notebooks/proposed-method-with-google-trends.ipynb

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 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import lightgbm as lgb\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import KFold\n",
    "import datetime\n",
    "import gc\n",
    "import pickle\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import seaborn as sns; sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly\n",
    "import chart_studio.plotly as py\n",
    "import plotly.express as px\n",
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "\n",
    "from pathlib import Path\n",
    "import os\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 201 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "root = Path(r'C:\\Users\\patri\\Google trends for buildings\\Dataset\\GEPIII_dataset')\n",
    "\n",
    "df_train = pd.read_feather(root/'train.feather')\n",
    "\n",
    "weather_train = pd.read_feather(root/'weather_train.feather')\n",
    "weather_test = pd.read_feather(root/'weather_test.feather')\n",
    "\n",
    "building = pd.read_feather(root/'building_metadata.feather')\n",
    "le = LabelEncoder()\n",
    "building.primary_use = le.fit_transform(building.primary_use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_train = df_train.query('not (building_id <= 104 & meter == 0 & timestamp <= \"2016-05-20 18\")')\n",
    "df_train = df_train.query('not (building_id == 681 & meter == 0 & timestamp <= \"2016-04-27\")')\n",
    "df_train = df_train.query('not (building_id == 761 & meter == 0 & timestamp <= \"2016-09-02\")')\n",
    "df_train = df_train.query('not (building_id == 799 & meter == 0 & timestamp <= \"2016-09-02\")')\n",
    "df_train = df_train.query('not (building_id == 802 & meter == 0 & timestamp <= \"2016-08-24\")')\n",
    "df_train = df_train.query('not (building_id == 1073 & meter == 0 & timestamp <= \"2016-10-26\")')\n",
    "df_train = df_train.query('not (building_id == 1094 & meter == 0 & timestamp <= \"2016-09-08\")')\n",
    "df_train = df_train.query('not (building_id == 29 & meter == 0 & timestamp <= \"2016-08-10\")')\n",
    "df_train = df_train.query('not (building_id == 40 & meter == 0 & timestamp <= \"2016-06-04\")')\n",
    "df_train = df_train.query('not (building_id == 45 & meter == 0 & timestamp <= \"2016-07\")')\n",
    "df_train = df_train.query('not (building_id == 106 & meter == 0 & timestamp <= \"2016-11\")')\n",
    "df_train = df_train.query('not (building_id == 107 & meter == 0 & timestamp >= \"2016-11-10\")')\n",
    "df_train = df_train.query('not (building_id == 112 & meter == 0 & timestamp < \"2016-10-31 15\")')\n",
    "df_train = df_train.query('not (building_id == 144 & meter == 0 & timestamp > \"2016-05-14\" & timestamp < \"2016-10-31\")')\n",
    "df_train = df_train.query('not (building_id == 147 & meter == 0 & timestamp > \"2016-06-05 19\" & timestamp < \"2016-07-18 15\")')\n",
    "df_train = df_train.query('not (building_id == 171 & meter == 0 & timestamp <= \"2016-07-05\")')\n",
    "df_train = df_train.query('not (building_id == 177 & meter == 0 & timestamp > \"2016-06-04\" & timestamp < \"2016-06-25\")')\n",
    "df_train = df_train.query('not (building_id == 258 & meter == 0 & timestamp > \"2016-09-26\" & timestamp < \"2016-12-12\")')\n",
    "df_train = df_train.query('not (building_id == 258 & meter == 0 & timestamp > \"2016-08-30\" & timestamp < \"2016-09-08\")')\n",
    "df_train = df_train.query('not (building_id == 258 & meter == 0 & timestamp > \"2016-09-18\" & timestamp < \"2016-09-25\")')\n",
    "df_train = df_train.query('not (building_id == 260 & meter == 0 & timestamp <= \"2016-05-11\")')\n",
    "df_train = df_train.query('not (building_id == 269 & meter == 0 & timestamp > \"2016-06-04\" & timestamp < \"2016-06-25\")')\n",
    "df_train = df_train.query('not (building_id == 304 & meter == 0 & timestamp >= \"2016-11-20\")')\n",
    "df_train = df_train.query('not (building_id == 545 & meter == 0 & timestamp > \"2016-01-17\" & timestamp < \"2016-02-10\")')\n",
    "df_train = df_train.query('not (building_id == 604 & meter == 0 & timestamp < \"2016-11-21\")')\n",
    "df_train = df_train.query('not (building_id == 693 & meter == 0 & timestamp > \"2016-09-07\" & timestamp < \"2016-11-23\")')\n",
    "df_train = df_train.query('not (building_id == 693 & meter == 0 & timestamp > \"2016-07-12\" & timestamp < \"2016-05-29\")')\n",
    "df_train = df_train.query('not (building_id == 723 & meter == 0 & timestamp > \"2016-10-06\" & timestamp < \"2016-11-22\")')\n",
    "df_train = df_train.query('not (building_id == 733 & meter == 0 & timestamp > \"2016-05-29\" & timestamp < \"2016-06-22\")')\n",
    "df_train = df_train.query('not (building_id == 733 & meter == 0 & timestamp > \"2016-05-19\" & timestamp < \"2016-05-20\")')\n",
    "df_train = df_train.query('not (building_id == 803 & meter == 0 & timestamp > \"2016-9-25\")')\n",
    "df_train = df_train.query('not (building_id == 815 & meter == 0 & timestamp > \"2016-05-17\" & timestamp < \"2016-11-17\")')\n",
    "df_train = df_train.query('not (building_id == 848 & meter == 0 & timestamp > \"2016-01-15\" & timestamp < \"2016-03-20\")')\n",
    "df_train = df_train.query('not (building_id == 857 & meter == 0 & timestamp > \"2016-04-13\")')\n",
    "df_train = df_train.query('not (building_id == 909 & meter == 0 & timestamp < \"2016-02-02\")')\n",
    "df_train = df_train.query('not (building_id == 909 & meter == 0 & timestamp < \"2016-06-23\")')\n",
    "df_train = df_train.query('not (building_id == 1008 & meter == 0 & timestamp > \"2016-10-30\" & timestamp < \"2016-11-21\")')\n",
    "df_train = df_train.query('not (building_id == 1113 & meter == 0 & timestamp < \"2016-07-27\")')\n",
    "df_train = df_train.query('not (building_id == 1153 & meter == 0 & timestamp < \"2016-01-20\")')\n",
    "df_train = df_train.query('not (building_id == 1169 & meter == 0 & timestamp < \"2016-08-03\")')\n",
    "df_train = df_train.query('not (building_id == 1170 & meter == 0 & timestamp > \"2016-06-30\" & timestamp < \"2016-07-05\")')\n",
    "df_train = df_train.query('not (building_id == 1221 & meter == 0 & timestamp < \"2016-11-04\")')\n",
    "df_train = df_train.query('not (building_id == 1225 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1234 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1233 & building_id <= 1234 & meter == 0 & timestamp > \"2016-01-13 22\" & timestamp < \"2016-03-08 12\")')\n",
    "df_train = df_train.query('not (building_id == 1241 & meter == 0 & timestamp > \"2016-07-14\" & timestamp < \"2016-11-19\")')\n",
    "df_train = df_train.query('not (building_id == 1250 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1255 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1264 & meter == 0 & timestamp > \"2016-08-23\")')\n",
    "df_train = df_train.query('not (building_id == 1265 & meter == 0 & timestamp > \"2016-05-06\" & timestamp < \"2016-05-26\")')\n",
    "df_train = df_train.query('not (building_id == 1272 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1275 & building_id <= 1280 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1283 & meter == 0 & timestamp > \"2016-07-08\" & timestamp < \"2016-08-03\")')\n",
    "df_train = df_train.query('not (building_id >= 1291 & building_id <= 1302 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1303 & meter == 0 & timestamp > \"2016-07-25 22\" & timestamp < \"2016-07-27 16\")')\n",
    "df_train = df_train.query('not (building_id == 1303 & meter == 0 & timestamp > \"2016-01-26\" & timestamp < \"2016-06-02 12\")')\n",
    "df_train = df_train.query('not (building_id == 1319 & meter == 0 & timestamp > \"2016-05-17 16\" & timestamp < \"2016-06-07 12\")')\n",
    "df_train = df_train.query('not (building_id == 1319 & meter == 0 & timestamp > \"2016-08-18 14\" & timestamp < \"2016-09-02 14\")')\n",
    "df_train = df_train.query('not (building_id == 1322 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "\n",
    "# 2nd cleaning\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 0 & timestamp > \"2016-10-14 22\" & timestamp < \"2016-10-17 08\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 0 & timestamp > \"2016-07-01 14\" & timestamp < \"2016-07-05 06\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 1 & timestamp > \"2016-10-14 22\" & timestamp < \"2016-10-17 08\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 1 & timestamp > \"2016-07-01 14\" & timestamp < \"2016-07-05 06\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 2 & timestamp > \"2016-10-14 22\" & timestamp < \"2016-10-17 08\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 2 & timestamp > \"2016-07-01 14\" & timestamp < \"2016-07-05 06\")')\n",
    "df_train = df_train.query('not (building_id == 1272 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1291 & building_id <= 1297 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1300 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1302 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1291 & building_id <= 1299 & meter == 2 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1221 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1225 & building_id <= 1226 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1233 & building_id <= 1234 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1241 & meter == 0 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1223 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1226 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1233 & building_id <= 1234 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1225 & building_id <= 1226 & meter == 2 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1305 & meter == 2 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1307 & meter == 2 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1223 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1231 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1233 & building_id <= 1234 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1272 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id >= 1275 & building_id <= 1297 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1300 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1302 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1293 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-25 12\")')\n",
    "df_train = df_train.query('not (building_id == 1302 & meter == 3 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-25 12\")')\n",
    "df_train = df_train.query('not (building_id == 1223 & meter == 0 & timestamp > \"2016-9-28 07\" & timestamp < \"2016-10-11 18\")')\n",
    "df_train = df_train.query('not (building_id == 1225 & meter == 1 & timestamp > \"2016-8-22 23\" & timestamp < \"2016-10-11 14\")')\n",
    "df_train = df_train.query('not (building_id == 1230 & meter == 1 & timestamp > \"2016-8-22 08\" & timestamp < \"2016-10-05 18\")')\n",
    "df_train = df_train.query('not (building_id == 904 & meter == 0 & timestamp < \"2016-02-17 08\")')\n",
    "df_train = df_train.query('not (building_id == 986 & meter == 0 & timestamp < \"2016-02-17 08\")')\n",
    "df_train = df_train.query('not (building_id == 954 & meter == 0 & timestamp < \"2016-08-08 11\")')\n",
    "df_train = df_train.query('not (building_id == 954 & meter == 0 & timestamp < \"2016-06-23 08\")')\n",
    "df_train = df_train.query('not (building_id >= 745 & building_id <= 770 & meter == 1 & timestamp > \"2016-10-05 01\" & timestamp < \"2016-10-10 09\")')\n",
    "df_train = df_train.query('not (building_id >= 774 & building_id <= 787 & meter == 1 & timestamp > \"2016-10-05 01\" & timestamp < \"2016-10-10 09\")')\n",
    "\n",
    "# 3rd cleaning hourly spikes\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 0 & timestamp > \"2016-05-11 09\" & timestamp < \"2016-05-12 01\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 1 & timestamp > \"2016-05-11 09\" & timestamp < \"2016-05-12 01\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 2 & timestamp > \"2016-05-11 09\" & timestamp < \"2016-05-12 01\")')\n",
    "\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 0 & timestamp == \"2016-02-26 01\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 1 & timestamp == \"2016-02-26 01\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 2 & timestamp == \"2016-02-26 01\")')\n",
    "\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 0 & timestamp > \"2016-03-29 10\" & timestamp < \"2016-03-30 12\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 1 & timestamp > \"2016-03-29 10\" & timestamp < \"2016-03-30 12\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 2 & timestamp > \"2016-03-29 10\" & timestamp < \"2016-03-30 12\")')\n",
    "\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 0 & timestamp > \"2016-01-19 23\" & timestamp < \"2016-01-28 15\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 1 & timestamp > \"2016-01-19 23\" & timestamp < \"2016-01-28 15\")')\n",
    "df_train = df_train.query('not (building_id >= 874 & building_id <= 997 & meter == 2 & timestamp > \"2016-01-19 23\" & timestamp < \"2016-01-28 15\")')\n",
    "\n",
    "df_train = df_train.query('not (building_id != 1227 & building_id != 1281 & building_id != 1314 & building_id >=1223 & building_id < 1335 & meter==0 & meter_reading==0)')\n",
    "\n",
    "# 4th cleaning (using hindsight from leaks)\n",
    "df_train = df_train.query('not (building_id >= 1223 & building_id <= 1324 & meter==1 & timestamp > \"2016-07-16 04\" & timestamp < \"2016-07-19 11\")')\n",
    "df_train = df_train.query('not (building_id == 107 & meter == 0 & timestamp <= \"2016-07-06\")')\n",
    "# df_train = df_train.query('not (building_id == 53 & meter == 0)')\n",
    "df_train = df_train.query('not (building_id == 180 & timestamp >= \"2016-02-17 12\")')\n",
    "df_train = df_train.query('not (building_id == 182 & meter == 0)')\n",
    "df_train = df_train.query('not (building_id == 191 & meter == 0 & timestamp >= \"2016-12-22 09\")')\n",
    "df_train = df_train.query('not (building_id == 192 & meter == 1 & timestamp >= \"2016-05-09 18\")')\n",
    "df_train = df_train.query('not (building_id == 192 & meter == 3 & timestamp >= \"2016-03-29 05\" & timestamp <= \"2016-04-04 08\")')\n",
    "df_train = df_train.query('not (building_id == 207 & meter == 1 & timestamp > \"2016-07-02 20\" & timestamp < \"2016-08-25 12\")')\n",
    "# df_train = df_train.query('not (building_id == 218)')\n",
    "df_train = df_train.query('not (building_id == 258 & timestamp > \"2016-09-18\" & timestamp < \"2016-12-12 13\")')\n",
    "df_train = df_train.query('not (building_id == 258 & timestamp > \"2016-08-29 08\" & timestamp < \"2016-09-08 14\")')\n",
    "df_train = df_train.query('not (building_id == 257 & meter == 1 & timestamp < \"2016-03-25 16\")')\n",
    "df_train = df_train.query('not (building_id == 260 & meter == 1 & timestamp > \"2016-05-10 17\" & timestamp < \"2016-08-17 11\")')\n",
    "df_train = df_train.query('not (building_id == 260 & meter == 1 & timestamp > \"2016-08-28 01\" & timestamp < \"2016-10-31 13\")')\n",
    "# df_train = df_train.query('not (building_id == 279 & meter == 3)')\n",
    "# df_train = df_train.query('not (building_id == 287 & meter == 1)')\n",
    "# df_train = df_train.query('not (building_id == 287 & meter == 3)')\n",
    "df_train = df_train.query('not (building_id == 220 & meter == 1 & timestamp > \"2016-09-23 01\" & timestamp < \"2016-09-23 12\")')\n",
    "df_train = df_train.query('not (building_id == 281 & meter == 1 & timestamp > \"2016-10-25 08\" & timestamp < \"2016-11-04 15\")')\n",
    "df_train = df_train.query('not (building_id == 273 & meter == 1 & timestamp > \"2016-04-03 04\" & timestamp < \"2016-04-29 15\")')\n",
    "df_train = df_train.query('not (building_id == 28 & meter == 0 & timestamp < \"2016-10-14 20\")')\n",
    "df_train = df_train.query('not (building_id == 71 & meter == 0 & timestamp < \"2016-08-18 20\")')\n",
    "df_train = df_train.query('not (building_id == 76 & meter == 0 & timestamp > \"2016-06-04 09\" & timestamp < \"2016-06-04 14\")')\n",
    "df_train = df_train.query('not (building_id == 101 & meter == 0 & timestamp > \"2016-10-12 13\" & timestamp < \"2016-10-12 18\")')\n",
    "df_train = df_train.query('not (building_id == 7 & meter == 1 & timestamp > \"2016-11-03 09\" & timestamp < \"2016-11-28 14\")')\n",
    "df_train = df_train.query('not (building_id == 9 & meter == 1 & timestamp > \"2016-12-06 08\")')\n",
    "df_train = df_train.query('not (building_id == 43 & meter == 1 & timestamp > \"2016-04-03 08\" & timestamp < \"2016-06-06 13\")')\n",
    "df_train = df_train.query('not (building_id == 60 & meter == 1 & timestamp > \"2016-05-01 17\" & timestamp < \"2016-05-01 21\")')\n",
    "df_train = df_train.query('not (building_id == 75 & meter == 1 & timestamp > \"2016-08-05 13\" & timestamp < \"2016-08-26 12\")')\n",
    "df_train = df_train.query('not (building_id == 95 & meter == 1 & timestamp > \"2016-08-08 10\" & timestamp < \"2016-08-26 13\")')\n",
    "df_train = df_train.query('not (building_id == 97 & meter == 1 & timestamp > \"2016-08-08 14\" & timestamp < \"2016-08-25 14\")')\n",
    "df_train = df_train.query('not (building_id == 1232 & meter == 1 & timestamp > \"2016-06-23 16\" & timestamp < \"2016-08-31 20\")')\n",
    "df_train = df_train.query('not (building_id == 1236 & meter == 1 & meter_reading >= 3000)')\n",
    "df_train = df_train.query('not (building_id == 1239 & meter == 1 & timestamp > \"2016-03-11 16\" & timestamp < \"2016-03-27 17\")')\n",
    "df_train = df_train.query('not (building_id == 1264 & meter == 1 & timestamp > \"2016-08-22 17\" & timestamp < \"2016-09-22 20\")')\n",
    "df_train = df_train.query('not (building_id == 1264 & meter == 1 & timestamp > \"2016-09-28 07\" & timestamp < \"2016-10-20 13\")')\n",
    "df_train = df_train.query('not (building_id == 1269 & meter == 1 & meter_reading >= 2000)')\n",
    "df_train = df_train.query('not (building_id == 1272 & meter == 1 & timestamp > \"2016-08-11 12\" & timestamp < \"2016-08-30 19\")')\n",
    "df_train = df_train.query('not (building_id == 1273 & meter == 1 & timestamp > \"2016-05-31 14\" & timestamp < \"2016-06-17\")')\n",
    "df_train = df_train.query('not (building_id == 1276 & meter == 1 & timestamp < \"2016-02-03 23\")')\n",
    "df_train = df_train.query('not (building_id == 1280 & meter == 1 & timestamp > \"2016-05-18\" & timestamp < \"2016-05-26 09\")')\n",
    "df_train = df_train.query('not (building_id == 1280 & meter == 1 & timestamp > \"2016-02-28 23\" & timestamp < \"2016-05-02 05\")')\n",
    "df_train = df_train.query('not (building_id == 1280 & meter == 1 & timestamp > \"2016-06-12 01\" & timestamp < \"2016-7-07 06\")')\n",
    "df_train = df_train.query('not (building_id == 1288 & meter == 1 & timestamp > \"2016-07-07 15\" & timestamp < \"2016-08-12 17\")')\n",
    "df_train = df_train.query('not (building_id == 1311 & meter == 1 & timestamp > \"2016-04-25 18\" & timestamp < \"2016-05-13 14\")')\n",
    "df_train = df_train.query('not (building_id == 1099 & meter == 2)')\n",
    "\n",
    "df_train = df_train.query('not (building_id == 1329 & meter == 0 & timestamp > \"2016-04-28 00\" & timestamp < \"2016-04-28 07\")')\n",
    "df_train = df_train.query('not (building_id == 1331 & meter == 0 & timestamp > \"2016-04-28 00\" & timestamp < \"2016-04-28 07\")')\n",
    "df_train = df_train.query('not (building_id == 1427 & meter == 0 & timestamp > \"2016-04-11 10\" & timestamp < \"2016-04-11 14\")')\n",
    "df_train = df_train.query('not (building_id == 1426 & meter == 2 & timestamp > \"2016-05-03 09\" & timestamp < \"2016-05-03 14\")')\n",
    "df_train = df_train.query('not (building_id == 1345 & meter == 0 & timestamp < \"2016-03-01\")')\n",
    "df_train = df_train.query('not (building_id == 1346 & timestamp < \"2016-03-01\")')\n",
    "df_train = df_train.query('not (building_id == 1359 & meter == 0 & timestamp > \"2016-04-25 17\" & timestamp < \"2016-07-22 14\")')\n",
    "df_train = df_train.query('not (building_id == 1365 & meter == 0 & timestamp > \"2016-08-19 00\" & timestamp < \"2016-08-19 07\")')\n",
    "df_train = df_train.query('not (building_id == 1365 & meter == 0 & timestamp > \"2016-06-18 22\" & timestamp < \"2016-06-19 06\")')\n",
    "\n",
    "df_train = df_train.query('not (building_id == 18 & meter == 0 & timestamp > \"2016-06-04 09\" & timestamp < \"2016-06-04 16\")')\n",
    "df_train = df_train.query('not (building_id == 18 & meter == 0 & timestamp > \"2016-11-05 05\" & timestamp < \"2016-11-05 15\")')\n",
    "df_train = df_train.query('not (building_id == 101 & meter == 0 & meter_reading > 800)')\n",
    "\n",
    "df_train = df_train.query('not (building_id == 1384 & meter == 0 & meter_reading == 0 )')\n",
    "df_train = df_train.query('not (building_id >= 1289 & building_id <= 1301 & meter == 2 & meter_reading == 0)')\n",
    "df_train = df_train.query('not (building_id == 1243 & meter == 2 & meter_reading == 0)')\n",
    "df_train = df_train.query('not (building_id == 1263 & meter == 2 & meter_reading == 0)')\n",
    "df_train = df_train.query('not (building_id == 1284 & meter == 2 & meter_reading == 0)')\n",
    "df_train = df_train.query('not (building_id == 1286 & meter == 2 & meter_reading == 0)')\n",
    "df_train = df_train.query('not (building_id == 1263 & meter == 0 & timestamp > \"2016-11-10 11\" & timestamp < \"2016-11-10 15\")')\n",
    "\n",
    "df_train = df_train.query('not (building_id == 1238 & meter == 2 & meter_reading == 0)')\n",
    "df_train = df_train.query('not (building_id == 1329 & meter == 2 & timestamp > \"2016-11-21 12\" & timestamp < \"2016-11-29 12\")')\n",
    "df_train = df_train.query('not (building_id == 1249 & meter == 2 & meter_reading == 0)')\n",
    "\n",
    "df_train = df_train.query('not (building_id == 1250 & meter == 2 & meter_reading == 0)')\n",
    "df_train = df_train.query('not (building_id == 1256 & meter == 2 & timestamp > \"2016-03-05 18\" & timestamp < \"2016-03-05 22\")')\n",
    "df_train = df_train.query('not (building_id == 1256 & meter == 2 & timestamp > \"2016-03-27 00\" & timestamp < \"2016-03-27 23\")')\n",
    "df_train = df_train.query('not (building_id == 1256 & meter == 2 & timestamp > \"2016-04-11 09\" & timestamp < \"2016-04-13 03\")')\n",
    "df_train = df_train.query('not (building_id == 1256 & meter == 2 & timestamp > \"2016-04-29 00\" & timestamp < \"2016-04-30 15\")')\n",
    "df_train = df_train.query('not (building_id == 1303 & meter == 2 & timestamp < \"2016-06-06 19\")')\n",
    "df_train = df_train.query('not (building_id >= 1223 & building_id <= 1324 & meter == 1 & timestamp > \"2016-08-11 17\" & timestamp < \"2016-08-12 17\")')\n",
    "df_train = df_train.query('not (building_id >= 1223 & building_id <= 1324 & building_id != 1296 & building_id != 129 & building_id != 1298 & building_id != 1299 & meter == 2 & timestamp > \"2016-08-11 17\" & timestamp < \"2016-08-12 17\")')\n",
    "df_train = df_train.query('not (building_id >= 1223 & building_id <= 1324 & meter == 3 & timestamp > \"2016-08-11 17\" & timestamp < \"2016-08-12 17\")')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Remove outliers\n",
    "# df_train = df_train [ df_train['building_id'] != 1099 ]\n",
    "# df_train = df_train.query('not (building_id <= 104 & meter == 0 & timestamp <= \"2016-05-20\")')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # building_meter map\n",
    "\n",
    "# bm_cols = ['bm', 'weekday', 'hour',]\n",
    "# df_train['hour'] = df_train['timestamp'].dt.hour\n",
    "# df_train['weekday'] = df_train['timestamp'].dt.weekday\n",
    "# df_train['bm'] = df_train['building_id'].apply(lambda x: str(x)) + '_' + df_train['meter'].apply(lambda x: str(x))\n",
    "# bm = df_train.groupby(bm_cols)['meter_reading'].mean().rename('bm_week_hour').to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train = df_train.merge(bm, right_index=True, left_on=bm_cols, how='left')\n",
    "# df_train.drop(['bm'], axis=1, inplace=True)\n",
    "# df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original code from https://www.kaggle.com/aitude/ashrae-missing-weather-data-handling by @aitude\n",
    "\n",
    "def fill_weather_dataset(weather_df):\n",
    "    \n",
    "    # Find Missing Dates\n",
    "    time_format = \"%Y-%m-%d %H:%M:%S\"\n",
    "    start_date = datetime.datetime.strptime(weather_df['timestamp'].min(),time_format)\n",
    "    end_date = datetime.datetime.strptime(weather_df['timestamp'].max(),time_format)\n",
    "    total_hours = int(((end_date - start_date).total_seconds() + 3600) / 3600)\n",
    "    hours_list = [(end_date - datetime.timedelta(hours=x)).strftime(time_format) for x in range(total_hours)]\n",
    "    \n",
    "    missing_hours = []\n",
    "    for site_id in range(16):\n",
    "        site_hours = np.array(weather_df[weather_df['site_id'] == site_id]['timestamp'])\n",
    "        new_rows = pd.DataFrame(np.setdiff1d(hours_list,site_hours),columns=['timestamp'])\n",
    "        new_rows['site_id'] = site_id\n",
    "        weather_df = pd.concat([weather_df,new_rows])\n",
    "\n",
    "        weather_df = weather_df.reset_index(drop=True)           \n",
    "\n",
    "    # Add new Features\n",
    "    weather_df[\"timestamp\"] = pd.to_datetime(weather_df[\"timestamp\"])\n",
    "    weather_df[\"day\"] = weather_df[\"timestamp\"].dt.day\n",
    "    weather_df[\"week\"] = weather_df[\"timestamp\"].dt.week\n",
    "    weather_df[\"month\"] = weather_df[\"timestamp\"].dt.month\n",
    "    \n",
    "    # Reset Index for Fast Update\n",
    "    weather_df = weather_df.set_index(['site_id','day','month'])\n",
    "\n",
    "    air_temperature_filler = pd.DataFrame(weather_df.groupby(['site_id','day','month'])['air_temperature'].mean(),columns=[\"air_temperature\"])\n",
    "    weather_df.update(air_temperature_filler,overwrite=False)\n",
    "\n",
    "    # Step 1\n",
    "    cloud_coverage_filler = weather_df.groupby(['site_id','day','month'])['cloud_coverage'].mean()\n",
    "    # Step 2\n",
    "    cloud_coverage_filler = pd.DataFrame(cloud_coverage_filler.fillna(method='ffill'),columns=[\"cloud_coverage\"])\n",
    "\n",
    "    weather_df.update(cloud_coverage_filler,overwrite=False)\n",
    "\n",
    "    due_temperature_filler = pd.DataFrame(weather_df.groupby(['site_id','day','month'])['dew_temperature'].mean(),columns=[\"dew_temperature\"])\n",
    "    weather_df.update(due_temperature_filler,overwrite=False)\n",
    "\n",
    "    # Step 1\n",
    "    sea_level_filler = weather_df.groupby(['site_id','day','month'])['sea_level_pressure'].mean()\n",
    "    # Step 2\n",
    "    sea_level_filler = pd.DataFrame(sea_level_filler.fillna(method='ffill'),columns=['sea_level_pressure'])\n",
    "\n",
    "    weather_df.update(sea_level_filler,overwrite=False)\n",
    "\n",
    "    wind_direction_filler =  pd.DataFrame(weather_df.groupby(['site_id','day','month'])['wind_direction'].mean(),columns=['wind_direction'])\n",
    "    weather_df.update(wind_direction_filler,overwrite=False)\n",
    "\n",
    "    wind_speed_filler =  pd.DataFrame(weather_df.groupby(['site_id','day','month'])['wind_speed'].mean(),columns=['wind_speed'])\n",
    "    weather_df.update(wind_speed_filler,overwrite=False)\n",
    "\n",
    "    # Step 1\n",
    "    precip_depth_filler = weather_df.groupby(['site_id','day','month'])['precip_depth_1_hr'].mean()\n",
    "    # Step 2\n",
    "    precip_depth_filler = pd.DataFrame(precip_depth_filler.fillna(method='ffill'),columns=['precip_depth_1_hr'])\n",
    "\n",
    "    weather_df.update(precip_depth_filler,overwrite=False)\n",
    "\n",
    "    weather_df = weather_df.reset_index()\n",
    "    weather_df = weather_df.drop(['day','week','month'],axis=1)\n",
    "        \n",
    "    return weather_df\n",
    "\n",
    "# Original code from https://www.kaggle.com/gemartin/load-data-reduce-memory-usage by @gemartin\n",
    "\n",
    "from pandas.api.types import is_datetime64_any_dtype as is_datetime\n",
    "from pandas.api.types import is_categorical_dtype\n",
    "\n",
    "def reduce_mem_usage(df, use_float16=False):\n",
    "    \"\"\"\n",
    "    Iterate through all the columns of a dataframe and modify the data type to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    \n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print(\"Memory usage of dataframe is {:.2f} MB\".format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        if is_datetime(df[col]) or is_categorical_dtype(df[col]):\n",
    "            continue\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == \"int\":\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if use_float16 and c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype(\"category\")\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print(\"Memory usage after optimization is: {:.2f} MB\".format(end_mem))\n",
    "    print(\"Decreased by {:.1f}%\".format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "def features_engineering(df):\n",
    "    \n",
    "    # Sort by timestamp\n",
    "    df.sort_values(\"timestamp\")\n",
    "    df.reset_index(drop=True)\n",
    "    \n",
    "    # Add more features\n",
    "    df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"],format=\"%Y-%m-%d %H:%M:%S\")\n",
    "    df[\"hour\"] = df[\"timestamp\"].dt.hour\n",
    "    df[\"weekday\"] = df[\"timestamp\"].dt.weekday\n",
    "    \n",
    "    df['square_feet'] =  np.log1p(df['square_feet'])\n",
    "    df['sm'] = df['site_id'].apply(lambda x: str(x)) + '_' + df['meter'].apply(lambda x: str(x))\n",
    "    \n",
    "    \n",
    "    # Remove Unused Columns\n",
    "    drop = [\"timestamp\",'site_id',\"sea_level_pressure\", \"wind_direction\", \"wind_speed\",\"year_built\",\"floor_count\"]\n",
    "    df = df.drop(drop, axis=1)\n",
    "    gc.collect()\n",
    "    \n",
    "    # Encode Categorical Data\n",
    "    le = LabelEncoder()\n",
    "    df[\"primary_use\"] = le.fit_transform(df[\"primary_use\"])\n",
    "    \n",
    "    # reduce memory\n",
    "    df = reduce_mem_usage(df, use_float16=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(ytrue, ypred):\n",
    "    return np.sqrt(np.mean(np.square(ypred - ytrue), axis=0))\n",
    "def rmsle(ytrue, ypred):\n",
    "    return np.sqrt(np.mean(np.square(np.log1p(ypred) - np.log1p(ytrue)), axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill Weather Information\n",
    "\n",
    "I'm using [this kernel](https://www.kaggle.com/aitude/ashrae-missing-weather-data-handling) to handle missing weather information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_train['timestamp'] = weather_train['timestamp'].astype('str')\n",
    "weather_train = fill_weather_dataset(weather_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Memory Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 426.99 MB\n",
      "Memory usage after optimization is: 426.99 MB\n",
      "Decreased by 0.0%\n",
      "Memory usage of dataframe is 0.03 MB\n",
      "Memory usage after optimization is: 0.02 MB\n",
      "Decreased by 36.7%\n",
      "Memory usage of dataframe is 5.90 MB\n",
      "Memory usage after optimization is: 3.08 MB\n",
      "Decreased by 47.7%\n"
     ]
    }
   ],
   "source": [
    "df_train = reduce_mem_usage(df_train,use_float16=True)\n",
    "building = reduce_mem_usage(building,use_float16=True)\n",
    "weather_train = reduce_mem_usage(weather_train,use_float16=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Data\n",
    "\n",
    "We need to add building and weather information into training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.merge(building, left_on='building_id',right_on='building_id',how='left')\n",
    "df_train = df_train.merge(weather_train,how='left',left_on=['site_id','timestamp'],right_on=['site_id','timestamp'])\n",
    "del weather_train\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add google trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>-1.833676</td>\n",
       "      <td>-1.960736</td>\n",
       "      <td>-1.129372</td>\n",
       "      <td>-1.531382</td>\n",
       "      <td>0.110405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.953329</td>\n",
       "      <td>-1.699726</td>\n",
       "      <td>-0.706947</td>\n",
       "      <td>-1.377623</td>\n",
       "      <td>-1.428354</td>\n",
       "      <td>-1.227533</td>\n",
       "      <td>-1.070068</td>\n",
       "      <td>-1.041021</td>\n",
       "      <td>-1.531826</td>\n",
       "      <td>-1.878145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204981</td>\n",
       "      <td>0.046385</td>\n",
       "      <td>-1.391724</td>\n",
       "      <td>-1.164136</td>\n",
       "      <td>-0.847735</td>\n",
       "      <td>-1.291567</td>\n",
       "      <td>-1.906615</td>\n",
       "      <td>-1.184466</td>\n",
       "      <td>-0.651313</td>\n",
       "      <td>0.110405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.742953</td>\n",
       "      <td>-1.389145</td>\n",
       "      <td>-0.934356</td>\n",
       "      <td>-1.428898</td>\n",
       "      <td>-1.083383</td>\n",
       "      <td>-0.935830</td>\n",
       "      <td>-1.006042</td>\n",
       "      <td>-0.951320</td>\n",
       "      <td>-1.264034</td>\n",
       "      <td>-1.721785</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.983600</td>\n",
       "      <td>-0.105918</td>\n",
       "      <td>-0.671917</td>\n",
       "      <td>-0.267400</td>\n",
       "      <td>-0.654401</td>\n",
       "      <td>-0.957961</td>\n",
       "      <td>-1.203041</td>\n",
       "      <td>-0.716166</td>\n",
       "      <td>-0.777037</td>\n",
       "      <td>-0.441035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.045242</td>\n",
       "      <td>0.598570</td>\n",
       "      <td>0.884921</td>\n",
       "      <td>1.545045</td>\n",
       "      <td>0.871449</td>\n",
       "      <td>0.425451</td>\n",
       "      <td>-0.002967</td>\n",
       "      <td>-0.263615</td>\n",
       "      <td>0.572261</td>\n",
       "      <td>0.258778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.813092</td>\n",
       "      <td>-0.277259</td>\n",
       "      <td>1.607470</td>\n",
       "      <td>3.639809</td>\n",
       "      <td>0.505607</td>\n",
       "      <td>0.710068</td>\n",
       "      <td>0.204105</td>\n",
       "      <td>-0.523337</td>\n",
       "      <td>0.354480</td>\n",
       "      <td>-0.091790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>1.728964</td>\n",
       "      <td>0.847035</td>\n",
       "      <td>1.112331</td>\n",
       "      <td>1.339946</td>\n",
       "      <td>0.353993</td>\n",
       "      <td>0.425451</td>\n",
       "      <td>-0.024309</td>\n",
       "      <td>-0.024413</td>\n",
       "      <td>0.266212</td>\n",
       "      <td>0.779978</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.458413</td>\n",
       "      <td>-0.239183</td>\n",
       "      <td>1.067615</td>\n",
       "      <td>1.910389</td>\n",
       "      <td>1.085611</td>\n",
       "      <td>0.584966</td>\n",
       "      <td>0.853558</td>\n",
       "      <td>-0.688619</td>\n",
       "      <td>0.647837</td>\n",
       "      <td>0.183930</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>2018-12-27</th>\n",
       "      <td>-1.527578</td>\n",
       "      <td>-1.076350</td>\n",
       "      <td>-2.031472</td>\n",
       "      <td>-0.869256</td>\n",
       "      <td>-0.681949</td>\n",
       "      <td>-0.925210</td>\n",
       "      <td>-1.177585</td>\n",
       "      <td>-1.368253</td>\n",
       "      <td>-1.510374</td>\n",
       "      <td>-0.684214</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401073</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>-1.520183</td>\n",
       "      <td>-1.268215</td>\n",
       "      <td>-1.326939</td>\n",
       "      <td>-1.542094</td>\n",
       "      <td>-1.399374</td>\n",
       "      <td>-0.369563</td>\n",
       "      <td>0.047030</td>\n",
       "      <td>-0.379788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>-1.108932</td>\n",
       "      <td>-1.145681</td>\n",
       "      <td>-1.264956</td>\n",
       "      <td>-1.200809</td>\n",
       "      <td>-1.550535</td>\n",
       "      <td>-1.137309</td>\n",
       "      <td>-1.243969</td>\n",
       "      <td>-1.477271</td>\n",
       "      <td>-1.729988</td>\n",
       "      <td>-0.367104</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.464454</td>\n",
       "      <td>0.309800</td>\n",
       "      <td>-1.520183</td>\n",
       "      <td>-1.172772</td>\n",
       "      <td>-1.428614</td>\n",
       "      <td>-1.481530</td>\n",
       "      <td>-0.901392</td>\n",
       "      <td>-0.077421</td>\n",
       "      <td>-0.120080</td>\n",
       "      <td>-0.442056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>-1.527578</td>\n",
       "      <td>-1.423004</td>\n",
       "      <td>-1.913546</td>\n",
       "      <td>-1.808655</td>\n",
       "      <td>-2.018235</td>\n",
       "      <td>-1.349407</td>\n",
       "      <td>-1.210777</td>\n",
       "      <td>-1.331914</td>\n",
       "      <td>-1.598220</td>\n",
       "      <td>-2.206343</td>\n",
       "      <td>...</td>\n",
       "      <td>1.246818</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>-1.675955</td>\n",
       "      <td>-1.459102</td>\n",
       "      <td>-1.479452</td>\n",
       "      <td>-1.481530</td>\n",
       "      <td>-1.676031</td>\n",
       "      <td>-1.070705</td>\n",
       "      <td>-1.874744</td>\n",
       "      <td>2.110938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>-1.946223</td>\n",
       "      <td>-2.116311</td>\n",
       "      <td>-1.323919</td>\n",
       "      <td>-1.642878</td>\n",
       "      <td>-2.151864</td>\n",
       "      <td>-1.561506</td>\n",
       "      <td>-1.310353</td>\n",
       "      <td>-1.440931</td>\n",
       "      <td>-1.773911</td>\n",
       "      <td>-0.557370</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.492628</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>-1.675955</td>\n",
       "      <td>-1.411381</td>\n",
       "      <td>-1.479452</td>\n",
       "      <td>-1.602657</td>\n",
       "      <td>-2.008019</td>\n",
       "      <td>-0.194278</td>\n",
       "      <td>-1.373411</td>\n",
       "      <td>-0.753397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>-1.228545</td>\n",
       "      <td>-2.046980</td>\n",
       "      <td>-1.913546</td>\n",
       "      <td>-1.256067</td>\n",
       "      <td>-0.748763</td>\n",
       "      <td>-1.561506</td>\n",
       "      <td>-1.210777</td>\n",
       "      <td>-1.477271</td>\n",
       "      <td>-1.598220</td>\n",
       "      <td>-1.889233</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401073</td>\n",
       "      <td>0.118117</td>\n",
       "      <td>-1.675955</td>\n",
       "      <td>-1.411381</td>\n",
       "      <td>-1.326939</td>\n",
       "      <td>-1.723784</td>\n",
       "      <td>-1.676031</td>\n",
       "      <td>-1.362848</td>\n",
       "      <td>-1.456967</td>\n",
       "      <td>0.118357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1096 rows × 137 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CA_Career  CA_Company  CA_Education  CA_Employment  CA_Enterprise  \\\n",
       "date                                                                            \n",
       "2016-01-01  -1.637050   -2.010306     -1.218619      -1.839097      -2.233285   \n",
       "2016-01-02  -0.953329   -1.699726     -0.706947      -1.377623      -1.428354   \n",
       "2016-01-03  -0.742953   -1.389145     -0.934356      -1.428898      -1.083383   \n",
       "2016-01-04   1.045242    0.598570      0.884921       1.545045       0.871449   \n",
       "2016-01-05   1.728964    0.847035      1.112331       1.339946       0.353993   \n",
       "...               ...         ...           ...            ...            ...   \n",
       "2018-12-27  -1.527578   -1.076350     -2.031472      -0.869256      -0.681949   \n",
       "2018-12-28  -1.108932   -1.145681     -1.264956      -1.200809      -1.550535   \n",
       "2018-12-29  -1.527578   -1.423004     -1.913546      -1.808655      -2.018235   \n",
       "2018-12-30  -1.946223   -2.116311     -1.323919      -1.642878      -2.151864   \n",
       "2018-12-31  -1.228545   -2.046980     -1.913546      -1.256067      -0.748763   \n",
       "\n",
       "            CA_Google  CA_Google Classroom  CA_Google Docs  CA_Google Drive  \\\n",
       "date                                                                          \n",
       "2016-01-01  -1.033064            -0.984700       -1.190522        -1.570083   \n",
       "2016-01-02  -1.227533            -1.070068       -1.041021        -1.531826   \n",
       "2016-01-03  -0.935830            -1.006042       -0.951320        -1.264034   \n",
       "2016-01-04   0.425451            -0.002967       -0.263615         0.572261   \n",
       "2016-01-05   0.425451            -0.024309       -0.024413         0.266212   \n",
       "...               ...                  ...             ...              ...   \n",
       "2018-12-27  -0.925210            -1.177585       -1.368253        -1.510374   \n",
       "2018-12-28  -1.137309            -1.243969       -1.477271        -1.729988   \n",
       "2018-12-29  -1.349407            -1.210777       -1.331914        -1.598220   \n",
       "2018-12-30  -1.561506            -1.310353       -1.440931        -1.773911   \n",
       "2018-12-31  -1.561506            -1.210777       -1.477271        -1.598220   \n",
       "\n",
       "            CA_Health Care  ...  US_Residential building  US_Retail  \\\n",
       "date                        ...                                       \n",
       "2016-01-01       -2.242986  ...                -0.292565   0.655597   \n",
       "2016-01-02       -1.878145  ...                 0.204981   0.046385   \n",
       "2016-01-03       -1.721785  ...                -0.983600  -0.105918   \n",
       "2016-01-04        0.258778  ...                 0.813092  -0.277259   \n",
       "2016-01-05        0.779978  ...                -0.458413  -0.239183   \n",
       "...                    ...  ...                      ...        ...   \n",
       "2018-12-27       -0.684214  ...                -0.401073   0.501484   \n",
       "2018-12-28       -0.367104  ...                -0.464454   0.309800   \n",
       "2018-12-29       -2.206343  ...                 1.246818   0.501484   \n",
       "2018-12-30       -0.557370  ...                -2.492628   0.501484   \n",
       "2018-12-31       -1.889233  ...                -0.401073   0.118117   \n",
       "\n",
       "            US_School  US_School district  US_Science  US_Secondary school  \\\n",
       "date                                                                         \n",
       "2016-01-01  -1.751627           -1.548452   -1.331072            -1.833676   \n",
       "2016-01-02  -1.391724           -1.164136   -0.847735            -1.291567   \n",
       "2016-01-03  -0.671917           -0.267400   -0.654401            -0.957961   \n",
       "2016-01-04   1.607470            3.639809    0.505607             0.710068   \n",
       "2016-01-05   1.067615            1.910389    1.085611             0.584966   \n",
       "...               ...                 ...         ...                  ...   \n",
       "2018-12-27  -1.520183           -1.268215   -1.326939            -1.542094   \n",
       "2018-12-28  -1.520183           -1.172772   -1.428614            -1.481530   \n",
       "2018-12-29  -1.675955           -1.459102   -1.479452            -1.481530   \n",
       "2018-12-30  -1.675955           -1.411381   -1.479452            -1.602657   \n",
       "2018-12-31  -1.675955           -1.411381   -1.326939            -1.723784   \n",
       "\n",
       "            US_Technology  US_Utility  US_Warehouse  US_storage  \n",
       "date                                                             \n",
       "2016-01-01      -1.960736   -1.129372     -1.531382    0.110405  \n",
       "2016-01-02      -1.906615   -1.184466     -0.651313    0.110405  \n",
       "2016-01-03      -1.203041   -0.716166     -0.777037   -0.441035  \n",
       "2016-01-04       0.204105   -0.523337      0.354480   -0.091790  \n",
       "2016-01-05       0.853558   -0.688619      0.647837    0.183930  \n",
       "...                   ...         ...           ...         ...  \n",
       "2018-12-27      -1.399374   -0.369563      0.047030   -0.379788  \n",
       "2018-12-28      -0.901392   -0.077421     -0.120080   -0.442056  \n",
       "2018-12-29      -1.676031   -1.070705     -1.874744    2.110938  \n",
       "2018-12-30      -2.008019   -0.194278     -1.373411   -0.753397  \n",
       "2018-12-31      -1.676031   -1.362848     -1.456967    0.118357  \n",
       "\n",
       "[1096 rows x 137 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_trends = r'C:\\Users\\patri\\Google trends for buildings\\Dataset'\n",
    "file = 'df_googleTrend_topic_20210406.pickle'\n",
    "df_googleTrend_topic_merged = pd.read_pickle(os.path.join(path_trends, file)).sort_index()\n",
    "df_googleTrend_topic_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_googleTrend_topic_merged = df_googleTrend_topic_merged.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>geo_topic</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>CA_Career</td>\n",
       "      <td>-1.637050</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>CA_Career</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>CA_Career</td>\n",
       "      <td>-0.742953</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
       "      <th>150147</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>US_storage</td>\n",
       "      <td>-0.379788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150148</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>US_storage</td>\n",
       "      <td>-0.442056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150149</th>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>US_storage</td>\n",
       "      <td>2.110938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150150</th>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>US_storage</td>\n",
       "      <td>-0.753397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150151</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>US_storage</td>\n",
       "      <td>0.118357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150152 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date   geo_topic  calendar_value\n",
       "0      2016-01-01   CA_Career       -1.637050\n",
       "1      2016-01-02   CA_Career       -0.953329\n",
       "2      2016-01-03   CA_Career       -0.742953\n",
       "3      2016-01-04   CA_Career        1.045242\n",
       "4      2016-01-05   CA_Career        1.728964\n",
       "...           ...         ...             ...\n",
       "150147 2018-12-27  US_storage       -0.379788\n",
       "150148 2018-12-28  US_storage       -0.442056\n",
       "150149 2018-12-29  US_storage        2.110938\n",
       "150150 2018-12-30  US_storage       -0.753397\n",
       "150151 2018-12-31  US_storage        0.118357\n",
       "\n",
       "[150152 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_googleTrend_topic_merged = df_googleTrend_topic_merged.reset_index().melt(id_vars='date',var_name='topic',value_name='calendar_value')\n",
    "df_googleTrend_topic_merged = df_googleTrend_topic_merged.rename(columns={'topic':'geo_topic'})\n",
    "df_googleTrend_topic_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>geo_topic</th>\n",
       "      <th>merged_id</th>\n",
       "      <th>corr</th>\n",
       "      <th>corr_abs</th>\n",
       "      <th>count</th>\n",
       "      <th>site_id</th>\n",
       "      <th>building_id</th>\n",
       "      <th>meter</th>\n",
       "      <th>primary_use</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>year_built</th>\n",
       "      <th>floor_count</th>\n",
       "      <th>country</th>\n",
       "      <th>topic</th>\n",
       "      <th>topic_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>US_Lodging</td>\n",
       "      <td>site0_bldg0_meter0</td>\n",
       "      <td>0.623851</td>\n",
       "      <td>0.623851</td>\n",
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       "      <td>Lodging</td>\n",
       "      <td>Google trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>US_Retail</td>\n",
       "      <td>site0_bldg100_meter0</td>\n",
       "      <td>0.311244</td>\n",
       "      <td>0.311244</td>\n",
       "      <td>5405</td>\n",
       "      <td>site0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Lodging/residential</td>\n",
       "      <td>24456</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Retail</td>\n",
       "      <td>Google trend</td>\n",
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       "      <td>US_Education</td>\n",
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       "      <td>0.462664</td>\n",
       "      <td>0.462664</td>\n",
       "      <td>5401</td>\n",
       "      <td>site0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>Office</td>\n",
       "      <td>18860</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Education</td>\n",
       "      <td>Google trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>US_Lodging</td>\n",
       "      <td>site0_bldg102_meter0</td>\n",
       "      <td>0.759592</td>\n",
       "      <td>0.759592</td>\n",
       "      <td>5405</td>\n",
       "      <td>site0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>Office</td>\n",
       "      <td>15876</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Lodging</td>\n",
       "      <td>Google trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>US_Google Docs</td>\n",
       "      <td>site0_bldg103_meter0</td>\n",
       "      <td>0.494782</td>\n",
       "      <td>0.494782</td>\n",
       "      <td>5404</td>\n",
       "      <td>site0</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>Education</td>\n",
       "      <td>21657</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Google Docs</td>\n",
       "      <td>Google trend</td>\n",
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       "    <tr>\n",
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       "      <th>2375</th>\n",
       "      <td>2375</td>\n",
       "      <td>US_Google Docs</td>\n",
       "      <td>site15_bldg1444_meter0</td>\n",
       "      <td>0.472436</td>\n",
       "      <td>0.472436</td>\n",
       "      <td>7445</td>\n",
       "      <td>site15</td>\n",
       "      <td>1444</td>\n",
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       "      <td>Entertainment/public assembly</td>\n",
       "      <td>19619</td>\n",
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       "      <td>Google trend</td>\n",
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       "    <tr>\n",
       "      <th>2376</th>\n",
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       "      <td>US_Science</td>\n",
       "      <td>site15_bldg1445_meter0</td>\n",
       "      <td>0.238057</td>\n",
       "      <td>0.238057</td>\n",
       "      <td>7449</td>\n",
       "      <td>site15</td>\n",
       "      <td>1445</td>\n",
       "      <td>0</td>\n",
       "      <td>Education</td>\n",
       "      <td>4298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Science</td>\n",
       "      <td>Google trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2377</th>\n",
       "      <td>2377</td>\n",
       "      <td>US_Education</td>\n",
       "      <td>site15_bldg1446_meter0</td>\n",
       "      <td>0.823792</td>\n",
       "      <td>0.823792</td>\n",
       "      <td>7472</td>\n",
       "      <td>site15</td>\n",
       "      <td>1446</td>\n",
       "      <td>0</td>\n",
       "      <td>Entertainment/public assembly</td>\n",
       "      <td>11265</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Education</td>\n",
       "      <td>Google trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2378</th>\n",
       "      <td>2378</td>\n",
       "      <td>US_Google Docs</td>\n",
       "      <td>site15_bldg1447_meter0</td>\n",
       "      <td>0.508164</td>\n",
       "      <td>0.508164</td>\n",
       "      <td>7471</td>\n",
       "      <td>site15</td>\n",
       "      <td>1447</td>\n",
       "      <td>0</td>\n",
       "      <td>Lodging/residential</td>\n",
       "      <td>29775</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Google Docs</td>\n",
       "      <td>Google trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379</th>\n",
       "      <td>2379</td>\n",
       "      <td>US_Microsoft Excel</td>\n",
       "      <td>site15_bldg1448_meter0</td>\n",
       "      <td>0.832529</td>\n",
       "      <td>0.832529</td>\n",
       "      <td>7452</td>\n",
       "      <td>site15</td>\n",
       "      <td>1448</td>\n",
       "      <td>0</td>\n",
       "      <td>Office</td>\n",
       "      <td>92271</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Microsoft Excel</td>\n",
       "      <td>Google trend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2380 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      level_0           geo_topic               merged_id      corr  corr_abs  \\\n",
       "0           0          US_Lodging      site0_bldg0_meter0  0.623851  0.623851   \n",
       "1           1           US_Retail    site0_bldg100_meter0  0.311244  0.311244   \n",
       "2           2        US_Education    site0_bldg101_meter0  0.462664  0.462664   \n",
       "3           3          US_Lodging    site0_bldg102_meter0  0.759592  0.759592   \n",
       "4           4      US_Google Docs    site0_bldg103_meter0  0.494782  0.494782   \n",
       "...       ...                 ...                     ...       ...       ...   \n",
       "2375     2375      US_Google Docs  site15_bldg1444_meter0  0.472436  0.472436   \n",
       "2376     2376          US_Science  site15_bldg1445_meter0  0.238057  0.238057   \n",
       "2377     2377        US_Education  site15_bldg1446_meter0  0.823792  0.823792   \n",
       "2378     2378      US_Google Docs  site15_bldg1447_meter0  0.508164  0.508164   \n",
       "2379     2379  US_Microsoft Excel  site15_bldg1448_meter0  0.832529  0.832529   \n",
       "\n",
       "      count site_id  building_id  meter                    primary_use  \\\n",
       "0      5405   site0            0      0                      Education   \n",
       "1      5405   site0          100      0            Lodging/residential   \n",
       "2      5401   site0          101      0                         Office   \n",
       "3      5405   site0          102      0                         Office   \n",
       "4      5404   site0          103      0                      Education   \n",
       "...     ...     ...          ...    ...                            ...   \n",
       "2375   7445  site15         1444      0  Entertainment/public assembly   \n",
       "2376   7449  site15         1445      0                      Education   \n",
       "2377   7472  site15         1446      0  Entertainment/public assembly   \n",
       "2378   7471  site15         1447      0            Lodging/residential   \n",
       "2379   7452  site15         1448      0                         Office   \n",
       "\n",
       "      square_feet  year_built  floor_count country            topic  \\\n",
       "0            7432      2008.0          NaN      US          Lodging   \n",
       "1           24456      1968.0          NaN      US           Retail   \n",
       "2           18860      1986.0          NaN      US        Education   \n",
       "3           15876      1983.0          NaN      US          Lodging   \n",
       "4           21657      2016.0          NaN      US      Google Docs   \n",
       "...           ...         ...          ...     ...              ...   \n",
       "2375        19619      1914.0          NaN      US      Google Docs   \n",
       "2376         4298         NaN          NaN      US          Science   \n",
       "2377        11265      1997.0          NaN      US        Education   \n",
       "2378        29775      2001.0          NaN      US      Google Docs   \n",
       "2379        92271      2001.0          NaN      US  Microsoft Excel   \n",
       "\n",
       "        topic_type  \n",
       "0     Google trend  \n",
       "1     Google trend  \n",
       "2     Google trend  \n",
       "3     Google trend  \n",
       "4     Google trend  \n",
       "...            ...  \n",
       "2375  Google trend  \n",
       "2376  Google trend  \n",
       "2377  Google trend  \n",
       "2378  Google trend  \n",
       "2379  Google trend  \n",
       "\n",
       "[2380 rows x 16 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster_topics = pd.read_pickle(os.path.join(path_trends, 'df_corr_idxmax_merged_20210406.pickle')).reset_index()\n",
    "df_cluster_topics = df_cluster_topics.rename(columns={'index':'geo_topic', 'R2':'corr'})\n",
    "df_cluster_topics['corr_abs'] = df_cluster_topics['corr'].abs()\n",
    "#df_cluster_topics = df_cluster_topics.merge(building_meta_df, on='merged_id')\n",
    "df_cluster_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_cluster_topics = df_cluster_topics[df_cluster_topics['corr']>0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>1.700195</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.601562</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>8.796875</td>\n",
       "      <td>-1.204204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19466504</th>\n",
       "      <td>1445</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>4.825000</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.700195</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.601562</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>8.796875</td>\n",
       "      <td>-1.452725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19466505</th>\n",
       "      <td>1446</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>11265</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.700195</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.601562</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>8.796875</td>\n",
       "      <td>-2.053481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19466506</th>\n",
       "      <td>1447</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>159.574997</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>29775</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.700195</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.601562</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>8.796875</td>\n",
       "      <td>-1.204204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19466507</th>\n",
       "      <td>1448</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>92271</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.700195</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.601562</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>8.796875</td>\n",
       "      <td>-1.700967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19466508 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          building_id  meter           timestamp  meter_reading  site_id  \\\n",
       "0                 105      0 2016-01-01 00:00:00      23.303600        1   \n",
       "1                 106      3 2016-01-01 00:00:00       0.000000        1   \n",
       "2                 108      0 2016-01-01 00:00:00      91.265297        1   \n",
       "3                 109      0 2016-01-01 00:00:00      80.930000        1   \n",
       "4                 109      3 2016-01-01 00:00:00       0.000000        1   \n",
       "...               ...    ...                 ...            ...      ...   \n",
       "19466503         1444      0 2016-12-31 23:00:00       8.750000       15   \n",
       "19466504         1445      0 2016-12-31 23:00:00       4.825000       15   \n",
       "19466505         1446      0 2016-12-31 23:00:00       0.000000       15   \n",
       "19466506         1447      0 2016-12-31 23:00:00     159.574997       15   \n",
       "19466507         1448      0 2016-12-31 23:00:00       2.850000       15   \n",
       "\n",
       "          primary_use  square_feet  year_built  floor_count  air_temperature  \\\n",
       "0                   0        50623         NaN          5.0         3.800781   \n",
       "1                   0         5374         NaN          4.0         3.800781   \n",
       "2                   0        81580      1913.0          5.0         3.800781   \n",
       "3                   0        56995      1953.0          6.0         3.800781   \n",
       "4                   0        56995      1953.0          6.0         3.800781   \n",
       "...               ...          ...         ...          ...              ...   \n",
       "19466503            1        19619      1914.0          NaN         1.700195   \n",
       "19466504            0         4298         NaN          NaN         1.700195   \n",
       "19466505            1        11265      1997.0          NaN         1.700195   \n",
       "19466506            4        29775      2001.0          NaN         1.700195   \n",
       "19466507            6        92271      2001.0          NaN         1.700195   \n",
       "\n",
       "          cloud_coverage  dew_temperature  precip_depth_1_hr  \\\n",
       "0                    0.0         2.400391                0.0   \n",
       "1                    0.0         2.400391                0.0   \n",
       "2                    0.0         2.400391                0.0   \n",
       "3                    0.0         2.400391                0.0   \n",
       "4                    0.0         2.400391                0.0   \n",
       "...                  ...              ...                ...   \n",
       "19466503             2.0        -5.601562               -1.0   \n",
       "19466504             2.0        -5.601562               -1.0   \n",
       "19466505             2.0        -5.601562               -1.0   \n",
       "19466506             2.0        -5.601562               -1.0   \n",
       "19466507             2.0        -5.601562               -1.0   \n",
       "\n",
       "          sea_level_pressure  wind_direction  wind_speed  calendar_value  \n",
       "0                     1021.0           240.0    3.099609       -1.634459  \n",
       "1                     1021.0           240.0    3.099609       -0.240210  \n",
       "2                     1021.0           240.0    3.099609       -1.634459  \n",
       "3                     1021.0           240.0    3.099609       -1.634459  \n",
       "4                     1021.0           240.0    3.099609       -0.240210  \n",
       "...                      ...             ...         ...             ...  \n",
       "19466503              1008.5           180.0    8.796875       -1.204204  \n",
       "19466504              1008.5           180.0    8.796875       -1.452725  \n",
       "19466505              1008.5           180.0    8.796875       -2.053481  \n",
       "19466506              1008.5           180.0    8.796875       -1.204204  \n",
       "19466507              1008.5           180.0    8.796875       -1.700967  \n",
       "\n",
       "[19466508 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.merge(df_cluster_topics[['building_id','meter','geo_topic']], on=['building_id','meter'],how='left')\n",
    "df_train['date'] = pd.to_datetime(df_train['timestamp'].dt.date)\n",
    "df_train = df_train.merge(df_googleTrend_topic_merged, on=['date','geo_topic'],how='left')\n",
    "df_train = df_train.drop(['geo_topic', 'date'],axis=1)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 1318.09 MB\n",
      "Memory usage after optimization is: 575.51 MB\n",
      "Decreased by 56.3%\n",
      "Wall time: 18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_train = features_engineering(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    }\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>building_id</th>\n",
       "      <th>meter</th>\n",
       "      <th>meter_reading</th>\n",
       "      <th>primary_use</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>cloud_coverage</th>\n",
       "      <th>dew_temperature</th>\n",
       "      <th>precip_depth_1_hr</th>\n",
       "      <th>calendar_value</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>sm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>23.303600</td>\n",
       "      <td>0</td>\n",
       "      <td>10.835938</td>\n",
       "      <td>3.800781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.634766</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>8.585938</td>\n",
       "      <td>3.800781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.240234</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>91.265297</td>\n",
       "      <td>0</td>\n",
       "      <td>11.312500</td>\n",
       "      <td>3.800781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.634766</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>80.930000</td>\n",
       "      <td>0</td>\n",
       "      <td>10.953125</td>\n",
       "      <td>3.800781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.634766</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>109</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>10.953125</td>\n",
       "      <td>3.800781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.240234</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1_3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   building_id  meter  meter_reading  primary_use  square_feet  \\\n",
       "0          105      0      23.303600            0    10.835938   \n",
       "1          106      3       0.000000            0     8.585938   \n",
       "2          108      0      91.265297            0    11.312500   \n",
       "3          109      0      80.930000            0    10.953125   \n",
       "4          109      3       0.000000            0    10.953125   \n",
       "\n",
       "   air_temperature  cloud_coverage  dew_temperature  precip_depth_1_hr  \\\n",
       "0         3.800781             0.0         2.400391                0.0   \n",
       "1         3.800781             0.0         2.400391                0.0   \n",
       "2         3.800781             0.0         2.400391                0.0   \n",
       "3         3.800781             0.0         2.400391                0.0   \n",
       "4         3.800781             0.0         2.400391                0.0   \n",
       "\n",
       "   calendar_value  hour  weekday   sm  \n",
       "0       -1.634766     0        4  1_0  \n",
       "1       -0.240234     0        4  1_3  \n",
       "2       -1.634766     0        4  1_0  \n",
       "3       -1.634766     0        4  1_0  \n",
       "4       -0.240234     0        4  1_3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features & Target Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.log1p(df_train[\"meter_reading\"])\n",
    "X_train = df_train.drop('meter_reading', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  KFOLD LIGHTGBM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.057281 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2869\n",
      "[LightGBM] [Info] Number of data points in the train set: 12977672, number of used features: 12\n",
      "[LightGBM] [Info] Start training from score 4.303446\n",
      "Training until validation scores don't improve for 50 rounds\n",
      "[25]\ttraining's rmse: 0.953945\tvalid_1's rmse: 1.13763\n",
      "[50]\ttraining's rmse: 0.732865\tvalid_1's rmse: 0.99852\n",
      "[75]\ttraining's rmse: 0.67286\tvalid_1's rmse: 0.973202\n",
      "[100]\ttraining's rmse: 0.641479\tvalid_1's rmse: 0.965982\n",
      "[125]\ttraining's rmse: 0.619613\tvalid_1's rmse: 0.963987\n",
      "[150]\ttraining's rmse: 0.600842\tvalid_1's rmse: 0.964101\n",
      "[175]\ttraining's rmse: 0.586941\tvalid_1's rmse: 0.964196\n",
      "Early stopping, best iteration is:\n",
      "[131]\ttraining's rmse: 0.61445\tvalid_1's rmse: 0.963733\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.055526 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2844\n",
      "[LightGBM] [Info] Number of data points in the train set: 12977672, number of used features: 12\n",
      "[LightGBM] [Info] Start training from score 4.230456\n",
      "Training until validation scores don't improve for 50 rounds\n",
      "[25]\ttraining's rmse: 0.984045\tvalid_1's rmse: 1.0867\n",
      "[50]\ttraining's rmse: 0.772339\tvalid_1's rmse: 0.907405\n",
      "[75]\ttraining's rmse: 0.714762\tvalid_1's rmse: 0.863123\n",
      "[100]\ttraining's rmse: 0.681759\tvalid_1's rmse: 0.843742\n",
      "[125]\ttraining's rmse: 0.658762\tvalid_1's rmse: 0.834245\n",
      "[150]\ttraining's rmse: 0.639191\tvalid_1's rmse: 0.831675\n",
      "[175]\ttraining's rmse: 0.624714\tvalid_1's rmse: 0.830767\n",
      "[200]\ttraining's rmse: 0.61245\tvalid_1's rmse: 0.831058\n",
      "[225]\ttraining's rmse: 0.602335\tvalid_1's rmse: 0.831553\n",
      "Early stopping, best iteration is:\n",
      "[180]\ttraining's rmse: 0.621791\tvalid_1's rmse: 0.830424\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.054769 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2835\n",
      "[LightGBM] [Info] Number of data points in the train set: 12977672, number of used features: 12\n",
      "[LightGBM] [Info] Start training from score 4.264084\n",
      "Training until validation scores don't improve for 50 rounds\n",
      "[25]\ttraining's rmse: 0.966995\tvalid_1's rmse: 1.11111\n",
      "[50]\ttraining's rmse: 0.740663\tvalid_1's rmse: 0.967623\n",
      "[75]\ttraining's rmse: 0.677545\tvalid_1's rmse: 0.942144\n",
      "[100]\ttraining's rmse: 0.642561\tvalid_1's rmse: 0.933222\n",
      "[125]\ttraining's rmse: 0.619206\tvalid_1's rmse: 0.930768\n",
      "[150]\ttraining's rmse: 0.599713\tvalid_1's rmse: 0.931007\n",
      "[175]\ttraining's rmse: 0.58479\tvalid_1's rmse: 0.931478\n",
      "Early stopping, best iteration is:\n",
      "[128]\ttraining's rmse: 0.616758\tvalid_1's rmse: 0.930622\n",
      "Wall time: 5min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "categorical_features = ['sm', \"building_id\", \"meter\", \"primary_use\", \"weekday\"]\n",
    "params = {\n",
    "    \"objective\": \"regression\",\n",
    "    \"boosting\": \"gbdt\",\n",
    "    \"num_leaves\": 1280,\n",
    "    \"learning_rate\": 0.05,\n",
    "    \"feature_fraction\": 0.85,\n",
    "    \"reg_lambda\": 2,\n",
    "    \"metric\": \"rmse\",\n",
    "}\n",
    "\n",
    "pred_L1 = []\n",
    "valid_L1 = []\n",
    "seed = None\n",
    "kf = KFold(n_splits=3, random_state=seed)\n",
    "\n",
    "models = []\n",
    "for train_index,test_index in kf.split(X_train):\n",
    "    train_features = X_train.loc[train_index]\n",
    "    train_target = y_train.loc[train_index]\n",
    "    \n",
    "    test_features = X_train.loc[test_index]\n",
    "    test_target = y_train.loc[test_index]\n",
    "    \n",
    "    d_training = lgb.Dataset(train_features, label=train_target,categorical_feature=categorical_features, free_raw_data=False)\n",
    "    d_test = lgb.Dataset(test_features, label=test_target,categorical_feature=categorical_features, free_raw_data=False)\n",
    "    \n",
    "    model = lgb.train(params, train_set=d_training, num_boost_round=1000, valid_sets=[d_training,d_test], verbose_eval=25, early_stopping_rounds=50)\n",
    "    models.append(model)\n",
    "    pred_L1.append(model.predict(test_features))\n",
    "    valid_L1.append(test_target)\n",
    "    \n",
    "    del train_features, train_target, test_features, test_target, d_training, d_test\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del X_train, y_train\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model in models:\n",
    "    lgb.plot_importance(model)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 437.43 MB\n",
      "Memory usage after optimization is: 437.43 MB\n",
      "Decreased by 0.0%\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_feather(root/'test.feather')\n",
    "\n",
    "row_ids = df_test[\"row_id\"]\n",
    "df_test.drop(\"row_id\", axis=1, inplace=True)\n",
    "df_test = reduce_mem_usage(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_test['hour'] = df_test['timestamp'].dt.hour\n",
    "# df_test['weekday'] = df_test['timestamp'].dt.weekday\n",
    "# df_test['bm'] = df_test['building_id'].apply(lambda x: str(x)) + '_' + df_test['meter'].apply(lambda x: str(x))\n",
    "# df_test = df_test.merge(bm, right_index=True, left_on=bm_cols, how='left')\n",
    "# df_test.drop('bm', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Building Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.merge(building,left_on='building_id',right_on='building_id',how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill Weather Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 11.76 MB\n",
      "Memory usage after optimization is: 9.89 MB\n",
      "Decreased by 15.9%\n"
     ]
    }
   ],
   "source": [
    "weather_test['timestamp'] = weather_test['timestamp'].astype('str')\n",
    "weather_test = fill_weather_dataset(weather_test)\n",
    "weather_test = reduce_mem_usage(weather_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = df_test.merge(weather_test,how='left',on=['timestamp','site_id'])\n",
    "del weather_test\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add google trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "    }\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>building_id</th>\n",
       "      <th>meter</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>site_id</th>\n",
       "      <th>primary_use</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>year_built</th>\n",
       "      <th>floor_count</th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>cloud_coverage</th>\n",
       "      <th>dew_temperature</th>\n",
       "      <th>precip_depth_1_hr</th>\n",
       "      <th>sea_level_pressure</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>calendar_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7432</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>1021.400024</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.111146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2720</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>1021.400024</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.111146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5376</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>1021.400024</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>-1.285704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23685</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>1021.400024</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.111146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116607</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>1021.400024</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.111146</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697595</th>\n",
       "      <td>1444</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-05-09 07:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>19619</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.031111</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>3.355556</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1017.053345</td>\n",
       "      <td>184.883728</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>1.048834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697596</th>\n",
       "      <td>1445</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-05-09 07:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.031111</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>3.355556</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1017.053345</td>\n",
       "      <td>184.883728</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>0.817816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697597</th>\n",
       "      <td>1446</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-05-09 07:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>11265</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.031111</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>3.355556</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1017.053345</td>\n",
       "      <td>184.883728</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>0.992121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697598</th>\n",
       "      <td>1447</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-05-09 07:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>29775</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.031111</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>3.355556</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1017.053345</td>\n",
       "      <td>184.883728</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>1.048834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697599</th>\n",
       "      <td>1448</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-05-09 07:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>92271</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.031111</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>3.355556</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1017.053345</td>\n",
       "      <td>184.883728</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>0.697558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41697600 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          building_id  meter           timestamp  site_id  primary_use  \\\n",
       "0                   0      0 2017-01-01 00:00:00        0            0   \n",
       "1                   1      0 2017-01-01 00:00:00        0            0   \n",
       "2                   2      0 2017-01-01 00:00:00        0            0   \n",
       "3                   3      0 2017-01-01 00:00:00        0            0   \n",
       "4                   4      0 2017-01-01 00:00:00        0            0   \n",
       "...               ...    ...                 ...      ...          ...   \n",
       "41697595         1444      0 2018-05-09 07:00:00       15            1   \n",
       "41697596         1445      0 2018-05-09 07:00:00       15            0   \n",
       "41697597         1446      0 2018-05-09 07:00:00       15            1   \n",
       "41697598         1447      0 2018-05-09 07:00:00       15            4   \n",
       "41697599         1448      0 2018-05-09 07:00:00       15            6   \n",
       "\n",
       "          square_feet  year_built  floor_count  air_temperature  \\\n",
       "0                7432      2008.0          NaN        17.799999   \n",
       "1                2720      2004.0          NaN        17.799999   \n",
       "2                5376      1991.0          NaN        17.799999   \n",
       "3               23685      2002.0          NaN        17.799999   \n",
       "4              116607      1975.0          NaN        17.799999   \n",
       "...               ...         ...          ...              ...   \n",
       "41697595        19619      1914.0          NaN        12.031111   \n",
       "41697596         4298         NaN          NaN        12.031111   \n",
       "41697597        11265      1997.0          NaN        12.031111   \n",
       "41697598        29775      2001.0          NaN        12.031111   \n",
       "41697599        92271      2001.0          NaN        12.031111   \n",
       "\n",
       "          cloud_coverage  dew_temperature  precip_depth_1_hr  \\\n",
       "0               4.000000        11.700000           0.282609   \n",
       "1               4.000000        11.700000           0.282609   \n",
       "2               4.000000        11.700000           0.282609   \n",
       "3               4.000000        11.700000           0.282609   \n",
       "4               4.000000        11.700000           0.282609   \n",
       "...                  ...              ...                ...   \n",
       "41697595        1.727273         3.355556          -1.000000   \n",
       "41697596        1.727273         3.355556          -1.000000   \n",
       "41697597        1.727273         3.355556          -1.000000   \n",
       "41697598        1.727273         3.355556          -1.000000   \n",
       "41697599        1.727273         3.355556          -1.000000   \n",
       "\n",
       "          sea_level_pressure  wind_direction  wind_speed  calendar_value  \n",
       "0                1021.400024      100.000000    3.600000        0.111146  \n",
       "1                1021.400024      100.000000    3.600000        0.111146  \n",
       "2                1021.400024      100.000000    3.600000       -1.285704  \n",
       "3                1021.400024      100.000000    3.600000        0.111146  \n",
       "4                1021.400024      100.000000    3.600000        0.111146  \n",
       "...                      ...             ...         ...             ...  \n",
       "41697595         1017.053345      184.883728    2.702222        1.048834  \n",
       "41697596         1017.053345      184.883728    2.702222        0.817816  \n",
       "41697597         1017.053345      184.883728    2.702222        0.992121  \n",
       "41697598         1017.053345      184.883728    2.702222        1.048834  \n",
       "41697599         1017.053345      184.883728    2.702222        0.697558  \n",
       "\n",
       "[41697600 rows x 16 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = df_test.merge(df_cluster_topics[['building_id','meter','geo_topic']], on=['building_id','meter'],how='left')\n",
    "df_test['date'] = pd.to_datetime(df_test['timestamp'].dt.date)\n",
    "df_test = df_test.merge(df_googleTrend_topic_merged, on=['date','geo_topic'],how='left')\n",
    "df_test = df_test.drop(['geo_topic', 'date'],axis=1)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 2982.44 MB\n",
      "Memory usage after optimization is: 1073.68 MB\n",
      "Decreased by 64.0%\n"
     ]
    }
   ],
   "source": [
    "df_test = features_engineering(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41697600 entries, 0 to 41697599\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Dtype   \n",
      "---  ------             -----   \n",
      " 0   building_id        int16   \n",
      " 1   meter              int8    \n",
      " 2   primary_use        int8    \n",
      " 3   square_feet        float16 \n",
      " 4   air_temperature    float16 \n",
      " 5   cloud_coverage     float16 \n",
      " 6   dew_temperature    float16 \n",
      " 7   precip_depth_1_hr  float16 \n",
      " 8   calendar_value     float16 \n",
      " 9   hour               int8    \n",
      " 10  weekday            int8    \n",
      " 11  sm                 category\n",
      "dtypes: category(1), float16(6), int16(1), int8(4)\n",
      "memory usage: 1.0 GB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
       "    }\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>building_id</th>\n",
       "      <th>meter</th>\n",
       "      <th>primary_use</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>cloud_coverage</th>\n",
       "      <th>dew_temperature</th>\n",
       "      <th>precip_depth_1_hr</th>\n",
       "      <th>calendar_value</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>sm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.914062</td>\n",
       "      <td>17.796875</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.282715</td>\n",
       "      <td>0.111145</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.910156</td>\n",
       "      <td>17.796875</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.282715</td>\n",
       "      <td>0.111145</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.593750</td>\n",
       "      <td>17.796875</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.282715</td>\n",
       "      <td>-1.286133</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.070312</td>\n",
       "      <td>17.796875</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.282715</td>\n",
       "      <td>0.111145</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.664062</td>\n",
       "      <td>17.796875</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.703125</td>\n",
       "      <td>0.282715</td>\n",
       "      <td>0.111145</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0_0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   building_id  meter  primary_use  square_feet  air_temperature  \\\n",
       "0            0      0            0     8.914062        17.796875   \n",
       "1            1      0            0     7.910156        17.796875   \n",
       "2            2      0            0     8.593750        17.796875   \n",
       "3            3      0            0    10.070312        17.796875   \n",
       "4            4      0            0    11.664062        17.796875   \n",
       "\n",
       "   cloud_coverage  dew_temperature  precip_depth_1_hr  calendar_value  hour  \\\n",
       "0             4.0        11.703125           0.282715        0.111145     0   \n",
       "1             4.0        11.703125           0.282715        0.111145     0   \n",
       "2             4.0        11.703125           0.282715       -1.286133     0   \n",
       "3             4.0        11.703125           0.282715        0.111145     0   \n",
       "4             4.0        11.703125           0.282715        0.111145     0   \n",
       "\n",
       "   weekday   sm  \n",
       "0        6  0_0  \n",
       "1        6  0_0  \n",
       "2        6  0_0  \n",
       "3        6  0_0  \n",
       "4        6  0_0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffb88cf2941a43e696854dfb5a3278c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pred = []\n",
    "for model in tqdm(models):\n",
    "    if  pred == []:\n",
    "        pred = np.expm1(model.predict(df_test, num_iteration=model.best_iteration)) / len(models)\n",
    "    else:\n",
    "        pred += np.expm1(model.predict(df_test, num_iteration=model.best_iteration)) / len(models)\n",
    "    del model\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\"row_id\": row_ids, \"meter_reading\": np.clip(pred, 0, a_max=None)})\n",
    "submission['meter_reading'] = submission['meter_reading'].astype('float32')\n",
    "submission['row_id'] = submission['row_id'].astype('int32')\n",
    "submission.to_csv(\"submission_googleTrend_allTopics_20210406.csv\", index=False, chunksize=25000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>meter_reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>188.195740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>68.501534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>11.738132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>213.563736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>850.077026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id  meter_reading\n",
       "0       0     188.195740\n",
       "1       1      68.501534\n",
       "2       2      11.738132\n",
       "3       3     213.563736\n",
       "4       4     850.077026"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "submission mean: 357.0862\n",
      "submission std: 1846.5231\n",
      "submission min: 0.0000\n",
      "submission max: 418860.8750\n"
     ]
    }
   ],
   "source": [
    "print(f\"submission mean: {submission['meter_reading'].mean():.4f}\")\n",
    "print(f\"submission std: {submission['meter_reading'].std():.4f}\")\n",
    "print(f\"submission min: {submission['meter_reading'].min():.4f}\")\n",
    "print(f\"submission max: {submission['meter_reading'].max():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log1p(submission['meter_reading'].values), kde=False);\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 954.38 MB\n",
      "Memory usage after optimization is: 357.89 MB\n",
      "Decreased by 62.5%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>meter_reading</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>173.369995</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53.512699</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6.143000</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>101.700996</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1141.239990</td>\n",
       "      <td>Public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>41697595</th>\n",
       "      <td>41697595</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>Ignored</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697596</th>\n",
       "      <td>41697596</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>Ignored</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697597</th>\n",
       "      <td>41697597</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>Ignored</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697598</th>\n",
       "      <td>41697598</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>Ignored</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41697599</th>\n",
       "      <td>41697599</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>Ignored</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41697600 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id  meter_reading    Usage\n",
       "0                0     173.369995   Public\n",
       "1                1      53.512699   Public\n",
       "2                2       6.143000   Public\n",
       "3                3     101.700996   Public\n",
       "4                4    1141.239990   Public\n",
       "...            ...            ...      ...\n",
       "41697595  41697595   -9999.000000  Ignored\n",
       "41697596  41697596   -9999.000000  Ignored\n",
       "41697597  41697597   -9999.000000  Ignored\n",
       "41697598  41697598   -9999.000000  Ignored\n",
       "41697599  41697599   -9999.000000  Ignored\n",
       "\n",
       "[41697600 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_solution = pd.read_csv(os.path.join(root, 'solution.csv'))\n",
    "df_solution = reduce_mem_usage(df_solution, use_float16=True)\n",
    "df_solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>building_id</th>\n",
       "      <th>meter</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>meter_reading</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Ignored</td>\n",
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       "      <td>-9999.000000</td>\n",
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       "      <th>41697599</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41697600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id  building_id  meter           timestamp  meter_reading  \\\n",
       "0                0            0      0 2017-01-01 00:00:00     173.369995   \n",
       "1                1            1      0 2017-01-01 00:00:00      53.512699   \n",
       "2                2            2      0 2017-01-01 00:00:00       6.143000   \n",
       "3                3            3      0 2017-01-01 00:00:00     101.700996   \n",
       "4                4            4      0 2017-01-01 00:00:00    1141.239990   \n",
       "...            ...          ...    ...                 ...            ...   \n",
       "41697595  41697595         1444      0 2018-05-09 07:00:00   -9999.000000   \n",
       "41697596  41697596         1445      0 2018-05-09 07:00:00   -9999.000000   \n",
       "41697597  41697597         1446      0 2018-05-09 07:00:00   -9999.000000   \n",
       "41697598  41697598         1447      0 2018-05-09 07:00:00   -9999.000000   \n",
       "41697599  41697599         1448      0 2018-05-09 07:00:00   -9999.000000   \n",
       "\n",
       "            Usage  \n",
       "0          Public  \n",
       "1          Public  \n",
       "2          Public  \n",
       "3          Public  \n",
       "4          Public  \n",
       "...           ...  \n",
       "41697595  Ignored  \n",
       "41697596  Ignored  \n",
       "41697597  Ignored  \n",
       "41697598  Ignored  \n",
       "41697599  Ignored  \n",
       "\n",
       "[41697600 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_feather(root/'test.feather')\n",
    "merged = df_test.merge(df_solution,  on='row_id', how='left')\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrue = df_solution.loc[merged['Usage']=='Private', 'meter_reading']\n",
    "pred = submission.loc[merged['Usage']=='Private', 'meter_reading']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE: 1.3008\n"
     ]
    }
   ],
   "source": [
    "print(f'RMSLE: {rmsle(ytrue, pred):.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nbuilding = pd.read_feather(root/'building_metadata.feather')\\nmerged['meter_reading_pred'] = submission['meter_reading']\\n\\nfor building_id in building['building_id'].unique():\\n    for meter in np.arange(0, 4):\\n        df_plot = merged[(merged['building_id']==building_id)&(merged['meter']==meter)].sort_values('timestamp').set_index('timestamp').copy()\\n        df_plot = df_plot[df_plot['meter_reading']>0].loc['2017']\\n        #df_plot = df_plot[df_plot['Usage']!='Ignored']\\n\\n        if len(df_plot)>0:\\n            print('R-squared: '+str(df_plot[['meter_reading','meter_reading_pred']].corr().iloc[0,1].round(3)))\\n            print('RMSLE: '+str(round(rmsle(df_plot['meter_reading'], df_plot['meter_reading_pred']), 2)))\\n            df_plot[['meter_reading','meter_reading_pred']].plot(figsize=(15,3),alpha=0.5,title='building_id: '+str(building_id)+' - meter: '+str(meter));plt.show()\\n            print('--------------------------------------------------------------------------------------------------------------------------') \\n\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "building = pd.read_feather(root/'building_metadata.feather')\n",
    "merged['meter_reading_pred'] = submission['meter_reading']\n",
    "\n",
    "for building_id in building['building_id'].unique():\n",
    "    for meter in np.arange(0, 4):\n",
    "        df_plot = merged[(merged['building_id']==building_id)&(merged['meter']==meter)].sort_values('timestamp').set_index('timestamp').copy()\n",
    "        df_plot = df_plot[df_plot['meter_reading']>0].loc['2017']\n",
    "        #df_plot = df_plot[df_plot['Usage']!='Ignored']\n",
    "\n",
    "        if len(df_plot)>0:\n",
    "            print('R-squared: '+str(df_plot[['meter_reading','meter_reading_pred']].corr().iloc[0,1].round(3)))\n",
    "            print('RMSLE: '+str(round(rmsle(df_plot['meter_reading'], df_plot['meter_reading_pred']), 2)))\n",
    "            df_plot[['meter_reading','meter_reading_pred']].plot(figsize=(15,3),alpha=0.5,title='building_id: '+str(building_id)+' - meter: '+str(meter));plt.show()\n",
    "            print('--------------------------------------------------------------------------------------------------------------------------') \n",
    "'''"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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