 col10\" >0.175325</td>\n", " <td id=\"T_87d01_row28_col11\" class=\"data row28 col11\" >0.531062</td>\n", " <td id=\"T_87d01_row28_col12\" class=\"data row28 col12\" >-0.119638</td>\n", " <td id=\"T_87d01_row28_col13\" class=\"data row28 col13\" >0.554897</td>\n", " <td id=\"T_87d01_row28_col14\" class=\"data row28 col14\" >0.538166</td>\n", " <td id=\"T_87d01_row28_col15\" class=\"data row28 col15\" >-0.102007</td>\n", " <td id=\"T_87d01_row28_col16\" class=\"data row28 col16\" >0.483208</td>\n", " <td id=\"T_87d01_row28_col17\" class=\"data row28 col17\" >0.440472</td>\n", " <td id=\"T_87d01_row28_col18\" class=\"data row28 col18\" >0.602450</td>\n", " <td id=\"T_87d01_row28_col19\" class=\"data row28 col19\" >-0.030413</td>\n", " <td id=\"T_87d01_row28_col20\" class=\"data row28 col20\" >0.215204</td>\n", " <td id=\"T_87d01_row28_col21\" class=\"data row28 col21\" >0.787424</td>\n", " <td id=\"T_87d01_row28_col22\" class=\"data row28 col22\" >0.359755</td>\n", " <td id=\"T_87d01_row28_col23\" class=\"data row28 col23\" >0.816322</td>\n", " <td id=\"T_87d01_row28_col24\" class=\"data row28 col24\" >0.747419</td>\n", " <td id=\"T_87d01_row28_col25\" class=\"data row28 col25\" >0.547691</td>\n", " <td id=\"T_87d01_row28_col26\" class=\"data row28 col26\" >0.801080</td>\n", " <td id=\"T_87d01_row28_col27\" class=\"data row28 col27\" >0.855434</td>\n", " <td id=\"T_87d01_row28_col28\" class=\"data row28 col28\" >1.000000</td>\n", " <td id=\"T_87d01_row28_col29\" class=\"data row28 col29\" >0.502528</td>\n", " <td id=\"T_87d01_row28_col30\" class=\"data row28 col30\" >0.511114</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row29\" class=\"row_heading level0 row29\" >symmetry_worst</th>\n", " <td id=\"T_87d01_row29_col0\" class=\"data row29 col0\" >0.416294</td>\n", " <td id=\"T_87d01_row29_col1\" class=\"data row29 col1\" >0.163953</td>\n", " <td id=\"T_87d01_row29_col2\" class=\"data row29 col2\" >0.105008</td>\n", " <td id=\"T_87d01_row29_col3\" class=\"data row29 col3\" >0.189115</td>\n", " <td id=\"T_87d01_row29_col4\" class=\"data row29 col4\" >0.143570</td>\n", " <td id=\"T_87d01_row29_col5\" class=\"data row29 col5\" >0.394309</td>\n", " <td id=\"T_87d01_row29_col6\" class=\"data row29 col6\" >0.510223</td>\n", " <td id=\"T_87d01_row29_col7\" class=\"data row29 col7\" >0.409464</td>\n", " <td id=\"T_87d01_row29_col8\" class=\"data row29 col8\" >0.375744</td>\n", " <td id=\"T_87d01_row29_col9\" class=\"data row29 col9\" >0.699826</td>\n", " <td id=\"T_87d01_row29_col10\" class=\"data row29 col10\" >0.334019</td>\n", " <td id=\"T_87d01_row29_col11\" class=\"data row29 col11\" >0.094543</td>\n", " <td id=\"T_87d01_row29_col12\" class=\"data row29 col12\" >-0.128215</td>\n", " <td id=\"T_87d01_row29_col13\" class=\"data row29 col13\" >0.109930</td>\n", " <td id=\"T_87d01_row29_col14\" class=\"data row29 col14\" >0.074126</td>\n", " <td id=\"T_87d01_row29_col15\" class=\"data row29 col15\" >-0.107342</td>\n", " <td id=\"T_87d01_row29_col16\" class=\"data row29 col16\" >0.277878</td>\n", " <td id=\"T_87d01_row29_col17\" class=\"data row29 col17\" >0.197788</td>\n", " <td id=\"T_87d01_row29_col18\" class=\"data row29 col18\" >0.143116</td>\n", " <td id=\"T_87d01_row29_col19\" class=\"data row29 col19\" >0.389402</td>\n", " <td id=\"T_87d01_row29_col20\" class=\"data row29 col20\" >0.111094</td>\n", " <td id=\"T_87d01_row29_col21\" class=\"data row29 col21\" >0.243529</td>\n", " <td id=\"T_87d01_row29_col22\" class=\"data row29 col22\" >0.233027</td>\n", " <td id=\"T_87d01_row29_col23\" class=\"data row29 col23\" >0.269493</td>\n", " <td id=\"T_87d01_row29_col24\" class=\"data row29 col24\" >0.209146</td>\n", " <td id=\"T_87d01_row29_col25\" class=\"data row29 col25\" >0.493838</td>\n", " <td id=\"T_87d01_row29_col26\" class=\"data row29 col26\" >0.614441</td>\n", " <td id=\"T_87d01_row29_col27\" class=\"data row29 col27\" >0.532520</td>\n", " <td id=\"T_87d01_row29_col28\" class=\"data row29 col28\" >0.502528</td>\n", " <td id=\"T_87d01_row29_col29\" class=\"data row29 col29\" >1.000000</td>\n", " <td id=\"T_87d01_row29_col30\" class=\"data row29 col30\" >0.537848</td>\n", " </tr>\n", " <tr>\n", " <th id=\"T_87d01_level0_row30\" class=\"row_heading level0 row30\" >fractal_dimension_worst</th>\n", " <td id=\"T_87d01_row30_col0\" class=\"data row30 col0\" >0.323872</td>\n", " <td id=\"T_87d01_row30_col1\" class=\"data row30 col1\" >0.007066</td>\n", " <td id=\"T_87d01_row30_col2\" class=\"data row30 col2\" >0.119205</td>\n", " <td id=\"T_87d01_row30_col3\" class=\"data row30 col3\" >0.051019</td>\n", " <td id=\"T_87d01_row30_col4\" class=\"data row30 col4\" >0.003738</td>\n", " <td id=\"T_87d01_row30_col5\" class=\"data row30 col5\" >0.499316</td>\n", " <td id=\"T_87d01_row30_col6\" class=\"data row30 col6\" >0.687382</td>\n", " <td id=\"T_87d01_row30_col7\" class=\"data row30 col7\" >0.514930</td>\n", " <td id=\"T_87d01_row30_col8\" class=\"data row30 col8\" >0.368661</td>\n", " <td id=\"T_87d01_row30_col9\" class=\"data row30 col9\" >0.438413</td>\n", " <td id=\"T_87d01_row30_col10\" class=\"data row30 col10\" >0.767297</td>\n", " <td id=\"T_87d01_row30_col11\" class=\"data row30 col11\" >0.049559</td>\n", " <td id=\"T_87d01_row30_col12\" class=\"data row30 col12\" >-0.045655</td>\n", " <td id=\"T_87d01_row30_col13\" class=\"data row30 col13\" >0.085433</td>\n", " <td id=\"T_87d01_row30_col14\" class=\"data row30 col14\" >0.017539</td>\n", " <td id=\"T_87d01_row30_col15\" class=\"data row30 col15\" >0.101480</td>\n", " <td id=\"T_87d01_row30_col16\" class=\"data row30 col16\" >0.590973</td>\n", " <td id=\"T_87d01_row30_col17\" class=\"data row30 col17\" >0.439329</td>\n", " <td id=\"T_87d01_row30_col18\" class=\"data row30 col18\" >0.310655</td>\n", " <td id=\"T_87d01_row30_col19\" class=\"data row30 col19\" >0.078079</td>\n", " <td id=\"T_87d01_row30_col20\" class=\"data row30 col20\" >0.591328</td>\n", " <td id=\"T_87d01_row30_col21\" class=\"data row30 col21\" >0.093492</td>\n", " <td id=\"T_87d01_row30_col22\" class=\"data row30 col22\" >0.219122</td>\n", " <td id=\"T_87d01_row30_col23\" class=\"data row30 col23\" >0.138957</td>\n", " <td id=\"T_87d01_row30_col24\" class=\"data row30 col24\" >0.079647</td>\n", " <td id=\"T_87d01_row30_col25\" class=\"data row30 col25\" >0.617624</td>\n", " <td id=\"T_87d01_row30_col26\" class=\"data row30 col26\" >0.810455</td>\n", " <td id=\"T_87d01_row30_col27\" class=\"data row30 col27\" >0.686511</td>\n", " <td id=\"T_87d01_row30_col28\" class=\"data row30 col28\" >0.511114</td>\n", " <td id=\"T_87d01_row30_col29\" class=\"data row30 col29\" >0.537848</td>\n", " <td id=\"T_87d01_row30_col30\" class=\"data row30 col30\" >1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n" ], "text/plain": [ "<pandas.io.formats.style.Styler at 0x7fa6be276050>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.corr().style.background_gradient()" ] }, { "cell_type": "code", "execution_count": 10, "id": "a5ff45f8", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.924145Z", "iopub.status.busy": "2022-01-28T14:18:46.923259Z", "iopub.status.idle": "2022-01-28T14:18:46.926306Z", "shell.execute_reply": "2022-01-28T14:18:46.926820Z", "shell.execute_reply.started": "2022-01-28T14:07:24.053474Z" }, "papermill": { "duration": 0.042857, "end_time": "2022-01-28T14:18:46.927013", "exception": false, "start_time": "2022-01-28T14:18:46.884156", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "table_corr = df.corr()" ] }, { "cell_type": "code", "execution_count": 11, "id": "f13df32e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:46.997182Z", "iopub.status.busy": "2022-01-28T14:18:46.996251Z", "iopub.status.idle": "2022-01-28T14:18:47.008111Z", "shell.execute_reply": "2022-01-28T14:18:47.008686Z", "shell.execute_reply.started": "2022-01-28T14:07:24.063115Z" }, "papermill": { "duration": 0.048822, "end_time": "2022-01-28T14:18:47.008897", "exception": false, "start_time": "2022-01-28T14:18:46.960075", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "table_corr = pd.DataFrame(table_corr).query('diagnosis>0.5')" ] }, { "cell_type": "code", "execution_count": 12, "id": "c1e7bb2e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:47.080065Z", "iopub.status.busy": "2022-01-28T14:18:47.079114Z", "iopub.status.idle": "2022-01-28T14:18:47.084988Z", "shell.execute_reply": "2022-01-28T14:18:47.085494Z", "shell.execute_reply.started": "2022-01-28T14:07:24.079572Z" }, "papermill": { "duration": 0.043869, "end_time": "2022-01-28T14:18:47.085687", "exception": false, "start_time": "2022-01-28T14:18:47.041818", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n", " 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n", " 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n", " 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n", " 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n", " 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n", " 'perimeter_worst', 'area_worst', 'smoothness_worst',\n", " 'compactness_worst', 'concavity_worst', 'concave points_worst',\n", " 'symmetry_worst', 'fractal_dimension_worst'],\n", " dtype='object')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table_corr.columns" ] }, { "cell_type": "code", "execution_count": 13, "id": "623b8a30", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:47.157149Z", "iopub.status.busy": "2022-01-28T14:18:47.156207Z", "iopub.status.idle": "2022-01-28T14:18:47.162767Z", "shell.execute_reply": "2022-01-28T14:18:47.163218Z", "shell.execute_reply.started": "2022-01-28T14:07:24.087157Z" }, "papermill": { "duration": 0.044351, "end_time": "2022-01-28T14:18:47.163403", "exception": false, "start_time": "2022-01-28T14:18:47.119052", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df = df[['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n", " 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n", " 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n", " 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n", " 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n", " 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n", " 'perimeter_worst', 'area_worst', 'smoothness_worst',\n", " 'compactness_worst', 'concavity_worst', 'concave points_worst',\n", " 'symmetry_worst', 'fractal_dimension_worst']]" ] }, { "cell_type": "code", "execution_count": 14, "id": "3ea4b50c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:47.234030Z", "iopub.status.busy": "2022-01-28T14:18:47.233072Z", "iopub.status.idle": "2022-01-28T14:18:47.242445Z", "shell.execute_reply": "2022-01-28T14:18:47.242913Z", "shell.execute_reply.started": "2022-01-28T14:07:24.101288Z" }, "papermill": { "duration": 0.046396, "end_time": "2022-01-28T14:18:47.243113", "exception": false, "start_time": "2022-01-28T14:18:47.196717", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X = df.drop('diagnosis',axis = 1)\n", "y = df[['diagnosis']]\n", "X_train, X_test,y_train,y_test = train_test_split(X,y,test_size = 0.33)" ] }, { "cell_type": "code", "execution_count": 15, "id": "253487d9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:47.314139Z", "iopub.status.busy": "2022-01-28T14:18:47.313209Z", "iopub.status.idle": "2022-01-28T14:18:47.319664Z", "shell.execute_reply": "2022-01-28T14:18:47.318969Z", "shell.execute_reply.started": "2022-01-28T14:07:24.115446Z" }, "papermill": { "duration": 0.043296, "end_time": "2022-01-28T14:18:47.319879", "exception": false, "start_time": "2022-01-28T14:18:47.276583", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(381, 30)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": 16, "id": "78c3818e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:47.414685Z", "iopub.status.busy": "2022-01-28T14:18:47.404920Z", "iopub.status.idle": "2022-01-28T14:18:53.809313Z", "shell.execute_reply": "2022-01-28T14:18:53.808702Z", "shell.execute_reply.started": "2022-01-28T14:07:24.124430Z" }, "papermill": { "duration": 6.450213, "end_time": "2022-01-28T14:18:53.809503", "exception": false, "start_time": "2022-01-28T14:18:47.359290", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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FLxp4KgD4auOsfF1eVf95YpPAvbRp06bMzq7+fWJ2djabN28eeCIAuLtx4usnkyxW1Z3eaoK1aGFhIevWrf4rPTMzky1btgw8EQDc3VHH1+itJda11tYf6q0mquqbJjMiHJ25ubnMz8+nqjI/P+9kewDWpHFWvo7kd47jY8ExWVhYyKMf/WirXgCsWeOccH8kdRwfC47J3NxcLr744qHHAIB7dDxXvrz5KgDAERzP+AIA4AiO52HHrxzHxwKYOnv27MmXbpvJK6/2sbjQ0/W3zeT+e/YMPca/OeqVr6p6UlXdf3T5R6rqtVV1zv79rbVvm8SAAADTZJyVr99I8piqekySFyf57SSXJvmOSQwGMG02bNiQu1ZuyMu+xVskQk+vvPq03GfDhqHH+DfjnPO10lprSZ6Z5A2ttV+Pz3YEABjLOCtft1XVS5P8SJKnVNW6JF8zmbEAAKbTOCtfP5jkriTPa619NslDk/zaRKYCAJhSR73yNQqu1x5w/VNZPecLAICjNM6rHW8bfaD2rVX15araW1VfnORwMK7l5eVceOGFWV5eHnoUADikcT9Y+7TRh2mvT/L9WX0FJKwZO3bsyK5du3LppRZlAVibjukd7tuqP0nytOM7Dhy75eXlLC4uprWWxcVFq18ArEnjHHb8vgO+nlNVr0ry5QnOBmPZsWNH9u3blyTZu3ev1S8A1qRxVr6+54CvpyW5Lavv+QVrwhVXXJGVlZUkycrKSnbu3DnwRABwd+O82vHHJjkI3FubNm3K5ZdfnpWVlczOzmbz5s1DjwQAd3PE+Kqqn22t/WpVbU/SDt7fWrtwIpPBmBYWFrK4uJgkmZmZyZYtWwaeCADu7mhWvj42+n7lJAeBe2tubi7z8/O57LLLMj8/n7m5uaFHAoC7OWJ8tdYuG33fMflx4N5ZWFjI7t27rXoBsGYdzWHHy3KIw437tda+97hOxMRt3749S0tLQ48xEXv27EmSvOIVrxh4ksnZuHFjtm7dOvQYAByjozns+OrR9+9L8nVJfnd0/YIkN05iKDhWd95559AjAMBhHc1hx/clSVW9prV2/gG7Lqsq54GdgKZ51WTbtm1JkosuumjgSQDg0MZ5n6/7V9U37r9SVd+Q5P7HfyQAgOl11O/zleRFSd5bVZ9IUknOSfKCiUwFADClxnmT1cWqOi/JI0abPt5au2syYwEATKdxVr6S5LwkD09y3ySPqaq01nyAHgDAUTrq+KqqX0zynUkemeTyJE9P8sEk4gsA4CiNc8L9c5I8NclnR5/z+JgkD5jIVAAAU2qc+Ppya21fkpWqOi3JTUkeNpmxAACm01EddqyqSvLhqnpgkkuSXJXk9iQfmtxoAADT56jiq7XWquoJrbUvJPnNqlpMclpr7cMTnQ4AYMqMc9jx6qp6fJK01nYLLwCA8Y3zVhNPTPLDVXV9ki9l9Y1WW2vtmycyGQDAFBonvp42sSkAAE4S47zD/fWTHAQA4GQwzjlfAADcS+ILAKAj8QUA0JH4AgDoSHwBAHQkvgAAOhJfAAAdiS8AgI7EFwBARxONr6p6c1XdVFXXHrDt5VW1p6quGX09Y5IzAACsJZNe+XprkvlDbH9da+2xo6/LJzwDAMCaMc4Ha4+ttfb+qjp3ks8BcCL51O0zeeXVpw09BmO68Y7VtYoz77dv4Ek4Fp+6fSbnDT3EASYaX4fxwqrakuTKJC9urX1+oDkAutm4cePQI3CMvrK0lCS5zzn+DE9E52Vt/fc3RHz9RpJfStJG31+T5L8e6oZV9fwkz0+Ss88+u9d8ABOxdevWoUfgGG3bti1JctFFFw08CdOg+6sdW2s3ttb2ttb2JbkkyRMOc9s3ttbOb62df8YZZ/QbEgBgQrrHV1WddcDVZye59p5uCwAwbSZ62LGqfj/JdyY5vao+k+QXk3xnVT02q4cddyd5wSRnAABYSyb9ascLDrH5TZN8TgCAtcw73AMAdCS+AAA6El8AAB2JLwCAjsQXAEBH4gsAoCPxBQDQkfgCAOhIfAEAdCS+AAA6El8AAB2JLwCAjsQXAEBH4gsAoCPxBQDQkfgCAOhIfAEAdCS+AAA6El8AAB2JLwCAjsQXAEBH4gsAoCPxBQDQkfgCAOhodugB1qLt27dnaWlp6DE4Bvv/3LZt2zbwJByrjRs3ZuvWrUOPATAx4usQlpaWcs21H8ve+z146FEY07qvtCTJVZ+4ceBJOBYzd9wy9AgAEye+7sHe+z04dz7iGUOPASeV9R+/fOgRACbOOV8AAB2JLwCAjsQXAEBH4gsAoCPxBQDQkfgCAOhIfAEAdCS+AAA6El8AAB2JLwCAjsQXAEBH4gsAoCPxBQDQkfgCAOhIfAEAdCS+AAA6El8AAB2JLwCAjsQXAEBHs0MPsBbt2bMnM3d8Mes/fvnQo8BJZeaO5ezZszL0GAATZeULAKAjK1+HsGHDhnz2rtnc+YhnDD0KnFTWf/zybNhw5tBjAEyUlS8AgI7EFwBAR+ILAKAj8QUA0JH4AgDoSHwBAHQkvgAAOhJfAAAdiS8AgI7EFwBAR+ILAKAj8QUA0JH4AgDoSHwBAHQkvgAAOhJfAAAdiS8AgI7EFwBAR7NDD7BWzdxxS9Z//PKhx2BM6758a5Jk331PG3gSjsXMHbckOXPoMQAmSnwdwsaNG4cegWO0tHRbkmTjN/of+InpTP/9AVNPfB3C1q1bhx6BY7Rt27YkyUUXXTTwJABwaOILgONi+/btWVpaGnqMidj/c+3/C9402rhxo8WHTsQXABzB+vXrhx6BKSK+ADgurJrA0fFWEwAAHYkvAICOxBcAQEfiCwCgI/EFANCR+AIA6Eh8AQB0JL4AADoSXwAAHYkvAICOxBcAQEfiCwCgo4nGV1W9uapuqqprD9j24KraWVXXjb4/aJIzAACsJZNe+XprkvmDtr0kybtba+cleffoOgDASWGi8dVae3+SWw7a/MwkO0aXdyR51iRnAABYS4Y45+vM1toNo8ufTXLmADMAAAxi0BPuW2stSbun/VX1/Kq6sqquvPnmmztOBgAwGUPE141VdVaSjL7fdE83bK29sbV2fmvt/DPOOKPbgAAAkzJEfP1pkoXR5YUk7xpgBgCAQUz6rSZ+P8mHkjy8qj5TVc9L8qokm6vquiSbRtcBAE4Ks5N88NbaBfew66mTfF4AgLXKO9wDAHQkvgAAOhJfAAAdiS8AgI7EFwBAR+ILAKAj8QUA0JH4AgDoSHwBAHQkvgAAOhJfAAAdiS8AgI7EFwBAR+ILAKAj8QUA0JH4AgDoSHwBAHQkvgAAOhJfAAAdiS8AgI7EFwBAR+ILAKAj8QUA0JH4AgDoSHwBAHQkvgAAOhJfAAAdiS8AgI7EFwBAR+ILAKAj8QUA0JH4AgDoSHwBAHQkvgAAOhJfAAAdiS8AgI7EFwBAR7NDD0B/27dvz9LS0tBjTMT+n2vbtm0DTzI5GzduzNatW4ceA4BjJL6YKuvXrx96BAA4LPF1ErJqAgDDcc4XAEBH4gsAoCPxBQDQkfgCAOhIfAEAdCS+AAA6El8AAB2JL6bK8vJyLrzwwiwvLw89CgAckvhiquzYsSO7du3KpZdeOvQoAHBI4oupsby8nMXFxbTWsri4aPULgDVJfDE1duzYkX379iVJ9u7da/ULgDVJfDE1rrjiiqysrCRJVlZWsnPnzoEnAoC7E19MjU2bNmV2dvWz4mdnZ7N58+aBJwKAuxNfTI2FhYWsW7f6r/TMzEy2bNky8EQAcHfii6kxNzeX+fn5VFXm5+czNzc39EgAcDezQw8Ax9PCwkJ2795t1QuANUt8MVXm5uZy8cUXDz0GANwjhx0BADoSXwAAHYkvAICOxBcAQEfiCwCgI/EFANCR+AIA6Eh8AQB0JL4AADoSXwAAHYkvAICOxBcAQEfiCwCgI/EFANBRtdaGnuGoVNXNSa4feg5OCKcn+dzQQwBTx+8WxnFOa+2MQ+04YeILjlZVXdlaO3/oOYDp4ncLx4vDjgAAHYkvAICOxBfT6I1DDwBMJb9bOC6c8wUA0JGVLwCAjsQXU6Wq5qvqH6tqqapeMvQ8wImvqt5cVTdV1bVDz8J0EF9MjaqaSfLrSZ6e5JFJLqiqRw47FTAF3ppkfughmB7ii2nyhCRLrbVPtNa+kuQPkjxz4JmAE1xr7f1Jbhl6DqaH+GKabEjy6QOuf2a0DQDWDPEFANCR+GKa7EnysAOuP3S0DQDWDPHFNPm7JOdV1TdU1dcm+aEkfzrwTADwVcQXU6O1tpLkhUn+MsnHkry9tfaRYacCTnRV9ftJPpTk4VX1map63tAzcWLzDvcAAB1Z+QIA6Eh8AQB0JL4AADoSXwAAHYkvAICOZoceAGBcVfXyJLcnOS3J+1trVww4yyuGngE4sYgv4ITVWvsFMwAnGocdgRNCVf2PqvqnqvpgkoePtr21qp4zuvwLVfV3VXVtVb2xqmq0/fFV9eGquqaqfq2qrh1tf25VvbOqFqvquqr61QOe64Kq2jV6rF8ZbZsZPd+1o30vOsQMr6qqj46e79Vd/wEBJwwrX8CaV1XfmtWPi3psVn9vXZ3kqoNu9obW2itGt/+dJN+d5LIkb0ny4621D1XVqw66z2OTPC7JXUn+saq2J9mb5FeSfGuSzyf5q6p6VpJPJ9nQWnvU6DkeeNCMc0meneQRrbV28H6A/ax8ASeCJyf5P621O1prt+bQn9n5H6vqb6tqV5L/lOSbRgF0amvtQ6Pb/N5B93l3a+2LrbUvJ/loknOSPD7Je1trN48+suptSZ6S5BNJvrGqtlfVfJJbD3qsLyb5cpI3VdX3Jbnj3v7QwHQSX8AJr6rum+R/J3lOa+3RSS5Jct+juOtdB1zem8McDWitfT7JY5K8N8lPJPntg/avJHlCkj/O6qrb4tH/BMDJRHwBJ4L3J3lWVa2vqlOTfM9B+/eH1ueq6pQkz0mS1toXktxWVU8c7f+ho3iu/5fkO6rq9KqaSXJBkvdV1elJ1rXW3pHk55N8y4F3Gj3vA1prlyd5UVZDDeBunPMFrHmttaur6g+T/EOSm5L83UH7v1BVlyS5NslnD9r/vCSXVNW+JO/L6uHBwz3XDVX1kiTvSVJJ/ry19q6qekySt1TV/r+0vvSgu56a5F2jVbhK8jPH8KMCJ4FqrQ09A8DEVNUprbXbR5dfkuSs1tq2gccCTmJWvoBp911V9dKs/r67Pslzhx0HONlZ+QIA6MgJ9wAAHYkvAICOxBcAQEfiCwCgI/EFANCR+AIA6Oj/Ax6AbWi5fL7hAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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AgFPslBa1qnr6opevTnL4jtAbk7ymqp5YVc9KclGSv0ryqSQXVdWzquoJWbjh4MZTmRkAYFxGdjNBVf1Bkh9Ocl5V3ZnkTUl+uKouSdKS7Evyc0nSWvtsVX0gCzcJHEzyutba/HCc1yf5cJKJJO9prX12VJkBAHryuEWtqtYn+dkkGxdv31r7Z8fbr7V25TGGf/c4278tyduOMX5TkpseLycAwGqzlBm1Dyb5eJLdSeZHGwcAgMOWUtTObK390siTAABwhKXcTPAnix/1BADAqbGUorYtC2Xtoaq6r6rur6r7Rh0MAGCte9xTn621s05FEAAAjrSkj+cYHvV0UZInHR5rrX1sVKEAAFjax3P88yyc/rwgyS1JXpjkE0n+8UiTAQCscUu9Ru35SW5vrf1Ikucm+fooQwEAsLSi9nBr7eEkqaontta+kOTZo40FAMBSrlG7s6rOTvIfk+yqqq8luX2UoQAAWNpdn68eFt9cVR9N8rQkO0eaCgCAJd/1+aIkF7XW3js8+3NDki+PNBkAwBr3uNeoVdWbkvxSkjcMQ6cn+XejDAUAwNJuJnh1kh9L8s0kaa39XRIfggsAMGJLKWrfaq21JC1JqurJo40EAECytKL2gar6nSRnV9XPJtmd5IbRxgIAYCl3ff5GVV2e5L4sfH7aG1tru0aeDABgjVvSXZ+ttV1V9ZeHt6+qc1tr9440GQDAGreUZ33+XJJfTfJwkkNJKgvXq33vaKMBAKxtS5lR+1+T/GBr7aujDgMAwN9bys0Ef5PkwVEHAQDgSEuZUXtDkr8YrlF75PBga+3qkaUCAGBJRe13kvw/SfZm4Ro1AABOgaUUtdNba78w8iQAABxhKdeo/d9VdVVVPb2qzj38NfJkAABr3FJm1K4cvr9h0ZiP5wAAGLGlPJngWcdbX1WXe1IBAMDyW8qpz8fz68twDAAAjrIcRa2W4RgAABxlOYpaW4ZjAABwlOUoagAAjMByFLV9y3AMAACOspSP50hV/WCSi5M86fBYa23H8P2fjCYaAMDa9rhFrarelOSHs1DUbkpyRZL/nGTHSJMBAKxxSzn1+eNJfjTJV1prP53kh5I8baSpAABYUlF7qLV2KMnBqnpqknuSXDjaWAAALOUatT1VdXaSG5LcnOSBJJ8YZSgAAJb2CKnXDov/pqp2Jnlqa+0zo40FAMDjnvqsBf9TVb2xtbYvyder6tLRRwMAWNuWco3au5L8oyRXDq/vT/LOkSUCACDJ0q5Re0Fr7XlV9ddJ0lr7WlU9YcS5AADWvKXMqH27qiYyPNOzqtYnOTTSVAAALKmoXZfkj5N8T1W9LQsfdvv2kaYCAOD4pz6r6rQkX07yv2XhQ28ryataa58/BdkA1qzt27dndnZ23DE4QYf/223btm3MSThRU1NT2bp167hjHL+otdYOVdU7W2vPTfKFU5QJYM2bnZ3NbZ/96zzjKfPjjsIJeMK3F05YPXL7njEn4UT87QMT447wHUu5meAjVfVPk/xRa62NOhAAC57xlPn88vPuG3cMWHPe/umnjjvCdyzlGrWfS/IfkjxSVfdV1f1V5TcHAMCILeXJBGdV1blJLkrypNFHAgAgWUJRq6p/nmRbkguS3JLkhUn+Igs3FwAAMCJLOfW5Lcnzk9zeWvuRJM9N8o2RpgIAYElF7eHW2sNJUlVPbK19IcmzRxsLAICl3PV5Z1WdneQ/JtlVVV9LcvsoQwEAsLSbCV49LL65qj6a5GlJdo40FQAAS5pR+47W2p+PKggAAEdayjVqAACMgaIGANApRQ0AoFOKGgBApxQ1AIBOKWoAAJ1S1AAAOqWoAQB0SlEDAOiUogYA0ClFDQCgU4oaAECnFDUAgE4pagAAnVLUAAA6pagBAHRKUQMA6JSiBgDQKUUNAKBTihoAQKcUNQCATo2sqFXVe6rqnqq6ddHYuVW1q6puG76fM4xXVV1XVbNV9Zmqet6ifbYM299WVVtGlRcAoDejnFH7vSTTR41dk+QjrbWLknxkeJ0kVyS5aPi6Ksm7k4Vil+RNSV6Q5NIkbzpc7mA5zM3N5eqrr87c3Ny4owDAo4ysqLXWPpbk3qOGX5lkZlieSfKqReM72oJPJjm7qp6e5KVJdrXW7m2tfS3Jrjy6/MEJm5mZyd69e7Njx45xRwGARznV16id31q7a1j+SpLzh+UNSe5YtN2dw9hjjT9KVV1VVXuqas+BAweWNzWr0tzcXHbu3JnWWnbu3GlWDYDujO1mgtZaS9KW8XjXt9Y2tdY2rV+/frkOyyo2MzOTQ4cOJUnm5+fNqgHQnVNd1O4eTmlm+H7PML4/yYWLtrtgGHuscThpu3fvzsGDB5MkBw8ezK5du8acCACOdKqL2o1JDt+5uSXJBxeNbx7u/nxhkm8Mp0g/nOQlVXXOcBPBS4YxOGmXXXZZJicnkySTk5O5/PLLx5wIAI40yo/n+IMkn0jy7Kq6s6p+JsmvJbm8qm5LctnwOkluSvKlJLNJbkjy2iRprd2b5F8l+dTw9ZZhDE7ali1bctppC38FJiYmsnnz5jEnAoAjTY7qwK21Kx9j1Y8eY9uW5HWPcZz3JHnPMkaDJMm6desyPT2dD33oQ5mens66devGHQkAjjCyogYrwZYtW7Jv3z6zaQB0SVFjTVu3bl2uu+66cccAgGPyrE8AgE4pagAAnVLUAAA65Ro1gA7t378/37x/Im//9FPHHQXWnNvvn8iT9/fx+fpm1AAAOmVGDaBDGzZsyCMH78ovP+++cUeBNeftn35qnrhhw7hjJDGjBgDQLUUNAKBTihpr2tzcXK6++urMzc2NOwoAPIqixpo2MzOTvXv3ZseOHeOOAgCPoqixZs3NzWXnzp1prWXnzp1m1QDojqLGmjUzM5NDhw4lSebn582qAdAdRY01a/fu3Tl48GCS5ODBg9m1a9eYEwHAkRQ11qzLLrssk5MLHyU4OTmZyy+/fMyJAOBIihpr1pYtW3LaaQt/BSYmJrJ58+YxJwKAIylqrFnr1q3L9PR0qirT09NZt27duCMBwBEUNda0H/uxH8uZZ56ZV7ziFeOOAgCPoqixpt1444158MEH86EPfWjcUQDgURQ11iyfowZA7xQ11qyZmZnMz88nWfh4Dp+jBkBvFDXWrN27d3+nqM3Pz/scNQC6o6ixZr3oRS864vWLX/ziMSUBgGNT1FizqmrcEQDguBQ11qyPf/zjx30NAOOmqLFmeYQUAL2bHHcA+rZ9+/bMzs6OO8ZIfPvb3/7OQ9nn5+dz2223Zdu2bWNOtfympqaydevWcccA4ASYUWPNOv30078zo3buuefm9NNPH3MiADiSGTWOa7XPxLz2ta/N7bffnuuvv96zPgHojhk11rTTTz89U1NTShoAXVLUAAA6pagBAHRKUQMA6JSiBgDQKUUNAKBTihoAQKcUNQCATilqAACdUtQAADqlqAEAdEpRAwDolKIGANCpyXEHAODY/vaBibz9008ddwxOwN0PLsyDnH/moTEn4UT87QMTuWjcIQaKGkCHpqamxh2Bk/Ct2dkkyROf6b/jSnRR+vk7qKgBdGjr1q3jjsBJ2LZtW5Lk2muvHXMSVjrXqAEAdEpRAwDolKIGANApRQ0AoFOKGgBApxQ1AIBOKWoAAJ1S1AAAOqWoAQB0SlEDAOiUogYA0ClFDQCgU4oaAECnFDUAgE4pagAAnVLUAAA6pagBAHRKUQMA6JSiBgDQKUUNAKBTihoAQKcUNQCATilqAACdmhx3gJVu+/btmZ2dHXcMTtDh/3bbtm0bcxJO1NTUVLZu3TruGAAjoaidpNnZ2dxy6+czf+a5447CCTjtWy1JcvOX7h5zEk7ExIP3jjsCwEgpastg/sxz89D3v2zcMWDNOeMLN407AsBIuUYNAKBTihoAQKcUNQCATo2lqFXVvqraW1W3VNWeYezcqtpVVbcN388Zxquqrquq2ar6TFU9bxyZAQBOtXHOqP1Ia+2S1tqm4fU1ST7SWrsoyUeG10lyRZKLhq+rkrz7lCcFABiDnk59vjLJzLA8k+RVi8Z3tAWfTHJ2VT19DPkAAE6pcRW1luQ/VdXNVXXVMHZ+a+2uYfkrSc4fljckuWPRvncOY0eoqquqak9V7Tlw4MCocgMAnDLj+hy1F7XW9lfV9yTZVVVfWLyytdaqqn03B2ytXZ/k+iTZtGnTd7UvAECPxjKj1lrbP3y/J8kfJ7k0yd2HT2kO3+8ZNt+f5MJFu18wjAEArGqnvKhV1ZOr6qzDy0lekuTWJDcm2TJstiXJB4flG5NsHu7+fGGSbyw6RQoAsGqN49Tn+Un+uKoOv//7Wms7q+pTST5QVT+T5PYkPzFsf1OSlyWZTfJgkp8+9ZEBAE69U17UWmtfSvJDxxifS/KjxxhvSV53CqIBAHTFQ9lP0v79+zPx4Dc8HBrGYOLBuezff3DcMQBGpqfPUQMAYBEzaidpw4YN+cojk3no+1827iiw5pzxhZuyYcP5j78hwAplRg0AoFOKGgBApxQ1AIBOKWoAAJ1S1AAAOqWoAQB0SlEDAOiUogYA0ClFDQCgU4oaAECnFDUAgE551ucymHjw3pzxhZvGHYMTcNrD9yVJDj3pqWNOwomYePDeJJ71CaxeitpJmpqaGncETsLs7P1Jkqnv9Y/9ynS+v4PAqlattXFnWHabNm1qe/bsGXcMVoBt27YlSa699toxJ4G1Zfv27ZmdnR13jJE5/LOt5v+RmJqaytatW8cdY1Woqptba5uOtc6MGgAsszPOOGPcEVglFDUATjkzMbA07voEAOiUogYA0ClFDQCgU4oaAECnFDUAgE4pagAAnVLUAAA6pagBAHRKUQMA6JSiBgDQKUUNAKBTihoAQKcUNQCATilqAACdUtQAADqlqAEAdEpRAwDolKIGANApRQ0AoFOKGgBApxQ1AIBOKWoAAJ1S1AAAOqWoAQB0SlEDAOiUogYA0ClFDQCgU4oaAECnFDUAgE4pagAAnVLUAAA6pagBAHRKUQMA6JSiBgDQqclxB6Bv27dvz+zs7LhjjMzhn23btm1jTjI6U1NT2bp167hjAHACFDXWtDPOOGPcEQDgMSlqHJeZGAAYH9eoAQB0SlEDAOiUogYA0ClFDQCgU4oaAECnFDUAgE4pagAAnVLUAAA6pagBAHRKUQMA6JSiBgDQKUUNAKBTihoAQKcUNQCATilqAACdUtQAADqlqAEAdEpRAwDoVLXWxp1h2VXVgSS3jzsHK8Z5Sb467hDAquN3C0v1zNba+mOtWJVFDb4bVbWntbZp3DmA1cXvFpaDU58AAJ1S1AAAOqWoQXL9uAMAq5LfLZw016gBAHTKjBoAQKcUNdasqpquqi9W1WxVXTPuPMDqUFXvqap7qurWcWdh5VPUWJOqaiLJO5NckeTiJFdW1cXjTQWsEr+XZHrcIVgdFDXWqkuTzLbWvtRa+1aS9yd55ZgzAatAa+1jSe4ddw5WB0WNtWpDkjsWvb5zGAOAbihqAACdUtRYq/YnuXDR6wuGMQDohqLGWvWpJBdV1bOq6glJXpPkxjFnAoAjKGqsSa21g0len+TDST6f5AOttc+ONxWwGlTVHyT5RJJnV9WdVfUz487EyuXJBAAAnTKjBgDQKUUNAKBTihoAQKcUNQCATilqAACdmhx3AIBRqqo3J3kgyVOTfKy1tnuMWd4y7gzAyqKoAWtCa+2NMgArjVOfwKpTVf+yqv6/qvrPSZ49jP1eVf34sPzGqvpUVd1aVddXVQ3jz6+qz1TVLVX1f1TVrcP4T1XVH1XVzqq6rar+90XvdWVV7R2O9evD2MTwfrcO637+GBl+rao+N7zfb5zSPyBgxTCjBqwqVfUPs/BIsEuy8Dvu00luPmqz326tvWXY/t8m+e+TfCjJe5P8bGvtE1X1a0ftc0mS5yZ5JMkXq2p7kvkkv57kHyb5WpL/VFWvSnJHkg2ttR8c3uPsozKuS/LqJN/fWmtHrwc4zIwasNq8OMkft9YebK3dl2M/w/VHquovq2pvkn+c5AeGsnRWa+0TwzbvO2qfj7TWvtFaezjJ55I8M8nzk/xZa+3A8Fiy30/y3yb5UpLvrartVTWd5L6jjvWNJA8n+d2q+idJHjzZHxpYnRQ1YE2pqicleVeSH2+tPSfJDUmetIRdH1m0PJ/jnJForX0tyQ8l+bMk/3OS//Oo9QeTXJrk/8rCbN7Opf8EwFqiqAGrzceSvKqqzqiqs5K84qj1h0vZV6vqKUl+PElaa19Pcn9VvWBY/5olvNdfJfnvquq8qppIcmWSP6+q85Kc1lr7wyS/kuR5i3ca3vdprbWbkvx8FkodwKO4Rg1YVVprn66qf5/k/01yT5JPHbX+61V1Q5Jbk3zlqPU/k+SGqjqU5M+zcIryeO91V1Vdk+SjSSrJn7bWPlhVP5TkvVV1+H+G33DUrmcl+eAwu1dJfuEEflRgDajW2rgzAHShqp7SWntgWL4mydNba9vGHAtYw8yoAfy9l1fVG7Lwu/H2JD813jjAWmdGDQCgU24mAADolKIGANApRQ0AoFOKGgBApxQ1AIBOKWoAAJ36/wEj+oXlZS1lOgAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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jo8MuRx3miJskSS3ba6+9mD9/vqFNu83gJkmS1BEGN0mSpI4wuEmSJHWEwU2SJKkjDG6SJEkdYXCTJEnqCIObJElSRxjcJEmSOsLgJkmS1BEGN0mSpI4wuEmSJHWEwU2SJKkjDG6SJEkdYXCTJEnqiNaDW5LFSW5JMpbk3CnWvyzJdUkmkpw0ad3GJNc30/K+9iOSfLPZ5+eS7N32cUiSJA1bq8EtyWzgI8CrgaOA05IcNWmz7wFnAJ+ZYhc/qapjmumEvvYPAB+qqvnAfcCb9njxkiRJ00zbI27HAmNVdXtVPQpcCpzYv0FV3VFVNwCbBtlhkgC/AlzeNC0DXrfHKpYkSZqm2g5u84C7+pbXNW2D2jfJmiTfSPK6pm0UuL+qJna0zyRnNv3XbNiwYSdLlyRJml5Ghl3ADjyzqtYneRbwlSRrgR8N2rmqlgJLARYsWFAt1ShJkvS4aHvEbT1wWN/yoU3bQKpqffPzduBq4AXAOPDUJJtD507tU5IkqavaDm7XAEc2d4HuDZwKLN9BHwCSHJhkn2b+YOA/ATdXVQH/CGy+A3UJ8MU9XrkkSdI002pwa65DexuwEvg2cFlV3ZTkgiQnACR5UZJ1wMnAxUluarr/B2BNkn+hF9T+tKpubtb9AfCOJGP0rnn7eJvHIUmSNB20fo1bVV0JXDmp7fy++Wvone6c3O+fgKO3sc/b6d2xKkmS9IThmxMkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeqIkUE2SjIXeDNweH+fqvrtdsqSJEnSZIOOuH0ROABYDfxD37RDSRYnuSXJWJJzp1j/siTXJZlIclJf+zFJvp7kpiQ3JHlD37pPJflukuub6ZgBj0OSJKmzBhpxA/arqj/Y2Z0nmQ18BFgErAOuSbK8qm7u2+x7wBnA70/q/hBwelXdmuTpwLVJVlbV/c36d1bV5TtbkyRJUlcNOuL2P5O8Zhf2fywwVlW3V9WjwKXAif0bVNUdVXUDsGlS+79W1a3N/N3AD4C5u1CDJEnSjDBocDuHXnj7SZIHkjyY5IEB+s0D7upbXte07ZQkxwJ7A7f1Nf/X5hTqh5Lss41+ZyZZk2TNhg0bdvZjJUmSppWBgltVPaWqZlXVnKrav1nev+3iAJI8Dfg08FtVtXlU7jzgOcCLgIOAKU/jVtXSqlpQVQvmznWwTpIkddug17iR5EDgSGDfzW1V9bUddFsPHNa3fGjTNuhn7k/vJoh3VdU3+j73nmb2kSSfZOvr4yRJkmacQR8H8jv0TpceClwPvBj4OvArO+h6DXBkkiPoBbZTgV8f8DP3Br4AXDL5JoQkT6uqe5IEeB1w4yD7lCRJ6rKducbtRcCdVfUK4AXA/TvqVFUTwNuAlcC3gcuq6qYkFyQ5ASDJi5KsA04GLk5yU9P9FOBlwBlTPPbj75KsBdYCBwPvG/A4JEmSOmvQU6UPV9XDSUiyT1V9J8mzB+lYVVcCV05qO79v/hp6I3mT+/0t8Lfb2OeORvokSZJmnEGD27okTwX+B7AqyX3AnW0VJUmSpK0NFNyq6tea2T9J8o/03qKworWqJEmStJWduav0JcCRVfXJ5t2l84DvtlaZJEmStjDQzQlJ/pjes9LOa5r2YhvXn0mSJKkdg95V+mvACcC/w2OvoHpKW0VJkiRpa4MGt0erqoACSPKk9kqSJEnSVAYNbpcluRh4apI3A6uBj7VXliRJkiYb9K7SP0+yCHgAeDZwflWtarUySZIkbWHgu0qralWSb27uk+Sgqrq3tcokSZK0hUHfVfoW4D3Aw8AmIPSud3tWe6VJkiSp36Ajbr8PPK+qfthmMZIkSdq2QW9OuA14qM1CJEmStH2DjridB/xTc43bI5sbq+rsVqqSJEnSVgYNbhcDXwHW0rvGTZIkSY+zQYPbXlX1jlYrkSRJ0nYNeo3bl5KcmeRpSQ7aPLVamSRJkrYw6Ijbac3P8/rafByIJEnS42jQNyccsb31SRb5JgVJkqR2DXqqdEc+sIf2I0mSpG3YU8Ete2g/kiRJ2oY9FdxqD+1HkiRJ27CngpskSZJatqeC2x17aD+SJEnahoGCW5KTkzylmX93kr9P8kub11fV69sqUJIkST2Djrj9UVU9mOQlwELg48BftVeWJEmSJhs0uG1sfv5nYGlV/QOwdzslSZIkaSqDBrf1SS4G3gBcmWSfnegrSZKkPWDQ8HUKsBJ4VVXdDxwEvLOtoiRJkrS1QYPbxVX191V1K0BV3QP8ZntlSZIkabJBg9tz+xeSzAZeuOfLkSRJ0rZsN7glOS/Jg8DzkzzQTA8CPwC++LhUKEmSJGAHwa2q/t+qegrwZ1W1fzM9papGq+q8x6lGSZIkASODbFRV5yWZBzyzv09Vfa2twiRJkrSlgYJbkj8FTgVu5mfPdCvA4CZJkvQ4GSi4Ab8GPLuqHmmzGEmSJG3boHeV3g7s1WYhkiRJ2r5BR9weAq5PchXw2KhbVZ3dSlWSpK1cdNFFjI2NDbsM7YLNv7dzzjlnyJVoV82fP5+zzjpr2GUMHNyWN5MkaUjGxsa49aZ/5hlP3rjjjTWt7P3T3gmuR+5cM+RKtCu+9+PZwy7hMYPeVbqs7UIkSTv2jCdv5A9/6YFhlyE9obz/uv2HXcJjthvcklxWVackWUvvLtItVNXzW6tMkiRJW9jRzQmbT8b/KvDaKaYdSrI4yS1JxpKcO8X6lyW5LslEkpMmrVuS5NZmWtLX/sIka5t9Xpgkg9QiSZLUZTt6c8I9zc87gYeBo5vpJ03bdjXvNP0I8GrgKOC0JEdN2ux7wBnAZyb1PQj4Y+A44Fjgj5Mc2Kz+K+DNwJHNtHhHtUiSJHXdQI8DSXIK8C3gZOAU4JuTR8e24VhgrKpur6pHgUuBE/s3qKo7quoGYNOkvq8CVlXVvVV1H7AKWJzkacD+VfWNqirgEuB1gxyHJElSlw16V+m7gBdV1Q8AkswFVgOX76DfPOCuvuV19EbQBjFV33nNtG6KdkmSpBlt0Afwztoc2hrjO9F3aJKcmWRNkjUbNmwYdjmSJEm7ZdDwtSLJyiRnJDkD+AfgygH6rQcO61s+tGkbxLb6rm/md7jPqlpaVQuqasHcuXMH/FhJkqTpaaDgVlXvBC4Gnt9MS6vqDwboeg1wZJIjkuxN70X1gz7IdyXwyiQHNjclvBJY2dww8UCSFzd3k54OfHHAfUqSJHXWoNe4AfwTsJHeTQTXDNKhqiaSvI1eCJsNfKKqbkpyAbCmqpYneRHwBeBA4LVJ3lNVz62qe5O8t++zLqiqe5v5twKfAuYAX2omSZKkGW2g4Jbkd4Dzga8AAS5KckFVfWJHfavqSiadVq2q8/vmr2HLU5/9230C2OozqmoN8LxBapckSZopBh1xeyfwgqoaB0gySm8EbofBTZIkSXvGoDcnjAMP9i0/2LRJkiTpcTLoiNsYvYfufpHeO0tPBG5I8g6AqvqLluqTJElSY9Dgdlszbbb5Ls6n7NlyJEmStC0DBbeqek/bhUiSJGn7pv3bDyRJktRjcJMkSeoIg5skSVJHDBTckvxCkquS3NgsPz/Ju9stTZIkSf0GHXH7GHAe8FOAqrqB3ntHJUmS9DgZNLjtV1XfmtQ2saeLkSRJ0rYNGtx+mOTn6T18lyQnAfe0VpUkSZK2MugDeH8PWAo8J8l64LvAG1urSpIkSVsZNLjdWVULkzwJmFVVD+6whyRJkvaoQU+VfjfJUuDFwI9brEeSJEnbMGhwew6wmt4p0+8m+cskL2mvLEmSJE02UHCrqoeq6rKqej3wAmB/4KutViZJkqQtDPzmhCS/nOSjwLXAvsAprVUlSZKkrQx0c0KSO4B/Bi4D3llV/95mUZIkSdraoCNuz6+qX6uqzxraNJONj49z9tlnMz4+PuxSJEnayqCPA3k0ye8Bz6V3mhSAqvrtVqqShmTZsmWsXbuWSy65hLe//e3DLkfawvr16/n3B2fz/uv2H3Yp0hPKnQ/O5knr1w+7DGDwEbdPA/8X8Cp6NyUcCvgsN80o4+PjrFixgqpixYoVjrpJkqadQUfc5lfVyUlOrKplST4D/K82C5Meb8uWLWPTpk0AbNy40VE3TTvz5s3jkYl7+MNfemDYpUhPKO+/bn/2mTdv2GUAg4+4/bT5eX+S5wEHAIe0U5I0HKtXr2ZiYgKAiYkJVq1aNeSKJEna0qDBbWmSA4F3A8uBm4EPtlaVNAQLFy5kZKQ3CD0yMsKiRYuGXJEkSVsa9AG8f1NV91XV16rqWVV1SFX9ddvFSY+nJUuWMGtW74/E7NmzOf3004dckSRJWxoouCV5f5Kn9i0fmOR9rVUlDcHo6CiLFy8mCYsXL2Z0dHTYJUmStIVBT5W+uqru37xQVfcBr2mlImmIlixZwtFHH+1omyRpWhr0rtLZSfapqkcAkswB9mmvLGk4RkdHufDCC4ddhiRJUxo0uP0dcFWSTzbLvwUsa6ckSZIkTWWg4FZVH0hyA3B80/TeqlrZXlmSJEmabNARN6rqS8CXWqxFkiRJ2zHoXaWvT3Jrkh8leSDJg0l8dLckSdLjaNC7Sj8InFBVB1TV/lX1lKryLceaccbHxzn77LN9T6kkaVoaNLh9v6q+3Wol0jSwdOlSbrjhBpYuXTrsUiRJ2sqgwW1Nks8lOa05bfr6JK9vtTLpcTY+Pv7Y+0lXrVrlqJskadoZNLjtDzwEvBJ4bTP9altFScOwdOlSNm3aBMCmTZscdZMkTTuDPg7kt9ouRBq2q666aqvl8847b0jVSJK0tUHvKj00yReS/KCZPp/k0LaLkx5PVbXdZUmShm3QU6WfBJYDT2+mK5o2acY4/vjjt1heuHDhkCqRJGlqgwa3uVX1yaqaaKZPAXMH6ZhkcZJbkowlOXeK9fs0Nz6MJflmksOb9jcmub5v2pTkmGbd1c0+N687ZMDjkLbpLW95C7Nm9f5IzJo1izPPPHPIFUmStKVBg9t4kt9IMruZfgPY4S13SWYDHwFeDRwFnJbkqEmbvQm4r6rmAx8CPgBQVX9XVcdU1THAbwLfrarr+/q9cfP6qvrBgMchbdPo6Ohjo2yLFi1idHR0yBVJkrSlQYPbbwOnAP8G3AOcRO9F8ztyLDBWVbdX1aPApcCJk7Y5kZ+9sP5y4PgkmbTNaU1fqVWnnHIKT3rSkzj55JOHXYokSVsZKLhV1Z1VdUJVza2qQ6rqdVX1vQG6zgPu6lte17RNuU1VTQA/AiYPdbwB+Oyktk82p0n/aIqgJ+2S5cuX89BDD3HFFVcMuxRJkrYy6F2ly5I8tW/5wCSfaK2qLT/7OOChqrqxr/mNVXU08NJm+s1t9D0zyZokazZs2PA4VKsuGx8fZ8WKFVQVK1as8AG8kqRpZ9BTpc+vqvs3L1TVfcALBui3Hjisb/nQpm3KbZKMAAew5fVzpzJptK2q1jc/HwQ+Q++U7FaqamlVLaiqBXPnDnQvhZ7Ali1b9tgDeDdu3Mgll1wy5IokSdrSoMFtVpIDNy8kOYjBHt57DXBkkiOS7E0vhC2ftM1yYEkzfxLwlWoeoJVkFr1r6x67vi3JSJKDm/m96L3B4Uak3bR69WomJiYAmJiYeOz1V5IkTReDBrf/D/h6kvcmeS/wT8AHd9SpuWbtbcBK4NvAZVV1U5ILkpzQbPZxYDTJGPAOoP+RIS8D7qqq2/va9gFWJrkBuJ7eiN3HBjwOaZsWLlzIyEjv/0dGRkZYtGjRkCuSJGlLg77y6pIka4BfaZpeX1U3D9j3SuDKSW3n980/DEx5C19VXQ28eFLbvwMvHOSzpZ2xZMkSVqxYAfSe43b66acPuSJJkrY0UHADaILaQGFN6qLR0VEOOeQQ1q1bxyGHHOJz3CRJ086gp0qlGW98fJz163v3zqxfv967SiVJ047BTWpcfPHFj71YvqpYunTpkCuSJGlLBjepcdVVV22xvHr16iFVIknS1AxuUmPzM9y2tSxJ0rAZ3KTGrFmztrssSdKw+S+T1Dj++OO3WF64cOGQKpEkaWoGN6nxlre8hSRAb7TtzDPPHHJFkiRtyeAmNUZHRznuuOMAOO6443yOmyRp2jG4SX3uvPPOLX5KkjSdGNykxtjYGPfccw8Ad999N2NjY0OuSJKkLRncpMZ73vOe7S5LkjRsBjepcdddd213WZKkYTO4SZIkdcTIsAuQpotZs2Zt8bYEH8Cr6eh7P57N+6/bf9hlaCd9/6He3yc/t59vZOmi7/14NkcOu4iGwU1qLFq0iJUrV26xLE0n8+fPH3YJ2kWPNjc77fNMf4dddCTT58+fwU1qnHzyyVsEt5NPPnmI1UhbO+uss4ZdgnbROeecA8CHP/zhIVeirvNckNRYvnz5FstXXHHFkCqRJGlqBjepsWrVqi2Wv/zlLw+pEkmSpmZwkxqTX3HlK68kSdONwU1qbH5rwraWJUkaNoOb1Eiy3WVJkobN4CY1XvKSl2x3WZKkYTO4SY2HH354i+VHHnlkSJVIkjQ1g5vU+MY3vrHF8te//vUhVSJJ0tQMbpIkSR1hcJMahx122HaXJUkaNl95pZ1y0UUXMda8c2+m2XfffbdYnjNnzmOvqZlJ5s+f76uTJKmjHHGTGvvtt99j83vvvTdz5swZYjWSJG3NETftlJk+UvPmN7+Z2267jY9+9KPMnz9/2OVIkrQFR9ykPvvttx9HH320oU2SNC0Z3CRJkjrC4CZJktQRBjdJkqSOMLhJkiR1hMFNkiSpIwxukiRJHWFwkyRJ6giDmyRJUkcY3CRJkjqi9eCWZHGSW5KMJTl3ivX7JPlcs/6bSQ5v2g9P8pMk1zfTX/f1eWGStU2fC5Ok7eOQJEkatlaDW5LZwEeAVwNHAaclOWrSZm8C7quq+cCHgA/0rbutqo5ppt/ta/8r4M3Akc20uK1jkCRJmi7aHnE7Fhirqtur6lHgUuDESducCCxr5i8Hjt/eCFqSpwH7V9U3qqqAS4DX7fHKJUmSppm2g9s84K6+5XVN25TbVNUE8CNgtFl3RJJ/TvLVJC/t237dDvYpSZI044wMu4DtuAd4RlWNJ3kh8D+SPHdndpDkTOBMgGc84xktlChJkvT4aXvEbT1wWN/yoU3blNskGQEOAMar6pGqGgeoqmuB24BfaLY/dAf7pOm3tKoWVNWCuXPn7oHDkSRJGp62g9s1wJFJjkiyN3AqsHzSNsuBJc38ScBXqqqSzG1ubiDJs+jdhHB7Vd0DPJDkxc21cKcDX2z5OCRJkoau1VOlVTWR5G3ASmA28ImquinJBcCaqloOfBz4dJIx4F564Q7gZcAFSX4KbAJ+t6rubda9FfgUMAf4UjNJkiTNaK1f41ZVVwJXTmo7v2/+YeDkKfp9Hvj8Nva5Bnjenq1UkiRpevPNCZIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6YmTYBcxEF110EWNjY8MuQ7tg8+/tnHPOGXIl2lXz58/nrLPOGnYZktQKg1sLxsbGuP7Gb7Nxv4OGXYp20qxHC4Brb//+kCvRrpj90L3DLkGSWmVwa8nG/Q7iJ895zbDLkJ5Q5nznymGXIEmt8ho3SZKkjjC4SZIkdYTBTZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEQY3SZKkjmg9uCVZnOSWJGNJzp1i/T5JPtes/2aSw5v2RUmuTbK2+fkrfX2ubvZ5fTMd0vZxSJIkDVurr7xKMhv4CLAIWAdck2R5Vd3ct9mbgPuqan6SU4EPAG8Afgi8tqruTvI8YCUwr6/fG6tqTZv1S5IkTSdtj7gdC4xV1e1V9ShwKXDipG1OBJY185cDxydJVf1zVd3dtN8EzEmyT8v1SpIkTVttB7d5wF19y+vYctRsi22qagL4ETA6aZv/AlxXVY/0tX2yOU36R0myZ8uWJEmafqb9zQlJnkvv9Olb+prfWFVHAy9tpt/cRt8zk6xJsmbDhg3tFytJktSitoPbeuCwvuVDm7Ypt0kyAhwAjDfLhwJfAE6vqts2d6iq9c3PB4HP0Dslu5WqWlpVC6pqwdy5c/fIAUmSJA1LqzcnANcARyY5gl5AOxX49UnbLAeWAF8HTgK+UlWV5KnAPwDnVtX/2bxxE+6eWlU/TLIX8KvA6paPY6esX7+e2Q/9iDnfuXLYpUhPKLMfGmf9+olhlyFJrWl1xK25Zu1t9O4I/TZwWVXdlOSCJCc0m30cGE0yBrwD2PzIkLcB84HzJz32Yx9gZZIbgOvpBcKPtXkckiRJ00HbI25U1ZXAlZPazu+bfxg4eYp+7wPet43dvnBP1rinzZs3j397ZISfPOc1wy5FekKZ850rmTfv54ZdhiS1ZtrfnCBJkqQeg5skSVJHGNwkSZI6ovVr3CRJ2pGLLrqIsbGxYZfRms3Hds455wy5kvbMnz+fs846a9hlzHgGN0mSWjZnzpxhl6AZwuAmSRo6R2qkwXiNmyRJUkcY3CRJkjrC4CZJktQRBjdJkqSOMLhJkiR1hMFNkiSpIwxukiRJHWFwkyRJ6ggfwNuS2Q/dy5zvXDnsMrSTZj38AACb9t1/yJVoV8x+6F7g54ZdhiS1xuDWgvnz5w+7BO2isbEHAZj/LP/x76af88+fpBnN4NYCX93SXZtfAP3hD394yJVIkrQ1r3GTJEnqCIObJElSRxjcJEmSOsLgJkmS1BEGN0mSpI4wuEmSJHWEwU2SJKkjDG6SJEkdYXCTJEnqCIObJElSRxjcJEmSOsLgJkmS1BEGN0mSpI4wuEmSJHWEwU2SJKkjDG6SJEkdYXCTJEnqCIObJElSRxjcJEmSOsLgJkmS1BEGN0mSpI4wuEmSJHWEwU2SJKkjWg9uSRYnuSXJWJJzp1i/T5LPNeu/meTwvnXnNe23JHnVoPuUJEmaiVoNbklmAx8BXg0cBZyW5KhJm70JuK+q5gMfAj7Q9D0KOBV4LrAY+GiS2QPuU5IkacZpe8TtWGCsqm6vqkeBS4ETJ21zIrCsmb8cOD5JmvZLq+qRqvouMNbsb5B9SpIkzTgjLe9/HnBX3/I64LhtbVNVE0l+BIw27d+Y1HdeM7+jfaolF110EWNjY8MuozWbj+2cc84ZciXtmT9/Pmedddawy5Ak7YK2g9tQJTkTOBPgGc94xpCrURfMmTNn2CVIkrRNbQe39cBhfcuHNm1TbbMuyQhwADC+g7472icAVbUUWAqwYMGC2rVDUD9HaiRJGp62r3G7BjgyyRFJ9qZ3s8HySdssB5Y08ycBX6mqatpPbe46PQI4EvjWgPuUJEmacVodcWuuWXsbsBKYDXyiqm5KcgGwpqqWAx8HPp1kDLiXXhCj2e4y4GZgAvi9qtoIMNU+2zwOSZKk6SC9wa2Zb8GCBbVmzZphlyFJkrRDSa6tqgWT231zgiRJUkcY3CRJkjrC4CZJktQRBjdJkqSOMLhJkiR1hMFNkiSpIwxukiRJHWFwkyRJ6giDmyRJUkcY3CRJkjrC4CZJktQRBjdJkqSOMLhJkiR1hMFNkiSpIwxukiRJHZGqGnYNj4skG4A7h12HOuFg4IfDLkLSjOPfLdoZz6yquZMbnzDBTRpUkjVVtWDYdUiaWfy7RXuCp0olSZI6wuAmSZLUEQY3aWtLh12ApBnJv1u027zGTZIkqSMccZMkSeoIg5vUJ8niJLckGUty7rDrkdR9ST6R5AdJbhx2Leo+g5vUSDIb+AjwauAo4LQkRw23KkkzwKeAxcMuQjODwU36mWOBsaq6vaoeBS4FThxyTZI6rqq+Btw77Do0MxjcpJ+ZB9zVt7yuaZMkaVowuEmSJHWEwU36mfXAYX3LhzZtkiRNCwY36WeuAY5MckSSvYFTgeVDrkmSpMcY3KRGVU0AbwNWAt8GLquqm4ZblaSuS/JZ4OvAs5OsS/KmYdek7vLNCZIkSR3hiJskSVJHGNwkSZI6wuAmSZLUEQY3SZKkjjC4SZIkdcTIsAuQpMdTkj8BfgzsD3ytqlYPsZYLhl2DpG4xuEl6Qqqq861BUtd4qlTSjJfkXUn+Ncn/Bp7dtH0qyUnN/PlJrklyY5KlSdK0vyjJDUmuT/JnSW5s2s9I8vdJViS5NckH+z7rtCRrm319oGmb3Xzejc26t09Rw58mubn5vD9/XL8gSZ3hiJukGS3JC+m9vuwYen/nXQdcO2mzv6yqC5rtPw38KnAF8EngzVX19SR/OqnPMcALgEeAW5JcBGwEPgC8ELgP+HKS1wF3AfOq6nnNZzx1Uo2jwK8Bz6mqmrxekjZzxE3STPdS4AtV9VBVPcDU7599RZJvJlkL/Arw3CY8PaWqvt5s85lJfa6qqh9V1cPAzcAzgRcBV1fVhuYVan8HvAy4HXhWkouSLAYemLSvHwEPAx9P8nrgod09aEkzk8FN0hNakn2BjwInVdXRwMeAfQfo+kjf/Ea2cwajqu4DfhG4Gvhd4G8mrZ8AjgUupzfat2LwI5D0RGJwkzTTfQ14XZI5SZ4CvHbS+s0h7YdJngycBFBV9wMPJjmuWX/qAJ/1LeCXkxycZDZwGvDVJAcDs6rq88C7gV/q79R87gFVdSXwdnohT5K24jVukma0qrouyeeAfwF+AFwzaf39ST4G3Aj826T1bwI+lmQT8FV6pzS391n3JDkX+EcgwD9U1ReT/CLwySSb/2f5vEldnwJ8sRn9C/COXThUSU8Aqaph1yBJ01KSJ1fVj5v5c4GnVdU5Qy5L0hOYI26StG3/Ocl59P6uvBM4Y7jlSHqic8RNkiSpI7w5QZIkqSMMbpIkSR1hcJMkSeoIg5skSVJHGNwkSZI6wuAmSZLUEf8/lI2VxSVvT7cAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for col in df.columns:\n", " plt.figure(figsize=(10,8))\n", " plt.title(col)\n", " sns.boxplot(x = 'diagnosis',y = col,data = df)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "id": "f2c07d95", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:53.932141Z", "iopub.status.busy": "2022-01-28T14:18:53.931490Z", "iopub.status.idle": "2022-01-28T14:18:54.121383Z", "shell.execute_reply": "2022-01-28T14:18:54.121862Z", "shell.execute_reply.started": "2022-01-28T14:07:29.778396Z" }, "papermill": { "duration": 0.252605, "end_time": "2022-01-28T14:18:54.122066", "exception": false, "start_time": "2022-01-28T14:18:53.869461", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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XpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfp/W5jn2/nxWF0AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(df['radius_mean'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "id": "764283a5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:54.241795Z", "iopub.status.busy": "2022-01-28T14:18:54.241076Z", "iopub.status.idle": "2022-01-28T14:18:54.800204Z", "shell.execute_reply": "2022-01-28T14:18:54.800723Z", "shell.execute_reply.started": "2022-01-28T14:07:29.978271Z" }, "papermill": { "duration": 0.620808, "end_time": "2022-01-28T14:18:54.800900", "exception": false, "start_time": "2022-01-28T14:18:54.180092", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:8: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " \n", "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " return f(**kwargs)\n", "/opt/conda/lib/python3.7/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n", " warnings.warn(label_encoder_deprecation_msg, UserWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[14:18:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n" ] } ], "source": [ "clf_forest = RandomForestClassifier()\n", "clf_log = LogisticRegression(solver='liblinear')\n", "clf_xgb = XGBClassifier()\n", "\n", "clf = [clf_forest,clf_log,clf_xgb]\n", "\n", "for i in clf:\n", " i.fit(X_train,y_train)\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "4433abdc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:54.924571Z", "iopub.status.busy": "2022-01-28T14:18:54.923396Z", "iopub.status.idle": "2022-01-28T14:18:54.962522Z", "shell.execute_reply": "2022-01-28T14:18:54.963143Z", "shell.execute_reply.started": "2022-01-28T14:07:30.483778Z" }, "papermill": { "duration": 0.103276, "end_time": "2022-01-28T14:18:54.963357", "exception": false, "start_time": "2022-01-28T14:18:54.860081", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "ac = []\n", "m = ['random forest','logistic regression','xgboost']\n", "for i in clf:\n", " b = i.score(X_train,y_train)\n", " ac.append(b)" ] }, { "cell_type": "code", "execution_count": 20, "id": "60c689ea", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:55.138034Z", "iopub.status.busy": "2022-01-28T14:18:55.137060Z", "iopub.status.idle": "2022-01-28T14:18:55.140661Z", "shell.execute_reply": "2022-01-28T14:18:55.140114Z", "shell.execute_reply.started": "2022-01-28T14:15:46.632846Z" }, "papermill": { "duration": 0.071477, "end_time": "2022-01-28T14:18:55.140806", "exception": false, "start_time": "2022-01-28T14:18:55.069329", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "accuracy = pd.concat([pd.DataFrame(ac,columns = ['score']),pd.DataFrame(m,columns=['model'])],axis=1)" ] }, { "cell_type": "code", "execution_count": 21, "id": "3dcdbb71", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:55.288533Z", "iopub.status.busy": "2022-01-28T14:18:55.283893Z", "iopub.status.idle": "2022-01-28T14:18:55.415526Z", "shell.execute_reply": "2022-01-28T14:18:55.414848Z", "shell.execute_reply.started": "2022-01-28T14:18:06.222145Z" }, "papermill": { "duration": 0.214956, "end_time": "2022-01-28T14:18:55.415676", "exception": false, "start_time": "2022-01-28T14:18:55.200720", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='score', ylabel='model'>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(y = 'model',x='score',data = accuracy)" ] }, { "cell_type": "code", "execution_count": 22, "id": "2ecbf155", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:55.541597Z", "iopub.status.busy": "2022-01-28T14:18:55.540869Z", "iopub.status.idle": "2022-01-28T14:18:55.589531Z", "shell.execute_reply": "2022-01-28T14:18:55.588573Z", "shell.execute_reply.started": "2022-01-28T14:07:30.931497Z" }, "papermill": { "duration": 0.113095, "end_time": "2022-01-28T14:18:55.589768", "exception": false, "start_time": "2022-01-28T14:18:55.476673", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "y_fores = clf_forest.predict(X_train)\n", "y_log = clf_log.predict(X_train)\n", "y_xgb = clf_xgb.predict(X_train)\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 23, "id": "80a5409e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:55.767321Z", "iopub.status.busy": "2022-01-28T14:18:55.766297Z", "iopub.status.idle": "2022-01-28T14:18:55.862352Z", "shell.execute_reply": "2022-01-28T14:18:55.861364Z", "shell.execute_reply.started": "2022-01-28T14:07:30.933657Z" }, "papermill": { "duration": 0.165654, "end_time": "2022-01-28T14:18:55.862578", "exception": false, "start_time": "2022-01-28T14:18:55.696924", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "predict = pd.DataFrame()\n", "for i in clf:\n", " b = pd.DataFrame(i.predict(X_train))\n", " predict.append(b)" ] }, { "cell_type": "code", "execution_count": 24, "id": "f2fa424e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:56.019748Z", "iopub.status.busy": "2022-01-28T14:18:56.019039Z", "iopub.status.idle": "2022-01-28T14:18:56.101949Z", "shell.execute_reply": "2022-01-28T14:18:56.102876Z", "shell.execute_reply.started": "2022-01-28T14:07:30.935180Z" }, "papermill": { "duration": 0.154242, "end_time": "2022-01-28T14:18:56.103211", "exception": false, "start_time": "2022-01-28T14:18:55.948969", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "y_forest = clf_forest.predict(X_train)\n", "y_logreg = clf_log.predict(X_train)\n", "y_xbg = clf_xgb.predict(X_train)\n", "\n", "cm_forest = confusion_matrix(y_train,y_forest,labels = clf_forest.classes_)\n", "cm_logreg = confusion_matrix(y_train,y_logreg,labels = clf_log.classes_)\n", "cm_xgb = confusion_matrix(y_train,y_xbg,labels = clf_xgb.classes_)\n", "\n", "disp_forest = ConfusionMatrixDisplay(confusion_matrix=cm_forest,display_labels=clf_forest.classes_)\n", "disp_logreg = ConfusionMatrixDisplay(confusion_matrix=cm_logreg,display_labels=clf_log.classes_)\n", "disp_xgb = ConfusionMatrixDisplay(confusion_matrix=cm_xgb,display_labels=clf_xgb.classes_)" ] }, { "cell_type": "code", "execution_count": 25, "id": "263f4b13", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:56.314337Z", "iopub.status.busy": "2022-01-28T14:18:56.299103Z", "iopub.status.idle": "2022-01-28T14:18:57.192248Z", "shell.execute_reply": "2022-01-28T14:18:57.191684Z", "shell.execute_reply.started": "2022-01-28T14:07:30.936232Z" }, "papermill": { "duration": 1.004305, "end_time": "2022-01-28T14:18:57.192401", "exception": false, "start_time": "2022-01-28T14:18:56.188096", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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Idhe5sK6tMrPmFQWaRY2IGyXNIdkyScCHI8KZ7c36s6L04CRNAV4HflVeFhEL69kwM2tiRQlwwG94K/nM9sA04Blgnzq2y8yaWGHuwUXEfuXv011GzunhdDOzppHlObgtpNskvbsObTGzVlGjLct7yIs6WtIsSc+mf+6YlkvSFZLmpzlTe9zSrSTLPbgvlL0dABwAvNJ7082skGo7i3o9W+dFPR+YHRGXSjo/ff8VkoRX09Pj3cBV9NLZytKDG152DCa5J3dCrh/BzIqlRj24iLgf6Lrt+AnADenrG4APl5X/OBIPA6MkTaxUf8UeXPqA7/CI+FLvTTWz/kDUfZJhQkQsTl8vASakrycBL5Wd93JatpgeVNqyfLuIaJf0nj421syKZhslfo6IkKoPp5V6cI+Q3G+bK+lO4FZgfdmFf1HtRc2sheXbKaSaxM9LJU2MiMXpEHRZWr6ILVOW7pKW9SjLPbjtgZUkORg+BPxV+qeZ9VedGY/q3Amcnr4+HbijrPxv09nUg4E1ZUPZblXqwY1PZ1D/wlsP+pa0yGN+ZlYPdc6Leilwi6QzgReBk9PT7wKOB+aTrK46o7f6KwW4gcAwtgxsJQ5wZv1ZffOiQrL2veu5AZybp/5KAW5xRFyUpzIz6wcKklWr8dtxmllTKsJa1K26iGZmQOv34CKi69PFZmZAgTa8NDPbQkHuwZmZbUW0zg16Bzgzy889ODMrqiLMopqZdc8BzswKqUhpA83MtuIenJkVle/BmVlxOcCZWVG5B2dmxRT0ZTPLbcoBzsxy2QZJZ2rGAc7M8qtBgJP0duDmsqJdga8Bo4C/B5an5V+NiLuquYYDnJnlpuh7hIuIZ4AZsDlF6SLgdpKtyC+PiO/09RoOcGaWT312EzkSWBARL0q1W8qfJauWmdkWFNkO0ryoZcdZPVR5KnBT2fvzJD0h6TpJO1bbTgc4M8tNndkO0ryoZcdWSZ8lDQL+miT3MsBVwG4kw9fFwGXVttMBzszyi4xHNscBj0fEUoCIWBoRHRHRCfwIOKjaZjrAmVk+GYenOR4lOY2y4Wmazb7kRJLczFXxJIOZ5Ve7xM9DgQ8CZ5cVf0vSjPQqL3T5LBcHODPLpZYP+kbEemBMl7JP1KZ2Bzgzq4I6W2MpgwOcmeXjrFr91+e//TzvPmI1q1e2MfPofQEYNrKdr/5gARN22cjSlwfzr+fsxmtr/atvlPHXvMjQuWvoGLEdC/91bwBG3/YKwx5fDQNE+/DtWPr3b6Njx0HsMG8dE/9tAe3jBgPw2rtGserDEyvU3j+0yo6+dZtFTR/QWyap6hmQVjTr1rFcePoeW5Sdcs5i5v5+BGce/g7m/n4EJ5+zuEGtM4C17x3NK1/afYuy1cdPYOEle7Pw4r1YP2MkY+5YsvmzN/YYxsKL92LhxXs5uJXU9jGRuqnnYyLXA8fWsf6m9JdHhrNu9Za9s0M+uJr7bkvuo9532xgOPXp1A1pmJW/sOZyOoQO3KOvc4a33AzZ2NsO/zaZW48dE6qZu46SIuF/S1HrV30pGjd3EqmWDAFi1rI1RYzc1uEXWnTE/X8Tw36+ic4eBLDp/+uby7eevZ8qF82gf1caKUyfx5i47NLCVTSCAGiy23xYa/qCvpLNK69Q2xRuNbs42IPcOmtTKkybxwuX7se6Q0Yy8L9mpZ+PUITz/3X1Z+C97sfqD45h4xXMNbmVzyLFUq6EaHuAi4urSOrU2bd/o5tTF6hVtjB7/JgCjx7/JmhVtDW6RVbLu0NEMe2w1kAxdY/tk+Pr6/iNRRzBgXXsDW9d4pefgWmGI2vAA1x88fN8ojvroSgCO+uhKHpo1qrENsq20LXlr9DD08dW8OTH5n+3A1Zs2D8cGL1gPnUHnsIHd1tFvRGQ/GszPKtTY+Vcs4B2HrGPEju3858Nz+cnlk7j5yol89cr5HHPKcpYtGswl5+zW6Gb2aztd+Tw7PL2Oga+1M/Vzf2bViRMZ+sRa2ha/AYJNYwex7PQpAAx79FVG/nYFDBSdg8SSc6ZBDfcra1XN0DvLom4BTtJNwOEk+0G9DHw9Iq6t1/WaxaWf6T54XfCxPbdxS6wnS86ZtlXZ2veP7fbcNR8cz5oPjq93k1pPfw9wEXFaveo2s8bq9z04MyuoADpaI8I5wJlZbu7BmVlxNcEMaRYOcGaWW616cJJeANYBHUB7RBwoaTRJvtSpJBtenhwRr1ZTv5+DM7N8si60zx4EPxARMyLiwPT9+cDsiJgOzE7fV8UBzsxyEaCOyHRU6QTghvT1DcCHq63IAc7MclNEpoPe86IGcK+kOWWfTYiI0p5iS4AJ1bbT9+DMLJ98w88VZUPP7rw3IhZJGg/MkvT0FpeKCKn6O37uwZlZTrVbixoRi9I/lwG3k+RAXVpKHZj+uazaljrAmVlutdhNRNJQScNLr4GjSXKg3gmcnp52OnBHte30ENXM8qvNc3ATgNuVbF6wHfDTiLhb0qPALZLOBF4ETq72Ag5wZpZP0JcZ0reqiXgO2L+b8pXAkX2+AA5wZlaN1ljI4ABnZvnJS7XMrLAc4MyskAJogoQyWTjAmVkuIjxENbMC62yNLpwDnJnl4yGqmRWZh6hmVlwOcGZWTM2R1DkLBzgzy8dZtcysyHwPzsyKywHOzAopgE4HODMrpNaZZPCOvmaWXw22LJc0WdJ/S3pK0pOSPpuWf0PSIklz0+P4apvpHpyZ5RNAR02WMrQDX4yIx9Oty+dImpV+dnlEfKevF3CAM7OcAqLvAS5NDbg4fb1O0jxgUp8rLuMhqpnll32I2lteVAAkTQXeCfwhLTpP0hOSrpO0Y7XNdIAzs3xKs6hZjjQvatlxddfqJA0DbgM+FxFrgauA3YAZJD28y6ptqoeoZpZfjWZRJbWRBLcbI+IXSdWxtOzzHwG/rrZ+9+DMLL/azKIKuBaYFxHfLSufWHbaiSS5UqviHpyZ5RMBHR21qOk9wCeAP0uam5Z9FThN0gySwfALwNnVXsABzszyq8EQNSIeBNTNR3f1ufKUA5yZ5dciKxkc4Mwsp/BaVDMrqICowYO+24IDnJnlV5ulWnXnAGdm+UQ4baCZFZgnGcysqMI9ODMrptbZ8NIBzszy8ZblZlZUAURtlmrVnQOcmeUTtdnwcltwgDOz3MJDVDMrrBbpwSmaaDZE0nLgxUa3ow7GAisa3QjLpah/Z2+LiHF9qUDS3SS/nyxWRMSxfbleXzRVgCsqSY9FxIGNbodl57+zYvCOvmZWWA5wZlZYDnDbxlaZhKzp+e+sAHwPzswKyz04MyssBzgzKywHuDqSdKykZyTNl3R+o9tjvZN0naRlkqrOxWnNwwGuTiQNBH4AHAfsTZLrce/GtsoyuB5o2IOpVlsOcPVzEDA/Ip6LiDeBnwEnNLhN1ouIuB9Y1eh2WG04wNXPJOClsvcvp2Vmto04wJlZYTnA1c8iYHLZ+13SMjPbRhzg6udRYLqkaZIGAacCdza4TWb9igNcnUREO3AecA8wD7glIp5sbKusN5JuAh4C3i7pZUlnNrpNVj0v1TKzwnIPzswKywHOzArLAc7MCssBzswKywHOzArLAa6FSOqQNFfSXyTdKmlIH+q6XtJJ6etrKm0EIOlwSYdWcY0XJG2Vfamn8i7nvJbzWt+Q9KW8bbRic4BrLRsiYkZE7Au8Ccws/1BSVXluI+JTEfFUhVMOB3IHOLNGc4BrXQ8Au6e9qwck3Qk8JWmgpG9LelTSE5LOBlDi++n+dPcB40sVSfqdpAPT18dKelzSnyTNljSVJJB+Pu09vk/SOEm3pdd4VNJ70u+OkXSvpCclXQOotx9C0i8lzUm/c1aXzy5Py2dLGpeW7Sbp7vQ7D0jasya/TSskZ7ZvQWlP7Tjg7rToAGDfiHg+DRJrIuJ/SRoM/F7SvcA7gbeT7E03AXgKuK5LveOAHwGHpXWNjohVkn4IvBYR30nP+ylweUQ8KGkKyWqNvYCvAw9GxEWS/jeQZRXAJ9Nr7AA8Kum2iFgJDAUei4jPS/paWvd5JMlgZkbEs5LeDVwJHFHFr9H6AQe41rKDpLnp6weAa0mGjo9ExPNp+dHAO0r314CRwHTgMOCmiOgAXpH0227qPxi4v1RXRPS0L9pRwN7S5g7aCEnD0mt8JP3ubyS9muFn+oykE9PXk9O2rgQ6gZvT8p8Av0ivcShwa9m1B2e4hvVTDnCtZUNEzCgvSP+hry8vAv4hIu7pct7xNWzHAODgiHijm7ZkJulwkmB5SES8Lul3wPY9nB7pdVd3/R2Y9cT34IrnHuDTktoAJO0haShwP3BKeo9uIvCBbr77MHCYpGnpd0en5euA4WXn3Qv8Q+mNpBnpy/uBj6VlxwE79tLWkcCraXDbk6QHWTIAKPVCP0Yy9F0LPC/p/6TXkKT9e7mG9WMOcMVzDcn9tcfTxCn/l6SnfjvwbPrZj0l2zNhCRCwHziIZDv6Jt4aIvwJOLE0yAJ8BDkwnMZ7irdncfyYJkE+SDFUX9tLWu4HtJM0DLiUJsCXrgYPSn+EI4KK0/OPAmWn7nsTbwFsF3k3EzArLPTgzKywHODMrLAc4MyssBzgzKywHODMrLAc4MyssBzgzK6z/D+sdeINVdF4xAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "disp = [disp_forest,disp_logreg,disp_xgb]\n", "for i in disp:\n", " i.plot()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "id": "ba6dc1a1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:57.326395Z", "iopub.status.busy": "2022-01-28T14:18:57.325426Z", "iopub.status.idle": "2022-01-28T14:18:57.336200Z", "shell.execute_reply": "2022-01-28T14:18:57.335551Z", "shell.execute_reply.started": "2022-01-28T14:07:30.937965Z" }, "papermill": { "duration": 0.07926, "end_time": "2022-01-28T14:18:57.336351", "exception": false, "start_time": "2022-01-28T14:18:57.257091", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "fpr_f,tpr_f, _ = roc_curve(y_train, y_forest,pos_label=clf_forest.classes_[1])\n", "fpr_l,tpr_l, _ = roc_curve(y_train, y_log,pos_label=clf_log.classes_[1])\n", "fpr_x,tpr_x, _ = roc_curve(y_train, y_xbg,pos_label=clf_xgb.classes_[1])" ] }, { "cell_type": "code", "execution_count": 27, "id": "ee62247a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:57.465673Z", "iopub.status.busy": "2022-01-28T14:18:57.464708Z", "iopub.status.idle": "2022-01-28T14:18:57.704041Z", "shell.execute_reply": "2022-01-28T14:18:57.704545Z", "shell.execute_reply.started": "2022-01-28T14:07:30.939233Z" }, "papermill": { "duration": 0.305729, "end_time": "2022-01-28T14:18:57.704745", "exception": false, "start_time": "2022-01-28T14:18:57.399016", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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pOYdrSl3jdCyPoTKVxakypYUYRURETvrb9Tf95/Zn0ZZF9KvVjw86fkCRwCJOx/IoKlNZuFwuva2HiIhIpnX71hEZE8muI7t4+/a3GXnTSP2MPAeVqSxcLhehob6zyJiIiMj5TFo3iTsW3UHp0NIsG7yMWyre4nQkj6UylYXKlIiI+LoT6ScYvWQ078e/T/NKzZkeNZ1yYeWcjuXRVKayUJkSERFftvPITrrP7M7qPat5qNFDPNPiGQL8VBUuRl+hLFSmRETEV3297Wt6z+7NifQTzO4xm4hqEU5HKjT0XiFZqEyJiIivcVs3z33/HG2ntKV8WHnWDF+jIpVDmpnKQmVKRER8yeGUwwycN5AFvy2gV81efNjpQ8KDwp2OVeioTGXhcrkoVaqU0zFERETy3fr964mMiWTH4R280e4N7m5wt5Y9uEQqU1loZkpERHzBlPVTiF4YTYmQEsQOjKXRlY2cjlSoqUxloTIlIiLeLDUjlfu+uI931rxDk6uaMCNqBhXCKzgdq9BTmcpCZUpERLzV7qO76T6zO6t2r+Lft/6b/7b8L4H+gU7H8goqU1moTImIiDf6dvu39JzVE1e6i5ioGLrX6O50JK+ipRGyUJkSERFvYq3lxRUv0uqTVpQuUprVw1arSOUDzUxlstaSkpKiMiUiIl7h6ImjDJ4/mDmb5tC9enc+7vwxRYOLOh3LK6lMZUpJSQFQmRIRkUJvw4ENRMRE8Ptfv/NKm1e495Z7texBPlKZyuRyuQCVKRERKdym/zKdoQuGUjSoKN8M/IYmVzVxOpLX0zlTmVSmRESkMEvLSGP0ktH0nt2buhXqkjAiQUWqgGhmKpPKlIiIFFZ7j+2l+8zurNi1gntuvoeXWr+kZQ8KkMpUJp0zJSIihdGyP5bRY2YPjqUeY1rkNHrV7OV0JJ+jw3yZNDMlIiKFibWWV1e+SotJLSgeUpzVw1arSDlEM1OZVKZERKSwOHbiGEMXDGXmxpl0q9qNiV0nUiy4mNOxfJbKVCaVKRERKQw2HdxEREwEmxM382KrF7m/4f1a9sBhKlOZVKZERMTTzdo4i8HzBxMaEMrX/b+meeXmTkcSdM7UaSpTIiLiqdLd6dz/5f10n9mdmuVqkjAiQUXKg2hmKpPKlIiIeKJ9SfvoOasny/5Yxl033cUrbV8hyD/I6ViShcpUJpUpERHxNCt2rqD7zO4cTjnMJ90+oV+tfk5HknPQYb5MKlMiIuIprLW8seoNmk1qRlhQGKuGrVKR8mCamcqkMiUiIp4gKTWJ4QuHM/2X6XS+vjOTuk6iREgJp2PJBahMZXK5XAQEBBAQoC+JiIg4Y3PiZiJmRLDp0Caea/EcD932EH5GB5E8nZpDJpfLpVkpERFxzNxNcxk4byDBAcF80e8LWlVp5XQkySbV3UwqUyIi4oR0dzoPffUQETERVC1TlfjoeBWpQkYzU5lUpkREpKAdSD5Ar1m9+HbHt9xx4x283u51ggOCnY4lOaQylUllSkRECtLKXSvpPrM7ia5EJnaZyMA6A52OJJdIh/kyqUyJiEhBsNbyzup3aDqxKUH+QawculJFqpDTzFQmlSkREclvyanJ3LHoDqasn0KHazvwSbdPKBla0ulYkksqU5lcLhchISFOxxARES+19a+tRMyI4JcDv/B086d5tPGjWvbAS6hMZXK5XJQsqd8OREQk7y34bQED5g7A38+fz/t+Tttr2jodSfKQKnEmHeYTEZG8luHO4LGlj9FleheuKXUN8dHxKlJeSDNTmVSmREQkLx06fojes3vz9bavGVZ3GG+1f4uQAJ1O4o1UpjKpTImISF5ZvWc1UTFRHEg+wEedPmJovaFOR5J8pMN8mVSmREQkt6y1fBD3AY0nNMbfz58VQ1aoSPkAzUxlUpkSEZHccKW5uHPRnUz6aRLtrmnHlG5TKF2ktNOxpACoTAHp6emkp6erTImIyCXZ9vc2ImMi+WnfTzzZ9EmeaPIE/n7+TseSAqIyxclZKUBlSkREcmzR5kX0m9sPgM/6fEb7a9s7nEgKms6ZQmVKRERyLsOdwZPfPknHaR2pVKIS8dHxKlI+KltlyhjTzhjzmzFmqzHm4XM8fqUx5ltjzFpjzHpjTKH616QyJSIiOZF4PJEOUzswdtlYBtUZxA9DfqBKySpOxxKHXPQwnzHGH3gHaA3sBtYYYxZYazdm2exxIMZa+54xpjqwGKiUD3nzhcqUiIhkV/yf8UTGRLI3aS8fdPyA4fWGY4xxOpY4KDszUw2ArdbabdbaVGA60OWMbSxQLPPz4sCfeRcx/6lMiYhIdizau4hG4xvhtm6WD15O9I3RKlKSrRPQLwd2Zbm9G7j5jG3+A3xpjLkbCANa5Um6AqIyJSIiF5KSnsJdi+/i480f07pKa6ZGTqVMkTJOxxIPkVdX8/UGJlprXzHG3Ap8Yoypaa11Z93IGBMNRAOUL1+e2NjYPNr92cLS08HabO0jPj4egF9//ZWQEC31n9+SkpLydewl5zQmnknj4hn2pexjzIYxbEnaQo8KPYiuGM0vq39xOpZk4fT3SnbK1B7giiy3K2bel9VQoB2AtXalMSYEKAMcyLqRtXYcMA6gfv36tlmzZpeWOhvW7gggPT2d7OwjOTkZgIYNG9KgQYN8yyQnxcbGZmtcpOBoTDyTxsV5S7Yu4V9z/kWGO4MFvRZQdG9RjYkHcvp7JTvnTK0BrjXGVDbGBAG9gAVnbLMTaAlgjKkGhAAH8zJoftJhPhERycpt3Yz9biztP21PxWIViYuOo9P1nZyOJR7qojNT1tp0Y8xdwBeAPzDeWrvBGDMWiLPWLgD+DXxojLmXkyejD7LW2vwMnpdUpkRE5JS/XX/Tb24/Fm9ZTL9a/fig4wcUCSzidCzxYNk6Z8pau5iTyx1kvW9Mls83Ao3yNlrBUZkSERGAtXvXEhkTye6ju3mn/TvcWf9OXa0nF6W3k0FlSkREYOK6idy56E5Kh5Zm2eBl3FLxFqcjSSGhMoXKlIiILzuRfoJRn49iXMI4WlRuwbTIaZQLK+d0LClEVKb4X5nSsggiIr5l55GdRMVEsebPNTzc6GGebvE0AX760Sg5o38xnCxTISEhOi4uIuJDvt72Nb1m9SLNncbcnnPpWrWr05GkkMrWGx17O5fLpUN8IiI+wm3dPPf9c7Sd0pYK4RVYM3yNipTkimamUJkSEfEVh1MOM2DuABZuXkjvmr35sNOHhAWFOR1LCjmVKVSmRER8wfr964mYEcEfR/7gzXZvcleDu3R6h+QJlSlUpkREvN2U9VOIXhhNydCSxA6MpdGVhXZpRPFAKlOoTImIeKvUjFTuXXIv78a9S9OrmjI9ajoVwis4HUu8jMoUKlMiIt5o99HddJ/ZnVW7V3H/rffz31b/1bIHki/0r4qTZapkyZJOxxARkTzyzfZv6DWrF650FzO7zySqepTTkcSLaWkENDMlIuItrLW8uOJFWn/SmjJFyrBm+BoVKcl3mplCZUpExBscSTnC4PmDmfvrXLpX787HnT+maHBRp2OJD1CZQmVKRKSw++XAL0TMiGDb39t4tc2rjL5ltJY9kAKjMoXKlIhIYTbt52kMWziMYsHF+GbgNzS5qonTkcTH6JwpVKZERAqj1IxU7vn8HvrM6UO9y+qREJ2gIiWO8PmZKWvt6Tc6FhGRwuHPY3/SfWZ3ftj1A6NvHs2LrV8k0D/Q6Vjio3y+TJ04cQJAM1MiIoXEdzu+o+esniSlJjE9cjo9a/Z0OpL4OJ8/zOdyuQCVKRERT2et5ZUfXqHl5JaUCCnBj8N+VJESj+DzM1MqUyIinu/YiWMMWTCEWRtnEVEtggldJlAsuJjTsUQAlSmVKRERD7fp4CYiYiLYnLiZF1u9yP0N79eyB+JRVKZUpkREPFbMhhiGzB9CWFAYX/f/muaVmzsdSeQsOmdKZUpExOOkZaRx3xf30XNWT2qVr0VCdIKKlHgszUypTImIeJR9SfvoMbMH3+/8nrsb3M3LbV4myD/I6Vgi56UypTIlIuIxlu9cTo+ZPThy4ghTuk2hb62+TkcSuSgd5lOZEhFxnLWWN1a9QfNJzQkLCmPV0FUqUlJoaGZKZUpExFFJqUkMWzCMGRtm0OX6LkzqOoniIcWdjiWSbSpTKlMiIo757dBvRMRE8OuhX/lvy//yYKMH8TM+f9BEChmfL1MpKSmAypSISEGbs2kOg+YNIjggmC/7fUnLKi2djiRySXy+/mtmSkSkYKW703nwqweJjImkWtlqJEQnqEhJoebzM1MqUyIiBWd/0n56ze5F7I5Y7qx/J6+1fY3ggGCnY4nkisqUy4W/vz+BgYFORxER8Word60kamYUf7n+YlLXSQyoPcDpSCJ5Qof5XC7NSomI5CNrLW+vfpumE5sSEhDCyqErVaTEq2hmSmVKRCTfJKcmM+KzEXz686d0vK4jk7tOpmRoSadjieQplSmVKRGRfLElcQsRMRFsOLCBp5s/zaONH9WyB+KVVKZUpkRE8tz8X+czYN4AAvwC+Lzv57S9pq3TkUTyjc//iqAyJSKSdzLcGTy69FG6zujKdaWvIyE6QUVKvJ5mplSmRETyxMHkg/Se3Zul25cyvN5w3rz9TUICQpyOJZLvVKZUpkREcm31ntVExkRyMPkgH3f+mCF1hzgdSaTA6DCfypSIyCWz1vJ+3PvcNv42AvwC+GHoDypS4nM0M6UyJSJySY6nHefORXcy+afJtLumHZ9GfEqp0FJOxxIpcCpTKlMiIjn2+1+/ExkTyfr963my6ZOMaTpGyx6Iz1KZUpkSEcmRzzZ/Rr85/fAzfizqs4jbr73d6UgijvL5XyNUpkREsifDncET3zxBp2mdqFKyCvHR8SpSImhmSmVKRCQbEo8n0mdOH778/UsG1xnMO+3fITRQ/3eKgI+XqYyMDNLS0lSmREQuIO7POKJiotibtJdxHccxrN4wjDFOxxLxGD59mM/lcgGoTImInMdHCR/RaHwjLJblg5cz/MbhKlIiZ/DpmSmVKRGRc3Olubhr8V2MXzeeNle34dOITylTpIzTsUQ8ksoUKlMiIllt/3s7UTOjSNibwOONH+c/zf6Dv5+/07FEPJbKFCpTIiKnfL7lc/rO6YvbulnQawGdru/kdCQRj6dzplCZEhFxWzdPxT5Fh6kduLL4lcRHx6tIiWSTZqZQmRIR3/aX6y/6z+3P4i2LGVB7AO91eI8igUWcjiVSaKhMASEhIQ4nERFxxtq9a4mIiWDP0T281+E9Rtw4QlfrieSQyhSamRIR3zRh7QRGLh5JmSJl+H7w99xc8WanI4kUSipTqEyJiG9JSU/hns/vYVzCOFpUbsH0yOmUDSvrdCyRQktlCpUpEfEdfxz+g6iZUcT9GcfDjR7m6RZPE+Dn0z8KRHLNp7+DVKZExJd8+fuX9JndhzR3GnN7zqVr1a5ORxLxCloaAZUpEfFubuvm2WXP0m5KOy4rehlxw+NUpETykGamUJkSEe91OOUwA+YOYOHmhfS5oQ/jOo4jLCjM6VgiXkVlCi2NICLe6ad9PxEZE8kfR/7grdvf4l83/UvLHojkA58uUykpKQQHB+Pn59NHO0XEC03+aTJ3fHYHJUNL8t2g72h4RUOnI4l4LZ8uUy6XS4f4RMSrnEg/wb1f3Mt7ce/R9KqmzIiaQfnw8k7HEvFqKlMqUyLiJXYd2UX3md35cc+PPNDwAZ5r+ZyWPRApAD79XaYyJSLeYum2pfSa3YuU9BRmdZ9FZPVIpyOJ+AyfPllIZUpECjtrLc8vf542U9pQtkhZ1gxfoyIlUsA0M6UyJSKF1JGUIwyaP4h5v86jZ42efNT5I8KDwp2OJeJzVKZUpkSkEPrlwC9EzIhg++HtvNb2Ne65+R4teyDiEJ8vU8WLF3c6hohIjkz9eSrDFw6nWHAxvhnwDY2vaux0JBGfpnOmNDMlIoVEakYqoz4fRd85fbnxshtJiE5QkRLxAD4/M6UyJSKFwZ6je+gxqwc/7PqBe2+5lxdavUCgf6DTsUSEbM5MGWPaGWN+M8ZsNcY8fJ5tehhjNhpjNhhjpuZtzPyhMiUihUHsjljqjavHT/t+YkbUDF5t+6qKlIgHuejMlDHGH3gHaA3sBtYYYxZYazdm2eZa4BGgkbX2b2NMufwKnJdUpkTEk1lrefmHl3n464e5ptQ1fDvwW6qXre50LBE5Q3YO8zUAtlprtwEYY6YDXYCNWbYZDrxjrf0bwFp7IK+D5geVKRHxVMdOHOM/G//DskPLiKwWyfgu4ykWXMzpWCJyDtk5zHc5sCvL7d2Z92V1HXCdMWaFMWaVMaZdXgXML9ZalSkR8UgbD27kpg9vYvmh5bzc+mVmdp+pIiXiwfLqBPQA4FqgGVARWGaMucFaezjrRsaYaCAaoHz58sTGxubR7s8Wlp4O1p53H6mpqVhr2bt3b77mkLMlJSXpa+5hNCae49sD3/Liby8S6h/KM9c9w42pN/Ldd985HUsy6XvFMzk9LtkpU3uAK7Lcrph5X1a7gR+ttWnAdmPMZk6WqzVZN7LWjgPGAdSvX982a9bsEmNf3NodAaSnp3O+fRw+fBiAGjVqnHcbyR+xsbH6mnsYjYnz0jLSePCrB3l90+s0vKIhMVExbEnYonHxMPpe8UxOj0t2DvOtAa41xlQ2xgQBvYAFZ2wzj5OzUhhjynDysN+2vIuZ91wuF4AO84mI4/Ye20uLyS14/cfXubvB3Xw78FsuL3bm2RQi4qkuOjNlrU03xtwFfAH4A+OttRuMMWOBOGvtgszH2hhjNgIZwAPW2sT8DJ5bKlMi4gm+/+N7eszqwdETR/k04lP63NDH6UgikkPZOmfKWrsYWHzGfWOyfG6B+zI/CgWVKRFxkrWWN358g/u/vJ8qJavwVf+vqFmuptOxROQS+OwK6CpTIuKUpNQkhi0YxowNM+hatSsTu0ykeIjeJ1SksFKZUpkSkQL066FfiZgRwW+Jv/F8y+d5sNGDGGOcjiUiuaAypTIlIgVk9sbZDJo/iNCAUL7s9yUtq7R0OpKI5IFsvTefN1KZEpGCku5O54EvHyBqZhQ1ytYgPjpeRUrEi2hmSmVKRPLR/qT99Jrdi9gdsYysP5JX275KcECw07FEJA+pTKlMiUg++WHXD3Sf2Z2/XX8zuetk+tfu73QkEckHOsynMiUiecxay9ur36bpxKaEBoSycuhKFSkRL6aZKZUpEclDyanJRH8WzdSfp9Lpuk5M7jaZEiElnI4lIvlIZUplSkTyyJbELUTERLDhwAaeaf4MjzR+BD/jswcARHyGT5cpPz8/AgJ89ksgInlo3q/zGDhvIIF+gSzpt4Q2V7dxOpKIFBCf/ZXJ5XIRGhqqxfJEJFfS3ek88vUjdJvRjetKX0d8dLyKlIiP8dlpmVNlSkTkUh1IPkDv2b35Zvs3RNeL5o3b3yAkIMTpWCJSwFSmREQuwY+7fyRqZhSHjh9ifOfxDK472OlIIuIQnz3Ml5KSojIlIjlmreW9Ne/ReEJjAv0C+WHIDypSIj5OM1MiItl0PO04dy66k8k/Tab9te35pNsnlAot5XQsEXGYypSISDb8/tfvRMRE8PP+n3mq2VM83uRxLXsgIoDKlNMxRKQQWPjbQvrP7Y+f8WNRn0Xcfu3tTkcSEQ/is79WqUyJyMVkuDN4/JvH6Ty9M1eXupr46HgVKRE5i2amRETO4dDxQ/SZ3Yevtn3FkDpDeKfDO1r2QETOSWVKROQMa/asIWpmFPuT9vNhpw8ZVm+Y05FExIPpMJ+ISCZrLR/Gf8htE24DYPmQ5SpSInJRmpkSEQFcaS7+tfhfTFg3gTZXt+HTiE8pU6SM07FEpBBQmRIRn7f97+1ExkSydt9anmjyBE82fRJ/P3+nY4lIIeGTZSojI4PU1FSVKRFh8ZbF9JvTD4tlYe+FdLyuo9ORRKSQ8clzplJSUgBUpkR8mNu6+U/sf+g4tSNXFr+SuOFxKlIickl8cmbK5XIBKlMivuov11/0ndOXJVuXMLD2QN7t8C5FAos4HUtECimVKRHxKQl7E4iMiWTP0T283+F9om+MxhjjdCwRKcRUpkTEZ0xYO4E7F91J2bCyfD/4e26ueLPTkUTEC6hMiYjXS0lPYdTno/gw4UNaVm7JtMhplA0r63QsEfESKlMi4tX+OPwHkTGRxO+N55HbHuHp5k9r2QMRyVMqUyLitb78/Ut6z+5NujudeT3n0aVqF6cjiYgX8smlEVSmRLyb27p5ZtkztJvSjsuLXk7c8DgVKRHJN5qZEhGv8rfrbwbMG8Bnmz+j7w19+aDjB4QFhTkdS0S8mMqUiHiNn/b9RERMBLuO7OLt299m5E0jteyBiOQ7lSkR8QqTf5rMiM9GUCq0FN8N+o5br7jV6Ugi4iNUpkSkUDuRfoJ7v7iX9+Leo1mlZkyPnE758PJOxxIRH6IyJSKF1q4ju4iaGcXqPat5sOGDPNvyWQL8fPK/NRFxkE/+r3OqTIWEhDicREQu1dJtS+k1uxcn0k8wq/ssIqtHOh1JRHyUzy6NEBwcjJ+fT/71RQo1t3Xz3+//S5spbSgXVo41w9eoSImIo3x2ZkqH+EQKnyMpRxg4byDzf5tPzxo9+ajzR4QHhTsdS0R8nMqUiBQKP+//mYiYCHYc3sHrbV9n1M2jtOyBiHgElSkR8Xifrv+U4QuHUzykON8O/JbbrrzN6UgiIqf55ElDKlMihUNqRip3L76bfnP7Uf//6pMQnaAiJSIeRzNTIuKR9hzdQ/eZ3Vm5eyX33XIfz7d6nkD/QKdjiYicRWVKRDzOt9u/pdfsXiSnJjMjagY9avRwOpKIyHn55GG+lJQUrTEl4oGstby04iVafdKKUqGlWDN8jYqUiHg8n52ZKleunNMxRCSLoyeOMnj+YOZsmkNU9SjGdx5P0eCiTscSEbkony1TOswn4jk2HNhAREwEv//1O6+0eYV7b7lXyx6ISKGhMiUijprxywyGLhhKeFA4SwcspWmlpk5HEhHJEZ88Z0plSsR5aRlp3LvkXnrN7kXtCrVJGJGgIiUihZJmpkSkwO09tpces3qwfOdyRjUYxUttXiLIP8jpWCIil8TnypS1VmVKxEHf//E9PWb14OiJo0yNmErvG3o7HUlEJFd87jBfWloabrdbZUqkgFlreW3lazSf1JxiwcX4cdiPKlIi4hV8bmbK5XIBqEyJFKBjJ44xbOEwYjbE0K1qNyZ2nUix4GJOxxIRyRMqUyKSr3499CsRMyL4LfE3Xmj1Ag80fEDLHoiIV1GZEpF8M2vjLAbPH0xoQChf9f+KFpVbOB1JRCTP+dw5UypTIvkv3Z3O/V/eT/eZ3alZriYJIxJUpETEa2lmSkTy1L6kffSa1Yvv/viOf930L15t+6qWPRARr6YyJSJ5ZsXOFXSf2Z3DKYeZ3HUy/Wv3dzqSiEi+02E+Eck1ay1v/vgmzSY1o0hgEVYNW6UiJSI+QzNTIpIryanJDF84nGm/TKPTdZ2Y3G0yJUJKOB1LRKTAqEyJyCXbnLiZiBkRbDq0iWdbPMvDtz2Mn/G5CW8R8XEqUyJySeZumsvAeQMJ8g9iSd8ltL66tdORREQc4XO/QqpMieROujudh79+mIiYCKqWqUrCiAQVKRHxaZqZEpFsO5B8gF6zevHtjm8ZceMI3mj3BsEBwU7HEhFxlMqUiGTLqt2riIqJItGVyIQuExhUZ5DTkUREPIJPHubz8/MjMDDQ6SgihYK1lnfXvEuTCU0I8g9i5dCVKlIiIln45MxUaGio3mhVJBuOpx1nxGcjmLJ+Cu2vbc+UblMoGVrS6VgiIh7FZ8uUiFzY1r+2EhkTyc/7f2Zss7E81uQxLXsgInIOKlMicpaFvy2k/9z++Pv5s7jvYtpd087pSCIiHitbv2YaY9oZY34zxmw1xjx8ge0ijTHWGFM/7yLmLZUpkfPLcGfw+DeP03l6Z64udTXx0fEqUiIiF3HRmSljjD/wDtAa2A2sMcYssNZuPGO7osA9wI/5ETSvqEyJnNuh44foM7sPX237iqF1h/J2+7cJCQhxOpaIiMfLzsxUA2CrtXabtTYVmA50Ocd2TwMvACl5mC/PqUyJnO3Xo79y47gbWfbHMj7s9CEfdf5IRUpEJJuyU6YuB3Zlub07877TjDH1gCustYvyMFu+UJkS+R9rLePixzFq3SgMhhVDVjCs3jCnY4mIFCq5PgHdGOMHvAoMysa20UA0QPny5YmNjc3t7s8rLD0drD1rHwcOHKBkyZL5um+5sKSkJH39PcCJjBO8vuV1luxfQr1i9RhTYwzHNh8jdnOs09Ekk75XPI/GxDM5PS7ZKVN7gCuy3K6Yed8pRYGaQGzm2k0VgAXGmM7W2risL2StHQeMA6hfv75t1qzZpSe/iLU7AkhPT+fMfQQEBFCxYsWz7peCExsbq6+/w7b9vY3ImEjW7V/HmCZjaEITWjZv6XQsOYO+VzyPxsQzOT0u2TnMtwa41hhT2RgTBPQCFpx60Fp7xFpbxlpbyVpbCVgFnFWkPIUO84mvW7xlMTeOu5Edh3fwWe/PeKr5U/gbf6djiYgUWhctU9badOAu4AtgExBjrd1gjBlrjOmc3wHzmsqU+KoMdwZPfvskHaZ2oFKJSsRHx9Phug5OxxIRKfSydc6UtXYxsPiM+8acZ9tmuY+Vf1JSUlSmxOckHk+k39x+LNm6hIG1B/Jeh/cIDdT3gYhIXtAK6CJeLv7PeCJjItmbtJf3O7xP9I3Rem9KEZE85FNlyu12c+LECUJCtH6O+IaPEz7mX4v/Rbmwcnw/+HsaXN7A6UgiIl7Hp8pUSsrJ9UQ1MyXeLiU9hbsX381Haz+iVZVWTIucRpkiZZyOJSLilXyqTLlcLkBlSrzbjsM7iIqJIn5vPI/e9ihjm4/F309X64mI5BeVKREv8sXWL+gzpw/p7nTm95pP5+sL3QW3IiKFTnbWmfIaKlPirdzWzdPfPc3tn97O5UUvJz46XkVKRKSAaGZKpJD72/U3/ef2Z9GWRfSr1Y8POn5AkcAiTscSEfEZKlMihdi6feuIjIlk15FdvH3724y8aaSWPRARKWAqUyKF1KR1k7hj0R2UDi3NssHLuKXiLU5HEhHxSSpTIoXMifQT3LPkHj6I/4DmlZozPWo65cLKOR1LRMRnqUyJFCI7j+wkKiaKNX+u4aFGD/FMi2cI8POpb2MREY/jU/8Lq0xJYfb1tq/pPbs3J9JPMKfHHLpV6+Z0JBERQUsjiHg8t3Xz3PfP0XZKW8qHlScuOk5FSkTEg2hmSsSDHU45zMB5A1nw2wJ61+zNuE7jCA8KdzqWiIhkoTIl4qHW719PZEwkOw7v4I12b3B3g7u17IGIiAdSmRLxQFPWTyF6YTQlQkoQOzCWRlc2cjqSiIich8+VqaCgIPz8fOpUMSlEUjNSue+L+3hnzTs0uaoJM6JmUCG8gtOxRETkAnyuTGlWSjzV7qO76T6zO6t2r+Lft/6b/7b8L4H+gU7HEhGRi1CZEvEA327/lp6zeuJKdzGz+0yiqkc5HUlERLLJp453qUyJp7HW8uKKF2n1SStKFynN6mGrVaRERAoZzUyJOOToiaMMnj+YOZvm0L16dz7u/DFFg4s6HUtERHJIZUrEARsObCAiJoLf//qdV9q8wr233KtlD0RECimVKZECNv2X6QxdMJSiQUX5ZuA3NLmqidORREQkF3TOlEgBSctIY/SS0fSe3Zu6FeqSMCJBRUpExAv43MxUuXLlnI4hPujPY3/SY2YPVuxawT0338NLrV/SsgciIl7C58qUZqakoC37Yxk9ZvbgWOoxpkVOo1fNXk5HEhGRPKTDfCL5xFrLKz+8QotJLSgeUpzVw1arSImIeCHNTInkg2MnjjFkwRBmbZxFt6rdmNh1IsWCizkdS0RE8oHKlEge23RwExExEWxO3MyLrV7k/ob3a9kDEREv5jNlylqrMiX5buaGmQxZMITQgFC+7v81zSs3dzqSiIjkM585ZyotLQ23260yJfkiLSONf3/xb3rM6kHNcjVJGJGgIiUi4iN8ZmYqJSUFQGVK8ty+pH30nNWTZX8s466b7uKVtq8Q5B/kdCwRESkgPlOmXC4XoDIleWvFzhV0n9mdwymHmdJtCn1r9XU6koiIFDCfOcynMiV5yVrLG6veoNmkZoQFhbFq2CoVKRERH6WZKZEcSkpNYvjC4Uz/ZTpdru/CxK4TKRFSwulYIiLiEJUpkRzYnLiZiBkRbDq0iedaPMdDtz2En/GZCV4RETkHlSmRbJq7aS4D5w0kOCCYL/p9QasqrZyOJCIiHsBnfqU+VaZCQkIcTiKFTbo7nYe+eoiImAiqla1GQnSCipSIiJymmSmRCziQfIBes3rx7Y5vuePGO3i93esEBwQ7HUtERDyIypTIeazctZLuM7uT6EpkYpeJDKwz0OlIIiLigXzuMJ/KlFyMtZZ3Vr9D04lNCfIPYuXQlSpSIiJyXpqZEskiOTWZOxbdwZT1U+hwbQc+6fYJJUNLOh1LREQ8mMqUSKatf20lYkYEvxz4haebP82jjR/VsgciInJRKlMiwILfFjBg7gD8/fz5vO/ntL2mrdORRESkkPCZX7tdLhfGGIKC9Aa08j8Z7gweW/oYXaZ34ZpS1xAfHa8iJSIiOeJTM1OhoaEYY5yOIh7iYPJB+szpw9fbvmZY3WG81f4tQgK0DpmIiOSMz5UpEYDVe1YTFRPFgeQDfNTpI4bWG+p0JBERKaR86jCfypRYa3k/7n0aT2iMv58/K4asUJESEZFc0cyU+AxXmos7F93JpJ8m0e6adkzpNoXSRUo7HUtERAo5lSnxCdv+3kZkTCQ/7fuJJ5s+yRNNnsDfz9/pWCIi4gVUpsTrLdq8iH5z+wHwWZ/PaH9te4cTiYiIN9E5U+K1MtwZjPl2DB2ndaRSiUrER8erSImISJ7zqZmpsmXLOh1DCkji8UT6zunLF79/waA6g3i3/buEBqpMi4hI3vOpMqWZKd8Q/2c8kTGR7E3aywcdP2B4veFaX0xERPKNypR4lY8SPuKuxXdRLqwcywcv56bLb3I6koiIeDmVKfEKKekp3LX4Lj5e+zGtq7RmauRUyhQp43QsERHxASpTUujtOLyDyJhIEvYm8Fjjx3iq2VNa9kBERAqMypQUaku2LqHvnL5kuDNY0GsBna7v5HQkERHxMT6xNILb7ebEiRMqU17Ebd2M/W4s7T9tT8ViFYmLjlOREhERR/jEzFRKSgqAypSX+Nv1N/3m9mPxlsX0r9Wf9zu+T5HAIk7HEhERH+UTZcrlcgEqU95g7d61RMZEsvvobt5t/y531L9Dyx6IiIijVKak0Ji4biJ3LrqT0qGlWTZ4GbdUvMXpSCIiIipT4vlOpJ9g1OejGJcwjhaVWzAtchrlwso5HUtERATwkTKlc6YKr51HdhIVE8WaP9fwcKOHebrF0wT4+cQ/WxERKSR84qeSZqYKp69+/4res3uT5k5jbs+5dK3a1elIIiIiZ/GJpRFUpgoXt3Xz3PfP0XZKWyqEV2DN8DUqUiIi4rE0MyUe5XDKYQbMHcDCzQvpXbM3H3b6kLCgMKdjiYiInJfKlHiM9fvXEzEjgj+O/MGb7d7krgZ3adkDERHxeCpT4hE++ekTRnw2gpKhJYkdGEujKxs5HUlERCRbVKbEUakZqdy75F7ejXuXplc1ZXrUdCqEV3A6loiISLZl6wR0Y0w7Y8xvxpitxpiHz/H4fcaYjcaY9caYpcaYq/I+6qVTmfJMu4/upsmEJrwb9y7333o/Xw/4WkVKREQKnYvOTBlj/IF3gNbAbmCNMWaBtXZjls3WAvWttceNMXcCLwI98yPwpVCZ8jzfbP+GXrN64Up3MbP7TKKqRzkdSURE5JJkZ2aqAbDVWrvNWpsKTAe6ZN3AWvuttfZ45s1VQMW8jZk7p8pUSEiIw0nEWsu0ndNo/UlryhQpw5rha1SkRESkUMtOmboc2JXl9u7M+85nKPB5bkLlNZfLRWBgIP7+/k5H8WlHUo4QGRPJuO3jiKoexerhq6lapqrTsURERHIlT09AN8b0A+oDTc/zeDQQDVC+fHliY2Pzcvf/EJaeDtYSGxvLli1bCAoKytf9yYVtT97OmA1j+NP1J0MrDqVv6b7E/RDndCzJlJSUpO8PD6Rx8TwaE8/k9Lhkp0ztAa7Icrti5n3/YIxpBTwGNLXWnjjXC1lrxwHjAOrXr2+bNWuW07zZtnZHAOnp6TRr1oxp06YRHh5Ofu5Pzm/az9O464e7KBZcjNhBsWRsz9BYeJjY2FiNiQfSuHgejYlncnpcsnOYbw1wrTGmsjEmCOgFLMi6gTGmLvAB0NlaeyDvY+aOy+XSyecOSM1I5Z7P76HPnD7Uu6weCdEJNL6qsdOxRERE8tRFZ6astenGmLuALwB/YLy1doMxZiwQZ61dALwEhAMzM1es3mmt7ZyPuXNEZarg/XnsT7rP7M4Pu37g3lvu5YVWLxDoH+h0LBERkTyXrXOmrLWLgcVn3Dcmy+et8jhXnlKZKljf7fiOnrN6kpSaxPTI6fSs6TGrZIiIiOS5bC3aWdipTBUMay2v/PAKLSe3pERICVYPX60iJSIiXs9n3k4mLCzM6Rhe7diJYwxZMIRZG2cRUS2CCV0mUCy4mNOxRERE8p3PlKkyZco4HcNrbTq4iYiYCDYnbual1i/x71v/Tea5cyIiIl7PZ8qUDvPlj5gNMQyZP4SwoDC+7v81zSs3dzqSiIhIgdI5U3JJ0jLSuO+L++g5qye1ytciITpBRUpERHySZqYkx/Yl7aPHzB58v/N77m5wNy+3eZkg/yCnY4mIiDhCZUpyZPnO5XSf2Z2jJ44ypdsU+tbq63QkERERR+kwn2SLtZY3Vr1B80nNCQ8KZ9XQVSpSIiIi+MDMVFpaGhkZGSpTuZCUmsSwBcOYsWEGXa7vwqSukygeUtzpWCIiIh7B68uUy+UCUJm6RL8d+o2ImAh+PfQr/235Xx5s9CB+xicmNEVERLJFZUrOa/bG2QyeP5jggGC+7PclLau0dDqSiIiIx/H6KQaVqZxLd6fz4FcPEjUzimplq5EQnaAiJSIich6amZJ/2J+0n16zexG7I5Y769/Ja21fIzgg2OlYIiIiHktlSk5buWslUTOj+Mv1F5O6TmJA7QFORxIREfF4OswnWGt5e/XbNJ3YlJCAEFYNXaUiJSIikk2amfJxyanJjPhsBJ/+/Ckdr+vI5K6TKRla0ulYIiIihYbXl6mUlBRAZepctiRuISImgg0HNvB086d5tPGjWvZAREQkh7y+TGlm6tzm/zqfAfMGEOAXwJJ+S2hzdRunI4mIiBRKXj8NoTL1TxnuDB5d+ihdZ3TlutLXkRCdoCIlIiKSC5qZ8iEHkw/Se3Zvlm5fSnS9aN64/Q1CAkKcjiUiIlKoqUz5iNV7VhMZE8nB5IOM7zyewXUHOx1JRETEK+gwn5ez1vJ+3PvcNv42AvwC+GHoDypSIiIiecgnZqaMMQQFBTkdpcAdTzvOnYvuZPJPk7n9mtuZEjGFUqGlnI4lIiLiVXyiTIWGhmKMcTpKgfr9r9+JjIlk/f71/Kfpf3ii6RNa9kBERCQf+EyZ8iWfbf6MfnP64Wf8WNRnEbdfe7vTkURERLyW109V+FKZynBn8MQ3T9BpWieqlKxCfHS8ipSIiEg+08yUl0g8nkifOX348vcvGVxnMO+0f4fQQO//e4uIiDhNZcoLxP0ZR2RMJPuS9jGu4ziG1Rvmc+eIiYiIOEWH+Qq5jxI+otH4RgAsH7yc4TcOV5ESEREpQJqZKqRcaS7uWnwX49eNp83Vbfg04lPKFCnjdCwRERGf4xNlqnTp0k7HyFPb/95OZEwka/et5fHGj/OfZv/B38/f6VgiIiI+ySfKlDfNTH2+5XP6zumL27pZ0GsBna7v5HQkERERn6ZzpgoJt3XzVOxTdJjagSuLX0l8dLyKlIiIiAfQzFQh8JfrL/rN6cfnWz9nQO0BvNfhPYoEFnE6loiIiKAy5fHW7l1LREwEe47u4b0O7zHixhG6Wk9ERMSDqEx5sAlrJzBy8UjKFCnD94O/5+aKNzsdSURERM7g3WXKQkpKSqErUynpKdzz+T2MSxhHi8otmB45nbJhZZ2OJSIiIufg1WXKnflnYSpTfxz+g6iZUcT9GcfDjR7m6RZPE+Dn1cMkIiJSqHn1T2k3Fig8ZerL37+kz+w+pLnTmNdzHl2qdnE6koiIiFyEVy+N4D7ZpTy+TLmtm2eXPUu7Ke24rOhlxA2PU5ESEREpJDQz5bDDKYcZMHcACzcvpM8NfRjXcRxhQWFOxxIREZFs8u4y5eEzUz/t+4nImEj+OPIHb93+Fv+66V9a9kBExEOkpaWxe/duUlJSTt9XvHhxNm3a5GAqOZe8HJeQkBAqVqxIYGBgtp/j3WUq809PLFOTf5rMHZ/dQcnQknw36DsaXtHQ6UgiIpLF7t27KVq0KJUqVTr9i+6xY8coWrSow8nkTHk1LtZaEhMT2b17N5UrV87287z7nCkPPMx3Iv0EIxeNZOC8gdxc8WYSohNUpEREPFBKSgqlS5fWEQMfYoyhdOnS/5iNzA7vnpnysMN8u47sovvM7vy450ceaPgAz7V8TsseiIh4MBUp33MpY66ZqQKydNtS6o2rx4aDG5jVfRYvtn5RRUpERApUpUqVOHToUJ6/bu/evalVqxavvfZanr82QGxsLD/88EO+vHZe8Oqf5hkeMDNlreWFFS/w2DePcX3p65nTcw5Vy1R1LI+IiBQ+1lqstfj5ed4cyL59+1izZg1bt27N9nPS09MJCMh+BYmNjSU8PJyGDT3ztBjPG5U85PTM1JGUI0TERPDI0kfoXr07q4evVpESEZFs2bFjB9dffz0DBgygZs2a7Nq1izvvvJP69etTo0YNnnzyydPbVqpUiSeffJJ69epxww038OuvvwKQmJhImzZtqFGjBsOGDcNae/o5r776KjVr1qRmzZq8/vrrp/dZtWpVBg0axHXXXUffvn35+uuvadSoEddeey2rV68+K2ebNm3Ys2cPderU4fvvv2fdunXccsst1KpVi27duvH3338D0KxZM0aPHk39+vV54403iI+Pp2nTptx44420bduWvXv3AvDmm29SvXp1atWqRa9evdixYwfvv/8+r7322ul9eBqvnply8mq+n/f/TGRMJNsPb+e1tq9xz8336Ni7iEhhNXo0rFtHaEYG+PvnzWvWqQOZJeZ8tmzZwqRJk7jlllsAePbZZylVqhQZGRm0bNmS9evXU6tWLQDKlClDQkIC7777Li+//DIfffQRTz31FLfddhtjxoxh0aJFfPzxxwDEx8czYcIEfvzxR6y13HzzzTRt2pSSJUuydetWZs6cyfjx47npppuYOnUqy5cvZ8GCBTz33HPMmzfvHxkXLFhAx44dWbduHQC1atXirbfeomnTpowZM4annnrqdFlLTU0lLi6OtLQ0mjZtyvz58ylbtiwzZszgscceY/z48Tz//PNs376d4OBgDh8+TIkSJbjjjjsIDw/n/vvvz5uvfR7z7pkphw7zTf15Krd8fAvHUo/xzYBvGH3LaBUpERHJsauuuup0kQKIiYmhXr161K1blw0bNrBx48bTj0VERABw4403smPHDgCWLVtGv379AOjQoQMlS5YEYPny5XTr1o2wsDDCw8OJiIg4PeNTuXJlbrjhBvz8/KhRowYtW7bEGMMNN9xw+nXP58iRIxw+fJimTZsCMHDgQJYtW3b68Z49ewLw22+/8csvv9C6dWvq1KnDM888w+7du4GTZaxv375MmTIlR4cCnVQ4Ul6igj7Ml5qRyv1f3s9bq9+i8ZWNmRE1g8uKXlYg+xYRkXyUObPiKuB1psLC/veOGNu3b+fll19mzZo1lCxZkkGDBv3jEv7g4GAA/P39SU9Pv+R9nnodAD8/v9O3/fz8cvW68L+/j7WWGjVqsHLlyrO2WbRoEcuWLWPhwoU8++yz/Pzzz7naZ0Hw+pmpwMBA/PNqSvYC9hzdQ7OJzXhr9Vvce8u9LB2wVEVKRETyzNGjRwkLC6N48eLs37+fzz///KLPadKkCVOnTgXg888/P33+UuPGjZk3bx7Hjx8nOTmZuXPn0rhx41xnLF68OCVLljw9y/XJJ5+cnqXK6vrrr+fgwYOny1RaWhobNmzA7Xaza9cumjdvzgsvvMCRI0dISkqiaNGiHDt2LNf58ovXz0yFhhbJ9/3E7oil56yeJKcmMyNqBj1q9Mj3fYqIiG+pXbs2devWpWrVqlxxxRU0atToos958skn6d27NzVq1KBhw4ZceeWVANSrV49BgwbRoEEDAIYNG0bdunUvehgvOyZNmsQdd9zB8ePHqVKlChMmTDhrm6CgIGbNmsWoUaM4cuQI6enpjB49muuuu45+/fpx5MgRrLWMGjWKEiVK0KlTJ6Kiopg/fz5vvfVWnhS/vGSyntlfkOrXr2/j4uLy7fXXTizBb8nJjH66NPv27cuXfVhreWXlKzz89cNcU+oa5vScQ/Wy1fNlX94kNjaWZs2aOR1DstCYeCaNi7M2bdpEtWrV/nGf3k7GM+X1uJxr7I0x8dba+ufa3rtnpmz+nS919MRRhswfwuxNs4msFsn4LuMpFlwsX/YlIiIinsu7yxQ2X8rUxoMbiZgRwda/tvJy65e579b7dLWeiIiIj/LuMpUPM1MxG2IYMn8IYUFhLB2wlKaVzj6xTkRERHyHd1/Nl4czU2kZady75F56zupJ7Qq1SYhOUJESERER756Zysijmam9x/bSY1YPlu9czqgGo3ipzUsE+QflQUIREREp7Ly6TLnJfZn6/o/v6TGrB0dPHGVqxFR639A7b8KJiIiIV9BhvvOw1vL6qtdpPqk5RYOK8uOwH1WkRESkQIWHh1/yc4cNG/aPt5s508SJE/nzzz+zvb2nW7BgAc8//7wj+/bumalLPMyXlJrEsAXDmLFhBl2rdmVil4kUDymeDwlFRETyx0cffXTBxydOnEjNmjX5v//7v2xtfz7p6em5fg+9jIyMXL9bSefOnencuXOuXuNSaWbqDL8e+pUGHzZg5saZPN/yeeb0mKMiJSIijrLW8sADD1CzZk1uuOEGZsyYAYDb7WbkyJFUrVqV1q1b0759e2bNmgVAs2bNiIuLIyMjg0GDBp1+7muvvcasWbOIi4ujb9++1KlTB5fLdXp7gCVLllCvXj1q165Ny5Ytz8ozceJEOnfuTIsWLWjZsiXJyckMGTKEBg0aULduXebPnw/A8ePH6dGjB9WrV6dbt27cfPPNp/cRHh7Ov//9b2rXrs3KlSuZMmUKDRo0oE6dOowYMYKMjIxzZgd48803qV69OrVq1aJXr16nM911110A7NixgxYtWlCrVi1atmzJzp07ARg0aBCjRo2iYcOGVKlS5fTXKrc0M5XF7I2zGTR/EKEBoXzV/ytaVG6Rj+lERKSwGD16NOvWrcuTGZRT6tSpw+uZb6B8MXPmzGHdunX89NNPHDp0iJtuuokmTZqwYsUKduzYwcaNGzlw4ADVqlVjyJAh/3juunXr2LNnD7/88gsAhw8fpkSJErz99tu8/PLL1K//z0W9Dx48yPDhw1m2bBmVK1fmr7/+OmemhIQE1q9fT6lSpXj00Udp0aIF48eP5/DhwzRo0IBWrVrx3nvvUbJkSTZu3Mgvv/xCnTp1Tj8/OTmZm2++mVdeeYVNmzbxwgsvsGLFCgIDAxk5ciSffvopNWrUOCs7wPPPP8/27dsJDg4+fV9Wd999NwMHDmTgwIGMHz+eUaNGMW/ePAD27t3L8uXL+fXXX+ncuTNRUVHZGoML8fKZqeyVqXR3Og98+QBRM6OoUbYGCSMSVKRERMRjLF++nN69e+Pv70/58uVp2rQpa9asYfny5XTv3h0/Pz8qVKhA8+bNz3pulSpV2LZtG3fffTdLliyhWLELv1vHqlWraNKkCZUrVwagVKlS59yudevWpx/78ssvef7556lTpw7NmjUjJSWFnTt3snz58tMzRzVr1qRWrVqnn+/v709kZCQAS5cuJT4+nptuuok6deqwdOlStm3bdt7stWrVom/fvkyZMuWchxhXrlxJnz59AOjfvz/Lly8//VjXrl3x8/OjevXq7N+//4Jfi+zy2pkpa8ECISEhF9xuf9J+es3uReyOWEbWH8mrbV8lOCC4YEKKiEihcGoGqTC+N1/JkiX56aef+OKLL3j//feJiYlh/PjxuX7dsLCw059ba5k9ezbXX399tp8fEhJyepbPWsvAgQP573//e9Z258q+aNEili1bxsKFC3n22Wf54Ycfsr3f4OD//YzPq/cn9tqZKTcnv0AXmpn6YdcP1BtXjx93/8jkrpN5p8M7KlIiIuJxGjduzIwZM8jIyODgwYMsW7aMBg0a0KhRI2bPno3b7Wb//v3Exsae9dxDhw7hdruJjIzkmWeeISEhAYCiRYty7Nixs7a/5ZZbWLZsGdu3bwc472G+rNq2bctbb711upysXbsWgEaNGhETEwPAxo0b+fnnn8/5/JYtWzJr1iwOHDhwep9//PHHObO73W527dpF8+bNeeGFFzhy5AhJSUn/eL2GDRsyffp0AD799FMaN2580b9DbnjtzJQ7s2yeq0xZa3l79dvc9+V9XFX8KhYPXUztCrULOKGIiEj2dOvWjZUrV1K7dm2MMbz44otUqFCByMhIli5dSvXq1bniiiuoV68exYv/86KpPXv2MHjwYNxuN8Dp2Z9BgwZxxx13EBoaysqVK09vX7ZsWcaNG0dERARut5ty5crx1VdfXTDfE088wejRo6lVqxZut5vKlSvz2WefMXLkSAYOHEj16tWpWrUqNWrUOCsfQPXq1XnmmWdo06YNbrebwMBA3nnnHUJDQ8/KnpGRQb9+/Thy5AjWWkaNGkWJEiX+8XpvvfUWgwcP5qWXXqJs2bJMmDAhx1/znDB5NcWVU/Xr17enzujPD6vGF2PV0WMUK/bxP07GS05NJvqzaKb+PJVO13VicrfJlAgpkW855GyxsbE0a9bM6RiShcbEM2lcnLVp0yaqVav2j/s88TBfUlIS4eHhJCYm0qBBA1asWEGFChWcjgWcXPIgLS2NkJAQfv/9d1q1asVvv/1GUFDevotIXo/LucbeGBNvra1/ru29dmYq4xwzU5sTNxMZE8mGAxt4pvkzPNL4EfyM1x7pFBERH9CxY0cOHz5MamoqTzzxhMcUKTi5NELz5s1JS0vDWsu7776b50XKE2SrTBlj2gFvAP7AR9ba5894PBiYDNwIJAI9rbU78jZqzrjtP8+ZmvfrPAbOG0igXyBL+i2hzdVtnIwnIiKSJ851npSnKFq0KPl5FMpTXHRaxhjjD7wD3A5UB3obY6qfsdlQ4G9r7TXAa8ALeR00p06dgB4UEsTDXz9MtxnduK70dcRHx6tIiYiISJ7JzsxUA2CrtXYbgDFmOtAFyPoGPl2A/2R+Pgt42xhjrFMnZHHyBPRjbnjitydIOJxAdL1o3rj9DUICLrxUgoiIiEhOZKdMXQ7synJ7N3Dz+bax1qYbY44ApYFDeRHyUmxMTefVJHCZDYzvPJ7BdQc7FUVERES8WIGegG6MiQaiAcqXL5+vx3ndh4sRmH6I+ys9QeUjlT36mLKvSUpK0nh4GI2JZ9K4OKt48eJnrcOUkZFxzrWZxFl5PS4pKSk5+t7LTpnaA1yR5XbFzPvOtc1uY0wAUJyTJ6L/g7V2HDAOTi6NkJ+X/DZrdoAa335Di+Z6WxhPo8u9PY/GxDNpXJy1adOmsy6398SlEU6pVKkScXFxlClTJs9fe926dfz555+0b98+z187L+T1uISEhFC3bt1sb5+ddQHWANcaYyobY4KAXsCCM7ZZAAzM/DwK+MbJ86VO0bIHIiIiubdu3ToWL17sdAyPddG2Ya1NB+4CvgA2ATHW2g3GmLHGmM6Zm30MlDbGbAXuAx7Or8AiIiK+YM2aNdSqVYuUlBSSk5OpUaMG69evZ+TIkVStWpXWrVvTvn17Zs2adfo5L774IjfccAMNGjRg69atAOzYsYMWLVpQq1YtWrZsyc6dOy94/8yZM6lZsya1a9emSZMmpKamMmbMGGbMmEGdOnWYMWNGwX8xPFy2zpmy1i4GFp9x35gsn6cA3fM2moiIiGfYsmU0SUnryMjIOP3mvLkVHl6Ha699/byP33TTTXTu3JnHH38cl8tFv3792Lx5Mzt27GDjxo0cOHCAatWq/eNdPooXL87PP//M5MmTGT16NJ999hl33303AwcOZODAgYwfP55Ro0Yxb968894/duxYvvjiCy6//HIOHz5MUFAQY8eOJS4ujrfffjtP/u7eRsfBREREPNSYMWP46quviIuL48EHH2T58uV0794dPz8/KlSoQPPmzf+xfe/evU//eer99lauXEmfPn0A6N+/P8uXL7/g/Y0aNWLQoEF8+OGHZGRkFMjfs7Dz2reTERERySunZpAK+gT0xMREkpKSSEtLIyUl5aLbG2PO+XlOvP/++/z4448sWrSIG2+8kfj4+Et6HV+imSkREREPNWLECJ5++mn69u3LQw89RKNGjZg9ezZut5v9+/efdfn+qfOZZsyYwa233gpAw4YNmT59OgCffvopjRs3vuD9v//+OzfffDNjx46lbNmy7Nq1i6JFi2pJiAvQzJSIiIgHmjx5MoGBgfTp04eMjAwaNmxIREQEFStWpHr16lxxxRXUq1eP4sWLn37O33//Ta1atQgODmbatGkAvPXWWwwePJiXXnqJsmXLMmHChAve/8ADD7BlyxastbRs2ZLatWtz5ZVX8vzzz1OnTh0eeeQRevbsWfBfEA+mMiUiIuKBBgwYwIABAwDw9/fnxx9/BKBBgwaEh4eTmJhIgwYNuOGGG4CTV+cBvPDCP98e96qrruKbb7456/XPd/+cOXPOuq9UqVKsWbMmV38fb6YyJSIiUoh07NiRw4cPk5qayhNPPEGFChWcjuTzVKZEREQKEb3FkOfRCegiIiIiuaAyJSIich4e8M5oUsAuZcxVpkRERM4hJCSExMREFSofYq0lMTGRkJCQHD1P50yJiIicQ8WKFdm9ezcHDx48fV9KSkqOf9BK/svLcQkJCaFixYo5eo7KlIiIyDkEBgZSuXLlf9wXGxtL3bp1HUok5+P0uOgwn4iIiEguqEyJiIiI5ILKlIiIiEguGKeuUjDGHAT+yOfdlAEO5fM+JOc0Lp5HY+KZNC6eR2PimQpiXK6y1pY91wOOlamCYIyJs9bWdzqH/JPGxfNoTDyTxsXzaEw8k9PjosN8IiIiIrmgMiUiIiKSC95epsY5HUDOSePieTQmnknj4nk0Jp7J0XHx6nOmRERERPKbt89MiYiIiOQrryhTxph2xpjfjDFbjTEPn+PxYGPMjMzHfzTGVHIgps/JxrjcZ4zZaIxZb4xZaoy5yomcvuRiY5Jlu0hjjDXG6KqlfJadMTHG9Mj8XtlgjJla0Bl9UTb+/7rSGPOtMWZt5v9h7Z3I6UuMMeONMQeMMb+c53FjjHkzc8zWG2PqFVS2Ql+mjDH+wDvA7UB1oLcxpvoZmw0F/rbWXgO8BrxQsCl9TzbHZS1Q31pbC5gFvFiwKX1LNscEY0xR4B7gx4JN6HuyMybGmGuBR4BG1toawOiCzulrsvm98jgQY62tC/QC3i3YlD5pItDuAo/fDlyb+RENvFcAmQAvKFNAA2CrtXabtTYVmA50OWObLsCkzM9nAS2NMaYAM/qii46LtfZba+3xzJurgJy9TbfkVHa+VwCe5uQvHCkFGc5HZWdMhgPvWGv/BrDWHijgjL4oO+NigWKZnxcH/izAfD7JWrsM+OsCm3QBJtuTVgEljDGXFUQ2byhTlwO7stzenXnfObex1qYDR4DSBZLOd2VnXLIaCnyer4nkomOSOS1+hbV2UUEG82HZ+T65DrjOGLPCGLPKGHOh38wlb2RnXP4D9DPG7AYWA3cXTDS5gJz+3MkzAQWxE5ELMcb0A+oDTZ3O4suMMX7Aq8Agh6PIPwVw8rBFM07O3i4zxtxgrT3sZCihNzDRWvuKMeZW4BNjTE1rrdvpYFLwvGFmag9wRZbbFTPvO+c2xpgATk7JJhZIOt+VnXHBGNMKeAzobK09UUDZfNXFxqQoUBOINcbsAG4BFugk9HyVne+T3cACa22atXY7sJmT5UryT3bGZSgQA2CtXQmEcPL94cQ52fq5kx+8oUytAa41xlQ2xgRx8kTABWdsswAYmPl5FPCN1QJb+e2i42KMqQt8wMkipfNA8t8Fx8Rae8RaW8ZaW8laW4mT57F1ttbGORPXJ2Tn/695nJyVwhhThpOH/bYVYEZflJ1x2Qm0BDDGVONkmTpYoCnlTAuAAZlX9d0CHLHW7i2IHRf6w3zW2nRjzF3AF4A/MN5au8EYMxaIs9YuAD7m5BTsVk6evNbLucS+IZvj8hIQDszMvB5gp7W2s2OhvVw2x0QKUDbH5AugjTFmI5ABPGCt1cx6PsrmuPwb+NAYcy8nT0YfpF/S85cxZhonf7Eok3mu2pNAIIC19n1OnrvWHtgKHAcGF1g2jb2IiIjIpfOGw3wiIiIijlGZEhEREckFlSkRERGRXFCZEhEREckFlSkRERGRXFCZEhEREckFlSkRERGRXFCZEhEREcmF/weMGwjEMaeceQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "plt.title('ROC curve')\n", "l1, = plt.plot(fpr_f,tpr_f,color = 'r',label='random forest')\n", "l2, = plt.plot(fpr_l,tpr_l,color = 'k',label='logistic regression')\n", "l3, = plt.plot(fpr_x,tpr_x,color = 'y',label='xgboost')\n", "plt.plot([0, 1], [0, 1],color = 'g')\n", "plt.legend(handles = [l1,l2,l3])\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "id": "84a6b2b9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:57.840299Z", "iopub.status.busy": "2022-01-28T14:18:57.839646Z", "iopub.status.idle": "2022-01-28T14:18:57.921686Z", "shell.execute_reply": "2022-01-28T14:18:57.922342Z", "shell.execute_reply.started": "2022-01-28T14:07:30.940539Z" }, "papermill": { "duration": 0.153999, "end_time": "2022-01-28T14:18:57.922547", "exception": false, "start_time": "2022-01-28T14:18:57.768548", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "User settings:\n", "\n", " KMP_AFFINITY=granularity=fine,verbose,compact,1,0\n", " KMP_BLOCKTIME=0\n", " KMP_DUPLICATE_LIB_OK=True\n", " KMP_INIT_AT_FORK=FALSE\n", " KMP_SETTINGS=1\n", " KMP_WARNINGS=0\n", "\n", "Effective settings:\n", "\n", " KMP_ABORT_DELAY=0\n", " KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n", " KMP_ALIGN_ALLOC=64\n", " KMP_ALL_THREADPRIVATE=128\n", " KMP_ATOMIC_MODE=2\n", " KMP_BLOCKTIME=0\n", " KMP_CPUINFO_FILE: value is not defined\n", " KMP_DETERMINISTIC_REDUCTION=false\n", " KMP_DEVICE_THREAD_LIMIT=2147483647\n", " KMP_DISP_NUM_BUFFERS=7\n", " KMP_DUPLICATE_LIB_OK=true\n", " KMP_ENABLE_TASK_THROTTLING=true\n", " KMP_FORCE_REDUCTION: value is not defined\n", " KMP_FOREIGN_THREADS_THREADPRIVATE=true\n", " KMP_FORKJOIN_BARRIER='2,2'\n", " KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n", " KMP_GTID_MODE=3\n", " KMP_HANDLE_SIGNALS=false\n", " KMP_HOT_TEAMS_MAX_LEVEL=1\n", " KMP_HOT_TEAMS_MODE=0\n", " KMP_INIT_AT_FORK=true\n", " KMP_LIBRARY=throughput\n", " KMP_LOCK_KIND=queuing\n", " KMP_MALLOC_POOL_INCR=1M\n", " KMP_NUM_LOCKS_IN_BLOCK=1\n", " KMP_PLAIN_BARRIER='2,2'\n", " KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n", " KMP_REDUCTION_BARRIER='1,1'\n", " KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n", " KMP_SCHEDULE='static,balanced;guided,iterative'\n", " KMP_SETTINGS=true\n", " KMP_SPIN_BACKOFF_PARAMS='4096,100'\n", " KMP_STACKOFFSET=64\n", " KMP_STACKPAD=0\n", " KMP_STACKSIZE=8M\n", " KMP_STORAGE_MAP=false\n", " KMP_TASKING=2\n", " KMP_TASKLOOP_MIN_TASKS=0\n", " KMP_TASK_STEALING_CONSTRAINT=1\n", " KMP_TEAMS_THREAD_LIMIT=4\n", " KMP_TOPOLOGY_METHOD=all\n", " KMP_USE_YIELD=1\n", " KMP_VERSION=false\n", " KMP_WARNINGS=false\n", " OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n", " OMP_ALLOCATOR=omp_default_mem_alloc\n", " OMP_CANCELLATION=false\n", " OMP_DEFAULT_DEVICE=0\n", " OMP_DISPLAY_AFFINITY=false\n", " OMP_DISPLAY_ENV=false\n", " OMP_DYNAMIC=false\n", " OMP_MAX_ACTIVE_LEVELS=1\n", " OMP_MAX_TASK_PRIORITY=0\n", " OMP_NESTED: deprecated; max-active-levels-var=1\n", " OMP_NUM_THREADS: value is not defined\n", " OMP_PLACES: value is not defined\n", " OMP_PROC_BIND='intel'\n", " OMP_SCHEDULE='static'\n", " OMP_STACKSIZE=8M\n", " OMP_TARGET_OFFLOAD=DEFAULT\n", " OMP_THREAD_LIMIT=2147483647\n", " OMP_WAIT_POLICY=PASSIVE\n", " KMP_AFFINITY='verbose,warnings,respect,granularity=fine,compact,1,0'\n", "\n", "2022-01-28 14:18:57.879474: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n" ] } ], "source": [ "model = keras.Sequential([layers.BatchNormalization()\n", " ,layers.Dense(4, activation = 'relu', input_shape = [30]),\n", " layers.BatchNormalization(),\n", " layers.Dense(4, activation = 'relu'),\n", " layers.BatchNormalization(),\n", " layers.Dense(1, activation = 'sigmoid')])\n", "model.compile(optimizer='adam',\n", " loss = 'binary_crossentropy',\n", " metrics = ['binary_accuracy'])" ] }, { "cell_type": "code", "execution_count": 29, "id": "b88968fe", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:18:58.058047Z", "iopub.status.busy": "2022-01-28T14:18:58.056906Z", "iopub.status.idle": "2022-01-28T14:19:00.772509Z", "shell.execute_reply": "2022-01-28T14:19:00.771901Z", "shell.execute_reply.started": "2022-01-28T14:07:30.942639Z" }, "papermill": { "duration": 2.784641, "end_time": "2022-01-28T14:19:00.772675", "exception": false, "start_time": "2022-01-28T14:18:57.988034", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-01-28 14:18:58.157005: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/30\n", "12/12 [==============================] - 1s 3ms/step - loss: 0.6602 - binary_accuracy: 0.6273\n", "Epoch 2/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.6027 - binary_accuracy: 0.7165\n", "Epoch 3/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.5599 - binary_accuracy: 0.7927\n", "Epoch 4/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.5255 - binary_accuracy: 0.8163\n", "Epoch 5/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.4880 - binary_accuracy: 0.8635\n", "Epoch 6/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.4719 - binary_accuracy: 0.8793\n", "Epoch 7/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.4425 - binary_accuracy: 0.9003\n", "Epoch 8/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.4347 - binary_accuracy: 0.8871\n", "Epoch 9/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.4103 - binary_accuracy: 0.9160\n", "Epoch 10/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.4009 - binary_accuracy: 0.8924\n", "Epoch 11/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.3749 - binary_accuracy: 0.8976\n", "Epoch 12/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.3499 - binary_accuracy: 0.9213\n", "Epoch 13/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.3346 - binary_accuracy: 0.9108\n", "Epoch 14/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.3186 - binary_accuracy: 0.9344\n", "Epoch 15/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.3231 - binary_accuracy: 0.9081\n", "Epoch 16/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.3106 - binary_accuracy: 0.9108\n", "Epoch 17/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2808 - binary_accuracy: 0.9239\n", "Epoch 18/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2659 - binary_accuracy: 0.9396\n", "Epoch 19/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2514 - binary_accuracy: 0.9449\n", "Epoch 20/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2302 - binary_accuracy: 0.9554\n", "Epoch 21/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2337 - binary_accuracy: 0.9344\n", "Epoch 22/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2214 - binary_accuracy: 0.9528\n", "Epoch 23/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2152 - binary_accuracy: 0.9449\n", "Epoch 24/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2095 - binary_accuracy: 0.9449\n", "Epoch 25/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.2170 - binary_accuracy: 0.9265\n", "Epoch 26/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.1881 - binary_accuracy: 0.9528\n", "Epoch 27/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.1980 - binary_accuracy: 0.9318\n", "Epoch 28/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.1701 - binary_accuracy: 0.9580\n", "Epoch 29/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.1690 - binary_accuracy: 0.9580\n", "Epoch 30/30\n", "12/12 [==============================] - 0s 2ms/step - loss: 0.1680 - binary_accuracy: 0.9685\n" ] } ], "source": [ "history = model.fit(X_train,y_train,\n", " epochs=30)" ] }, { "cell_type": "code", "execution_count": 30, "id": "e9f7f965", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:00.949060Z", "iopub.status.busy": "2022-01-28T14:19:00.948367Z", "iopub.status.idle": "2022-01-28T14:19:00.959593Z", "shell.execute_reply": "2022-01-28T14:19:00.958925Z", "shell.execute_reply.started": "2022-01-28T14:07:30.944813Z" }, "papermill": { "duration": 0.104221, "end_time": "2022-01-28T14:19:00.959738", "exception": false, "start_time": "2022-01-28T14:19:00.855517", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>loss</th>\n", " <th>binary_accuracy</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.660234</td>\n", " <td>0.627297</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.602686</td>\n", " <td>0.716535</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.559949</td>\n", " <td>0.792651</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.525467</td>\n", " <td>0.816273</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.487959</td>\n", " <td>0.863517</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.471871</td>\n", " <td>0.879265</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0.442516</td>\n", " <td>0.900262</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0.434685</td>\n", " <td>0.887139</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0.410273</td>\n", " <td>0.916010</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0.400882</td>\n", " <td>0.892388</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0.374910</td>\n", " <td>0.897638</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>0.349935</td>\n", " <td>0.921260</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>0.334625</td>\n", " <td>0.910761</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>0.318631</td>\n", " <td>0.934383</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>0.323132</td>\n", " <td>0.908136</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>0.310583</td>\n", " <td>0.910761</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>0.280775</td>\n", " <td>0.923885</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>0.265853</td>\n", " <td>0.939633</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>0.251391</td>\n", " <td>0.944882</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>0.230226</td>\n", " <td>0.955381</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>0.233662</td>\n", " <td>0.934383</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>0.221442</td>\n", " <td>0.952756</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>0.215247</td>\n", " <td>0.944882</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>0.209508</td>\n", " <td>0.944882</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>0.217025</td>\n", " <td>0.926509</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>0.188124</td>\n", " <td>0.952756</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>0.198037</td>\n", " <td>0.931759</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>0.170129</td>\n", " <td>0.958005</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>0.169047</td>\n", " <td>0.958005</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>0.168027</td>\n", " <td>0.968504</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " loss binary_accuracy\n", "0 0.660234 0.627297\n", "1 0.602686 0.716535\n", "2 0.559949 0.792651\n", "3 0.525467 0.816273\n", "4 0.487959 0.863517\n", "5 0.471871 0.879265\n", "6 0.442516 0.900262\n", "7 0.434685 0.887139\n", "8 0.410273 0.916010\n", "9 0.400882 0.892388\n", "10 0.374910 0.897638\n", "11 0.349935 0.921260\n", "12 0.334625 0.910761\n", "13 0.318631 0.934383\n", "14 0.323132 0.908136\n", "15 0.310583 0.910761\n", "16 0.280775 0.923885\n", "17 0.265853 0.939633\n", "18 0.251391 0.944882\n", "19 0.230226 0.955381\n", "20 0.233662 0.934383\n", "21 0.221442 0.952756\n", "22 0.215247 0.944882\n", "23 0.209508 0.944882\n", "24 0.217025 0.926509\n", "25 0.188124 0.952756\n", "26 0.198037 0.931759\n", "27 0.170129 0.958005\n", "28 0.169047 0.958005\n", "29 0.168027 0.968504" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(history.history)" ] }, { "cell_type": "code", "execution_count": null, "id": "e6595318", "metadata": { "papermill": { "duration": 0.084295, "end_time": "2022-01-28T14:19:01.127251", "exception": false, "start_time": "2022-01-28T14:19:01.042956", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a1a09515", "metadata": { "papermill": { "duration": 0.082502, "end_time": "2022-01-28T14:19:01.293236", "exception": false, "start_time": "2022-01-28T14:19:01.210734", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "1c932dc6", "metadata": { "papermill": { "duration": 0.08268, "end_time": "2022-01-28T14:19:01.458972", "exception": false, "start_time": "2022-01-28T14:19:01.376292", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ec073948", "metadata": { "papermill": { "duration": 0.082944, "end_time": "2022-01-28T14:19:01.624962", "exception": false, "start_time": "2022-01-28T14:19:01.542018", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "3e79c500", "metadata": { "papermill": { "duration": 0.083108, "end_time": "2022-01-28T14:19:01.792092", "exception": false, "start_time": "2022-01-28T14:19:01.708984", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 37.772148, "end_time": "2022-01-28T14:19:04.947413", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:18:27.175265", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/399/86399553.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "id": "7d65b4b7", "metadata": { "id": "QRcqbpLpFK5o", "papermill": { "duration": 0.034893, "end_time": "2022-01-28T14:19:09.971255", "exception": false, "start_time": "2022-01-28T14:19:09.936362", "status": "completed" }, "tags": [] }, "source": [ "# **Análise Exploratória de Dados de Logística**" ] }, { "cell_type": "markdown", "id": "cddaed1b", "metadata": { "id": "6-CvdKwqFPiW", "papermill": { "duration": 0.027116, "end_time": "2022-01-28T14:19:10.023749", "exception": false, "start_time": "2022-01-28T14:19:09.996633", "status": "completed" }, "tags": [] }, "source": [ "## 1\\. Contexto" ] }, { "cell_type": "markdown", "id": "d82d262a", "metadata": { "id": "XRURE1uUFXGw", "papermill": { "duration": 0.022425, "end_time": "2022-01-28T14:19:10.069069", "exception": false, "start_time": "2022-01-28T14:19:10.046644", "status": "completed" }, "tags": [] }, "source": [ "## Intro:\n", "O Loggi Benchmark for Urban Deliveries (BUD) é um repositório do GitHub[(link)](https://github.com/loggi/loggibud) com dados e\n", "códigos para problemas típicos que empresas de logística enfrentam: otimização das rotas de\n", "entrega, alocação de entregas nos veículos da frota com capacidade limitada, etc. Os dados\n", "são sintetizados de fontes públicas (IBGE, IPEA, etc.) e são representativos dos desafios que a\n", "startup enfrenta no dia a dia, especialmente com relação a sua escala.\n", "\n", "## Objetivo da análise:\n", "Essa análise consiste em encontrar possíveis otimizações nas entregas através dos dados coletados." ] }, { "cell_type": "markdown", "id": "1fbd4a9a", "metadata": { "id": "QxukLHaqFnkU", "papermill": { "duration": 0.023586, "end_time": "2022-01-28T14:19:10.115524", "exception": false, "start_time": "2022-01-28T14:19:10.091938", "status": "completed" }, "tags": [] }, "source": [ "## 2\\. Pacotes e bibliotecas" ] }, { "cell_type": "code", "execution_count": 1, "id": "0dd188ed", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:10.171621Z", "iopub.status.busy": "2022-01-28T14:19:10.171020Z", "iopub.status.idle": "2022-01-28T14:19:30.646034Z", "shell.execute_reply": "2022-01-28T14:19:30.646804Z" }, "id": "C-RWo4YTEHfR", "outputId": "f300fb8c-8df5-4579-818b-4124ca4d0042", "papermill": { "duration": 20.508788, "end_time": "2022-01-28T14:19:30.647241", "exception": false, "start_time": "2022-01-28T14:19:10.138453", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: haversine in /opt/conda/lib/python3.7/site-packages (2.5.1)\r\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n", "Requirement already satisfied: geopandas in /opt/conda/lib/python3.7/site-packages (0.10.2)\r\n", "Requirement already satisfied: shapely>=1.6 in /opt/conda/lib/python3.7/site-packages (from geopandas) (1.8.0)\r\n", "Requirement already satisfied: fiona>=1.8 in /opt/conda/lib/python3.7/site-packages (from geopandas) (1.8.20)\r\n", "Requirement already satisfied: pandas>=0.25.0 in /opt/conda/lib/python3.7/site-packages (from geopandas) (1.3.5)\r\n", "Requirement already satisfied: pyproj>=2.2.0 in /opt/conda/lib/python3.7/site-packages (from geopandas) (3.1.0)\r\n", "Requirement already satisfied: munch in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (2.5.0)\r\n", "Requirement already satisfied: click>=4.0 in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (8.0.3)\r\n", "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (59.5.0)\r\n", "Requirement already satisfied: click-plugins>=1.0 in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (1.1.1)\r\n", "Requirement already satisfied: six>=1.7 in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (1.16.0)\r\n", "Requirement already satisfied: attrs>=17 in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (21.2.0)\r\n", "Requirement already satisfied: certifi in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (2021.10.8)\r\n", "Requirement already satisfied: cligj>=0.5 in /opt/conda/lib/python3.7/site-packages (from fiona>=1.8->geopandas) (0.7.2)\r\n", "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.25.0->geopandas) (2.8.0)\r\n", "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.25.0->geopandas) (2021.3)\r\n", "Requirement already satisfied: numpy>=1.17.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.25.0->geopandas) (1.20.3)\r\n", "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from click>=4.0->fiona>=1.8->geopandas) (4.10.1)\r\n", "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->click>=4.0->fiona>=1.8->geopandas) (3.6.0)\r\n", "Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->click>=4.0->fiona>=1.8->geopandas) (3.10.0.2)\r\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n" ] } ], "source": [ "#Instalar o módulo haversine para cálculo de dinstância entre hub e entrega\n", "!pip install haversine\n", "\n", "#Instalar o módulo geopandas para visualização de entregas no mapa de Brasília \n", "!pip install geopandas" ] }, { "cell_type": "code", "execution_count": 2, "id": "1eb54c8c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:30.718095Z", "iopub.status.busy": "2022-01-28T14:19:30.712805Z", "iopub.status.idle": "2022-01-28T14:19:54.863192Z", "shell.execute_reply": "2022-01-28T14:19:54.862491Z" }, "id": "bN2KFGFdy1t7", "papermill": { "duration": 24.188367, "end_time": "2022-01-28T14:19:54.863407", "exception": false, "start_time": "2022-01-28T14:19:30.675040", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#download dataset e pacote support_project\n", "!wget -q \"https://raw.githubusercontent.com/andre-marcos-perez/ebac-course-utils/main/dataset/deliveries.json\" -O deliveries.json \n", "!wget -q \"https://raw.githubusercontent.com/AnthonyCavalcante/Estudos_python/main/EBAC/Web-scraping/support_project.py\" -O support_project.py\n", "\n", "#download do pacote de dados para criação do mapa através do ibge\n", "!wget -q \"https://geoftp.ibge.gov.br/cartas_e_mapas/bases_cartograficas_continuas/bc100/go_df/versao2016/shapefile/bc100_go_df_shp.zip\" -O distrito-federal.zip\n", "!unzip -q distrito-federal.zip -d ./maps\n", "!cp ./maps/LIM_Unidade_Federacao_A.shp ./distrito-federal.shp\n", "!cp ./maps/LIM_Unidade_Federacao_A.shx ./distrito-federal.shx" ] }, { "cell_type": "code", "execution_count": 3, "id": "c3a10338", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:54.926365Z", "iopub.status.busy": "2022-01-28T14:19:54.925286Z", "iopub.status.idle": "2022-01-28T14:19:56.845445Z", "shell.execute_reply": "2022-01-28T14:19:56.844827Z" }, "id": "VXUEW0VrF7XW", "papermill": { "duration": 1.954488, "end_time": "2022-01-28T14:19:56.845633", "exception": false, "start_time": "2022-01-28T14:19:54.891145", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/geopandas/_compat.py:115: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n", " shapely_geos_version, geos_capi_version_string\n" ] } ], "source": [ "import json \n", "\n", "import pandas as pd\n", "import geopandas\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from haversine import haversine\n", "\n", "from support_project import cal_lat_lng, my_figsize\n" ] }, { "cell_type": "markdown", "id": "da64249a", "metadata": { "id": "irQxHW1zGkdZ", "papermill": { "duration": 0.02681, "end_time": "2022-01-28T14:19:56.901829", "exception": false, "start_time": "2022-01-28T14:19:56.875019", "status": "completed" }, "tags": [] }, "source": [ "## 3\\. Exploração de dados\n", "\n" ] }, { "cell_type": "markdown", "id": "b36080af", "metadata": { "id": "Gg39GmOPya0r", "papermill": { "duration": 0.027751, "end_time": "2022-01-28T14:19:56.955988", "exception": false, "start_time": "2022-01-28T14:19:56.928237", "status": "completed" }, "tags": [] }, "source": [ "### Coleta de Dados " ] }, { "cell_type": "code", "execution_count": 4, "id": "c56c3d85", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:57.014347Z", "iopub.status.busy": "2022-01-28T14:19:57.013624Z", "iopub.status.idle": "2022-01-28T14:20:00.085391Z", "shell.execute_reply": "2022-01-28T14:20:00.084769Z" }, "id": "k2Curlvwy8aq", "outputId": "42298a32-fca4-4d9f-9c6d-701d644e3ed2", "papermill": { "duration": 3.102072, "end_time": "2022-01-28T14:20:00.085549", "exception": false, "start_time": "2022-01-28T14:19:56.983477", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "with open('deliveries.json', 'r', encoding='utf8') as json_file:\n", " data_loggi = json.load(json_file)\n" ] }, { "cell_type": "markdown", "id": "10c8c9e6", "metadata": { "id": "V4GMo9Yiz81w", "papermill": { "duration": 0.027902, "end_time": "2022-01-28T14:20:00.140218", "exception": false, "start_time": "2022-01-28T14:20:00.112316", "status": "completed" }, "tags": [] }, "source": [ "### Wrangling\n", "Organizando a estrutura de dados" ] }, { "cell_type": "code", "execution_count": 5, "id": "33730723", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.195708Z", "iopub.status.busy": "2022-01-28T14:20:00.195070Z", "iopub.status.idle": "2022-01-28T14:20:00.533083Z", "shell.execute_reply": "2022-01-28T14:20:00.532396Z" }, "id": "QGlpVfbA0MZb", "outputId": "c5246dd6-0a04-4a0a-b7a7-ebfd7c91f6db", "papermill": { "duration": 0.366475, "end_time": "2022-01-28T14:20:00.533229", "exception": false, "start_time": "2022-01-28T14:20:00.166754", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>region</th>\n", " <th>origin</th>\n", " <th>vehicle_capacity</th>\n", " <th>deliveries</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>{'lng': -48.05498915846707, 'lat': -15.8381445...</td>\n", " <td>180</td>\n", " <td>[{'id': '313483a19d2f8d65cd5024c8d215cfbd', 'p...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>cvrp-2-df-73</td>\n", " <td>df-2</td>\n", " <td>{'lng': -48.05498915846707, 'lat': -15.8381445...</td>\n", " <td>180</td>\n", " <td>[{'id': 'bf3fc630b1c29601a4caf1bdd474b85', 'po...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>cvrp-2-df-20</td>\n", " <td>df-2</td>\n", " <td>{'lng': -48.05498915846707, 'lat': -15.8381445...</td>\n", " <td>180</td>\n", " <td>[{'id': 'b30f1145a2ba4e0b9ac0162b68d045c3', 'p...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>cvrp-1-df-71</td>\n", " <td>df-1</td>\n", " <td>{'lng': -47.89366206897872, 'lat': -15.8051175...</td>\n", " <td>180</td>\n", " <td>[{'id': 'be3ed547394196c12c7c27c89ac74ed6', 'p...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>cvrp-2-df-87</td>\n", " <td>df-2</td>\n", " <td>{'lng': -48.05498915846707, 'lat': -15.8381445...</td>\n", " <td>180</td>\n", " <td>[{'id': 'a6328fb4dc0654eb28a996a270b0f6e4', 'p...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name region origin \\\n", "0 cvrp-2-df-33 df-2 {'lng': -48.05498915846707, 'lat': -15.8381445... \n", "1 cvrp-2-df-73 df-2 {'lng': -48.05498915846707, 'lat': -15.8381445... \n", "2 cvrp-2-df-20 df-2 {'lng': -48.05498915846707, 'lat': -15.8381445... \n", "3 cvrp-1-df-71 df-1 {'lng': -47.89366206897872, 'lat': -15.8051175... \n", "4 cvrp-2-df-87 df-2 {'lng': -48.05498915846707, 'lat': -15.8381445... \n", "\n", " vehicle_capacity deliveries \n", "0 180 [{'id': '313483a19d2f8d65cd5024c8d215cfbd', 'p... \n", "1 180 [{'id': 'bf3fc630b1c29601a4caf1bdd474b85', 'po... \n", "2 180 [{'id': 'b30f1145a2ba4e0b9ac0162b68d045c3', 'p... \n", "3 180 [{'id': 'be3ed547394196c12c7c27c89ac74ed6', 'p... \n", "4 180 [{'id': 'a6328fb4dc0654eb28a996a270b0f6e4', 'p... " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loggi_df = pd.DataFrame(data_loggi)\n", "loggi_df.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "a0ef0d6f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.603219Z", "iopub.status.busy": "2022-01-28T14:20:00.595230Z", "iopub.status.idle": "2022-01-28T14:20:00.609766Z", "shell.execute_reply": "2022-01-28T14:20:00.610328Z" }, "id": "jCHxO9080ZTM", "outputId": "8e42c98f-6d55-413a-f47f-3bd6a6faaff2", "papermill": { "duration": 0.050211, "end_time": "2022-01-28T14:20:00.610499", "exception": false, "start_time": "2022-01-28T14:20:00.560288", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "normalize_origin = pd.json_normalize(loggi_df['origin'])\n", "\n", "#concat. os dois dataframes\n", "loggi_df = pd.merge(left=loggi_df, right=normalize_origin, how='inner', left_index=True, right_index=True)\n", "\n", "loggi_df.rename(columns={'lng': 'origin_lng', 'lat':'origin_lat'}, inplace=True)\n", "loggi_df = loggi_df[['name', 'region', 'origin_lat','origin_lng', 'vehicle_capacity', 'deliveries']]\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "a3975575", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.708491Z", "iopub.status.busy": "2022-01-28T14:20:00.667140Z", "iopub.status.idle": "2022-01-28T14:20:01.841267Z", "shell.execute_reply": "2022-01-28T14:20:01.841726Z" }, "id": "lPjEZrjmBkE1", "outputId": "be7d322a-7ce0-46b7-84fa-c0c68592652d", "papermill": { "duration": 1.204558, "end_time": "2022-01-28T14:20:01.841900", "exception": false, "start_time": "2022-01-28T14:20:00.637342", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>delivery_lat</th>\n", " <th>delivery_lng</th>\n", " <th>delivery_size</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-15.848929</td>\n", " <td>-48.116189</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>-15.850772</td>\n", " <td>-48.118195</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>-15.847871</td>\n", " <td>-48.112483</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>-15.846471</td>\n", " <td>-48.118023</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>-15.858055</td>\n", " <td>-48.114898</td>\n", " <td>7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " delivery_lat delivery_lng delivery_size\n", "0 -15.848929 -48.116189 9\n", "0 -15.850772 -48.118195 2\n", "0 -15.847871 -48.112483 1\n", "0 -15.846471 -48.118023 2\n", "0 -15.858055 -48.114898 7" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Flatten na coluna deliveries\n", "data = pd.DataFrame(loggi_df['deliveries']).explode('deliveries')\n", "\n", "exploded_deliveries = pd.concat([\n", " pd.DataFrame(data['deliveries'].apply(lambda row : row['point']['lat'])).rename(columns=({'deliveries':'delivery_lat'})),\n", " pd.DataFrame(data['deliveries'].apply(lambda row : row['point']['lng'])).rename(columns=({'deliveries':'delivery_lng'})),\n", " pd.DataFrame(data['deliveries'].apply(lambda row : row['size'])).rename(columns=({'deliveries':'delivery_size'}))], axis=1\n", ")\n", "\n", "exploded_deliveries.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "ac1fae6f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:01.903627Z", "iopub.status.busy": "2022-01-28T14:20:01.902909Z", "iopub.status.idle": "2022-01-28T14:20:02.128994Z", "shell.execute_reply": "2022-01-28T14:20:02.128303Z" }, "id": "Zoubww4USajN", "outputId": "a6d014c1-ae77-4df5-9ab4-c7baa013b449", "papermill": { "duration": 0.259438, "end_time": "2022-01-28T14:20:02.129139", "exception": false, "start_time": "2022-01-28T14:20:01.869701", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>region</th>\n", " <th>origin_lat</th>\n", " <th>origin_lng</th>\n", " <th>vehicle_capacity</th>\n", " <th>delivery_lat</th>\n", " <th>delivery_lng</th>\n", " <th>delivery_size</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.848929</td>\n", " <td>-48.116189</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.850772</td>\n", " <td>-48.118195</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.847871</td>\n", " <td>-48.112483</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.846471</td>\n", " <td>-48.118023</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.858055</td>\n", " <td>-48.114898</td>\n", " <td>7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name region origin_lat origin_lng vehicle_capacity \\\n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "\n", " delivery_lat delivery_lng delivery_size \n", "0 -15.848929 -48.116189 9 \n", "0 -15.850772 -48.118195 2 \n", "0 -15.847871 -48.112483 1 \n", "0 -15.846471 -48.118023 2 \n", "0 -15.858055 -48.114898 7 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#retornando coluna para o dataframe original\n", "loggi_df = pd.merge(left=loggi_df, right=exploded_deliveries, how='inner', left_index=True, right_index=True)\n", "loggi_df = loggi_df[['name', 'region', 'origin_lat', 'origin_lng', 'vehicle_capacity',\n", " 'delivery_lat', 'delivery_lng', 'delivery_size']]\n", "\n", "loggi_df.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "2cf48db7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:02.349041Z", "iopub.status.busy": "2022-01-28T14:20:02.257596Z", "iopub.status.idle": "2022-01-28T14:20:06.041825Z", "shell.execute_reply": "2022-01-28T14:20:06.042419Z" }, "id": "bnJwcPGPiAT0", "outputId": "7d18db29-94bd-4493-b34d-7f248b557155", "papermill": { "duration": 3.884983, "end_time": "2022-01-28T14:20:06.042599", "exception": false, "start_time": "2022-01-28T14:20:02.157616", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>region</th>\n", " <th>origin_lat</th>\n", " <th>origin_lng</th>\n", " <th>vehicle_capacity</th>\n", " <th>delivery_lat</th>\n", " <th>delivery_lng</th>\n", " <th>delivery_size</th>\n", " <th>distance_delivery(km)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.848929</td>\n", " <td>-48.116189</td>\n", " <td>9</td>\n", " <td>6.66</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.850772</td>\n", " <td>-48.118195</td>\n", " <td>2</td>\n", " <td>6.91</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.847871</td>\n", " <td>-48.112483</td>\n", " <td>1</td>\n", " <td>6.24</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.846471</td>\n", " <td>-48.118023</td>\n", " <td>2</td>\n", " <td>6.81</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>cvrp-2-df-33</td>\n", " <td>df-2</td>\n", " <td>-15.838145</td>\n", " <td>-48.054989</td>\n", " <td>180</td>\n", " <td>-15.858055</td>\n", " <td>-48.114898</td>\n", " <td>7</td>\n", " <td>6.78</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name region origin_lat origin_lng vehicle_capacity \\\n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "0 cvrp-2-df-33 df-2 -15.838145 -48.054989 180 \n", "\n", " delivery_lat delivery_lng delivery_size distance_delivery(km) \n", "0 -15.848929 -48.116189 9 6.66 \n", "0 -15.850772 -48.118195 2 6.91 \n", "0 -15.847871 -48.112483 1 6.24 \n", "0 -15.846471 -48.118023 2 6.81 \n", "0 -15.858055 -48.114898 7 6.78 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#criando coluna de distância entre hub e entrega\n", "lat1 = loggi_df['origin_lat'].tolist()\n", "lon1 = loggi_df['origin_lng'].tolist()\n", "lat2 = loggi_df['delivery_lat'].tolist()\n", "lon2 = loggi_df['delivery_lng'].tolist()\n", "\n", "distance = []\n", "for lat_a, lat_b, lon_a, lon_b in zip(lat1,lat2, lon1, lon2):\n", " x = cal_lat_lng(lat_a, lon_a,lat_b, lon_b)\n", " distance.append(x)\n", "\n", "loggi_df['distance_delivery(km)'] = distance\n", "loggi_df.head()" ] }, { "cell_type": "markdown", "id": "95f265b0", "metadata": { "id": "Diw55KUIma1c", "papermill": { "duration": 0.035967, "end_time": "2022-01-28T14:20:06.113328", "exception": false, "start_time": "2022-01-28T14:20:06.077361", "status": "completed" }, "tags": [] }, "source": [ "## 4\\. Schema" ] }, { "cell_type": "code", "execution_count": 10, "id": "3255481e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.184091Z", "iopub.status.busy": "2022-01-28T14:20:06.183343Z", "iopub.status.idle": "2022-01-28T14:20:06.379477Z", "shell.execute_reply": "2022-01-28T14:20:06.380036Z" }, "id": "m8irBIGMRwx4", "outputId": "7c771bc3-e979-4229-8778-80019bd0ecd5", "papermill": { "duration": 0.231011, "end_time": "2022-01-28T14:20:06.380214", "exception": false, "start_time": "2022-01-28T14:20:06.149203", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 636149 entries, 0 to 198\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 name 636149 non-null object \n", " 1 region 636149 non-null object \n", " 2 origin_lat 636149 non-null float64\n", " 3 origin_lng 636149 non-null float64\n", " 4 vehicle_capacity 636149 non-null int64 \n", " 5 delivery_lat 636149 non-null float64\n", " 6 delivery_lng 636149 non-null float64\n", " 7 delivery_size 636149 non-null int64 \n", " 8 distance_delivery(km) 636149 non-null float64\n", "dtypes: float64(5), int64(2), object(2)\n", "memory usage: 48.5+ MB\n" ] } ], "source": [ "loggi_df.info()" ] }, { "cell_type": "code", "execution_count": 11, "id": "ce75c710", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.443404Z", "iopub.status.busy": "2022-01-28T14:20:06.442726Z", "iopub.status.idle": "2022-01-28T14:20:06.593642Z", "shell.execute_reply": "2022-01-28T14:20:06.593113Z" }, "id": "_gbJ1A7XZd-I", "outputId": "d5c614d0-7169-4ed7-ce3d-a6d373a7d07a", "papermill": { "duration": 0.18383, "end_time": "2022-01-28T14:20:06.593775", "exception": false, "start_time": "2022-01-28T14:20:06.409945", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "name False\n", "region False\n", "origin_lat False\n", "origin_lng False\n", "vehicle_capacity False\n", "delivery_lat False\n", "delivery_lng False\n", "delivery_size False\n", "distance_delivery(km) False\n", "dtype: bool" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loggi_df.isna().any()" ] }, { "cell_type": "markdown", "id": "d0d0829a", "metadata": { "id": "-YOmM6LrR3aL", "papermill": { "duration": 0.03001, "end_time": "2022-01-28T14:20:06.653682", "exception": false, "start_time": "2022-01-28T14:20:06.623672", "status": "completed" }, "tags": [] }, "source": [ "* Não existe dados faltantes\n", "* Nomes das colunas de acordo com seu tipo de dado" ] }, { "cell_type": "code", "execution_count": 12, "id": "1b01d9c5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.721905Z", "iopub.status.busy": "2022-01-28T14:20:06.720357Z", "iopub.status.idle": "2022-01-28T14:20:07.074605Z", "shell.execute_reply": "2022-01-28T14:20:07.074056Z" }, "id": "mK4xkIFlSF1P", "outputId": "dfa425a5-11d9-4a83-ca3e-8e6e6bde566d", "papermill": { "duration": 0.39095, "end_time": "2022-01-28T14:20:07.074745", "exception": false, "start_time": "2022-01-28T14:20:06.683795", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>count</th>\n", " <th>unique</th>\n", " <th>top</th>\n", " <th>freq</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>name</th>\n", " <td>636149</td>\n", " <td>199</td>\n", " <td>cvrp-1-df-87</td>\n", " <td>5636</td>\n", " </tr>\n", " <tr>\n", " <th>region</th>\n", " <td>636149</td>\n", " <td>3</td>\n", " <td>df-1</td>\n", " <td>304708</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count unique top freq\n", "name 636149 199 cvrp-1-df-87 5636\n", "region 636149 3 df-1 304708" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loggi_df.select_dtypes('object').describe().transpose()" ] }, { "cell_type": "markdown", "id": "e80be315", "metadata": { "id": "FxvG-2qwSSsI", "papermill": { "duration": 0.030427, "end_time": "2022-01-28T14:20:07.136048", "exception": false, "start_time": "2022-01-28T14:20:07.105621", "status": "completed" }, "tags": [] }, "source": [ "* Existem 199 names id onde o **cvrp-1-df-87** surge com maior frequência\n", "* Existem 3 Hubs de distribuição, onde o que tem mair frequência é o **df-1**, logo podemos entender que é o hub com maior volume de entregas" ] }, { "cell_type": "code", "execution_count": 13, "id": "4c7e57db", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:07.210592Z", "iopub.status.busy": "2022-01-28T14:20:07.209539Z", "iopub.status.idle": "2022-01-28T14:20:07.259830Z", "shell.execute_reply": "2022-01-28T14:20:07.259313Z" }, "id": "1IUH4K_kTBQH", "outputId": "f2b8f53d-30ac-4978-ad8c-09741da00aa1", "papermill": { "duration": 0.092924, "end_time": "2022-01-28T14:20:07.259995", "exception": false, "start_time": "2022-01-28T14:20:07.167071", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>count</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " <th>min</th>\n", " <th>25%</th>\n", " <th>50%</th>\n", " <th>75%</th>\n", " <th>max</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>vehicle_capacity</th>\n", " <td>636149.0</td>\n", " <td>180.000000</td>\n", " <td>0.000000</td>\n", " <td>180.0</td>\n", " <td>180.0</td>\n", " <td>180.0</td>\n", " <td>180.0</td>\n", " <td>180.0</td>\n", " </tr>\n", " <tr>\n", " <th>delivery_size</th>\n", " <td>636149.0</td>\n", " <td>5.512111</td>\n", " <td>2.874557</td>\n", " <td>1.0</td>\n", " <td>3.0</td>\n", " <td>6.0</td>\n", " <td>8.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count mean std min 25% 50% 75% \\\n", "vehicle_capacity 636149.0 180.000000 0.000000 180.0 180.0 180.0 180.0 \n", "delivery_size 636149.0 5.512111 2.874557 1.0 3.0 6.0 8.0 \n", "\n", " max \n", "vehicle_capacity 180.0 \n", "delivery_size 10.0 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loggi_df.select_dtypes('int').describe().transpose()" ] }, { "cell_type": "markdown", "id": "e3005f15", "metadata": { "id": "noYGWKzHTrmH", "papermill": { "duration": 0.030808, "end_time": "2022-01-28T14:20:07.321885", "exception": false, "start_time": "2022-01-28T14:20:07.291077", "status": "completed" }, "tags": [] }, "source": [ "* Nota-se que a capacidade dos veículos é a mesma para todo o dataset\n", "* No tamanho da entrega, encontramos uma boa variação no tamanho, podemos entender a partir desse ponto como essa distribuição pode impactar na otimização das entregas." ] }, { "cell_type": "code", "execution_count": 14, "id": "0e77390e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:07.409624Z", "iopub.status.busy": "2022-01-28T14:20:07.408550Z", "iopub.status.idle": "2022-01-28T14:20:07.507658Z", "shell.execute_reply": "2022-01-28T14:20:07.507149Z" }, "id": "GovTfYJyUMQs", "outputId": "23cf1ef0-a455-47c0-c848-8f6a19dbad79", "papermill": { "duration": 0.154812, "end_time": "2022-01-28T14:20:07.507808", "exception": false, "start_time": "2022-01-28T14:20:07.352996", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>count</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " <th>min</th>\n", " <th>25%</th>\n", " <th>50%</th>\n", " <th>75%</th>\n", " <th>max</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>origin_lat</th>\n", " <td>636149.0</td>\n", " <td>-15.802359</td>\n", " <td>0.053463</td>\n", " <td>-15.838145</td>\n", " <td>-15.838145</td>\n", " <td>-15.805118</td>\n", " <td>-15.805118</td>\n", " <td>-15.657014</td>\n", " </tr>\n", " <tr>\n", " <th>origin_lng</th>\n", " <td>636149.0</td>\n", " <td>-47.949902</td>\n", " <td>0.091875</td>\n", " <td>-48.054989</td>\n", " <td>-48.054989</td>\n", " <td>-47.893662</td>\n", " <td>-47.893662</td>\n", " <td>-47.802665</td>\n", " </tr>\n", " <tr>\n", " <th>delivery_lat</th>\n", " <td>636149.0</td>\n", " <td>-15.809492</td>\n", " <td>0.082462</td>\n", " <td>-16.050028</td>\n", " <td>-15.842795</td>\n", " <td>-15.814033</td>\n", " <td>-15.769516</td>\n", " <td>-15.500355</td>\n", " </tr>\n", " <tr>\n", " <th>delivery_lng</th>\n", " <td>636149.0</td>\n", " <td>-47.946087</td>\n", " <td>0.112769</td>\n", " <td>-48.280779</td>\n", " <td>-48.035911</td>\n", " <td>-47.928967</td>\n", " <td>-47.883394</td>\n", " <td>-47.310611</td>\n", " </tr>\n", " <tr>\n", " <th>distance_delivery(km)</th>\n", " <td>636149.0</td>\n", " <td>7.095050</td>\n", " <td>5.341920</td>\n", " <td>0.000000</td>\n", " <td>3.680000</td>\n", " <td>5.530000</td>\n", " <td>8.890000</td>\n", " <td>67.920000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count mean std min 25% \\\n", "origin_lat 636149.0 -15.802359 0.053463 -15.838145 -15.838145 \n", "origin_lng 636149.0 -47.949902 0.091875 -48.054989 -48.054989 \n", "delivery_lat 636149.0 -15.809492 0.082462 -16.050028 -15.842795 \n", "delivery_lng 636149.0 -47.946087 0.112769 -48.280779 -48.035911 \n", "distance_delivery(km) 636149.0 7.095050 5.341920 0.000000 3.680000 \n", "\n", " 50% 75% max \n", "origin_lat -15.805118 -15.805118 -15.657014 \n", "origin_lng -47.893662 -47.893662 -47.802665 \n", "delivery_lat -15.814033 -15.769516 -15.500355 \n", "delivery_lng -47.928967 -47.883394 -47.310611 \n", "distance_delivery(km) 5.530000 8.890000 67.920000 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loggi_df.select_dtypes('float64').describe().transpose()" ] }, { "cell_type": "markdown", "id": "284d33bf", "metadata": { "id": "GTXJUPsMUxyI", "papermill": { "duration": 0.031426, "end_time": "2022-01-28T14:20:07.571064", "exception": false, "start_time": "2022-01-28T14:20:07.539638", "status": "completed" }, "tags": [] }, "source": [ "* Por mais que a média da distância seja de aproximadamente 7km, existem locais de entrega bem distante (67km), que pode impactar de maneira negativa na otimização das entregas. Levando em consideração os custos para viabilizar as entregas, vale a pena verificar se há um hub mais próximo ou até mesmo determinar um raio de ação limite para cada hub, otimizando a alocação de entregas por hub, caso não seja viável, mapear as entregas mais distantes para aproveitar a entrega.\n", "\n", "* Observe abaixo a quantidade de entregas a partir de 30km\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "c2c071ae", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:07.642737Z", "iopub.status.busy": "2022-01-28T14:20:07.642032Z", "iopub.status.idle": "2022-01-28T14:20:07.654860Z", "shell.execute_reply": "2022-01-28T14:20:07.654255Z" }, "id": "0SCqfOroXOJH", "outputId": "455dc440-b6f8-44eb-f791-6a637672eaf9", "papermill": { "duration": 0.052059, "end_time": "2022-01-28T14:20:07.655025", "exception": false, "start_time": "2022-01-28T14:20:07.602966", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quantidade de entregas com distância acima de 60km: 45\n", "Quantidade de entregas com distância entre 50km e 60km: 100\n", "Quantidade de entregas com distância entre 40km e 50km: 392\n", "Quantidade de entregas com distância entre 30km e 40km: 533\n" ] } ], "source": [ "dist_60km = loggi_df['distance_delivery(km)'][loggi_df['distance_delivery(km)'] > 60]\n", "dist_50km = loggi_df['distance_delivery(km)'][(loggi_df['distance_delivery(km)'] > 50) & (loggi_df['distance_delivery(km)'] < 60)]\n", "dist_40km = loggi_df['distance_delivery(km)'][(loggi_df['distance_delivery(km)'] > 40) & (loggi_df['distance_delivery(km)'] < 50)]\n", "dist_30km = loggi_df['distance_delivery(km)'][(loggi_df['distance_delivery(km)'] > 30) & (loggi_df['distance_delivery(km)'] < 40)]\n", "print(f'Quantidade de entregas com distância acima de 60km: {dist_60km.size}')\n", "print(f'Quantidade de entregas com distância entre 50km e 60km: {dist_50km.size}')\n", "print(f'Quantidade de entregas com distância entre 40km e 50km: {dist_40km.size}')\n", "print(f'Quantidade de entregas com distância entre 30km e 40km: {dist_30km.size}')" ] }, { "cell_type": "markdown", "id": "ec71738e", "metadata": { "id": "Ml2tb7XRnCGi", "papermill": { "duration": 0.032104, "end_time": "2022-01-28T14:20:07.719935", "exception": false, "start_time": "2022-01-28T14:20:07.687831", "status": "completed" }, "tags": [] }, "source": [ "# 5 Visualização" ] }, { "cell_type": "code", "execution_count": 16, "id": "c333a6b3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:07.863513Z", "iopub.status.busy": "2022-01-28T14:20:07.862391Z", "iopub.status.idle": "2022-01-28T14:20:08.169723Z", "shell.execute_reply": "2022-01-28T14:20:08.168991Z" }, "id": "rdkMYp4SnOc5", "papermill": { "duration": 0.417804, "end_time": "2022-01-28T14:20:08.169870", "exception": false, "start_time": "2022-01-28T14:20:07.752066", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#criando mapa de brasilia com pontos de entrega e localização dos hubs\n", "map_df = geopandas.read_file('distrito-federal.shp')\n", "map_df = map_df.loc[[0]]\n", "\n", "hub_df = loggi_df[['region', 'origin_lat', 'origin_lng']].drop_duplicates().reset_index(drop=True)\n", "geo_hub_df = geopandas.GeoDataFrame(hub_df, geometry=geopandas.points_from_xy(hub_df[\"origin_lng\"], hub_df[\"origin_lat\"]))\n", "geo_loggi_df = geopandas.GeoDataFrame(loggi_df, geometry= geopandas.points_from_xy(loggi_df['delivery_lng'], loggi_df['delivery_lat']))\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "4f22b575", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:08.250044Z", "iopub.status.busy": "2022-01-28T14:20:08.245463Z", "iopub.status.idle": "2022-01-28T14:22:10.748270Z", "shell.execute_reply": "2022-01-28T14:22:10.748790Z" }, "id": "kNeu3L86s7SS", "outputId": "4c2ebea7-5cb2-4733-ab04-9a63edcfab4d", "papermill": { "duration": 122.545651, "end_time": "2022-01-28T14:22:10.748996", "exception": false, "start_time": "2022-01-28T14:20:08.203345", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1417.32x1417.32 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# cria o plot vazio\n", "fig, ax = plt.subplots(figsize = (50/2.54, 50/2.54))\n", "\n", "# plot mapa do distrito federal\n", "map_df.plot(ax=ax, alpha=0.4, color=\"lightgrey\")\n", "\n", "# plot das entregas\n", "geo_loggi_df.query(\"region == 'df-0'\").plot(ax=ax, markersize=1, color=\"red\", label=\"df-0\")\n", "geo_loggi_df.query(\"region == 'df-1'\").plot(ax=ax, markersize=1, color=\"blue\", label=\"df-1\")\n", "geo_loggi_df.query(\"region == 'df-2'\").plot(ax=ax, markersize=1, color=\"seagreen\", label=\"df-2\")\n", "\n", "# plot dos hubs\n", "geo_hub_df.plot(ax=ax, markersize=30, marker=\"x\", color=\"black\", label=\"hub\")\n", "\n", "# plot da legenda\n", "plt.title(\"Entregas no Distrito Federal por Região\", fontdict={\"fontsize\": 16})\n", "lgnd = plt.legend(prop={\"size\": 15})\n", "for handle in lgnd.legendHandles:\n", " handle.set_sizes([50])" ] }, { "cell_type": "markdown", "id": "f620d2d2", "metadata": { "id": "pwDcKA252y64", "papermill": { "duration": 0.046691, "end_time": "2022-01-28T14:22:10.842586", "exception": false, "start_time": "2022-01-28T14:22:10.795895", "status": "completed" }, "tags": [] }, "source": [ "* Como Visto na etapa de Schema, existe uma grande quantidade de entregas com uma distância maior, e aparentemente as entregas mais distântes estão registradas para o hub **df-0**.\n", "\n", "* No entanto, a distribuição das entregas está bem coerente, visto que os pontos de cada hub não tem uma tendência significativa de se misturarem entre os hubs de entrega. \n", "\n", "* Porém se olharmos para a canto inferior direito do mapa (entre -47,6 e -47,4) existem pontos de entrega do df-0 que podem ser realocados para o df-1, fortalecendo a proposta dita acima também na etapa de Schema, onde uma possibilidade de definir pontos de alcance por hub, pode otimizar a organização das entregas e, por consequência, custos para cada operação" ] }, { "cell_type": "code", "execution_count": 18, "id": "8c0f78de", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:22:10.925406Z", "iopub.status.busy": "2022-01-28T14:22:10.924578Z", "iopub.status.idle": "2022-01-28T14:22:11.026281Z", "shell.execute_reply": "2022-01-28T14:22:11.026753Z" }, "id": "Vzf_ibNliQYS", "outputId": "52838081-9da1-4072-a20a-30dec5b414fe", "papermill": { "duration": 0.144617, "end_time": "2022-01-28T14:22:11.026949", "exception": false, "start_time": "2022-01-28T14:22:10.882332", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>region</th>\n", " <th>vehicle_capacity</th>\n", " <th>region_percent</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>df-1</td>\n", " <td>180</td>\n", " <td>0.478988</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>df-2</td>\n", " <td>180</td>\n", " <td>0.410783</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>df-0</td>\n", " <td>180</td>\n", " <td>0.110229</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " region vehicle_capacity region_percent\n", "0 df-1 180 0.478988\n", "1 df-2 180 0.410783\n", "2 df-0 180 0.110229" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = loggi_df[['region','vehicle_capacity' ]].value_counts(normalize=True).reset_index()\n", "data.rename(columns={0:'region_percent'}, inplace=True)\n", "data" ] }, { "cell_type": "code", "execution_count": 19, "id": "fd87b450", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:22:11.129718Z", "iopub.status.busy": "2022-01-28T14:22:11.128636Z", "iopub.status.idle": "2022-01-28T14:22:11.346311Z", "shell.execute_reply": "2022-01-28T14:22:11.345642Z" }, "id": "-3VqsfRhjLgj", "outputId": "66aa7bc3-abac-4e1e-9be1-90c19d3e6cf1", "papermill": { "duration": 0.280058, "end_time": "2022-01-28T14:22:11.346471", "exception": false, "start_time": "2022-01-28T14:22:11.066413", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.axes_style('whitegrid'):\n", " my_figsize(13,6)\n", " graffic = sns.barplot(data=data, x='region', y='region_percent', ci=None, palette=['b','g', 'r']);\n", " graffic.set_title('Média de entregas por região', size=16, fontweight='bold');\n", " graffic.set(xlabel='Região', ylabel='Média de entregas')" ] }, { "cell_type": "markdown", "id": "03ed02b8", "metadata": { "id": "UA8SwnXbkkID", "papermill": { "duration": 0.040533, "end_time": "2022-01-28T14:22:11.427839", "exception": false, "start_time": "2022-01-28T14:22:11.387306", "status": "completed" }, "tags": [] }, "source": [ "* A maior concentração de entregas está entre o **df-1 e df-0**, porém a capacidade de veículos é a mesma para todas, pode-se avaliar a possibilidade de transferir veículos do **df-0** para as outras regiões.\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "3b51529d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:22:11.522134Z", "iopub.status.busy": "2022-01-28T14:22:11.521376Z", "iopub.status.idle": "2022-01-28T14:22:13.474399Z", "shell.execute_reply": "2022-01-28T14:22:13.474849Z" }, "id": "H2OZRAeCnhN7", "outputId": "f80a4467-0bb6-45ed-c3c5-b8fb89531ed9", "papermill": { "duration": 2.006587, "end_time": "2022-01-28T14:22:13.475052", "exception": false, "start_time": "2022-01-28T14:22:11.468465", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.axes_style('whitegrid'):\n", " my_figsize(15,6)\n", " graffic = sns.boxplot(x=loggi_df['distance_delivery(km)'], y=loggi_df['region'], palette=['g','b','r'])\n", " graffic.set_title('Distância de entregas por Região', size=16, fontweight='bold')\n", " graffic.set(xlabel='Distância de entrega (Km)', ylabel='Regiões')" ] }, { "cell_type": "markdown", "id": "58bd18d2", "metadata": { "id": "_vSJp9j1pnjT", "papermill": { "duration": 0.041711, "end_time": "2022-01-28T14:22:13.558319", "exception": false, "start_time": "2022-01-28T14:22:13.516608", "status": "completed" }, "tags": [] }, "source": [ "* Observa-se que a distância média das entregas entre todos os hubs fica entre 5 a 15 km\n", "* Por mais que não seja comum, as entregas a acima de 30km são feitas com maior frequência pelo **df-0**, onde uma possível medida para entregas tão distantes é ter veículos com maior capacidade de armazenamento, desta forma ele pode conseguir entregar mais com menos viagens" ] }, { "cell_type": "code", "execution_count": 21, "id": "b36cf472", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:22:13.646825Z", "iopub.status.busy": "2022-01-28T14:22:13.646071Z", "iopub.status.idle": "2022-01-28T14:22:14.281022Z", "shell.execute_reply": "2022-01-28T14:22:14.280412Z" }, "id": "nUq_PMgk-T1k", "outputId": "230de563-b0de-40db-93fb-27f89abe418e", "papermill": { "duration": 0.68109, "end_time": "2022-01-28T14:22:14.281171", "exception": false, "start_time": "2022-01-28T14:22:13.600081", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = loggi_df[['region', 'delivery_size']].value_counts().reset_index()\n", "data.rename(columns={0:'qtd_deliveries'}, inplace=True)\n", "with sns.axes_style('darkgrid'):\n", " my_figsize(15,6)\n", " graffic = sns.barplot(data=data, x='delivery_size', y='qtd_deliveries', hue='region', palette=['b','g','r'])\n", " graffic.set_title('Quantidade de entregar por tamanho')\n", " graffic.set(xlabel='tamanho das entregas', ylabel='quantidade de entregas')" ] }, { "cell_type": "markdown", "id": "105c4731", "metadata": { "id": "sL_MNs68tzQD", "papermill": { "duration": 0.044225, "end_time": "2022-01-28T14:22:14.370131", "exception": false, "start_time": "2022-01-28T14:22:14.325906", "status": "completed" }, "tags": [] }, "source": [ "* Por fim, vemos que os tamanhos das entregas são bem distribuídos entre os hubs, os 3 contém entregas de todos os tamanhos, com praticamente a mesma propoção.\n", "\n", "* Isso pode reforçar a necessidade de realocação de veículos do hub **df-0** para o **df-1**, como também é viável avaliar ter veículos com maior capacidade, visto que o **df-0** tem entregas mais distantes que, somado a quantidade menor de entregas, se os veículos puderem alocar mais entregas, podem ir para mais locais, aproveitando uma viagem só." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 195.611203, "end_time": "2022-01-28T14:22:15.932097", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:19:00.320894", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/399/86399602.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "4c55c41b", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "papermill": { "duration": 0.050224, "end_time": "2022-01-28T14:19:56.355112", "exception": false, "start_time": "2022-01-28T14:19:56.304888", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "id": "aae933bd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:56.460809Z", "iopub.status.busy": "2022-01-28T14:19:56.459693Z", "iopub.status.idle": "2022-01-28T14:19:56.463886Z", "shell.execute_reply": "2022-01-28T14:19:56.464480Z", "shell.execute_reply.started": "2022-01-28T13:35:03.180914Z" }, "papermill": { "duration": 0.061357, "end_time": "2022-01-28T14:19:56.464810", "exception": false, "start_time": "2022-01-28T14:19:56.403453", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "1ef3d245", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:56.567552Z", "iopub.status.busy": "2022-01-28T14:19:56.566849Z", "iopub.status.idle": "2022-01-28T14:19:56.638601Z", "shell.execute_reply": "2022-01-28T14:19:56.637891Z", "shell.execute_reply.started": "2022-01-28T13:37:20.509289Z" }, "papermill": { "duration": 0.124072, "end_time": "2022-01-28T14:19:56.638752", "exception": false, "start_time": "2022-01-28T14:19:56.514680", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>country</th>\n", " <th>store</th>\n", " <th>product</th>\n", " <th>num_sold</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Mug</td>\n", " <td>329</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Hat</td>\n", " <td>520</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Sticker</td>\n", " <td>146</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Mug</td>\n", " <td>572</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Hat</td>\n", " <td>911</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date country store product num_sold\n", "0 0 2015-01-01 Finland KaggleMart Kaggle Mug 329\n", "1 1 2015-01-01 Finland KaggleMart Kaggle Hat 520\n", "2 2 2015-01-01 Finland KaggleMart Kaggle Sticker 146\n", "3 3 2015-01-01 Finland KaggleRama Kaggle Mug 572\n", "4 4 2015-01-01 Finland KaggleRama Kaggle Hat 911" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = pd.read_csv('../input/tabular-playground-series-jan-2022/train.csv')\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "803f3cfe", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:56.744215Z", "iopub.status.busy": "2022-01-28T14:19:56.743317Z", "iopub.status.idle": "2022-01-28T14:19:56.773530Z", "shell.execute_reply": "2022-01-28T14:19:56.774135Z", "shell.execute_reply.started": "2022-01-28T13:37:23.171819Z" }, "papermill": { "duration": 0.084936, "end_time": "2022-01-28T14:19:56.774341", "exception": false, "start_time": "2022-01-28T14:19:56.689405", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>country</th>\n", " <th>store</th>\n", " <th>product</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>26298</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Mug</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>26299</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Hat</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>26300</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Sticker</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>26301</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Mug</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>26302</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Hat</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date country store product\n", "0 26298 2019-01-01 Finland KaggleMart Kaggle Mug\n", "1 26299 2019-01-01 Finland KaggleMart Kaggle Hat\n", "2 26300 2019-01-01 Finland KaggleMart Kaggle Sticker\n", "3 26301 2019-01-01 Finland KaggleRama Kaggle Mug\n", "4 26302 2019-01-01 Finland KaggleRama Kaggle Hat" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testd = pd.read_csv('../input/tabular-playground-series-jan-2022/test.csv')\n", "testd.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "1d33f27e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:56.877761Z", "iopub.status.busy": "2022-01-28T14:19:56.877063Z", "iopub.status.idle": "2022-01-28T14:19:56.914193Z", "shell.execute_reply": "2022-01-28T14:19:56.914871Z", "shell.execute_reply.started": "2022-01-28T13:37:28.316974Z" }, "papermill": { "duration": 0.09152, "end_time": "2022-01-28T14:19:56.915083", "exception": false, "start_time": "2022-01-28T14:19:56.823563", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>num_sold</th>\n", " <th>country_Finland</th>\n", " <th>country_Norway</th>\n", " <th>country_Sweden</th>\n", " <th>store_KaggleMart</th>\n", " <th>store_KaggleRama</th>\n", " <th>product_Kaggle Hat</th>\n", " <th>product_Kaggle Mug</th>\n", " <th>product_Kaggle Sticker</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>2015-01-01</td>\n", " <td>329</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2015-01-01</td>\n", " <td>520</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>2015-01-01</td>\n", " <td>146</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>2015-01-01</td>\n", " <td>572</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>2015-01-01</td>\n", " <td>911</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date num_sold country_Finland country_Norway \\\n", "0 0 2015-01-01 329 1 0 \n", "1 1 2015-01-01 520 1 0 \n", "2 2 2015-01-01 146 1 0 \n", "3 3 2015-01-01 572 1 0 \n", "4 4 2015-01-01 911 1 0 \n", "\n", " country_Sweden store_KaggleMart store_KaggleRama product_Kaggle Hat \\\n", "0 0 1 0 0 \n", "1 0 1 0 1 \n", "2 0 1 0 0 \n", "3 0 0 1 0 \n", "4 0 0 1 1 \n", "\n", " product_Kaggle Mug product_Kaggle Sticker \n", "0 1 0 \n", "1 0 0 \n", "2 0 1 \n", "3 1 0 \n", "4 0 0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d_encoded = pd.get_dummies(d, columns = ['country' , 'store' , 'product']) \n", "d_encoded.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "0622af3c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:57.020390Z", "iopub.status.busy": "2022-01-28T14:19:57.019666Z", "iopub.status.idle": "2022-01-28T14:19:57.029952Z", "shell.execute_reply": "2022-01-28T14:19:57.030520Z", "shell.execute_reply.started": "2022-01-28T13:37:33.409263Z" }, "papermill": { "duration": 0.063067, "end_time": "2022-01-28T14:19:57.030718", "exception": false, "start_time": "2022-01-28T14:19:56.967651", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d_encoded\n", "testd=pd.get_dummies(testd, columns = ['country' , 'store' , 'product']) " ] }, { "cell_type": "code", "execution_count": 6, "id": "9911dbfe", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:57.133237Z", "iopub.status.busy": "2022-01-28T14:19:57.132541Z", "iopub.status.idle": "2022-01-28T14:19:57.148934Z", "shell.execute_reply": "2022-01-28T14:19:57.149639Z", "shell.execute_reply.started": "2022-01-28T13:37:35.571612Z" }, "papermill": { "duration": 0.069456, "end_time": "2022-01-28T14:19:57.149831", "exception": false, "start_time": "2022-01-28T14:19:57.080375", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d['date']=pd.to_datetime(d['date'])\n", "testd['date']=pd.to_datetime(testd['date'])" ] }, { "cell_type": "code", "execution_count": 7, "id": "af4a3dcc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:57.254145Z", "iopub.status.busy": "2022-01-28T14:19:57.253382Z", "iopub.status.idle": "2022-01-28T14:19:57.273386Z", "shell.execute_reply": "2022-01-28T14:19:57.273948Z", "shell.execute_reply.started": "2022-01-28T13:37:38.135857Z" }, "papermill": { "duration": 0.073757, "end_time": "2022-01-28T14:19:57.274166", "exception": false, "start_time": "2022-01-28T14:19:57.200409", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d['day']=d['date'].dt.day\n", "d['month']=d['date'].dt.month\n", "d['year']=d['date'].dt.year\n", "testd['day']=testd['date'].dt.day\n", "testd['month']=testd['date'].dt.month\n", "testd['year']=testd['date'].dt.year" ] }, { "cell_type": "code", "execution_count": 8, "id": "5336f374", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:57.377108Z", "iopub.status.busy": "2022-01-28T14:19:57.376407Z", "iopub.status.idle": "2022-01-28T14:19:57.712430Z", "shell.execute_reply": "2022-01-28T14:19:57.713085Z", "shell.execute_reply.started": "2022-01-28T14:05:48.100341Z" }, "papermill": { "duration": 0.389138, "end_time": "2022-01-28T14:19:57.713272", "exception": false, "start_time": "2022-01-28T14:19:57.324134", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='date'>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "davg=d.groupby('date')['num_sold'].mean()\n", "davg.plot()" ] }, { "cell_type": "code", "execution_count": 9, "id": "344860e9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:57.819459Z", "iopub.status.busy": "2022-01-28T14:19:57.818702Z", "iopub.status.idle": "2022-01-28T14:19:57.828208Z", "shell.execute_reply": "2022-01-28T14:19:57.828757Z", "shell.execute_reply.started": "2022-01-28T14:05:59.855997Z" }, "papermill": { "duration": 0.064569, "end_time": "2022-01-28T14:19:57.828953", "exception": false, "start_time": "2022-01-28T14:19:57.764384", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>num_sold</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>598.222222</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-02</th>\n", " <td>555.444444</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-03</th>\n", " <td>615.277778</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-04</th>\n", " <td>595.944444</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-05</th>\n", " <td>437.111111</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sold\n", "date \n", "2015-01-01 598.222222\n", "2015-01-02 555.444444\n", "2015-01-03 615.277778\n", "2015-01-04 595.944444\n", "2015-01-05 437.111111" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "davg=pd.DataFrame(davg)\n", "davg.head()" ] }, { "cell_type": "code", "execution_count": 10, "id": "58527967", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:57.938713Z", "iopub.status.busy": "2022-01-28T14:19:57.938035Z", "iopub.status.idle": "2022-01-28T14:19:57.948257Z", "shell.execute_reply": "2022-01-28T14:19:57.948863Z", "shell.execute_reply.started": "2022-01-28T14:11:26.835942Z" }, "papermill": { "duration": 0.068188, "end_time": "2022-01-28T14:19:57.949079", "exception": false, "start_time": "2022-01-28T14:19:57.880891", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>num_sold</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2019-01-01</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-02</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-03</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-04</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-05</th>\n", " <td>0.333333</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sold\n", "date \n", "2019-01-01 0.333333\n", "2019-01-02 0.333333\n", "2019-01-03 0.333333\n", "2019-01-04 0.333333\n", "2019-01-05 0.333333" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt=pd.DataFrame(testd.groupby('date')['country_Norway'].mean())\n", "dt.columns=['num_sold']\n", "dt.head()" ] }, { "cell_type": "code", "execution_count": 11, "id": "0c8e5190", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:58.057228Z", "iopub.status.busy": "2022-01-28T14:19:58.056514Z", "iopub.status.idle": "2022-01-28T14:19:58.061255Z", "shell.execute_reply": "2022-01-28T14:19:58.061872Z", "shell.execute_reply.started": "2022-01-28T14:12:28.212503Z" }, "papermill": { "duration": 0.061419, "end_time": "2022-01-28T14:19:58.062095", "exception": false, "start_time": "2022-01-28T14:19:58.000676", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "dtall=pd.concat([davg,dt],axis=0)" ] }, { "cell_type": "code", "execution_count": 12, "id": "31fb7b37", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:58.172238Z", "iopub.status.busy": "2022-01-28T14:19:58.171537Z", "iopub.status.idle": "2022-01-28T14:19:58.541040Z", "shell.execute_reply": "2022-01-28T14:19:58.539934Z", "shell.execute_reply.started": "2022-01-28T14:13:14.358487Z" }, "papermill": { "duration": 0.424671, "end_time": "2022-01-28T14:19:58.541295", "exception": false, "start_time": "2022-01-28T14:19:58.116624", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from statsmodels.tsa.deterministic import CalendarFourier, DeterministicProcess\n", "\n", "fourier = CalendarFourier(freq=\"M\", order=4) # 10 sin/cos pairs for \"A\"nnual seasonality\n", "\n", "dp = DeterministicProcess(\n", " index=dtall.index,\n", " constant=True, # dummy feature for bias (y-intercept)\n", " order=1, # trend (order 1 means linear)\n", " seasonal=True, # weekly seasonality (indicators)\n", " additional_terms=[fourier], # annual seasonality (fourier)\n", " drop=True, # drop terms to avoid collinearity\n", ")\n", "\n", "X = dp.in_sample() # create features for dates in tunnel.index" ] }, { "cell_type": "code", "execution_count": 13, "id": "310038b5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:58.709863Z", "iopub.status.busy": "2022-01-28T14:19:58.708439Z", "iopub.status.idle": "2022-01-28T14:19:58.712043Z", "shell.execute_reply": "2022-01-28T14:19:58.712595Z", "shell.execute_reply.started": "2022-01-28T14:14:41.557326Z" }, "papermill": { "duration": 0.068576, "end_time": "2022-01-28T14:19:58.712780", "exception": false, "start_time": "2022-01-28T14:19:58.644204", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "Xtimetrain=X.loc[:'2018-12-31']\n", "Xtimetest=X.loc['2019-01-01':]" ] }, { "cell_type": "code", "execution_count": 14, "id": "42aaefde", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:58.820475Z", "iopub.status.busy": "2022-01-28T14:19:58.819804Z", "iopub.status.idle": "2022-01-28T14:19:58.836955Z", "shell.execute_reply": "2022-01-28T14:19:58.836308Z", "shell.execute_reply.started": "2022-01-28T14:16:38.352321Z" }, "papermill": { "duration": 0.072251, "end_time": "2022-01-28T14:19:58.837125", "exception": false, "start_time": "2022-01-28T14:19:58.764874", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>num_sold</th>\n", " <th>country_Finland</th>\n", " <th>country_Norway</th>\n", " <th>country_Sweden</th>\n", " <th>store_KaggleMart</th>\n", " <th>store_KaggleRama</th>\n", " <th>product_Kaggle Hat</th>\n", " <th>product_Kaggle Mug</th>\n", " <th>product_Kaggle Sticker</th>\n", " <th>day</th>\n", " <th>month</th>\n", " <th>year</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>0</td>\n", " <td>2015-01-01</td>\n", " <td>329</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>1</td>\n", " <td>2015-01-01</td>\n", " <td>520</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>2</td>\n", " <td>2015-01-01</td>\n", " <td>146</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>3</td>\n", " <td>2015-01-01</td>\n", " <td>572</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>4</td>\n", " <td>2015-01-01</td>\n", " <td>911</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date num_sold country_Finland country_Norway \\\n", "date \n", "2015-01-01 0 2015-01-01 329 1 0 \n", "2015-01-01 1 2015-01-01 520 1 0 \n", "2015-01-01 2 2015-01-01 146 1 0 \n", "2015-01-01 3 2015-01-01 572 1 0 \n", "2015-01-01 4 2015-01-01 911 1 0 \n", "\n", " country_Sweden store_KaggleMart store_KaggleRama \\\n", "date \n", "2015-01-01 0 1 0 \n", "2015-01-01 0 1 0 \n", "2015-01-01 0 1 0 \n", "2015-01-01 0 0 1 \n", "2015-01-01 0 0 1 \n", "\n", " product_Kaggle Hat product_Kaggle Mug product_Kaggle Sticker \\\n", "date \n", "2015-01-01 0 1 0 \n", "2015-01-01 1 0 0 \n", "2015-01-01 0 0 1 \n", "2015-01-01 0 1 0 \n", "2015-01-01 1 0 0 \n", "\n", " day month year \n", "date \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.index=d['date']\n", "testd.index=testd['date']\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 15, "id": "0354097b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:58.947279Z", "iopub.status.busy": "2022-01-28T14:19:58.946573Z", "iopub.status.idle": "2022-01-28T14:19:58.966271Z", "shell.execute_reply": "2022-01-28T14:19:58.966822Z", "shell.execute_reply.started": "2022-01-28T14:18:05.599997Z" }, "papermill": { "duration": 0.077922, "end_time": "2022-01-28T14:19:58.967033", "exception": false, "start_time": "2022-01-28T14:19:58.889111", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d.join(Xtimetrain)\n", "testd=testd.join(Xtimetest)" ] }, { "cell_type": "code", "execution_count": null, "id": "286deaf1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:17:18.770983Z", "iopub.status.busy": "2022-01-28T14:17:18.770127Z", "iopub.status.idle": "2022-01-28T14:17:18.777623Z", "shell.execute_reply": "2022-01-28T14:17:18.776532Z", "shell.execute_reply.started": "2022-01-28T14:17:18.770934Z" }, "papermill": { "duration": 0.052363, "end_time": "2022-01-28T14:19:59.071821", "exception": false, "start_time": "2022-01-28T14:19:59.019458", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "id": "fc782e7e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:19:59.180780Z", "iopub.status.busy": "2022-01-28T14:19:59.179857Z", "iopub.status.idle": "2022-01-28T14:20:00.255431Z", "shell.execute_reply": "2022-01-28T14:20:00.254347Z", "shell.execute_reply.started": "2022-01-28T14:18:12.013363Z" }, "papermill": { "duration": 1.131042, "end_time": "2022-01-28T14:20:00.255641", "exception": false, "start_time": "2022-01-28T14:19:59.124599", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " import sys\n", "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \"\"\"\n", "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " if sys.path[0] == '':\n", "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " # Remove the CWD from sys.path while we load stuff.\n" ] } ], "source": [ "d['ng_coef']=0\n", "testd['ng_coef']=0\n", "for i in range(d.shape[0]):\n", " if(d['month'][i]==12 and d['day'][i]>=20):\n", " d['ng_coef'][i]=d['day'][i]/30\n", " if(d['month'][i]==1 and d['day'][i]<=5):\n", " d['ng_coef'][i]=(5-d['day'][i])/4\n", "for i in range(testd.shape[0]):\n", " if(testd['month'][i]==12 and testd['day'][i]>=20):\n", " testd['ng_coef'][i]=testd['day'][i]/30\n", " if(testd['month'][i]==1 and testd['day'][i]<=5):\n", " testd['ng_coef'][i]=(5-testd['day'][i])/4\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "57b3f81e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.393225Z", "iopub.status.busy": "2022-01-28T14:20:00.392366Z", "iopub.status.idle": "2022-01-28T14:20:00.397647Z", "shell.execute_reply": "2022-01-28T14:20:00.397084Z", "shell.execute_reply.started": "2022-01-28T14:18:22.375235Z" }, "papermill": { "duration": 0.088177, "end_time": "2022-01-28T14:20:00.397817", "exception": false, "start_time": "2022-01-28T14:20:00.309640", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>country_Finland</th>\n", " <th>country_Norway</th>\n", " <th>country_Sweden</th>\n", " <th>store_KaggleMart</th>\n", " <th>store_KaggleRama</th>\n", " <th>product_Kaggle Hat</th>\n", " <th>product_Kaggle Mug</th>\n", " <th>product_Kaggle Sticker</th>\n", " <th>...</th>\n", " <th>s(7,7)</th>\n", " <th>sin(1,freq=M)</th>\n", " <th>cos(1,freq=M)</th>\n", " <th>sin(2,freq=M)</th>\n", " <th>cos(2,freq=M)</th>\n", " <th>sin(3,freq=M)</th>\n", " <th>cos(3,freq=M)</th>\n", " <th>sin(4,freq=M)</th>\n", " <th>cos(4,freq=M)</th>\n", " 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<td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-01</th>\n", " <td>26302</td>\n", " <td>2019-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " row_id date country_Finland country_Norway \\\n", "date \n", "2019-01-01 26298 2019-01-01 1 0 \n", "2019-01-01 26299 2019-01-01 1 0 \n", "2019-01-01 26300 2019-01-01 1 0 \n", "2019-01-01 26301 2019-01-01 1 0 \n", "2019-01-01 26302 2019-01-01 1 0 \n", "\n", " country_Sweden store_KaggleMart store_KaggleRama \\\n", "date \n", "2019-01-01 0 1 0 \n", "2019-01-01 0 1 0 \n", "2019-01-01 0 1 0 \n", "2019-01-01 0 0 1 \n", "2019-01-01 0 0 1 \n", "\n", " product_Kaggle Hat product_Kaggle Mug product_Kaggle Sticker \\\n", "date \n", "2019-01-01 0 1 0 \n", "2019-01-01 1 0 0 \n", "2019-01-01 0 0 1 \n", "2019-01-01 0 1 0 \n", "2019-01-01 1 0 0 \n", "\n", " ... s(7,7) sin(1,freq=M) cos(1,freq=M) sin(2,freq=M) \\\n", "date ... \n", "2019-01-01 ... 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 0.0 0.0 1.0 0.0 \n", "\n", " cos(2,freq=M) sin(3,freq=M) cos(3,freq=M) sin(4,freq=M) \\\n", "date \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "\n", " cos(4,freq=M) ng_coef \n", "date \n", "2019-01-01 1.0 1 \n", "2019-01-01 1.0 1 \n", "2019-01-01 1.0 1 \n", "2019-01-01 1.0 1 \n", "2019-01-01 1.0 1 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testd.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "d75a45d2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.511468Z", "iopub.status.busy": "2022-01-28T14:20:00.510791Z", "iopub.status.idle": "2022-01-28T14:20:00.521255Z", "shell.execute_reply": "2022-01-28T14:20:00.521812Z", "shell.execute_reply.started": "2022-01-28T14:18:29.253988Z" }, "papermill": { "duration": 0.069347, "end_time": "2022-01-28T14:20:00.522016", "exception": false, "start_time": "2022-01-28T14:20:00.452669", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d.drop('date',axis=1)\n", "testd=testd.drop('date',axis=1)" ] }, { "cell_type": "code", "execution_count": 19, "id": "3514be3a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.635270Z", "iopub.status.busy": "2022-01-28T14:20:00.632842Z", "iopub.status.idle": "2022-01-28T14:20:00.640938Z", "shell.execute_reply": "2022-01-28T14:20:00.641570Z", "shell.execute_reply.started": "2022-01-28T14:18:31.315925Z" }, "papermill": { "duration": 0.06655, "end_time": "2022-01-28T14:20:00.641758", "exception": false, "start_time": "2022-01-28T14:20:00.575208", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d.drop('row_id',axis=1)" ] }, { "cell_type": "code", "execution_count": 20, "id": "da082719", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.757502Z", "iopub.status.busy": "2022-01-28T14:20:00.754886Z", "iopub.status.idle": "2022-01-28T14:20:00.762662Z", "shell.execute_reply": "2022-01-28T14:20:00.763224Z", "shell.execute_reply.started": "2022-01-28T14:18:33.024641Z" }, "papermill": { "duration": 0.06585, "end_time": "2022-01-28T14:20:00.763401", "exception": false, "start_time": "2022-01-28T14:20:00.697551", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X=d.drop('num_sold',axis=1)\n", "y=d['num_sold']" ] }, { "cell_type": "code", "execution_count": 21, "id": "6a79e2c6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:00.876568Z", "iopub.status.busy": "2022-01-28T14:20:00.875869Z", "iopub.status.idle": "2022-01-28T14:20:01.663103Z", "shell.execute_reply": "2022-01-28T14:20:01.663643Z", "shell.execute_reply.started": "2022-01-28T14:18:37.868329Z" }, "papermill": { "duration": 0.845296, "end_time": "2022-01-28T14:20:01.663840", "exception": false, "start_time": "2022-01-28T14:20:00.818544", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33, random_state=42)" ] }, { "cell_type": "code", "execution_count": 22, "id": "eb17596e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:01.780981Z", "iopub.status.busy": "2022-01-28T14:20:01.779887Z", "iopub.status.idle": "2022-01-28T14:20:05.910671Z", "shell.execute_reply": "2022-01-28T14:20:05.911371Z", "shell.execute_reply.started": "2022-01-28T14:18:40.198129Z" }, "papermill": { "duration": 4.192844, "end_time": "2022-01-28T14:20:05.911557", "exception": false, "start_time": "2022-01-28T14:20:01.718713", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate set to 0.064424\n", "0:\tlearn: 250.2545078\ttotal: 54.3ms\tremaining: 54.3s\n", "1:\tlearn: 237.0017669\ttotal: 57.9ms\tremaining: 28.9s\n", "2:\tlearn: 224.4799253\ttotal: 61.2ms\tremaining: 20.3s\n", "3:\tlearn: 212.9032106\ttotal: 64.7ms\tremaining: 16.1s\n", "4:\tlearn: 202.0630930\ttotal: 68.2ms\tremaining: 13.6s\n", "5:\tlearn: 192.0434976\ttotal: 72ms\tremaining: 11.9s\n", "6:\tlearn: 182.8427061\ttotal: 75.8ms\tremaining: 10.8s\n", "7:\tlearn: 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"cell_type": "code", "execution_count": 23, "id": "dce50915", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.046136Z", "iopub.status.busy": "2022-01-28T14:20:06.045051Z", "iopub.status.idle": "2022-01-28T14:20:06.056456Z", "shell.execute_reply": "2022-01-28T14:20:06.057066Z", "shell.execute_reply.started": "2022-01-28T14:18:53.749101Z" }, "papermill": { "duration": 0.082301, "end_time": "2022-01-28T14:20:06.057257", "exception": false, "start_time": "2022-01-28T14:20:05.974956", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "pv=cat.predict(X_valid)" ] }, { "cell_type": "code", "execution_count": 24, "id": "5486207d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.192336Z", "iopub.status.busy": "2022-01-28T14:20:06.191341Z", "iopub.status.idle": "2022-01-28T14:20:06.195802Z", "shell.execute_reply": "2022-01-28T14:20:06.196384Z", "shell.execute_reply.started": "2022-01-28T14:18:55.931479Z" }, "papermill": { "duration": 0.076268, "end_time": "2022-01-28T14:20:06.196566", "exception": false, "start_time": "2022-01-28T14:20:06.120298", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18.303582203961366\n" ] } ], "source": [ "from sklearn.metrics import mean_absolute_error\n", "print(mean_absolute_error(pv,y_valid))" ] }, { "cell_type": "code", "execution_count": 25, "id": "b70b1785", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.330775Z", "iopub.status.busy": "2022-01-28T14:20:06.330068Z", "iopub.status.idle": "2022-01-28T14:20:06.353843Z", "shell.execute_reply": "2022-01-28T14:20:06.353225Z", "shell.execute_reply.started": "2022-01-28T14:19:02.349465Z" }, "papermill": { "duration": 0.093846, "end_time": "2022-01-28T14:20:06.354014", "exception": false, "start_time": "2022-01-28T14:20:06.260168", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " 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}, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.read_csv('../input/tabular-playground-series-jan-2022/sample_submission.csv')\n", "s.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "62eca774", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.529978Z", "iopub.status.busy": "2022-01-28T14:20:06.528928Z", "iopub.status.idle": "2022-01-28T14:20:06.532927Z", "shell.execute_reply": "2022-01-28T14:20:06.533499Z", "shell.execute_reply.started": "2022-01-28T14:19:07.080216Z" }, "papermill": { "duration": 0.109756, "end_time": "2022-01-28T14:20:06.533697", "exception": false, "start_time": "2022-01-28T14:20:06.423941", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th 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<td>0.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-01</th>\n", " <td>26300</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-01</th>\n", " <td>26301</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-01</th>\n", " <td>26302</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 29 columns</p>\n", "</div>" ], "text/plain": [ " row_id country_Finland country_Norway country_Sweden \\\n", "date \n", "2019-01-01 26298 1 0 0 \n", "2019-01-01 26299 1 0 0 \n", "2019-01-01 26300 1 0 0 \n", "2019-01-01 26301 1 0 0 \n", "2019-01-01 26302 1 0 0 \n", "\n", " store_KaggleMart store_KaggleRama product_Kaggle Hat \\\n", "date \n", "2019-01-01 1 0 0 \n", "2019-01-01 1 0 1 \n", "2019-01-01 1 0 0 \n", "2019-01-01 0 1 0 \n", "2019-01-01 0 1 1 \n", "\n", " product_Kaggle Mug product_Kaggle Sticker day ... s(7,7) \\\n", "date ... \n", "2019-01-01 1 0 1 ... 0.0 \n", "2019-01-01 0 0 1 ... 0.0 \n", "2019-01-01 0 1 1 ... 0.0 \n", "2019-01-01 1 0 1 ... 0.0 \n", "2019-01-01 0 0 1 ... 0.0 \n", "\n", " sin(1,freq=M) cos(1,freq=M) sin(2,freq=M) cos(2,freq=M) \\\n", "date \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "\n", " sin(3,freq=M) cos(3,freq=M) sin(4,freq=M) cos(4,freq=M) \\\n", "date \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "2019-01-01 0.0 1.0 0.0 1.0 \n", "\n", " ng_coef \n", "date \n", "2019-01-01 1 \n", "2019-01-01 1 \n", "2019-01-01 1 \n", "2019-01-01 1 \n", "2019-01-01 1 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testd.head()" ] }, { "cell_type": "code", "execution_count": 27, "id": "bec81ec7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.686027Z", "iopub.status.busy": "2022-01-28T14:20:06.685190Z", "iopub.status.idle": "2022-01-28T14:20:06.690653Z", "shell.execute_reply": "2022-01-28T14:20:06.691190Z", "shell.execute_reply.started": "2022-01-28T14:19:11.865116Z" }, "papermill": { "duration": 0.089298, "end_time": "2022-01-28T14:20:06.691379", "exception": false, "start_time": "2022-01-28T14:20:06.602081", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X_test=testd.drop('row_id',axis=1)\n", "pvtest=cat.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 28, "id": "765d0cd7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:06.839978Z", "iopub.status.busy": "2022-01-28T14:20:06.838878Z", "iopub.status.idle": "2022-01-28T14:20:06.867878Z", "shell.execute_reply": "2022-01-28T14:20:06.867262Z", "shell.execute_reply.started": "2022-01-28T14:19:13.569103Z" }, "papermill": { "duration": 0.111598, "end_time": "2022-01-28T14:20:06.868077", "exception": false, "start_time": "2022-01-28T14:20:06.756479", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "w=pd.DataFrame()\n", "w['row_id']=testd['row_id']" ] }, { "cell_type": "code", "execution_count": 29, "id": "d0adfd30", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:07.010717Z", "iopub.status.busy": "2022-01-28T14:20:07.009850Z", "iopub.status.idle": "2022-01-28T14:20:07.013071Z", "shell.execute_reply": "2022-01-28T14:20:07.013595Z", "shell.execute_reply.started": "2022-01-28T14:19:15.513630Z" }, "papermill": { "duration": 0.079275, "end_time": "2022-01-28T14:20:07.013782", "exception": false, "start_time": "2022-01-28T14:20:06.934507", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: 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0086/399/86399670.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "cfb7b72c", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:20:51.364900Z", "iopub.status.busy": "2022-01-28T14:20:51.364110Z", "iopub.status.idle": "2022-01-28T14:20:51.377037Z", "shell.execute_reply": "2022-01-28T14:20:51.377548Z", "shell.execute_reply.started": "2022-01-28T14:18:13.069028Z" }, "papermill": { "duration": 0.037567, "end_time": "2022-01-28T14:20:51.377844", "exception": false, "start_time": "2022-01-28T14:20:51.340277", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/dataset/dataset.csv\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" ] }, { "cell_type": "code", "execution_count": 2, "id": "241e33d2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:51.406517Z", "iopub.status.busy": "2022-01-28T14:20:51.405919Z", "iopub.status.idle": "2022-01-28T14:20:51.435463Z", "shell.execute_reply": "2022-01-28T14:20:51.435954Z", "shell.execute_reply.started": "2022-01-28T14:18:13.088231Z" }, "papermill": { "duration": 0.044235, "end_time": "2022-01-28T14:20:51.436140", "exception": false, "start_time": "2022-01-28T14:20:51.391905", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>age</th>\n", " <th>gender</th>\n", " <th>fever</th>\n", " <th>cough</th>\n", " <th>city</th>\n", " <th>has_covid</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>60</td>\n", " <td>Male</td>\n", " <td>103.0</td>\n", " <td>Mild</td>\n", " <td>Kolkata</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>27</td>\n", " <td>Male</td>\n", " <td>100.0</td>\n", " <td>Mild</td>\n", " <td>Delhi</td>\n", " <td>Yes</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>42</td>\n", " <td>Male</td>\n", " <td>101.0</td>\n", " <td>Mild</td>\n", " <td>Delhi</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>31</td>\n", " <td>Female</td>\n", " <td>98.0</td>\n", " <td>Mild</td>\n", " <td>Kolkata</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>65</td>\n", " <td>Female</td>\n", " <td>101.0</td>\n", " <td>Mild</td>\n", " <td>Mumbai</td>\n", " <td>No</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 age gender fever cough city has_covid\n", "0 0 60 Male 103.0 Mild Kolkata No\n", "1 1 27 Male 100.0 Mild Delhi Yes\n", "2 2 42 Male 101.0 Mild Delhi No\n", "3 3 31 Female 98.0 Mild Kolkata No\n", "4 4 65 Female 101.0 Mild Mumbai No" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"/kaggle/input/dataset/dataset.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "050821c3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:51.464662Z", "iopub.status.busy": "2022-01-28T14:20:51.463718Z", "iopub.status.idle": "2022-01-28T14:20:51.486730Z", "shell.execute_reply": "2022-01-28T14:20:51.487240Z", "shell.execute_reply.started": "2022-01-28T14:18:13.117209Z" }, "papermill": { "duration": 0.038688, "end_time": "2022-01-28T14:20:51.487427", "exception": false, "start_time": "2022-01-28T14:20:51.448739", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 100 entries, 0 to 99\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Unnamed: 0 100 non-null int64 \n", " 1 age 100 non-null int64 \n", " 2 gender 100 non-null object \n", " 3 fever 90 non-null float64\n", " 4 cough 100 non-null object \n", " 5 city 100 non-null object \n", " 6 has_covid 100 non-null object \n", "dtypes: float64(1), int64(2), object(4)\n", "memory usage: 5.6+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "id": "b1c1f53b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:51.529403Z", "iopub.status.busy": "2022-01-28T14:20:51.528507Z", "iopub.status.idle": "2022-01-28T14:20:51.532669Z", "shell.execute_reply": "2022-01-28T14:20:51.532190Z", "shell.execute_reply.started": "2022-01-28T14:18:13.134755Z" }, "papermill": { "duration": 0.032996, "end_time": "2022-01-28T14:20:51.532816", "exception": false, "start_time": "2022-01-28T14:20:51.499820", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>gender</th>\n", " <th>fever</th>\n", " <th>cough</th>\n", " <th>city</th>\n", " <th>has_covid</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>60</td>\n", " <td>Male</td>\n", " <td>103.0</td>\n", " <td>Mild</td>\n", " <td>Kolkata</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>27</td>\n", " <td>Male</td>\n", " <td>100.0</td>\n", " <td>Mild</td>\n", " <td>Delhi</td>\n", " <td>Yes</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>42</td>\n", " <td>Male</td>\n", " <td>101.0</td>\n", " <td>Mild</td>\n", " <td>Delhi</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>31</td>\n", " <td>Female</td>\n", " <td>98.0</td>\n", " <td>Mild</td>\n", " <td>Kolkata</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>65</td>\n", " <td>Female</td>\n", " <td>101.0</td>\n", " <td>Mild</td>\n", " <td>Mumbai</td>\n", " <td>No</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age gender fever cough city has_covid\n", "0 60 Male 103.0 Mild Kolkata No\n", "1 27 Male 100.0 Mild Delhi Yes\n", "2 42 Male 101.0 Mild Delhi No\n", "3 31 Female 98.0 Mild Kolkata No\n", "4 65 Female 101.0 Mild Mumbai No" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop(columns = df.columns[0],axis = 1,inplace = True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "25d2e9bb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:51.561600Z", "iopub.status.busy": "2022-01-28T14:20:51.560900Z", "iopub.status.idle": "2022-01-28T14:20:51.569129Z", "shell.execute_reply": "2022-01-28T14:20:51.568574Z", "shell.execute_reply.started": "2022-01-28T14:18:13.157885Z" }, "papermill": { "duration": 0.023859, "end_time": "2022-01-28T14:20:51.569279", "exception": false, "start_time": "2022-01-28T14:20:51.545420", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "age 0\n", "gender 0\n", "fever 10\n", "cough 0\n", "city 0\n", "has_covid 0\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 6, "id": "be35311f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:51.600973Z", "iopub.status.busy": "2022-01-28T14:20:51.600225Z", "iopub.status.idle": "2022-01-28T14:20:52.883142Z", "shell.execute_reply": "2022-01-28T14:20:52.882392Z", "shell.execute_reply.started": "2022-01-28T14:18:13.175139Z" }, "papermill": { "duration": 1.300594, "end_time": "2022-01-28T14:20:52.883285", "exception": false, "start_time": "2022-01-28T14:20:51.582691", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.preprocessing import OrdinalEncoder" ] }, { "cell_type": "markdown", "id": "e6e7a2d3", "metadata": { "papermill": { "duration": 0.013138, "end_time": "2022-01-28T14:20:52.909454", "exception": false, "start_time": "2022-01-28T14:20:52.896316", "status": "completed" }, "tags": [] }, "source": [ "# LONG METHOND" ] }, { "cell_type": "markdown", "id": "a5ca9ed4", "metadata": { "papermill": { "duration": 0.012749, "end_time": "2022-01-28T14:20:52.935935", "exception": false, "start_time": "2022-01-28T14:20:52.923186", "status": "completed" }, "tags": [] }, "source": [ "### Using Simple Imputer to find missing values in column 'fever'" ] }, { "cell_type": "code", "execution_count": 7, "id": "1831e329", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:52.967462Z", "iopub.status.busy": "2022-01-28T14:20:52.966788Z", "iopub.status.idle": "2022-01-28T14:20:52.974975Z", "shell.execute_reply": "2022-01-28T14:20:52.975538Z", "shell.execute_reply.started": "2022-01-28T14:18:13.700309Z" }, "papermill": { "duration": 0.026684, "end_time": "2022-01-28T14:20:52.975717", "exception": false, "start_time": "2022-01-28T14:20:52.949033", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "SI = SimpleImputer()\n", "df_fever = SI.fit_transform(df[['fever']])" ] }, { "cell_type": "markdown", "id": "bd4e7465", "metadata": { "papermill": { "duration": 0.013041, "end_time": "2022-01-28T14:20:53.001773", "exception": false, "start_time": "2022-01-28T14:20:52.988732", "status": "completed" }, "tags": [] }, "source": [ "### Using Ordinal Encoding in column 'fever'" ] }, { "cell_type": "code", "execution_count": 8, "id": "1a90fcd2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:53.035846Z", "iopub.status.busy": "2022-01-28T14:20:53.035216Z", "iopub.status.idle": "2022-01-28T14:20:53.037219Z", "shell.execute_reply": "2022-01-28T14:20:53.037818Z", "shell.execute_reply.started": "2022-01-28T14:18:13.711075Z" }, "papermill": { "duration": 0.022698, "end_time": "2022-01-28T14:20:53.038028", "exception": false, "start_time": "2022-01-28T14:20:53.015330", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "OE = OrdinalEncoder(categories=[['Mild','Strong']])\n", "df_cough = OE.fit_transform(df[['cough']])" ] }, { "cell_type": "markdown", "id": "bac50013", "metadata": { "papermill": { "duration": 0.012871, "end_time": "2022-01-28T14:20:53.064071", "exception": false, "start_time": "2022-01-28T14:20:53.051200", "status": "completed" }, "tags": [] }, "source": [ "### Using One Hot Encoding in column 'gender' , 'city'" ] }, { "cell_type": "code", "execution_count": 9, "id": "bf5938d4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:53.093082Z", "iopub.status.busy": "2022-01-28T14:20:53.092459Z", "iopub.status.idle": "2022-01-28T14:20:53.099719Z", "shell.execute_reply": "2022-01-28T14:20:53.100192Z", "shell.execute_reply.started": "2022-01-28T14:18:13.722925Z" }, "papermill": { "duration": 0.023428, "end_time": "2022-01-28T14:20:53.100374", "exception": false, "start_time": "2022-01-28T14:20:53.076946", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "OHE = OneHotEncoder(drop='first',sparse=False)\n", "df_gender_city = OHE.fit_transform(df[['gender','city']])" ] }, { "cell_type": "code", "execution_count": 10, "id": "aeed903e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:53.132557Z", "iopub.status.busy": "2022-01-28T14:20:53.131873Z", "iopub.status.idle": "2022-01-28T14:20:53.133883Z", "shell.execute_reply": "2022-01-28T14:20:53.134350Z", "shell.execute_reply.started": "2022-01-28T14:18:13.742602Z" }, "papermill": { "duration": 0.021093, "end_time": "2022-01-28T14:20:53.134514", "exception": false, "start_time": "2022-01-28T14:20:53.113421", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df_age = df.drop(columns=['gender','fever','cough','city']).values" ] }, { "cell_type": "code", "execution_count": 11, "id": "7e5732be", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:53.165439Z", "iopub.status.busy": "2022-01-28T14:20:53.163593Z", "iopub.status.idle": "2022-01-28T14:20:53.168296Z", "shell.execute_reply": "2022-01-28T14:20:53.168741Z", "shell.execute_reply.started": "2022-01-28T14:18:13.756713Z" }, "papermill": { "duration": 0.021352, "end_time": "2022-01-28T14:20:53.168939", "exception": false, "start_time": "2022-01-28T14:20:53.147587", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df_transformed = np.concatenate((df_age,df_fever,df_gender_city,df_cough),axis=1)" ] }, { "cell_type": "code", "execution_count": 12, "id": "63c51f8e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:53.201228Z", "iopub.status.busy": "2022-01-28T14:20:53.199366Z", "iopub.status.idle": "2022-01-28T14:20:53.204385Z", "shell.execute_reply": "2022-01-28T14:20:53.204863Z", "shell.execute_reply.started": "2022-01-28T14:18:13.771773Z" }, "papermill": { "duration": 0.022084, "end_time": "2022-01-28T14:20:53.205049", "exception": false, "start_time": "2022-01-28T14:20:53.182965", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(100, 8)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_transformed.shape" ] }, { "cell_type": "code", "execution_count": 13, "id": "cf8d3284", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:53.243595Z", "iopub.status.busy": "2022-01-28T14:20:53.234863Z", "iopub.status.idle": "2022-01-28T14:20:53.247088Z", "shell.execute_reply": "2022-01-28T14:20:53.247535Z", "shell.execute_reply.started": "2022-01-28T14:18:13.788307Z" }, "papermill": { "duration": 0.028825, "end_time": "2022-01-28T14:20:53.247716", "exception": false, "start_time": "2022-01-28T14:20:53.218891", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>gender</th>\n", " <th>fever</th>\n", " <th>cough</th>\n", " <th>city</th>\n", " <th>has_covid</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>60</td>\n", " <td>Male</td>\n", " <td>103.0</td>\n", " <td>Mild</td>\n", " <td>Kolkata</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>27</td>\n", " <td>Male</td>\n", " <td>100.0</td>\n", " <td>Mild</td>\n", " <td>Delhi</td>\n", " <td>Yes</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>42</td>\n", " <td>Male</td>\n", " <td>101.0</td>\n", " <td>Mild</td>\n", " <td>Delhi</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>31</td>\n", " <td>Female</td>\n", " <td>98.0</td>\n", " <td>Mild</td>\n", " <td>Kolkata</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>65</td>\n", " <td>Female</td>\n", " <td>101.0</td>\n", " <td>Mild</td>\n", " <td>Mumbai</td>\n", " <td>No</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age gender fever cough city has_covid\n", "0 60 Male 103.0 Mild Kolkata No\n", "1 27 Male 100.0 Mild Delhi Yes\n", "2 42 Male 101.0 Mild Delhi No\n", "3 31 Female 98.0 Mild Kolkata No\n", "4 65 Female 101.0 Mild Mumbai No" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "4f8dfe59", "metadata": { "papermill": { "duration": 0.014218, "end_time": "2022-01-28T14:20:53.278267", "exception": false, "start_time": "2022-01-28T14:20:53.264049", "status": "completed" }, "tags": [] }, "source": [ "# Column Transformer" ] }, { "cell_type": "markdown", "id": "638222e5", "metadata": { "papermill": { "duration": 0.013017, "end_time": "2022-01-28T14:20:53.305881", "exception": false, "start_time": "2022-01-28T14:20:53.292864", "status": "completed" }, "tags": [] }, "source": [ "Import ColumnTransformer class and make an object. The object will take 2 inputs - `transformers` and `remainder`. Transfomations are passed inside tuples and each tuple contains **name , transformers object , column**" ] }, { "cell_type": "code", "execution_count": 14, "id": "6e318a0b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:20:53.339480Z", "iopub.status.busy": "2022-01-28T14:20:53.338779Z", "iopub.status.idle": "2022-01-28T14:20:53.349950Z", "shell.execute_reply": "2022-01-28T14:20:53.350628Z", "shell.execute_reply.started": "2022-01-28T14:18:13.807972Z" }, "papermill": { "duration": 0.030875, "end_time": "2022-01-28T14:20:53.350816", "exception": false, "start_time": "2022-01-28T14:20:53.319941", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.compose import ColumnTransformer\n", "\n", "transformer = ColumnTransformer(transformers=[\n", " ('tnf1',SimpleImputer(),['fever']),\n", " ('tnf2',OrdinalEncoder(categories=[['Mild','Strong']]),['cough']),\n", " ('tnf3',OneHotEncoder(sparse=False,drop='first'),['gender','city'])\n", "],remainder='passthrough')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 11.448072, "end_time": "2022-01-28T14:20:54.075737", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:20:42.627665", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/399/86399767.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**LAB 07**\n**Choose any dataset from Kaggle or any other source and apply following tasks on them**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nHeart_failure_data = pd.read_csv('../input/heart-failure-clinical-records-dataset/Heart_failure_clinical_records_dataset.csv')\nHeart_failure_data.keys()\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:17:49.825395Z","iopub.execute_input":"2022-01-28T14:17:49.825713Z","iopub.status.idle":"2022-01-28T14:17:49.845787Z","shell.execute_reply.started":"2022-01-28T14:17:49.825678Z","shell.execute_reply":"2022-01-28T14:17:49.845233Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n 'ejection_fraction', 'high_blood_pressure', 'platelets',\n 'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n 'DEATH_EVENT'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"Heart_failure_data","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:17:57.091785Z","iopub.execute_input":"2022-01-28T14:17:57.092413Z","iopub.status.idle":"2022-01-28T14:17:57.115785Z","shell.execute_reply.started":"2022-01-28T14:17:57.092363Z","shell.execute_reply":"2022-01-28T14:17:57.114910Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":" age anaemia creatinine_phosphokinase diabetes ejection_fraction \\\n0 75.0 0 582 0 20 \n1 55.0 0 7861 0 38 \n2 65.0 0 146 0 20 \n3 50.0 1 111 0 20 \n4 65.0 1 160 1 20 \n.. ... ... ... ... ... \n294 62.0 0 61 1 38 \n295 55.0 0 1820 0 38 \n296 45.0 0 2060 1 60 \n297 45.0 0 2413 0 38 \n298 50.0 0 196 0 45 \n\n high_blood_pressure platelets serum_creatinine serum_sodium sex \\\n0 1 265000.00 1.9 130 1 \n1 0 263358.03 1.1 136 1 \n2 0 162000.00 1.3 129 1 \n3 0 210000.00 1.9 137 1 \n4 0 327000.00 2.7 116 0 \n.. ... ... ... ... ... \n294 1 155000.00 1.1 143 1 \n295 0 270000.00 1.2 139 0 \n296 0 742000.00 0.8 138 0 \n297 0 140000.00 1.4 140 1 \n298 0 395000.00 1.6 136 1 \n\n smoking time DEATH_EVENT \n0 0 4 1 \n1 0 6 1 \n2 1 7 1 \n3 0 7 1 \n4 0 8 1 \n.. ... ... ... \n294 1 270 0 \n295 0 271 0 \n296 0 278 0 \n297 1 280 0 \n298 1 285 0 \n\n[299 rows x 13 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>age</th>\n <th>anaemia</th>\n <th>creatinine_phosphokinase</th>\n <th>diabetes</th>\n <th>ejection_fraction</th>\n <th>high_blood_pressure</th>\n <th>platelets</th>\n <th>serum_creatinine</th>\n <th>serum_sodium</th>\n <th>sex</th>\n <th>smoking</th>\n <th>time</th>\n <th>DEATH_EVENT</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>75.0</td>\n <td>0</td>\n <td>582</td>\n <td>0</td>\n <td>20</td>\n <td>1</td>\n <td>265000.00</td>\n <td>1.9</td>\n <td>130</td>\n <td>1</td>\n <td>0</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>55.0</td>\n <td>0</td>\n <td>7861</td>\n <td>0</td>\n <td>38</td>\n <td>0</td>\n <td>263358.03</td>\n <td>1.1</td>\n <td>136</td>\n <td>1</td>\n <td>0</td>\n <td>6</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>65.0</td>\n <td>0</td>\n <td>146</td>\n <td>0</td>\n <td>20</td>\n <td>0</td>\n <td>162000.00</td>\n <td>1.3</td>\n <td>129</td>\n <td>1</td>\n <td>1</td>\n <td>7</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>50.0</td>\n <td>1</td>\n <td>111</td>\n <td>0</td>\n <td>20</td>\n <td>0</td>\n <td>210000.00</td>\n <td>1.9</td>\n <td>137</td>\n <td>1</td>\n <td>0</td>\n <td>7</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>65.0</td>\n <td>1</td>\n <td>160</td>\n <td>1</td>\n <td>20</td>\n <td>0</td>\n <td>327000.00</td>\n <td>2.7</td>\n <td>116</td>\n <td>0</td>\n <td>0</td>\n <td>8</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>294</th>\n <td>62.0</td>\n <td>0</td>\n <td>61</td>\n <td>1</td>\n <td>38</td>\n <td>1</td>\n <td>155000.00</td>\n <td>1.1</td>\n <td>143</td>\n <td>1</td>\n <td>1</td>\n <td>270</td>\n <td>0</td>\n </tr>\n <tr>\n <th>295</th>\n <td>55.0</td>\n <td>0</td>\n <td>1820</td>\n <td>0</td>\n <td>38</td>\n <td>0</td>\n <td>270000.00</td>\n <td>1.2</td>\n <td>139</td>\n <td>0</td>\n <td>0</td>\n <td>271</td>\n <td>0</td>\n </tr>\n <tr>\n <th>296</th>\n <td>45.0</td>\n <td>0</td>\n <td>2060</td>\n <td>1</td>\n <td>60</td>\n <td>0</td>\n <td>742000.00</td>\n <td>0.8</td>\n <td>138</td>\n <td>0</td>\n <td>0</td>\n <td>278</td>\n <td>0</td>\n </tr>\n <tr>\n <th>297</th>\n <td>45.0</td>\n <td>0</td>\n <td>2413</td>\n <td>0</td>\n <td>38</td>\n <td>0</td>\n <td>140000.00</td>\n <td>1.4</td>\n <td>140</td>\n <td>1</td>\n <td>1</td>\n <td>280</td>\n <td>0</td>\n </tr>\n <tr>\n <th>298</th>\n <td>50.0</td>\n <td>0</td>\n <td>196</td>\n <td>0</td>\n <td>45</td>\n <td>0</td>\n <td>395000.00</td>\n <td>1.6</td>\n <td>136</td>\n <td>1</td>\n <td>1</td>\n <td>285</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>299 rows × 13 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**Separate data and labels and split them into train and test data (If data is not split)**","metadata":{}},{"cell_type":"code","source":"X, y = Heart_failure_data.loc[:, Heart_failure_data.columns!= 'DEATH_EVENT'],Heart_failure_data.DEATH_EVENT\nX.shape, y.shape\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:18:15.103864Z","iopub.execute_input":"2022-01-28T14:18:15.104190Z","iopub.status.idle":"2022-01-28T14:18:15.117081Z","shell.execute_reply.started":"2022-01-28T14:18:15.104143Z","shell.execute_reply":"2022-01-28T14:18:15.116002Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"((299, 12), (299,))"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nXtrain,Xtest,ytrain,ytest = train_test_split(X, y ,test_size = 0.25,shuffle= True)\nXtrain.shape, Xtest.shape\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:18:29.342546Z","iopub.execute_input":"2022-01-28T14:18:29.342817Z","iopub.status.idle":"2022-01-28T14:18:29.353564Z","shell.execute_reply.started":"2022-01-28T14:18:29.342788Z","shell.execute_reply":"2022-01-28T14:18:29.352725Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"((224, 12), (75, 12))"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nmodel_log = LogisticRegression(solver='liblinear')\nmodel_log.fit(Xtrain,ytrain)\nmodel_log.score(Xtest,ytest)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:18:40.221924Z","iopub.execute_input":"2022-01-28T14:18:40.222239Z","iopub.status.idle":"2022-01-28T14:18:40.235490Z","shell.execute_reply.started":"2022-01-28T14:18:40.222205Z","shell.execute_reply":"2022-01-28T14:18:40.234710Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"0.7866666666666666"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nimport numpy as np\nimport matplotlib.pyplot as plt\narr = 5*(np.arange(10))\ntest_score = []\ntrain_score = []\nfor i in arr:\n model_log = LogisticRegression(solver='liblinear',max_iter=i)\n model_log.fit(Xtrain,ytrain)\n test_score.append(model_log.score(Xtest,ytest))\n train_score.append(model_log.score(Xtrain,ytrain))\n\nplt.xlabel('Iterations')\nplt.ylabel('Accuracy')\nplt.title('Logistic Regression Accuracy Graph')\nplt.plot(arr,train_score,label= 'Training Accuracy')\nplt.plot(arr,test_score,label= 'Testing Accuracy')\nplt.legend(loc='upper left')","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:18:52.254837Z","iopub.execute_input":"2022-01-28T14:18:52.255628Z","iopub.status.idle":"2022-01-28T14:18:52.597869Z","shell.execute_reply.started":"2022-01-28T14:18:52.255585Z","shell.execute_reply":"2022-01-28T14:18:52.597041Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n \"the number of iterations.\", ConvergenceWarning)\n/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n \"the number of iterations.\", ConvergenceWarning)\n/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n \"the number of iterations.\", ConvergenceWarning)\n/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n \"the number of iterations.\", ConvergenceWarning)\n/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n \"the number of iterations.\", ConvergenceWarning)\n","output_type":"stream"},{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"<matplotlib.legend.Legend at 0x7f976ecb0410>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"Heart_failure_data.keys()\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:19:07.870220Z","iopub.execute_input":"2022-01-28T14:19:07.870892Z","iopub.status.idle":"2022-01-28T14:19:07.877058Z","shell.execute_reply.started":"2022-01-28T14:19:07.870858Z","shell.execute_reply":"2022-01-28T14:19:07.876385Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n 'ejection_fraction', 'high_blood_pressure', 'platelets',\n 'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n 'DEATH_EVENT'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\nmin_max = MinMaxScaler()\nXtrain['age'] = min_max.fit_transform(Xtrain[['age']])\nXtrain['creatinine_phosphokinase'] = min_max.fit_transform(Xtrain[['creatinine_phosphokinase']])\nXtrain['ejection_fraction'] = min_max.fit_transform(Xtrain[['ejection_fraction']])\nXtrain['platelets'] = min_max.fit_transform(Xtrain[['platelets']])\nXtrain['serum_creatinine'] = min_max.fit_transform(Xtrain[['serum_creatinine']])\nXtrain['serum_sodium'] = min_max.fit_transform(Xtrain[['serum_sodium']])\nXtrain['time'] = min_max.fit_transform(Xtrain[['time']])\nXtest['age'] = min_max.fit_transform(Xtest[['age']])\nXtest['creatinine_phosphokinase'] = min_max.fit_transform(Xtest[['creatinine_phosphokinase']])\nXtest['ejection_fraction'] = min_max.fit_transform(Xtest[['ejection_fraction']])\nXtest['platelets'] = min_max.fit_transform(Xtest[['platelets']])\nXtest['serum_creatinine'] = min_max.fit_transform(Xtest[['serum_creatinine']])\nXtest['serum_sodium'] = min_max.fit_transform(Xtest[['serum_sodium']])\nXtest['time'] = min_max.fit_transform(Xtest[['time']])","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:19:36.637976Z","iopub.execute_input":"2022-01-28T14:19:36.638514Z","iopub.status.idle":"2022-01-28T14:19:36.701515Z","shell.execute_reply.started":"2022-01-28T14:19:36.638474Z","shell.execute_reply":"2022-01-28T14:19:36.700762Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n This is separate from the ipykernel package so we can avoid doing imports until\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n after removing the cwd from sys.path.\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \"\"\"\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n import sys\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n if __name__ == '__main__':\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n # Remove the CWD from sys.path while we load stuff.\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n # This is added back by InteractiveShellApp.init_path()\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n if sys.path[0] == '':\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n del sys.path[0]\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n from ipykernel import kernelapp as app\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n app.launch_new_instance()\n","output_type":"stream"}]},{"cell_type":"code","source":"Xtrain.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:19:57.470743Z","iopub.execute_input":"2022-01-28T14:19:57.471047Z","iopub.status.idle":"2022-01-28T14:19:57.487519Z","shell.execute_reply.started":"2022-01-28T14:19:57.471012Z","shell.execute_reply":"2022-01-28T14:19:57.486392Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":" age anaemia creatinine_phosphokinase diabetes ejection_fraction \\\n203 0.363636 0 0.003703 0 0.166667 \n96 0.418182 1 0.061806 1 0.166667 \n122 0.363636 0 0.008428 1 0.363636 \n21 0.454545 1 0.012514 1 0.242424 \n298 0.181818 0 0.021198 0 0.469697 \n\n high_blood_pressure platelets serum_creatinine serum_sodium sex \\\n203 1 0.226573 0.337079 0.657143 1 \n96 1 0.277488 0.089888 0.600000 1 \n122 0 0.245969 0.028090 0.771429 0 \n21 1 0.329616 0.123596 0.657143 0 \n298 0 0.448418 0.123596 0.657143 1 \n\n smoking time \n203 1 0.651246 \n96 0 0.281139 \n122 0 0.323843 \n21 0 0.056940 \n298 1 1.000000 ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>age</th>\n <th>anaemia</th>\n <th>creatinine_phosphokinase</th>\n <th>diabetes</th>\n <th>ejection_fraction</th>\n <th>high_blood_pressure</th>\n <th>platelets</th>\n <th>serum_creatinine</th>\n <th>serum_sodium</th>\n <th>sex</th>\n <th>smoking</th>\n <th>time</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>203</th>\n <td>0.363636</td>\n <td>0</td>\n <td>0.003703</td>\n <td>0</td>\n <td>0.166667</td>\n <td>1</td>\n <td>0.226573</td>\n <td>0.337079</td>\n <td>0.657143</td>\n <td>1</td>\n <td>1</td>\n <td>0.651246</td>\n </tr>\n <tr>\n <th>96</th>\n <td>0.418182</td>\n <td>1</td>\n <td>0.061806</td>\n <td>1</td>\n <td>0.166667</td>\n <td>1</td>\n <td>0.277488</td>\n <td>0.089888</td>\n <td>0.600000</td>\n <td>1</td>\n <td>0</td>\n <td>0.281139</td>\n </tr>\n <tr>\n <th>122</th>\n <td>0.363636</td>\n <td>0</td>\n <td>0.008428</td>\n <td>1</td>\n <td>0.363636</td>\n <td>0</td>\n <td>0.245969</td>\n <td>0.028090</td>\n <td>0.771429</td>\n <td>0</td>\n <td>0</td>\n <td>0.323843</td>\n </tr>\n <tr>\n <th>21</th>\n <td>0.454545</td>\n <td>1</td>\n <td>0.012514</td>\n <td>1</td>\n <td>0.242424</td>\n <td>1</td>\n <td>0.329616</td>\n <td>0.123596</td>\n <td>0.657143</td>\n <td>0</td>\n <td>0</td>\n <td>0.056940</td>\n </tr>\n <tr>\n <th>298</th>\n <td>0.181818</td>\n <td>0</td>\n <td>0.021198</td>\n <td>0</td>\n <td>0.469697</td>\n <td>0</td>\n <td>0.448418</td>\n <td>0.123596</td>\n <td>0.657143</td>\n <td>1</td>\n <td>1</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nencoder = LabelEncoder()\nytrain= encoder.fit_transform(ytrain)\nytest= encoder.fit_transform(ytest)\nytrain,ytest\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:20:06.606811Z","iopub.execute_input":"2022-01-28T14:20:06.607078Z","iopub.status.idle":"2022-01-28T14:20:06.614765Z","shell.execute_reply.started":"2022-01-28T14:20:06.607049Z","shell.execute_reply":"2022-01-28T14:20:06.614215Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"(array([0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n 0, 0, 0, 1]),\n array([0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n 0, 1, 1, 0, 1, 0, 0, 0, 0]))"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nmodel_log = LogisticRegression(solver='liblinear')\nmodel_log.fit(Xtrain,ytrain)\nmodel_log.score(Xtest,ytest)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:20:17.135003Z","iopub.execute_input":"2022-01-28T14:20:17.136032Z","iopub.status.idle":"2022-01-28T14:20:17.149976Z","shell.execute_reply.started":"2022-01-28T14:20:17.135988Z","shell.execute_reply":"2022-01-28T14:20:17.149103Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"0.7733333333333333"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import plot_confusion_matrix\nplot_confusion_matrix(model_log,Xtest,ytest)","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:20:24.847098Z","iopub.execute_input":"2022-01-28T14:20:24.847939Z","iopub.status.idle":"2022-01-28T14:20:25.103004Z","shell.execute_reply.started":"2022-01-28T14:20:24.847898Z","shell.execute_reply":"2022-01-28T14:20:25.102155Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9766beae50>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"result1 = model_log.predict(Xtest)\nresult1","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:20:34.203755Z","iopub.execute_input":"2022-01-28T14:20:34.204023Z","iopub.status.idle":"2022-01-28T14:20:34.211717Z","shell.execute_reply.started":"2022-01-28T14:20:34.203995Z","shell.execute_reply":"2022-01-28T14:20:34.211155Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n 0, 1, 1, 0, 1, 0, 0, 0, 0])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(ytest,result1))\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:20:45.871452Z","iopub.execute_input":"2022-01-28T14:20:45.871916Z","iopub.status.idle":"2022-01-28T14:20:45.881640Z","shell.execute_reply.started":"2022-01-28T14:20:45.871868Z","shell.execute_reply":"2022-01-28T14:20:45.880861Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":" precision recall f1-score support\n\n 0 0.77 0.94 0.84 49\n 1 0.80 0.46 0.59 26\n\n accuracy 0.77 75\n macro avg 0.78 0.70 0.71 75\nweighted avg 0.78 0.77 0.75 75\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nmodel_NB = GaussianNB()\nmodel_NB.fit(Xtrain,ytrain)\nmodel_NB.score(Xtest,ytest)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:20:55.260090Z","iopub.execute_input":"2022-01-28T14:20:55.260433Z","iopub.status.idle":"2022-01-28T14:20:55.276430Z","shell.execute_reply.started":"2022-01-28T14:20:55.260394Z","shell.execute_reply":"2022-01-28T14:20:55.275635Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"0.8"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import plot_confusion_matrix\nplot_confusion_matrix(model_NB,Xtest,ytest)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:21:10.939584Z","iopub.execute_input":"2022-01-28T14:21:10.939866Z","iopub.status.idle":"2022-01-28T14:21:11.183589Z","shell.execute_reply.started":"2022-01-28T14:21:10.939837Z","shell.execute_reply":"2022-01-28T14:21:11.182715Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9766a319d0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"result2 = model_NB.predict(Xtest)\nresult2\n","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:21:18.913937Z","iopub.execute_input":"2022-01-28T14:21:18.914468Z","iopub.status.idle":"2022-01-28T14:21:18.929566Z","shell.execute_reply.started":"2022-01-28T14:21:18.914418Z","shell.execute_reply":"2022-01-28T14:21:18.928669Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0,\n 0, 1, 1, 0, 0, 0, 0, 0, 0])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(ytest,result2))","metadata":{"execution":{"iopub.status.busy":"2022-01-28T14:21:26.460357Z","iopub.execute_input":"2022-01-28T14:21:26.460684Z","iopub.status.idle":"2022-01-28T14:21:26.471481Z","shell.execute_reply.started":"2022-01-28T14:21:26.460610Z","shell.execute_reply":"2022-01-28T14:21:26.470591Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":" precision recall f1-score support\n\n 0 0.81 0.90 0.85 49\n 1 0.76 0.62 0.68 26\n\n accuracy 0.80 75\n macro avg 0.79 0.76 0.77 75\nweighted avg 0.80 0.80 0.79 75\n\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
0086/399/86399854.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "2690227c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:14.839266Z", "iopub.status.busy": "2022-01-28T14:26:14.838312Z", "iopub.status.idle": "2022-01-28T14:26:16.977242Z", "shell.execute_reply": "2022-01-28T14:26:16.976345Z", "shell.execute_reply.started": "2022-01-28T13:59:57.834618Z" }, "papermill": { "duration": 2.156857, "end_time": "2022-01-28T14:26:16.977478", "exception": false, "start_time": "2022-01-28T14:26:14.820621", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<style type='text/css'>\n", ".datatable table.frame { margin-bottom: 0; }\n", ".datatable table.frame thead { border-bottom: none; }\n", ".datatable table.frame tr.coltypes td { color: #FFFFFF; line-height: 6px; padding: 0 0.5em;}\n", ".datatable .bool { background: #DDDD99; }\n", ".datatable .object { background: #565656; }\n", ".datatable .int { background: #5D9E5D; }\n", ".datatable .float 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inline-block; opacity: 0.6; padding: 1px 10px 1px 5px;}\n", "</style>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "from sklearn.model_selection import GroupKFold\n", "import lightgbm as lgb\n", "from tqdm.notebook import tqdm\n", "import random\n", "import joblib\n", "import warnings\n", "import gc\n", "warnings.filterwarnings('ignore')\n", "pd.set_option('display.max_columns', 500)\n", "pd.set_option('display.max_rows', 100)" ] }, { "cell_type": "code", "execution_count": 2, "id": "76d1f3b4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:16.994582Z", "iopub.status.busy": "2022-01-28T14:26:16.993572Z", "iopub.status.idle": "2022-01-28T14:26:16.997559Z", "shell.execute_reply": "2022-01-28T14:26:16.998032Z", "shell.execute_reply.started": "2022-01-28T13:59:59.953297Z" }, "papermill": { "duration": 0.014147, "end_time": "2022-01-28T14:26:16.998232", "exception": false, "start_time": "2022-01-28T14:26:16.984085", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Model Seed \n", "MODEL_SEED = 42\n", "# Learning rate\n", "LR = 0.05\n", "# Folds\n", "FOLDS = 5" ] }, { "cell_type": "code", "execution_count": 3, "id": "bb93d433", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:17.041829Z", "iopub.status.busy": "2022-01-28T14:26:17.013470Z", "iopub.status.idle": "2022-01-28T15:44:37.234691Z", "shell.execute_reply": "2022-01-28T15:44:37.233523Z" }, "papermill": { "duration": 4700.23165, "end_time": "2022-01-28T15:44:37.235460", "exception": false, "start_time": "2022-01-28T14:26:17.003810", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0c77871f62a6482d920dce9e6440c900", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/100 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "We added 100 features related to time_id\n", "Training fold 1\n", "Training with 2501556 rows\n", "Validating with 639854 rows\n", "Training light gradient boosting model with 400 features...\n", "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 8.938289 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 101993\n", "[LightGBM] [Info] Number of data points in the train set: 2501556, number of used features: 400\n", "[LightGBM] [Info] Start training from score -0.021775\n", "Training until validation scores don't improve for 100 rounds\n", "[100]\ttraining's l2: 0.779452\tvalid_1's l2: 0.835931\n", "[200]\ttraining's l2: 0.746061\tvalid_1's l2: 0.833724\n", "[300]\ttraining's l2: 0.721485\tvalid_1's l2: 0.832823\n", "[400]\ttraining's l2: 0.701007\tvalid_1's l2: 0.832505\n", "[500]\ttraining's l2: 0.684727\tvalid_1's l2: 0.832553\n", "Early stopping, best iteration is:\n", "[407]\ttraining's l2: 0.699848\tvalid_1's l2: 0.83243\n", "Training fold 2\n", "Training with 2508116 rows\n", "Validating with 633294 rows\n", "Training light gradient boosting model with 400 features...\n", "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 9.886855 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 101981\n", "[LightGBM] [Info] Number of data points in the train set: 2508116, number of used features: 400\n", "[LightGBM] [Info] Start training from score -0.016391\n", "Training until validation scores don't improve for 100 rounds\n", "[100]\ttraining's l2: 0.791575\tvalid_1's l2: 0.801787\n", "[200]\ttraining's l2: 0.757833\tvalid_1's l2: 0.801413\n", "Early stopping, best iteration is:\n", "[147]\ttraining's l2: 0.774194\tvalid_1's l2: 0.801071\n", "Training fold 3\n", "Training with 2511769 rows\n", "Validating with 629641 rows\n", "Training light gradient boosting model with 400 features...\n", "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 6.865505 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 101977\n", "[LightGBM] [Info] Number of data points in the train set: 2511769, number of used features: 400\n", "[LightGBM] [Info] Start training from score -0.020096\n", "Training until validation scores don't improve for 100 rounds\n", "[100]\ttraining's l2: 0.773867\tvalid_1's l2: 0.855495\n", "[200]\ttraining's l2: 0.740554\tvalid_1's l2: 0.853783\n", "[300]\ttraining's l2: 0.715219\tvalid_1's l2: 0.853281\n", "[400]\ttraining's l2: 0.695522\tvalid_1's l2: 0.852848\n", "[500]\ttraining's l2: 0.678282\tvalid_1's l2: 0.852765\n", "[600]\ttraining's l2: 0.663339\tvalid_1's l2: 0.852824\n", "Early stopping, best iteration is:\n", "[518]\ttraining's l2: 0.675512\tvalid_1's l2: 0.852697\n", "Training fold 4\n", "Training with 2518985 rows\n", "Validating with 622425 rows\n", "Training light gradient boosting model with 400 features...\n", "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 11.574600 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 101992\n", "[LightGBM] [Info] Number of data points in the train set: 2518985, number of used features: 400\n", "[LightGBM] [Info] Start training from score -0.023439\n", "Training until validation scores don't improve for 100 rounds\n", "[100]\ttraining's l2: 0.774993\tvalid_1's l2: 0.858722\n", "[200]\ttraining's l2: 0.742971\tvalid_1's l2: 0.856598\n", "[300]\ttraining's l2: 0.718749\tvalid_1's l2: 0.855792\n", "[400]\ttraining's l2: 0.699152\tvalid_1's l2: 0.855201\n", "[500]\ttraining's l2: 0.682823\tvalid_1's l2: 0.855045\n", "Early stopping, best iteration is:\n", "[488]\ttraining's l2: 0.68466\tvalid_1's l2: 0.854948\n", "Training fold 5\n", "Training with 2525214 rows\n", "Validating with 616196 rows\n", "Training light gradient boosting model with 400 features...\n", "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 14.163910 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 101992\n", "[LightGBM] [Info] Number of data points in the train set: 2525214, number of used features: 400\n", "[LightGBM] [Info] Start training from score -0.023734\n", "Training until validation scores don't improve for 100 rounds\n", "[100]\ttraining's l2: 0.788566\tvalid_1's l2: 0.804017\n", "[200]\ttraining's l2: 0.753657\tvalid_1's l2: 0.803179\n", "[300]\ttraining's l2: 0.728069\tvalid_1's l2: 0.803128\n", "[400]\ttraining's l2: 0.707425\tvalid_1's l2: 0.803221\n", "Early stopping, best iteration is:\n", "[362]\ttraining's l2: 0.714593\tvalid_1's l2: 0.802897\n", "Our out of folds mean pearson correlation coefficient is 0.1394856781214322\n" ] } ], "source": [ "# Function to seed everything\n", "def seed_everything(seed):\n", " random.seed(seed)\n", " np.random.seed(seed)\n", " os.environ['PYTHONHASHSEED'] = str(seed)\n", " \n", "# Calculate pearson correlation coefficient\n", "def pearson_coef(data):\n", " return data.corr()['target']['prediction']\n", "\n", "# Calculate mean pearson correlation coefficient\n", "def comp_metric(valid_df):\n", " return np.mean(valid_df.groupby(['time_id']).apply(pearson_coef))\n", "\n", "# Function to train and evaluate\n", "def train_and_evaluate():\n", " # Seed everything\n", " seed_everything(MODEL_SEED)\n", " # Read data\n", " train = pd.read_pickle('../input/ubiquant-market-prediction-half-precision-pickle/train.pkl')\n", " # Feature list\n", " features = [col for col in train.columns if col not in ['row_id', 'time_id', 'investment_id', 'target']]\n", " # Some feature engineering\n", " # Get the correlations with the target to encode time_id\n", " corr1 = train[features[0:100] + ['target']].corr()['target'].reset_index()\n", " corr2 = train[features[100:200] + ['target']].corr()['target'].reset_index()\n", " corr3 = train[features[200:] + ['target']].corr()['target'].reset_index()\n", " corr = pd.concat([corr1, corr2, corr3], axis = 0, ignore_index = True)\n", " corr['target'] = abs(corr['target'])\n", " corr.sort_values('target', ascending = False, inplace = True)\n", " best_corr = corr.iloc[3:103, 0].to_list()\n", " del corr1, corr2, corr3, corr\n", " # Add time id related features (market general features to relate time_ids)\n", " time_id_features = []\n", " for col in tqdm(best_corr):\n", " mapper = train.groupby(['time_id'])[col].mean().to_dict()\n", " train[f'time_id_{col}'] = train['time_id'].map(mapper)\n", " train[f'time_id_{col}'] = train[f'time_id_{col}'].astype(np.float16)\n", " time_id_features.append(f'time_id_{col}')\n", " print(f'We added {len(time_id_features)} features related to time_id')\n", " # Update feature list\n", " features += time_id_features\n", " np.save('features.npy', np.array(features))\n", " np.save('best_corr.npy', np.array(best_corr))\n", " # Store out of folds predictions\n", " oof_predictions = np.zeros(len(train))\n", " # Initiate GroupKFold\n", " kfold = GroupKFold(n_splits = FOLDS)\n", " # Create groups based on time_id\n", " train.loc[(train['time_id'] >= 0) & (train['time_id'] < 280), 'group'] = 0\n", " train.loc[(train['time_id'] >= 280) & (train['time_id'] < 585), 'group'] = 1\n", " train.loc[(train['time_id'] >= 585) & (train['time_id'] < 825), 'group'] = 2\n", " train.loc[(train['time_id'] >= 825) & (train['time_id'] < 1030), 'group'] = 3\n", " train.loc[(train['time_id'] >= 1030) & (train['time_id'] < 1400), 'group'] = 4\n", " train['group'] = train['group'].astype(np.int16)\n", " #Lightgbm hyperparammeters\n", " params = {\n", " 'boosting_type': 'gbdt',\n", " 'metric': 'mse',\n", " 'objective': 'regression',\n", " 'n_jobs': -1,\n", " 'seed': MODEL_SEED,\n", " 'num_leaves': 200,\n", " 'learning_rate': LR,\n", " 'feature_fraction': 0.5,\n", " 'bagging_freq': 7,\n", " 'bagging_fraction': 0.80,\n", " 'lambda_l1': 2,\n", " 'lambda_l2': 4,\n", " }\n", " for fold, (trn_ind, val_ind) in enumerate(kfold.split(train, groups = train['group'])):\n", " print(f'Training fold {fold + 1}')\n", " x_train, x_val = train[features].loc[trn_ind], train[features].loc[val_ind]\n", " y_train, y_val = train['target'].loc[trn_ind], train['target'].loc[val_ind]\n", " n_training_rows = x_train.shape[0]\n", " n_validation_rows = x_val.shape[0]\n", " # Build lgbm dataset\n", " train_set, val_set = lgb.Dataset(x_train, y_train), lgb.Dataset(x_val, y_val)\n", " print(f'Training with {n_training_rows} rows')\n", " print(f'Validating with {n_validation_rows} rows')\n", " print(f'Training light gradient boosting model with {len(features)} features...')\n", " # Train and evaluate\n", " model = lgb.train(\n", " params, \n", " train_set, \n", " num_boost_round = 10000, \n", " early_stopping_rounds = 100, \n", " valid_sets = [train_set, val_set], \n", " verbose_eval = 100\n", " )\n", " # Predict validation set\n", " val_pred = model.predict(x_val)\n", " # Add validation prediction to out of folds array\n", " oof_predictions[val_ind] = val_pred\n", " # Save model to disk for inference\n", " joblib.dump(model, f'lgbm_{fold + 1}.pkl')\n", " del x_train, x_val, y_train, y_val, train_set, val_set\n", " gc.collect()\n", " # Compute out of folds Pearson Correlation Coefficient (for each time_id)\n", " oof_df = pd.DataFrame({'time_id': train['time_id'], 'target': train['target'], 'prediction': oof_predictions})\n", " score = comp_metric(oof_df)\n", " print(f'Our out of folds mean pearson correlation coefficient is {score}') \n", " \n", "train_and_evaluate()" ] }, { "cell_type": "code", "execution_count": null, "id": "21af164a", "metadata": { "papermill": { "duration": 0.023173, "end_time": "2022-01-28T15:44:37.282075", "exception": false, "start_time": "2022-01-28T15:44:37.258902", 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0086/400/86400177.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "id": "0c5bba76", "metadata": { "papermill": { "duration": 0.013502, "end_time": "2022-01-28T14:26:22.344124", "exception": false, "start_time": "2022-01-28T14:26:22.330622", "status": "completed" }, "tags": [] }, "source": [ "**THALES FILTERS**" ] }, { "cell_type": "code", "execution_count": 1, "id": "9e11a9b0", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:26:22.379699Z", "iopub.status.busy": "2022-01-28T14:26:22.379008Z", "iopub.status.idle": "2022-01-28T14:26:22.391497Z", "shell.execute_reply": "2022-01-28T14:26:22.392080Z", "shell.execute_reply.started": "2022-01-28T14:06:53.738774Z" }, "papermill": { "duration": 0.035035, "end_time": "2022-01-28T14:26:22.392412", "exception": false, "start_time": "2022-01-28T14:26:22.357377", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/images/boat.ppm\n" ] } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "31009c36", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:22.421519Z", "iopub.status.busy": "2022-01-28T14:26:22.420855Z", "iopub.status.idle": "2022-01-28T14:26:23.051698Z", "shell.execute_reply": "2022-01-28T14:26:23.052223Z", "shell.execute_reply.started": "2022-01-28T14:06:53.751182Z" }, "papermill": { "duration": 0.646865, "end_time": "2022-01-28T14:26:23.052423", "exception": false, "start_time": "2022-01-28T14:26:22.405558", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f064fb5a810>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import cv2\n", "import matplotlib.pyplot as plt\n", "img = cv2.imread(\"/kaggle/input/images/boat.ppm\")\n", "\n", "# opencv by default reads images in BGR rather than RGB\n", "img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n", "\n", "# Now, for small images like yours or any similar ones we use for example purpose to understand image processing operations or computer graphics\n", "# Using opencv's cv2.imshow()\n", "# Or google.colab.patches.cv2_imshow() [in case we are on Google Colab]\n", "# Would not be of much use as the output would be very small to visualize\n", "# Instead using matplotlib.pyplot.imshow() would give a decent visualization\n", "plt.imshow(img, cmap='gray')" ] }, { "cell_type": "markdown", "id": "e6a7848b", "metadata": { "papermill": { "duration": 0.014969, "end_time": "2022-01-28T14:26:23.083354", "exception": false, "start_time": "2022-01-28T14:26:23.068385", "status": "completed" }, "tags": [] }, "source": [ "**NOISE**" ] }, { "cell_type": "code", "execution_count": 3, "id": "d6d76c59", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:23.123465Z", "iopub.status.busy": "2022-01-28T14:26:23.122756Z", "iopub.status.idle": "2022-01-28T14:26:23.125431Z", "shell.execute_reply": "2022-01-28T14:26:23.124922Z", "shell.execute_reply.started": "2022-01-28T14:06:54.038574Z" }, "papermill": { "duration": 0.026299, "end_time": "2022-01-28T14:26:23.125586", "exception": false, "start_time": "2022-01-28T14:26:23.099287", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import copy\n", "import math\n", "import random\n", "\n", "def noise_gauss(image, sigma):\n", " s = image.shape\n", " noise_image = image.copy()\n", " for j in range(s[0]):\n", " for i in range(s[1]):\n", " v = int(math.floor(image[j][i] + random.gauss(0, sigma)))\n", " if v > 255:\n", " v = 255\n", " if v < 0:\n", " v = 0\n", " noise_image[j][i] = v\n", " return noise_image" ] }, { "cell_type": "code", "execution_count": 4, "id": "a2d53f94", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:23.159599Z", "iopub.status.busy": "2022-01-28T14:26:23.158935Z", "iopub.status.idle": "2022-01-28T14:26:29.080975Z", "shell.execute_reply": "2022-01-28T14:26:29.081500Z", "shell.execute_reply.started": "2022-01-28T14:06:54.049528Z" }, "papermill": { "duration": 5.940748, "end_time": "2022-01-28T14:26:29.081692", "exception": false, "start_time": "2022-01-28T14:26:23.140944", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "noise_image10 = noise_gauss(img, 10)\n", "plt.imshow(noise_image10, cmap = 'gray')\n", "cv2.imwrite('noise_gauss_10.png', noise_image10)\n", "noise_image20 = noise_gauss(img, 20)\n", "plt.imshow(noise_image20, cmap = 'gray')\n", "cv2.imwrite('noise_gauss_20.png', noise_image20)\n", "noise_image30 = noise_gauss(img, 30)\n", "plt.imshow(noise_image30, cmap = 'gray')\n", "cv2.imwrite('noise_gauss_30.png', noise_image30)" ] }, { "cell_type": "markdown", "id": "67987cc7", "metadata": { "papermill": { "duration": 0.018384, "end_time": "2022-01-28T14:26:29.119608", "exception": false, "start_time": "2022-01-28T14:26:29.101224", "status": "completed" }, "tags": [] }, "source": [ "**Bilateral Filter**" ] }, { "cell_type": "code", "execution_count": 5, "id": "9be0f51f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:29.166515Z", "iopub.status.busy": "2022-01-28T14:26:29.165753Z", "iopub.status.idle": "2022-01-28T14:26:29.170454Z", "shell.execute_reply": "2022-01-28T14:26:29.170991Z", "shell.execute_reply.started": "2022-01-28T14:07:00.012244Z" }, "papermill": { "duration": 0.032841, "end_time": "2022-01-28T14:26:29.171168", "exception": false, "start_time": "2022-01-28T14:26:29.138327", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[0.01831564, 0.082085 , 0.13533528, 0.082085 , 0.01831564],\n", " [0.082085 , 0.36787944, 0.60653066, 0.36787944, 0.082085 ],\n", " [0.13533528, 0.60653066, 1. , 0.60653066, 0.13533528],\n", " [0.082085 , 0.36787944, 0.60653066, 0.36787944, 0.082085 ],\n", " [0.01831564, 0.082085 , 0.13533528, 0.082085 , 0.01831564]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def gauss_kernel(d=5, sig=1.):\n", " \"\"\"\\\n", " creates gaussian kernel with side length `l` and a sigma of `sig`\n", " \"\"\"\n", " ax = np.linspace(-(d - 1) / 2., (d - 1) / 2., d)\n", " gauss = np.exp(-0.5 * np.square(ax) / np.square(sig))\n", " kernel = np.outer(gauss, gauss)\n", " return kernel\n", "\n", "gk = gauss_kernel()\n", "gk" ] }, { "cell_type": "code", "execution_count": 6, "id": "8cf8afca", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:29.212978Z", "iopub.status.busy": "2022-01-28T14:26:29.212286Z", "iopub.status.idle": "2022-01-28T14:26:29.223295Z", "shell.execute_reply": "2022-01-28T14:26:29.222761Z", "shell.execute_reply.started": "2022-01-28T14:07:00.026459Z" }, "papermill": { "duration": 0.033527, "end_time": "2022-01-28T14:26:29.223447", "exception": false, "start_time": "2022-01-28T14:26:29.189920", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[0. , 0. , 0. , 0. , 0. ],\n", " [0. , 0. , 0.60653066, 0. , 0. ],\n", " [0. , 0.60653066, 1. , 0.60653066, 0. ],\n", " [0. , 0. , 0.60653066, 0. , 0. ],\n", " [0. , 0. , 0. , 0. , 0. ]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def range_weight(sub_matrix, sigma_color):\n", " kernel = sub_matrix.copy()\n", " s = sub_matrix.shape\n", " p = sub_matrix[s[0]//2, s[0]//2]\n", " for j in range(s[0]):\n", " for i in range(s[1]):\n", " if abs(p - sub_matrix[i, j]) < sigma_color:\n", " kernel[i, j] = 1.\n", " else:\n", " kernel[i, j] = 0.\n", " return kernel\n", "\n", "rw = range_weight(gk, 0.40)\n", "rw * gk" ] }, { "cell_type": "code", "execution_count": 7, "id": "d478e5ea", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:29.393564Z", "iopub.status.busy": "2022-01-28T14:26:29.382775Z", "iopub.status.idle": "2022-01-28T14:26:59.625368Z", "shell.execute_reply": "2022-01-28T14:26:59.624768Z", "shell.execute_reply.started": "2022-01-28T14:07:00.047490Z" }, "papermill": { "duration": 30.382312, "end_time": "2022-01-28T14:26:59.625527", "exception": false, "start_time": "2022-01-28T14:26:29.243215", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#Filtre Bilatéral\n", "def bilateral_filter(im, d, sigma_space, sigma_color):\n", " f_im = im.copy()\n", " h = d//2\n", " \n", " # boucle for et padding\n", " for i in range(h, im.shape[1] - h):\n", " for j in range(h, im.shape[0] - h):\n", " mat = im[i - h : i + h + 1, j - h : j + h + 1].astype(np.float)\n", " gk = gauss_kernel(d)\n", " rw = range_weight(mat, sigma_color)\n", " prod = gk * rw * mat\n", " n_mat = np.sum(prod) / (np.sum(gk) - gk[h,h])\n", " \n", " f_im[i, j] = n_mat\n", "\n", " return f_im\n", "\n", "d, sigma_space, sigma_color = (5, 50, 50)\n", "denoised = bilateral_filter(noise_image30, d, sigma_space, sigma_color)" ] }, { "cell_type": "code", "execution_count": 8, "id": "2136d5fb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:59.687309Z", "iopub.status.busy": "2022-01-28T14:26:59.684279Z", "iopub.status.idle": "2022-01-28T14:26:59.908818Z", "shell.execute_reply": "2022-01-28T14:26:59.909522Z", "shell.execute_reply.started": "2022-01-28T14:07:36.666160Z" }, "papermill": { "duration": 0.264602, "end_time": "2022-01-28T14:26:59.909714", "exception": false, "start_time": "2022-01-28T14:26:59.645112", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f064f9e7b10>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.imshow(denoised, cmap = 'gray')" ] }, { "cell_type": "code", "execution_count": 9, "id": "7009c895", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:26:59.960395Z", "iopub.status.busy": "2022-01-28T14:26:59.959336Z", "iopub.status.idle": "2022-01-28T14:26:59.972776Z", "shell.execute_reply": "2022-01-28T14:26:59.973334Z", "shell.execute_reply.started": "2022-01-28T14:20:23.374140Z" }, "papermill": { "duration": 0.040438, "end_time": "2022-01-28T14:26:59.973522", "exception": false, "start_time": "2022-01-28T14:26:59.933084", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import math\n", "\n", "def distance(x, y, i, j):\n", " return np.sqrt((x-i)**2 + (y-j)**2)\n", "\n", "\n", "def gaussian(x, sigma):\n", " return (1.0 / (2 * math.pi * (sigma ** 2))) * math.exp(- (x ** 2) / (2 * sigma ** 2))\n", "\n", "\n", "def apply_bilateral_filter(source, filtered_image, x, y, diameter, sigma_i, sigma_s):\n", " hl = diameter//2\n", " i_filtered = 0\n", " Wp = 0\n", " i = 0\n", " while i < diameter:\n", " j = 0\n", " while j < diameter:\n", " neighbour_x = x - (hl - i)\n", " neighbour_y = y - (hl - j)\n", " if neighbour_x >= len(source):\n", " neighbour_x -= len(source)\n", " if neighbour_y >= len(source[0]):\n", " neighbour_y -= len(source[0])\n", " gi = gaussian(source[neighbour_x][neighbour_y] - source[x][y], sigma_i)\n", " gs = gaussian(distance(neighbour_x, neighbour_y, x, y), sigma_s)\n", " w = int(gi) * int(gs)\n", " i_filtered += int(source[neighbour_x][neighbour_y]) * w\n", " Wp += w\n", " j += 1\n", " i += 1\n", " i_filtered = i_filtered / 1\n", " filtered_image[x][y] = int(round(i_filtered))\n", "\n", "\n", "def bilateral_filter_own(source, filter_diameter, sigma_i, sigma_s):\n", " filtered_image = np.zeros(source.shape)\n", "\n", " i = 0\n", " while i < len(source):\n", " j = 0\n", " while j < len(source[0]):\n", " apply_bilateral_filter(source, filtered_image, i, j, filter_diameter, sigma_i, sigma_s)\n", " j += 1\n", " i += 1\n", " return " ] }, { "cell_type": "code", "execution_count": 10, "id": "828b57a8", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:00.024533Z", "iopub.status.busy": "2022-01-28T14:27:00.023822Z", "iopub.status.idle": "2022-01-28T14:27:00.256003Z", "shell.execute_reply": "2022-01-28T14:27:00.256525Z", "shell.execute_reply.started": "2022-01-28T14:20:23.389474Z" }, "papermill": { "duration": 0.260697, "end_time": "2022-01-28T14:27:00.256713", "exception": false, "start_time": "2022-01-28T14:26:59.996016", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "img = cv2.imread(\"/kaggle/working/./noise_gauss_30.png\")\n", "\n", "plt.imshow(img[:,:,0], cmap = 'gray')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "id": "f230c7e0", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:00.311747Z", "iopub.status.busy": "2022-01-28T14:27:00.311030Z", "iopub.status.idle": "2022-01-28T14:27:00.317907Z", "shell.execute_reply": "2022-01-28T14:27:00.317312Z", "shell.execute_reply.started": "2022-01-28T14:20:23.646888Z" }, "papermill": { "duration": 0.035735, "end_time": "2022-01-28T14:27:00.318054", "exception": false, "start_time": "2022-01-28T14:27:00.282319", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[106, 198, 149, ..., 174, 174, 198],\n", " [170, 165, 183, ..., 132, 154, 116],\n", " [121, 177, 82, ..., 127, 149, 117],\n", " ...,\n", " [ 70, 141, 128, ..., 120, 160, 124],\n", " [181, 112, 168, ..., 152, 148, 146],\n", " [ 76, 166, 122, ..., 127, 186, 154]], dtype=uint8)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img[:,:,0]" ] }, { "cell_type": "code", "execution_count": 12, "id": "87ebb7f1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:00.395164Z", "iopub.status.busy": "2022-01-28T14:27:00.373727Z", "iopub.status.idle": "2022-01-28T14:29:12.657246Z", "shell.execute_reply": "2022-01-28T14:29:12.656234Z", "shell.execute_reply.started": "2022-01-28T14:20:23.656894Z" }, "papermill": { "duration": 132.313773, "end_time": "2022-01-28T14:29:12.657452", "exception": false, "start_time": "2022-01-28T14:27:00.343679", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:25: RuntimeWarning: overflow encountered in ubyte_scalars\n" ] } ], "source": [ "d, sigma_space, sigma_color = (5, 12., 16.)\n", "denoised = bilateral_filter_own(img[:,:,0], d, sigma_color, sigma_space)\n", "denoised" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 181.507565, "end_time": "2022-01-28T14:29:13.396772", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:26:11.889207", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/400/86400190.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1a68a93e", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:27:35.764105Z", "iopub.status.busy": "2022-01-28T14:27:35.763222Z", "iopub.status.idle": "2022-01-28T14:27:35.774745Z", "shell.execute_reply": "2022-01-28T14:27:35.774016Z", "shell.execute_reply.started": "2022-01-28T14:25:42.275187Z" }, "papermill": { "duration": 0.050931, "end_time": "2022-01-28T14:27:35.774926", "exception": false, "start_time": "2022-01-28T14:27:35.723995", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/bank-note-authentication-uci-data/BankNote_Authentication.csv\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" ] }, { "cell_type": "markdown", "id": "3d068570", "metadata": { "papermill": { "duration": 0.023684, "end_time": "2022-01-28T14:27:35.824033", "exception": false, "start_time": "2022-01-28T14:27:35.800349", "status": "completed" }, "tags": [] }, "source": [ "## Importing Data" ] }, { "cell_type": "code", "execution_count": 2, "id": "75b0f7f2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:35.877502Z", "iopub.status.busy": "2022-01-28T14:27:35.876797Z", "iopub.status.idle": "2022-01-28T14:27:35.931908Z", "shell.execute_reply": "2022-01-28T14:27:35.932615Z", "shell.execute_reply.started": "2022-01-28T14:25:42.290624Z" }, "papermill": { "duration": 0.085226, "end_time": "2022-01-28T14:27:35.932825", "exception": false, "start_time": "2022-01-28T14:27:35.847599", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " variance skewness curtosis entropy class\n", "0 3.62160 8.6661 -2.8073 -0.44699 0\n", "1 4.54590 8.1674 -2.4586 -1.46210 0\n", "2 3.86600 -2.6383 1.9242 0.10645 0\n", "3 3.45660 9.5228 -4.0112 -3.59440 0\n", "4 0.32924 -4.4552 4.5718 -0.98880 0\n", "Dimension : (1372, 5) \n", "\n", " variance skewness curtosis entropy class\n", "count 1372.000000 1372.000000 1372.000000 1372.000000 1372.000000\n", "mean 0.433735 1.922353 1.397627 -1.191657 0.444606\n", "std 2.842763 5.869047 4.310030 2.101013 0.497103\n", "min -7.042100 -13.773100 -5.286100 -8.548200 0.000000\n", "25% -1.773000 -1.708200 -1.574975 -2.413450 0.000000\n", "50% 0.496180 2.319650 0.616630 -0.586650 0.000000\n", "75% 2.821475 6.814625 3.179250 0.394810 1.000000\n", "max 6.824800 12.951600 17.927400 2.449500 1.000000\n" ] } ], "source": [ "# importing csv data into pandas dataframe\n", "data=pd.read_csv(\"/kaggle/input/bank-note-authentication-uci-data/BankNote_Authentication.csv\")\n", "\n", "# printing first few data\n", "print(data.head())\n", "\n", "# printing dimension of data\n", "print('Dimension :',data.shape,'\\n')\n", "\n", "# printing statistical summary of data\n", "print(data.describe())" ] }, { "cell_type": "code", "execution_count": 3, "id": "e2a24587", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:35.987560Z", "iopub.status.busy": "2022-01-28T14:27:35.986925Z", "iopub.status.idle": "2022-01-28T14:27:35.994008Z", "shell.execute_reply": "2022-01-28T14:27:35.994528Z", "shell.execute_reply.started": "2022-01-28T14:25:42.329291Z" }, "papermill": { "duration": 0.037341, "end_time": "2022-01-28T14:27:35.994698", "exception": false, "start_time": "2022-01-28T14:27:35.957357", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NA values\n", "--------------\n", "variance => 0\n", "skewness => 0\n", "curtosis => 0\n", "entropy => 0\n", "class => 0\n" ] } ], "source": [ "# checking for NA values\n", "\n", "cols=data.columns\n", "\n", "print(\"NA values\")\n", "print(\"--------------\")\n", "for col in cols:\n", " print(col,'=>',sum(data[col].isna()))" ] }, { "cell_type": "code", "execution_count": 4, "id": "06a4b055", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:36.050974Z", "iopub.status.busy": "2022-01-28T14:27:36.050125Z", "iopub.status.idle": "2022-01-28T14:27:36.053642Z", "shell.execute_reply": "2022-01-28T14:27:36.053004Z", "shell.execute_reply.started": "2022-01-28T14:25:42.340119Z" }, "papermill": { "duration": 0.034565, "end_time": "2022-01-28T14:27:36.053795", "exception": false, "start_time": "2022-01-28T14:27:36.019230", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# feature and target selection\n", "\n", "X=data.iloc[:,:-1] # all columns except last as features\n", "y=data.iloc[:,-1:] # last column as target" ] }, { "cell_type": "markdown", "id": "9b18722f", "metadata": { "papermill": { "duration": 0.024893, "end_time": "2022-01-28T14:27:36.104304", "exception": false, "start_time": "2022-01-28T14:27:36.079411", "status": "completed" }, "tags": [] }, "source": [ "## Data split" ] }, { "cell_type": "code", "execution_count": 5, "id": "62913348", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:36.159100Z", "iopub.status.busy": "2022-01-28T14:27:36.158456Z", "iopub.status.idle": "2022-01-28T14:27:37.338318Z", "shell.execute_reply": "2022-01-28T14:27:37.337358Z", "shell.execute_reply.started": "2022-01-28T14:25:42.346865Z" }, "papermill": { "duration": 1.209166, "end_time": "2022-01-28T14:27:37.338463", "exception": false, "start_time": "2022-01-28T14:27:36.129297", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# spliting 80% data as training data and remaining 20% data as testing data\n", "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=4)" ] }, { "cell_type": "markdown", "id": "4881656f", "metadata": { "papermill": { "duration": 0.024374, "end_time": "2022-01-28T14:27:37.387350", "exception": false, "start_time": "2022-01-28T14:27:37.362976", "status": "completed" }, "tags": [] }, "source": [ "# 1. K-Nearest Neighbor Classifiaction\n", "\n", "***" ] }, { "cell_type": "markdown", "id": "33c5dc6a", "metadata": { "papermill": { "duration": 0.024664, "end_time": "2022-01-28T14:27:37.438232", "exception": false, "start_time": "2022-01-28T14:27:37.413568", "status": "completed" }, "tags": [] }, "source": [ "> ## *fitting*" ] }, { "cell_type": "code", "execution_count": 6, "id": "60244197", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:37.496070Z", "iopub.status.busy": "2022-01-28T14:27:37.495295Z", "iopub.status.idle": "2022-01-28T14:27:37.670718Z", "shell.execute_reply": "2022-01-28T14:27:37.671270Z", "shell.execute_reply.started": "2022-01-28T14:25:42.798279Z" }, "papermill": { "duration": 0.207337, "end_time": "2022-01-28T14:27:37.671454", "exception": false, "start_time": "2022-01-28T14:27:37.464117", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " after removing the cwd from sys.path.\n" ] } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "knn=KNeighborsClassifier()\n", "knn=knn.fit(X_train,y_train)" ] }, { "cell_type": "markdown", "id": "eee8d412", "metadata": { "papermill": { "duration": 0.025363, "end_time": "2022-01-28T14:27:37.722737", "exception": false, "start_time": "2022-01-28T14:27:37.697374", "status": "completed" }, "tags": [] }, "source": [ "> ## prediction" ] }, { "cell_type": "code", "execution_count": 7, "id": "ef956bd2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:37.781730Z", "iopub.status.busy": "2022-01-28T14:27:37.779754Z", "iopub.status.idle": "2022-01-28T14:27:37.795769Z", "shell.execute_reply": "2022-01-28T14:27:37.795119Z", "shell.execute_reply.started": "2022-01-28T14:25:42.842269Z" }, "papermill": { "duration": 0.048061, "end_time": "2022-01-28T14:27:37.795906", "exception": false, "start_time": "2022-01-28T14:27:37.747845", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# predict the testing data\n", "\n", "y_pred=knn.predict(X_test)" ] }, { "cell_type": "markdown", "id": "1d5b2010", "metadata": { "papermill": { "duration": 0.025296, "end_time": "2022-01-28T14:27:37.846647", "exception": false, "start_time": "2022-01-28T14:27:37.821351", "status": "completed" }, "tags": [] }, "source": [ "> ## Accuracy" ] }, { "cell_type": "code", "execution_count": 8, "id": "f449d6cc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:37.906041Z", "iopub.status.busy": "2022-01-28T14:27:37.905231Z", "iopub.status.idle": "2022-01-28T14:27:38.177279Z", "shell.execute_reply": "2022-01-28T14:27:38.176732Z", "shell.execute_reply.started": "2022-01-28T14:25:42.858933Z" }, "papermill": { "duration": 0.305301, "end_time": "2022-01-28T14:27:38.177422", "exception": false, "start_time": "2022-01-28T14:27:37.872121", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of our trained model is 1.0\n" ] }, { "data": { "image/png": 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biLha0kMk/c4EHB4RT+YemZlZZ3pTy1bSrsB64JbSbRHxfJ6BmZl1qjclW+A/2Tzx4wCSZ4eXAh/KMS4zs071qpptRPxN6Xo6GtjJuUVkZtYLlf0EWUQ8LGnfPIIxMytLb2rZSvqnktU6YC9gZW4RmZll0dt6IwA7lLxuIqnh3pRPOGZmZegtLdv0YYYdIuKMKsVjZpaJ6CU3yFoHzJU0tZoBmZll1huSLfAgSX12kaQ5JNNEvNW6MyJ+k3NsZmbt60EjemWRpWY7AHiFZM6x1v62ATjZmllt9ZIbZDunPREeZ3OSbVWg3ydm1lv1lpZtPTCILZNsqwJ9RDPrtQqUiTpKtqsi4rtVi8TMrByVnV0XSUOBS4E90jN/mWRoguuBccBzwJER8WpXzt/RTA09Y3hzM7N2VHhanJ8At0XEB4A9gSeBM4G5ETEBmJuud0lHyfbArp7UzKwqKjSVuaQhwCdJZ8+NiMaIWAfMAGanh80GDu9qqO0m24hY29WTmplVQxlTmQ+XtLBkmbXVqcYDLwO/lPSIpEslbQ+MjIhV6TGrgZFdjdVTmZtZMZVXs10TEVM62N+H5LmCUyNivqSfsFXJICJC6nr/hyyz65qZ9TgqY8lgBbAiIuan6zeSJN+/ShoFkP75UlfjdbI1s+KqUM02IlYDL0iamG46EFgCzAFmpttmAjd3NVSXEcyssCr8UMOpwNWS+gHPAMeTNEhvkHQCsBw4sqsnd7I1s+KqYLKNiEVAW3XdivTMcrI1s2LqhYOHm5n1TL3kcV0zsx6ttwxEY2bWsznZmpnlzy1bM7O8Bb1m8HAzsx6r10z4aGbW4znZmpnlT1GcbOtka2bFVOGZGvLmZGtmheWarZlZFfhxXTOzanDL1swsZ+VN5lhzTrZmVlxOtmZm+fJDDWZmVaKW4mRbJ1szKyb3s7WeaMq01znpeyuprwtuvXYYN1w4stYhbfPePP8tGu/bRN2OYuivhgCwcV4jb1/2Ns3LWxhyyQ70+WDyI7rx9o28fc3Gd97b/Jdmhly+A33ev23/CBep61ehZ9eVdKmkSbWOo6erqwtOOf9Fzj52PCdOm8gBM9ax64QNtQ5rm9f/M/0Y/G+DtthWv3s9O5w/iD6Tt0yi/Q/pz9DZgxk6ezCDvrM9daPqtvlEC1Rsdl0ASfWSHpH0H+n6eEnzJS2TdH06EWSXFTrZRsRXImJJrePo6SZ+ZD0rn+vH6uf707SpjrtuHsr+h7xW67C2eX0n90WDtcW2PuPqqd+tvsP3Nd7ZSP+DuvVz32sosi0ZfR14smT9h8AFEfE+4FXghO7EmmuylfQvkpZKulfStZLOkPReSbdJekjSPZI+kB57haSfSvqTpGckHZFun9b6myZdv1DScenruyRNSV+/Ken7kh6V9ICkken2EZJukrQgXabm+Zl7op3es4mXV27+4Vyzqi/DR22qYUTWHRvnNtLv0062Sas1si2dkDQG+Dvg0nRdwKeAG9NDZgOHdyfc3JKtpI8C/wDsCRzK5imCG4BTI2Jv4AzgopK3jQI+DhwG/KDMS24PPBARewJ3Ayem239C8tupNZ5L24l3lqSFkhZuYmNbh5jV3KYnmtAA6LN7x63fbYVasi3A8Naf73SZtdWp/h34JpuHI98JWBcRTen6CmB0d2LNs+gzFbg5IjYAGyTdAgwAPgb8OvnFAUD/kvf8LiJagCWtLdMyNAKtLeCHgE+nrw8CJpVcb7CkQRHxZumbI6KB5BcBgzWsQPc4O/fK6r6M2KXxnfXhozaxZlXfGkZkXdX4h0b6uYQAlN3Pdk1ETGlrh6TDgJci4iFJ0yoSXBuqXWGvI/ltMbmd/aVNytbs2MSWLfAB7bx3U8Q73xea2fzZ6oD90qS/TVq6aCCjxzcycuxGXlndl2kz1vGDU3ardVhWpmgJNs5rZMhFO9Q6lJ4hY4kgg6nAZyV9hiS/DCb5RjxUUp+0dTsGeLE7F8mzZnsf8PeSBkgaRFIaWA88K+nzkNRFJO3ZyXmWk7RM+0saChxYZhx3AKe2rkiaXOb7C6+lWfzs26M5/5pnuOS/lnL3LUNZ/lR7v7OsWt44501e++obND/fwquHr2PDLRvZ+F+NvHr4Opoeb+L1//0mr5/+xjvHNy1qon7nOupHu4TQqhI3yCLirIgYExHjgKOBeRFxLPBH4Ij0sJnAzd2JNbeWbUQskDQHWAz8FXgMeA04FrhY0tlAX+A64NEOzvOCpBuAx4FngUfKDOU04GeSFpN83ruBk8o8R+EtmDeYBfMG1zoMK7HDeYPa3N7/b9suE/Tdqy9DLnH5Zwv5Fvz+GbhO0v8hyTuXdedkeZcRfhQR50oaSJLkHoqIZ4HpWx8YEcdttT6o5PU3SYrXW79nWjvH30h6FzEi1gBHdfeDmFnPU+mxESLiLuCu9PUzwD6VOnfeybYhfehgADA7Ih7O+Xpmtq0IoLk497JzTbYR8YU8z29m2zaP+mVmVg2eXdfMLH9u2ZqZ5c1DLJqZ5U+AfIPMzCx/cs3WzCxnLiOYmVVDxcZGqAonWzMrLPdGMDOrBrdszcxyFu6NYGZWHcXJtU62ZlZc7vplZlYNTrZmZjkLNk/PWABOtmZWSCJcRjAzq4qW4jRt85zw0cwsP61lhCxLJySNlfRHSUskPSHp6+n2YZLulPR0+ueOXQ3XydbMCksRmZYMmoBvRMQkYD/glHRKrzOBuRExAZibrneJk62ZFVdEtqXT08Sq1jkSI+IN4ElgNDADmJ0eNhs4vKuhumZrZgVV1kA0wyUtLFlviIiGtg6UNA74CDAfGBkRq9Jdq4GRXQzWydbMCqq82XXXRMSUzg6SNAi4CfhfEfG6pM2Xiwip60PfuIxgZoVVwZotkvqSJNqrI+I36ea/ShqV7h8FvNTVWJ1szay4KlSzVdKEvQx4MiL+rWTXHGBm+nomcHNXQ3UZwcyKKYCWij3UMBX4EvCYpEXptm8BPwBukHQCsBw4sqsXcLI1s4Kq3EwNEXEvyRySbTmwEtdwsjWz4vLjumZmOQuguTiP6zrZmllBBYSTrZlZ/lxGMDPLWWV7I+TOydbMisstWzOzKnCyNTPLWQQ0N9c6isycbM2suNyyNTOrAidbM7O8hXsjmJnlLiD8UIOZWRX4cV0zs5xFFGoqcydbMysu3yAzM8tfuGVrZpa3yg0eXg1OtmZWTB6IxswsfwFEgR7X9ey6ZlZMkQ4enmXJQNJ0SUslLZN0ZqXDdcvWzAorKlRGkFQP/Az4NLACWCBpTkQsqcgFcMvWzIqsci3bfYBlEfFMRDQC1wEzKhmqokB386pF0sskc8T3NsOBNbUOwsrSW//NdouIEd05gaTbSP5+shgAbChZb4iIhpJzHQFMj4ivpOtfAvaNiK91J8ZSLiO0obv/CXoqSQsjYkqt47Ds/G/WvoiYXusYyuEygpkZvAiMLVkfk26rGCdbMzNYAEyQNF5SP+BoYE4lL+AywralofNDrIfxv1kVRESTpK8BtwP1wOUR8UQlr+EbZGZmVeAygplZFTjZmplVgZNtwUg6TdKTkq6u0vWOk3RhNa5l2Ui6VNKkWsdh5fENsuI5GTgoIlZ0dqCkPhHRVIWYrIpaO95bsbhlWyCSfg7sDtwq6RuSfidpsaQHJH04PeZcSVdJug+4StIISXdKeiJtES2XNDw99ouSHpS0SNIv0ufDkXS8pKckPQhMrdXnLSpJ/5IOaHKvpGslnSHpvZJuk/SQpHskfSA99gpJP5X0J0nPpE8yIWmapP8oOeeFko5LX98laUr6+k1J35f0aPr/YGS6fYSkmyQtSBf/O9aYk22BRMRJwErgAGAc8EhEfBj4FnBlyaGTSFq/xwDnAPMi4kPAjcCuAJI+CBwFTI2IyUAzcKykUcB5JEn24+m5LCNJHwX+AdgTOBRoffqrATg1IvYGzgAuKnnbKJK/68OAH5R5ye2BByJiT+Bu4MR0+0+ACyKiNZ5Ly/80VkkuIxTXx0l+iIiIeZJ2kjQ43TcnIt4uOe5z6XG3SXo13X4gsDfJ6EYA2wEvAfsCd0XEywCSrgfeX4XP01tMBW6OiA3ABkm3kDyX/zHg1+nfNUD/kvf8LpI5uZe0tkzL0Ai0toAfIhm1CuAgYFLJ9QZLGhQRb5Z5fqsQJ9ve6a0MxwiYHRFnbbFROjyXiLZtdcC69BtEWzaWvG7Njk1s+c1zQDvv3RSbO8s3s/lnug7YL0361gO4jFBc9wDHQlLfA9ZExOttHHcfcGR63MHAjun2ucARknZO9w2TtBswH/jbtKXcF/h8nh+iF7oP+HtJAyQNIikNrAeelfR5ACX27OQ8y0lapv0lDSX5JlKOO4BTW1ckTS7z/VZhTrbFdS6wt6TFJHW+me0cdx5wsKTHSRLnauCNdFDks4E70nPcCYyKiFXpue8nSRxP5vkhepuIWEDyTP1i4FbgMeA1kl+MJ0h6FHiCTsZKjYgXgBuAx9M/HykzlNOAKekN1CXASWW+3yrMj+v2cpL6A83ps9/7Axd38HXWKqC1NippIMlNq1kR8XCt47Lacs2299sVuEFSHcnNlBM7Od66ryF96GAASV3cidbcsjUzqwbXbM3MqsDJ1sysCpxszcyqwMnWyiapOR1P4XFJv07vunf1XFeUjAfQ4WhW6XgBH+vCNZ5rHQ8iy/atjinriat0bIozyo3Rej8nW+uKtyNickTsQdLDYYs+nJK61MslIr6S9v9tzzSSx17NCsfJ1rrrHuB9aavzHklzSJ7xr5f0r+mIU4slfRXeeXrqwnRUrD8AO7eeaKvRrKZLejgdzWqupHEkSf30tFX9ifZGtkqffrtD6UhnbH4Etl1KRlB7KH3PrK32XZBunytpRLqtzVG8zNrjfrbWZWkL9lDgtnTTXsAeEfFsmrBei4iPpg9W3CfpDuAjwESS0cRGAkuAy7c67wjgEuCT6bmGRcRaJUNMvhkRP0qPu4ZkZKt7Je1KMlnfB0lGOrs3Ir4r6e+AEzJ8nC+n19iOZHCemyLiFZJRtRZGxOmSvpOe+2sko3idFBFPS9qXZBSvT3Xhr9G2EU621hXbSVqUvr4HuIzk6/2DEfFsuv1g4MOt9VhgCDAB+CRwbUQ0AyslzWvj/PsBd7eeKyLWthNHmyNbpdf4H+l7/7NkpLOOnCbpc+nrsWmsrwAtwPXp9l8Bv0mv0dEoXmbv4mRrXfH21o/8pkmndLQxkYzfevtWx32mgnG0ObJVSQLMJB3I5yBg/4hYL+ku2h9lK+h8FC+zd3HN1vJyO/CP6chhSHq/pO1Jxgo4Kq3pjiIZCH1rDwCflDQ+fe+wdPsbwA4lx7U3stXdwBfSbYeyeaSz9gwBXk0T7QdIWtat6oDW1vkXSMoTr1P+KF62jXOytbxcSlKPfTgdcewXJN+kfgs8ne67kmR0sS2kA5fPIvnK/iibv8bfAnyu9QYZ7Y9sdR5Jsn6CpJzwfCex3gb0kfQkyQhqD5TsewvYJ/0MnwK+m24vaxQvM4+NYGZWBW7ZmplVgZOtmVkVONmamVWBk62ZWRU42ZqZVYGTrZlZFTjZmplVwX8DfErt1lkC3R8AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn import metrics\n", "\n", "labels=['forged','genuine']\n", "\n", "# ploting confusion matrix\n", "metrics.plot_confusion_matrix(knn,X_test,y_test,display_labels=labels)\n", "\n", "# printing accuracy score\n", "accuracy_score=metrics.accuracy_score(y_test,y_pred)\n", "print(\"Accuracy of our trained model is\",accuracy_score)" ] }, { "cell_type": "markdown", "id": "64371e79", "metadata": { "papermill": { "duration": 0.025532, "end_time": "2022-01-28T14:27:38.228949", "exception": false, "start_time": "2022-01-28T14:27:38.203417", "status": "completed" }, "tags": [] }, "source": [ "# 2. Decision Tree Classifiaction\n", "\n", "***" ] }, { "cell_type": "markdown", "id": "d65f57b0", "metadata": { "papermill": { "duration": 0.024313, "end_time": "2022-01-28T14:27:38.277375", "exception": false, "start_time": "2022-01-28T14:27:38.253062", "status": "completed" }, "tags": [] }, "source": [ "> ## fitting" ] }, { "cell_type": "code", "execution_count": 9, "id": "e780f91c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:38.332580Z", "iopub.status.busy": "2022-01-28T14:27:38.331713Z", "iopub.status.idle": "2022-01-28T14:27:38.370258Z", "shell.execute_reply": "2022-01-28T14:27:38.370775Z", "shell.execute_reply.started": "2022-01-28T14:25:43.110492Z" }, "papermill": { "duration": 0.067918, "end_time": "2022-01-28T14:27:38.370948", "exception": false, "start_time": "2022-01-28T14:27:38.303030", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn import tree\n", "\n", "clf=tree.DecisionTreeClassifier()\n", "clf=clf.fit(X_train,y_train)" ] }, { "cell_type": "markdown", "id": "fb3e6d0d", "metadata": { "papermill": { "duration": 0.027215, "end_time": "2022-01-28T14:27:38.424417", "exception": false, "start_time": "2022-01-28T14:27:38.397202", "status": "completed" }, "tags": [] }, "source": [ "> ## prediction" ] }, { "cell_type": "code", "execution_count": 10, "id": "db11f46a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:38.487726Z", "iopub.status.busy": "2022-01-28T14:27:38.486859Z", "iopub.status.idle": "2022-01-28T14:27:38.488442Z", "shell.execute_reply": "2022-01-28T14:27:38.489016Z", "shell.execute_reply.started": "2022-01-28T14:25:43.128260Z" }, "papermill": { "duration": 0.037872, "end_time": "2022-01-28T14:27:38.489195", "exception": false, "start_time": "2022-01-28T14:27:38.451323", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# predict the testing data\n", "\n", "y_pred=clf.predict(X_test)" ] }, { "cell_type": "markdown", "id": "1796019f", "metadata": { "papermill": { "duration": 0.027132, "end_time": "2022-01-28T14:27:38.542386", "exception": false, "start_time": "2022-01-28T14:27:38.515254", "status": "completed" }, "tags": [] }, "source": [ "> ## Accuracy" ] }, { "cell_type": "code", "execution_count": 11, "id": "c2de7b6c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:38.598634Z", "iopub.status.busy": "2022-01-28T14:27:38.597940Z", "iopub.status.idle": "2022-01-28T14:27:38.810641Z", "shell.execute_reply": "2022-01-28T14:27:38.811124Z", "shell.execute_reply.started": "2022-01-28T14:25:43.133956Z" }, "papermill": { "duration": 0.242394, "end_time": "2022-01-28T14:27:38.811309", "exception": false, "start_time": "2022-01-28T14:27:38.568915", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of our trained model is 0.9963636363636363\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn import metrics\n", "\n", "labels=['forged','genuine']\n", "\n", "# ploting confusion matrix\n", "metrics.plot_confusion_matrix(clf,X_test,y_test,display_labels=labels)\n", "\n", "# printing accuracy score\n", "accuracy_score=metrics.accuracy_score(y_test,y_pred)\n", "print(\"Accuracy of our trained model is \",accuracy_score)" ] }, { "cell_type": "markdown", "id": "875d3064", "metadata": { "papermill": { "duration": 0.027999, "end_time": "2022-01-28T14:27:38.866962", "exception": false, "start_time": "2022-01-28T14:27:38.838963", "status": "completed" }, "tags": [] }, "source": [ "# 3. Gaussian Naive Bayes Classifier\n", "\n", "***" ] }, { "cell_type": "markdown", "id": "b74621c1", "metadata": { "papermill": { "duration": 0.028851, "end_time": "2022-01-28T14:27:38.923854", "exception": false, "start_time": "2022-01-28T14:27:38.895003", "status": "completed" }, "tags": [] }, "source": [ "> ## fitting" ] }, { "cell_type": "code", "execution_count": 12, "id": "24066b12", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:38.985051Z", "iopub.status.busy": "2022-01-28T14:27:38.984292Z", "iopub.status.idle": "2022-01-28T14:27:38.997112Z", "shell.execute_reply": "2022-01-28T14:27:38.996463Z", "shell.execute_reply.started": "2022-01-28T14:25:43.360696Z" }, "papermill": { "duration": 0.045205, "end_time": "2022-01-28T14:27:38.997242", "exception": false, "start_time": "2022-01-28T14:27:38.952037", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " return f(**kwargs)\n" ] } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "clf = GaussianNB()\n", "clf=clf.fit(X_train,y_train)" ] }, { "cell_type": "markdown", "id": "3c3937d7", "metadata": { "papermill": { "duration": 0.026965, "end_time": "2022-01-28T14:27:39.051651", "exception": false, "start_time": "2022-01-28T14:27:39.024686", "status": "completed" }, "tags": [] }, "source": [ "> ## prediction" ] }, { "cell_type": "code", "execution_count": 13, "id": "25967df0", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:39.116144Z", "iopub.status.busy": "2022-01-28T14:27:39.115433Z", "iopub.status.idle": "2022-01-28T14:27:39.118624Z", "shell.execute_reply": "2022-01-28T14:27:39.118093Z", "shell.execute_reply.started": "2022-01-28T14:25:43.373734Z" }, "papermill": { "duration": 0.039035, "end_time": "2022-01-28T14:27:39.118761", "exception": false, "start_time": "2022-01-28T14:27:39.079726", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# predict the testing data\n", "\n", "y_pred=clf.predict(X_test)" ] }, { "cell_type": "markdown", "id": "14bb12a4", "metadata": { "papermill": { "duration": 0.027482, "end_time": "2022-01-28T14:27:39.174896", "exception": false, "start_time": "2022-01-28T14:27:39.147414", "status": "completed" }, "tags": [] }, "source": [ "> ## Accuracy" ] }, { "cell_type": "code", "execution_count": 14, "id": "0085d67e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:27:39.239841Z", "iopub.status.busy": "2022-01-28T14:27:39.239124Z", "iopub.status.idle": "2022-01-28T14:27:39.444588Z", "shell.execute_reply": "2022-01-28T14:27:39.445109Z", "shell.execute_reply.started": "2022-01-28T14:25:43.380245Z" }, "papermill": { "duration": 0.242618, "end_time": "2022-01-28T14:27:39.445276", "exception": false, "start_time": "2022-01-28T14:27:39.202658", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of our trained model is 0.850909090909091\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn import metrics\n", "\n", "labels=['forged','genuine']\n", "\n", "# ploting confusion matrix\n", "metrics.plot_confusion_matrix(clf,X_test,y_test,display_labels=labels)\n", "\n", "# printing accuracy score\n", "accuracy_score=metrics.accuracy_score(y_test,y_pred)\n", "print(\"Accuracy of our trained model is \",accuracy_score)" ] }, { "cell_type": "markdown", "id": "f36915a8", "metadata": { "papermill": { "duration": 0.027785, "end_time": "2022-01-28T14:27:39.501684", "exception": false, "start_time": "2022-01-28T14:27:39.473899", "status": "completed" }, "tags": [] }, "source": [ "***\n", "\n", "***" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 16.018164, "end_time": "2022-01-28T14:27:40.544374", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:27:24.526210", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/400/86400275.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "id": "8fb1fb11", "metadata": { "papermill": { "duration": 0.030559, "end_time": "2022-01-28T14:33:39.668276", "exception": false, "start_time": "2022-01-28T14:33:39.637717", "status": "completed" }, "tags": [] }, "source": [ "# WINE CLASSIFICATION" ] }, { "cell_type": "code", "execution_count": 1, "id": "a1b63595", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:39.735588Z", "iopub.status.busy": "2022-01-28T14:33:39.733739Z", "iopub.status.idle": "2022-01-28T14:33:39.758729Z", "shell.execute_reply": "2022-01-28T14:33:39.757575Z", "shell.execute_reply.started": "2022-01-28T14:27:43.924157Z" }, "papermill": { "duration": 0.060378, "end_time": "2022-01-28T14:33:39.759033", "exception": false, "start_time": "2022-01-28T14:33:39.698655", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" ] }, { "cell_type": "markdown", "id": "de4d550f", "metadata": { "papermill": { "duration": 0.033715, "end_time": "2022-01-28T14:33:39.824620", "exception": false, "start_time": "2022-01-28T14:33:39.790905", "status": "completed" }, "tags": [] }, "source": [ "## Context" ] }, { "cell_type": "markdown", "id": "03ad53fa", "metadata": { "papermill": { "duration": 0.030361, "end_time": "2022-01-28T14:33:39.884890", "exception": false, "start_time": "2022-01-28T14:33:39.854529", "status": "completed" }, "tags": [] }, "source": [ "Context\n", "The two datasets are related to red and white variants of the Portuguese \"Vinho Verde\" wine. For more details, consult the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).\n", "\n", "These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones)." ] }, { "cell_type": "markdown", "id": "ed6c6af4", "metadata": { "papermill": { "duration": 0.030842, "end_time": "2022-01-28T14:33:39.945568", "exception": false, "start_time": "2022-01-28T14:33:39.914726", "status": "completed" }, "tags": [] }, "source": [ "## Content" ] }, { "cell_type": "markdown", "id": "446c1c2d", "metadata": { "papermill": { "duration": 0.032238, "end_time": "2022-01-28T14:33:40.008431", "exception": false, "start_time": "2022-01-28T14:33:39.976193", "status": "completed" }, "tags": [] }, "source": [ "For more information, read [Cortez et al., 2009].\n", "\n", "Input variables (based on physicochemical tests):\n", "\n", "1 - fixed acidity\n", "\n", "2 - volatile acidity\n", "\n", "3 - citric acid\n", "\n", "4 - residual sugar\n", "\n", "5 - chlorides\n", "\n", "6 - free sulfur dioxide\n", "\n", "7 - total sulfur dioxide\n", "\n", "8 - density\n", "\n", "9 - pH\n", "\n", "10 - sulphates\n", "\n", "11 - alcohol\n", "\n", "Output variable (based on sensory data):\n", "\n", "12 - quality (score between 0 and 10)" ] }, { "cell_type": "markdown", "id": "76d3a415", "metadata": { "papermill": { "duration": 0.030447, "end_time": "2022-01-28T14:33:40.069026", "exception": false, "start_time": "2022-01-28T14:33:40.038579", "status": "completed" }, "tags": [] }, "source": [ "## Tips" ] }, { "cell_type": "markdown", "id": "4a5db4ab", "metadata": { "papermill": { "duration": 0.029662, "end_time": "2022-01-28T14:33:40.129084", "exception": false, "start_time": "2022-01-28T14:33:40.099422", "status": "completed" }, "tags": [] }, "source": [ "What might be an interesting thing to do, is aside from using regression modelling, is to set an arbitrary cutoff for your dependent variable (wine quality) at e.g. 7 or higher getting classified as 'good/1' and the remainder as 'not good/0'.\n", "This allows you to practice with hyper parameter tuning on e.g. decision tree algorithms looking at the ROC curve and the AUC value.\n", "Without doing any kind of feature engineering or overfitting you should be able to get an AUC of .88 (without even using random forest algorithm)\n", "\n", "KNIME is a great tool (GUI) that can be used for this.\n", "\n", "1 - File Reader (for csv) to linear correlation node and to interactive histogram for basic EDA.\n", "\n", "2- File Reader to 'Rule Engine Node' to turn the 10 point scale to dichtome variable (good wine and rest), the code to put in the rule engine is something like this:\n", "\n", "$quality$ > 6.5 => \"good\"\n", "TRUE => \"bad\"\n", "\n", "3- Rule Engine Node output to input of Column Filter node to filter out your original 10point feature (this prevent leaking)\n", "\n", "4- Column Filter Node output to input of Partitioning Node (your standard train/tes split, e.g. 75%/25%, choose 'random' or 'stratified')\n", "\n", "5- Partitioning Node train data split output to input of Train data split to input Decision Tree Learner node and\n", "\n", "6- Partitioning Node test data split output to input Decision Tree predictor Node\n", "\n", "7- Decision Tree learner Node output to input Decision Tree Node input\n", "\n", "8- Decision Tree output to input ROC Node.. (here you can evaluate your model base on AUC value)" ] }, { "cell_type": "markdown", "id": "f8b87262", "metadata": { "papermill": { "duration": 0.031403, "end_time": "2022-01-28T14:33:40.190691", "exception": false, "start_time": "2022-01-28T14:33:40.159288", "status": "completed" }, "tags": [] }, "source": [ "## IMPORT LIBRARY" ] }, { "cell_type": "code", "execution_count": 2, "id": "3158b7a4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:40.260908Z", "iopub.status.busy": "2022-01-28T14:33:40.259963Z", "iopub.status.idle": "2022-01-28T14:33:42.244112Z", "shell.execute_reply": "2022-01-28T14:33:42.243470Z", "shell.execute_reply.started": "2022-01-28T14:27:43.934157Z" }, "papermill": { "duration": 2.023702, "end_time": "2022-01-28T14:33:42.244297", "exception": false, "start_time": "2022-01-28T14:33:40.220595", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.model_selection import train_test_split\n", "from xgboost import XGBClassifier, plot_importance\n", "from sklearn import svm\n", "from catboost import CatBoostClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "import sys\n", "import warnings\n", "\n", "if not sys.warnoptions:\n", " warnings.simplefilter(\"ignore\")" ] }, { "cell_type": "markdown", "id": "a65f1c50", "metadata": { "papermill": { "duration": 0.02953, "end_time": "2022-01-28T14:33:42.304330", "exception": false, "start_time": "2022-01-28T14:33:42.274800", "status": "completed" }, "tags": [] }, "source": [ "## ACQUIRE DATA" ] }, { "cell_type": "code", "execution_count": 3, "id": "b79ea43b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:42.370973Z", "iopub.status.busy": "2022-01-28T14:33:42.370182Z", "iopub.status.idle": "2022-01-28T14:33:42.396536Z", "shell.execute_reply": "2022-01-28T14:33:42.397080Z", "shell.execute_reply.started": "2022-01-28T14:27:45.678725Z" }, "papermill": { "duration": 0.063078, "end_time": "2022-01-28T14:33:42.397314", "exception": false, "start_time": "2022-01-28T14:33:42.334236", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [], "source": [ "wine_df = pd.read_csv(\"/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "23840349", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:42.461000Z", "iopub.status.busy": "2022-01-28T14:33:42.460269Z", "iopub.status.idle": "2022-01-28T14:33:42.500917Z", "shell.execute_reply": "2022-01-28T14:33:42.501514Z", "shell.execute_reply.started": "2022-01-28T14:27:45.707637Z" }, "papermill": { "duration": 0.074355, "end_time": "2022-01-28T14:33:42.501740", "exception": false, "start_time": "2022-01-28T14:33:42.427385", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fixed acidity</th>\n", " <th>volatile acidity</th>\n", " <th>citric acid</th>\n", " <th>residual sugar</th>\n", " <th>chlorides</th>\n", " <th>free sulfur dioxide</th>\n", " <th>total sulfur dioxide</th>\n", " <th>density</th>\n", " <th>pH</th>\n", " <th>sulphates</th>\n", " <th>alcohol</th>\n", " <th>quality</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7.4</td>\n", " <td>0.700</td>\n", " <td>0.00</td>\n", " <td>1.9</td>\n", " <td>0.076</td>\n", " <td>11.0</td>\n", " <td>34.0</td>\n", " <td>0.99780</td>\n", " <td>3.51</td>\n", " <td>0.56</td>\n", " <td>9.4</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>7.8</td>\n", " <td>0.880</td>\n", " <td>0.00</td>\n", " <td>2.6</td>\n", " <td>0.098</td>\n", " <td>25.0</td>\n", " <td>67.0</td>\n", " <td>0.99680</td>\n", " <td>3.20</td>\n", " <td>0.68</td>\n", " <td>9.8</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>7.8</td>\n", " <td>0.760</td>\n", " <td>0.04</td>\n", " <td>2.3</td>\n", " <td>0.092</td>\n", " <td>15.0</td>\n", " <td>54.0</td>\n", " <td>0.99700</td>\n", " <td>3.26</td>\n", " <td>0.65</td>\n", " <td>9.8</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11.2</td>\n", " <td>0.280</td>\n", " <td>0.56</td>\n", " <td>1.9</td>\n", " <td>0.075</td>\n", " <td>17.0</td>\n", " <td>60.0</td>\n", " <td>0.99800</td>\n", " <td>3.16</td>\n", " <td>0.58</td>\n", " <td>9.8</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>7.4</td>\n", " <td>0.700</td>\n", " <td>0.00</td>\n", " <td>1.9</td>\n", " <td>0.076</td>\n", " <td>11.0</td>\n", " <td>34.0</td>\n", " <td>0.99780</td>\n", " <td>3.51</td>\n", " <td>0.56</td>\n", " <td>9.4</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1594</th>\n", " <td>6.2</td>\n", " <td>0.600</td>\n", " <td>0.08</td>\n", " <td>2.0</td>\n", " <td>0.090</td>\n", " <td>32.0</td>\n", " <td>44.0</td>\n", " <td>0.99490</td>\n", " <td>3.45</td>\n", " <td>0.58</td>\n", " <td>10.5</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1595</th>\n", " <td>5.9</td>\n", " <td>0.550</td>\n", " <td>0.10</td>\n", " <td>2.2</td>\n", " <td>0.062</td>\n", " <td>39.0</td>\n", " <td>51.0</td>\n", " <td>0.99512</td>\n", " <td>3.52</td>\n", " <td>0.76</td>\n", " <td>11.2</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1596</th>\n", " <td>6.3</td>\n", " <td>0.510</td>\n", " <td>0.13</td>\n", " <td>2.3</td>\n", " <td>0.076</td>\n", " <td>29.0</td>\n", " <td>40.0</td>\n", " <td>0.99574</td>\n", " <td>3.42</td>\n", " <td>0.75</td>\n", " <td>11.0</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1597</th>\n", " <td>5.9</td>\n", " <td>0.645</td>\n", " <td>0.12</td>\n", " <td>2.0</td>\n", " <td>0.075</td>\n", " <td>32.0</td>\n", " <td>44.0</td>\n", " <td>0.99547</td>\n", " <td>3.57</td>\n", " <td>0.71</td>\n", " <td>10.2</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1598</th>\n", " <td>6.0</td>\n", " <td>0.310</td>\n", " <td>0.47</td>\n", " <td>3.6</td>\n", " <td>0.067</td>\n", " <td>18.0</td>\n", " <td>42.0</td>\n", " <td>0.99549</td>\n", " <td>3.39</td>\n", " <td>0.66</td>\n", " <td>11.0</td>\n", " <td>6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1599 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", "0 7.4 0.700 0.00 1.9 0.076 \n", "1 7.8 0.880 0.00 2.6 0.098 \n", "2 7.8 0.760 0.04 2.3 0.092 \n", "3 11.2 0.280 0.56 1.9 0.075 \n", "4 7.4 0.700 0.00 1.9 0.076 \n", "... ... ... ... ... ... \n", "1594 6.2 0.600 0.08 2.0 0.090 \n", "1595 5.9 0.550 0.10 2.2 0.062 \n", "1596 6.3 0.510 0.13 2.3 0.076 \n", "1597 5.9 0.645 0.12 2.0 0.075 \n", "1598 6.0 0.310 0.47 3.6 0.067 \n", "\n", " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", "0 11.0 34.0 0.99780 3.51 0.56 \n", "1 25.0 67.0 0.99680 3.20 0.68 \n", "2 15.0 54.0 0.99700 3.26 0.65 \n", "3 17.0 60.0 0.99800 3.16 0.58 \n", "4 11.0 34.0 0.99780 3.51 0.56 \n", "... ... ... ... ... ... \n", "1594 32.0 44.0 0.99490 3.45 0.58 \n", "1595 39.0 51.0 0.99512 3.52 0.76 \n", "1596 29.0 40.0 0.99574 3.42 0.75 \n", "1597 32.0 44.0 0.99547 3.57 0.71 \n", "1598 18.0 42.0 0.99549 3.39 0.66 \n", "\n", " alcohol quality \n", "0 9.4 5 \n", "1 9.8 5 \n", "2 9.8 5 \n", "3 9.8 6 \n", "4 9.4 5 \n", "... ... ... \n", "1594 10.5 5 \n", "1595 11.2 6 \n", "1596 11.0 6 \n", "1597 10.2 5 \n", "1598 11.0 6 \n", "\n", "[1599 rows x 12 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_df" ] }, { "cell_type": "code", "execution_count": 5, "id": "4d864545", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:42.568886Z", "iopub.status.busy": "2022-01-28T14:33:42.566249Z", "iopub.status.idle": "2022-01-28T14:33:42.572094Z", "shell.execute_reply": "2022-01-28T14:33:42.572614Z", "shell.execute_reply.started": "2022-01-28T14:27:45.752006Z" }, "papermill": { "duration": 0.040437, "end_time": "2022-01-28T14:33:42.572840", "exception": false, "start_time": "2022-01-28T14:33:42.532403", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(1599, 12)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_df.shape" ] }, { "cell_type": "code", "execution_count": 6, "id": "7ba6afca", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:42.655474Z", "iopub.status.busy": "2022-01-28T14:33:42.654473Z", "iopub.status.idle": "2022-01-28T14:33:42.657164Z", "shell.execute_reply": "2022-01-28T14:33:42.657660Z", "shell.execute_reply.started": "2022-01-28T14:27:45.760515Z" }, "papermill": { "duration": 0.053564, "end_time": "2022-01-28T14:33:42.657879", "exception": false, "start_time": "2022-01-28T14:33:42.604315", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "wine_df['quality'] = wine_df['quality'].map({5:0,4:0,3:0,6:0,7:1,8:1})\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "d4af5412", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:42.725305Z", "iopub.status.busy": "2022-01-28T14:33:42.724543Z", "iopub.status.idle": "2022-01-28T14:33:42.731838Z", "shell.execute_reply": "2022-01-28T14:33:42.732395Z", 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"name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 1599 entries, 0 to 1598\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fixed acidity 1599 non-null float64\n", " 1 volatile acidity 1599 non-null float64\n", " 2 citric acid 1599 non-null float64\n", " 3 residual sugar 1599 non-null float64\n", " 4 chlorides 1599 non-null float64\n", " 5 free sulfur dioxide 1599 non-null float64\n", " 6 total sulfur dioxide 1599 non-null float64\n", " 7 density 1599 non-null float64\n", " 8 pH 1599 non-null float64\n", " 9 sulphates 1599 non-null float64\n", " 10 alcohol 1599 non-null float64\n", " 11 quality 1599 non-null int64 \n", "dtypes: float64(11), int64(1)\n", "memory usage: 150.0 KB\n" ] } ], "source": [ "wine_df.info()" ] }, { "cell_type": "code", "execution_count": 9, "id": "c8019c59", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:42.895746Z", "iopub.status.busy": "2022-01-28T14:33:42.894519Z", "iopub.status.idle": "2022-01-28T14:33:42.939657Z", "shell.execute_reply": "2022-01-28T14:33:42.940237Z", "shell.execute_reply.started": "2022-01-28T14:27:45.822406Z" }, "papermill": { "duration": 0.088526, "end_time": "2022-01-28T14:33:42.940483", "exception": false, "start_time": "2022-01-28T14:33:42.851957", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fixed acidity</th>\n", " <th>volatile acidity</th>\n", " <th>citric acid</th>\n", " <th>residual sugar</th>\n", " <th>chlorides</th>\n", " <th>free sulfur dioxide</th>\n", " <th>total sulfur dioxide</th>\n", " <th>density</th>\n", " <th>pH</th>\n", " <th>sulphates</th>\n", " <th>alcohol</th>\n", " <th>quality</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " <td>1599.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>8.319637</td>\n", " <td>0.527821</td>\n", " <td>0.270976</td>\n", " <td>2.538806</td>\n", " <td>0.087467</td>\n", " <td>15.874922</td>\n", " <td>46.467792</td>\n", " <td>0.996747</td>\n", " <td>3.311113</td>\n", " <td>0.658149</td>\n", " <td>10.422983</td>\n", " <td>0.135710</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.741096</td>\n", " <td>0.179060</td>\n", " <td>0.194801</td>\n", " <td>1.409928</td>\n", " <td>0.047065</td>\n", " <td>10.460157</td>\n", " <td>32.895324</td>\n", " <td>0.001887</td>\n", " <td>0.154386</td>\n", " <td>0.169507</td>\n", " <td>1.065668</td>\n", " <td>0.342587</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>4.600000</td>\n", " <td>0.120000</td>\n", " <td>0.000000</td>\n", " <td>0.900000</td>\n", " <td>0.012000</td>\n", " <td>1.000000</td>\n", " <td>6.000000</td>\n", " <td>0.990070</td>\n", " <td>2.740000</td>\n", " <td>0.330000</td>\n", " <td>8.400000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>7.100000</td>\n", " <td>0.390000</td>\n", " <td>0.090000</td>\n", " <td>1.900000</td>\n", " <td>0.070000</td>\n", " <td>7.000000</td>\n", " <td>22.000000</td>\n", " <td>0.995600</td>\n", " <td>3.210000</td>\n", " <td>0.550000</td>\n", " <td>9.500000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>7.900000</td>\n", " <td>0.520000</td>\n", " <td>0.260000</td>\n", " <td>2.200000</td>\n", " <td>0.079000</td>\n", " <td>14.000000</td>\n", " <td>38.000000</td>\n", " <td>0.996750</td>\n", " <td>3.310000</td>\n", " <td>0.620000</td>\n", " <td>10.200000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>9.200000</td>\n", " <td>0.640000</td>\n", " <td>0.420000</td>\n", " <td>2.600000</td>\n", " <td>0.090000</td>\n", " <td>21.000000</td>\n", " <td>62.000000</td>\n", " <td>0.997835</td>\n", " <td>3.400000</td>\n", " <td>0.730000</td>\n", " <td>11.100000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>15.900000</td>\n", " <td>1.580000</td>\n", " <td>1.000000</td>\n", " <td>15.500000</td>\n", " <td>0.611000</td>\n", " <td>72.000000</td>\n", " <td>289.000000</td>\n", " <td>1.003690</td>\n", " <td>4.010000</td>\n", " <td>2.000000</td>\n", " <td>14.900000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 0.135710 \n", "std 0.154386 0.169507 1.065668 0.342587 \n", "min 2.740000 0.330000 8.400000 0.000000 \n", "25% 3.210000 0.550000 9.500000 0.000000 \n", "50% 3.310000 0.620000 10.200000 0.000000 \n", "75% 3.400000 0.730000 11.100000 0.000000 \n", "max 4.010000 2.000000 14.900000 1.000000 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_df.describe()" ] }, { "cell_type": "code", "execution_count": 10, "id": "0806b960", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:43.022421Z", "iopub.status.busy": "2022-01-28T14:33:43.014970Z", "iopub.status.idle": "2022-01-28T14:33:46.370526Z", "shell.execute_reply": "2022-01-28T14:33:46.369861Z", "shell.execute_reply.started": "2022-01-28T14:27:45.878676Z" }, "papermill": { "duration": 3.397788, "end_time": "2022-01-28T14:33:46.370739", "exception": false, "start_time": "2022-01-28T14:33:42.972951", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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QJLUyLCRJrbyDW4BPsZM0s4GHRZK9gLOAPYECVlfVaUl2BT4DrAB+ChxdVb8YdH2LlU+xkzSTYZyG2gz8TVXtBxwEvDHJfsDbgcuqal/gsua9JGkEDDwsqmpdVV3XLP8HcAuwDDgSOLNZ7UzgqEHXJkma3lAHuJOsAJ4BXA3sWVXrmq71dE5TTbfNCUkmkkxMTk4OplBJWuSGFhZJHg5cAJxYVf/e3VdVRWc8YwtVtbqqxqtqfGxsbACVSpKGEhZJ/oBOUJxTVZ9vmu9KsrTpXwrMfGmOJGlgBh4WSQJ8Erilqt7f1XUxsKpZXgVcNOjaJEnTG8Z9Fs8BjgVuSPK9pu0k4FTg/CTHAbcDRw+hNklD0vboVR+7OlwDD4uq+iaQrXQfNshaJI2Otkev+tjV4XK6D0lSK8NCktTKuaG2A73M6+T5XknbwrDYDvQyr5PneyVtC09DSZJaGRaSpFaGhSSplWEhSWrlALekBaHtDm/wqr9+MiwWibYvmo9M1ahru8MbvOqvnwyLRaLti+YjUyXNxLAYMm+ok7QQGBZD5g110vxx5tr+MSwkbTecubZ/vHRWktTKIwtJmoW2ccZeTnXNxz4GbdGGRS8Dy7/85d086lFLtto/H78UvVyy6mWv0uD08p19yT9dstX+C9+ysvV+kLZ9jOLpspELiyQrgdOABwOfqKpT+/E5vQwsX3Di4TOuMx+/FL1csuplr9L86OXGvm39zvZyP0jbPkbxBsSRCoskDwb+N/AnwFrgO0kurqqbh1vZ9Objl0LS4CyU7+wo3oA4agPcBwJrquq2qroXOA84csg1SdKil6oadg0PSPIyYGVVHd+8PxZ4VlW9qWudE4ATmrdPBH44h4/aHdi4jeUO2kKreaHVC9Y8CAutXtg+a35sVY3NZocjdRqqF1W1Gli9LftIMlFV4/NU0kAstJoXWr1gzYOw0OoFa77fqJ2GuhPYq+v98qZNkjREoxYW3wH2TbJ3kh2BY4CLh1yTJC16I3Uaqqo2J3kT8BU6l86eXlU39eGjtuk01pAstJoXWr1gzYOw0OoFawZGbIBbkjSaRu00lCRpBBkWkqRWCz4skqxM8sMka5K8fZr+xya5LMn3k3w9yfKuvvckubF5vaKrfe8kVzf7/Ewz2D7qNZ/T7PPGJKcn+YNRr7mr/0NJ/nPU603HKUl+lOSWJH+1AGo+LMl1Sb6X5JtJZp5DYnb1np5kQ5Ibt9Kf5r/tmqbmA7r6ViW5tXmt6mr/wyQ3NNt8KEnmq95+1Jxk5yRfTPKDJDclmffpifrx99zVf/HW9ruFqlqwLzqD4D8GHgfsCFwP7Ddlnc8Cq5rlQ4Gzm+UXAZfSGeR/GJ0rsR7Z9J0PHNMs/zPw+gVQ8xFAmte5C6Hmpn8cOBv4z1GvF3gNcBbwoOb9Hgug5h8BT26W3wCcMY81Pxc4ALhxK/1HAF9qficPAq5u2ncFbmt+7tIs79L0XdOsm2bbF85Xvf2oGdgZeEGzzo7AlaNec9d2LwU+vbX9Tn0t9COLXqYH2Q/4WrN8eVf/fsAVVbW5qn4FfB9Y2fxL5lDgc816ZwJHjXLNAFV1STXofOGWM3/6UnM6c4G9F3jbPNbat3qB1wP/WFX3AVTVzNMWj0bNBTyyWX4U8G/zVXBVXQH8fIZVjgTOan4trwKWJFkKHA5cWlU/r6pf0Am6lU3fI6vqqub3+Czm97s37zVX1T1VdXmz73uB65jf79681wyQ5OHAW4B39VrHQg+LZcAdXe/XNm3drqeToAAvAR6RZLemfWVzGLk78AI6NwTuBtxdVZtn2Oeo1fyA5vTTscCXF0DNbwIurqp181hrP+t9PPCKJBNJvpRk3wVQ8/HAJUnW0vm96MsszluxtT/TTO1rp2kfpNnW/IAkS4A/BS7rb4lbmEvN/xP4J+CeXj9koYdFL/4WeF6S7wLPo3NH+G+r6qvAJcC36Jy2+Tbw26FV+fu2peaP0vlX5pUDrBdmWXOSxwAvBz484DrnVG+zzU7A/6vONAofB05fADX/NXBEVS0HPgW8f8A1LwpJdqDzd/+hqrpt2PXMJMn+wOOr6sLZbLfQw6J1epCq+reqemlVPQN4Z9N2d/PzlKrav6r+hM75vh8Bm+gcxu2wtX2OYM0AJDkZGKNzeDmf+lHzM4B9gDVJfgrsnGTNCNcLnX+Zfb5ZvhB42jzV25eak4wBT6+qq5tdfAY4eB5rbrO1P9NM7cunaR+k2dZ8v9XArVX1wX4XOI3Z1vxsYLz53n0TeEKSr7d+Si8DG6P6ojOgdxuwN78bFHzKlHV253cDkqfQOecMnQHF3ZrlpwE3Ajs07z/L7w9wv2EB1Hw8nX9ZPnSh/D1P2X4+B7j79Xd8KvDaZvn5wHdGuebmtRF4QtN3HHDBPP9urGDrA68v4vcHXq9p2ncFfkJn0HWXZnnXpm/qAPcRffh9nu+a3wVccP9/m3685rvmXva7xef06w83qBedKwF+ROdKknc2bf8I/Fmz/DLg1madTwA7Ne0PAW5uXlcB+3ft83HNL+0aOsGx0wKoeXOzv+81r78f9Zqn7H/ewqKPf8dLgC8CN9A51fP0BVDzS5p6rwe+DjxuHus9F1gH/IbOUddxwOuA1zX9ofMwsx83NYx3bfva5vu1BnhNV/s4nbD7MfARmlkmRrVmOv9aL+AWfvfdO36Ua56y7xX0GBZO9yFJarXQxywkSQNgWEiSWhkWkqRWhoUkqZVhIUlqZVhILZL8Q5K/ncf9XZJkSfN6w3ztV+onw0IasKo6ojp3Xi+hMxOsNPIMC2kaSd6ZznMrvgk8sWl7fJIvJ7k2yZVJntS0n9E8T+BbSW5L8rKmfWmSK5pnSdyY5JCm/afNhH+nAo9v+t+b5KwkR3XVcE6SqTPPSkOxQ/sq0uKS5A+BY4D96XxHrgOupTP/z+uq6tYkz6IzaeOhzWZLgT8CngRcTGeK+z8HvlJVpzTTse885aPeDjy1qvZvPvd5dCb++0KSR9GZx2lVn/6Y0qwYFtKWDgEurKp7oPM0MTpTahwMfLbr4W07dW3zheo85+LmJHs2bd8B7n9q4Req6nszfWhVfSPJR5sJAP8bnXmcNs+0jTQonoaSevMgOs852b/r9eSu/l93LQceeGjNc+nM9HlGklf38DlnAX9B56l8g54CXdoqw0La0hXAUUkemuQRdB5ocw/wkyQvhweee/z0mXaS5LHAXVX1cToT/x0wZZX/AB4xpe0M4ESAqrp5G/8c0rwxLKQpquo6Os9+uJ7O1M/fabpeBRyX5HrgJrZ87OlUzweubx5W9ArgtCmfswn4P83g93ubtrvozGD6qfn500jzw1lnpRGSZGc600wfUFW/HHY90v08spBGRJI/pnNU8WGDQqPGIwtJUiuPLCRJrQwLSVIrw0KS1MqwkCS1MiwkSa3+PwqupecqPPCIAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for i in wine_df: \n", " sns.histplot(wine_df[i])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "id": "1a6d8adc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:46.462219Z", "iopub.status.busy": "2022-01-28T14:33:46.461510Z", "iopub.status.idle": "2022-01-28T14:33:51.956180Z", "shell.execute_reply": "2022-01-28T14:33:51.955567Z", "shell.execute_reply.started": "2022-01-28T14:27:49.677988Z" }, "papermill": { "duration": 5.544534, "end_time": "2022-01-28T14:33:51.956357", "exception": false, "start_time": "2022-01-28T14:33:46.411823", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for i in wine_df.drop(columns = 'quality'):\n", " g = sns.FacetGrid(wine_df, col='quality')\n", " g.map(plt.hist, i, bins=20)" ] }, { "cell_type": "code", "execution_count": 12, "id": "5742cdcd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:52.063644Z", "iopub.status.busy": "2022-01-28T14:33:52.062537Z", "iopub.status.idle": "2022-01-28T14:33:52.067958Z", "shell.execute_reply": "2022-01-28T14:33:52.067299Z", "shell.execute_reply.started": "2022-01-28T14:27:55.307563Z" }, "papermill": { "duration": 0.061303, "end_time": "2022-01-28T14:33:52.068135", "exception": false, "start_time": "2022-01-28T14:33:52.006832", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "wine_df_test = wine_df.copy()" ] }, { "cell_type": "code", "execution_count": 13, "id": "04d914ce", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:52.171116Z", "iopub.status.busy": "2022-01-28T14:33:52.170209Z", "iopub.status.idle": "2022-01-28T14:33:52.262171Z", "shell.execute_reply": "2022-01-28T14:33:52.262880Z", "shell.execute_reply.started": "2022-01-28T14:27:55.314670Z" }, "papermill": { "duration": 0.146454, "end_time": "2022-01-28T14:33:52.263130", "exception": false, "start_time": "2022-01-28T14:33:52.116676", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "for i in wine_df_test.drop(columns = 'quality'):\n", " wine_df_test['{} Band'.format(i)] = pd.qcut(wine_df_test[i], 5)\n", " wine_df_test[['{} Band'.format(i), 'quality']].groupby(['{} Band'.format(i)], as_index=False).mean().sort_values(by='{} Band'.format(i), ascending=True)" ] }, { "cell_type": "code", "execution_count": 14, "id": "c0b03c90", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:52.374591Z", "iopub.status.busy": "2022-01-28T14:33:52.373390Z", "iopub.status.idle": "2022-01-28T14:33:52.379261Z", "shell.execute_reply": "2022-01-28T14:33:52.379797Z", "shell.execute_reply.started": "2022-01-28T14:27:55.410689Z" }, "papermill": { "duration": 0.06244, "end_time": "2022-01-28T14:33:52.379997", "exception": false, "start_time": "2022-01-28T14:33:52.317557", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array(['fixed acidity', 'volatile acidity', 'citric acid',\n", " 'residual sugar', 'chlorides', 'free sulfur dioxide',\n", " 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n", " 'quality', 'fixed acidity Band', 'volatile acidity Band',\n", " 'citric acid Band', 'residual sugar Band', 'chlorides Band',\n", " 'free sulfur dioxide Band', 'total sulfur dioxide Band',\n", " 'density Band', 'pH Band', 'sulphates Band', 'alcohol Band'],\n", " dtype=object)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_df_test.columns.values" ] }, { "cell_type": "code", "execution_count": 15, "id": "e46b03c4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:52.494542Z", "iopub.status.busy": "2022-01-28T14:33:52.493418Z", "iopub.status.idle": "2022-01-28T14:33:52.499112Z", "shell.execute_reply": "2022-01-28T14:33:52.499756Z", "shell.execute_reply.started": "2022-01-28T14:27:55.420455Z" }, "papermill": { "duration": 0.06052, "end_time": "2022-01-28T14:33:52.499969", "exception": false, "start_time": "2022-01-28T14:33:52.439449", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "wine_df_test.columns.values\n", "band = []\n", "for i in wine_df_test.columns.values:\n", " if (i.find('Band') != -1):\n", " band.append(i)\n", " \n", "band = np.array(band)" ] }, { "cell_type": "code", "execution_count": 16, "id": "b811eb9d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:52.605487Z", "iopub.status.busy": "2022-01-28T14:33:52.604380Z", "iopub.status.idle": "2022-01-28T14:33:52.631848Z", "shell.execute_reply": "2022-01-28T14:33:52.632332Z", "shell.execute_reply.started": "2022-01-28T14:27:55.433704Z" }, "papermill": { "duration": 0.082247, "end_time": "2022-01-28T14:33:52.632579", "exception": false, "start_time": "2022-01-28T14:33:52.550332", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fixed acidity\n", " [(4.598999999999999, 7.0], (7.0, 7.6], (7.6, 8.3], (8.3, 9.7], (9.7, 15.9]]\n", "Categories (5, interval[float64, right]): [(4.598999999999999, 7.0] < (7.0, 7.6] < (7.6, 8.3] < (8.3, 9.7] < (9.7, 15.9]]\n", "volatile acidity\n", " [(0.119, 0.37], (0.37, 0.47], (0.47, 0.57], (0.57, 0.66], (0.66, 1.58]]\n", "Categories (5, interval[float64, right]): [(0.119, 0.37] < (0.37, 0.47] < (0.47, 0.57] < (0.57, 0.66] < (0.66, 1.58]]\n", "citric acid\n", " [(-0.001, 0.07], (0.07, 0.21], (0.21, 0.32], (0.32, 0.46], (0.46, 1.0]]\n", "Categories (5, interval[float64, right]): [(-0.001, 0.07] < (0.07, 0.21] < (0.21, 0.32] < (0.32, 0.46] < (0.46, 1.0]]\n", "residual sugar\n", " [(0.899, 1.8], (1.8, 2.1], (2.1, 2.3], (2.3, 2.7], (2.7, 15.5]]\n", "Categories (5, interval[float64, right]): [(0.899, 1.8] < (1.8, 2.1] < (2.1, 2.3] < (2.3, 2.7] < (2.7, 15.5]]\n", "chlorides\n", " [(0.011, 0.067], (0.067, 0.076], (0.076, 0.082], (0.082, 0.094], (0.094, 0.611]]\n", "Categories (5, interval[float64, right]): [(0.011, 0.067] < (0.067, 0.076] < (0.076, 0.082] < (0.082, 0.094] < (0.094, 0.611]]\n", "free sulfur dioxide\n", " [(0.999, 6.0], (6.0, 11.0], (11.0, 16.0], (16.0, 24.0], (24.0, 72.0]]\n", "Categories (5, interval[float64, right]): [(0.999, 6.0] < (6.0, 11.0] < (11.0, 16.0] < (16.0, 24.0] < (24.0, 72.0]]\n", "total sulfur dioxide\n", " [(5.999, 19.0], (19.0, 30.0], (30.0, 45.8], (45.8, 69.0], (69.0, 289.0]]\n", "Categories (5, interval[float64, right]): [(5.999, 19.0] < (19.0, 30.0] < (30.0, 45.8] < (45.8, 69.0] < (69.0, 289.0]]\n", "density\n", " [(0.989, 0.995], (0.995, 0.996], (0.996, 0.997], (0.997, 0.998], (0.998, 1.004]]\n", "Categories (5, interval[float64, right]): [(0.989, 0.995] < (0.995, 0.996] < (0.996, 0.997] < (0.997, 0.998] < (0.998, 1.004]]\n", "pH\n", " [(2.7390000000000003, 3.18], (3.18, 3.28], (3.28, 3.35], (3.35, 3.424], (3.424, 4.01]]\n", "Categories (5, interval[float64, right]): [(2.7390000000000003, 3.18] < (3.18, 3.28] < (3.28, 3.35] < (3.35, 3.424] < (3.424, 4.01]]\n", "sulphates\n", " [(0.329, 0.54], (0.54, 0.59], (0.59, 0.65], (0.65, 0.76], (0.76, 2.0]]\n", "Categories (5, interval[float64, right]): [(0.329, 0.54] < (0.54, 0.59] < (0.59, 0.65] < (0.65, 0.76] < (0.76, 2.0]]\n", "alcohol\n", " [(8.399000000000001, 9.5], (9.5, 9.9], (9.9, 10.5], (10.5, 11.3], (11.3, 14.9]]\n", "Categories (5, interval[float64, right]): [(8.399000000000001, 9.5] < (9.5, 9.9] < (9.9, 10.5] < (10.5, 11.3] < (11.3, 14.9]]\n" ] } ], "source": [ "for i in zip(band, wine_df.drop(columns = 'quality').columns.values):\n", " print(i[1]+'\\n',wine_df_test[i[0]]\\\n", " .unique()\\\n", " .sort_values(ascending = True))\n", " " ] }, { "cell_type": "markdown", "id": "8c8c3c8d", "metadata": { "papermill": { "duration": 0.04919, "end_time": "2022-01-28T14:33:52.730610", "exception": false, "start_time": "2022-01-28T14:33:52.681420", "status": "completed" }, "tags": [] }, "source": [ "Creating definition band for the range number" ] }, { "cell_type": "code", "execution_count": 17, "id": "5ddd596e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:52.840270Z", "iopub.status.busy": "2022-01-28T14:33:52.839451Z", "iopub.status.idle": "2022-01-28T14:33:52.930445Z", "shell.execute_reply": "2022-01-28T14:33:52.929722Z", "shell.execute_reply.started": "2022-01-28T14:27:55.472269Z" }, "papermill": { "duration": 0.148142, "end_time": "2022-01-28T14:33:52.930642", "exception": false, "start_time": "2022-01-28T14:33:52.782500", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#fixed acidity\n", "wine_df.loc[ wine_df['fixed acidity'] <= 7.0, 'fixed acidity'] = 0\n", "wine_df.loc[(wine_df['fixed acidity'] > 7.0) & (wine_df['fixed acidity'] <= 7.6), 'fixed acidity'] = 1\n", "wine_df.loc[(wine_df['fixed acidity'] > 7.6) & (wine_df['fixed acidity'] <= 8.3), 'fixed acidity'] = 2\n", "wine_df.loc[(wine_df['fixed acidity'] > 8.3) & (wine_df['fixed acidity'] <= 9.7), 'fixed acidity'] = 3\n", "wine_df.loc[ wine_df['fixed acidity'] > 9.7, 'fixed acidity'] = 4\n", "wine_df['fixed acidity'] = wine_df['fixed acidity'].astype(int)\n", "#volatile acidity\n", "wine_df.loc[ wine_df['volatile acidity'] <= 0.37, 'volatile acidity'] = 0\n", "wine_df.loc[(wine_df['volatile acidity'] > 0.37) & (wine_df['volatile acidity'] <= 0.47), 'volatile acidity'] = 1\n", "wine_df.loc[(wine_df['volatile acidity'] > 0.47) & (wine_df['volatile acidity'] <= 0.57), 'volatile acidity'] = 2\n", "wine_df.loc[(wine_df['volatile acidity'] > 0.57) & (wine_df['volatile acidity'] <= 0.66), 'volatile acidity'] = 3\n", "wine_df.loc[ wine_df['volatile acidity'] > 0.66, 'volatile acidity'] = 4\n", "wine_df['volatile acidity'] = wine_df['volatile acidity'].astype(int)\n", "#citric acid\n", "wine_df.loc[ wine_df['citric acid'] <= 0.07, 'citric acid'] = 0\n", "wine_df.loc[(wine_df['citric acid'] > 0.07) & (wine_df['citric acid'] <= 0.21), 'citric acid'] = 1\n", "wine_df.loc[(wine_df['citric acid'] > 0.21) & (wine_df['citric acid'] <= 0.32), 'citric acid'] = 2\n", "wine_df.loc[(wine_df['citric acid'] > 0.32) & (wine_df['citric acid'] <= 0.46), 'citric acid'] = 3\n", "wine_df.loc[ wine_df['citric acid'] > 0.46, 'citric acid'] = 4\n", "wine_df['citric acid'] = wine_df['citric acid'].astype(int)\n", "#residual sugar\n", "wine_df.loc[ wine_df['residual sugar'] <= 1.8, 'residual sugar'] = 0\n", "wine_df.loc[(wine_df['residual sugar'] > 1.8) & (wine_df['residual sugar'] <= 2.1), 'residual sugar'] = 1\n", "wine_df.loc[(wine_df['residual sugar'] > 2.1) & (wine_df['residual sugar'] <= 2.3), 'residual sugar'] = 2\n", "wine_df.loc[(wine_df['residual sugar'] > 2.3) & (wine_df['residual sugar'] <= 2.7), 'residual sugar'] = 3\n", "wine_df.loc[ wine_df['residual sugar'] > 2.7, 'residual sugar'] = 4\n", "wine_df['residual sugar'] = wine_df['residual sugar'].astype(int)\n", "#chlorides\n", "wine_df.loc[ wine_df['chlorides'] <= 0.067, 'chlorides'] = 0\n", "wine_df.loc[(wine_df['chlorides'] > 0.067) & (wine_df['chlorides'] <= 0.076), 'chlorides'] = 1\n", "wine_df.loc[(wine_df['chlorides'] > 0.076) & (wine_df['chlorides'] <= 0.082), 'chlorides'] = 2\n", "wine_df.loc[(wine_df['chlorides'] > 0.082) & (wine_df['chlorides'] <= 0.094), 'chlorides'] = 3\n", "wine_df.loc[ wine_df['chlorides'] > 0.094, 'chlorides'] = 4\n", "wine_df['chlorides'] = wine_df['chlorides'].astype(int)\n", "#free sulfur dioxide\n", "wine_df.loc[ wine_df['free sulfur dioxide'] <= 6.0, 'free sulfur dioxide'] = 0\n", "wine_df.loc[(wine_df['free sulfur dioxide'] > 6.0) & (wine_df['free sulfur dioxide'] <= 11.0), 'free sulfur dioxide'] = 1\n", "wine_df.loc[(wine_df['free sulfur dioxide'] > 11.0) & (wine_df['free sulfur dioxide'] <= 16.0), 'free sulfur dioxide'] = 2\n", "wine_df.loc[(wine_df['free sulfur dioxide'] > 16.0) & (wine_df['free sulfur dioxide'] <= 24.0), 'free sulfur dioxide'] = 3\n", "wine_df.loc[ wine_df['free sulfur dioxide'] > 24.0, 'free sulfur dioxide'] = 4\n", "wine_df['free sulfur dioxide'] = wine_df['free sulfur dioxide'].astype(int)\n", "#total sulfur dioxide\n", "wine_df.loc[ wine_df['total sulfur dioxide'] <= 19.0, 'total sulfur dioxide'] = 0\n", "wine_df.loc[(wine_df['total sulfur dioxide'] > 19.0) & (wine_df['total sulfur dioxide'] <= 30.0), 'total sulfur dioxide'] = 1\n", "wine_df.loc[(wine_df['total sulfur dioxide'] > 30.0) & (wine_df['total sulfur dioxide'] <= 45.8), 'total sulfur dioxide'] = 2\n", "wine_df.loc[(wine_df['total sulfur dioxide'] > 45.8) & (wine_df['total sulfur dioxide'] <=69.0 ), 'total sulfur dioxide'] = 3\n", "wine_df.loc[ wine_df['total sulfur dioxide'] > 69.0, 'total sulfur dioxide'] = 4\n", "wine_df['total sulfur dioxide'] = wine_df['total sulfur dioxide'].astype(int)\n", "#density\n", "wine_df.loc[ wine_df['density'] <=0.995, 'density'] = 0\n", "wine_df.loc[(wine_df['density'] > 0.995) & (wine_df['density'] <= 0.996), 'density'] = 1\n", "wine_df.loc[(wine_df['density'] > 0.996) & (wine_df['density'] <= 0.997), 'density'] = 2\n", "wine_df.loc[(wine_df['density'] > 0.997) & (wine_df['density'] <= 0.998), 'density'] = 3\n", "wine_df.loc[ wine_df['density'] > 0.998, 'density'] = 4\n", "wine_df['density'] = wine_df['density'].astype(int)\n", "#pH\n", "wine_df.loc[ wine_df['pH'] <= 3.18, 'pH'] = 0\n", "wine_df.loc[(wine_df['pH'] > 3.18) & (wine_df['pH'] <= 3.28), 'pH'] = 1\n", "wine_df.loc[(wine_df['pH'] > 3.28) & (wine_df['pH'] <= 3.35), 'pH'] = 2\n", "wine_df.loc[(wine_df['pH'] > 3.35) & (wine_df['pH'] <= 3.424), 'pH'] = 3\n", "wine_df.loc[ wine_df['pH'] > 3.424, 'pH'] = 4\n", "wine_df['pH'] = wine_df['pH'].astype(int)\n", "#sulphates\n", "wine_df.loc[ wine_df['sulphates'] <= 0.54, 'sulphates'] = 0\n", "wine_df.loc[(wine_df['sulphates'] > 0.54) & (wine_df['sulphates'] <= 0.59), 'sulphates'] = 1\n", "wine_df.loc[(wine_df['sulphates'] > 0.59) & (wine_df['sulphates'] <= 0.65), 'sulphates'] = 2\n", "wine_df.loc[(wine_df['sulphates'] > 0.65) & (wine_df['sulphates'] <= 0.76), 'sulphates'] = 3\n", "wine_df.loc[ wine_df['sulphates'] > 0.76, 'pH'] = 4\n", "wine_df['sulphates'] = wine_df['sulphates'].astype(int)\n", "#alcohol\n", "wine_df.loc[ wine_df['alcohol'] <= 9.5, 'alcohol'] = 0\n", "wine_df.loc[(wine_df['alcohol'] > 9.5) & (wine_df['alcohol'] <= 9.9), 'alcohol'] = 1\n", "wine_df.loc[(wine_df['alcohol'] > 9.9) & (wine_df['alcohol'] <= 10.5), 'alcohol'] = 2\n", "wine_df.loc[(wine_df['alcohol'] > 10.5) & (wine_df['alcohol'] <= 11.3), 'alcohol'] = 3\n", "wine_df.loc[ wine_df['alcohol'] > 11.3, 'alcohol'] = 4\n", "wine_df['alcohol'] = wine_df['alcohol'].astype(int)" ] }, { "cell_type": "code", "execution_count": 18, "id": "3883c0c0", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:53.041943Z", "iopub.status.busy": "2022-01-28T14:33:53.041259Z", "iopub.status.idle": "2022-01-28T14:33:53.059085Z", "shell.execute_reply": "2022-01-28T14:33:53.059658Z", "shell.execute_reply.started": "2022-01-28T14:27:55.557235Z" }, "papermill": { "duration": 0.076225, "end_time": "2022-01-28T14:33:53.059875", "exception": false, "start_time": "2022-01-28T14:33:52.983650", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fixed acidity</th>\n", " <th>volatile acidity</th>\n", " <th>citric acid</th>\n", " <th>residual sugar</th>\n", " <th>chlorides</th>\n", " <th>free sulfur dioxide</th>\n", " <th>total sulfur dioxide</th>\n", " <th>density</th>\n", " <th>pH</th>\n", " <th>sulphates</th>\n", " <th>alcohol</th>\n", " <th>quality</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1594</th>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1595</th>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1596</th>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1597</th>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1598</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1599 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", "0 1 4 0 1 4 \n", "1 2 4 0 4 4 \n", "2 2 4 0 2 4 \n", "3 4 0 4 1 4 \n", "4 1 4 0 1 4 \n", "... ... ... ... ... ... \n", "1594 0 4 4 1 4 \n", "1595 0 4 4 2 0 \n", "1596 0 4 4 2 4 \n", "1597 0 4 4 1 4 \n", "1598 0 0 4 4 0 \n", "\n", " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", "0 1 2 4 4 1 \n", "1 4 3 4 4 3 \n", "2 2 3 4 4 2 \n", "3 3 3 4 4 1 \n", "4 1 2 4 4 1 \n", "... ... ... ... .. ... \n", "1594 4 2 0 4 1 \n", "1595 4 3 4 4 3 \n", "1596 4 2 4 4 3 \n", "1597 4 2 4 4 3 \n", "1598 3 2 4 4 3 \n", "\n", " alcohol quality \n", "0 0 0 \n", "1 1 0 \n", "2 1 0 \n", "3 1 0 \n", "4 0 0 \n", "... ... ... \n", "1594 2 0 \n", "1595 3 0 \n", "1596 3 0 \n", "1597 2 0 \n", "1598 3 0 \n", "\n", "[1599 rows x 12 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_df" ] }, { "cell_type": "code", "execution_count": 19, "id": "45a6d556", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:53.162064Z", "iopub.status.busy": "2022-01-28T14:33:53.161154Z", "iopub.status.idle": "2022-01-28T14:33:53.167281Z", "shell.execute_reply": "2022-01-28T14:33:53.167888Z", "shell.execute_reply.started": "2022-01-28T14:27:55.576963Z" }, "papermill": { "duration": 0.059151, "end_time": "2022-01-28T14:33:53.168091", "exception": false, "start_time": "2022-01-28T14:33:53.108940", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X = wine_df.drop(\"quality\", axis=1)\n", "y = wine_df['quality']\n" ] }, { "cell_type": "markdown", "id": "3fe721dc", "metadata": { "papermill": { "duration": 0.048689, "end_time": "2022-01-28T14:33:53.267074", "exception": false, "start_time": "2022-01-28T14:33:53.218385", "status": "completed" }, "tags": [] }, "source": [ "## Model" ] }, { "cell_type": "markdown", "id": "7fd7623b", "metadata": { "papermill": { "duration": 0.049445, "end_time": "2022-01-28T14:33:53.366568", "exception": false, "start_time": "2022-01-28T14:33:53.317123", "status": "completed" }, "tags": [] }, "source": [ "### Train/test split" ] }, { "cell_type": "code", "execution_count": 20, "id": "9889db3b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:53.468878Z", "iopub.status.busy": "2022-01-28T14:33:53.468050Z", "iopub.status.idle": "2022-01-28T14:33:53.478660Z", "shell.execute_reply": "2022-01-28T14:33:53.477793Z", "shell.execute_reply.started": "2022-01-28T14:27:55.584491Z" }, "papermill": { "duration": 0.063263, "end_time": "2022-01-28T14:33:53.478827", "exception": false, "start_time": "2022-01-28T14:33:53.415564", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X = wine_df.drop(columns = 'quality')\n", "y = wine_df['quality']\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)" ] }, { "cell_type": "markdown", "id": "0915a73c", "metadata": { "papermill": { "duration": 0.051325, "end_time": "2022-01-28T14:33:53.581175", "exception": false, "start_time": "2022-01-28T14:33:53.529850", "status": "completed" }, "tags": [] }, "source": [ "### DecisionTreeClassifier" ] }, { "cell_type": "code", "execution_count": 21, "id": "e0108836", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:53.692551Z", "iopub.status.busy": "2022-01-28T14:33:53.687901Z", "iopub.status.idle": "2022-01-28T14:33:53.704384Z", "shell.execute_reply": "2022-01-28T14:33:53.705008Z", "shell.execute_reply.started": "2022-01-28T14:27:55.600168Z" }, "papermill": { "duration": 0.074197, "end_time": "2022-01-28T14:33:53.705271", "exception": false, "start_time": "2022-01-28T14:33:53.631074", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 82.77%\n" ] } ], "source": [ "model = DecisionTreeClassifier()\n", "model.fit(X_train,y_train)\n", "print(\"Accuracy: {}%\".format(round(model.score(X_test,y_test)*100,2)))" ] }, { "cell_type": "markdown", "id": "aeb72a29", "metadata": { "papermill": { "duration": 0.051305, "end_time": "2022-01-28T14:33:53.807000", "exception": false, "start_time": "2022-01-28T14:33:53.755695", "status": "completed" }, "tags": [] }, "source": [ "### XGBoost" ] }, { "cell_type": "code", "execution_count": 22, "id": "bc0a32bd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:53.915256Z", "iopub.status.busy": "2022-01-28T14:33:53.914320Z", "iopub.status.idle": "2022-01-28T14:33:54.287883Z", "shell.execute_reply": "2022-01-28T14:33:54.288508Z", "shell.execute_reply.started": "2022-01-28T14:27:55.620397Z" }, "papermill": { "duration": 0.430445, "end_time": "2022-01-28T14:33:54.288749", "exception": false, "start_time": "2022-01-28T14:33:53.858304", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 85.61%\n" ] } ], "source": [ "model = XGBClassifier(eval_metric='mlogloss')\n", "model.fit(X_train, y_train)\n", "y_pred = model.predict(X_test)\n", "predictions = [round(value) for value in y_pred]\n", "accuracy = accuracy_score(y_test, predictions)\n", "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))" ] }, { "cell_type": "code", "execution_count": 23, "id": "7c5f9872", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:54.393799Z", "iopub.status.busy": "2022-01-28T14:33:54.393018Z", "iopub.status.idle": "2022-01-28T14:33:54.546026Z", "shell.execute_reply": "2022-01-28T14:33:54.547002Z", "shell.execute_reply.started": "2022-01-28T14:27:55.979172Z" }, "papermill": { "duration": 0.206936, "end_time": "2022-01-28T14:33:54.547340", "exception": false, "start_time": "2022-01-28T14:33:54.340404", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.07412022 0.14326912 0.04955326 0.07273688 0.06613933 0.06818143\n", " 0.06950161 0.06481194 0.11635959 0.05865109 0.2166755 ]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(model.feature_importances_)\n", "# plot\n", "plt.bar(range(len(model.feature_importances_)), model.feature_importances_)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "id": "fce5fb49", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:54.655397Z", "iopub.status.busy": "2022-01-28T14:33:54.653448Z", "iopub.status.idle": "2022-01-28T14:33:54.920277Z", "shell.execute_reply": "2022-01-28T14:33:54.920952Z", "shell.execute_reply.started": "2022-01-28T14:27:56.182804Z" }, "papermill": { "duration": 0.322481, "end_time": "2022-01-28T14:33:54.921164", "exception": false, "start_time": "2022-01-28T14:33:54.598683", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_importance(model)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "8aa9f625", "metadata": { "papermill": { "duration": 0.05176, "end_time": "2022-01-28T14:33:55.025551", "exception": false, "start_time": "2022-01-28T14:33:54.973791", "status": "completed" }, "tags": [] }, "source": [ "### CatBoost" ] }, { "cell_type": "code", "execution_count": 25, "id": "4e18df94", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:33:55.141559Z", "iopub.status.busy": "2022-01-28T14:33:55.140471Z", "iopub.status.idle": "2022-01-28T14:33:55.303624Z", "shell.execute_reply": "2022-01-28T14:33:55.302611Z", "shell.execute_reply.started": "2022-01-28T14:27:56.459035Z" }, "papermill": { "duration": 0.226503, "end_time": "2022-01-28T14:33:55.303859", "exception": false, "start_time": "2022-01-28T14:33:55.077356", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9680f6c6e7154825a591281474cb3cd1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "0:\tlearn: 0.5973298\ttotal: 51.7ms\tremaining: 982ms\n", "1:\tlearn: 0.5268292\ttotal: 52.7ms\tremaining: 474ms\n", "2:\tlearn: 0.4744002\ttotal: 53.1ms\tremaining: 301ms\n", "3:\tlearn: 0.4355445\ttotal: 53.5ms\tremaining: 214ms\n", "4:\tlearn: 0.4059002\ttotal: 53.9ms\tremaining: 162ms\n", "5:\tlearn: 0.3786499\ttotal: 54.3ms\tremaining: 127ms\n", "6:\tlearn: 0.3583230\ttotal: 54.6ms\tremaining: 101ms\n", "7:\tlearn: 0.3419222\ttotal: 55ms\tremaining: 82.5ms\n", "8:\tlearn: 0.3293313\ttotal: 55.3ms\tremaining: 67.6ms\n", "9:\tlearn: 0.3179416\ttotal: 55.7ms\tremaining: 55.7ms\n", "10:\tlearn: 0.3106634\ttotal: 55.9ms\tremaining: 45.7ms\n", "11:\tlearn: 0.3029725\ttotal: 56.9ms\tremaining: 37.9ms\n", "12:\tlearn: 0.2968714\ttotal: 57.7ms\tremaining: 31.1ms\n", "13:\tlearn: 0.2892533\ttotal: 58.5ms\tremaining: 25.1ms\n", "14:\tlearn: 0.2821918\ttotal: 59.2ms\tremaining: 19.7ms\n", "15:\tlearn: 0.2771839\ttotal: 59.6ms\tremaining: 14.9ms\n", "16:\tlearn: 0.2751512\ttotal: 60.6ms\tremaining: 10.7ms\n", "17:\tlearn: 0.2715680\ttotal: 61.3ms\tremaining: 6.82ms\n", "18:\tlearn: 0.2681186\ttotal: 62.3ms\tremaining: 3.28ms\n", "19:\tlearn: 0.2633026\ttotal: 63.1ms\tremaining: 0us\n", "Accuracy: 87.69%\n" ] } ], "source": [ "model = CatBoostClassifier(iterations=20, learning_rate=0.2)\n", "model.fit(X_train, y_train, plot=True)\n", "predicted = model.predict(X_test)\n", "accuracy = accuracy_score(y_test, predicted)\n", "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))" ] }, { "cell_type": "markdown", "id": "d46ef869", "metadata": { "papermill": { "duration": 0.052301, "end_time": "2022-01-28T14:33:55.411422", "exception": false, "start_time": "2022-01-28T14:33:55.359121", "status": "completed" }, "tags": [] }, "source": [ "The accuracy for all models are 82.95%, 85.61%, and 87.69%." ] }, { "cell_type": "code", "execution_count": null, "id": "99794d36", "metadata": { "papermill": { "duration": 0.0529, "end_time": "2022-01-28T14:33:55.517675", "exception": false, "start_time": "2022-01-28T14:33:55.464775", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 28.515156, "end_time": "2022-01-28T14:33:56.887067", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:33:28.371911", "version": "2.3.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "3452d5f5724748f39e81bb9543ab1b8c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": "stretch", "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": "500px", "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "9680f6c6e7154825a591281474cb3cd1": { "model_module": "catboost-widget", "model_module_version": "^1.0.0", "model_name": "CatboostWidgetModel", "state": { "_dom_classes": [], "_model_module": "catboost-widget", "_model_module_version": "^1.0.0", "_model_name": "CatboostWidgetModel", "_view_count": null, "_view_module": "catboost-widget", "_view_module_version": "^1.0.0", "_view_name": "CatboostWidgetView", "data": { "catboost_info": { "content": { "data": { "iterations": [ { "iteration": 0, "learn": [ 0.5973298206 ], "passed_time": 0.05169358127, "remaining_time": 0.9821780441 }, { "iteration": 1, "learn": [ 0.526829151 ], "passed_time": 0.05271280687, "remaining_time": 0.4744152618 }, { "iteration": 2, "learn": [ 0.4744001641 ], "passed_time": 0.05312782304, "remaining_time": 0.3010576639 }, { "iteration": 3, "learn": [ 0.4355445134 ], "passed_time": 0.05352288121, "remaining_time": 0.2140915248 }, { "iteration": 4, "learn": [ 0.405900153 ], "passed_time": 0.05390761039, "remaining_time": 0.1617228312 }, { "iteration": 5, "learn": [ 0.3786499334 ], "passed_time": 0.05428605326, "remaining_time": 0.1266674576 }, { "iteration": 6, "learn": [ 0.358322958 ], "passed_time": 0.05464583403, "remaining_time": 0.1014851203 }, { "iteration": 7, "learn": [ 0.3419222165 ], "passed_time": 0.05500480254, "remaining_time": 0.08250720381 }, { "iteration": 8, "learn": [ 0.3293313281 ], "passed_time": 0.05534821846, "remaining_time": 0.06764782256 }, { "iteration": 9, "learn": [ 0.317941556 ], "passed_time": 0.05571749415, "remaining_time": 0.05571749415 }, { "iteration": 10, "learn": [ 0.3106633535 ], "passed_time": 0.05590690285, "remaining_time": 0.04574201143 }, { "iteration": 11, "learn": [ 0.302972496 ], "passed_time": 0.05690584409, "remaining_time": 0.03793722939 }, { "iteration": 12, "learn": [ 0.2968713556 ], "passed_time": 0.05772949104, "remaining_time": 0.03108511056 }, { "iteration": 13, "learn": [ 0.2892533121 ], "passed_time": 0.05848829495, "remaining_time": 0.02506641212 }, { "iteration": 14, "learn": [ 0.2821918453 ], "passed_time": 0.05921841594, "remaining_time": 0.01973947198 }, { "iteration": 15, "learn": [ 0.2771838948 ], "passed_time": 0.0596142641, "remaining_time": 0.01490356603 }, { "iteration": 16, "learn": [ 0.2751512258 ], "passed_time": 0.06060885902, "remaining_time": 0.010695681 }, { "iteration": 17, "learn": [ 0.2715680497 ], "passed_time": 0.06134729584, "remaining_time": 0.006816366205 }, { "iteration": 18, "learn": [ 0.2681186251 ], "passed_time": 0.06230380293, "remaining_time": 0.003279147523 }, { "iteration": 19, "learn": [ 0.2633026428 ], "passed_time": 0.06307385173, "remaining_time": 0 } ], "meta": { "iteration_count": 20, "launch_mode": "Train", "learn_metrics": [ { "best_value": "Min", "name": "Logloss" } ], "learn_sets": [ "learn" ], "name": "experiment", "parameters": "", "test_metrics": [], "test_sets": [] } }, "passed_iterations": 19, "total_iterations": 20 }, "name": "catboost_info", "path": "catboost_info" } }, "layout": "IPY_MODEL_3452d5f5724748f39e81bb9543ab1b8c" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }
0086/400/86400737.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "dbc9d792", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:38:17.225209Z", "iopub.status.busy": "2022-01-28T14:38:17.224019Z", "iopub.status.idle": "2022-01-28T14:38:18.227843Z", "shell.execute_reply": "2022-01-28T14:38:18.228653Z", "shell.execute_reply.started": "2022-01-28T14:30:15.368503Z" }, "papermill": { "duration": 1.027071, "end_time": "2022-01-28T14:38:18.229081", "exception": false, "start_time": "2022-01-28T14:38:17.202010", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/recruitment/sample_submission.csv\n", "/kaggle/input/recruitment/final_train.csv\n", "/kaggle/input/recruitment/final_test.csv\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "sns.set_style('darkgrid')\n", "\n", "# Input data files are available in the read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" ] }, { "cell_type": "code", "execution_count": 2, "id": "812c1fc5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:18.271076Z", "iopub.status.busy": "2022-01-28T14:38:18.270325Z", "iopub.status.idle": "2022-01-28T14:38:18.320385Z", "shell.execute_reply": "2022-01-28T14:38:18.320905Z", "shell.execute_reply.started": "2022-01-28T14:30:16.552196Z" }, "papermill": { "duration": 0.071686, "end_time": "2022-01-28T14:38:18.321097", "exception": false, "start_time": "2022-01-28T14:38:18.249411", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " 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"2022-01-28T14:38:18.361775Z", "iopub.status.busy": "2022-01-28T14:38:18.361110Z", "iopub.status.idle": "2022-01-28T14:38:18.387339Z", "shell.execute_reply": "2022-01-28T14:38:18.386743Z", "shell.execute_reply.started": "2022-01-28T14:30:16.609077Z" }, "papermill": { "duration": 0.048097, "end_time": "2022-01-28T14:38:18.387538", "exception": false, "start_time": "2022-01-28T14:38:18.339441", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>feature_0</th>\n", " <th>feature_1</th>\n", " <th>feature_2</th>\n", " <th>feature_3</th>\n", " <th>feature_4</th>\n", 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feature_5 \\\n", "0 10001 4 0 0 0 0 0 \n", "1 10002 2 0 28 0 1 0 \n", "2 10003 5 4 0 1 1 0 \n", "3 10004 1 1 13 0 0 14 \n", "4 10005 0 1 27 1 9 1 \n", "\n", " feature_6 feature_7 feature_8 feature_9 feature_10 feature_11 \\\n", "0 0 0 0 14 0 0 \n", "1 0 1 0 0 4 0 \n", "2 0 2 0 1 0 0 \n", "3 0 0 1 2 0 0 \n", "4 1 2 1 2 0 13 \n", "\n", " feature_12 feature_13 feature_14 \n", "0 0 8 0 \n", "1 0 4 0 \n", "2 1 0 0 \n", "3 2 13 2 \n", "4 0 6 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data = pd.read_csv(\"/kaggle/input/recruitment/final_test.csv\")\n", "test_data.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "af6b34e7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:18.431169Z", "iopub.status.busy": "2022-01-28T14:38:18.430469Z", "iopub.status.idle": "2022-01-28T14:38:18.442652Z", "shell.execute_reply": "2022-01-28T14:38:18.442017Z", "shell.execute_reply.started": "2022-01-28T14:30:16.639841Z" }, "papermill": { "duration": 0.035853, "end_time": "2022-01-28T14:38:18.442831", "exception": false, "start_time": "2022-01-28T14:38:18.406978", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "submission = pd.read_csv(\"/kaggle/input/recruitment/sample_submission.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "3c2fdd73", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:18.488406Z", "iopub.status.busy": "2022-01-28T14:38:18.487683Z", "iopub.status.idle": "2022-01-28T14:38:18.490486Z", "shell.execute_reply": "2022-01-28T14:38:18.489801Z", "shell.execute_reply.started": "2022-01-28T14:30:16.657376Z" }, "papermill": { "duration": 0.028099, "end_time": "2022-01-28T14:38:18.490677", "exception": false, "start_time": "2022-01-28T14:38:18.462578", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "columns = train_data.columns\n", "features = [col for col in columns if (col != 'id' and col != 'target')]" ] }, { "cell_type": "code", "execution_count": 6, "id": "8371d136", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:18.540664Z", "iopub.status.busy": "2022-01-28T14:38:18.539888Z", "iopub.status.idle": "2022-01-28T14:38:22.478060Z", "shell.execute_reply": "2022-01-28T14:38:22.478560Z", "shell.execute_reply.started": "2022-01-28T14:30:16.664268Z" }, "papermill": { "duration": 3.968507, "end_time": "2022-01-28T14:38:22.478749", "exception": false, "start_time": "2022-01-28T14:38:18.510242", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x1080 with 15 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(25,15))\n", "for i,col in enumerate(features):\n", " plt.subplot(3, 5, i+1)\n", " sns.distplot(a = train_data[col], label = col, kde = False)\n", " plt.title(col)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f27c8528", "metadata": { "papermill": { "duration": 0.022239, "end_time": "2022-01-28T14:38:22.523017", "exception": false, "start_time": "2022-01-28T14:38:22.500778", "status": "completed" }, "tags": [] }, "source": [ "The figure shows that the data is almost entirely skewed and will need to be standardised" ] }, { "cell_type": "code", "execution_count": 7, "id": "7cf3709f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:22.570452Z", "iopub.status.busy": "2022-01-28T14:38:22.569475Z", "iopub.status.idle": "2022-01-28T14:38:22.603357Z", "shell.execute_reply": "2022-01-28T14:38:22.603984Z", 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"text": [ "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x1080 with 15 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(25,15))\n", "for i,col in enumerate(features):\n", " plt.subplot(3, 5, i+1)\n", " sns.distplot(a = train_data[col], label = col, kde = False)\n", " plt.title(col)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "id": "875ff50a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:26.570131Z", "iopub.status.busy": "2022-01-28T14:38:26.569144Z", "iopub.status.idle": "2022-01-28T14:38:26.606766Z", "shell.execute_reply": "2022-01-28T14:38:26.607246Z", "shell.execute_reply.started": "2022-01-28T14:30:24.763800Z" }, "papermill": { "duration": 0.067144, "end_time": "2022-01-28T14:38:26.607416", "exception": false, "start_time": "2022-01-28T14:38:26.540272", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>feature_0</th>\n", " <th>feature_1</th>\n", " <th>feature_2</th>\n", " <th>feature_3</th>\n", " <th>feature_4</th>\n", " <th>feature_5</th>\n", " <th>feature_6</th>\n", " <th>feature_7</th>\n", " <th>feature_8</th>\n", " <th>...</th>\n", " <th>feature_10</th>\n", " <th>feature_11</th>\n", " <th>feature_12</th>\n", " <th>feature_13</th>\n", " <th>feature_14</th>\n", " <th>target</th>\n", " <th>New_feature1</th>\n", " <th>New_feature2</th>\n", " <th>New_feature3</th>\n", " <th>New_feature4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>0.693147</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.386294</td>\n", " <td>0.693147</td>\n", " <td>0</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>0.693147</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1.945910</td>\n", " <td>0.000000</td>\n", " <td>1.098612</td>\n", " <td>1.098612</td>\n", " <td>0.693147</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.098612</td>\n", " <td>0.000000</td>\n", " <td>1</td>\n", " <td>1.945910</td>\n", " <td>3.044522</td>\n", " <td>3.044522</td>\n", " <td>2.639057</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1.386294</td>\n", " <td>0.000000</td>\n", " <td>1.609438</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>1.098612</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>1.791759</td>\n", " <td>0.693147</td>\n", " <td>1</td>\n", " <td>1.386294</td>\n", " <td>2.995732</td>\n", " <td>1.386294</td>\n", " <td>1.386294</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1.098612</td>\n", " <td>0.000000</td>\n", " <td>2.079442</td>\n", " <td>1.386294</td>\n", " <td>1.791759</td>\n", " <td>0.0</td>\n", " <td>0.693147</td>\n", " <td>0.693147</td>\n", " <td>1.386294</td>\n", " <td>...</td>\n", " <td>2.197225</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>2.197225</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " <td>1.098612</td>\n", " <td>3.178054</td>\n", " <td>2.484907</td>\n", " <td>2.890372</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>1.098612</td>\n", " <td>1.098612</td>\n", " <td>1.386294</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>0.000000</td>\n", " <td>0.693147</td>\n", " <td>0</td>\n", " <td>0.693147</td>\n", " <td>1.791759</td>\n", " <td>1.791759</td>\n", " <td>2.079442</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " id feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n", "0 1 0.000000 0.693147 0.000000 0.693147 0.693147 0.0 \n", "1 2 1.945910 0.000000 1.098612 1.098612 0.693147 0.0 \n", "2 3 1.386294 0.000000 1.609438 0.000000 0.000000 0.0 \n", "3 4 1.098612 0.000000 2.079442 1.386294 1.791759 0.0 \n", "4 5 0.693147 0.000000 1.098612 1.098612 1.386294 0.0 \n", "\n", " feature_6 feature_7 feature_8 ... feature_10 feature_11 feature_12 \\\n", "0 0.000000 0.000000 0.000000 ... 0.693147 0.000000 0.000000 \n", "1 0.000000 0.693147 0.000000 ... 0.693147 0.000000 0.000000 \n", "2 0.000000 1.098612 0.000000 ... 0.000000 0.000000 0.693147 \n", "3 0.693147 0.693147 1.386294 ... 2.197225 0.693147 0.000000 \n", "4 0.000000 0.693147 0.000000 ... 0.000000 0.000000 0.693147 \n", "\n", " feature_13 feature_14 target New_feature1 New_feature2 New_feature3 \\\n", "0 1.386294 0.693147 0 0.693147 0.000000 0.693147 \n", "1 1.098612 0.000000 1 1.945910 3.044522 3.044522 \n", "2 1.791759 0.693147 1 1.386294 2.995732 1.386294 \n", "3 2.197225 0.000000 0 1.098612 3.178054 2.484907 \n", "4 0.000000 0.693147 0 0.693147 1.791759 1.791759 \n", "\n", " New_feature4 \n", "0 0.693147 \n", "1 2.639057 \n", "2 1.386294 \n", "3 2.890372 \n", "4 2.079442 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def addFeature(df):\n", " df[\"New_feature1\"] = df['feature_0'] + df['feature_1']\n", " df[\"New_feature2\"] = df['feature_0'] + df['feature_2']\n", " df[\"New_feature3\"] = df['feature_0'] + df['feature_3']\n", " df[\"New_feature4\"] = df['feature_0'] + df['feature_4']\n", " return df\n", "\n", "train_data = addFeature(train_data)\n", "test_data = addFeature(test_data)\n", "new_features = ['New_feature1','New_feature2','New_feature3','New_feature4']\n", "train_data.head()" ] }, { "cell_type": "code", "execution_count": 11, "id": "d1a732aa", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:26.664452Z", "iopub.status.busy": "2022-01-28T14:38:26.663837Z", "iopub.status.idle": "2022-01-28T14:38:27.641585Z", "shell.execute_reply": "2022-01-28T14:38:27.642174Z", "shell.execute_reply.started": "2022-01-28T14:30:24.808020Z" }, "papermill": { "duration": 1.008063, "end_time": "2022-01-28T14:38:27.642351", "exception": false, "start_time": "2022-01-28T14:38:26.634288", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x1080 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(25,15))\n", "for i,col in enumerate(new_features):\n", " plt.subplot(3, 5, i+1)\n", " sns.distplot(a = train_data[col], label = col, kde = False)\n", " plt.title(col)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "id": "d4ffe4a5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:27.738182Z", "iopub.status.busy": "2022-01-28T14:38:27.731132Z", "iopub.status.idle": "2022-01-28T14:38:31.218586Z", "shell.execute_reply": "2022-01-28T14:38:31.219109Z", "shell.execute_reply.started": "2022-01-28T14:30:25.814885Z" }, "papermill": { "duration": 3.548034, "end_time": "2022-01-28T14:38:31.219272", "exception": false, "start_time": "2022-01-28T14:38:27.671238", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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JKHte3ncMx9xB3D11ZM4XFhDlM7YEScMf32nGJWeU471mZ7ZDIaKTTJo0CaWlpQkdW19fj9mzZ2sckfr+eaQLZp2IySPL0XCoM9vhENEg9HmrG5eMKkdLlw++oJztcIioQB1y+lBRZMBf3j+U7VCIaBDr8gWx/5gb/pCMQ52+bIdDVNBYYZ2iUFjB/mMefH/qKPy8/mMoisKna0R5yOv14tVXX8VPf/rTPq/fcsstEAQB119/Pa6//vq4nyNJAmy2zFb8tO1rw7AKC8ZUFeOfrW78S4bPrzVJEjP+N9VaIX4noHC/F8Xm8ofg9AZxqs2MoSUm7D/mxrihJdkOi4gK0KEuHy4bOwQNzXxAT0TZ81ZjB8ZWWlFaZMDuAx0YZjNnOySigsWEdYqOufwoNulgLzZCEgQ0OX04rYyLFVG+efnll3H++ef3aQeyfv162O12tLW14eabb8aoUaMwadKkmJ8jywqcTo/G0fb16eFOlOgljKksQn3D4YyfX2s2m4XfKU8UwveqrCzOdgh5Z99RN4bZTBBFAcNsZnx2lAlrItJGS5cPV5xdhdf2tWU7FCIaxP5xuAujhlgwpMSEd5o6UXfuKdkOiahgsSVIihzdflRYDBAEAcPLzNh/zJ3tkIgoBfX19Zg1a1af1+x2OwCgoqIC06dPR0NDQzZCi6upw4tKqwFDbSa0uQPwBLgdn4gy57OjLpxSagIAnFJqwicOV5YjIqJCJIcVHHUFcPqQInR6g9kOh4gGsVZXAGVmPaqKjWjp8mc7HKKCxoR1ilq6/Ci36AEApSY92tyBLEdERMnq7u7Gnj17MG3atN7XPB4PXC5X779ff/11jBkzJlshxtTc6UOl1QhREFBmMeAY1yEiyqD9x9wYWtKTsDbiizY+vCci9R1zB2A16lBi0iEUVtgvn4iy5pg7gFKzHqUWPdo9vPci0hJbgqSopdsPmzmSsLYaJRxz8ekaUS5ZunQpdu/ejY6ODkydOhVLlixBKBQCACxYsAAA8NJLL2HKlCmwWL7qvdvW1obFixcDAGRZxuzZszF16tTMf4E4FEXB4U4fKq0GAIDNrMNRl5+tiYgoY1pdAYyrjrRSKTXp+dCMiDTR0uVDRZEegiCgxKSD0xtEtV7KdlhENAi1uwMoMelgMzNhTaS1rCSsly1bhldeeQUVFRXYunVrv/eff/55PPHEEwCAoqIiPPDAAzjrrLMyHWZMR7p8KOupsDbrcZQ3aUQ5ZdWqVXGPqaurQ11dXZ/Xhg8fjueff16rsFTT6QtBUYAiQ+SGrdSkxzEX1yEiypxj7gBKTZFLyVKzDu1ubtUnIvUd7vKhwhJ5QF9sPJ6wPr67g4gok9o8AZSa9DDrJchhBd6gDDMfoBFpIistQerq6rBmzZoB3x82bBjWrVuHLVu24LbbbsNPf/rTDEaXmBMT1iUmHY4yUUREGXTI6UVVcaSPPnB8HeKDMyLKoEiVUeRayKKXEAqHuVWfiFR3tDuSIAIA6/GENRFRpnmDMuSwArNehCAIKDWzNSyRlrKSsJ40aRJKS0sHfP/888/vfX/ixIloaWnJVGgJYw9rIsomR7cf5cerjYBIwrq1m62JiCgzFEVBhzeIUnOkwrrnxo1tQYhIbd3+EEz6yG2r1SihgwlrIsqCtuP9q3sKhpgHItJWzg9d3LhxY072j211fZUsKjXp0Mb+RUSUQR3eIKyGr7aflZr1aGUvfSLKkC5fCAZJhF766lLSxkojItJAtz/Uu+W+yKBDh4cJayLKvGOuQO8cMyBSMNTG9YhIMzk9dPGtt97Cxo0b8ac//Smh4yVJgM1miX9gmvxBGf5QGPZyC0RRxNAhRej0hlBSYoYoCpqfXw2SJGbkb6WFfI09X+MG8jv2QuX0BmE5IWFtM+vxblNnFiMiosHk2PEqoxOVmHSssCYi1XX7QhhaagQQmd3BliBElA3Hjg9c7FFs0vFBPZGGcjZh/cknn+AnP/kJnnjiCZSVlSX0O7KswOn0aBwZ0Nrth9Wgg9sdgNVqhN8bhEkv4kBLJ8pO2KKfy2w2S0b+VlrI19jzNW4gM7FXVhZr+vmFpt0dhNX41RJeygsmIsqgNncANlPfy8gSo47DX4lymN/vxw033IBAIABZljFjxgzceeedfY4JBAK45557sHfvXthsNjzyyCMYNmxYliKOcAVCMOsjhRNWIwe8ElF2tLkDKDnh/qvYqEMbd7gSaSYnW4IcPnwYS5YswS9/+UuMHDky2+H00+ULocjYdxJsZBssL56IKDPaPQEUn5As6tmKryhKFqMiosHimLvvGgREKo04/JUodxkMBjz11FN4/vnn8dxzz+HVV1/FBx980OeYDRs2oKSkBC+99BK+973v4de//nV2gj2B2y/DpIvce7GHNRFly9GTrn049J5IW1mpsF66dCl2796Njo4OTJ06FUuWLEEoFAIALFiwAL/97W/hdDrx4IMPAgAkScKmTZuyEWpUnb4gigx9/3SlJj2Ouf0YXVmUpaiIaDBxeoM42/5VVbpJLwEC4A7IfSqviYi0EKky6tsSpNSkx1EOfyXKWYIgoKgocq8SCoUQCoV6h4f12LFjB+644w4AwIwZM7BixQooitLvuEyKVFj3DF1kD2siyo5jLj/KLH17WDe25+cOaqJ8kJWsxqpVq2K+v3LlSqxcuTJD0SSvyxdCkaFvhXWJSccKayLKmA5vENaTdnqUmvTo8ASZsCYizR119a+wLjHr8EmrK0sREVEiZFlGXV0dDh48iO985zs499xz+7zvcDgwdOhQAIBOp0NxcTE6OjpQXl6ejXABRCqse4YuWo06OL2saCSizOv0hTDMZu79ucSoQzsfoBFphlmNFHT5gr0XTT0segmdPi5WRJQZnd5Qv8S0xSChyx/KUkRENJgcdfkxvMzc5zX20ifKfZIkYfPmzejq6sLixYvx2WefYezYsWl+praD7z1BGUNsZoiiiCGlJvhC4YwOA8/W8PFsDj0fbN+Z56VEuPyhPnkgs0GCm/deRJphwjoFnd4QLCdVWJv0IlxcrIgoAxRFQacviOKTEtZFBgldfHBGRBng9IZwtr3vGmQ16NDNayGivFBSUoKLLroIr776ap+Etd1ux5EjR1BdXY1QKITu7m6UlZXF/CwtB98rigK3X4YcCCFs1iMUCMETkDM6yDxbg9OzObB9sH3nwX7eysriGEdTD5dfhsXw1Rg4s16CKyBnMSKiwpaTQxdzXaSH9UkV1gYJnT7epBGR9jxBGaIgwKDru4Rb9BK6vFyHiEh7Xb5gv4f3FoOEbl4LEeWs9vZ2dHV1AQB8Ph/eeOMNjBo1qs8xtbW1ePbZZwEAL774IiZPnpzV/tX+UBiiCOilyDWPSSfBG5Q5ZJqIMu7kCmuLXoLbz4Q1kVZYYZ0Cp7d/j1izXkKri9tgiUh7Tm//6mogsi2NLUGIKBO6/f13mxl1IoKygkAo3O+BGhFlX2trK370ox9BliMJ36uuugqXX345Vq9ejfHjx2PatGmYN28e/v3f/x3Tp09HaWkpHnnkkazG7PKHYDkhQSSJAiRRgD8UjgycJqKcs2zZMrzyyiuoqKjA1q1b+72/Zs0abNmyBUCkr/7+/fvx5ptvwmazoba2FkVFRRBFEZIkYdOmTZkOf0CRAbBfrTt6SYAc5nUPkVaYsE5BpzcEe7Gxz2usKiKiTHF6gv2GnQGRB2dsCUJEmdDt7z+AWhAEWI2RB2dDdIYsRUZEAznrrLPw3HPP9Xv9rrvu6v230WjEf/7nf2YwqtgiW/BPasV4vMqaCWui3FRXV4eFCxfi3nvvjfr+okWLsGjRIgDAjh078OSTT8Jms/W+/9RTT2V10Gs0YUWBJyD3SVgLgoAiowRXIIRyXvcQqY6PgVIQtSWIXmLfRiLKCKc3BKuh/02aRS/ByZYgRKSxnps2S5RkUREf4BORitwnVTQCkdlBbvaNJcpZkyZNQmlpaULH1tfXY/bs2RpHlD5PQIZBEiGJfVskmfUSXGwLQqQJVlinoMsXgsXQ90/HCmsiypQObwBFUVqCFBkktLnZmoiItOXyh2DSSxDF/n1ti4w67vQgItW4/P0rqU06Ed4gE0RE+c7r9eLVV1/FT3/60z6v33LLLRAEAddffz2uv/76uJ8jSQJsNotWYQIAvJ1eWE06WK2RnfaiKMJqNUZ2vep1mp8/EyRJLIjvcaJC/E5A4X6vkzFhnYIuX/9tsBZ9ZCsIEZHWunyh6JWNRglftHEdIiJtdfmi7/IAOISaiNQV6Rnbd1OwUSfBwwprorz38ssv4/zzz+/TDmT9+vWw2+1oa2vDzTffjFGjRmHSpEkxP0eWFTidHk1jPXTUDbNOhMvlBwBYrUa4XH4YJBFH2lw4zarX9PyZYLNZNP87Zlohfieg8L5XZWVx1NfZEiQF0fo2mg0SXGwJQkQZ4PbLMOn7L98WvYROVjYSkcYiD+6j1zxY2EufiFTk8odg0vVvCeJhhTVR3quvr8esWbP6vGa32wEAFRUVmD59OhoaGrIRWj/d/v7tiQDArBeZByLSCBPWSfKHwpDDCoy6k5/0i/CHwgiFlSxFRkSDRacvGPWCib1jifLDrl27MGPGDEyfPh2PP/541GNeeOEFzJw5E7NmzcLdd9+d4Qhj64oyy6NHJGHNdYiI1OEOyDBFue/yssKaKK91d3djz549mDZtWu9rHo8HLper99+vv/46xowZk60Q++j2h2COcu1j0rFwkUgrbAmSpO7jN2mC0LdvoygIsByvsraZ8387CBHlLpc/hDJL/3XGYuDwV6JcJ8syVqxYgbVr18Jut2PevHmora3F6NGje49pbGzE448/jvXr16O0tBRtbW1ZjLi/Ll/0mzYgsuOsy8sKayJSR6Rnft+EtUFihTVRLlu6dCl2796Njo4OTJ06FUuWLEEoFLlHWbBgAQDgpZdewpQpU2CxfNWHt62tDYsXLwYQuV6aPXs2pk6dmvkvEIUrRoV1N4cuEmmCCeskufwyLAPcpBUZdExYE5Hmuv0yTik19Xu9Zw0iotzV0NCAESNGYPjw4QCAWbNmYfv27X0S1n/5y19www03oLS0FEBkW2wuGaiPPhDZ6eFkhTURqaTbL8N4cksQncge1kQ5bNWqVXGPqaurQ11dXZ/Xhg8fjueff16rsNLS7evfTx8AzHoJ3WyFRqQJJqyT5A5Ef7IGcBssEWXGQD3U9JIABYAvKMM0wDpFRNnlcDhQXV3d+7Pdbu/Xn7GxsREA8O1vfxvhcBh33HFH3AojSRIyNi08AAElRQZYrUYAgCiKvf8uLzGhsc2TE5PLc3mCeq7GxriSk6txFRJvoH+xkJ4JayLKMFcg1O/hGQCY9NzhSqQVJqyT5AoMnAjidnwiygSXPxR1p4cgCCgy6NDlCzFhTZTHZFnGgQMH8Mwzz6ClpQULFy7Eli1bUFJSEuN3lIxNC3c4PdBDgcvlBwBYrcbef4vhMNq6fDkxuTyXJ6jnamyMKzlqxVVZWaxCNIXJG5T7tUEzsiUIEWVYZHdZlKH3BhEtXf4sRERU+Dh0MUnRBn/0MOvZcJ+ItDdQDzUAsBq404Mol9ntdrS0tPT+7HA4YLfb+x1TW1sLvV6P4cOH4/TTT++tus4Fnd4gigzRax6KuAYRkYq8IRmGk+69THoRbvaMJaIM6vIN1MNagovrEZEmmLBOkicQipGwFnmTRpQjli1bhosvvhizZ8+O+v7bb7+Nr3/965gzZw7mzJmDRx99tPe9Xbt2YcaMGZg+fToef/zxTIWcMHdAHjBhbTbwwRlRLpswYQIaGxvR1NSEQCCA+vp61NbW9jnmiiuuwO7duwEA7e3taGxs7O15nQs6vdF3eQDo3eVBRKQGXzAMg9R32L2RLUGIKMO6fdGvfcxsCUKkGbYESZLbL/ebVN0j0nCfixVRLqirq8PChQtx7733DnjMBRdcgN///vd9XpNlGStWrMDatWtht9sxb9481NbW9hmIlk2KosDtl6MO/QAiD87cvIkjylk6nQ7Lly/HokWLIMsy5s6dizFjxmD16tUYP348pk2bhm984xt4/fXXMXPmTEiShHvuuQdlZWXZDr1X1wBtiYDIPA/euBGRWrxBGcaTioWMOgmeoC9LERHRYOQaYJYZd9kTaYcJ6yS5AzIMUZrtA5Gn/e4AFyuiXDBp0iQ0Nzcn/XsNDQ0YMWJEbzXjrFmzsH379pxJWPtDYYgioJeiJ6xNOonrEFGOq6mpQU1NTZ/X7rrrrt5/C4KAZcuWYdmyZZkOLSGx2hKZ9JHKR0VRIAhC1GOIiBIVqbA+qSWIjg/niSizBmoNa9aLTFgTaYQtQZLk8odgHqAliEkvsn8RUR754IMPcO2112LRokX4/PPPAUT6yVZXV/ceY7fb4XA4shViPy5/CEUDVDYCkQdnLt7EEZGGIm2Jol8L6SURohh5uEZElC5flB7WkZYgXGOIKHO8gf67PYBIsZAvyPWISAussE6Syx+CaaCqIp2Edk8gwxERUSrGjRuHHTt2oKioCDt37sTixYuxbdu2lD9PkgTYbBYVI4zuWCCMIqMeVqux9zVRFHt/LrYYEBbFjMSiNUkqjO9xokL8TkDhfi+KzhOjjz4QaQsS63qJiChRvmAYRql/wtob5MN5Isocb1CGMcp1jVEnwhviekSkBSask+QKhGCz6KO+Z2TvWKK8YbVae/9dU1ODBx98EO3t7bDb7Whpael9z+FwwG63x/08WVbgdHo0ifVER465YNIJcLn8va9Zrcben0VFwTGnJyOxaM1msxTE9zhRIX4noDC+V2VlcbZDyAuKosAdYwA1AFgMElwBGUMyGBcRFSZ/KNyvwtqklzh0kYgyyhsMR7320UsC5LCCkByGboCWjUSUGv43Kkku/8BVReynRpQ/jh49CkVRAET6VofDYZSVlWHChAlobGxEU1MTAoEA6uvrUVtbm+Vov9Ido3csEOmj1s11iIg04g+FoROFmDdlJr0EN/s5EpEKfCG5Xw9rVjQSUaZFGwALROaOmPQSPNz1QaQ6VlgnaaBm+0CkJQif9hPlhqVLl2L37t3o6OjA1KlTsWTJEoRCkQTKggUL8OKLL2L9+vWQJAkmkwmrVq2CIAjQ6XRYvnw5Fi1aBFmWMXfuXIwZMybL3+YrsYadAZF1qM3N1kREpA2XPwRLjD76AGDWS+ylT0Rpk8MK5LACvdR3gKtRJ8LLNYaIMiQoh6EogE6MPkzarIsMnC4xRd+JT0SpYcI6Se5ACKYBBg2Z9CIT1kQ5YtWqVTHfX7hwIRYuXBj1vZqaGtTU1GgRVtpcAXnANQg4PvyV6xARacQVp381ENnpwQprIkpXz8BFQeibJDJIIgJyGIqi9HuPiEhtPcOmB1pvjKywJtIEW4IkyROQYdINPHTRHeANGhFpx+ULDbgGAZHKRq5DRKQVd0COO0zRpBPh8vPGjYjS440ycBEAJFGAKAgIykoWoiKiwWaggYs9TNz1QaQJJqyT5Ilxo2bSi/AGwxmOiIgGE1ecYWcmvQg3E0VEpJF4bYmAyAN8Fx+cEVGafAP0jAUAg06Ej32siSgD3AEZ5lj3X5xlRqQJJqyT5AlGtoNEY9SJ8HIrCBFpyO2P0xKEOz2ISEOxZnn0MOr44IyI0ucLhmEYYL0xSiL8IRYKEZH2vAEZxhj3XwbmgYg0kZWE9bJly3DxxRdj9uzZUd9XFAU///nPMX36dFxzzTXYu3dvhiOMLhCK3Wxff3zLWoAXT0SkEZc/XksQ9tInIu1EKqxjXz6a9BK62cOaiNLkjVdhzZ2tRJQBnoAMY4z7L6NOZA9rIg1kJWFdV1eHNWvWDPj+rl270NjYiG3btuFnP/sZHnjggcwFF4Pn+KChWMM9OHiRiLTkjvOE38ShH0SkIXecmzYg8uCMCWsiSpcvJEftYQ0ABklgSxAiyghPMPbuMqOOOSAiLWQlYT1p0iSUlpYO+P727dtx3XXXQRAETJw4EV1dXWhtbc1ghNG5AvGrisw6Ce4gb9KISBuRHmqxn/D7Q2HIYQ4iIiL1RXZ5xLkW0ktwMWFNRGnyBsPQs8KaiLLMG5QHbE8EAEZJYsKaSAM52cPa4XCgurq69+fq6mo4HI4sRhThjjFwsYfJILFvIxFpxhMMxaywFgUBJh0vmohIGy5/KGYffaAnYc01iIjS4wvGqrBmD2siyozI7rJYPawF3nsRaUCX7QDUJEkCbDaLZp8vOn2wmnSwWo1fvSaKfX4uMuogGvWaxqEGSRJzPsaB5Gvs+Ro3kN+xFxpPAgPPzHoR7kAIxaaCWuKJKAe4/CEUG2OvLSa9CBeHvxJRmnzBcO+MoJMZJJEtQYgoI7wBGQZp4LawRp0ENxPWRKrLyWyG3W5HS0tL788tLS2w2+1xf0+WFTidHs3iau3wQCcALpe/9zWr1djnZ70AONrdcJYao31EzrDZLJr+rbSUr7Hna9xAZmKvrCzW9PMLRSRhHa9/rAQXL5qISAMufwiV1tjXOGa9BDdbghDlnCNHjuCee+5BW1sbBEHAt771Ldx00019jnn77bdx++23Y9iwYQCA6dOn44477shGuMe34Q887J4tQYhy07Jly/DKK6+goqICW7du7fd+rHVm165dWLlyJcLhMObPn49bb701o7FH4w7EHnpv0olMWBNpICcT1rW1tVi3bh1mzZqFDz/8EMXFxaiqqsp2WJEna3ESRUY9t+ITkXa8wXDc7fgmJouISCMuvxx/ngcHUBPlJEmS8KMf/Qjjxo2Dy+XC3LlzMWXKFIwePbrPcRdccAF+//vfZynKr/hCA1dY6zl0kShn1dXVYeHChbj33nsHPCbaOiPLMlasWIG1a9fCbrdj3rx5qK2t7bdGZVq8liBGvYjWE4oYiUgdWUlYL126FLt370ZHRwemTp2KJUuWIBSKJFcWLFiAmpoa7Ny5E9OnT4fZbMZDDz2UjTD78cToo9bDKInwcBssEWlAURR44lwwAZFkESusiUgLrkAo/jwPPbfGEuWiqqqq3iIgq9WKUaNGweFwZD0ZNBBvIBSzhzUrrIly06RJk9Dc3Jz07zU0NGDEiBEYPnw4AGDWrFnYvn171tcod0BGaYxWi0adCE+Q1z1EastKwnrVqlUx3xcEAffff3+GokmcNyDDOMC2tB7cDkJEWgnICgQBA1Yb9TDqJHi5DhGRBtx+GeY4CWujTkRADiMUVqATY183EVF2NDc34+OPP8a5557b770PPvgA1157LaqqqnDvvfdizJgxMT9LqzlCYUmC1aLvnRd04uwgi0kHUS9lZMZKtma5ZHOGzGD7zjxv5kVbZxwOB6qrq3uPsdvtaGhoiPtZWs8yCylAqdU44CwzW7EJgXBX1v+m6cqF/1yorRC/E1C43+tkOdkSJFd5gjIMcSobDUxYE5FGPIFQ3EQRcPwpP9chItKAJxh/8KsoCDDqRHgDMoe/EuUgt9uNO++8E/fddx+sVmuf98aNG4cdO3agqKgIO3fuxOLFi7Ft27aYn6fVHCFntw8mvdQ7L+jE2UFKWEFHty8j82GyNYcmm/NvBtt3HuznzfQsoVTWmVi0nmXmdPuBcvOAs8yUoIwuTyBv51X1yOeZWwMpxO8EFN73GmgNin3HQX14AjKMUpxtsDqRvWOJSBPugBy3fzUAGHUCt6URkSY8Ca5DZr0EN1ukEeWcYDCIO++8E9dccw2uvPLKfu9brVYUFRUBAGpqahAKhdDe3p7pMAEA3lB4wKGLBklgSxCiPDXQOmO329HS0tJ7nMPhgN1uz1aYvTzBcOwe1joRXt57EamOCeskxGu2D0Qa7rPCmoi0EEkUxa+wNkissCYi9SmKAm9QhinOAGog0seaD86IcouiKPjxj3+MUaNG4eabb456zNGjR6EoCoBIP9lwOIyysrJMhtnLF5RhiNHDmmsMUX4aaJ2ZMGECGhsb0dTUhEAggPr6etTW1mY52uP3YDHyQCY91yMiLXCfZhI8gRDKLYaYx5h0EtrdwQxFRESDSbyLpR5GHSsbiUh9/lAYOlGAlEBf6siOM968EeWSd999F5s3b8bYsWMxZ84cAMDSpUtx+PBhAMCCBQvw4osvYv369ZAkCSaTCatWrYIgZKcXvS8oDzi3w8A1hihnLV26FLt370ZHRwemTp2KJUuWIBSK3JvEWmd0Oh2WL1+ORYsWQZZlzJ07N24P/UzwBmMXLhp1HAJLpAUmrJPgCcgYWhpv2BmfrhGRNtxBGeYEtuKbuNODiDTgCSa2ywOItAThTg+i3HLBBRfg008/jXnMwoULsXDhwgxFFJsvFI5ZYX0sGMhwRESUiFWrVsV8P9Y6U1NTg5qaGi3CSpkvziwzk06CNyhDUZSsPeAjKkRsCZIET5wnawD7FxGRdtz+EIwJbMU3cvgrEWnAE5ATGvwKHF+HeD1ERGnwh8IwSNGTP3pJgC/EikYi0p4vFLuHtSQKEAUBQVnJYFREhY8J6yQksh3fqBPhZaKIiDTgSaCPPnB8pwfXIaKctWvXLsyYMQPTp0/H448/3u/9TZs2YfLkyZgzZw7mzJmDDRs2ZCHK/twJtiUCetYhtiYiotT5Q+GYLUF8fChGRBngC4ZhHGAt6sHCRSL1sSVIErxxpsMCPQsVn/YTkfo8QTnuxRJwfNgZE0VEOUmWZaxYsQJr166F3W7HvHnzUFtbi9GjR/c5bubMmVi+fHmWoowuMvg1sYS1iQ/OiChNkYR19AprgyTCzwprItJYUI6sM7oEE9alZn0mwiIaFFhhnYRIs/3YW2H5ZI2ItOJOpsKa6xBRTmpoaMCIESMwfPhwGAwGzJo1C9u3b892WAmJ7PJIsCUIe+kTUZr8cXpY+0JcY4hIW96gDGMCD+s5eJFIfaywTkK86bAAE9ZEpB23P5RQdSMrG4lyl8PhQHV1de/PdrsdDQ0N/Y7btm0b9uzZg5EjR2LZsmUYOnRozM+VJAE2m0X1ePvQd8Jq0sFqNfZ5WRTFfq8VWwwIIQMxxSBJYlbPH0uuxsa4kpOrcRWKQCg84KAzgyQwOUREmktklz3AgiEiLTBhnYTEEtYSW4IQkSbcCQ48Y2siovx2+eWXY/bs2TAYDPjzn/+Me++9F08//XTM35FlBU6nR9O4jnZ4IAFwufx9Xrdajf1eE8MK2rp9mscUi81myer5Y8nV2BhXctSKq7KyWIVoCo9fjt3Dmi1BiEhr3gTnd0TyQExYE6mJCesEhRUloWb7eklAKByGHFYgidF7rhGR9pYtW4ZXXnkFFRUV2Lp1a7/3n3/+eTzxxBMAgKKiIjzwwAM466yzAAC1tbUoKiqCKIqQJAmbNm3KaOwDcQdklFni90Uz6SQOfyXKUXa7HS0tLb0/OxwO2O32PseUlZX1/nv+/Pn41a9+lbH4YvEk8OC+h1EnotMV0DgiIipUiqIgwB7WRJRl3pA8YGuiExl13PVBpDb2sE5Qz5RqMU4SWhQE9lQjygF1dXVYs2bNgO8PGzYM69atw5YtW3Dbbbfhpz/9aZ/3n3rqKWzevDlnktVA5Ak/t6QR5bcJEyagsbERTU1NCAQCqK+vR21tbZ9jWltbe/+9Y8cOnHHGGZkOMyp3QB5we/7JzHoJbj+HvxJRagJypPhHFJiwJqLsSWSXPQAYJFZYE6mNFdYJ8gTkhHrHAoBJL8IbkFFk4J+XKFsmTZqE5ubmAd8///zze/89ceLEPhWPucoTTGxLml4SEFYUBGNspSWi7NDpdFi+fDkWLVoEWZYxd+5cjBkzBqtXr8b48eMxbdo0PPPMM9ixYwckSUJpaSkefvjhbIcN4Hgf/SQqrDl0kYhS5Q/FfkDGliBElAmJ9rA2SAIT1kQqY0Y1Qd5gYr1jgUj/Ig+3gxDljY0bN2Lq1Kl9XrvlllsgCAKuv/56XH/99VmKrC9PQIZRF38dEgQBZr0ET0BGqZkJa6JcU1NTg5qamj6v3XXXXb3/vvvuu3H33XdnOqy43AEZlgSvhUx6Dn8lotT5Q7FbMUqiAAUKQnIYOj6cJyKN+IKJ7S4zcIYQkeqYsE6QJ8Gt+ABg0ol8ukaUJ9566y1s3LgRf/rTn3pfW79+Pex2O9ra2nDzzTdj1KhRmDRpUszPkSQBNptF01j9YQVlJSZYrcY+r4ui2O81s0GC3myAzWbWNCYtSZKo+d800wrxOwGF+72oL3cglFAffSDSS5+tiYgoVf5QOG6SyKgT4QuFYWXCmog04g0m1sPaIInw8bqHSFVMWCco0d5FwPGna6wqIsp5n3zyCX7yk5/giSee6DPkrGcAWkVFBaZPn46Ghoa4CWtZVuB0ejSN1+0LQg6E4HL5+7xutRr7vWaURBw55oIFiqYxaclms2j+N820QvxOQGF8r8rK4myHkPPcgcTaEgGRCmt3gD2siSg1vuDAAxd7ROYGhXHSM3siItV4g/EfngGRlowsWiRSFx9HJyjR3rFApMKaVUVEue3w4cNYsmQJfvnLX2LkyJG9r3s8Hrhcrt5/v/766xgzZky2wuwj0R5qQKTqiBdNRKSmyDyPBFuC6CR4A9waS0Sp8YfiVzVGBi/yWoeItOMNJFZhbeTOMiLVscI6Qd5AYr2LgJ7+RVysiLJp6dKl2L17Nzo6OjB16lQsWbIEoVCk2m/BggX47W9/C6fTiQcffBAAIEkSNm3ahLa2NixevBgAIMsyZs+e3a+/dbYks9PDpJc48IyIVJVMhXVkq74MRVEgCLGrJImITuYLheMmifQcvEhEGou0BIl/HWPkLnsi1TFhnSBPgr2LgMhWfCasibJr1apVMd9fuXIlVq5c2e/14cOH4/nnn9cqrJQF5cgNmT7RdUjHgWdEpC5vEvM8JFGA/vh2/USHVhMR9fCHEmkJIjBhTUSa8iQ4dNGoE9HuCWQgIqLBgy1BEpRo7yIgUmHt4TZYIlKRJ4nKRoAtQYhIfZ5g4i1BAMCsF+H2s481ESXPFwrHfUivl0T4g7znIiLtJPqw3sC2sESqY8I6Qd6ADGOClY1sCUJEavMGZRiTSBQZdSJbghCRqrxJzPMAIn2suQ4RUSr8ITmhoYussCYiLXkSbMlolETO7iBSGRPWCfIG41809TBKIjwBVhQRkXqS6R0LRNYhHx+cEZFKwooCfyjx3WYAYNLzAT4RpcYfTKTCWoCPCWsi0pA3GL+fPhApWvRxCCyRqpiwTpA7IMOoS6y6kZWNRKS2ZAYuAj2tibgOEZE6fMdv2MQkBiiywpqIUuUPheMOOtNLIvxMEBGRhhK9BzPqBD6kJ1IZE9YJSiZZxN6xRKQ2TyC53rF8cEZEavIEQkmtQQBg1PPBGRGlxhcKQxevwlrk0EUi0lbiCWsJPvbUJ1KVLtsB5At3QIbNrE/oWINOhKebN2hEpJ5kK6yNOhFuP9chIlKHJxhOqi0R0NMijesQESXPH5KhFxOpsGaCiCjXLFu2DK+88goqKiqwdevWfu8///zzeOKJJwAARUVFeOCBB3DWWWcBAGpra1FUVARRFCFJEjZt2pTR2E/mDcqJtQRhO0Yi1TFhnaBkK6w5IZaI1OQJJj74FYisQ0ddfg0jIqLBJFJhnWTCWi/CzeshIkqBL8Ee1kxYE+Weuro6LFy4EPfee2/U94cNG4Z169ahtLQUO3fuxE9/+lNs2LCh9/2nnnoK5eXlmQo3Jl8wnGCFtcie+kQqY8I6Qb6gnPCgoUhLEC5WRKQeT0CGQZd471i2BCEiNXmCic/y6MEKayJKlS8kx01Y69gShCgnTZo0Cc3NzQO+f/755/f+e+LEiWhpaclEWClJNA9k1IlsCUKksqz0sN61axdmzJiB6dOn4/HHH+/3/uHDh3HjjTfiuuuuwzXXXIOdO3dmIcq+PMn2sOYNGhGpyBOQYZQSTxaZOHSRiFTkCcjJtwTRifAEQhpFRESFzBtMbOiij0MXifLaxo0bMXXq1D6v3XLLLairq8P//u//Zimqr/hC4YR2uUqiAEEAgjKT1kRqyXiFtSzLWLFiBdauXQu73Y558+ahtrYWo0eP7j3md7/7Ha6++mp85zvfwb59+3Drrbdix44dmQ61D18wsYUKiFQUcegiEanJG0y2wlriOkREqvEEkuujD0TWIRd76RNRCvwhGQadIeYxeom7Wony2VtvvYWNGzfiT3/6U+9r69evh91uR1tbG26++WaMGjUKkyZNivk5kiTAZrNoEqMvFEa5zdxvx4coirBajX1eM+kl6M0G2Cyx165cJUmiZn/HbCnE7wQU7vc6WcYT1g0NDRgxYgSGDx8OAJg1axa2b9/eJ2EtCAJcLhcAoLu7G1VVVZkOs5/kelgzUURE6nIHktuOz9ZERKSmVBLWJr0Il58V1kSUPF8ofg9rgyTAz3suorz0ySef4Cc/+QmeeOIJlJWV9b5ut9sBABUVFZg+fToaGhriJqxlWYHT6VE9xqAchqIo8HkC8At9C4esViNcJ80LMkgCHG1uIE93l9lsFk3+jtlUiN8JKLzvVVlZHPX1jCesHQ4Hqqure3+22+1oaGjoc8wdd9yBW265BevWrYPX68XatWsT+mwtn6z5Q2GUlZpgPelpWbQna7Ikwi8rOf3EI5+fyORr7PkaN5DfsRcKt19GkSHZhDVv4ohIHZ6gDEMSg1+ByDrU2s11iChXHDlyBPfccw/a2togCAK+9a1v4aabbupzjKIoWLlyJXbu3AmTyYRf/OIXGDduXMZj9QfD0CfQEqTTl5+JIaLB7PDhw1iyZAl++ctfYuTIkb2vezwehMNhWK1WeDwevP7667j99tuzFqc3KMOkkyAIie1y5f0XkbpycuhifX09vvnNb+Jf/uVf8P777+Oee+7B1q1bIYqxb5S0erIGRCqLZH8IrrDS5/VoT9bkUBjegJzTTzzy+YlMvsaer3EDmYl9oKdqFOEJhlBepE/4eBMvmIhIRSlVWHP4K1FOkSQJP/rRjzBu3Di4XC7MnTsXU6ZM6bPTddeuXWhsbMS2bdvw4Ycf4oEHHsCGDRsyHqsvFI77kMygE+BnD2uinLN06VLs3r0bHR0dmDp1KpYsWYJQKPJwacGCBfjtb38Lp9OJBx98EEBkbdq0aRPa2tqwePFiAJFWsrNnz+7X3zqTfMFwUtc+kcGLXJOI1JLxhLXdbu8zBdbhcPRu++ixceNGrFmzBgBw3nnnwe/3o6OjAxUVFRmNtUdYUeBP4KKph14SEJTDkMMKJDHxnrNERAPxBBJfgwBOqiYidblTagkicfgrUQ6pqqrqbbVotVoxatQoOByOPgnr7du347rrroMgCJg4cSK6urrQ2tqa8RaN/pCcUIW1L8RrHaJcs2rVqpjvr1y5EitXruz3+vDhw/H8889rFVbSkmkLC7AlI5HaMp6wnjBhAhobG9HU1AS73Y76+nr85je/6XPM0KFD8eabb6Kurg779++H3+9HeXl5pkPt5Q9FtqSJCSafBUGIJItCMooMOVnETkR5xhOUYdInfsGkO57cDsrxe0ASEcXjDoRgTGINAiIV1h5WGhHlpObmZnz88cc499xz+7x+cvvG6upqOByOmAlrLdoyhhSg1Grq03rx5FaMJUVGyNCuJWSPbLXGy2ZLvsH2nXleiibZCmuDxB2uRGrKeDZVp9Nh+fLlWLRoEWRZxty5czFmzBisXr0a48ePx7Rp0/CjH/0IP/nJT/Dkk09CEAT84he/SLhvkBa8QRkmfeK9Y4HIoCFvgAlrIlKHNxDpoZYMk06EJyCj1MyENRGlx+2XUWpKvC0REKk0YoU1Ue5xu9248847cd9998Fqtab9eVq0ZfT4QwgFQn1aL57cilEOynD7gpq3rctWW79sthMcbN95sJ+XrRmj8wZlGJJJWLMlCJGqspJNrampQU1NTZ/X7rrrrt5/jx49Gn/+858zHdaAvEEZxqQHDUncDkJEqkl2SxoAGPUSvEEZpebkkkxERCfzpLAGmfQSK6yJckwwGMSdd96Ja665BldeeWW/909u39jS0tKvfWMmBEJhGOK0BDFIAvxsCUJEGvGGkrv2iVRYc00iUgvL7hLgDYST3gbLCbFEpKZUEtbcjk9Eakl16KKXFdZEOUNRFPz4xz/GqFGjcPPNN0c9pra2Fs899xwURcEHH3yA4uLijPevBgB/Ai3N9JLIhDURacYbTG6GEFuCEKmL/SoSkFJlIxPWRKQib5I91IDj6xCTRUSkAk8guT76QGRrrD8URlhRIGaxtRsRRbz77rvYvHkzxo4dizlz5gAAli5disOHDwMAFixYgJqaGuzcuRPTp0+H2WzGQw89lJVYAwkMvNezwpqINOQLykxYE2VR2gnrO+64A/PmzcPUqVMhioVZsB3ZBptc71gmrInUMxjWmXhYYU2Ue1Jdm3bt2oWVK1ciHA5j/vz5uPXWW6Me9+KLL+LOO+/Exo0bMWHCBLXCTok3hWsh8fgQak9AhtXIGgkiNaWy/lxwwQX49NNPYx4jCALuv/9+NUJMmaIoCMoKdHFbgogIMGFNpKnBfB/mDcpxWxOdSK8T4OOaRKSatFec73znO9iyZQuuvPJK/PrXv8YXX3yhRlw5xZfkQgVELqA87F9EpIrBsM7EEpTDUBRAJya5DulEeAJch4i0ksraJMsyVqxYgTVr1qC+vh5bt27Fvn37+h3ncrnw9NNP49xzz9Ui9KR5gjJMST40A473seZODyLVFfK1kT8Uhk4S4u7MYEsQIu0V8loTjzcYTmroolHi7lYiNaVd7nLJJZfgkksuQXd3N7Zu3Yqbb74ZQ4cOxfz583HttddCr8//YV+pDBoyckIskWoGwzoTS89WfCHJLfXc6UGkrVTWpoaGBowYMQLDhw8HAMyaNQvbt2/H6NGj+xy3evVqfP/738cf/vCHjHyXeFLZ5QEAZj13ehBpoZCvjfwJtAMBIi1BAjIT1kRaKuS1Jh5fUI7bS/9EBu5uJVKVKns6Ojo6sGnTJmzYsAFnn302vvvd7+Kjjz7Cv/zLv6jx8VmX7JM14HiFNZ+uEamm0NeZWLwpVjYaJV40EWkt2bXJ4XCgurq692e73Q6Hw9HnmL1796KlpQWXXXaZlqEnTFEUeAPh1CqsdaywJtJKoV4bJZqw1okC5LACOaxkICqiwatQ15p4kn1Yb2CFNZGq0q6wXrx4Mb788kvMmTMHjz32WO8U6ZkzZ6Kuri7tAHOBNyDDmMSTNSDydI2VjUTqGAzrTCyeoAyjPrnesQBg1Em8aCLSkBZrUzgcxi9+8Qs8/PDDSf2eJAmw2SwpnTMefygMQQBspeao74uiCKvVGPW9IpMOokGnWWyxSJKYlfMmIldjY1zJyWZchXxt5A8lViwkCELvcFeLIfnrJCKKr5DXmng8geSGLnJ3K5G60k5Yf+tb30JNTU2f1wKBAAwGAzZt2pTux+cEb5JbQQDAIAnwsYc1kSpSWWeWLVuGV155BRUVFdi6dWu/9xVFwcqVK7Fz506YTCb84he/wLhx4wAAzz77LH73u98BAG677TZ885vfVPkbJccbSK3C2qATWNlIpKFU1ia73Y6Wlpbenx0OB+x2e+/Pbrcbn332Gb773e8CAI4ePYrbbrsNv/vd72IOXpRlBU6nJ52vMyCnJwiTToTL5Y/6vtVqHPA9nQA42t1wlkdPdmvJZrNo9jdJV67GxriSo1ZclZXFSf9OId+D+UNh6BOcH2SQRPhDMhPWRBop5LUmHm9QRrnFkPDxLFokUlfaLUH+3//7f/1eu/7669P92JziCSTft9GgE+EOhDSKiGhwSWWdqaurw5o1awZ8f9euXWhsbMS2bdvws5/9DA888AAAwOl04tFHH8Vf/vIXbNiwAY8++ig6OzvTCT9tqfTRByJP+bkOEWknlbVpwoQJaGxsRFNTEwKBAOrr61FbW9v7fnFxMd5++23s2LEDO3bswMSJE+Mmq7XmDoZgSmGXB9CzDvHmjUhthXwP5g8lXtVokAQOXiTSUCGvNfF4g3JyQxd1IrwsWiRSTcoV1kePHoXD4YDP58NHH30ERYn0DnO5XPB6vaoFmAtSHbrIp2tE6UlnnZk0aRKam5sHfH/79u247rrrIAgCJk6ciK6uLrS2tmL37t2YMmUKbDYbAGDKlCl49dVXMXv2bNW+V7I8gXCKCWv2jiXSQjprk06nw/Lly7Fo0SLIsoy5c+dizJgxWL16NcaPH49p06Zl4iskxRsIw6RPrcaBPayJ1DUY7sF8oXDCu1sNOpG7Wok0MBjWmng8wTAMCe72AI73sGYOiEg1KSesX3vtNWzatAktLS19+iwWFRVh6dKlqgSXKzwBGZXWxLeCAJGEdYcnqFFERIODluvMyYPPqqur4XA4EhqIFo2W/WMFvRNFJv2APWIH6h9bajWgu8OXk30/E5GrPUvTUYjfCSjc7zWQdNemmpqafttr77rrrqjHPvPMM+kFqwJ3IASTLvUKayasidQzGO7BIkMXE0sS6SWRFdZEGhgMa008vmSHLupE+LgeEakm5YT1N7/5TXzzm9/Eiy++iBkzZqgZU85JdjoscHzYGZ/2E6Uln9YZLfvHHuv0QlSU5PvHymE4Xf6c7PuZiFztWZqOQvxOQGF8r2R6yObT2qSGVNsSAZGEtYutiYhUMxjWH38yFdaSCF+ID8WI1DYY1pp4km4JIonwscKaSDUpJ6w3b96MOXPm4NChQ1i7dm2/92+++ea0AsslnkByCxUQWay4HYQoPVquMycPPmtpaYHdbofdbsfu3bt7X3c4HLjwwgtTPo8avCmsQcDxykauQ0SqG0zXQEDkOijVliBGnQi3n+sQkVoGw/qT3NBF9rAm0sJgWGvi8QXDMCb48AxghTWR2lJOWPf0LfJ48ruiKhGeoAwTe1gTZZyW60xtbS3WrVuHWbNm4cMPP0RxcTGqqqpw6aWXYtWqVb2DFl977bWsb3tLZ+giE9ZE6htM10DA8eHTSdywncisl9DuCagcEdHgNRjWH19Qhk5MbM1hSxAibQyGtSYeXyic9NBF9tQnUk/KCetvf/vbAIA77rhDtWBylS8YTnhSdQ8mrInSl846s3TpUuzevRsdHR2YOnUqlixZglAosi19wYIFqKmpwc6dOzF9+nSYzWY89NBDAACbzYbbb78d8+bNAwAsXry4dwBjtrj9qSasJXjZO5ZIdYPpGgg4nrBOtcJaL8LNdYhINYNh/UmqhzUrGok0MRjWmniS7WGtlwQE5TDCigJRSHxYIxFFl9rdxwl++ctfwuVyIRgM4qabbsLkyZOxefNmNWLLGan0sDboRHgDvHgiUkMq68yqVavw2muvYe/evdi1axfmz5+PBQsWYMGCBQAAQRBw//334+9//zu2bNmCCRMm9P7uvHnz8NJLL+Gll17C3LlzNf1uifAEQylXWPPBGZF2BsM1EBDZ5ZHsg/seJp0IF1uCEKmukNcffygMXYJrjl4U4GcPayLNFPJaE48vlFzhoigIkbYgrLImUkXaCevXX38dVqsVr7zyCk499VS89NJL+MMf/qBGbDkj2SdrQOQGzcuLJyJVDIZ1JhZPQIZJJyX9eyadyOGvRBoaLGuTJ5D60EWTXoInyKGLRGor5PXHHwpDLyZYYc0e1kSaKuS1JpawoiCQZEsQgAVDRGpKO2Ety5H/Mr7yyiu46qqrUFxcnHZQucYbDMOYZLIo0r+ICxWRGgbDOhNLqskiXjARaWuwrE0ufyilh2ZA5MGZhy1BiFRXyOuPLyRDn3CFNXtYE2mpkNeaWHqGvybb2sMo8f6LSC1pJ6wvu+wyXHXVVdi7dy8uvvhitLe3w2g0qhFbTlAUBb5QCi1Bjg8ACSuKRpERDR6Fvs7Ek0pbIgDQiQIUBQjKvJEj0sJgWZvc6VRY6yQmrIk0UMjrjy8YSRQlQi8J3H5PpKFU1pply5bh4osvxuzZs6O+rygKfv7zn2P69Om45pprsHfv3t73nn32WVx55ZW48sor8eyzz6r6XZLhDcow6ZN/WM/Bi0TqSXnoYo8f/vCHWLRoEYqLiyFJEsxmM/77v/9bjdhyQkCONMyXEtyW1kMUhd6p1eYUFjoi+kqhrzPxuANy0tvRgEifbpM+Ut1Yak77+SQRnWSwrE3ugIxhtlRbgojwsNKISHWFvP74QjKsxsRuU/WSCB/bMBJpJpW1pq6uDgsXLsS9994b9f1du3ahsbER27Ztw4cffogHHngAGzZsgNPpxKOPPoq//vWvEAQBdXV1qK2tRWlpqRZfLSZPQIYxhfkdRj0rrInUknbCGgC++OILHDp0qHe7CABcd911anx01nkDMkz61G7SerbjM2FNlL5CXmfi8QZlmNLoH+sNyig161WOioiAwbE2pdpHHzh+LRQIQ1EUCEluqyWi2Ap1/fEFwyi3JNgSRGI1I5HWkl1rJk2ahObm5gHf3759O6677joIgoCJEyeiq6sLra2t2L17N6ZMmQKbzQYAmDJlCl599dUBK7W15AuGYUwhD8SWIETqSTth/e///u9oamrCWWedBUmK3MwIglAQF0sA4A3JSfev7tHTt7HconJQRINMoa8z8aSTLDLpWN1IpJXBsjZ5gqGUbtqASDJJECI71ow6JqyJ1FLI609P79hEGCSBc4OINKTFWuNwOFBdXd37c3V1NRwOR7/X7XY7HA5H3M+TJAE2m7pJF6k7AItBB6s1evsTURSjvmcx6SEadKrHkwmSJOZl3LEU4ncCCvd7nSzthPU///lPvPDCCwVbNRNJFKVYYX28spGI0lPo60w8kcGvqe/0YP9YIm0MlrUpnWshADDrJXgCIRh1BhWjIhrcCnn98ScxdNGgE9HpC2kcEdHglQ9rjSwrcDo9qn7m0XY3dALgcvmjvm+1GqO+J0HBMadX9XgywWaz5GXcsRTidwIK73tVVkYf5pp2U9MxY8bg6NGj6X5Mzkp12BnwVYU1EaWn0NeZeCJDP7gOEeWawbI2pfPQDIj0sXZzHSJSVSGvP/5QGIaEhy6K8LOHNZFmtFhr7HY7Wlpaen9uaWmB3W7v97rD4YDdblf13InyBlObIWRgSxAi1aRdYd3R0YFZs2bhnHPOgV7/VY/Uxx57LN2PzgnpJKx7elgTUXoKfZ2JJSiHoSiALsnBrz2404NIO4NlbfKm0ZYIiFRYM2FNpK5CXn98oXDCFdZ6SYAvxB7WRFrRYq2pra3FunXrMGvWLHz44YcoLi5GVVUVLr30UqxatQqdnZ0AgNdeew1Lly5N+zukItWH9UxYE6kn7YT1kiVL1IgjZ3kCqVcVGSQRHg4BIUpboa8zsXiOD35NdRueUWJlI5FWBsva5Enj4T3AnR5EWijk9cefRMLawKGLRJpKZa1ZunQpdu/ejY6ODkydOhVLlixBKBRp3bNgwQLU1NRg586dmD59OsxmMx566CEAgM1mw+2334558+YBABYvXtw7gDHTvEEZhgTXoRPpWbRIpJq0E9YXXnghDh06hAMHDuCSSy6B1+vtMz023/lSXKiASIU1h4AQpa/Q15lYPEEZJn3qlY0GncCLJiKNDIa1KSiHEVaUhAegRRNpCcIes0RqKuT1J5BESxCDJMLPCmsizaSy1qxatSrm+4Ig4P7774/63rx583oT1tmUasLaKPEhPZFa0u5h/Ze//AV33nknli9fDiDSZ2jx4sUxf2fXrl2YMWMGpk+fjscffzzqMS+88AJmzpyJWbNm4e677043zJSlU1VkYEURkSpSWWcKhTsgw5xGZaNRkrgOEWlkMKxNnoAMs15Ka9iSScd1iEhthbz++ELhhBNFeklgwppIQ4W81sTiDcrQp3APxoH3ROpJO2H9xz/+EevXr4fVagUAnH766Whvbx/weFmWsWLFCqxZswb19fXYunUr9u3b1+eYxsZGPP7441i/fj3q6+tx3333pRtmylJ9sgZEnq6xspEofcmuM4XEG5BhTHHgIsAKayItDYa1Kd1dHkDk5s3lZ4U1kZoKef0JhMIJDzvj0EUibRXyWhOLJyDDmEqFNVuCEKkm7YS1wWCAwWDo/bmnN9FAGhoaMGLECAwfPhwGgwGzZs3C9u3b+xzzl7/8BTfccANKS0sBABUVFemGmbJUn6wBrLAmUkuy60wh8aQ57Myok+Dycx0i0sJgWJvcARmmNHZ5AICJQxeJVJfq+rNs2TJcfPHFmD17dtT33377bXz961/HnDlzMGfOHDz66KOqxJuMZCqsDTq2BCHS0mC41onGE5ATfnB2IgMT1kSqSbuH9aRJk/DYY4/B5/Ph9ddfx5/+9CfU1tYOeLzD4UB1dXXvz3a7HQ0NDX2OaWxsBAB8+9vfRjgcxh133IGpU6fGjUWSBNhsltS+yABkQUSJRQ+r1Rj1fVEUB3yv2KJHSFA/JjVIkpiTcSUiX2PP17iB7Mee7DpTSNQYduZi71giTQyGtSnSEiTNhLVORDcrrIlUler6U1dXh4ULF+Lee+8d8JgLLrgAv//979UMN2FhRUFQDkOXcA9rtgQh0tJguNaJxhuUUW4xxD/wJGwJQqSetBPWP/zhD7Fx40aMHTsW//u//4uamhrMnz8/rc+UZRkHDhzAM888g5aWFixcuBBbtmxBSUlJnN9T4HR60jr3ydq6fNBLAlwuf9T3rVbjgO8JsoIOr1/1mNRgs1lyMq5E5Gvs+Ro3kJnYKyuLB3xPi3UmX3gC6SWsjXoRrS5eNBFpYTCsTe5AKO2WICY9d3oQqS3V9WfSpElobm7OQISpCYTC0EsCxAT75uslEQGZCWsirQyGa51ovMHUKqwjLUG4JhGpIe2EtSiKuOKKK3DFFVegvLw87vF2ux0tLS29PzscDtjt9n7HnHvuudDr9Rg+fDhOP/10NDY24pxzzkk33KR5gjKqjMk/WQMiiaKj7oDKERENPsmuM4XEnWbCOlJhzUQRkRYGw9qU7kMzADDrRbS6eD1EpCYt158PPvgA1157LaqqqnDvvfdizJgxqn5+LL5g4v2rAcAgRVqCKIqS1nBYIopuMFzrRJPq9Y9BEuFhSxAiVaScsFYUBY8++ijWrVsHRVEARBazhQsX4o477hjw9yZMmIDGxkY0NTXBbrejvr4ev/nNb/occ8UVV6C+vh5z585Fe3s7GhsbMXz48FRDTYsnkPqwIaNOhJeJIqKUpbrOFJJUn+73MOklbksjUtlgWpvc/vT66AOAWS9x6CKRSrRef8aNG4cdO3agqKgIO3fuxOLFi7Ft27aYv6NmW0av4IVJL0VtuThQK0ZBEFBUbE7reimWbLXGy2ZLvsH2nXne/gbTtU403mA4pYS1USfCx4Q1kSpSTlg/+eSTeO+997Bx48beZHJTUxMeeOABPPnkk/je974X/YQ6HZYvX45FixZBlmXMnTsXY8aMwerVqzF+/HhMmzYN3/jGN/D6669j5syZkCQJ99xzD8rKylINNS2eQCjlyiI+XSNKT6rrTCFxB0IpTajuYWIfNSLVDaa1yZ1mH30gUmHNoYtE6tB6/bFarb3/rqmpwYMPPoj29vaYlZVqtmV0tHugF6O3YxyoFaNBEtDa5oLVmPbm4aiy1dYvm+0EB9t3HuznjdaacTBd60TjDcoJD389kVEnwse++kSqSPkOZPPmzfjNb37Tp/J5+PDh+NWvfoXnnnsu5u/W1NTgxRdfxN///nfcdtttAIC77roL06ZNAxB5Sr5s2TK88MIL2LJlC2bNmpVqmGlLZyusSceENVE60llnCoXbL8OUxsAzDv4gUt9gWpvc/tQf3PcwscKaSDVarz9Hjx7traZsaGhAOBzOaOGQP8mWIECkSIgJIiJ1DaZrnWi8KT6wj/Sw5r0XkRpSfgwdCoWiPmkvLy9HKFQ4NyWeoAxTijdqbAlClJ7Bss7E4gnIKLPoU/59k17iRRORytJdm3bt2oWVK1ciHA5j/vz5uPXWW/u8v379evzpT3+CKIqwWCz42c9+htGjR6sWfzJcaT40AyItQfjgjEgd6a4/S5cuxe7du9HR0YGpU6diyZIlvb+3YMECvPjii1i/fj0kSYLJZMKqVasy2hvaF0q+qtHALfhEqhvs92G+UDotQfgAjUgNKSes9fqBEyix3ss3qfYuAgCjjokionQMlnUmFncghOqS/v0aE8WdHkTqS2dtkmUZK1aswNq1a2G32zFv3jzU1tb2SUhfc801WLBgAQBg+/btePjhh/GHP/xBneCT5A6EYE5xlkcPk06EO6DOze3fPz2KZ/Y0YcHXh+Gqs6tU+UyifJLutdGqVativr9w4UIsXLgw6bjU4guGk09YHx+8SETqGez3Yb4U80A6UUBYURCSw9Cl0daRiNJIWH/yySc4//zz+72uKAoCgcKZBO8NpD5sKLIdhBdPRKkaLOtMLJ6gDGOaLUGCchhyWIEkZq5CiqiQpbM2NTQ0YMSIEb1bbGfNmoXt27f3SVif2EPW6/VmtLrxZC5/KK1dHoB6FdaftrrwH9s/x3UThuK/dn0BnSjgijMrE/79L9rc+PO7hxBWgPkTT8GZdmv8XyLKMYV+beQLhaGXklvzmLAmUl+hrzXxpLLbA4i0t+3JAxUzYU2UlpQT1h9//LGaceQsbyiNHtZ69i8iSke660y8bfcPPfQQ3n77bQCAz+dDW1sb3nnnHQDA2WefjbFjxwIAhg4disceeyytWFLlTuOhGXDiRZOs2TAiosEmnbXJ4XCgurq692e73Y6GhoZ+x/3xj3/E2rVrEQwG8dRTT8X9XEkSYLNZUo5rIAEFsBUbYbUOvNNDFMWY7yuKAlkBLFZT0r1pT/TH//sUV48fiivGV2PYkCKseuULXDXx1AHXNkkSe/8mHzY78YP/bcC0s6tgFEUs/us/8Jt556BmbOIJbzWdGFsuYVzJyUZchX4P5k+pJYgAX4j3XERqKvS1JpZQWIEcVpJ+eNbDdHynfbGJ915E6eB/g2IIHa9KTHWh0okCFAUIhJIfHkJE6Ulk2/19993X++9nnnkGH330Ue/PJpMJmzdvzmjM0aQz+LWHSS/BHWDCmiif3HDDDbjhhhuwZcsW/O53v8N//Md/xDxelhU4nR7V43C6A0AoDJfLP+AxVqsx5vsAYNFLONzaDVuK1dpHXX68uu8Yfj7zLLhcfgyzGjCqwoLfv/w5bpk8Iurv2GwWOJ0edHqD+MEz7+I7Xz8V55xSAgA4rcSAuzd8iCdvOA/DbOaUYkpHT2y5hnElR624KiuLVYimMKTaEoQ9Y4lILb7jAxdT3eHGwYtE6mAWNQZPUIZJL6W8UAmCALOe/WOJsuHEbfcGg6F32/1A6uvrMXv27AxGmBhvGoNfe5hV7B9LROmx2+1oaWnp/dnhcMButw94/KxZs/D3v/89E6FF5QnIMKc5dBEAzHoRrjTWobcPdOBsezFMJ/TTnnFWJf783qG4w9Z+vWMfJp5a2pusBoBRQ4pw5VlV+En9JwgrSspxEZG6fCEZel1y9156SYSPLUGISCXeoAxjGjtcmbAmUgfL7WLwBGSYVahs9ARk2MyFP5iAKJckuu0eAA4dOoTm5mZMnjy59zW/34+6ujrodDrceuutuOKKK2KeT6vt+L5QGBU2M6wx1pB42/GLTHqIBn1ObqeOJVe3gKejEL8TULjfSwsTJkxAY2MjmpqaYLfbUV9fj9/85jd9jmlsbMTpp58OAHjllVcwYkT0CuJMcAdCad209TAbJLj9qd+8vdXYgbGVRX1eO6XUhFEVRXiu4Qi+/fVhUX/vw0Od2HPQieVXndnvvcvHVOD95k5sbjiCb557SsqxEZF6/KEw9GKyLUHEuA+uiIgS5QmkVzDEWWZE6mDCOobIsLP0btJMelGVQUNEpJ36+nrMmDEDkvTVf99ffvll2O12NDU14aabbsLYsWNx2mmnDfgZWm3Hd/tDkP0huOSBL3ribcfXC4Cj3Q1nsUH1+LSUq1vA01GI3wkojO+VqS35Op0Oy5cvx6JFiyDLMubOnYsxY8Zg9erVGD9+PKZNm4Z169bhzTffhE6nQ0lJSdx2IFryBlWqsNZJKVdYK4qC3QedWHrZqH7vTT+zEv/z9kHMnXgK9Ce1EQiHFfx6x35cO6E6amslURDw7fNPwaOvfonasZUoZXEBUdb5gikMXRQFJqyJSDWRnfbpJay5y54ofUxYx+BN88kaEGm4z634RJmXzLb7F154AcuXL+/3+wAwfPhwXHjhhfjoo49iJqy1EJTDkMNIuY9+D+PxnR5ElBtqampQU1PT57W77rqr998/+clPMh3SgDxpDn7tYdaLcKVYYf1Fmwd6UUBllJ0kIyssqLIaUb/XgevOGdrnvb++fwjBcBiTTrMN+NnDbGZMPLUUj73RiHunjUkpvmR0eoN4t7kT3jBQJAKTRthQZODlOFEPbzD5oYtsCUJEaorMEEqnJQjvvYjUwB7WMbhVSFjz6RpRdpy47T4QCKC+vh61tbX9jtu/fz+6urpw3nnn9b7W2dmJQCAAAGhvb8d7773XZ1hjprgDMsyG1Ad+9DDquNODiJIXksMIpTF8+kRGfeoP8D9xuDCyYuCWM7PH2fG71xvh8n/1+e2eAH790qf41sRTIMZZQ2ePs+OlT47iU4crpfgS0e4J4KFtn2HOmt344zvNeG3fMTy1pwlzntiNDe8fgsI+2kQAjiesk7z/0rNfLBGpSJWWILz3IkobSzpi8LIlCFHeSmTbPRCprp45c2afpPD+/ftx//33QxAEKIqC73//+1lKWIdgTnMNAiIXTW6uQ0SUJHdAhiWN4dMnMuvEPgnlZHzs6MYppaYB3x9ZYcHX7MX45fZ9eODqMyGHFfxk6ye4dPQQjCiP31vdatTh2vHVWPnSZ1j7nfMgiel/3x6KomDrXgdW7/wCk06z4YGrzkSxSdfbyulwpw9P7m7CJ60u/PjKsXGT60SFzhcMo8ySXHsegyTAx36xRKSSyNDF9BLWbj5EI0obE9YxeIIyjEluSTuZUWKiiChb4m27B4AlS5b0+73zzz8fW7Zs0TS2RHgCMkwqJaw9bE1EREmK9HBMfw0CIhXW3SkmrD9xuFAzuiLmMfMmDsV/v9aIRes/gCsgo9ysx9zzToXXE0joHBePLMOeJiee2tOEf7lInfZPrd1+/HzbZzjU6cMd3xiJ4WXmfsecUmrC0stG4b9fa8Qjr3yBuy8/Q5VzE+UrX0iGQZfczA3uaCXKLbt27cLKlSsRDocxf/583HrrrX3ef+ihh/D2228DAHw+H9ra2vDOO+8AAM4++2yMHTsWADB06FA89thjmQ0ekQf26SSsDZLACmsiFTBhHYMnzYUKAIyssCaiFLn9MsxprkEAYGKFNRGlwO1XZ+AiAFj0Ejq9ySesFUXBvmNuLLxgWMzjTHoJd3xjJD5ydEMUBEwYWpxUpbQoCPjupGH4j7/vw4ShxZh0WlnSsfbwBWVs+OAwntzdhKlnVOCGr58KXYwCCJNewg+mjMCvtu/H+OpizDi7KuVzE+W7yNDF5NYdgySiy8cH80S5QJZlrFixAmvXroXdbse8efNQW1vbZ7fqfffd1/vvZ555Bh999FHvzyaTCZs3b85ozCdLpZf+iUxptEEjoq+wh3UMnkDyPdROZmRPNSJKkVu1CmuJCWsiSpo7EFKtwrrIIKE7hYTS4S4fjDoRxab4NRYGnYiJp5binFNKUmpjUm4x4JbJp2HZlo/xzkFnUr8blMNoONyF/9z5Ba59Yjde/aINSy8bhdnj7DGT1T2KDDr8y+TT8Ksd+9DS5Us6dqJC4Qslnygy6ET4QrzOIcoFDQ0NGDFiBIYPHw6DwYBZs2Zh+/btAx5fX1+P2bNnZzDC+NKtsDbqUh80napAKIx/HunK6DmJtMYK6xhUqbCWpJR7NhLR4OYOhNIe/ApEKqzb3Iltiyci6uFSYfh0D7NBQmOHJ+nf+7zVjeG2/q00tDK2yoqbLxqOH239CFeMrcScCdUYWW6BIAjo8ARwqNOHQ04fDnd6caTLj1aXHy3H/9+hJSacXV2MJVNHxuy5PZDTysyoGV2Bh1/6HP+vbrwqvcOJ8o0vFE4+YS2J8AbYw5ooFzgcDlRXV/f+bLfb0dDQEPXYQ4cOobm5GZMnT+59ze/3o66uDjqdDrfeeiuuuOKKuOeUJAE2W/yZFYmSBQHFRQZYrcYBjxFFccD3S61GtHT6VI0pljaXH9f/YQ8c3T78bcmlGFFRlNLnSJKYsZgzpRC/E1C43+tkTFjHkO6TNSAydJGVjUSUCrWSRSa9CBfXISJKktuvzuBXINISJJUK68Z2D+zFA98wauEsezF+PH0MdnzehmVbPkaryw9FAUpMOgwpMmBIkQFlFj0qigw4Y0gRyix6VFoNSbcxiObKMyvx8N/3Ydf+9rh9u4kKkS8YhkGX3MMaVlgT5af6+nrMmDEDkvTVtcbLL78Mu92OpqYm3HTTTRg7dixOOy32bAlZVuB0Jv9QfCDtXT4Y9SJcLv+Ax/QMT44eUBgdbr+qMcXy7AeHcWqpEROGFuO/d+zDsuljUvocm82SsZgzpRC/E1B436uysjjq60xYx+AOhGA1pvcnMupEdLq0qWxUFAUbPjiCS0aWYVgGq4+IKDPc/hCMKiSLTHqJQxeJKGkuvzq7PADAYpBS6jH7ZZsHVcXJDWBTQ4lJj+smVOO6CdXxD1aRThJRd85QPPLKflwyskyVJDhRPvGn0BLEKInwhVhhTZQL7HY7Wlpaen92OByw2+1Rj33hhRewfPnyfr8PAMOHD8eFF16Ijz76KG7CWm2ugIxSsz7l3zfqxIwOXXxlXxsmnFKCMUOK8OD/fYZ/u2yUai3diLKJV8ExuAPpDxsy6kTNGu7v+PwY/vDWAdy47j180ebW5BxElD1qtCUCALM+833UiCj/ufzq9NEHIgnrVFqkfdnuQXVx8u018tm4ocUoM+vxbMORbIdClHH+UApDF3UCZwYR5YgJEyagsbERTU1NCAQCqK+vR21tbb/j9u/fj66uLpx33nm9r3V2diIQiBT7tbe347333uszrDFTvGneg2Vy4L0nIKPhcCfG2YtRatbDXmLEp62ujJybSGussI4hUlmU3o1apLJR/cUqFFaweucX+O6kYfi01Y3nGlqw9PIzVD8PEWVPt0rVjSadBBcrrIkoSd3+EExpPrjvYdFLST84UxQFTU5vxluC5IJrxtvx+BsHcO34alZJ0aCSag9rf5AV1kS5QKfTYfny5Vi0aBFkWcbcuXMxZswYrF69GuPHj8e0adMARKqrZ86c2Wdew/79+3H//fdDEAQoioLvf//7WUlYu4PpD13UIgcUzfuHOjGizAKzIXKtMKLMjL0t3Tj31NKMnJ9IS0xYxxCpsE4zYa3R07VPHN3QiQLOshejzGLAI6/sx51TRyY0iZ6I8oPbL6PMkvp2tB5mvcRe+kSUtG4Ve1ib9JEes3JYgSQm1p+2wxsEAFiNgy9hO6LcgtMrLNj4wWEsnDQ82+EQZUykh3WyFdYivOxhTZQzampqUFNT0+e1u+66q8/PS5Ys6fd7559/PrZs2aJpbInwBuS0HtibdFLGdn3sO+rGMNtXO9FOKzPjH4e7gK9n5PREmmJ2Mwa3P72FCogkrLXoX/TOQSfGVFoBAPZiI4YUGfFuU6fq5yGi7HEF1KluNOsz95SfiApHt0+9hLUoCMerrBPf7XGg3YuhJaY+1VeDydVnV+GZd5rhY6sDGiTksIJQOAyDlOTQRUmEnz2siUglnqAMYxo77Y06Ed4M7fr4rNWFU0q/SlifXm7B3pbujJybSGtMWMfgDoTS3oZp1EvwaHCjseegE2Mqi3p/Hl1pwQeHmLAmKiRq9Y816kQE5TBCMm/miChxaj0062ExSOhOKmHtQZV18LUD6THMZsZpZWY89w/2sqbBwXd84GKyD6mMOhE+tgQhIpV4g3JabRlNejFzFdbH3Dj1hIR1dbERTm8QzuO71IjyGRPWMXiCMsxp9o816dRfrEJyGP840tUnYT2qogjvM2FNVFDcgVDaaxAACIIAs16Ci1XWRJQElz/91mgnKjLo0OVLPGHd5PRiSJFBtfPnoxlnVeGZPc184EiDgjcYTukhmU4UjldnKxpERUSDjSeQXsJaJwpQFCCo8f92h+Qwmp2R3Wg9RFHAqaUmHGj3aHpuokxgwnoAiqJEFioVelh7A2EoinoXUPuPeVBuMcBq/KoF+agKCz5q6YbMCzWiguEJyDCqlCyy6CW4OXiRiJLgUrGHNXC8wjqJhPWBdi8qrYM7YT2ywoKKIgP+75PWbIdCpDlvILVt+IIgwKgT4WcfayJKk6Io8AXDaQ1dFAQBJr02s8xOdNDpRUWRoV/f/0qrEQc6vJqemygTmLAegD8Uhk4UEx4MNBCdJEISoWpftf1tfRvrA4DVqEOZxYB9x9yqnYeIskuNXR49zAYJLj9v5IgocZHh0+pdKpr1ybUEae5kwhoArjizEk/tblK1+IEoF3mDcspJokz2jCWiwhWQFQhCJI+TDpNe+8GL+495+vSv7lFpNeAgE9ZUAJiwHoArIMNiUOfPYzEkN2Qonn1H3bAX9+/pOJIN9okKihq7PHqY9aKq6xARFT53QN0Ka7NeRFeC65CiKDjc6cMQJqzxNbsVYQV4o7Ej26EQaSqdhLVBJ3JAKRGlzRuQVZnfYdJpX2HdPEDrtCqrEY1tbAlC+Y8J6wG4VdwGa9arW9n4+TF31Cdpp5Qa8VmrS7XzEFH2hBUlrRu3k5n0kuYXTT0URcGu/W3o8nHYB1G+6mmNpmrCOomWIE5vEJIgoMigi39wgRMEAdPGDsGTbx/MdihEmkq3wjoXBi/6grKqO2uJKLPcwRDMKbQmOplJL8KrdUuQjkhLkJNVFRtZYU0FISsJ6127dmHGjBmYPn06Hn/88QGPe/HFF3HmmWfiH//4Rwaji3CrWtkowaVi79gvjrlxSkn/hPWppWZ81ppaSxBFUbDxg8P43h/fx679bemGSERp8gQiN23ptiXqYdapu9MjlqXP7sWK//sU9239GGFuYSfKS95gGAZJvTUIiPTS70xwan2z04eqKLvJBqsLhtvQ5PTiYwd30lHh8hxfd1JhkET4cqCH9f1/+xQzfvcm/v4p+84T5SNvIAyjKhXWEjwaJ6wPDVhhbcChTh/vwyjvZTxhLcsyVqxYgTVr1qC+vh5bt27Fvn37+h3ncrnw9NNP49xzz810iACOb4NVq3esXoJbpQprdyCETl8o6sI0zGbCF23ulHoc/u3jVjyzpwkXjrBhxf99yhsioixz+UOwqFhZaNKLGelh/bGjG5+2uvDzWWehzR3E3z7iDRtRPnL5QzAb1KuuBgCrUUKHJ8GENftX9yGJAi4bPQRP7W7KdihEmvHleYW1LyjjrcYOXDu+GpsaWrIaCxGlxh0IwaRCHsioE+HWuE3RoU5f1Gslk15CkVFCa7df0/MTaS3jCeuGhgaMGDECw4cPh8FgwKxZs7B9+/Z+x61evRrf//73YTRmp7rG7VevwtqkF1WrsP6yzYOhJUaIUSqerEYdDDoRLUkuTGFFwR/ePIj5552Ci0aUYc6Eaqzc9hmfyBFlUbc/BIuKySKjToRbxZ0eA9n4wWFcMqoceklEzegK/N/HTFgT5aNufwgWFduBAJHrlHZvIKFjmzu8KLcwYX2iS0eV4+0DHTjS5ct2KJTHli1bhosvvhizZ8+O+r6iKPj5z3+O6dOn45prrsHevXszFpsnIKdVYa31gLN43mjswOkVZkw6zYZ/HuliaxCiPORSqR2aUSfCo+G9V0gOo90THPBayW41osnJtiCU3zLeGNDhcKC6urr3Z7vdjoaGhj7H7N27Fy0tLbjsssvwhz/8IeHPliQBNptFlTgVnQSrWQ+rNXbCXBTFuMcUmw0IS5IqsbU1OnFqmWXAc55eUYTDnhDOPi32uSRJ7I1n+yetMBokXDCqAoIgYPr4auz6oh17j3nwjTGVacesthNjzyf5GjeQ37Hnq25/CGYVtqP1iPTS1zZhHZLD+Punx3D/VWMBABOGFuNP7zaj2xdCsYl9aInyiUvFWR49rEYdnAlWWDc5o1cNDWZmvYRLTi/HH99pxg9rR2c7HMpTdXV1WLhwIe69996o7+/atQuNjY3Ytm0bPvzwQzzwwAPYsGFDRmLzBmUYUqxs1EtC1hPWL392FOeeUgqLQcKppWY0HO7EpNPKshoTESXH7Q+pUrho0ouq7bKPpqXbjzKzfsDWbeUWPY50scKa8lvOZRDC4TB+8Ytf4OGHH076d2VZgdOpzjTUo04PJChwuWL/l9xqNcY9RgcFrR0eVWL79LATJQZpwHNWFunxQWMbvl5tjfk5NpulN57n3mvG14eVwO3+qurp0pFleGLXF5hQWZR2zGo7MfZ8kq9xA5mJvbKyWNPPzzfdPlnV6kaTXkx42FmqPm11oaLIgFKz/vg5JZxZZcWrX7Rh5tfsmp6biNQVqTBSdyNesVEHpzexdajJ6cXZ9tjXMoPR5WMqsPKlz/GDS07ng0BKyaRJk9Dc3Dzg+9u3b8d1110HQRAwceJEdHV1obW1FVVVVZrH5g3KMEip9c036qSsJ6w/drhw46RhAICxVUV4+4CTCWuiPOMKyDCpcP1j1Kk7x+xkh+I82LdZ9DjSyR1ZlN8yfqVrt9vR0vJVTy+HwwG7/atEhtvtxmeffYbvfve7AICjR4/itttuw+9+9ztMmDAhY3G6A6n3UDuZUSeqVtl4sMMLe4whRKeUmLDvaOKDF0NhBW980Y4fTe9bqXPhaWV4/p8OHOr04tRSc8rxElFq1K5uNOsltLkT24qfqveaOzF6SN9K/LOqrHjnoJMJa6I849aiwtogodOXWIX1kU4fhrDCup8yiwEThpbgrx8exvcuOi3b4VABOnk3bHV1NRwOR8yEtVq7XGVBRHGRYcCdpLF2tlrNeig6dXa0niyRnYZBOYwj3X6Mqi6BQSfizKEl2HOgI614srnDMVvn5nkL+7z5wO0PwSSlf/1j1mlbLHSo04vyKHPNelRYIoMXifJZxhPWEyZMQGNjI5qammC321FfX4/f/OY3ve8XFxfj7bff7v35xhtvxD333JPRZDUQSRaZdOrcqJn1ErpVSlg3Ob0YP3TgStShJUbs2t+W8Of943AXyiz6fr2PDDoRXx9Wivq9Dtx6yemphktEKXJp0BKkW+Ohi+80OfG16r7r08gKC9a/e0jT8xLlm127dmHlypUIh8OYP38+br311j7vr127Fhs2bIAkSSgvL8dDDz2EU089NaMxdvtDqlQYnchskOALhhGSw9DF6FPrC8ro8odgM+lVPX+hmDZ2CP77tUZ85+vDUm6fQKQmtXa5dnT7YNSLA+4kjbWzVVAUtHV6NdkRmMhOw8Y2D8rNegR8QQQAlOpF7G91pRVPNndnZuvcPG92zsudrl9xqXT9o2YOKJpDnT6UmQe+Tqoo0uMfR7o0Oz9RJmT8Klen02H58uVYtGgRZs6ciauvvhpjxozB6tWrow5fzJZun3o3amr2jj3c6UNljJ7Z1SUmNDt9kMOJDUx8/ct2fG2ABPjk08uw5Z8ODl8kyoJuFR+aAYBZL8Kt4UVTWFHQcLgLY4b0bSM0zGZGS7df8/7ZRPlClmWsWLECa9asQX19PbZu3Yp9+/b1Oebss8/GX//6V2zZsgUzZszAr371q4zH2eVTd/ArAIiCgGKTBKc3dpX1kS4/hhQZog6Ypsi6ekqpCX/72JHtUKgAnbwbtqWlpc9uWC15gnLK1z4mnbb9YuP5os2NoaWm3p+HFBlwzB1AgIMXifJKt19WpYe12SDCpeGadKTLj/KiWAlrA3tYU97LSllGTU0NXnzxRfz973/HbbfdBgC46667MG3atH7HPvPMMxmvrgYAV0C9rbAmvahKZaM7EIInEEZpjJ6FRp0Im1mH5gQnwr7b5MSZA/SpPq3MDL0k4L2mzpTiJcq2Xbt2YcaMGZg+fToef/zxfu9v2rQJkydPxpw5czBnzpw+Q4WeffZZXHnllbjyyivx7LPPZjJsAJFkkRoXSz00f8rv9MGok3r7V/eQRAEjyi3Ye6Rbs3MT5ZOGhgaMGDECw4cPh8FgwKxZs/o9sJ88eTLM5kg7rokTJ/ZJHmVKpzeoeksQIDJ4sSNOwvpQp5cDF+OYfmYlntrdxKICUl1tbS2ee+45KIqCDz74AMXFxRnpXw1EEtap7how6kS4A9lMWHtgL/5q3dJJIoYUGdDcmdg9GRHlBrUG35s0Hnjv6PajzDzwtVKZWY82dwChBAsZiXIRp7UMwOVXr4e1WhXWh5w+VBUbIAixK45OKTXhizYPRpTH7kvlC8rYd9SN7188Iur7giDgohFleO4fR3DBabZUwybKip4qxrVr18Jut2PevHmora3F6NF9+7XPnDkTy5cv7/Oa0+nEo48+ir/+9a8QBAF1dXWora1FaWlpxuLv9oVgs6i3RFsM2iasPz/mxnCbKep7p5eb0XC4ExedzsFDRCf3h7Xb7WhoaBjw+I0bN2Lq1KlxP1etHrI9vGGgvNg0YL/YE8XqK3sym8WAkBS7z2x74CiqS80Jf6YacWVaurGdX2TA1o9asedwN2aMq47/CwnK1b6mjEs9S5cuxe7du9HR0YGpU6diyZIlCIUi1wcLFixATU0Ndu7cienTp8NsNuOhhx7KWGzeQDjl+y+TXoTTm71qwn3H3DitrO/cH3uxEQfavRhVkXtD7IkoOpc/hBFl6c/wMuu1Hbro6Paj3DJwhbVOElFi0uGYy4/qkuj3aES5jgnrAXT7QyhSaSusWS/BrcJi1RynHUiPqmIj9h9z4/IxQ2Iet7elG8Ns5pgXhpNPL8P9f/sU3b4Qp9FTXjmxihFAbxXjyQnraF577TVMmTIFNpsNADBlyhS8+uqrmD17tpYh99HlD2FoqXqJFote0nRb2uetrj5bYU80vMyMj1tdmp2bqFBt3rwZ//znP7Fu3bq4x6rVQ7bH0S4vhph1A/aLPVGsvrInM0kCmlq7cXb5wDeDnx7uhFUvJfyZasSVaWrEVju6Ao/u2IcLTymOW8yQqGz2zI2l0OPKZP/YVatWxXxfEATcf//9GYqmL29QhjFGf/tYjDoJnixWWDe2e3DhSQU+lVYjDrTn3n9uiWhgbpUqrM0atikKKwra3AGUxUhYA1+1BWHCmvIVM5AD6Pap1xIk0js2/cXqcKcP5TEa6/c4pcSEz4+64x73XpMTo4bErgqxGnX4WnUx6ve24NtfH5ZwrETZlmgV47Zt27Bnzx6MHDkSy5Ytw9ChQ6P+rsMRu1eo6tWNchgVJepVNxYpCuSwAovVpMmQrv0dXowbWhI1jrFDS/C3j48m/ffJx8q1eArxOwGF+720cHJ/WIfDEbU/7BtvvIHHHnsM69atg8GQ+fYY3b4QLBq1BHF6Yz/Eb+rw4txTS1Q/d6E599QSbNnrwO6DTlw0gjtYKP95g6nvcDXpRHiC2UtYt3T5UVHUd62ushrwZRsT1kT5xBVQqYe1XtKsTVG7O4AigwR9nAd85RY9jnT5cB4yt0uYSE1MWA/AFVBv2JBai9XhTl/Mxvo9Ti014eXPj8U97v1DXZiUQKuPy8dU4OndzZh33qnQcQASFZDLL78cs2fPhsFgwJ///Gfce++9ePrpp1P6LLWrG53uAAQ5rGp1o8UgocnR1e+GSg0fH+5CzciyqHEUiQKOufw43Nqd1LqaqxV16SjE7wQUxvfKVIXjhAkT0NjYiKamJtjtdtTX1+M3v/lNn2M++ugjLF++HGvWrEFFRUVG4jpZl0+9nWYnKjJI6PAEYh7T7PTiijMrVT93oREFAVeeWYk/vHWQCWsqCN40elibstjD2uUPQQ4r/dbMqmIjGg5zhgdRPnH7ZVUKF9XaZR+No9uP8gTu50rNeji6c3OnGVEisjJ0MR+4/LJqlUVqLVaHOr0ot8RfmKpLjDjS5Ys5lTocVvBRSzdGxulzDQCjKopQYtJh+6dHk4qXKJsSqWIsKyvrrVycP38+9u7dm/Dvas3lV2+XR48ig4Run/oXTi5/CB3eIKoGqPKWRAFDSyKtiogGO51Oh+XLl2PRokWYOXMmrr76aowZMwarV6/uHb74y1/+Eh6PB3fddRfmzJmD/+//+/8yHme3X70H9yeyGnVo9ww8dFEOK3B0+1GpwYO1QjTpNBuanV58eIgDsin/+UJhmNIYuujNUsK6pcuPIUX95wyVW/RwdPuyEhNRtu3atQszZszA9OnT8fjjj/d7f9OmTZg8eTLmzJmDOXPmYMOGDb3vPfvss7jyyitx5ZVX4tlnn81k2HAH1GkJYtSL8IfCkDUYetgSp391j3KzHkc6uQZR/mKFdRRBOYygnPrQj5PpJQFyOPK58bZtxNLS5cdlo+PfwOklEZVWIxrbPRhbZY16zL6jLhQbdQn3pZ41zo7/3PUFakZXqLJFhkhriVQxtra29k6+37FjB8444wwAwKWXXopVq1ahszOSAHjttdewdOnSjMbvUunp/omKDBK6NBi82NjuwdASI8QYOzBOLTXj82NuTDiF2/yJampqUFNT0+e1u+66q/ffTz75ZIYj6q/bp03CusSkwycxetofdflRZNRp0rqoEEmigOljK7HmzYP4r3kTsh0OUVp8aVRYG/USvFlqCXK4yxe12tFm1qPdE4QcViBxlyoNIrIsY8WKFVi7di3sdjvmzZuH2trafrOEZs6cieXLl/d5zel04tFHH8Vf//pXCIKAuro61NbWorQ0M20tPEEZJl361z+iIMCoE+EJyKrPAnN0+2EzxU9Yl1n0eP9Ql6rnJsok3g1EERm4qFNtgI0gCLAYJLjSSBQpihLpjZbAkzQg0hZkf9vA1YzvNzkxsiLxfqNnVllxerkFj7zyBRRF/aeERGpLpIrxmWeewaxZs3Dttdfi6aefxsMPPwwAsNlsuP322zFv3jzMmzcPixcv7h3AmAmKoqj2dP9EFo0qrA+0e1FdHHuYx9ASIz51cPAiUT6IPLhXUq50jKXMokdrjO2pTU4v7An07qevXDyyDJ8ddeGjFrYeoPylKAp8wXDKQxez2cO6pcsXtdpRL4koNupwzB27DRJRoWloaMCIESMwfPhwGAwGzJo1q/f+K57XXnsNU6ZMgc1mQ2lpKaZMmYJXX31V44gjAqEwFCVScKgGi0ZtQY50+VGawGyzcosBji62BKH8xQrrKLTo22g1SujyhVCWQEuPaLr9IShQEq52qi4xYt9RN3B29PffOdCB0xNoB3Kib59/Kh55ZT8eeeUL3H7p6ay0ppwXr4rx7rvvxt133x31d3uS1dngDUZ2Y+jS2JERjVkvocs/8Fb8VH3Z7kGlNfbadkqpCbv2t6l+biJSX891kFoP7k9UZtbjmGvg5E2z04chcdYT6ksvibhibCWeePMAHvnm+GyHQ5QSfygMvSTE3K0Vi1EnwhccuB2ilg53+lA2QPKookiPli4f7MV8EEeDR7QB9g0NDf2O27ZtG/bs2YORI0di2bJlGDp0aNTfdTgcGYm7Z46ZWtc/ZoMEl1/9B2mOLh/OGFIU97gyix6tCcw5IspVTFhH4dKgb2ORIZKwTtWRAXqjDWSYzYzdBzoGfP+dAx24edKwpGKwGCQsmToS//v+Ycx6/G18fVgpTq+wYFRFES4YXoohrIgiUkWXL6jJsDOLQUKXV/2n/F+0uXHmAO2HegwtMaGxPb+H8hENFt2+EIqM2jyULjHp0ekLISSHoz6Ua+rwoiLFh/uD2aWjyrH8b5/i01ZX3PWYKBe5Aum1QutpuxgIhTPeUuhwpw+jBkgelVkMHHpGFMXll1+O2bNnw2Aw4M9//jPuvfdePP300yl9liQJsNmSK8aLplNWYDHoYE0gryGKYtzjrCY9YNCpEtuJ2rwhfKPCEvf8RUUGhMIKdGYDrMb4qT9JElWPNdsK8TsBhfu9TsaEdRRdGvRttKSbsO70oSKJAUQjysx4ek8TFEXpl+Q+5g6g0xPE0NLYW/ijKTHp8f2LR+CYO4D9x9xo7faj4XAX/uPvn+P84aVYMnVU0pXbRNRXpzeU0EVFssw6bXpYH2j3ouaMipjH2Mw6BEIKnJ4gbAm2NiKi7Ojyq7/TrIckCig2RbbIV5f0vw450OHF2Mr4VUPUl0En4oqxQ/DEGwfw6+vGZTscoqSpUTBk0kfagmQ8Yd3lw6TTbFHfs5n1aOGWfBpkEhlgX1ZW1vvv+fPn41e/+lXv7+7evbvP71544YUxzyfLCpzO9AtjDh9zwaQT4EqgKtlqNcY9ziAKcLS54SxVt7DvSKcXRiChOCssenzW3IFRFfGvrWw2iyp/x1xSiN8JKLzvVVlZHPV19rCOwuUPwaJyuwuLXodOX+pb8Q93DbzVLJpSsx4mnYRmZ/+psB8e6sRYuxViGltdhhQZcNGIMlwzvhrfv3gEVs4+G5VWI/7lTx/gT+82s881URqcGlZYd3rVbQkSCis40uVDVZwn/IIg4JRSE75klTVRzuvyBTUZuNij3KJH6wBtQQ60e1Bdwh1bqZh6RgU+PNyFz49yXgDlH3dATrvdoEknwRPIfB/r1u5A1KGLQKQN0uGu/vdjRIVswoQJaGxsRFNTEwKBAOrr61FbW9vnmNbW1t5/79ixA2eccQYA4NJLL8Vrr72Gzs5OdHZ24rXXXsOll16akbjdKg+9N+nEtOaYRSOHFXR4gihNcJAjd3lQPmOFdRRdvhBMqg87E9Madnak04eyJKsSTy83Y29LN4aXmfu8/l5zJ0arvF3UqBMx/cxKnHdqKf7w1kF8eKgLD1x9pqoLPtFgEekfq/7ybDFIOBqjd2wqDnf6YDPrE6pmqi4x4st2D84blpkp30SUmi5fCBa9dpeINnP0wYshOYwj7PWaMoNORO2YIXj8jQP41RxWWVN+cflDad83mPRixhPWQTmMbn8IJQPsjCsv0qPhcFdGYyLKNp1Oh+XLl2PRokWQZRlz587FmDFjsHr1aowfPx7Tpk3DM888gx07dkCSJJSWluLhhx8GANhsNtx+++29s4QWL14Mm82Wkbhd/pCqc7pMehEulYcutnsCsBqlhGcdlVm4y4PyFxPWUXSrcMF0MrM+vZYgh7v8GFuV3BbZ08rM+MeRLlx1dlWf19856MSNF49IOZZYhlgNWHr5KPzp3UO4Zf0HeOSb43njSZSkTm8QFoP6G2DSbU0UzYF2D6oT/O+4vdiI/cfcqp6fiNTX5QvBrMEa1KPUFH0IULPThzKLobcXLSWvZnQFlv/tE+w75sboBAYyEeUKd0CGKc1WHiadBE8wswnrY+4ASs36AYdFljNZRINUTU0Nampq+rx211139f777rvvxt133x31d+fNm9ebsM6kLpV32pv0UlpFi9G0ugIoS2LWR6lJBwd3eVCe4h1BFF0+9RPWFoMOzjS24h/p8iU9hGhkRRHeb+7s81pLlw9HXYGEpsqmSi+J+O6kYZgwtBg3rXsP/2BVAVFSOn1B1dsSAYBFL6ErjdZE0TQ5vRiSYH/9oSVGfMGENVHO6/QGNd0hVWrWRd2e+mW7B0PZDiQtxuNV1k+8cSDboRAlxeUPpZ2wNupEeFSuZoyntdsfs22jzWzAMbe6u9uISBuRHWbqXf8UpVm0GE28Nedk5RYDDvOhGeUpJqyj0KJ3Y5FBSith3drtR3mSLUFGVVhwpNOHoydUMb3+ZTvGDS0esApALYIg4MqzqnD9+afiX5/9J7b8syX+LxERAMDpCaJIg6GLFoOEbpX7qB1o92JIApO0AaC6xITGdq+q5yci9bV7AijWYA3qUWbWoyVKtU9juwdV3JWVtpozKvBukxNftPEBIeUPVXpYZ6EliKPbD5t54PXSaoz01faHwhmMiohS0ekNqrrDzJJmDiia1m4/SmOsOScrL4p+zUWUD5iwjkLtJ2tAz1b81BYrX1CGJyijOMHG+j0kUcD4oSV49Yv23td27mvDuOroEzi1cM4pJfjXmlH4/RsH8Osd+xAKcxgjUTwdXm2GLhZp0BLkYIcHVcWJVViXW/Rw+UOqDx8hInW1e4KwapiwHmI1oCnKUOj9x9wJtxiigZn0Ei4/3suaKF+4/CEY062wlsSMtwQ56grAFqPaURQElJn1fQqIiCg3dXqDqs4RKjLq0KnyvZej249SU3IV1i0cukh5ignrKLp8IU0qrFNNFLV0+VFhMUAUkq+KHje0GC9/fgwAcKjTi38c6cL4DCasAeCUUhPumXYG/nmkC/+66R9MVhHF0enVJllkNerQ5QtBUdR7cNTk9KEqwQprURAwtMSEA+0e1c5PROpzeoMoNmrXEmRoiQkHO7wIn7QW7T/mwdASk2bnHUwuG12BPQed+LKN6y3lh8jQRTVagmQ2Yd3S7UdJnORRmSV6334iyi2dPnWLhorSKFocSEu3L+ZDspOVmfU45g70u+YiygdMWEehxdDFIoOErhQTtS3dPlQk2CP2ZBOGFuPzVhfebXLif946iKlnVMCsQeVmPEUGHW6/dCRMOgm3rP8ArXzKRzQgpy+kSYW1XhKhEwW4VbqZC4TCaHMHEu5hDQDVJUZ8wQQKUU7r0LjC2qyXYDXqcLjzqyprX1BGk9OLU0uZsFZDT5X1E2+yypryQ7cvlHZLEKNOhMuf2YR1a7cPZXHaNtrMehztZh9rolzXqXLhoha7W1u6AnHXnBMZdCLMegntHnUT50SZwIR1FJHqRvVbgqQ6IfZIlz+pp2gnMuklXH/+qbhj4z/wVmMHascMSelz1CCJAr59/ik499QS/Mv6D9DIpBVRVF0aJawBoMSU3gDYEx3q9GFIkQFSEj3xq6xGVvwR5bhOX1DTHtYAcGqpEfuPfbUWfNrqwimlJhjSbAlAX7lsdAXePtDBXS2UF9yB9AuG0mnBmCpHd+yWIEDk2osV1kS5T+17MItBQqfKa1KrK/nZZhVFBjjYx5ryEO8Kouj0hVS/UbPoJbj8ckpb8Vu64j+5j+WcU0pw/1Vj8cDVZ2paMZUIQRAw46wqzDirErf+74fYe6Qrq/EQ5aJuX0iz/64Wm3ToUOkJe5PTm/SAtKGlRuw7xkFgRLkqrCjo9smaXy/Yi019hgLubenG6WVmTc852Jj1Ei4bXYE1bx3MdihEcXX7ZZjSbAli1kuq94uN56jLj7I4CWubWc8eskR5IJKwVrGHtUGHbp96uz7CioI2d/yHZCcrt3ANovzEhPVJfEEZclhJe+jHyXSSCL2U2lb8Q52+pJ+inazSakypB7ZWLhlZjgVfPxV3bvonXv+yPf4vEA0ScliBO6D+4NceVqMOHSpVWDd1eFGZZLui6mIjGlntR5SzunwhmPRiUjsnUlFdbMS+o18lrD881IXTypmwVtvlo4fgjS/b0ez0ZjsUopjUqrBOdUdrKsKKgnZPEKXm2Akum1nPdohEeaBb5ZYgBklAWFHgU2kYbLsnCItBgl5KLldlM+nR0sU1iPIPE9YncXqDKDHpIGiQ3LUadehOoY/14U5/yj2sc9k5p5TgB5eMwAN/+xR/ef+QqoPgiPJVtz+SrBY1ShZZDRKcKlVYH+zwYIg1ubWp0mpEmzuY8aFIRJQYpydyHaS1YTYTPml1AQAURcE/jnRhZLlF8/MONmaDhKlnlOMPrLKmHOf2yzCnWTBkMUgp3WulKtHkkc2sh4MJa6KcFpLD8IXS3+lxIkEQUJxiDigaR3fy7UCAyODXQ51sCUL5hwnrkzi92vVttBqllHrHpjN0MdedMaQId18+CuvfO4QH/vYpvCo9fSTKV5Ee+toliyyG1NahaA50eFFlTa4liCQKOKXUiC/b2BaEKBc5NV6DegwvM8MdkLHvmBvvH+qEQRJhT7LFECXm8jFDsHNfG6usKae5A3L6Fdb6zCasE2kHAgA2ix5HXRy6SJTLuv2RdiBq70ovMqrXqqi1248yS/J5oSFWA68BKC8xYX0SLRPWxUZd0tNZQ3IYHZ5gQhdD+arSasQPLz8DTl8QC556Fx80d2Y7JKKs0TpZZDXq0O5R56ap2elDVXHyF02nlJjYx5ooR3VkKGEtCgIuGF6Kv33kwHMNLZh8epkmu9so0kNz6hkV+B9WWVMOcwdCMKnQEsSVwYR1JHmUQML6+MBrOczdpES5qtMXgtWofkvGIoNOtWGwjm4/bCnsghtSZMBhVlhTHmLC+iQd3iCKNFiogOOJIndyiSKHyw+bWa95L8lsM+klfHfScMweZ8c9Wz7Civ/7FB0qJdWI8kmbO6DpdvxUHpxFEwiF0e4JoDyFp/zVJUZ8fjT5hPXz/ziCBU+9i5/Uf4wWTrom0kSHNwiriv0bY7nwNBvWv3cIu/a34aIRtoycc7CqHVOBV/a14VAnK6wo94QVBf5QGKY0W4KYM1xh3eoKoNQUP2Gtk0QUGSTVCgaISH2RgYtaJKwldHnVWZdaunwoTaGQsaLIAEe3ny1YKe8wYX0Sp1fdybAnKjbq0JFkouhIpx9DCrQdSDTnDSvF8ivHwhuUMW/tO1j/bjNCrEagQaTNo90uD+D40EUVbpgOd/pQYTGk9DDt1FJz0gnrrXtb8PibBzHza1UQBGDxxn+oVq1ARF9xerR7cH+yU21mPHj1mVg56yyUJJD0odQVGXWYOrocT7xxINuhEPXjCcgwSGLa8zvMegmegIxwhpIyjm5/3IGLPcotBrSyLQhRzuryBWHRIA9k1kvoUqklSEuCuzqixaCXRHSo1BaSKFOYsD5JhyegyZM1INLDui3JRNHhLl9Ki1I+MxskzJt4Cv61ZhT+9nErbnzmPXx6fDATUaFrdwc02Y7Wo9ioU+Vi5aDTi6oU+82eUmrC/mPuhJ/yH3X5serl/fjBJSPwtepiXDdhKEaVW/DrHftTOj8RDazNE4BVowf30ZRbDGm3AaDETBtTiV3723Gwg1XWlFtc/hAsKtx/SaIAo07M2GBnR3dkJ2wibBY9Wjl4kShndfnUWYdOZjFI6FSxJUiqrWIrrQYccia/Q1VRFATlcErnJEpXVhLWu3btwowZMzB9+nQ8/vjj/d5fu3YtZs6ciWuuuQY33XQTDh06lLHYOjza9W4sMelxLMkn60c6B1/CuscppSbcOXUkpowqw+0bGvDU7qaMVUwQZctRV0DTSkOrUUKnCtvSmp3elHd/2Mw6KEDCA4jWvHkAF59ejlNKTb2vXXdONd5q7MDeI10pxUBE0R1z+VPabkq5z2KQUDu2Ao+91pjtUIj66FYpYQ0AFoMuY21BkkpYm3RMWBPlMKc3CIsGD9AtevUG3rd0+VGe4v1XRQp9rN9tcmLG795C7aNv4O+fHk3pvETpyHjCWpZlrFixAmvWrEF9fT22bt2Kffv29Tnm7LPPxl//+lds2bIFM2bMwK9+9auMxdfuCWpW3ZhK79hDx7fdD1aCIODi08tx77TReOEjB370/EfwZqhqgigbMtHDWo2n/I1tHlRZU1ubBEHAyHILPmrpjntsa7cf2z49iivPquzzukkvYebXqvBbJl6IVBXpyZq5CmvKrMtHD8Hupg58fpQ71wareIVDmzZtwuTJkzFnzhzMmTMHGzZs0DwmNQdOFxkkdKu0/T6eo0kkrEvNejiYsCbKWW0a7XK1GiVV5gcF5TCc3mDKFdblFj0OJzEDqKXLh2VbPsbCScOw9PIz8Iu/f57QvRuRmjKesG5oaMCIESMwfPhwGAwGzJo1C9u3b+9zzOTJk2E2mwEAEydOREtLS8biU/OC6WTFpuSHbTQ7vRiSYlKokFQUGfBvl42CJyjjxrW72buWClabR9uEtVEnQg4j7e2yje2elFuCAMAwmwkfOeJf9DzbcASThtuirssXn16GL9o8vHgiUlGbO8AK6wJm0ku46qwqrN75RbZDoSxIpHAIAGbOnInNmzdj8+bNmD9/vuZxdXpDqrUismRo8KKiKDjmDiS8E9Zm1qOlmwOjiXKVVkVDJSYdjrnT71/fs6MjlflBQCSfkkxLsLVvH8RFI2wYV12M08rMmDOhGg+/9Dl3vFNGZbyExuFwoLq6uvdnu92OhoaGAY/fuHEjpk6dmtBnS5IAm82SVnzdARlVZWZYrYklYkRRTPjYakFApy+UVIyHu/wYUWVN+ByJSibuXLK4djT+vKcZizf+E+tuuTCvbqolSUz7P5/Zks+x55t2T1DThLUgCCiz6HHMHcBpBnPKn3PQ6YU9jYT1iHIL9hx0xjwmKIexqeEIFn9jZNT3dZKI2jFD8D9vHcSvrxuXcixEmbZr1y6sXLkS4XAY8+fPx6233trn/T179uChhx7Cp59+ilWrVuGqq67KSFyKokQS1qywLmjfGFWOV/a14e0DHbhoRFm2w6EMOrFwCEBv4dDo0aOzGpfTG4TFoE4dlSVDFdYuvwwIkWFmibCZ9Xi/uVPjqIgoVW3uAEaUq3+/W2LSo12FhHVLlx8VKbYDAQB7sRENhxNrpdjuCeDFT47i/qvG9r42+fQyvLq/Ha/ub0PN6CEpx0GUjJy+I9m8eTP++c9/Yt26dQkdL8sKnE5PWudsc/khyWG4XIlt2bJajQkfK8phOD1BtHe4IQrxn4z5gjK6fEEYwkrC50hUMnHnmgWThuHp1w9g4Zq38dj156Aog8Oh0mGzWdL+z2e2ZCL2yspi1T8zXlJo7dq12LBhAyRJQnl5OR566CGceuqpACKticaOjfyP9NChQ/HYY4+pHt/JFEVBhyeIYo12efQoM+tx1OXHaWWpJaw9ARndvlBa/fVPKzNj3Z5mKIoCYYD18OVPj2JIkQGnntC7+mRTRpZh+d8+RbPTi2G21BPwRJnSU+G4du1a2O12zJs3D7W1tX0SRkOHDsXDDz+M//mf/8lobC6/DJ0ocghigdNJIq6bUI1f79iH9TddAF2K1VqUfxItHNq2bRv27NmDkSNHYtmyZRg6dGjMz023aMgPAbYiY9ximkQKbooteoRULrSIVrjhaOlGpTV+zD1OlRUc+/BIUnFls2AkW+fmeQv7vLmsTaOioRKTOgPvj3T5UJ7GvVd1sTHhCuvN/2jB+cNK+8xVEgUB08YOwVO7m5iwpozJeKbPbrf3afHhcDhgt9v7HffGG2/gsccew7p162AwZKYlRiAUhicga9YSRCf9/+zdeXxU5dk38N+ZM1sykx0yCRCWsKnsm2xKFAyorCJUbVGrUn3csMUVq7jbvlaxWq212rpWW6FsghYUBNxRBCPIDhFCkknIPvt23j+GREK22c7Mmcnv+3n6eUjmZM51YnLlPte57+tWIUmjQr3DE1C/sxN1DnQ1aKHijUQzgiBg7rAcvLPjBO5avQfPXz4EGjEm+4eSggVSFGrsl5+UlIR33nkHf/rTn/DnP/8ZAKDX67FmzZqoxmxzeyEIkL1YlJakDnjDw9YcPzW7OpAHb21JT9JAq1bhp2o7eme1PmD+97fHMb5P+7P/9BoRE/pk4p0dJ3DPlNjOECMKRCAzHHv06AHAX5yJpkqrE+lJ8fEQmMIzvHsqPj1ShRU7T+DKUT1iHQ4pyIUXXogZM2ZAq9Xi3//+N+699168+eab7X5NuJOGSqut0ArocDJNIBNuNAAqamwRnWjR2sSNg6W1yEhSBzwBSCtJqGhwoKbG2uaD+kDOGy2xOjfPG5vzyjFxKN7U2NxIlaEOlKpXo8bmbneSTiDK6h0B98xvKw6PT0KtzY30DgrfH/xoxrxh3Vp8fkSPNKz5oRx7yhswKIc/MyS/qN+VDBkyBMXFxTh+/DhMJhPWr1+PZ555ptkxP/74I5YuXYpXX30VWVlZUYutyubv2xhOEaYjqXo1qm2ugJJNSa0DXeOwbUc0CIKAq0Z2x9+//AmPbTiARy4ZGNYfAEo8gRSFxo0b1/Tv4cOHY+3atVGP83RVVndYA5FAper8M6xDdazGHlb/6kYDsw3Yfqy21YJ1pcWJncdqMW/IWR2+zwX9svD4xgO4cXyvDgdgRLEWbGu0QEWiLZq9yoasIGYMNlJqmzGlxgUoI7ZfT+iNJz7cjzljeiIn1b+SRamz7hhXZAQycSgj4+cHxfPnz8ef/vQn2eOqsbnbXU0VDL1GRH0EZjN2pLw+8A0XAf8eIjq1CtU2d1jL+oko8iRJQq3djRQZZlhrRBW0ahUanJ5mM5aDVVoX3gxrQRDQLVWP4mobhientXncoUorGpxe5Hdp+bdNVAmYmJ+J5TtPYNAlHd+jEYUr6gVrtVqNpUuXYuHChfB6vbj88svRv39/PPfccxg8eDCmTJmCp556CjabDXfccQeA6C3Hr7S4kCHzzKIUvQbVVjfyA6jDn6izc0DTDpVKwPVje+LZLUfwz6+P4YZxvWIdEilIuP3ynU4n5s6dC7VajRtvvBEXXXRRu+eLRLHoUJ0TGcla2YtFXdP0qHP5Qo7XbHOjW0Zy2MWWYT0z8O2JOtx4YcuZ0W/vLMW4/ExkBdDmw2jU4dzemViztwJ3TOkfVkxyi7fiRqAS9briSSTaohWXN8CgFYNuGabUNmNKjQtQRmypahXOz8/EfSu+x7OXDYYgCIptX5bocUVrdmMgE4cqKiqQnZ0NANi8eTP69u0re1x1djcGZBsi8l4GrRiR5fcdCWW2YxeDFuX1Dt7fUcKLt7aMDU4PtGqVbKu20/RqVFvdYRes+3YJL0+aUnT+gnWPtgvWG/ZVYFReWpuTOCf0ycQj/9uPekd410MUiJis+ywoKEBBQUGzzzUWpwHg9ddfj3JEfpUWp+yb+KUnqVER4A3K8Vo7sgxMAu3RqlX4v4m98NTmQ+iTmYzJA7rGOiSKQ631y//kk09gMplw/PhxXHvttRgwYAB69uzZ5ntEolh0pKwOxigUi5JE4GiVJeR4fzxRi+5pSWEXW3qn6fDGlz/hZLW1WQ9Vr0/CO9uP4Y4p/QI+R0F+Jp755DAuG5St6MGTUosu4UqE64pWwSjQ1mixUGlxyrrpKynPxWd1xVObD2Pt7nLMHtJ+n2KKf4FMHHrrrbewefNmiKKItLQ0/OEPf5A9rlq7G8YI7UmTqlfjQIU1Iu/VnrJ6B7qnBbd3RmayFmX1TgzirxolsHhsy1htdcu64XSaXoMqm6vNNoiBKG8Ib9NFAMg2anG0uv3x+uaDJ3HVyO5tvp6qV2NIbirW/lCOBWPywoqHqCNs/HuaSotL9hu19CQNzA0BFqxr7GwJEoC0JA1umtALT3x0EAcqLLEOhxQi2H75L730UrN++Y3H5uXl4dxzz8WPP/4oe8zmhuj0j01P0qAijB7WR6psyE0NPzel6jXoatRix/HaZp//7EgVUnVq9M4KfBaBKUWHod1S8cb242HHRSSn02c4ulwurF+/HpMnT451WABOjYNk3vSVlEUtqnDtuXl4fttRHKmSv8hHsVdQUIANGzbg448/xs033wzAP3FoypQpAIA777wT69evx9q1a/HWW29FZYZ1vcMDoy4y+3ek6tWosoU+xglUWb0z6OX5GUkalNU7ZIqISBlOb8uo1Wqb2jKebty4cUhK8j/wGT58eLN7tliosrlknfCSolejyhp6XvJ4fai0uJAVZutDU6oOhyrb/lt/pNKCBocHvTLbfxhX0C8L/9lZCq9PCiseoo7wruQ0lRYn0mSemZeRrEFpXWADlWM1dlx6jjJmXSldz4xkzB/eDXeu3oO3FoxkH1sKq19+XV0dkpKSoNVqUV1dje+++w4LFy6UPebSegcykuVfJpqm14Q8aPL6JByvccAUgR7WADCudwZW7CrF2F7+npmSJOHlL37CRQODXy0x/ZxsPPnRIcwcnIPemWxPQcoUyAzHoqIi3Hbbbaivr8cnn3yCv/zlL1i/fr3ssZU3ODGga2SW5VP86J6mx5whObhz9R6svmVirMOhTqjO4YYhQjOsU3T+Dc7kVtHgRGaQY7aMZA1KWbCmBBfttoxA+K0ZHcfrkGkMri1jMC0ZM4w62CWEHONPVVZkGbVID3JVx5nO7pGOf+8sRVpaUqv7fy3/7AhG985Aakr7ewoMMeqQ+kM5dpRbMFXh9apEbVuYqNd1JhasT2NucCInArMG25OZ3HI2YWucHh+qrC50YY+zgI3pmY4TtXbc+/6PeHH+0GYtBqjzCadf/uHDh/HQQw9BEARIkoTf/OY3zZaxyaWszonBufK3JUhL8hesQ9mtuqzegTS9GnpNZGZCje2ZgQc+2IdKixNdjTpsOnASLo8Pw7qnBv1eGclaXHpONh76cB9evXK4bH3oiMLVUWu0oUOHYtu2bdEOC2X1DozvndHxgZRwJvTJRGmdA//3r+/w5zmDoFMzf1J0uL0+uDwSkjSR+ZlL0ctfsPb4JFTb3EGvissyaPFDWb1MURHFn0i0ZQTCb814vNICvSgE1e4wmJaMehVw/KQ15Bj3HKtBl2Rt2O0YtZIEQQL2HqtBt1Y2ul3/Qzmm9MsK6DyT+2dh2cb9GJ1rbLPftRIkQtvC1iTadbXVmpEF69NUWFwYmG2U9RyZyRqU13ecAI7V2JBt1EFk0TUoswbn4MXPivGXbUfwuwvkX8JIyhZqv/yRI0fi/ffflzO0VpU3ODCpb6bs59GpVVCLKtQ5PEFvGHSkyobcVgY4oUrSipjcvwvuXvMjfn1uHv748UHcNKFXyAOfgn5Z2F9hwdIP9uGx6WfzwRVREMrqHXxQ3onNHZqLt747gbvX7MGfZrNoTdFRZ3cjRS8G/QC9LckaES6vD06PT7af4ZMWJ1L0aqiDfDAe6H0gUTwLti3j22+/3WFbxo4K1uGqsrqQImNLtFS9GifDKDaX1NrRxRj++EwQBPTOSsae8oYWBevyegeOV9swMLtXQO81rFsqNu6rxMZ9lbj47OxWj/H6JFicHqTo1YouapNycSR6mpMWZ9DFm2BlJGtQGUDv2OJqO0wyz/ZORCqVgOvG5uGj/ZX4eH9lrMMhCkpFgyvo5aWh6mrQhtRH8WiVLWLtQBpdck42clJ1+OfXx3DlyO7ID2MHbJUg4PqxPVFhceH6d3bii6PVcHp8EYyWKDFZnB54vFLE+shS/FGpBNw0qQ88Pgm3//cHNDg8sQ6JOoFahydiGy4C/oJMml6Nahn7WJeG+HAvy6ANeC+jtkiShKNVNhyrsYf1PkRyCWSvjsa2jC+99FKLtowul/93t7EtYzRWuZ6oc8h6D+bfxyz0nHSsxh6xCQU905Owu5WVHlsOVWFkz/SAJ0wKgoB5w3PxzCeHUH7GPeXxGjse/nAfJr/wBWa9sh1T//olXvj0KGwub0SugToPzrA+TZXVLXvBOlkjwif5nzQZ23mKV1xlRTY3XAyJUafGwnE98YePD6JfF0NYu/ESRYvN5YXT441asaiLUYuSWgfONgXXguRgpQU5ES5YqwQB84d3i9j7adUq3DyxF74srsHz247geI0dKTo1jDo1MpM1yMtIwpDcVIzrnYGc1MjNFieKZyfqHOhq1EZsliPFJ7VKhevOzcN/vy/DNf/6Dk/PHoS+YTxEJOpIjc0FY4Q3vU9N0qDa5kauTH/jS2pDK1gbtGLTjMP27gPb88wnh/HR/kp4fBIeuWQgzsvP6viLiKIoHtsyltc7METGtoxZBm2Lom4wjtXYQ2qX2JreWUnYdOBki8//b28FLh6S08pXtC0/y4ApA7ripv98jzsn94NRJ+KDPRXYfPAkLuifhYcuHoC0JA0qLU6s21OBX721g+MKCgoL1qc0ODzwSZHrn9YWQRDQxaBFeb0T/bq2/e0/XGVDXnp4TfU7s16ZyZg5yIS71/o3YYxUv10iuZgbnMgyRK9YlJmswYna4GfnHKiwYmSPNBkiiixBEDChTyYm9MmEx+tDvdMDu8uLeqcH5nonNh04ib9sO4rcNB0uPisbFw3syuI1dWqldQ5ksR0IwT/Tev6IbvjiaDVu/M/3uHlib1w+LJcPM0gWlRZXxCcMperUqA5xc+lAHA9xtqMgCOiaokNJrR1nBTlhAAA+O1KFTQdO4oGpA1Be78BDH+7H6hvORUqEC/5E4Yq/toxOZMo4BspM1qLC4gxp/yDA3xIklA3pW9M3y4BXqo6h2vbzyt6jVTaU1jkwtHsa7EGuTikc2BWZyRq88sVPcHl9OCfHiIcuHtDsoVxXow7Xjc3Dl8XVuOk/3+Ox6WdhfG/522BS/ONft1NK6uwwpeiiMhjPNGhQ3uBAv65tP1k6WmXDmJ7psseSyM7Lz8Thkzb84eODeOSSs2IdDlG7yhvkXYp2pq4GHY4FWbB2enw4UWdvdZMOJVOLKv/3NhnoDjTNKvf6JOyvsGBHSR3++fVxdDVqMa53BoZ3T8Pg3BR05SoX6kRK6xzIimIOIuWb0CcTfbKS8eb2Enx8oBIPTB2AHpxMQRFmbnAiLcIFV6NO3pYgx2rs6JUZ2u+CyajDsZrgC9aSJOGvnxVj/vBcJGtF5HcxYGhuKt785jhuPb9PSLEQEeDx+lBjcyNDxpX2OrUKeo2IKps76IddHp8Ec4MTXSNUUNeqVRiSm4JPDp7E5cP8K1xX/1CGsb0zQt4/bVReOkblpXd43Pjemehq0GHpB/vx63Pz8MtR3fkwnNrFHtanlNQ6ItLIPhCZyVocr217SYjb60NJrQPdONsvLIIg4MqR3bCrpA7r95R3/AVEMVRW50BGsrwtiU7X2BIkGEerrDCl6qEJcpMhpRJVAs7JScGC0T3wx5lnY+6wXFicHrz1zXFc8foOXPy3r3Dv2j1YVVQW1kYpRPGgpNaOTEP0chDFh9xUPe6a3Be9M5Nxzds78fcviuFwswclRU55feT3EErRi6i2uSP6nqcrqbWH3Lqxi1ETUv/p70/Uo8HhwZBuP7cFuOScbKz4vpS/k0RhqLC4kJakCblYG6guIbYFKamxIyNZC20EN5Ed0SMNH/xYAQCotDjx/m4zJvbJiNj7t6dfVwPumtwXa3eX49YVP+BIlTUq56X4xBnWp5TU2qO2FNaUosORk23/Yh45aUN2SmSTUmel14i4flxPLNtyBEO6paFnBmcGkTIdrbYjO0oPzQCgq1EbdEuQ/RUW9Iiz2dWBElUC+nUxoN+pnmqSJOGk1YWDlVZsOlCJ57cewYBsI+YOzcWF/bswP1PCKalzYHiE+iNSYhFVAgoHdsXIHmlYWVSGtbvLsWhSPi4a2BUqzoyiMJkbnBjSLbK9Y41aNU4GsMl9qBp7/oci26hDcbUt6K97b1cpJvXNavY7l2XQok9mMjYfPIlLzzGFFA9RZ1dW70CXKDywzzRoUVrnwODc4MZaR6qsEV/dOignBWt3m/H618ew60QdzsvPjOrK0i4GLe68sC82HzyJ37z7Pfp1NWBS3yyck5OCPlnJsu8rR/GDBetTIrnzakdyU/WtNrpvtL/Cwv7VEdQjPQmXnp2N+97/EW/8akTCzA6lxHK0Krq9oTOTtai2ueH2+gL+ndhXYUH3BC1Yn0kQBHQ16tDVqMOEPplwe30oKq3HO9+V4JlPDuPyYbm4fHi3qP3dIJLbT9U2TDsrMv0RKTFlGbT4zfheOFBhwatfHsMb24/jlvP6YEKfDC7ppZCZG5yYlBTZXqZpSRocbmdyUDjq7G5I8G+gGIpsow7fHKsN6mvsbi++OFqNhy8e2OK1sb0zsOaHchasiUJUXu9ERhRaomUkaVBeH/yKzUMnrRHf8F4jqvB/E3rhH18dQ+/MZFxydnZE3z8QjQ/DL+iXhR/KGrDrRB3W7TGjtM4Brdo/kWhwbipG9EjD8O5pSA4x51J8Y8H6lJJaOy7oF51dlnNT/U/W22q6/6O5odMUhaKloF8W9lVY8OKnxfjtBfmxDoeohZ+q7VG92RBVAjKTNSirdwa88uDHckunLWhpRFVTf7bSOge2HqrCvH9+g/G9M/GLEd0wvHsqCzYUtxxuL6qsrpCXuFPnMiDbiLun9MXOkjo8vfkQ9BoRvxrdHRcN6MpNrilolZbItwTpYtDi08NVEX3PRiW14e17lJ2iC7ol22dHqtEnK7nVzRWHdUvFv3ecQEWDE9kRLmoRdQZl9Q5Z+1c3ykzW4ERd8O2ADlVa0ScrOeLxZKfosKSwf8TfN1gaUYWRPdKaJm5JkoRauxvHax0orrbhpc+KUVxjw1nZRhT0zcIF/btwP41OhAXrU07UOdAlSjdqqXo1JEiotrlbbUOyz2zB1E5aFJKLIAj41ageePLjgxjfJwNje0WnRxNRIBxuL6psrqjP1s1N1ePISWtABWuXx4fDJ63oldEzCpEpW7c0Pa4a1R2zhpjwVXENHvnffkgALjk7G1MGdEG/LgYWrymuHK22ITdVL3v/RkocKkHAqLx0jOiRhj1lDfjv92V45pPDOD8/CwX9sjCqRzrSo7gvA8Unp8cHq8vbaiE2HF0MWpSFMJMxEMVhtnBL0Ynw+iTU2d1IC7BItmFvBUZ0b30VnkZUYWi3VGw+eBJXjuweclxEndWJWgcyo/D3KsugxY7jtUF/3eEqGybmR3YVipIJgoCMZC0ykrUYeqpnv9Pjw/4KC3aW1OG17cfRI02Py4d3w7SzsqFjm8aExoI1/IWYWrsbmVHqlSMIArql6XG0ytaiYO32+nCkyoq8dBaFIi1Fr8bVo3vg4Q/3491rRvFGihSjpNaBbKMu6sWi3DQd9ldYcEH/Lh0eu6/CgtxUHWfPncagVWPKgK6Y3L8LfqqxY8exOvx25W5IAMacmo09KDcFvTOTWQgkRTt80opcruyiEKgEAUO6pWJIt1TU2NzYdaIO7+w4gUf/dwApejXys5LRMyMJual6dDVqkWXQIitZiy5GLQxakQ/3OrlKixMZyZqI90I36kS4vT40ODwRL4YfqLQgNzX0fCkIAnJOrbYd1kYR+nQOtxffHKvFzMFtr8Ib0SMNG/dVsGBNFILDVdaorHLNNmqD3nDV7fWhvN4BUydfPaFT+x/MDe2Wiit8EvaUN2BVURn++ulRXD+uJy4bmsu2rwmKBWsAxdU2ZBt1UEWxoJCTosPhk1aM7pne7PN7yhpgStGxR49MzslJwcgeaXh0w348M2cQb5RIEYqrbchJjf5ApEd6EvabLQEd+0NpvSzL0RKBIAjonZmM3pnJmDssB+YGJ/ZVWLBxfwVe+fIn1Njd6JWRhL5dDBjWKwN5Ri3ONqVE/CaaKFSHKiPfH5E6n4xkDS7s3wUX9u8Cn09ChcWJsnonTlpd+KG0HvVOD+ocbtTbPai1uwEAXYxamFJ06JaqR15GErqn6dEzIwm9MpORxAekCa/iVME60gRBQHaKDqV1DgzUGyP63gcqrBjbKz2s9+iepseBSmtABeuvimvQKzMJRl3bY4azTUa8sf0424IQBUmSJBRX29AtCvdhXY06VFndsLu9Af99O1BpRU6qnsXY04gqoal4fazGhrU/mPHv707g3in9MbY3V9EnGt4tIzabHPbKSMbOE3W44own4d8cq8GA7MgOrKi5mYNNeHrzYazYVYr5IzgTgWLv8ElryLvNh6NHmh7rdpsDOnZnSR36dmHBuiP+mVN65KTqcUE//+ccbi9K6xw4UefAzmO1WHnSgmM1dnRL1WN870xc2D8LQ7uxBzbFzsHKlg/QicKhUv2cC9tid3tRY3OjxuZGlc2FQyet+PqnGlRYXDDXO9DVqMM5OUaMykvHtKHdYGSKTDhldU7Zesd2MWhxos6OgabI3lcdqbJi7tCcsN6je7oee80NAR276eDJpmXxbVGLKgztnoqPD1Til6N6hBUbUWdibnBCpxZhaOeBUKSIKgG5qTocqbJhUE5KQF+zp6wevQPca6gz6pmRjFvP740fyhrwyIb9GN49DXdP7huVTTQpOliwhn+pe7e06D6NHmgy4v095S02Xvz6p1pM6hudzR87K42owvXjeuKZTw5jWPc0PiCgmPuhrB7DA5hlE2nZRh1q7W5YnJ52Z+74JAm7TtSxt36I9BoR+V0MyO9igNGog8XihNcn4adqG/aUN+ChD/fD65MwZ2gO5gzJbXVvAyK5SJKEgyetmD0kvAIMUbCSNCKS0kR0a6UdjdcnwdzgxNEqG7YeqsJLn/8Ek1GLWYNzMH2Qqd2/WRQ/jlRZZVvq7t/gLLjNDTtSa3PD4fGFPSu8Z3oSVv9Q3uFxLo8Pnx+pxpLCfh0eO7JHGjbsY8GaKBiHq2zoHsWWaN3S9DhcaQ24YP39iXr0yuSEofYIgn/G9cBsI9bvMeMXr3+L3xb0xaXnZHMyUALg2gL4NznMi/KTqy4GLXRqFY5U2Zo+Z3F6sL/SwlmMUWBK0eHyYbm4Z+2PsDg9sQ6HOjFJkrDXbIlJuw2VSkD3ND0OVlrbPW6f2QKDTs1CagSJKgH5XQyYOTgHD0ztj+vG5uHHsgZc/s9v8PCH+3D0tL8NRHJqLOhEY8MhokCJKv9+LxPzM3HtuXn4yxXDcPHZ2dhyqAqzX9mOFz49inqHO9ZhUpgOVlpbfWARCVkGLY4H2S+2I4dOWtEjTR92EaR7ehKKq+3weH3tHvf10Spkp2iRGcBswbNNKTheY0dphIv0RInsyMnotkTLSdXh4Mn277tOt7u8Ab3ZkjEgOrUKc4fl4uaJvfHa18fwf+8V4UhV4N9rUqZOX7D2SRIOVVqj3hIEAAZ0NeKbY7VNH2/YV4HBOSnc1CxKxvbKQL8uBjz04X74JCnW4VAndaLOAY1KQHqUNn09U5+sZHxXUtvuMV8crcY5EV5SSz8TBAG9MpPxy9E98MglAyGqBPzm37vwu1W7UVRaH+vwKMF9f6Ie/boYOAuFFE2lEnBOTgoWju+Je6b0w5GTVsz9xzd4Z0dJh0U/Uq4jVbawNjBsTxejFsdqI1uw3mtuQPf08OPVqVXoYtTiaHX7D6c/2F2OYd0CW4EnqgSMzkvDuj0dz9wmIr+Dldao7iPULU2PQ5WB7R9Ua3ejxuZCLvvSB6VXZjLumdIPfbsY8Jt/f4+H39+DKqsr1mFRiDp9wbq0zoEkjSomSwuHdU/FyqKypmLpf78vw4Q+mVGPozObNzwXJ+rsePXLn2IdCnVSu8saYrqZ4UCTEV8V17R7zGdHqnFOgEvXKDxGnRqXnmPCY5eehR5peix5/0f8+l87selAJTw+PlijyPuupBb5nL1DcaSLUYtfje6BOwry8dG+Slz5xg7sOF4b67AoSDaXv4d5V5lWb+WlJ+FgpRVSBCel7Dheh75Zhoi8V+/MJHx/ou2H0h6vDx/vq8DIHoG3jBvfJwNrd5dzIg5RgH4sb0DPKK60z0tPwv4Ka0C/o98eq0W/LgaoVJxQECxRJWDKgC5YOm0AqqwuzH/tGzzzyWGU13MFSrzp9AXrHcdr0bdLZAYewRqUkwIBwMf7K7F+TzkaHB6cxVmMUaURVbhxfC+sKirH//ZWxDoc6oR2naiL6kDpTP27GLDPbIHd7W31dXODEz9V29C/a2zyZGelVatwQf8ueOjigZjQJwP//OoYZv79a/z9i2KYG5yxDo8SyK4T9WxFRnGpW5oet57fGxefnY0H1u/D/ev24qSF+TFeHK22ITdVJ1sxJj1JA1EloDRCBQqfJKGotB79IjQeGphtxJfF1W2+/vnRGuSm6tEliE25e2YkI0kj4suj7U9EICKg2uZCpdUV1ZX26UkaGHQiDnXQjhEAPjtSxQlDYTLq1FgwtifuL+yPKosLV725A3f89wdsPlAJRxv3vqQsnX7Hks+PVMesSCwIAmYPycEfPz4EAPjdBflQcUlu1KUlaXDzeb3x9OZDSNWrOcudokaSJHx2pBo3TugVsxj0GhE9M5Kws6Su1Z/9D340Y1ReOjRip3++GROiSsCovHSMykvH8Ro7PjtajStf/xbn5KRg5uAcFPTLQhLbSFGISuscqLW50T2NO9BTfBIEASN6pOGcnBR8+KMZv3h9B359bh6uHNkdWjX/binZkZNW5Mq82VnvzGT8WG6JSI47UmVDslaMWAu3s7KNeG9nKTw+CepWivZrd5dhQt+soN/3ogFd8fcvijGhT0arrZ6sLg9e+/o4PvjRjDq7B+fkGHH9uJ4Y35v3P9S57CypQ/8YzGAe0NWAb4/XYkB22zUonyThi+Ia/K4gP4qRJa6MZC0uH56L6YOy8V1JHd745jge3XAAY3tl4ML+XTCxTyZS9J2+NKpInXok5/VJ+PZ4Hc42xe7J1cBsI/4w4yw8cslA2TYdoY51T9Pjpgm98OAH+/D5kbZnOxBF0qGT/qWq3aLYO601Q7ulYv0ec4vPS5KEtT+UY2zv9OgHRS3kZSThqpHd8eSMszE4NwXLd5Xi4pe+wr1rf8SmA5WwuThTgIKz+UAlhnVPhcjlphTndGoV5gzNxV2T+2Lb4Spc/s9v8OFeM7xspaRY35XUoZfMK8zy0vXYUxaZvSC+O16HfhFclZuWpEGWQYsfyxtavFZpceLb43UY2ycj6Pcd2SMNdQ4Pth2uavHanrJ6XPn6DhyssODmib3x/2aejTE90/H4hgP4w0cH2A+eOpVvY7TSvn9XI7b/VNvuMfsrLNCpVchm/+qI0mtETOiTiUWT8vHQxQPRLU2HFd+XYvrfv8LN732Pld+XotbGDZ2VpFMXrH8sb0BakhoZybHZ7KyRWlQhWcsZcrGW38WA/5vYCw99uA9rfyiLdTjUCWw9VIUhuakx3+xsfO8MfHa0GjW25htSbDpwEhpRhT6ZbBegJFq1Cuf2ysAt5/XGI5cMRG6qDm9/W4KL//Ylbl1ehHd2lOBgpYU9LKlDH+2vxPAg+qMSKZ0pRYebz+uNq0Z1x5vbj+MXr3+LD340sxCnMJIk4aviGtmXu/fOTMYPZS0LwqHYdKAS5+REdlXu4NyUVlsS/uvbExjXKwPJ2uBn/KlUAq4c2R1PbDyIE3X+TSclScJ/vjuBRSt3Y/aQHFxzbh66pemRpBUxKi8dSwr74/BJG25Z8QPqHSzWUOKTJAlfHa3BwOzoF6wHZhuw60QdnJ62/y6t+aEco/I4PpNTql6N8/KzcPPE3vjjzHMwokcaPj5wErNf3Y7frdyNrYdOcv8gBejU897X7i4PaiMLSnz5WQb89oJ8/O3zn7Cn3ILfXZAPPZfbkwx8koT1e8y4cmT3WIcCg06NEd1T8a9vT+C2SX0AAC6PD3/59Ch+MTw35gV1aluKXo3z+2bh/L5ZsLu8+NHcgB3HavHujhNocPr3RTgrOwX9uxrQOzMJeRlJSNXH9iEtKUNxlQ0ldQ6c1c6SVKJ4NTDbiAEX9sVeswX//u4E/rLtKK4Y0Q1zhuQiPcYTVQgorrZDAJAdRH/mUORnJePVL4+hzu5GWhitPCobnNhfYcGvz82LYHTAefmZePKjg7jlvN4w6vy35TU2F9buLsN9F/UP+X37dzXgknOycc3bOzGxTyb2mS1QqYA7L+wLUyszNpM0Im6a0Asri8pwzds78fTsQRidzskKlLh2lzXAI0kx2UcoVa9Bn6xkbD5YiUvONrV43e72YsO+CtwfRg6g4OjUqqYWjA63FzuO1+HlL37CHz46iDlDczFnSA5yUtkNIRY6bcHa4vTgo/2VWDptQKxDIYXJTdXj3in98O/vTuCqN3fg3in9MI593SjCPj1cDY1apZjNzmYO9vfTH9Y9FWN6puPBD/ahR5oeZ8WwZRIFp3Gm1Ki8dABAvcODn6ptOF5rx4d7zahocKG8wQFRJSA3VY8eaXrkZSShZ0YSemYko1dmEjKSNHxA0Um8tv0YLuiXxXYglLAEQcA5OSk4JycFx2ps2HKwCq9vP47z8jNx+bBuGN499iucOqsvi6txtsko+/dfrxExKDcFmw+exGVDc0N+n/U/lGFYt9SI90XPTNbiHFMK3tlRghsn9IYkSXhs4wFM6JOJLEN4xfxJfbMwoKsBh05aMXtIDgZmG9r9fqtUAuYN74avimtw43++x/xRPXDpwC7omZHU9HVenwRJkiCqBP7uUFxbu7scY3u23uc9Gsb3zsB/d5W1WrBe80MZ+mYZkBlmDqDQ6DUiJuZnYmJ+Jk7UOfD5kSpc9eYODMlNxZwhOTgvP4t7ZERRpy1YL99VirNNKWE9bafElawVcf24nvj+RB0e33gQ3dL0uHp0D0zok6nom3uPT4K5wYHjNXYcr3XgRK0d5Q1O1NjcsLm88EoStKIKKXo1clJ06J2ZjH5dDRiUk9I0s4Pk5/VJ+MdXP2Fy/yzFDPjTkzS4bmwe/vDxQdQ7PBjWLRVXj+kR67AoDKl6NYZ0S8WQbqlNn5MkCRanFyetLpy0OlHR4MKhSisqLC6U1zsAAchLT0LvzGT0zkxGj3Q9TCk6dDXqkJms4YqTBFFcbcO2w1V45JKBsQ6FKCp6ZiTjmnOTYXF68FVxDR7+336IAjBrcA4uPcfEPqFR5PVJWLGrFJcP6xaV843skYYPfzSHXLB2enz4x+dHcfVoecZEc4bm4OnNhyFJwE81dhyvsePOC/tG5L1zUvVBzwoc1zsDZ5mM2HKkBr/5z/ewu7zQqlVwenxweXwQBEAjqtA9TY8huakY2zsDY3qmR2wzSiK5nbS6sOlAJZYUxm4G87BuqVhZVIbtP9Xg3F4/96qvtrnw6pfHsIibLSpC9zQ9fjGiO+YMycWOklq8vv04Ht94EOflZ+KC/l1wbs901lBkFpPv7rZt2/DEE0/A5/Nh/vz5uPHGG5u97nK5cM8992DPnj1IT0/Hs88+ix49IjdIKKm1461vSnDPlMgMBihxDeuehsG5qfjmWC1e/PQoHtt4AOfnZ+Lcnhk4OycF3dP0YRWw7W4vTlpcOGl1ocrqQo3djXqHGw0OD+xuL1xeCb5TvZNUKgGiIEA89UDP6wMcHi8sTi9q7e6m90hL0sBk1KKLQYtMgxY9M5IwKCcFeo0KoiDA7ZVgdXlQY3Pj+xN1+GCvGceq7eiersfYXhkY3zsDI3qkQ5cATw7DyTUvv/wyVqxYAZVKhQceeADnn39+xOJ6Z0cJfBKaZsIqxcBsIx6+eCBcHh+S2Fc/IQmCgBS9Gil6NfpkNZ/dL0kSGpxemBucMDc4cKTKim+P16LG5kKNzY06hwcqwb90WK8RoREFaFQqqEUBOo0IQQJ0agE6tYhkrQoGrRopOjVS9WqkJmmQnqRBRpIGGckadDFoYdCKinlgE22xHAe5vT78ft1ezDjHBEMI/VGJ4plRp8ZFA7tiyoAuOFJlw1fFNXhj+3EMyDbi0nOycUG/Lgk/mSXW92FbD1dBK6qi1jt2cG4KVuwqxXcltRjZIz3or39v5wn0yEiWbXO2zGQtbju/Dz49XAW9RsTiC/pCI8Z2DJ6epMGCsXmYMygbdrcXbq8PWlEFnVoFQRBgd3tRXu/EkSor/vPdCTy2YT9yUvQ4J8eIPlkGdDVqkZH889/9LIM25tdE0afU+7Dntx7B+D6ZyEyO3QxmtajCVSO749EN+/HaL0egq1EHm8uLu9f8iAl9MtE9je0nlESrVmF870yM752JGpsLu07U461vjuOhD/ehZ3oSBuemYqDJiD6nJvtkGbRQddJ7nEiL+p2K1+vFo48+itdeew0mkwnz5s3D5MmT0a9fv6Zjli9fjtTUVHz00UdYv349nn76afz5z3+OWAwb9lVgTM80dDVyNgV1TFQJGNc7A+N6Z8Dc4MTusnqs+qEMz209glqHG12NOmQla5Cm1yBZKyLpVCFHpRIAyT/r2eX1wQOg1uJEvdNfLK61u+H1SUhP0iAtSYNUvRpGrRpJWhFJGhV0ogpJGqEp2fkkCZKEpo3UVGoBaUlq9M4UYdSpkZ6kRnqSJqQBocfrw081duwzW/D8tqMoqbVjaLc0TMzPxLk90zEyLfr9vcIVTq45dOgQ1q9fj/Xr18NsNuO6667Dhg0bIIqRKeIu31WK68bmKfIPmagSWKzupARB8BeX9Wr079ryxlySJLi90qmHaT54fBK8p/6n1WvQYHHC7ZPg9vrg9PjgcPvQ4PTAbHHC7vLC6vKiwelBg8ODWrsbPgnoYtDClKL1tyhJ1yMvI/lUi5KkhJ2xEOtx0O6yBjg8PhT0y4rI+xHFI0EQ0LeLAX27GDB/eDf8UFaP/+2twLJPjmBgtgHn983C2F4Z6NfVoMi/1aGKdf4BgK0HT2Jsr+gtxdeIKlw+vBue3HgQr/9qRFB/W7495p9R9+D0s2WMEOiWpscVCtjTpDVJGv+9zZmf65OVjD5ZyZgyoCu8PgnHa+04VmPH/goLvjnmhtV56m++04N6hwepejW6penRPU2PvPQk5Kbq0TXFP8Gmi0GLtCRNQv2udXZKvQ/zSRI27KvAsjmDwn6vcA3OTcWJOgd++eZ3GNMzDTtL6jEoJwUzB7dsE0LKkZGsxYX9u+DC/l3gPlVD+anahm2HqrDCUoqKBiesLi/S9GqkJWmQplcjRa9Bik6EQadGqs4/ccioVcOoVyNFJyJFp4ZBq4ZBJyJZI0KrVgWcDyXJfy/WuD2kAP9Ex0TJp1G/GywqKkKvXr2Ql+fftGL69OnYtGlTs+S1efNm3HbbbQCAadOm4dFHH4UkSREb2PgkcFkzhcSUooMppSumnPrY5fGh2uZCvcMDq8sLh8cLt0eCx+efGe1fNicgSaNGmlEHZCQhWSvCqBWRqtcgSaNSxAxDtahqunGbPsgEq8uDfWYLvvmpBm99cxxun4RzTEYMzklB365G9ExPQk6qTtEFpXByzaZNmzB9+nRotVrk5eWhV69eKCoqwogRIyISm08CkjXK/d4RtUYQBGjVQqt924xGHSxBbmTmcPtXh9TY3aiyunCs1o6dJ+pR0eBEeYMTyVoRvTKS0OtUf+3uaf6lzdlGLdKTtVAruD1Te2I9DvJJEpI78ex2ojNpT9tsyeXxYa+5AUWl9Vi+qxT1Dg/ONhlxTk4K+nYxoHuaHrmpemQa4jMHxTr/AIAPgEYd3e/diO6pOFRpwQ3v7sLdk/thZF5auzfzlRYnVn5fhvd2leKGcT2Rm6aHxeKMYsTxRVQJTa3EWuPzSah1uJtWlZbWO7DX3IA6uwe1Dv8kHrvLh9QkNboYdUjTichI1iBVr0GKTo1krQi1SoB4arXpmf/pBEGAKPjjUKtU0IgC1KIKWlGARvR/rBVVTf/WNB5z6nhRJUDt9MDh9kJ12vv7Tk0U8ngluH0+uL3+h/JenwSvJDWdXwX/1wiCAEny3wc2Pbw/9QDf/6Dfh8YvU6v8MWak6uF2uP3xqVXQqH6OWSP6JzDpNaqgClhKoOT7MElBtaBpZ2VjcG4qjtXYcG7PDOTFYBNICp1GVKFfFwP6nbECx+P1od7pgcXpge3UpB2H2we724uyegeOVvlzg93thd3thc3l///+fOGF2ytBfSpX+fcM8BeiJQnwShJ8kgSv5D+PTwJUApryQ+MxKgFNOc6fb4SmPKo+lWe0p/JLstZfNM9I0iDToEVWshZZBi2yDBpkJmuRnqSJWd/uqFdMzGYzcnJymj42mUwoKipqcUxurr/PmFqtRkpKCmpqapCZ2f7GdxqNiK5dO94g7IYL+qHG6oImUt/0DGVsmha0eI0biO/Y48Twvl2b/u3xSnB6vM1ebwDQ4PSiPaJKwFk5KRBV0U9w4eQas9mMYcOGNftas9nc7vkCzT8A8Mb150ItqloMuMOSqL8TiXhdiXhNQMSvy3PqxtB32k0hALgBVLp9Ab2HKVUPk8J29ZZrHBRoDjovNQl5OamRv1lT6s+1UuMClBtbJ49rUJ+fVx94fRKcbi9Oz0Je/JyDyswN7b5Xv2wjkhXUeifW+QcAfj9zEGwuL9RiiIOgEH8Oft8zE1anBwBgdnXwN0SjxqzReZg/rvfPccbq9yKWv4/RPLfk/33zNM4WPONvvwyng/vU/wDAUms/7ZUzCAIgioD//9DRX8/gG020XqT0AbADsPsAdPQz24pkrRp9u7a92Wagv7OhUOp9mCRJWHPbRBgiNekqAr8j/fMiEEckKXUMEI44vCZJQlMePD0rNf42C4Lg/3dbf0ol/9dJ0qnZ140fn/pH42uND+ba4gVQ5ZUAb/t1n9b0yEgOe/NQ5YygokiJN7BE1HmclZva8UFERDIw6tQY1C0t1mEQUSeV18YsXCIiuQmCgKEh9LInotiI+rRHk8mE8vLypo/NZjNMJlOLY8rKygAAHo8HDQ0NyMjIABFRoMLJNYF8LRFRKDgOIqJYYf4homjgfRgRRULUC9ZDhgxBcXExjh8/DpfLhfXr12Py5MnNjpk8eTJWrVoFANiwYQPGjRvHXotEFJRwcs3kyZOxfv16uFwuHD9+HMXFxRg6dGgsLoOIEgzHQUQUK8w/RBQNvA8jokgQJLkbRLVi69atePLJJ+H1enH55Zfj5ptvxnPPPYfBgwdjypQpcDqduPvuu7F3716kpaXh2WefbWrYT0QUqHByzUsvvYT//ve/EEUR999/PwoKCmJ8NUSUKDgOIqJYYf4homjgfRgRhSsmBWsiIiIiIiIiIiIiojNFvSUIEREREREREREREVFrWLAmIiIiIiIiIiIiIkVgwToM27Ztw7Rp01BYWIi///3vsQ4nIEuWLMH48eMxY8aMWIcSlLKyMlx99dW49NJLMX36dLzxxhuxDilgTqcT8+bNw6xZszB9+nQ8//zzsQ4pKF6vF3PmzMFNN90U61DoDPGYgzoSrzmqI/Gcw9oT7/mNwqfEPKTUPKLUPKD032OljkMmT56MmTNnYvbs2Zg7d26sw0koHeUVl8uF3/72tygsLMT8+fNRUlISlfOuXLkS48aNw+zZszF79mwsX748IuftKGdJkoTHH38chYWFmDlzJvbs2ROV83799dcYNWpU0/W+8MILETlvILlQjmsO5LxyXHMgOVaOn+lAzivXz3QiU+K4J1xKHTeFQ6ljrnAofbwmC4lC4vF4pClTpkjHjh2TnE6nNHPmTOngwYOxDqtD27dvl3bv3i1Nnz491qEExWw2S7t375YkSZIaGhqkqVOnxsX3W5IkyefzSRaLRZIkSXK5XNK8efOknTt3xjaoIPzzn/+UFi9eLN14442xDoVOE685qCPxmqM6Es85rD3xnt8oPErNQ0rNI0rNA0r/PVbqOOTCCy+UqqqqYh1Gwgkkr7z99tvSgw8+KEmSJK1bt0664447onLe//73v9IjjzwS9rnO1FHO2rJli3TDDTdIPp9P2rlzpzRv3ryonPerr76S5fcukFwoxzUHcl45rjmQHCvHz3Qg55XrZzpRKXXcEy6ljpvCodQxVziUPl6TA2dYh6ioqAi9evVCXl4etFotpk+fjk2bNsU6rA6NGTMGaWlpsQ4jaNnZ2Rg0aBAAwGg0Ij8/H2azOcZRBUYQBBgMBgCAx+OBx+OBIAgxjiow5eXl2LJlC+bNmxfrUOgM8ZqDOhKvOaoj8ZzD2hPP+Y3Cp9Q8pNQ8otQ8oOTfY45DOp9A8srmzZtx2WWXAQCmTZuGL7/8EpIkyX5euXSUszZt2oQ5c+ZAEAQMHz4c9fX1qKiokP28cgkkF8pxzbHKwYHkWDl+ppWc2+OVUsc94VLquCkcSh1zhaMz/k6zYB0is9mMnJycpo9NJlPc/wLEi5KSEuzduxfDhg2LdSgB83q9mD17NiZMmIAJEybETexPPvkk7r77bqhUTBVKwxwUv+Ixh7UnXvMbhY95KHRKywNK/T1W+jjkhhtuwNy5c/Gf//wn1qEkjEDyitlsRm5uLgBArVYjJSUFNTU1sp8XADZu3IiZM2di0aJFKCsrC+ucocaWk5MTtVy7a9cuzJo1CwsXLsTBgwcj/v5t5UK5r7m9HCzHNXeUY+X4mQ7kvEBsfqbjFcc98UlpY65wKHW8Jhdljv6I2mC1WrFo0SLcf//9MBqNsQ4nYKIoYs2aNdi6dSuKiopw4MCBWIfUoU8++QSZmZkYPHhwrEMhShjxmsPaE4/5jSiWlJgHlPh7rPRxyLvvvotVq1bhlVdewb/+9S988803sQ6JZHbhhRdi8+bNeP/99zFhwgTce++9sQ5JVoMGDcLmzZuxdu1aXH311bj11lsj+v6xyoXtnVeua45Vju3ovJ3tZ5o6HyWOucKhxPGanFiwDpHJZEJ5eXnTx2azGSaTKYYRJT63241FixZh5syZmDp1aqzDCUlqairGjh2LTz/9NNahdOi7777D5s2bMXnyZCxevBhfffUV7rrrrliHRacwB8WfRMhh7Ymn/EaRwTwUPKXnASX9Hit9HNL4s56VlYXCwkIUFRXFOKLEEEheMZlMTTNBPR4PGhoakJGRIft5MzIyoNVqAQDz58+P2OaHwcZWXl4elVxrNBqblp8XFBTA4/Gguro6Iu/dUS6U65o7Oq+c1wy0nWPl+JkO5Lyx+pmOVxz3xBelj7nCoaTxmpxYsA7RkCFDUFxcjOPHj8PlcmH9+vWYPHlyrMNKWJIk4fe//z3y8/Nx3XXXxTqcoFRXV6O+vh4A4HA48MUXXyA/Pz/GUXXszjvvxLZt27B582YsW7YM48aNw9NPPx3rsOgU5qD4Es85rD3xmt8oMpiHgqPUPKDU32Mlj0NsNhssFkvTvz///HP0798/xlElhkDyyuTJk7Fq1SoAwIYNGzBu3Liw+3gGct7Teyhv3rwZffv2DeucgZo8eTJWr14NSZKwa9cupKSkIDs7W/bzVlZWNvVRLioqgs/ni0gRNZBcKMc1B3JeOa45kBwrx890IOeN1c90vOK4J34odcwVDqWO1+SkjnUA8UqtVmPp0qVYuHAhvF4vLr/88rgYqC5evBjbt29HTU0NJk2ahNtvvx3z58+PdVgd2rFjB9asWYMBAwZg9uzZAPzXUlBQEOPIOlZRUYH77rsPXq8XkiTh4osvxoUXXhjrsCjOxWsO6ki85qiOxHMOaw/zW+em1Dyk1Dyi1DzA3+PgVVVVNbUK8Hq9mDFjBiZNmhTjqBJDW3nlueeew+DBgzFlyhTMmzcPd999NwoLC5GWloZnn302Kud96623sHnzZoiiiLS0NPzhD3+IwBW3nrM8Hg8A4KqrrkJBQQG2bt2KwsJCJCUl4cknn4zKeTds2IB3330XoihCr9dj2bJlEdngq61cWFpa2nRuOa45kPPKcc1t5Vi5f6YDOa9cP9OJSqnjnnApddwUDqWOucLRGcdrghTu9rNERERERERERERERBHAliBEREREREREREREpAgsWBMRERERERERERGRIrBgTURERERERERERESKwII1ERERERERERERESkCC9ZEREREREREREREpAgsWBMRERERERERERGRIrBgTRH15ptv4pJLLsGdd94Z1NeVlJTg/ffflymqn+3evRszZ85EYWEhHn/8cUiSJPs5iSh6lJ6Dnn32WRQUFGDEiBGyn4uIok/JOchut+PGG2/ExRdfjOnTp+Ppp5+W9XxEFF1Kzj8AcMMNN2DWrFmYPn06li5dCq/XK/s5iSh6lJ6DGv3f//0fZsyYEbXzUehYsKaIeuedd/Daa6/hmWeeCerrTpw4gXXr1gV9vmAHOg8//DAee+wxbNy4EcXFxdi2bVvQ5yQi5VJ6DrrwwguxfPnyoM9DRPFB6Tno+uuvx//+9z+sWrUK3333HbZu3Rr0OYlImZSef5577jmsXbsW69atQ01NDf73v/8FfU4iUi6l5yAA2LhxIwwGQ9BfR7GhjnUAlDiWLl2KkpIS/OY3v8Gll16KY8eO4eDBg/B4PLjttttw0UUXoaSkBPfccw/sdjsA4MEHH8TIkSPxzDPP4PDhw5g9ezYuu+wypKamYvfu3Vi6dCkA4KabbsL111+PsWPHYsSIEbjiiivwxRdfYOnSpThx4gTeeustuN1uDBs2DA899BBEUWwRX0VFBSwWC4YPHw4AmDNnDjZt2oSCgoKofY+ISD5Kz0EAmvIPESUepeegpKQkjBs3DgCg1WpxzjnnwGw2R+8bRESyUXr+AQCj0QgA8Hg8cLvdEAQhOt8cIpJdPOQgq9WK1157DY899hh++9vfRutbQ2HgDGuKmEcffRTZ2dl44403YLfbMW7cOKxYsQJvvvkm/vSnP8FmsyErKwuvvfYaVq1ahWeffRaPP/44AODOO+/E6NGjsWbNGvz6179u9zw2mw1Dhw7F2rVrkZGRgQ8//BDvvvsu1qxZA5VK1eZyErPZjJycnKaPc3JyeKNGlECUnoOIKLHFUw6qr6/HJ598gvHjx0fi0okoxuIl/9xwww2YMGECDAYDpk2bFqnLJ6IYi4cc9Nxzz+H666+HXq+P5KWTjDjDmmTx2WefYfPmzfjnP/8JAHA6nSgrK0N2djYeffRR7Nu3DyqVCsXFxUG/tyiKTQOcL7/8Ert378a8efMAAA6HA1lZWRG7DiKKT8xBRBRLSs5BHo8HixcvxtVXX428vLygz09Eyqbk/POPf/wDTqcTd911F7766itMnDgx6BiISNmUmIP27t2LY8eO4f7770dJSUloF0ZRx4I1yeb5559Hfn5+s8/95S9/QZcuXbBmzRr4fD4MHTq01a8VRRE+n6/pY6fT2fRvnU7XtMxDkiRcdtllATX2N5lMKC8vb/q4vLwcJpMpqGsiovihtBxERJ2LUnPQgw8+iN69e3c4i4mI4pdS80/je0yZMgWbNm1iwZooQSktB+3cuRO7d+/G5MmT4fF4UF1djauvvhpvvfVWKJdHUcKWICSL8847D2+//TYkSQIA/PjjjwCAhoYGdO3aFSqVCmvWrGlqlG8wGGC1Wpu+vnv37ti3bx98Ph/KyspQVFTU6nnGjx+PDRs2oKqqCgBQW1uLEydOtHpsdnY2jEYjdu3aBUmSsHr1akyZMiVi10xEyqHEHEREnYdSc9Czzz4Li8WC+++/PyLXSUTKo8T8Y7VaUVFRAcC/ymPLli0tillElBiUmIN++ctfNs38fuedd9C7d28Wq+MAC9Yki1tuuQUejwezZs3C9OnT8dxzzwHwJ4pVq1Zh1qxZOHLkCJKTkwEAAwcOhEqlwqxZs/D6669j1KhR6N69Oy699FI8/vjjGDRoUKvn6devH37729/i+uuvx8yZM3H99dejsrKyzbgeeughPPDAAygsLETPnj0xadKkyF88EcWcUnPQU089hUmTJsFut2PSpEn4y1/+EvmLJ6KYU2IOKi8vx9/+9jccOnQIl112GWbPno3ly5fL8w0gophRYv6x2+24+eabMXPmTMyZMwdZWVm48sor5fkGEFFMKTEHUXwSpMbHHkREREREREREREREMcQZ1kRERERERERERESkCNx0kRLS/Pnz4XK5mn3uqaeewsCBA2MUERF1JsxBRBRLzEFEFCvMP0QUS8xBiYMtQShkR44cwe9+9zscO3YMv/vd73DNNdfEOiQi6kSYg4golpiDiChWmH+IKJaYgyga2BKEQvbqq69i7Nix2LlzZ1gJ6uqrr47Jpj8lJSUYOHAgRowY0fS/F198MepxEFFo4j0HAf5NiB5++GGMHTsWo0aNwq9+9auYxEFEwYv3HLR27dpmY6Bhw4Zh4MCB2L17d9RjIaLgxHv+AYAPPvgAl1xyCUaMGIFLL70UH3/8cUziIKLgJUIOWr58OQoLCzFixAjccMMNMJvNMYmD2saWIBSy0tJSTJ8+PdZhwOPxQK0O/Uf5m2++CevriSg2EiEHPfjgg/B6vfjwww+RlpaGvXv3Rjg6IpJLvOegWbNmYdasWU0fr1y5En/9618xaNCgSIZHRDKI9/xjNptxzz334MUXX8SkSZOwdetW3HHHHdi8eTOysrJkiJSIIinec9DXX3+NZcuW4c0330SvXr3wxBNP4M4778Tbb78tQ5QUKs6wppBcc801+Prrr/Hoo49ixIgROHLkCP7f//t/uOCCCzBhwgQsXboUDocDAFBXV4ebbroJ48aNw5gxY3DTTTehvLwcAPDss8/i22+/bXqfRx99tGnms8fjaTrf6U/eVq5ciSuvvBJPPvkkxo4di7/85S9wuVxtnp+IEk8i5KDDhw9j8+bNeOyxx5CZmQlRFDF48GCZvmNEFEmJkIPOtGrVKsyZMweCIETou0REckiE/FNeXo6UlBQUFBRAEARccMEFSEpKwrFjx2T6rhFRpCRCDtqyZQsuvvhi9O/fH1qtFrfccgu++eYb5iCFYcGaQvLmm29i9OjRWLp0KXbu3Il///vfOHr0KFavXo2NGzeioqKiqb2Gz+fD3Llz8cknn+CTTz6BTqfDo48+CgD43e9+1+x9li5dGtD5i4qKkJeXh88//xw333wznn766TbP35ELL7wQkyZNwpIlS1BdXR3aN4SIoioRctAPP/yA7t274/nnn8fYsWMxc+ZMbNiwIbxvDBFFRSLkoNOdOHEC3377LWbPnh38N4OIoioR8s/gwYPRt29fbNq0CV6vFx9//DG0Wi03RSOKA4mQgwCgte38Dhw4EMR3guTGgjWFTZIkvPfee7j//vuRnp4Oo9GIm266CevXrwcAZGRkYNq0aUhKSoLRaMTNN9+Mb775JqxzZmdn4+qrr4ZarYZOp2v3/G3JyMjAihUr8Mknn2DlypWwWq24++67w4qLiKIvXnNQeXk5Dhw4gJSUFHz66ad48MEHcd999+Hw4cNhxUZE0RWvOeh0q1evxujRo5GXlxdWXEQUXfGaf0RRxOzZs3HXXXdhyJAhuPPOO/Hoo48iOTk5rNiIKLriNQedf/75+PDDD7Fv3z44HA68+OKLEASBq/QVho17KWzV1dWw2+2YO3du0+ckSYLP5wPg31TsD3/4Az799FPU1dUBAKxWK7xeL0RRDOmcOTk5AZ+/LQaDAUOGDAEAdOnSBQ8++CDOO+88WCwWGI3GkOIiouiL1xyk1+uh0Whw8803Q61W49xzz8XYsWPx2WefoW/fviHFRUTRF6856HRr1qzBTTfdFFIsRBQ78Zp/vvjiCzz99NN48803MWjQIOzevRu33HILXnnlFZx99tkhxUVE0RevOWjChAlYtGgRFi1aBIvFgmuvvRYGg6HZe1PssWBNYcvIyIBer8f69ethMplavP7Pf/4TR48exXvvvYeuXbti7969mDNnTqtLMAA0PVl3OBxNhePKyspmx5zeX7Gj8weq8T3biouIlClecxCXvRIlhnjNQY127NiBiooKTJs2LeivJaLYitf8s3fvXowePbpp8tDQoUMxdOhQfPHFFyxYE8WReM1BAPCrX/0Kv/rVrwAAR48exUsvvYT+/fsH9R4kL7YEobCpVCrMnz8fTz75JKqqqgD4d37+9NNPAfifoOl0OqSmpqK2thYvvPBCs6/v0qULjh8/3vRxZmYmTCYT1qxZA6/XixUrVjR7Pdjzt+X777/HkSNH4PP5UFNTg8cffxznnnsuUlJSQvo+EFFsxGsOGj16NHJzc/Hyyy/D4/Fgx44d+Prrr3HeeeeF9H0gotiI1xzUaPXq1Zg6dSpXlxHFoXjNP0OGDMG3336LvXv3AgB+/PFH7Nixgw/zieJMvOYgp9OJAwcOQJIklJaWYunSpbjmmmuQlpYW0veB5MGCNUXE3XffjV69euEXv/gFRo4ciV//+tc4evQoAODaa6+F0+nEuHHjcMUVV+D8889v9rXXXHMNNmzYgDFjxuDxxx8HADz22GP4xz/+gbFjx+LQoUMYMWJEyOdvy/Hjx7Fw4UKMHDkSM2fOhFarxbJly8L4LhBRrMRjDtJoNPjrX/+Kbdu2YfTo0XjwwQfx1FNPsR0IURyKxxwE+G/YPvzwQ1x22WUhXjkRxVo85p9zzz0Xt99+OxYtWoQRI0bg9ttvx0033cSH9kRxKB5zkNPpxJ133okRI0Zg/vz5GD58OO64444wvgskB0Fi/wMiIiIiIiIiIiIiUgDOsCYiIiIiIiIiIiIiReCmi5TQ/va3v+Hll19u8flRo0bh1VdfjUFERNSZMAcRUSwxBxFRrDD/EFEsMQfFP7YEISIiIiIiIiIiIiJFiMkM6yVLlmDLli3IysrCunXrWj3m66+/xpNPPgmPx4OMjAy8/fbbHb6vz+eD1xu9+rsoClE9X7CUHJ+SYwOUHZ+SYwNCj0+jEWWIJnqimX+U/jMQCZ3hGoHOcZ3xco3MQYGJl/+e4eos1wl0nmtV8nUy/wRPyf89Q8HrUbZEvx7moODF+89EPMfP2GNDztjbykExmWH9zTffIDk5Gffee2+rBev6+npceeWVePXVV9GtWzdUVVUhKyurw/d1u72orbXJEXKr0tOTo3q+YCk5PiXHBig7PiXHBoQeX9euKTJEEz3RzD9K/xmIhM5wjUDnuM54uUbmoMDEy3/PcHWW6wQ6z7Uq+TqZf4Kn5P+eoeD1KFuiXw9zUPDi/WcinuNn7LEhZ+xt5aCYbLo4ZswYpKWltfn6+++/j8LCQnTr1g0AAipWExERERERdXZlZWW4+uqrcemll2L69Ol44403WhwjSRIef/xxFBYWYubMmdizZ0/Ta6tWrcLUqVMxdepUrFq1KpqhExEREQFQ6KaLxcXF8Hg8uPrqq2G1WnHNNddgzpw5HX6dKApIT0+WP8Cm86mier5gKTk+JccGKDs+JccGKD8+IiIiokQmiiLuu+8+DBo0CBaLBZdffjkmTpyIfv36NR2zbds2FBcXY+PGjfj+++/x8MMPY/ny5aitrcULL7yA//73vxAEAXPnzsXkyZPbnWxEREREFGmKLFh7vV7s2bMHr7/+OhwOB6688koMGzYMffr06eDrJLYEOY2S41NybICy41NybEDnbQlCREREpATZ2dnIzs4GABiNRuTn58NsNjcrWG/atAlz5syBIAgYPnw46uvrUVFRge3bt2PixIlIT08HAEycOBGffvopZsyYEYtLISIiok5KkQXrnJwcpKenIzk5GcnJyRg9ejT27dvXYcGaiIiIiIiI/EpKSrB3714MGzas2efNZjNycnKaPs7JyYHZbG7xeZPJBLPZ3O45or3K1X/OxFrRx+tRNl4PEVH0KbJgPWXKFDz66KPweDxwu90oKirCr3/961iHRUREREREFBesVisWLVqE+++/H0ajUbbzRHuVK6D8FYfB4vUoW6JfD1e6EpESxaRgvXjxYmzfvh01NTWYNGkSbr/9dng8HgDAVVddhb59++L888/HrFmzoFKpMG/ePAwYMCAWoRIREREREcUVt9uNRYsWYebMmZg6dWqL100mE8rLy5s+Li8vh8lkgslkwvbt25s+bzabce6550YlZiIiIqJGMSlYL1u2rMNjFi5ciIULF0YhGiIiIiIiosQgSRJ+//vfIz8/H9ddd12rx0yePBlvv/02pk+fju+//x4pKSnIzs7Geeedh2XLlqGurg4A8Nlnn2Hx4sXRDJ+IiIhImS1BiIiIiBKR0+nEr371K7hcLni9XkybNg2LFi1qdozL5cI999yDPXv2ID09Hc8++yx69OgRo4iJKN7s2LEDa9aswYABAzB79mwA/hWupaWlAPwrWgsKCrB161YUFhYiKSkJTz75JAAgPT0dt9xyC+bNmwcAuPXWW5s2YCQiIiKKFhasiYiIiKJEq9XijTfegMFggNvtxi9/+UtMmjQJw4cPbzpm+fLlSE1NxUcffYT169fj6aefxp///OeYxUxE8WX06NHYv39/u8cIgoCHHnqo1dfmzZvXVLAmIoqUJUuWYMuWLcjKysK6detavL527Vq88sorAACDwYCHH34YZ511VrTDJCKFUMU6gHh2qMICSZJiHQYRdSJWlwdfH61Ceb0j1qEQUQgEQYDBYAAAeDweeDweCILQ7JjNmzfjsssuAwBMmzYNX375pWLGGxz7EJESlNQkzgZ4RJ3F3Llz8eqrr7b5eo8ePfD222/j/fffx80334wHH3wwitEFx+n2oqLBGeswiBIaC9Yhcri9mPXXz3GgwhrrUIioE3lr+3Hc+PZ3eH7b0ViHQkQh8nq9mD17NiZMmIAJEyZg2LBhzV43m83Izc0FAKjVaqSkpKCmpiYWoTZTZ3djzktf4HAVC0VEFDtl9Q5MeXYbjtfYYx0KEQVhzJgxSEtLa/P1kSNHNr0+fPjwZhvDKs1TG/fjgQ/2xToMooTGliAh2nmiDm6vhM+OVmGgyRjrcIiokyipc2BUrwyUcYY1UdwSRRFr1qxBfX09br31Vhw4cAADBgwI8z0FpKcnRyjC1v1391E4PT7UuHyynyvWRFGV8NfYqLNca2e5zs5gT1kDJAl457sS3Dulf6zDISIZrFixApMmTQro2GiMgU53otaOVbtK4fVJSElNgqgSOv4ihYnnv4mMPTZiETsL1iH6urgGA7KN+PRwNW4Y1yvW4RBRJ2FucGJU70y8X1Yf61CIKEypqakYO3YsPv3002YFa5PJhLKyMuTk5MDj8aChoQEZGRntvpfXK6G2Vt6Zz//6+if07WrA/tI6jOmWIuu5Yi09PVn276dSdJZrVfJ1du2a2L9Pkba7rAGTz8rGhz9W4LcFfaFTc9EwUSL56quvsGLFCrzzzjsBHR+NMdDp/rv9OMb1ycQPJXXYdeQk+nYxRO3ckaLkv4kdYeyxIWfsbY2D+Nc9RF8W1+Dykd1xpMqKWrs71uEQUSdhbnCiX7YB1TY3vD72kSWKN9XV1aiv9z9wcjgc+OKLL5Cfn9/smMmTJ2PVqlUAgA0bNmDcuHEt+lxHm8vjQ1m9E+PzM3GcvWOJKIb2lNdjVM90GLQie8gSJZh9+/bhgQcewF//+tcOH9bHyok6O3LT9OidmYw9ZQ2xDocoYbFgHQKnx4fjtXYMzDGiR3oSitnLkYiiwCdJOGl1ITtFhxSdGietrliHRERBqqiowDXXXIOZM2di3rx5mDBhAi688EI899xz2LRpEwBg3rx5qK2tRWFhIV577TXcddddMY4aqLG7kaJXw5Sqx7EatiQiotjwSRIOVFjRp4sBGclamFmwJkoYpaWluP322/HUU0+hT58+sQ6nTaV1DmQZteiZkYQfuOqVSDZsCRKCKqsLqXoN1CoVUvUsGhFRdNTY3NCrRejUIrIMGpTXO2BK0cU6LCIKwllnnYXVq1e3+Pwdd9zR9G+dTofnn38+ilF1rNbmRqpODVOKDifquNEZEcXGiVoHDFoRKXo10pPUqLCwYE0ULxYvXozt27ejpqYGkyZNwu233w6PxwMAuOqqq/Diiy+itrYWjzzyCAD/nh8rV66MZcitKqt3ootBB9ErYf2P5liHQ5SwWLAOQZXVhfQk/7culbMciShKzA1OZCZrAICziogoqqrtLqTo1eiaokOlxQWPT4I6DjcZIqL4Vml1IuPUWCgtScOxEFEcWbZsWbuvP/HEE3jiiSeiFE1oJElChcWJLkYtfG4PqlgLIpINW4KEoHGGNQCk6NWo5JN9IooCc4MTmQYtACBdz5s0IoqeGpsbRq0aGlGFtCT/Cg8iomirsfnbEwFARpIG5fUcCxFR9NQ7PBAgwKBTI1WvQa3dDUnivkJEcmDBOgQnrS6k6EQAQJpeg0oLn6oRkfzMDc6m1R3pyRqU1rFgRETRUWNzw3Bq7JNl4AMzIoqNKqsbRt2psRBnWBNRlJXXO5Fl9E8g0qlVUAkCrC5vjKMiSkwsWIegyupCyqmBUmoSW4IQUXSU1zuQfmp1R2YyZxURUfRU234e+yRrRFicvDkjouirsblg1PofnmUka9jDmoiiqqzegaxTbYkA/4Mz1oOI5MGCdQgqLT+3BEnTa9i3iIiiovq0ZbBGnYg6hyfGERFRZ1FldcF4aoZ1kkaExcn8Q0TRV2V1NWsJwtaMRBRNZQ0/99EHgLQkNaptrAcRyYEF6xCctLqQemqglKZXs2BNFEecTifmzZuHWbNmYfr06Xj++edbHONyufDb3/4WhYWFmD9/PkpKSmIQaUsNDg+SNCwYEVH0VdvcTTOskzQiGph/iCgGTp622iNFr4bF6YXL44txVETUWZjrHUjT/1ywTtFpUGV1xzAiosTFgnUIqqwupJ3qI5ui8w+UPF4OlIjigVarxRtvvIG1a9di9erV+PTTT7Fr165mxyxfvhypqan46KOP8Otf/xpPP/10bII9g8XlQbL2tIK1iwUjIoqOmtMK1nqNigVrIoqJGpu7aaWrShCQnqRBpZWzrIkoOursnqYVZwCQohc5gZFIJixYh6DK9nNLEJVKQIpejSobn6oRxQNBEGAwGAAAHo8HHo8HgiA0O2bz5s247LLLAADTpk3Dl19+qYjdnxucHiRp/Gk7mTOsiSiKauw/tyRK1oioZ0siIooB/2qP04pFOjXq7MxHRBQdtXY3DFp108dGLVfcE8lF3fEhdDpJkvxP9nU/f+vST228aErRxTAyIgqU1+vF3LlzcezYMfzyl7/EsGHDmr1uNpuRm5sLAFCr1UhJSUFNTQ0yMzPbfE9RFJCenixr3Da3D13SkqBSqZCVkQSnx4eU1CSIKqHjL44zoqiS/fupBJ3hOjvDNXYGdXY3jKe1BKliv0YiioHa02ZYA0CyVkQDH6ARUZTUOTwwaH9+aJaqV+OkhWMiIjmwYB2keocHWrUKWvXPk9NT9WpU86kaUdwQRRFr1qxBfX09br31Vhw4cAADBgwI6z29Xgm1tbYIRdi6BocbPrcXPp8PNqsLeo2IExX1zW7cEkV6erLs308l6AzXGS/X2LVrSqxDUCyH2wuPT4L+1NgnSSuioZoFIiKKLqfHB5fX17TaDPDno3quOCOiKKl3uGE4bfJiql6NwyetMYyIKHGxJUiQ6hyeph6OjdhLlig+paamYuzYsfj000+bfd5kMqGsrAyAv21IQ0MDMjIyYhFiE58kwebyNm26CAAGbnxGRFHQ4PT3a2xsn5TMHtZEFAM1NhfS9OpmrdyS1So0ONiakYiio9555gxrDdvDEskkJgXrJUuWYPz48ZgxY0a7xxUVFeGcc87B//73vyhF1rEG58+bnjVK0ohocHhjFBERBaO6uhr19fUAAIfDgS+++AL5+fnNjpk8eTJWrVoFANiwYQPGjRvXos91tNlcXmjVqmbtP5K1IixO5h4ikpfV5YVe/fPYJ0nD3ENE0Vd9RjsQANBr2VOfiKJDkiQ0nNESJEWvRq2dBWsiOcSkYD137ly8+uqr7R7j9Xrx9NNPY+LEiVGKKjAWh6fZDEcA0GtU3PyMKE5UVFTgmmuuwcyZMzFv3jxMmDABF154IZ577jls2rQJADBv3jzU1taisLAQr732Gu66664YRw1YnB4kn5F7krTceJGI5GdzeaE/bQl+MnMPEcVAncMNg675WIibwBJRtNjdPogqARrxtDGRhmMiIrnEpIf1mDFjUFJS0u4xb731FqZNm4YffvghSlEFxuLyNOubBgBJahH1XIpGFBfOOussrF69usXn77jjjqZ/63Q6PP/881GMqmMWp7fF6o5kDTcaIiL52VqbYc1WaESKtWTJEmzZsgVZWVlYt25di9dfffVVvP/++wD8k4QOHz6ML7/8Eunp6Zg8eTIMBgNUKhVEUcTKlSujHX6bLE5vi4lDyVoRdbwPI6IoqHe4YdS2nLzo9Pjg8UlQq2K7Ipco0Shy00Wz2YyPP/4Yb775puIK1g2tzLBO4lI0IpJZQyszrPVq9pElIvlZz5hhnaQRYXN54ZMkqGLcLomIWpo7dy4WLFiAe++9t9XXFy5ciIULFwIANm/ejNdffx3p6elNr7/xxhvIzMyMRqhBaXB6muUiwF+wLq1zxCgiIupM6hweGM/Yz0wlCNBrRFidHqQladr4SiIKhSIL1k888QTuuusuqFTBdSwRRQHp6ckyReXnUamQkqSB0aiDSqWC0ahDZooOJ21u2c8dLFFUKS6mRkqODVB2fEqODVB+fPHK4mzlYZlGhMXFPrJEJC+b2wOd+ucxmagSoBVVsLm8LW7ciCj2AlnN2mj9+vUd7iukFFanp9lqD8C/2qyOE4eIKArq7O5m/asbGTQiGliwJoo4Rd5l7N69G4sXLwYA1NTUYOvWrVCr1bjooova/TqvV0JtrU3W2CprbVADsFicMBp1sFicEHwSqi1O2c8drPT0ZMXF1EjJsQHKjk/JsQGhx9e1a4oM0SSOhlYK1nqNChbepBGRzGwub7OCNQAYTvWxZsGaKH7Z7XZ8+umnePDBB5t9/oYbboAgCLjiiitwxRVXxCi6lhqcHuhbyUVsj0ZE0VDv8CBZ23Lcw709iOShyLuMzZs3N/37vvvuwwUXXNBhsTpa6ltpCZJ86okaEZFcLK0sg03SsG8jEcnPX7Bu2Te2welBToxiIqLwffLJJxg5cmSzdiDvvvsuTCYTqqqqcN111yE/Px9jxoxp932iscoVAJwSkG7UNVvp2sUnwer2xv3qvkRbocjrUbZEu55oqXe4kaxt2QUgSct6EJEcYlKwXrx4MbZv346amhpMmjQJt99+Ozwe/y/4VVddFYuQAtbg9CAnVdfsc0kaFSxOLssnIvlYnN4Ws4qSNCKqbK4YRUREnYXV6WkxwzqZN2dEcW/9+vWYPn16s8+ZTCYAQFZWFgoLC1FUVNRhwToaq1wBoKregZxUXbOVrnB7UW93K3r1YSCUvoIyWLweZTvzerjSNTB1Dg+SW2kJ4p/AyHoQUaTFpGC9bNmygI/94x//KGMkwWtwetBH0/xpZJKGS0CISF71DjeStC1nOBZXM/cQkbwsrtYfmHEZPlH8amhowDfffIM//elPTZ+z2Wzw+XwwGo2w2Wz4/PPPccstt8Qwyubaug+zu73w+iSIKm4CS0TyqbW7kaxpWUJL0ohs00gkA0W2BFGyBocXyWe2BNGKsLq8kCQJgsCBEhFFXr3DgxR985SdxHZERBQFVpcXaWfkH51aBZubs4mIlCiQ1awfffQRJk6ciOTknwvAVVVVuPXWWwEAXq8XM2bMwKRJk6J/AW2wOL0tWjOqVAL0pyYPccMzIpJTrd2NrGRti88naVS8JyOSAQvWQbK6Wvaw1ogqCALg9Pig17RcIkJEFC6L04PsFLYjIqLos7k8MJ2Rf7RqFexuX4wiIqL2BLKade7cuZg7d26zz+Xl5WHt2rVyhRU2SysbUAOnNl5kwZqIZGZxetEjvWUO0mtENHBfIaKIa9kxntplcXpaLMsH/AMltgUhIrlYXd4Wmy7q1CrYOcORiGRmbaUliFalgt3F/ENE0WN1eZGkaXn7atCKqOdyfCKSmdXlaTEeAvyrXus5iYgo4liwDpLV1bIlCMBG+0QkL5u7ZcFIpxbhYMGaiGTW2gMzLR+YEVGUtbbSFWi8D2PBmojkZW2lLREAJGtVqOcMa6KIY8E6CD5Jgt3d8qYN8Pex5kCJiORid3uhO6NgreeSfCKKApvLC526+Q0aV3gQUTRJkgSbq/VikU4jwsYVH0QkM6vL02otiPsKEcmDBesgWJ1e6NUiVK1srMgkRURycrh90IotZzg6PP4NX4mI5GJvZYWHVlSxQEREUWNze6ERVRBVLe/DdGrmIyKSn83lrwedKVkjosHBHEQUaSxYB8Hi8iC5lf7VgL9gbWXBmohk4p9h3Tz/iCoBokqAy8uCNRHJx9ZGD30WiIgoWixOb5v3YTq1ClbmIyKSmc3deh/9ZO5nRiQLFqyD0OBou2CtV6uYpIhINg63r0VLEADQq0VufEZEsvLPsGZLECKKHYvT0+o+QkDjAzTehxGRfLw+CU6PD9o2Nl20MAcRRRwL1kFoa2dqwL8038ZeskQkA0mS4PC07GENnCoaeVg0IiJ5eHwSPD4JGrH5MnxuukhE0WRxtr7hIgDoRM6wJiJ5NT68b6s9LCcvEkUeC9ZBsLWy6VkjPtknIrk4Pb6m9h9n0nNZPlFcKSsrw9VXX41LL70U06dPxxtvvNHimK+//hqjRo3C7NmzMXv2bLzwwgsxiNTP5vJArxEhnHGDpuODeiKKIouz7YlDeg0L1kTxYMmSJRg/fjxmzJjR6uuSJOHxxx9HYWEhZs6ciT179kQ5wrb5H5q1kYPUKjjcPvi4rxBRRKljHUA8sbm80LbSZB/w37hZnBwoEVHktdUOBAB0Gs5yJIonoijivvvuw6BBg2CxWHD55Zdj4sSJ6NevX7PjRo8ejZdffjlGUf7M5vIiqa3VHcw9RBQlVlfbM6z1ahFVVleUIyKiYM2dOxcLFizAvffe2+rr27ZtQ3FxMTZu3Ijvv/8eDz/8MJYvXx7lKFvnX23feg5SqYSmlWcGLUtsRJHCGdZBsLu80IttPVUTOcuRiGRha6V/bCOdyKIRUTzJzs7GoEGDAABGoxH5+fkwm80xjqptVpcXulZu0LSiCg7mHiKKEqur7ZWunGFNFB/GjBmDtLS0Nl/ftGkT5syZA0EQMHz4cNTX16OioiKKEbbN6vJC30bBGgCSNFz1ShRpLFgHwer2ttpkH/DPcrSyJQgRycDeTjsirVoFm4vL8oniUUlJCfbu3Ythw4a1eG3Xrl2YNWsWFi5ciIMHD8YgOj+Hxwd9qxu+quDwMPcQUXTY3V5o25k4xII1Ufwzm83Iyclp+jgnJ0cxD/X9qzzaLp8xDxFFHtcrBMHuaqdgrVbBxplGRCSD9grWXJZPFJ+sVisWLVqE+++/H0ajsdlrgwYNwubNm2EwGLB161bceuut2LhxY7vvJ4oC0tOTIx6nusaOJJ0aRqMOAKBSqWA06iCpRTg8PlnOqQSiqErYaztTZ7nWznKdiaq9GdY6jQo2NycOEXVWco2BmlHXw5ikaTEeamTQqSHqNHHzdyae/yYy9tiIRewsWAfB6vK0OssIYEsQIpKPzeVtM/do+bCMKO643W4sWrQIM2fOxNSpU1u8fnoBu6CgAI888giqq6uRmZnZ5nt6vRJqa20Rj/VkjR0iJFgszlOx6WCxOOHx+mB3eWU5pxKkpycn7LWdqbNcq5Kvs2vXlFiHoHi29lqCcANqooRgMplQXl7e9HF5eTlMJlOHXyfXGOh0FTVWiECL8VAjjUqAucqKPING1jgiRcl/EzvC2GNDztjbGgexJUgQ2n2yz4ESEcnE7va1vbqDfWSJ4ookSfj973+P/Px8XHfdda0eU1lZCenUTvNFRUXw+XzIyMiIZphNHB4vNK0sw1erBPgkCR4v24IQkfysLk87BWtOHCJKBJMnT8bq1ashSRJ27dqFlJQUZGdnxzosAKd6WLeRg4DGFrHMQ0SRxBnWQbC6vMg2alt9Ta/hLEcikkd7LUE0Ih+WEcWTHTt2YM2aNRgwYABmz54NAFi8eDFKS0sBAFdddRU2bNiAd999F6IoQq/XY9myZRAEISbxOty+VvvGCoIAvVqE3e1DSht9ZYmIIsXq9CItrfWZi7wPI4oPixcvxvbt21FTU4NJkybh9ttvh8fjb+dz1VVXoaCgAFu3bkVhYSGSkpLw5JNPxjjin1mdbd+PAadWerA1EVFEsWAdBFs7O8OyjywRycXe3oavXN1BFFdGjx6N/fv3t3vMggULsGDBgihF1L72NjprHPuk6DmcJCJ52Traz8PlgyRJMXu4R0QdW7ZsWbuvC4KAhx56KErRBKfB6WmzFgT4V73aeU9GFFGcEhOE9nunibC7uCyWiCLP7vZCq+KGr0QUfQ6PDxqx9QKQXsOH9UQUHR2tNhMEwOWVohwVEXUWFmfb+5kBgE4tsiUIUYSxYB0Em7vt3mk6tQoOj7ep5yQRUaTYXJxhTUSx4WhnhrVWrYLDzYf1RCS/jvrH6jUq2Fxcjk9E8rC62p9hrVULvCcjijCu4QyC3eVrc6AkqgSoVQKcHl+7iYyIYqusrAz33HMPqqqqIAgCfvGLX+Daa69tdszXX3+NW265BT169AAAFBYW4rbbbotFuADan1XEgjURyandlkQiV3gQUXTY21npCgBJp2Y3ZiRHMSgi6jQszo4emomw8J6MKKJiUrBesmQJtmzZgqysLKxbt67F62vXrsUrr7wCADAYDHj44Ydx1llnRTvMFtrrnQb4k5TN3XafayKKPVEUcd9992HQoEGwWCy4/PLLMXHiRPTr16/ZcaNHj8bLL78coyibs7Zzk8b++UQkJ7vbB00HPayJiOTmf3jf9j2Wf4Y18xERyaOjOo9erUK9g6s8iCIpJi1B5s6di1dffbXN13v06IG3334b77//Pm6++WY8+OCDUYyubXa3F/r2Bkqc6UikeNnZ2Rg0aBAAwGg0Ij8/H2azOcZRta+9WUXsYU1EcvIXiVrvYe1vCcL8Q0Tyc7h9HU8c4n0YEcmkvf3MAP9DM/awJoqsmBSsx4wZg7S0tDZfHzlyZNPrw4cPR3l5ebRCa1d7y/IBDpSI4k1JSQn27t2LYcOGtXht165dmDVrFhYuXIiDBw/GILqf2dpbks8ZjkQko3Z7WLMlCJEiLVmyBOPHj8eMGTNaff3rr7/GqFGjMHv2bMyePRsvvPBC02vbtm3DtGnTUFhYiL///e/RCrlD/tmN7dyHqVWwMh8RkUw6qgXp1CL76BNFmOJ7WK9YsQKTJk0K6FhRFJCeLk/jMrfXB69PQkaaHoLgn2mkUqlgNOqajknWqaHSaWSLIViiqFJMLGdScmyAsuNTcmyA8uNrZLVasWjRItx///0wGo3NXhs0aBA2b94Mg8GArVu34tZbb8XGjRvbfT8584/LB6QbdU355vTck+GV4PJKcfE9D0a8/ByFqzNcZ2e4xkTmcPs6eGDGTReJlGbu3LlYsGAB7r333jaPaa31mdfrxaOPPorXXnsNJpMJ8+bNw+TJk1u0TYs2t9cHnyRBrWp9tQcA6DhxiIhk5HC3vZ8ZcOqhmYtjIqJIUnTB+quvvsKKFSvwzjvvBHS81yuhttYmSyz1Djf0GhFWq6vpc0ajDhaLs+ljjQBUVFtRm6Zr7S2iLj09WbbvR7iUHBug7PiUHBsQenxdu6bIEE3r3G43Fi1ahJkzZ2Lq1KktXj+9gF1QUIBHHnkE1dXVyMzMbPM95cw/DXYXfG5vU745Pfd4nB5YnR5F/0yEQuk/55HSGa4zXq4xmjkontg9bc+w1ohsCUKkRGPGjEFJSUnQX1dUVIRevXohLy8PADB9+nRs2rQp5gVr+6nesY2ThlqjE1Wws2BNRDLpeLW9CjY3Z1gTRZJiC9b79u3DAw88gFdeeQUZGRmxDgc2V/u7wgJ8sk8UDyRJwu9//3vk5+fjuuuua/WYyspKdOnSBYIgoKioCD6fL6Z5yOFpu2+jTq2C08On+UQkj/ZmWGtEgfmHKE41tj7Lzs7Gvffei/79+8NsNiMnJ6fpGJPJhKKiog7fS85VZgBgq7UjWSs2W9l65kpXQ5IG0Ihxu6In0VYj8XqULdGuR24+SYLT0/Z4CGhsCcJaEFEkKbJgXVpaittvvx1PPfUU+vTpE+twAHS8Kyzgf7LPXo5EyrZjxw6sWbMGAwYMwOzZswEAixcvRmlpKQDgqquuwoYNG/Duu+9CFEXo9XosW7as3Vk9cnO008Nay4IREcnI0c4Ma63IHvpE8SiU1mftkXOVGQCUV1mhFVXNVraeudJV8PlQVWePixU9rYmX1UiB4vUo25nXw1Vm7Wvc9FXVzv2gnvsKEUVcTArWixcvxvbt21FTU4NJkybh9ttvh8fjXz5x1VVX4cUXX0RtbS0eeeQRAIAoili5cmUsQm1iD2SGtVrFp2pECjd69Gjs37+/3WMWLFiABQsWRCmijjk9vjYLRqJKgE+S4PH6oG7jGCKiUDndPmjF1m/Q/C1B+MCMKN601frMZDI12+zebDbDZDLFIsRmArkP4wM0IpJLR5u+AoBeI8LGHtZEERWTgvWyZcvaff2JJ57AE088EaVoAmMNaKAksGBNRBHnaKdgLQgCdGoVHB4fjCxYE1GE2T1t92zUigIanOzXSBRv2mp9lpqaiuLiYhw/fhwmkwnr16/HM888E+twA7oP06lVsPI+jIhkYHd5oVO3v9peKwrw+Hzw+NrfIJaIAqfIliBKZG9nSX4jrZotQYgo8trrIQv4b9Icbi+MOqZ0Ioqs9lZ4aNUq2C2cTUSkNB2tZm2r9ZlarcbSpUuxcOFCeL1eXH755ejfv3+Mryaw+zCdWoV6Bx+gEVHk2dwdPzQTBAF6tQi7y4sUPe/JiCKBv0kBsnWwKyzgb7TP3amJKJJ8kgS31wdNG0vyAf8yWAf7WBORDNrbZEgr+h+WEZGydLSatb3WZwUFBSgoKJAjrJAFdh/GliBEJA+bq+McBAA6jT8PsWBNFBlcPx4gu7vtGUaNdGqBM6yJKKJcHn+xur1NPvwzrFmwJqLI8vokeNtZ2sqHZUQUDYEUi7jSlYjkEshDM8C/8SLzEFHksGAdIIfbG0DBmk/2iSiyOmoHAvhv0hwe5h4iiiyHx78MX2jjgZlGFOBkwZqIZGZzdXwfplerYOfDeyKSQSA9rAH4W4KwHkQUMSxYB8ju9kITwO7U3HSRiCLJ4fFC18FNmn9ZPm/SiCiyHG5fu/lHq2ZLECKSX6AP79makYjk4J9h3fFGilpOYCSKKBasA2RzdVw04gxrIoo0h9vX8TJYkTOsiSjyGmdYt0UrqjjDmohkZ3MHch/GmY1EJA+7q+ONX4FT9SAXx0VEkcKCdYDsATzZ16lFznIkoojqqGAEABougyUiGdg7eGDGliBEFA22AIpFOpETh4hIHrYA2sMC/oI1e1gTRQ4L1gHy9y1qfxkIl4AQUaQ5AtjwVasSuCyfiCLO2cGsRs6wJqJoCGTDM52am8ASkTwC2fgV8I+L2JqIKHJYsA6Qze3peKDEJ/tEFGGBzrDmTRoRRZrD42t3/w5u+EpETE80kAABAABJREFU0RDIcnwde+oTkUwCLVjr1ALrQUQRxIJ1gGyBzHJk0YiIIswe0Axr3qQRUeR1tMJDK6rg8khRjIiIOiN7ADOsNaIAj0+C18ecRESRZXN5oVeLHR6nZUsQoohiwTpAjkCXorGPLBFFkMPjhUZsvx2RRi3wYRkRRZyzg/wjqvyvebzMP0QkH7vb1+Gmi4IgsD0jEckikNX2gP9Bvo0tQYgihgXrAPmf7Lf/VE0jCnB7ffBJfLJPRJERUA9rkTOsiSjyHJ6O8w/7xhKR3ByewJbj69Uix0NEFHGBtwThDGuiSGLBOkAOt6/D3mkqQeAsayKKKIfHB00ABWvOKCKiSAso/3CFBxHJLJAe1oC/WGTnfRgRRZjN7Qu8YM0Z1kQRw4J1gOxub4dL0QA+VSOiyHK4O24JolULvEEjoohzuL3QqDrIP1zhQUQys3sCuw/T8z6MiGQQSB99ANCpRRasiSKIBesAOTwdz7AGuEM1EUWWw+0NqCUIZ1gTUaQ5PT5o1AEUrDnDmohk5AhwdqOW92FEJAM7W4IQxQQL1gEKZNNFoHEpGpMUEUWGPYB2RFq2IiIiGTjdXmhUHecfJ8c9RCQjZ4AThzjDmojkEPgMaz40I4okFqwD4PVJcHulDpflA+ydRkSRFegMa4eHgyMiiiy7x9dxSyLOsCYiGXlObWiv7qA9EeB/gMb7MCKKtGBW27MlCFHksGAdAIfHv9GHSgikYC3CziRFRBFi9/igDWRJPm/QiOJCWVkZrr76alx66aWYPn063njjjRbHSJKExx9/HIWFhZg5cyb27NkTg0hPbTjdwQMzjchNF4lIPna3D3q1CCGA+zCtqOJ9GJHCbdu2DdOmTUNhYSH+/ve/t3i9tLQUV199NebMmYOZM2di69atMYjyZ5IkBbyfGds0EkWWOtYBxAP/QCmw2r6WLUGIKILsrgBmWKs5w5ooXoiiiPvuuw+DBg2CxWLB5ZdfjokTJ6Jfv35Nx2zbtg3FxcXYuHEjvv/+ezz88MNYvnx51GN1uL0waLXtHsOWIEQkJ1uAS/EB3ocRKZ3X68Wjjz6K1157DSaTCfPmzcPkyZObjYFeeuklXHLJJfjlL3+JQ4cO4cYbb8TmzZtjFrPbK0ElCFAHuPErH+ITRQ5nWAcg0P7VAKAVBdhZOCKiCHF4AmkJIsDJGdZEcSE7OxuDBg0CABiNRuTn58NsNjc7ZtOmTZgzZw4EQcDw4cNRX1+PioqKqMcayBJYDVuCEJGMAu0dC3B2I5HSFRUVoVevXsjLy4NWq8X06dOxadOmZscIggCLxQIAaGhoQHZ2dixCbWJzewOevMj9zIgiizOsAxDUQIm904goghyBbrrIghFR3CkpKcHevXsxbNiwZp83m83Iyclp+jgnJwdms7ndmzZRFJCenhzR+HwCkGrQwmjUNX1OpVI1+zhZp4ZKo474uWNNFFUJd01t6SzX2lmuM9E43F7oNEFMHGKxiEixzhzfmEwmFBUVNTvmtttuww033IC3334bdrsdr732WrTDbCaoyYtqtmkkiqSYFKyXLFmCLVu2ICsrC+vWrWvxuiRJeOKJJ7B161bo9Xr88Y9/bJqNFAt2ty/wJKVi7zQiipzAZlir4GTBmiiuWK1WLFq0CPfffz+MRmPY7+f1SqittUUgsp812N3wur2wWJxNnzMadc0+FnwSquvtET93rKWnJyfcNbWls1yrkq+za9eUWIegWHa3L6DesYC/WMQNz4ji2/r163HZZZfh+uuvx86dO3HPPfdg3bp1UKnazgNyPLRvdNLlQ7JO3exhPdDyAT7gr2NJAJKN+oA2aYyleH6Iy9hjIxaxx6RgPXfuXCxYsAD33ntvq68rpXdjI7vLC61aDOhY9k4jokjyb3oWwKaLbEVEFDfcbjcWLVqEmTNnYurUqS1eN5lMKC8vb/q4vLwcJpMpmiECAJyeADZdVAt8YEakMB1NDlq7di1eeeUVAIDBYMDDDz+Ms846CwAwefJkGAwGqFQqiKKIlStXRjX2MwWz0lXHliBEinbm+MZsNrcY36xYsQKvvvoqAGDEiBFwOp2oqalBVlZWm+8rx0P7phirrVCrhGYP64GWD/Ab6dUqlJ9sQKpeI0s8kaLkh7gdYeyxIWfsbT24j8ljnzFjxiAtLa3N15XSu7GR3e3tsGDUiH2LiCiSnAH1kBXg8Urw+qQoRUVEoZIkCb///e+Rn5+P6667rtVjJk+ejNWrV0OSJOzatQspKSkx6eHocPug6WD8o1Fx3EOkNHPnzm0q+LSmR48eePvtt/H+++/j5ptvxoMPPtjs9TfeeANr1qyJebEa8C/HD3Smolatgo3L8YkUa8iQISguLsbx48fhcrmwfv16TJ48udkxubm5+PLLLwEAhw8fhtPpRGZmZizCBXCqJUiAqzwAfz2IKz2IIkORPaxD6d0IyLcURNCqYdBrAloGkpKshUeSFDHNX8nLDZQcG6Ds+JQcG6D8+OJNIDMcBUGAVq2Cy+tDkiqw1SBEFBs7duzAmjVrMGDAAMyePRsAsHjxYpSWlgIArrrqKhQUFGDr1q0oLCxEUlISnnzyyZjE6vR4oelohrXIfo1ESjNmzBiUlJS0+frIkSOb/j18+PBmMx6VxubuuDVaI52arRmJlEytVmPp0qVYuHAhvF4vLr/8cvTv3x/PPfccBg8ejClTpuC+++7DAw88gNdffx2CIOCPf/wjBCGwyYNysLkCX+UB+GdYc08zoshQZME6VHItBamqs0MlSYEtA/H6UGdzKWKav5KXGyg5NkDZ8Sk5NiD0+Ni/sXWB9LAG/DdpDrcXSRoWrImUbPTo0di/f3+7xwiCgIceeihKEbUtkBUeWlGAky2JiOLWihUrMGnSpGafu+GGGyAIAq644gpcccUVHb6HnP1jBXXgE4fSjDq4zda4nDiRaBM+eD3KFsvrKSgoQEFBQbPP3XHHHU3/7tevH/79739HO6w22YNY5QEAOrXIlWdEEaLIgrVSejc2cgTxZJ89rImUraysDPfccw+qqqogCAJ+8Ytf4Nprr212jFI2fpUkKaCCEdDYx5pP84kocvwrPDpoCSKqUO/wRCkiIoqkr776CitWrMA777zT9Ll3330XJpMJVVVVuO6665Cfn48xY8a0+z5y9o+trrdDCHDikM/tRYPdreiJHW1R+oSUYPF6lO3M6+HEobb59xMKriUI60FEkaHIrUuV0ruxkd3t7bCHYyMde6cRKZooirjvvvvwwQcf4D//+Q/eeecdHDp0qNkxp2/8+thjj+Hhhx+OSawenwQBAkRVx/lHKwpclk9EEeXydnyTplVz01eieLRv3z488MAD+Otf/4qMjIymzzdOEsrKykJhYSGKiopiFSKA4PcSYj4iokiyub3QqQNvScIJjESRE5MZ1osXL8b27dtRU1ODSZMm4fbbb4fH45+do6TejY1sriA2+xD9y/KJSJmys7ObHoAZjUbk5+fDbDajX79+Tce0tfFrtB+cOT2+gHumsWhERJHUuMKj4x7WAld3EMWZ0tJS3H777XjqqafQp0+fps/bbDb4fD4YjUbYbDZ8/vnnuOWWW2IYaZD3YSwUEVGEBdsSRCsK3HSRKEJiUrBetmxZu68rpXdjI7s7mKKRwIESUZwoKSnB3r17MWzYsGafD2XjVzn6N7oaHNBrWvZobK1vY5JWDY1emzD99RKtV2BbOsN1doZrTESBrvDQiio4WbAmUpSOJge9+OKLqK2txSOPPALAv/ps5cqVqKqqwq233goA8Hq9mDFjRov+1tFmd3uhC3TTRW4CS0QRZnMF3h4W8PewZh4iigxF9rBWGrvbizR9YN8qDpSI4oPVasWiRYtw//33w2g0hv1+cvRvrKi1QyOqAurbKArAyRoratOaF7LjVaL1CmxLZ7jOeLlG9m9sLtAVHlqOe4gUp6PJQU888QSeeOKJFp/Py8vD2rVr5QorJDa3D0ZdYPdhXG1GRJHmbwkSxAxrTmAkihhF9rBWGnsQSYpL0YiUz+12Y9GiRZg5cyamTp3a4nWlbPzqcPsC7p/PohERRZLD7YU2gJ6NGlHgDGsiko3dFfh9mE7NsRARRZY9yBnWWlEFG+tBRBHBgnUAgulbpFNzaSyRkkmShN///vfIz8/Hdddd1+oxStn41eEJfIDEPrJEFEmOAPpXA4BGVMHJGY1EJJNg7sPUKgE+SYLHJ8kcFRF1FkHPsBY5gZEoUtgSJABB9bAWVSwaESnYjh07sGbNGgwYMACzZ88G4O/1WFpaCkBZG7863D5u+EpEMeHw+ALqG6vlg3oikpHdHVguAvz7IPlnWXsDbiNCRNSeYFbbA/4JjNx0kSgy+Jc8AMFs9qFVq+Dy+OCTJKiEwJbyE1H0jB49Gvv372/3GKVs/Or0+DjDmohiwunxQRNQD2u2BCEi+Tg8wRaLRNhZsCaiCAmmLRHgL1jXOzwyRkTUebAlSAAcQSxFUwnCqeWxvHkjovAE0xJEzbxDRBHkDDD/aEQVXF7mHiKSRzAtQQB/scjOPtZEFCH+1fZiwMdzTzOiyAm7YH3bbbdhy5Yt8PkSd2DgcAc+yxEA9BomKaJoSPT8E/ymi8w7RNGUyDnIP/bpOP9oTz0skyT2jCWSQyLnmUA4gmgJAjQWrDkeIpJbZ8lNoTw046aLRJERdsH6l7/8Jd5//31MnToVTz/9NI4cORKJuBQl6KVobLRPFBWJnn8cHm8QBWuBeYcoyhI5BzkD3HRRVAkQIHCTMyKZJHKeCYTDE3yxiA/wieTXWXJTKD2sucqDKDLCbu41YcIETJgwAQ0NDVi3bh2uu+465ObmYv78+Zg1axY0Gk0k4owppyfwjc+AxmUgTFJEckv0/BPM6g7/jtTMO0TRlMg5KKgHZmoh4AI3EQUnkfNMIBxuHycOESlQZ8lNgW5C3Yg5iChyInJnUVNTg5UrV2L58uU4++yzcc011+DHH3/E9ddfH4m3jylJkuAMNknxyT5R1CRy/nF4vFAHseEr8w5R9CVqDnK6Ay9Ac9xDJK9EzTMd8fgk+CQJalXgG9lz4hBR9HSG3OQIZYa1i2MiokgIe4b1rbfeiqNHj2L27Nn429/+huzsbADApZdeirlz54YdYKw5PT6IKgGqoAdKTFJEckv0/BNoD1nAv/GZg5suEkVVIucgh4f5h0gJEjnPdKSxUCQIQdyHcXYjUVR0htzk9Unw+KSAV5wBpyYRcUxEFBFhF6x/8YtfoKCgoNnnXC4XtFotVq5cGe7bx1ywy9AA/zIQB5/sE8ku0fOP3e0NoiWIAIeHN2hE0ZTIOcjp8QW+wuPUxotEFHmJnGc64u8dKwb1NVpR4IoPoijoDLnJHsJDM646I4qcsFuC/PnPf27xuSuuuCLct1UMuyeEgRJnWBNFRaLnH4c78P75/pYgLBgRRVMi5yCH2wtNgKvLOJuISD6JnGc6Yg9h4hBbghBFR2fITY4QHprp1CJzEFGEhDzDurKyEmazGQ6HAz/++CMkyb87vMVigd1uj1iAsWZ3e6ELYgkI4J9pxMIRkXw6U/7JSA5swxKtqOIMa6Io6Qw5yN8SJPAVHk7mH6KI6gx5piP2IHvHAv4WRZw4RCSfzpSbbG4f9EHnIAFurw9enwQxiLayRNRSyAXrzz77DCtXrkR5eTn+8Ic/NH3eYDBg8eLFEQlOCfxP9oOdYS1woEQko86SfxweX8A90/igjCh6OkMOsru9Aa/w0LAlCFHEdYY805FgNzsDTm14xvswItl0ptxkdwWfg1SCcGrlmRcGbdgdeIk6tZB/gy677DJcdtll2LBhA6ZNmxbJmBTFEcQNWyM+2SeSF/NPS1q1Ci4WjIiiojPkIKfbh6TkwIaJfGBGFHmdIc90JJi9PBppRRVsLt6HEcmlM+WmUFZ5AID+VGsig1aGoIg6kZAL1mvWrMHs2bNx4sQJvPbaay1ev+6668IKTCn8my4Gt5RDx4I1kaw6Tf4JYkm+RhTYQ5YoSjpDDrJ7vMgWA7vT0ogCZ1gTRVhnyDMdCaWHtU6tQrXNJVNERNSZcpMtxII1N14kioyQC9aN/YlsNlvEglEi/5P94FqCaEQVbJxpRCSbzpJ/nJ7AZxZpuSSfKGo6Qw5yun3QBJh/1CoVe1gTRVhnyDMdCaY1USOtmjOsieTUmXJTKKvtAUCnEZmHiCIg5IL1lVdeCQC47bbbIhaMEvkL1kHOsOZAiUhWnSX/ONy+wFuCiCwYEUVLZ8hBDk8wLYkEtgQhirDOkGc64gjxPowrXYnk05lyk83thS7ItkQAV9wTRUrwv31neOqpp2CxWOB2u3Httddi3LhxWLNmTSRiUwR7EAWjRjq1CjYmKCLZJXr+cXp8Ad+o+XekluA7tVM3EckvkXOQI5j8o+IKDyK5JHKe6YjdHXhrtEa6U71jiUhenSE3hVILAhp7WLMeRBSusAvWn3/+OYxGI7Zs2YLu3bvjo48+wj/+8Y92v2bbtm2YNm0aCgsL8fe//73F66Wlpbj66qsxZ84czJw5E1u3bg03zJA5QtnsgwmKKCpCyT/xxBlED2vh1I7U3HiRKHoSOQcFk3/U7GFNJJtEzjMdCXXTRd6HEcmvM+Qmuyu0liBaPjgjioiwC9Zer39AsGXLFlx88cVISUnp8PhHH30Ur776KtavX49169bh0KFDzY556aWXcMkll2D16tV49tln8cgjj4QbZsjsbi80ISxFY5N9IvkFm3/ijSOIHtZAY+7h4IgoWhI5Bzk9gfewZoGISD6h5JklS5Zg/PjxmDFjRquvS5KExx9/HIWFhZg5cyb27NnT9NqqVaswdepUTJ06FatWrYrMRYQolB7WbAlCFB2JPAZqZHN5gn5oBnACI1GkhF2wvuCCC3DxxRdjz549GD9+PKqrq6HT6do8vqioCL169UJeXh60Wi2mT5+OTZs2NTtGEARYLBYAQENDA7Kzs8MNM2Sh7Azr71nEohGR3ILNP/FEkqRTBaPAH5hpRRXs7GNNFDWJnIOCWWGmEVVwcIY1kSxCyTNz587Fq6++2ubr27ZtQ3FxMTZu3IjHHnsMDz/8MACgtrYWL7zwAt577z0sX74cL7zwAurq6iJ5OUGxuUK4D1MzHxFFQyKPgRrZ3L6gcxDgrwdxTzOi8IW86WKju+66CwsXLkRKSgpEUURSUhL++te/tnm82WxGTk5O08cmkwlFRUXNjrnttttwww034O2334bdbsdrr70Wbpghs7u80Gn4ZJ9IiYLNP/HE45MgQIA6iKf6Ws6wJoqqRM5BLm/gD8y0ooA6h0fmiIg6p1DyzJgxY1BSUtLm65s2bcKcOXMgCAKGDx+O+vp6VFRUYPv27Zg4cSLS09MBABMnTsSnn37a5kxtudncXmQla4P6Gq42I4qORB4DNbK5vDBoxaC/jjOsiSIj7II1ABw5cgQnTpxoWhYCAHPmzAn5/davX4/LLrsM119/PXbu3Il77rkH69atg0rVfuFGFAWkpyeHfN7WuCEg26CD0djyaaFKpWr18xleCU6vFPFYgiWKqpjH0BYlxwYoOz4lxwZEP75I5x+lcLh9wT8sE1VwcIY1UVQlag5yegLfaEirVsHJ3EMkm0jnmTMnEOXk5MBsNrc6schsNod8nnA5QljpquVYiChqEnUM1Mjm9iLToAn667SiwII1UQSEXbC+++67cfz4cZx11lkQRf/TJ0EQ2kxUJpMJ5eXlTR+bzWaYTKZmx6xYsaJpGduIESPgdDpRU1ODrKysdmPxeiXU1trCuJqW6mxOdEvRwmJxtnjNaNS1+nmPywObyxPxWIKVnp4c8xjaouTYAGXHp+TYgNDj69o1+L5nweafeGJ3e6ELYcNXzioiip5QctCSJUuwZcsWZGVlYd26dS1e//rrr3HLLbegR48eAIDCwkLcdtttssTflsaWRIG2BNGyFRqRbOJhrCPHpCEAcElAWkpwE4ckSYLXJ8GYog9qlVqsKX1CSrB4PcoWiesJNTdt27YNTzzxBHw+H+bPn48bb7yxxTEffPABXnjhBQiCgLPOOgvPPPNMWLGGyu4KfuNXwL/Sw8qWIERhC7tgvXv3bnzwwQcQhMCWjQ4ZMgTFxcU4fvw4TCYT1q9f3yIB5ebm4ssvv8TcuXNx+PBhOJ1OZGZmhhtqSOyu4PsWcdNFougINv/EE0cQsxsb8Wk+UXSFkoPmzp2LBQsW4N57723zmNGjR+Pll1+ORIghaWxJJKoCbQmigpM9Y4lkIcdY58wJROXl5TCZTDCZTNi+fXvT581mM84999wO30+OSUMA0GBzw+f2BjVxCPDfi5WftMCoi8hi4qhQ+oSUYPF6lO3M6wll4lAoucnr9eLRRx/Fa6+9BpPJhHnz5mHy5Mno169f0zHFxcX4+9//jnfffRdpaWmoqqoKOrZIsXuCX+UBADq1yB7WRBEQ9mPn/v37o7KyMuDj1Wo1li5dioULF+LSSy/FJZdcgv79++O5555r2nzxvvvuw3vvvYdZs2Zh8eLF+OMf/xizglQosxx1p27cJEmSKSoiAoLPP/EkpGWw3GiIKKpCyUFjxoxBWlqaTBFFhr8dSBAbvqoFPqgnkokcY53Jkydj9erVkCQJu3btQkpKCrKzs3Heeefhs88+Q11dHerq6vDZZ5/hvPPOi+i5g2Fze6EPsVjEB/hE8golNxUVFaFXr17Iy8uDVqvF9OnTm2pAjd577z386le/ahordbTKXk6hbPwKcIY1UaSE/di5pqYG06dPx9ChQ6HR/Nzf529/+1ubX1NQUICCgoJmn7vjjjua/t2vXz/8+9//Dje0iLC7vUHPclSpBKhVApweH/Sa4Jv0E1FgQsk/8cLuDn4Jmkbk6g6iaJIrB+3atQuzZs1CdnY27r33XvTv37/Dr4nkknxXgwN6jRjwMvw0hwduCVwuHac6y7XG63WGkmcWL16M7du3o6amBpMmTcLtt98Oj8e/MepVV12FgoICbN26FYWFhUhKSsKTTz4JAEhPT8ctt9yCefPmAQBuvfXWpg0YYyGU+zDAXyzi7EYieYWSm1rrk19UVNTsmOLiYgDAlVdeCZ/Ph9tuuw2TJk1qNxbZ2hL5JGSk6oNqSwQAaUYdimsdiv6bE69/EwHGHiuxiD3sgvXtt98eiTgUyx7CLEfg5yf7LFgTySeR80+oLUFYsCaKHjly0KBBg7B582YYDAZs3boVt956KzZu3Njh10VySX5FrR1qlRDwMnyP0wObM/Z7d0RSoi3/bk9nuVYlX2d7y/FDyTPLli1r93VBEPDQQw+1+tq8efOaCtax5nAH35oRAPTc04NIdnLdh3m9Xvz000946623UF5ejgULFuD9999HampqO18jT1siq8MDr8sTdFsiyeNFg92l2L85gLL/JnaEsceGnLG3NQ4Ku2B97rnn4sSJE/jpp58wYcIE2O32ZrvExjuHJ/SBkt3tQ4YMMRGRXyLnH4fbB60YXCskrciWIETRJEcOMhqNTf8uKCjAI488gurq6qju5eHw+IJqh6bl6g4i2STyWKcjoU4c0qpVbAlCJLNQctOZ/fPNZjNMJlOLY4YNGwaNRoO8vDz07t0bxcXFGDp0qCzX0Z5Qc5CeqzyIIiLsHtbvvfceFi1ahKVLlwLwJ51bb7017MCUIpQ+ssCppWgcKBHJKpHzjyOEliBqkTdoRNEkRw6qrKxs2gOjqKgIPp8PGRnRffzt9PigCWLso1UL3HSRSCaJPNbpiDPIh2eNdGoV7B6Oh4jkFEpuGjJkCIqLi3H8+HG4XC6sX78ekydPbnbMRRdd1LT5a3V1NYqLi5GXlyfPRXTA4fEFfT8G8KEZUaSEXbD+17/+hXfffbdpRlDv3r1RXV0ddmBKIElSyAMlrZqzjYjkFkr+WbJkCcaPH48ZM2a0+vrXX3+NUaNGYfbs2Zg9ezZeeOGFiMcdCIfHC00IG75yCSxR9ISSgxYvXowrr7wSR48exaRJk7B8+XK8++67ePfddwEAGzZswIwZMzBr1iw8/vjjWLZsWdQ3ng72gZn21GbTRBR5iXyv1R63159T1KHch4kq2Dm7kUhWoeQmtVqNpUuXYuHChbj00ktxySWXoH///njuueeaNl88//zzkZ6ejksvvRTXXnst7rnnnqg/uAcAnyTBFUKLRuDUQzPekxGFLeyWIFqtFlqttunjxg09EoHT44OoEqBSBX+jqOdTNSLZhZJ/5s6diwULFuDee+9t85jRo0fj5ZdfjkiMofL3bQyyJYha4MoOoigKJQd11Ft2wYIFWLBgQdixhcPpCa4lkeZUwVqSpKgX14kSXSLfa7XH5gptlSvAYhFRNISamwoKClBQUNDsc3fccUfTvwVBwJIlS7BkyZLIBBqixk1fVSGMaxr3MyOi8IRdsB4zZgz+9re/weFw4PPPP8c777zTYllHvHK4fdCrQ9s0UatWwebiQIlITqHknzFjxqCkpCRKEYbO7vYGPatIK6pQ7+gcN7JESpCoYyCHxxfUCg9RJUBUCXB7JWiDfNBGRO1L1DzTEbvbC32IBWstW6QRyS7Rc5Pd5UWSJrQcpI/CantJkrD9WC0EAOf24s5plJjCLljfddddWLFiBQYMGID//Oc/KCgowPz58yMRW8zZQuxfDfif7LMlCJG85Mo/u3btwqxZs5CdnY17770X/fv37/BrRFFAenpy2Of++Q1VMCZpYDTqWrykUqla/XyqUYcapzeyccSIKKoS4jo60hmuM5GvMVHHQE6PF5ogN33VqVVweLwhLZ0lorYlap7piN0d2sb3AAvWRNGQ6LnJ6vKGPHlRIwrw+CR4fRLEEFbrB+Lf353Am9+UQBCAtb8ZC7VM5yGKpbAL1iqVChdddBEuuuiiqO5gHw3hPtnn0nwiecmRfwYNGoTNmzfDYDBg69atuPXWW7Fx48YOv87rlVBba4tIDABQY3ECXgkWi7PFa0ajrtXP+9xe1NtcEY0jVtLTkxPiOjrSGa4zXq6xa9eUoL8mUcdADnfwmwz59+7wIVUvU1BEnVSi5pmO2N1e6DQhFovUAgvWRDJL9Nzkz0Gh1YIEQWh6cGbUhV1ya9X/9lXil6O648O9Ffj0cBUu7N9FlvMQxVLIvz2SJOGFF17A22+/3bSbvUqlwoIFC3DbbbdFLMBYcrhDnynEJ/tE8pEz/zRuHAL4e6w98sgjqK6ujvpAzO4KfpDEzV6JoiPRx0BOjy/4GdaiCg5uvEgUMYmeZzpiD3Olq42bLhLJorPkpnBW2wOAXiPKVrCutblRXGVD/64GNDgzsaqojAVrSkgh/wa+/vrr+O6777BixQps374d27dvx/Lly7Fz5068/vrrEQwxdsJJUppTM42IKPLkzD+VlZVNg6+ioiL4fL6Y7Ext9wQ/w5EFI6LoSPQxkCOUHvp8YEYUUYmeZzpic3mhCzIPNdJx4hCRbDpLbrKF0RIE8PexluvB2Zc/VWOgyQiNqMJZ2UbsLmtoun8lSiQhF6zXrFmDZ555Bnl5eU2fy8vLw5/+9CesXr06ErHFXDi903SiClYXNz8jkkM4+Wfx4sW48sorcfToUUyaNAnLly/Hu+++i3fffRcAsGHDBsyYMQOzZs3C448/jmXLlkEIYXfocDlCeGCmVQt8UEYUBYk+BnK4vcG3BBEFPjAjiqBEzzMdCWeGtVatgpUzrIlk0Vlyk80V3gxrnUa+B2ffHKvFwK4GAEBakgYaUUBpvUOWcxHFUsjrEzweT6tL5DMzM+HxJEah1u4K/oatkU6tgp2FIyJZ/H/27jtOqvLeH/jnnDO9b51dYFl6Eak2bBBXEQURRUjTxBQ1MUa90cRE7w3J9apJ1MRrkvuL8WqsiYmaCCrmqgEVrNgXQaQubO/T+8z5/THsyrJtypm6n/frlVfcmdk5zyzsl+d8n+/zfdKJP7/5zW9GfP6yyy7DZZddltb4lOBPKWHECkeibCj2OZAvlR7WjD9Eiir2ODMafzgKdYr3YfGt+LwPI8qEsRKbUrkXO5oug/mgA10+nDe7ov/r2lIDdrV5MN6qz8j1iHIl5d9AtVqd0nOFJN3eaX6u7BNlxFiIP4FwLOke+hoVW4IQZUOxx6D4GR7J7SxRM/4QKarY48xo0trpqhLh405XoowYK7HJG4pCl0aFtUYS4cvAQr4sy2jo8aH6qFOua2w67Gx1K34tolxLucJ69+7dWLRo0aDHZVlGKBRKa1D5IpUesn20KhG+MCdKRJkwFuJPIJJahXWQCSOijCv2GOQLR2HRJzdFZIU1kbKKPc6MJl5hnVpLtvh9GOMRUSaMldjkD0ehTqcliErKSAFjtzcESRQGHOZYW2rAtv3dil+LKNdSTlh/+umnSo4jL/lDqU+UNCoRPm5FI8qIsRB/UqqwlkQEIrxBI8q0Yo9BgZRagghcMCNSULHHmdGk0z9WpxLhDzEeEWXCWIlNvjQrrLUqISOHLh7o9mGcVTfgsQlWHRp6fIpfiyjXUv8NHAN84Si0KZ4My5YgRJSOQCQGTZILZmpJQCQqIxrjKdFElDp/OJr0gplaYksQIlJOOglrVlgTUbo8wTQPXVSJ8GYgDh3s9sFu1g54zKJTIRCOwR3gDn8qLkxYj8AfSv6GrQ8nSkSUjmAk+UmSIAjQqlhlTUTpCaTQEk3NliBEpKD0EtYS/IxHRJQGXxrnmQFHelhnoJf+/i4vqo5JWAuCgCqLFocdfsWvR5RLTFiPID5RSq0lSPxUWE6UiCg1qWzJB44cvMh2RESUhlQOnVZLAiusiUgx6ex01am4gEZE6Uln0QwAdGoJ3qDycehQrx+VJu2gxytNWjT2MmFNxYUJ6xF4QxHo2BKEiLJMlmUEUzz0lRXWRJSuVOIPD10kIiWlU92olgREYjIibJFGRCnyhSLQqVPLBQFHWoJkIB/U5g6i1Kge9Hi5SYND7GNNRYYJ6xH4wlFo1ake9iHBzypHIkpBKCpDJQkQxeR3eGglkbGHiNISSLGHNXeWEZFS/KFoSgv3QHx7vE4lsXiIiFKWbksQnVqEV+GWIDFZRqcniFKDZtBzlSYtD16kosOE9Qi8oWhaFdaBSBSyzJV9IkqOP5z6TZqG22CJKE2p9LDWSAIXy4hIMb5wFLoUC4eAeLKI5wkRUar8oRh06SSsVZLiFdY93hD0KmnIRLrdrMUhtgShIpOThPXWrVuxfPlyLFu2DPfff/+Qr3nhhRewYsUKrFy5EjfeeGOWRxiXTt8iURSgEkX2cySipAXC0ZQnSFr2zyeiNAUjsaTnP1wsIyIl+UKpz4WAI+cJscKaiFKUboW1ViXCp3AManUFUWYaXF0NAJUmDZodARZMUlFRZfuC0WgUt956Kx566CHY7XasXbsWdXV1mDZtWv9rGhoacP/99+OJJ56A1WpFd3d3tocJIF7lmM5ESa+O9y3Sp9H7iIjGnng7otR3d/hCXCgjotTEZBmhSAxqKbmWRPEe1ow9RPli69atuP322xGLxbBu3TpcddVVA56/44478M477wAAAoEAuru78d577wEAZs+ejRkzZgAAqqurcd9992V38Ejv0EUA0KoleLmIRkQp8qcZg3TqTCSsAygboh0IABg08bG6gxFYdIN7XBMVoqwnrOvr61FbW4uamhoAwMqVK7F58+YBCesnn3wSl156KaxWKwCgrKws28ME0Bek0jsZ1h+KAkYFB0VERc8fTn0LGg8+I6J0xJPVIkQhyYQ1D3wlyhuJFAjdcsst/f/92GOPYdeuXf1f63Q6bNy4MatjPlYg3ZYgrLAmojSkW7yoU0mKtyVqcwVRoh86GS0IAipMGrQ4A0xYU9HIesK6vb0dVVVV/V/b7XbU19cPeE1DQwMA4Mtf/jJisRi+//3vY8mSJaO+tyQJsNkMio01EI6hzKYf9nRYURRhMmmH/X6DVgVRq1Z0TMmQJDFn1x5NPo8NyO/x5fPYgPwfXyHwh5I/8KyPVsWejUSUulQX63ngK1H+SKRA6GibNm3Ctddem80hjigmyym1Jjoa50NElKpINIZoTE56t9nRdBloCdLsDKDEMHwyusyoQYsriFl2s6LXJcqVrCesExGNRnHo0CE89thjaGtrw2WXXYbnnnsOFotllO+T4XAoczJqNCYjFI0hHAgjEhz6dFeTSQuPJzjse2hEoL3Hg/GG3PyYbTaDYj8PpeXz2ID8Hl8+jw1IfXwVFfyHtU86PdM0EntYE1HqApFYSgtm7J9PlD8SKRDq09zcjKamJixevLj/sWAwiDVr1kClUuGqq67COeecM+L1lC4a8gQj0KhEWMy6YV8zWuGQUaeGoJYKpoii2Ao++HnyW7F9HqXFd7tKEJLcbXY0rVr5eVGrK4D544fPiZUa1GhxBhS9JlEuZT2Tarfb0dbW1v91e3s77Hb7oNfMnz8farUaNTU1mDRpEhoaGjBv3rysjbOvwiitIKUS4WcvWSJKkj8UhVZKLWGtZtKIiNKQaoW1hrGHqCBt2rQJy5cvhyR9vqP0lVdegd1uR2NjIy6//HLMmDEDEydOHPY9lCwaAoBOTxA6lThiYdBohUMSgE6HP6+LPI6W7wUpyeLnyW/Hfh4WDg3kDUWgTaMlERBvCeIPRyHLclo5paO1u4MoHaHCusSgQZPDr8i1iPJBer+FKZg7dy4aGhrQ2NiIUCiETZs2oa6ubsBrzjnnHGzfvh0A0NPTg4aGhv4tbdniC0WHbQWSKK1Kgjc0dHU2EdFw/OHUW4JkYvsZEY0dgXAspQUzrUpEIMJFeqJ8kEiBUJ8XXngBK1euHPT9AFBTU4OTTz55QH/rbPCGotClcdgZAGgkgT2siSglvnAU+jRzQZIoQCUKCCo4N+ryhmAbpoc1AJQb1WhmhTUVkawnrFUqFdavX48rrrgCK1aswPnnn4/p06fj3nvvxebNmwEAZ555Jmw2G1asWIHLL78cN910E0pKSrI6Tl8ovSb7wJHeaZwoEVGSfOHUK6y1KhFexh0iSlGqC2ZaFQ98JcoXiRQIAcD+/fvhcrmwcOHC/secTidCoRCAeOHQBx98MGzv60yJFw6ldx+mYQ9rIkqRNxiFPs0YBAA6taTYfVkoEoMvFIVJO3yThDKjBq1MWFMRyUlz5aVLl2Lp0qUDHrv++uv7/1sQBNx88824+eabsz20ft6wAhXWEidKRJQ8fzgKdRo9rN2BkMIjIqKxIhCJQZPCIUNaSUQwElN06ysRpeboAqFoNIpLLrmkv0Do+OOPx9lnnw0gXl29YsWKAb+z+/fvx89+9jMIggBZlnHllVfmJmGdZoW1TiXCO8w5RJnQ2OtHtVUHlcj4R1TovKEI9GnGIADQqyX4QlGUGdMfU7cvBKtOBXGEOVaZUYM2d5BzMSoaeXnoYj7wh1I/9KyPhhXWRJQCXxo9rLVqLpQRUeoCKVZYi6IA6cjW13QX/IkofaMVCAHAtddeO+j7Fi1ahOeeey6jYxuNLxxNu3+sViVl7T7s42Ynrn6qHl89YQK+f+bkrFyTiDLHG0o/BgHKtmrscAdRMkL/aiCeIFdLAnr9YZQaNIpclyiXst4SpFB4FWgJouPWfCJKgS+NBTMtDz4jojQEwjFoUlww6ztgiIgoHUq1ZszGfVhMlvGT5z7FVxaNx7M72vBpuzvj1ySizPIG09/lAQA6tQhvWJmdHl3eEKwj9K/uU2HUotU1/IG0w/nd1gP46qPv48VP21MZHlFGMGE9DF84knaFdbyHNQ9dJKLkpJWwlrizg4hS5w9HU05YxxfMePAiEaXHF4qkvNOsjy5LO872dnihkgScNrkUiyeXYMueroxfk4gyyxOKpL1oBii706PTE4JFN3qDhFKjOuk+1u8dduCFXR1YNrMCd27Zjxb2waY8wYT1MPxpbMnvo1NL8ASZOCKi5PjT2ArLhBERpSMQiUGdQg9rgDs8iEgZ3lBqrYmOlq0K67caejC70gQAmFVpwjuHejN+TSLKrHSKh46mZEuQTk8QFu3oFdYlejVaXcklnO997QAunleFeeMsWDK1DPe/2ZDiKImUxYT1MLyhKLRpbgPRsoc1EaXAF06jh7VKRIAJI6K8dfPNN+PUU0/FBRdcMOTzsizjtttuw7Jly7Bq1Srs3Lkzq+NLv8Ka8YeI0qNEskinkrKy0/XNgz2YXWUGAEwpM+Bgjw/uAHfYEg1n69atWL58OZYtW4b7779/2Ne9+OKLmDlzJnbs2JHF0cV5ghHoFOhhreTCWZsrCJshgQprgwbNSVRINzv9aHEFsHC8FQBw5pRSvLqvm/M5ygtMWA/DH45Co0rvZFWtioefEVHyfKEYe1gTFak1a9bggQceGPb5rVu3oqGhAS+99BL+67/+Cz//+c+zNzikfugiwPhDRMrwKFA4pFdnvsI6HI1hZ5sHMyqMAAC1JGJauRHvNzoyel2iQhWNRnHrrbfigQcewKZNm/D8889j3759g17n8Xjw6KOPYv78+TkYJeBRqIe1kgWMnd4QrLrRK6zLjOqkEtav7O3C/HEWiGI892XVqzGt3IhX9rK9EeUeE9bD8ATTnyjp1KywJqLk+cPpHLoosSUIUR476aSTYLVah31+8+bNuOiiiyAIAhYsWACXy4WOjo6sjc8XYg9rIsotbyj96kadWrnescNp6PGh3KSBTv35PWNtiR67ePAi0ZDq6+tRW1uLmpoaaDQarFy5Eps3bx70unvvvRdXXnkltFptDkZ5pIe1QhXWnqBChy56grAlcOhimVGTVA/rzXu6MH+8ZcBjJ0604p+fZm/uSTSc0fcUjFHeUAQmbXo/Hq1KYoU1ESUtkFbCWkQgEoUsyxCE9HaJEFH2tbe3o6qqqv/rqqoqtLe3o7KycsTvkyQBNpsh7euHAVSaNDCZhr5JFEVx2OcMOjUEtaTIOHJNksSi+ByJGCufdax8zmLgC0VRYdSk9R5K9o4dzr4uL8ZbdQMeG2fV4dN2T0avS1Sojp3j2O121NfXD3jNzp070dbWhi984Qt48MEHE3pfpeZAfYIxGSVm3bDzHWDk+VAfq0mLEKDI2Hr9YYwrN46ao5qokdDhCcJq1Y94LyhJItR6DfZ1enHDshkDdtedPLUcT3zQAp1RO2BBLl8U8r/nHHtymLAehi8URVkBTJSIKDk333wzXn31VZSVleH5558f9Lwsy7j99tvx2muvQafT4Ze//CXmzJmT1TGmU2EtiQJEQUAoKkObZlsjIioc0agMh8OX9vs4PCFUGtTweIJDPm8yaYd9TpRldDv8iowj12w2Q1F8jkSMlc+az5+zosKc6yHkFW8omnaSRKsSEYrGEI3JkMTMzIf2dXpRZR6YsBpv1eH5ne0ZuR5RsYvFYvjlL3+JX/ziF0l9n1JzoD4ObwiIRoed7wAjz4f6SDEZ3Z5g2mMLhKMIhGOQQxF4EiiIFAUBh1pdsBmGr8i22Qx4Y3c7JpboEQqEETrm+RqbHps/acXpk0vTGnsm5PO/56Ph2Ic23DyILUGG4Q1FoUvzsA+9WoI3C4d9EFHi8r1/LAD4w7G0WhLp1CL8XCwjKkh2ux1tbW39X7e1tcFut2ft+vH5T2rxRyuJ8EfYEoSI0qPEoYuCIECf4bYgezu9GG8bWGFdYdKi2xtmP3+iIRw7x2lvbx8wx/F6vdizZw++/vWvo66uDh999BGuvvrqrB+86EtjLnQ0nVqEO5h+LOj2hWDTqxPePVth0qDZNXpbkI+anZhSNnTF7Gy7Cdv2dyc1TiKlMWE9DE8w/ZV9nUpEMBJf2Sei/JDv/WNlWUYgkt6Nmk4lwR/hjRJRIaqrq8OGDRsgyzI++ugjmM3mUduBKCmdHR5qSeBiGRGlzadA4RAQLx7yZLB4aH+XF+MsAxPWkiig2qLFge7CrKAjyqS5c+eioaEBjY2NCIVC2LRpE+rq6vqfN5vNeOedd7BlyxZs2bIFCxYswB/+8AfMnTs3q+OM7/JQKAYp0MO6yxOCTZ94c4RyowbNDv+or/ugyYkp5cYhn5tTZcbbDb0JX5MoE9gSZBjeUAT6NBPWoigcOYAomnY/bCLKjlT7xyolFJUhCkJa21eVPJGaiJR1ww03YPv27ejt7cWSJUtw7bXXIhKJ38x85StfwdKlS/Haa69h2bJl0Ov1uOOOO7I6Pl8aN2kalcizO4gobT4FWoIAgF4twqtAdeNQPMEInIEIyk2DW0iOs+qwv8uLOVVs9UJ0NJVKhfXr1+OKK65ANBrFJZdcgunTp+Pee+/F8ccfj7PPPjvXQwQQj0Hp5oIA5RLW3b4wLLrRD1zsU2bUoMkxcoV1NCZjV5sbX100fsjnx9t0cAcjaHUFUH3MwhxRtjCLOgxvKAq9AqtqhiNBiglrouKm1GEfPd4Q9BoprUM+DFoVJK26YA90AAr7QIpkjIXPORY+YzJ+85vfjPi8IAj42c9+lqXRDJZOhbVWJbHCmojS5glFFLkP02WwPWOjw48qsxbiEFv07WYt9nd5M3JdokK3dOlSLF26dMBj119//ZCvfeyxx7IxpAFkWVakLRFwZNFMgXlRlycEsy65CuvDvSPv8tjX4YFNr4ZxmDyVKAiYbTfhnYZeXDSvOqnxEimFWdRhKFFhDfStqvHmjahQpNo/VqnDPtpdAWgkMa1DPlQC0NnrhcOc3sGxuVTIB1IkYyx8zkL5jDz0LC6esE6xh7VKgMPHszuIKHV9ySJl7sNEeDK0iNbkCAxZXQ3E+8fubvdk5LpElFnBSAyiCKglZVqCKLFo1u0NwpJEAWSFSYNPWt0jvqa+2YnakpELSmZWmvBWQw8T1pQz7GE9BEUnShpltoEQUXbkun+sEiv6bAlCRKnyh1PvHatVSWwJQkRpCR45uFWJZJFOJcGbofuwJocfZcahE9aVJu2o2/GJKD95FcoDAX0J6/TnRZ2eECzJVFibtGh2jhyDPm5yoKZEP+JrZtvNeO+wk2eyUc6wwnoIgUgMKlFMq4dsn/jKPhPWRPmiEPrHGtLcBqtTaHJERGNLJCYjEpOhllKb//Sd20FElCpPKAqDRplkkVah7fhDOdzrR/kwCesKkwYtrgBkWYYwRMsQIspfSias1ZKAWAwIRWLQpFGQ1OkNjZpcPlqpXg1nIIxgJDZsIVR9kxMXHDdyUVaJQQ2rXoVP2904vtqS1JiJlMCE9RA8wQgMGmWKz3UqtgQhyif53j/WG4qkfdCQTpW5GzQiKl7+UBQ6lZRygkUjMWFNROnxBCMwKJQs0kmZW8BvcvixdFrZ0NdVS9CpRHR5Q6gY4cwRIso/vlAk5cOnjyUIAgwaCZ5QBKWq1Fs1dnuTq7AWRQHlRg1anAFMLhvc9iMUieFAlxc1ttGT4LPtZrx5sIcJa8oJtgQZgieo3KqaTi0q0hKkoceHj5qcCoyIiPKZJxhPGKVDqxIztgWWiIqXLxxN6yYtHyqs3YEI9nXysDOiQuUNRqBXqMJap87cfKjJEUCFcfhkdKWZbUGICpFbwUUzAPGEdZoFjD3eEKxJJKyBeAw61DP0GTJ7Oj0YZ9UnVPV9XJUJrx/oSeraREphwnoISh24CCizNb/XF8L3n96BH27ciUe3NyoyLiLKT95QJO0e1jqVlLFDhoioePlCqfevBnLfP//TdjcuenA7vvPkxzlPnBNRapQuHHJlIGEdjMTg8IdRYlAP+5oKowaNDr/i1yaizHIrGIOAIy1i04hDMVmGMxCBOYlDFwHAbtZiX9fQC/g7W92YUmFM6H2mlhtxuNePTk8wqesTKYEJ6yF4ggomrFXpV1j/z7YGzK0240dnT8PD2xt5E0ZUxLxpJowA5XZ2ENHYEq+wTn3+o1eLOT10ceOONpw1rRzTyo3YtLM9Z+MgotR5QhHoFdqOH2/NqPx8qMUZQLlRM+J5R2VGDZqYsCYqOO5AWOGEdXpxyOEPw6CRoEryINpxFt2wCesdrS5MLh/cKmQoaknE3HEWvLK3O6nrEymBCesheILpbYk9ml4twRVIPUBFYjJe3deFL0wrR7lRg6nlBry8u1ORsRFR/vEGo9CmGX/SnRgR0djkS3OHh04t5azCWpZlvLavGwsmWPCFaWX424fNORkHEaXHE4yk3RqtT7wliPIxqcUVQLlp5H605SYNDvcyYU1UaOIV1sqlyfTq9Ha+dqfQDgQAxlm1ONA1dEuQna1uTC1PrMIaABaOt+DlzzqSHgNRunKSsN66dSuWL1+OZcuW4f777x/2dS+++CJmzpyJHTt2ZHF0Rw49U2iipFdLcKeROPq42YkSg7p/UnTa5FJs2NGqyNiIKP+4FbhRy9QNGhEVN19o+NPkE6FViQhGYojJsoKjSszuDg9UkoAqsxbTyo1odwfh9IezPg6ifDHa/dY//vEPLF68GKtXr8bq1avx1FNP9T/3zDPP4Nxzz8W5556LZ555JpvDPtISRLnCIW9I+QX8NlcAJfrh24EAQLlRg2Yne1gTFRqXwhXW6e647/KGYB0l3gylyqxDs9OPSDQ24HFXIIwuXwjjEzhwsc9xVWbs6/Ki1cWYRtmV9YR1NBrFrbfeigceeACbNm3C888/j3379g16ncfjwaOPPor58+dne4iKTpTS3Zq/ZU8X5o37/ETWWZUm7O305rRHJBFlTrwlkRI9rFlhTUTJ8YejaSWsRUHIWR/rdw85MMduhiAIEEUBk0oN2NXuzvo4iPJBovdbK1aswMaNG7Fx40asW7cOAOBwOPD73/8eTz75JJ566in8/ve/h9OZvYPfPcH0z/Loo1OLGTnTo8UZGLF/NRBvCdLuYs9XokLjCkRgUOjgVyC++yydfFC3N5R0/2oA0KhElBg0aDzm8Nf6FhcmlxogjtDS6FhqScSptaX42wctSY+DKB1ZT1jX19ejtrYWNTU10Gg0WLlyJTZv3jzodffeey+uvPJKaLXDn76cKUpOlOJb81OfKH3Q5MQsu6n/a41KxKRSAz5szt7EkYiyR4lDX/VqMe3DXolo7Em3JQjQV9GY/fizu8ONCSW6/q8nluixs5UJaxqbEr3fGsrrr7+O008/HTabDVarFaeffjq2bduW4RF/zqXoWUISvBnqYV1qGLkliEWngjcUQYBnDxEVlExUWKez87XLE4I5hZYgADDeqsPeTs+Ax9497MC0JNqB9FkyrRTPfdLGtpOUVan9zU9De3s7qqqq+r+22+2or68f8JqdO3eira0NX/jCF/Dggw8m/N6SJMBmS6x5/EiCMmA1aWEyjZwsF0Vx1NeUBSPwR2IpjcsfiqLJ4ces8TZojrqBnDPeivo2D1YunDDi90uSqMjPIxPyeWxAfo8vn8cG5P/48p0SPfR1aoktQYgoab5wDFop3ZZEfVvws1twsLfDi5MmlvR/XVtqQH2LK6tjIMoXidxvAcBLL72Ed999F5MnT8bNN9+M6urqIb+3vX3kQ0yVugcDgFAMqDArcx9WIQrwhqKKz0s7fGEsnlY++vXNOrhlAVUJXL/Y5s/8PPmt2D6PktyBCAwVylZYu9JI8nZ6QrCkUGENAJPLDHjvsAPnzqrsf2z7IQcumls1wncNrcKkxbxxFvx+20H85Jzpw75OlmW0u4P4rMOLFlcA4UgMlWYtTppoQ5lx5IU+omNlPWE9mlgshl/+8pf4xS9+kfT3RqMyHI6hG8sno8cdXzX3eEbexmUyaUd9jRyOwh0IpzSuj5qcGGfVIRQII3TU45NLdNi4ow1XnzpxxO+32QyK/DwyIZ/HBuT3+PJ5bEDq46uoMGdgNIXHG4pCl+aqvk4lwhfm6jcRJccXikCjSnyL6FD0OeihH4rE0OIKoNryefJoUqkeT37YDFmWIQjpfSaiYnTWWWfhggsugEajwV//+lf8+Mc/xqOPPprSeyl1DwYA3Z4AKo1qRe7DEI3BFYigt9eraBxo7vXBIGDU65fqVdjT5ECFZvRChHyf3yeLnye/Hft5eB/2OXcwCoOCFdZGjYRub2j0Fw6jyxvE5LLkK6IBYGalEY++29T/tdMfRrPTj0mlifevPtrF86tw20t7cXy1GRfM+Tzp7QqE8XZDL7bt78a7jU5EYjHUlhhQZlRDLYp4s6EHv/zXXlwwx47vnzk57XtdGjuynrC22+1oa2vr/7q9vR12u73/a6/Xiz179uDrX/86AKCzsxNXX301/vCHP2Du3LlZGaM7GMU4a+4P+/ikzY3aksHBpLZEj0M9fgTC6Se2iCi/eEMR6BXYks8+90SULG8oCm26h76qMnPI2UgOdvtQadZCLX0eO0v0aoSjMhz+MEpG2bpPVGxGu98CgJKSz3ckrFu3DnfddVf/927fvn3A95588skZHvHnlDxLSC2JEAUgGIkpds8Ujsbg9EcSOgSt1MCDF4kKjTuobA9ro1bCge50KqzDmD8+tbTdBKseDl8YXZ4gyk1avN3Qi+kVJqik1GKsUaPCtWdOxh9eb8CG+jaMs+rQ0ONDQ48PMypMOK7KjOuXTEaFSTNokdATjOCvH7Tgu0/W4/dr58KUYtU4jS1Z72E9d+5cNDQ0oLGxEaFQCJs2bUJdXV3/82azGe+88w62bNmCLVu2YMGCBVlNVgN9h54pE6T6EkcxWU76e3e0OFFbOnirjloSMd6mw2cdniG+i4gKmRIV1mpJQCQmI3zMqdBERCPxBqPQpblgpstBD/29XR5MsA5c4BcEAdUWLQ73+rM6FqJ8MNr9FgB0dHT0//eWLVswdepUAMAZZ5yB119/HU6nE06nE6+//jrOOOOMrI3dE4woXN2ogiug3CJauzuIEr0aUgIHlpUa1Gh2MgYRFRIlc0FAvMLaGQin/P09vhCsKfawFkUB0ytNeOeQAwDw9MctOKXWlvJYAGCcVYefLp+B06eUosKkwXmzK/GrVcfh6jMmYem0MlSatUPuaDFpVfj24hqUmzS4ccNORGLJ58do7Mn6soZKpcL69etxxRVXIBqN4pJLLsH06dNx77334vjjj8fZZ5+d7SENomSQkkThyIEf0aSb5e/p8OL0KWVDPldbosfONjfmj7cqMUwiyhO+UPo9rAVBgEETP/jMps/6uiQRFShfOIJS4+hVgyOJV1hnN2G9r9OLKsvgXrIVJi0O9fg5V6IxJ5H7rcceewxbtmyBJEmwWq397RhtNhu+973vYe3atQCAa665BjabLWtj9wQjiu4gNWri/WMrzcr01W9zBRPuw1pm1GBPp1eR6xJRdniCERgVrLA2aFRwp7FoFk9Ypz43O2NyKe5/6xAmlerR0OPHFYtHbiubCLUkYt44S9LfJwgCvrRgHP7n9Qbc93oDvr9kctpjoeKWkzr8pUuXYunSpQMeu/7664d87WOPPZaNIQ3gCUYV3wbiDISTSlgHwlF0eIKwm4aeEE0s0eOTVh4mRFRMZFmGPxyFLs0t+cDn7YhsCWxZJSIC4i3R0l2w16myX2F9qNeP2XbToMcrTBo09BRPz1GiZIx2v3XjjTfixhtvHPJ7165d25+wzjZvKP04dDSDRoIrjerGY7W6AigxJDa3KjNq0Lq/W7FrE1FmBSMxxOT4blWl9C2apcIfjiIak9MqZppTbca2A9347pP1+OLCcSm3A1GKKAr4xsk1uO3lPfjC9DIcX5184pvGDpbeDcGt8KqaSauCM8lVtUM9ftjN2mEDyqRSA3a2sSUIUTHxh2PQSGJC20xHo1dLWT/4jIgKmxI7zLQqEd4Ub8xS1dgbnzMdy27W4iAT1kQFQ5ZleINRGBTqYQ3EE9bpVDceq80VSLgYoNyoQZtrlIMhiShv9OWBlDyk1aCOxyA5hRaxnZ4QSgyD+0En6/KTa3Dbylk4bXJpWu+jFLNOhbXzx+E//+8zRNjCkkbAhPUxojE5fuiZ0n2L/Mmt7O/v9mKcVTfs81VmLXq8IUUnYESUW0rGHn0O+sgSUWGL945Nv4e1J4sJ65gso9UVRPkQW/SrzFocYsKaqGB4Q1GoVYKiFYB6taRoD+sWVxClCVZYm7QSwtFYVmMiEaXOE1D2wEUA0KhEiIKAQCT5xGynJwibPv2mCHq1lHeHHJ5YY4VRo8KTH7XkeiiUx5iwPkZf3zQlKhz7GDXJT5T2dXqHrBbqI4oCakr02NPJKmuiYuENRqFXqKoo3keWN0hElDhPMAp9mjdqOrUETxZ3d3R6QjBopCF73laYtGhzB3mwD1GBcAbCMGuUTaoY1BLcCiaM21wBlBoS62EtCALKTRq0OAOKXZ+IMkfpnfZ9TNrkCxgBoNubXv/qfCYIAtYtqMaDbx9Gry+U6+FQnmLC+hiuQARmhVefDOrkA9TeTs+IFdYAMMGqw+52JqyJioU3pNxBQzq1yJYgRJQUbygCQ5oxSK8W4cniYlmTw49K89DJI41KhFWnRpuLySKiQuAKRGDUKpss0qlFuFJIFA2nzR1MuIc1cKSPNWMQUUFwKdAabShGrSqlnR6dnhAsSZyDVmiqLDqcVGPDfW8cyvVQKE8xYX0MVyCs+KqaPoUK6wPdPlRbRk5Y15TosbPNnc7QiCiPeEJRxRLWWlV2k0ZEVNgiMRnBSAxaVZotQVRSVntYH+71o8I4/I60ciOrG4kKhdMfhknhCmujJvmzhIYjyzI63KGEW4IAQKlejWbGIKKC4AqEFW8JAqS24x4AOjzBok5YA8D5x1XiX3s6cbCbLdxoMCasj+EMKL8NxKiR0JvEyn4gHIXDHx6yH+PRJpbo8VkHK6yJioVbwb5phhQnRkQ0NsX7V6d/0JBOLcGTxf75jb3+EedLZUYNk0VEBcKVgf6xBo2kWMLa6Y9AIwlJFReUMgYRFQyHP6L4ohnQl7BOfqdHhzsIa4KHvBYqk1aFc2dW4J5X9+d6KJSHmLA+RiYmSkaNCo4kEtaHe/2oNGtH7aNdbdGh3R2EP8xt/0TFwBUIp33gWZ9UWhER0djlCSoz/9GpRPiymLA+3OtHuWn4hHWpgdWNRIXC4c9MwjqVRNFQ2twBlI1SUHSscqMGzQ7GIKJC0OsLZaTC2qBObeGs01O8PayPtnRaGfZ3efHOod5cD4XyDBPWx4gnjBROWCfZZL+hxzfigYt9JFHAeKsOe1hlTVQUnAoumBk1Kjj9rLAmosS4lUpYq0V4s5iwbnEFRqywLjdp0Njrz9p4iCh1rkBY8f6xSu44a3MFUZpkwjq+y4MxiKgQ9PrCMCl8nhmQWotYAOjyhmDTF3dLEABQSyIunleNX2/Zx4OyaQAmrI/hDCjfaD/ZnkUNPT5UmkZPWAPxtiC7ePAiUVFw+pVbMDNoJDgUqigiouLX1xIkXXq1BE8We1i3uYIjVjxWsIc1UcHo9St/lpBBLcGtUExqdQeTTh5VmDRodQUhy0zCEOW7eMI6Az2sU9j5Kssyur2hom8J0mfBeAt0agl//6gl10OhPMKE9TEcfuUb7ccP+0g8QB3sTqzCGgBqbHp80uJKdWhElEec/giMCvVNS/VwDyIam9zBKPQKzH8Magn+cBTRLFTIeIIRhKMxmEe4uSwzatDiYsKaqBA4/WEYFU4WGTXKLaK1OAMo0SdXYa1XS9CqRHR7Q4qMgYgyx+HPTIW1QSMl1SIWQP9uNV2ah2EXCkEQsG7BONz/1iF0MV7SEWPjb38SMjVRSq7C2o8qS2IJ69pSPXa1u1MdGhHlEYeCJ1OnergHEY1NnkAEOlX68UcUhaxVWbc4Ayg3aUY8KNKiUyEYicEb4gIeUb5z+sOKLdz3MWokuANRxBSocG52+lFmTL7a0W7WopF9rInyXqYS1iatCr2+5O7LujwhlBjUaR+GXUjGWXU4dVIJ7t6yL9dDoTzBhPUxnAHlKhz7JFNtFJNlNDn8sCfYEqTaokOXJ5TV7bdElBmuQESxrbAGjQQ3K6yJKEHuYAQ6hQ59NWlVKR0ulKxWVwBlhpGrHQVBYFsQogLhVHAe1EclidCqRUXmRG2uIEpHiTlDKTdq0ORgH2uifOcMhGHKwKGLZq0KPf7kqobb3UGUjJF2IEdbeZwdn7S68dq+7lwPhfIAE9bHcAWU750mikLCJ1R3uIPQqaWEt+VKooCJpXrsbGOVNVGhUzL+GDUqxXo2EpGytm7diuXLl2PZsmW4//77Bz3/j3/8A4sXL8bq1auxevVqPPXUUxkfk1vBw86ytcOj2RlAqWH0m7kyExPWRIVAyYX7o1l1yVc3DqV9lJ75wykzatDIhDURgNHnQA899BBWrFiBVatW4fLLL0dzc3NWxhWTZbgD0YxUWJtTiEFt7gBKEpjjFBuNSsRlJ47HHS/vQa+PrUHGOiasj5GpiZJNp0a3d/QgdajHj+oE24H0mVJmwEdNzlSHRkR5wh2IwKDQDg+1JEAGEAhHFXk/IlJGNBrFrbfeigceeACbNm3C888/j337Bm99XLFiBTZu3IiNGzdi3bp1GR+XKxhVNGGdjQrrZkcgoeRRqV6NFlcw4+MhovS4M7DTFYhXN/Ym2T/2WL5QFIHIyD3zh1Nh0uBwLxPWRInMgWbPno2///3veO6557B8+XLcddddWRmb58hOM0lUvgWHJZWEtSsIq27sJawBYHqFCSdNtOHn//yMB9aOcUxYH8PlVy5hdDSLXp3QYRuHen2oTLAdSJ+pZUZ8wIQ1UcLysboRiG/JV2rBTBAEmDQqHrxIlGfq6+tRW1uLmpoaaDQarFy5Eps3b871sOAKhKFXqCVIorvK0tXk9CeUsC7jdnyivBeTZXhCEcXO8jhavH9sepV6ra4Ayo2p9ZOtMGnQyIQ1UUJzoMWLF0Ov1wMAFixYgLa2tqyMrdcXhjkD1dVA/ODEmCzDn0QhUZsrOCYrrPtceHwV2j1BPPpuU66HQjmUmd/IAhWJxuANRTPSt8iiUyV02unBHh8qzcltNZtSbsCf3jmMSDQGlcQ1CKKR9K3sP/TQQ7Db7Vi7di3q6uowbdq0Aa9bsWIF1q9fn7Vx9VVCaxQ8CdqoleAKRlBpTm4RjIgyp729HVVVVf1f2+121NfXD3rdSy+9hHfffReTJ0/GzTffjOrq6hHfV5IE2GyGlMcVjMkotehgGmXRXBTFUV9jM2oRQnrjSUS7J4SVFaZRxzO+zICPm5xJjUeSxIyPP1+Mlc86Vj5noXIFItCrpYxUN5q0EnrSbAnSlmI7EACoNGnR5AhAluWkEt6RaAySKIypQ9eouCU6B+rz9NNPY8mSJaO+b7pzIAA44ArCalCPOqfok8h86Gg2gwYRlZTwOLv8Ycweb0nqGslIdvy5cG3dNNz6/KdYNKUMZ04r73+8kP8959iTw4T1UXr9YZh1EsQMTJTMWlVCFdYHu304dVJJUu9t1KhQbtTgsw4P5lRbUh0i0Zhw9Mo+gP6V/WMT1tnmCkRgVHhV36hRZaXKkYiUddZZZ+GCCy6ARqPBX//6V/z4xz/Go48+OuL3RKMyHA5fytfsdocgRGPweEZunWEyaUd9jQoy2nt9aY1nNLIso7nXD4OAUcdjlEQc7k5uPDabIaPjzydj5bPm8+esqDDnegg51+0NwZahA8aMmvRbgrS6Uu8na9KqoJYEdHlDqEgwQfTS7g7c9tIeqCUR65fPwNKjkjVEY8HGjRvxySef4PHHHx/1tenOgQCgscMDvUocdU7RJ5H50IDXayQ0tLpgTjDV1Nzrg04sT+oayUh2/LmgA/CNk2tww5Mf4f4vLcDksniyNJ//PR8Nxz604eZBTFgfpccbzlifIItOhY4EAsLhXj8umls16uuONavSjDcO9jBhTTSKfK1ubA9EYdEltqqf6Iq41aBGVEx8JV8J2xt68MGhXqw7sSblSiSgsFefkzEWPudY+IzJsNvtA7a3tre3w263D3hNScnnC9fr1q3LSv9Ghz+k2EFDBo0KDn9m2xE5/GGoRCGhQ6rLjRq0upKvbiSi7On2hmDVZebWNNHCoZE0OwMo0ac+rxln1eFAly+hhPWOFhfu2rIfN541FeGojFtf3IP/Nmgwdxzv86iwJTIHAoA333wT9913Hx5//HFoNKn/3iXD4Q9lpId+H4tOhZ4EWxPJsoxOTwilGVrEKyQzKk1YPbcK1/19Bx766gKU53lVOCmLCeujdPlCsGRoomTVqdDQM3LvMn84Cqc/jDJD8kH5+HFmbNrZjqtOm5TiCImoTy6qG5s63Qmv6ie6Iq4WBLR2e7O2ilvf4sINz3yCKeVGvLCjFfd/aT50KR7iVsirz8kYC5+zUD5jtioc586di4aGBjQ2NsJut2PTpk349a9/PeA1HR0dqKysBABs2bIFU6dOzfi4nP6IYr0bjRoJzc6AIu81nBZnIOFKRYNGgigIcAYiGavgJKL0dPtCMGcqYa1T4WC3N633aOz1Y3qFMeXvrzJrcaDHh1NG2Ukbjcn4xb/2Ys28Kkywxfv4XjK/Gr/811489rVFELnoRgUskTnQrl27sH79ejzwwAMoKyvL2th6fPHd9pli1qrQnWBrIncwAkFAQovyY8Gpk0rh8IdxzdM78L9fng9brgdEWcOGx0fp8SpXXXQsi270QxcPdvtQbdGl1JJkWrkRLc4AOvN8WwdRriVa3di3mr9u3Trs3Lkz4+NyBZQ/aMigkeDMUkuQaEzGbS/uwbqF43DVqROhVYn4+8etWbk2USFRqVRYv349rrjiCqxYsQLnn38+pk+fjnvvvbf/4KHHHnsMK1euxIUXXohHH30Uv/jFLzI6pkgsftiZUmd4GLUSnGluvx9NszOQ1C6OCpMGLRlOohNR6nq84Yzdh5kV6GHd7Ayg3JR6pafdrMX+ztGT5tv2dyMWk3HSRFv/YydPtCEmA//3aUfK1yfKB4nMge688074fD5cf/31WL16Nb773e9mZWzt7mDGdtsDgFGrQk+COz3a3an3zC9W582qxJQyA677+yfwBjO7i4/yR04qrLdu3Yrbb78dsVgM69atw1VXXTXg+YceeghPPfUUJElCaWkp7rjjDowfPz7j4+rJ4MmwVv3oW9EOdvtQZUlti4MkCjh+nAUv7e7EpSdOSOk9iMaCfK1udPjDiiesjZr0b9AS9e7hXsiQccIEKwRBwLkzK/D4e034ygnjWQ1EdIylS5di6dKlAx67/vrr+//7xhtvxI033pi18Tj98USRUmd4GDUSXIHM3ky0OAMoMSQ+ZyszatDsDOC4KvYKprEhnfut2bNnY8aMGQCA6upq3HfffRkfb6cnBEvGEtYq9KYxH5JlGS3OAMrTSCBVW3V4+bPOUV/35/eb8IXp5QPaFwmCgPOPq8RD7xzG+bMr2dqICtpoc6CHH344yyOK63AHMTuDcwSLVoWuBBPWra4gSlPYdV/MBEHAJfOr8Zf3m3HFY+/jntXHpbyTlwpH1iuso9Eobr31VjzwwAPYtGkTnn/+eezbt2/Aa2bPno2///3veO6557B8+fKs9G4EgK6MVlir0D1Kz6ID3V7Yzan35FkypRR/+7AZ0Zic8nsQFbt8rG4E4r0blV4ws+gSnxil69lP2nFKbUn/TdTkMgO0KglvNfRm5fpElLpev7IL9vEDXzObsG5yJFdhXWbUoNkxcms2omKR7v2WTqfDxo0bsXHjxqwkqwGgyxuERZ+5liCONHZ9uAIRQIgvxqWq2qJDQ48Psjz8fdq+Li8O9fqxaIJ10HPH2U2IycCbnFcRZUSnNwRbhtoSAfE4lGgv/fguMrYwO5YgCPjKovHQq0Xc8MxOBCOxXA+JMizrCev6+nrU1taipqYGGo0GK1eu7E8S9Vm8eDH0+njPrgULFgzYvp9J3d5gxnpYG9QSIjEZ/nB02Nfs6/Ki2qJL+RqTywwwqCW8srcr5fcgGguWLl2KF198Ef/6179w9dVXA4iv7J999tkA4tWNmzZtwrPPPovHHnssKxXWnR7le+hbdWp0eTKfsPaGInjzYM+A7auCIOCEGis27xm9moiIcsuh8A4zo0aCO8PbNZudflQYE1/kLzdqcKg3vYT1B00OfP3xD/Dfrx5gcQDltXy+3xpOtzcES4a24xs1KriDkZR/b5ucAVSaNGlVNpu1EtSiiFbX8O0bn/ukDYtrSyANsdtFEATUTS/Hn99rSnkMRDS8Lk8I1gyec2FJImHd1OtP6VyzsUAUBVxxxmRAAG7c8AlCTFoXtay3BGlvb0dVVVX/13a7HfX19cO+/umnn8aSJUsSem9JEmCzGVIemyMYxaISPUwJHuIjimLCrwWAUqMGIUlC9TBjPNwbwEULxyf1nsf6yikTcdeWfTh1ZiXKJBE2mwHBSAyHe3wwaCSMP3J4R65JR8aWr/J5fPk8NiD/x5evOr1BzKo0KfqeyZxGnY7thxyYXGYYtENl/ngLfvPKAcRkmW1BiPJYjy8Ek4ILZga1BG8onhwaKvGihGSrjypMGuxqc6d8vUM9Pty0cRcunleNNw724IkP1LjsxJqU348ok9K93woGg1izZg1UKhWuuuoqnHPOORkdLwB0e8OwZqhwSBIFmLXxOVGih7UerdnhR3kSC2RDEQQBU8oM+KTVhXHWwQVKkWgM/9zVgX/7wpRh3+OEGiueqW9FQ48Pk0o51yZSSkyW4fCHM1a8CAClBjXa3YmdN3bY4ceC8ZaMjaXQSaKAb5xcg4feacSPNu7EXavnQKPi8XzFKCc9rBO1ceNGfPLJJ3j88ccTen00KsPh8KV8vQ5nAGpZhifBgwtNJm3CrwWAEp0anzX2wjrE71IgHEWHOwCjKCT1nseaYNJg2cwKrPr961g8pQxtDj/2dHpRalDDF46iwqjFT86ZhvnjB281yyabzZDWn1Wm5fP48nlsQOrjq6gY231Fu73Kr+pnK2H9xoGeIZPtFSYtDBoJu9rcOL6aky6ifOXwhxU7cBGIV7+YtPEt+Jk4NCgSjaHLG0qqn2yFUYvmNA5dfHh7I5ZOK8PiSSWYWm7AnZv34fzZdh6KRAVvqPutV155BXa7HY2Njbj88ssxY8YMTJw4cdj3SLdoCAAcgTCqy4wwJTgXSrZwqMKshQ+pjbMnGIXdpkurqAgApleZsbvLhy8OMYZX93ai0qLD1FHmS2fNqsQ/PmnHrRfOSWssmVZsBSz8PMWt1xeGQS1BLWUu6WnTq9HjCye0mN/iDGDZjPKMjaUYSKKAb55SgwffPowfP7cLd154XEb//Cg3sp6wttvtA7actbe3w263D3rdm2++ifvuuw+PP/44NJrs3Az0ZnhVrdykQZMzgJOHeO5gjw92s1aRSqSzppdjbrUFLd4Qjqs04opTa6FViYjJMj5scuKHG3fiuiVTsOr4qtHfjIiyoicDlUVmrQruQBSRmAxVhqocZVnGmw09+N4Zk4Z8fm61Gdv2dzNhTZTHen3htHqzDsWmj7ckykRCt9UVRIleDVUSNyYlBjUc/jBCkVjSVThd3hBe3deFn583E0B8MW7+OCue39mGy08ePolHlCvp3m/1vbampgYnn3wydu3aNWLCOt2ioUhMhssfgRCOwhNNbHt3soVDFq0K+1ucqDUlH5M+a3HBqlelVVQEAOPNGrywq2PIn9WT7zbixAmWUa9xao0V//XSHnz7pAkZbV+QrnwvsElWsX+esV441OUJwWbI7O+TWhJh1qrQ6QmiaoQ2sDFZRpsruXM6xipJFPCto5PWq45Lam5I+S/rf5pz585FQ0MDGhsbEQqFsGnTJtTV1Q14za5du7B+/Xr84Q9/QFlZWVbGFY7G4AtFYdJkMGFt1OBwz9D9Ez9r92CCgu06yk0anDa1DLPtZmiP3JiJgoATamz4t6VT8PttB/HS7g7FrkdEqZNlGT0+5Xs3iqIAs06F3gxWWR/s8UGWgaphDoydaTdh+2FHxq5PROnLxKGv1gwe+nrY4UdlkodUS6KAMoMGLSlUWb+wsx0LxlsHtD06bUoJ/lHfNuIBakS5ks79ltPpRCgU/93t6enBBx98gGnTpmV0vA5fCGadBDFDi+tAPCa1p3iuR0NvvLAoXRNLDDjQ7Rt0UJjDH8abB7pxYo1t1Pew6tWYN86Cpz9uSXs8RBTX6Q3CloUFoDKjZtS2IF2eEAwaCTq1soUExUolifj24olwByL48XOfIpLgoicVhqwnrFUqFdavX48rrrgCK1aswPnnn4/p06fj3nvv7T8M5M4774TP58P111+P1atX47vf/W7Gx9XujgepTE6UKkwaHB5mZfbTdjcm2FI/cDEZVRYdvnfGJPxq8z583OzMyjWJaHjeUBSSKPQvLinJmsQBH6l477ADsypNwx5ENLXMiH2dXvhCwx84S0S51eMLK9rDGgAsOjU606xGHE5Trx8VKVQeVZg0aHImf/Div/Z0YtGEga3UJpcaIACob3El/X5EmZbO/db+/ftxySWX4MILL8Tll1+OK6+8MuMJ63Z3ECX6zFYT2gxqtLlSawt0uNc/7MJ8MrQqEZNKDdh+qHfA45t2tmPhBBv0Ce50WTazAk+83wx3ILOH2xKNFZ2eUMZ66B+t1KBG2wgHrwLxMzpS6bU/lvUlrV2BMG56bhfCTFoXjZz0sF66dCmWLl064LHrr7++/78ffvjhLI8IaM3CtosKkwb/+mzoidKn7R6cP7syo9c/2gSbHpefVIMfPbsLf/rKAkWru4koOV3eUMZW9a16Fbq94Yy8NxA/cHFahXHY5zVHbs4+bHbi9MmlGRsHEaWu1x9WvMLanMkK615/SnO2cqMGh3uTS1i3u4NodPgx85g+/YIgYMEEC7bs7cr5uSBEQ0n1fmvRokV47rnnMjm0QVpcwYzfh5Xo1WgYZqfrSBy+MCJRWbG2kQvHW/Di7g6cOTVe1R6JyXjigyZcvXRqwu9RbdHh+GozHtneiO8vmazIuIjGsg53UPF50FBsBjVaR1k4a3T4kzqjg+LUkogrFk/Eg+804ocbd+LOC+dkpBiMsot/gke0uoIozXDfogqjFi2uwKDto9GYjIPdPtRkOWk8p9qM5bMq8P2nd2S0ApOIRtbtVb4dSB+zVp2x3++YLOPDZidmVA6fsAaA6RXGQdVERJQ/en2ZaQnSMcq211Qd7vUl3RIEAOxmLQ50JdeD9LV9XZg3zjLkGSMLxlnxyt4utgUhSlOrM4CSDN+HlRhG34o/lEO9PlRbtcPuJEvWohorXj/Qg0A4vvPsX591wqZXY9oQh1eP5II5djyzoxW7292KjItoLGtyZqdndIl+9IT1/i6vIi2IxiLVkaR1OCrj+n/s4A7fIsCE9RGtzgBKMty3SK+RoFWJg5JHh3v9sOrVCW8DU9IXppVj4QQrvvO3j1OaxBFR+rq9mduGZtZJGatyPNDtg14todQw8gRvRqUJ77GPNVFekmU5I7s8bHo1OlPsFzuaRkcAFSkcnFZl0eFAd3IJ6237ezCnaujDqCbYdIjGZOzr8iY9FiL6XLMzkPHCoRJ9am2KDvX4YVdwe75Fp8Ysuwn3vXEIXZ4gfvPqflwwpyrp9ykxaLBuQTV+/OyuURNgRDSyJocf5SnMK5JVZhz9LI19nV5UW5iwTpUkCvjmyTXQqyV898mP4fBlbqcxZR4T1ke0OAMoNWa+0b7drMWhY7ajftTsxORSQ8avPZyVx1XihBorLn/8A2zb381KIaIs68rAgWd9LFo1OjLUR/aDRgemj9AOpM+kUj0OO/zwBNlrkSjfuAIRCBBgUHjRPFOHLoYiMXR4giltl622aHG4N/GEdTASw8ctTsyyD135KAgC5lZbsG1/d9JjIaLPtThTa/OTDJtehR5fGNFYcvc5B3t8iveT/cqi8XhxdwcuevBdLJlaltBcaignTSzBGVPLcPmfP8RD7xzGWw09ePNgD17a3YGXdnfg42bnoAMeiWiwFmdqC+HJKjWo0TpKD+uDPT6Ms2bnbLNiJYoCLj1hPCaVGvDNJz5EkyP5dlCUH3LSwzoftboCI/ZhVcoEqw672tw44ahToLcfSizpkymCIODcWZWYWGLAnZv34cG3NTh/diWOrzajyqKDVaeCSuLaBlGmtDoDsGZoh0epUY2PmzNzKNi7hx2YVj567FJLIqaUGfBxswunT2Efa6J80uYOoiwDN2kWvTojCetDvfHkkTqFeYlFp0I4KsPhDydUUf5RkxMTbHoYNcNPl+dUm/GvPV341uLapMdDRHGtWehhrZJEmLTxg6iTaSm0v8uLeeMsio7FpFXhP86dDhmAXp3eYmHd9HJMKzfijYM9eG1fN0QB0B15zy5vCJ2eIFbMtuObp9SgnAe5EQ0SCEfhCUYydp7Q0SpNWrS6AojEZKiGaDXmCUbgCkSy0p6k2AmCgNVzq2DVq/DtJz7C3avnYK7CsZwyjwnrI7IxUQKAKeVGfNDkxNdOqgEQ34r7fpMDN3xhSsavPZpZdhN+unwGdrS68HZDL57+uAXd3jC8oQhEQYBWJcKokWDVq1Fl1mJquRHHV1twQo0VpiwcUkBUrA47/JhbnZl/QCsS2HqWClmW8WGTE+fOrEjo9VPLjHj3cC8T1kR5ps2Vma34Vp0KDn8YMVmGqFDvVwA42O3DuBS3ygqCgGqrFg3dPiyYMPpBiW8c7MGsUfrKTq8w4oG3DqPXF0LJKO2RiGgwWZbjC2cZbgkCAJUmDRod/qQS1vu6vFhxXKXiY9Glmag+2sQSPSaWjB/yuR5fCFv2dOGLD7+Pr580AZeeOCGlBT+iYtXkDKDcpFV0rjIcjUpEiUGDpl4/JpUN3mEfn+PosjKWseIL08pRZtDg3575BDfVTcPy2crHc8ocZhkRP/SwyxvKeO80AJhWbsBTH7VAlmUIgoCGHj8kQcibk2AlUcCC8VYsOOrEe1mWEYnJCEVi8EdicAci6PGF0OIM4OHth/HTF7w4rsqM82dX4uwZFUxeEyWp2RFA3fTyjLx3mVGDVldQ+aRRjw8alYjSBGPXjEoTnvukTbHrE5Ey2lxBlGagqkgtidCpRTj84VH73CdjX5qHEVWZdTjYk1jC+q2GHnxp4dBJoD5qScTsKhNeP9CDVccn34eWaKxz+MPQSKKiCdzh2M1aHOrxDdjpOhKHPwxvMFrQ1Y6lBg3WLhiHJVPL8NRHLXjuk3b82xem4IwppYodJElUyJod/qy0A+kzzqrDgW7vkAnrA93sX50Jc8dZcP2Sybh36wHs7fTie2dO4qJAgWBmEUCnJwiTVpWV1eYSgwZaScShXj8mlRqwZW8njqsy5fWEQRAEqCUBakmEUQuUGzWYXGbACfEicQQjMXzS6sI/d3XgN68cwKmTS3D+bDsWTyqBVsUVfKKRyLKMVldq/VgToVNLMGjih70q2YPx/UZnUq2MJpfq0ejwwxUIw6JLPjkWk2U89WEL3jjYg+OrzfjqCRO4OEakgBZXIGPbYCuM2vgZIQonrGeOUvU8kmqLFns6PaO+rt0VQJcnhNoS/aivnVttwZa9XUxYE6WgxRnIWuFOhUmDgz2J9zLd0+FBTYm+KBIblWYtvnfGJOxodeM3r+zHH99swFcWTcBZ08sVP8OAqJA0OQIoz+IOqSqzFvu6vKibMXiX6s5WN8bb2L86E8bb9LipbhoeePsw9nR6cMcFs3kvWQCYTQSwv9uH8dbsrWTNqDRhy55ORGIynv6oFWdOKcvatTNBqxJxQo0NV55Wi1tXzESlSYv/fesQzv1/b+Hap3fgT28fwtb93djX5UW3N4RAOIpYkgeeEBWrLm8IenVmK4sqTFo0O5RtC/JOQy9mVCSeNFJJIqaVG/F+ozPpa8VkGetf2I0NO9owp8qMj5pd+N5T9TzEkUgBrc5AwjslklVp1uBwr7IH3Rzs8qHakvrN3MQSPXa2ukd93Rv7uzHLboI4RI/JY82tNuP9RgcC4WjK4yIaqw50+9LaNZGMKosOB7u9Cb/+sw4PJhTR4WeCIGDeOAtuOXc66qaX45n6Vpx/39v44YadeGFXO+dVNCYd7vVnbB40lGqLFvs6h45DH7e4MDWB84EoNWadCtctmQydSsTXHv8AB5L494Byg0sKAA50eVFlzt5k5NxZFbh7y364AhGUGtSoSaB6p1CYtCosnVaGpdPK4AlG8FmHB/u6fHjjYA+6vWG4gxH4w1GEozIExFuQqCUBOrUEi1aFCpMWE0t0mF5hwtxqC6ZVGCElcLNIVKiaHQHFT58/VplRg2ZnIKEt8ImIxuK9989Psqfj9Aojth/uxVlJtj/52wfN2NflxfVLpkCjEnFCjRV/eb8Z//l/n+HOC4/L6x0qRPmu1RXEiRNtGXnvMqMGh3t8ir1fIBxFhycIexpbd2tK9DjY7Rv2wKM+r+3pTLiS26hVYXKZAW829GasvRNRsdrX5UVVlrbA283apBbRdrd7irLaURQEzB9vxfzxVniCEdS3uPBMfSt+9a99OLnWhkvmV+Pk2pKiqCwnGs3eTi/OnpG9AsJxVh3+tadr0OOeYAQtzgBqbMWTG8pHkijgS4vG482DPbjyrx/jP86dkfS9KWUPE9aIB6lsTZSA+GTpnBnl2NPpxaUnTsjadbPNpFXhhBrbkH3ijEYN3O4gorKMSFRGIBKDNxSBwx9GhzuENw724LH3muAJRnDa5FKcN7sSp9SWjHhzSVSImpx+lGe4b1qZQY0mh3JVjp91eGDVqZNuIzDLbsYj2xuT+p4mhx//+9Zh3HT2VGiOtBgSBAFfXDgOv/rXPry0u5OHZxClod0TVLRlx9HsZm1S2+9H81mHBxNseqjSaOGmV0soNajR0O3DtGHaGkViMl7f14Wbz5mW8PsuHG/FCzvbmbAmStKeDi9OqbVl5VrlRg26vCEEI7GE2hbuanPj5CyNLVdMWhVOm1yK0yaXwhuK4P3DTty1ZT9iMRlfOWE8Vs6xw6hhyoCKU0yWsb/Li8tPzl5Oxm7WosMdhCcYGdCSYmerG5NKDSzWy5LTJpdinFWHX23ei51tblx9+iT+7PMQ//UBsL/Li7lzs9t3cKwnWARBgCgKECFALQF6jYQSgxoTbHqg+vPX9fhC+LjZhd9tPYDb/BFcMr8al8yvRkkW+0wRZVJTrz/jB76WmzQ4pOC2/O2HelPqIVtj08EXjuJQjw+1pYMPGhnKva8dQN2MskFV6GpJxKUnTsA9r+7HGVNLeTNFlAJ3IL7ryarPzO9PpUmL1w/0KPZ+O9vcCfWUHs3EUgN2tbuHTVjvaHGh3KRNaq5xYo0Nz+zYDYcvDFsWDvEmKhYHur24eF527sMkUUCFSYvDvT5MH6WtWbc3hB5/GOPSaEFUaIwaFZZMK8OZU0uxr8uHLXu78Ic3GnDuzEpcNK8Ksyrz+9wlomS1OAPQa6Ss9jJWSyKmVRjx3mEHvnDUIvdHzU7UlrK6OpsmlRrw47On48G3D2N3uxu3r5wNa4bOdaHUjPke1jFZxqFeP6qLqD9ZMSk1aHDW9HL8qG4avnt6LXa2uXHxg+/iV5v3otWlbE9eolzY1+XL+GnQ1RYd9ncp16Nr6/5uHFeVfMJaEATMrTLjjYOJJbB2tLiwo8WFs6cPPpQEACaXGTCj0oQH3zqc9FiIKL4Vf4I1cweKVZo0aHL4IcvKnFtR3+LCRAVu5mpsOuxocQ37/Lb93ZiXZAslvUbC3GoLXvi0fdjXRGIyXv6sE394/SA27mhlv1ga8xz+MPzhaMYX7o9WY9Nhd/voB69+3OzE9HJjQn3si40gCJheYcSVp9bilmXTEYrG8MMNO7HmwXdx72sH8EGTA5FoLNfDJErbng4PanLQ9mdGhRFvNfQOeGzzni4cX23J+ljGOsuRvtZWnRqXPfYBPm0f/ZwTyp4xn7BucQZg1qqgz+CBZ6SMCTY9LjtxAtYvnwF3IIKvPvo+1r+wG/sUTMQRZdun7W5MLEms2jhV4606NDkCihwI5vCFsb/Ll1KFNQDMqTbj1b2D+7YN5f+9fhDnza7sbwUylNVzq7BhRyuancoe7EY0Fuzt9GBcBg+dNmpVUIsiun1hRd5vV1t8u2y6ZlWa8HZD75CJdFmWsWVvFxbWJN/z/6zpZXjs3aYhY+3Bbh++8sh7ePidw+jwhPDCrg6se+g9vHfYkcpHICoK+7u8mGDTZ7Vqt7bEgI+aRz8A+v1GJ6aUZXZ+VghKDRpcMMeO/zx/Ji47aQJ6fCH84uW9OPt/3sK1T+/A4+81YU+HBzGFFiaJsml3hwfjc1C4ONtuxjuHPk9Y7+/ywhUIY2o5Y04uSKKANfOrsep4O77/9A48+WGzYsUWlJ4xn7Cub3Fx60WBserVuHheNf7z/JnQqERc/WQ9vvO3j/GvzzoRjHC1nwpHry8ETyiKigz3sFZLIsZZtYos7rxxsAez7CaoU+whO9tuxp5OL7o8wRFf987BHhzu9eO0yaUjvs6mV+Os6eX471cPpDQeorHssw4PxmX4Rq3KqsUBBWJPjy8Epz8Cuzn9BPs4qw7BaAyNjsE7tXa1uRGNyZhSPnS7kJFMKjVgYokeT3zQPODxNw724Mq/foQzp5ThhrOm4oI5dlx1Wi2+esJ4/OS5XXhpd0fKn4WokO1ocWFClpNFU8sNqB9hh0Wf95scw7YNGosEQcCkUgMuPL4KPzlnOm5dMRNzx5nxUbMTN27YifP+8DZ++sJubN7TCW+Iu0eoMOxqc8dbkmbZeJsOwUgMHx9ZPPvnpx1YVGPjQac5dkKNDTeeNRVPfdSCGzfsRK8vlOshjXljPmH93mEHpqVwU0K5Z9SocP7sSty2YiYWTrDgsfcacf598cnSlr1d3GpLee/Tdg8mlWRuO/7Ramx67GobfQvsaDbv6cTx1eaUv1+jErFgvAX/9+nwCRpZlnHXS59hxXH2hA6/OGdGBXa1uRNuNUJEcXs64y1BMmlSSWLJodG8c6gXM+0mReKlIAg4zm7C2w2DY8YLuzpw4kRryhWfF8+rwhPvN2Pjjla0uQL4/daD+Pk/P8NVp9Xi9CkDF+COqzLj2iWTcfeW/Xh+Z1tK1yMqZNsPOzAjxR1bqZpg06PNFRzxPqHNFUC7O6jIjo5iZdKqcEKNDV9ZNB4/P38mbjxrKmw6FR5/rwnn3/c2vv3oe3h+ZxvcAd6PUX4KR2PY0eLG9BwsTImCgBXHVeK/XzuAna0uPFPfijOnjFykQ9lhN2tx41lTYdBI+NIj7+PlzzpZbZ1DTFg3OjCDq+cFTSWJOGliCa5bMgW3LJsOq06Fx95txPl/fBtfe+wD/G7rAbx5sIer/ZR3drW5MUGBA8QSUVOix6629JJGDn8Y7zc6sXB88lvlj3ZybQme/aR92H/8t+7vQa8vhJMn2hJ6P41KxJcWjccvXt4DV0CZ1gNExS4my2jo9mV8K+zkMgM+bBp9+/1oXt/fg9l25RJbc6otePmzzgGP+UJRvLi7AydPLEn5fStMWnzvjEl48sMWfOWR97Gn04Obz5mGqcMUR0yw6XHd0sn4/baDePTdRt4U0ZgRicbwSasr68kiSRRQW2rAjtbh50Sv7OvGvHGWhBbNKa7cpMEXppfj+2dOxu0rZ+O4ajOe3dGGlfe/jWueqsc/Pm5Bu3vk3XVE2VTf4kKVRZvVAxePtri2BJIg4LtP1mPNvGpUKrCDjJShlkRcPK8aVy6eiP/3+kF876l67O1Mv/CLkpeb38480e4OwhOM8sDFIlJiiLcHOGt6OcLRGA50+7Cn04M/vN6Ahh4fppQbceqkEpw2uRRzqsyciFJOfdTswrxxqVcrJ2NSqQGPbG9M6z1e/ixeXa3XpNfzf3qFEeFYDG829OL0Y1p+BCMx/PqVffja4tqkDjqaU2XG3GoLfrppN3590RyoUmxZQjRWfNruQZlRk/bv82imlhvw+HtNiMlyytXRMVnG24d68ZOzpyk2rnnVZjz9UQs+6/D09+R/+qMWzKg0pX3TWFOixw1nTU349dUWHW48ayruf/MQ3j3swDVnTMLMSlNW+/oSZdvONjcqTblJFh1nN+GVPV04ddLQFY3/+qwTZ7DaMWUGjYQzppVjQZUZgXAUn7S68crebvxu20GUGTQ4caINC8ZbMafKjPE2HdsgUE683dCb8pk8ShBFAVefMQmyLPPf+zw1pdyIm8+Zjq37u3H1U/WYP86CS0+cgIXjU9+JR8kZ0wnrvl6s/EeyOKklETMrTf3/EIUiMezv9uLTdg9+/s/dcAUiOH1yKepmlOOU2hLoePAmZZE3FEF9ixNfXjguK9ebYNPBH46ioduHSSkcIiTLMv7xcQvOn21PeyyiIOD82ZW47/UGnDqpZEAMvu+NBoyz6DBvghWeUfpcH+uiuVW4/63D+OHGXVh/3gyUGuK9wWOyjANdPtS3OHGwx49AOIoSgxozK004YYINNoM67c9EVGhe39+NOVWZXzCz6NQwaVU40O1LuQXbjhYXLDoVSo3K9ftXSSKWTi3DA28dwp0XHod2dxCPvdeI7585WbFrJKPUoMEPz5qKLXu78INndiISi6HMqIEoCBAQP8ByUokeJ9eW4IwppZyzUMF742BPzpJFJ0y04c7N+3DT2dMGLXAf6vGhoduHK0+dmJOxFRudWsKJE204caINsZiMRocfn3V4sWFHK+597QC8oQgmlRowrcKIaeVGTC0zYmqFEWUGNRNClFHbDnRj9fFVuR4G/57nOUkUcNb0cpw+uRRvHOzBrf+3B7Iso25GBU6fXIq54yzQqlgolSljOmH97I42LJ1WluthUJZoVCJm282YbTcD86rR5Q2hvtmFB98+jPUvfIYTJ1px1vRynDa5FDYbe9ZRZr15sBfTyo0Zr27sIwoC5o+z4JV9XfhmWfI3YW819CIYiWF2lTI3lwsnWPHK3m48+PZhXHlqLQDgpd0d+L9PO/DjFKsoVZKIq06diI072nHJn97FtHIjBAD7u33Qq0VMKzfBbtZCpxbR4Q7ig0Ynbv2/PagtjSeBZlWaUGHSQBIFeENROP1hOAMRhKMx6NUSqi1azKg09SfCiQrZtgPdWHFc+gtQiZhZacTr+7tTTlj//ePWtNp0DGfptDL8dutB/HDjTuzv8uGcmRU5OXypj0oSce6sSiybWYFefxjuQASxIx1C/OEoWl0B/Pn9Jtzx8l6snGPHV08Yj2oLdwlS4YnGZDz3STu+e3ptTq5fbtSg0qTFO4ccg3rLP/ZuI86cVpry4dI0PPFIO5bao3qDe4MRtLgCaHYG8FGTE//3aQcaHX6oRAEzK02YW23B8eMsmFtthkXHAgNSxq42N1yBCM8yo4RpVCLOml6OL0wrQ6PDj4+aXbhryz40OwOYXGrAcVVmzLSbML3ciCnlBhg1YzrVqpgx+1Ns6Pah2RnISnUR5adyowZ1M8pRN6McnmAEO1pd2LSzHXdt2Y+JpQYsGm/B/PFWzKo0ocqiZSU+KWrznk7MG2fJ6jXnj7fghV0d+OYpySWsZVnG/751COfMrFDs90AUBFx56kTctWU/9nd5IYkC3m904DunTYJZl/o/TSpJxCULqnHurAo0OvwQBOBLi8bDph/6JicSjWFflw97uzx46iMXXIEIYrIMnUqEUauCQS1BEgUEIzH0+kI41OtHiV6Nk2tLcOqkEiyqsfIGigpOpyeIRkcgazdqJ9eW4G8ftODyk2uSriRy+sPYur8b/3n+TMXHpVNLuPbMyXj9YA+OqzJjfpZj8nAEQUCpQTNocey4KjPOnlGBbm8Ir+3rxlcffR8nTLDh4vnVOHmijQk2KhjbD/fCrFPldIHozKmluO+NBpw6+fOdXod6fNi8pws/O0/5eENDM2pVmF5hwvSKzwsiZFlGrz+Mw71+NPT48ObBHhzs8aHSpMW8cRbMG2fB7CozppQZGPcoJU991ILTJ5cm1X6QCIjP0SaWGDCxxIALjwcC4SgaHX4c7vVj675u/PX9ZjQ7A7DqVagt0aO21IDxVh3sZi1KDGqM84UR8ochCkBMjncB8IWj8f+FovCFIgiEYwjHZMRiMgQh3jnAoJZg1qlQalDDbtaiwqQdE+1tx2zC+oG3D+H0yaVj4g+ZRmfSqnDqpFKcOqkUkWgMbf4I6g/34i/vNeGwww9/KAq7WYsyowYWnQp6tQSVKEAQgFgMiMoyIjEZsiz3V0OJQnwLiVoUoZYEqCURkihAFAAZQCQqIxiJwReOwB2MwhOMwB2IwBOMwBeOIhyVEZNlqEQBBo0Eq06NaosWs8ZZMdmmw5wqMyaW6LmNqAA19vqx/ZADPz9vRlavO73CBFegBdsP9eLk2sSrFTfsaIMvFMVJNTZFx2PVq/Ef507HGwd7IQnAT86enlay+mhmnQrHJbAgqZJEzLKbMCvBw9xisowmRwCftrvxyLuN+Ok/d6PcqMG0ciNqSw2wm7UoNahh0alg1MRjhVoSoBIFyIhXlUViMiJRGeFYDDp3CC6XH4IQf43+yGTEpldDxX+fKEP+/F4TTploy9ocaGqZAZFYDDta3Ukv1D28vRELxlsy1udWr5GwbGZFRt47U8qMGqyZX40Vx1XinUMO/H7rQbS6AvFKxGozplUYUVtiwASbjq1DKO/EZBn/++YhnJnjHtEnTbRh6/5uPPF+My49cQI8wQh+8tynWHV8lWJzEUrN0Yt2C44c9B2NyWh2+rG/y4cte7vw8PZGdHqCGGfRoaZEj3FWHSpNWtgMali0Khi1EgyaeOGBQSPBqIn/PwuQ6FCPD6/t68ZPl2f3PoyKk04tDVp0i8VkdPtCaHMH0eEOYVebG28e7IEnFEUoKiMQjiIWkyEeSUZrVCJ0KhFalQitSoJGEo7kjQTIMhCVY0fyRjG4A2H0+MJwByOotugwudSAWfZ4G9zZdhPKTcV1eGdO/jXeunUrbr/9dsRiMaxbtw5XXXXVgOdDoRBuuukm7Ny5EzabDffccw8mTJig2PU/anLivUYH1i/n6jkNppJEzKoyY4Lp88omfziKbm8IrkA8mRyKxBCNyZART0wLiAcVQYh/DQiIyfGEc1+CKhqTEYzIkCFDQDyZbdRIKDWqoVOJ8UmVRoJeLUKniie5REFANCbDH47CHYygyxtCTyCK53e247dbDyIai2HRBCtOmVSKk2psmGDTMYGdgFzHoPveaMBZ08tgzPJBQ5IoYOWcSvxu20E8VGNLKCH6WbsHv992ENctmZyRKgSdWsLZM8oVf99MEQUBE0v0mFiix/JZ8RuoVlcALc4AOj0hNHT74AlF4A1FETgSK/oWnyAIkI4sZEliPEGtUUmIxWQA8TgRjMTgC0XhCUZh1atQZdGixha/3gSbvn+FvtyoGfZgyZgsH3mPeLwKhGMIR2P949eqRJi08aS4IUstaWigXMYghy+MZz9pwy3LpivyfokQBAFLp5Xhv189gAe+Mj/hhMHeTg+e/aQN/3Fu9sZaSHRqCUunlWHptDK4AhHs6/KisdePD5qcaHMH0ekJwqJTY7xVh/FWHcZZtKi06FBh1KDcpEG5cXAVNxWPdOLMH//4Rzz99NMQRRH/8R//gTPPPFOxcf1zVwe8oSgWJ7FwngmiIODrJ9XgD683YMveLjQ7A1gw3oIlU3nYYj6SxM+rGvuEIjG0u4Po8ATR4wtjd4cH3r65TySGYDiGQCT+3/5QFKEjLd4sRwoD4klxNcpNGpQZNCgxqGHVqWHR9xUdiNAcKThSBeNFRTH588KDSEw+cr+H/oPzRAFQifFCJa1KhO5IkRN9Ltf3Ybe/vBfLZ1XAwoUpyhBRFFBhildBo3rgcyaTNulzmoYSisTQ4Qmi1RVAQ48PbxzswaEeP9RSvJ3SLLsJ08qNmFxmQI1NX7AFDFn/LY1Go7j11lvx0EMPwW63Y+3atairq8O0aZ/3LH3qqadgsVjw8ssvY9OmTbj77rvx3//934qN4d1GB06qsbE5OiVMr5Zytm1REgVoVCKsejUm2PQDgly3N4Q9nR68srcL973eAEkUsGiCFQsmWHGc3YQp5UboCzQ4ZUo+xKCPW1y45oxJir1fMk6oseHthl788uW9uOXc6SMmjrYf6sV/vLAbX1k0PqfbdvOZJAqYYNOn/PMZbtISjclwBcLo8obR6QmiyRHAx80udPtC6PGG4QpEoFWJ0KvF/irZcCy+Yh+MxKCRROg1EvRqCdojN1uCEE9mh6PxhLYrEIEoABUmDezmeEJrvE2HaosOFSYNyowalOjVMOtUrEhSUK5j0P5uL8ZZ9SjJcqJyydQyvN/oxP1vHsJ3TqsddXH1YLcP1/39E6xbUM22Owmw6FRYNMGKRROs/Y/FYvFt9Z2eELq9IbS6g/is0wtXIAynPwKHPwxvKAqbQY0ygxqVJi3sZm3/VtNy45E4YFDDqlMNu0hG+SedOLNv3z5s2rQJmzZtQnt7O775zW/ixRdfhCQpM598+1Bv3mzFt5u1+PE507CvywurTjUgGUr5T6MSUVOiR01JYnOwWEyGLxyFNxQvBHIHIvGCoP6Cg/hzvlAE/nC8ojEcjSEqy/3vIR7ZEScJQn8BQvzx+Pb+o4uVQtEYQpEYREGATi32V3ubtCqYtCpYjhQPlBrVKDFojvRW16DKrINVryrKIqRcz4FisowPm5y4YjEPVaXCplGJ/fegJx356yzLMnp88XZKTQ4/6ltcaHPFF/XMWhUqzVrYTVpUmDWoMMZ3BZcYNCgzqlF2pIgh33KkWU9Y19fXo7a2FjU1NQCAlStXYvPmzQOC1JYtW/D9738fALB8+XLceuut/auWSsmHSRJRusqMGpxqjLcykWUZ7e4g9nV58eaBHvztg2a0ugKw6tWoMutQYVLDplfDqFFBqxKhkgQIECBDhnxkgnWkCLS/PYH2yPYUvUaCQa2CSRufZPVtqzOoJWhUYkEls/ImBuXoZyYKAq44tRb3vXEIlz/+Ib68aDzmVJlh06sRg4webxi7O9x4cXcn9nR48LUTJyTUWoOUJYkCSgwalBg0mF4xuM9wTJYRCMcQjEQRjQE4UtGjkeK/s4n+G+cPR9HrC6PbF09o7e304L3DDjiOHDjpDkYQCEf7q4S0RyqG1FI8hmgksb/liSQKECEAAo5sX4v3XhMlEaFwtH9HivpIZblBI8GkiVc6mXVqmDRSvG/4UTtNtEe2x2mk+P/Uqvg14y2ZCifuHC0fYlAufnSiIOBbp0zEfW80YFebG19aOB6zq0yw6dUQhfiuJJc/gv3dXryytwsv7OrAmvlVOCkDhy2OFaIooOxI0nk40ZiMiCiguduLXl8YDn8Eezo9eL/RCVcgvjjmCkTgCUX6F8K0/b+DR/2uy/H3GpjYAdRiX5WhCKPmSJJGp4JVp4JVH2+fZNYeqWbUiPFFNpUItfj5Qpt8VBIoHJMRPpIECkRiR3axxHeyhGNHdr/1t2b7fDeLWhJgNesRCISgOtKeDUD/uMPRGEJR+ch7xfrfoy/OGTR9MSveasCoVkGnjn+2fIxF6cSZzZs3Y+XKldBoNKipqUFtbS3q6+uxcOFCxcYn5tH9sF4tYW51fvSvp8wSRaE/WWw3J7dtPtWqSFn+fAddIBJDIByFPxyDPxyFNxSBNxhFQ48fO1vdcAbiC4k93hBCURl2swbVFh3GWXX9LU/KjGqU6DWw9lWBawqrgjsv5kAA28JSURKEz+d9C48pYHAEwujxhuE4UrRwsMeL+pYjbWmDETiP3Pvp1CLKDBpUmDRHqsTj/19qUKOm0gwxEj0yb5P6W19mch6U9YR1e3s7qqqq+r+22+2or68f9Jrq6njtvEqlgtlsRm9vL0pLR96ipVZLqKgYPbFyxVnT0OEKKpIwKjXl9+ns+Ty+fB4bkN/jG25stdXAyUe145IRv8HLhCCAYAxAKDbg8dZ294CvjxtngSqP7kwyFYMSjT8A8Oi3T0EkFoOA1GNQun8/7/lyKSKxz//s/Ef+32iRcIJFhxOmVcSTBWmMMV35/DuopPz/nHJ/1VCuRBH/O+qXAUTiLVQSYdNrMLEsvyrmch2DzrToMb7SktbNWqp/ZycC+H+Ty/qTmkEA7Uf/GyKJGFdpxqWVZlx6+uSc/9uR/7+bypleZR31NX2tzrJNACAd+V/6+wKU2S3kB+AfYg50rCnlRphysEMgnTjT3t6O+fPnD/je9vb2Ea+XzBzoPy6cA4cvPCbuw5LFz5Pfsvl5Rou3MgAPAE945Bg0d7x12GRSor+zSsr1HEiWZTx77RmQFEqwFfrf8UIeP8eeGVE5fi7baHwAfFEZiA7/WrtFB7slvc86Jhv3VJp1qDTn718SIipuM1mxTEQ5YtSqMHfC6MlJIqJMmFBiwARunCCiHBAEAXPHcw5EVCiyXrpit9vR1tbW/3V7ezvsdvug17S2tgIAIpEI3G43Sko4syGi9DEGEVEuMQYRUaalE2cS+V4iolRwDkREych6wnru3LloaGhAY2MjQqEQNm3ahLq6ugGvqaurwzPPPAMAePHFF7F48eK87A9HRIWHMYiIcokxiIgyLZ04U1dXh02bNiEUCqGxsRENDQ2YN29eLj4GERUZzoGIKBmCnEiDEoW99tpruOOOOxCNRnHJJZfg6quvxr333ovjjz8eZ599NoLBIH70ox/h008/hdVqxT333NPfmJ+IKF2MQUSUS4xBRJRp6cSZP/zhD/j73/8OSZJwyy23YOnSpTn+NERULDgHIqJE5SRhTURERERERERERER0rNwev05EREREREREREREdAQT1kRERERERERERESUF1S5HkCh2rp1K26//XbEYjGsW7cOV111Va6H1O/mm2/Gq6++irKyMjz//PO5Hs4Ara2tuOmmm9Dd3Q1BEPDFL34Rl19+ea6HBQAIBoO49NJLEQqFEI1GsXz5clx33XW5HtYgff2+7HY7/vjHP+Z6OAPU1dXBaDRCFEVIkoR//OMfuR5S0cnn2KOEfI5fSsnnOKikQomplJhijz19xkIM6sNYRIWqmOJRscWcYosrxRg/8vleslAUcgwq1JhT6LGlGGJJTmKHTEmLRCLy2WefLR8+fFgOBoPyqlWr5L179+Z6WP22b98uf/LJJ/LKlStzPZRB2tvb5U8++USWZVl2u93yueeemzc/u1gsJns8HlmWZTkUCslr166VP/zww9wOagh/+tOf5BtuuEG+6qqrcj2UQc466yy5u7s718MoWvkee5SQz/FLKfkcB5VUKDGVRjcWYk+fsRCD+jAWUSEqtnhUbDGn2OJKMcaPfL6XLASFHoMKNeYUemwphliSi9jBliApqK+vR21tLWpqaqDRaLBy5Ups3rw518Pqd9JJJ8FqteZ6GEOqrKzEnDlzAAAmkwlTpkxBe3t7jkcVJwgCjEYjACASiSASiUAQhByPaqC2tja8+uqrWLt2ba6HQjmQ77FHCfkcv5SSz3FQSYUQUykxYyH29BkLMagPYxEVomKLR8UWc4otrhRb/OC9ZPoKPQYVaswp9NhS6LEkV7GDCesUtLe3o6qqqv9ru91eUL8s+aKpqQmffvop5s+fn+uh9ItGo1i9ejVOO+00nHbaaXk1NgC444478KMf/QiimL+/ut/+9rexZs0a/O1vf8v1UIoOY0/xycc4qKR8j6mUGMae4sdYRIWC8ahwFEtcKab4UQj3kvmOMSj3CjW2FHIsyVXsYKSinPB6vbjuuutwyy23wGQy5Xo4/SRJwsaNG/Haa6+hvr4ee/bsyfWQ+r3yyisoLS3F8ccfn+uhDOuJJ57AM888g//93//Fn//8Z7z77ru5HhJR3srXOKikfI6pRBTHWERESiumuFIs8aMQ7iWJRlPIsaVQY0kuYwcT1imw2+1oa2vr/7q9vR12uz2HIyos4XAY1113HVatWoVzzz0318MZksViwSmnnIJt27bleij9PvjgA2zZsgV1dXW44YYb8Pbbb+OHP/xhroc1QN/vQVlZGZYtW4b6+vocj6i4MPYUj0KIg0rKx5hKiWPsKV6MRVRoGI/yX7HGlUKPH4VwL1kIGINyp1hiS6HFklzGDiasUzB37lw0NDSgsbERoVAImzZtQl1dXa6HVRBkWca///u/Y8qUKfjmN7+Z6+EM0NPTA5fLBQAIBAJ48803MWXKlByP6nM33ngjtm7dii1btuA3v/kNFi9ejLvvvjvXw+rn8/ng8Xj6//uNN97A9OnTczyq4sLYUxzyOQ4qKd9jKiWOsac4MRZRIWI8ym/FFleKKX7k+71koWAMyo1Cjy2FHEtyGTtUWblKkVGpVFi/fj2uuOIKRKNRXHLJJXmVmLvhhhuwfft29Pb2YsmSJbj22muxbt26XA8LAPD+++9j48aNmDFjBlavXg0gPt6lS5fmeGRAR0cHfvKTnyAajUKWZZx33nk466yzcj2sgtHd3Y1rrrkGQLw/0wUXXIAlS5bkeFTFJd9jjxLyOX4pJZ/joJIYU4vHWIg9fcZCDOrDWESFqNjiUbHFnGKLK4wfdKxCj0GFGnMKPbYwlqRGkGVZzvUgiIiIiIiIiIiIiIjYEoSIiIiIiIiIiIiI8gIT1kRERERERERERESUF5iwJiIiIiIiIiIiIqK8wIQ1EREREREREREREeUFJqyJiIiIiIiIiIiIKC8wYU1EREREREREREREeYEJa1LUo48+ivPPPx833nhjUt/X1NSE5557LkOj+tzXvvY1LF++HKtXr8bq1avR3d2d8WsSUfbkewwKhUL46U9/iuXLl+O8887Diy++mPFrElH25HMM8ng8/fOf1atX45RTTsHtt9+e0WsSUfbkc/wBgOeffx6rVq3CqlWr8O1vfxs9PT0ZvyYRZU++x6AXXngBq1atwsqVK3HXXXdl/HqUPlWuB0DF5S9/+QsefvhhVFVVJfV9zc3N/ZOYZESjUUiSlNT33H333Zg7d25S30NEhSHfY9B9992H0tJSvPjii4jFYnA4HEldj4jyWz7HIJPJhI0bN/Z/vWbNGpx77rlJXY+I8lc+x59IJILbb78dmzZtQmlpKe688078+c9/xrXXXpvUNYkof+VzDOrt7cWdd96Jf/zjHygtLcWPf/xjvPXWWzj11FOTuiZlFxPWpJj169ejqakJV155JVasWIHDhw9j7969iEQi+P73v49zzjkHTU1NuOmmm+D3+wEAP/3pT7Fo0SL8+te/xv79+7F69WpcfPHFsFgs+OSTT7B+/XoAwHe+8x1861vfwimnnIKFCxfiS1/6Et58802sX78ezc3NeOyxxxAOhzF//nz87Gc/SzqJTUSFrxBi0N///nf885//BACIoojS0tLs/HCIKOMKIQb1OXjwILq7u3HiiSdm/OdCRJmX7/FHlmXIsgy/3w9ZluHxeFBbW5vVnxERZU6+x6DGxkbU1tb233udeuqpePHFF5mwznNsCUKKufXWW1FZWYlHHnkEfr8fixcvxtNPP41HH30Ud911F3w+H8rKyvDQQw/hmWeewT333IPbbrsNAHDjjTfixBNPxMaNG/GNb3xjxOv4fD7MmzcPzz77LEpKSvDPf/4TTzzxBDZu3AhRFEfdTnLLLbdg9erV+J//+R/IsqzUxyeiHMv3GORyuQAA9957Ly6++GJcd9116OrqUvRnQES5k+8x6GibNm3CihUrIAiCEh+diHIs3+OPWq3Gz3/+c6xatQpnnnkm9u/fj7Vr1yr9YyCiHMn3GFRbW4uDBw+iqakJkUgEmzdvRltbm9I/BlIYK6wpI15//XVs2bIFf/rTnwAAwWAQra2tqKysxK233ordu3dDFEU0NDQk/d6SJGH58uUAgLfeeguffPJJ/4QnEAigrKxs2O+9++67Ybfb4fF4cN1112Hjxo246KKLkh4DEeW3fIxBkUgEbW1tWLhwIW6++WY89NBD+NWvfsUeakRFKB9j0NFeeOEF3HnnnUlfm4jyXz7Gn3A4jCeeeAIbNmxATU0N/uu//gt//OMf8b3vfS+1D0lEeSsfY5DVasXPf/5z/OAHP4Aoili4cCEOHz6c2gekrGHCmjLmt7/9LaZMmTLgsd/97ncoLy/Hxo0bEYvFMG/evCG/V5IkxGKx/q+DwWD/f2u12v5tHrIs4+KLL064sb/dbgcQ7+N4wQUXoL6+nglroiKVbzGopKQEer2+v2fseeedh6effjrpz0VEhSHfYlCf3bt3IxqN4vjjj0/m4xBRAcm3+PPpp58CACZOnAgAOP/883H//fcn96GIqGDkWwwCgLq6OtTV1QEA/va3v0EU2XAi3/FPiDLijDPOwOOPP97fcmPXrl0AALfbjYqKCoiiiI0bNyIajQIAjEYjvF5v//ePHz8eu3fvRiwWQ2trK+rr64e8Tl/voe7ubgCAw+FAc3PzkK+NRCL9p1GHw2G8+uqrmD59ujIfmIjySj7GIEEQcNZZZ+Gdd94BEK8KmDp1qjIfmIjySj7GoD7PP/88Vq5cmfZnJKL8lI/xx263Y//+/f33Ym+88QbnQERFKh9jEID+1zmdTvzlL3/BunXr0v+wlFGssKaM+N73voc77rgDF154IWKxGCZMmIA//vGP+OpXv4prr70WGzZswJlnngmDwQAAmDlzJkRRxIUXXog1a9bg8ssvx/jx47FixQpMnToVc+bMGfI606ZNw7/927/hW9/6FmKxGNRqNdavX4/x48cPem0oFMIVV1yBcDiMWCyGU089FV/84hcz+nMgotzIxxgEAD/84Q9x00034Y477kBpaSl+8YtfZOxnQES5k68xCAD++c9/srKRqIjlY/yx2+245pprcOmll0KlUmH8+PGcAxEVqXyMQQBw++23Y/fu3QCAa665BpMnT87MD4AUI8g8dY6IiIiIiIiIiIiI8gBbghARERERERERERFRXmBLECpK69atQygUGvDYnXfeiZkzZ+ZoREQ0ljAGEVEuMQYRUa4w/hBRLjEGFQ+2BKGkHDhwAD/4wQ9w+PBh/OAHP8DXv/71XA+JiMYIxh8iyiXGICLKFcYfIsolxiDKBbYEoaQ88MADOOWUU/Dhhx+mFaS+9rWv4amnnlJwZIkJhUK47rrrUFdXh5kzZ+Kdd94Z8Lwsy7jrrrtwyimn4JRTTsFdd90FrukQ5Ydijz9vv/02vva1r+GEE05AXV1d1sdHRCMr9hj0wAMP4IILLsDChQtRV1eHBx54IOtjJKKhFXv8efjhh3H22Wdj0aJFOOOMM3DHHXcgEolkfZxENLRij0FHv+7888/HkiVLsjxCGgoT1pSUlpYWTJ8+PdfDSGsCs2jRItx5552oqKgY9Nzf/vY3/Otf/8LGjRvx7LPP4pVXXsFf//rXdIZKRAop9vhjMBhwySWX4KabbkpneESUIcUeg2RZxq9+9Su8++67eOCBB/DnP/8ZmzZtSmeoRKSQYo8/dXV1eOaZZ/DBBx/g+eefx+7du/HYY4+lM1QiUlCxx6A+Dz74IEpLS1O+BimLCWtK2Ne//nW88847uPXWW7Fw4UIcOHAAv/rVr/CFL3wBp512GtavX49AIAAAcDqd+M53voPFixfjpJNOwne+8x20tbUBAO655x689957/e9z6623oqmpCTNnzhwQgI5effvHP/6BL3/5y7jjjjtwyimn4He/+x1CodCw1x+ORqPBN77xDZx44okQxcF//Tds2IBvfetbqKqqgt1uxze/+U0888wzSv0IiShFYyH+zJs3DxdddBFqamqU+rERkULGQgy68sorMWfOHKhUKkyZMgVnn302PvjgA6V+hESUorEQfyZOnAiLxQIgvngmiiIOHTqkyM+PiNIzFmIQADQ2NuLZZ5/FVVddpcSPjRTAhDUl7NFHH8WJJ56I9evX48MPP8Rf//pXHDx4EBs2bMBLL72Ejo4O/M///A8AIBaLYc2aNXjllVfwyiuvQKvV4tZbbwUA/OAHPxjwPuvXr0/o+vX19aipqcEbb7yBq6++Gnffffew10/V3r17MWvWrP6vZ82ahb1796b1nkSUvrEQf4gof421GCTLMt577z1MmzZNsfckotSMlfjz3HPPYdGiRVi8eDF2796NL3/5y2m/JxGlb6zEoNtuuw033HADdDpd2u9FymDCmlIiyzKefPJJ3HLLLbDZbDCZTPjOd77Tv3W0pKQEy5cvh16vh8lkwtVXX4133303rWtWVlbia1/7GlQqFbRa7YjXT5XP54PJZOr/2mw2w+fzsY81UR4p1vhDRIVhLMSg3/3ud4jFYrjkkksUe08iSl8xx59Vq1bhgw8+wIsvvogvf/nLKCsrS/s9iUhZxRqDXn75ZUSjUSxbtiyt9yFlqXI9ACpMPT098Pv9WLNmTf9jsiwjFosBAPx+P37xi19g27ZtcDqdAACv14toNApJklK6ZlVVVcLXT5XBYIDX6+3/2uPxwGAwQBCEtN6XiJRTrPGHiApDscegxx9/HBs2bMBf/vIXaDQaRd6TiJRR7PEHACZNmoTp06fjP//zP/H73/9esfclovQVYwzy+Xy46667cP/996f8HpQZTFhTSkpKSqDT6bBp0ybY7fZBz//pT3/CwYMH8eSTT6KiogKffvopLrroomErlQ0GAwAgEAj0Vzh3dnYOeM3RSePRrp+q6dOnY/fu3Zg3bx4AYPfu3XlxuAARfa5Y4w8RFYZijkFPP/007r//fvz5z38ecINIRPmhmOPP0SKRCA4fPpyx9yei1BRjDDp06BCam5tx6aWXAgDC4TDcbjdOP/10/O1vf8OECRMUuQ4ljy1BKCWiKGLdunW444470N3dDQBob2/Htm3bAMRX0bRaLSwWCxwOx6DV8fLycjQ2NvZ/XVpaCrvdjo0bNyIajeLpp58e8Hyy1x9JKBRCMBgEEA9GwWCwP4CuXr0aDz30ENrb29He3o6HHnoIF198cRI/GSLKtGKNP7FYDMFgEOFwGLIsIxgMIhQKJfGTIaJsKNYY9Oyzz+Kee+7BQw89xMNfifJUscafp556qv/99u3bh/vvvx+nnnpqoj8WIsqSYoxB06dPx6uvvooNGzZgw4YNuO2221BWVoYNGzaguro6uR8QKYoJa0rZj370I9TW1uKLX/wiFi1ahG984xs4ePAgAODyyy9HMBjE4sWL8aUvfQlnnnnmgO/9+te/jhdffBEnnXQSbrvtNgDAf/3Xf+HBBx/EKaecgn379mHhwoUpX38k5513HubNm4f29nZ8+9vfxrx589Dc3AwA+PKXv4yzzjoLq1atwqpVq7B06VIe+EGUh4ox/rz77ruYN28errrqKrS0tGDevHn49re/ncqPh4gyrBhj0H//93/D4XBg7dq1WLhwIRYuXJjwgUhElD3FGH8++OADrFq1CgsWLMBVV12FJUuW4IYbbkjlx0NEGVZsMUilUqGioqL/f1arFaIooqKiIuU2JqQMQeZpckRERERERERERESUB1hhTURERERERERERER5gYcuUtG577778Mc//nHQ4yeccAIeeOCBHIyIiMYKxh8iyiXGICLKFcYfIsolxqDiw5YgRERERERERERERJQXiqrCOhaLIRrNTv5dkoSsXUsJhTZeoPDGXGjjBfJrzGp1YR9okK34k09/Zkoops9TTJ8FKK7Pk8hnYQwaWTH9fejDz1QYivEzAQM/F+NP8vLh7wXHwDEUyxgYgxKTD3/GyeB4M6eQxgrk/3iHi0FFlbCORmU4HL6sXMtmM2TtWkootPEChTfmQhsvkF9jrqgw53oIaclW/MmnPzMlFNPnKabPAhTX50nkszAGjayY/j704WcqDMX4mYCBn4vxJ3n58PeCY+AYimUMjEGJyYc/42RwvJlTSGMF8n+8w8UgHrpIRERERERERERERHmBCWsiIiIiIiIiIiIiygtMWBMRERERERERERFRXmDCmoiIiIiIiIiIiIjyAhPWRERERERERERERJQXmLAmIiIiIiIiIiIiorzAhDURERERERERERER5QUmrImIiIiIiIiIiIgoLzBhTURERERERERERER5gQnrFATCUWz8qCXXwyCiFASDQaxduxYXXnghVq5cid/+9reDXhMKhfBv//ZvWLZsGdatW4empqYcjHSwj5ud+J9X92N/lzfXQyGiMSQmy9j4Mec9RJQfAuEonq9vzfUwiGiMebehBy3OQK6HQTRmMGGdpEg0hu8/vQM//Hs9PMFIrodDREnSaDR45JFH8Oyzz2LDhg3Ytm0bPvroowGveeqpp2CxWPDyyy/jG9/4Bu6+++7cDPYYf/uwGY++fQjP8CaNiLKozRXED5+ux+Fef66HQkRjXCQaww+e+QQ/eOpj+MPRXA+HiMaQezfvw4YdvA8jypaMJaxbW1vxta99DStWrMDKlSvxyCOPDHqNLMu47bbbsGzZMqxatQo7d+7sf+6ZZ57Bueeei3PPPRfPPPNMpoaZtEO9frS6AphYqkczV9eICo4gCDAajQCASCSCSCQCQRAGvGbLli24+OKLAQDLly/HW2+9BVmWsz7WY3V6Qpg7zoJOTyjXQyGiMaTDHQQA/HNXe45HQkRj3Z5OL1qcAdSU6LmIRpSnbr75Zpx66qm44IILhnz+gQcewOrVq7F69WpccMEFmD17NhwOBwCgrq4Oq1atwurVq7FmzZosjnpksizj0zYX3m905nooRGOGKlNvLEkSfvKTn2DOnDnweDy45JJLcPrpp2PatGn9r9m6dSsaGhrw0ksv4eOPP8bPf/5zPPXUU3A4HPj973+Pv//97xAEAWvWrEFdXR2sVmumhpuwTk8QFSYtDDoVWpwBzKw05XpIRJSkaDSKNWvW4PDhw/jqV7+K+fPnD3i+vb0d1dXVAACVSgWz2Yze3l6UlpYO+56SJMBmM2R03L3+CBbWluKjRkfGr5UtkiTys+SpYvo8xfRZcqHDE4TdosULn3bgqtNqBy3yERFlS5s7iCqLDmq1hEM9Pt6LEeWhNWvW4LLLLsOPf/zjIZ+/4oorcMUVVwCIFwo9/PDDsNls/c8/8sgjI9535UK7OwgZwJ4OD4KRGLQqNisgyrSMJawrKytRWVkJADCZTJgyZQra29sHJKw3b96Miy66CIIgYMGCBXC5XOjo6MD27dtx+umn9wet008/Hdu2bRt2hS6bOjwh2PQqmA1aVlgTFShJkrBx40a4XC5cc8012LNnD2bMmJHWe0ajMhwOn0IjHFqHO4DaUj1e/KQt49fKFpvNwM+Sp4rp8yTyWSoqzFkaTeHp9ISwsKYEr+3pgCcYhVmXsekjEdGI2lwB2PRqmAxqHOopjn+jiIrNSSedlPAZQJs2bcqLPM9oPuvwYFqFCU5fCDvbXFg0wZbrIREVvazccTQ1NeHTTz8dsoqxqqqq/+uqqiq0t7cPetxut6O9ffRtqNmocHSFYygz62A1aNDpCxdMxVYhVpcV2pgLbbxAYY5ZSRaLBaeccgq2bds2IGFtt9vR2tqKqqoqRCIRuN1ulJSU5HCkgC8URTQGjLPp0eMLQZZlVjkSUVa0uYOwGVQwaVVwBcNMWBNRzrQ4AygxqFFm0eFAuzvXwyGiNPj9fmzbtg0//elPBzz+7W9/G4Ig4Etf+hK+9KUvjfo+2cgDNTiDmFhmQMiixX5HEHXH5/89dKHd6xfSeAtprEDhjbdPxu84vF4vrrvuOtxyyy0wmTK7ZSsbFY6HOj0wqEWUm9R4a29XwVSfFWKlXKGNudDGC+TXmLNV3djT0wOVSgWLxYJAIIA333wTV1555YDX1NXV4ZlnnsHChQvx4osvYvHixTlPDnd5QygxqKFXx7efeUNRmLRMGhFR5nW4A5g1zgqjRoIrEMH43HdoI6IxqtUVxMxKI8ZZdXhld0euh0NEaXjllVewaNGiAe1AnnjiCdjtdnR3d+Ob3/wmpkyZgpNOOmnE98lGHujjw72YP7EELT0+NHV58uYeeiT5dK+fiEIabyGNFcj/8Q6XC8po451wOIzrrrsOq1atwrnnnjvoebvdjra2tv6v29raYLfbBz3e3t4Ou92eyaEmrN0dhE2vRqVZixZXMNfDIaIkdXR04Otf/zpWrVqFtWvX4rTTTsNZZ52Fe++9F5s3bwYArF27Fg6HA8uWLcNDDz2EH/7whzkedbx/vlWvgiAIsOnV6PLy4EUiyo4OTwilRg2MGhVc/kiuh0NEY1irK4BSgwbVVh2aHP68OBSbiFKzadMmrFy5csBjfXmfsrIyLFu2DPX19bkY2iCd3hDKTRqYtBJ6feFcD4doTMhYeZ4sy/j3f/93TJkyBd/85jeHfE1dXR0ef/xxrFy5Eh9//DHMZjMqKytxxhln4De/+Q2czvgJrK+//jpuuOGGTA01KZ2eEEr0alSYtGhzBbgtn6jAzJo1Cxs2bBj0+PXXX9//31qtFr/97W+zOKrRdXtDsOnUABBPWHtCmFRaeNt6iKjwdLiDKDWoYdBIcAWZsCai3OmLR0atCqIgwOmPwGZQ53pYRJQkt9uNd999F3fddVf/Yz6fD7FYDCaTCT6fD2+88Qa+973v5XCUn3MHIjBq4+3R9nR6cz0cojEhYwnr999/Hxs3bsSMGTOwevVqAMANN9yAlpYWAMBXvvIVLF26FK+99hqWLVsGvV6PO+64AwBgs9nwve99D2vXrgUAXHPNNQO2ieRSlzdeYa1TS9CqJPT6wyg1aHI9LCIqcp2eEMxHWoBYdCpWWBNRVsRkGT2+MGwGDfRqEa4Aq4qIKDcC4Sh84c8PfjVrVXAEwkxYE+WZG264Adu3b0dvby+WLFmCa6+9FpFIfMH7K1/5CgDg5Zdfxumnnw6D4fMCnO7ublxzzTUAgGg0igsuuABLlizJ/gcYgisQgUkrwaSR4PBzLkSUDRlLWJ944on47LPPRnyNIAj42c9+NuRza9eu7U9Y54tQJAZPMNqfNDJpJbj8ESasiSjjOj1BWPRHbtB0KnR62JKIqBAFg0FceumlCIVCiEajWL58Oa677roBrwmFQrjpppuwc+dO2Gw23HPPPZgwYUJOxuvwh2HQSNCoxHiFdYAV1kSUG23uIMoMGohHdreatBKcTBwR5Z3f/OY3o75mzZo1WLNmzYDHampq8Oyzz2ZqWCmLyTK8oQiMGhWMWhXjDlGWZLSHdbHp8oZg1ashivFJErfGElG2dHiCsB5pCWLVqdDpYYU1USHSaDR45JFH8Oyzz2LDhg3Ytm0bPvroowGveeqpp2CxWPDyyy/jG9/4Bu6+++7cDBZAjzcMy5FqRr2aySEiyp1OT3BANbVRo4KTi2hElGHuQAR6tQRRFOJFi4w7RFnBhHUSur0h2PSfF6Ub1BLcTFgTURY4/PFtaABg0qrQ42PCmqgQCYIAo9EIAIhEIohEIoPOwtiyZQsuvvhiAMDy5cvx1ltv5exgMW8ofpMGAEaNBCcPXSSiHHEFIjBqpP6vDRouohFR5sXbgXy+eB8IxxCJxnI8KqLix4R1EtzBCAzqgZMkN1fXiCgLfKEodEfij14twRuK5nhERJSqaDSK1atX47TTTsNpp52G+fPnD3i+vb0d1dXVAACVSgWz2Yze3t5cDBXeUBQ6VXy6GK9mZHKIiHLDFRh8L8YKayLKNGcg3L9YJgoCjFrGHqJsyFgP62LkCUb6E0YAoFNzOwgRZYc3FIH+SNJIpxaZsCYqYJIkYePGjXC5XLjmmmuwZ88ezJgxI833FGCzGUZ/YZIEtQtGnRqiKKLMqoOvyZmR6+SCJIlF81n68DMVjmL9XJnUty2/j5EV1kSUBc5jdneYtSo4/GGUGXmWGVEmMWGdhHjC+vOidL1KhIctQYgoC46usNapJHgZe4gKnsViwSmnnIJt27YNSFjb7Xa0traiqqoKkUgEbrcbJSUlI75XNCrD4fApPsZOhw8SgFgsBjEaQ48nlJHr5ILNZiiaz9KHn6lwHP25KirMWbvu1q1bcfvttyMWi2HdunW46qqrBr3mhRdewO9//3sIgoBZs2bh17/+ddbGNxJnIDwgYW3SqNDrY8KaiDLL6Q/DqPk8dWY6krAmosxiwjoJnmAUetWx29AYqIgo847elq9Ti/CFWWFNVIh6enqgUqlgsVgQCATw5ptv4sorrxzwmrq6OjzzzDNYuHAhXnzxRSxevHhQn+ts8YWi0Ko+P2yaZ3cQFa5oNIpbb70VDz30EOx2O9auXYu6ujpMmzat/zUNDQ24//778cQTT8BqtaK7uzuHIx7IFYjAcFSVo1ErockZyOGIiGgsiMeezwsXTVru7iDKBiask+A+tsJazcOHiCjzZFmGPzywwtrHliBEBamjowM/+clPEI1GIcsyzjvvPJx11lm49957cfzxx+Pss8/G2rVr8aMf/QjLli2D1WrFPffck7Px+kJRaKT43IeHTRMVtvr6etTW1qKmpgYAsHLlSmzevHlAwvrJJ5/EpZdeCqvVCgAoKyvLyViH4goM3IJv1KiYNCKijHP6B+7uMKolVlgTZQET1klwBwf2TTNoJDQ6uKpPRJkViMSgEkVIYrzKUa8WmbAmKlCzZs3Chg0bBj1+/fXX9/+3VqvFb3/72yyOanje0Ofnd2hVIqIxGaFIDBoVz+0mKjTt7e2oqqrq/9put6O+vn7AaxoaGgAAX/7ylxGLxfD9738fS5YsyeYwh+U8psLapOVuVyLKPMcxLUEMWgkOFi4SZRwT1klwBSIYb9X1f23USHBzkkREGeYNRQdsQ9OoRISiMURjcn8Sm4goEzzBKLRHktOCIMCkleAKhFFu0uZ4ZESUCdFoFIcOHcJjjz2GtrY2XHbZZXjuuedgsViG/Z5MHfp6LG84hnKrDiaTFqIoorLEAHcwmrPDK/Ph4EyOgWPItzEUI2cgjAk2ff/XRo0Kvb5QDkdENDYwYZ0ETzAC/TEtQVzcGktEGeY9ZneHKAjQqkT4w1GYtAzjRJQ5vnAUdt3nW/D1agneUBTlORwTEaXGbrejra2t/+v29nbY7fZBr5k/fz7UajVqamowadIkNDQ0YN68ecO+b6YOfT2W0xeCEInB4wnCZNJCiETh9IfR2+vNSZ//fDgQlGPgGJQYQzYPfi1EDn8EMyo/vxfTqUS2hiXKAu7nTIInGB3UEsQT5LZ8Isqs+IGL0oDH9GoJHi6YEVGGeUORAfFHoxIRCMdyOCIiStXcuXPR0NCAxsZGhEIhbNq0CXV1dQNec84552D79u0A4ofENjQ09Pe8zjV3cGBLELUUb5fmZ0wiogxyBcIwHdUSRKeW4A3xPowo01ialwRv6Jge1kwYEVEWxHvIDlxf1B2pciQiyiRf6POWIACgleK7O4io8KhUKqxfvx5XXHEFotEoLrnkEkyfPn3Aoa9nnnkm3njjDaxYsQKSJOGmm25CSUlJroeOmCzDF4rCoB64gG/SquAMhAcksomIlBQvXPx8LqRTifBxLkSUcUxYJ8FzzLZ8vVqCPxxlH1kiyihvMNp/6FkfHrxIRNkQ3+ExsIe+P8LYQ1Soli5diqVLlw547OhDXwVBwM0334ybb74520MbkTcY320mHnPPFd/xygIiIsocfzgK7VG7zXRqkYVDRFnAliBJ8Iai0B918JkoCtwOQkQZ5wsPTBgBgF7F2ENEmecPRaE9asFMI4ncfk9EWecKhmEcooparxLhZYtGIsog/zH3YloVC4eIsoEJ6wTFZBn+cBT6Y/rIGtUSXAEmjYgoczzBgVvyAUDLlX0iyoJjb9LiPawZe4gou9yBCAzawQlrVjoSUSbJsoxgJDawPZpKYns0oixgwjpBvlAUGpXIbWhElHXeUGRQwlqn4g0aEWXesTs8NJLAhDURZZ0rEIFRPUTCmjvOiCiDApEYVOLAPJBOxfM8iLKBCesEeYKRQYd8APFVfTbcJ6JM8gQj0B2zu0Or4qGLRJRZsizDHxpYVaRmSxAiygF3MAL9EC1BuOOMiDLJF4pCpz6mcEgtwRfiXIgo05iwTpAnGB3y9GmtSoSfwYqIMsgTHDxR0qpEeLm7g4gyKBSVIQiASjq6wppVRUSUfX2HLh5LKzFhTUSZ4wsNPktIIwmIxGKIxOQcjYpobGDCOkGeYAT6ISqsNSqJFdZElFHe0OD4o1OJ8PCQISLKIN8QsUfLbbBElAPe8ODzPAAu4BNRZvnCUeiOmQsJgnCkcJHzIaJMUmXqjW+++Wa8+uqrKCsrw/PPPz/o+QceeADPPfccACAajWL//v146623YLPZUFdXB6PRCFEUIUkS/vGPf2RqmAlzD5Ow1koMVESUWd4hDl3UqdmzkYgyyzvENlhWWBNRLvhCEWiGSFjr1RI8vBcjogzxh4ZeLNOp44WLZl3GUmpEY17GfrvWrFmDyy67DD/+8Y+HfP6KK67AFVdcAQDYsmULHn74Ydhstv7nH3nkEZSWlmZqeEnzhwdvBQEArUpghTURZVS8wvrYhLWITk8wRyMiorHAH44OWqzXqAQ4fJz3EFF2xVuCDF1h7QxwAZ+IMuPYw6f76FUifFwsI8qojLUEOemkk2C1WhN67aZNm3DBBRdkaiiK8IWiQ67qa7g1logyzBuKQqsaoiUIJ0lElEG+IaqKtCoeNk1E2ecdoo8sEK+wZksQIsoU3xD3YQCgVUvwcbcrUUblfP/C/2fvzuOjqu/98b/OMmfWZCbrJJCwBIIioIhgbbFEg2gLriz1+r3cem0p3+v+/WmvVtuitUV77bdU79dbba+92GrV1q2o2IqCgLjhBlH2LZAAmezLLGdmzvL7Y0hMSGbmzMw5s+X9fDx8XDIzmXmPt3w85/15f97vQCCAd999Fz/96U+HPP79738fDMPg2muvxbXXXqvpvTiOgctlMyJMwMTBbjHB4TADAFiWhcNhRoFVgMwY+Lk64Tg262M8Xa7FnGvxArkZ82gkSsqISSORkkaEEAP5R+gZSy1BCCGZ4AtJKCy0DHvcYqKhi4QQ40SuhZhhj1t4WnsIMVrGE9bvvPMOZs2aNaQdyHPPPQe3242Ojg7ccMMNqKmpwZw5c+K+lyyr6O72GxJnZ48IRlHgPXUE3+EwR/6sqOjsCxr2uXpxuWxZH+Ppci3mXIsXyK6Yy8oKMh1C1hLDMgTutKQRz0IMKxmKiBAyGgTDyrC1hzbLCCGZ4IvWR5bnKGlESJaJN8/so48+wk033YSqqioAwIIFC3DLLbcAALZu3YrVq1dDURQsW7YMK1euTGvspwtEOWlPQ6gJMZ5hLUG0Wr9+PRYtWjTkMbfbDQAoKSnBggUL0NDQkInQhgiER16oLDxLR0EIIYYKSsqw9UfgWIgSXSQRQowjSgoEbmhVkcCzCEi0WUYISa+RWhQB/RXWdC9GSDZZvHgxnnzyyZivmT17NtatW4d169YNJKtlWcYDDzyAJ598EuvXr8frr7+OgwcPpiPkqEY6bQbQZhkh6ZDRhHVfXx8+/vhjzJ8/f+Axv98Pr9c78Of33nsPtbW1mQpxwEg9ZIFTvRxpoSKEGGikpJGZZxGkpBEhxEBBSQZ/+ukOjiqsCSHp5w/JsJhGSlhzdC9GSJZJZJ7ZYA0NDRg/fjyqq6shCAIWLVqEjRs3GhChdv7Q8JOuQGQINa09hBjLsJYgd9xxB7Zv346uri7MmzcPt956KyQpsvt93XXXAQDeeustzJ07FzbbVz10Ozo6cPPNNwOI7LBdfvnlmDdvnlFhahYIyXDZhv/rouFDhOSWkydP4q677kJHRwcYhsF3vvMdXH/99UNeE+uYWrqpqoqQNPxYfiRpRAlrQohxgpIC0wibZbT2EELSzR+WYRmheMhC92KE5KQdO3bgyiuvRHl5Oe6++27U1tbC4/GgoqJi4DVut1vTaXsjZ5mFwaDQLsDhMA/MMQOAAqsAJcvnQeXavKpcijeXYgVyL95+hiWs16xZE/c1ixcvxuLFi4c8Vl1djVdffdWosJLmD0tw8+Zhj1OFNSG5heM4/OhHP8K0adPg9XqxZMkSzJ07F5MnTx7yutmzZ+N3v/tdhqL8Sn/CiGVPO5ZPLUEIIQYTwwpMI22W0dpDCEkzf0iGJWp7RhmqqoJhhg9GI4Rkn2nTpmHTpk2w2+3YsmULbr75ZmzYsCHp9zNylllXn4gSuwCvN/jVHDMAjKqisyeQNfOgRpJN86q0yKV4cylWIPvjjTbPLOM9rHOFP6yM2Lso0myfKo0IyRXl5eWYNm0aAMDhcKCmpgYejyfDUUUnhof3rwYifWSDkgJVVTMQFSFkNAhKCkwsVVgTQjIvEKWPLM+x4FmG2qQRkkMcDgfsdjsAoK6uDpIkobOzE263Gy0tLQOv83g8A/PNMsUflmEeqR0Rz8JLhYuEGIoS1hoFogz6oOmwhOSu5uZm7NmzB+ecc86w5/qPqa1YsQIHDhzIQHQRoiTDPELfNI5lwDIMwjIlrAkhxgiE5RErrGmzjBCSTqqqIhCWYTENbwkCRPpY0/AzQnJHW1vbwHVEQ0MDFEVBUVERZsyYgcbGRjQ1NSEUCmH9+vWor6/PaKzRBr6aeZbWHUIMZlhLkHwTCI/cbN/Mc5SwJiQH+Xw+3Hbbbbj33nvhcDiGPJfMMTWjeqe1hxRYBX6gX9rg3mlmnoXZbobTatL9c9MlV/tpjSSfvguQX98nn75LOokj9M9nWQY8F6lmjJY8IoQQPQUlBRzLgGNHbvlhPZWwLrGnOTBCyIjizTN788038dxzz4HjOFgsFqxZswYMw4DneaxatQorVqyALMtYsmQJamtrM/pdovbPN7E42StlICJCRg9KWGsU7RiahSqsCck54XAYt912G6644gpceumlw54fnMCuq6vDz372M3R2dqK4uDjqexrVO62tyweexUC/tMG90wSeRUu7F2rB8P76uSLb+2klIp++C5Bf30fLd4nWO200E8MyCi0jD5wWw5SwJoSkhz8swxpjvbGaWPhClDgiJFvEm2e2fPlyLF++fMTn6urqUFdXZ0RYSYlWYS1w1BqWEKNRSxCNRGnkHtbUR5aQ3KKqKn784x+jpqYGN9xww4iviXZMLRPE8PAKx35mjoVIG2aEEIOIkjxiD30zzyFAgxcJIWkSbeBiPwvPwRekNYkQor9ohYuRzXtadwgxElVYaxRtoeJYBjzL0tFYQnLEp59+inXr1mHKlCm46qqrAESOrZ04cQJA7GNqmRBt7QFOXSjRkCFCiEGibZiZOYZOlxFC0sYXkmGOcZ9FM4UIIUYRw8qIG2YCxyJA92GEGIoS1hqJ4ZErrIFI/yJ/jEEghJDsMXv2bOzbty/ma2IdU0s3UVKGDT3rJ9DOPiHEQJH1Z/hmnZnnINIxWEJImgTiVFgLlLAmhBhElKK0BOFZBOlaiBBDUcJaA0lWoKgq+CiDPiw8C39IRjHNcyKE6EwMj3wkH4js7FPSiJDccvLkSdx1113o6OgAwzD4zne+g+uvv37Iaz766CPcdNNNqKqqAgAsWLAAt9xyS9pjDY4wdBEABJ4qrAkh6eMLx0lYc5SwJoToT1VVBKMUDwkcC5HaoxFiKEpYaxAIK7DwXNSWAGYTRxdJhBBDiGEZQpTNMqooIiT3cByHH/3oR5g2bRq8Xi+WLFmCuXPnYvLkyUNeN3v2bPzud7/LUJQRYlge8SbNxLEIybRZRghJD3/cliAMDT8jhOguJKtgGQbcCPdiArVmJMRwNHRRA39YhtkUa9BHpMKaEEL0FrMlCMfQhRIhOaa8vBzTpk0DADgcDtTU1MDj8WQ4qpFFKqyH36SZODoGSwhJH39IgjnKtRBAG/iEEGOIsWYJ0bUQIYajhLUGcfumcZEe1oQQojcxrMAUsyUIrT2E5Krm5mbs2bMH55xzzrDnduzYgSuvvBIrVqzAgQMHMhAdEJKVEVsSmVgGQdosI4SkiS8UPWkEnLoXo+IhQojORCn6HLNIhbUMVVXTHBUhowe1BNEgEKXRfj8zT31kCSHGCITlESscgUiVI02nJiQ3+Xw+3Hbbbbj33nvhcDiGPDdt2jRs2rQJdrsdW7Zswc0334wNGzbEfD+OY+By6TtMIySrcBVa4LAJYFkWDocZAGC18ODMvO6fl24cx+b8dzgdfafckanvtXXrVqxevRqKomDZsmVYuXLlkOdffvllPPzww3C73QAig6iXLVuW9jgHE8Mjn/boZ6bTroQQA8SqsOZYBgwYSIo64oBqQkjqKGGtgT8kw8xH75tGDfcJIUbxh+URh54BgIljqMKakBwUDodx22234YorrsCll1467PnBCey6ujr87Gc/Q2dnJ4qLi6O+pyyr6O726xqnGJYRFiV4FRUOhxlebzDyhKKiq1fU/fPSzeWy5fx3OB19p9wx+HuVlRWk5TNlWcYDDzyAtWvXwu12Y+nSpaivrx/WQ3/hwoVYtWpVWmLSIhBjADUQuRfrEaU0RkQIGQ0im2UxChdNkcLFaO0bCSGpob9ZGohhBWY++q6ZiQZ9EEIMEohxDFbgqGcjIblGVVX8+Mc/Rk1NDW644YYRX9PW1jZwxLShoQGKoqCoqCidYQKI9LAeqWqIWoIQkpsaGhowfvx4VFdXQxAELFq0CBs3bsx0WHHFawliMVGFNSFEf6IUe7PMTPdihBiKKqw18IdlCLEqrFnqI0sIMYYoySjlhRGfiwwZoqQRIbnk008/xbp16zBlyhRcddVVAIA77rgDJ06cAABcd911ePPNN/Hcc8+B4zhYLBasWbMGDJPe46ayokJWVPDs8M/lWQZBOllGSM7xeDyoqKgY+NntdqOhoWHY6zZs2ICPP/4YEydOxD333IPKysp0hjlMICTDaY1+20ob+IQQI8StsOZZiLSBT4hhKGGtQSAswxyjL5GJo0ojQogxAjEulMw08JWQnDN79mzs27cv5muWL1+O5cuXpymikQVPDRoaKVFu4li67iEkT1188cW4/PLLIQgCnn/+edx9993405/+FPN3jOihP5gEwOkwD/TRBzCkr76rwIKQ0pP2nuDZ0F+dYqAYsi2GfBKvwlrgqXCRECNRwloDMUYPWSCyUNExNEKIEcQYfRsFnkW7L5TmiAgho0GsmzQTR8OmCclFbrcbLS0tAz97PJ6B4Yr9BrcfWrZsGX71q1/FfV8jeugP1uMPQZEsX/XRB4b01VfCMrxiOO29zrOhvzrFQDHoEUO6+ujnmngDX+l0ByHGoh7WGohhBaY4gz5ooSKEGEGUoldYmziGjqERQgwRjLH2CBy1BCEkF82YMQONjY1oampCKBTC+vXrUV9fP+Q1ra2tA3/etGkTJk2alO4whxHDsXtYm6lFGiHEAIGwHHOgIrUEIcRYVGGtgSjJMI3Qw7GfwLPwBenGjRCiv1hDX810DI0QYpBYVUUmjkVvUEpzRISQVPE8j1WrVmHFihWQZRlLlixBbW0tHn30UUyfPh3z58/H008/jU2bNoHjODidTjz00EOZDhuBsBIzYU3H8gkhRogUDsVuDUsnzggxjmEJ63vuuQebN29GSUkJXn/99WHPf/TRR7jppptQVVUFAFiwYAFuueUWAMDWrVuxevVqKIqCZcuWYeXKlUaFqYk/pMTuXcSxaAvTsXxCiP5itSSKnO6giyRCiP6Ckhz1dBnN7iAkd9XV1aGurm7IY7fffvvAn++8807ceeed6Q4rpsg8oej3YhaqsCaEGEAMy+BjtYblaLOMECMZlrBevHgxli9fjrvvvjvqa2bPno3f/e53Qx6TZRkPPPAA1q5dC7fbjaVLl6K+vh6TJ082KtS4Ahp6WIt0NJYQYoBYLUFo7SGEGEWMMfDVxLEIUnKIEJImohS/eIiuhwghehPjbJbRvRghxjKsh/WcOXPgdDoT/r2GhgaMHz8e1dXVEAQBixYtwsaNGw2IULu4QxfpKAghxCBBSYnaO03gWKpyJIQYInYPa+rZSAhJn3g9rE0cA1lRISlqGqMihOS7QIz7MIDyQIQYLaM9rHfs2IErr7wS5eXluPvuu1FbWwuPx4OKioqB17jdbjQ0NGh6P45j4HLZdI9TZhgUOgQ4HOaBx1iWHfjZVWBBWFEN+Wy9cByb1fGNJNdizrV4gdyMebQJxqgqomP5hBCjiJICU5S+jTzHICTT2kMISY9Yp80AgGGYgbkeDjONaCKE6CMQkmE2xdosYxGgliCEGCZj/0WfNm0aNm3aBLvdji1btuDmm2/Ghg0bUnpPWVbR3e3XKcKv9PpDUMIWeL3BgcccDvPAz3JIgi8oGfLZenG5bFkd30hyLeZcixfIrpjLygoyHULWUVUVYTl60kjgWIQoYU0IMUBQit0/P0hHYAkhaaCoKkJxWoIAgIXn4A9RwpoQop+ApKDAEn1NoR7WhBjLsJYg8TgcDtjtdgCR4R+SJKGzsxNutxstLS0Dr/N4PHC73ZkKE0BkVz/2MTRaqAgh+gvJKjiWAcuMnLA2Uc9GQohBREkBz0Zbe+h0ByEkPfpbo0W7Fupn5qnSkRCiLzEsx9wsM3EsAnQ9RIhhMpawbmtrg6pG+ow1NDRAURQUFRVhxowZaGxsRFNTE0KhENavX4/6+vpMhQkgfg9rM099ZAkh+ot3kSRwDEIS9WskhOgvFKNvo4n65xNC0sQfkmGJcSS/n9lECWtCiL7iDV008ywCIVp3CDGKYWem7rjjDmzfvh1dXV2YN28ebr31VkiSBAC47rrr8Oabb+K5554Dx3GwWCxYs2YNGIYBz/NYtWoVVqxYAVmWsWTJEtTW1hoVpiaxesgC/UdB6MaNEKKvoKTEvEjiWAYqIkOGolVCEkJIMkIx2hGZqIc1ISRNAnESRv3MHIsA3Y8RQnQkxssD8Qy6ArTuEGIUwxLWa9asifn88uXLsXz58hGfq6urQ11dnRFhJSUQlmNPh+VZiFRpRAjRWbzNMoZhBnrJ8gL1bCSE6CcoKeBj9s+n0x2EEOOJ4ditGfuZeRZ+qrAmJCvcc8892Lx5M0pKSvD6668Pe/7VV1/Ff//3fwMA7HY77r//fpx55pkAgPr6etjtdrAsC47j8PLLL6c19sECcU7aUw9rQoxFGQ4NgnEmU5s4BmFZgaKqcfurEUKIVpG+jbHXFOHU0Xy7kKagCCGjQjAsw8RSSxBCSGYF4rRH6yfwlDgiJFssXrwYy5cvx9133z3i81VVVXjmmWfgdDqxZcsW/PSnP8ULL7ww8Pwf//hHFBcXpyvcqMSwAiHGvZhAvfMJMVTGeljnkkiVY/SFimUYunkjhOhOlGLv6gP9N2i09hBC9CXGqLA2sZGN+v5ZJIQQYpRAWNZUYS1wlDgiJFvMmTMHTqcz6vOzZs0aeH7mzJloaWlJV2gJ0dIalloREWIcqrDWIF6FNRA5hiaGZVhNXJqiIoTkOy1rj8AxtFlGCNFdUFKiVlizLAOOZRCW1Zgb+oQQkqqAxpYgkUpHuh4iJNe8+OKLmDdv3pDHvv/974NhGFx77bW49tpr474HxzFwuWy6xxaSFRQVWuGwmQAALMvC4TAPPO8KhCGpqiGfrQeOY7M2tpHkUry5FCuQe/H2o4R1HNKpVh/xBpqZqY81IURnooaWICY+0sOaEEL0FJQUFFqiXyb2tyPSclSfEEKSJcbpIduPeskSkns+/PBDvPjii3j22WcHHnvuuefgdrvR0dGBG264ATU1NZgzZ07M95FlFd3dft3jC4RlhINheJVInsfhMMPrDQ48L4Vk+IOSIZ+tB5fLlrWxjSSX4s2lWIHsj7esrGDEx+kuIw5RiuzqM3F6U1P/IkKI3iI9rONVWFM7IkKI/sQ464/AM7RZRggxnNYe1iaOoXsxQnLI3r178ZOf/AS//e1vUVRUNPC42+0GAJSUlGDBggVoaGjISHyqqsY97UonXQkxFiWs4+hPWMdj5qiPLCFEX1qqigRaewghBgjFOeFh4uhkGSHEeAEN7dGAyL2YP0RrEiG54MSJE7j11lvx8MMPY+LEiQOP+/1+eL3egT+/9957qK2tzUiMYVkFg0gLtGgEuhYixFDUEiQOzcfQeBYiVRoRQnQU1NIShGPoQokQorugHLvC2sSxCMm09hBCjCWGZU298um0KyHZ44477sD27dvR1dWFefPm4dZbb4UkSQCA6667Dv/1X/+F7u5u/OxnPwMAcByHl19+GR0dHbj55psBALIs4/LLLx/W3zpdREmG2RQ7D2TiWIToPowQw1DCOg4xrK0/o8AxNOiDEKIrbQlr6mFNCNFfvPWHjsESQtLBH9JWPGTmWfSKoTRERAiJZ82aNTGfX716NVavXj3s8erqarz66qtGhZWQeO1AALoWIsRo1BIkDlHSXmEdpF19QrLeyZMn8S//8i9YuHAhFi1ahD/+8Y/DXqOqKn7xi19gwYIFuOKKK7Br164MRBq5UOLjrD8mli6UCCH6C8XpYW3iWARpo54QYjB/AqddqcKaEKIXMRy/NSzHMlBUFZKipikqQkYXqrCOQ3uFNUsV1oTkAI7j8KMf/QjTpk2D1+vFkiVLMHfuXEyePHngNVu3bkVjYyM2bNiAnTt34v7778cLL7yQ9ljFsAxTjL5pAPWRJYQYIygpMdcfEw18JYSkQSAko9AS/5bVzFHCmhCiHy0nXRmGiRQuSjJ4gVJrhOiNKqzjECUZZg27+iaOoWP5hOSA8vJyTJs2DQDgcDhQU1MDj8cz5DUbN27E1VdfDYZhMHPmTPT29qK1tTXtsYoajqKZ6CgaIcQAobg9rBkEqYc1IcRgoiRrKx7iaQg1IUQ/Wk/amzlaewgxCm0DxSGGFZg0VlhTlSMhuaW5uRl79uzBOeecM+Rxj8eDioqKgZ8rKirg8XhQXl4e9b04joHLZdM1PoVl4LAJcDjMA4+xLDvkZ5vFBIbndP/sdOE4NmdjP10+fRcgv75PPn2XdAnFqSyiCmtCSDoEQvE374H+065UPEQI0UdQ0n7Snq6HCDEGJazjECUZQpwj+QDA084aITnF5/Phtttuw7333guHw5Hy+8myiu5uvw6RfaXXG4LTysPrDQ485nCYh/wMRUF3n6j7Z6eLy2XL2dhPl0/fBciv76Plu5SVFaQpmtwQjFdhzTII0Q0aIcRgoiTH7SMLRIYuUvEQIUQvYliBEKclCHDqdAedtCfEENQSJI6A5gprhnb1CckR4XAYt912G6644gpceumlw553u91oaWkZ+LmlpQVutzudIQLQdgyWelgTQowQitfDmqWWIIQQ4wXCGiuseRYi3YsRQnQSlOSYG/f9qMKaEONQwjoOMSxDYOkYGiH5QlVV/PjHP0ZNTQ1uuOGGEV9TX1+Pv/3tb1BVFTt27EBBQUHMdiBG0dLDWuDoBo0Qor9wnAprnm7QCCFpEAhr62FNfWQJIXoSpdjXQf0EnqG1hxCDUEuQOERJAa/lKAjHwheipBEh2e7TTz/FunXrMGXKFFx11VUAgDvuuAMnTpwAAFx33XWoq6vDli1bsGDBAlitVjz44IMZiVXLdGoTx6ArQBdJhOSKkydP4q677kJHRwcYhsF3vvMdXH/99UNeo6oqVq9ejS1btsBiseCXv/zlwLDYdFBVFWFZjXn9w3PUEoQQYrygpO1YPrUEIYToKVK4GH/tiZx2pTwQIUaghHUcYlhb3zSBZ9HuC6UhIkJIKmbPno19+/bFfA3DMLjvvvvSFFF0Yjj+zr7AsQhShTUhOYPjOPzoRz/CtGnT4PV6sWTJEsydOxeTJ08eeM3WrVvR2NiIDRs2YOfOnbj//vvxwgsvpC3G0KlkNcvEbglCCWtCiNG03ouZOAZhWYGiqjHXLkII0UJzhTXHIkgV1oQYglqCxBHQkDACIhdJdDSWEKKnoCTHbQli4hiqKCIkh5SXlw9USzscDtTU1MDj8Qx5zcaNG3H11VeDYRjMnDkTvb29aG1tTVuMWvo2UkURISQdREnR1BKEYZhTfazpmogQkrqgpMDEa6mwpnsxQoxCFdZxBMIynJb4/5poOiwhRG9ajsHS0EVCcldzczP27NmDc845Z8jjHo8HFRUVAz9XVFTA4/HE7KXPcQxcLpsucYX6RJh5Fg6HeeAxlh36s8Nmggr9PjMTOI7N6fhHQt8pd2Tqe23duhWrV6+GoihYtmwZVq5cOeLr3nzzTdx222148cUXMWPGjDRH+ZWghnke/cx8ZKaQTeAMjooQku/EsAyThllmJponRIhhDEtY33PPPdi8eTNKSkrw+uuvD3v+1VdfxX//938DAOx2O+6//36ceeaZACIDz+x2O1iWBcdxePnll40KMy4xLKPMIcR9nUCDPgghOgvFGXoGRDbLgrRZRkjO8fl8uO2223DvvffC4XCk/H6yrKK7269DZEBbTwA8y8DrDQ485nCYh/yshBX4w7Jun5kJLpctp+MfCX2n3DH4e5WVFaTlM2VZxgMPPIC1a9fC7XZj6dKlqK+vH9KSCAC8Xi/+9Kc/DdtMSzfpVIsPXkMfWeCrhDUhhKRK60l7gU7aE2IYw1qCLF68GE8++WTU56uqqvDMM8/gtddew4033oif/vSnQ57/4x//iHXr1mU0WQ2cOoamsXcRVVgTQvSk5RgsXSQRknvC4TBuu+02XHHFFbj00kuHPe92u9HS0jLwc0tLC9xud9riC2ro28jTEVhCck5DQwPGjx+P6upqCIKARYsWYePGjcNe9+ijj+IHP/gBzGbzCO+SPqKkwMyzYDT2pKbBi4QQvYiSrGngK0+nXQkxjGEJ6zlz5sDpdEZ9ftasWQPPz5w5c8iNWTYRw9r6plHPNEKI3kKSApOGliCUsCYkd6iqih//+MeoqanBDTfcMOJr6uvr8be//Q2qqmLHjh0oKCiI2Q5EbyFJgSlORaPAsQjRRj0hOeX0dkNut3tYD/1du3ahpaUFF110UZqjGy4QlmHmtbf3MPN0NJ8Qog+teSATy9BpV0IMkhU9rF988UXMmzdvyGPf//73wTAMrr32Wlx77bWa3kfP/o39JFWF02Ee0rcRGN7LsUhREVbUrO2xl4v9/3It5lyLF8jNmEcTLX0bTRyLECWsCckZn376KdatW4cpU6bgqquuAgDccccdOHHiBADguuuuQ11dHbZs2YIFCxbAarXiwQcfTGuMQQ2nO3iOQVBS0xQRISQdFEXBL3/5Szz00EMJ/Z4R92AA0COrsArcsPswYPi9GADYBB6c2ZS2a9tsuI6mGCiGbIshXwQlWfNJe3+I7sUIMULGE9YffvghXnzxRTz77LMDjz333HNwu93o6OjADTfcgJqaGsyZMyfue+nZv7GfLyhBDklD+jYCw3s5hoMSAqHs7eWYi/3/ci3mXIsXyK6Y09W/MVfIigpZid+3UaBjaITklNmzZ2Pfvn0xX8MwDO677740RTRcUEOFdeR0B1UUEZJLTm835PF4hrQb8vl82L9/P7773e8CANra2nDjjTfi8ccfjzl40Yh7MADwdPiG9dPvd/q9GACwDNDW5Ud3t1X3WEaSDdfRFAPFoEcMdB82XKSHdfyWIAJPrWEJMYphLUG02Lt3L37yk5/gt7/9LYqKigYe779wKikpwYIFC9DQ0JCpECGGZe0tQWihIoTopL+HbLy+jQLHUIU1IURXmga+Uv98QnLOjBkz0NjYiKamJoRCIaxfvx719fUDzxcUFOCjjz7Cpk2bsGnTJsycOTNustpIoqTArKHCsZ+Zo5YghBB9aJklBESKh4LUGpYQQ2QsYX3ixAnceuutePjhhzFx4sSBx/1+P7xe78Cf33vvPdTW1mYqTE1H8oH+Xo50NJYQoo+gJMOspW8axyIkK1BVWn8IIfoISgr4OFVFPBtZewghuYPneaxatQorVqzAwoUL8e1vfxu1tbV49NFHRxy+mGmRHtbab1dNHM0UIoToIyjJcTfvAcDEMQhQ4SIhhtDUEuSWW27B0qVLMW/ePLCstouGO+64A9u3b0dXVxfmzZuHW2+9FZIkAYj0Z/yv//ovdHd342c/+xkAgOM4vPzyy+jo6MDNN98MAJBlGZdffvmw/tbpJGpMWHOnjs5KsgI+gUoAQkjyklmbckVksyz+MTSOZcCAgaSomo6tEUL0k69rUKQlSLz++VRhTUimJbMG1dXVoa6ubshjt99++4ivffrpp1OOMRWixiP5/cw8gwBVWBOiq3y91olHDGssXOSpwpoQo2hKWP+v//W/8NJLL+EXv/gFvvWtb2Hx4sWoqamJ+Ttr1qyJ+fzq1auxevXqYY9XV1fj1Vdf1RJWWmgZPNTPzEd6yTooYU1IWiSzNuUKrcfQAEDgmVM3dbT2EJJO+boGhaT4SSITnSwjJOPydQ3qJyZRYU0Ja0L0le/rTDQhjcVDJo5awxJiFE0J62984xv4xje+gb6+Prz++uu44YYbUFlZiWXLluHKK6+EyWQyOs6MUFVV001bP4GP9E1zmDM+y5KQUSGf16b+HtZamPnI8LOCzM/RJWRUydc1SEtLEBPHUEsQQjIsX9egfoGwtiP5/QRKWBOiu3xfZ6LRetJe4CJFi4QQ/Wm+Aujq6sLLL7+MF154AVOnTsV3v/td7N69G9/73veMjC+jQrIKnmPAxhl61q+/wpoQkj75ujaJYVnTRRLQv7NPaw8hmZCPa1BIVsDHOfYbmd1B6w4hmZaPa1A/rQmjfmaehZ+O5hOiu3xeZ6IJaSweoiHUhBhHUznezTffjCNHjuCqq67CE088gfLycgDAwoULsXjxYkMDzCQxLCc0mVrgGBr0QUga5fPapLWHNXBqOjVdKBGSdvm6BomSAhMbb+giVVgTkmn5ugb1C4RlCLz2HtYCz8IXpAprQvSU7+tMNFrbM0aGvdK6Q4gRNCWsv/Od7wwbzhEKhSAIAl5++WVDAssGifSQBU61BKH+RYSkTT6vTYm0BKGENSGZka9rkLaWICwlrAnJsHxdg/qJYTnuANjBzDyLNm/QwIgIGX2SWWfuuecebN68GSUlJXj99deHPa+qKlavXo0tW7bAYrHgl7/8JaZNmwYAeOWVV/D4448DAG688UZcc801On+j+FRVRVjW1hpW4Ok+jBCjaLoCeOSRR4Y9du211+odS9ZJdNCHwLFUYU1IGuXz2pRIwtrEMbRZRkgG5OsapKUlEccyYMBAoqQ1IRmTr2tQP39YSfheLBCiNYkQPSWzzixevBhPPvlk1Oe3bt2KxsZGbNiwAT//+c9x//33AwC6u7vx2GOP4a9//SteeOEFPPbYY+jp6Ukl/KT0b9xraQ1LhUOEGCdmhXVbWxs8Hg9EUcTu3buhqpFp8F6vF4FAIC0BZlKifdNosSIkPUbD2iRKckIDX2ntISR98n0NCkkKzCYNx2B5BkFZAZ/AtRIhJHX5vgb1C4Rk2AVO8+vNPIsAbeATootU1pk5c+agubk56vMbN27E1VdfDYZhMHPmTPT29qK1tRXbt2/H3Llz4XK5AABz587Fu+++i8svv1y376VFUFI0t4alHtaEGCdmwnrbtm14+eWX0dLSgoceemjgcbvdjjvuuMPw4DJNDMsJtQQxUUsQQtJiNKxNkQprbQlrE8ciSKc7CEmbfF+DRElBgSV+17j+wYt2IQ1BEUIG5Psa1C8QllFkM2l+vcCzCFAvWUJ0YeQ64/F4UFFRMfBzRUUFPB7PsMfdbjc8Hk/c9+M4Bi6XLaWYBgswIswmDg6HecjjLMsOe8yqqAhKCpxOKxgNFdnpxHGsrv9ejJZL8eZSrEDuxdsv5t3INddcg2uuuQZvvvkmLrvssnTFlDUSrrBmaegiIekwGtamoKRo7ttoYmlnn5B0yvc1SGtLIhOdLCMkI/J9DeoXSLA9o5naMxKim1xaZ2RZRXe3X7f3a+30w8Qy8J7WE9/hMA97DIi0SWvr8CVU7JgOLpdN138vRsuleHMpViD74y0rKxjx8ZgJ63Xr1uGqq67C8ePHsXbt2mHP33DDDfpEl6XEsPYKR4AqrAlJl9GwNgUlBSY+gQprShoRkjb5vgZpPeFBx2AJyYx8X4P6BcJKQgkgqrAmRD9GrjNutxstLS0DP7e0tMDtdsPtdmP79u0Dj3s8Hpx//vlJf06yglJia4/5VB4o2xLWhOS6mAnr/t5Efn/2ZuKNFJTiDx0azEQV1oSkxWhYm8SwrLnCWqChi4SkVb6vQVorrHmORYiGLhKSdvm+BvUTJVlzH1mgP2lEaxIhejBynamvr8czzzyDRYsWYefOnSgoKEB5eTkuvPBCrFmzZmDQ4rZt2zLS5iiZWWZiWEGhxcCgCBmFYias/+mf/gkAcMstt6QlmGwTqbBOIGHN0a4+IekwGtamQAInPHiqsCYkrfJ9DYq0JKIKa0KyVb6vQf0CYRmCxtNmQH9LELoXI0QPqawzd9xxB7Zv346uri7MmzcPt956KyRJAgBcd911qKurw5YtW7BgwQJYrVY8+OCDAACXy4WbbroJS5cuBQDcfPPNAwMY0ynRWWYCbZYRYghNfwsffvhheL1ehMNhXH/99bjggguwbt06o2PLOFFSErtIooWKkLTK57UpkRMeJpahtYeQDMjXNSgky9p6WLO0WUZIJuXrGtRPDCdY5chH1iRVVQ2MipDRJZl1Zs2aNdi2bRt27dqFrVu3YtmyZbjuuutw3XXXAQAYhsF9992Ht99+G6+99hpmzJgx8LtLly7FW2+9hbfeegtLliwx9LtFE6mw1p4HEnjaLCPECJquAN577z04HA5s3rwZY8eOxVtvvYU//OEPRseWcUFJ2w1bPxPHIhCihYqQdMnntSmREx4mqigiJCPydQ3S2sPaxDHUEoSQDMrXNaifmODQRY5lwDIMQjIlrAnRS76vMyMRw4nlgcx04owQQ2j6WyjLkUTI5s2b8a1vfQsFBSNPcMw3Yljbkdh+AsdQSxBC0iif16ZETniYOOqfT0gm5OsapLWHtYljEaS1h5CMydc1qJ+Y4OAzALDQ4EVCdJXv68xIEq6w5liaJ0SIATRdAVx00UX41re+hV27duHrX/86Ojs7YTabjY4t4/xJ9S6ihYqQdMnntUlrwgigtYeQTMnXNSgka6uw5lmqsCYkk/J1DeonhpWEhi4Cp1o0UsKaEN3k+zozkmRmmVHxECH6izl0sd8Pf/hDrFixAgUFBeA4DlarFb/97W+Nji3jxLD2HrJA5AIpEKKFipB0yee1SUygJZFAF0mEZES+rkEhSdV0/WOiga+EZFS+rkEAED61GcYnkbAO0DURIbrJ53UmmoRbw9IsM0IMoSlhDQCHDx/G8ePHB46EAMDVV19tRExZQwwrKLKZNL+ejoIQkn75ujYFJe2DhkzUN42QjMnHNSgkK+A1tETjae0hJOPycQ0CTlVXmxJLVgOAmefgpwprQnSVr+tMNKLGWR79BJZBkPJAhOhOU8L63//939HU1IQzzzwTHMcBiEx2zedFCoi0BHHz2o+7RBLWdONGSLrk89oUTKB3WqTKkS6SCEm3fFyDVFVFSGsPa2oJQkhG5eMa1C+Q4MDFfgK1BCFEV/m8zkST6El7E0+nXQkxgqaE9Zdffok33ngDDKN9lykfiJKcWLN9GvJBSFolszbdc8892Lx5M0pKSvD6668Pe/6jjz7CTTfdhKqqKgDAggULcMstt+gWs1YJ9bDmGNosIyQD8vH6SFJUsAwDTkuFNUsV1oRkUj6uQf0CYTnh/tUAYOYZuh8jREf5vM5EEwgn2BKEpXsxQoyg6W9hbW0t2traEn7ze+65B1//+tdx+eWXj/i8qqr4xS9+gQULFuCKK67Arl27Bp575ZVXcOmll+LSSy/FK6+8kvBn6yEQVhLa2Rc4BkHaWSMkbZJZmxYvXownn3wy5mtmz56NdevWYd26dRlJVgP9CWttF4Z0uoOQzEj2+iibBSUFAq/9dAdVMhKSOfm4BvUTE7wP6yfwHPWwJkRH+bzORBMIaz/pCtD1ECFG0VRh3dXVhUWLFuHss8+GyfRVT+cnnngi5u8tXrwYy5cvx9133z3i81u3bkVjYyM2bNiAnTt34v7778cLL7yA7u5uPPbYY3jppZfAMAwWL16M+vp6OJ3OBL5a6hI9CiJQs31C0iqZtWnOnDlobm5OR3gpCUra1x8TxyJEaw8haZfs9VE2S6x/Pl33EJJJ+bgG9QuEZQhJJKzNXG5UWPtDMl5pOImthzrgD8mYUm7HP80ai9oyR6ZDI2SIfF5nohHDMsocgubXCxydtCfECJoS1rfeemtSbx4vMbRx40ZcffXVYBgGM2fORG9vL1pbW7F9+3bMnTsXLpcLADB37ly8++67USu1jSKGlYQulGjoIiHplezaFM+OHTtw5ZVXory8HHfffTdqa2vj/g7HMXC5bLrFEJJVFDktsAlDl2mWZeFwDO2t75QVhBVV189PF45jczLukeTTdwHy6/sY9V2MWoMyKeF2RFTJSEjG5OMa1C8gJdnDOgcqHbcc7MAv3z6ACcU2XDChCA6Bw/42H278awOunTUWKy4YN6raL5Dsls/rTDRiWPvmPQAIPLUEIcQImhLW559/Po4fP46jR4/iG9/4BgKBwJAJscnyeDyoqKgY+LmiogIej2fY4263Gx6PJ+776Z4wUlS4Ci3DkkPAyEkjwWJCSFKy8gY/FxMPuRZzrsUL5GbMgxmxNk2bNg2bNm2C3W7Hli1bcPPNN2PDhg1xf0+WVXR3+1P67MGCYQWhQBhKaOj3cTjM8HqDQx6TRAmBkKzr56eLy2XLybhHkk/fBciv76Plu5SVFST8vkZdH2VSKMGBr73BkMEREUKiycc1qF8gwYRRPxPPZm1LkD5Rwm82H8JHR7tw/flVQ6qpa0rtuGBCEX7//lGc6BHxk0unaJolQIjR8nmdiUaUEitcNHEsvEHJwIgIGZ00Jaz/+te/4i9/+Qt6enrw9ttvw+Px4L777sMf//hHo+NLiN4JI39IghyUhiWHgJGTRqqqIiyr6Oj0Zd0FRi4mHnIt5lyLF8iumJNJFhmxNjkcX9081NXV4Wc/+xk6OztRXFyc9HsmSpIVqFA1ryMCz9LgM0IyIJk1KNsHvyZUYc3T0EVCMilX7tGSISbZEkTgWARCmUsciWEZmw92oKk7gBKbCVUuK1iGwWfN3Xhx50mcPaYQ9y6ohcXEDftdl9WE2+ZNxBPvH8UvNuzHTy+bApYqrUmG5fM6E42YQGtGoL8lCF0PEaI3TX8L//znP+O5554bSORMmDABnZ2dKX+42+1GS0vLwM8tLS1wu93DHvd4PHC73Sl/XqKCCe7sMwwDM09tQQhJFyPWpra2NqiqCgBoaGiAoigoKipKOdZEiKd6yGo9DmriKGFNSCYkswZl++DXoKw9YW3iWBo2TUgGGXWPlg0CYTmhoWf9BI6FL0Prkjco4XvP7cDznx1Hc1cA7x7uxGPvHsEjWw7jULsft1w4AdfNGjtisrqfxcTh374xHns9Xjy88eDANSkhmZLP60w0wQROmwH9s8woB0SI3jRVWAuCAEH4qum8JOmza11fX49nnnkGixYtws6dO1FQUIDy8nJceOGFWLNmDXp6egAA27Ztwx133KHLZ2qlqmpkZy3BnX0zz0IMK7Br79FPCElSMmvTHXfcge3bt6Orqwvz5s3DrbfeOvB71113Hd58800899xz4DgOFosFa9asSXsfwUSGngGAiWMQlhWoqko9DwlJo2TWoGwf/BqUZJgSaAlCPRsJyZxk1qCtW7di9erVUBQFy5Ytw8qVK4c8/9xzz+HZZ58Fy7Kw2Wz4+c9/jsmTJ+seezzJtgSx8Cx8ocwkjn72j30Y47Tgn84dk9L1mMXE4eYLJ+A/tx7Bf2w8iLvmT6ZKa5IxRuWCspkYljVv3gORmR60gU+I/jQlrOfMmYMnnngCoijivffew7PPPov6+vq4vxcvMVRXV4ctW7ZgwYIFsFqtePDBBwEALpcLN910E5YuXQoAuPnmmwcGMKaLpKhgGSbh1h60u0ZI+iSzNq1Zsybm88uXL8fy5cv1DDNhQUlJaNAQyzDgucjR/FhVO4QQfSV7fRRPJge/8h4vrAI/bE7HiANfHSHIQM7OQsj1OQ4joe+UO/T4XomuQbIs44EHHsDatWvhdruxdOlS1NfXD0lIX3HFFbjuuusAABs3bsRDDz2EP/zhDynFmYxIhXXiCWuziUWbb3g7R6Pt9fRh54lePPDtM3QpHrAKHG6bNxGPv9eIH67bhZ9960wUWDTduhOiK6OudbJZMMEe1gJt4BNiCE3/1fvhD3+IF198EVOmTMFf/vIX1NXVYdmyZXF/L15iiGEY3HfffSM+t3Tp0oGEdSYEwslNpjZT/yJC0ibZtSnbBZLo22g+daFECWtC0seINSjTg1+7egJgoA6b0zHS7I5wUIJPDGfNLIREZdMcB73Qd8odg79XMnM8gMTXoIaGBowfPx7V1dUAgEWLFmHjxo1DEtaDZ3kEAoGMndwKhJLrYW3mWfhD6b8Xe/KDY7hkSllCVZnxWAUOt86biFcaTmLJ2o/x3dlVuGxqOcpO2zwkxEj5er8Vi5jwaVcWYpiKFgnRm6aENcuyuOSSS3DJJZekdfBYJonhxHbV+gk8iyAtVoSkRb6uTYlOpgZOne4Iy4DVZFBUhJDTGbEGZXrwqygpMLEahy5yNHSRkExKdA3yeDyoqKgY+NntdqOhoWHY6/785z9j7dq1CIfDGRus5k+ywtrCs/CneehiTyCMj49148HLz9T9vU0ci++cOxZfG1+EzQc78IcPj6HAwmN6ZSHmTizG4jnjdP9MQgbL1/utWJLpYU3XQ4ToL2bCWlVVPPbYY3jmmWcGBj6wLIvly5enfQhQugXCMixJJqzpOAghxsr3tUlM4ibNTGsPIWlj5BrU1taG0tJSMAyTkcGvIUkBn0AP65BM6w4h6Wb0ddA///M/45//+Z/x2muv4fHHH8d//Md/xHy9Xi2JBpMZBkUOYVgron4jtSkCAJc/jKCipqWNTH9bl7cONWNGlROlRcZ95jSHGdPGFUFRVJzsEXGgzYtXd3vw2LYj+MWV0zF/arlhnx1PNrTtoRj0jyHf77eiUVUVoQQrrGkDnxBjxExYP/XUU/jss8/w4osvDhwda2pqwv3334+nnnoK//qv/5qOGDNCTHLQh8CxCFCFNSGGyve1SUxwVx/oP91BF0qEpEMqa1C2D34NSkpCQxfpBo2Q9Et2DXK73WhpaRn42ePxwO12R/2cRYsW4f77748bj14tiQbr9QVRYuGHtSLqN1KbIgBQwjK8opSWNjL9bV1e+awZ54wtjBqr3pwmFrPHFGL2mEIc94bw4799geb2CbhqRmVaPv902dC2h2JILYaR2hLl+/1WNCFZBc8xYBOYZSbQ9RAhhoiZsF63bh3+53/+Z8jRj+rqavzqV7/C9773vbxdpABAlJLrmyZwLERKGhFiqHxfm4JJ9LCODPugzTJC0iGVNSjbB7+GZAU8tQQhJKsluwbNmDEDjY2NaGpqgtvtxvr16/HrX/96yGsaGxsxYcIEAMDmzZsxfvx4o75GTP5wYgOo+5n59BYP+UMyvjjRi38+b2zaPnOwMyoKcFtdDX6z+TDGOq2YPc6VkThI/sn3+61oxLAMc4KFi3QfRogxYiasJUkasU9RcXHxQDVQvgoksVABkZs3WqwIMVa+r02ipCQ8tMdMm2WEpE0+r0GRHtbUEoSQbJbsGsTzPFatWoUVK1ZAlmUsWbIEtbW1ePTRRzF9+nTMnz8fzzzzDD744APwPI/CwsK47UCMkkx7NCDSwzqdCevPm3swodiW0aHX7gIz/mV2FX76xl789V9no8CiaUwVITHl87VOLKKkwJTELKGgpEBV1YwNqiUkH8X8r5nJFH14V6zn8oEYTnyhAgATzyJASSNCDJXva5MYliEkcAwNiKw9tFlGSHrk8xoUTKiHNQNJVqGoKli6QSMkbVJZg+rq6lBXVzfksdtvv33gzz/5yU9SC04n/pCcdIV1OjfwP2jsxJRye9o+L5pplQWY6nbgvz84ijsunpTpcEgeyOdrnViSqbDmWAYswyAsqxB4uh4iRC8xE9Z79+7FrFmzhj2uqipCoZBhQWWDgCQn3EMWAASWhUg9rAkxVL6vTclUWAscQxXWhKRJPq9BiVQ1MgwDE8cgJCkZrS4kZLTJ5zWoXyCcXMKaP7V+hSQlqfaOifqosQvXzspMO5DTXTHdjZ+/eQDLZo5BdZE10+GQHDca1pmRiEmuHf3tiNKx7hAyWsRMWO/ZsyddcWSdZIcumngGIvVzJMRQ+b42JXPCw0S90whJm3xeg4KSAlsCyedI30ZKWBOSTvm8BvULhBVYkkz8WHgW/jQkjtq9QbT5QhifJcnhQosJdZNL8IcPj+L+b5+Z6XBIjktlndm6dStWr14NRVGwbNkyrFy5csjzDz74ID766CMAgCiK6OjowCeffAIAmDp1KqZMmQIAqKysxBNPPJF0HMlIth1Rf8Laac3f6nNC0o0aXEURSHKhEjgWgRAljQghyYusP4md8KAKa0KIHiI3W9ovD02n+jYSQoiekq2wBgCziUMgLMNlcOLo48Yu1JbZwSbYxs1IF08uwX3/2IeTvSIqCy2ZDoeMQrIs44EHHsDatWvhdruxdOlS1NfXY/LkyQOvuffeewf+/PTTT2P37t0DP1ssFqxbty6tMQ8mpjDwle7FCNEXnVeIIpEejoMJXGRHnxBCkpXMhhl/qsqREEJSkegJM4GjhDUhRH/JJo2AUxXWaSgg+vBwB2pKbIZ/TiLsZh7fmFiMZz89nulQyCjV0NCA8ePHo7q6GoIgYNGiRdi4cWPU169fvx6XX355GiOMLdm2HmaeRYBOuxKiK6qwjiKVQR8d/vzt6UQIMZ4YVmA3J3a8XuAix9AIISQVQSmxDTOBYxCkGzRCiI7CsgKG+aofdaL6j+YbbXtjJ5acXWn45yTqokkleOjtg/i3ueNhF+h2n6SXx+NBRUXFwM9utxsNDQ0jvvb48eNobm7GBRdcMPBYMBjE4sWLwfM8Vq5ciUsuuSTuZ3IcA5dLn80jRuBhM/NwOMzDnmNZdsTHAcAq8ODNJt3i0APHsVkVTzy5FG8uxQrkXrz96L9gUSTbEsTMU0sQQkhqAmEZRbbEjrFSwpoQoodEh76aOBYhqrAmhOjIH5KT7l8NABYTZ3iFdXcgjBM9IsZlSf/qwYrtAs4ot+O1L1vwT7OqMh0OIVGtX78el112GTjuq0Kdd955B263G01NTbj++usxZcoUjBs3Lub7yLKK7m6/LjF19ATAQoXXGxz2nMNhHvFxAOAZoK3Lj26X/q14VFXF4Q4/akpsYBjtXQBcLptu/17SIZfizaVYgeyPt6ysYMTHqSVIFMkeBRHStKNPCMlfyUynFngGIq09hJAUiWEFAq/9ZshE7YgIIToLhGWYUxjkak5DS5DPmntwhtsBLov6Vw/2zUkleGHHSaiqmulQyCjjdrvR0tIy8LPH44Hb7R7xtW+88QYWLVo07PcBoLq6Gueff/6Q/tbpIIZlCGxyeSAj7sUUVcV3n/kc1/3xU+w43qv7+xOSzShhHUUgwR6O/cw8Cz812yeEpEBMaugiiwCtPYSQFAWlRHtYM9TDmhCiK384tQprcxpmCn18rAtT3CNXhGWDKWV2SIqKz5p7Mh0KGWVmzJiBxsZGNDU1IRQKYf369aivrx/2ukOHDqG3txfnnnvuwGM9PT0IhSLtVTs7O/HZZ58NGdaYDoGwDFMyhYsG3Ysd7vCjKxDGt88qx9ZDHbq/PyHZjBLWUSQ7mTpdPdMIIflLTDBhBBi3q08IGV1ESU64JQglrAkhegokOUuon8Azht+PfXK0G2dVZm/CmmEYzJ1YjBd2nMh0KGSU4Xkeq1atwooVK7Bw4UJ8+9vfRm1tLR599NEhwxffeOMNLFy4cEiLi0OHDmHJkiW48sorcf311+MHP/hB2hPW/pAMcxKFi0a1Z/ysqQe1pXbMqCykhDUZdaiHdRRiKj2sKWlECEmBmERLIoFjEaCkESEkRYlWWJuowpoQorPUK6yN7WHd6Q+h1RfChBI7Av6QYZ+TqgsmuPDTN/ahyx9CkU3IdDhkFKmrq0NdXd2Qx26//fYhP996663Dfm/WrFl47bXXDI0tnkA48daMgHEJ60+bujCpzIbxxVb0iGE0dwdQ5cq+3vmEGIEqrKMIhBWYTUkkrDkWIh3LJ4SkIKkKa44qrAkhqQtKifWw5lkWQYnWHkKIfvyh5BJG/YyusP7gSBfOLM/e/tX97AKPmWML8dqXnkyHQkjOiJzwSPzvtolndJ/poaoqPj/ei9pSB1iGwaQSO/Z6vLp+BiHZjBLWUQSS3NmnCmtCSKqSOeEhGHCRRAgZfaiHNSEk05JtzdjPzHPwGVhh/c6BdkzP4nYgg104sRgvNZyEQsMXCdHEn+T6I3AsAjqvO23eEGRFRYndBAAocwho7PTr+hmEZDNKWEeR7IWSwFMvR0JIahKtcAQiF0lBOt1BCEmBrKhQVBV8AlWDPPWwJoTozJ9ka8Z+Fp6FL5ha4qhPlKCOkOQNSgo+PtaNGZWFKb1/ukwssYFnGXx8rDvToRCSEwIptIbVe9hrc08A7gLzQJ9vd4GZEtZkVDG0h/XWrVuxevVqKIqCZcuWYeXKlUOef/DBB/HRRx8BAERRREdHBz755BMAwNSpUzFlyhQAQGVlJZ544gkjQx0m2YWKZxmoKhCWlYSGFhFCSL9EKxyBUy1B6Fg+ISQF/WvP4AFI8ZhYOt1BCNFXIJT4LI/BzCYWrd5gUr+rqCrue2MvNuxrw0WTS3Hft86ATeAGnv/gSCeqiywosOTGKCiGYXDhxGK88PkJfG18UabDISTriSlUWPeJ+va0b+4WUWr/qv+8u8CMj4526foZhGQzw/5LK8syHnjgAaxduxZutxtLly5FfX39kCmv995778Cfn376aezevXvgZ4vFgnXr1hkVXlyBsJLUQsUwDMymSFsQSlgTQhKlqiqCUuIbXmY63UEISZEoJZ4kMlH/fEKIzvxhGeZUK6yTPJq/5WAH9rX68PCVZ+GFHSfw0zf24ldXnQX21Ebenz5uwjdrSpKOLRPOH+/CT9bvRUuviIpCS6bDISSrJTt00YjWsM3dARTbTAM/uwvMaOoSoapqQsUFhOQqwzKqDQ0NGD9+PKqrqyEIAhYtWoSNGzdGff369etx+eWXGxVOQmRFRUhKftiHhWcNnUxNCMlfIVkFxzIJD/IR6Fg+ISRFSZ3u4BkaNk0I0ZU/lFoPa6uJgzeJliCqqmLtR8ew4MwyOMw8/mV2FU72inhky2GoqortR7vg6Qvi3Cpn0rFlgsXE4fzxRXhx58lMh0JI1gskuWEm8CwCOt+LNXUFUOYwD/zsMPPgWAYd/rCun0NItjKswtrj8aCiomLgZ7fbjYaGhhFfe/z4cTQ3N+OCCy4YeCwYDGLx4sXgeR4rV67EJZdcEvczOY6By2VLOXZvUILZxKKwIPoONMuycAxaPAazCjxMVkGXWPTCcWxWxaNFrsWca/ECuRlzvkv6GBpVWBNCUiQmUVUkcCx6RMmgiAgho5E/JKWUsLaYOPhCia9LR7sC8PQFMXNMpD81z7H4t2+MxyNbDmN3Sx8aO/z47vnVCRcVZIN5k4rxyJbDWHHBOFhMXPxfIGSUEpMsXDTz+p84a+4Wh22QVRaacbTTP6RVCCH5Kiuab61fvx6XXXYZOO6r/3i+8847cLvdaGpqwvXXX48pU6Zg3LhxMd9HllV0d6fehL7dG4SFZ+GN0fvM4TBHfV5gGXg6fCgxZU9LEJfLpsu/m3TKtZhzLV4gu2IuK8uNaetGS/YiiTvVP1+SFfDUjogQkgRRSryqyIgbNELI6OYLySgoTP421WpKriXIZ03dOLPcAXZQQtpu5vHD+snY4+nDFdPcqHJZk44rkyoKLRhfZMPf97TimrMrMx0OIVkrlR7WercEOdEjDktMl9oFHO8WcV61rh9FSFYyLKvhdrvR0tIy8LPH44Hb7R7xtW+88QYWLVo07PcBoLq6Gueff/6Q/tZG84cVmPnkd54FA/oXEUJGBzEsw5JkVZGZZ2n4GSEkacGwAhOXYDsinkWAWoIQQnTkD8sp3YtZTVxS7Rm3H+vG5DL7sMfNPIuZY505m6zuVz+lFE9/3AxFVTMdCiFZS0yiPRoQuR7Ss0WaNyghJCsoPG3Aq9NqQkufqNvnEJLNDEtYz5gxA42NjWhqakIoFML69etRX18/7HWHDh1Cb28vzj333IHHenp6EApFJqx2dnbis88+GzKs0WiBFBJGgDEN9wkho4MYTnzgYj/aLCOEpCKZoYtGVBQRQka3QIo9rCNDFyWoCSRmVVXFZ009mFLmSPpzs92UMjt4jsHWgx2ZDoWQrKSoyc8yM+t8PXSiR0SpQxg2XLHIZsLJ3uidAAjJJ4a1BOF5HqtWrcKKFSsgyzKWLFmC2tpaPProo5g+fTrmz58PIFJdvXDhwiF/EQ8dOoT77rsPDMNAVVX84Ac/SG/COsWLJIFjqNqIEJKUVDbMLDrv7BNCRhcxnMzQRRaiRAlrQoh+AmElpXsxnmPBswyCkqK5X/OxrgBYlkGJ3ZT052Y7hmGw4Iwy/OGjY6ibXDIsEUbIaBeUIoVDbBJ/N/SeJ9TmDaHIOnw9KrKZsL/Vp9vnEJLNDO1hXVdXh7q6uiGP3X777UN+vvXWW4f93qxZs/Daa68ZGVpM/iT7FvUz8xwCSRxDI4QY75577sHmzZtRUlKC119/fdjzqqpi9erV2LJlCywWC375y19i2rRpaYsvlfXHbGLhp0pHQkiSIjdqid2kmTnaKCOE6CvV064AYBM4eIOS5oT1/jYfxhdZ8z6JO3NsIV7f5cH2o9342oSiTIdDSFZJZe3RuzVjmzeIQsvwhHWx1YTWGLPWCMknNJlrBIFw4kdiBxN4hpJGhGSpxYsX48knn4z6/NatW9HY2IgNGzbg5z//Oe6///70BYfU1h8zx9JmGSEkaaIkJ9ySSO+ejYQQ4k/xtCsQ6WPtTeCa6GCbFxUF5pQ+MxewDIPLzizDE+83JtQyhZDRIJBC4ZCJYxCSFN16xLd6g8P6VwNAkU1AmzdIf3/JqEAJ6xH4QzLMSfaQBaifIyHZbM6cOXA6nVGf37hxI66++mowDIOZM2eit7cXra2taYsvlfXHzFOFNSEkeZGWIAkOXeSoJQghRF/+kAxLCkMXgUjC2pdAwvpAmw9jXJaUPjNXzKl2ocMXwvZj3ZkOhZCskko7IpZhYOL0awvS2heCa4SWIFYTC1UFvEG69iL5jxLWI0hlZw04lTSiKkdCcpLH40FFRcXAzxUVFfB4PGn7/FTWHzPP0WYZISRpQUkBn3CFNaNrz0ZCyOimqir8YRkWU2q3qRZTpCWIVgfbfRjrHB0Ja5Zl8O2p5fjtu0eoSpOQQcQUT9pbTPrlgTzeIJzW4RXWDMOgxC7A00dtQUj+M7SHda7yh2SYUhq6SFWOhIw2HMfA5bKl/D4qx8FuFeBwjHwslWXZqM/ZrTzAc7rEkS4cx+ZUvLHk03cB8uv7ZNN3yeY++qIkw8RSD2tCSOaE5EgCNdH2RKezmljNFdaBsIwOXxjlUa6v8tHsahc27GvD+0e6MLemONPhEJIVUu2fbzVx8IdklNhTj6U9ytBFACi2meDpC2JymQ4fREgWo4T1CPzh1FqCmHkWPaL2HX1CSPZwu91oaWkZ+LmlpQVutzvu78myiu5uf8qf39kbAKMo8EYZpuFwmKM+x6oqOnoCusSRLi6XLafijSWfvguQX99Hy3cpKytISyyLFy/G8uXLcffdd4/4/OA++jt37sT999+PF154IS2x+UNKwpVFHMtAhQpJTrw6mxBCTucLSbAJqbUDAQALz2qusD7c4UdloRlcght2uWygynrbEXxjYlHeD5skRItAOPHroMEsOrZnbPcF4YySsHZZTfD0ibp8DiHZjO4sRpDqoA8zTz2sCclV9fX1+Nvf/gZVVbFjxw4UFBSgvLw8bZ/vS2H9EThqR0RItsvmPvpBSYaQYNKZYRhYeA4BqrImJCds3boVl112GRYsWIDf//73w55fu3YtFi5ciCuuuALXX389jh8/ntb4fEEZVpMeCWvtPayPdvpRUTh6qqv7nVvlRFBSsOVgR6ZDISQrBFLMA+nVEkSSFfSJMgrNI9eXFlp4tHlDKX8OIdmOKqxH4A/JI05k1cqS4JAPQkj63HHHHdi+fTu6urowb9483HrrrZCkSAXOddddh7q6OmzZsgULFiyA1WrFgw8+mNb4Ull/aOgiIbkvWh/9eBtnerQlkhkWLrtpxLZDsdoRWUwsBJsAV2Fu9X/NplYxeqHvlDsy8b1kWcYDDzyAtWvXwu12Y+nSpaivr8fkyZMHXjN16lS89NJLsFqtePbZZ/GrX/0KjzzySNpi9IWklPtXA5FrIq0V1sc6/SixCyl/Zq5hGQYLzyrHE+81Yt7kErBUZU1GOV/KJ+05XRLW7b4QCq082CinPiIV1tTDmuQ/SliPwB+WUV6Q/EULDV0kJHutWbMm5vMMw+C+++5LUzTDpbL+mHnt/RoJIflFj7ZEvf4giszsiG2HYrUjMnEsPB0+mJXcqrLOp7Y3/eg75Y7B3ytdLYkaGhowfvx4VFdXAwAWLVqEjRs3DklYX3DBBQN/njlzJl599dW0xNbPF5Jh5XWosE5g6OLRrgCqXNaUPzMXnT2mEH/f04rNBztQX1ua6XAIyShfUII5hfUnci+WemvYthj9qwHAaTXhULsv5c8hJNtRwnoE/lDiR2IHiwz5oB7WhJDERVoSJXehZOZZdAdo7SEklyXbR18PgXByR2EFnoVIpzsIyXqnn+Bwu91oaGiI+voXX3wR8+bNi/u+eg2eBgC0eOGwjnzSY7BYpz4AwOUQ0BOQNMV1vDeIuVPK4n5mojGkgx4xXDNrLP7noyZcM7s6qV7W2XAKgmLInhhymT8kQ+CTP2mgV+Fiuy8UtX81ALisPNp81BKE5D9KWI8gcsOW/M6aRaejIISQ0SfZhBFApzsIyQf19fV45plnsGjRIuzcuTOtffST3TAzczS7g5B8s27dOnz55Zd45pln4r5Wr8HTANDa5QMPRD3R0S/WqQ8AYBUVXV4xblyqquJYpx8FHBP3MxONIR30iKG2yIpASML6z5txYU1Jwr+fDacgKIbUYkjXKY9s5w3JsKTQQ9/M6dOesdMfQkGM4bNOiwkdlLAmowAlrEeQ6tBFi4kS1oSQ5KSy/ph5jnpYE5LlsrmPfkoV1lJutQMhZDQ6/QSHx+MZ8QTH+++/jyeeeALPPPMMBCG9vZ19QRlmHXpYW0ws+oLxr4l6Tp1Ms8dIDuU7lmFw6Rll+MOHx5JKWBOSL3xBKaVZZnq1Z2z3huCIEUeBmUefKEOSFfApdAYgJNtRwnoE/pAMawoXShYafEYISVIgnHxLIjMdyyck62VzH/1AWEkuYc0xaV97QpKC32w5hGtmVGJKuSOtn01IrpoxYwYaGxvR1NQEt9uN9evX49e//vWQ1+zevRurVq3Ck08+iZKS9CcvfSkWDvWzCRz6xPht0o51B+AuMCfVCiOfnFvlxKtftmBHcw9mVjkzHQ7JA1u3bsXq1auhKAqWLVuGlStXDnn+5ZdfxsMPPzywabZ8+XIsW7YMAPDKK6/g8ccfBwDceOONuOaaa9ISsy8ko7wg+RY7ZhMLn8be+bF0+EIxE+csy6DQyqPDH4Y7hXgJyXaUsB6BL5zaURATx0BRIjdTgg4XXISQ0UOUFFioJQghJAMCYTmp9UfgWIjh9FZY3/f3ffi0qRuBkIz7v31mWj+bkJs6LNEAAFgKSURBVFzF8zxWrVqFFStWQJZlLFmyBLW1tXj00Ucxffp0zJ8/Hw8//DD8fj9uv/12AEBlZSWeeOKJtMXoDUpJXwcNZhd49GpIWDd3B1DmSG8VeTbiWAbzp5Thqe3H8EjVjEyHQ3KcLMt44IEHsHbtWrjdbixduhT19fVDBrwCwMKFC7Fq1aohj3V3d+Oxxx7DSy+9BIZhsHjxYtTX18PpNH4jxRdKbf3R616szRfCWRWx27QUWU1o9wYpYU3yGiWsR+ALyrCmkLBmGAZWgT3VtJ8S1oQQ7VLtYU19ZAkhyUqlJUg6156QpGDb4Q7cu6AW/7HxIHwhCXaBLmkJ0aKurg51dXVDHutPTgPAU089leaIhookrFNvz2EXOPQGw3Ff19wdQLGNEtYA8PUJRVi/24OjnX6ML6bBfSR5DQ0NGD9+PKqrqwEAixYtwsaNG4clrEeybds2zJ07Fy6XCwAwd+5cvPvuu7j88suNDBlApMLaktJJew4dvvjrTjzxKqwBwGk1oc1LfaxJfqOr+9NIsgJJUSBwqR0Ls5o4+MISXIg+3ZUQQk4nJnkkH+hPWFMfWUJIcpJdfwSORSCNPaz3tnpRUWhGeYEZk8vs2HqoA9+eOrwPLyEk9/hCsi4Vz7ZTM4VkRQXHRr+va+4WUWqnhDUQ2Xz8Zk0x/vxJM+69dEqmwyE5zOPxoKKiYuBnt9uNhoaGYa/bsGEDPv74Y0ycOBH33HMPKisrR/xdj8cT8/M4joHLlfomS1BWUVRogcMxctUyy7JRnwMAV4EZjd2BlGPpESVUFttiflZpgRk+BTE/i+NYXf69pEsuxZtLsQK5F28/SlifxheKVFen2sfMYuLg0zDogxBC+oXlSMIn2eEZVGFNCElWKuuPKc09rHce78HEU9V/NcU27GnxUsKakDzhDUoYV2RN+X1YlhnoY+2yRS8gau4Wcaab+uD3mzepBA+8uR+3zJuIQgsVXhHjXHzxxbj88sshCAKef/553H333fjTn/6U1HvJsorubn/KMXnFMJSQDK83OOLzDoc56nMAAElGjy+UUiyqqqLdGwInKzE/y8IyaGzti/lZLpdNl38v6ZJL8eZSrED2x1tWNnILHOpXcRpvSIIthXYg/aw6TYglhIwe/lBy/WP7mU/1kVVVVceoCCGjQSrrj8Cld7Pss+Ye1JTaAQBjnBYcaPOm7bMJIcZK9Uj+YA6BR48Y+3j+id4AVVgP4rSaMKOyEK9+0ZLpUEgOc7vdaGn56n9DHo9nYLhiv6KiIghC5O/esmXLsGvXLs2/axR/iuuPmedSzgH5wzJYBnFnqrmsJrT2xUieE5IHKGF9Gl9QhkVIPWFtOXUMjRBCtAqEZZhT2DDjORYMA4RlSlgTQhKTyvojpLkd0a6WPtSURCqsxzotONSRvRUjhJDERIaepX4vBgAOM4eeGIMXg5KCnoCEIitVEg92UW0Jnv/8BGSFridJcmbMmIHGxkY0NTUhFAph/fr1qK+vH/Ka1tbWgT9v2rQJkyZNAgBceOGF2LZtG3p6etDT04Nt27bhwgsvTEvckeHTya8/Zp6FP8UN/A5fGE4Na5LTyqOVeliTPEctQU7jC8mw6jAo0cKz8IXiT6YmhJB+/nBqFdZAZO3xh2ngKyEkMamsP2YufafKvEEJ/pCM4lNH/ItsJgQlBd3+cMxj/4SQ3BBpz6jPNYxd4NETiF5hfbJXRIldABujx/VoNKHYBoeZw7bDHaibXJrpcEgO4nkeq1atwooVKyDLMpYsWYLa2lo8+uijmD59OubPn4+nn34amzZtAsdxcDqdeOihhwAALpcLN910E5YuXQoAuPnmmwcGMBopMstMhSmFWWYWE4tAitdDWgYuApEK63YfVViT/GZownrr1q1YvXo1FEXBsmXLsHLlyiHPv/zyy3j44YcHjngsX74cy5YtAwC88sorePzxxwEAN954I6655hojQx3gDUqw6tASxGLi4KUKa0JIAgJhJeWEtdnEIRCW4aJqIUJIAgIhOemBrwLPoi1NN03N3QG4C8wDs0YYhkGV04JDHT6cZ3OlJQZCiHEiR/L1qbC2CRx6Y1RYn+ihgYvRzJtUguc+PU4Ja5K0uro61NXVDXns9ttvH/jznXfeiTvvvHPE3126dOlAwjpd9JhlZuG5lE+cdfoTSVhThTXJb4YlrGVZxgMPPIC1a9fC7XZj6dKlqK+vx+TJk4e8buHChVi1atWQx7q7u/HYY4/hpZdeAsMwWLx4Merr6+F0Oo0Kd4BefdPMHAtfkCqsCSHapZIw6mfhWWpHRAhJWCoV1hZT+iqsj3UFUO4wD3msstCCg20+nFftSksMhBDj+FKc5zGYzcTF7GF9vEdEiZ02+Ecyq8qJlxtO4kiHHxNPtWAiJJ/5w6lvlpl5FqIkQ1XVpBPfHb4QCszx03R2gUNQUiDqEDch2cqwM+MNDQ0YP348qqurIQgCFi1ahI0bN2r63W3btmHu3LlwuVxwOp2YO3cu3n33XaNCHUKvvmlmEyWNCCGJ8YWklFt5WGjtIYQkwR9Skl5/LHz65nY0dw9PMJU5BBzrCqTl8wkhxgnLCiRZTXnzvp9N4NAdoyVIc3cAxTaqsB6JiWNx4cRiPP/Z8UyHQkha6NEalmMZ8CwLUUq+yrrDF4JDQ8KaYRiqsiZ5z7AKa4/Hg4qKioGf3W43Ghoahr1uw4YN+PjjjzFx4kTcc889qKysHPF3PR5P3M/kOAYuV2o7wBLDwmEzwXFa9c7pWJaN+RqnXUAISDme07X1BeHpFTF9bGLV5hzH6h6L0XIt5lyLF8jNmPOZNyin3JLIwnPwUv98QkiCAuHkT3hY01hh3djpH1ZhXWIX8MXJ3rR8PiHEOL2iBLs5tSP5g9kFDt3+6Anrpu4Azix36PJZ+eibk0rwiw37ceu8iZoSaITkMr9OJ+37r4mSvadr94VQqPHvm8tqQps3hCqXNanPIiTbZfS/PBdffDEuv/xyCIKA559/HnfffTf+9Kc/Jf1+sqyiuzu1SfHtPQFwqgqvN3YvRofDHPM1rKKi0xtKOZ7BPmrswo9e3w0GDJ75l1kY47Ro/l2Xy6ZrLOmQazHnWrxAdsVcVlaQ6RAyzhuUUr5Qspo4eINUYU0ISYw/LMPMJTl0MY2tiJq6A5jqHppgKrULONlDg4cIyXV9QQl2Qb+j7XYzj1avL+rzx7tFfLOmWLfPyzcuqwlnuQvw6pct+F/nVWU6HEIM5QtJMOtw0t5iOnXqzJ7c73f4wprzPJGENV3/kPxlWEsQt9uNlpaWgZ89Hs/AcMV+RUVFEITIMaxly5Zh165dmn/XKHoNXTSbWN2rHJ/a3oRlM8dg/pRS/MfbB3R9b0JI5nl1uFCymlh4qX8+ISRBgZCcdEsQqymdLUECKC8YXmHd0idCVdW0xEAIMUafKMGmZ8Ja4NAdGPmaSFVVnOwVUWqPfap2tLtocgme/+w4ZIXWV5Lf9K2wTv5erCOBCutCM482L7UEIfnLsIT1jBkz0NjYiKamJoRCIaxfvx719fVDXtPa2jrw502bNmHSpEkAgAsvvBDbtm1DT08Penp6sG3bNlx44YVGhTpEnw4VjgBg5Tldj8c2dwewv9WLWVVOzJ9Sih3He2MecSOE5J4+UYI1xfXHwnOUsCaEJMwfliEkWWFtMXHwh41PWPtDMvwhGU7L0Bs5mxBpIdAj0tpHSC7rDUqw6Tg8zC5EH7rYHQiDYxhdE+T5aGKJDVYTh3cPdWQ6FEIM5Qslf9JsMJvAozeF65FOfwgFFm0Ja6eVRytVWJM8ZlhLEJ7nsWrVKqxYsQKyLGPJkiWora3Fo48+iunTp2P+/Pl4+umnsWnTJnAcB6fTiYceeggA4HK5cNNNN2Hp0qUAgJtvvhkul8uoUIfw6dBDFogMPtMzafSPPa2YM84F06lFdKrbgfcbO7HwrPRUnhNCjNcXlFLuEaj32kMIGR18weR7WAscExmWpqjgWX16z47E0xdEiV0Ysb9tmV3AyV4RLqtphN8khOQCvSusCy08OqMU+BzvEYed1iDDMQyDi2tL8cwnzbiotjTT4RBiGJ+OFdbJ3oupqoruQBiFFm3XMi6rCY2dNHSa5C9De1jX1dWhrq5uyGO33377wJ/vvPNO3HnnnSP+7tKlSwcS1unkDUmw6NC7yCboW+X4aVM35oxzDfx8VkUBNh9op4Q1IXnEG5RQFmfgazwWE4c+6mFNCEmQPywlnbBmGAYWE4dASNZcFZSMlj4RxTZhxOdK7AJO9IiY6qZ5CITkql5Rn9aM/QotJvSJEiRZAX9a5eTxbhGl9pHXEzLUrCon1n3Rgl0tfZhWQWssyU99YhgWHdYfq4lDX5IV1r6QDJZlNF+PFdsEfHi0S9Nr/SEZj245hEPtfjyyeDoNUiU5wbCWILkqMtFVh6MgJg59OiWsJUXFrpY+TCr9qnP/9MoCbD/WTf3ECMkjfcHU1x+riUVvlOOvhBASjT8kw5zC+mPlU+vZqEVLbxBFtpGrjopsJpzoEQ39fEKIsfqC+iSM+nEsgwILj3bf8B6vTd0BFNvpRIYWHMvg4toSPPXRsUyHQohhekR9WhJZ+eTzQB2+EJwaq6sBoNhmgqdPW0uQ37/fiMMdfrisJtz16u6k4iMk3ShhfRpfUNblQilSYS3rMgBof6sXJXZhyC6Y02qCw8yjsdOf8vsTQrKDHkNfrabI2kMIIYnwh+SUhr5aTRy8Bg9ebOkVh/Wv7ldiE9DcTQlrQnJZT0DfHtYAUGQ1oXWEoWTHugIoo4GLml1YU4LPmntwlO49SZ7q1aklkcXEJt3DutMfRmECJ9WcVhN6AhLCshLzdb6QhFe/9GDpzDH4zrljcKjdh4NtvqRiJCSdKGF9Gl8o9aFnAGDiWHAsg0A49uKhxY7jPUOqq/tNLLZh18m+lN+fEJIdIic89EhYUw9rQkhiAuHke1gDkRs0v8EJ6xO9wagtQYpsJrT0UcKakFzWK4Zh13kIostqQtsIQ8mOdPrhLqSEtVZmnsVFk0vwhw+pyprkp14xrEvC2iZwKSSsQwklrDmWgctqijt4cf2uVpxRbkepXQDHMvj6xCK81HAiqRgJSSdKWA+iqiq8QRl2QZ9+PnaB0+Vo/pcnezGh2Drs8eoiK7442Zvy+xNCsoMvmPqGmdXEwmvwsXxCSP7xhVJNWHNpaAkiRj3Cn8ixWEJIdurReegiADgt/LAKa1VV0dQVQAUNXUzIRZNL8d6RTjR305A3kn/6dGoJYhM49AWTywF1+EIJ95YutpvQ0hv7+uedA204r9o18PPcCcX4x55WSNRelmQ5SlgP4gvJEHgGnE4T7u2CPn2sD7T6UOUcnrCeWEIJa0LyiV4V1j5qCUIISZA3mNqNmoXnDK+w9vTFrrBuG+HYPyEkd/QZkbC2mtB62mZWhz8MlmFo6FiCbAKHeZNK8Lv3GjMdCiG66wvqs/6kMnSx3RdCYaIJa5sQM2HtD8n4sqUPZ7odX/2OXUCpXcAXJyiXRLIbJawH6RHDul642AU+6eMg/YKSghO9QVSMcGSt2mVFU5cIMUzJKUJynaSoCMlKShWOwKmEtcFJI0JI/vGG5JRu1Mw8a+hmmaKqaPeFUGQducLaYebhD8l0TURIDutLceNsJC6rCS29Q9sFHe30o5LagSRl/pRSvN/YhUPt1P+W5Be91h+biUNfktdDbX2JtQQBAJeVj9kS7ZOmbkwstg0rijqrogDbDnckFSch6UIJ60F6AhIcOrUDAVLrX9TvSIcP7kIzTNzw/1eZOBaVhWYc6qDhF4TkOt+pgYsMk9oJD6uJhS8k6TLwNZ6WXhEfNXYZ/jmEEOP5Uq6wZuEzMFnc6Q/DynMQomzqsQyDYtvIw9UIIblBrwrHwVwjDF082ulHObUDSYrVxGHBGWV4bOuRTIdCiK68wdQ27vtZUzhl3+YLwhllYz6aYquA4zGGTr9/pHNIdXW/6RUF2Ha4M+EYCUknSlgPovegj1SOg/Tb3+bD2EJL1OfHuizY3+pN6TMIIZnnDemzq89zLHiWgSilPvA1ljZvECv/shP3vL4Hf/nsuKGfRQgxVlBSoKiAiUt+w0zgWfgMHPjq6QtG7V/dr9gmwEODFwnJWV4DEtaRdkFDj8sf6fCj3EEJ62RdNLkEe1u9+Ky5O9OhEKKLsKwgrMNJVwCwmVh4k7weaveF4EywwrrYbsKJ3ujXPp8e68YZ5cMT1hOKbWjtC444lJaQbEEJ60F6de6bZjNx6Elx6OK+Vi/GOKNfUI11WrDXQwlrQnKdNyjDqtP6YxO4pC+UtPqfj45hWkUB7po/CY+/14gAHcMnJGf1J4lSOeFhMbGGtiPy9AVRFKV/dT8XDV4kJGdJioqgpMCiQ8JosCKrCR2+EMLyVxv5+9p8qIxxf0ViM3Esrprhxq83HYKShhN9hBitLyjBLvApn3QFAJvAw5tkS5BOXzjhCmt3gRnHukYehNrpC8HjDaLaNXweGssyOKPcgU+aupMJlZC0oIT1IN0BfRPWVoFNuSXIwTYfxo4wcLFflcuKfVRhTUhCtm7dissuuwwLFizA73//+2HPv/zyy7jgggtw1VVX4aqrrsILL7xgeEzeUy1B9GA1cUlfKGkhyQre3teGusklKHOYManUjncOtBv2eYQQY0Vu1FJbfyy8sRtlrX1BuKyxq45cVh6tfdQShJBc1BMIo8CsT8JoMIFnUeYw4/CpFoqyomKfx4uJxTZdP2e0mV3tggrg9S89mQ6FkJT1iqlfB/UTOAayoiKU4GlXSVHRK0ooSHCmWolNQE9AGnHw9adHuzC51A6OHXldnVxmp/aOJKtRwnqQXjGsW8IIiFQ5plph3RhnKEiVy4LDHT7a3SZEI1mW8cADD+DJJ5/E+vXr8frrr+PgwYPDXrdw4UKsW7cO69atw7JlywyPK5Kw1mdJjiSsjUscfXSsG6V2M8pOHac9f7wLr37ZYtjnEUKMpccxfMup/vlGaekV4bTErjpyWU04GeNYLCEke3X5wyhI8Ci8VtWDCnyOdPpRaOHhSDApRIZiGAZLz6nEf207gt5Aave7hGRan44n7RmGgV3g4E3wmqjLH0KBhYuaXI6GZRlUFJpxtGv4XLOPjnSipsQe9XfPdDvw8bHutMw+IiQZlLAepDugbw9ru8ChJ5D8zVuvGIY/JKPIFv0GzS7wsAs8mmM02ieEfKWhoQHjx49HdXU1BEHAokWLsHHjxkyHBW9QhkXPCmsDE0ebD7Rj5tjCgZ/PrizErpN9EKktCCGaZNspj74UBy4Cxp/sONkbjHk9BER6WLdQwpqQnNTpD6HQoCTyWNdXLRS/PNGLiSVUXa2H8cU2TK8swG/ePpDpUAhJid4DX20Cl/BJ+0j/6sTagfSrKDDjSMfwhPUHRzpQWx49YV1RYEZYVimXRLIWbS0P0h0IoyJGNXOibCY+pZYgRzr8GOO0xD0aV+Wy4ECbF+OKorcOIYREeDweVFRUDPzsdrvR0NAw7HUbNmzAxx9/jIkTJ+Kee+5BZWVlzPflOAYuV/I3QCGGgctuhiPOECCWZeO+xmkzQWLZlOKJZceJXnx/7oQhcUwoteNwbwjfmFSS0HtxnHFxpls+fRcgv75PNn2X/lMea9euhdvtxtKlS1FfX4/JkycPed3ChQuxatWqtMTUJ6bekshq4tBnZEsQbxDnVhXGfE2R1QQPtQQhJKatW7di9erVUBQFy5Ytw8qVK4c8//HHH+PBBx/Evn37sGbNGnzrW99KS1xd/rBhVc/VLis27m8DADSc7MV4umfSzZXTK/CLDQdw2ZQSnOkuyHQ4hCSlT0x9436wZOYJtXtDCfev7lfmENDYOTRh3RMIo6kzgAkx1juGYXCm24Htx7pQTesiyUKUsB6kR5QwqTT6DlSiIjtryR+ROtIRux1IvzFOC/a1ejF/SlnSn0UI+crFF1+Myy+/HIIg4Pnnn8fdd9+NP/3pTzF/R5ZVdHcP39nW6kSHDwKjwhtnUrPDYY77GjPH4Hi7L6V4oun2h9HaG0SJwA2JY0KRBVv3enBWSWIXOy6XzZA4MyGfvguQX99Hy3cpK0vPjfbgUx4ABk55nJ6wTqe+oARLii2JIqfKjDsWHulhHa/C2oRWmnZPSFRaNswqKyvx0EMP4X/+53/SGltXwMCEdZEFB9t98AYlvHe4E7d8c6IhnzMaOcw8lp1XhdVvHcBT/+vchNsZEJINenWcJQScuiZKosK6MMm2SJWFFuxv8w157PPmHpzhdoDnYl/fnVHuwAdHurDknDFJfTYhRqKE9SC9OrcEsQmpVRsd7vCjvCB+wrrKZcWXJ3uT/hxCRhO3242Wlq/6LXs8Hrjd7iGvKSoqGvjzsmXL8Ktf/crwuLr8YTgEfZZkm4lDt9+YKsMdx3swqdQG9rQbktpSB7Ye6gDmGvKxhOSNbDzlITEsCm2xT3jEO91RzrLwhmRDKtllRUWnP4yqMgdMMW687HYBsqKCtwqaEl/ZVHmvF/pOuSMT30vLhllVVRWAyN/5dOr0h+Aw63cfNphd4DHVXYDr//w5JhTbMMZpMeRzRqtv1pZgy75WvLjjBK6dNTbT4RCSsD5RglXQb81zmHl0+xPbxG/zBhMeuNiv0mnBG3tahzz28bFuTNFw6mGq24EXd56ArKi04USyDiWsB9FzOiwAFJh5dKdQbXSo3YfZ41xxX1ftsuCVhpNJfw4ho8mMGTPQ2NiIpqYmuN1urF+/Hr/+9a+HvKa1tRXl5eUAgE2bNmHSpEmGx9UVCMOtU0uiAjOPLoMqHT8/3jNi78eaUht+/8FRSLISdyefEBJbuk95tHb7wSP2CY+4pztkBb0BCV1dvritzBLV5g3CYeYQDIQRr3662GbC/uaumEOG+uXTKYJ+9J1yx+Dvla4THlo3zBKVals0AOgLKygptMRte9ZPS4u0wVbW1eDXbx3AsjlVCf2enjEYIVti+P43J+LBv+/DledVozIDGwLZsLFFMXwlXuuhtWvX4oUXXgDHcSguLsaDDz6IsWMjmx1Tp07FlClTAEROfDzxxBOGx9sVCOvaEsQucOhMsHiotS8EpzXZCmsz+kQJLb0iKgotUFUVHzR24gfzauL+rtNqQpFVwJcne3HOWGdSn0+IUShhPUhvUIJNpwpHALCaWEiKCjGc3DC1xk4/rpjujvu6ErsAb1BCTyCsqe+Rqqr4y+fH8cWJPlx6ZjnqJifWc5aQXMbzPFatWoUVK1ZAlmUsWbIEtbW1ePTRRzF9+nTMnz8fTz/9NDZt2gSO4+B0OvHQQw8ZHld3IAyHThtmDjOPg+2++C9Mwhcn+jB/yvA1w2riUGoXcKjDjzPKHYZ8NiH5IBtPefTq0MOa51gIPANfSNb9WL+nL4gim6DptUU2AZ6+oKaENSFEH6m2RQOAlq4ASioL4rY966elRdrp7rwokrxJ9Pf0jEFv2RKDk2fxzZpi3PPSTvzmmum6b1zGkw0bW7kcg56bZlpaD02dOhUvvfQSrFYrnn32WfzqV7/CI488AgCwWCxYt26dbvFo0eELYayOGy0OM4/OBCusW/pEnFftSurz2FO9qD8+1o0rplfgQJsPoqRgYokNPl/8xPmMMQV450AHJaxJ1qEyuFMUVYUvKOtaYc0wDJwWU8KLFQD4QzK6AxJKNNygsQyDapcV+9u8mt77d+8fxV8/P4Eyh4AH39qPdV9QdTYZXerq6vDmm2/i7bffxo033ggAuP322zF//nwAwJ133on169fj1VdfxdNPP52WCutuHXs3OsxcSqc7olFUFQfbvVGHcowrsmJPS5/un0tIPhl8yiMUCmH9+vWor68f8prW1q+OdabjlEevKMGmw1FYR4ony6Jp6Q2i2KZtEFGRzYSWXupjTchItGyYZUpnIISCJPu3kuzwrTPLcLQzgDf3tmU6FJJBg1sPCYIw0HposAsuuABWa+R+YubMmUPWpUzo9Ou7/hSYeXRoSBQP5ukLoijJoYsAMKXMjg8buwAAG/a2YVaVU/PG0cyxTrxzoA2qqib9+YQYga4KTukOhGETON379hRaIotVor3Sjnb5UVFoHtYnNppqlxV7WryYM64o5uuOdPjxwo4T+OmlU1Bg4VFbZsev3zmM86pdqHLRZFhCMqUnENatd6NRSaNjnQEUWkywRzmJUuWyYndLH64+O3avXUJGs2w85dGn07Ahh8CjR5RQpUNMg53sFTXfxDktPDy9os4REJIftLRFy5Ruv5R0/1aSHXiOxb/MqcKv3zmI2eNcKLVrOxlD8kuirYdefPFFzJs3b+DnYDCIxYsXg+d5rFy5Epdcckncz0y1LVGPKMFdZI3bXkdrC55ylxWHuwIJxdTuC6GqzAFHkonz82pK8LPXd6MtKOONPa24ff4kzfFOtQuQPwJaRBlTKwuT+nw9ZEtLGy1yKVYg9+LtZ+hVQS71LurwJd8zKJZCC59w/yIgkliu0DBwsd+4Yiu+0DB48T+3HsalZ5QN7CBWFFqw4IxS/MfbB/H/ls5IOE5CSOoUVYU3KEdNBCfKYebRE0h+4Gs0e1r7UO2Kvvk2vtiKV7/IbIUEIbmgrq4OdXV1Qx67/fbbB/5855134s4770xbPH1BSZfejXYzhx4DNstO9Ioo0lhhXWwTcJIqrAkZkZYNs4aGBtxyyy3o7e3FO++8g//3//4f1q9fb3hs3YEwCilhnfPGF9vwjYnFeOAf+/Do4vS3BiG5Zd26dfjyyy/xzDPPDDz2zjvvwO12o6mpCddffz2mTJmCcePGxXyfVNsSdXhD4JXYszwA7S14eFVFW6+oOSZfSEJIUoGwBK8ka/qd09kYoG5SCS7/r/ew4IwylJp5KIqiuWXQeVVO/On9I7h7fm1Sn6+HbGiro1UuxQpkf7zR2hIZdlWQa72L2n0hOC3JH8GIpsDCoyOJliCHO/woTyBhPaHYhvW7PDFfc6wrgIYTvbh20ZlDHq+vLcUDb+7HJ8e6NQ15JIToK9I/ltXthIdD4NArSlBVVdcbhd0t3pgnMapdVjR2BhCWFZho8CIhOcMblGDVoSWa/dTao7cTPSJmaKz4KbKZ8CW1JiIkqngbZmeffTa2bt2a1piCkoKQrMBiomuHfLBwajl+tekQXm44iSXnjMl0OCTNtLYeev/99/HEE0/gmWeegSAIQ34fAKqrq3H++edj9+7dcRPWqZAVFb2ivic8CiyJnXb19AVRajelfN/27anlGFdkxVkVifck/2ZNMVa/dQA3XzhR91kkhCTLsKuCXOtd1O4NodCAvmkOgUdngv2LAOBwuw8VhdoT1mUOAb6QHLOa+8UdJ/CNCUXDEkk8x+KK6W48uuUw9S0iJAO6A2Fd+6bxHAszz8IbTG6HPpo9LX0YF6V/NQCYeRZlDgGHO7J395YQMlx3IKzLjZpNMKbCuqU3iBK7tqKCEruAFmoJQkhO6TrVP5aqcfMDz7G44WvV+O22RhwyaAg4yV5aZnXs3r0bq1atwuOPP46Skq+Guff09CAUiuQzOjs78dlnnw0peDRCrxiGVdCvcAiI9LDuDoQ151YSGS4dC8MwmF5ZCDaJtbTIJmCquwB//fx4ynEQohfDtk5yrXeRT1ZRUmDR1OMH0N6/qMxpQV9YSTiuY90iFp5dqTkeAJhc7kBjXwg1Y1zDnpMUFW/s8eC+y88a8T3rprrxzsFOfHi8D9+eXjHs+UzItT47uRYvkJsx56MeHQcu9iuw8OjSMRGuqioOtvuwfHbs7rTVLiv2tXpxRrlDl88lhBgrLCsIhBTYdKiwtpmMGfjq6QuiWOONXLHNhDZvCLKi6j6XhBBijDZvKKVhYyT7VBRacM3ZFfj3dbvwp+WzqGJzFNHSeujhhx+G3+8fON3R3wL20KFDuO+++8AwDFRVxQ9+8APDE9Yd/rDuJ+3NPAuGYeAPa2v56OkNwpUFa+CV0914eOMhLDijHNUxipQISZes+C9HNvQuamr3wcpBc48frf2LBAY42ulPKK6gpOBkTwAFHKM5HgAY57Rg824PZrmHJ4q2HetBldMKGxP9O15+Vjke+vsenOu2w6JDL8tUZXufndPlWrxAdsUcrW/RaNBtRMLaHEkcxaqITsTJ3iDMPBs3AT7GacFejxdXTtflYwkhBuv0h1Fo5ZOqxjmdXdB/4Ks3KEFSVNg1JtRNHItCC49WbxCVhYkNvCaEZEakujDzyRqir69PKEZjRwA/fWMv/u9V02gTcRSJ13roqaeeGvH3Zs2ahddee83I0Ibp9IV0Penar9DCo8sf1paw7gvCZcA8tUSVOcxYNK0ct770BR7/ztl0HUUyzrCWIIn2Lnr88cfj9i4yUps3iEIDelg7LTw6Ehy62NgZ6V/NJ9gDdmqFAx8d7Rrxub980oQLJhTF/P2zKgow1mnFkx8cS+hzCSGpiVzM6LtJZDfrmzja1+pFdYz+1f3GFVmw10P9YwnJFR2+EJw63ajZzfpXWLf0BVFqFxJqFVDqEHCih9qCEJIrWvqCVGGdp5bNrES7L4TfbD5ErSdJVuryGzPwtT9hrcXJBIZLG+2iyaW4sKYY//ynz/Cn7U0Qw/q2mCQkEYYlrHOtd1GbjjdsgxWYtS9U/Q61+zAmid2s8UU2ePqC6DitZ/bxngB2n+zFzLHxBxYtm1mJdV+24N1DHQl/PiEkOV0B/RPWDoFDV4KbZbHsb/VirCv+ulTtsuJguw8K3ZQQkhM6/frN8LALHLoD+g5dPNkjau5f3a/UJuB4kglrSVEpqUJImp3sFbPiODzRH8+x+MHXx+G9I5148kMqiiLZp8MfMqRlTYGZR6fGPFBzt4hSe+o9rPVycW0p7ry4Bh80duKaP3yMjfvbMh0SGaUMO3eQc72LfCE4DbhQKrSYYg5CHMmhBAcu9uNYBmeUR6qsF571VTX7C5+fwDcnlw4btjgSp9WE//2N8bj/H/uw8uvjceWMClizoD0IIfmsxYC+ZYUWE1q9+iWs93i8mFYZv22L3czDLvBo7hZ1a0dCCDFOp0+fgYtApCVIj6hvhXVzj4iSBG/iimwmHO8OJPxZjR1+/OAvOyErKn6+8EzMrSlO+D0IIYlr6Q3ijHJ7psMgBrELPG795kQ8uuUwZFnF/547ngZskqzR5Q/DYdY/31Fg5tHu09betbkngLIEZpelQ0WhBSu+Ph4H2314ZPNh7Djei//vohpdWsgRopWhjXJypXeRqqro9IcNqbC2mlioaqQHo9adu/1tPk3V0CM5t6oQf/vi5EDCOhCW8douD+6/fKrm95hYYsMdF9XgxZ0n8di7R2AVOJhYBkU2E86tcmHpOZUYX0yD+gjRS0ufiLPHJPd3Ppoiqwkne/U7Er+v1YtvTS3T9NrxxVbs9fRRwpqQHKBnZZHTwie8SR/P0U5/wlVHpQ4BTd2JrX8hScGtL3+Bq2a4UWwT8LN/7MOz352F0iy7gSQkH7X0irhggivTYRADOa0m/J+LavBf7zai1RvEPQtqNRVTEWK0Nq8xAw+LbCZN7cnEsIw+UcqaliCnm1xqx7/Pn4TfvXcUqzfsx48vnUJJa5I29F8JAP6wDAYwZNAgwzAoKzAndDT1cJItQQDg3LFONHYEcLDdBwD4w4fHMNXtQHmC71dRaMEt35yIh688C3fPn4zb5tXgyukV6BHD+N5zO/A/Hx2jI7OE6MRjQO/GIpsJLb3ah7bG0ukPIRCWNSeNxjot2N1CfawJyQXt3pBuFdZOqwk9gciQRL0c6wqgPMGkcak98R7Wf9/jQaldwNcnFOOMcgfOq3biqe1NCb0HISQ5bd4QiqzZcxyeGKPQYsL/d1ENmntErHh+B473JH4ShhC9negNJnySS4tim7ZrkeYeEWUOc1Ynge0Cj5sunIDdLV48tvVIpsMhowglrAGc7A2i2MAdrRKNu2sA0CuG0RuUku5hxHMsLqotwS/e3I83dnvw8s6TuObsyqTeCwAEnoXLakKpQ8CkUjuuml6Bey6ZjA17W/Hj9XshyUrS700IiWjrC6HYpu+FUrHNhNY+fRLWez1ejC+2aT6+Ob7IRglrQnJEu0+/HtYcy6DAwqPdq8/aAwDN3QGUORKssLabE0pYK6qKP25vwoIzvjpFUl9bijd2t8IfomFDhBgpJCnoC0qGnHQl2cdi4vCDr4/DWe4CfPeZz/HSzhM094RkVEtv4q3HtCixm3BSQ/FQc1fi1zmZYDFx+Le54/H2/jb89fPjmQ6HjBKUsAZwvDuAsgLjjnyWaNxdAyLH7scVWcGyye+wLTijDOOKrPjzJ834wdfH6X7Epcgm4PZ5NWjtC+Ku13YjTElrQpImhmWIkqx777Qim4BWnZJGezx9qNIwcLHfuCIr9rfR4EVCcoGeQxeBU5tlOvXPl2QFbd5Qwpv4LisPUZLRq7GfdsPxXigqMKXsqx66xXYBU8rt+PseT0KfTQhJTKs3csoslXsfkltYhsElZ5Th9roavLDjBK7/8+doONGb6bDIKCQrKtq8IUOKF4ttAlr64ueAmroDWTVwMRaHmcfNF07Ekx8cw6YD7ZkOh4wClLDGqamsOlc3DlZsN6FZ45GnvR4vqp2p9X1lGQZXzajAHRdPwpRyR0rvFY3ARyY+e0UJd726GyGJktaEJKOlL4him6D78JnB/fNTtaulD9Uu7etSgYWHxcShOcEesoSQ9OvwhVBg0e9GzWXV73THiVMn4PgE+5wyDIMxTguOdPg1vf4fe1txXrVz2Dr8tfFFeH0XJawJMdLJXhHFOZKsIfoa67Tgzosn4YLxRbjr1V24/aUv8OVJSlyT9GnzBlFg4Q3pp+461SYtXnHfsa4ASnOgwrpfqUPAjRdOwOoN+7HtcEemwyF5jhLWiCwSRhwD6VdiFzQnbna39KGqKLn+1enGcyy+f8E4BMIy/s/LX9KxWUKS4OkzpiURwzAotQu69LHe5/EmPEBxYrGVbjoIyQFdgTAKdephDQAuiwkenRLWTV0BlCd5As5dYNaUsJYUFRv3t2F2tWvYc2e5HTjaGUCLjgNsCSFDHekIwJ1DyRqiL5ZhcMGEItz/rTMwrsiKu17djRue/Rz/2NOKIBVEEYOd6BUNq27mWAZF1vjXREe7AijLsQHP44qsuHHuBNz/93147cuWTIdD8hglrBE5hmFk36BSu4CTGluC7PV4MS6BSsZM4zkW3/vaOFhNLL7/3A6cpJs6QhLi6TNmMjUQGbyYauKo3RuEPyyjPME1ckKxDTuae1L6bEKIsbxBCWFZ1bUlkdNqQotOCeujXf6kr8/cBWYc1pCw3tHcA5fVNGJinOdYnDu2EG/ubU0qBkJIfIc7fElvTJH8YeJYXFRbivu/dQbmTizG858dx7ef+BC/eHM/Pm3qpjZzxBAne4wZuNivJE7xkKqqONTuw1hnbhQsDjaxxIbb62rwu/eP4sEN+yGGqXiR6I8S1gCO94iGJ6xb+oJQ4/yH1huU0O4LoaIwtxYsjmVw3XljMbOqEN995jNspn5GhGjm6RXhNChh7bKa4NHQOy2WhpN9mFRiT7hlyeQyOz4/nljCOhCW8dLOE/jde43Y3+pN6HcJIYlr7g7AXWDWtSVRkc0Ej06b13s9XoxJ8pqostCCwx2+uK/bdKAdZ48pjPr8rGoX/rGnLakYCCHxHe7wozIHkzXEGBzL4NwqJ26dNxH3XDIZDAM8+NYBLPrdR/jPLYdxtFNbqydCtDjRE0CRQfdhQOSa6ESMa6K2UzM/cnXo7BinBXfPn4yTvUFc+8dP8cmx7kyHRPLMqE9YS4qKNq+xO2sWEwczz6LdF3sI0c4TvZhYYgOXg0NHGIbBJVPKsPLr4/F/3zmEH7++B91+bcOOCBnNmrpFlNiNq7A+rvF0RzQ7j/dgQrEt4d+rdlnR0hfUPPSsJxDGDc9+jr/vbkVzTwA3vtCA13fRETNCjHTMgMn0RVYTPDoNfN3f5kVVkqfOKgvjtwRRVRWbD7Zj5lhn1NdMKbOjzRfEsS5ts0gIIYk52ulHJVVYkxEU2wV8a2o57l1Qi5sunACPN4jvP7cDt7zYgJ0JFkUQMpJmA+/DgEjhYqzrhwNtPowrsuo+yyidbAKHf/1aNa6eUYGfvLEHP1m/J27eixCtRn3CuqVXhNNiMqTR/mDVLiv2t8au9PmsqRs1pYknhrJJTakd9y6ohaKq+M5Tn+C1L1viVpYTMpodavdhjEGVRWOcFuxvi19hGMuO4z1JrUscy2BisU3T1HdFVfHDdbtQU2zHv80dj6tnVOKOi2rw6JbDtFNPiIGau/Xv3eiymdDal/qNiiQraOoSk14fS2wC+oISegLRN812negFxzCoLIyeLGNZBrPGOrGB2oIQojtvUII/JKPIgFkeJL+McVqw+OxK/GLRmZhcasePXtuDO/+2i2YMkJQc6za2f/RYlwX7PH1Rn9/X2mfYfWC6nT2mED+9dApUANc+9Qn+8tlxyArlgUhqRn3C+lC7H2Ocxu/qj3VZsCfGYgUAnzX3YHKp3fBYjGbmWSydOQY3Xjgez3zSjO8/txMH2uh4PyGnkxUVR7sCSR95j2es04LD7cknrMWwjMPt/qQqrAFgSpkDHxzpivu65z87Dn9IxjVnVwxUGFQUWvCv51fjx+v3oMtPu/SEGOFoZ/I9oqMptprQK0rwhaSU3qexK4ASuwlmPrlLVZZlMLHEhi9bol97vfFlC2ZWOeNWNs0e58L6XR7agCdEZ0dOtQPJ5epCkl4mjsU3J5Xgvm9NgcvK45+f/oxO5JGkKKf6R1cZmDCucllxIEbx0F6PNyf7V0djMXFYfHYl/k9dDV7f5cG/PPOZpuIlQqIZ9QnrXS29qE7DkMNqlxV7PNGTtmJYxoE2H2pKcj9h3W9ckQ0/vHgSZowpwL/9tQEPvbUfnZR4ImTA8R4RhRYeFpN+A88GK7EL6BUleIPJJY4+P96DccXWpBNGM8YUYOuhjphJnpZeEU9+cAz/PLsK7GntkM50F2B2tQu/fPtgUp9PCInNiMoilmUw1mXBofbU+oweaEv9Jm58sQ0NUY6Nq6qKv+9qwbljo/ev7ldTYoOiqnTTRYjO9rcl36eejG4mjsXCs9y4bd5E/Pf7R3HPK18gJCmZDovkkOZuEQ6Bh91sXP/oEpsJAUmJWnyzt9WbllxUuo1xWnB73URcWFOMH67bhZ++sQetOg3kJqPLqE9Yf3myD+OK0pGwtmBfjCFinzb3YHxR8omhbMWyDOZNKsF9l01BT0DC0v/5GP+55TDadepvSUguM3oqNMswGOO04FCSVdbvH+nCGWWOpD9/rNOCsKygsTN677Zf/mMfLqwphjtK/8rLp7mx19NHx/EJMcDxbhHlBgydHlNowcEUTncAwO6TfRjrTO36rKbYhh3HR04yH2jzISwrmq4BGYbB1yYU4eWGkynFQwgZ6rOmHtSU5HY7RJJZVS4r7po/Gcc6/bjxhQZ0x2gDRchg+1q9qDY4D8QwDMa5rCO2aDzZK8IfklERoy1ZLmMYBl8bX4T7LpsCBgz+6Y+f4LF3jyRdSEVGp/zKjiZIVVXsa/VifJLH3RNRXmBGjxiOOoDsnf3tmF4Zv8onV9nNPJadOwb3XFKL5p4Alq39BD96bTfeO9IJSabdcDI6HWzzGn6RUlmYfML6gyOdOKuiIOnPZhgG08cUYtOBthGf33m8Bx8e7sBlZ5ZFfQ+BZ/Hd86vxq00HcZL6FBKimz5Rgj8sw2nVv3dsZaEZ+2Ns0mux/Vg3zihP7dTZxBIb9nj6Ruyh+PouD742sVhzK4JvTCjG1kMdMful+kMyWnpFSNSzkRBNdhzvwaQcn99DMs9i4nDLxZNQUWDG9579nK4XiSZ7PenpHz3WaRnxmmj70S6c6XaAzfOWSBYTh6tmVOCeS2pxoM2Lq5/cjrUfHUu5dRwZHUZ1wvpErwiOZeAy4GbtdCzDoKbEhk+bhh9NlRUVWw91YKaGY6m5rtgu4Npzx+JnC89AmV3AY1uPYMHjH+Ce1/bg9V0tNDiDjCp7Wo0/CjvGacbeGO2IojnZK6LTH8b4FCsPvjGhCC/vPDksgSMrKn616SCWnTc2bkuUCcU2XDKlDHf+bVfUi5tAWMaXJ3uxo7mHqmsI0eCLk72YWGwz5EZprMsSs2djPN2BME72iikXFDjMPNwFZnzePPTaKyQpeGO3B/NqSzW/V4GFx4U1JXjyg2PDnmvtC+LHr+/BZU98gO/++XMs+t2H+Ovnx6nnNSExtPSKECUl6gkrQhLBMgwWn1OJr08sxvee3YGDKQ4dJ/lvr8eLapfxCeuJJTZsP9o97PEPGrswJYWTrLmm2C7gu3Oq8X8uqsGnTd248r+34/FtR9Duo5axJDrjGvbkgB3NvWk9hjajshCbDrTj4tNukHYc74HDzKF8FF2w2QUeF9WW4qLaUnT5w/jyZC/e2N2K32w+DAvPYnplIS6YXIrJLgumlDvyrlUKIZKi4vPmHiw6y23o50wpc+Cp7U0J/97fd3swq8o5rK90osYX21BoMeHdQx1D1r5nPmkCyzCYO6kEPg0XKvOnlKLdF8IPnt+Jny88E5NK7QhKCt470olXvziJT5t6UFloAccyONEr4sxyB5bPrsKFNdorKAkZTT5v7kGNQZWNY50WHO7wQVXVpP7+fdbUjdoyB7gU1x8AmFXlxBu7PZg9zjXw2OaD7RjjtKCi0AJvAi3KLplSiofePoBN+9tQP6Us0gd7TyvWvHMIc2uK8dDlU2E1cWjuDuDZT4/jo6PdWL3oTMPmFBCSyz4/3oPaUjv9N5roqr62FAVmHje+sBP/ceVZmFXlynRIJAuFJAW7WvqwdOYYwz9rWmUB/vxpM7xBCY5T/bLDsoKPj3XjR/MnG/752aay0IIbvjYOnr4gNu1vx9L/+RgXTCjCVTMqsGAGzTQgQ43qhPXb+9swI41tOGaOLcSDbx2EJCvgua8SsH/+pBlza4rTFke2KbKZ8M1JJfjmpBKoqopWbwiHO3zYfqQTz7b24WRvEDUlNswc68S5VU6cO9YJl834qnhCjLS7pQ/FNsHwEx5VLgt8IRnN3QFUaRzqoaoqXt/Vimtn6XMRd+mZZViz+RBmV7tQYOHx8bEu/OnjZtw1f7LmG1WGYXDtuWOw+WAH/vdfd4IFA39YxsRiG84f78KymWMGkkJhWcGO4z14ZMthPP5eI773tXG4qLYUvA7JL0LyxWfNPaibXGLIexdaTLALHA60+TClPPHqoQ8auzBZp2T6nHEurH7rAO6WamHmWUiKiifea8RVMyoSfi+Hmcf//sZ4/GLDAbz6ZQtaeoOQFBU3XThhSDV4lcuKOy6qwZ8/PY6Vf9mJRxdPR5FN/17hhOSyt/e1YWrF6KkuJOkzZ5wLDjOHf1+3G3fVT8ZlU8szHRLJMp81d2OM04JCi/HpMKuJQ22ZA+8f6cSlZ0b+t7hxfzvGOi0oto/eawN3gRnXnTcWV82owEdHu/DI5sO47+/7cOHEYtRNLsHscS7YhVGdriQYxQlrf0jGZ009uObsxG9YklVkE1DuELDtcCcuOlVpeKwrgJ0netOyu5cLGIaBu8AMd4EZDocZXm8QQUlBY6cfh9v9eOaTJtz/930ocwg4r9qJ86pdmDnWOaqq00l+eP9IJ6a6jb9RYxgGZ1U48EFjF5bN1JawbjjRi6AkY5JOJ1DOHlOIPZ4+/O+/7sTMsU68ubcVKy4Yh9IEL9IYhsHFtaWYN6kEfUEJNhMHYYTTFyaOxZxxRTiv2oWdx3ux9qNjeOjtA5gzzoUzyh0otplgNXGwmDg4zByKbQLcBWZYqQqSjBIhScH+Vi9u+Fq1YZ8xo7IQWw91JJywDkoKNu1vx92X6FN1VGQTUFtmxx8+PIqbLpyIP3/ShAILj2lJ9ucfV2TDA98+Aw0ne3H++CLUltpHPInCcyy+O6cKr+3y4Po/f47/uPIsTHUnPxOAkHzSEwjjk2M9uHpGZaZDIXlqqrsAt8+biEe2HMb+Ni9uvHAiFS6QAVsOdSR9HZCMc8YU4m8NLVhwRmRuz7OfNhtWNJBrbAKHi2tLcXFtKfwq8P7+Nqz9qAk/Wb8Xk0rtmDPOhXOrnDh7TOFAhToZPUbt/8ffPdSBmlJb2ndtFp7lxm82H8LXJxaDY4BVb+zFpWeUUcuLGMw8izPKHTjj1E2vrKho7g5gf5sPL+48iV++fRBWE4vplQWYXlmIM90OTClzGDJIihA9qKqKTfvbcXWaNszOchdg4742LNO4Mfb794/ikjPKdD2mu+ycMdhxohcnekT8e/0klDmS32TSOnuAZZjIqYwqJ9p9IRxo8+Jguw++oIyQrCAkKwiEZPSKEjr9IdgEDpWFFoxxWjDW+dX/nVBsg7vATMeWSd748GgXql1WQzdpZowpxBu7PVjx9fEJ/d6Wg+2oLrKiRMeqo386dyx++fZBNBzvRVN3ALfOm5jS32erwOFr44vivo5hGFw5vQJjCi245cUvML+2FFfNqMAZ5Y4hJ+0IGW3e2teGaZUFsAm0UUyMM9Zlxd3zJ+Op7U1Y+fwO/OzbZ6I6xdksJPdJsoLNBzpw04UT0vaZXxvvwpZDHXj20+PoFcPwh2ScncaT/rmivMCMS84owyVnlCEkKTjU4cP+Nh8e39aIxk4/ygvMmFZRgGkVBagtt2NyqR2FFsr55LNRmbCWFRW//+AorphubO/YkUyrLMBHR6343rOfg2UZmDkW9VO0D/0hkWTV+GLbwPHb/jYiRzr8+PJkH/6xpxVN3QFYTRwmlthQU2LHhGIbxhVZUOWyoqLQQjvsJKPeP9IFSVFQW2ZPy+edM7YQrzScxO6WPpwVp5rgk2PdONzhx3fnVOkaA8symFXlxKwqp67vq1WpXUCpPXrrJUVV0StKaPeG0OEPocMXWVM6fCGc6A1CUhScWV6AmWMLMb2yEGeU21FiF6ImvSRFRUhSoEKFmWMpOUWyyvOfHTe8FdnkUjtO9ARxtNOveXiioqp45pNmzJ2ob2xOqwl3zZ+Exs4Arp01Ju03N7NPne7YfLAdP16/F56+IJwWHi6bCe4CM6aUOXBetROzqlwjnhohJJ8EwjL+8OEx/KuBJzwI6Vdg4XHzNydg0/52XP/nz3HN2RX4lznVhrfkI9lr/W4Pyh0CxjjT1y+Z51hcP6cKf/n8BFQAN39zQspzgvKdwLOY6i4YOJ0mKyqO94g42unHx8e68bcvWtDcHYBV4DDOZUW1y4pKpxllDjOKrCYUWHhYTBzMHAuOZTD4X7cKQFUBFV8Nx2bAgGMZmDgGFp6FTeAhcAwVLGWYoQnrrVu3YvXq1VAUBcuWLcPKlSuHPB8KhXDXXXdh165dcLlc+M1vfoOqqkiS5He/+x1efPFFsCyLn/zkJ/jmN7+pW1xv7WuLVOSm8RjIYP/6tWo0nOgFAJxdWQiW/hKkZHAbkQsmRCqeVFVFpz+Mk70iWnqD2H60C+t3h9DaF0R3IIxSu4DKQvNANaW7wIzyAjPKHWaU2gUUWvms+/+LqqroESW09gXR7guhyx9GjxhGICxDklUwDCBwLOxmHkVWE0rsAsocAsodZroBPk0qa5Mentp+DJeeWZa2/42ZOBb1U8rw+LZGPLpketTP7fSH8NM39uKfZo0ZdQlWlolUbbusJkzG8I2EnkAYR7sCONzhwweNnTjWJUJWVJTYI+1FGAYIK0BvIIy+oISwrMDEsWAAhGQFZp5FiU1ApdOC8cVWTCy2YVyRFdVFVrgLaBNttMnkGtTcHcD+Vq/um1Kn41gG86eU4j+3HsGvr56m6XfWfdGCkKxgVrX+G1tFNiGjfaQLLDyumF6BK6ZXQJIV9IoS+oISOv1hNHcH8J9bj+BEj4i5E4sx/4wynD/ORUdfiSEyfQ307KfNqCmxYXJpejbtCWEZBpecUYbzqp34++5WXP3kdlxYU4z62lKcM9ap64ke8pVszAWpqor//uAY/mW2sddAIxnrsuKOiyel/XPzBccyGFdkxbhBpyQUVUW3PwyPN4h2bwjN3SL2eLzwhWQEQjKCsoKwrEBRVCinctP9t8HMqR8YRBLYUFXIaqQCPygrEMMKAMBh5uAw8yjo/8fCwy5wKC6wgFdVWE0crAIHm4mDTTj1j4mD1cTBzLMwcQx4jh1ImCtq5H+HsqJC6v9HVk99pgxvUEavGEZ3IIxOfxht3uBA3scfkiErAM8xcJg5lDvMmFBsQ22ZHVPdBahyWfIuwW7YlbAsy3jggQewdu1auN1uLF26FPX19Zg8+auehC+88AIKCwvx1ltvYf369fi///f/4pFHHsHBgwexfv16rF+/Hh6PBzfccAPefPNNcJw+x8aaugOYlMGp1CzDYObYzFQZjhYMw6DELqDELmD6ae3xwrKCTn8YHb4QOv0hnOyNLGw9Yhjd/sjiIEoKCi08XFYTnBYeBRYTCswc7GYeDuHUAnRqETJzkYXIVWhFMBAa2MFjGAb9m3YKVChKZFFV1Mj/VU8tiqo60kIloUeU0OUPod0XRrs3hHZfEDzHotgWSaoVmHnYTBxMPAvu1P+Uw4oKMSzDd6rNQdep7+Mwcyi1m1HqEFBqF1BiM8FpNaGyxA5eVlBg4VFo4VFoiXxfSx738k1lbdLLyd4gJhan90Zt3qRiPLrlCB7dchi3zasBd1qC9FC7Dz9ctwvfmFiE6XREbRin1YSzrSacPearfze+kISegISgFLmgcRaYgbAMmxBZG/r/G6OqKsSwgq5AGB3+yMbZ9mPdWL/LA09fED2iBKeVR7FNgPPUWmPrX2d4DhYTCzPPRqoEeBaWU//0P2fhWZhN3KnHIv8IHAueZcCyDNRTa05YVhGUZIiSgkBYhj806J9w5PGQpEBSIuuT3SZACsmwCSwKzJH1sNgWWVetJjbuf0NVVYU/LCMQjlwsyooKnmUg8CzsAj9qW2Fleg3y9AVR6bTAlIZNqflTSvHAm/vxys4TuOac2C2J3jvcicfePYLb5k3Mug1jvf3/7d17cBN1vwbwZ5M0vaQX21LSCrz1RZRXuRS8DIVxihRKgVAqWsaDHBURRBSR4TaCWt7TcUS5yAB/4OhIBUGOClMLMsI4VEAFhaO8UyswoJweKNNG3lKgTdNc9/yxSWiFtklz2d3wfGaYtiG7+20mv6e73939RafVIM2gR5pBj+w0YLjnzpNrVgdOXrqGbccvYuXXZ9A3JR5/T09AVnIskmJ1vpPPLrc0nh0uN1yiCK0gIFanQXKcDn/rnYQEAchMjsUd8TFR/1r6QxRF2F3SXS8uUYQA6cqtWJ3mtnt95M4fAKi9YuWHLZIsUhP0eOqhviganIn/uXgVO36uQ9mBs9BppIufUhNikBirkz7nRKeRGlGeZpRBr/U1rJLjpeOmlLgYJOi1t12O+EOpvSARwJ/NNgyI0F2uFF4aQfDtTyEMkyc4XG7fcZL3q9UuHTPZnC5ctth9+xc2l3QcZfP8n83pht27r+a+cS234KlbEKQmvFYQoNMK0Gul47e4GCl7DHqpUW5MisWADAMMei3idFpoNQJcbhGtdhearA6Ym234tf46aq+ch93pxj+MiRiclYR7MxJxV3oC+qbEqbq3E7aGdXV1NbKzs9Gvn3S7l8lkwsGDBzuEVFVVFebPnw8AKCwsRFlZGURRxMGDB2EymaDX69GvXz9kZ2ejuroaw4cPD1l9bQ43mlodPV7eLgiwBLF8pKmtXiC8Neu1GmQlxyEr+da3AjndbrTYXGi2OWGxO9Fqlxo8Vyx21F+T5r71Hiw63NIZMjcAh9MTSKIUSu3P3Hmb2O1D6sZXQKfR+Jo5sToNDHot7ojXIzstASlxMbgjXodYXeBhI0JEc5sTTVYHrlmduN7mwP9escJib4b9j0Y0Wx2weJpWFrvLt5zOM09wakIM0hP0SE2Qvk+Ji0Fi7I2zh3qdBjEaDXRaARpB6PB7JsXpcJeft4JHSjDZFMqTXNfbHDc1jf0RzLj4jwfuxIfHLmBPTQMeHdALvRP1aLa58K+6azj3bwsmDzJi5F1pQWVjoNSYTe15D2YAwBCrg8XphtUh/bvVc/umxKNvSsf5E11uEdfaHLjeJuVNm0Pa2Wm2OdHYar+RNZ6v3vyR5uGWdpIcLmkZp1u8abt/JQi40fDWaRDraXrrPVOXaD1jWKPVwGaXGvKt9hsn0rzbEAAY9FrExkhNH7co1dLqkHLwr9vUCALc7vY330nrSInXISU+BnfExSDZc9VCgqehrddpoPfuxHlq9H3VCojxnDDUagTfTp+0PSmD/pYar7jPM1BCBjldYsDjvKdj9T8f6ovVVX+g4tcG5N/TC/1SpbmzRQAtbU5caLLi0O//xtnLFrwwKhsGvS5iGaTE/Mm5MwU5d6bA6Xaj7mob6q+34f+uWGF1uuBySaNH43mve0+Qu0XA4Zbm5G/5Vz0aW2y43uYEAN/f4gTPCS+dVoDOs7w0fjSI9xwcxXtOlHnzoP0ttCI8J93dgFOUTrI73d59Hun/AGmcazSebbT7KkDKAbcIuD1XFLXPNLvnQM/uOcDzHuzZnG44RBGtNhfaHNJjDj9yLhCxOo20v+O5yyY5TgdDrHTCUO+5MkqnERDj2UfTazueHIzRSf/vfV0FT4bGaAUM7J2oqGaWEvIHgHSgHeTYU8L4ZQ3qrWFYnxTPBWSi57NMpDvk2hwu2F3SY40WO+ye/av2J/stdhcsNhdc4o0sitEKSIqLgcHzgd5JsTokxnr3aaQP+o7x5qEAuN1SbntzzpdvnmwFpDzVtcvo9ldwxre7glPaf5MyKPlqG1otNgBAr0Q9+qTIM2e3kntBItDj/FHCez0QrDc04nRSs7j9hHUGgx4Wi12egrTScWW6Qd/hbiWpz9OKs3+24NDvjai/1oY2z4VV0kk2XYdsivP0cnSe/TQANx2naTU3jsPiPBdQeY8fY3XSlCs6jXBTLwgA7skwBP15OWFrWJvNZmRm3vhAMaPRiOrq6puek5UlXf6q0+mQlJSEpqYmmM1m5OTkdFjWbDZ3u82YGC0yMrqf5mPe2HtRf63N31+F6LYjXQkemgPC1HQDdBrlXEkZTDalpXU+r6q/+QMA/z13JKztTg5E0qh/ZPp2hL2ehqfJoKCDalI26Q6RzjNCEATPnR+dv6e6W0co6OP1yEiXTpr5Oz7DTe4MevSOBGRnpSDML30HY4fc2enflKEAJj/Y19PUZAa192CQy4sQbzp5FC28J6VuxXfBAKQmeVfvK5fnjrdwMSTHI7GLOdMjnUty5w8A/NfUIWhskekgnyjkbtwxqxSJ7U7U9+qVKMtd5ZHuBfmbQaIo4sDCvKj920h0q8lzpCu8I/+ej0uMQ0YnF4j667acHC/VoEcq56oiIpn8nfM2EpFM4mK0uI/T/hCRTIzJcTAGeQBLRNQTgiBgoEyfY0ZEgQvbZY9GoxENDQ2+n81mM4xG403Pqa+vBwA4nU40NzcjNTXVr2WJiHoimGwiIgoWM4iI5ML8IaJIYC+IiEIhbA3rIUOGoLa2FhcvXoTdbse+ffuQn5/f4Tn5+fmoqKgAABw4cAC5ubkQBAH5+fnYt28f7HY7Ll68iNraWgwdOjRcpRLRbSSYbCIiChYziIjkwvwhokhgL4iIQkEQwzjh0uHDh/H222/D5XLhiSeewLx587BhwwYMHjwYY8eOhc1mw9KlS3H69GmkpKRg/fr1von5N2/ejN27d0Or1WLFihUYPXp0uMokottMMNlERBQsZhARyYX5Q0SRwF4QEQUrrA1rIiIiIiIiIiIiIiJ/hW1KECIiIiIiIiIiIiKiQLBhTURERERERERERESKwIZ1gI4cOYLCwkIUFBTggw8+kLucbi1fvhwjR47E5MmT5S7FL/X19Xj66acxadIkmEwmbN26Ve6SumWz2VBSUoIpU6bAZDJh48aNcpfkF5fLhcceewxz586VuxTyg9qypytqy6XuqDG3OqPWPOsO8y440ZQ/QPRlEBBdOeQVrXkEMJOCoYQ8kjtDlDDelTQ+5R5P+fn5KCoqQnFxMR5//HFZarh+/ToWLFiACRMmYOLEiTh58qQsdUQzJWSPv+TOqEAoIc8CoaTs85fcGRkUkfzmdDrFsWPHihcuXBBtNptYVFQknjt3Tu6yunT8+HGxpqZGNJlMcpfiF7PZLNbU1IiiKIrNzc3i+PHjFf8au91usaWlRRRFUbTb7WJJSYl48uRJeYvyw5YtW8RFixaJL7zwgtylUDfUmD1dUVsudUeNudUZteZZd5h3PRdt+SOK0ZdBohhdOeQVrXkkisyknlJKHsmdIUoY70oan3KPpzFjxoiNjY2ybNtr2bJl4ueffy6KoijabDbx2rVrstYTbZSSPf6SO6MCoYQ8C4SSss9fcmdkMHiFdQCqq6uRnZ2Nfv36Qa/Xw2Qy4eDBg3KX1aWHH34YKSkpcpfht969e2PQoEEAgMTERPTv3x9ms1nmqromCAIMBgMAwOl0wul0QhAEmavqWkNDAw4dOoSSkhK5SyE/qDF7uqK2XOqOGnOrM2rMs+4w74ITbfkDRF8GAdGVQ17RmEcAMykYSskjuTNECeNdKeOT4wlobm7GiRMnfK+BXq9HcnKyzFVFF6Vkj7/kzqhAKCHPAqGU7POX2jOSDesAmM1mZGZm+n42Go2KHkxqV1dXh9OnTyMnJ0fuUrrlcrlQXFyMUaNGYdSoUYqv+e2338bSpUuh0TAC1IDZox5qyq3OqC3PusO8Cw7zR32iIYe8oi2PAGZSMJhHN5NzvCthfCplPD3//PN4/PHH8dlnn0V823V1dUhLS8Py5cvx2GOP4fXXX0dra2vE64hmzJ7IUMv+ixKyz19KycieUmfVFPUsFgsWLFiAFStWIDExUe5yuqXValFZWYnDhw+juroaZ8+elbukTn377bdIS0vD4MGD5S6FKKqoLbc6o6Y86w7zjm430ZJDXtGURwAziUJL7vEu9/hUynjauXMnKioq8OGHH2LHjh04ceJERLfvdDpx6tQpTJ8+HV9++SXi4+MVP8cy0V/JnWeBkDv7/KWUjAwGG9YBMBqNaGho8P1sNpthNBplrCg6ORwOLFiwAEVFRRg/frzc5QQkOTkZI0aMwHfffSd3KZ365ZdfUFVVhfz8fCxatAg//vgjlixZIndZ1AVmj/KpObc6o4Y86w7zLnjMH/WIxhzyioY8AphJwWIe3aCk8S7X+FTKePK+B9PT01FQUIDq6uqIbj8zMxOZmZm+qzwnTJiAU6dORbSGaMfsCS8l5VkglL5vopSMDAYb1gEYMmQIamtrcfHiRdjtduzbtw/5+flylxVVRFHE66+/jv79++O5556Tuxy/XLlyBdevXwcAtLW14ejRo+jfv7/MVXVu8eLFOHLkCKqqqvDee+8hNzcXa9eulbss6gKzR9nUmFudUVuedYd5FzzmjzpEUw55RVseAcykYDGPJEoY70oYn0oYT62trWhpafF9/8MPP+Cee+6JaA0ZGRnIzMzE+fPnAQDHjh3D3XffHdEaoh2zJ3yUkGeBUEL2+UsJGRksndwFqIlOp0NpaSlmz54Nl8uFJ554IuJ/kAK1aNEiHD9+HE1NTcjLy8Mrr7yCadOmyV1Wp37++WdUVlbi3nvvRXFxMQDpdxg9erTMlXXuzz//xGuvvQaXywVRFDFhwgSMGTNG7rIoiqgxe7qitlzqjhpzqzPMM/qraMsfIPoyCIiuHPJiHtFfKSWP5M4QJYx3jk9JY2MjXn75ZQDSvLaTJ09GXl5exOt48803sWTJEjgcDvTr1w+rVq2KeA3RTCnZ4y+5MyoQSsizQDD7IksQRVGUuwgiIiIiIiIiIiIiIk4JQkRERERERERERESKwIY1ERERERERERERESkCG9ZEREREREREREREpAhsWBMRERERERERERGRIrBhTURERERERERERESKwIY1ERERERERERERESkCG9YUtG3btmHixIlYvHhxQMvV1dVh7969YarqhvXr12P06NEYPnx4h8ftdjsWLlyIgoICTJs2DXV1dWGvhYhCT60ZdOLECUydOhX3338/9u/fH/Y6iCj01Jo/5eXlmDRpEoqKivDss8/i0qVLYa+FiEJPrRm0c+dOFBUVobi4GNOnT8fvv/8e9lqIKPTUmkFeBw4cwMCBA/Hrr7+GvRYKHBvWFLRPP/0U5eXlWLduXUDLXbp0CV999VXA23O5XAE9f8yYMfjiiy9uevyLL75AcnIyvvnmG8ycORNr164NuBYikp9aMygrKwurVq3C5MmTA66BiJRBrflz3333Yffu3di7dy8KCwuxZs2agGshIvmpNYOKioqwd+9eVFZWYvbs2Vi1alXAtRCR/NSaQQDQ0tKCbdu2IScnJ+A6KDJ0chdA6lZaWoq6ujrMmTMHkyZNwoULF3Du3Dk4nU7Mnz8f48aNQ11dHZYtWwar1QoAePPNN/HAAw9g3bp1+OOPP1BcXIypU6ciOTkZNTU1KC0tBQDMnTsXs2bNwogRIzB8+HA8+eSTOHr0KEpLS3Hp0iV88skncDgcyMnJwcqVK6HVam9Z47Bhw275eFVVFebPnw8AKCwsRFlZGURRhCAIoX+hiCgs1JxBffv2BQBoNDx3TKRGas6f3NzcDs/Zs2dPaF8cIgo7NWdQYmKi73ur1crjLyIVUnMGAcCGDRswZ84cfPTRRyF/bSg0eJRMQSkrK0Pv3r2xdetWWK1W5ObmYteuXdi2bRvWrFmD1tZWpKeno7y8HBUVFVi/fj3eeustAMDixYvx0EMPobKyEjNnzuxyO62trRg6dCj27NmD1NRUfP3119i5cycqKyuh0Wh6dDuJ2WxGVlYWAECn0yEpKQlNTU0Br4eI5KPmDCIidYuW/Nm1axfy8vKCWgcRRZ7aM2jHjh0YN24c1qxZgzfeeKNH6yAi+ag5g3777Tc0NDTg0Ucf7cFvTpHCK6wpZL7//ntUVVVhy5YtAACbzYb6+nr07t0bZWVlOHPmDDQaDWprawNet1arRWFhIQDg2LFjqKmpQUlJCQCgra0N6enpIfs9iEidmEFEJBe15k9lZSVqamqwffv2Hq+DiOSnxgyaMWMGZsyYgb1792Lz5s149913e7QeIpKfmjLI7XbjnXfe4VREKsCGNYXUxo0b0b9//w6Pbdq0Cb169UJlZSXcbjeGDh16y2W1Wi3cbrfvZ5vN5vs+NjbWd5uHKIqYOnVqwBP7/5XRaER9fT0yMzPhdDrR3NyM1NTUoNZJRPJSUwYRUXRRW/4cPXoU77//PrZv3w69Xh/0+ohIXmrLIC+TyYR//vOfIVsfEclDLRlksVhw9uxZPPPMMwCAy5cvY968edi8eTOGDBnS4/VS6HFKEAqZRx55BNu3b4coigCAU6dOAQCam5uRkZEBjUaDyspK30T5BoMBFovFt3yfPn1w5swZuN1u1NfXo7q6+pbbGTlyJA4cOIDGxkYAwNWrV3v06fb5+fmoqKgAIH06bG5uLudPI1IxtWUQEUUPteXPqVOnUFpais2bN/MOEaIooLYMan+V5aFDh5CdnR3wOohIOdSUQUlJSfjpp59QVVWFqqoqDBs2jM1qhWLDmkLmpZdegtPpxJQpU2AymbBhwwYAwFNPPYWKigpMmTIF58+fR0JCAgBg4MCB0Gg0mDJlCj7++GM8+OCD6NOnDyZNmoS33noLgwYNuuV2BgwYgIULF2LWrFkoKirCrFmzcPny5U7rWr16NfLy8mC1WpGXl4dNmzYBAEpKSnD16lUUFBSgvLwcS5YsCfErQkSRpLYMqq6uRl5eHvbv34+VK1fCZDKF+BUhokhRW/6sXr0ara2tePXVV1FcXIwXX3wxxK8IEUWS2jJo+/btMJlMKC4uRnl5OacDIVI5tWUQqYMgek+BEBERERERERERERHJiFdYExEREREREREREZEi8EMXKWpMmzYNdru9w2OrV6/GwIEDZaqIiG4nzCAikgvzh4jkxAwiIjkxg6ITpwQhIiIiIiIiIiIiIkXglCBEREREREREREREpAhsWBMRERERERERERGRIrBhTURERERERERERESKwIY1ERERERERERERESnC/wMwddMJO87c1AAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 1800x1080 with 15 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(25,15))\n", "for i,col in enumerate(features):\n", " plt.subplot(3, 5, i+1)\n", " sns.kdeplot(data = train_data[col], label = col, shade = True)\n", " plt.title(col)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "id": "42d379f4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:31.319679Z", "iopub.status.busy": "2022-01-28T14:38:31.316294Z", "iopub.status.idle": "2022-01-28T14:38:32.133871Z", "shell.execute_reply": "2022-01-28T14:38:32.133344Z", "shell.execute_reply.started": "2022-01-28T14:30:29.357660Z" }, "papermill": { "duration": 0.881378, "end_time": "2022-01-28T14:38:32.134013", "exception": false, "start_time": "2022-01-28T14:38:31.252635", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1800x1080 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(25,15))\n", "for i,col in enumerate(new_features):\n", " plt.subplot(3, 5, i+1)\n", " sns.kdeplot(data = train_data[col], label = col, shade = True)\n", " plt.title(col)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "id": "b5b7091d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:32.208251Z", "iopub.status.busy": "2022-01-28T14:38:32.207260Z", "iopub.status.idle": "2022-01-28T14:38:32.254047Z", "shell.execute_reply": "2022-01-28T14:38:32.254544Z", "shell.execute_reply.started": "2022-01-28T14:30:30.239316Z" }, "papermill": { "duration": 0.085573, "end_time": "2022-01-28T14:38:32.254711", "exception": false, "start_time": "2022-01-28T14:38:32.169138", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>feature_0</th>\n", " <th>feature_1</th>\n", " <th>feature_2</th>\n", " <th>feature_3</th>\n", " <th>feature_4</th>\n", " <th>feature_5</th>\n", " <th>feature_6</th>\n", " <th>feature_7</th>\n", " <th>feature_8</th>\n", " <th>...</th>\n", " <th>feature_10</th>\n", " <th>feature_11</th>\n", " <th>feature_12</th>\n", " <th>feature_13</th>\n", " <th>feature_14</th>\n", " <th>target</th>\n", " <th>New_feature1</th>\n", " <th>New_feature2</th>\n", " <th>New_feature3</th>\n", " <th>New_feature4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>-0.952229</td>\n", " <td>0.141619</td>\n", " <td>-1.117889</td>\n", " <td>0.725384</td>\n", " <td>0.218739</td>\n", " <td>-0.674964</td>\n", " <td>-0.502166</td>\n", " <td>-0.647598</td>\n", " <td>-0.729977</td>\n", " <td>...</td>\n", " <td>0.274006</td>\n", " <td>-0.505800</td>\n", " <td>-0.586893</td>\n", " <td>0.064019</td>\n", " <td>0.189520</td>\n", " <td>0</td>\n", " <td>-0.574658</td>\n", " <td>-1.368309</td>\n", " <td>-0.412773</td>\n", " <td>-0.565270</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1.370171</td>\n", " <td>-0.768569</td>\n", " <td>-0.135003</td>\n", " <td>1.524032</td>\n", " <td>0.218739</td>\n", " <td>-0.674964</td>\n", " <td>-0.502166</td>\n", " <td>0.629709</td>\n", " <td>-0.729977</td>\n", " <td>...</td>\n", " <td>0.274006</td>\n", " <td>-0.505800</td>\n", " <td>-0.586893</td>\n", " <td>-0.199267</td>\n", " <td>-0.740923</td>\n", " <td>1</td>\n", " <td>0.468673</td>\n", " <td>0.666421</td>\n", " <td>1.846548</td>\n", " <td>1.135495</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>0.702282</td>\n", " <td>-0.768569</td>\n", " <td>0.322013</td>\n", " <td>-0.639914</td>\n", " <td>-0.784601</td>\n", " <td>-0.674964</td>\n", " <td>-0.502166</td>\n", " <td>1.376885</td>\n", " <td>-0.729977</td>\n", " <td>...</td>\n", " <td>-0.640501</td>\n", " <td>-0.505800</td>\n", " <td>0.710571</td>\n", " <td>0.435100</td>\n", " <td>0.189520</td>\n", " <td>1</td>\n", " <td>0.002612</td>\n", " <td>0.633813</td>\n", " <td>0.253238</td>\n", " <td>0.040554</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>0.358940</td>\n", " <td>-0.768569</td>\n", " <td>0.742508</td>\n", " <td>2.090682</td>\n", " <td>1.808994</td>\n", " <td>-0.674964</td>\n", " <td>0.682856</td>\n", " <td>0.629709</td>\n", " <td>1.194542</td>\n", " <td>...</td>\n", " <td>2.258417</td>\n", " <td>0.640809</td>\n", " <td>-0.586893</td>\n", " <td>0.806181</td>\n", " <td>-0.740923</td>\n", " <td>0</td>\n", " <td>-0.236977</td>\n", " <td>0.755663</td>\n", " <td>1.308841</td>\n", " <td>1.355149</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>-0.124973</td>\n", " <td>-0.768569</td>\n", " <td>-0.135003</td>\n", " <td>1.524032</td>\n", " <td>1.222078</td>\n", " <td>-0.674964</td>\n", " <td>-0.502166</td>\n", " <td>0.629709</td>\n", " <td>-0.729977</td>\n", " <td>...</td>\n", " <td>-0.640501</td>\n", " <td>-0.505800</td>\n", " <td>0.710571</td>\n", " <td>-1.204715</td>\n", " <td>0.189520</td>\n", " <td>0</td>\n", " <td>-0.574658</td>\n", " <td>-0.170831</td>\n", " <td>0.642830</td>\n", " <td>0.646379</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " id feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n", "0 1 -0.952229 0.141619 -1.117889 0.725384 0.218739 -0.674964 \n", "1 2 1.370171 -0.768569 -0.135003 1.524032 0.218739 -0.674964 \n", "2 3 0.702282 -0.768569 0.322013 -0.639914 -0.784601 -0.674964 \n", "3 4 0.358940 -0.768569 0.742508 2.090682 1.808994 -0.674964 \n", "4 5 -0.124973 -0.768569 -0.135003 1.524032 1.222078 -0.674964 \n", "\n", " feature_6 feature_7 feature_8 ... feature_10 feature_11 feature_12 \\\n", "0 -0.502166 -0.647598 -0.729977 ... 0.274006 -0.505800 -0.586893 \n", "1 -0.502166 0.629709 -0.729977 ... 0.274006 -0.505800 -0.586893 \n", "2 -0.502166 1.376885 -0.729977 ... -0.640501 -0.505800 0.710571 \n", "3 0.682856 0.629709 1.194542 ... 2.258417 0.640809 -0.586893 \n", "4 -0.502166 0.629709 -0.729977 ... -0.640501 -0.505800 0.710571 \n", "\n", " feature_13 feature_14 target New_feature1 New_feature2 New_feature3 \\\n", "0 0.064019 0.189520 0 -0.574658 -1.368309 -0.412773 \n", "1 -0.199267 -0.740923 1 0.468673 0.666421 1.846548 \n", "2 0.435100 0.189520 1 0.002612 0.633813 0.253238 \n", "3 0.806181 -0.740923 0 -0.236977 0.755663 1.308841 \n", "4 -1.204715 0.189520 0 -0.574658 -0.170831 0.642830 \n", "\n", " New_feature4 \n", "0 -0.565270 \n", "1 1.135495 \n", "2 0.040554 \n", "3 1.355149 \n", "4 0.646379 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def Normalisation(df):\n", " for col in (features+new_features):\n", " df[col] = ((df[col] - df[col].mean()) / df[col].std())\n", " return df\n", "train_data = Normalisation(train_data)\n", "test_data = Normalisation(test_data)\n", "train_data.head()" ] }, { "cell_type": "code", "execution_count": 15, "id": "1abab17f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:32.333431Z", "iopub.status.busy": "2022-01-28T14:38:32.332794Z", "iopub.status.idle": "2022-01-28T14:38:32.505259Z", "shell.execute_reply": "2022-01-28T14:38:32.504639Z", "shell.execute_reply.started": "2022-01-28T14:30:30.292827Z" }, "papermill": { "duration": 0.214819, "end_time": "2022-01-28T14:38:32.505423", "exception": false, "start_time": "2022-01-28T14:38:32.290604", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(train_data.drop(['target'], axis=1),train_data.target, \n", " random_state=1,test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 16, "id": "50a34615", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:32.581799Z", "iopub.status.busy": "2022-01-28T14:38:32.581123Z", "iopub.status.idle": "2022-01-28T14:38:32.763690Z", "shell.execute_reply": "2022-01-28T14:38:32.764281Z", "shell.execute_reply.started": "2022-01-28T14:30:30.506929Z" }, "papermill": { "duration": 0.222973, "end_time": "2022-01-28T14:38:32.764485", "exception": false, "start_time": "2022-01-28T14:38:32.541512", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0.6295961295961296" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from xgboost import XGBRegressor\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.metrics import roc_auc_score,confusion_matrix\n", "\n", "forest_model = XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n", " max_depth = 5, alpha = 10, n_estimators = 10)\n", "forest_model.fit(X_train, y_train)\n", "Y_pred = forest_model.predict(X_test)\n", "roc_auc_score(y_test, Y_pred)" ] }, { "cell_type": "code", "execution_count": 17, "id": "565e6094", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:32.844535Z", "iopub.status.busy": "2022-01-28T14:38:32.843899Z", "iopub.status.idle": "2022-01-28T14:38:32.862437Z", "shell.execute_reply": "2022-01-28T14:38:32.863042Z", "shell.execute_reply.started": "2022-01-28T14:30:30.731022Z" }, "papermill": { "duration": 0.060918, "end_time": "2022-01-28T14:38:32.863207", "exception": false, "start_time": "2022-01-28T14:38:32.802289", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>feature_0</th>\n", " <th>feature_1</th>\n", " <th>feature_2</th>\n", " <th>feature_3</th>\n", " <th>feature_4</th>\n", " <th>feature_5</th>\n", " <th>feature_6</th>\n", " <th>feature_7</th>\n", " <th>feature_8</th>\n", " <th>feature_9</th>\n", " <th>feature_10</th>\n", " <th>feature_11</th>\n", " <th>feature_12</th>\n", " <th>feature_13</th>\n", " <th>feature_14</th>\n", " <th>New_feature1</th>\n", " <th>New_feature2</th>\n", " <th>New_feature3</th>\n", " <th>New_feature4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>10001</td>\n", " <td>1.020929</td>\n", " <td>-0.756866</td>\n", " <td>-1.132386</td>\n", " <td>-0.633374</td>\n", " <td>-0.796251</td>\n", " <td>-0.665731</td>\n", " <td>-0.498449</td>\n", " <td>-0.651277</td>\n", " <td>-0.726976</td>\n", " <td>2.309094</td>\n", " <td>-0.640565</td>\n", " <td>-0.509769</td>\n", " <td>-0.581277</td>\n", " <td>0.815803</td>\n", " <td>-0.719640</td>\n", " <td>0.236369</td>\n", " <td>-0.283864</td>\n", " <td>0.517714</td>\n", " <td>0.259160</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10002</td>\n", " <td>0.399949</td>\n", " <td>-0.756866</td>\n", " <td>1.901830</td>\n", " <td>-0.633374</td>\n", " <td>0.216546</td>\n", " <td>-0.665731</td>\n", " <td>-0.498449</td>\n", " <td>0.622976</td>\n", " <td>-0.726976</td>\n", " <td>-1.003609</td>\n", " <td>1.586090</td>\n", " <td>-0.509769</td>\n", " <td>-0.581277</td>\n", " <td>0.274261</td>\n", " <td>-0.719640</td>\n", " <td>-0.194322</td>\n", " <td>1.661247</td>\n", " <td>0.013645</td>\n", " <td>0.419394</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10003</td>\n", " <td>1.242567</td>\n", " <td>1.420382</td>\n", " <td>-1.132386</td>\n", " <td>0.759569</td>\n", " <td>0.216546</td>\n", " <td>-0.665731</td>\n", " <td>-0.498449</td>\n", " <td>1.368367</td>\n", " <td>-0.726976</td>\n", " <td>-0.155696</td>\n", " <td>-0.640565</td>\n", " <td>-0.509769</td>\n", " <td>0.694951</td>\n", " <td>-1.208552</td>\n", " <td>-0.719640</td>\n", " <td>1.747051</td>\n", " <td>-0.159712</td>\n", " <td>1.381603</td>\n", " <td>1.028571</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>10004</td>\n", " <td>-0.092950</td>\n", " <td>0.180824</td>\n", " <td>1.245626</td>\n", " <td>-0.633374</td>\n", " <td>-0.796251</td>\n", " <td>2.157702</td>\n", " <td>-0.498449</td>\n", " <td>-0.651277</td>\n", " <td>0.225927</td>\n", " <td>0.340301</td>\n", " <td>-0.640565</td>\n", " <td>-0.509769</td>\n", " <td>1.441497</td>\n", " <td>1.222874</td>\n", " <td>0.715865</td>\n", " <td>0.048230</td>\n", " <td>0.889253</td>\n", " <td>-0.386457</td>\n", " <td>-0.546128</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>10005</td>\n", " <td>-0.935568</td>\n", " <td>0.180824</td>\n", " <td>1.870209</td>\n", " <td>0.759569</td>\n", " <td>2.568187</td>\n", " <td>0.056949</td>\n", " <td>0.671594</td>\n", " <td>1.368367</td>\n", " <td>0.225927</td>\n", " <td>0.340301</td>\n", " <td>-0.640565</td>\n", " <td>3.792040</td>\n", " <td>-0.581277</td>\n", " <td>0.584261</td>\n", " <td>0.186063</td>\n", " <td>-0.536181</td>\n", " <td>0.889253</td>\n", " <td>-0.386457</td>\n", " <td>0.868337</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n", "0 10001 1.020929 -0.756866 -1.132386 -0.633374 -0.796251 -0.665731 \n", "1 10002 0.399949 -0.756866 1.901830 -0.633374 0.216546 -0.665731 \n", "2 10003 1.242567 1.420382 -1.132386 0.759569 0.216546 -0.665731 \n", "3 10004 -0.092950 0.180824 1.245626 -0.633374 -0.796251 2.157702 \n", "4 10005 -0.935568 0.180824 1.870209 0.759569 2.568187 0.056949 \n", "\n", " feature_6 feature_7 feature_8 feature_9 feature_10 feature_11 \\\n", "0 -0.498449 -0.651277 -0.726976 2.309094 -0.640565 -0.509769 \n", "1 -0.498449 0.622976 -0.726976 -1.003609 1.586090 -0.509769 \n", "2 -0.498449 1.368367 -0.726976 -0.155696 -0.640565 -0.509769 \n", "3 -0.498449 -0.651277 0.225927 0.340301 -0.640565 -0.509769 \n", "4 0.671594 1.368367 0.225927 0.340301 -0.640565 3.792040 \n", "\n", " feature_12 feature_13 feature_14 New_feature1 New_feature2 \\\n", "0 -0.581277 0.815803 -0.719640 0.236369 -0.283864 \n", "1 -0.581277 0.274261 -0.719640 -0.194322 1.661247 \n", "2 0.694951 -1.208552 -0.719640 1.747051 -0.159712 \n", "3 1.441497 1.222874 0.715865 0.048230 0.889253 \n", "4 -0.581277 0.584261 0.186063 -0.536181 0.889253 \n", "\n", " New_feature3 New_feature4 \n", "0 0.517714 0.259160 \n", "1 0.013645 0.419394 \n", "2 1.381603 1.028571 \n", "3 -0.386457 -0.546128 \n", "4 -0.386457 0.868337 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test = test_data\n", "test.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "84e4e1b3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:32.943958Z", "iopub.status.busy": "2022-01-28T14:38:32.943274Z", "iopub.status.idle": "2022-01-28T14:38:32.965270Z", "shell.execute_reply": "2022-01-28T14:38:32.965840Z", "shell.execute_reply.started": "2022-01-28T14:30:30.755737Z" }, "papermill": { "duration": 0.065306, "end_time": "2022-01-28T14:38:32.966082", "exception": false, "start_time": "2022-01-28T14:38:32.900776", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([0.52553093, 0.48950678, 0.45979574, ..., 0.45594886, 0.4611154 ,\n", " 0.4324836 ], dtype=float32)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pred = forest_model.predict(test)\n", "pred" ] }, { "cell_type": "code", "execution_count": 19, "id": "fcc263f9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:33.046645Z", "iopub.status.busy": "2022-01-28T14:38:33.045950Z", "iopub.status.idle": "2022-01-28T14:38:33.072078Z", "shell.execute_reply": "2022-01-28T14:38:33.071553Z", "shell.execute_reply.started": "2022-01-28T14:30:30.791729Z" }, "papermill": { "duration": 0.069147, "end_time": "2022-01-28T14:38:33.072210", "exception": false, "start_time": "2022-01-28T14:38:33.003063", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>target</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>10001</td>\n", " <td>0.525531</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10002</td>\n", " <td>0.489507</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10003</td>\n", " <td>0.459796</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>10004</td>\n", " <td>0.435077</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>10005</td>\n", " <td>0.436578</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>4995</th>\n", " <td>14996</td>\n", " <td>0.498208</td>\n", " </tr>\n", " <tr>\n", " <th>4996</th>\n", " <td>14997</td>\n", " <td>0.586506</td>\n", " </tr>\n", " <tr>\n", " <th>4997</th>\n", " <td>14998</td>\n", " <td>0.455949</td>\n", " </tr>\n", " <tr>\n", " <th>4998</th>\n", " <td>14999</td>\n", " <td>0.461115</td>\n", " </tr>\n", " <tr>\n", " <th>4999</th>\n", " <td>15000</td>\n", " <td>0.432484</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5000 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " id target\n", "0 10001 0.525531\n", "1 10002 0.489507\n", "2 10003 0.459796\n", "3 10004 0.435077\n", "4 10005 0.436578\n", "... ... ...\n", "4995 14996 0.498208\n", "4996 14997 0.586506\n", "4997 14998 0.455949\n", "4998 14999 0.461115\n", "4999 15000 0.432484\n", "\n", "[5000 rows x 2 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submission['target'] = pred\n", "submission.to_csv(\"submission.csv\", index = False)\n", "submission" ] }, { "cell_type": "code", "execution_count": 20, "id": "27ede743", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:38:33.150113Z", "iopub.status.busy": "2022-01-28T14:38:33.149419Z", "iopub.status.idle": "2022-01-28T14:38:33.484112Z", "shell.execute_reply": "2022-01-28T14:38:33.483484Z", "shell.execute_reply.started": "2022-01-28T14:30:30.825735Z" }, "papermill": { "duration": 0.374814, "end_time": "2022-01-28T14:38:33.484248", "exception": false, "start_time": "2022-01-28T14:38:33.109434", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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0086/401/86401100.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
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<td>-0.138020</td>\n", " <td>0.912726</td>\n", " <td>-0.551904</td>\n", " <td>-1.220772</td>\n", " <td>-1.060166</td>\n", " <td>-0.219097</td>\n", " <td>-1.087009</td>\n", " <td>-0.612428</td>\n", " <td>-0.113944</td>\n", " <td>0.243608</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0_7</td>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>-1.064780</td>\n", " <td>-2.343535</td>\n", " <td>-0.011870</td>\n", " <td>1.874606</td>\n", " <td>-0.606346</td>\n", " <td>-0.586827</td>\n", " <td>-0.815737</td>\n", " <td>...</td>\n", " <td>0.382201</td>\n", " <td>0.912726</td>\n", " <td>-0.266359</td>\n", " <td>-1.220772</td>\n", " <td>0.941183</td>\n", " <td>-0.609113</td>\n", " <td>0.104928</td>\n", " <td>-0.783423</td>\n", " <td>1.151730</td>\n", " <td>-0.773309</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0_8</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>-0.531940</td>\n", " <td>0.842057</td>\n", " <td>-0.262993</td>\n", " <td>2.330030</td>\n", " <td>-0.583422</td>\n", " <td>-0.618392</td>\n", " <td>-0.742814</td>\n", " <td>...</td>\n", " <td>-0.170365</td>\n", " <td>0.912726</td>\n", " <td>-0.741355</td>\n", " <td>-1.220772</td>\n", " <td>0.941183</td>\n", " <td>-0.588445</td>\n", " <td>0.104928</td>\n", " <td>0.753279</td>\n", " <td>1.345611</td>\n", " <td>-0.737624</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 304 columns</p>\n", "</div>" ], "text/plain": [ " row_id time_id investment_id target f_0 f_1 f_2 \\\n", "0 0_1 0 1 -0.300875 0.932573 0.113691 -0.402206 \n", "1 0_2 0 2 -0.231040 0.810802 -0.514115 0.742368 \n", "2 0_6 0 6 0.568807 0.393974 0.615937 0.567806 \n", "3 0_7 0 7 -1.064780 -2.343535 -0.011870 1.874606 \n", "4 0_8 0 8 -0.531940 0.842057 -0.262993 2.330030 \n", "\n", " f_3 f_4 f_5 ... f_290 f_291 f_292 f_293 \\\n", "0 0.378386 -0.203938 -0.413469 ... 0.366028 -1.095620 0.200075 0.819155 \n", "1 -0.616673 -0.194255 1.771210 ... -0.154193 0.912726 -0.734579 0.819155 \n", "2 -0.607963 0.068883 -1.083155 ... -0.138020 0.912726 -0.551904 -1.220772 \n", "3 -0.606346 -0.586827 -0.815737 ... 0.382201 0.912726 -0.266359 -1.220772 \n", "4 -0.583422 -0.618392 -0.742814 ... -0.170365 0.912726 -0.741355 -1.220772 \n", "\n", " f_294 f_295 f_296 f_297 f_298 f_299 \n", "0 0.941183 -0.086764 -1.087009 -1.044826 -0.287605 0.321566 \n", "1 0.941183 -0.387617 -1.087009 -0.929529 -0.974060 -0.343624 \n", "2 -1.060166 -0.219097 -1.087009 -0.612428 -0.113944 0.243608 \n", "3 0.941183 -0.609113 0.104928 -0.783423 1.151730 -0.773309 \n", "4 0.941183 -0.588445 0.104928 0.753279 1.345611 -0.737624 \n", "\n", "[5 rows x 304 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import cudf\n", "\n", "df = cudf.read_parquet(\"../input/ubiquant-parquet/train_low_mem.parquet\")\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 2, "id": "39202293", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:43:23.265647Z", "iopub.status.busy": "2022-01-28T14:43:23.265064Z", "iopub.status.idle": "2022-01-28T14:43:23.274411Z", "shell.execute_reply": "2022-01-28T14:43:23.273966Z", "shell.execute_reply.started": "2022-01-28T13:48:41.308299Z" }, "papermill": { "duration": 0.019887, "end_time": "2022-01-28T14:43:23.274514", "exception": false, "start_time": "2022-01-28T14:43:23.254627", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "1219" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"time_id\"].max()" ] }, { "cell_type": "code", "execution_count": 3, "id": "fe1d7456", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:43:23.295082Z", "iopub.status.busy": "2022-01-28T14:43:23.294440Z", "iopub.status.idle": "2022-01-28T14:43:27.216876Z", "shell.execute_reply": "2022-01-28T14:43:27.217300Z", "shell.execute_reply.started": "2022-01-28T13:48:55.778037Z" }, "papermill": { "duration": 3.935715, "end_time": "2022-01-28T14:43:27.217451", "exception": false, "start_time": "2022-01-28T14:43:23.281736", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cuML version: 21.10.02\n", "621517 64756\n", "686273 65904\n", "752177 66446\n", "818623 64866\n", "883489 67265\n", "950754 65144\n" ] } ], "source": [ "import cupy\n", "import cuml\n", "\n", "print(\"cuML version:\", cuml.__version__)\n", "\n", "WINDOW = 20\n", "DROP_BEFORE = 900\n", "START = 1100\n", "N_SPLITS = 6\n", "\n", "cv = []\n", "\n", "for i in range(N_SPLITS):\n", " train_ind = cupy.where((df[\"time_id\"].values <= START + i*WINDOW) & (df[\"time_id\"].values > DROP_BEFORE))[0]\n", " val_ind = cupy.where((df[\"time_id\"].values > START + i*WINDOW) & (df[\"time_id\"].values <= START + (i+1)*WINDOW))[0]\n", " cv.append((cupy.asnumpy(train_ind), cupy.asnumpy(val_ind)))\n", " print(len(train_ind), len(val_ind))" ] }, { "cell_type": "code", "execution_count": 4, "id": "23671091", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:43:27.239158Z", "iopub.status.busy": "2022-01-28T14:43:27.238332Z", "iopub.status.idle": "2022-01-28T14:43:27.241534Z", "shell.execute_reply": "2022-01-28T14:43:27.241923Z", "shell.execute_reply.started": "2022-01-28T13:49:29.469880Z" }, "papermill": { "duration": 0.016672, "end_time": "2022-01-28T14:43:27.242044", "exception": false, "start_time": "2022-01-28T14:43:27.225372", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features = [col for col in df.columns if col not in {\"row_id\", \"target\", \"investment_id\", \"time_id\"}]\n", "len(features)" ] }, { "cell_type": "code", "execution_count": 5, "id": "612dc2cf", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:43:27.266391Z", "iopub.status.busy": "2022-01-28T14:43:27.265563Z", "iopub.status.idle": "2022-01-28T18:14:53.223966Z", "shell.execute_reply": "2022-01-28T18:14:53.222190Z", "shell.execute_reply.started": "2022-01-28T13:49:56.679236Z" }, "papermill": { "duration": 12685.974089, "end_time": "2022-01-28T18:14:53.224136", "exception": false, "start_time": "2022-01-28T14:43:27.250047", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 6/6 [3:31:25<00:00, 2114.32s/it]\n" ] } ], "source": [ "from tqdm import tqdm\n", "\n", "\n", "def evaluate(val_df):\n", " scores = []\n", " for time_id in val_df[\"time_id\"].unique().values_host:\n", " time_df = val_df[val_df[\"time_id\"] == time_id]\n", " scores.append(time_df[\"target\"].corr(time_df[\"pred\"]))\n", "\n", " return cupy.mean(cupy.array(scores))\n", "\n", "\n", "val_scores = []\n", "\n", "\n", "for f, (train_ind, val_ind) in tqdm(enumerate(cv), total=len(cv)):\n", " train_df, val_df = df.iloc[train_ind], df.iloc[val_ind]\n", "\n", " model = cuml.SVR(C=1.0, cache_size=3000.0)\n", " model.fit(train_df[features], train_df[\"target\"])\n", "\n", " y_pred = model.predict(val_df[features])\n", " val_df[\"pred\"] = y_pred.values\n", " \n", " val_scores.append(evaluate(val_df).item())\n", " \n", "val_scores = cupy.array(val_scores)" ] }, { "cell_type": "code", "execution_count": 6, "id": "7ba0d379", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:14:53.286400Z", "iopub.status.busy": "2022-01-28T18:14:53.285632Z", "iopub.status.idle": "2022-01-28T18:14:54.437191Z", "shell.execute_reply": "2022-01-28T18:14:54.437809Z", "shell.execute_reply.started": "2022-01-28T14:41:24.921404Z" }, "papermill": { "duration": 1.196398, "end_time": "2022-01-28T18:14:54.437982", "exception": false, "start_time": "2022-01-28T18:14:53.241584", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation scores: [0.1100807 0.13784313 0.09660739 0.12025975 0.14713392 0.09811604]\n", "Mean: 0.11834015473106958\n", "STD: 0.018985427576698858\n" ] } ], "source": [ "print(\"Validation scores:\", val_scores)\n", "print(\"Mean:\", cupy.mean(val_scores))\n", "print(\"STD:\", cupy.std(val_scores))" ] }, { "cell_type": "code", "execution_count": 7, "id": "3a5f85f2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T18:14:54.467752Z", "iopub.status.busy": "2022-01-28T18:14:54.467025Z", "iopub.status.idle": "2022-01-28T18:14:54.718606Z", "shell.execute_reply": "2022-01-28T18:14:54.717815Z", "shell.execute_reply.started": "2022-01-28T14:41:41.246928Z" }, "papermill": { "duration": 0.268651, "end_time": "2022-01-28T18:14:54.718873", "exception": true, "start_time": "2022-01-28T18:14:54.450222", "status": "failed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This version of the API is not optimized and should not be used to estimate the runtime of your code on the hidden test set.\n" ] }, { "ename": "AttributeError", "evalue": "'numpy.ndarray' object has no attribute 'values'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_25/1677583273.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_prediction_df\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miter_test\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0msample_prediction_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_prediction_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'" ] } ], "source": [ "import ubiquant\n", "env = ubiquant.make_env()\n", "iter_test = env.iter_test() \n", "\n", "for (test_df, sample_prediction_df) in iter_test:\n", " sample_prediction_df['target'] = model.predict(test_df[features]).values\n", " env.predict(sample_prediction_df) " ] }, { "cell_type": "code", "execution_count": null, "id": "0264c6a6", "metadata": { "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 12763.064258, "end_time": "2022-01-28T18:14:56.162003", "environment_variables": {}, "exception": true, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:42:13.097745", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/401/86401441.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a3f75edc", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:42:32.818302Z", "iopub.status.busy": "2022-01-28T14:42:32.817371Z", "iopub.status.idle": "2022-01-28T14:42:34.216637Z", "shell.execute_reply": "2022-01-28T14:42:34.215829Z", "shell.execute_reply.started": "2022-01-28T14:41:12.549196Z" }, "papermill": { "duration": 1.430906, "end_time": "2022-01-28T14:42:34.216807", "exception": false, "start_time": "2022-01-28T14:42:32.785901", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.model_selection import train_test_split\n", "\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from xgboost import XGBClassifier\n", "\n", "SEED = 2456455732" ] }, { "cell_type": "markdown", "id": "b7e334d2", "metadata": { "papermill": { "duration": 0.0169, "end_time": "2022-01-28T14:42:34.252734", "exception": false, "start_time": "2022-01-28T14:42:34.235834", "status": "completed" }, "tags": [] }, "source": [ "# Load data\n", "\n", "## Training data" ] }, { "cell_type": "code", "execution_count": 2, "id": "ab3e1372", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:34.290707Z", "iopub.status.busy": "2022-01-28T14:42:34.289757Z", "iopub.status.idle": "2022-01-28T14:42:34.338886Z", "shell.execute_reply": "2022-01-28T14:42:34.338232Z", "shell.execute_reply.started": "2022-01-28T14:41:12.557762Z" }, "papermill": { "duration": 0.069203, "end_time": "2022-01-28T14:42:34.339031", "exception": false, "start_time": "2022-01-28T14:42:34.269828", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "1 0 3 \n", "2 1 1 \n", "3 1 3 \n", "4 1 1 \n", "5 0 3 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris male 22.0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", "3 Heikkinen, Miss. Laina female 26.0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", "5 Allen, Mr. William Henry male 35.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked \n", "PassengerId \n", "1 1 0 A/5 21171 7.2500 NaN S \n", "2 1 0 PC 17599 71.2833 C85 C \n", "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", "4 1 0 113803 53.1000 C123 S \n", "5 0 0 373450 8.0500 NaN S " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')\n", "train_df.head()" ] }, { "cell_type": "markdown", "id": "84445bcb", "metadata": { "papermill": { "duration": 0.019037, "end_time": "2022-01-28T14:42:34.378714", "exception": false, "start_time": "2022-01-28T14:42:34.359677", "status": "completed" }, "tags": [] }, "source": [ "## Test data" ] }, { "cell_type": "code", "execution_count": 3, "id": "bec266cb", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:34.427509Z", "iopub.status.busy": "2022-01-28T14:42:34.426545Z", "iopub.status.idle": "2022-01-28T14:42:34.455160Z", "shell.execute_reply": "2022-01-28T14:42:34.455711Z", "shell.execute_reply.started": "2022-01-28T14:41:12.607716Z" }, "papermill": { "duration": 0.05734, "end_time": "2022-01-28T14:42:34.456012", "exception": false, "start_time": "2022-01-28T14:42:34.398672", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>892</th>\n", " <td>3</td>\n", " <td>Kelly, Mr. James</td>\n", " <td>male</td>\n", " <td>34.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>330911</td>\n", " <td>7.8292</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>893</th>\n", " <td>3</td>\n", " <td>Wilkes, Mrs. James (Ellen Needs)</td>\n", " <td>female</td>\n", " <td>47.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>363272</td>\n", " <td>7.0000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>894</th>\n", " <td>2</td>\n", " <td>Myles, Mr. Thomas Francis</td>\n", " <td>male</td>\n", " <td>62.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>240276</td>\n", " <td>9.6875</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>895</th>\n", " <td>3</td>\n", " <td>Wirz, Mr. Albert</td>\n", " <td>male</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>315154</td>\n", " <td>8.6625</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>896</th>\n", " <td>3</td>\n", " <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n", " <td>female</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3101298</td>\n", " <td>12.2875</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass Name Sex \\\n", "PassengerId \n", "892 3 Kelly, Mr. James male \n", "893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "894 2 Myles, Mr. Thomas Francis male \n", "895 3 Wirz, Mr. Albert male \n", "896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "PassengerId \n", "892 34.5 0 0 330911 7.8292 NaN Q \n", "893 47.0 1 0 363272 7.0000 NaN S \n", "894 62.0 0 0 240276 9.6875 NaN Q \n", "895 27.0 0 0 315154 8.6625 NaN S \n", "896 22.0 1 1 3101298 12.2875 NaN S " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')\n", "test_df.head()" ] }, { "cell_type": "markdown", "id": "e4a09d5b", "metadata": { "papermill": { "duration": 0.020359, "end_time": "2022-01-28T14:42:34.500358", "exception": false, "start_time": "2022-01-28T14:42:34.479999", "status": "completed" }, "tags": [] }, "source": [ "# Prepare data\n", "Before fitting the model we create the training set with all the features prepared.\n", "\n", "## Derive additional features" ] }, { "cell_type": "code", "execution_count": 4, "id": "52379273", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:34.543889Z", "iopub.status.busy": "2022-01-28T14:42:34.543011Z", "iopub.status.idle": "2022-01-28T14:42:34.584372Z", "shell.execute_reply": "2022-01-28T14:42:34.583810Z", "shell.execute_reply.started": "2022-01-28T14:41:12.635892Z" }, "papermill": { "duration": 0.064579, "end_time": "2022-01-28T14:42:34.584506", "exception": false, "start_time": "2022-01-28T14:42:34.519927", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>family_size</th>\n", " <th>fare_per_passenger</th>\n", " <th>no_of_cabins</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>2</td>\n", " <td>3.62500</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " <td>2</td>\n", " <td>35.64165</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>1</td>\n", " <td>7.92500</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " <td>2</td>\n", " <td>26.55000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>1</td>\n", " <td>8.05000</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "1 0 3 \n", "2 1 1 \n", "3 1 3 \n", "4 1 1 \n", "5 0 3 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris male 22.0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", "3 Heikkinen, Miss. Laina female 26.0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", "5 Allen, Mr. William Henry male 35.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked \\\n", "PassengerId \n", "1 1 0 A/5 21171 7.2500 NaN S \n", "2 1 0 PC 17599 71.2833 C85 C \n", "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", "4 1 0 113803 53.1000 C123 S \n", "5 0 0 373450 8.0500 NaN S \n", "\n", " family_size fare_per_passenger no_of_cabins \n", "PassengerId \n", "1 2 3.62500 0 \n", "2 2 35.64165 1 \n", "3 1 7.92500 0 \n", "4 2 26.55000 1 \n", "5 1 8.05000 0 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>family_size</th>\n", " <th>fare_per_passenger</th>\n", " <th>no_of_cabins</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>892</th>\n", " <td>3</td>\n", " <td>Kelly, Mr. James</td>\n", " <td>male</td>\n", " <td>34.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>330911</td>\n", " <td>7.8292</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " <td>1</td>\n", " <td>7.829200</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>893</th>\n", " <td>3</td>\n", " <td>Wilkes, Mrs. James (Ellen Needs)</td>\n", " <td>female</td>\n", " <td>47.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>363272</td>\n", " <td>7.0000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>2</td>\n", " <td>3.500000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>894</th>\n", " <td>2</td>\n", " <td>Myles, Mr. Thomas Francis</td>\n", " <td>male</td>\n", " <td>62.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>240276</td>\n", " <td>9.6875</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " <td>1</td>\n", " <td>9.687500</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>895</th>\n", " <td>3</td>\n", " <td>Wirz, Mr. Albert</td>\n", " <td>male</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>315154</td>\n", " <td>8.6625</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>1</td>\n", " <td>8.662500</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>896</th>\n", " <td>3</td>\n", " <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n", " <td>female</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3101298</td>\n", " <td>12.2875</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>3</td>\n", " <td>4.095833</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass Name Sex \\\n", "PassengerId \n", "892 3 Kelly, Mr. James male \n", "893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "894 2 Myles, Mr. Thomas Francis male \n", "895 3 Wirz, Mr. Albert male \n", "896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked family_size \\\n", "PassengerId \n", "892 34.5 0 0 330911 7.8292 NaN Q 1 \n", "893 47.0 1 0 363272 7.0000 NaN S 2 \n", "894 62.0 0 0 240276 9.6875 NaN Q 1 \n", "895 27.0 0 0 315154 8.6625 NaN S 1 \n", "896 22.0 1 1 3101298 12.2875 NaN S 3 \n", "\n", " fare_per_passenger no_of_cabins \n", "PassengerId \n", "892 7.829200 0 \n", "893 3.500000 0 \n", "894 9.687500 0 \n", "895 8.662500 0 \n", "896 4.095833 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def derive_features(df_in):\n", " df = df_in.copy()\n", " df['family_size'] = df['SibSp'] + df['Parch'] + 1\n", " df['fare_per_passenger'] = df['Fare'] / df['family_size']\n", " df['no_of_cabins'] = (\n", " df['Cabin']\n", " .str.count('([ABCDEFG]{1})([0-9]*)\\w')\n", " .fillna(0)\n", " .astype('int')\n", " )\n", " \n", " return(df)\n", "\n", "train_df_enriched = derive_features(train_df)\n", "test_df_enriched = derive_features(test_df)\n", "\n", "display(train_df_enriched.head())\n", "display(test_df_enriched.head())" ] }, { "cell_type": "markdown", "id": "95679506", "metadata": { "papermill": { "duration": 0.0197, "end_time": "2022-01-28T14:42:34.623333", "exception": false, "start_time": "2022-01-28T14:42:34.603633", "status": "completed" }, "tags": [] }, "source": [ "## Split training data" ] }, { "cell_type": "code", "execution_count": 5, "id": "29a87f33", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:34.664079Z", "iopub.status.busy": "2022-01-28T14:42:34.663388Z", "iopub.status.idle": "2022-01-28T14:42:34.700744Z", "shell.execute_reply": "2022-01-28T14:42:34.700091Z", "shell.execute_reply.started": "2022-01-28T14:41:12.678104Z" }, "papermill": { "duration": 0.059183, "end_time": "2022-01-28T14:42:34.700927", "exception": false, "start_time": "2022-01-28T14:42:34.641744", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# features = ['Pclass', 'Sex', 'Age', 'family_size', 'fare_per_passenger', 'no_of_cabins']\n", "# features = ['Pclass', 'Sex', 'Age', 'family_size', 'fare_per_passenger']\n", "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\", 'Embarked', 'Age', 'Fare']\n", "\n", "y_train_dev = train_df_enriched[\"Survived\"]\n", "\n", "# Keep only selected 'features' and transform to dummy variable if necessary\n", "X_train_dev_feats = pd.get_dummies(train_df_enriched[features], drop_first=True)\n", "X_test_feats = pd.get_dummies(test_df_enriched[features], drop_first=True)\n", "\n", "imp = SimpleImputer(missing_values=np.nan, strategy='median')\n", "imp.fit(X_train_dev_feats)\n", "\n", "# Impute missing values and put back in dataframes\n", "X_train_dev = pd.DataFrame(\n", " imp.transform(X_train_dev_feats), \n", " index=X_train_dev_feats.index,\n", " columns=X_train_dev_feats.columns\n", ")\n", "X_test = pd.DataFrame(\n", " imp.transform(X_test_feats), \n", " index=X_test_feats.index,\n", " columns=X_test_feats.columns\n", ")\n", "\n", "# Split all data available for training in a train and a dev set\n", "X_train, X_dev, y_train, y_dev = train_test_split(\n", " X_train_dev, y_train_dev, \n", " test_size=0.25, \n", " random_state=SEED,\n", " stratify=y_train_dev\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "id": "546fac6e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:34.745134Z", "iopub.status.busy": "2022-01-28T14:42:34.744366Z", "iopub.status.idle": "2022-01-28T14:42:34.752423Z", "shell.execute_reply": "2022-01-28T14:42:34.753080Z", "shell.execute_reply.started": "2022-01-28T14:41:12.712851Z" }, "papermill": { "duration": 0.032356, "end_time": "2022-01-28T14:42:34.753314", "exception": false, "start_time": "2022-01-28T14:42:34.720958", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The size of X_train: (668, 8)\n", "The size of y_train: (668,)\n", "The size of X_dev: (223, 8)\n", "The size of y_dev: (223,)\n", "The size of X_test: (418, 8)\n" ] } ], "source": [ "print(\"The size of X_train: {}\".format(X_train.shape))\n", "print(\"The size of y_train: {}\".format(y_train.shape))\n", "print(\"The size of X_dev: {}\".format(X_dev.shape))\n", "print(\"The size of y_dev: {}\".format(y_dev.shape))\n", "print(\"The size of X_test: {}\".format(X_test.shape))" ] }, { "cell_type": "code", "execution_count": 7, "id": "bd302cc1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:34.799198Z", "iopub.status.busy": "2022-01-28T14:42:34.798428Z", "iopub.status.idle": "2022-01-28T14:42:34.833747Z", "shell.execute_reply": "2022-01-28T14:42:34.833215Z", "shell.execute_reply.started": "2022-01-28T14:41:12.724870Z" }, "papermill": { "duration": 0.059275, "end_time": "2022-01-28T14:42:34.833966", "exception": false, "start_time": "2022-01-28T14:42:34.774691", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Pclass</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Age</th>\n", " <th>Fare</th>\n", " <th>Sex_male</th>\n", " 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7.7500 0.0 1.0 \n", "95 3.0 0.0 0.0 59.0 7.2500 1.0 0.0 \n", "360 3.0 0.0 0.0 28.0 7.8792 0.0 1.0 \n", "\n", " Embarked_S \n", "PassengerId \n", "670 1.0 \n", "615 1.0 \n", "574 0.0 \n", "95 1.0 \n", "360 0.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PassengerId\n", "670 1\n", "615 0\n", "574 1\n", "95 0\n", "360 1\n", "Name: Survived, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(X_train.head())\n", "display(y_train.head())\n", "display(X_dev.head())\n", "display(y_dev.head())" ] }, { "cell_type": "markdown", "id": "36c7f54f", "metadata": { "papermill": { "duration": 0.021028, "end_time": "2022-01-28T14:42:34.876835", "exception": false, "start_time": "2022-01-28T14:42:34.855807", "status": "completed" }, "tags": [] }, "source": [ "# Fit model\n", "\n", "Now that the data is prepared and split into a train and dev set, we can start to fit models. \n", "\n", "## Gradient boosting with manual hyperparameter tuning\n", "\n", "First, we will try fitting a model and tune the hyperparameters manually. We try to get a good estimate of the performance of the model by using cross-validation to determine the accuracy. We can further verify this by calculating the accuracy on the dev-set. This is data never seen by the model as we did not use it during training." ] }, { "cell_type": "code", "execution_count": 8, "id": "730b35ad", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:34.923198Z", "iopub.status.busy": "2022-01-28T14:42:34.922517Z", "iopub.status.idle": "2022-01-28T14:42:35.327043Z", "shell.execute_reply": "2022-01-28T14:42:35.327774Z", "shell.execute_reply.started": "2022-01-28T14:41:12.761958Z" }, "papermill": { "duration": 0.429692, "end_time": "2022-01-28T14:42:35.327969", "exception": false, "start_time": "2022-01-28T14:42:34.898277", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.7761194 0.84328358 0.80597015 0.84210526 0.81954887]\n", "0.8174054539333409\n" ] } ], "source": [ "gb = GradientBoostingClassifier(\n", " learning_rate=0.05,\n", " n_estimators=80,\n", " subsample=0.8,\n", " max_depth=3,\n", " random_state=SEED,\n", " max_features=5\n", "# min_samples_leaf=25,\n", "# min_samples_split=100\n", ")\n", "\n", "# Fit the model on different folds of the training data and get the score \n", "# Score is using accuracy by default for GBC\n", "gb_scores = cross_val_score(gb, X_train, y_train, cv=5)\n", "print(gb_scores)\n", "print(np.mean(gb_scores))\n" ] }, { "cell_type": "markdown", "id": "b2b78e25", "metadata": { "papermill": { "duration": 0.020375, "end_time": "2022-01-28T14:42:35.368763", "exception": false, "start_time": "2022-01-28T14:42:35.348388", "status": "completed" }, "tags": [] }, "source": [ "Now we also fit the model on the full training data and get the accuracy on that dataset. The accuracy on the full data should not be signicantly higher or lower than the CV estimate of the accuracy. Otherwise we are probably respectively overfitting or underfitting. \n", "In addition, we will do a final estimate of the model accuracy on data that the model has never seen: the dev-set. " ] }, { "cell_type": "code", "execution_count": 9, "id": "17e88396", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:35.413493Z", "iopub.status.busy": "2022-01-28T14:42:35.412552Z", "iopub.status.idle": "2022-01-28T14:42:35.499465Z", "shell.execute_reply": "2022-01-28T14:42:35.498921Z", "shell.execute_reply.started": "2022-01-28T14:41:13.168128Z" }, "papermill": { "duration": 0.110464, "end_time": "2022-01-28T14:42:35.499597", "exception": false, "start_time": "2022-01-28T14:42:35.389133", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8562874251497006\n", "0.8295964125560538\n" ] } ], "source": [ "gb.fit(X_train, y_train)\n", "\n", "score = gb.score(X_train, y_train)\n", "print(score)\n", "\n", "score = gb.score(X_dev, y_dev)\n", "print(score)" ] }, { "cell_type": "markdown", "id": "e61861cd", "metadata": { "papermill": { "duration": 0.023503, "end_time": "2022-01-28T14:42:35.543931", "exception": false, "start_time": "2022-01-28T14:42:35.520428", "status": "completed" }, "tags": [] }, "source": [ "## Feature importance\n", "\n", "Determine importance of the selected features in building the gradient boost model." ] }, { "cell_type": "code", "execution_count": 10, "id": "4d58beba", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:35.593389Z", "iopub.status.busy": "2022-01-28T14:42:35.592483Z", "iopub.status.idle": "2022-01-28T14:42:35.833158Z", "shell.execute_reply": "2022-01-28T14:42:35.832586Z", "shell.execute_reply.started": "2022-01-28T14:41:34.645410Z" }, "papermill": { "duration": 0.266939, "end_time": "2022-01-28T14:42:35.833375", "exception": false, "start_time": "2022-01-28T14:42:35.566436", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gb_feature_importance = pd.Series(gb.feature_importances_, index=X_train.columns)\n", "ax = (\n", " gb_feature_importance\n", " .sort_values()\n", " .plot(kind='barh')\n", ")" ] }, { "cell_type": "markdown", "id": "14e3bc88", "metadata": { "papermill": { "duration": 0.021362, "end_time": "2022-01-28T14:42:35.878106", "exception": false, "start_time": "2022-01-28T14:42:35.856744", "status": "completed" }, "tags": [] }, "source": [ "## Gradient boosting with grid search for hyperparameters\n", "\n", "## Hyperparameter tuning\n", "To fit a model, we are first going to search for the optimal parameters to train the gradient boost classifier with. " ] }, { "cell_type": "code", "execution_count": 11, "id": "db1fc393", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:35.924739Z", "iopub.status.busy": "2022-01-28T14:42:35.924166Z", "iopub.status.idle": "2022-01-28T14:42:35.927334Z", "shell.execute_reply": "2022-01-28T14:42:35.927763Z", "shell.execute_reply.started": "2022-01-28T14:41:13.505864Z" }, "papermill": { "duration": 0.02843, "end_time": "2022-01-28T14:42:35.927965", "exception": false, "start_time": "2022-01-28T14:42:35.899535", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# import scipy.stats as stats\n", "\n", "# from sklearn.model_selection import GridSearchCV\n", "# from sklearn.ensemble import GradientBoostingClassifier\n", "\n", "# gbs = GradientBoostingClassifier(random_state=SEED)\n", "\n", "# # specify parameters and distributions to sample from\n", "# param_dist = {\n", "# 'learning_rate': [*np.arange(0.05, 0.30, 0.05)],\n", "# 'n_estimators': [50, 75, 100, 125, 150],\n", "# 'subsample': [0.7, 0.8, 0.9, 1.0],\n", "# 'max_depth': [*range(2, 6)],\n", "# # 'max_features': ['sqrt', 'log2', 2, 3, 4, 5]\n", "# }\n", "\n", "# # run search\n", "# grid_search = GridSearchCV(\n", "# gbs, param_grid=param_dist, \n", "# cv=5,\n", "# verbose=1\n", "# )\n", "# grid_search.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 12, "id": "f005c9b6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:35.976030Z", "iopub.status.busy": "2022-01-28T14:42:35.975382Z", "iopub.status.idle": "2022-01-28T14:42:35.977686Z", "shell.execute_reply": "2022-01-28T14:42:35.978153Z", "shell.execute_reply.started": "2022-01-28T14:41:13.511216Z" }, "papermill": { "duration": 0.028916, "end_time": "2022-01-28T14:42:35.978313", "exception": false, "start_time": "2022-01-28T14:42:35.949397", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# print(grid_search.best_estimator_)\n", "# print(grid_search.best_params_)\n", "# print(grid_search.best_score_)" ] }, { "cell_type": "markdown", "id": "ccf5e1d4", "metadata": { "papermill": { "duration": 0.021358, "end_time": "2022-01-28T14:42:36.021336", "exception": false, "start_time": "2022-01-28T14:42:35.999978", "status": "completed" }, "tags": [] }, "source": [ "## Cross-validation score" ] }, { "cell_type": "code", "execution_count": 13, "id": "b0910bb9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:36.069075Z", "iopub.status.busy": "2022-01-28T14:42:36.068437Z", "iopub.status.idle": "2022-01-28T14:42:36.071211Z", "shell.execute_reply": "2022-01-28T14:42:36.070697Z", "shell.execute_reply.started": "2022-01-28T14:41:13.523784Z" }, "papermill": { "duration": 0.028196, "end_time": "2022-01-28T14:42:36.071354", "exception": false, "start_time": "2022-01-28T14:42:36.043158", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "\n", "\n", "\n", "# gb = grid_search.best_estimator_\n", "\n", "\n", "# print(grid_search.best_score_)" ] }, { "cell_type": "markdown", "id": "125b9b11", "metadata": { "papermill": { "duration": 0.02162, "end_time": "2022-01-28T14:42:36.114798", "exception": false, "start_time": "2022-01-28T14:42:36.093178", "status": "completed" }, "tags": [] }, "source": [ "## Score on dev-set" ] }, { "cell_type": "code", "execution_count": 14, "id": "02b6de65", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:36.163230Z", "iopub.status.busy": "2022-01-28T14:42:36.162401Z", "iopub.status.idle": "2022-01-28T14:42:36.165400Z", "shell.execute_reply": "2022-01-28T14:42:36.164819Z", "shell.execute_reply.started": "2022-01-28T14:41:13.536797Z" }, "papermill": { "duration": 0.029063, "end_time": "2022-01-28T14:42:36.165543", "exception": false, "start_time": "2022-01-28T14:42:36.136480", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# gb.fit(X_train, y_train)\n", "\n", "# score = gb.score(X_train, y_train)\n", "# print(score)\n", "\n", "# score = gb.score(X_dev, y_dev)\n", "# print(score)" ] }, { "cell_type": "markdown", "id": "29aa870a", "metadata": { "papermill": { "duration": 0.021693, "end_time": "2022-01-28T14:42:36.209334", "exception": false, "start_time": "2022-01-28T14:42:36.187641", "status": "completed" }, "tags": [] }, "source": [ "## Prediction errors" ] }, { "cell_type": "code", "execution_count": 15, "id": "1eba0d41", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:36.263928Z", "iopub.status.busy": "2022-01-28T14:42:36.263250Z", "iopub.status.idle": "2022-01-28T14:42:36.286602Z", "shell.execute_reply": "2022-01-28T14:42:36.287135Z", "shell.execute_reply.started": "2022-01-28T14:41:13.547737Z" }, "papermill": { "duration": 0.05567, "end_time": "2022-01-28T14:42:36.287307", "exception": false, "start_time": "2022-01-28T14:42:36.231637", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>Pclass</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Age</th>\n", " <th>Fare</th>\n", " <th>Sex_male</th>\n", " <th>Embarked_Q</th>\n", " <th>Embarked_S</th>\n", " <th>Survived_pred</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>66</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Moubarek, Master. Gerios</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2661</td>\n", " <td>15.2458</td>\n", " <td>NaN</td>\n", " <td>C</td>\n", " <td>3.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>28.0</td>\n", " <td>15.2458</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>126</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Nicola-Yarred, Master. Elias</td>\n", " <td>male</td>\n", " <td>12.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2651</td>\n", " <td>11.2417</td>\n", " <td>NaN</td>\n", " <td>C</td>\n", " <td>3.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>12.0</td>\n", " <td>11.2417</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>550</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>Davies, Master. John Morgan Jr</td>\n", " <td>male</td>\n", " <td>8.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>C.A. 33112</td>\n", " <td>36.7500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>8.0</td>\n", " <td>36.7500</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Name Sex Age \\\n", "PassengerId \n", "66 1 3 Moubarek, Master. Gerios male NaN \n", "126 1 3 Nicola-Yarred, Master. Elias male 12.0 \n", "550 1 2 Davies, Master. John Morgan Jr male 8.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked Pclass SibSp \\\n", "PassengerId \n", "66 1 1 2661 15.2458 NaN C 3.0 1.0 \n", "126 1 0 2651 11.2417 NaN C 3.0 1.0 \n", "550 1 1 C.A. 33112 36.7500 NaN S 2.0 1.0 \n", "\n", " Parch Age Fare Sex_male Embarked_Q Embarked_S \\\n", "PassengerId \n", "66 1.0 28.0 15.2458 1.0 0.0 0.0 \n", "126 0.0 12.0 11.2417 1.0 0.0 0.0 \n", "550 1.0 8.0 36.7500 1.0 0.0 1.0 \n", "\n", " Survived_pred \n", "PassengerId \n", "66 0 \n", "126 0 \n", "550 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_dev_pred = pd.Series(gb.predict(X_dev), index=X_dev.index, name='Survived_pred')\n", "\n", "dev_df_pred = pd.concat(\n", " [train_df, X_dev, y_dev_pred],\n", " axis=1,\n", " join='inner'\n", ")\n", "\n", "display(dev_df_pred[\n", " (dev_df_pred['Survived'] != dev_df_pred['Survived_pred'])\n", " & (dev_df_pred['Name'].str.contains('master', case=False))\n", "])\n" ] }, { "cell_type": "markdown", "id": "dec70ec6", "metadata": { "papermill": { "duration": 0.022269, "end_time": "2022-01-28T14:42:36.331717", "exception": false, "start_time": "2022-01-28T14:42:36.309448", "status": "completed" }, "tags": [] }, "source": [ "# XGBoost\n", "\n", "## Cross-validation score" ] }, { "cell_type": "code", "execution_count": 16, "id": "21f29628", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:36.381343Z", "iopub.status.busy": "2022-01-28T14:42:36.380653Z", "iopub.status.idle": "2022-01-28T14:42:36.383514Z", "shell.execute_reply": "2022-01-28T14:42:36.383038Z", "shell.execute_reply.started": "2022-01-28T14:41:13.589371Z" }, "papermill": { "duration": 0.029421, "end_time": "2022-01-28T14:42:36.383643", "exception": false, "start_time": "2022-01-28T14:42:36.354222", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# xgb = XGBClassifier(\n", "# learning_rate=0.3,\n", "# # min_split_loss=2,\n", "# max_depth=3,\n", "# subsample=0.6,\n", "# colsample_bytree=0.8,\n", "# reg_lambda=1, # L2 regularization\n", "# # reg_alpha=2,\n", "# n_estimators=100,\n", "# objective='reg:logistic',\n", "# use_label_encoder=False,\n", "# random_state=SEED\n", "# )\n", "\n", "# # gb = grid_search.best_estimator_\n", "\n", "# scores = cross_val_score(xgb, X_train, y_train, cv=5)\n", "# print(scores)\n", "# print(np.mean(scores))" ] }, { "cell_type": "markdown", "id": "6a2d6bc7", "metadata": { "papermill": { "duration": 0.021515, "end_time": "2022-01-28T14:42:36.427274", "exception": false, "start_time": "2022-01-28T14:42:36.405759", "status": "completed" }, "tags": [] }, "source": [ "## Score on dev-set" ] }, { "cell_type": "code", "execution_count": 17, "id": "9ebf3077", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:36.474533Z", "iopub.status.busy": "2022-01-28T14:42:36.473979Z", "iopub.status.idle": "2022-01-28T14:42:36.477259Z", "shell.execute_reply": "2022-01-28T14:42:36.477702Z", "shell.execute_reply.started": "2022-01-28T14:41:13.594747Z" }, "papermill": { "duration": 0.028682, "end_time": "2022-01-28T14:42:36.477876", "exception": false, "start_time": "2022-01-28T14:42:36.449194", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# xgb.fit(X_train, y_train)\n", "\n", "# score = xgb.score(X_train, y_train)\n", "# print(score)\n", "\n", "# score = xgb.score(X_dev, y_dev)\n", "# print(score)" ] }, { "cell_type": "markdown", "id": "c4959879", "metadata": { "papermill": { "duration": 0.021441, "end_time": "2022-01-28T14:42:36.521472", "exception": false, "start_time": "2022-01-28T14:42:36.500031", "status": "completed" }, "tags": [] }, "source": [ "# Create Submission" ] }, { "cell_type": "code", "execution_count": 18, "id": "1efbb1e9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:36.568604Z", "iopub.status.busy": "2022-01-28T14:42:36.567948Z", "iopub.status.idle": "2022-01-28T14:42:36.580215Z", "shell.execute_reply": "2022-01-28T14:42:36.579344Z", "shell.execute_reply.started": "2022-01-28T14:41:13.610384Z" }, "papermill": { "duration": 0.037028, "end_time": "2022-01-28T14:42:36.580397", "exception": false, "start_time": "2022-01-28T14:42:36.543369", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Your submission was successfully saved!\n" ] } ], "source": [ "# Submit using XGBoost\n", "y_test_pred = gb.predict(X_test)\n", "\n", "output = pd.DataFrame({'PassengerId': test_df.index, 'Survived': y_test_pred})\n", "output.to_csv('submission.csv', index=False)\n", "print(\"Your submission was successfully saved!\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 13.110984, "end_time": "2022-01-28T14:42:37.315218", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:42:24.204234", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/401/86401452.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "040a3d6c", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "papermill": { "duration": 0.063705, "end_time": "2022-01-28T14:42:49.456803", "exception": false, "start_time": "2022-01-28T14:42:49.393098", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "id": "954e4b41", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:49.582132Z", "iopub.status.busy": "2022-01-28T14:42:49.580538Z", "iopub.status.idle": "2022-01-28T14:42:49.589102Z", "shell.execute_reply": "2022-01-28T14:42:49.589694Z", "shell.execute_reply.started": "2022-01-28T14:25:13.521425Z" }, "papermill": { "duration": 0.072672, "end_time": "2022-01-28T14:42:49.589961", "exception": false, "start_time": "2022-01-28T14:42:49.517289", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "ff8e2f31", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:49.721119Z", "iopub.status.busy": "2022-01-28T14:42:49.720238Z", "iopub.status.idle": "2022-01-28T14:42:49.783916Z", "shell.execute_reply": "2022-01-28T14:42:49.783418Z", "shell.execute_reply.started": "2022-01-28T14:25:15.198356Z" }, "papermill": { "duration": 0.129437, "end_time": "2022-01-28T14:42:49.784047", "exception": false, "start_time": "2022-01-28T14:42:49.654610", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>country</th>\n", " <th>store</th>\n", " <th>product</th>\n", " <th>num_sold</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Mug</td>\n", " <td>329</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Hat</td>\n", " <td>520</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Sticker</td>\n", " <td>146</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Mug</td>\n", " <td>572</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>2015-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Hat</td>\n", " <td>911</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date country store product num_sold\n", "0 0 2015-01-01 Finland KaggleMart Kaggle Mug 329\n", "1 1 2015-01-01 Finland KaggleMart Kaggle Hat 520\n", "2 2 2015-01-01 Finland KaggleMart Kaggle Sticker 146\n", "3 3 2015-01-01 Finland KaggleRama Kaggle Mug 572\n", "4 4 2015-01-01 Finland KaggleRama Kaggle Hat 911" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = pd.read_csv('../input/tabular-playground-series-jan-2022/train.csv')\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "10fdabe5", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:49.864304Z", "iopub.status.busy": "2022-01-28T14:42:49.863748Z", "iopub.status.idle": "2022-01-28T14:42:49.891727Z", "shell.execute_reply": "2022-01-28T14:42:49.892306Z", "shell.execute_reply.started": "2022-01-28T14:25:20.576294Z" }, "papermill": { "duration": 0.070625, "end_time": "2022-01-28T14:42:49.892521", "exception": false, "start_time": "2022-01-28T14:42:49.821896", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>country</th>\n", " <th>store</th>\n", " <th>product</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>26298</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Mug</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>26299</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Hat</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>26300</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleMart</td>\n", " <td>Kaggle Sticker</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>26301</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Mug</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>26302</td>\n", " <td>2019-01-01</td>\n", " <td>Finland</td>\n", " <td>KaggleRama</td>\n", " <td>Kaggle Hat</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date country store product\n", "0 26298 2019-01-01 Finland KaggleMart Kaggle Mug\n", "1 26299 2019-01-01 Finland KaggleMart Kaggle Hat\n", "2 26300 2019-01-01 Finland KaggleMart Kaggle Sticker\n", "3 26301 2019-01-01 Finland KaggleRama Kaggle Mug\n", "4 26302 2019-01-01 Finland KaggleRama Kaggle Hat" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testd = pd.read_csv('../input/tabular-playground-series-jan-2022/test.csv')\n", "testd.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "18304ef7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:50.017873Z", "iopub.status.busy": "2022-01-28T14:42:50.017285Z", "iopub.status.idle": "2022-01-28T14:42:50.021692Z", "shell.execute_reply": "2022-01-28T14:42:50.021068Z", "shell.execute_reply.started": "2022-01-28T14:34:19.939848Z" }, "papermill": { "duration": 0.069225, "end_time": "2022-01-28T14:42:50.021832", "exception": false, "start_time": "2022-01-28T14:42:49.952607", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/tabular-playground-series-jan-2022/sample_submission.csv\n", "/kaggle/input/tabular-playground-series-jan-2022/train.csv\n", "/kaggle/input/tabular-playground-series-jan-2022/test.csv\n" ] } ], "source": [ "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))" ] }, { "cell_type": "code", "execution_count": null, "id": "98129179", "metadata": { "papermill": { "duration": 0.037528, "end_time": "2022-01-28T14:42:50.097062", "exception": false, "start_time": "2022-01-28T14:42:50.059534", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "id": "1a5b33df", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:50.180712Z", "iopub.status.busy": "2022-01-28T14:42:50.180054Z", "iopub.status.idle": "2022-01-28T14:42:50.210203Z", "shell.execute_reply": "2022-01-28T14:42:50.210853Z", "shell.execute_reply.started": "2022-01-28T14:34:57.324107Z" }, "papermill": { "duration": 0.075771, "end_time": "2022-01-28T14:42:50.211027", "exception": false, "start_time": "2022-01-28T14:42:50.135256", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>num_sold</th>\n", " <th>country_Finland</th>\n", " <th>country_Norway</th>\n", " <th>country_Sweden</th>\n", " <th>store_KaggleMart</th>\n", " <th>store_KaggleRama</th>\n", " <th>product_Kaggle Hat</th>\n", " <th>product_Kaggle Mug</th>\n", " <th>product_Kaggle Sticker</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>2015-01-01</td>\n", " <td>329</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2015-01-01</td>\n", " <td>520</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>2015-01-01</td>\n", " <td>146</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>2015-01-01</td>\n", " <td>572</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>2015-01-01</td>\n", " <td>911</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date num_sold country_Finland country_Norway \\\n", "0 0 2015-01-01 329 1 0 \n", "1 1 2015-01-01 520 1 0 \n", "2 2 2015-01-01 146 1 0 \n", "3 3 2015-01-01 572 1 0 \n", "4 4 2015-01-01 911 1 0 \n", "\n", " country_Sweden store_KaggleMart store_KaggleRama product_Kaggle Hat \\\n", "0 0 1 0 0 \n", "1 0 1 0 1 \n", "2 0 1 0 0 \n", "3 0 0 1 0 \n", "4 0 0 1 1 \n", "\n", " product_Kaggle Mug product_Kaggle Sticker \n", "0 1 0 \n", "1 0 0 \n", "2 0 1 \n", "3 1 0 \n", "4 0 0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d_encoded = pd.get_dummies(d, columns = ['country' , 'store' , 'product']) \n", "d_encoded.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "a0452524", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:50.338759Z", "iopub.status.busy": "2022-01-28T14:42:50.338038Z", "iopub.status.idle": "2022-01-28T14:42:50.351231Z", "shell.execute_reply": "2022-01-28T14:42:50.350694Z", "shell.execute_reply.started": "2022-01-28T14:35:02.457839Z" }, "papermill": { "duration": 0.078754, "end_time": "2022-01-28T14:42:50.351374", "exception": false, "start_time": "2022-01-28T14:42:50.272620", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d_encoded\n", "testd=pd.get_dummies(testd, columns = ['country' , 'store' , 'product']) " ] }, { "cell_type": "code", "execution_count": 7, "id": "56ba1b2f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:50.436858Z", "iopub.status.busy": "2022-01-28T14:42:50.436317Z", "iopub.status.idle": "2022-01-28T14:42:50.445510Z", "shell.execute_reply": "2022-01-28T14:42:50.446064Z", "shell.execute_reply.started": "2022-01-28T14:35:04.276653Z" }, "papermill": { "duration": 0.055518, "end_time": "2022-01-28T14:42:50.446258", "exception": false, "start_time": "2022-01-28T14:42:50.390740", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d['date']=pd.to_datetime(d['date'])\n", "testd['date']=pd.to_datetime(testd['date'])" ] }, { "cell_type": "code", "execution_count": 8, "id": "6afa12b3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:50.575537Z", "iopub.status.busy": "2022-01-28T14:42:50.574820Z", "iopub.status.idle": "2022-01-28T14:42:50.597063Z", "shell.execute_reply": "2022-01-28T14:42:50.596372Z", "shell.execute_reply.started": "2022-01-28T14:35:07.783678Z" }, "papermill": { "duration": 0.088297, "end_time": "2022-01-28T14:42:50.597236", "exception": false, "start_time": "2022-01-28T14:42:50.508939", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d['day']=d['date'].dt.day\n", "d['month']=d['date'].dt.month\n", "d['year']=d['date'].dt.year\n", "testd['day']=testd['date'].dt.day\n", "testd['month']=testd['date'].dt.month\n", "testd['year']=testd['date'].dt.year" ] }, { "cell_type": "code", "execution_count": 9, "id": "8d4044ee", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:50.725585Z", "iopub.status.busy": "2022-01-28T14:42:50.724918Z", "iopub.status.idle": "2022-01-28T14:42:50.977712Z", "shell.execute_reply": "2022-01-28T14:42:50.977229Z", "shell.execute_reply.started": "2022-01-28T14:35:13.374265Z" }, "papermill": { "duration": 0.318813, "end_time": "2022-01-28T14:42:50.977834", "exception": false, "start_time": "2022-01-28T14:42:50.659021", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='date'>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "davg=d.groupby('date')['num_sold'].mean()\n", "davg.plot()" ] }, { "cell_type": "code", "execution_count": 10, "id": "4fa1b87c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:51.069144Z", "iopub.status.busy": "2022-01-28T14:42:51.063789Z", "iopub.status.idle": "2022-01-28T14:42:51.072285Z", "shell.execute_reply": "2022-01-28T14:42:51.072803Z", "shell.execute_reply.started": "2022-01-28T14:35:18.169630Z" }, "papermill": { "duration": 0.054412, "end_time": "2022-01-28T14:42:51.072945", "exception": false, "start_time": "2022-01-28T14:42:51.018533", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>num_sold</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>598.222222</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-02</th>\n", " <td>555.444444</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-03</th>\n", " <td>615.277778</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-04</th>\n", " <td>595.944444</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-05</th>\n", " <td>437.111111</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sold\n", "date \n", "2015-01-01 598.222222\n", "2015-01-02 555.444444\n", "2015-01-03 615.277778\n", "2015-01-04 595.944444\n", "2015-01-05 437.111111" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "davg=pd.DataFrame(davg)\n", "davg.head()" ] }, { "cell_type": "code", "execution_count": 11, "id": "08cc6d4a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:51.154812Z", "iopub.status.busy": "2022-01-28T14:42:51.154288Z", "iopub.status.idle": "2022-01-28T14:42:51.163974Z", "shell.execute_reply": "2022-01-28T14:42:51.164656Z", "shell.execute_reply.started": "2022-01-28T14:35:25.429969Z" }, "papermill": { "duration": 0.052307, "end_time": "2022-01-28T14:42:51.164823", "exception": false, "start_time": "2022-01-28T14:42:51.112516", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>num_sold</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2019-01-01</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-02</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-03</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-04</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>2019-01-05</th>\n", " <td>0.333333</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sold\n", "date \n", "2019-01-01 0.333333\n", "2019-01-02 0.333333\n", "2019-01-03 0.333333\n", "2019-01-04 0.333333\n", "2019-01-05 0.333333" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt=pd.DataFrame(testd.groupby('date')['country_Norway'].mean())\n", "dt.columns=['num_sold']\n", "dt.head()" ] }, { "cell_type": "code", "execution_count": 12, "id": "8e9dbdf3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:51.298672Z", "iopub.status.busy": "2022-01-28T14:42:51.297782Z", "iopub.status.idle": "2022-01-28T14:42:51.300491Z", "shell.execute_reply": "2022-01-28T14:42:51.300930Z", "shell.execute_reply.started": "2022-01-28T14:35:30.500551Z" }, "papermill": { "duration": 0.072406, "end_time": "2022-01-28T14:42:51.301077", "exception": false, "start_time": "2022-01-28T14:42:51.228671", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "dtall=pd.concat([davg,dt],axis=0)" ] }, { "cell_type": "code", "execution_count": 13, "id": "e93cae50", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:51.437052Z", "iopub.status.busy": "2022-01-28T14:42:51.436296Z", "iopub.status.idle": "2022-01-28T14:42:51.740049Z", "shell.execute_reply": "2022-01-28T14:42:51.740787Z", "shell.execute_reply.started": "2022-01-28T14:35:32.372471Z" }, "papermill": { "duration": 0.375092, "end_time": "2022-01-28T14:42:51.740993", "exception": false, "start_time": "2022-01-28T14:42:51.365901", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from statsmodels.tsa.deterministic import CalendarFourier, DeterministicProcess\n", "\n", "fourier = CalendarFourier(freq=\"M\", order=4) # 10 sin/cos pairs for \"A\"nnual seasonality\n", "\n", "dp = DeterministicProcess(\n", " index=dtall.index,\n", " constant=True, # dummy feature for bias (y-intercept)\n", " order=1, # trend (order 1 means linear)\n", " seasonal=True, # weekly seasonality (indicators)\n", " additional_terms=[fourier], # annual seasonality (fourier)\n", " drop=True, # drop terms to avoid collinearity\n", ")\n", "\n", "X = dp.in_sample() # create features for dates in tunnel.index" ] }, { "cell_type": "code", "execution_count": 14, "id": "8a655528", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:51.889750Z", "iopub.status.busy": "2022-01-28T14:42:51.889219Z", "iopub.status.idle": "2022-01-28T14:42:51.894385Z", "shell.execute_reply": "2022-01-28T14:42:51.893989Z", "shell.execute_reply.started": "2022-01-28T14:35:37.740626Z" }, "papermill": { "duration": 0.0784, "end_time": "2022-01-28T14:42:51.894521", "exception": false, "start_time": "2022-01-28T14:42:51.816121", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "Xtimetrain=X.loc[:'2018-12-31']\n", "Xtimetest=X.loc['2019-01-01':]" ] }, { "cell_type": "code", "execution_count": 15, "id": "a2d94ed7", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:51.993143Z", "iopub.status.busy": "2022-01-28T14:42:51.992564Z", "iopub.status.idle": "2022-01-28T14:42:51.996117Z", "shell.execute_reply": "2022-01-28T14:42:51.995622Z", "shell.execute_reply.started": "2022-01-28T14:36:32.834927Z" }, "papermill": { "duration": 0.061185, "end_time": "2022-01-28T14:42:51.996233", "exception": false, "start_time": "2022-01-28T14:42:51.935048", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>date</th>\n", " <th>num_sold</th>\n", " <th>country_Finland</th>\n", " <th>country_Norway</th>\n", " <th>country_Sweden</th>\n", " <th>store_KaggleMart</th>\n", " <th>store_KaggleRama</th>\n", " <th>product_Kaggle Hat</th>\n", " <th>product_Kaggle Mug</th>\n", " <th>product_Kaggle Sticker</th>\n", " <th>day</th>\n", " <th>month</th>\n", " <th>year</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>0</td>\n", " <td>2015-01-01</td>\n", " <td>329</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>1</td>\n", " <td>2015-01-01</td>\n", " <td>520</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>2</td>\n", " <td>2015-01-01</td>\n", " <td>146</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>3</td>\n", " <td>2015-01-01</td>\n", " <td>572</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>4</td>\n", " <td>2015-01-01</td>\n", " <td>911</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2015</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id date num_sold country_Finland country_Norway \\\n", "date \n", "2015-01-01 0 2015-01-01 329 1 0 \n", "2015-01-01 1 2015-01-01 520 1 0 \n", "2015-01-01 2 2015-01-01 146 1 0 \n", "2015-01-01 3 2015-01-01 572 1 0 \n", "2015-01-01 4 2015-01-01 911 1 0 \n", "\n", " country_Sweden store_KaggleMart store_KaggleRama \\\n", "date \n", "2015-01-01 0 1 0 \n", "2015-01-01 0 1 0 \n", "2015-01-01 0 1 0 \n", "2015-01-01 0 0 1 \n", "2015-01-01 0 0 1 \n", "\n", " product_Kaggle Hat product_Kaggle Mug product_Kaggle Sticker \\\n", "date \n", "2015-01-01 0 1 0 \n", "2015-01-01 1 0 0 \n", "2015-01-01 0 0 1 \n", "2015-01-01 0 1 0 \n", "2015-01-01 1 0 0 \n", "\n", " day month year \n", "date \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 \n", "2015-01-01 1 1 2015 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.index=d['date']\n", "testd.index=testd['date']\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 16, "id": "1e6f8907", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:52.083705Z", "iopub.status.busy": "2022-01-28T14:42:52.083181Z", "iopub.status.idle": "2022-01-28T14:42:52.100148Z", "shell.execute_reply": "2022-01-28T14:42:52.100650Z", "shell.execute_reply.started": "2022-01-28T14:36:37.882318Z" }, "papermill": { "duration": 0.062567, "end_time": "2022-01-28T14:42:52.100815", "exception": false, "start_time": "2022-01-28T14:42:52.038248", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d.join(Xtimetrain)\n", "testd=testd.join(Xtimetest)" ] }, { "cell_type": "code", "execution_count": null, "id": "01b42a15", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:17:18.770983Z", "iopub.status.busy": "2022-01-28T14:17:18.770127Z", "iopub.status.idle": "2022-01-28T14:17:18.777623Z", "shell.execute_reply": "2022-01-28T14:17:18.776532Z", "shell.execute_reply.started": "2022-01-28T14:17:18.770934Z" }, "papermill": { "duration": 0.040044, "end_time": "2022-01-28T14:42:52.181962", "exception": false, "start_time": "2022-01-28T14:42:52.141918", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "8a7b9ebb", "metadata": { "papermill": { "duration": 0.04093, "end_time": "2022-01-28T14:42:52.264000", "exception": false, "start_time": "2022-01-28T14:42:52.223070", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "id": "00de9228", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:52.348359Z", "iopub.status.busy": "2022-01-28T14:42:52.347784Z", "iopub.status.idle": "2022-01-28T14:42:52.378309Z", "shell.execute_reply": "2022-01-28T14:42:52.378918Z", "shell.execute_reply.started": "2022-01-28T14:37:14.888290Z" }, "papermill": { "duration": 0.074374, "end_time": "2022-01-28T14:42:52.379087", "exception": false, "start_time": "2022-01-28T14:42:52.304713", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " 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<td>26302</td>\n", " <td>2019-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 29 columns</p>\n", "</div>" ], "text/plain": [ " row_id date country_Finland country_Norway \\\n", "date \n", "2019-01-01 26298 2019-01-01 1 0 \n", "2019-01-01 26299 2019-01-01 1 0 \n", "2019-01-01 26300 2019-01-01 1 0 \n", "2019-01-01 26301 2019-01-01 1 0 \n", "2019-01-01 26302 2019-01-01 1 0 \n", "\n", " country_Sweden store_KaggleMart store_KaggleRama \\\n", "date \n", "2019-01-01 0 1 0 \n", "2019-01-01 0 1 0 \n", "2019-01-01 0 1 0 \n", "2019-01-01 0 0 1 \n", "2019-01-01 0 0 1 \n", "\n", " product_Kaggle Hat product_Kaggle Mug product_Kaggle Sticker \\\n", "date \n", "2019-01-01 0 1 0 \n", "2019-01-01 1 0 0 \n", "2019-01-01 0 0 1 \n", "2019-01-01 0 1 0 \n", "2019-01-01 1 0 0 \n", "\n", " ... s(6,7) s(7,7) sin(1,freq=M) cos(1,freq=M) sin(2,freq=M) \\\n", "date ... \n", "2019-01-01 ... 1.0 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 1.0 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 1.0 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 1.0 0.0 0.0 1.0 0.0 \n", "2019-01-01 ... 1.0 0.0 0.0 1.0 0.0 \n", "\n", " cos(2,freq=M) sin(3,freq=M) cos(3,freq=M) sin(4,freq=M) \\\n", "date \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "\n", " cos(4,freq=M) \n", "date \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testd.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "622526d3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:52.466410Z", "iopub.status.busy": "2022-01-28T14:42:52.465722Z", "iopub.status.idle": "2022-01-28T14:42:52.476273Z", "shell.execute_reply": "2022-01-28T14:42:52.476785Z", "shell.execute_reply.started": "2022-01-28T14:37:22.675613Z" }, "papermill": { "duration": 0.05599, "end_time": "2022-01-28T14:42:52.476939", "exception": false, "start_time": "2022-01-28T14:42:52.420949", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d.drop('date',axis=1)\n", "testd=testd.drop('date',axis=1)" ] }, { "cell_type": "code", "execution_count": 19, "id": "9313b761", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:52.564860Z", "iopub.status.busy": "2022-01-28T14:42:52.564165Z", "iopub.status.idle": "2022-01-28T14:42:52.571080Z", "shell.execute_reply": "2022-01-28T14:42:52.571588Z", "shell.execute_reply.started": "2022-01-28T14:37:25.461380Z" }, "papermill": { "duration": 0.052761, "end_time": "2022-01-28T14:42:52.571749", "exception": false, "start_time": "2022-01-28T14:42:52.518988", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "d=d.drop('row_id',axis=1)" ] }, { "cell_type": "code", "execution_count": 20, "id": "d6f5295e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:52.659792Z", "iopub.status.busy": "2022-01-28T14:42:52.659060Z", "iopub.status.idle": "2022-01-28T14:42:52.666857Z", "shell.execute_reply": "2022-01-28T14:42:52.667305Z", "shell.execute_reply.started": "2022-01-28T14:37:27.552429Z" }, "papermill": { "duration": 0.05392, "end_time": "2022-01-28T14:42:52.667496", "exception": false, "start_time": "2022-01-28T14:42:52.613576", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X=d.drop('num_sold',axis=1)\n", "y=d['num_sold']" ] }, { "cell_type": "code", "execution_count": 21, "id": "ec4bd8dc", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:52.757521Z", "iopub.status.busy": "2022-01-28T14:42:52.756837Z", "iopub.status.idle": "2022-01-28T14:42:53.391714Z", "shell.execute_reply": "2022-01-28T14:42:53.391179Z", "shell.execute_reply.started": "2022-01-28T14:37:31.757043Z" }, "papermill": { "duration": 0.682014, "end_time": "2022-01-28T14:42:53.391831", "exception": false, "start_time": "2022-01-28T14:42:52.709817", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33, random_state=42)" ] }, { "cell_type": "code", "execution_count": 22, "id": "aed1873b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:53.482415Z", "iopub.status.busy": "2022-01-28T14:42:53.481748Z", "iopub.status.idle": "2022-01-28T14:42:57.673810Z", "shell.execute_reply": "2022-01-28T14:42:57.674285Z", "shell.execute_reply.started": "2022-01-28T14:41:46.449495Z" }, "papermill": { "duration": 4.240737, "end_time": "2022-01-28T14:42:57.674491", "exception": false, "start_time": "2022-01-28T14:42:53.433754", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate set to 0.064424\n", "0:\tlearn: 249.7227016\ttotal: 54.5ms\tremaining: 54.4s\n", "1:\tlearn: 236.3250201\ttotal: 59.4ms\tremaining: 29.6s\n", "2:\tlearn: 224.2299506\ttotal: 63.8ms\tremaining: 21.2s\n", "3:\tlearn: 212.7346426\ttotal: 68.1ms\tremaining: 17s\n", "4:\tlearn: 202.2853586\ttotal: 72.5ms\tremaining: 14.4s\n", "5:\tlearn: 192.4564121\ttotal: 76.8ms\tremaining: 12.7s\n", "6:\tlearn: 183.3675289\ttotal: 81.1ms\tremaining: 11.5s\n", "7:\tlearn: 175.0994453\ttotal: 85.1ms\tremaining: 10.5s\n", "8:\tlearn: 167.2812520\ttotal: 89.3ms\tremaining: 9.83s\n", "9:\tlearn: 160.3482386\ttotal: 93.2ms\tremaining: 9.23s\n", "10:\tlearn: 153.5134869\ttotal: 97.2ms\tremaining: 8.74s\n", "11:\tlearn: 147.6502608\ttotal: 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3.71s\tremaining: 26.2ms\n", "993:\tlearn: 23.1975508\ttotal: 3.72s\tremaining: 22.5ms\n", "994:\tlearn: 23.1912062\ttotal: 3.72s\tremaining: 18.7ms\n", "995:\tlearn: 23.1888901\ttotal: 3.73s\tremaining: 15ms\n", "996:\tlearn: 23.1851291\ttotal: 3.73s\tremaining: 11.2ms\n", "997:\tlearn: 23.1779440\ttotal: 3.73s\tremaining: 7.48ms\n", "998:\tlearn: 23.1738265\ttotal: 3.74s\tremaining: 3.74ms\n", "999:\tlearn: 23.1657328\ttotal: 3.74s\tremaining: 0us\n" ] }, { "data": { "text/plain": [ "<catboost.core.CatBoostRegressor at 0x7f49e1713050>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from catboost import CatBoostRegressor\n", "cat = CatBoostRegressor()\n", "cat.fit(X_train,y_train)" ] }, { "cell_type": "code", "execution_count": 23, "id": "710b094d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:57.781245Z", "iopub.status.busy": "2022-01-28T14:42:57.773258Z", "iopub.status.idle": "2022-01-28T14:42:57.792490Z", "shell.execute_reply": "2022-01-28T14:42:57.791801Z", "shell.execute_reply.started": "2022-01-28T14:41:56.908380Z" }, "papermill": { "duration": 0.07033, "end_time": "2022-01-28T14:42:57.792661", "exception": false, "start_time": "2022-01-28T14:42:57.722331", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "pv=cat.predict(X_valid)" ] }, { "cell_type": "code", "execution_count": 24, "id": "ce47cd24", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:57.949861Z", "iopub.status.busy": "2022-01-28T14:42:57.948996Z", "iopub.status.idle": "2022-01-28T14:42:57.952643Z", "shell.execute_reply": "2022-01-28T14:42:57.953265Z", "shell.execute_reply.started": "2022-01-28T14:41:58.412977Z" }, "papermill": { "duration": 0.085368, "end_time": "2022-01-28T14:42:57.953456", "exception": false, "start_time": "2022-01-28T14:42:57.868088", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18.324975081475703\n" ] } ], "source": [ "from sklearn.metrics import mean_absolute_error\n", "print(mean_absolute_error(pv,y_valid))" ] }, { "cell_type": "code", "execution_count": 25, "id": "1e19282c", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:58.109607Z", "iopub.status.busy": "2022-01-28T14:42:58.108760Z", "iopub.status.idle": "2022-01-28T14:42:58.128228Z", "shell.execute_reply": "2022-01-28T14:42:58.128785Z", "shell.execute_reply.started": "2022-01-28T14:42:03.458610Z" }, "papermill": { "duration": 0.100543, "end_time": "2022-01-28T14:42:58.128960", "exception": false, "start_time": "2022-01-28T14:42:58.028417", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>num_sold</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>26298</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>26299</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>26300</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>26301</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>26302</td>\n", " <td>100</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " row_id num_sold\n", "0 26298 100\n", "1 26299 100\n", "2 26300 100\n", "3 26301 100\n", "4 26302 100" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.read_csv('../input/tabular-playground-series-jan-2022/sample_submission.csv')\n", "s.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "b1dd553b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:58.285324Z", "iopub.status.busy": "2022-01-28T14:42:58.284785Z", "iopub.status.idle": "2022-01-28T14:42:58.317334Z", "shell.execute_reply": "2022-01-28T14:42:58.317901Z", "shell.execute_reply.started": "2022-01-28T14:42:07.279392Z" }, "papermill": { "duration": 0.112587, "end_time": "2022-01-28T14:42:58.318100", "exception": false, "start_time": "2022-01-28T14:42:58.205513", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>row_id</th>\n", " <th>country_Finland</th>\n", " <th>country_Norway</th>\n", " <th>country_Sweden</th>\n", " <th>store_KaggleMart</th>\n", " <th>store_KaggleRama</th>\n", " <th>product_Kaggle Hat</th>\n", " <th>product_Kaggle Mug</th>\n", " <th>product_Kaggle Sticker</th>\n", " <th>day</th>\n", " <th>...</th>\n", " <th>s(6,7)</th>\n", " <th>s(7,7)</th>\n", " <th>sin(1,freq=M)</th>\n", " <th>cos(1,freq=M)</th>\n", " <th>sin(2,freq=M)</th>\n", " <th>cos(2,freq=M)</th>\n", " <th>sin(3,freq=M)</th>\n", " <th>cos(3,freq=M)</th>\n", " <th>sin(4,freq=M)</th>\n", " <th>cos(4,freq=M)</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2019-01-01</th>\n", " 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"2019-01-01 0.0 0.0 1.0 0.0 \n", "2019-01-01 0.0 0.0 1.0 0.0 \n", "2019-01-01 0.0 0.0 1.0 0.0 \n", "2019-01-01 0.0 0.0 1.0 0.0 \n", "\n", " cos(2,freq=M) sin(3,freq=M) cos(3,freq=M) sin(4,freq=M) \\\n", "date \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "2019-01-01 1.0 0.0 1.0 0.0 \n", "\n", " cos(4,freq=M) \n", "date \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "2019-01-01 1.0 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testd.head()" ] }, { "cell_type": "code", "execution_count": 27, "id": "cb07fedd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:58.475733Z", "iopub.status.busy": "2022-01-28T14:42:58.475035Z", "iopub.status.idle": "2022-01-28T14:42:58.490393Z", "shell.execute_reply": "2022-01-28T14:42:58.489744Z", "shell.execute_reply.started": "2022-01-28T14:42:10.607638Z" }, "papermill": { "duration": 0.095241, "end_time": "2022-01-28T14:42:58.490573", "exception": false, "start_time": "2022-01-28T14:42:58.395332", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X_test=testd.drop('row_id',axis=1)\n", "pvtest=cat.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 28, "id": "9b74970e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:42:58.641695Z", "iopub.status.busy": "2022-01-28T14:42:58.636683Z", "iopub.status.idle": "2022-01-28T14:42:58.657772Z", "shell.execute_reply": "2022-01-28T14:42:58.657189Z", "shell.execute_reply.started": "2022-01-28T14:42:15.328657Z" }, "papermill": { "duration": 0.090473, "end_time": "2022-01-28T14:42:58.657893", "exception": false, "start_time": "2022-01-28T14:42:58.567420", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "w=pd.DataFrame()\n", "w['row_id']=testd['row_id']" ] }, { "cell_type": "code", "execution_count": 29, 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0086/401/86401475.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "id": "c84ced13", "metadata": { "execution": { "iopub.execute_input": "2022-01-27T14:30:10.050721Z", "iopub.status.busy": "2022-01-27T14:30:10.050414Z", "iopub.status.idle": "2022-01-27T14:30:28.630956Z", "shell.execute_reply": "2022-01-27T14:30:28.630079Z", "shell.execute_reply.started": "2022-01-27T14:30:10.050691Z" }, "papermill": { "duration": 0.108033, "end_time": "2022-01-28T14:47:32.251427", "exception": false, "start_time": "2022-01-28T14:47:32.143394", "status": "completed" }, "tags": [] }, "source": [ "* !pip install missingno\n", "* !pip install outlier_utils\n", "* !pip install optuna" ] }, { "cell_type": "code", "execution_count": 1, "id": "fe6b9861", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2022-01-28T14:47:32.441599Z", "iopub.status.busy": "2022-01-28T14:47:32.439533Z", "iopub.status.idle": "2022-01-28T14:47:34.467562Z", "shell.execute_reply": "2022-01-28T14:47:34.468476Z", "shell.execute_reply.started": "2022-01-28T14:14:19.318841Z" }, "papermill": { "duration": 2.129847, "end_time": "2022-01-28T14:47:34.468933", "exception": false, "start_time": "2022-01-28T14:47:32.339086", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/song-popularity-prediction/sample_submission.csv\n", "/kaggle/input/song-popularity-prediction/train.csv\n", "/kaggle/input/song-popularity-prediction/test.csv\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "from sklearn import preprocessing\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import missingno as msno\n", "%matplotlib inline\n", "# Input data files are available in the read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "from collections import Counter\n", "import random\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import metrics\n", "from sklearn.metrics import classification_report,confusion_matrix\n", "from scipy import stats\n", "import optuna\n", "import sys\n", "import os\n", "from sklearn.model_selection import KFold\n", "from sklearn.metrics import mean_squared_error\n", "\n", "pd.options.mode.chained_assignment = None \n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" ] }, { "cell_type": "code", "execution_count": 2, "id": "9c654718", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:47:34.576801Z", "iopub.status.busy": "2022-01-28T14:47:34.575804Z", "iopub.status.idle": "2022-01-28T14:47:34.846482Z", "shell.execute_reply": "2022-01-28T14:47:34.847070Z", "shell.execute_reply.started": "2022-01-28T14:14:19.337271Z" }, "papermill": { "duration": 0.326202, "end_time": "2022-01-28T14:47:34.847250", "exception": false, "start_time": "2022-01-28T14:47:34.521048", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>song_duration_ms</th>\n", " <th>acousticness</th>\n", " <th>danceability</th>\n", " <th>energy</th>\n", " <th>instrumentalness</th>\n", " <th>key</th>\n", " <th>liveness</th>\n", " <th>loudness</th>\n", " <th>audio_mode</th>\n", " <th>speechiness</th>\n", " <th>tempo</th>\n", " <th>time_signature</th>\n", " <th>audio_valence</th>\n", " <th>song_popularity</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>212990.0</td>\n", " <td>0.642286</td>\n", " <td>0.856520</td>\n", " <td>0.707073</td>\n", " <td>0.002001</td>\n", " <td>10.0</td>\n", " <td>NaN</td>\n", " <td>-5.619088</td>\n", " <td>0</td>\n", " <td>0.082570</td>\n", " <td>158.386236</td>\n", " <td>4</td>\n", " <td>0.734642</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " 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0.000608 0.0 0.094805 -7.893694 0 0.035618 \n", "4 0.002033 10.0 0.094891 -2.684095 0 0.050746 \n", "\n", " tempo time_signature audio_valence song_popularity \n", "0 158.386236 4 0.734642 0 \n", "1 102.752988 3 0.711531 1 \n", "2 178.685791 3 0.425536 0 \n", "3 128.715630 3 0.453597 0 \n", "4 121.928157 4 0.741311 0 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df=pd.read_csv(\"/kaggle/input/song-popularity-prediction/train.csv\")\n", "test_df=pd.read_csv(\"/kaggle/input/song-popularity-prediction/test.csv\")\n", "train_df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "154ba596", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:47:34.966626Z", "iopub.status.busy": "2022-01-28T14:47:34.950592Z", "iopub.status.idle": "2022-01-28T14:47:34.981483Z", "shell.execute_reply": "2022-01-28T14:47:34.982032Z", "shell.execute_reply.started": "2022-01-28T14:14:19.469558Z" }, "papermill": { "duration": 0.086101, "end_time": "2022-01-28T14:47:34.982222", "exception": false, "start_time": "2022-01-28T14:47:34.896121", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>song_duration_ms</th>\n", " <th>acousticness</th>\n", " <th>danceability</th>\n", " 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"metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df=train_df.drop(['id'], 1)\n", "train_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "156f4f40", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:47:35.098130Z", "iopub.status.busy": "2022-01-28T14:47:35.097027Z", "iopub.status.idle": "2022-01-28T14:47:35.110533Z", "shell.execute_reply": "2022-01-28T14:47:35.109926Z", "shell.execute_reply.started": "2022-01-28T14:14:19.490720Z" }, "papermill": { "duration": 0.078003, "end_time": "2022-01-28T14:47:35.110695", "exception": false, "start_time": "2022-01-28T14:47:35.032692", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>song_duration_ms</th>\n", " <th>acousticness</th>\n", " <th>danceability</th>\n", " <th>energy</th>\n", " <th>instrumentalness</th>\n", " <th>key</th>\n", " <th>liveness</th>\n", " <th>loudness</th>\n", " <th>audio_mode</th>\n", " <th>speechiness</th>\n", " <th>tempo</th>\n", " <th>time_signature</th>\n", " <th>audio_valence</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>308523.0</td>\n", " <td>0.019845</td>\n", " <td>NaN</td>\n", " <td>0.908939</td>\n", " <td>0.001438</td>\n", " <td>NaN</td>\n", " <td>0.112832</td>\n", " <td>-8.890172</td>\n", " <td>0</td>\n", " <td>0.082714</td>\n", " <td>126.129304</td>\n", " <td>4</td>\n", " <td>0.399620</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>200011.0</td>\n", " <td>0.070119</td>\n", " <td>0.731256</td>\n", " <td>0.444655</td>\n", " <td>0.002020</td>\n", " <td>10.0</td>\n", " <td>0.139040</td>\n", " <td>-6.301214</td>\n", " <td>0</td>\n", " <td>0.061685</td>\n", " <td>86.448149</td>\n", " <td>3</td>\n", " <td>0.499424</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>279758.0</td>\n", " <td>0.810637</td>\n", " <td>0.568858</td>\n", " <td>0.125466</td>\n", " <td>0.898841</td>\n", " <td>0.0</td>\n", " <td>0.226614</td>\n", " <td>-11.542478</td>\n", " <td>0</td>\n", " <td>0.041868</td>\n", " <td>99.544351</td>\n", " <td>3</td>\n", " <td>0.564951</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>249197.0</td>\n", " <td>NaN</td>\n", " <td>0.871789</td>\n", " <td>0.557342</td>\n", " <td>0.000715</td>\n", " <td>4.0</td>\n", " <td>0.325391</td>\n", " <td>-7.905546</td>\n", " <td>1</td>\n", " <td>0.046815</td>\n", " <td>123.063854</td>\n", " <td>4</td>\n", " <td>0.906485</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>NaN</td>\n", " <td>0.765568</td>\n", " <td>0.624687</td>\n", " <td>0.710794</td>\n", " <td>0.000346</td>\n", " <td>8.0</td>\n", " <td>0.308284</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0.129284</td>\n", " <td>88.703121</td>\n", " <td>3</td>\n", " <td>0.935571</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id song_duration_ms acousticness danceability energy \\\n", "0 0 308523.0 0.019845 NaN 0.908939 \n", "1 1 200011.0 0.070119 0.731256 0.444655 \n", "2 2 279758.0 0.810637 0.568858 0.125466 \n", "3 3 249197.0 NaN 0.871789 0.557342 \n", "4 4 NaN 0.765568 0.624687 0.710794 \n", "\n", " instrumentalness key liveness loudness audio_mode speechiness \\\n", "0 0.001438 NaN 0.112832 -8.890172 0 0.082714 \n", "1 0.002020 10.0 0.139040 -6.301214 0 0.061685 \n", "2 0.898841 0.0 0.226614 -11.542478 0 0.041868 \n", "3 0.000715 4.0 0.325391 -7.905546 1 0.046815 \n", "4 0.000346 8.0 0.308284 NaN 0 0.129284 \n", "\n", " tempo time_signature audio_valence \n", "0 126.129304 4 0.399620 \n", "1 86.448149 3 0.499424 \n", "2 99.544351 3 0.564951 \n", "3 123.063854 4 0.906485 \n", "4 88.703121 3 0.935571 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.head()" ] }, { "cell_type": "markdown", "id": "f41268ce", "metadata": { "papermill": { "duration": 0.050793, "end_time": "2022-01-28T14:47:35.212068", "exception": false, "start_time": "2022-01-28T14:47:35.161275", "status": "completed" }, "tags": [] }, "source": [ "## 1. Data Preprocessing " ] }, { "cell_type": "code", "execution_count": 5, "id": "ebbc26a4", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:47:35.322558Z", "iopub.status.busy": "2022-01-28T14:47:35.321469Z", "iopub.status.idle": "2022-01-28T14:47:35.339909Z", "shell.execute_reply": "2022-01-28T14:47:35.340532Z", "shell.execute_reply.started": "2022-01-28T14:14:19.513152Z" }, "papermill": { "duration": 0.078545, "end_time": "2022-01-28T14:47:35.340738", "exception": false, "start_time": "2022-01-28T14:47:35.262193", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 40000 entries, 0 to 39999\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 song_duration_ms 35899 non-null float64\n", " 1 acousticness 36008 non-null float64\n", " 2 danceability 35974 non-null float64\n", " 3 energy 36025 non-null float64\n", " 4 instrumentalness 36015 non-null float64\n", " 5 key 35935 non-null float64\n", " 6 liveness 35914 non-null float64\n", " 7 loudness 36043 non-null float64\n", " 8 audio_mode 40000 non-null int64 \n", " 9 speechiness 40000 non-null float64\n", " 10 tempo 40000 non-null float64\n", " 11 time_signature 40000 non-null int64 \n", " 12 audio_valence 40000 non-null float64\n", " 13 song_popularity 40000 non-null int64 \n", "dtypes: float64(11), int64(3)\n", "memory usage: 4.3 MB\n" ] } ], "source": [ "train_df.info()" ] }, { "cell_type": "markdown", "id": "7991bbbe", "metadata": { "papermill": { "duration": 0.051624, "end_time": "2022-01-28T14:47:35.443395", "exception": false, "start_time": "2022-01-28T14:47:35.391771", "status": "completed" }, "tags": [] }, "source": [ "# 1. Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 6, "id": "b7539ad6", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:47:35.551186Z", "iopub.status.busy": "2022-01-28T14:47:35.550071Z", "iopub.status.idle": "2022-01-28T14:47:35.555888Z", "shell.execute_reply": "2022-01-28T14:47:35.556461Z", "shell.execute_reply.started": "2022-01-28T14:14:19.527383Z" }, "papermill": { "duration": 0.062183, "end_time": "2022-01-28T14:47:35.556636", "exception": false, "start_time": "2022-01-28T14:47:35.494453", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "cate_features=['audio_mode','key','time_signature']\n", "nominal_features=['song_duration_ms','instrumentalness','acousticness','danceability','audio_valence','liveness','loudness','speechiness','tempo','energy']\n", "target_feature=['song_popularity']\n", "features=nominal_features+cate_features+target_feature" ] }, { "cell_type": "code", "execution_count": 7, "id": "02b50aef", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:47:35.669650Z", "iopub.status.busy": "2022-01-28T14:47:35.668703Z", "iopub.status.idle": "2022-01-28T14:47:37.775471Z", "shell.execute_reply": "2022-01-28T14:47:37.774872Z", "shell.execute_reply.started": "2022-01-28T14:14:19.534224Z" }, "papermill": { "duration": 2.167951, "end_time": "2022-01-28T14:47:37.775629", "exception": false, "start_time": "2022-01-28T14:47:35.607678", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x864 with 10 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize =(25,12))\n", "for j,i in enumerate(nominal_features):\n", " plt.subplot(5,3,j+1)\n", " sns.set_theme('notebook')\n", " sns.boxplot(data = train_df ,x = i)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3c897fc3", "metadata": { "papermill": { "duration": 0.052294, "end_time": "2022-01-28T14:47:37.881273", "exception": false, "start_time": "2022-01-28T14:47:37.828979", "status": "completed" }, "tags": [] }, "source": [ "Below Features have outliers:\n", "* song_duration_ms \n", "* instrumentalness \n", "* liveness \n", "* loudness \n", "* speechiness \n", "* tempo" ] }, { "cell_type": "markdown", "id": "2506a0e7", "metadata": { "papermill": { "duration": 0.052295, "end_time": "2022-01-28T14:47:37.985838", "exception": false, "start_time": "2022-01-28T14:47:37.933543", "status": "completed" }, "tags": [] }, "source": [ "### 1.1 Skew Data in Train" ] }, { "cell_type": "code", "execution_count": 8, "id": "55aefd40", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:47:38.120719Z", "iopub.status.busy": "2022-01-28T14:47:38.119434Z", "iopub.status.idle": "2022-01-28T14:48:10.479881Z", "shell.execute_reply": "2022-01-28T14:48:10.480463Z", "shell.execute_reply.started": "2022-01-28T14:14:21.385359Z" }, "papermill": { "duration": 32.442304, "end_time": "2022-01-28T14:48:10.480678", "exception": false, "start_time": "2022-01-28T14:47:38.038374", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x1080 with 15 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(5, 3, figsize=(20, 15))\n", "sns.histplot(train_df[\"song_duration_ms\"],kde=True, ax=axes[0, 0])\n", "sns.histplot(train_df[\"acousticness\"],kde=True, ax=axes[0, 1])\n", "sns.histplot(train_df[\"danceability\"], kde=True,ax=axes[0, 2])\n", "sns.histplot(train_df[\"audio_valence\"],kde=True, ax=axes[1, 0])\n", "sns.histplot(train_df[\"liveness\"],kde=True, ax=axes[1,1])\n", "sns.histplot(train_df[\"loudness\"],kde=True, ax=axes[1,2])\n", "sns.histplot(train_df[\"speechiness\"],kde=True, ax=axes[2, 0])\n", "sns.histplot(train_df[\"tempo\"],kde=True, ax=axes[2,1])\n", "sns.histplot(train_df[\"audio_valence\"],kde=True, ax=axes[2, 2])\n", "sns.histplot(train_df[\"audio_mode\"],kde=False, ax=axes[3, 0])\n", "sns.histplot(train_df[\"key\"],kde=False, ax=axes[3,1])\n", "sns.histplot(train_df[\"time_signature\"],kde=False, ax=axes[3, 2])\n", "sns.histplot(train_df[\"instrumentalness\"],kde=True, ax=axes[4, 0])\n", "sns.histplot(train_df[\"song_popularity\"],kde=True, ax=axes[4, 1])\n", "sns.histplot(train_df[\"energy\"],kde=True, ax=axes[4, 1])\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 9, "id": "c24c687b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:10.604207Z", "iopub.status.busy": "2022-01-28T14:48:10.603181Z", "iopub.status.idle": "2022-01-28T14:48:11.465427Z", "shell.execute_reply": "2022-01-28T14:48:11.464824Z", "shell.execute_reply.started": "2022-01-28T14:14:44.855451Z" }, "papermill": { "duration": 0.928904, "end_time": "2022-01-28T14:48:11.465587", "exception": false, "start_time": "2022-01-28T14:48:10.536683", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='loudness', ylabel='Count'>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(train_df[\"loudness\"],kde=True)" ] }, { "cell_type": "code", "execution_count": 10, "id": "79ff98c3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:11.587649Z", "iopub.status.busy": "2022-01-28T14:48:11.586761Z", "iopub.status.idle": "2022-01-28T14:48:11.607955Z", "shell.execute_reply": "2022-01-28T14:48:11.608844Z", "shell.execute_reply.started": "2022-01-28T14:14:45.502854Z" }, "papermill": { "duration": 0.086483, "end_time": "2022-01-28T14:48:11.609130", "exception": false, "start_time": "2022-01-28T14:48:11.522647", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "song_duration_ms : 0.6329315750087531\n", "instrumentalness : 4.947630571834681\n", "acousticness : 1.0041944479340874\n", "danceability : -0.39838573844116837\n", "audio_valence : -0.1810057441435877\n", "liveness : 2.165545687789332\n", "loudness : -1.4301003331775142\n", "speechiness : 1.7201205396556827\n", "tempo : 0.7641500817330785\n", "energy : -0.5828321649140663\n" ] } ], "source": [ "for x in nominal_features:\n", " print(x,\" : \",train_df[x].skew())" ] }, { "cell_type": "markdown", "id": "563ec77c", "metadata": { "papermill": { "duration": 0.058853, "end_time": "2022-01-28T14:48:11.726061", "exception": false, "start_time": "2022-01-28T14:48:11.667208", "status": "completed" }, "tags": [] }, "source": [ "1. Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. </br>\n", "1. If the skewness is between -0.5 and 0.5, the data are fairly symmetrical. If the skewness is between -1 and – 0.5 or between 0.5 and 1, the data are moderately skewed. If the skewness is less than -1 or greater than 1, the data are highly skewed. </br>\n", "1. Data needs Transformation </br>\n", "1. For More info </br>\n", "* https://opendatascience.com/transforming-skewed-data-for-machine-learning/\n", "* https://towardsdatascience.com/top-3-methods-for-handling-skewed-data-1334e0debf45" ] }, { "cell_type": "markdown", "id": "c2c7a35e", "metadata": { "papermill": { "duration": 0.05879, "end_time": "2022-01-28T14:48:11.843012", "exception": false, "start_time": "2022-01-28T14:48:11.784222", "status": "completed" }, "tags": [] }, "source": [ "#### Handling Highly Skewed Data\n", "Skewed Features\n", "* acousticness \n", "* liveness \n", "* instrumentalness \n", "* loudness \n", "* speechiness \n", "<br/><br/>\n", "Log Transformation doesn't work on negatively skewed data" ] }, { "cell_type": "code", "execution_count": 11, "id": "cfb3cb15", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:11.964297Z", "iopub.status.busy": "2022-01-28T14:48:11.963222Z", "iopub.status.idle": "2022-01-28T14:48:11.965937Z", "shell.execute_reply": "2022-01-28T14:48:11.966478Z", "shell.execute_reply.started": "2022-01-28T14:14:45.521273Z" }, "papermill": { "duration": 0.065754, "end_time": "2022-01-28T14:48:11.966638", "exception": false, "start_time": "2022-01-28T14:48:11.900884", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "skewed_features=['acousticness','liveness','instrumentalness','loudness','speechiness']" ] }, { "cell_type": "code", "execution_count": 12, "id": "067419f9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:12.088126Z", "iopub.status.busy": "2022-01-28T14:48:12.086976Z", "iopub.status.idle": "2022-01-28T14:48:12.110604Z", "shell.execute_reply": "2022-01-28T14:48:12.109969Z", "shell.execute_reply.started": "2022-01-28T14:14:45.526979Z" }, "papermill": { "duration": 0.087618, "end_time": "2022-01-28T14:48:12.110746", "exception": false, "start_time": "2022-01-28T14:48:12.023128", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "acousticness -0.5546673403380031\n", "liveness 0.942533923167347\n", "instrumentalness 1.5440220030695357\n", "loudness nan\n", "speechiness 0.7075261765246131\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: invalid value encountered in log\n", " result = getattr(ufunc, method)(*inputs, **kwargs)\n" ] } ], "source": [ "#log transformation\n", "for x in skewed_features:\n", " log_inst=np.log(train_df[x])\n", " print(x,\" \",log_inst.skew())\n", " if not np.isnan(log_inst.skew()):\n", " train_df[x]=log_inst" ] }, { "cell_type": "code", "execution_count": 13, "id": "6fcc3b1a", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:12.236580Z", "iopub.status.busy": "2022-01-28T14:48:12.235854Z", "iopub.status.idle": "2022-01-28T14:48:26.454351Z", "shell.execute_reply": "2022-01-28T14:48:26.453250Z", "shell.execute_reply.started": "2022-01-28T14:14:45.549550Z" }, "papermill": { "duration": 14.285385, "end_time": "2022-01-28T14:48:26.454515", "exception": false, "start_time": "2022-01-28T14:48:12.169130", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data= np.array(train_df[\"loudness\"])\n", "data=data.reshape(-1, 1)\n", "from sklearn.preprocessing import PowerTransformer\n", "power = PowerTransformer(method='yeo-johnson', standardize=True)\n", "data_trans = power.fit_transform(data)\n", "data_trans=data_trans.reshape(1, -1)\n", "i=0\n", "for num in data_trans[0]:\n", " train_df[\"loudness\"][i]=num\n", " i=i+1" ] }, { "cell_type": "code", "execution_count": 14, "id": "fedbce48", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:26.580219Z", "iopub.status.busy": "2022-01-28T14:48:26.579125Z", "iopub.status.idle": "2022-01-28T14:48:27.598213Z", "shell.execute_reply": "2022-01-28T14:48:27.599544Z", "shell.execute_reply.started": "2022-01-28T14:14:53.856233Z" }, "papermill": { "duration": 1.086463, "end_time": "2022-01-28T14:48:27.599820", "exception": false, "start_time": "2022-01-28T14:48:26.513357", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.013394988387723239\n" ] }, { "data": { "text/plain": [ "<AxesSubplot:xlabel='loudness', ylabel='Count'>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(train_df[\"loudness\"].skew())\n", "sns.histplot(train_df[\"loudness\"],kde=True)" ] }, { "cell_type": "markdown", "id": "92b1420d", "metadata": { "papermill": { "duration": 0.070724, "end_time": "2022-01-28T14:48:27.775164", "exception": false, "start_time": "2022-01-28T14:48:27.704440", "status": "completed" }, "tags": [] }, "source": [ "### 1.2 Null value handling" ] }, { "cell_type": "code", "execution_count": 15, "id": "e06d3e0b", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:27.903172Z", "iopub.status.busy": "2022-01-28T14:48:27.902116Z", "iopub.status.idle": "2022-01-28T14:48:27.912157Z", "shell.execute_reply": "2022-01-28T14:48:27.911572Z", "shell.execute_reply.started": "2022-01-28T14:14:54.454695Z" }, "papermill": { "duration": 0.076524, "end_time": "2022-01-28T14:48:27.912297", "exception": false, "start_time": "2022-01-28T14:48:27.835773", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "instrumentalness 7201\n", "acousticness 4364\n", "song_duration_ms 4101\n", "liveness 4086\n", "key 4065\n", "danceability 4026\n", "energy 3975\n", "loudness 3957\n", "audio_mode 0\n", "speechiness 0\n", "tempo 0\n", "time_signature 0\n", "audio_valence 0\n", "song_popularity 0\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.isnull().sum().sort_values(ascending = False)" ] }, { "cell_type": "code", "execution_count": 16, "id": "cd1b1e67", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:28.041003Z", "iopub.status.busy": "2022-01-28T14:48:28.039869Z", "iopub.status.idle": "2022-01-28T14:48:29.459487Z", "shell.execute_reply": "2022-01-28T14:48:29.460042Z", "shell.execute_reply.started": "2022-01-28T14:14:54.467076Z" }, "papermill": { "duration": 1.487854, "end_time": "2022-01-28T14:48:29.460243", "exception": false, "start_time": "2022-01-28T14:48:27.972389", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1728x720 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msno.bar(train_df)" ] }, { "cell_type": "code", "execution_count": 17, "id": "736f9db1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:29.592972Z", "iopub.status.busy": "2022-01-28T14:48:29.592261Z", "iopub.status.idle": "2022-01-28T14:48:30.450797Z", "shell.execute_reply": "2022-01-28T14:48:30.451366Z", "shell.execute_reply.started": "2022-01-28T14:14:55.500873Z" }, "papermill": { "duration": 0.927897, "end_time": "2022-01-28T14:48:30.451565", "exception": false, "start_time": "2022-01-28T14:48:29.523668", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x720 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msno.matrix(train_df)" ] }, { "cell_type": "code", "execution_count": 18, "id": "3f16dd64", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:30.590505Z", "iopub.status.busy": "2022-01-28T14:48:30.589656Z", "iopub.status.idle": "2022-01-28T14:48:30.596483Z", "shell.execute_reply": "2022-01-28T14:48:30.595868Z", "shell.execute_reply.started": "2022-01-28T14:14:56.170961Z" }, "papermill": { "duration": 0.078318, "end_time": "2022-01-28T14:48:30.596621", "exception": false, "start_time": "2022-01-28T14:48:30.518303", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(40000, 14)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.shape" ] }, { "cell_type": "markdown", "id": "10d6515a", "metadata": { "papermill": { "duration": 0.066667, "end_time": "2022-01-28T14:48:30.729762", "exception": false, "start_time": "2022-01-28T14:48:30.663095", "status": "completed" }, "tags": [] }, "source": [ "<li>song_duration_ms, liveness, key, danceability, acousticness, instrumentalness, energy, loudness columns have Null values </li>\n", "<li>Replacing key with median as it is categorical variable </li>\n", "<li>Remaining columns will be replaced with Mean value </li>" ] }, { "cell_type": "code", "execution_count": 19, "id": "3542e8f3", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:30.868685Z", "iopub.status.busy": "2022-01-28T14:48:30.867921Z", "iopub.status.idle": "2022-01-28T14:48:30.880908Z", "shell.execute_reply": "2022-01-28T14:48:30.880275Z", "shell.execute_reply.started": "2022-01-28T14:14:56.178150Z" }, "papermill": { "duration": 0.085062, "end_time": "2022-01-28T14:48:30.881054", "exception": false, "start_time": "2022-01-28T14:48:30.795992", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.experimental import enable_iterative_imputer\n", "from sklearn.impute import IterativeImputer" ] }, { "cell_type": "code", "execution_count": 20, "id": "1745f2b9", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:31.025095Z", "iopub.status.busy": "2022-01-28T14:48:31.023887Z", "iopub.status.idle": "2022-01-28T14:48:32.828537Z", "shell.execute_reply": "2022-01-28T14:48:32.829490Z", "shell.execute_reply.started": "2022-01-28T14:14:56.185985Z" }, "papermill": { "duration": 1.882388, "end_time": "2022-01-28T14:48:32.829806", "exception": false, "start_time": "2022-01-28T14:48:30.947418", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "it_imputer = IterativeImputer(max_iter=1000)\n", "train_imp = it_imputer.fit_transform(train_df[features])\n", "train_df = pd.DataFrame(train_imp, columns=features)" ] }, { "cell_type": "code", "execution_count": 21, "id": "7029e606", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:33.021030Z", "iopub.status.busy": "2022-01-28T14:48:33.020104Z", "iopub.status.idle": "2022-01-28T14:48:33.023770Z", "shell.execute_reply": "2022-01-28T14:48:33.024309Z", "shell.execute_reply.started": "2022-01-28T14:14:57.552217Z" }, "papermill": { "duration": 0.077353, "end_time": "2022-01-28T14:48:33.024513", "exception": false, "start_time": "2022-01-28T14:48:32.947160", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(40000, 14)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.shape" ] }, { "cell_type": "code", "execution_count": 22, "id": "be6767cd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:33.164113Z", "iopub.status.busy": "2022-01-28T14:48:33.162648Z", "iopub.status.idle": "2022-01-28T14:48:33.907484Z", "shell.execute_reply": "2022-01-28T14:48:33.906866Z", "shell.execute_reply.started": "2022-01-28T14:14:57.559600Z" }, "papermill": { "duration": 0.816926, "end_time": "2022-01-28T14:48:33.907630", "exception": false, "start_time": "2022-01-28T14:48:33.090704", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WfpU73Fd4+eWX4957742xY8dGSin+9Kc/xamnnhpNmzb9we/5zDPPxMMPPxwTJ06MUqVKxW677RZnn312HH744avdfunSpfHQQw/F008/HVOmTIkNNtgg9t5777jgggtin332We1zvvvuu7jrrrti6NChMX369KhUqVLsv//+ceGFF8bOO+/883YGAAAAAAB/aL8ouAMAAAAAAMuZOwUAAAAAADIguAMAAAAAQAYEdwAAAAAAyIDgDgAAAAAAGRDcAQAAAAAgA4I7AAAAAABkQHAHAAAAAIAMCO4AAAAAAJABwR0AAAAAADIguAMAAAAAQAb+H0coi71kofQuAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1800x720 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msno.matrix(train_df)" ] }, { "cell_type": "code", "execution_count": 23, "id": "ba8a13e0", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:34.055624Z", "iopub.status.busy": "2022-01-28T14:48:34.054511Z", "iopub.status.idle": "2022-01-28T14:48:34.063634Z", "shell.execute_reply": "2022-01-28T14:48:34.062993Z", "shell.execute_reply.started": "2022-01-28T14:14:58.248701Z" }, "papermill": { "duration": 0.086344, "end_time": "2022-01-28T14:48:34.063791", "exception": false, "start_time": "2022-01-28T14:48:33.977447", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "train_df['song_duration_ms'].fillna(train_df['song_duration_ms'].mean(), inplace=True)\n", "train_df['liveness'].fillna(train_df['liveness'].mean(), inplace=True)\n", "train_df['danceability'].fillna(train_df['danceability'].mean(), inplace=True)\n", "train_df['acousticness'].fillna(train_df['acousticness'].mean(), inplace=True)\n", "train_df['instrumentalness'].fillna(train_df['instrumentalness'].mean(), inplace=True)\n", "train_df['energy'].fillna(train_df['energy'].mean(), inplace=True)\n", "train_df['loudness'].fillna(train_df['loudness'].mean(), inplace=True)\n", "train_df['key'].fillna(train_df['key'].median(), inplace=True)" ] }, { "cell_type": "markdown", "id": "8abf40ae", "metadata": { "papermill": { "duration": 0.069199, "end_time": "2022-01-28T14:48:34.201722", "exception": false, "start_time": "2022-01-28T14:48:34.132523", "status": "completed" }, "tags": [] }, "source": [ "### 1.3 Data Corelation" ] }, { "cell_type": "code", "execution_count": 24, "id": "d3359151", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:34.367683Z", "iopub.status.busy": "2022-01-28T14:48:34.345335Z", "iopub.status.idle": "2022-01-28T14:48:36.133213Z", "shell.execute_reply": "2022-01-28T14:48:36.133778Z", "shell.execute_reply.started": "2022-01-28T14:14:58.266406Z" }, "papermill": { "duration": 1.863617, "end_time": "2022-01-28T14:48:36.133960", "exception": false, "start_time": "2022-01-28T14:48:34.270343", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(figsize=(20, 20))\n", "corr = train_df.corr()\n", "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool),annot=True, cmap=sns.diverging_palette(220, 10, as_cmap=True),square=True, ax=ax)" ] }, { "cell_type": "code", "execution_count": 25, "id": "13498b0e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:36.292430Z", "iopub.status.busy": "2022-01-28T14:48:36.291451Z", "iopub.status.idle": "2022-01-28T14:48:36.321452Z", "shell.execute_reply": "2022-01-28T14:48:36.322277Z", "shell.execute_reply.started": "2022-01-28T14:14:59.583627Z" }, "papermill": { "duration": 0.113776, "end_time": "2022-01-28T14:48:36.322549", "exception": false, "start_time": "2022-01-28T14:48:36.208773", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "cor_matrix = train_df.corr().abs()\n", "upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape),k=1).astype(np.bool))\n", "to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > 0.7)]\n", "print(to_drop)" ] }, { "cell_type": "markdown", "id": "7f1f6693", "metadata": { "papermill": { "duration": 0.07537, "end_time": "2022-01-28T14:48:36.474526", "exception": false, "start_time": "2022-01-28T14:48:36.399156", "status": "completed" }, "tags": [] }, "source": [ "### 1.4 Normalization" ] }, { "cell_type": "code", "execution_count": 26, "id": "19acb418", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:36.634552Z", "iopub.status.busy": "2022-01-28T14:48:36.633384Z", "iopub.status.idle": "2022-01-28T14:48:36.720479Z", "shell.execute_reply": "2022-01-28T14:48:36.721027Z", "shell.execute_reply.started": "2022-01-28T14:14:59.617791Z" }, "papermill": { "duration": 0.171816, "end_time": "2022-01-28T14:48:36.721233", "exception": false, "start_time": "2022-01-28T14:48:36.549417", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>song_duration_ms</th>\n", " <th>instrumentalness</th>\n", " <th>acousticness</th>\n", " <th>danceability</th>\n", " <th>audio_valence</th>\n", " <th>liveness</th>\n", " <th>loudness</th>\n", " <th>speechiness</th>\n", " <th>tempo</th>\n", " <th>energy</th>\n", " <th>audio_mode</th>\n", " <th>key</th>\n", " <th>time_signature</th>\n", " <th>song_popularity</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>-4.147339e-16</td>\n", " <td>-2.489897e-16</td>\n", " <td>2.883194e-16</td>\n", " <td>2.640832e-16</td>\n", " <td>-4.594578e-16</td>\n", " <td>1.293549e-17</td>\n", " <td>-1.991740e-17</td>\n", " <td>-3.962594e-17</td>\n", " <td>-7.664785e-16</td>\n", " <td>1.927347e-17</td>\n", " <td>-1.329270e-16</td>\n", " <td>2.117750e-16</td>\n", " <td>-6.773693e-16</td>\n", " <td>-4.556910e-16</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-3.849128e+00</td>\n", " <td>-6.310202e+00</td>\n", " <td>-5.674797e+00</td>\n", " <td>-2.889336e+00</td>\n", " <td>-2.389940e+00</td>\n", " <td>-3.237828e+00</td>\n", " <td>-3.050671e+00</td>\n", " <td>-2.086949e+00</td>\n", " <td>-2.082998e+00</td>\n", " <td>-3.302727e+00</td>\n", " <td>-6.878077e-01</td>\n", " <td>-1.571330e+00</td>\n", " <td>-2.659001e+00</td>\n", " <td>-7.571767e-01</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-5.663283e-01</td>\n", " <td>-4.213683e-01</td>\n", " <td>-6.239039e-01</td>\n", " <td>-7.202857e-01</td>\n", " <td>-7.667037e-01</td>\n", " <td>-6.542518e-01</td>\n", " <td>-7.708623e-01</td>\n", " <td>-8.076286e-01</td>\n", " <td>-7.477766e-01</td>\n", " <td>-6.706202e-01</td>\n", " <td>-6.878077e-01</td>\n", " <td>-9.482149e-01</td>\n", " <td>-7.520527e-01</td>\n", " <td>-7.571767e-01</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>-1.097891e-01</td>\n", " <td>-1.148327e-01</td>\n", " <td>1.053014e-01</td>\n", " <td>1.608691e-01</td>\n", " <td>7.660547e-02</td>\n", " <td>-2.220921e-01</td>\n", " <td>5.793080e-02</td>\n", " <td>-2.996706e-01</td>\n", " <td>-1.057360e-01</td>\n", " <td>9.301288e-02</td>\n", " <td>-6.878077e-01</td>\n", " <td>4.560762e-02</td>\n", " <td>-7.520527e-01</td>\n", " <td>-7.571767e-01</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>4.167883e-01</td>\n", " <td>1.606453e-01</td>\n", " <td>8.837133e-01</td>\n", " <td>7.577276e-01</td>\n", " <td>7.541266e-01</td>\n", " <td>3.770908e-01</td>\n", " <td>7.043286e-01</td>\n", " <td>7.291410e-01</td>\n", " <td>4.568465e-01</td>\n", " <td>8.683199e-01</td>\n", " <td>1.453895e+00</td>\n", " <td>9.211313e-01</td>\n", " <td>1.154896e+00</td>\n", " <td>1.320696e+00</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>6.859263e+00</td>\n", " <td>3.798258e+00</td>\n", " <td>1.656587e+00</td>\n", " <td>2.117309e+00</td>\n", " <td>1.861878e+00</td>\n", " <td>3.431837e+00</td>\n", " <td>4.025888e+00</td>\n", " <td>2.844471e+00</td>\n", " <td>3.920911e+00</td>\n", " <td>2.231412e+00</td>\n", " <td>1.453895e+00</td>\n", " <td>1.855804e+00</td>\n", " <td>3.061844e+00</td>\n", " <td>1.320696e+00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " song_duration_ms instrumentalness acousticness danceability \\\n", "count 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 \n", "mean -4.147339e-16 -2.489897e-16 2.883194e-16 2.640832e-16 \n", "std 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 \n", "min -3.849128e+00 -6.310202e+00 -5.674797e+00 -2.889336e+00 \n", "25% -5.663283e-01 -4.213683e-01 -6.239039e-01 -7.202857e-01 \n", "50% -1.097891e-01 -1.148327e-01 1.053014e-01 1.608691e-01 \n", "75% 4.167883e-01 1.606453e-01 8.837133e-01 7.577276e-01 \n", "max 6.859263e+00 3.798258e+00 1.656587e+00 2.117309e+00 \n", "\n", " audio_valence liveness loudness speechiness tempo \\\n", "count 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 \n", "mean -4.594578e-16 1.293549e-17 -1.991740e-17 -3.962594e-17 -7.664785e-16 \n", "std 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 \n", "min -2.389940e+00 -3.237828e+00 -3.050671e+00 -2.086949e+00 -2.082998e+00 \n", "25% -7.667037e-01 -6.542518e-01 -7.708623e-01 -8.076286e-01 -7.477766e-01 \n", "50% 7.660547e-02 -2.220921e-01 5.793080e-02 -2.996706e-01 -1.057360e-01 \n", "75% 7.541266e-01 3.770908e-01 7.043286e-01 7.291410e-01 4.568465e-01 \n", "max 1.861878e+00 3.431837e+00 4.025888e+00 2.844471e+00 3.920911e+00 \n", "\n", " energy audio_mode key time_signature \\\n", "count 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 \n", "mean 1.927347e-17 -1.329270e-16 2.117750e-16 -6.773693e-16 \n", "std 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 \n", "min -3.302727e+00 -6.878077e-01 -1.571330e+00 -2.659001e+00 \n", "25% -6.706202e-01 -6.878077e-01 -9.482149e-01 -7.520527e-01 \n", "50% 9.301288e-02 -6.878077e-01 4.560762e-02 -7.520527e-01 \n", "75% 8.683199e-01 1.453895e+00 9.211313e-01 1.154896e+00 \n", "max 2.231412e+00 1.453895e+00 1.855804e+00 3.061844e+00 \n", "\n", " song_popularity \n", "count 4.000000e+04 \n", "mean -4.556910e-16 \n", "std 1.000013e+00 \n", "min -7.571767e-01 \n", "25% -7.571767e-01 \n", "50% -7.571767e-01 \n", "75% 1.320696e+00 \n", "max 1.320696e+00 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stand_scaler = preprocessing.StandardScaler()\n", "scaler = stand_scaler.fit(train_df)\n", "train_scaled = scaler.transform(train_df)\n", "train_scaled_df = pd.DataFrame(train_scaled, columns = train_df.columns)\n", "train_scaled_df.describe()" ] }, { "cell_type": "code", "execution_count": 27, "id": "852ac88f", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:36.895569Z", "iopub.status.busy": "2022-01-28T14:48:36.894543Z", "iopub.status.idle": "2022-01-28T14:48:36.898637Z", "shell.execute_reply": "2022-01-28T14:48:36.899206Z", "shell.execute_reply.started": "2022-01-28T14:14:59.704854Z" }, "papermill": { "duration": 0.103216, "end_time": "2022-01-28T14:48:36.899404", "exception": false, "start_time": "2022-01-28T14:48:36.796188", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>song_duration_ms</th>\n", " <th>instrumentalness</th>\n", " <th>acousticness</th>\n", " <th>danceability</th>\n", " <th>audio_valence</th>\n", " <th>liveness</th>\n", " <th>loudness</th>\n", " <th>speechiness</th>\n", " <th>tempo</th>\n", " <th>energy</th>\n", " <th>audio_mode</th>\n", " <th>key</th>\n", " <th>time_signature</th>\n", " <th>song_popularity</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.455525</td>\n", " <td>-0.163597</td>\n", " <td>1.135194</td>\n", " <td>1.565691</td>\n", " <td>0.648823</td>\n", " <td>0.072572</td>\n", " <td>0.308727</td>\n", " <td>0.232641</td>\n", " <td>1.598291</td>\n", " <td>0.112316</td>\n", " <td>-0.687808</td>\n", " <td>1.544247</td>\n", " <td>1.154896</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.093153</td>\n", " <td>-0.603605</td>\n", " <td>-0.502009</td>\n", " <td>0.890049</td>\n", " <td>0.551451</td>\n", " <td>1.798680</td>\n", " <td>0.459737</td>\n", " <td>0.823504</td>\n", " <td>-0.527746</td>\n", " <td>0.731341</td>\n", " <td>1.453895</td>\n", " <td>0.921131</td>\n", " <td>-0.752053</td>\n", " <td>1.320696</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.001074</td>\n", " <td>0.236215</td>\n", " <td>-0.677076</td>\n", " <td>-2.097488</td>\n", " <td>-0.653509</td>\n", " <td>0.078575</td>\n", " <td>0.580148</td>\n", " <td>-0.390442</td>\n", " <td>2.374043</td>\n", " <td>0.480683</td>\n", " <td>-0.687808</td>\n", " <td>-0.013542</td>\n", " <td>-0.752053</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.303509</td>\n", " <td>-0.914395</td>\n", " <td>0.953272</td>\n", " <td>0.078305</td>\n", " <td>-0.535282</td>\n", " <td>-0.995519</td>\n", " <td>-0.409899</td>\n", " <td>-0.913735</td>\n", " <td>0.464423</td>\n", " <td>-0.631582</td>\n", " <td>-0.687808</td>\n", " <td>-1.571330</td>\n", " <td>-0.752053</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-0.624959</td>\n", " <td>-0.153678</td>\n", " <td>0.959180</td>\n", " <td>0.475471</td>\n", " <td>0.676921</td>\n", " <td>-0.993861</td>\n", " <td>1.896348</td>\n", " <td>-0.431085</td>\n", " <td>0.205038</td>\n", " <td>0.275700</td>\n", " <td>-0.687808</td>\n", " <td>1.544247</td>\n", " <td>1.154896</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " song_duration_ms instrumentalness acousticness danceability \\\n", "0 0.455525 -0.163597 1.135194 1.565691 \n", "1 0.093153 -0.603605 -0.502009 0.890049 \n", "2 0.001074 0.236215 -0.677076 -2.097488 \n", "3 1.303509 -0.914395 0.953272 0.078305 \n", "4 -0.624959 -0.153678 0.959180 0.475471 \n", "\n", " audio_valence liveness loudness speechiness tempo energy \\\n", "0 0.648823 0.072572 0.308727 0.232641 1.598291 0.112316 \n", "1 0.551451 1.798680 0.459737 0.823504 -0.527746 0.731341 \n", "2 -0.653509 0.078575 0.580148 -0.390442 2.374043 0.480683 \n", "3 -0.535282 -0.995519 -0.409899 -0.913735 0.464423 -0.631582 \n", "4 0.676921 -0.993861 1.896348 -0.431085 0.205038 0.275700 \n", "\n", " audio_mode key time_signature song_popularity \n", "0 -0.687808 1.544247 1.154896 -0.757177 \n", "1 1.453895 0.921131 -0.752053 1.320696 \n", "2 -0.687808 -0.013542 -0.752053 -0.757177 \n", "3 -0.687808 -1.571330 -0.752053 -0.757177 \n", "4 -0.687808 1.544247 1.154896 -0.757177 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_scaled_df.head()" ] }, { "cell_type": "markdown", "id": "08b52bb9", "metadata": { "papermill": { "duration": 0.081643, "end_time": "2022-01-28T14:48:37.056858", "exception": false, "start_time": "2022-01-28T14:48:36.975215", "status": "completed" }, "tags": [] }, "source": [ "# Test Data preparation" ] }, { "cell_type": "code", "execution_count": 28, "id": "efd1bfdf", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:37.226126Z", "iopub.status.busy": "2022-01-28T14:48:37.224497Z", "iopub.status.idle": "2022-01-28T14:48:40.856683Z", "shell.execute_reply": "2022-01-28T14:48:40.857248Z", "shell.execute_reply.started": "2022-01-28T14:14:59.726230Z" }, "papermill": { "duration": 3.718377, "end_time": "2022-01-28T14:48:40.857569", "exception": false, "start_time": "2022-01-28T14:48:37.139192", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: invalid value encountered in log\n", " result = getattr(ufunc, method)(*inputs, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "acousticness -0.5980174749932947\n", "liveness 0.9503113380758493\n", "instrumentalness 1.5051342456009544\n", "speechiness 0.7200896535402886\n" ] } ], "source": [ "skewed_features=['acousticness','liveness','instrumentalness','speechiness']\n", "#log transformation\n", "for x in skewed_features:\n", " log_inst=np.log(test_df[x])\n", " print(x,\" \",log_inst.skew())\n", " if not np.isnan(log_inst.skew()):\n", " test_df[x]=log_inst\n", " \n", "data= np.array(test_df[\"loudness\"])\n", "data=data.reshape(-1, 1)\n", "from sklearn.preprocessing import PowerTransformer\n", "power = PowerTransformer(method='yeo-johnson', standardize=True)\n", "data_trans = power.fit_transform(data)\n", "data_trans=data_trans.reshape(1, -1)\n", "i=0\n", "for num in data_trans[0]:\n", " test_df[\"loudness\"][i]=num\n", " i=i+1\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "f24948bd", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:41.018989Z", "iopub.status.busy": "2022-01-28T14:48:41.018091Z", "iopub.status.idle": "2022-01-28T14:48:41.022509Z", "shell.execute_reply": "2022-01-28T14:48:41.021934Z", "shell.execute_reply.started": "2022-01-28T14:15:01.907001Z" }, "papermill": { "duration": 0.087663, "end_time": "2022-01-28T14:48:41.022683", "exception": false, "start_time": "2022-01-28T14:48:40.935020", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "features=test_df.columns.to_list()" ] }, { "cell_type": "code", "execution_count": 30, "id": "5d760211", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:41.185558Z", "iopub.status.busy": "2022-01-28T14:48:41.184441Z", "iopub.status.idle": "2022-01-28T14:48:41.342036Z", "shell.execute_reply": "2022-01-28T14:48:41.342878Z", "shell.execute_reply.started": "2022-01-28T14:15:01.912776Z" }, "papermill": { "duration": 0.243148, "end_time": "2022-01-28T14:48:41.343199", "exception": false, "start_time": "2022-01-28T14:48:41.100051", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "it_imputer = IterativeImputer(max_iter=1000)\n", "test_imp = it_imputer.fit_transform(test_df[features])\n", "test_df = pd.DataFrame(test_imp, columns=features)" ] }, { "cell_type": "code", "execution_count": 31, "id": "63598d9e", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:41.562746Z", "iopub.status.busy": "2022-01-28T14:48:41.561518Z", "iopub.status.idle": "2022-01-28T14:48:41.640631Z", "shell.execute_reply": "2022-01-28T14:48:41.641224Z", "shell.execute_reply.started": "2022-01-28T14:15:02.051156Z" }, "papermill": { "duration": 0.165486, "end_time": "2022-01-28T14:48:41.641427", "exception": false, "start_time": "2022-01-28T14:48:41.475941", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>song_duration_ms</th>\n", " <th>acousticness</th>\n", " <th>danceability</th>\n", " <th>energy</th>\n", " <th>instrumentalness</th>\n", " <th>key</th>\n", " <th>liveness</th>\n", " <th>loudness</th>\n", " <th>audio_mode</th>\n", " <th>speechiness</th>\n", " <th>tempo</th>\n", " <th>time_signature</th>\n", " <th>audio_valence</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " <td>4.000000e+04</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>-4.147339e-16</td>\n", " <td>-2.489897e-16</td>\n", " <td>2.883194e-16</td>\n", " <td>2.640832e-16</td>\n", " <td>-4.594578e-16</td>\n", " <td>1.293549e-17</td>\n", " <td>-1.991740e-17</td>\n", " <td>-3.962594e-17</td>\n", " <td>-7.664785e-16</td>\n", " <td>1.927347e-17</td>\n", " <td>-1.329270e-16</td>\n", " <td>2.117750e-16</td>\n", " <td>-6.773693e-16</td>\n", " <td>-4.556910e-16</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " <td>1.000013e+00</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-3.849128e+00</td>\n", " <td>-6.310202e+00</td>\n", " <td>-5.674797e+00</td>\n", " <td>-2.889336e+00</td>\n", " <td>-2.389940e+00</td>\n", " <td>-3.237828e+00</td>\n", " <td>-3.050671e+00</td>\n", " <td>-2.086949e+00</td>\n", " <td>-2.082998e+00</td>\n", " <td>-3.302727e+00</td>\n", " <td>-6.878077e-01</td>\n", " <td>-1.571330e+00</td>\n", " <td>-2.659001e+00</td>\n", " <td>-7.571767e-01</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-5.663283e-01</td>\n", " <td>-4.213683e-01</td>\n", " <td>-6.239039e-01</td>\n", " <td>-7.202857e-01</td>\n", " <td>-7.667037e-01</td>\n", " <td>-6.542518e-01</td>\n", " <td>-7.708623e-01</td>\n", " <td>-8.076286e-01</td>\n", " <td>-7.477766e-01</td>\n", " <td>-6.706202e-01</td>\n", " <td>-6.878077e-01</td>\n", " <td>-9.482149e-01</td>\n", " <td>-7.520527e-01</td>\n", " <td>-7.571767e-01</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>-1.097891e-01</td>\n", " <td>-1.148327e-01</td>\n", " <td>1.053014e-01</td>\n", " <td>1.608691e-01</td>\n", " <td>7.660547e-02</td>\n", " <td>-2.220921e-01</td>\n", " <td>5.793080e-02</td>\n", " <td>-2.996706e-01</td>\n", " <td>-1.057360e-01</td>\n", " <td>9.301288e-02</td>\n", " <td>-6.878077e-01</td>\n", " <td>4.560762e-02</td>\n", " <td>-7.520527e-01</td>\n", " <td>-7.571767e-01</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>4.167883e-01</td>\n", " <td>1.606453e-01</td>\n", " <td>8.837133e-01</td>\n", " <td>7.577276e-01</td>\n", " <td>7.541266e-01</td>\n", " <td>3.770908e-01</td>\n", " <td>7.043286e-01</td>\n", " <td>7.291410e-01</td>\n", " <td>4.568465e-01</td>\n", " <td>8.683199e-01</td>\n", " <td>1.453895e+00</td>\n", " <td>9.211313e-01</td>\n", " <td>1.154896e+00</td>\n", " <td>1.320696e+00</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>6.859263e+00</td>\n", " <td>3.798258e+00</td>\n", " <td>1.656587e+00</td>\n", " <td>2.117309e+00</td>\n", " <td>1.861878e+00</td>\n", " <td>3.431837e+00</td>\n", " <td>4.025888e+00</td>\n", " <td>2.844471e+00</td>\n", " <td>3.920911e+00</td>\n", " <td>2.231412e+00</td>\n", " <td>1.453895e+00</td>\n", " <td>1.855804e+00</td>\n", " <td>3.061844e+00</td>\n", " <td>1.320696e+00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id song_duration_ms acousticness danceability \\\n", "count 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 \n", "mean -4.147339e-16 -2.489897e-16 2.883194e-16 2.640832e-16 \n", "std 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 \n", "min -3.849128e+00 -6.310202e+00 -5.674797e+00 -2.889336e+00 \n", "25% -5.663283e-01 -4.213683e-01 -6.239039e-01 -7.202857e-01 \n", "50% -1.097891e-01 -1.148327e-01 1.053014e-01 1.608691e-01 \n", "75% 4.167883e-01 1.606453e-01 8.837133e-01 7.577276e-01 \n", "max 6.859263e+00 3.798258e+00 1.656587e+00 2.117309e+00 \n", "\n", " energy instrumentalness key liveness \\\n", "count 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 \n", "mean -4.594578e-16 1.293549e-17 -1.991740e-17 -3.962594e-17 \n", "std 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 \n", "min -2.389940e+00 -3.237828e+00 -3.050671e+00 -2.086949e+00 \n", "25% -7.667037e-01 -6.542518e-01 -7.708623e-01 -8.076286e-01 \n", "50% 7.660547e-02 -2.220921e-01 5.793080e-02 -2.996706e-01 \n", "75% 7.541266e-01 3.770908e-01 7.043286e-01 7.291410e-01 \n", "max 1.861878e+00 3.431837e+00 4.025888e+00 2.844471e+00 \n", "\n", " loudness audio_mode speechiness tempo time_signature \\\n", "count 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 4.000000e+04 \n", "mean -7.664785e-16 1.927347e-17 -1.329270e-16 2.117750e-16 -6.773693e-16 \n", "std 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 1.000013e+00 \n", "min -2.082998e+00 -3.302727e+00 -6.878077e-01 -1.571330e+00 -2.659001e+00 \n", "25% -7.477766e-01 -6.706202e-01 -6.878077e-01 -9.482149e-01 -7.520527e-01 \n", "50% -1.057360e-01 9.301288e-02 -6.878077e-01 4.560762e-02 -7.520527e-01 \n", "75% 4.568465e-01 8.683199e-01 1.453895e+00 9.211313e-01 1.154896e+00 \n", "max 3.920911e+00 2.231412e+00 1.453895e+00 1.855804e+00 3.061844e+00 \n", "\n", " audio_valence \n", "count 4.000000e+04 \n", "mean -4.556910e-16 \n", "std 1.000013e+00 \n", "min -7.571767e-01 \n", "25% -7.571767e-01 \n", "50% -7.571767e-01 \n", "75% 1.320696e+00 \n", "max 1.320696e+00 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_scaled = scaler.transform(test_df)\n", "test_scaled_df = pd.DataFrame(train_scaled, columns = test_df.columns)\n", "test_scaled_df.describe()" ] }, { "cell_type": "markdown", "id": "ab46de5b", "metadata": { "papermill": { "duration": 0.078988, "end_time": "2022-01-28T14:48:41.797798", "exception": false, "start_time": "2022-01-28T14:48:41.718810", "status": "completed" }, "tags": [] }, "source": [ "## 2. Model Building" ] }, { "cell_type": "code", "execution_count": 32, "id": "9e2975a1", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:41.963013Z", "iopub.status.busy": "2022-01-28T14:48:41.961978Z", "iopub.status.idle": "2022-01-28T14:48:41.964632Z", "shell.execute_reply": "2022-01-28T14:48:41.965146Z", "shell.execute_reply.started": "2022-01-28T14:15:02.173701Z" }, "papermill": { "duration": 0.090144, "end_time": "2022-01-28T14:48:41.965342", "exception": false, "start_time": "2022-01-28T14:48:41.875198", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data=train_scaled_df.drop(['song_popularity'],axis=1)\n", "target=train_scaled_df['song_popularity']" ] }, { "cell_type": "code", "execution_count": 33, "id": "28fd58f2", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:42.143502Z", "iopub.status.busy": "2022-01-28T14:48:42.139211Z", "iopub.status.idle": "2022-01-28T14:48:42.147279Z", "shell.execute_reply": "2022-01-28T14:48:42.147807Z", "shell.execute_reply.started": "2022-01-28T14:27:07.850135Z" }, "papermill": { "duration": 0.104593, "end_time": "2022-01-28T14:48:42.148001", "exception": false, "start_time": "2022-01-28T14:48:42.043408", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>song_duration_ms</th>\n", " <th>instrumentalness</th>\n", " <th>acousticness</th>\n", " <th>danceability</th>\n", " <th>audio_valence</th>\n", " <th>liveness</th>\n", " <th>loudness</th>\n", " <th>speechiness</th>\n", " <th>tempo</th>\n", " <th>energy</th>\n", " <th>audio_mode</th>\n", " <th>key</th>\n", " <th>time_signature</th>\n", " <th>song_popularity</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.455525</td>\n", " <td>-0.163597</td>\n", " <td>1.135194</td>\n", " <td>1.565691</td>\n", " <td>0.648823</td>\n", " <td>0.072572</td>\n", " <td>0.308727</td>\n", " <td>0.232641</td>\n", " <td>1.598291</td>\n", " <td>0.112316</td>\n", " <td>-0.687808</td>\n", " <td>1.544247</td>\n", " <td>1.154896</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.093153</td>\n", " <td>-0.603605</td>\n", " <td>-0.502009</td>\n", " <td>0.890049</td>\n", " <td>0.551451</td>\n", " <td>1.798680</td>\n", " <td>0.459737</td>\n", " <td>0.823504</td>\n", " <td>-0.527746</td>\n", " <td>0.731341</td>\n", " <td>1.453895</td>\n", " <td>0.921131</td>\n", " <td>-0.752053</td>\n", " <td>1.320696</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.001074</td>\n", " <td>0.236215</td>\n", " <td>-0.677076</td>\n", " <td>-2.097488</td>\n", " <td>-0.653509</td>\n", " <td>0.078575</td>\n", " <td>0.580148</td>\n", " <td>-0.390442</td>\n", " <td>2.374043</td>\n", " <td>0.480683</td>\n", " <td>-0.687808</td>\n", " <td>-0.013542</td>\n", " <td>-0.752053</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.303509</td>\n", " <td>-0.914395</td>\n", " <td>0.953272</td>\n", " <td>0.078305</td>\n", " <td>-0.535282</td>\n", " <td>-0.995519</td>\n", " <td>-0.409899</td>\n", " <td>-0.913735</td>\n", " <td>0.464423</td>\n", " <td>-0.631582</td>\n", " <td>-0.687808</td>\n", " <td>-1.571330</td>\n", " <td>-0.752053</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-0.624959</td>\n", " <td>-0.153678</td>\n", " <td>0.959180</td>\n", " <td>0.475471</td>\n", " <td>0.676921</td>\n", " <td>-0.993861</td>\n", " <td>1.896348</td>\n", " <td>-0.431085</td>\n", " <td>0.205038</td>\n", " <td>0.275700</td>\n", " <td>-0.687808</td>\n", " <td>1.544247</td>\n", " <td>1.154896</td>\n", " <td>-0.757177</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " song_duration_ms instrumentalness acousticness danceability \\\n", "0 0.455525 -0.163597 1.135194 1.565691 \n", "1 0.093153 -0.603605 -0.502009 0.890049 \n", "2 0.001074 0.236215 -0.677076 -2.097488 \n", "3 1.303509 -0.914395 0.953272 0.078305 \n", "4 -0.624959 -0.153678 0.959180 0.475471 \n", "\n", " audio_valence liveness loudness speechiness tempo energy \\\n", "0 0.648823 0.072572 0.308727 0.232641 1.598291 0.112316 \n", "1 0.551451 1.798680 0.459737 0.823504 -0.527746 0.731341 \n", "2 -0.653509 0.078575 0.580148 -0.390442 2.374043 0.480683 \n", "3 -0.535282 -0.995519 -0.409899 -0.913735 0.464423 -0.631582 \n", "4 0.676921 -0.993861 1.896348 -0.431085 0.205038 0.275700 \n", "\n", " audio_mode key time_signature song_popularity \n", "0 -0.687808 1.544247 1.154896 -0.757177 \n", "1 1.453895 0.921131 -0.752053 1.320696 \n", "2 -0.687808 -0.013542 -0.752053 -0.757177 \n", "3 -0.687808 -1.571330 -0.752053 -0.757177 \n", "4 -0.687808 1.544247 1.154896 -0.757177 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_scaled_df.head()" ] }, { "cell_type": "markdown", "id": "938d1cfc", "metadata": { "papermill": { "duration": 0.079775, "end_time": "2022-01-28T14:48:42.305454", "exception": false, "start_time": "2022-01-28T14:48:42.225679", "status": "completed" }, "tags": [] }, "source": [ "# IN PROGRESS <br/>\n", "\n", "Took reference of Optuna code from https://www.kaggle.com/hamzaghanmi/xgboost-hyperparameter-tuning-using-optuna" ] }, { "cell_type": "code", "execution_count": 34, "id": "978c4a84", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:42.468874Z", "iopub.status.busy": "2022-01-28T14:48:42.468102Z", "iopub.status.idle": "2022-01-28T14:48:42.708907Z", "shell.execute_reply": "2022-01-28T14:48:42.708111Z", "shell.execute_reply.started": "2022-01-28T14:15:02.182409Z" }, "papermill": { "duration": 0.324745, "end_time": "2022-01-28T14:48:42.709108", "exception": false, "start_time": "2022-01-28T14:48:42.384363", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import xgboost as xgb\n", "from catboost import CatBoostRegressor" ] }, { "cell_type": "code", "execution_count": 35, "id": "07ee6e25", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:42.872224Z", "iopub.status.busy": "2022-01-28T14:48:42.871128Z", "iopub.status.idle": "2022-01-28T14:48:42.883133Z", "shell.execute_reply": "2022-01-28T14:48:42.882482Z", "shell.execute_reply.started": "2022-01-28T14:15:02.192853Z" }, "papermill": { "duration": 0.094874, "end_time": "2022-01-28T14:48:42.883278", "exception": false, "start_time": "2022-01-28T14:48:42.788404", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def objective(trial,data=data,target=target):\n", " \n", " train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.15,random_state=42)\n", " param = {\n", " 'tree_method':'gpu_hist', # this parameter means using the GPU when training our model to speedup the training process\n", " 'lambda': trial.suggest_loguniform('lambda', 1e-3, 10.0),\n", " 'alpha': trial.suggest_loguniform('alpha', 1e-3, 10.0),\n", " 'colsample_bytree': trial.suggest_categorical('colsample_bytree', [0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.0]),\n", " 'subsample': trial.suggest_categorical('subsample', [0.4,0.5,0.6,0.7,0.8,1.0]),\n", " 'learning_rate': trial.suggest_categorical('learning_rate', [0.008,0.009,0.01,0.012,0.014,0.016,0.018, 0.02]),\n", " 'n_estimators': 4000,\n", " 'max_depth': trial.suggest_categorical('max_depth', [5,7,9,11,13,15,17,20]),\n", " 'random_state': trial.suggest_categorical('random_state', [24, 48,2020]),\n", " 'min_child_weight': trial.suggest_int('min_child_weight', 1, 300),\n", " }\n", " model = xgb.XGBRegressor(**param) \n", " model.fit(train_x,train_y,eval_set=[(test_x,test_y)],early_stopping_rounds=100,verbose=False)\n", " preds = model.predict(test_x)\n", " rmse = mean_squared_error(test_y, preds,squared=False)\n", " return rmse" ] }, { "cell_type": "code", "execution_count": 36, "id": "4a8fc683", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:48:43.048982Z", "iopub.status.busy": "2022-01-28T14:48:43.047893Z", "iopub.status.idle": "2022-01-28T14:52:21.612340Z", "shell.execute_reply": "2022-01-28T14:52:21.613137Z", "shell.execute_reply.started": "2022-01-28T14:15:02.208173Z" }, "papermill": { "duration": 218.649655, "end_time": "2022-01-28T14:52:21.613509", "exception": false, "start_time": "2022-01-28T14:48:42.963854", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2022-01-28 14:48:43,045]\u001b[0m A new study created in memory with name: no-name-e8f4cf0c-3e82-406d-8f7e-573045fdbdbf\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:48:50,593]\u001b[0m Trial 0 finished with value: 0.9897768959410994 and parameters: {'lambda': 0.002252770170785709, 'alpha': 4.832081949777727, 'colsample_bytree': 0.3, 'subsample': 0.8, 'learning_rate': 0.018, 'max_depth': 20, 'random_state': 48, 'min_child_weight': 130}. Best is trial 0 with value: 0.9897768959410994.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:48:55,069]\u001b[0m Trial 1 finished with value: 0.9891787374681663 and parameters: {'lambda': 0.14176217422813112, 'alpha': 7.32982576940872, 'colsample_bytree': 0.8, 'subsample': 0.4, 'learning_rate': 0.02, 'max_depth': 20, 'random_state': 24, 'min_child_weight': 69}. Best is trial 1 with value: 0.9891787374681663.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:48:59,392]\u001b[0m Trial 2 finished with value: 0.9887519880296619 and parameters: {'lambda': 0.0015148905132489963, 'alpha': 0.03433597771043349, 'colsample_bytree': 0.5, 'subsample': 0.7, 'learning_rate': 0.018, 'max_depth': 20, 'random_state': 2020, 'min_child_weight': 154}. Best is trial 2 with value: 0.9887519880296619.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:02,930]\u001b[0m Trial 3 finished with value: 0.9897721221179644 and parameters: {'lambda': 0.055247639472521023, 'alpha': 1.1293144736145355, 'colsample_bytree': 0.8, 'subsample': 0.7, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 2020, 'min_child_weight': 33}. Best is trial 2 with value: 0.9887519880296619.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:04,915]\u001b[0m Trial 4 finished with value: 0.9886023982874098 and parameters: {'lambda': 1.2472108476266748, 'alpha': 0.901700921162489, 'colsample_bytree': 0.6, 'subsample': 0.5, 'learning_rate': 0.012, 'max_depth': 5, 'random_state': 2020, 'min_child_weight': 247}. Best is trial 4 with value: 0.9886023982874098.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:06,812]\u001b[0m Trial 5 finished with value: 0.9894587259880139 and parameters: {'lambda': 0.0017224393668374574, 'alpha': 0.0022889828993921035, 'colsample_bytree': 0.7, 'subsample': 1.0, 'learning_rate': 0.012, 'max_depth': 7, 'random_state': 24, 'min_child_weight': 112}. Best is trial 4 with value: 0.9886023982874098.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:08,674]\u001b[0m Trial 6 finished with value: 0.9887059504997825 and parameters: {'lambda': 0.020013076633394782, 'alpha': 0.0014002951603614212, 'colsample_bytree': 0.8, 'subsample': 0.5, 'learning_rate': 0.012, 'max_depth': 7, 'random_state': 48, 'min_child_weight': 259}. Best is trial 4 with value: 0.9886023982874098.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:10,783]\u001b[0m Trial 7 finished with value: 0.9885307737993445 and parameters: {'lambda': 0.0010015523659598485, 'alpha': 1.4373308128594358, 'colsample_bytree': 0.7, 'subsample': 0.5, 'learning_rate': 0.014, 'max_depth': 13, 'random_state': 48, 'min_child_weight': 300}. Best is trial 7 with value: 0.9885307737993445.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:22,859]\u001b[0m Trial 8 finished with value: 0.9920864217683288 and parameters: {'lambda': 0.04844392453043617, 'alpha': 0.019294082817959513, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 20, 'random_state': 2020, 'min_child_weight': 62}. Best is trial 7 with value: 0.9885307737993445.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:25,856]\u001b[0m Trial 9 finished with value: 0.9889348016313564 and parameters: {'lambda': 0.0013911223994628088, 'alpha': 0.5091515480156145, 'colsample_bytree': 0.8, 'subsample': 0.5, 'learning_rate': 0.018, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 107}. Best is trial 7 with value: 0.9885307737993445.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:27,936]\u001b[0m Trial 10 finished with value: 0.9885997724837895 and parameters: {'lambda': 3.881787560550954, 'alpha': 0.12442172602131324, 'colsample_bytree': 0.7, 'subsample': 0.6, 'learning_rate': 0.014, 'max_depth': 13, 'random_state': 48, 'min_child_weight': 297}. Best is trial 7 with value: 0.9885307737993445.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:30,370]\u001b[0m Trial 11 finished with value: 0.988544845191299 and parameters: {'lambda': 8.296664566847541, 'alpha': 0.2148546767790952, 'colsample_bytree': 0.7, 'subsample': 0.6, 'learning_rate': 0.014, 'max_depth': 13, 'random_state': 48, 'min_child_weight': 296}. Best is trial 7 with value: 0.9885307737993445.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:33,785]\u001b[0m Trial 12 finished with value: 0.9882934794536041 and parameters: {'lambda': 0.5028833446383132, 'alpha': 0.179251520514611, 'colsample_bytree': 0.7, 'subsample': 0.6, 'learning_rate': 0.014, 'max_depth': 13, 'random_state': 48, 'min_child_weight': 196}. Best is trial 12 with value: 0.9882934794536041.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:36,958]\u001b[0m Trial 13 finished with value: 0.988701222972205 and parameters: {'lambda': 0.46950318494244586, 'alpha': 2.5457598141929187, 'colsample_bytree': 0.4, 'subsample': 0.6, 'learning_rate': 0.014, 'max_depth': 17, 'random_state': 48, 'min_child_weight': 193}. Best is trial 12 with value: 0.9882934794536041.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:41,741]\u001b[0m Trial 14 finished with value: 0.9885602065376221 and parameters: {'lambda': 0.009963184922449197, 'alpha': 0.03386549201542094, 'colsample_bytree': 1.0, 'subsample': 0.8, 'learning_rate': 0.008, 'max_depth': 13, 'random_state': 48, 'min_child_weight': 194}. Best is trial 12 with value: 0.9882934794536041.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:44,958]\u001b[0m Trial 15 finished with value: 0.9879774893595104 and parameters: {'lambda': 0.3501050953541883, 'alpha': 0.22545393302117198, 'colsample_bytree': 0.7, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 220}. Best is trial 15 with value: 0.9879774893595104.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:47,995]\u001b[0m Trial 16 finished with value: 0.9881564294020456 and parameters: {'lambda': 0.3135975463903011, 'alpha': 0.006972688991212811, 'colsample_bytree': 0.7, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 207}. Best is trial 15 with value: 0.9879774893595104.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:50,637]\u001b[0m Trial 17 finished with value: 0.9881488626739141 and parameters: {'lambda': 0.20895277261532652, 'alpha': 0.007111968113219199, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 234}. Best is trial 15 with value: 0.9879774893595104.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:52,915]\u001b[0m Trial 18 finished with value: 0.9885652979599978 and parameters: {'lambda': 1.767649743252341, 'alpha': 0.006402371918655641, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 254}. Best is trial 15 with value: 0.9879774893595104.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:49:56,517]\u001b[0m Trial 19 finished with value: 0.9879661968353494 and parameters: {'lambda': 0.14547989705235234, 'alpha': 0.06260916909061803, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 231}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:00,703]\u001b[0m Trial 20 finished with value: 0.9887208443685994 and parameters: {'lambda': 0.01154941724434982, 'alpha': 0.06460452383289925, 'colsample_bytree': 0.3, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 167}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:04,397]\u001b[0m Trial 21 finished with value: 0.9880049389041629 and parameters: {'lambda': 0.1808184076924712, 'alpha': 0.006439661965539506, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 233}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:07,710]\u001b[0m Trial 22 finished with value: 0.9884255638962168 and parameters: {'lambda': 0.0631117083189738, 'alpha': 0.3878453135896288, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 230}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:10,546]\u001b[0m Trial 23 finished with value: 0.9883680605981375 and parameters: {'lambda': 0.8145807728992548, 'alpha': 0.04641692187005007, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 221}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:13,264]\u001b[0m Trial 24 finished with value: 0.9882090274928922 and parameters: {'lambda': 0.12414673564639041, 'alpha': 0.012768494733406213, 'colsample_bytree': 0.4, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 275}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:14,445]\u001b[0m Trial 25 finished with value: 0.9884292198829362 and parameters: {'lambda': 0.02738940695187962, 'alpha': 0.08998321598202788, 'colsample_bytree': 0.5, 'subsample': 0.4, 'learning_rate': 0.02, 'max_depth': 5, 'random_state': 24, 'min_child_weight': 175}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:16,157]\u001b[0m Trial 26 finished with value: 0.9880864114254201 and parameters: {'lambda': 0.24661116848149262, 'alpha': 0.003690980187684463, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 273}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:19,884]\u001b[0m Trial 27 finished with value: 0.9881528854210344 and parameters: {'lambda': 0.09071303621971114, 'alpha': 0.016282840208649497, 'colsample_bytree': 0.9, 'subsample': 0.4, 'learning_rate': 0.008, 'max_depth': 17, 'random_state': 24, 'min_child_weight': 217}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:24,792]\u001b[0m Trial 28 finished with value: 0.9916482246694411 and parameters: {'lambda': 2.320561847956506, 'alpha': 0.24435288125067664, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 15, 'random_state': 24, 'min_child_weight': 177}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:30,718]\u001b[0m Trial 29 finished with value: 0.9893995159618694 and parameters: {'lambda': 0.004920674851343665, 'alpha': 2.9694774203634795, 'colsample_bytree': 0.3, 'subsample': 0.8, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 2020, 'min_child_weight': 127}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:34,998]\u001b[0m Trial 30 finished with value: 0.988659104922605 and parameters: {'lambda': 0.7507742930180525, 'alpha': 0.4917715597390373, 'colsample_bytree': 1.0, 'subsample': 0.7, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 145}. Best is trial 19 with value: 0.9879661968353494.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:37,111]\u001b[0m Trial 31 finished with value: 0.9878566439690453 and parameters: {'lambda': 0.22287786370311125, 'alpha': 0.0026914683774543, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 274}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:38,969]\u001b[0m Trial 32 finished with value: 0.9882199563297643 and parameters: {'lambda': 0.20819863954558265, 'alpha': 0.0011337428867189683, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 268}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:40,736]\u001b[0m Trial 33 finished with value: 0.9884680307049861 and parameters: {'lambda': 0.11781309218253622, 'alpha': 0.002732359512357555, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 242}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:42,518]\u001b[0m Trial 34 finished with value: 0.98820616696663 and parameters: {'lambda': 0.35069774831952644, 'alpha': 0.023777271508166765, 'colsample_bytree': 0.5, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 280}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:45,876]\u001b[0m Trial 35 finished with value: 0.9889269823340935 and parameters: {'lambda': 0.04227542890320932, 'alpha': 0.01218276811779815, 'colsample_bytree': 0.6, 'subsample': 0.7, 'learning_rate': 0.02, 'max_depth': 20, 'random_state': 24, 'min_child_weight': 246}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:46,866]\u001b[0m Trial 36 finished with value: 0.9901723498952215 and parameters: {'lambda': 0.08029931002554073, 'alpha': 0.00494751479085547, 'colsample_bytree': 1.0, 'subsample': 0.8, 'learning_rate': 0.018, 'max_depth': 5, 'random_state': 2020, 'min_child_weight': 2}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:49,555]\u001b[0m Trial 37 finished with value: 0.988318559626098 and parameters: {'lambda': 0.1741558819958348, 'alpha': 0.0028327080831170954, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.009, 'max_depth': 7, 'random_state': 24, 'min_child_weight': 214}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:51,795]\u001b[0m Trial 38 finished with value: 0.9890482791531914 and parameters: {'lambda': 0.9689207334670444, 'alpha': 0.0017748279618005694, 'colsample_bytree': 0.9, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 11, 'random_state': 2020, 'min_child_weight': 257}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:54,927]\u001b[0m Trial 39 finished with value: 0.9883175017862382 and parameters: {'lambda': 0.5166770916646309, 'alpha': 0.7857078405710813, 'colsample_bytree': 0.4, 'subsample': 1.0, 'learning_rate': 0.012, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 228}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:50:58,412]\u001b[0m Trial 40 finished with value: 0.9881988242814939 and parameters: {'lambda': 1.5465139639112455, 'alpha': 0.11562085918984474, 'colsample_bytree': 0.8, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 287}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:00,189]\u001b[0m Trial 41 finished with value: 0.9881374875411307 and parameters: {'lambda': 0.2637666842821444, 'alpha': 0.004273391163508371, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 269}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:02,151]\u001b[0m Trial 42 finished with value: 0.9883866482189838 and parameters: {'lambda': 0.14809866327672858, 'alpha': 0.0030856551029151092, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 263}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:03,952]\u001b[0m Trial 43 finished with value: 0.9884478561288058 and parameters: {'lambda': 0.3215440764159795, 'alpha': 0.00968996171258326, 'colsample_bytree': 0.6, 'subsample': 0.5, 'learning_rate': 0.016, 'max_depth': 9, 'random_state': 24, 'min_child_weight': 239}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:05,401]\u001b[0m Trial 44 finished with value: 0.9883183739987047 and parameters: {'lambda': 0.03494426134125058, 'alpha': 0.0010134342354491818, 'colsample_bytree': 0.7, 'subsample': 0.4, 'learning_rate': 0.018, 'max_depth': 7, 'random_state': 24, 'min_child_weight': 285}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:08,633]\u001b[0m Trial 45 finished with value: 0.9886212470844393 and parameters: {'lambda': 0.07198972459910243, 'alpha': 0.00169498686818063, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 20, 'random_state': 48, 'min_child_weight': 204}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:13,474]\u001b[0m Trial 46 finished with value: 0.9883701882380076 and parameters: {'lambda': 0.6533656519385255, 'alpha': 0.030265794760906947, 'colsample_bytree': 0.3, 'subsample': 0.4, 'learning_rate': 0.008, 'max_depth': 17, 'random_state': 2020, 'min_child_weight': 257}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:16,056]\u001b[0m Trial 47 finished with value: 0.9886736101041923 and parameters: {'lambda': 0.40715154352234095, 'alpha': 6.84600117610974, 'colsample_bytree': 0.7, 'subsample': 0.5, 'learning_rate': 0.009, 'max_depth': 9, 'random_state': 48, 'min_child_weight': 247}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:18,911]\u001b[0m Trial 48 finished with value: 0.9880933856305457 and parameters: {'lambda': 0.20986197624335512, 'alpha': 0.004488021985901496, 'colsample_bytree': 0.5, 'subsample': 0.6, 'learning_rate': 0.012, 'max_depth': 11, 'random_state': 24, 'min_child_weight': 294}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:20,902]\u001b[0m Trial 49 finished with value: 0.9894778696084768 and parameters: {'lambda': 3.0102168958929494, 'alpha': 0.3008602559441237, 'colsample_bytree': 0.8, 'subsample': 1.0, 'learning_rate': 0.009, 'max_depth': 5, 'random_state': 24, 'min_child_weight': 188}. Best is trial 31 with value: 0.9878566439690453.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:22,713]\u001b[0m Trial 50 finished with value: 0.9877187745110819 and parameters: {'lambda': 1.1393568931596074, 'alpha': 0.0686238951129839, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 277}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:25,456]\u001b[0m Trial 51 finished with value: 0.989196030373001 and parameters: {'lambda': 1.0933274235415442, 'alpha': 0.06605535434346808, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 90}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:27,303]\u001b[0m Trial 52 finished with value: 0.9878557313626946 and parameters: {'lambda': 0.555773783016304, 'alpha': 0.14406609154436723, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 267}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:28,657]\u001b[0m Trial 53 finished with value: 0.9891660991605384 and parameters: {'lambda': 5.352319100438553, 'alpha': 0.15524493407182635, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.02, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 228}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:30,365]\u001b[0m Trial 54 finished with value: 0.9881590594304968 and parameters: {'lambda': 0.5865119008543597, 'alpha': 0.050258262752753274, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.016, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 300}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:33,828]\u001b[0m Trial 55 finished with value: 0.9886101231054508 and parameters: {'lambda': 1.6108139225351497, 'alpha': 0.08455648035065413, 'colsample_bytree': 1.0, 'subsample': 0.8, 'learning_rate': 0.009, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 260}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:35,974]\u001b[0m Trial 56 finished with value: 0.9879764834220782 and parameters: {'lambda': 0.10530625999126107, 'alpha': 0.1585396297590307, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 280}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:37,756]\u001b[0m Trial 57 finished with value: 0.9879609766905654 and parameters: {'lambda': 0.13042352920208147, 'alpha': 0.14618416452928393, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 287}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:39,837]\u001b[0m Trial 58 finished with value: 0.9883175243108037 and parameters: {'lambda': 0.018452129118379977, 'alpha': 0.15555719385760292, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 285}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:41,857]\u001b[0m Trial 59 finished with value: 0.9882098729707544 and parameters: {'lambda': 0.10335234504346577, 'alpha': 0.7611948290617423, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 13, 'random_state': 48, 'min_child_weight': 278}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:44,347]\u001b[0m Trial 60 finished with value: 0.9884468233042099 and parameters: {'lambda': 0.054494573298350196, 'alpha': 1.3217092678807945, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 251}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:46,591]\u001b[0m Trial 61 finished with value: 0.9884359494037751 and parameters: {'lambda': 0.11708793070077574, 'alpha': 0.3113481525683086, 'colsample_bytree': 0.7, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 288}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:48,642]\u001b[0m Trial 62 finished with value: 0.9879952895659248 and parameters: {'lambda': 0.3592586339186782, 'alpha': 0.2075078952380091, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 269}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:50,644]\u001b[0m Trial 63 finished with value: 0.9882201933565067 and parameters: {'lambda': 0.26708606796092044, 'alpha': 0.12660995680464762, 'colsample_bytree': 1.0, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 300}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:52,693]\u001b[0m Trial 64 finished with value: 0.9878395266651582 and parameters: {'lambda': 0.15334025641587495, 'alpha': 0.044960510727182657, 'colsample_bytree': 0.9, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 276}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:54,618]\u001b[0m Trial 65 finished with value: 0.9884134881025112 and parameters: {'lambda': 0.16375978187094709, 'alpha': 0.044990527008026374, 'colsample_bytree': 0.9, 'subsample': 0.5, 'learning_rate': 0.014, 'max_depth': 11, 'random_state': 48, 'min_child_weight': 276}. Best is trial 50 with value: 0.9877187745110819.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:57,107]\u001b[0m Trial 66 finished with value: 0.9877108278537992 and parameters: {'lambda': 0.08313861956034789, 'alpha': 0.06737932206729562, 'colsample_bytree': 0.9, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 265}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:51:59,372]\u001b[0m Trial 67 finished with value: 0.9880230238108515 and parameters: {'lambda': 0.059795832634042444, 'alpha': 0.07292769192586583, 'colsample_bytree': 0.9, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 266}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:52:02,709]\u001b[0m Trial 68 finished with value: 0.9879637884517003 and parameters: {'lambda': 0.03600631330117258, 'alpha': 0.026967396789990997, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 291}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:52:05,562]\u001b[0m Trial 69 finished with value: 0.9881058655935695 and parameters: {'lambda': 0.016310911801090296, 'alpha': 0.023848624815174743, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 290}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:52:09,011]\u001b[0m Trial 70 finished with value: 0.9888241805424097 and parameters: {'lambda': 0.04010691752884385, 'alpha': 0.03696784966672737, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 255}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:52:11,989]\u001b[0m Trial 71 finished with value: 0.9887206679604648 and parameters: {'lambda': 0.0883749541910483, 'alpha': 0.05717595839681089, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 272}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:52:15,609]\u001b[0m Trial 72 finished with value: 0.9883586563510788 and parameters: {'lambda': 0.030145235845904925, 'alpha': 0.11006838785886, 'colsample_bytree': 0.9, 'subsample': 0.4, 'learning_rate': 0.008, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 290}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:52:18,842]\u001b[0m Trial 73 finished with value: 0.9886908301765712 and parameters: {'lambda': 0.15787466147777623, 'alpha': 0.08690993796576882, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.016, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 240}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n", "\u001b[32m[I 2022-01-28 14:52:21,580]\u001b[0m Trial 74 finished with value: 0.9895309989156617 and parameters: {'lambda': 0.005305164908662099, 'alpha': 0.03467281686798228, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.018, 'max_depth': 17, 'random_state': 48, 'min_child_weight': 280}. Best is trial 66 with value: 0.9877108278537992.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of finished trials: 75\n", "Best trial: {'lambda': 0.08313861956034789, 'alpha': 0.06737932206729562, 'colsample_bytree': 0.9, 'subsample': 0.4, 'learning_rate': 0.014, 'max_depth': 15, 'random_state': 48, 'min_child_weight': 265}\n" ] } ], "source": [ "study = optuna.create_study(direction='minimize')\n", "study.optimize(objective, n_trials=75)\n", "print('Number of finished trials:', len(study.trials))\n", "print('Best trial:', study.best_trial.params)" ] }, { "cell_type": "code", "execution_count": 37, "id": "00040241", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:52:21.830369Z", "iopub.status.busy": "2022-01-28T14:52:21.829578Z", "iopub.status.idle": "2022-01-28T14:52:22.000540Z", "shell.execute_reply": "2022-01-28T14:52:22.001104Z", "shell.execute_reply.started": "2022-01-28T14:19:05.946553Z" }, "papermill": { "duration": 0.282079, "end_time": "2022-01-28T14:52:22.001305", "exception": false, "start_time": "2022-01-28T14:52:21.719226", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ " <script type=\"text/javascript\">\n", " window.PlotlyConfig = {MathJaxConfig: 'local'};\n", " if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n", " if (typeof require !== 'undefined') {\n", " require.undef(\"plotly\");\n", " requirejs.config({\n", " paths: {\n", " 'plotly': ['https://cdn.plot.ly/plotly-2.6.3.min']\n", " }\n", " });\n", " require(['plotly'], function(Plotly) {\n", " window._Plotly = Plotly;\n", " });\n", " }\n", " </script>\n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div> <div id=\"8fcc7af1-d0bd-4281-9d7c-3b99dac6eb3b\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div> <script type=\"text/javascript\"> require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {}; if 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You can choose which hyperparameters you would like to explore.\n", "optuna.visualization.plot_contour(study, params=['alpha',\n", " #'max_depth',\n", " 'lambda',\n", " 'subsample',\n", " 'learning_rate',\n", " 'subsample'])" ] }, { "cell_type": "code", "execution_count": 41, "id": "e2f41d40", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:52:24.302605Z", "iopub.status.busy": "2022-01-28T14:52:24.301552Z", "iopub.status.idle": "2022-01-28T14:52:29.868163Z", "shell.execute_reply": "2022-01-28T14:52:29.867619Z", "shell.execute_reply.started": "2022-01-28T14:19:07.059707Z" }, "papermill": { "duration": 5.688536, "end_time": "2022-01-28T14:52:29.868379", "exception": false, "start_time": "2022-01-28T14:52:24.179843", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div> <div id=\"a924d6df-728e-49fb-b96b-6579d46e5476\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div> <script type=\"text/javascript\"> 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"optuna.visualization.plot_edf(study)" ] }, { "cell_type": "code", "execution_count": 43, "id": "4311152d", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:52:30.355974Z", "iopub.status.busy": "2022-01-28T14:52:30.354799Z", "iopub.status.idle": "2022-01-28T14:52:30.358049Z", "shell.execute_reply": "2022-01-28T14:52:30.357518Z", "shell.execute_reply.started": "2022-01-28T14:20:52.575644Z" }, "papermill": { "duration": 0.121783, "end_time": "2022-01-28T14:52:30.358211", "exception": false, "start_time": "2022-01-28T14:52:30.236428", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "Best_trial= {'lambda': 0.1407650276096794, 'alpha': 0.005402024537936522, 'colsample_bytree': 0.6, 'subsample': 0.4, 'learning_rate': 0.01, 'max_depth': 20, 'random_state': 24, 'min_child_weight': 186, 'tree_method':'gpu_hist'}" ] }, { "cell_type": "code", "execution_count": 44, "id": "cce12392", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:52:30.588631Z", "iopub.status.busy": "2022-01-28T14:52:30.587805Z", "iopub.status.idle": "2022-01-28T14:52:30.592557Z", "shell.execute_reply": "2022-01-28T14:52:30.591951Z", "shell.execute_reply.started": "2022-01-28T14:30:52.092056Z" }, "papermill": { "duration": 0.122605, "end_time": "2022-01-28T14:52:30.592724", "exception": false, "start_time": "2022-01-28T14:52:30.470119", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "columns=train_scaled_df.columns.to_list()\n", "columns.remove('song_popularity')" ] }, { "cell_type": "code", "execution_count": 45, "id": "b7696730", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:52:30.829878Z", "iopub.status.busy": "2022-01-28T14:52:30.828949Z", "iopub.status.idle": "2022-01-28T14:52:35.940624Z", "shell.execute_reply": "2022-01-28T14:52:35.939746Z", "shell.execute_reply.started": "2022-01-28T14:31:10.209302Z" }, "papermill": { "duration": 5.236733, "end_time": "2022-01-28T14:52:35.940883", "exception": false, "start_time": "2022-01-28T14:52:30.704150", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 1.010759622263783\n", "2 1.0084947404538425\n", "3 1.009858883066642\n", "4 1.010319201621299\n", "5 1.012485077712708\n" ] } ], "source": [ "preds = np.zeros(test_scaled_df.shape[0])\n", "kf = KFold(n_splits=5,random_state=48,shuffle=True)\n", "rmse=[] # list contains rmse for each fold\n", "n=0\n", "for trn_idx, test_idx in kf.split(train_scaled_df[columns],train_scaled_df['song_popularity']):\n", " X_tr,X_val=train_scaled_df[columns].iloc[trn_idx],train_scaled_df[columns].iloc[test_idx]\n", " y_tr,y_val=train_scaled_df['song_popularity'].iloc[trn_idx],train_scaled_df['song_popularity'].iloc[test_idx]\n", " model = xgb.XGBRegressor(**Best_trial)\n", " model.fit(X_tr,y_tr,eval_set=[(X_val,y_val)],early_stopping_rounds=100,verbose=False)\n", " preds+=model.predict(test_scaled_df[columns])/kf.n_splits\n", " rmse.append(mean_squared_error(y_val, model.predict(X_val), squared=False))\n", " print(n+1,rmse[n])\n", " n+=1" ] }, { "cell_type": "code", "execution_count": 46, "id": "15a3e648", "metadata": { "execution": { "iopub.execute_input": "2022-01-28T14:52:36.174945Z", "iopub.status.busy": "2022-01-28T14:52:36.173906Z", "iopub.status.idle": "2022-01-28T14:52:36.177729Z", "shell.execute_reply": "2022-01-28T14:52:36.178252Z", "shell.execute_reply.started": "2022-01-28T14:31:27.591242Z" }, "papermill": { "duration": 0.125433, "end_time": "2022-01-28T14:52:36.178435", "exception": false, "start_time": "2022-01-28T14:52:36.053002", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "1.010383505023655" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(rmse)" ] }, { "cell_type": "code", "execution_count": null, "id": "9e12cff1", "metadata": { "papermill": { "duration": 0.112877, "end_time": "2022-01-28T14:52:36.404875", "exception": false, "start_time": "2022-01-28T14:52:36.291998", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 317.058061, "end_time": "2022-01-28T14:52:39.119498", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-01-28T14:47:22.061437", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0086/401/86401860.ipynb
s3://data-agents/kaggle-outputs/sharded/018_00086.jsonl.gz