Hugging Face's logo

Datasets:
bigcomputer
/
kaggle-notebooks-outputs-filtered-23

Modalities:
Text
Formats:
parquet
Size:
1K - 10K
Libraries:
Datasets
Dask
Dataset card Data Studio Files Community
Dataset Viewer (First 5GB)
Auto-converted to Parquet
Search is not available for this dataset
text
stringlengths
1.7k
93M
id
stringlengths
22
24
file_path
stringclasses
54 values
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5fa56f08", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:14:30.198201Z", "iopub.status.busy": "2021-08-31T02:14:30.197617Z", "iopub.status.idle": "2021-08-31T02:14:32.557104Z", "shell.execute_reply": "2021-08-31T02:14:32.558256Z", "shell.execute_reply.started": "2021-08-31T01:59:17.853890Z" }, "papermill": { "duration": 2.380896, "end_time": "2021-08-31T02:14:32.558639", "exception": false, "start_time": "2021-08-31T02:14:30.177743", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from fastai.vision.all import *\n", "import sys\n", "import numpy as np\n", "np.set_printoptions(threshold=np.inf)" ] }, { "cell_type": "code", "execution_count": 2, "id": "3391a63a", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:14:32.583734Z", "iopub.status.busy": "2021-08-31T02:14:32.582841Z", "iopub.status.idle": "2021-08-31T02:14:32.596559Z", "shell.execute_reply": "2021-08-31T02:14:32.596983Z", "shell.execute_reply.started": "2021-08-31T01:59:17.861632Z" }, "papermill": { "duration": 0.029989, "end_time": "2021-08-31T02:14:32.597131", "exception": false, "start_time": "2021-08-31T02:14:32.567142", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import random\n", "df = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv', header=0, names=['id','true_value'], dtype=object)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "0086c3a7", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:14:32.620379Z", "iopub.status.busy": "2021-08-31T02:14:32.619629Z", "iopub.status.idle": "2021-08-31T02:14:32.632553Z", "shell.execute_reply": "2021-08-31T02:14:32.632992Z", "shell.execute_reply.started": "2021-08-31T01:59:17.883421Z" }, "papermill": { "duration": 0.028059, "end_time": "2021-08-31T02:14:32.633116", "exception": false, "start_time": "2021-08-31T02:14:32.605057", "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>true_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>00000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00002</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>00003</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>00005</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>00006</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id true_value\n", "0 00000 1\n", "1 00002 1\n", "2 00003 0\n", "3 00005 1\n", "4 00006 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "e7fc3279", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:14:32.655203Z", "iopub.status.busy": "2021-08-31T02:14:32.654653Z", "iopub.status.idle": "2021-08-31T02:14:32.668270Z", "shell.execute_reply": "2021-08-31T02:14:32.667861Z", "shell.execute_reply.started": "2021-08-31T01:59:17.899653Z" }, "papermill": { "duration": 0.027012, "end_time": "2021-08-31T02:14:32.668383", "exception": false, "start_time": "2021-08-31T02:14:32.641371", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df_test = pd.DataFrame(columns=['id', 'value'])\n", "df_test.id = os.listdir(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/test/\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "082953b1", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:14:32.688876Z", "iopub.status.busy": "2021-08-31T02:14:32.688197Z", "iopub.status.idle": "2021-08-31T02:14:32.690916Z", "shell.execute_reply": "2021-08-31T02:14:32.690416Z", "shell.execute_reply.started": "2021-08-31T01:59:17.910238Z" }, "papermill": { "duration": 0.014577, "end_time": "2021-08-31T02:14:32.691016", "exception": false, "start_time": "2021-08-31T02:14:32.676439", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#https://stackoverflow.com/a/4836734/8245487\n", "def natural_sort(l): \n", " convert = lambda text: int(text) if text.isdigit() else text.lower()\n", " alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]\n", " return sorted(l, key=alphanum_key)" ] }, { "cell_type": "code", "execution_count": 6, "id": "38b66acc", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:14:32.720592Z", "iopub.status.busy": "2021-08-31T02:14:32.719971Z", "iopub.status.idle": "2021-08-31T02:36:35.550245Z", "shell.execute_reply": "2021-08-31T02:36:35.549518Z" }, "papermill": { "duration": 1322.851483, "end_time": "2021-08-31T02:36:35.550467", "exception": false, "start_time": "2021-08-31T02:14:32.698984", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:78: RuntimeWarning: invalid value encountered in true_divide\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Its possible!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n", "Its possible!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Nope!\n", "Its possible!\n" ] } ], "source": [ "import os\n", "import pydicom\n", "import pandas as pd\n", "from pydicom.pixel_data_handlers.util import apply_voi_lut\n", "from tqdm import tqdm\n", "import binascii\n", "from PIL import Image\n", "from multiprocessing import Pool\n", "\n", "\n", "INPUT = '../input/rsna-miccai-brain-tumor-radiogenomic-classification'\n", "\n", "def get_dicom_files(dataset, df):\n", " for cur_id in df.id:\n", " pred = get_dicom_files_helper(dataset, cur_id)\n", " df.loc[df.id==cur_id,'value'] = pred\n", "\n", "def get_dark_cells(data):\n", " return np.sum((data > 60) & (data <= 230), axis=(0,1))\n", "\n", "def get_very_bright_cells(data):\n", " return np.sum((data > 230), axis=(0,1))\n", "\n", "def get_live_cells(data):\n", " return np.sum((data > 60), axis=(0,1))\n", "\n", "\n", "def get_dicom_files_helper(dataset, cur_id):\n", " root = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/{}'.format(dataset)\n", " for scan_type in ['FLAIR', 'T1w', 'T1wCE', 'T2w']:\n", " cur_dir = os.path.join(root, cur_id, scan_type)\n", " dicoms = os.listdir(cur_dir)\n", " if len(dicoms) == 0:\n", " print('Skipping {}'.format(cur_dir))\n", " continue\n", " \n", " dicoms = natural_sort(dicoms)\n", " clip = len(dicoms) // 3\n", " upper_clip = len(dicoms)-clip\n", " dicoms = dicoms[clip:upper_clip] #take middle 1/3\n", " num_dicoms = len(dicoms)\n", "\n", " possible_meth = 0 #per image type\n", "\n", " for dicom in dicoms:\n", " filepath = os.path.join(cur_dir, dicom)\n", " data = process_dicom(filepath)\n", " #print(data)\n", " live_cells = get_live_cells(data)\n", " if live_cells < 10:\n", " continue\n", " dark_cells = get_dark_cells(data)\n", " bright_cells = live_cells - dark_cells\n", " very_bright_cells = get_very_bright_cells(data)\n", " not_very_bright_cells = live_cells - very_bright_cells \n", " mostly_dark = (dark_cells/live_cells) > 0.5\n", " if mostly_dark:\n", " if (very_bright_cells/live_cells) > 0.20:\n", " possible_meth += 1\n", " #mostly bright picture\n", " elif (not_very_bright_cells/live_cells) > 0.20: \n", " possible_meth += 1\n", " \n", " if possible_meth >= (num_dicoms * 0.05):\n", " print('Its possible!')\n", " return 1\n", " \n", " print('Nope!')\n", " return 0\n", " \n", "\n", "def process_dicom(path):\n", " dicom = pydicom.read_file(path)\n", " data = apply_voi_lut(dicom.pixel_array, dicom)\n", " if dicom.PhotometricInterpretation == \"MONOCHROME1\":\n", " data = np.amax(data) - data\n", " data = data - np.min(data)\n", " data = data / np.max(data)\n", " data = (data * 255).astype(np.uint8)\n", " \n", " return data\n", "\n", "\n", "get_dicom_files('train', df)\n", "get_dicom_files('test', df_test)\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "a393f6e7", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:36:35.903148Z", "iopub.status.busy": "2021-08-31T02:36:35.902240Z", "iopub.status.idle": "2021-08-31T02:36:35.905960Z", "shell.execute_reply": "2021-08-31T02:36:35.906328Z" }, "papermill": { "duration": 0.185031, "end_time": "2021-08-31T02:36:35.906458", "exception": false, "start_time": "2021-08-31T02:36:35.721427", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0.0 541\n", "1.0 44\n", "Name: value, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.value.value_counts()" ] }, { "cell_type": "code", "execution_count": 8, "id": "adaf3415", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:36:36.261702Z", "iopub.status.busy": "2021-08-31T02:36:36.261183Z", "iopub.status.idle": "2021-08-31T02:36:36.265617Z", "shell.execute_reply": "2021-08-31T02:36:36.265995Z" }, "papermill": { "duration": 0.187251, "end_time": "2021-08-31T02:36:36.266126", "exception": false, "start_time": "2021-08-31T02:36:36.078875", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "1 307\n", "0 278\n", "Name: true_value, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.true_value.value_counts()" ] }, { "cell_type": "code", "execution_count": 9, "id": "e6c92858", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:36:36.612143Z", "iopub.status.busy": "2021-08-31T02:36:36.611580Z", "iopub.status.idle": "2021-08-31T02:36:36.630475Z", "shell.execute_reply": "2021-08-31T02:36:36.630931Z" }, "papermill": { "duration": 0.194232, "end_time": "2021-08-31T02:36:36.631125", "exception": false, "start_time": "2021-08-31T02:36:36.436893", "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>true_value</th>\n", " <th>value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>00000</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00002</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>00003</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>00005</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>00006</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>580</th>\n", " <td>01005</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>581</th>\n", " <td>01007</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>582</th>\n", " <td>01008</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>583</th>\n", " <td>01009</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>584</th>\n", " <td>01010</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>541 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " id true_value value\n", "0 00000 1 0.0\n", "1 00002 1 0.0\n", "2 00003 0 0.0\n", "3 00005 1 0.0\n", "4 00006 1 0.0\n", ".. ... ... ...\n", "580 01005 1 0.0\n", "581 01007 1 0.0\n", "582 01008 1 0.0\n", "583 01009 0 0.0\n", "584 01010 0 0.0\n", "\n", "[541 rows x 3 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.value==0]" ] }, { "cell_type": "code", "execution_count": 10, "id": "9426cbd0", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:36:37.021057Z", "iopub.status.busy": "2021-08-31T02:36:37.020425Z", "iopub.status.idle": "2021-08-31T02:36:37.025215Z", "shell.execute_reply": "2021-08-31T02:36:37.024810Z" }, "papermill": { "duration": 0.180216, "end_time": "2021-08-31T02:36:37.025333", "exception": false, "start_time": "2021-08-31T02:36:36.845117", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0 82\n", "1 5\n", "Name: value, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test.value.value_counts()" ] }, { "cell_type": "code", "execution_count": 11, "id": "b4f8fe35", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:36:37.375911Z", "iopub.status.busy": "2021-08-31T02:36:37.375355Z", "iopub.status.idle": "2021-08-31T02:36:37.383272Z", "shell.execute_reply": "2021-08-31T02:36:37.382844Z" }, "papermill": { "duration": 0.185768, "end_time": "2021-08-31T02:36:37.383399", "exception": false, "start_time": "2021-08-31T02:36:37.197631", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df_test.rename(columns={'id':'BraTS21ID','value':'MGMT_value'}).to_csv('submission.csv', index=False)" ] } ], "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": "3.7.10" }, "papermill": { "default_parameters": {}, "duration": 1334.954091, "end_time": "2021-08-31T02:36:38.667118", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2021-08-31T02:14:23.713027", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0073/574/73574080.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "62860b21", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2021-08-31T02:21:25.804261Z", "iopub.status.busy": "2021-08-31T02:21:25.802219Z", "iopub.status.idle": "2021-08-31T02:21:25.892755Z", "shell.execute_reply": "2021-08-31T02:21:25.891546Z", "shell.execute_reply.started": "2021-08-31T01:56:11.005778Z" }, "papermill": { "duration": 0.115678, "end_time": "2021-08-31T02:21:25.892968", "exception": false, "start_time": "2021-08-31T02:21:25.777290", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/input/tabular-playground-series-aug-2021/sample_submission.csv\n", "/kaggle/input/tabular-playground-series-aug-2021/train.csv\n", "/kaggle/input/tabular-playground-series-aug-2021/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", "\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": "f1883381", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:25.974398Z", "iopub.status.busy": "2021-08-31T02:21:25.973388Z", "iopub.status.idle": "2021-08-31T02:21:27.015163Z", "shell.execute_reply": "2021-08-31T02:21:27.014421Z", "shell.execute_reply.started": "2021-08-31T01:56:11.055483Z" }, "papermill": { "duration": 1.083032, "end_time": "2021-08-31T02:21:27.015387", "exception": false, "start_time": "2021-08-31T02:21:25.932355", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "# from catboost import Pool, CatBoostRegressor\n", "from sklearn.preprocessing import OrdinalEncoder, StandardScaler, RobustScaler,PowerTransformer,QuantileTransformer,OneHotEncoder\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error\n", "from xgboost import XGBRegressor" ] }, { "cell_type": "code", "execution_count": 3, "id": "ce4bfa35", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:27.058321Z", "iopub.status.busy": "2021-08-31T02:21:27.057512Z", "iopub.status.idle": "2021-08-31T02:21:37.342013Z", "shell.execute_reply": "2021-08-31T02:21:37.343692Z", "shell.execute_reply.started": "2021-08-31T01:56:15.364215Z" }, "papermill": { "duration": 10.30979, "end_time": "2021-08-31T02:21:37.344042", "exception": false, "start_time": "2021-08-31T02:21:27.034252", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# import data\n", "train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv',index_col = 0)\n", "test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv',index_col = 0)" ] }, { "cell_type": "code", "execution_count": 4, "id": "d9d14b0e", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:37.428690Z", "iopub.status.busy": "2021-08-31T02:21:37.427683Z", "iopub.status.idle": "2021-08-31T02:21:37.482592Z", "shell.execute_reply": "2021-08-31T02:21:37.483906Z", "shell.execute_reply.started": "2021-08-31T01:56:25.099729Z" }, "papermill": { "duration": 0.105075, "end_time": "2021-08-31T02:21:37.484138", "exception": false, "start_time": "2021-08-31T02:21:37.379063", "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>f0</th>\n", " <th>f1</th>\n", " <th>f2</th>\n", " <th>f3</th>\n", " <th>f4</th>\n", " <th>f5</th>\n", " <th>f6</th>\n", " <th>f7</th>\n", " <th>f8</th>\n", " <th>f9</th>\n", " <th>...</th>\n", " <th>f91</th>\n", " <th>f92</th>\n", " <th>f93</th>\n", " <th>f94</th>\n", " <th>f95</th>\n", " <th>f96</th>\n", " <th>f97</th>\n", " <th>f98</th>\n", " <th>f99</th>\n", " <th>loss</th>\n", " </tr>\n", " <tr>\n", " <th>id</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>0</th>\n", " <td>-0.002350</td>\n", " <td>59</td>\n", " <td>0.766739</td>\n", " <td>-1.350460</td>\n", " <td>42.2727</td>\n", " <td>16.68570</td>\n", " <td>30.3599</td>\n", " <td>1.267300</td>\n", " <td>0.392007</td>\n", " <td>1.09101</td>\n", " <td>...</td>\n", " <td>-42.43990</td>\n", " <td>26.854000</td>\n", " <td>1.45751</td>\n", " <td>0.696161</td>\n", " <td>0.941764</td>\n", " <td>1.828470</td>\n", " <td>0.924090</td>\n", " <td>2.29658</td>\n", " <td>10.48980</td>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.784462</td>\n", " <td>145</td>\n", " <td>-0.463845</td>\n", " <td>-0.530421</td>\n", " <td>27324.9000</td>\n", " <td>3.47545</td>\n", " <td>160.4980</td>\n", " <td>0.828007</td>\n", " <td>3.735860</td>\n", " <td>1.28138</td>\n", " <td>...</td>\n", " <td>-184.13200</td>\n", " <td>7.901370</td>\n", " <td>1.70644</td>\n", " <td>-0.494699</td>\n", " <td>-2.058300</td>\n", " <td>0.819184</td>\n", " <td>0.439152</td>\n", " <td>2.36470</td>\n", " <td>1.14383</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.317816</td>\n", " <td>19</td>\n", " <td>-0.432571</td>\n", " <td>-0.382644</td>\n", " <td>1383.2600</td>\n", " <td>19.71290</td>\n", " <td>31.1026</td>\n", " <td>-0.515354</td>\n", " <td>34.430800</td>\n", " <td>1.24210</td>\n", " <td>...</td>\n", " <td>7.43721</td>\n", " <td>37.218100</td>\n", " <td>3.25339</td>\n", " <td>0.337934</td>\n", " <td>0.615037</td>\n", " <td>2.216760</td>\n", " <td>0.745268</td>\n", " <td>1.69679</td>\n", " <td>12.30550</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.210753</td>\n", " <td>17</td>\n", " <td>-0.616454</td>\n", " <td>0.946362</td>\n", " <td>-119.2530</td>\n", " <td>4.08235</td>\n", " <td>185.2570</td>\n", " <td>1.383310</td>\n", " <td>-47.521400</td>\n", " <td>1.09130</td>\n", " <td>...</td>\n", " <td>9.66778</td>\n", " <td>0.626942</td>\n", " <td>1.49425</td>\n", " <td>0.517513</td>\n", " <td>-10.222100</td>\n", " <td>2.627310</td>\n", " <td>0.617270</td>\n", " <td>1.45645</td>\n", " <td>10.02880</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.439671</td>\n", " <td>20</td>\n", " <td>0.968126</td>\n", " <td>-0.092546</td>\n", " <td>74.3020</td>\n", " <td>12.30650</td>\n", " <td>72.1860</td>\n", " <td>-0.233964</td>\n", " <td>24.399100</td>\n", " <td>1.10151</td>\n", " <td>...</td>\n", " <td>290.65700</td>\n", " <td>15.604300</td>\n", " <td>1.73557</td>\n", " <td>-0.476668</td>\n", " <td>1.390190</td>\n", " <td>2.195740</td>\n", " <td>0.826987</td>\n", " <td>1.78485</td>\n", " <td>7.07197</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 101 columns</p>\n", "</div>" ], "text/plain": [ " f0 f1 f2 f3 f4 f5 f6 \\\n", "id \n", "0 -0.002350 59 0.766739 -1.350460 42.2727 16.68570 30.3599 \n", "1 0.784462 145 -0.463845 -0.530421 27324.9000 3.47545 160.4980 \n", "2 0.317816 19 -0.432571 -0.382644 1383.2600 19.71290 31.1026 \n", "3 0.210753 17 -0.616454 0.946362 -119.2530 4.08235 185.2570 \n", "4 0.439671 20 0.968126 -0.092546 74.3020 12.30650 72.1860 \n", "\n", " f7 f8 f9 ... f91 f92 f93 \\\n", "id ... \n", "0 1.267300 0.392007 1.09101 ... -42.43990 26.854000 1.45751 \n", "1 0.828007 3.735860 1.28138 ... -184.13200 7.901370 1.70644 \n", "2 -0.515354 34.430800 1.24210 ... 7.43721 37.218100 3.25339 \n", "3 1.383310 -47.521400 1.09130 ... 9.66778 0.626942 1.49425 \n", "4 -0.233964 24.399100 1.10151 ... 290.65700 15.604300 1.73557 \n", "\n", " f94 f95 f96 f97 f98 f99 loss \n", "id \n", "0 0.696161 0.941764 1.828470 0.924090 2.29658 10.48980 15 \n", "1 -0.494699 -2.058300 0.819184 0.439152 2.36470 1.14383 3 \n", "2 0.337934 0.615037 2.216760 0.745268 1.69679 12.30550 6 \n", "3 0.517513 -10.222100 2.627310 0.617270 1.45645 10.02880 2 \n", "4 -0.476668 1.390190 2.195740 0.826987 1.78485 7.07197 1 \n", "\n", "[5 rows x 101 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "bff405b4", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:37.563866Z", "iopub.status.busy": "2021-08-31T02:21:37.562655Z", "iopub.status.idle": "2021-08-31T02:21:37.638269Z", "shell.execute_reply": "2021-08-31T02:21:37.637684Z", "shell.execute_reply.started": "2021-08-31T01:56:29.497411Z" }, "papermill": { "duration": 0.121245, "end_time": "2021-08-31T02:21:37.638419", "exception": false, "start_time": "2021-08-31T02:21:37.517174", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#Split the target and featues\n", "y = train['loss']\n", "f = train.drop(['loss'],axis = 1)" ] }, { "cell_type": "code", "execution_count": 6, "id": "8adf7b7e", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:37.683308Z", "iopub.status.busy": "2021-08-31T02:21:37.682445Z", "iopub.status.idle": "2021-08-31T02:21:37.688669Z", "shell.execute_reply": "2021-08-31T02:21:37.688160Z", "shell.execute_reply.started": "2021-08-31T01:56:31.268622Z" }, "papermill": { "duration": 0.0308, "end_time": "2021-08-31T02:21:37.688809", "exception": false, "start_time": "2021-08-31T02:21:37.658009", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "id\n", "0 15\n", "1 3\n", "2 6\n", "3 2\n", "4 1\n", "Name: loss, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.head()" ] }, { "cell_type": "code", "execution_count": 7, "id": "4c1b0df3", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:37.735877Z", "iopub.status.busy": "2021-08-31T02:21:37.734789Z", "iopub.status.idle": "2021-08-31T02:21:37.740925Z", "shell.execute_reply": "2021-08-31T02:21:37.740390Z", "shell.execute_reply.started": "2021-08-31T01:56:32.852938Z" }, "papermill": { "duration": 0.03251, "end_time": "2021-08-31T02:21:37.741089", "exception": false, "start_time": "2021-08-31T02:21:37.708579", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "f0 float64\n", "f1 int64\n", "f2 float64\n", "f3 float64\n", "f4 float64\n", " ... \n", "f95 float64\n", "f96 float64\n", "f97 float64\n", "f98 float64\n", "f99 float64\n", "Length: 100, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.dtypes" ] }, { "cell_type": "code", "execution_count": 8, "id": "ad7a8df4", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:37.794148Z", "iopub.status.busy": "2021-08-31T02:21:37.793203Z", "iopub.status.idle": "2021-08-31T02:21:37.797513Z", "shell.execute_reply": "2021-08-31T02:21:37.798155Z", "shell.execute_reply.started": "2021-08-31T01:56:39.169217Z" }, "papermill": { "duration": 0.036938, "end_time": "2021-08-31T02:21:37.798315", "exception": false, "start_time": "2021-08-31T02:21:37.761377", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of catagorical features 0\n", "number of catagorical features 100\n" ] } ], "source": [ "#Check if we have any catagorical columns\n", "cat_col = [c for c in f.columns if f[c].dtype =='object']\n", "print(f\"number of catagorical features {len(cat_col)}\")\n", "#check number of numeric features\n", "num_col = [c for c in f.columns if f[c].dtype in ['int64','float64']]\n", "print(f\"number of catagorical features {len(num_col)}\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "56575de1", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:37.918451Z", "iopub.status.busy": "2021-08-31T02:21:37.916986Z", "iopub.status.idle": "2021-08-31T02:21:38.087499Z", "shell.execute_reply": "2021-08-31T02:21:38.086941Z", "shell.execute_reply.started": "2021-08-31T01:56:59.561003Z" }, "papermill": { "duration": 0.26906, "end_time": "2021-08-31T02:21:38.087664", "exception": false, "start_time": "2021-08-31T02:21:37.818604", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#Make a copy of the original data\n", "X = f[num_col].copy()\n", "X_test = test[num_col].copy()" ] }, { "cell_type": "code", "execution_count": 10, "id": "660c5577", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:38.133713Z", "iopub.status.busy": "2021-08-31T02:21:38.132980Z", "iopub.status.idle": "2021-08-31T02:21:38.140854Z", "shell.execute_reply": "2021-08-31T02:21:38.138372Z", "shell.execute_reply.started": "2021-08-31T01:57:02.905403Z" }, "papermill": { "duration": 0.032895, "end_time": "2021-08-31T02:21:38.141561", "exception": false, "start_time": "2021-08-31T02:21:38.108666", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#Data preprocessing\n", "preprocessor = ColumnTransformer([('num',StandardScaler(),num_col)])" ] }, { "cell_type": "code", "execution_count": null, "id": "f5a7de73", "metadata": { "papermill": { "duration": 0.047306, "end_time": "2021-08-31T02:21:38.235552", "exception": false, "start_time": "2021-08-31T02:21:38.188246", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "id": "9d04b23e", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:38.327237Z", "iopub.status.busy": "2021-08-31T02:21:38.326225Z", "iopub.status.idle": "2021-08-31T02:21:38.650033Z", "shell.execute_reply": "2021-08-31T02:21:38.651102Z", "shell.execute_reply.started": "2021-08-31T01:57:06.625266Z" }, "papermill": { "duration": 0.376024, "end_time": "2021-08-31T02:21:38.651429", "exception": false, "start_time": "2021-08-31T02:21:38.275405", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#Train test split\n", "X_t,X_v,y_t,y_v = train_test_split(X,y, train_size = 0.8,test_size = 0.2 ,random_state =10)" ] }, { "cell_type": "code", "execution_count": 12, "id": "0019c4a4", "metadata": { "collapsed": true, "execution": { "iopub.execute_input": "2021-08-31T02:21:38.737055Z", "iopub.status.busy": "2021-08-31T02:21:38.734747Z", "iopub.status.idle": "2021-08-31T02:21:38.738125Z", "shell.execute_reply": "2021-08-31T02:21:38.739026Z", "shell.execute_reply.started": "2021-08-31T02:16:56.395729Z" }, "jupyter": { "outputs_hidden": true }, "papermill": { "duration": 0.04851, "end_time": "2021-08-31T02:21:38.739297", "exception": false, "start_time": "2021-08-31T02:21:38.690787", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# #Find the best model by trying value in the parameter tuning\n", "# # l = list(np.arange(0.0,1,0.05))\n", "# l = list(range(0,100,5))\n", "# all_value={}\n", "# for n in l:\n", "# model = XGBRegressor(\n", "# # verbosity = 0,\n", "# n_estimators = 5000,\n", "# max_debth = 3,#5,\n", "# learning_rate = 0.05,#0.12,\n", "# n_jobs = -1, #10,\n", "# random_state = 95,#10, #30,\n", "# colsample_bytree = 0.4,#0.85,\n", "# subsample = 0.85,#0.6,#0.9,\n", "# booster='gbtree',\n", "# reg_lambda= 50,#30,\n", "# reg_alpha=30,\n", "# tree_method = 'gpu_hist', # optimizer\n", "# predictor = 'gpu_predictor', #Make prediction faster\n", "# gamma = n\n", "# )\n", "\n", "# #Define Pipeline\n", "# pipe = Pipeline(steps = [('preprocessor',preprocessor),('model',model)])\n", "\n", "# pipe[0].fit(X_t)\n", "\n", "# fit_param = {\"model__eval_set\":[(pipe[0].transform(X_v),y_v)],\n", "# \"model__early_stopping_rounds\":7}\n", "# pipe.fit(X_t,y_t,**fit_param)\n", "# predict_value = pipe.predict(X_v)\n", "# all_value[n]=mean_squared_error(predict_value,y_v)\n", "# print(\"Mean Square Error:\", mean_squared_error(predict_value,y_v))" ] }, { "cell_type": "code", "execution_count": 13, "id": "b22f30d9", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:38.813785Z", "iopub.status.busy": "2021-08-31T02:21:38.811975Z", "iopub.status.idle": "2021-08-31T02:21:38.815319Z", "shell.execute_reply": "2021-08-31T02:21:38.815886Z", "shell.execute_reply.started": "2021-08-31T02:18:42.490189Z" }, "papermill": { "duration": 0.038944, "end_time": "2021-08-31T02:21:38.816119", "exception": false, "start_time": "2021-08-31T02:21:38.777175", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# min(all_value, key = all_value.get)" ] }, { "cell_type": "markdown", "id": "692cefeb", "metadata": { "papermill": { "duration": 0.021721, "end_time": "2021-08-31T02:21:38.859871", "exception": false, "start_time": "2021-08-31T02:21:38.838150", "status": "completed" }, "tags": [] }, "source": [ "0.05: 61.09175065659344,\\n\n", "95: 61.01755668117088 \\n\n", "0.4: 61.003202057629714,\n", "0.8500000000000001: 61.003202057629714," ] }, { "cell_type": "code", "execution_count": 14, "id": "7dc4b16f", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:21:38.910367Z", "iopub.status.busy": "2021-08-31T02:21:38.908280Z", "iopub.status.idle": "2021-08-31T02:21:38.911531Z", "shell.execute_reply": "2021-08-31T02:21:38.912118Z", "shell.execute_reply.started": "2021-08-31T02:18:42.498096Z" }, "papermill": { "duration": 0.030464, "end_time": "2021-08-31T02:21:38.912290", "exception": false, "start_time": "2021-08-31T02:21:38.881826", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# all_value" ] }, { "cell_type": "code", "execution_count": 15, "id": "9ac878df", "metadata": { "collapsed": true, "execution": { "iopub.execute_input": "2021-08-31T02:21:39.028803Z", "iopub.status.busy": "2021-08-31T02:21:39.023119Z", "iopub.status.idle": "2021-08-31T02:33:34.219403Z", "shell.execute_reply": "2021-08-31T02:33:34.219848Z" }, "jupyter": { "outputs_hidden": true }, "papermill": { "duration": 715.286757, "end_time": "2021-08-31T02:33:34.220133", "exception": false, "start_time": "2021-08-31T02:21:38.933376", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[02:21:41] WARNING: ../src/learner.cc:573: \n", "Parameters: { \"max_debth\" } might not be used.\n", "\n", " This may not be accurate due to some parameters are only used in language bindings but\n", " passed down to XGBoost core. Or some parameters are not used but slip through this\n", " verification. Please open an issue if you find above cases.\n", "\n", "\n", "[0]\tvalidation_0-rmse:9.88475\n", "[1]\tvalidation_0-rmse:9.70673\n", "[2]\tvalidation_0-rmse:9.54343\n", "[3]\tvalidation_0-rmse:9.39425\n", "[4]\tvalidation_0-rmse:9.25620\n", "[5]\tvalidation_0-rmse:9.13021\n", "[6]\tvalidation_0-rmse:9.01498\n", "[7]\tvalidation_0-rmse:8.91062\n", "[8]\tvalidation_0-rmse:8.81463\n", "[9]\tvalidation_0-rmse:8.72713\n", "[10]\tvalidation_0-rmse:8.64734\n", "[11]\tvalidation_0-rmse:8.57478\n", "[12]\tvalidation_0-rmse:8.50863\n", "[13]\tvalidation_0-rmse:8.44850\n", "[14]\tvalidation_0-rmse:8.39371\n", "[15]\tvalidation_0-rmse:8.34413\n", "[16]\tvalidation_0-rmse:8.29889\n", "[17]\tvalidation_0-rmse:8.25792\n", "[18]\tvalidation_0-rmse:8.22078\n", "[19]\tvalidation_0-rmse:8.18727\n", "[20]\tvalidation_0-rmse:8.15705\n", "[21]\tvalidation_0-rmse:8.12902\n", "[22]\tvalidation_0-rmse:8.10359\n", "[23]\tvalidation_0-rmse:8.08081\n", "[24]\tvalidation_0-rmse:8.06059\n", "[25]\tvalidation_0-rmse:8.04184\n", "[26]\tvalidation_0-rmse:8.02473\n", "[27]\tvalidation_0-rmse:8.00939\n", "[28]\tvalidation_0-rmse:7.99575\n", "[29]\tvalidation_0-rmse:7.98298\n", "[30]\tvalidation_0-rmse:7.97172\n", "[31]\tvalidation_0-rmse:7.96130\n", "[32]\tvalidation_0-rmse:7.95202\n", "[33]\tvalidation_0-rmse:7.94349\n", "[34]\tvalidation_0-rmse:7.93589\n", "[35]\tvalidation_0-rmse:7.92874\n", "[36]\tvalidation_0-rmse:7.92210\n", "[37]\tvalidation_0-rmse:7.91627\n", "[38]\tvalidation_0-rmse:7.91122\n", "[39]\tvalidation_0-rmse:7.90643\n", "[40]\tvalidation_0-rmse:7.90209\n", "[41]\tvalidation_0-rmse:7.89838\n", "[42]\tvalidation_0-rmse:7.89493\n", "[43]\tvalidation_0-rmse:7.89172\n", "[44]\tvalidation_0-rmse:7.88872\n", "[45]\tvalidation_0-rmse:7.88596\n", "[46]\tvalidation_0-rmse:7.88356\n", "[47]\tvalidation_0-rmse:7.88129\n", "[48]\tvalidation_0-rmse:7.87914\n", "[49]\tvalidation_0-rmse:7.87732\n", "[50]\tvalidation_0-rmse:7.87573\n", "[51]\tvalidation_0-rmse:7.87415\n", "[52]\tvalidation_0-rmse:7.87268\n", "[53]\tvalidation_0-rmse:7.87114\n", "[54]\tvalidation_0-rmse:7.86973\n", "[55]\tvalidation_0-rmse:7.86874\n", "[56]\tvalidation_0-rmse:7.86758\n", "[57]\tvalidation_0-rmse:7.86652\n", "[58]\tvalidation_0-rmse:7.86544\n", "[59]\tvalidation_0-rmse:7.86454\n", "[60]\tvalidation_0-rmse:7.86367\n", "[61]\tvalidation_0-rmse:7.86287\n", "[62]\tvalidation_0-rmse:7.86214\n", "[63]\tvalidation_0-rmse:7.86134\n", "[64]\tvalidation_0-rmse:7.86072\n", "[65]\tvalidation_0-rmse:7.85996\n", "[66]\tvalidation_0-rmse:7.85929\n", "[67]\tvalidation_0-rmse:7.85866\n", "[68]\tvalidation_0-rmse:7.85809\n", "[69]\tvalidation_0-rmse:7.85759\n", "[70]\tvalidation_0-rmse:7.85708\n", "[71]\tvalidation_0-rmse:7.85639\n", "[72]\tvalidation_0-rmse:7.85590\n", "[73]\tvalidation_0-rmse:7.85548\n", "[74]\tvalidation_0-rmse:7.85498\n", "[75]\tvalidation_0-rmse:7.85459\n", "[76]\tvalidation_0-rmse:7.85405\n", "[77]\tvalidation_0-rmse:7.85370\n", "[78]\tvalidation_0-rmse:7.85322\n", "[79]\tvalidation_0-rmse:7.85280\n", "[80]\tvalidation_0-rmse:7.85253\n", "[81]\tvalidation_0-rmse:7.85229\n", "[82]\tvalidation_0-rmse:7.85186\n", "[83]\tvalidation_0-rmse:7.85157\n", "[84]\tvalidation_0-rmse:7.85136\n", "[85]\tvalidation_0-rmse:7.85124\n", "[86]\tvalidation_0-rmse:7.85078\n", "[87]\tvalidation_0-rmse:7.85037\n", "[88]\tvalidation_0-rmse:7.85011\n", "[89]\tvalidation_0-rmse:7.84983\n", "[90]\tvalidation_0-rmse:7.84949\n", "[91]\tvalidation_0-rmse:7.84915\n", "[92]\tvalidation_0-rmse:7.84875\n", "[93]\tvalidation_0-rmse:7.84846\n", "[94]\tvalidation_0-rmse:7.84811\n", "[95]\tvalidation_0-rmse:7.84792\n", "[96]\tvalidation_0-rmse:7.84766\n", "[97]\tvalidation_0-rmse:7.84749\n", "[98]\tvalidation_0-rmse:7.84731\n", "[99]\tvalidation_0-rmse:7.84692\n", "[100]\tvalidation_0-rmse:7.84657\n", "[101]\tvalidation_0-rmse:7.84628\n", "[102]\tvalidation_0-rmse:7.84577\n", "[103]\tvalidation_0-rmse:7.84559\n", "[104]\tvalidation_0-rmse:7.84534\n", "[105]\tvalidation_0-rmse:7.84516\n", "[106]\tvalidation_0-rmse:7.84481\n", "[107]\tvalidation_0-rmse:7.84455\n", "[108]\tvalidation_0-rmse:7.84463\n", "[109]\tvalidation_0-rmse:7.84422\n", "[110]\tvalidation_0-rmse:7.84404\n", "[111]\tvalidation_0-rmse:7.84370\n", "[112]\tvalidation_0-rmse:7.84334\n", "[113]\tvalidation_0-rmse:7.84314\n", "[114]\tvalidation_0-rmse:7.84289\n", "[115]\tvalidation_0-rmse:7.84263\n", "[116]\tvalidation_0-rmse:7.84232\n", "[117]\tvalidation_0-rmse:7.84216\n", "[118]\tvalidation_0-rmse:7.84172\n", "[119]\tvalidation_0-rmse:7.84145\n", "[120]\tvalidation_0-rmse:7.84125\n", "[121]\tvalidation_0-rmse:7.84114\n", "[122]\tvalidation_0-rmse:7.84072\n", "[123]\tvalidation_0-rmse:7.84066\n", "[124]\tvalidation_0-rmse:7.84029\n", "[125]\tvalidation_0-rmse:7.83997\n", "[126]\tvalidation_0-rmse:7.83983\n", "[127]\tvalidation_0-rmse:7.83956\n", "[128]\tvalidation_0-rmse:7.83933\n", "[129]\tvalidation_0-rmse:7.83923\n", "[130]\tvalidation_0-rmse:7.83901\n", "[131]\tvalidation_0-rmse:7.83891\n", "[132]\tvalidation_0-rmse:7.83866\n", "[133]\tvalidation_0-rmse:7.83833\n", "[134]\tvalidation_0-rmse:7.83813\n", "[135]\tvalidation_0-rmse:7.83808\n", "[136]\tvalidation_0-rmse:7.83790\n", "[137]\tvalidation_0-rmse:7.83760\n", "[138]\tvalidation_0-rmse:7.83737\n", "[139]\tvalidation_0-rmse:7.83716\n", "[140]\tvalidation_0-rmse:7.83712\n", "[141]\tvalidation_0-rmse:7.83681\n", "[142]\tvalidation_0-rmse:7.83652\n", "[143]\tvalidation_0-rmse:7.83636\n", "[144]\tvalidation_0-rmse:7.83609\n", "[145]\tvalidation_0-rmse:7.83598\n", "[146]\tvalidation_0-rmse:7.83585\n", "[147]\tvalidation_0-rmse:7.83555\n", "[148]\tvalidation_0-rmse:7.83542\n", "[149]\tvalidation_0-rmse:7.83513\n", "[150]\tvalidation_0-rmse:7.83475\n", "[151]\tvalidation_0-rmse:7.83430\n", "[152]\tvalidation_0-rmse:7.83403\n", "[153]\tvalidation_0-rmse:7.83372\n", "[154]\tvalidation_0-rmse:7.83330\n", "[155]\tvalidation_0-rmse:7.83315\n", "[156]\tvalidation_0-rmse:7.83306\n", "[157]\tvalidation_0-rmse:7.83289\n", "[158]\tvalidation_0-rmse:7.83269\n", "[159]\tvalidation_0-rmse:7.83247\n", "[160]\tvalidation_0-rmse:7.83239\n", "[161]\tvalidation_0-rmse:7.83235\n", "[162]\tvalidation_0-rmse:7.83248\n", "[163]\tvalidation_0-rmse:7.83218\n", "[164]\tvalidation_0-rmse:7.83177\n", "[165]\tvalidation_0-rmse:7.83153\n", "[166]\tvalidation_0-rmse:7.83131\n", "[167]\tvalidation_0-rmse:7.83105\n", "[168]\tvalidation_0-rmse:7.83095\n", "[169]\tvalidation_0-rmse:7.83080\n", "[170]\tvalidation_0-rmse:7.83082\n", "[171]\tvalidation_0-rmse:7.83068\n", "[172]\tvalidation_0-rmse:7.83039\n", "[173]\tvalidation_0-rmse:7.83028\n", "[174]\tvalidation_0-rmse:7.83015\n", "[175]\tvalidation_0-rmse:7.83017\n", "[176]\tvalidation_0-rmse:7.83001\n", "[177]\tvalidation_0-rmse:7.83008\n", "[178]\tvalidation_0-rmse:7.82989\n", "[179]\tvalidation_0-rmse:7.82967\n", "[180]\tvalidation_0-rmse:7.82946\n", "[181]\tvalidation_0-rmse:7.82931\n", "[182]\tvalidation_0-rmse:7.82907\n", "[183]\tvalidation_0-rmse:7.82887\n", "[184]\tvalidation_0-rmse:7.82887\n", "[185]\tvalidation_0-rmse:7.82857\n", "[186]\tvalidation_0-rmse:7.82820\n", "[187]\tvalidation_0-rmse:7.82814\n", "[188]\tvalidation_0-rmse:7.82804\n", "[189]\tvalidation_0-rmse:7.82800\n", "[190]\tvalidation_0-rmse:7.82789\n", "[191]\tvalidation_0-rmse:7.82774\n", "[192]\tvalidation_0-rmse:7.82757\n", "[193]\tvalidation_0-rmse:7.82737\n", "[194]\tvalidation_0-rmse:7.82728\n", "[195]\tvalidation_0-rmse:7.82718\n", "[196]\tvalidation_0-rmse:7.82712\n", "[197]\tvalidation_0-rmse:7.82703\n", "[198]\tvalidation_0-rmse:7.82685\n", "[199]\tvalidation_0-rmse:7.82677\n", "[200]\tvalidation_0-rmse:7.82688\n", "[201]\tvalidation_0-rmse:7.82682\n", "[202]\tvalidation_0-rmse:7.82681\n", "[203]\tvalidation_0-rmse:7.82660\n", "[204]\tvalidation_0-rmse:7.82643\n", "[205]\tvalidation_0-rmse:7.82619\n", "[206]\tvalidation_0-rmse:7.82612\n", "[207]\tvalidation_0-rmse:7.82597\n", "[208]\tvalidation_0-rmse:7.82580\n", "[209]\tvalidation_0-rmse:7.82557\n", "[210]\tvalidation_0-rmse:7.82564\n", "[211]\tvalidation_0-rmse:7.82565\n", "[212]\tvalidation_0-rmse:7.82568\n", "[213]\tvalidation_0-rmse:7.82552\n", "[214]\tvalidation_0-rmse:7.82545\n", "[215]\tvalidation_0-rmse:7.82521\n", "[216]\tvalidation_0-rmse:7.82522\n", "[217]\tvalidation_0-rmse:7.82532\n", "[218]\tvalidation_0-rmse:7.82519\n", "[219]\tvalidation_0-rmse:7.82494\n", "[220]\tvalidation_0-rmse:7.82471\n", "[221]\tvalidation_0-rmse:7.82462\n", "[222]\tvalidation_0-rmse:7.82452\n", "[223]\tvalidation_0-rmse:7.82443\n", "[224]\tvalidation_0-rmse:7.82442\n", "[225]\tvalidation_0-rmse:7.82450\n", "[226]\tvalidation_0-rmse:7.82461\n", "[227]\tvalidation_0-rmse:7.82442\n", "[228]\tvalidation_0-rmse:7.82430\n", "[229]\tvalidation_0-rmse:7.82430\n", "[230]\tvalidation_0-rmse:7.82421\n", "[231]\tvalidation_0-rmse:7.82424\n", "[232]\tvalidation_0-rmse:7.82399\n", "[233]\tvalidation_0-rmse:7.82399\n", "[234]\tvalidation_0-rmse:7.82368\n", "[235]\tvalidation_0-rmse:7.82356\n", "[236]\tvalidation_0-rmse:7.82345\n", "[237]\tvalidation_0-rmse:7.82345\n", "[238]\tvalidation_0-rmse:7.82350\n", "[239]\tvalidation_0-rmse:7.82322\n", "[240]\tvalidation_0-rmse:7.82304\n", "[241]\tvalidation_0-rmse:7.82306\n", "[242]\tvalidation_0-rmse:7.82294\n", "[243]\tvalidation_0-rmse:7.82290\n", "[244]\tvalidation_0-rmse:7.82274\n", "[245]\tvalidation_0-rmse:7.82256\n", "[246]\tvalidation_0-rmse:7.82231\n", "[247]\tvalidation_0-rmse:7.82230\n", "[248]\tvalidation_0-rmse:7.82215\n", "[249]\tvalidation_0-rmse:7.82194\n", "[250]\tvalidation_0-rmse:7.82190\n", "[251]\tvalidation_0-rmse:7.82171\n", "[252]\tvalidation_0-rmse:7.82171\n", "[253]\tvalidation_0-rmse:7.82169\n", "[254]\tvalidation_0-rmse:7.82159\n", "[255]\tvalidation_0-rmse:7.82167\n", "[256]\tvalidation_0-rmse:7.82153\n", "[257]\tvalidation_0-rmse:7.82147\n", "[258]\tvalidation_0-rmse:7.82153\n", "[259]\tvalidation_0-rmse:7.82141\n", "[260]\tvalidation_0-rmse:7.82138\n", "[261]\tvalidation_0-rmse:7.82123\n", "[262]\tvalidation_0-rmse:7.82105\n", "[263]\tvalidation_0-rmse:7.82094\n", "[264]\tvalidation_0-rmse:7.82088\n", "[265]\tvalidation_0-rmse:7.82061\n", "[266]\tvalidation_0-rmse:7.82048\n", "[267]\tvalidation_0-rmse:7.82037\n", "[268]\tvalidation_0-rmse:7.82014\n", "[269]\tvalidation_0-rmse:7.82017\n", "[270]\tvalidation_0-rmse:7.81998\n", "[271]\tvalidation_0-rmse:7.82003\n", "[272]\tvalidation_0-rmse:7.81983\n", "[273]\tvalidation_0-rmse:7.81984\n", "[274]\tvalidation_0-rmse:7.81971\n", "[275]\tvalidation_0-rmse:7.81973\n", "[276]\tvalidation_0-rmse:7.81942\n", "[277]\tvalidation_0-rmse:7.81929\n", "[278]\tvalidation_0-rmse:7.81921\n", "[279]\tvalidation_0-rmse:7.81904\n", "[280]\tvalidation_0-rmse:7.81902\n", "[281]\tvalidation_0-rmse:7.81890\n", "[282]\tvalidation_0-rmse:7.81888\n", "[283]\tvalidation_0-rmse:7.81871\n", "[284]\tvalidation_0-rmse:7.81870\n", "[285]\tvalidation_0-rmse:7.81869\n", "[286]\tvalidation_0-rmse:7.81872\n", "[287]\tvalidation_0-rmse:7.81863\n", "[288]\tvalidation_0-rmse:7.81854\n", "[289]\tvalidation_0-rmse:7.81846\n", "[290]\tvalidation_0-rmse:7.81840\n", "[291]\tvalidation_0-rmse:7.81834\n", "[292]\tvalidation_0-rmse:7.81836\n", "[293]\tvalidation_0-rmse:7.81837\n", "[294]\tvalidation_0-rmse:7.81834\n", "[295]\tvalidation_0-rmse:7.81822\n", "[296]\tvalidation_0-rmse:7.81818\n", "[297]\tvalidation_0-rmse:7.81801\n", "[298]\tvalidation_0-rmse:7.81780\n", "[299]\tvalidation_0-rmse:7.81774\n", "[300]\tvalidation_0-rmse:7.81769\n", "[301]\tvalidation_0-rmse:7.81770\n", "[302]\tvalidation_0-rmse:7.81756\n", "[303]\tvalidation_0-rmse:7.81745\n", "[304]\tvalidation_0-rmse:7.81748\n", "[305]\tvalidation_0-rmse:7.81749\n", "[306]\tvalidation_0-rmse:7.81748\n", "[307]\tvalidation_0-rmse:7.81742\n", "[308]\tvalidation_0-rmse:7.81747\n", "[309]\tvalidation_0-rmse:7.81728\n", "[310]\tvalidation_0-rmse:7.81712\n", "[311]\tvalidation_0-rmse:7.81719\n", "[312]\tvalidation_0-rmse:7.81722\n", "[313]\tvalidation_0-rmse:7.81710\n", "[314]\tvalidation_0-rmse:7.81694\n", "[315]\tvalidation_0-rmse:7.81685\n", "[316]\tvalidation_0-rmse:7.81680\n", "[317]\tvalidation_0-rmse:7.81687\n", "[318]\tvalidation_0-rmse:7.81689\n", "[319]\tvalidation_0-rmse:7.81692\n", "[320]\tvalidation_0-rmse:7.81681\n", "[321]\tvalidation_0-rmse:7.81668\n", "[322]\tvalidation_0-rmse:7.81668\n", "[323]\tvalidation_0-rmse:7.81658\n", "[324]\tvalidation_0-rmse:7.81642\n", "[325]\tvalidation_0-rmse:7.81624\n", "[326]\tvalidation_0-rmse:7.81621\n", "[327]\tvalidation_0-rmse:7.81627\n", "[328]\tvalidation_0-rmse:7.81616\n", "[329]\tvalidation_0-rmse:7.81627\n", "[330]\tvalidation_0-rmse:7.81617\n", "[331]\tvalidation_0-rmse:7.81620\n", "[332]\tvalidation_0-rmse:7.81620\n", "[333]\tvalidation_0-rmse:7.81624\n", "[334]\tvalidation_0-rmse:7.81621\n", "[335]\tvalidation_0-rmse:7.81612\n", "[336]\tvalidation_0-rmse:7.81618\n", "[337]\tvalidation_0-rmse:7.81612\n", "[338]\tvalidation_0-rmse:7.81608\n", "[339]\tvalidation_0-rmse:7.81592\n", "[340]\tvalidation_0-rmse:7.81577\n", "[341]\tvalidation_0-rmse:7.81557\n", "[342]\tvalidation_0-rmse:7.81556\n", "[343]\tvalidation_0-rmse:7.81557\n", "[344]\tvalidation_0-rmse:7.81546\n", "[345]\tvalidation_0-rmse:7.81558\n", "[346]\tvalidation_0-rmse:7.81558\n", "[347]\tvalidation_0-rmse:7.81546\n", "[348]\tvalidation_0-rmse:7.81560\n", "[349]\tvalidation_0-rmse:7.81558\n", "[350]\tvalidation_0-rmse:7.81557\n", "[351]\tvalidation_0-rmse:7.81564\n", "[352]\tvalidation_0-rmse:7.81544\n", "[353]\tvalidation_0-rmse:7.81542\n", "[354]\tvalidation_0-rmse:7.81529\n", "[355]\tvalidation_0-rmse:7.81527\n", "[356]\tvalidation_0-rmse:7.81520\n", "[357]\tvalidation_0-rmse:7.81510\n", "[358]\tvalidation_0-rmse:7.81509\n", "[359]\tvalidation_0-rmse:7.81505\n", "[360]\tvalidation_0-rmse:7.81502\n", "[361]\tvalidation_0-rmse:7.81494\n", "[362]\tvalidation_0-rmse:7.81503\n", "[363]\tvalidation_0-rmse:7.81507\n", "[364]\tvalidation_0-rmse:7.81503\n", "[365]\tvalidation_0-rmse:7.81501\n", "[366]\tvalidation_0-rmse:7.81495\n", "[367]\tvalidation_0-rmse:7.81499\n", "[368]\tvalidation_0-rmse:7.81485\n", "[369]\tvalidation_0-rmse:7.81495\n", "[370]\tvalidation_0-rmse:7.81494\n", "[371]\tvalidation_0-rmse:7.81501\n", "[372]\tvalidation_0-rmse:7.81488\n", "[373]\tvalidation_0-rmse:7.81484\n", "[374]\tvalidation_0-rmse:7.81480\n", "[375]\tvalidation_0-rmse:7.81466\n", "[376]\tvalidation_0-rmse:7.81453\n", "[377]\tvalidation_0-rmse:7.81444\n", "[378]\tvalidation_0-rmse:7.81433\n", "[379]\tvalidation_0-rmse:7.81422\n", "[380]\tvalidation_0-rmse:7.81424\n", "[381]\tvalidation_0-rmse:7.81435\n", "[382]\tvalidation_0-rmse:7.81440\n", "[383]\tvalidation_0-rmse:7.81437\n", "[384]\tvalidation_0-rmse:7.81437\n", "[385]\tvalidation_0-rmse:7.81443\n", "[386]\tvalidation_0-rmse:7.81448\n", "Mean Square Error: 61.06204867876849\n" ] } ], "source": [ "model = XGBRegressor(\n", "# verbosity = 0,\n", " n_estimators = 5000,\n", " max_debth = 3,#5,\n", " learning_rate = 0.05,#0.12,\n", " n_jobs = -1, #10,\n", " random_state = 95,#10, #30,\n", " colsample_bytree = 0.4,#0.85,\n", " subsample = 0.85,#0.6,#0.9,\n", " booster='gbtree',\n", " reg_lambda= 50,#30,\n", " reg_alpha=30,\n", "# tree_method = 'gpu_hist', # optimizer\n", "# predictor = 'gpu_predictor', #Make prediction faster\n", "# gamma = n\n", " )\n", "#Define Pipeline\n", "pipe = Pipeline(steps = [('preprocessor',preprocessor),('model',model)])\n", "\n", "pipe[0].fit(X_t)\n", "\n", "fit_param = {\"model__eval_set\":[(pipe[0].transform(X_v),y_v)],\n", " \"model__early_stopping_rounds\":7}\n", "pipe.fit(X_t,y_t,**fit_param)\n", "predict_value = pipe.predict(X_v)\n", "# all_value[n]=mean_squared_error(predict_value,y_v)\n", "print(\"Mean Square Error:\", mean_squared_error(predict_value,y_v))" ] }, { "cell_type": "code", "execution_count": 16, "id": "66f7d80e", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:33:34.574882Z", "iopub.status.busy": "2021-08-31T02:33:34.573312Z", "iopub.status.idle": "2021-08-31T02:33:36.376252Z", "shell.execute_reply": "2021-08-31T02:33:36.375621Z" }, "papermill": { "duration": 2.00711, "end_time": "2021-08-31T02:33:36.376408", "exception": false, "start_time": "2021-08-31T02:33:34.369298", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([7.9851785, 5.2491755, 7.1733794, ..., 6.2271237, 4.3847723,\n", " 7.240355 ], dtype=float32)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_test = pipe.predict(X_test)\n", "predict_test" ] }, { "cell_type": "code", "execution_count": 17, "id": "b5332c38", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:33:36.682569Z", "iopub.status.busy": "2021-08-31T02:33:36.681880Z", "iopub.status.idle": "2021-08-31T02:33:37.114402Z", "shell.execute_reply": "2021-08-31T02:33:37.113822Z" }, "papermill": { "duration": 0.589761, "end_time": "2021-08-31T02:33:37.114567", "exception": false, "start_time": "2021-08-31T02:33:36.524806", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "output = pd.DataFrame({'id': X_test.index,\n", " 'loss': predict_test})\n", "output.to_csv('submission.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 18, "id": "8a5aef75", "metadata": { "execution": { "iopub.execute_input": "2021-08-31T02:33:37.427693Z", "iopub.status.busy": "2021-08-31T02:33:37.426438Z", "iopub.status.idle": "2021-08-31T02:33:37.432181Z", "shell.execute_reply": "2021-08-31T02:33:37.431616Z" }, "papermill": { "duration": 0.168653, "end_time": "2021-08-31T02:33:37.432330", "exception": false, "start_time": "2021-08-31T02:33:37.263677", "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>loss</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>250000</td>\n", " <td>7.985178</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>250001</td>\n", " <td>5.249176</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>250002</td>\n", " <td>7.173379</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>250003</td>\n", " <td>7.389934</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>250004</td>\n", " <td>7.550790</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>149995</th>\n", " <td>399995</td>\n", " <td>7.876827</td>\n", " </tr>\n", " <tr>\n", " <th>149996</th>\n", " <td>399996</td>\n", " <td>7.302665</td>\n", " </tr>\n", " <tr>\n", " <th>149997</th>\n", " <td>399997</td>\n", " <td>6.227124</td>\n", " </tr>\n", " <tr>\n", " <th>149998</th>\n", " <td>399998</td>\n", " <td>4.384772</td>\n", " </tr>\n", " <tr>\n", " <th>149999</th>\n", " <td>399999</td>\n", " <td>7.240355</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>150000 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " id loss\n", "0 250000 7.985178\n", "1 250001 5.249176\n", "2 250002 7.173379\n", "3 250003 7.389934\n", "4 250004 7.550790\n", "... ... ...\n", "149995 399995 7.876827\n", "149996 399996 7.302665\n", "149997 399997 6.227124\n", "149998 399998 4.384772\n", "149999 399999 7.240355\n", "\n", "[150000 rows x 2 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output" ] }, { "cell_type": "code", "execution_count": null, "id": "c59b011c", "metadata": { "papermill": { "duration": 0.153124, "end_time": "2021-08-31T02:33:37.735965", "exception": false, "start_time": "2021-08-31T02:33:37.582841", "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": "3.7.10" }, "papermill": { "default_parameters": {}, "duration": 742.077418, "end_time": "2021-08-31T02:33:39.675097", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2021-08-31T02:21:17.597679", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }
0073/574/73574504.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed2b71e1\",\n \"metadata\": (...TRUNCATED)
0073/574/73574921.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"787658(...TRUNCATED)
0073/574/73574960.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"77684340\",\n \"metadata\": (...TRUNCATED)
0073/575/73575780.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"8b16c7(...TRUNCATED)
0073/576/73576168.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED)
0073/576/73576226.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"528c4aec\",\n \"metadata\": (...TRUNCATED)
0073/576/73576305.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"51aa05(...TRUNCATED)
0073/576/73576427.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED)
0073/576/73576571.ipynb
s3://data-agents/kaggle-outputs/sharded/012_00073.jsonl.gz
End of preview. Expand in Data Studio
README.md exists but content is empty.
Downloads last month
27
Size of the auto-converted Parquet files (First 5GB):
3.59 GB
Number of rows (First 5GB):
5,354
Estimated number of rows:
9,310