NN_Energy_Predictor_0.2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of NN Energy Predictor.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yovelop/NN/blob/master/NN_Energy_Predictor_0.2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zO1azhvf_t9_",
        "colab_type": "code",
        "outputId": "8c3b4389-daa4-42c4-95af-15888b131c3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "DIR = '/content/drive/My Drive/Colab Notebooks/ENSaver/'\n",
        "#drive.flush_and_unmount()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRgT9oDx_vpi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Импорты\n",
        "  import numpy as np # linear algebra\n",
        "  import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "  import gc\n",
        "\n",
        "  import matplotlib.pyplot as plt\n",
        "  import matplotlib.style\n",
        "  matplotlib.style.use('ggplot')\n",
        "\n",
        "  from sys import getsizeof\n",
        "\n",
        "  import os\n",
        "  for dirname, _, filenames in os.walk(DIR):\n",
        "      for filename in filenames:\n",
        "          print(os.path.join(dirname, filename))\n",
        "\n",
        "  pd.options.mode.chained_assignment = None  # default='warn'\n",
        "  import warnings\n",
        "  pd.set_option('display.max_rows', 500)\n",
        "  pd.set_option('display.max_columns', 500)\n",
        "  pd.set_option('display.width', 100)\n",
        "  warnings.filterwarnings('ignore')\n",
        "  np.set_printoptions (precision = 4, suppress  = True)\n",
        "\n",
        "  def reduce_mem_usage(df):\n",
        "      start_mem_usg = df.memory_usage().sum() / 1024**2 \n",
        "      print(\"Memory usage of properties dataframe is :\",start_mem_usg,\" MB\")\n",
        "      NAlist = [] # Keeps track of columns that have missing values filled in. \n",
        "      for col in df.columns:\n",
        "          if (df[col].dtype != object) &  (df[col].dtype != 'datetime64[ns]'):  # Exclude strings            \n",
        "              # Print current column type\n",
        "              print(\"******************************\")\n",
        "              print(\"Column: \",col)\n",
        "              print(\"dtype before: \",df[col].dtype)            \n",
        "              # make variables for Int, max and min\n",
        "              IsInt = False\n",
        "              mx = df[col].max()\n",
        "              mn = df[col].min()\n",
        "              print(\"min for this col: \",mn)\n",
        "              print(\"max for this col: \",mx)\n",
        "              # Integer does not support NA, therefore, NA needs to be filled\n",
        "              if not np.isfinite(df[col]).all(): \n",
        "                  NAlist.append(col)\n",
        "                  df[col].fillna(mn-1,inplace=True)  \n",
        "                    \n",
        "              # test if column can be converted to an integer\n",
        "              asint = df[col].fillna(0).astype(np.int64)\n",
        "              result = abs(df[col] - asint)\n",
        "              result = result.sum()\n",
        "              if result > -0.01 and result < 0.01:\n",
        "                  IsInt = True            \n",
        "              # Make Integer/unsigned Integer datatypes\n",
        "              if IsInt:\n",
        "                  if mn >= 0:\n",
        "                      if mx < 255:\n",
        "                          df[col] = df[col].astype(np.uint8)\n",
        "                      elif mx < 65535:\n",
        "                          df[col] = df[col].astype(np.uint16)\n",
        "                      elif mx < 4294967295:\n",
        "                          df[col] = df[col].astype(np.uint32)\n",
        "                      else:\n",
        "                          df[col] = df[col].astype(np.uint64)\n",
        "                  else:\n",
        "                      if mn > np.iinfo(np.int8).min and mx < np.iinfo(np.int8).max:\n",
        "                          df[col] = df[col].astype(np.int8)\n",
        "                      elif mn > np.iinfo(np.int16).min and mx < np.iinfo(np.int16).max:\n",
        "                          df[col] = df[col].astype(np.int16)\n",
        "                      elif mn > np.iinfo(np.int32).min and mx < np.iinfo(np.int32).max:\n",
        "                          df[col] = df[col].astype(np.int32)\n",
        "                      elif mn > np.iinfo(np.int64).min and mx < np.iinfo(np.int64).max:\n",
        "                          df[col] = df[col].astype(np.int64)    \n",
        "              # Make float datatypes 32 bit\n",
        "              else:\n",
        "                  df[col] = df[col].astype(np.float32)\n",
        "              \n",
        "              # Print new column type\n",
        "              print(\"dtype after: \",df[col].dtype)\n",
        "              print(\"******************************\")\n",
        "      # Print final result\n",
        "      print(\"___MEMORY USAGE AFTER COMPLETION:___\")\n",
        "      mem_usg = df.memory_usage().sum() / 1024**2 \n",
        "      print(\"Memory usage is: \",mem_usg,\" MB\")\n",
        "      print(\"This is \",100*mem_usg/start_mem_usg,\"% of the initial size\")\n",
        "      return df, NAlist\n",
        "\n",
        "  def show_plot(hist):\n",
        "    plt.plot(hist.history['loss'])\n",
        "    plt.plot(hist.history['val_loss'])\n",
        "    plt.title('Model NN')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend(['Train','Val'])\n",
        "    plt.show()\n",
        "    \n",
        "    plt.plot(hist.history['loss'])\n",
        "    plt.plot(hist.history['val_loss'])\n",
        "    plt.title('Model NN')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.ylim([1,2])\n",
        "    plt.legend(['Train','Val'])\n",
        "    plt.show()\n",
        "\n",
        "    plt.plot(hist.history['loss'])\n",
        "    plt.plot(hist.history['val_loss'])\n",
        "    plt.title('Model NN')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.ylim([0,1])\n",
        "    plt.legend(['Train','Val'])\n",
        "    plt.show()\n",
        "\n",
        "  #Импорты Керас:\n",
        "  from keras.models import Sequential, load_model\n",
        "\n",
        "  from keras.layers import Dense\n",
        "  from keras.initializers import TruncatedNormal, Constant\n",
        "  from keras.regularizers import l1,l2,l1_l2\n",
        "  from keras.optimizers import Adam\n",
        "  import keras.backend as K\n",
        "\n",
        "  from keras. callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
        "  #from keras.utils import plot_model\n",
        "  from keras.losses import mean_squared_error as mse #, mean_absolute_percentage_error as mape\n",
        "\n",
        "  def RMSLE(y_true, y_pred):\n",
        "    return K.pow( K.mean( K.pow(K.log(y_true+1) - K.log(y_pred+1),2.00000)),0.5000)\n",
        "\n",
        "  def tweedieloss(y_true, y_pred):\n",
        "      return K.mean(  K.pow(    K.pow(backend.maximum(0.000,K.maximum(0.013000,y_true)),0.5)  -   K.pow(K.maximum(0.013000,y_pred),0.5)   , 2 ) / K.pow(K.maximum(0.013000,y_pred),0.5)\n",
        "                  )\n",
        "\n",
        "  def tweedieloss_bkp(y_true, y_pred):\n",
        "      p=1.5\n",
        "      dev = 2 * (K.pow(K.maximum(0.000,y_true), 2-p)/((1-p) * (2-p)) -\n",
        "                    y_true * K.pow(y_pred, 1-p)/(1-p) +\n",
        "                    K.pow(y_pred, 2-p)/(2-p))\n",
        "      return K.mean(dev)\n",
        "\n",
        "  def VAL_ (y_true, y_pred):\n",
        "      return  K.maximum(0.0330000, K.sum(y_pred))/ K.maximum(0.033000, K.sum(y_true)) \n",
        "      \n",
        "  def VAL_2 (y_true, y_pred):\n",
        "      return  K.minimum( 5.000000, K.exp ( K.abs( K.log( VAL_ (y_true, y_pred))))-1)\n",
        "  \n",
        "  def VAL_3 (y_true, y_pred):\n",
        "      return  K.exp ( K.abs( K.log( VAL_ (y_true, y_pred))))-1\n",
        "\n",
        "  def MAPE_ (y_true, y_pred):\n",
        "      return K.mean( K.minimum( 5.000000,  K.abs(y_true - y_pred)/ K.maximum(0.033000, y_true)) )\n",
        "      \n",
        "  def MAE_(y_true, y_pred):\n",
        "      return K.sum( K.abs(y_true - y_pred))/ K.maximum(0.033000, K.sum(y_true))\n",
        "\n",
        "  def MSE_(y_true, y_pred):\n",
        "      return K.sum( K.pow(y_true - y_pred,2.00000))/ K.maximum(0.033000,  K.sum(K.pow(y_true,2.00000)))\n",
        "\n",
        "  def MAE_MAPE(y_true, y_pred):\n",
        "      return (\n",
        "                        MAE_(y_true, y_pred)\n",
        "          +  0.250000 * MAPE_ (y_true, y_pred)\n",
        "      )\n",
        "              \n",
        "  def MAE_VAL_MAPE(y_true, y_pred):\n",
        "      return (\n",
        "                        MAE_(y_true, y_pred)\n",
        "          + 1.800000 *   VAL_2 (y_true, y_pred)          \n",
        "          + 0.950000 *   MAPE_ (y_true, y_pred)\n",
        "          )\n",
        "\n",
        "  def MAE_VAL(y_true, y_pred):\n",
        "      return (\n",
        "                        MAE_(y_true, y_pred)\n",
        "          + 0.800000 *   VAL_2 (y_true, y_pred)                     \n",
        "              )\n",
        "\n",
        "  def MAPE_VAL(y_true, y_pred):\n",
        "      return (\n",
        "                      MAPE_ (y_true, y_pred)\n",
        "          + 0.4000 *  VAL_2 (y_true, y_pred)             \n",
        "              )\n",
        "\n",
        "  def MSE_VAL_MAPE(y_true, y_pred):\n",
        "      return (\n",
        "            5.000000 *   MSE_(y_true, y_pred)\n",
        "          + 2.200000 *   VAL_2 (y_true, y_pred)          \n",
        "          + 0.750000 *   MAPE_ (y_true, y_pred)\n",
        "          )\n",
        "\n",
        "  def MASPE_VAL(y_true, y_pred):\n",
        "      return (\n",
        "                        MAE_(y_true, y_pred)               \n",
        "          + 0.500000 *   MSE_(y_true, y_pred)\n",
        "          + 1.200000 *   VAL_2 (y_true, y_pred)          \n",
        "          + 0.250000 *   MAPE_ (y_true, y_pred)\n",
        "          )\n",
        "  \n",
        "  def MAE_RMSLE(y_true, y_pred):\n",
        "      return (\n",
        "            1.000000 *   MAE_   (y_true, y_pred)\n",
        "          + 2.000000 *   RMSLE  (y_true, y_pred)          \n",
        "          )  \n",
        "\n",
        "  def MAPE_RMSLE(y_true, y_pred):\n",
        "      return (\n",
        "            1.000000 *   MAPE_   (y_true, y_pred)\n",
        "          + 0.100000 *   RMSLE  (y_true, y_pred)          \n",
        "          )  \n",
        "  \n",
        "  def MAPE_VAL_RMSLE(y_true, y_pred):\n",
        "      return (\n",
        "            1.000000 *   MAPE_   (y_true, y_pred)\n",
        "          + 1.000000 *   RMSLE   (y_true, y_pred)    \n",
        "          + 1.000000 *   VAL_2   (y_true, y_pred)      \n",
        "          )  \n",
        " \n",
        "  import keras.metrics\n",
        "  keras.metrics.MAE_ = MAE_\n",
        "  keras.metrics.VAL_ = VAL_\n",
        "  keras.metrics.VAL_2 = VAL_2\n",
        "  keras.metrics.MAPE_ = MAPE_\n",
        "  keras.metrics.MSE_ = MSE_\n",
        "  keras.metrics.tweedieloss = tweedieloss\n",
        "  keras.metrics.MAE_RMSLE = MAE_RMSLE\n",
        "  keras.metrics.MAPE_RMSLE = MAPE_RMSLE\n",
        "  keras.metrics.MAPE_VAL_RMSLE = MAPE_VAL_RMSLE\n",
        "  keras.metrics.RMSLE = RMSLE\n",
        "\n",
        "  import keras.losses\n",
        "  keras.losses.MAE_VAL_MAPE = MAE_VAL_MAPE\n",
        "  keras.losses.MSE_VAL_MAPE = MSE_VAL_MAPE\n",
        "  keras.losses.MAE_ = MAE_\n",
        "  keras.losses.MASPE_VAL = MASPE_VAL\n",
        "  keras.losses.tweedieloss = tweedieloss\n",
        "  keras.losses.MAE_RMSLE = MAE_RMSLE\n",
        "  keras.losses.MAPE_RMSLE = MAPE_RMSLE\n",
        "  keras.losses.MAPE_VAL_RMSLE = MAPE_VAL_RMSLE\n",
        "  keras.losses.RMSLE = RMSLE\n",
        "\n",
        "  class MyCustomCallback(keras.callbacks.Callback):\n",
        "    \n",
        "    def __init__(self, epochs, stats_print_step): \n",
        "        \n",
        "        self.__epochs = epochs\n",
        "        self.__stats_print_step = stats_print_step\n",
        "    \n",
        "    def on_train_begin(self, logs={}):\n",
        "        pass\n",
        "        #print('on_train_begin', logs)\n",
        " \n",
        "    def on_train_end(self, logs={}):\n",
        "        pass\n",
        "        #print('on_train_end', logs)\n",
        " \n",
        "    def on_epoch_begin(self, epoch, logs={}):\n",
        "        pass\n",
        " \n",
        "    def on_epoch_end(self, epoch, logs={}):\n",
        "        if ((epoch < 5) or (epoch % self.__stats_print_step == 0)) :\n",
        "            print('# ' + str('{:04d}'.format(epoch + 1)) + ' | ' + self.get_stats_by_epoch(logs))\n",
        "        if epoch == 2:\n",
        "            print('.......')\n",
        "        if epoch == self.__epochs - 1:\n",
        "            print('# ' + str('{:04d}'.format(epoch + 1)) + ' | ' + self.get_stats_by_epoch(logs))\n",
        "        else:\n",
        "            print('# ' + str('{:04d}'.format(epoch + 1)) + ' | ' + self.get_stats_by_epoch(logs), end=\"\\r\")\n",
        " \n",
        "    def on_batch_begin(self, batch, logs={}):\n",
        "        pass\n",
        "        #print('on_batch_begin', batch, logs)\n",
        " \n",
        "    def on_batch_end(self, batch, logs={}):\n",
        "        pass\n",
        "        #print('on_batch_end', batch, logs)\n",
        "\n",
        "    def get_stats_by_epoch(self, logs):\n",
        "        \n",
        "        is_test = True\n",
        "        s = ''\n",
        "        \n",
        "        for key, value in logs.items(): \n",
        "            if is_test:\n",
        "                if 'val_' not in str(key):\n",
        "                    s += ' /// '\n",
        "                    is_test = False\n",
        "            if is_test:\n",
        "                s += ' ' + str(key).replace('val_', 'TST_') + ': ' + \"{0:.4f}\".format(value)\n",
        "            else:\n",
        "                s += ' ' + 'TRN_' + str(key) + ': ' + \"{0:.4f}\".format(value)\n",
        "\n",
        "        return s #'val_loss: ' + \"{0:.4f}\".format(logs['val_loss']) + ' | loss: ' + \"{0:.4f}\".format(logs['loss'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQppkzTWUjLm",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Fv4ErMY_vsQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#0: electricity, 1: chilledwater, 2: steam, 3: hotwater\n",
        "#  Подготовка данных\n",
        "  df = pd.read_csv(DIR + \"train.csv\", engine = 'python')\n",
        "  #df_test = pd.read_csv(DIR + \"test.csv\", engine = 'python')\n",
        "\n",
        "  df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
        "  #df_test['timestamp'] = pd.to_datetime(df_test['timestamp'])\n",
        "\n",
        "  #Очистка от корявых данных\n",
        "  #df = df.query('not (building_id <= 104 & meter == 0 & timestamp <= \"2016-05-20\")')\n",
        "\n",
        "  #df['meter_reading'] = np.log1p(df['meter_reading'])\n",
        "\n",
        "  #df = pd.concat([df_train, df_test], sort=False)\n",
        "\n",
        "  df['hour_cos'] = np.cos(df['timestamp'].dt.hour * 2 * np.pi / 24)\n",
        "  df['hour_sin'] = np.sin(df['timestamp'].dt.hour * 2 * np.pi / 24)\n",
        "\n",
        "  df['weekday_cos'] = np.cos(df['timestamp'].dt.weekday * 2 * np.pi / 7)\n",
        "  df['weekday_sin'] = np.sin(df['timestamp'].dt.weekday * 2 * np.pi / 7)\n",
        "\n",
        "  df['week_cos'] = np.cos(df['timestamp'].dt.week * 2 * np.pi / 53)\n",
        "  df['week_sin'] = np.sin(df['timestamp'].dt.week * 2 * np.pi / 53)\n",
        "\n",
        "  #df_train['weekends'] = (df_train['weekday'] >= 6) * 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3xRu7Y_2MP5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Очистка от подозрительных нулей\n",
        "  #print(df[df['building_id']==2][['timestamp','meter_reading','meter','ds_zero','de_zero','is_bad_zero']].head(20))\n",
        "  df = df.sort_values(by = ['meter','building_id','timestamp'])\n",
        "  df['meter_reading_prev'] = 0\n",
        "\n",
        "  #for bid in df['building_id'].unique():\n",
        "  #  for met in df['meter'].unique():\n",
        "  df['meter_reading_prev'] = df['meter_reading'].shift()\n",
        "  df['is_equal_prev']= (df['meter_reading_prev'] == df['meter_reading'] )*1\n",
        "\n",
        "  df['day'] = df['timestamp'].dt.dayofyear\n",
        "  df_bad_rows = df.groupby(by=['building_id','day','meter'], as_index = False)['is_equal_prev'].mean()\n",
        "  df_bad_rows.rename({\"is_equal_prev\": \"IS_BAD_PRCNT\"}, axis='columns', inplace=True)\n",
        "\n",
        "  df = pd.merge(df, df_bad_rows, how = 'inner', on = ['building_id','day','meter'])\n",
        "  #print(df_bad_rows[df_bad_rows['building_id']==109].head(365))\n",
        "  del df_bad_rows \n",
        "\n",
        "  #print(df[df['building_id']==2][['timestamp','meter_reading','is_equal_prev','IS_BAD_PRCNT','day']].head(30))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g52hlFMS2cpu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Добавление медианы по метрике постройки\n",
        "  df_median = df.groupby(by=['building_id','meter'])['meter_reading'].median()\n",
        "  df_median.name = 'building_meter_median'\n",
        "  df = pd.merge(df, df_median, how = 'inner', on = ['building_id','meter'])\n",
        "  del df_median \n",
        "# Добавление медианы по часу, по неделе, метрике постройки\n",
        "  df['hour'] = df['timestamp'].dt.hour\n",
        "  df_median = df.groupby(by=['building_id','hour','meter'])['meter_reading'].median()\n",
        "  df_median.name = 'building_meter_hour_median'\n",
        "  df = pd.merge(df, df_median, how = 'inner', on = ['building_id','hour','meter'])\n",
        "  del df_median \n",
        "\n",
        "  df['weekday'] = df['timestamp'].dt.weekday\n",
        "  df_median = df.groupby(by=['building_id','weekday','meter'])['meter_reading'].median()\n",
        "  df_median.name = 'building_meter_weekday_median'\n",
        "  df = pd.merge(df, df_median, how = 'inner', on = ['building_id','weekday','meter'])\n",
        "  del df_median \n",
        "#Подстановка параметров сооружения\n",
        "  building_df = pd.read_csv(DIR + \"building_metadata.csv\", engine = 'python')\n",
        "  from sklearn.preprocessing import LabelEncoder\n",
        "  le = LabelEncoder()\n",
        "  building_df[\"primary_use_ID\"] = le.fit_transform(building_df[\"primary_use\"])\n",
        "\n",
        "  building_df['primary_use'] = building_df['primary_use'].astype('category')\n",
        "  building_df = pd.get_dummies(building_df)\n",
        "\n",
        "  df = df.merge(building_df, left_on = \"building_id\", right_on = \"building_id\", how = \"left\")\n",
        "  df.head(5)\n",
        "  del building_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIDKj_YCb_bD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Вставка данных погоды\n",
        "#Восстановление пустых данных погоды через соседей по линейной инетрполяции\n",
        "  df_weather = pd.read_csv(DIR + \"weather_train.csv\", engine = 'python')\n",
        "  df_weather['timestamp'] = pd.to_datetime(df_weather['timestamp'])\n",
        "\n",
        "  #weather_train.groupby('site_id').apply(lambda group: group.isna().sum())\n",
        "  df_times = df.drop_duplicates(['site_id','timestamp'])[['site_id','timestamp']]\n",
        "  df_times = pd.merge(df_times, df_weather, how = 'left', on = ['site_id','timestamp'])\n",
        "  df_times = df_times.sort_values(by = ['site_id','timestamp'])\n",
        "  \n",
        "  df_times = df_times.groupby('site_id').apply(lambda group: group.interpolate(limit_direction='both'))\n",
        "  df = pd.merge(df, df_times, how = 'left', on = ['site_id','timestamp'])\n",
        "  #del df_weather"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vojXrtAOc0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Инфо о корявости данных:\n",
        "  print('0 Всего:',df[df['meter']==0].shape, 'Откинуть:', df[(df['meter']==0) & (df['IS_BAD_PRCNT']>0.45)].shape)\n",
        "  print('1 Всего:',df[df['meter']==1].shape, 'Откинуть:', df[(df['meter']==1) & (df['IS_BAD_PRCNT']>0.75)].shape)\n",
        "  print('2 Всего:',df[df['meter']==2].shape, 'Откинуть:', df[(df['meter']==2) & (df['IS_BAD_PRCNT']>0.75)].shape)\n",
        "  print('3 Всего:',df[df['meter']==3].shape, 'Откинуть:', df[(df['meter']==3) & (df['IS_BAD_PRCNT']>0.75)].shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2-83vuu0xOi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df_times = df.drop_duplicates(['site_id','hour','dayofyear'])[['site_id','hour','dayofyear']]\n",
        "# df_times = pd.merge(df_times, df_weather, how = 'left', on = ['site_id','timestamp'])\n",
        "# df_times = df_times.sort_values(by = ['site_id','timestamp'])\n",
        "# df_times = df_times.groupby('site_id').apply(lambda group: group.interpolate(limit_direction='both'))\n",
        "# df_times.to_feather(DIR + 'DF_WEATHER_BY_DAY_and_HOUR.FTHR')\n",
        "# del df_times"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmQtkGKQCueL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Вывести кол-во пустот по полям. Затем заполнить из средним значением по полю\n",
        "  for col in df.columns:\n",
        "    print(col)\n",
        "    for met in df['meter'].unique():\n",
        "      if np.sum(df[col].isnull()) > 0:\n",
        "        print(met)\n",
        "        print(np.sum(df[col].isnull()))\n",
        "\n",
        "        df_col = df.groupby(by=['building_id','meter'], as_index = False)[col].mean()\n",
        "        df_col.rename({col: \"tmp\"}, axis='columns', inplace=True)\n",
        "        df = df.merge(df_col, left_on = ['building_id','meter'], right_on = ['building_id','meter'], how = \"left\")\n",
        "        df[col].fillna( df_col['tmp'], inplace = True)\n",
        "        df.drop(columns = ['tmp'],inplace = True)\n",
        "        del df_col\n",
        "\n",
        "        df_col = df.groupby(by=['meter'], as_index = False)[col].mean()\n",
        "        df_col.rename({col: \"tmp\"}, axis='columns', inplace=True)\n",
        "        df = df.merge(df_col, left_on = ['meter'], right_on = ['meter'], how = \"left\")\n",
        "        df[col].fillna( df_col['tmp'], inplace = True)\n",
        "        df.drop(columns = ['tmp'],inplace = True)\n",
        "        del df_col\n",
        "\n",
        "        df[col].fillna( df[col].mean(), inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxPaU99v4PI-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df.to_feather(DIR + 'DF_TRAIN_EXTENDED2.FTHR')\n",
        "#df = pd.read_feather(DIR + 'DF_TRAIN_EXTENDED2.FTHR')\n",
        "reduce_mem_usage(df)\n",
        "df.to_feather(DIR + 'DF_TRAIN_REDUCED2.FTHR')\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6XbWpxAUnQb",
        "colab_type": "text"
      },
      "source": [
        "  Скорость"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asr3-4DHF_sx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df = pd.read_feather(DIR + 'DF_TRAIN_REDUCED2.FTHR')\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dKU2_Eb7NFo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRnpeXMU_vu5",
        "colab_type": "code",
        "outputId": "bc3fd8a9-cd01-4a83-d5ae-416f860f3c18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "source": [
        ""
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfToQh4ArZ3R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_Em3Xm6rdeV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Out_Columns = 'meter_reading'\n",
        "In_Columns = [ 'hour_cos','hour_sin', 'weekday_cos', 'weekday_sin', 'week_cos', 'week_sin',\n",
        "       'site_id', 'square_feet', 'year_built', 'floor_count', #'primary_use_ID',\n",
        "       'primary_use_Education', 'primary_use_Entertainment/public assembly',\n",
        "       'primary_use_Food sales and service', 'primary_use_Healthcare',\n",
        "       'primary_use_Lodging/residential',\n",
        "       'primary_use_Manufacturing/industrial', 'primary_use_Office',\n",
        "       'primary_use_Other', 'primary_use_Parking',\n",
        "       'primary_use_Public services', 'primary_use_Religious worship',\n",
        "       'primary_use_Retail', 'primary_use_Services',\n",
        "       'primary_use_Technology/science', 'primary_use_Utility',\n",
        "       'primary_use_Warehouse/storage', 'air_temperature', 'cloud_coverage',\n",
        "       'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n",
        "       'wind_direction', 'wind_speed'\n",
        "       , 'building_meter_median','building_meter_hour_median','building_meter_weekday_median']\n",
        "# Нормализация\n",
        "if 1==1:\n",
        "  from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, OneHotEncoder\n",
        "  scaler =  MinMaxScaler (copy=True, feature_range=(0, 1))                                  #quantile_range  = (15.0,85.0)) #Normalizer #(copy=True, feature_range=(-1, 1)) # MinMaxScaler(copy=True, feature_range=(-1, 1)) #StandardScaler() #MinMaxScaler(copy=True, feature_range=(-1, 1)) # RobustScaler()\n",
        "  scaler.fit( df[In_Columns] )\n",
        "  df[In_Columns]     = pd.DataFrame(data = scaler.transform( df[In_Columns])    , columns = df[In_Columns].columns   , index=df.index) \n",
        "\n",
        "  df.fillna(0.33)\n",
        "  gc.collect()\n",
        "\n",
        "  import pickle\n",
        "  with open(DIR+'scaler.pickle', 'wb') as handle:\n",
        "    pickle.dump(scaler, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "    pickle.dump(In_Columns, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "    pickle.dump(Out_Columns, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "  \n",
        "  #with open('filename.pickle', 'rb') as handle:\n",
        "  #  b = pickle.load(handle)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aup6pDdkDoZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#0: electricity, 1: chilledwater, 2: steam, 3: hotwater\n",
        "# РАСЧЁТ НЕЙРОНОК:\n",
        "  reg = 0.00001\n",
        "  batch_size = 2048\n",
        "  epochs = 50\n",
        "  meter = 0\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=5, verbose=1, min_delta=1e-3, mode='min')\n",
        "  model_checkpoint = ModelCheckpoint(monitor='loss', save_best_only=True, filepath = DIR + 'best_nn_' + str(meter) + '.model', save_weights_only = False, verbose=0, mode='min')\n",
        "\n",
        "  init = TruncatedNormal(mean=0.0, stddev=0.05, seed=159)\n",
        "  bias = Constant(value = 1e-3)\n",
        "  \n",
        "  if 0==0:\n",
        "    meter = 0\n",
        "    opt = Adam(lr = 0.009)\n",
        "\n",
        "    nn_0 = Sequential()\n",
        "    nn_0.add(Dense(60, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_regularizer=l1(reg), kernel_initializer = 'normal'))\n",
        "    nn_0.add(Dense(60, activation = 'tanh',  kernel_initializer = 'normal'))\n",
        "    nn_0.add(Dense(1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "    nn_0.compile(optimizer = opt, loss = MAE_RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "    \n",
        "    val = df[(df['meter']==meter) & (df['day']%6==0)]\n",
        "    hist = nn_0.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "                    , epochs = epochs, shuffle = True, callbacks=[earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                    , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                    ) \n",
        "    nn_0.save(DIR + str(meter) + 'HANDLY_SAVED2.MODEL')\n",
        "    gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nX1MEx4lPtK6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# hist = nn_0.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "#               , epochs = epochs, shuffle = True, callbacks=[earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "#               , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "#               ) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diaTWlaXC4_H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " #Продолжение расчёта\n",
        " if 9==9:\n",
        "    # nn_0.optimizer.lr = 0.007\n",
        "    # nn_0.compile(optimizer = opt, loss = MAE_RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse' ])\n",
        "    # batch_size = 2048\n",
        "    # epochs = 10\n",
        "    \n",
        "    # earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min',restore_best_weights = True)\n",
        "    # reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=5, verbose=1, min_delta=1e-3, mode='min')\n",
        "\n",
        "    # val = df[(df['meter']==meter) & (df['day']%6==0)]\n",
        "    # hist = nn_0.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "    #                 , epochs = epochs, shuffle = True, callbacks=[earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "    #                 , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "    #                 ) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mf8-ceMw8N1q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if 1==1:\n",
        "  reg = 0.00001\n",
        "  meter = 1\n",
        "  epochs = 100\n",
        "  batch_size = 512\n",
        "  opt = Adam(lr = 0.007)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=5, verbose=1, min_delta=1e-3, mode='min')\n",
        "  model_checkpoint = ModelCheckpoint(monitor='loss', save_best_only=True, filepath = DIR + 'best_nn_' + str(meter) + '.model', save_weights_only = False, verbose=0, mode='min')\n",
        "\n",
        "  nn_1 = Sequential()\n",
        "  nn_1.add(Dense(90, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_regularizer=l1(reg), kernel_initializer = 'normal'))\n",
        "  nn_1.add(Dense(90, activation = 'tanh',  kernel_initializer = 'normal'))\n",
        "  nn_1.add(Dense(90, activation = 'relu',  kernel_initializer = 'normal'))\n",
        "  nn_1.add(Dense(90, activation = 'relu',  kernel_initializer = 'normal'))\n",
        "  nn_1.add(Dense(1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "  nn_1.compile(optimizer = opt, loss = MAE_RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[(df['meter']==meter) & (df['day']%6==0)]\n",
        "  hist = nn_1.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_1.save(DIR + str(meter) + 'HANDLY_SAVED2.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHKu4TMG8ROI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if 2==2:\n",
        "  reg = 0.000001\n",
        "  meter = 2\n",
        "  epochs = 30\n",
        "  batch_size = 200\n",
        "  opt = Adam(lr = 0.002)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=30, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=55, verbose=1, min_delta=1e-3, mode='min')\n",
        "  model_checkpoint = ModelCheckpoint(monitor='loss', save_best_only=True, filepath = DIR + 'best_nn_' + str(meter) + '.model', save_weights_only = False, verbose=0, mode='min')\n",
        "\n",
        "  nn_2 = Sequential()\n",
        "  nn_2.add(Dense(90, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_regularizer=l1(reg), kernel_initializer = keras.initializers.RandomUniform(minval=-0.25, maxval=0.25, seed=None), bias_initializer=keras.initializers.Constant(0.1)))\n",
        "  nn_2.add(Dense(90, activation = 'tanh',  kernel_initializer = keras.initializers.RandomUniform(minval=-0.25, maxval=0.25, seed=None), bias_initializer=keras.initializers.Constant(0.1)))\n",
        "  nn_2.add(Dense( 1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "  nn_2.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[(df['meter']==meter) & (df['day']%6==0)]\n",
        "  hist = nn_2.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.75)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.75)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_2.save(DIR + str(meter) + 'HANDLY_SAVED2.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PlNOAoppy96",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# lr = 0.002 , 0.003, \n",
        "#nn_2 = keras.models.load_model (DIR + '2HANDLY_SAVED2.MODEL')\n",
        "opt = Adam(lr = 0.003)\n",
        "batch_size = 100\n",
        "epochs = 7\n",
        "nn_2.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "hist = nn_2.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.75)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.75)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "                , epochs = epochs, shuffle = True, callbacks=[ earlyStopping, reduce_lr_loss,model_checkpoint] #MyCustomCallback(epochs, 1),\n",
        "                , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                ) \n",
        "#nn_2.save(DIR + str(meter) + 'HANDLY_SAVED2.MODEL')\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdad7yTx8Uri",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if 3==3:\n",
        "  meter = 3\n",
        "  epochs = 30\n",
        "  batch_size = 200\n",
        "  opt = Adam(lr = 0.00002)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=50, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=10, verbose=1, min_delta=1e-3, mode='min')\n",
        "  model_checkpoint = ModelCheckpoint(monitor='loss', save_best_only=True, filepath = DIR + 'best_nn_' + str(meter) + '.model', save_weights_only = False, verbose=0, mode='min')\n",
        "\n",
        "  nn_3 = Sequential()\n",
        "  nn_3.add(Dense(45, input_shape = df[In_Columns].shape[1:], activation = 'relu', kernel_regularizer=l1(reg), kernel_initializer = 'normal', bias_initializer=keras.initializers.Constant(0.1)))\n",
        "  nn_3.add(Dense(45, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=keras.initializers.Constant(0.1)))\n",
        "  nn_3.add(Dense(70, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=keras.initializers.Constant(0.1)))\n",
        "  nn_3.add(Dense(1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "  nn_3.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[(df['meter']==meter) & (df['day']%6==0)]\n",
        "  hist = nn_3.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.25)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.25)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[earlyStopping, reduce_lr_loss]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_3.save(DIR + str(meter) + 'HANDLY_SAVED2.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38XziDvm_mhH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for lr in [0.00004,0.00007,0.0002,0.0007]:\n",
        "  opt = Adam(lr = lr)\n",
        "  epochs = 50\n",
        "  nn_3.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  hist = nn_3.fit( df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.25)][In_Columns], df[(df['meter']==meter) & (df['IS_BAD_PRCNT']<0.25)][Out_Columns], batch_size = batch_size, verbose = 1\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[earlyStopping, reduce_lr_loss]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_3.save(DIR + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H75KbUGtAdOG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#nn_3.save(DIR + str(meter) + 'HANDLY_SAVED.MODEL')\n",
        "# keras.initializers.RandomUniform(minval=-0.05, maxval=0.05, seed=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zWSKYPSOjpz",
        "colab_type": "code",
        "outputId": "c70e25c2-b1d6-46d7-9962-9809aadfa861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "nn_0 = keras.models.load_model (DIR + '0HANDLY_SAVED2.MODEL')\n",
        "nn_1 = keras.models.load_model (DIR + '1HANDLY_SAVED2.MODEL')\n",
        "nn_2 = keras.models.load_model (DIR + '2HANDLY_SAVED2.MODEL')\n",
        "nn_3 = keras.models.load_model (DIR + '3HANDLY_SAVED3.MODEL')\n",
        "\n",
        "# score = model.evaluate(X, Y, verbose=0)\n",
        "# print(\"%s: %.2f%%\" % (model.metrics_names[1], score[1]*100))\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "907"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xsuPcIPCvW2",
        "colab_type": "code",
        "outputId": "baaeacd8-f9fe-49be-8491-b69d1ea8b605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Предсказание из 4х значений:\n",
        "  df['NN_PRED'] = 0\n",
        "  df['NN_PRED_0'] = nn_0.predict(df[In_Columns], batch_size = 10000) \n",
        "  df['NN_PRED_1'] = nn_1.predict(df[In_Columns], batch_size = 10000) \n",
        "  df['NN_PRED_2'] = nn_2.predict(df[In_Columns], batch_size = 10000) \n",
        "  df['NN_PRED_3'] = nn_3.predict(df[In_Columns], batch_size = 10000) \n",
        "  df['NN_PRED'] = np.where(df['meter'] == 0, df['NN_PRED_0'], df['NN_PRED'])\n",
        "  df['NN_PRED'] = np.where(df['meter'] == 1, df['NN_PRED_1'], df['NN_PRED'])\n",
        "  df['NN_PRED'] = np.where(df['meter'] == 2, df['NN_PRED_2'], df['NN_PRED'])\n",
        "  df['NN_PRED'] = np.where(df['meter'] == 3, df['NN_PRED_3'], df['NN_PRED'])\n",
        "  df.drop(columns = ['NN_PRED_0', 'NN_PRED_1', 'NN_PRED_2', 'NN_PRED_3'], inplace = True)\n",
        "  #df.drop(columns = ['Value_x', 'Value_y'])\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9zp6mbpLAgV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1W6pmrQFgyS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[['meter','meter_reading','NN_PRED','NN_PRED_0','NN_PRED_1','NN_PRED_2','NN_PRED_3']].head(-10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jV_7qcb1EK2t",
        "colab_type": "code",
        "outputId": "94525e46-75b2-4b86-f38c-04a5f963e176",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df['NN_ERR'] = np.abs(df['NN_PRED']-df['meter_reading'])\n",
        "print( np.sqrt( np.mean( np.power(np.log(df['meter_reading']+1) - np.log(df['NN_ERR']+1),2.00000)))     )\n",
        "print('Ошибка 0: ', np.sqrt( np.mean( np.power(np.log(df[df['meter']==0]['meter_reading']+1) - np.log(df[df['meter']==0]['NN_ERR']+1),2.00000)))        )\n",
        "print('Ошибка 1: ', np.sqrt( np.mean( np.power(np.log(df[df['meter']==1]['meter_reading']+1) - np.log(df[df['meter']==1]['NN_ERR']+1),2.00000)))        )\n",
        "print('Ошибка 2: ', np.sqrt( np.mean( np.power(np.log(df[df['meter']==2]['meter_reading']+1) - np.log(df[df['meter']==2]['NN_ERR']+1),2.00000)))        )\n",
        "print('Ошибка 3: ', np.sqrt( np.mean( np.power(np.log(df[df['meter']==3]['meter_reading']+1) - np.log(df[df['meter']==3]['NN_ERR']+1),2.00000)))        )\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.31600296573249\n",
            "Ошибка 0:  2.367548887649364\n",
            "Ошибка 1:  2.3341838812051288\n",
            "Ошибка 2:  2.0793432874515667\n",
            "Ошибка 3:  2.238230002103621\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSg8qh29X2xl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Evaluate:\n",
        "  score = nn_0.evaluate(df[df['meter']==0][In_Columns], df[df['meter']==0][Out_Columns], verbose=1, batch_size = 30000)\n",
        "  print(0)\n",
        "  for i in range(len(nn_0.metrics_names)):\n",
        "    print(\"%s: %.2f%%\" % (nn_0.metrics_names[i], score[i]*100))\n",
        "\n",
        "  score = nn_1.evaluate(df[df['meter']==1][In_Columns], df[df['meter']==1][Out_Columns], verbose=1, batch_size = 30000)\n",
        "  print(1)\n",
        "  for i in range(len(nn_1.metrics_names)):\n",
        "    print(\"%s: %.2f%%\" % (nn_1.metrics_names[i], score[i]*100))\n",
        "\n",
        "  score = nn_2.evaluate(df[df['meter']==2][In_Columns], df[df['meter']==2][Out_Columns], verbose=1, batch_size = 30000)\n",
        "  print(2)\n",
        "  for i in range(len(nn_2.metrics_names)):\n",
        "    print(\"%s: %.2f%%\" % (nn_2.metrics_names[i], score[i]*100))\n",
        "\n",
        "  score = nn_3.evaluate(df[df['meter']==3][In_Columns], df[df['meter']==3][Out_Columns], verbose=1, batch_size = 30000)\n",
        "  print(3)\n",
        "  for i in range(len(nn_3.metrics_names)):\n",
        "    print(\"%s: %.2f%%\" % (nn_3.metrics_names[i], score[i]*100))\n",
        "  \n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsiTWGypOAHb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ИДЕИ:\n",
        "  1) Сделать не показатель, а его отклонение от медианы по строению/дню/часу\n",
        "  2) \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLLVe_MMYIz-",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6AezgKyEBi6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reduce_mem_usage(df_test)\n",
        "gc.collect()\n",
        "df_test.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWDYNM3xOBZx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Подготовка данных тест:\n",
        "  df_test = pd.read_csv(DIR + \"test.csv\", engine = 'python')\n",
        "  print('Забрали с диска')\n",
        "  df_test['timestamp'] = pd.to_datetime(df_test['timestamp'])\n",
        "\n",
        "  df_test['hour_cos'] = np.cos(df_test['timestamp'].dt.hour * 2. * np.pi / 24.)\n",
        "  df_test['hour_sin'] = np.sin(df_test['timestamp'].dt.hour * 2. * np.pi / 24.)\n",
        "\n",
        "  df_test['weekday_cos'] = np.cos(df_test['timestamp'].dt.weekday * 2. * np.pi / 7.)\n",
        "  df_test['weekday_sin'] = np.sin(df_test['timestamp'].dt.weekday * 2. * np.pi / 7.)\n",
        "\n",
        "  df_test['week_cos'] = np.cos(df_test['timestamp'].dt.week * 2. * np.pi / 53.)\n",
        "  df_test['week_sin'] = np.sin(df_test['timestamp'].dt.week * 2. * np.pi / 53.)\n",
        "\n",
        "  reduce_mem_usage(df_test)\n",
        "  gc.collect()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRkbaJjBUGZP",
        "colab_type": "code",
        "outputId": "ea6ccc3f-cecc-45c2-acba-822f0d1d1f4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "# Добавление медианы по метрике постройки\n",
        "  df_median = df.groupby(by=['building_id','meter'])['meter_reading'].median()\n",
        "  df_median.name = 'building_meter_median'\n",
        "  df_test = pd.merge(df_test, df_median, how = 'inner', on = ['building_id','meter'])\n",
        "  del df_median \n",
        "  gc.collect()\n",
        "  print('Построены медианы по сооружению')\n",
        "# Добавление медианы по часу, по неделе, метрике постройки\n",
        "  df_test['hour'] = df_test['timestamp'].dt.hour\n",
        "  df_median = df.groupby(by=['building_id','hour','meter'])['meter_reading'].median()\n",
        "  df_median.name = 'building_meter_hour_median'\n",
        "  df_test = pd.merge(df_test, df_median, how = 'inner', on = ['building_id','hour','meter'])\n",
        "  del df_median \n",
        "  gc.collect()\n",
        "  df_test['weekday'] = df_test['timestamp'].dt.weekday\n",
        "  df_median = df.groupby(by=['building_id','weekday','meter'])['meter_reading'].median()\n",
        "  df_median.name = 'building_meter_weekday_median'\n",
        "  df_test = pd.merge(df_test, df_median, how = 'inner', on = ['building_id','weekday','meter'])\n",
        "  del df_median \n",
        "  print('Построены медианы по сооружению/часу')\n",
        "  gc.collect()\n",
        "# Подстановка параметров сооружения\n",
        "  building_df = pd.read_csv(DIR + \"building_metadata.csv\", engine = 'python')\n",
        "  from sklearn.preprocessing import LabelEncoder\n",
        "  le = LabelEncoder()\n",
        "  building_df[\"primary_use_ID\"] = le.fit_transform(building_df[\"primary_use\"])\n",
        "\n",
        "  building_df['primary_use'] = building_df['primary_use'].astype('category')\n",
        "  building_df = pd.get_dummies(building_df)\n",
        "\n",
        "  df_test = df_test.merge(building_df, left_on = \"building_id\", right_on = \"building_id\", how = \"left\")\n",
        "  del building_df\n",
        "  print('Подставлены данные по сооружению')\n",
        "  gc.collect()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Построены медианы по сооружению\n",
            "Построены медианы по сооружению/часу\n",
            "Подставлены данные по сооружению\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i34pNJGAWNLV",
        "colab_type": "code",
        "outputId": "089280f0-88ec-4a97-9826-c5e1f22a0033",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# del df\n",
        "#del tmp\n",
        "gc.collect()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozAyo91RRIfR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gZDPAWaD0M5n",
        "colab": {}
      },
      "source": [
        "#Вставка данных погоды\n",
        "#Восстановление пустых данных погоды через соседей по линейной инетрполяции\n",
        "  df_weather = pd.read_csv(DIR + \"weather_test.csv\", engine = 'python')\n",
        "  df_weather['timestamp'] = pd.to_datetime(df_weather['timestamp'])\n",
        "  gc.collect()\n",
        "  #weather_train.groupby('site_id').apply(lambda group: group.isna().sum())\n",
        "  df_times = df_test.drop_duplicates(['site_id','timestamp'])[['site_id','timestamp']]\n",
        "  df_times = pd.merge(df_times, df_weather, how = 'left', on = ['site_id','timestamp'])\n",
        "  df_times = df_times.sort_values(by = ['site_id','timestamp'])\n",
        "  gc.collect()\n",
        "  df_times = df_times.groupby('site_id').apply(lambda group: group.interpolate(limit_direction='both'))\n",
        "  df_test = pd.merge(df_test, df_times, how = 'left', on = ['site_id','timestamp'])\n",
        "  del df_weather, df_times"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jblybbA1X8IM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reduce_mem_usage(df_test)\n",
        "df_test.to_feather(DIR + 'DF_TEST_REDUCED2.FTHR')\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjgCdq3PZh81",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_test = pd.read_feather(DIR + 'DF_TEST_REDUCED2.FTHR')\n",
        "gc.collect()\n",
        "\n",
        "import pickle\n",
        "with open(DIR+'scaler.pickle', 'rb') as handle:\n",
        "    scaler = pickle.load(handle)\n",
        "    In_Columns = pickle.load(handle)\n",
        "    Out_Columns = pickle.load(handle)\n",
        "  \n",
        "  #with open('filename.pickle', 'rb') as handle:\n",
        "  #  b = pickle.load(handle)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTDcNmss8mK5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "outputId": "e08ac5c4-b97d-4039-a9be-fe38fd9207e0"
      },
      "source": [
        "# Нормализация\n",
        "i=0\n",
        "for c in In_Columns:\n",
        "  print(i, c, scaler.scale_[i],scaler.min_[i])\n",
        "  df_test[c] = df_test[c] * scaler.scale_[i] + scaler.min_[i]\n",
        "  i+=1\n",
        "  gc.collect()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 hour_cos 0.5 0.5\n",
            "1 hour_sin 0.5 0.5\n",
            "2 weekday_cos 0.5260475468492817 0.4739524531507184\n",
            "3 weekday_sin 0.5128584368757862 0.5\n",
            "4 week_cos 0.5004394486055721 0.49956055139442795\n",
            "5 week_sin 0.5002196799875587 0.5\n",
            "6 site_id 0.06666666666666667 0.0\n",
            "7 square_feet 1.1432268950986433e-06 -0.00032353321131291604\n",
            "8 year_built 0.008547008547008548 -16.239316239316242\n",
            "9 floor_count 0.04 -0.04\n",
            "10 primary_use_Education 1.0 0.0\n",
            "11 primary_use_Entertainment/public assembly 1.0 0.0\n",
            "12 primary_use_Food sales and service 1.0 0.0\n",
            "13 primary_use_Healthcare 1.0 0.0\n",
            "14 primary_use_Lodging/residential 1.0 0.0\n",
            "15 primary_use_Manufacturing/industrial 1.0 0.0\n",
            "16 primary_use_Office 1.0 0.0\n",
            "17 primary_use_Other 1.0 0.0\n",
            "18 primary_use_Parking 1.0 0.0\n",
            "19 primary_use_Public services 1.0 0.0\n",
            "20 primary_use_Religious worship 1.0 0.0\n",
            "21 primary_use_Retail 1.0 0.0\n",
            "22 primary_use_Services 1.0 0.0\n",
            "23 primary_use_Technology/science 1.0 0.0\n",
            "24 primary_use_Utility 1.0 0.0\n",
            "25 primary_use_Warehouse/storage 1.0 0.0\n",
            "26 air_temperature 0.01314060440193505 0.3797634622031802\n",
            "27 cloud_coverage 0.1111111111111111 0.0\n",
            "28 dew_temperature 0.016366612009110198 0.5728314203188569\n",
            "29 precip_depth_1_hr 0.0029069767441860465 0.0029069767441860465\n",
            "30 sea_level_pressure 0.01293661265093965 -12.525228526557404\n",
            "31 wind_direction 0.002777777777777778 0.0\n",
            "32 wind_speed 0.05263157894736842 0.0\n",
            "33 building_meter_median 2.4980515685665876e-05 0.0\n",
            "34 building_meter_hour_median 2.2260881179590182e-05 0.0\n",
            "35 building_meter_weekday_median 2.4132730015082955e-05 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cG1CZk1i-CHl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Выбор нейронок:\n",
        "  nn_0 = keras.models.load_model (DIR + '0HANDLY_SAVED2.MODEL')\n",
        "  nn_1 = keras.models.load_model (DIR + '1HANDLY_SAVED2.MODEL')\n",
        "  nn_2 = keras.models.load_model (DIR + '2HANDLY_SAVED2.MODEL')\n",
        "  nn_3 = keras.models.load_model (DIR + '3HANDLY_SAVED3.MODEL')\n",
        "  # score = model.evaluate(X, Y, verbose=0)\n",
        "  # print(\"%s: %.2f%%\" % (model.metrics_names[1], score[1]*100))\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqmLUHDySwg-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2c1bc52b-9ac8-412f-c457-3b03ef37a151"
      },
      "source": [
        "#Предсказание из 4х значений:\n",
        "  df_test['NN_PRED'] = 0 \n",
        "  df_test['NN_PRED']  = df_test['NN_PRED'].astype('float16')\n",
        "  l = len(df_test)\n",
        "  print( len(df_test) )\n",
        "  step = 200000\n",
        "  for i in range(int(l/step)+1):\n",
        "    print(i*step, (i+1)*step)\n",
        "    tmp = df_test[i*step: (i+1)*step][In_Columns + ['row_id','meter']]\n",
        "    gc.collect() \n",
        "    tmp['NN_PRED'] = 0 \n",
        "    tmp['NN_PRED_0'] = nn_0.predict(tmp[In_Columns], batch_size = 20000) \n",
        "    tmp['NN_PRED_1'] = nn_1.predict(tmp[In_Columns], batch_size = 20000) \n",
        "    tmp['NN_PRED_2'] = nn_2.predict(tmp[In_Columns], batch_size = 20000) \n",
        "    tmp['NN_PRED_3'] = nn_3.predict(tmp[In_Columns], batch_size = 20000) \n",
        "    gc.collect()\n",
        "    tmp['NN_PRED'] = np.where(tmp['meter'] == 0, tmp['NN_PRED_0'], tmp['NN_PRED'])\n",
        "    tmp['NN_PRED'] = np.where(tmp['meter'] == 1, tmp['NN_PRED_1'], tmp['NN_PRED'])\n",
        "    tmp['NN_PRED'] = np.where(tmp['meter'] == 2, tmp['NN_PRED_2'], tmp['NN_PRED'])\n",
        "    tmp['NN_PRED'] = np.where(tmp['meter'] == 3, tmp['NN_PRED_3'], tmp['NN_PRED'])\n",
        "    gc.collect()\n",
        "    #print('спрогнозили')\n",
        "    #df_test[i*step: (i+1)*step]['NN_PRED'] = pd.merge(df_test, tmp[['row_id','NN_PRED']], how = 'left', on = ['row_id'])\n",
        "    #df_test.loc[i*step: (i+1)*step, 'NN_PRED'] = tmp['NN_PRED']\n",
        "    \n",
        "    if i==0:\n",
        "      a = tmp[['row_id','NN_PRED']].copy()\n",
        "    else:\n",
        "      a = pd.concat([a, tmp[['row_id','NN_PRED']].copy()], sort=False)\n",
        "    #del tmp\n",
        "    #gc.collect() "
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtxkAc9E00Yn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "fcceb21e-7a31-4588-aec3-e984b54b91aa"
      },
      "source": [
        "print(len(a))\n",
        "a.drop_duplicates(inplace =True)\n",
        "print(len(a))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "41697600\n",
            "41697600\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-Db2v_3u-_F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "outputId": "7ba1f2d4-b0dd-43a4-e1ba-fcc57f3d3d96"
      },
      "source": [
        "tmp"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        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>hour_cos</th>\n",
              "      <th>hour_sin</th>\n",
              "      <th>weekday_cos</th>\n",
              "      <th>weekday_sin</th>\n",
              "      <th>week_cos</th>\n",
              "      <th>week_sin</th>\n",
              "      <th>site_id</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>year_built</th>\n",
              "      <th>floor_count</th>\n",
              "      <th>primary_use_Education</th>\n",
              "      <th>primary_use_Entertainment/public assembly</th>\n",
              "      <th>primary_use_Food sales and service</th>\n",
              "      <th>primary_use_Healthcare</th>\n",
              "      <th>primary_use_Lodging/residential</th>\n",
              "      <th>primary_use_Manufacturing/industrial</th>\n",
              "      <th>primary_use_Office</th>\n",
              "      <th>primary_use_Other</th>\n",
              "      <th>primary_use_Parking</th>\n",
              "      <th>primary_use_Public services</th>\n",
              "      <th>primary_use_Religious worship</th>\n",
              "      <th>primary_use_Retail</th>\n",
              "      <th>primary_use_Services</th>\n",
              "      <th>primary_use_Technology/science</th>\n",
              "      <th>primary_use_Utility</th>\n",
              "      <th>primary_use_Warehouse/storage</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>building_meter_median</th>\n",
              "      <th>building_meter_hour_median</th>\n",
              "      <th>building_meter_weekday_median</th>\n",
              "      <th>row_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>NN_PRED</th>\n",
              "      <th>NN_PRED_0</th>\n",
              "      <th>NN_PRED_1</th>\n",
              "      <th>NN_PRED_2</th>\n",
              "      <th>NN_PRED_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>733.004395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
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              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.650460</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.818331</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.763259</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.163158</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>43344</td>\n",
              "      <td>0</td>\n",
              "      <td>251.292831</td>\n",
              "      <td>251.292831</td>\n",
              "      <td>100.346115</td>\n",
              "      <td>664.592407</td>\n",
              "      <td>691.125305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.968682</td>\n",
              "      <td>0.674177</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.685940</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.800327</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.542044</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.136842</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>65016</td>\n",
              "      <td>0</td>\n",
              "      <td>249.557755</td>\n",
              "      <td>249.557755</td>\n",
              "      <td>107.371262</td>\n",
              "      <td>637.268738</td>\n",
              "      <td>737.548157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.944780</td>\n",
              "      <td>0.728415</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.547963</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.545008</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.666236</td>\n",
              "      <td>0.861111</td>\n",
              "      <td>0.136842</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>86688</td>\n",
              "      <td>0</td>\n",
              "      <td>198.508957</td>\n",
              "      <td>198.508957</td>\n",
              "      <td>66.978920</td>\n",
              "      <td>564.799316</td>\n",
              "      <td>846.702820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.914628</td>\n",
              "      <td>0.779446</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.628121</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800327</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.708926</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>108360</td>\n",
              "      <td>0</td>\n",
              "      <td>242.850708</td>\n",
              "      <td>242.850708</td>\n",
              "      <td>109.067596</td>\n",
              "      <td>756.736572</td>\n",
              "      <td>755.723633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.878649</td>\n",
              "      <td>0.826554</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.642576</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.754501</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.716688</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.110526</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>130032</td>\n",
              "      <td>0</td>\n",
              "      <td>248.993134</td>\n",
              "      <td>248.993134</td>\n",
              "      <td>124.235741</td>\n",
              "      <td>767.401184</td>\n",
              "      <td>724.672424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.837349</td>\n",
              "      <td>0.869079</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.642576</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.609314</td>\n",
              "      <td>0.638889</td>\n",
              "      <td>0.110526</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>151704</td>\n",
              "      <td>0</td>\n",
              "      <td>242.528351</td>\n",
              "      <td>242.528351</td>\n",
              "      <td>119.515411</td>\n",
              "      <td>768.881287</td>\n",
              "      <td>750.239075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.791306</td>\n",
              "      <td>0.906422</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.700394</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.574385</td>\n",
              "      <td>0.805556</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>173376</td>\n",
              "      <td>0</td>\n",
              "      <td>264.144745</td>\n",
              "      <td>264.144745</td>\n",
              "      <td>147.889099</td>\n",
              "      <td>800.168152</td>\n",
              "      <td>715.593994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.741169</td>\n",
              "      <td>0.938060</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.628121</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.746318</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.793014</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.326316</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>195048</td>\n",
              "      <td>0</td>\n",
              "      <td>241.517212</td>\n",
              "      <td>241.517212</td>\n",
              "      <td>130.343201</td>\n",
              "      <td>836.487366</td>\n",
              "      <td>708.904602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.687639</td>\n",
              "      <td>0.963548</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.685940</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.772504</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.661061</td>\n",
              "      <td>0.194444</td>\n",
              "      <td>0.268421</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>216720</td>\n",
              "      <td>0</td>\n",
              "      <td>259.275177</td>\n",
              "      <td>259.275177</td>\n",
              "      <td>145.868576</td>\n",
              "      <td>836.949463</td>\n",
              "      <td>671.912598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.631470</td>\n",
              "      <td>0.982530</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.679369</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.690671</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.692109</td>\n",
              "      <td>0.805556</td>\n",
              "      <td>0.352632</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>238392</td>\n",
              "      <td>0</td>\n",
              "      <td>262.850739</td>\n",
              "      <td>262.850739</td>\n",
              "      <td>130.893158</td>\n",
              "      <td>838.763672</td>\n",
              "      <td>666.637207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.573449</td>\n",
              "      <td>0.994737</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.671485</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.772504</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.676585</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.268421</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>260064</td>\n",
              "      <td>0</td>\n",
              "      <td>264.340118</td>\n",
              "      <td>264.340118</td>\n",
              "      <td>113.950790</td>\n",
              "      <td>882.919922</td>\n",
              "      <td>645.887085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.514390</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.721419</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.851064</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.638292</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>281736</td>\n",
              "      <td>0</td>\n",
              "      <td>266.750427</td>\n",
              "      <td>266.750427</td>\n",
              "      <td>103.094238</td>\n",
              "      <td>833.652771</td>\n",
              "      <td>652.390442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.455124</td>\n",
              "      <td>0.998244</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.642576</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.654664</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.636482</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.326316</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>303408</td>\n",
              "      <td>0</td>\n",
              "      <td>254.988953</td>\n",
              "      <td>254.988953</td>\n",
              "      <td>30.753983</td>\n",
              "      <td>901.950562</td>\n",
              "      <td>656.249695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.396481</td>\n",
              "      <td>0.989493</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.685940</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.717982</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.242105</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>325080</td>\n",
              "      <td>0</td>\n",
              "      <td>261.805695</td>\n",
              "      <td>261.805695</td>\n",
              "      <td>23.593597</td>\n",
              "      <td>809.090027</td>\n",
              "      <td>680.220276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.339285</td>\n",
              "      <td>0.973871</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.716163</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.882160</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.566623</td>\n",
              "      <td>0.361111</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>346752</td>\n",
              "      <td>0</td>\n",
              "      <td>267.739197</td>\n",
              "      <td>267.739197</td>\n",
              "      <td>104.966454</td>\n",
              "      <td>750.929199</td>\n",
              "      <td>680.710815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.284339</td>\n",
              "      <td>0.951597</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.722733</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.900164</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.662355</td>\n",
              "      <td>0.305556</td>\n",
              "      <td>0.352632</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>368424</td>\n",
              "      <td>0</td>\n",
              "      <td>272.540222</td>\n",
              "      <td>272.540222</td>\n",
              "      <td>98.260262</td>\n",
              "      <td>754.183167</td>\n",
              "      <td>660.235046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.232414</td>\n",
              "      <td>0.922984</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.657030</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.644845</td>\n",
              "      <td>0.002907</td>\n",
              "      <td>0.564036</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>390096</td>\n",
              "      <td>0</td>\n",
              "      <td>264.299835</td>\n",
              "      <td>264.299835</td>\n",
              "      <td>13.075697</td>\n",
              "      <td>810.393494</td>\n",
              "      <td>702.019714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.801938</td>\n",
              "      <td>0.099031</td>\n",
              "      <td>0.184240</td>\n",
              "      <td>0.888433</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008173</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.657030</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.909984</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.522639</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.163158</td>\n",
              "      <td>0.00474</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004505</td>\n",
              "      <td>411768</td>\n",
              "      <td>0</td>\n",
              "      <td>273.611664</td>\n",
              "      <td>273.611664</td>\n",
              "      <td>48.685120</td>\n",
              "      <td>700.300171</td>\n",
              "      <td>699.162476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    hour_cos  hour_sin  weekday_cos  weekday_sin  week_cos  week_sin  site_id  square_feet  \\\n",
              "0        1.0       0.5     0.801938     0.099031  0.996487  0.440837      0.0     0.008173   \n",
              "1        1.0       0.5     0.801938     0.099031  0.996487  0.559163      0.0     0.008173   \n",
              "2        1.0       0.5     0.801938     0.099031  0.985999  0.617495      0.0     0.008173   \n",
              "3        1.0       0.5     0.801938     0.099031  0.968682  0.674177      0.0     0.008173   \n",
              "4        1.0       0.5     0.801938     0.099031  0.944780  0.728415      0.0     0.008173   \n",
              "5        1.0       0.5     0.801938     0.099031  0.914628  0.779446      0.0     0.008173   \n",
              "6        1.0       0.5     0.801938     0.099031  0.878649  0.826554      0.0     0.008173   \n",
              "7        1.0       0.5     0.801938     0.099031  0.837349  0.869079      0.0     0.008173   \n",
              "8        1.0       0.5     0.801938     0.099031  0.791306  0.906422      0.0     0.008173   \n",
              "9        1.0       0.5     0.801938     0.099031  0.741169  0.938060      0.0     0.008173   \n",
              "10       1.0       0.5     0.801938     0.099031  0.687639  0.963548      0.0     0.008173   \n",
              "11       1.0       0.5     0.801938     0.099031  0.631470  0.982530      0.0     0.008173   \n",
              "12       1.0       0.5     0.801938     0.099031  0.573449  0.994737      0.0     0.008173   \n",
              "13       1.0       0.5     0.801938     0.099031  0.514390  1.000000      0.0     0.008173   \n",
              "14       1.0       0.5     0.801938     0.099031  0.455124  0.998244      0.0     0.008173   \n",
              "15       1.0       0.5     0.801938     0.099031  0.396481  0.989493      0.0     0.008173   \n",
              "16       1.0       0.5     0.801938     0.099031  0.339285  0.973871      0.0     0.008173   \n",
              "17       1.0       0.5     0.801938     0.099031  0.284339  0.951597      0.0     0.008173   \n",
              "18       1.0       0.5     0.801938     0.099031  0.232414  0.922984      0.0     0.008173   \n",
              "19       1.0       0.5     0.801938     0.099031  0.184240  0.888433      0.0     0.008173   \n",
              "\n",
              "    year_built  floor_count  primary_use_Education  primary_use_Entertainment/public assembly  \\\n",
              "0     0.923077        -0.04                    1.0                                        0.0   \n",
              "1     0.923077        -0.04                    1.0                                        0.0   \n",
              "2     0.923077        -0.04                    1.0                                        0.0   \n",
              "3     0.923077        -0.04                    1.0                                        0.0   \n",
              "4     0.923077        -0.04                    1.0                                        0.0   \n",
              "5     0.923077        -0.04                    1.0                                        0.0   \n",
              "6     0.923077        -0.04                    1.0                                        0.0   \n",
              "7     0.923077        -0.04                    1.0                                        0.0   \n",
              "8     0.923077        -0.04                    1.0                                        0.0   \n",
              "9     0.923077        -0.04                    1.0                                        0.0   \n",
              "10    0.923077        -0.04                    1.0                                        0.0   \n",
              "11    0.923077        -0.04                    1.0                                        0.0   \n",
              "12    0.923077        -0.04                    1.0                                        0.0   \n",
              "13    0.923077        -0.04                    1.0                                        0.0   \n",
              "14    0.923077        -0.04                    1.0                                        0.0   \n",
              "15    0.923077        -0.04                    1.0                                        0.0   \n",
              "16    0.923077        -0.04                    1.0                                        0.0   \n",
              "17    0.923077        -0.04                    1.0                                        0.0   \n",
              "18    0.923077        -0.04                    1.0                                        0.0   \n",
              "19    0.923077        -0.04                    1.0                                        0.0   \n",
              "\n",
              "    primary_use_Food sales and service  primary_use_Healthcare  primary_use_Lodging/residential  \\\n",
              "0                                  0.0                     0.0                              0.0   \n",
              "1                                  0.0                     0.0                              0.0   \n",
              "2                                  0.0                     0.0                              0.0   \n",
              "3                                  0.0                     0.0                              0.0   \n",
              "4                                  0.0                     0.0                              0.0   \n",
              "5                                  0.0                     0.0                              0.0   \n",
              "6                                  0.0                     0.0                              0.0   \n",
              "7                                  0.0                     0.0                              0.0   \n",
              "8                                  0.0                     0.0                              0.0   \n",
              "9                                  0.0                     0.0                              0.0   \n",
              "10                                 0.0                     0.0                              0.0   \n",
              "11                                 0.0                     0.0                              0.0   \n",
              "12                                 0.0                     0.0                              0.0   \n",
              "13                                 0.0                     0.0                              0.0   \n",
              "14                                 0.0                     0.0                              0.0   \n",
              "15                                 0.0                     0.0                              0.0   \n",
              "16                                 0.0                     0.0                              0.0   \n",
              "17                                 0.0                     0.0                              0.0   \n",
              "18                                 0.0                     0.0                              0.0   \n",
              "19                                 0.0                     0.0                              0.0   \n",
              "\n",
              "    primary_use_Manufacturing/industrial  primary_use_Office  primary_use_Other  \\\n",
              "0                                    0.0                 0.0                0.0   \n",
              "1                                    0.0                 0.0                0.0   \n",
              "2                                    0.0                 0.0                0.0   \n",
              "3                                    0.0                 0.0                0.0   \n",
              "4                                    0.0                 0.0                0.0   \n",
              "5                                    0.0                 0.0                0.0   \n",
              "6                                    0.0                 0.0                0.0   \n",
              "7                                    0.0                 0.0                0.0   \n",
              "8                                    0.0                 0.0                0.0   \n",
              "9                                    0.0                 0.0                0.0   \n",
              "10                                   0.0                 0.0                0.0   \n",
              "11                                   0.0                 0.0                0.0   \n",
              "12                                   0.0                 0.0                0.0   \n",
              "13                                   0.0                 0.0                0.0   \n",
              "14                                   0.0                 0.0                0.0   \n",
              "15                                   0.0                 0.0                0.0   \n",
              "16                                   0.0                 0.0                0.0   \n",
              "17                                   0.0                 0.0                0.0   \n",
              "18                                   0.0                 0.0                0.0   \n",
              "19                                   0.0                 0.0                0.0   \n",
              "\n",
              "    primary_use_Parking  primary_use_Public services  primary_use_Religious worship  \\\n",
              "0                   0.0                          0.0                            0.0   \n",
              "1                   0.0                          0.0                            0.0   \n",
              "2                   0.0                          0.0                            0.0   \n",
              "3                   0.0                          0.0                            0.0   \n",
              "4                   0.0                          0.0                            0.0   \n",
              "5                   0.0                          0.0                            0.0   \n",
              "6                   0.0                          0.0                            0.0   \n",
              "7                   0.0                          0.0                            0.0   \n",
              "8                   0.0                          0.0                            0.0   \n",
              "9                   0.0                          0.0                            0.0   \n",
              "10                  0.0                          0.0                            0.0   \n",
              "11                  0.0                          0.0                            0.0   \n",
              "12                  0.0                          0.0                            0.0   \n",
              "13                  0.0                          0.0                            0.0   \n",
              "14                  0.0                          0.0                            0.0   \n",
              "15                  0.0                          0.0                            0.0   \n",
              "16                  0.0                          0.0                            0.0   \n",
              "17                  0.0                          0.0                            0.0   \n",
              "18                  0.0                          0.0                            0.0   \n",
              "19                  0.0                          0.0                            0.0   \n",
              "\n",
              "    primary_use_Retail  primary_use_Services  primary_use_Technology/science  primary_use_Utility  \\\n",
              "0                  0.0                   0.0                             0.0                  0.0   \n",
              "1                  0.0                   0.0                             0.0                  0.0   \n",
              "2                  0.0                   0.0                             0.0                  0.0   \n",
              "3                  0.0                   0.0                             0.0                  0.0   \n",
              "4                  0.0                   0.0                             0.0                  0.0   \n",
              "5                  0.0                   0.0                             0.0                  0.0   \n",
              "6                  0.0                   0.0                             0.0                  0.0   \n",
              "7                  0.0                   0.0                             0.0                  0.0   \n",
              "8                  0.0                   0.0                             0.0                  0.0   \n",
              "9                  0.0                   0.0                             0.0                  0.0   \n",
              "10                 0.0                   0.0                             0.0                  0.0   \n",
              "11                 0.0                   0.0                             0.0                  0.0   \n",
              "12                 0.0                   0.0                             0.0                  0.0   \n",
              "13                 0.0                   0.0                             0.0                  0.0   \n",
              "14                 0.0                   0.0                             0.0                  0.0   \n",
              "15                 0.0                   0.0                             0.0                  0.0   \n",
              "16                 0.0                   0.0                             0.0                  0.0   \n",
              "17                 0.0                   0.0                             0.0                  0.0   \n",
              "18                 0.0                   0.0                             0.0                  0.0   \n",
              "19                 0.0                   0.0                             0.0                  0.0   \n",
              "\n",
              "    primary_use_Warehouse/storage  air_temperature  cloud_coverage  dew_temperature  \\\n",
              "0                             0.0         0.613666        0.444444         0.764321   \n",
              "1                             0.0         0.482260        0.666667         0.608838   \n",
              "2                             0.0         0.650460        0.222222         0.818331   \n",
              "3                             0.0         0.685940        0.666667         0.800327   \n",
              "4                             0.0         0.547963        0.888889         0.545008   \n",
              "5                             0.0         0.628121        0.000000         0.800327   \n",
              "6                             0.0         0.642576        0.000000         0.754501   \n",
              "7                             0.0         0.642576        0.666667         0.846154   \n",
              "8                             0.0         0.700394        0.000000         0.808511   \n",
              "9                             0.0         0.628121        0.444444         0.746318   \n",
              "10                            0.0         0.685940        0.444444         0.772504   \n",
              "11                            0.0         0.679369        0.000000         0.690671   \n",
              "12                            0.0         0.671485        0.222222         0.772504   \n",
              "13                            0.0         0.721419        0.666667         0.851064   \n",
              "14                            0.0         0.642576        0.222222         0.654664   \n",
              "15                            0.0         0.685940        0.888889         0.808511   \n",
              "16                            0.0         0.716163        0.666667         0.882160   \n",
              "17                            0.0         0.722733        0.666667         0.900164   \n",
              "18                            0.0         0.657030        0.222222         0.644845   \n",
              "19                            0.0         0.657030        0.444444         0.909984   \n",
              "\n",
              "    precip_depth_1_hr  sea_level_pressure  wind_direction  wind_speed  building_meter_median  \\\n",
              "0            0.002907            0.688228        0.277778    0.189474                0.00474   \n",
              "1            0.002907            0.725744        0.972222    0.594737                0.00474   \n",
              "2            0.002907            0.763259        0.166667    0.163158                0.00474   \n",
              "3            0.002907            0.542044        0.611111    0.136842                0.00474   \n",
              "4            0.002907            0.666236        0.861111    0.136842                0.00474   \n",
              "5            0.002907            0.708926        1.000000    0.300000                0.00474   \n",
              "6            0.002907            0.716688        0.416667    0.110526                0.00474   \n",
              "7            0.002907            0.609314        0.638889    0.110526                0.00474   \n",
              "8            0.002907            0.574385        0.805556    0.300000                0.00474   \n",
              "9            0.002907            0.793014        0.083333    0.326316                0.00474   \n",
              "10           0.002907            0.661061        0.194444    0.268421                0.00474   \n",
              "11           0.002907            0.692109        0.805556    0.352632                0.00474   \n",
              "12           0.002907            0.676585        0.277778    0.268421                0.00474   \n",
              "13           0.002907            0.638292        0.277778    0.300000                0.00474   \n",
              "14           0.002907            0.636482        0.166667    0.326316                0.00474   \n",
              "15           0.002907            0.717982        0.250000    0.242105                0.00474   \n",
              "16           0.002907            0.566623        0.361111    0.300000                0.00474   \n",
              "17           0.002907            0.662355        0.305556    0.352632                0.00474   \n",
              "18           0.002907            0.564036        0.750000    0.300000                0.00474   \n",
              "19           0.000000            0.522639        0.611111    0.163158                0.00474   \n",
              "\n",
              "    building_meter_hour_median  building_meter_weekday_median  row_id  meter     NN_PRED  \\\n",
              "0                     0.004596                       0.004505       0      0  234.510788   \n",
              "1                     0.004596                       0.004505   21672      0  184.514343   \n",
              "2                     0.004596                       0.004505   43344      0  251.292831   \n",
              "3                     0.004596                       0.004505   65016      0  249.557755   \n",
              "4                     0.004596                       0.004505   86688      0  198.508957   \n",
              "5                     0.004596                       0.004505  108360      0  242.850708   \n",
              "6                     0.004596                       0.004505  130032      0  248.993134   \n",
              "7                     0.004596                       0.004505  151704      0  242.528351   \n",
              "8                     0.004596                       0.004505  173376      0  264.144745   \n",
              "9                     0.004596                       0.004505  195048      0  241.517212   \n",
              "10                    0.004596                       0.004505  216720      0  259.275177   \n",
              "11                    0.004596                       0.004505  238392      0  262.850739   \n",
              "12                    0.004596                       0.004505  260064      0  264.340118   \n",
              "13                    0.004596                       0.004505  281736      0  266.750427   \n",
              "14                    0.004596                       0.004505  303408      0  254.988953   \n",
              "15                    0.004596                       0.004505  325080      0  261.805695   \n",
              "16                    0.004596                       0.004505  346752      0  267.739197   \n",
              "17                    0.004596                       0.004505  368424      0  272.540222   \n",
              "18                    0.004596                       0.004505  390096      0  264.299835   \n",
              "19                    0.004596                       0.004505  411768      0  273.611664   \n",
              "\n",
              "     NN_PRED_0   NN_PRED_1   NN_PRED_2   NN_PRED_3  \n",
              "0   234.510788   38.851761  619.812805  733.004395  \n",
              "1   184.514343    0.700453  554.471619  831.921082  \n",
              "2   251.292831  100.346115  664.592407  691.125305  \n",
              "3   249.557755  107.371262  637.268738  737.548157  \n",
              "4   198.508957   66.978920  564.799316  846.702820  \n",
              "5   242.850708  109.067596  756.736572  755.723633  \n",
              "6   248.993134  124.235741  767.401184  724.672424  \n",
              "7   242.528351  119.515411  768.881287  750.239075  \n",
              "8   264.144745  147.889099  800.168152  715.593994  \n",
              "9   241.517212  130.343201  836.487366  708.904602  \n",
              "10  259.275177  145.868576  836.949463  671.912598  \n",
              "11  262.850739  130.893158  838.763672  666.637207  \n",
              "12  264.340118  113.950790  882.919922  645.887085  \n",
              "13  266.750427  103.094238  833.652771  652.390442  \n",
              "14  254.988953   30.753983  901.950562  656.249695  \n",
              "15  261.805695   23.593597  809.090027  680.220276  \n",
              "16  267.739197  104.966454  750.929199  680.710815  \n",
              "17  272.540222   98.260262  754.183167  660.235046  \n",
              "18  264.299835   13.075697  810.393494  702.019714  \n",
              "19  273.611664   48.685120  700.300171  699.162476  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXmjfkrQC61e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a['row_id']         = a['row_id'].astype('Int32')\n",
        "a['meter_reading']  = a['NN_PRED'].astype('float32')\n",
        "a[['row_id', 'meter_reading']].to_csv(DIR+'OUT_Ver0.21.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pSaG8s-5_yL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "f113d824-f725-4047-88d3-cfb2a59ebaed"
      },
      "source": [
        "a.head(-5)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>NN_PRED</th>\n",
              "      <th>meter_reading</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>234.510788</td>\n",
              "      <td>234.510788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21672</td>\n",
              "      <td>184.514343</td>\n",
              "      <td>184.514343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43344</td>\n",
              "      <td>251.292831</td>\n",
              "      <td>251.292831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>65016</td>\n",
              "      <td>249.557755</td>\n",
              "      <td>249.557755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>86688</td>\n",
              "      <td>198.508957</td>\n",
              "      <td>198.508957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41697590</th>\n",
              "      <td>41110039</td>\n",
              "      <td>255.980148</td>\n",
              "      <td>255.980148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41697591</th>\n",
              "      <td>41152039</td>\n",
              "      <td>259.096619</td>\n",
              "      <td>259.096619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41697592</th>\n",
              "      <td>41194037</td>\n",
              "      <td>263.612885</td>\n",
              "      <td>263.612885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41697593</th>\n",
              "      <td>41230287</td>\n",
              "      <td>278.547913</td>\n",
              "      <td>278.547913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41697594</th>\n",
              "      <td>41272287</td>\n",
              "      <td>304.864227</td>\n",
              "      <td>304.864227</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41697595 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            row_id     NN_PRED  meter_reading\n",
              "0                0  234.510788     234.510788\n",
              "1            21672  184.514343     184.514343\n",
              "2            43344  251.292831     251.292831\n",
              "3            65016  249.557755     249.557755\n",
              "4            86688  198.508957     198.508957\n",
              "...            ...         ...            ...\n",
              "41697590  41110039  255.980148     255.980148\n",
              "41697591  41152039  259.096619     259.096619\n",
              "41697592  41194037  263.612885     263.612885\n",
              "41697593  41230287  278.547913     278.547913\n",
              "41697594  41272287  304.864227     304.864227\n",
              "\n",
              "[41697595 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HcOED4IDv0k",
        "colab_type": "code",
        "outputId": "125bad6b-9605-4831-d451-f1428d5e1b45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        }
      },
      "source": [
        "show_plot(hist) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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odhS7qK7zY9RrnJsSw7heVoYlRbPH6WbL0Spy8qs5Vv8HQf8EE8nWKCwGHZYo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\nEEKIIAkFIYQQQf8/SUfcsaGe3N8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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na4dT8fHBHD4+kEugrx69VvUc5XZV4+/M36Cje4gv3UJ86R7iR/cQXzoH+ZJm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\nhBAuEgpCCCFc/j9ULh4wOxXj0AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lIiLfEJDpI7fbzWOPPcasWbPw+/0MHjyYDh06sHTpUtLS0khPT+fNN9+koqKC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\nEXEoFERExPF/kJ9R/u9OhAEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3y8HyLW1fPvb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsYpyA4omuiY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import model_from_json\n",
        "\n",
        "def load_model(dir = os.getcwd(), name = 'model'):\n",
        "  json_file = open(os.path.join(dir,name+'.json'), 'r')\n",
        "  loaded_model_json = json_file.read()\n",
        "  json_file.close()\n",
        "  model = model_from_json(loaded_model_json)\n",
        "  model.load_weights(os.path.join(dir,name+'.h5'))\n",
        "  print(\"Loading is complete.\")\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvoCZeopmulB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nn = load_model(DIR, 'model_nn')\n",
        "opt = Adam(lr = 0.05)\n",
        "nn.compile(optimizer = opt, loss = mse)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vze6V-2Dv3R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwGQ3nubDv6C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "k = nn.evaluate(X_val, y_val, batch_size = 800000, verbose = 0)\n",
        "print(k)\n",
        "for i in range(X_val.shape[1]):\n",
        "    X_val_2 = X_val.copy()\n",
        "    s = shuffle(X_val_2.iloc[:,i])\n",
        "    s.index = X_val_2.index\n",
        "    X_val_2.iloc[:, i] = s\n",
        "    print('{}: {:2.5}'.format(cols[i], nn.evaluate(X_val_2, y_val, batch_size = 800000, verbose = 0) - k))\n",
        "    X_val_2 = None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7cW0BjWDog5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rglOCdGlDojs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p = nn.predict(X_test, batch_size = 100000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHSFbHZiq7Of",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuyutB_Z1pu4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_sample_submission.to_csv(DIR+'out_file_int.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gQzWTyi1Vjb",
        "colab_type": "code",
        "outputId": "4a1a4271-a100-49b4-8b17-4effad4507ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df_sample_submission.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>8784</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>8785</th>\n",
              "      <td>129</td>\n",
              "      <td>170.970444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8786</th>\n",
              "      <td>258</td>\n",
              "      <td>158.120926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8787</th>\n",
              "      <td>387</td>\n",
              "      <td>150.405380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8788</th>\n",
              "      <td>516</td>\n",
              "      <td>149.041794</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      row_id  meter_reading\n",
              "8784       0     187.721802\n",
              "8785     129     170.970444\n",
              "8786     258     158.120926\n",
              "8787     387     150.405380\n",
              "8788     516     149.041794"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yaKZlONMsta8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_sample_submission['row_id'] = df_sample_submission['row_id'].astype('Int32')\n",
        "df_sample_submission['meter_reading'] = df_sample_submission['meter_reading'].astype('float16')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOIMC9y_steQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}