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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"from datetime import datetime \n",
"from datetime import date\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from keras.models import Sequential\n",
"from keras.layers import LSTM, Dense\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
"from keras.callbacks import ModelCheckpoint\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"merged = pd.read_csv(r'../data/long_merge.csv')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"ename": "MemoryError",
"evalue": "Unable to allocate 8.15 GiB for an array with shape (528, 2072154) and data type float64",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[10], line 23\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;66;03m# for rtu in rtus:\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;66;03m# for column in merged.columns:\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# if f\"rtu_00{rtu}_fltrd_sa\" in column:\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;66;03m# cols.append(column)\u001b[39;00m\n\u001b[0;32m 18\u001b[0m cols \u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m cols \u001b[38;5;241m+\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mair_temp_set_1\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 19\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mair_temp_set_2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 20\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdew_point_temperature_set_1d\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 21\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelative_humidity_set_1\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 22\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msolar_radiation_set_1\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m---> 23\u001b[0m input_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mmerged\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcols\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 24\u001b[0m input_dataset\u001b[38;5;241m.\u001b[39mcolumns\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\frame.py:4105\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m 4103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m-> 4105\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_with_is_copy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[0;32m 4108\u001b[0m \u001b[38;5;66;03m# What does looking for a single key in a non-unique index return?\u001b[39;00m\n\u001b[0;32m 4109\u001b[0m \u001b[38;5;66;03m# The behavior is inconsistent. It returns a Series, except when\u001b[39;00m\n\u001b[0;32m 4110\u001b[0m \u001b[38;5;66;03m# - the key itself is repeated (test on data.shape, #9519), or\u001b[39;00m\n\u001b[0;32m 4111\u001b[0m \u001b[38;5;66;03m# - we have a MultiIndex on columns (test on self.columns, #21309)\u001b[39;00m\n\u001b[0;32m 4112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns, MultiIndex):\n\u001b[0;32m 4113\u001b[0m \u001b[38;5;66;03m# GH#26490 using data[key] can cause RecursionError\u001b[39;00m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\generic.py:4150\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[1;34m(self, indices, axis)\u001b[0m\n\u001b[0;32m 4139\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 4140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_with_is_copy\u001b[39m(\u001b[38;5;28mself\u001b[39m, indices, axis: Axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[0;32m 4141\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 4142\u001b[0m \u001b[38;5;124;03m Internal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[0;32m 4143\u001b[0m \u001b[38;5;124;03m attribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 4148\u001b[0m \u001b[38;5;124;03m See the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[0;32m 4149\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4150\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4151\u001b[0m \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n\u001b[0;32m 4152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_get_axis(axis)\u001b[38;5;241m.\u001b[39mequals(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis(axis)):\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\generic.py:4130\u001b[0m, in \u001b[0;36mNDFrame.take\u001b[1;34m(self, indices, axis, **kwargs)\u001b[0m\n\u001b[0;32m 4125\u001b[0m \u001b[38;5;66;03m# We can get here with a slice via DataFrame.__getitem__\u001b[39;00m\n\u001b[0;32m 4126\u001b[0m indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\n\u001b[0;32m 4127\u001b[0m indices\u001b[38;5;241m.\u001b[39mstart, indices\u001b[38;5;241m.\u001b[39mstop, indices\u001b[38;5;241m.\u001b[39mstep, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mintp\n\u001b[0;32m 4128\u001b[0m )\n\u001b[1;32m-> 4130\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 4131\u001b[0m \u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4132\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_block_manager_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4133\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 4134\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39m__finalize__(\n\u001b[0;32m 4136\u001b[0m \u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 4137\u001b[0m )\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:894\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[1;34m(self, indexer, axis, verify)\u001b[0m\n\u001b[0;32m 891\u001b[0m indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[0;32m 893\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m--> 894\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex_indexer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 895\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_axis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 896\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 897\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 898\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_dups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 899\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 900\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:680\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[0;32m 677\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRequested axis not found in manager\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 680\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_slice_take_blocks_ax0\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 681\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 682\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 683\u001b[0m \u001b[43m \u001b[49m\u001b[43monly_slice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43monly_slice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 684\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_na_proxy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_na_proxy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 685\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 687\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 688\u001b[0m blk\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[0;32m 689\u001b[0m indexer,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 695\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[0;32m 696\u001b[0m ]\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:843\u001b[0m, in \u001b[0;36mBaseBlockManager._slice_take_blocks_ax0\u001b[1;34m(self, slice_or_indexer, fill_value, only_slice, use_na_proxy, ref_inplace_op)\u001b[0m\n\u001b[0;32m 841\u001b[0m blocks\u001b[38;5;241m.\u001b[39mappend(nb)\n\u001b[0;32m 842\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 843\u001b[0m nb \u001b[38;5;241m=\u001b[39m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake_nd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtaker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_mgr_locs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmgr_locs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 844\u001b[0m blocks\u001b[38;5;241m.\u001b[39mappend(nb)\n\u001b[0;32m 846\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m blocks\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:1307\u001b[0m, in \u001b[0;36mBlock.take_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_value)\u001b[0m\n\u001b[0;32m 1304\u001b[0m allow_fill \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 1306\u001b[0m \u001b[38;5;66;03m# Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype\u001b[39;00m\n\u001b[1;32m-> 1307\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43malgos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake_nd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_fill\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_fill\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\n\u001b[0;32m 1309\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1311\u001b[0m \u001b[38;5;66;03m# Called from three places in managers, all of which satisfy\u001b[39;00m\n\u001b[0;32m 1312\u001b[0m \u001b[38;5;66;03m# these assertions\u001b[39;00m\n\u001b[0;32m 1313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ExtensionBlock):\n\u001b[0;32m 1314\u001b[0m \u001b[38;5;66;03m# NB: in this case, the 'axis' kwarg will be ignored in the\u001b[39;00m\n\u001b[0;32m 1315\u001b[0m \u001b[38;5;66;03m# algos.take_nd call above.\u001b[39;00m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:117\u001b[0m, in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mtake(indexer, fill_value\u001b[38;5;241m=\u001b[39mfill_value, allow_fill\u001b[38;5;241m=\u001b[39mallow_fill)\n\u001b[0;32m 116\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(arr)\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_take_nd_ndarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_fill\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:157\u001b[0m, in \u001b[0;36m_take_nd_ndarray\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m 155\u001b[0m out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(out_shape, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mF\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 156\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 157\u001b[0m out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(out_shape, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m 159\u001b[0m func \u001b[38;5;241m=\u001b[39m _get_take_nd_function(\n\u001b[0;32m 160\u001b[0m arr\u001b[38;5;241m.\u001b[39mndim, arr\u001b[38;5;241m.\u001b[39mdtype, out\u001b[38;5;241m.\u001b[39mdtype, axis\u001b[38;5;241m=\u001b[39maxis, mask_info\u001b[38;5;241m=\u001b[39mmask_info\n\u001b[0;32m 161\u001b[0m )\n\u001b[0;32m 162\u001b[0m func(arr, indexer, out, fill_value)\n",
"\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 8.15 GiB for an array with shape (528, 2072154) and data type float64"
]
}
],
"source": [
"zones = [69, 68,67, 66,65.64, 42,41,40,39,38,37,36]\n",
"rtus = [1]\n",
"cols = []\n",
"\n",
"for zone in zones:\n",
" for column in merged.columns:\n",
" if f\"zone_0{zone}\" in column and 'co2' not in column and \"hw_valve\" not in column and \"cooling_sp\" not in column and \"heating_sp\" not in column:\n",
" cols.append(column)\n",
"\n",
"for zone in zones:\n",
" for column in merged.columns:\n",
" if f\"zone_0{zone}\" in column and \"cooling_sp\" in column or \"heating_sp\" in column:\n",
" cols.append(column)\n",
"# for rtu in rtus:\n",
"# for column in merged.columns:\n",
"# if f\"rtu_00{rtu}_fltrd_sa\" in column:\n",
"# cols.append(column)\n",
"cols =['date'] + cols + ['air_temp_set_1',\n",
" 'air_temp_set_2',\n",
" 'dew_point_temperature_set_1d',\n",
" 'relative_humidity_set_1',\n",
" 'solar_radiation_set_1']\n",
"input_dataset = merged[cols]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\arbal\\AppData\\Local\\Temp\\ipykernel_32464\\216607548.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\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",
" input_dataset['date'] = pd.to_datetime(input_dataset['date'], format = \"%Y-%m-%d %H:%M:%S\")\n"
]
},
{
"ename": "MemoryError",
"evalue": "Unable to allocate 8.15 GiB for an array with shape (528, 2070713) and data type float64",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[11], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m input_dataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(input_dataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY-\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm-\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mH:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mM:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m df_filtered \u001b[38;5;241m=\u001b[39m \u001b[43minput_dataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43mdate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2018\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdate\u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2021\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m df_filtered\u001b[38;5;241m.\u001b[39misna()\u001b[38;5;241m.\u001b[39many()\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThere are NA values in the DataFrame columns.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\frame.py:4081\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4079\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001b[39;00m\n\u001b[0;32m 4080\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[1;32m-> 4081\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_bool_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4083\u001b[0m \u001b[38;5;66;03m# We are left with two options: a single key, and a collection of keys,\u001b[39;00m\n\u001b[0;32m 4084\u001b[0m \u001b[38;5;66;03m# We interpret tuples as collections only for non-MultiIndex\u001b[39;00m\n\u001b[0;32m 4085\u001b[0m is_single_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(key)\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\frame.py:4143\u001b[0m, in \u001b[0;36mDataFrame._getitem_bool_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 4142\u001b[0m indexer \u001b[38;5;241m=\u001b[39m key\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m-> 4143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_with_is_copy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\generic.py:4150\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[1;34m(self, indices, axis)\u001b[0m\n\u001b[0;32m 4139\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 4140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_with_is_copy\u001b[39m(\u001b[38;5;28mself\u001b[39m, indices, axis: Axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[0;32m 4141\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 4142\u001b[0m \u001b[38;5;124;03m Internal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[0;32m 4143\u001b[0m \u001b[38;5;124;03m attribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 4148\u001b[0m \u001b[38;5;124;03m See the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[0;32m 4149\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4150\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4151\u001b[0m \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n\u001b[0;32m 4152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_get_axis(axis)\u001b[38;5;241m.\u001b[39mequals(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis(axis)):\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\generic.py:4130\u001b[0m, in \u001b[0;36mNDFrame.take\u001b[1;34m(self, indices, axis, **kwargs)\u001b[0m\n\u001b[0;32m 4125\u001b[0m \u001b[38;5;66;03m# We can get here with a slice via DataFrame.__getitem__\u001b[39;00m\n\u001b[0;32m 4126\u001b[0m indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\n\u001b[0;32m 4127\u001b[0m indices\u001b[38;5;241m.\u001b[39mstart, indices\u001b[38;5;241m.\u001b[39mstop, indices\u001b[38;5;241m.\u001b[39mstep, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mintp\n\u001b[0;32m 4128\u001b[0m )\n\u001b[1;32m-> 4130\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 4131\u001b[0m \u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4132\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_block_manager_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4133\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 4134\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39m__finalize__(\n\u001b[0;32m 4136\u001b[0m \u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 4137\u001b[0m )\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:894\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[1;34m(self, indexer, axis, verify)\u001b[0m\n\u001b[0;32m 891\u001b[0m indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[0;32m 893\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m--> 894\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex_indexer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 895\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_axis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 896\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 897\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 898\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_dups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 899\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 900\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:687\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[0;32m 680\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[0;32m 681\u001b[0m indexer,\n\u001b[0;32m 682\u001b[0m fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m 683\u001b[0m only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[0;32m 684\u001b[0m use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[0;32m 685\u001b[0m )\n\u001b[0;32m 686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 687\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake_nd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 690\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 691\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 692\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfill_value\u001b[49m\n\u001b[0;32m 693\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 694\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 695\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mblk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblocks\u001b[49m\n\u001b[0;32m 696\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 698\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m 699\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:688\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 680\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[0;32m 681\u001b[0m indexer,\n\u001b[0;32m 682\u001b[0m fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m 683\u001b[0m only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[0;32m 684\u001b[0m use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[0;32m 685\u001b[0m )\n\u001b[0;32m 686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 687\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m--> 688\u001b[0m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake_nd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 690\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 691\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 692\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfill_value\u001b[49m\n\u001b[0;32m 693\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 694\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 695\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[0;32m 696\u001b[0m ]\n\u001b[0;32m 698\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m 699\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:1307\u001b[0m, in \u001b[0;36mBlock.take_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_value)\u001b[0m\n\u001b[0;32m 1304\u001b[0m allow_fill \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 1306\u001b[0m \u001b[38;5;66;03m# Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype\u001b[39;00m\n\u001b[1;32m-> 1307\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43malgos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake_nd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_fill\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_fill\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\n\u001b[0;32m 1309\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1311\u001b[0m \u001b[38;5;66;03m# Called from three places in managers, all of which satisfy\u001b[39;00m\n\u001b[0;32m 1312\u001b[0m \u001b[38;5;66;03m# these assertions\u001b[39;00m\n\u001b[0;32m 1313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ExtensionBlock):\n\u001b[0;32m 1314\u001b[0m \u001b[38;5;66;03m# NB: in this case, the 'axis' kwarg will be ignored in the\u001b[39;00m\n\u001b[0;32m 1315\u001b[0m \u001b[38;5;66;03m# algos.take_nd call above.\u001b[39;00m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:117\u001b[0m, in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mtake(indexer, fill_value\u001b[38;5;241m=\u001b[39mfill_value, allow_fill\u001b[38;5;241m=\u001b[39mallow_fill)\n\u001b[0;32m 116\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(arr)\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_take_nd_ndarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_fill\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:157\u001b[0m, in \u001b[0;36m_take_nd_ndarray\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m 155\u001b[0m out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(out_shape, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mF\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 156\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 157\u001b[0m out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(out_shape, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m 159\u001b[0m func \u001b[38;5;241m=\u001b[39m _get_take_nd_function(\n\u001b[0;32m 160\u001b[0m arr\u001b[38;5;241m.\u001b[39mndim, arr\u001b[38;5;241m.\u001b[39mdtype, out\u001b[38;5;241m.\u001b[39mdtype, axis\u001b[38;5;241m=\u001b[39maxis, mask_info\u001b[38;5;241m=\u001b[39mmask_info\n\u001b[0;32m 161\u001b[0m )\n\u001b[0;32m 162\u001b[0m func(arr, indexer, out, fill_value)\n",
"\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 8.15 GiB for an array with shape (528, 2070713) and data type float64"
]
}
],
"source": [
"input_dataset['date'] = pd.to_datetime(input_dataset['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
"df_filtered = input_dataset[ (input_dataset.date.dt.date >date(2019, 1, 1)) & (input_dataset.date.dt.date< date(2021, 1, 1))]\n",
"\n",
"if df_filtered.isna().any().any():\n",
" print(\"There are NA values in the DataFrame columns.\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"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",
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"\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>zone_069_temp</th>\n",
" <th>zone_069_fan_spd</th>\n",
" <th>zone_068_temp</th>\n",
" <th>zone_068_fan_spd</th>\n",
" <th>zone_067_temp</th>\n",
" <th>zone_067_fan_spd</th>\n",
" <th>zone_066_temp</th>\n",
" <th>zone_066_fan_spd</th>\n",
" <th>zone_042_temp</th>\n",
" <th>...</th>\n",
" <th>zone_066_heating_sp</th>\n",
" <th>zone_067_heating_sp</th>\n",
" <th>zone_069_heating_sp</th>\n",
" <th>zone_070_heating_sp</th>\n",
" <th>zone_071_heating_sp</th>\n",
" <th>air_temp_set_1</th>\n",
" <th>air_temp_set_2</th>\n",
" <th>dew_point_temperature_set_1d</th>\n",
" <th>relative_humidity_set_1</th>\n",
" <th>solar_radiation_set_1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>2018-01-02 00:00:00</td>\n",
" <td>71.4</td>\n",
" <td>20.0</td>\n",
" <td>73.2</td>\n",
" <td>70.0</td>\n",
" <td>71.2</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>35.0</td>\n",
" <td>71.6</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.280</td>\n",
" <td>15.100</td>\n",
" <td>6.33</td>\n",
" <td>55.40</td>\n",
" <td>161.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>2018-01-02 00:01:00</td>\n",
" <td>71.4</td>\n",
" <td>20.0</td>\n",
" <td>73.2</td>\n",
" <td>70.0</td>\n",
" <td>71.2</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>35.0</td>\n",
" <td>71.6</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.280</td>\n",
" <td>15.100</td>\n",
" <td>6.33</td>\n",
" <td>55.40</td>\n",
" <td>161.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>2018-01-02 00:02:00</td>\n",
" <td>71.4</td>\n",
" <td>20.0</td>\n",
" <td>73.2</td>\n",
" <td>70.0</td>\n",
" <td>71.2</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>35.0</td>\n",
" <td>71.6</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.280</td>\n",
" <td>15.100</td>\n",
" <td>6.33</td>\n",
" <td>55.40</td>\n",
" <td>161.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>2018-01-02 00:03:00</td>\n",
" <td>71.4</td>\n",
" <td>20.0</td>\n",
" <td>73.2</td>\n",
" <td>70.0</td>\n",
" <td>71.2</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>35.0</td>\n",
" <td>71.6</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.280</td>\n",
" <td>15.100</td>\n",
" <td>6.33</td>\n",
" <td>55.40</td>\n",
" <td>161.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>2018-01-02 00:04:00</td>\n",
" <td>71.4</td>\n",
" <td>20.0</td>\n",
" <td>73.2</td>\n",
" <td>70.0</td>\n",
" <td>71.2</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>35.0</td>\n",
" <td>71.6</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.280</td>\n",
" <td>15.100</td>\n",
" <td>6.33</td>\n",
" <td>55.40</td>\n",
" <td>161.9</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>2072148</th>\n",
" <td>2020-12-31 23:57:00</td>\n",
" <td>68.8</td>\n",
" <td>20.0</td>\n",
" <td>71.7</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>20.0</td>\n",
" <td>68.6</td>\n",
" <td>35.0</td>\n",
" <td>71.4</td>\n",
" <td>...</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>65.0</td>\n",
" <td>67.0</td>\n",
" <td>13.994</td>\n",
" <td>13.528</td>\n",
" <td>4.11</td>\n",
" <td>51.61</td>\n",
" <td>188.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2072149</th>\n",
" <td>2020-12-31 23:58:00</td>\n",
" <td>68.8</td>\n",
" <td>20.0</td>\n",
" <td>71.7</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>20.0</td>\n",
" <td>68.6</td>\n",
" <td>35.0</td>\n",
" <td>71.4</td>\n",
" <td>...</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>65.0</td>\n",
" <td>67.0</td>\n",
" <td>13.994</td>\n",
" <td>13.528</td>\n",
" <td>4.11</td>\n",
" <td>51.61</td>\n",
" <td>188.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2072150</th>\n",
" <td>2020-12-31 23:58:00</td>\n",
" <td>68.8</td>\n",
" <td>20.0</td>\n",
" <td>71.7</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>20.0</td>\n",
" <td>68.6</td>\n",
" <td>35.0</td>\n",
" <td>71.4</td>\n",
" <td>...</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>65.0</td>\n",
" <td>67.0</td>\n",
" <td>13.994</td>\n",
" <td>13.528</td>\n",
" <td>4.11</td>\n",
" <td>51.61</td>\n",
" <td>188.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2072151</th>\n",
" <td>2020-12-31 23:59:00</td>\n",
" <td>68.8</td>\n",
" <td>20.0</td>\n",
" <td>71.7</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>20.0</td>\n",
" <td>68.6</td>\n",
" <td>35.0</td>\n",
" <td>71.4</td>\n",
" <td>...</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>65.0</td>\n",
" <td>67.0</td>\n",
" <td>13.994</td>\n",
" <td>13.528</td>\n",
" <td>4.11</td>\n",
" <td>51.61</td>\n",
" <td>188.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2072152</th>\n",
" <td>2020-12-31 23:59:00</td>\n",
" <td>68.8</td>\n",
" <td>20.0</td>\n",
" <td>71.7</td>\n",
" <td>20.0</td>\n",
" <td>70.4</td>\n",
" <td>20.0</td>\n",
" <td>68.6</td>\n",
" <td>35.0</td>\n",
" <td>71.4</td>\n",
" <td>...</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>65.0</td>\n",
" <td>67.0</td>\n",
" <td>13.994</td>\n",
" <td>13.528</td>\n",
" <td>4.11</td>\n",
" <td>51.61</td>\n",
" <td>188.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2070713 rows × 529 columns</p>\n",
"</div>"
],
"text/plain": [
" date zone_069_temp zone_069_fan_spd zone_068_temp \\\n",
"1440 2018-01-02 00:00:00 71.4 20.0 73.2 \n",
"1441 2018-01-02 00:01:00 71.4 20.0 73.2 \n",
"1442 2018-01-02 00:02:00 71.4 20.0 73.2 \n",
"1443 2018-01-02 00:03:00 71.4 20.0 73.2 \n",
"1444 2018-01-02 00:04:00 71.4 20.0 73.2 \n",
"... ... ... ... ... \n",
"2072148 2020-12-31 23:57:00 68.8 20.0 71.7 \n",
"2072149 2020-12-31 23:58:00 68.8 20.0 71.7 \n",
"2072150 2020-12-31 23:58:00 68.8 20.0 71.7 \n",
"2072151 2020-12-31 23:59:00 68.8 20.0 71.7 \n",
"2072152 2020-12-31 23:59:00 68.8 20.0 71.7 \n",
"\n",
" zone_068_fan_spd zone_067_temp zone_067_fan_spd zone_066_temp \\\n",
"1440 70.0 71.2 20.0 70.4 \n",
"1441 70.0 71.2 20.0 70.4 \n",
"1442 70.0 71.2 20.0 70.4 \n",
"1443 70.0 71.2 20.0 70.4 \n",
"1444 70.0 71.2 20.0 70.4 \n",
"... ... ... ... ... \n",
"2072148 20.0 70.4 20.0 68.6 \n",
"2072149 20.0 70.4 20.0 68.6 \n",
"2072150 20.0 70.4 20.0 68.6 \n",
"2072151 20.0 70.4 20.0 68.6 \n",
"2072152 20.0 70.4 20.0 68.6 \n",
"\n",
" zone_066_fan_spd zone_042_temp ... zone_066_heating_sp \\\n",
"1440 35.0 71.6 ... NaN \n",
"1441 35.0 71.6 ... NaN \n",
"1442 35.0 71.6 ... NaN \n",
"1443 35.0 71.6 ... NaN \n",
"1444 35.0 71.6 ... NaN \n",
"... ... ... ... ... \n",
"2072148 35.0 71.4 ... 68.0 \n",
"2072149 35.0 71.4 ... 68.0 \n",
"2072150 35.0 71.4 ... 68.0 \n",
"2072151 35.0 71.4 ... 68.0 \n",
"2072152 35.0 71.4 ... 68.0 \n",
"\n",
" zone_067_heating_sp zone_069_heating_sp zone_070_heating_sp \\\n",
"1440 NaN NaN NaN \n",
"1441 NaN NaN NaN \n",
"1442 NaN NaN NaN \n",
"1443 NaN NaN NaN \n",
"1444 NaN NaN NaN \n",
"... ... ... ... \n",
"2072148 68.0 68.0 65.0 \n",
"2072149 68.0 68.0 65.0 \n",
"2072150 68.0 68.0 65.0 \n",
"2072151 68.0 68.0 65.0 \n",
"2072152 68.0 68.0 65.0 \n",
"\n",
" zone_071_heating_sp air_temp_set_1 air_temp_set_2 \\\n",
"1440 NaN 15.280 15.100 \n",
"1441 NaN 15.280 15.100 \n",
"1442 NaN 15.280 15.100 \n",
"1443 NaN 15.280 15.100 \n",
"1444 NaN 15.280 15.100 \n",
"... ... ... ... \n",
"2072148 67.0 13.994 13.528 \n",
"2072149 67.0 13.994 13.528 \n",
"2072150 67.0 13.994 13.528 \n",
"2072151 67.0 13.994 13.528 \n",
"2072152 67.0 13.994 13.528 \n",
"\n",
" dew_point_temperature_set_1d relative_humidity_set_1 \\\n",
"1440 6.33 55.40 \n",
"1441 6.33 55.40 \n",
"1442 6.33 55.40 \n",
"1443 6.33 55.40 \n",
"1444 6.33 55.40 \n",
"... ... ... \n",
"2072148 4.11 51.61 \n",
"2072149 4.11 51.61 \n",
"2072150 4.11 51.61 \n",
"2072151 4.11 51.61 \n",
"2072152 4.11 51.61 \n",
"\n",
" solar_radiation_set_1 \n",
"1440 161.9 \n",
"1441 161.9 \n",
"1442 161.9 \n",
"1443 161.9 \n",
"1444 161.9 \n",
"... ... \n",
"2072148 188.8 \n",
"2072149 188.8 \n",
"2072150 188.8 \n",
"2072151 188.8 \n",
"2072152 188.8 \n",
"\n",
"[2070713 rows x 529 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_filtered"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp',\n",
" 'zone_070_heating_sp']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2019, 5, 1)) & (df_filtered.date.dt.date <date(2019,7, 1))]\n",
"\n",
"# traindataset_df = df_filtered[ (df_filtered.date.dt.date >date(2019, 11, 8))]\n",
"\n",
"traindataset_df = df_filtered[(df_filtered.date.dt.date >date(2019, 3, 1)) & (df_filtered.date.dt.date <date(2019, 5, 1))]\n",
"testdataset = testdataset_df.drop(columns=[\"date\"]).rolling(window = 5, step = 1, min_periods= 1).mean().values\n",
"traindataset = traindataset_df.drop(columns=[\"date\"]).rolling(window = 5, step = 1, min_periods= 1).mean().values\n",
"\n",
"columns_with_na = traindataset_df.columns[traindataset_df.isna().any()].tolist()\n",
"columns_with_na"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['date', 'zone_069_temp', 'zone_069_fan_spd', 'zone_068_temp',\n",
" 'zone_068_fan_spd', 'zone_067_temp', 'zone_067_fan_spd',\n",
" 'zone_066_temp', 'zone_066_fan_spd', 'zone_042_temp',\n",
" ...\n",
" 'zone_066_heating_sp', 'zone_067_heating_sp', 'zone_069_heating_sp',\n",
" 'zone_070_heating_sp', 'zone_071_heating_sp', 'air_temp_set_1',\n",
" 'air_temp_set_2', 'dew_point_temperature_set_1d',\n",
" 'relative_humidity_set_1', 'solar_radiation_set_1'],\n",
" dtype='object', length=529)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traindataset_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0\n"
]
}
],
"source": [
"print(traindataset_df.isna().sum().sum(), testdataset_df.isna().sum().sum())"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(86400, 86400)"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(traindataset), len(testdataset)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"traindataset = traindataset.astype('float32')\n",
"testdataset = testdataset.astype('float32')\n",
"\n",
"scaler = StandardScaler()\n",
"traindataset = scaler.fit_transform(traindataset)\n",
"testdataset = scaler.transform(testdataset)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(86400, 45)"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traindataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [],
"source": [
"train,test = traindataset,testdataset\n",
"\n",
"def create_dataset(dataset,time_step):\n",
" x = []\n",
" Y = []\n",
" for i in range(len(dataset) - time_step - 1):\n",
" x.append(dataset[i:(i+time_step),:])\n",
" Y.append(dataset[i+time_step,0:-5])\n",
" x= np.array(x)\n",
" Y = np.array(Y)\n",
" return x,Y\n",
"time_step = 30\n",
"X_train, y_train = create_dataset(train, time_step)\n",
"X_test, y_test = create_dataset(test, time_step)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((86369, 30, 45), (86369, 40))"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape, y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"674/675 [============================>.] - ETA: 0s - loss: 0.1090\n",
"Epoch 1: val_loss improved from inf to 0.26433, saving model to lstm_vav_01.tf\n",
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"675/675 [==============================] - 61s 84ms/step - loss: 0.1089 - val_loss: 0.2643\n",
"Epoch 2/5\n",
"675/675 [==============================] - ETA: 0s - loss: 0.0155\n",
"Epoch 2: val_loss improved from 0.26433 to 0.21391, saving model to lstm_vav_01.tf\n",
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"675/675 [==============================] - 45s 67ms/step - loss: 0.0155 - val_loss: 0.2139\n",
"Epoch 3/5\n",
"675/675 [==============================] - ETA: 0s - loss: 0.0081\n",
"Epoch 3: val_loss improved from 0.21391 to 0.17155, saving model to lstm_vav_01.tf\n",
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"675/675 [==============================] - 58s 86ms/step - loss: 0.0081 - val_loss: 0.1716\n",
"Epoch 4/5\n",
"675/675 [==============================] - ETA: 0s - loss: 0.0049\n",
"Epoch 4: val_loss improved from 0.17155 to 0.14438, saving model to lstm_vav_01.tf\n",
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"675/675 [==============================] - 54s 80ms/step - loss: 0.0049 - val_loss: 0.1444\n",
"Epoch 5/5\n",
"675/675 [==============================] - ETA: 0s - loss: 0.0030\n",
"Epoch 5: val_loss improved from 0.14438 to 0.12414, saving model to lstm_vav_01.tf\n",
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"675/675 [==============================] - 60s 89ms/step - loss: 0.0030 - val_loss: 0.1241\n"
]
},
{
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x1d5bf064950>"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"model = Sequential()\n",
"model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
"model.add(LSTM(units=50, return_sequences=True))\n",
"model.add(LSTM(units=30))\n",
"model.add(Dense(units=y_train.shape[1]))\n",
"\n",
"model.compile(optimizer='adam', loss='mean_squared_error')\n",
"\n",
"checkpoint_path = \"lstm_vav_01.tf\"\n",
"checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
"model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128, verbose=1, callbacks=[checkpoint_callback])"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x1d55c631f10>"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.load_weights(checkpoint_path)"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2700/2700 [==============================] - 25s 9ms/step\n"
]
}
],
"source": [
"test_predict1 = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1d5582d61d0>]"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.plot(y_test[:,3])\n",
"plt.plot(y_train[:,3])"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib qt\n",
"var = 1\n",
"plt.plot(y_test[:,var], label='Original Testing Data', color='blue')\n",
"plt.plot(test_predict1[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
"anomalies = np.where(abs(test_predict1[:,var] - y_test[:,var]) > 0.38)\n",
"plt.scatter(anomalies,test_predict1[anomalies,var], color='black',marker =\"o\",s=100 )\n",
"\n",
"\n",
"plt.title('Testing Data - Predicted vs Actual')\n",
"plt.xlabel('Time')\n",
"plt.ylabel('Value')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.mixture import GaussianMixture\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.decomposition import PCA\n",
"\n",
"# Generating random data for demonstration\n",
"np.random.seed(0)\n",
"X = test_predict1 - y_test\n",
"\n",
"\n",
"pca = PCA(n_components=2)\n",
"X = pca.fit_transform(X)\n",
"\n",
"\n",
"# Creating the GMM instance with desired number of clusters\n",
"gmm = GaussianMixture(n_components=2)\n",
"\n",
"# Fitting the model to the data\n",
"gmm.fit(X)\n",
"\n",
"# Getting the cluster labels\n",
"labels = gmm.predict(X)\n",
"\n",
"# Plotting the data points with colors representing different clusters\n",
"plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
"plt.title('GMM Clustering')\n",
"plt.xlabel('Feature 1')\n",
"plt.ylabel('Feature 2')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "operands could not be broadcast together with shapes (199403,51) (8,) ",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[19], line 6\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# Generating random data for demonstration\u001b[39;00m\n\u001b[0;32m 5\u001b[0m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mseed(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m----> 6\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43mtest_predict1\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvar_\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m+\u001b[39m scaler\u001b[38;5;241m.\u001b[39mmean_[\u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m8\u001b[39m]\n\u001b[0;32m 8\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m6\u001b[39m\n\u001b[0;32m 10\u001b[0m kmeans \u001b[38;5;241m=\u001b[39m KMeans(n_clusters\u001b[38;5;241m=\u001b[39mk)\n",
"\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (199403,51) (8,) "
]
}
],
"source": [
"from sklearn.cluster import KMeans\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# Generating random data for demonstration\n",
"np.random.seed(0)\n",
"X = (test_predict1 - y_test)\n",
"\n",
"k = 6\n",
"\n",
"kmeans = KMeans(n_clusters=k)\n",
"\n",
"kmeans.fit(X)\n",
"\n",
"\n",
"pca = PCA(n_components=2)\n",
"X = pca.fit_transform(X)\n",
"\n",
"\n",
"\n",
"# Getting the cluster centers and labels\n",
"centroids = kmeans.cluster_centers_\n",
"centroids = pca.transform(centroids)\n",
"labels = kmeans.labels_\n",
"\n",
"# Plotting the data points and cluster centers\n",
"plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
"plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
"plt.title('KMeans Clustering')\n",
"plt.xlabel('Feature 1')\n",
"plt.ylabel('Feature 2')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"k = 60\n",
"X= test_predict1 - y_test\n",
"processed_data = []\n",
"feat_df = pd.DataFrame(columns=[\"mean\",\"std\",])\n",
"for i in range(0,len(X), 60):\n",
" mean = X[i:i+k].mean(axis = 0)\n",
" std = X[i:i+k].std(axis = 0)\n",
" max = X[i:i+k].max(axis = 0)\n",
" min = X[i:i+k].min(axis = 0)\n",
" iqr = np.percentile(X[i:i+k], 75, axis=0) - np.percentile(X[i:i+k], 25,axis=0)\n",
" data = np.concatenate([mean, std, max, min, iqr])\n",
" processed_data.append([data])\n",
"processed_data = np.concatenate(processed_data,axis=0) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = processed_data\n",
"\n",
"kmeans = KMeans(n_clusters=3, algorithm='elkan', max_iter=1000, n_init = 5)\n",
"\n",
"kmeans.fit(X)\n",
"\n",
"pca = PCA(n_components=2)\n",
"X = pca.fit_transform(X)\n",
"\n",
"\n",
"# Getting the cluster centers and labels\n",
"centroids = kmeans.cluster_centers_\n",
"centroids = pca.transform(centroids)\n",
"labels = kmeans.labels_\n",
"\n",
"# Plotting the data points and cluster centers\n",
"plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
"plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
"plt.title('KMeans Clustering')\n",
"plt.xlabel('Feature 1')\n",
"plt.ylabel('Feature 2')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.mixture import GaussianMixture\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.decomposition import PCA\n",
"\n",
"# Generating random data for demonstration\n",
"np.random.seed(0)\n",
"X = processed_data\n",
"\n",
"# Creating the GMM instance with desired number of clusters\n",
"gmm = GaussianMixture(n_components=3, init_params='k-means++')\n",
"\n",
"# Fitting the model to the data\n",
"gmm.fit(X)\n",
"labels = gmm.predict(X)\n",
"\n",
"\n",
"pca = PCA(n_components=2)\n",
"X = pca.fit_transform(X)\n",
"\n",
"\n",
"# Getting the cluster labels\n",
"\n",
"# Plotting the data points with colors representing different clusters\n",
"plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
"plt.title('GMM Clustering')\n",
"plt.xlabel('Feature 1')\n",
"plt.ylabel('Feature 2')\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# Generating random data for demonstration\n",
"np.random.seed(0)\n",
"X = (test_predict1 * scaler.var_[0:8] + scaler.mean_[0:8]) - (y_test * scaler.var_[0:8] + scaler.mean_[0:8])\n",
"k = 6\n",
"\n",
"kmeans = KMeans(n_clusters=k)\n",
"\n",
"kmeans.fit(X)\n",
"\n",
"\n",
"pca = PCA(n_components=2)\n",
"X = pca.fit_transform(X)\n",
"\n",
"\n",
"\n",
"# Getting the cluster centers and labels\n",
"centroids = kmeans.cluster_centers_\n",
"centroids = pca.transform(centroids)\n",
"labels = kmeans.labels_\n",
"\n",
"# Plotting the data points and cluster centers\n",
"plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
"plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
"plt.title('KMeans Clustering')\n",
"plt.xlabel('Feature 1')\n",
"plt.ylabel('Feature 2')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "tensorflow",
"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": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|