NN_Energy_Predictor_0.33.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_0.33.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": "9b7c9879-7c03-46dc-de9b-43ea81808a35",
        "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",
        "DIR_SAVE = '/content/drive/My Drive/Colab Notebooks/ENSaver/32/'\n",
        "#drive.flush_and_unmount()"
      ],
      "execution_count": 3,
      "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 = 3, suppress  = True)\n",
        "  pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
        "\n",
        "  def reduce_mem_usage(df, verbose = False):\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",
        "              if verbose:  \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",
        "              if verbose:\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",
        "              if verbose:\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, Dropout\n",
        "  from keras.initializers import TruncatedNormal, Constant\n",
        "  from keras.regularizers import l1,l2,l1_l2\n",
        "  from keras.optimizers import Adam, Nadam, RMSprop\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 MSLE(y_true, y_pred):\n",
        "    return K.mean( K.pow(K.log(y_true+1) - K.log(y_pred+1),2.00000))\n",
        "\n",
        "  def MALE(y_true, y_pred):\n",
        "    return K.mean( K.abs(K.log(y_true+1) - K.log(y_pred+1)))\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 *   MSLE  (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",
        "          + 1.000000 *   RMSLE  (y_true, y_pred)          \n",
        "          )  \n",
        "  \n",
        "  def MAPE_MSLE(y_true, y_pred):\n",
        "      return (\n",
        "            1.000000 *   MAPE_   (y_true, y_pred)\n",
        "          + 1.000000 *   MSLE    (y_true, y_pred)          \n",
        "          )  \n",
        "  \n",
        "  def MAPE_MALE_MSLE(y_true, y_pred):\n",
        "      return (\n",
        "            1.000000 *   MAPE_   (y_true, y_pred)\n",
        "          + 1.000000 *   MSLE    (y_true, y_pred) \n",
        "          + 0.500000 *   MALE    (y_true, y_pred)\n",
        "          )  \n",
        "  \n",
        "  def MAPE_VAL_RMSLE(y_true, y_pred):\n",
        "      return (\n",
        "            0.500000 *   MAPE_   (y_true, y_pred)\n",
        "          + 1.500000 *   MSLE   (y_true, y_pred)    \n",
        "          + 0.300000 *   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",
        "  keras.metrics.MSLE = MSLE\n",
        "  keras.metrics.MALE = MALE\n",
        "  keras.metrics.MAPE_MALE_MSLE = MAPE_MALE_MSLE\n",
        "  keras.metrics.MAPE_MSLE = MAPE_MSLE\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.MSE_ = MSE_.MAE_ = MAE_\n",
        "  keras.losses.MSE_ = MSE_\n",
        "  keras.losses.MAPE_ = MAPE_\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",
        "  keras.losses.MSLE = MSLE\n",
        "  keras.losses.MALE = MALE\n",
        "  keras.losses.MAPE_MALE_MSLE = MAPE_MALE_MSLE\n",
        "  keras.losses.MAPE_MSLE = MAPE_MSLE\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": "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['timestamp'] = pd.to_datetime(df['timestamp'])\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['daymonth'] = df['timestamp'].dt.day\n",
        "  df['daymonth_cos'] = np.cos(df['timestamp'].dt.week * 2 * np.pi / 31)\n",
        "  df['daymonth_sin'] = np.sin(df['timestamp'].dt.week * 2 * np.pi / 31)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g52hlFMS2cpu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Очистка от подозрительных нулей\n",
        "  df = df.sort_values(by = ['meter','building_id','timestamp'])\n",
        "  df['meter_reading_prev'] = 0\n",
        "\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",
        "  del df_bad_rows \n",
        "# Добавление медиан\n",
        "  # Добавление медианы по метрике постройки\n",
        "  if 1==1:\n",
        "    #df_tmp = df[df['IS_BAD_PRCNT']<0.45]\n",
        "    df_median = df.groupby(by=['building_id','meter']) ['meter_reading'].median()\n",
        "    df_median.name = 'building_meter_median'\n",
        "    df_mean = df.groupby(by=['building_id','meter']) ['meter_reading'].mean()\n",
        "    df_mean.name = 'building_meter_mean'\n",
        "    df_median = pd.merge(df_median, df_mean, how = 'inner', on = ['building_id','meter'])\n",
        "    df_median['building_meter_median'] = ( df_median['building_meter_median'] * 27.000 + df_median['building_meter_mean'])/28.000\n",
        "    \n",
        "    df_median2 = df[df['IS_BAD_PRCNT']<0.45].groupby(by=['building_id','meter']) ['meter_reading'].median()\n",
        "    df_median2.name = 'building_meter_median_45'\n",
        "    df_mean2 = df[df['IS_BAD_PRCNT']<0.45].groupby(by=['building_id','meter']) ['meter_reading'].mean()\n",
        "    df_mean2.name = 'building_meter_mean_45'\n",
        "    df_median2 = pd.merge(df_median2, df_mean2, how = 'inner', on = ['building_id','meter'])\n",
        "    df_median2['building_meter_median_45'] = ( df_median2['building_meter_median_45'] * 27.000 + df_median2['building_meter_mean_45'])/28.000\n",
        "    df_median = pd.merge(df_median, df_median2, how = 'left', on = ['building_id','meter'])\n",
        "\n",
        "    df_median['building_meter_median'] = np.where(df_median['building_meter_median_45'] >= 0, df_median['building_meter_median_45'], df_median['building_meter_median'])\n",
        "    df_median.drop(columns=['building_meter_median_45'], inplace = True)\n",
        "\n",
        "    df = pd.merge(df, df_median, how = 'inner', on = ['building_id','meter'])\n",
        "    del df_median , df_mean, df_median2, df_mean2\n",
        "    gc.collect()\n",
        "    #print(len(df))\n",
        "  # Добавление медианы по часу, по неделе, метрике постройки\n",
        "  if 1==1:\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_mean       = df.groupby (by=['building_id','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean.name  = 'building_meter_hour_mean'\n",
        "    df_median     = pd.merge(df_median, df_mean, how = 'inner', on = ['building_id','hour','meter'])\n",
        "    df_median['building_meter_hour_median'] = (df_median['building_meter_hour_median'] * 9.000000 + df_median['building_meter_hour_mean'])/10.000000\n",
        "    #print(len(df))\n",
        "\n",
        "    df_median2      = df[df['IS_BAD_PRCNT']<0.75].groupby(by=['building_id','hour','meter'])['meter_reading'].median()\n",
        "    df_median2.name = 'building_meter_hour_median_all'\n",
        "    df_mean2        = df[df['IS_BAD_PRCNT']<0.75].groupby (by=['building_id','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean2.name   = 'building_meter_hour_mean_all'\n",
        "    df_median2      = pd.merge(df_median2, df_mean2, how = 'inner', on = ['building_id','hour','meter'])\n",
        "    df_median2['building_meter_hour_median_all'] = (df_median2['building_meter_hour_median_all'] * 9.000000 + df_median2['building_meter_hour_mean_all'])/10.000000\n",
        "    df_median2.drop(columns=['building_meter_hour_mean_all'], inplace = True)\n",
        "    df_median       = pd.merge(df_median, df_median2, how = 'left', on = ['building_id','hour','meter'])\n",
        "    \n",
        "    df_median['building_meter_hour_median'] = np.where(df_median['building_meter_hour_median_all'] >= 0, df_median['building_meter_hour_median_all'], df_median['building_meter_hour_median'])\n",
        "    #print(len(df))\n",
        "\n",
        "    df_median.drop(columns=['building_meter_hour_median_all'], inplace = True)\n",
        "    df_median2      = df[df['IS_BAD_PRCNT']<0.45].groupby(by=['building_id','hour','meter'])['meter_reading'].median()\n",
        "    df_median2.name = 'building_meter_hour_median_45'\n",
        "    df_mean2        = df[df['IS_BAD_PRCNT']<0.45].groupby (by=['building_id','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean2.name   = 'building_meter_hour_mean_45'\n",
        "    df_median2      = pd.merge(df_median2, df_mean2, how = 'inner', on = ['building_id','hour','meter'])\n",
        "    df_median2['building_meter_hour_median_45'] = (df_median2['building_meter_hour_median_45'] * 9.000000 + df_median2['building_meter_hour_mean_45'])/10.000000\n",
        "    df_median2.drop(columns=['building_meter_hour_mean_45'], inplace = True)\n",
        "    df_median       = pd.merge(df_median, df_median2, how = 'left', on = ['building_id','hour','meter'])\n",
        "    \n",
        "    df_median['building_meter_hour_median'] = np.where(df_median['building_meter_hour_median_45'] >= 0, df_median['building_meter_hour_median_45'], df_median['building_meter_hour_median'])\n",
        "    df_median.drop(columns=['building_meter_hour_median_45'], inplace = True)\n",
        "\n",
        "    df = pd.merge(df, df_median, how = 'left', on = ['building_id','hour','meter'])\n",
        "    #print(len(df))\n",
        "\n",
        "    del df_median , df_mean, df_median2, df_mean2\n",
        "    gc.collect()\n",
        "    df['k_fact_med_hour']=np.exp( np.abs( np.log( (df['meter_reading']+0.01)/(df['building_meter_hour_median']+0.01)) ) )\n",
        "  # Подстановка параметров сооружения\n",
        "  if 1==1:\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",
        "    df = df.merge(building_df, left_on = \"building_id\", right_on = \"building_id\", how = \"left\")\n",
        "    df.head(5)\n",
        "    del building_df\n",
        "    gc.collect()\n",
        "\n",
        "  if 2==2:\n",
        "    df_median       = df.groupby(by=['site_id','hour','meter'])['meter_reading'].median()\n",
        "    df_median.name  = 'site_meter_hour_median'\n",
        "    df_mean         = df.groupby (by=['site_id','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean.name    = 'site_meter_hour_mean'\n",
        "    df_median       = pd.merge(df_median, df_mean, how = 'inner', on = ['site_id','hour','meter'])\n",
        "    df_median['site_meter_hour_median'] = (df_median['site_meter_hour_median'] * 9.000000 + df_median['site_meter_hour_mean'])/10.000000\n",
        "    #print(len(df))\n",
        "\n",
        "    df_median2      = df[df['IS_BAD_PRCNT']<0.75].groupby(by=['site_id','hour','meter'])['meter_reading'].median()\n",
        "    df_median2.name = 'site_meter_hour_median_all'\n",
        "    df_mean2        = df[df['IS_BAD_PRCNT']<0.75].groupby (by=['site_id','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean2.name   = 'site_meter_hour_mean_all'\n",
        "    df_median2      = pd.merge(df_median2, df_mean2, how = 'inner', on = ['site_id','hour','meter'])\n",
        "    df_median2['site_meter_hour_median_all'] = (df_median2['site_meter_hour_median_all'] * 9.000000 + df_median2['site_meter_hour_mean_all'])/10.000000\n",
        "    df_median2.drop(columns=['site_meter_hour_mean_all'], inplace = True)\n",
        "    df_median       = pd.merge(df_median, df_median2, how = 'left', on = ['site_id','hour','meter'])\n",
        "    \n",
        "    df_median['site_meter_hour_median'] = np.where(df_median['site_meter_hour_median_all'] >= 0, df_median['site_meter_hour_median_all'], df_median['site_meter_hour_median'])\n",
        "    #print(len(df))\n",
        "\n",
        "    df_median.drop(columns=['site_meter_hour_median_all'], inplace = True)\n",
        "    df_median2      = df[df['IS_BAD_PRCNT']<0.45].groupby(by=['site_id','hour','meter'])['meter_reading'].median()\n",
        "    df_median2.name = 'site_meter_hour_median_45'\n",
        "    df_mean2        = df[df['IS_BAD_PRCNT']<0.45].groupby (by=['site_id','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean2.name   = 'site_meter_hour_mean_45'\n",
        "    df_median2      = pd.merge(df_median2, df_mean2, how = 'inner', on = ['site_id','hour','meter'])\n",
        "    df_median2['site_meter_hour_median_45'] = (df_median2['site_meter_hour_median_45'] * 9.000000 + df_median2['site_meter_hour_mean_45'])/10.000000\n",
        "    df_median2.drop(columns=['site_meter_hour_mean_45'], inplace = True)\n",
        "    df_median       = pd.merge(df_median, df_median2, how = 'left', on = ['site_id','hour','meter'])\n",
        "    \n",
        "    df_median['site_meter_hour_median'] = np.where(df_median['site_meter_hour_median_45'] >= 0, df_median['site_meter_hour_median_45'], df_median['site_meter_hour_median'])\n",
        "    df_median.drop(columns=['site_meter_hour_median_45'], inplace = True)\n",
        "\n",
        "    df = pd.merge(df, df_median, how = 'left', on = ['site_id','hour','meter'])\n",
        "    #print(len(df)\n",
        "\n",
        "  if 3==3:\n",
        "    df_median       = df.groupby(by=['primary_use_ID','hour','meter'])['meter_reading'].median()\n",
        "    df_median.name  = 'primary_meter_hour_median'\n",
        "    df_mean         = df.groupby (by=['primary_use_ID','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean.name    = 'primary_meter_hour_mean'\n",
        "    df_median       = pd.merge(df_median, df_mean, how = 'inner', on = ['primary_use_ID','hour','meter'])\n",
        "    df_median['primary_meter_hour_median'] = (df_median['primary_meter_hour_median'] * 9.000000 + df_median['primary_meter_hour_mean'])/10.000000\n",
        "    #print(len(df))\n",
        "\n",
        "    df_median2      = df[df['IS_BAD_PRCNT']<0.75].groupby(by=['primary_use_ID','hour','meter'])['meter_reading'].median()\n",
        "    df_median2.name = 'primary_meter_hour_median_all'\n",
        "    df_mean2        = df[df['IS_BAD_PRCNT']<0.75].groupby (by=['primary_use_ID','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean2.name   = 'primary_meter_hour_mean_all'\n",
        "    df_median2      = pd.merge(df_median2, df_mean2, how = 'inner', on = ['primary_use_ID','hour','meter'])\n",
        "    df_median2['primary_meter_hour_median_all'] = (df_median2['primary_meter_hour_median_all'] * 9.000000 + df_median2['primary_meter_hour_mean_all'])/10.000000\n",
        "    df_median2.drop(columns=['primary_meter_hour_mean_all'], inplace = True)\n",
        "    df_median       = pd.merge(df_median, df_median2, how = 'left', on = ['primary_use_ID','hour','meter'])\n",
        "    \n",
        "    df_median['primary_meter_hour_median'] = np.where(df_median['primary_meter_hour_median_all'] >= 0, df_median['primary_meter_hour_median_all'], df_median['primary_meter_hour_median'])\n",
        "    #print(len(df))\n",
        "\n",
        "    df_median.drop(columns=['primary_meter_hour_median_all'], inplace = True)\n",
        "    df_median2      = df[df['IS_BAD_PRCNT']<0.45].groupby(by=['primary_use_ID','hour','meter'])['meter_reading'].median()\n",
        "    df_median2.name = 'primary_meter_hour_median_45'\n",
        "    df_mean2        = df[df['IS_BAD_PRCNT']<0.45].groupby (by=['primary_use_ID','hour','meter']) ['meter_reading'].mean()\n",
        "    df_mean2.name   = 'primary_meter_hour_mean_45'\n",
        "    df_median2      = pd.merge(df_median2, df_mean2, how = 'inner', on = ['primary_use_ID','hour','meter'])\n",
        "    df_median2['primary_meter_hour_median_45'] = (df_median2['primary_meter_hour_median_45'] * 9.000000 + df_median2['primary_meter_hour_mean_45'])/10.000000\n",
        "    df_median2.drop(columns=['primary_meter_hour_mean_45'], inplace = True)\n",
        "    df_median       = pd.merge(df_median, df_median2, how = 'left', on = ['primary_use_ID','hour','meter'])\n",
        "    \n",
        "    df_median['primary_meter_hour_median'] = np.where(df_median['primary_meter_hour_median_45'] >= 0, df_median['primary_meter_hour_median_45'], df_median['primary_meter_hour_median'])\n",
        "    df_median.drop(columns=['primary_meter_hour_median_45'], inplace = True)\n",
        "\n",
        "    df = pd.merge(df, df_median, how = 'left', on = ['primary_use_ID','hour','meter'])\n",
        "    #print(len(df))\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",
        "\n",
        "    holidays = [\"2016-01-01\", \"2016-01-18\", \"2016-02-15\", \"2016-05-30\", \"2016-07-04\",\n",
        "                      \"2016-09-05\", \"2016-10-10\", \"2016-11-11\", \"2016-11-24\", \"2016-12-26\",\n",
        "                      \"2017-01-02\", \"2017-01-16\", \"2017-02-20\", \"2017-05-29\", \"2017-07-04\",\n",
        "                      \"2017-09-04\", \"2017-10-09\", \"2017-11-10\", \"2017-11-23\", \"2017-12-25\",\n",
        "                      \"2018-01-01\", \"2018-01-15\", \"2018-02-19\", \"2018-05-28\", \"2018-07-04\",\n",
        "                      \"2018-09-03\", \"2018-10-08\", \"2018-11-12\", \"2018-11-22\", \"2018-12-25\",\n",
        "                      \"2019-01-01\"]\n",
        "    df[\"is_holiday\"] = (df.timestamp.isin(holidays)).astype(int)\n",
        "    del holidays\n",
        "    #print(len(df))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIDKj_YCb_bD",
        "colab_type": "code",
        "outputId": "43e4a29e-49f3-47e2-de34-4faf657696fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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_times = df_times.groupby('timestamp').apply(lambda group: group.interpolate(limit_direction='both'))\n",
        "  df = pd.merge(df, df_times, how = 'left', on = ['site_id','timestamp'])\n",
        "  #print(df_times.describe())\n",
        "  del df_weather, df_times\n",
        "  gc.collect()\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRBy3w-itokX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Дополнительные поля\n",
        "  костыль = 0.033\n",
        "  df['k_over_med']              =  np.log( (df['meter_reading']+костыль)                 / (df['building_meter_median']+костыль))\n",
        "  df['k_over_med_hour']         =  np.log( (df['building_meter_hour_median']+костыль)    / (df['building_meter_median']+костыль))\n",
        "  df['k_over_med_weekday']      =  np.log( (df['building_meter_weekday_median']+костыль) / (df['building_meter_median']+костыль))\n",
        "  df['k_over_med_hour_site']    =  np.log( (df['site_meter_hour_median']+костыль)        / (df['building_meter_hour_median']+костыль))\n",
        "  df['k_over_med_hour_primary'] =  np.log( (df['primary_meter_hour_median']+костыль)     / (df['building_meter_hour_median']+костыль))\n",
        "\n",
        "  k = 8\n",
        "  df['k_over_med']              =  k * df['k_over_med']   / ( 1 + np.abs(k * df['k_over_med'])) \n",
        "  df['k_over_med_hour']         =  k * df['k_over_med_hour']   / ( 1 + np.abs(k * df['k_over_med'])) \n",
        "  df['k_over_med_weekday']      =  k * df['k_over_med_weekday']   / ( 1 + np.abs(k * df['k_over_med'])) \n",
        "  df['k_over_med_hour_site']    =  k * df['k_over_med_hour_site']   / ( 1 + np.abs(k * df['k_over_med'])) \n",
        "  df['k_over_med_hour_primary'] =  k * df['k_over_med_hour_primary']   / ( 1 + np.abs(k * df['k_over_med'])) \n",
        "\n",
        "\n",
        "  # df['k_over_med']              =  np.log(np.maximum(df['meter_reading'],костыль)                 / np.maximum(df['building_meter_median'],костыль))\n",
        "  # df['k_over_med_hour']         =  np.log(np.maximum(df['building_meter_hour_median'],костыль)    / np.maximum(df['building_meter_median'],костыль))\n",
        "  # df['k_over_med_weekday']      =  np.log(np.maximum(df['building_meter_weekday_median'],костыль) / np.maximum(df['building_meter_median'],костыль))\n",
        "  # df['k_over_med_hour_site']    =  np.log(np.maximum(df['site_meter_hour_median'],костыль)        / np.maximum(df['building_meter_hour_median'],костыль))\n",
        "  # df['k_over_med_hour_primary'] =  np.log(np.maximum(df['primary_meter_hour_median'],костыль)     / np.maximum(df['building_meter_hour_median'],костыль))\n",
        "\n",
        "  #df.drop(columns = ['k_over_med','k_over_med_hour','k_over_med_weekday','k_over_med_hour_site','k_over_med_hour_primary'], inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1-UqwK3ZxIg",
        "colab_type": "code",
        "outputId": "5eb04e80-8299-4df3-f0e2-8bfd3b3804c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "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>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</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>daymonth</th>\n",
              "      <th>daymonth_cos</th>\n",
              "      <th>daymonth_sin</th>\n",
              "      <th>meter_reading_prev</th>\n",
              "      <th>is_equal_prev</th>\n",
              "      <th>day</th>\n",
              "      <th>IS_BAD_PRCNT</th>\n",
              "      <th>building_meter_median</th>\n",
              "      <th>building_meter_mean</th>\n",
              "      <th>building_meter_mean_45</th>\n",
              "      <th>hour</th>\n",
              "      <th>building_meter_hour_median</th>\n",
              "      <th>building_meter_hour_mean</th>\n",
              "      <th>k_fact_med_hour</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_ID</th>\n",
              "      <th>site_meter_hour_median</th>\n",
              "      <th>site_meter_hour_mean</th>\n",
              "      <th>primary_meter_hour_median</th>\n",
              "      <th>primary_meter_hour_mean</th>\n",
              "      <th>weekday</th>\n",
              "      <th>building_meter_weekday_median</th>\n",
              "      <th>is_holiday</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>k_over_med</th>\n",
              "      <th>k_over_med_hour</th>\n",
              "      <th>k_over_med_weekday</th>\n",
              "      <th>k_over_med_hour_site</th>\n",
              "      <th>k_over_med_hour_primary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216099.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>19552715.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>8088455.000</td>\n",
              "      <td>3506933.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "      <td>20216100.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>799.278</td>\n",
              "      <td>0.662</td>\n",
              "      <td>2117.121</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>-0.003</td>\n",
              "      <td>-0.003</td>\n",
              "      <td>-0.022</td>\n",
              "      <td>-0.012</td>\n",
              "      <td>15.761</td>\n",
              "      <td>-0.099</td>\n",
              "      <td>0.110</td>\n",
              "      <td>2117.121</td>\n",
              "      <td>0.164</td>\n",
              "      <td>185.479</td>\n",
              "      <td>0.164</td>\n",
              "      <td>678.754</td>\n",
              "      <td>2117.121</td>\n",
              "      <td>2442.413</td>\n",
              "      <td>11.502</td>\n",
              "      <td>800.562</td>\n",
              "      <td>2117.121</td>\n",
              "      <td>22696.083</td>\n",
              "      <td>7.992</td>\n",
              "      <td>107782.981</td>\n",
              "      <td>1968.277</td>\n",
              "      <td>4.185</td>\n",
              "      <td>3.158</td>\n",
              "      <td>428.408</td>\n",
              "      <td>2117.121</td>\n",
              "      <td>402.784</td>\n",
              "      <td>2117.121</td>\n",
              "      <td>3.007</td>\n",
              "      <td>361.945</td>\n",
              "      <td>0.001</td>\n",
              "      <td>15.955</td>\n",
              "      <td>2.753</td>\n",
              "      <td>7.730</td>\n",
              "      <td>1.293</td>\n",
              "      <td>1016.112</td>\n",
              "      <td>171.341</td>\n",
              "      <td>3.377</td>\n",
              "      <td>-0.088</td>\n",
              "      <td>0.029</td>\n",
              "      <td>-0.591</td>\n",
              "      <td>0.395</td>\n",
              "      <td>0.629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>426.913</td>\n",
              "      <td>0.931</td>\n",
              "      <td>153235.625</td>\n",
              "      <td>0.707</td>\n",
              "      <td>0.707</td>\n",
              "      <td>0.707</td>\n",
              "      <td>0.707</td>\n",
              "      <td>0.704</td>\n",
              "      <td>0.710</td>\n",
              "      <td>8.821</td>\n",
              "      <td>0.708</td>\n",
              "      <td>0.691</td>\n",
              "      <td>153235.629</td>\n",
              "      <td>0.371</td>\n",
              "      <td>105.263</td>\n",
              "      <td>0.315</td>\n",
              "      <td>10885.105</td>\n",
              "      <td>79501.751</td>\n",
              "      <td>82602.238</td>\n",
              "      <td>6.922</td>\n",
              "      <td>13386.753</td>\n",
              "      <td>79645.514</td>\n",
              "      <td>990796.716</td>\n",
              "      <td>5.099</td>\n",
              "      <td>117142.444</td>\n",
              "      <td>30.208</td>\n",
              "      <td>4.008</td>\n",
              "      <td>3.451</td>\n",
              "      <td>1221.720</td>\n",
              "      <td>8990.673</td>\n",
              "      <td>1232.786</td>\n",
              "      <td>7011.973</td>\n",
              "      <td>1.997</td>\n",
              "      <td>1570.868</td>\n",
              "      <td>0.034</td>\n",
              "      <td>10.966</td>\n",
              "      <td>2.735</td>\n",
              "      <td>10.189</td>\n",
              "      <td>8.329</td>\n",
              "      <td>6.966</td>\n",
              "      <td>112.739</td>\n",
              "      <td>2.263</td>\n",
              "      <td>0.699</td>\n",
              "      <td>0.649</td>\n",
              "      <td>2.530</td>\n",
              "      <td>3.521</td>\n",
              "      <td>4.207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>-0.901</td>\n",
              "      <td>-0.975</td>\n",
              "      <td>-0.998</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>-0.995</td>\n",
              "      <td>-0.999</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>283.000</td>\n",
              "      <td>1900.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.529</td>\n",
              "      <td>16.674</td>\n",
              "      <td>3.081</td>\n",
              "      <td>3.093</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-28.900</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-35.000</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>968.200</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.993</td>\n",
              "      <td>-52.806</td>\n",
              "      <td>-131.289</td>\n",
              "      <td>-55.152</td>\n",
              "      <td>-56.673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>393.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>18.300</td>\n",
              "      <td>-0.707</td>\n",
              "      <td>-0.707</td>\n",
              "      <td>-0.901</td>\n",
              "      <td>-0.782</td>\n",
              "      <td>-0.718</td>\n",
              "      <td>-0.738</td>\n",
              "      <td>8.000</td>\n",
              "      <td>-0.759</td>\n",
              "      <td>-0.571</td>\n",
              "      <td>18.300</td>\n",
              "      <td>0.000</td>\n",
              "      <td>96.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>31.526</td>\n",
              "      <td>36.853</td>\n",
              "      <td>43.216</td>\n",
              "      <td>6.000</td>\n",
              "      <td>31.994</td>\n",
              "      <td>33.454</td>\n",
              "      <td>1.084</td>\n",
              "      <td>3.000</td>\n",
              "      <td>32527.000</td>\n",
              "      <td>1951.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>67.803</td>\n",
              "      <td>134.792</td>\n",
              "      <td>71.686</td>\n",
              "      <td>130.439</td>\n",
              "      <td>1.000</td>\n",
              "      <td>22.640</td>\n",
              "      <td>0.000</td>\n",
              "      <td>8.300</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1011.800</td>\n",
              "      <td>76.667</td>\n",
              "      <td>2.100</td>\n",
              "      <td>-0.765</td>\n",
              "      <td>-0.135</td>\n",
              "      <td>-0.203</td>\n",
              "      <td>-0.880</td>\n",
              "      <td>-0.933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>895.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>78.775</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.223</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.089</td>\n",
              "      <td>-0.059</td>\n",
              "      <td>16.000</td>\n",
              "      <td>-0.251</td>\n",
              "      <td>0.201</td>\n",
              "      <td>78.775</td>\n",
              "      <td>0.000</td>\n",
              "      <td>186.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>102.761</td>\n",
              "      <td>106.016</td>\n",
              "      <td>124.262</td>\n",
              "      <td>12.000</td>\n",
              "      <td>105.641</td>\n",
              "      <td>104.019</td>\n",
              "      <td>1.279</td>\n",
              "      <td>9.000</td>\n",
              "      <td>72709.000</td>\n",
              "      <td>1969.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>104.794</td>\n",
              "      <td>183.931</td>\n",
              "      <td>111.502</td>\n",
              "      <td>243.908</td>\n",
              "      <td>3.000</td>\n",
              "      <td>84.979</td>\n",
              "      <td>0.000</td>\n",
              "      <td>16.700</td>\n",
              "      <td>2.000</td>\n",
              "      <td>8.900</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1016.100</td>\n",
              "      <td>170.000</td>\n",
              "      <td>3.100</td>\n",
              "      <td>-0.237</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.019</td>\n",
              "      <td>0.228</td>\n",
              "      <td>0.326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1179.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>267.984</td>\n",
              "      <td>0.707</td>\n",
              "      <td>0.707</td>\n",
              "      <td>0.623</td>\n",
              "      <td>0.782</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.697</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.791</td>\n",
              "      <td>267.984</td>\n",
              "      <td>0.000</td>\n",
              "      <td>276.000</td>\n",
              "      <td>0.125</td>\n",
              "      <td>304.367</td>\n",
              "      <td>307.691</td>\n",
              "      <td>360.472</td>\n",
              "      <td>18.000</td>\n",
              "      <td>315.633</td>\n",
              "      <td>306.085</td>\n",
              "      <td>2.000</td>\n",
              "      <td>13.000</td>\n",
              "      <td>139113.000</td>\n",
              "      <td>1993.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>248.812</td>\n",
              "      <td>375.873</td>\n",
              "      <td>224.331</td>\n",
              "      <td>575.418</td>\n",
              "      <td>5.000</td>\n",
              "      <td>252.615</td>\n",
              "      <td>0.000</td>\n",
              "      <td>23.900</td>\n",
              "      <td>4.000</td>\n",
              "      <td>16.100</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1020.400</td>\n",
              "      <td>270.000</td>\n",
              "      <td>4.600</td>\n",
              "      <td>0.660</td>\n",
              "      <td>0.221</td>\n",
              "      <td>0.052</td>\n",
              "      <td>1.437</td>\n",
              "      <td>1.751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1448.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>21904700.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.975</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>31.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.999</td>\n",
              "      <td>21904700.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>366.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>517184.943</td>\n",
              "      <td>3813882.148</td>\n",
              "      <td>3866686.396</td>\n",
              "      <td>23.000</td>\n",
              "      <td>573294.611</td>\n",
              "      <td>4138801.770</td>\n",
              "      <td>57329462.143</td>\n",
              "      <td>15.000</td>\n",
              "      <td>875000.000</td>\n",
              "      <td>2017.000</td>\n",
              "      <td>26.000</td>\n",
              "      <td>15.000</td>\n",
              "      <td>6828.592</td>\n",
              "      <td>50891.359</td>\n",
              "      <td>32458.876</td>\n",
              "      <td>33148.728</td>\n",
              "      <td>6.000</td>\n",
              "      <td>41437.500</td>\n",
              "      <td>1.000</td>\n",
              "      <td>47.200</td>\n",
              "      <td>9.000</td>\n",
              "      <td>26.100</td>\n",
              "      <td>343.000</td>\n",
              "      <td>1045.500</td>\n",
              "      <td>360.000</td>\n",
              "      <td>19.000</td>\n",
              "      <td>0.984</td>\n",
              "      <td>19.738</td>\n",
              "      <td>15.109</td>\n",
              "      <td>68.871</td>\n",
              "      <td>75.525</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       building_id        meter  meter_reading     hour_cos     hour_sin  weekday_cos  \\\n",
              "count 20216100.000 20216100.000   20216100.000 20216100.000 20216100.000 20216100.000   \n",
              "mean       799.278        0.662       2117.121       -0.000       -0.000       -0.003   \n",
              "std        426.913        0.931     153235.625        0.707        0.707        0.707   \n",
              "min          0.000        0.000          0.000       -1.000       -1.000       -0.901   \n",
              "25%        393.000        0.000         18.300       -0.707       -0.707       -0.901   \n",
              "50%        895.000        0.000         78.775       -0.000        0.000       -0.223   \n",
              "75%       1179.000        1.000        267.984        0.707        0.707        0.623   \n",
              "max       1448.000        3.000   21904700.000        1.000        1.000        1.000   \n",
              "\n",
              "       weekday_sin     week_cos     week_sin     daymonth  daymonth_cos  daymonth_sin  \\\n",
              "count 20216100.000 20216100.000 20216100.000 20216100.000  20216100.000  20216100.000   \n",
              "mean        -0.003       -0.022       -0.012       15.761        -0.099         0.110   \n",
              "std          0.707        0.704        0.710        8.821         0.708         0.691   \n",
              "min         -0.975       -0.998       -1.000        1.000        -0.995        -0.999   \n",
              "25%         -0.782       -0.718       -0.738        8.000        -0.759        -0.571   \n",
              "50%          0.000       -0.089       -0.059       16.000        -0.251         0.201   \n",
              "75%          0.782        0.675        0.697       23.000         0.689         0.791   \n",
              "max          0.975        1.000        1.000       31.000         1.000         0.999   \n",
              "\n",
              "       meter_reading_prev  is_equal_prev          day  IS_BAD_PRCNT  building_meter_median  \\\n",
              "count        20216099.000   20216100.000 20216100.000  20216100.000           20216100.000   \n",
              "mean             2117.121          0.164      185.479         0.164                678.754   \n",
              "std            153235.629          0.371      105.263         0.315              10885.105   \n",
              "min                 0.000          0.000        1.000         0.000                  0.000   \n",
              "25%                18.300          0.000       96.000         0.000                 31.526   \n",
              "50%                78.775          0.000      186.000         0.000                102.761   \n",
              "75%               267.984          0.000      276.000         0.125                304.367   \n",
              "max          21904700.000          1.000      366.000         1.000             517184.943   \n",
              "\n",
              "       building_meter_mean  building_meter_mean_45         hour  building_meter_hour_median  \\\n",
              "count         20216100.000            19552715.000 20216100.000                20216100.000   \n",
              "mean              2117.121                2442.413       11.502                     800.562   \n",
              "std              79501.751               82602.238        6.922                   13386.753   \n",
              "min                  0.000                   0.000        0.000                       0.000   \n",
              "25%                 36.853                  43.216        6.000                      31.994   \n",
              "50%                106.016                 124.262       12.000                     105.641   \n",
              "75%                307.691                 360.472       18.000                     315.633   \n",
              "max            3813882.148             3866686.396       23.000                  573294.611   \n",
              "\n",
              "       building_meter_hour_mean  k_fact_med_hour      site_id  square_feet  year_built  \\\n",
              "count              20216100.000     20216100.000 20216100.000 20216100.000 8088455.000   \n",
              "mean                   2117.121        22696.083        7.992   107782.981    1968.277   \n",
              "std                   79645.514       990796.716        5.099   117142.444      30.208   \n",
              "min                       0.000            1.000        0.000      283.000    1900.000   \n",
              "25%                      33.454            1.084        3.000    32527.000    1951.000   \n",
              "50%                     104.019            1.279        9.000    72709.000    1969.000   \n",
              "75%                     306.085            2.000       13.000   139113.000    1993.000   \n",
              "max                 4138801.770     57329462.143       15.000   875000.000    2017.000   \n",
              "\n",
              "       floor_count  primary_use_ID  site_meter_hour_median  site_meter_hour_mean  \\\n",
              "count  3506933.000    20216100.000            20216100.000          20216100.000   \n",
              "mean         4.185           3.158                 428.408              2117.121   \n",
              "std          4.008           3.451                1221.720              8990.673   \n",
              "min          1.000           0.000                   3.529                16.674   \n",
              "25%          1.000           0.000                  67.803               134.792   \n",
              "50%          3.000           1.000                 104.794               183.931   \n",
              "75%          6.000           6.000                 248.812               375.873   \n",
              "max         26.000          15.000                6828.592             50891.359   \n",
              "\n",
              "       primary_meter_hour_median  primary_meter_hour_mean      weekday  \\\n",
              "count               20216100.000             20216100.000 20216100.000   \n",
              "mean                     402.784                 2117.121        3.007   \n",
              "std                     1232.786                 7011.973        1.997   \n",
              "min                        3.081                    3.093        0.000   \n",
              "25%                       71.686                  130.439        1.000   \n",
              "50%                      111.502                  243.908        3.000   \n",
              "75%                      224.331                  575.418        5.000   \n",
              "max                    32458.876                33148.728        6.000   \n",
              "\n",
              "       building_meter_weekday_median   is_holiday  air_temperature  cloud_coverage  \\\n",
              "count                   20216100.000 20216100.000     20216100.000    20216100.000   \n",
              "mean                         361.945        0.001           15.955           2.753   \n",
              "std                         1570.868        0.034           10.966           2.735   \n",
              "min                            0.000        0.000          -28.900           0.000   \n",
              "25%                           22.640        0.000            8.300           0.000   \n",
              "50%                           84.979        0.000           16.700           2.000   \n",
              "75%                          252.615        0.000           23.900           4.000   \n",
              "max                        41437.500        1.000           47.200           9.000   \n",
              "\n",
              "       dew_temperature  precip_depth_1_hr  sea_level_pressure  wind_direction   wind_speed  \\\n",
              "count     20216100.000       20216100.000        20216100.000    20216100.000 20216100.000   \n",
              "mean             7.730              1.293            1016.112         171.341        3.377   \n",
              "std             10.189              8.329               6.966         112.739        2.263   \n",
              "min            -35.000             -1.000             968.200           0.000        0.000   \n",
              "25%              0.000              0.000            1011.800          76.667        2.100   \n",
              "50%              8.900              0.000            1016.100         170.000        3.100   \n",
              "75%             16.100              0.000            1020.400         270.000        4.600   \n",
              "max             26.100            343.000            1045.500         360.000       19.000   \n",
              "\n",
              "        k_over_med  k_over_med_hour  k_over_med_weekday  k_over_med_hour_site  \\\n",
              "count 20216100.000     20216100.000        20216100.000          20216100.000   \n",
              "mean        -0.088            0.029              -0.591                 0.395   \n",
              "std          0.699            0.649               2.530                 3.521   \n",
              "min         -0.993          -52.806            -131.289               -55.152   \n",
              "25%         -0.765           -0.135              -0.203                -0.880   \n",
              "50%         -0.237            0.021              -0.019                 0.228   \n",
              "75%          0.660            0.221               0.052                 1.437   \n",
              "max          0.984           19.738              15.109                68.871   \n",
              "\n",
              "       k_over_med_hour_primary  \n",
              "count             20216100.000  \n",
              "mean                     0.629  \n",
              "std                      4.207  \n",
              "min                    -56.673  \n",
              "25%                     -0.933  \n",
              "50%                      0.326  \n",
              "75%                      1.751  \n",
              "max                     75.525  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxPaU99v4PI-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Сохранение выборки\n",
        "  reduce_mem_usage(df)\n",
        "  df.to_feather(DIR_SAVE + 'DF_TRAIN_REDUCED3.FTHR')"
      ],
      "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": [
        "# Загрузка выборки\n",
        "  df = pd.read_feather(DIR_SAVE + 'DF_TRAIN_REDUCED3.FTHR')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UH8hyTGnTcvD",
        "colab_type": "code",
        "outputId": "78be1b08-f5b1-4329-867c-abaf30039760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(df) # 20216100"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20216100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OVnA-YyWW9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[In_Columns].describe()"
      ],
      "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",
        "       'daymonth_cos', 'daymonth_sin',\n",
        "       'site_id', 'primary_use_ID', \n",
        "       'square_feet',\n",
        "       'is_holiday',\n",
        "       'air_temperature', 'cloud_coverage',\n",
        "       'dew_temperature', 'sea_level_pressure',\n",
        "       'wind_direction', 'wind_speed',\n",
        "       #'k_over_med_hour', 'k_over_med_weekday', 'k_over_med_hour_site', 'k_over_med_hour_primary',\n",
        "       'building_meter_median','building_meter_hour_median','building_meter_weekday_median',\n",
        "       'site_meter_hour_median','primary_meter_hour_median'\n",
        "       #КАНДИДАТЫ нА ИСКЛЮЧЕНИЕ:\n",
        "          #'year_built', 'floor_count', 'precip_depth_1_hr',\n",
        "          #  'primary_use_Education', 'primary_use_Entertainment/public assembly', 'primary_use_Food sales and service', 'primary_use_Healthcare',\n",
        "          #  'primary_use_Lodging/residential','primary_use_Manufacturing/industrial', 'primary_use_Office',\n",
        "          #  'primary_use_Other', 'primary_use_Parking', 'primary_use_Public services', 'primary_use_Religious worship',\n",
        "          #  'primary_use_Retail', 'primary_use_Services', 'primary_use_Technology/science', 'primary_use_Utility','primary_use_Warehouse/storage',\n",
        "       ]\n",
        "# Нормализация\n",
        "# if 1==1:\n",
        "  #   df.dropna(subset = In_Columns + [Out_Columns], inplace=True)\n",
        "\n",
        "  #   #print(df[['air_temperature', 'cloud_coverage','dew_temperature', 'sea_level_pressure','wind_direction', 'wind_speed']].describe())\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   ) \n",
        "  #   gc.collect()\n",
        "\n",
        "  #   #print(df[['air_temperature', 'cloud_coverage','dew_temperature', 'sea_level_pressure','wind_direction', 'wind_speed']].describe())\n",
        "\n",
        "  #   import pickle\n",
        "  #   with open(DIR_SAVE+'scaler32.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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laaKxqJQn2XC",
        "colab_type": "code",
        "outputId": "e11eac4c-6a1c-4de3-c56a-1eb73922f170",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Всякое инфо:\n",
        "  #df[['building_id','hour','meter','building_meter_hour_mean','building_meter_hour_median','meter_reading','k_fact_med_hour']].head(10)\n",
        "  #Инфо о корявости данных:\n",
        "  if 1== 1:\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)\n",
        "    # #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)\n",
        "\n",
        "  print(len(df[df['IS_BAD_PRCNT']<0.45]), len(df))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 Всего: (12060910, 49) Откинуть: (1401057, 49)\n",
            "1 Всего: (4182440, 49) Откинуть: (537615, 49)\n",
            "2 Всего: (2708713, 49) Откинуть: (223436, 49)\n",
            "3 Всего: (1264037, 49) Откинуть: (311410, 49)\n",
            "17270389 20216100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2WwbRnvu0Vfb",
        "colab_type": "text"
      },
      "source": [
        "**-- -- -- -- -- -- -- -- --РАСЧЁТ НЕЙРОНОК -- -- -- -- -- -- -- -- --**"
      ]
    },
    {
      "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 = 32048\n",
        "  epochs = 22\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=7, 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 = 0.0009)\n",
        "    bias = Constant(value = 0.1)\n",
        "\n",
        "    nn_0 = Sequential()\n",
        "    nn_0.add(Dense(60, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_regularizer=l2(reg), kernel_initializer = 'normal'))\n",
        "    nn_0.add(Dense(60, activation = 'tanh',  kernel_initializer = 'normal'))\n",
        "    nn_0.add(Dense(60, activation = 'tanh',  kernel_initializer = 'normal'))\n",
        "    nn_0.add(Dense(60, activation = 'tanh',  kernel_initializer = 'normal'))\n",
        "    nn_0.add(Dense(1, activation = 'linear', kernel_initializer = 'normal'))\n",
        "\n",
        "    nn_0.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_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 = 0\n",
        "                    , epochs = epochs, shuffle = True, callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                    , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                    ) \n",
        "    nn_0.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "    gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3l71K063-sz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Дообучение!\n",
        "  meter = 0\n",
        "  nn_tmp = keras.models.load_model (DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "  \n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience = 60, verbose=1, min_delta=1e-4, mode='min')\n",
        "  model_checkpoint = ModelCheckpoint(monitor='val_loss', save_best_only=True, filepath = DIR + 'best_nn_' + str(meter) + '.model', save_weights_only = False, verbose=0, mode='min')\n",
        "\n",
        "  for lr in [0.0005]:\n",
        "    opt = Nadam(lr = lr)\n",
        "    epochs =  100\n",
        "    batch_size = 720\n",
        "    \n",
        "    val = df[ ( df['meter'] == meter ) & (( df['building_id'] % 12 == 0 ) | ( df['day']%6 == 0     )) & ( df['IS_BAD_PRCNT'] < 0.65 ) ]\n",
        "    tar = df[ ( df['meter'] == meter ) & (  df['building_id'] % 12 != 0 ) & ( df['day']%6 != 0 )  & ( df['day']%6 != 0 ) & ( df['IS_BAD_PRCNT'] < 0.65 ) ]\n",
        "\n",
        "    print('*'*23, lr, '*'*33, len(tar), len(val))\n",
        "\n",
        "    nn_tmp.compile(optimizer = opt, loss = MAPE_VAL_RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "    hist = nn_tmp.fit( tar[In_Columns], tar[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                    , epochs = epochs, shuffle = True, callbacks=[ MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                    , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  )\n",
        "    \n",
        "    nn_tmp.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "    del val, tar\n",
        "    gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YIKx_MLrPz1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#История ошибок:\n",
        "    # 0 - 0.0009|  TST_loss: 0.3072 TST_MAE_: 0.1830 TST_RMSLE: 0.2885 TST_VAL_: 0.9286 TST_mean_squared_error: 27367.7978 TST_MAPE_: 0.2095 ///  TRN_loss: 0.3087 TRN_MAE_: 0.1822 TRN_RMSLE: 0.2900 TRN_VAL_: 0.9145 TRN_mean_squared_error: 28093.1744 TRN_MAPE_: 0.2099\n",
        "    #Ошибка 0:  1.0854703420517136 Ошибка 0 (VAL):  1.078260498883952  Ошибка 0 (VAL_чист):  0.3096183002113901  Ошибка 0 (VAL_чист):  0.28568778649613935\n",
        "    #Ошибка 0:  1.0561841191435806 Ошибка 0 (VAL):  1.0494709359899506 Ошибка 0 (VAL_чист):  0.3156408787681845  Ошибка 0 (VAL_чист):  0.2912135296204619\n",
        "    #Ошибка 0:  1.089517078219136  Ошибка 0 (VAL):  1.0830665788767133 Ошибка 0 (VAL_чист):  0.32307141312090654 Ошибка 0 (VAL_чист):  0.2984998363948374\n",
        "      #Ошибка 0:  1.1066134245452997 Ошибка 0 (VAL):  1.100076285523506  Ошибка 0 (VAL_чист):  0.3034311145371631  Ошибка 0 (VAL_чист):  0.2775469766783069\n",
        "      #Ошибка 0:  1.1030193587492334 Ошибка 0 (VAL):  1.0966634328629619 Ошибка 0 (VAL_чист):  0.31766628931864216 Ошибка 0 (VAL_чист):  0.2947707690483286\n",
        "      #Ошибка 0:  1.11466815037453   Ошибка 0 (VAL):  1.1077580860952245 Ошибка 0 (VAL_чист):  0.36128705026829394 Ошибка 0 (VAL_чист):  0.3413465001124272\n",
        "      #Ошибка 0:  1.1159494605817608 Ошибка 0 (VAL):  1.108724065391442  Ошибка 0 (VAL_чист):  0.41233170157550914 Ошибка 0 (VAL_чист):  0.39348967701947696\n",
        "      #Ошибка 0:  1.1161679797276878 Ошибка 0 (VAL):  1.1092097596156938 Ошибка 0 (VAL_чист):  0.44160974266951963 Ошибка 0 (VAL_чист):  0.42010303885086286\n",
        "\n",
        "    # 1 - 0.0005|  TST_loss: 0.8377 TST_MAE_: 0.3466 TST_RMSLE: 0.8257 TST_VAL_: 0.8746 TST_mean_squared_error: 73145611.7727 TST_MAPE_: 0.7010 ///  TRN_loss: 0.7481 TRN_MAE_: 0.4902 TRN_RMSLE: 0.8462 TRN_VAL_: 0.6074 TRN_mean_squared_error: 72128872.1765 TRN_MAPE_: 0.6687\n",
        "      # 1 - 0.0001|TST_loss: 0.7542 TST_MAE_: 0.3283 TST_RMSLE: 0.7290 TST_VAL_: 0.8683 TST_mean_squared_error: 76403735.2854 TST_MAPE_: 0.6021 ///  TRN_loss: 0.7802 TRN_MAE_: 0.4676 TRN_RMSLE: 0.7550 TRN_VAL_: 0.6303 TRN_mean_squared_error: 73674603.6638 TRN_MAPE_: 0.5863\n",
        "    #Ошибка 1:  1.3667812429174853 Ошибка 1 (VAL):  1.4029945499659757 Ошибка 1 (VAL_чист):  0.7281895570502563 Ошибка 1 (VAL_чист):  0.6149901419154421\n",
        "    #Ошибка 1:  1.4674246744239359 Ошибка 1 (VAL):  1.4998371692364791 Ошибка 1 (VAL_чист):  0.7936248109822508 Ошибка 1 (VAL_чист):  0.6763456453516548\n",
        "      #Ошибка 1:  1.4093558221005378 Ошибка 1 (VAL):  1.4441369966619106 Ошибка 1 (VAL_чист):  0.7674733833817231 Ошибка 1 (VAL_чист):  0.6515382783996846\n",
        "      #Ошибка 1:  1.4341247310132184 Ошибка 1 (VAL):  1.470060550523646  Ошибка 1 (VAL_чист):  0.7119020527708861 Ошибка 1 (VAL_чист):  0.5956327249468123\n",
        "      #Ошибка 1:  1.4425109745549276 Ошибка 1 (VAL):  1.4775752439079195 Ошибка 1 (VAL_чист):  0.7288509072605465 Ошибка 1 (VAL_чист):  0.6118551437247776\n",
        "      #Ошибка 1:  1.4794444121249946 Ошибка 1 (VAL):  1.5150122006735316 Ошибка 1 (VAL_чист):  0.756747550880491  Ошибка 1 (VAL_чист):  0.6370399718876337\n",
        "      #Ошибка 1:  1.478101110840682  Ошибка 1 (VAL):  1.511827390230529  Ошибка 1 (VAL_чист):  0.783690921235087  Ошибка 1 (VAL_чист):  0.6632605785239761\n",
        "      #Ошибка 1:  1.4972289288697942 Ошибка 1 (VAL):  1.5308990895435475 Ошибка 1 (VAL_чист):  0.7908108700644174 Ошибка 1 (VAL_чист):  0.6680700428184277\n",
        "      #Ошибка 1:  1.4817989404715222 Ошибка 1 (VAL):  1.5134076940639456 Ошибка 1 (VAL_чист):  0.8309019241778273 Ошибка 1 (VAL_чист):  0.7141481643815152\n",
        "      #Ошибка 1:  2.0022889004617257 Ошибка 1 (VAL):  2.0253074918080394 Ошибка 1 (VAL_чист):  1.2310513863169474 Ошибка 1 (VAL_чист):  0.9879544727674974\n",
        "      #Ошибка 1:  2.6107180409629787 Ошибка 1 (VAL):  2.62604593096904   Ошибка 1 (VAL_чист):  1.9195246386655913 Ошибка 1 (VAL_чист):  1.6554061978310448  \n",
        "\n",
        "    # 2 - 0.001   TST_loss: 0.7344 TST_MAE_: 0.3078 TST_RMSLE: 0.7344 TST_VAL_: 0.8726 TST_mean_squared_error: 58769617031.5805 TST_MAPE_: 0.5561 ///  TRN_loss: 0.8222 TRN_MAE_: 0.3435 TRN_RMSLE: 0.8222 TRN_VAL_: 0.9566 TRN_mean_squared_error: 49363914383.6177 TRN_MAPE_: 0.5457\n",
        "    #Ошибка 2:  1.412089120404965 Ошибка 2 (VAL):  1.4307337011199783 Ошибка 2 (VAL_чист):  0.7681072077180416 Ошибка 2 (VAL_чист):  0.5899942163118871\n",
        "      #Ошибка 2:  1.426513398531468  Ошибка 2 (VAL):  1.4374305117701076 Ошибка 2 (VAL_чист):  0.772188164217135  Ошибка 2 (VAL_чист):  0.5912252674990405\n",
        "      #Ошибка 2:  1.4711884860839508 Ошибка 2 (VAL):  1.4865450169842518 Ошибка 2 (VAL_чист):  0.7729726956881282 Ошибка 2 (VAL_чист):  0.5883201286605256\n",
        "    #Ошибка 2:  1.584895359471045  Ошибка 2 (VAL):  1.595279682390636  Ошибка 2 (VAL_чист):  0.8156651059087789 Ошибка 2 (VAL_чист):  0.6075341541971445\n",
        "      #Ошибка 2:  1.4342707989405115 Ошибка 2 (VAL):  1.445994967988859 Ошибка 2 (VAL_чист):  0.7751574158428917 Ошибка 2 (VAL_чист):  0.5934912395707151\n",
        "      #Ошибка 2:  1.4827505038656164 Ошибка 2 (VAL):  1.485921011505542 Ошибка 2 (VAL_чист):  0.776992332783179  Ошибка 2 (VAL_чист):  0.5982294414393752\n",
        "      #Ошибка 2:  1.5380867615202842 Ошибка 2 (VAL):  1.534869021740785 Ошибка 2 (VAL_чист):  0.8030714183598706 Ошибка 2 (VAL_чист):  0.6100693949499996\n",
        "      #Ошибка 2:  1.544696542956114  Ошибка 2 (VAL):  1.542512084385534 Ошибка 2 (VAL_чист):  0.8014154858977849 Ошибка 2 (VAL_чист):  0.6164432453479144\n",
        "      #Ошибка 2:  1.7437661049074722 Ошибка 2 (VAL):  1.739711067404998 Ошибка 2 (VAL_чист):  0.9019782041199934 Ошибка 2 (VAL_чист):  0.5395988401269427\n",
        "      #Ошибка 2:  1.896885277819035  Ошибка 2 (VAL):  1.886131540810365 Ошибка 2 (VAL_чист):  0.9932058892117468 Ошибка 2 (VAL_чист):  0.5522634244728574\n",
        "      #Ошибка 2:  2.0635301300600255 Ошибка 2 (VAL):  2.053288059566308 Ошибка 2 (VAL_чист):  1.110914319240778  Ошибка 2 (VAL_чист):  0.6531255552895722\n",
        "\n",
        "    #3 - 0.001  TST_loss: 1.0529 TST_MAE_: 0.4362 TST_RMSLE: 1.0238 TST_VAL_: 0.8601 TST_mean_squared_error: 9330985.8708 TST_MAPE_: 0.7937 ///  TRN_loss: 1.1771 TRN_MAE_: 0.4945 TRN_RMSLE: 1.1481 TRN_VAL_: 0.6708 TRN_mean_squared_error: 8529478.6350 TRN_MAPE_: 0.7768\n",
        "    #Ошибка 3:  1.377944923928185  Ошибка 3 (VAL):  1.4566577965681673 Ошибка 3 (VAL_чист):  1.0939884892538    Ошибка 3 (VAL_чист):  0.7449018059731893\n",
        "      #Ошибка 3:  1.399828310279418  Ошибка 3 (VAL):  1.4663188962845461 Ошибка 3 (VAL_чист):  1.0923668037824816 Ошибка 3 (VAL_чист):  0.7516940691334648\n",
        "    #Ошибка 3:  1.5811641937262615 Ошибка 3 (VAL):  1.6275882770258792 Ошибка 3 (VAL_чист):  1.1791615642131834 Ошибка 3 (VAL_чист):  0.8287557439810707\n",
        "      #Ошибка 3:  1.4481746307415786 Ошибка 3 (VAL):  1.5064615469019023 Ошибка 3 (VAL_чист):  1.0977130637106953 Ошибка 3 (VAL_чист):  0.7436043184254891\n",
        "      #Ошибка 3:  1.705222807620306 Ошибка 3 (VAL):  1.7366032113308998 Ошибка 3 (VAL_чист):  1.0745064725069604 Ошибка 3 (VAL_чист):  0.7306249462611814\n",
        "      #Ошибка 3:  1.8077285134043073 Ошибка 3 (VAL):  1.8127781024652065 Ошибка 3 (VAL_чист):  1.110416279220095 Ошибка 3 (VAL_чист):  0.7830760480518036\n",
        "      #Ошибка 3:  2.4125767152544086 Ошибка 3 (VAL):  2.4083597850289715 Ошибка 3 (VAL_чист):  1.4406713860219067 Ошибка 3 (VAL_чист):  0.6769461395256884"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKq7llhGn682",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Предсказание из 4х значений = 0:\n",
        "  meter = 0\n",
        "  nn_tmp = keras.models.load_model (DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "  \n",
        "  gc.collect()\n",
        "  col_name_tmp = 'NN_PRED_'+str(meter) \n",
        "  df['NN_PRED'] = 0.0000\n",
        "  df[col_name_tmp] = nn_tmp.predict(df[In_Columns], batch_size = 24000) \n",
        "  df['NN_PRED'] = np.where(df['meter'] == meter, df[col_name_tmp], df['NN_PRED']) #* np.maximum(df['building_meter_median'],костыль) \n",
        "  #df['NN_PRED'] = 1. /k * df['NN_PRED'] / (1-np.abs(df['NN_PRED'])) \n",
        "  #df['NN_PRED'] = np.exp(df['NN_PRED']) * (df['building_meter_median']+костыль)\n",
        "  df.drop(columns = [col_name_tmp], inplace = True) #, 'NN_PRED_1', 'NN_PRED_2', 'NN_PRED_3'\n",
        "  #df.drop(columns = ['Value_x', 'Value_y'])\n",
        "  gc.collect()\n",
        "\n",
        "  df['NN_ERR'] = np.abs(df['NN_PRED']-df['meter_reading'])\n",
        "  df['k_NN_ERR'] = np.maximum(df['NN_PRED'],0.33)/np.maximum(df['meter_reading'],0.33)\n",
        "\n",
        "  #print( np.sqrt( np.mean( np.power(np.log(df['meter_reading']+1) - np.log(df['NN_PRED']+1),2.00000)))     )\n",
        "  print('Ошибка ' + str(meter) + ': '  , np.sqrt( np.mean( np.power(np.log(df[df['meter']==meter]['meter_reading']+1) - np.log(df[df['meter']==meter]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка ' + str(meter) + ' (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==meter) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==meter) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка ' + str(meter) + ' (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==meter) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==meter) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка ' + str(meter) + ' (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==meter) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==meter) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        )\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sw9D0BC18aD7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Ошибка 0:  0.8909700105613897 Ошибка 0 (VAL):  0.8803848561122335 Ошибка 0 (VAL_чист):  0.6035943377193838 Ошибка 0 (VAL_чист):  0.5365194138119987\n",
        "Ошибка 0:  1.1448721048780457 Ошибка 0 (VAL):  1.136991328548529 Ошибка 0 (VAL_чист):  0.44455567927115286 Ошибка 0 (VAL_чист):  0.4229856862273071\n",
        "Ошибка 0:  1.1352649331738924 Ошибка 0 (VAL):  1.1274714230652931 Ошибка 0 (VAL_чист):  0.4744999862766928 Ошибка 0 (VAL_чист):  0.4557145402838548\n",
        "Ошибка 0:  1.1437964644861591 Ошибка 0 (VAL):  1.1359205098329244 Ошибка 0 (VAL_чист):  0.44646548817270737 Ошибка 0 (VAL_чист):  0.4250778220458699"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1046Sz92wLmY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[df['meter']==1][In_Columns + [Out_Columns, 'k_over_med_hour','building_meter_median','meter_reading']].describe()"
      ],
      "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 = 0\n",
        "  epochs = 70\n",
        "  batch_size = 14800\n",
        "  opt = Adam(lr = 0.0007, amsgrad = True )\n",
        "  bias = Constant(value = 1)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=10, verbose=1, min_delta=1e-5, mode='min')\n",
        "  model_checkpoint = ModelCheckpoint(monitor='val_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(77, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_regularizer=l1_l2(reg,reg), kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(77, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(77, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(77, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias, kernel_regularizer=l1_l2(reg,reg)))\n",
        "  nn_1.add(Dense( 1, activation = 'relu', kernel_initializer = 'normal', bias_initializer=bias))\n",
        "\n",
        "  nn_1.compile(optimizer = opt, loss = MSLE , metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[ ( df['meter'] == meter ) & (( df['building_id'] % 12 == 0 ) | ( df['day']%6 == 0     )) & ( 0.10 < np.exp(df['k_over_med_hour'] )) & ( np.exp(df['k_over_med_hour']) < 10 )]\n",
        "  tar = df[ ( df['meter'] == meter ) & (  df['building_id'] % 12 != 0 ) & ( df['day']%6 != 0     )  & ( 0.10 < np.exp(df['k_over_med_hour'] )) & ( np.exp(df['k_over_med_hour']) < 10 )]\n",
        "\n",
        "  hist = nn_1.fit( tar[In_Columns], tar[Out_Columns]\n",
        "                  , batch_size = batch_size\n",
        "                  , verbose = 0\n",
        "                  , epochs = epochs, shuffle = True\n",
        "                  , callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_1.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6iiQ7a07ve3J",
        "colab_type": "code",
        "outputId": "7c3915ca-42fa-4a1b-9761-12bf82b49e5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "del val, tar\n",
        "gc.collect()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1508"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7EreUBaUoeP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nn_tmp.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED3best.MODEL')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "3c6a4073-15d2-407f-c0bf-f043338cd5fb",
        "id": "7oD_wHAeyts3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Дообучение!\n",
        "  meter = 0\n",
        "  nn_tmp = keras.models.load_model (DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=9, verbose=1, min_delta=2e-4, 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",
        "  for lr in [0.000031]:\n",
        "    opt = Adam(lr = lr, amsgrad = True)\n",
        "    epochs = 4\n",
        "    batch_size = 128\n",
        "    \n",
        "    val = df[ ( df['meter'] == meter ) & (( df['building_id'] % 12 == 0 ) | ( df['day']%12 == 0     )) & ( 0.10 < np.exp(df['k_over_med_hour'] )) & ( np.exp(df['k_over_med_hour']) < 10 )]\n",
        "    tar = df[ ( df['meter'] == meter ) & (  df['building_id'] % 12 != 0 ) & ( df['day']%12 != 0     )  & ( 0.10 < np.exp(df['k_over_med_hour'] )) & ( np.exp(df['k_over_med_hour']) < 10 )]\n",
        "\n",
        "    print('*'*23, lr, '*'*33, len(tar), len(val))\n",
        "    nn_tmp.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "    hist = nn_tmp.fit( tar[In_Columns], tar[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                    , epochs = epochs, shuffle = True, callbacks=[ MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                    , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  )\n",
        "    \n",
        "    nn_tmp.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "    del val, tar\n",
        "    gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*********************** 3.1e-05 ********************************* 10106323 1909514\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHKu4TMG8ROI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if 2==2:\n",
        "  reg = 0.00001\n",
        "  meter = 2\n",
        "  epochs = 15\n",
        "  batch_size = 200\n",
        "  opt = Adam(lr = 0.003)\n",
        "  bias = Constant(value = 1)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=9, 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(99, input_shape = df[In_Columns].shape[1:], activation = 'relu', kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(99, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(99, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(99, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(1, activation = 'relu', kernel_initializer = 'normal', bias_initializer=bias))\n",
        "\n",
        "  nn_1.compile(optimizer = opt, loss = MALE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[ ( df['meter'] == meter ) & ( df['day']%6 == 0 ) & ( df['IS_BAD_PRCNT'] < 0.15 ) ]\n",
        "  tar = df[ ( df['meter'] == meter ) & ( df['day']%6 != 0 ) & ( df['IS_BAD_PRCNT'] < 0.15 ) & (df['k_fact_med_hour']<50) ]\n",
        "\n",
        "  hist = nn_1.fit( tar[In_Columns], tar[Out_Columns]\n",
        "                  , batch_size = batch_size\n",
        "                  , verbose = 0\n",
        "                  , epochs = epochs, shuffle = True\n",
        "                  , callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_1.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdad7yTx8Uri",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if 3==3:  \n",
        "  reg = 0.00001\n",
        "  meter = 3\n",
        "  epochs = 60\n",
        "  batch_size = 200\n",
        "  opt = Adam(lr = 0.000004)\n",
        "  bias = Constant(value = 1)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=9, 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(99, input_shape = df[In_Columns].shape[1:], activation = 'relu', kernel_regularizer=l2(reg) , kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_3.add(Dense(99, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_3.add(Dense(99, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=bias, kernel_regularizer=l1(reg)))\n",
        "  nn_3.add(Dense(99, activation = 'relu',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_3.add(Dense(1, activation = 'relu', kernel_initializer = 'normal', bias_initializer=bias))\n",
        "\n",
        "  nn_3.compile(optimizer = opt, loss = MALE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[ ( df['meter'] == meter ) & ( df['day']%6 == 0 ) & ( df['IS_BAD_PRCNT'] < 0.15 ) ]\n",
        "  tar = df[ ( df['meter'] == meter ) & ( df['day']%6 != 0 ) & ( df['IS_BAD_PRCNT'] < 0.15 ) & (df['k_fact_med_hour']<50) ]\n",
        "\n",
        "  hist = nn_3.fit( tar[In_Columns], tar[Out_Columns]\n",
        "                  , batch_size = batch_size\n",
        "                  , verbose = 0\n",
        "                  , epochs = epochs, shuffle = True\n",
        "                  , callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_3.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQcBV7uNxXWG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[(df['meter']==met) & (df['building_id']==building_id)].head(100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3GoK9la567l",
        "colab_type": "code",
        "outputId": "3bef8a17-e739-4e0c-ea3c-b0c05dafed31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "source": [
        "#ГРАФИК ПЛАН-ФАКТ\n",
        "  plt.figure(figsize=(20,9))\n",
        "  df['log1p_fact'] = np.log(1+df['meter_reading'])\n",
        "  df['log1p_plan'] = np.log(1+df['NN_PRED'])\n",
        "  met = 0\n",
        "  building_id = 1099\n",
        "  #1099, 106\n",
        "  plt.plot( df[(df['meter']==met) & (df['building_id']==building_id)]['timestamp']\n",
        "          , df[(df['meter']==met) & (df['building_id']==building_id)]['log1p_fact'] , 'ro', markersize = 1   , color = 'blue' ,label = 'Факт', alpha = 0.3)\n",
        "  plt.plot( df[(df['meter']==met) & (df['building_id']==building_id)]['timestamp']\n",
        "           , df[(df['meter']==met) & (df['building_id']==building_id)]['log1p_plan'] , 'ro', markersize = 1  , color = 'green',label = 'План', alpha = 0.3)\n",
        "  plt.plot( df[(df['meter']==met) & (df['building_id']==building_id)]['timestamp']\n",
        "           , df[(df['meter']==met) & (df['building_id']==building_id)]['is_equal_prev'] , 'ro', markersize = 1  , color = 'black',label = 'Выкинуто', alpha = 0.3)\n",
        "  plt.plot( df[(df['meter']==met) & (df['building_id']==building_id)]['timestamp']\n",
        "           , df[(df['meter']==met) & (df['building_id']==building_id)]['IS_BAD_PRCNT'] , 'ro', markersize = 1  , color = 'brown',label = 'Выкинуто', alpha = 0.3)\n",
        "  plt.show()\n",
        "  gc.collect()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kk3nYuze7d8vYGCttWbtWwbVrkqN3EC8Tu3BhcfeIl5qtXKng7/9+Gn/zN7MAci8bGRuT\ncPSojK4uFcXFOqamWHmZWJrCUVXvd1hRgHfekfHTn0YyvGbEY2Urh8vlfJdSEjMywgJkABm+LnNz\nEnTdh7m5pbd/555LV64w0W7t2szSncV4Molz6tiYhA8+COD48QC+9718PPVUQU5lRZxYTEdtrYaL\nF/24cmXpPjHcBLulRUdDAzPFXrNGwZEjMkZHgcnJPIyMSGhoyN2bTJaB6modc3N52LMniGPHZLz0\nUgQvvhhZUrmTqgK3buUhmbTm6hUrgP/5P2fw1a/Ooa5OR22tbopkdnj5VXm5njEO+J+vXJGwf38Q\n27eHsHNnCP/8z1G8/npoyd8DcRwHAkBREZuj3K6Nl31u3TqPb35zGlu3fnxhx8mMfbHcT78xgiAe\nHnISd+bn53HixAmsX7/+Xp8PQRDEpxp7oMdbGb//vowf/zgf77/vvGUnLloX49XzIDAyIuGZZ/Lx\ns59FPnbg8SByNxb29xpZBhIJHYmEjpISYNMm953y+nodf/u3M/jmN6fR06NkCJKFhWwMtrTo+K//\ndRYDAwr+5m9m8fd/7y4W7dgRwlNPRbFjR8gMbquqLJ+dQACoqkpj69YUAG8xJRZjwXBPj4LubmWB\nd5CiAJcvS9A0SwzNBUUBtm8P4V/+pQCnTzNBq6WFBd+5+pbE45Z/ysyMhOJi9nLz6xwbk3DihIzH\nH2fX6RXIjY1J2LUriCtXfJ5mth+nFXospqOtTTE9lBZDOs3ucyxm3R/R72QpwpGisLni5k0WfE9M\nSDh+XEYggIxnkOtzFefUeFxHZ6eKkpI0JiZ8uHjRvygPn4kJCWNjfqxbx278Un1inIS+igodf/qn\nc2b52BtvhPDHP4YW5U1WX6/jv/yXWfzlXybR06OYQm48ri9JaMvLY2bZzz7L5m3xWisrdWzZkkJV\nlfM7UVen4wtfmEddnZ7xreJipKoCf/InKRQUGCgtTaOyUsMHHwSW/D0Q56dsHeQCAZhzWEEB0Nen\nLsj6W4pwJ5pFLxVVhSkIP8jc682lh23ziiA+aaR/+Id/+IdsP+T3+/GFL3wBATep24Hp6emPc14P\nBJFIBMlk8n6fBkEsgMbmp4No1EBTE9sFHRmRcOBAEBs2KGhqWrgwjUYNNDaynz1zJoDt28Po6lIR\nj+sYHZUQiRjQdZh/lu6xbiKOUeZj4H4Ok5M+nD4dQEeHioGBFJqadHR1qait1e/5eX4SHDrEuizV\n1uqoq7v/JXMi/Nn4/QbGxnIbJ7oOTE/7UFhooLTUgCwDxcUGioqs8cr/mywDZWXGgoA183gGRkf9\neOKJOei6D/G4jvFxCdu2hZFOA4mEhtZWHR0dGoqLDVRV6YjHncfGyIiE3/42DFlmwsa+fSE0NemI\nRg2MjkqYnPRh9+4QSkrS2LIlAMPIbR4dHZXwzjtBtLaqSCRUvPVWGFVV7LjT0z5Eo9nfKUligW91\ntY6WFg0tLTpUFXjppQhqath9i8d1tLdrmJz04d13ZXR3qygrW6jQ+P0Gbt3yIZn0YcMGBZ2dmtkx\nS7xviYS+5PdobEzCL34RwY4dYTQ2WmN3ZoaN6ZIS52cajRoIBoGjR2XU1enmeU1N+fDOOyHU1LD/\n5vUcnRgdlfC734WhaXno7WURbnOzhqoqHckkewZ+P3senZ0sa8yOOBeJc2pvr4aVKzUUFBgYG/Pj\ns5+dx5YtqZzLX/x+A3l5zHQ7GmXPq6lJW1T5jNN9HR2VsH17GO3tGhKJMCYm5jA15cOjj6bQ3a2h\np0fJ6XdIElBaar2v/M+SxH4HHzOVlc7PQzy3ZNKH8+cDiMd1nD7tR00NEy15lpYkAcePy6ip0U0B\nU2RkRMJrr4XR0KDj0iX2u7u6VMiygV27glizRsGaNRqam9m7sGqVhtWr3b8H4jN1+ntJYnORJGX+\n2Ym8PAMzM2z8XLnCxn9NjY5g0MChQzLm5oAdO9i773RtbuemaQaGhvzYvDmFysqlKa7Xr/tw9KiM\n3l7nOeFBIBKJYGgohddfd79H2Z5Xtr8fHpbw059GMDnpQ0PD4t4xglhO8VM0GnX87zmJO0uBxB2C\nuHfc7bGZbbFOfLLwxU00aiAaZUF3Q4OG9nYdfX3Oi3lJAiIR9rNVVRqKigwMDKRw8SLLiqmv1zA/\n7/NcdH3c8xUXY+IYHR2VzN87OekzF8w84F6xgv33Q4dkNDVpZsq81yL8QUe8J0VFOkIhA+vWpcxg\n6m4LbNkWxG7/Zu9eGS+8EEE4bODAgRCqqnRMT/s8g73RURb0nDoVQG0t+/lIxBJ5FntdJSUGenpU\nBALA9u1snMRiOsbHfRgcDKC+npWo5BKcTU76cPAga6W8YkUaH3wQQHe3islJH559Nh8tLSomJiTs\n3h1CW1seKipym0f9fgMTEz5MTEjo6tLQ3s7ElJERCT//eQS1tTpKS7O/U1wYKy5mwfWtWz4cPx5A\naWkatbW6+b4nk8CJE+6BHCs/C2LTJgWrVmlmoB6JGEingSNHZFRVsXsWDBo4f56VHcViuc/xkYiB\n+noNXV0q2toUHDvGvhG8JbybWCmKWFzYef31MJqbrfs2MwPs3x9EdbXmKMI44fezMbZpUwqBAPOi\nKSw0MDsL/OhHbI4bHg7ghz8sQEOD7uhNduqUhKefLkBDg4bubiZ+9fUpkCT2XKqqdLS16Vi3Ts3p\nXeXvnShcTU35TPFCHBPZ3lEuAldVMdFvctKHigodFRU60mlAVcM4eDCNTZsUdHRo8Ptze9+y/d5I\nxICiALt2BVFX5zyO9+6V8a1vRVFbq2P1ag2NjToiEQMHDgRx8mQQjY068vMNNDdrSCQ0U8Bz+n2T\nkz6cOBFASUkahYU6zp8PYMuWedy8KWHXrjA6O9lcEI/rCIczhSgnhoctESaXd9CLI0dkPP98Phoa\ndBQUGDh1KoCuLhUXLgTwgx8UoKWFXVsuwt3MDPDb34awY0cIFRVpTE1J6O1VHc8xl/n71i0fLlxg\n89nHvc57RSQSATC7QLi1i6pezyvb85yc9GHnziAOHmTC/VI2TJbyvSSWB8sptidxZwkspwFALC/u\n5thUFGDbthCefbYAdXXOC+LlwsPwQedeGq++GkZdHdslff31MOrrdXR3ey8ouYgSCAAXLrCgeGTE\n2hltbdUALH5HORuieMNFI3GMRiJWtsXEBAu+u7tVTE/78POfRxAKAW+/HcTQUAA1NTr27g3h1KkA\nmpoWilBuWUCfZFaSG+K5nT/PShbq6zXMzPjwu9+FkUjcO4FtaMgS8XLd1R0dlfDccxGcPBnAhg0K\nNm5kJVbRqCUQVFcvPM9IxEBNDcuuEjNPFhPIinDBJhq1xsn4uITDh4Po61PQ3q6ZxxCFaH4N4u+I\nRg20tGjo7laRn2/gww9ZMPTRRxJeeSWM6modt29L2LAhhc9/Xoam5TaPjo1Z59PZaYkpk5M+fPAB\nCwC5WOl1zfxdKSrSMTQUQH29hmiUZbmUl+s4ezaA3btDKCoycO2a5BrIRSKGmeUjvsuisJJOM7EM\nAF58MYLt2zMzcESc7ms0aqCkhF3LmTMBfPe7LLjv6lJMsdJNmBFFOP7+V1RYGTbbt4fw9NNRVFSk\n0dGh5fwM9u8PobNTQ22tbo7Rubk8/PGPIXR1MR+XkREJmzalHN+Dt98O4tVXI0gkNHR1acjLY+fJ\nBaiKChaQRqPsHf7BD/Ihy2y8u2UR/PznEbS1qWb21JUrPrz1VhA9PQpKSqwxwX+H27tfUsKyrGIx\nHS+/zMTTRIL93u3bw+js9KOpaRrt7RouXcpdVHSan0Xm5oD33gtgeNjvKiaePSth794g+vsVhMNM\neCkpMZBIaFi1it33t95iGUbFxd7ZbGJmV2GhgUuXJKxdq2LlSiYalZfrePNN70wikclJnynC8Hsx\nOQm8/noIlZWZ4mG2eYk/g54eBaWlViYiF+hrazXs3x/C9es+1NV5f0cPHZLx7LOspmvLlnnE42kk\nEs7/JpfsqWDQQCgE12M8CEQiEaRSyQWiozgG02kseF4ily75sGvXwveHf+MrKnhGl4o1a3LLXLPD\n39tcRXli+bCcYnsSd5bAchoAxPLibo7N0VEJO3aEkJdnYOtW5wXxcuFh+KCPjrKyktnZPHR3W6UH\n2UorFIUtcu07p6rKvC82bEhheDiAXbvYLuvdvH5RvHHK3JmbA86eDaCsjIk7+/cH73R/YbvFAwMp\nrF+voqdHxcaN3mVZ4iJ4bs4SFq5c8eE73ylAY6O25LT3j4t4btevs937ri4Vc3M+s0yuo0NbdDlK\nLhw/HjBFPKeyPREe4MRibLd9etqHxx5Lob6eZVlEowaqq63MC/t5iiUeYuYJ91Kx787mInrw44qC\nQFkZ2z0XAwWxxC0vDwuCVvHcioqsUsXRUQnvvRfE1q0pDAwoqKxMo7Y2jFQqt3nUTUyJRlngDwAT\nEz68+KL7/KIorIV6fr6BqSkf/u3fWIbJunUKqqt1pFLA734XxsAA8wsKBoFwmF2H0zMQ74sYtPIM\nqmCQnUNHh4Lqag2ybGDz5nlHL6V33pHx9NNMvJmZAb7znShqalSMjDCxqbxcx40bPmzalMoQK52+\nF3YBlnf4EQWJzk4VFRVpbN06n3PmjjjPyLIlYnV0qGhoYKJAXZ2Ori4N9fXO71c8rqGqKo3HH583\ny4j4MXngybPHRkYkvPxyGFNTPnR2ao7CCA9E16xR0N7OfuepUwH8/vdhrF6tIpkEnn66ANXVLCPR\nay7n1zQ56UN+fhr19TpaW61SxJUrwwiHZ01RkWe/ZJtLIhEDsRi7tmh0oRB+6JCMH/6wAKGQgU2b\nFMexOzsLXLjgR2enhnffZRl+paWGWXJZVGSYGXdHjsjYvTvkKAwDmQJkKGRgeDiA1atVVFUZqKtj\nx+LCXXk5ux+Tk85ikaIAV674YBhAU5OKEyeYQPmHP1jiYWWlZgo94jN3OjdZhil+8nELAMeOyRga\nCqCjQ8PcXB5eeSWCujrvrJGSEvb3n/nMPEIhmJldTr9XzLhzu29jYxL27WPHiERym1PvBorC1k5u\nz0AknY5gzx4NJSVsHuH3XSxVFEUzp2OdORPArl3s/ZEka44fH/fhmWfy0dSkoaND9yz1zcb4uHfZ\nK7F8WU6xPYk7S2A5DQBieXE3xyZLvdexcSPzcnlQM1ruBuIu+4Mq7vj9Bnw+oKWFLYh27GClB9XV\n3s+G74wVFhqorLR2yEtKDKxcyTIftm0L4+pVCf39CoqKvBeHS8m8EH9OHKNi4Dg/7zNT70dH/Th4\nMIQ1a1T092toaMiehu/3G2a5TmGhgZERP7q6VJw8GTB35FtbtfuSxSOeW1mZjitXJPzJn6TQ1cV2\npLu6WAvidPrul5zNzwOXLkl49NHsAi0fKyz7RjMFJzEo8/KSEccGExHYLrzPxzxu7LuzPPtsMR4M\nvEyGB9o8mKmpYeOkp0dx9N9xEjkkiXXoSSSYkJJKseOWl/tx5IiGZBKOAoqIm5gSjRpIJtnx0mkD\nb78dQm9vCvG4c7nBj36Uj7ExCWvXKigvT2PdOubrMj3tg6oCQ0Msi0rTmDg4OOicweb2TPm9Gh6W\nMDQUwNGjMhobdUxOSvjNbyKuZQzJJMyMlw8+CODVV8MoLk5jcDCAgQFmUF1UZCA/n2Wg7NjBhESn\nTE/xXMQSP5/PwHvvyVi5UoWm+VBZmUZ5efasO36vg0HDnNckoUSPi0M7dmRm3jgdi/9daWlmphgf\nK+J/48L4n/3ZPLq6tKyBKPc3kyQDV6+yd3FwkM1LpaVpnDol4/JlCYmEsx8Qv3cvvRTB4GAA169L\nSCSYiMLOzZpTg0EDt2/7cOZMwFUwEK95epq9g+k0oGlMwIrF2POpqWHz09atKdc1wPS0D6OjfgwM\nWP5Oum4F/bzMcGxMwltvhVBTw7LnnIJvboydTgM1Ney9rKiwhGFRuEunmYDvlsk5OsreqX37QlCU\nPLzwQj6CQQP9/fNIpXx48skk9u2zhB6+YZIte1UU6jUN2LOHXVNvr4qWFg11dXpOfkeSxMyss3mF\n6Tr7PYmEu/jHxc1YTMf+/ayctqEh90zNpTI8zLJCh4YCaG72HmtHj0bw7W8HIMsGzpzx4+mnoygv\nTyMaNcxSRXtml33+j8V0NDYyY3y/38D16z60t6s4e5ZtYLS3qzAM5CQ2uXHrlg/nzjFRMdfNB2J5\nsJxiexJ3lsByGgDE8uJujhS4F7YAACAASURBVE1dB5JJn2mC6sRy8eQRTYcf1I/42BgzDR0cDKC3\nV0VxsWHuYHJvE7eFH9/5E4NsHqTzoGdkRMKaNVZJlFuWQbZU/myIY3RqCjh9mplJcqGjvFzHsWNB\nbN6cwqOP5m5cKpbHsPIQlt3Q0KBCVX34D/8haXZxWUyJ0t1APLfKSh2XL/tNj4W8PGBmxpcRqCxm\n9zWb2FZUxEQ8t4wF8d87BbViUNberuLYMffddzHwqa7WUVamw+cDEgkFgQBQU6Ph9m0fYrE02tqy\nmyDbhQn7eYrZAI2NLHtmfFxCXp6BCxeYkMbHj1d2Hi/B4ce9fj2Mp56SMTIiYeVK58wMt/svnjPP\n+jh3zo833wyjrY2V/Njh4nJnp4qqqjROnQqivl43A+/WVuZvA7AOSfX17hls9vMRA78jR2T89rdh\nfPSRH5s2KWhq0qAoQHu75lrGEA4zAaGtTUNDg4Z4PI3ubgXnzgXQ0KDD72ceN4ODAaxfr6Cnx70k\nwu+3dumLi615qajIwPi4hPLyNN58M2y+B/z63eYaXnIYClm+UFzA4gEef6Zi5o3TscTnxkUT8d6K\nIl44zMTxvj7FVYwRA9HxcXbs9nYNAwMq6utZe/SqqjQ+//k5DA/78fzz+aisdC9F4yWPq1erWL1a\nzRA9gkFmVhuJGK5li26I34dEQkNzs2aW7HLxwUmk5/CSIP4+j42xe8/ns9palmGTSrFuU+fOBVBX\n5/4MuGdXIsEyMOwla/xceGac23sg+kI1Nak4e9aPZNKHdNqHvXtDSCR09Pay78vnPjeH27et7Bcv\noTkWY5mB/H4VFRkYGmLXVF7OMoyyfbOyjTWnn/XayOH3ZGyMdRU7cyaAnh5ngfVulqBPTvpw9mwA\nW7ak0NHhPdbq6yPQtFlcvOhHfb2Gmzd9+Pzn5xEIwNxYm5rKXHs4rTX4XH3sGPNAqqlh7/bNmz60\ntWn4/e/DroJfLgSDTBROJBZX4kg8/Cyn2J7EnSWwnAYAsby422VZ2YJ4MfPiYfbkmZpavIlnLmTz\nAVkM/N+NjUno6WE+Drxkw6vkQ9z5q69nC7CmJs0MOGpqWAr88ePMpFWS4JnFJAZofBHrJvJlM1Tm\nu769vZmp9zU1Ovr62LnwQC0YZCU9brtyYrnO3JzPDDhV1VrM37zpy7lEaTGMjwMvvBBBba3qWNqS\nTjPT3XXrWEcULiSKhrJNTWzXsaNDzVoiwFEUdt+9Sh24Ua/bTqb4njtl5UQi7JhDQwEEAqxN9Nq1\nzoGjvYRgaooF55EIcPKkDEkCXnuNLb7b2jSUl3sHNuJYu3TJKufi/25kRMKvfx1GU5OGnh7VHNOj\noz7867+yEi3+nN2y85wCrcbGCEpLp7FpU8pVFHP698XFRkaJEMDufVubipoaVvLjNL9Eo6zF8uHD\nMjo7LY8WLkzU1upmFlBNjY7mZt01g00U2EpKWMlTOs0ExD17Qmhs1LB1awqrVrE5YMeOMNatU1Be\n7jzOxJIPntVSV6cjEmFZWQ0NGkpL0+jvV1BXp3ua+Y6NSdizJ4S8PJjlfdXVOurqNBQUAF1dill6\nuWKFbho9t7Y6Z1PwksP+fgX9/QrSaWBqKlMoLS1lz2RqyudZ+iQ+N54x5Pc7zzkjI5Lpd+Y0R87M\nsHIdnsHBy2L5z/PsomjUQHm5gURCRWWldymadKessKzMyoTh405RwvjlL/OQTrN3pbZ2YZmgG5JQ\nCsXH2auvhjE+zjZ2sgXL4viYnPSZPkPd3aopSPKMo40bmeGzm5jr9xvw+4H+fgVVVWx+jMV0s5RY\nzOSyd/Zyui5eGhaLGejoYJ21OjtVM8Pv9m0JQ0Msgy0WY++rW+YOf69457O6Ona/vMpU3bCXK3sJ\nLrmYWovCU3MzmwvdGiycOiV5likvRvyJRg00N7NuhdnGWnFxBPH4bdTUsLG5dq2GpiY9o4vdjRuW\np05lpbHgPokCPc9kisV0vPMO89XKVrrthnjNly5Z77aqAgcPyli9mkq0Pg0sp9iexJ0lsJwGALG8\ncBubS9mtsdfjO/07MfPifvmZuLGYa962LYRvf5ulCTvtrC/194k+IHNzwDPP5CMeZ+a5uXRasZej\nxGIsqEokmAhVXMy8TbzEGLFlbirlMxfiPKsgFtNx7lzAFI3q63XPLCanwMatpbdTtoTfb+0yRyKW\nESQ/V7ETmLgDHA6zGnu3QEPXgcHBAH73uzBWrrSCi9ZWFZEIM3mdm2OteJmJ5d0bry+8EMH3vx9F\nYaGBvj51wd+LnVYaG60daNFQ9tChIH7zG3aveHeZbAvU0VEWLNvFFnH8ZDNrFQWUDz9cmNkkBn/Z\nAkfxZ+NxJu6cOhVAf7+C1lYNwaCBFSvSuHpVwsqVKmZnvd8DPtbicR1Xr0oYHvZnGAnztuBTUz60\ntloBmqYZ2L8/hOZm1Sw9FD1w7OKVPXuouDiC8vJplJVlnzvsogD345BlS/hpbNSxcaN7yY0kAaoK\nnDwZQFlZ2gyWxIwJMQOHZ2zkkqnH35muLhVFRSxzYtUqVtrJRQevcWbP/Nm9O4S6OjYGqqvZ/Tx4\nkJkZZyuzs4t/XEwbH2cCQTyuIxAAamt1HD4s45//OYqbN31Ytco5e4qXHG7ZkkIoxDJz6uossam+\nnj2TQ4dk7NsXQkeH5roDL4qg/J0BMuccnj2WSrG5xm3Otfs/8flX/FmedRSPaxmlaEv5Pjc1hTE3\nl8SRI7LZ/GAxGwj2ceb3AxMTEgYGFKxcqSIWS6OlxfmdF8fH0FAAH37oN78jyaQvI9NMFLecEI2x\n+ViqqbGuZ6lZo/z956V7XIDk3lNNTcxzxytzRyytXUyXPrfz4XOz3294ehHxueH0ae9vPL9XXIwu\nLnYua7Qbh7sdK5d7rOtsDp6ayl4G5fdHcOJECpcvs/mRZxoNDQXws5+xb+Pt20zAXr1aNcsAxXtr\nN6m/elVCYyMbl0NDAXPdIpZo5oLd1Jl7xcmy4dmZkFheLKfYnsSdJbCcBgCxvHAbm0tZFEkSM5fj\nrWSdPm5i5sWDlra6uEUK27H5/OfnTZFqsYKY0+8LBnUkk3l45JF5XLjAdpnLynTs2ROCJMF1t8+t\nw5GTcWJFhY6WFncxRgw+xcWsrjMz49lZ4Pe/D2PLlhTa2thuPg9OnXDqQCJ2EuH3gu+W24Wna9ci\neOWVPFRVsUUYv57xcZ8pLPDOUc3NLK2+q0u9k3nkbajMDafXrVNNT4qJCQlHj7Iyl2vXJPzxjyH0\n9jqnrC+VykoV6XQevvSlWRQWLvx78f7YOxjxRe677zID6YYGzewuk+2d8vvZAj4W083uRfxe2EuD\n3Mba+fMsI6apScPYmOSY2WRv0+31PogL8miUGWTyQG/HjjDWrlWwdi0TwLjXh1sXGD7WYrE0TpyQ\n8cgjzNdD7I7S1sY8PCoqWCvuI0dk00h7YkJCa6vVwcrJq4f7rIiBUDAYwalTqZy8G8TrtWfN8GyR\nFSuYMOJVvprNK0UcK17zml2MEwNrMctgbExyFB3s8Gc/NcXeVS4k2s2ZeamV11gTzy0W082smIoK\n3Syv4M9Hlll5VX+/gjVrnD1axJIxnmEWDhs4dChkdi4bHWXt7d2yzTjiO8NFwkRCQSQCUyji9761\nlXmsuM254vsu3hNxjJ05EzD9bV57LezqW+L2HRLHc3OzHw0N0+azXWyHQP47mIAhoaVFQ2sre2/n\n533Yv99d9BDH5d69IfMd5ffKnmmWLUuF3yuxRNRJiM3Fj0kszxMFO16azDMLWZmc9zwpltbyuVLs\n1OT3sy5qPNssWyYLb+vt97MsL/v4FLNho1HvbliiQM+zF2Mxq8OeWLrNywHdsgj9fgOaBsiygXDY\n8BSSh4dZ17jDh1mJmtc8cu1aBN/9rmR25uOm921tlsdYXh7zsnLzhxMF+qGhAJ55psDMwvL5Fl6/\nV7m6iCiUsm8Dy0rM1pmQWF4sp9iexJ0lsJwGALG8cBubTrvTboiLinPnAmY3H6dg+H551bgtEN38\nQ9wWf/xny8oMrF7N0oT5Ym183LvDjf1YfBdcDOQGB2W88gozKy0vZyUSq1ap+OCDAM6e9bsuiD74\nwPm+i89RXDx77dSKu4Q8QMvLYzvezzxTgNLSNCYnJWzcyAxls7Vd5QG7+Mx5JxExW6GqSseKFbqZ\ncs0XkuXlERQVTWcs4GMxHfv2BbF3bwirVjFjSgCor2e72rW1uRkqc8Pp5ma2yLOPg4oKywdjqR5R\nYiDEF75TUxIuXgygpcW900plJWvjLf47MWjhpWgFBQvL3twYG2OC1rvvyqiv18xMGDHg5hlebuND\nHGvMD2jh4po/01jMu0ONHafME/4+ZWurDlhjraUlM2NI3KnmO7Wi+NDXx0x+z59nWQT8WpyCIC6S\niONWUcL48Y99OXk3iEFkRYXlx+HzsS447e0aLlwIOGa2icc4ckTG4GAA69axIM8teM0lo5Lfd3vp\niv15ZDsOYN1r3m2voMAw5xPu8cIFWi+zbfu5jY1l+qvE46xjXn4+a1lfVsaEmwsX3D1auNhdUaHj\nwoUAjhyR0d6uob3dMvadnPQhkdDQ2en9PolzK7+mQICVE3KhyD6G3a5RnA/t4h8fYy0t2p1vp4YP\nP3T3LfHapODn09AQxocfphZkjOWyscE7Fx08GIQkwXyW/JvitXawj33xHXX7d17nJt4rLiqK5bii\nuJytRbjo31Nby8Y5H8MVFex/J5NsrbN6tYrKSu8SUbErniggMGPsMGQZ+OUvI6Z44dUtC7CE6/5+\nBW1t2oLuf3v3yvjWt1jJeygE16wiPnfwrC0uTAaDLHtx7VoFBQWGKZryuddt/PJuhufOBSDLrEuf\nuAYS101TUz4cPsxeqv5+bwGkvDyCsrLbdzZrFMzMsPVSIACcOME2X+rrdU9/OFHQ7OpS0NWloadH\nyci64psZN28C//f/5tYlc26OleW/9x47D56VWFXFMsjE9QuxfFlOsT2JO0tgOQ2AbNxN87VcWC4G\nvR8Xe8vYbM+A/3xhYRjnz6ccd/lyTR0WfXT6+hTPYHgxx72b8F0v7gPBWYxRoXgM0fuDHyM/38BH\nH/lRUpLOWgohmh6Ojkp4+ukogkHjTiYG21kqK2PGtu3tGgoLDVy54r4jVFjIvGMGBlLmokJRMks+\n+OK5sDB7VoAYoBUWGjh0SMaKFToaGjRs2TKPjg79TgkB62Rx4YLfNaDi7cuLinRTQBG7o6xYwRZm\nTU0aTp60ypH4YjcajUCWZzOCzbExCQcPBtHRoWLr1nncvMkWazzYyCVIGRuT8MYbYYyO+lFQwLqY\ncGPfaJQFCDMzrHVxLgs1p3dQTKFPJoEf/jAfDQ1MFMwmnvJnACy8JvE9yubnIeL3G7h504dbt3wo\nKUnjjTeY4DEz4zM7kGQ7hmj8ysdoaSnrujI8LKG83Orokq1Djf2+iffC7v2TSxcYMWOI7777/UaG\nCMADreZm1t2rvZ09X10HLlxgXU/4PRAz38SyRLu3R1VVBCUl0zl5N4hBZEMDe28Ticwgsr7e6uTl\n5uexZ08IfX0K1q1j2W+HDsnYuTMEIDN4FQOcpZiai928pqayC9iioMF9fwBrDMfjuikSTU3lfl5c\n0OT3eGyMdYMaHvYjEsn05HF6r2ZmeGmUAkkC3norhEgkfcevxZrLeXZSts43YokJzyRqatLMMWEX\nx7Ktjfjfp9OGOT9HIiwrIi/PQCrF5qKyMm/fEi9/Fp49deOGlQ1p933K9m0+dEjGT36Sj9ZWFb29\n8zh3zo+SEuZTY79mO6OjzMD3nXdktLVpaG7OHKdO/y7bufHrm5y0xppTOa69xM9J+OLjC2Ad09rb\nmc8Xf3+iUSZOinOEG6LYxDPyAOYhNjubh02bUujtZVmmbW0Kjh3z/ibbsxrFLmWRiIELFyTs3RvE\nhg0Kurs113tmL829dEnCa6+FkUiwMkw+r/FxPDLibRIstgIvLtbwhz+E0NOjmGXMoqiWSLD5tr9f\nzWrYX1ERRmHhLBoadHPOLiw0MubJSMTIWlLFx09pqY7r1yVUVmZ2HOPvxO7dQfz61xE0N2vo7l5Y\nfia+R0eOWO/AmjVMLIrHdcf1C7F8WU6xPYk7S2A5DYBsLLXGOZfMCqfJ28m7424ITJ+0SLVU+HmO\nj/vws59FIEnI2uUDsJ5TIODHzp1skef3Gzmb3IqIrW+rqxd2fxAFOG7o+kmLcU6lQcDiMpTEY/CF\nv99v4MoVH5LJPPT3p5CfD+zZEzSDWrfyNLEev7RUx9CQH6lUHlpbdaxYYZjPjQeqtbUsVd0tcLx0\nScLbb4dQW6vj1i2fGXSI44Avno8ccfa7EREDNO6TsXdvEDdvSujosHZpjxyR8atfRbBxY8o0NLbD\n39FQyMDRo0GzrTEPcEMhdmy2E6chFGJ+N1xQcZo/IxHWNWp01J9hbtnRwcohcnmekYiBigodsVga\nXV0sQOYmlMmkdX65dtEYGrL8Z3gHsbw8dsymJg2Tk3l49VW2eOzt1bKKnJa/joYbN3xoblZx5Ypl\nFs3Fw4kJH/bvl1FenkZNjfd1j41JeP/9IDZtUlBerps+IKoKvPsuay09PZ0904Z3IOEL4yNH2K7x\ngQNBNDayc+A+CV4dagD3Dlf2ciIx8HYLrMRj8c5JQKYpMy+nqKhgZRZTU+x+zsz4zHIkfp5OZsf8\nvRJbZRcXRxAOz2YtQePH9AoiX3stjEAAWLfOvbOSaAbOxU7eYnloKIDqauduYdmyKewm5Nx/hvt7\npNPe5umAFaRz8YNnP/BgkbdrZ+VBVtZMto0IbibMRbtYTL8jaKZx9GimJ4/bPPTMMwXo6mLnc+aM\nH7t3h9DUpKOyUjcNZvl5Zitns4t0/Lr5+dm/l9nWRvzvr1/34fnn81Fby8ymX3stjEOHZJw/bxk+\ne80ddkHJyUBdzIa0+z5lo6REhyQZGB7248oVP557jrW2Li9PZy1/Ek3+vUpXnASpbCI4H0tNTdod\nATFtZnTy8cO78TllOfJ3mfv+iCIdnwdaWjQkErllHoviPM/Iq6hg93jTphRaWnRUVjID52PHvL/J\nbhs1MzNsw6C+XjM7yG3ZkkJJiXtGnJhRBDDB88MP/YjF0njjjRDOnQvcKXuWUFbGhH+vd/7WLR+O\nH5cxPe3DzIwPu3eHzE55hw7JqKnR4PeztUJVleWR5TbW+CZaU5Mf+flJ85y5MFdVxc5t795QRjdP\nt2+007pH9LHjGIaBy5f9ePLJOcfMHfH9ranRkEzmYXraZ2ZJVVVZHnO5tLb/NPKwxFW5spxiexJ3\nlsByGgDZuvnkustizzARdwfy8gy8/noIpaVsJ9+rs4uTN0W29Fs3FmMqupjjOZVVAEvPOhL/Hd/R\nCAYNvPuujOvXJXR0sB0YtzINsSSotzeMkhK2yHv33YWdrHLpriP6GPDfZ9/l4B/Vq1elrMLCvSAa\nZQIJ/3O23UInxJaX/L4DwK9+FcG+fSE0N7Pjf/SRH+EwS3F2K0+zt7q+csWPrVtTCIcNvPQSyw4S\nzUazlS+IXiOvvRbGO+/Idzp9LAyc3PxcRMT7IknsPaqrWxikl5Sw/75pU8o1EOW/r7eX1cjLMpsj\nuLdHOGyYIoOm+Uy/G2vH1Zo/FYWJKGfOsB34xsbMsgjRuJL/vNtiQpKA2VnmDxGPMzHgo4+YAW9t\nLVvct7WxADyXkiKxXMnvN7BrVxCNjRrOnw/g9m0fVq5kos+TT85nTfsWF/N8NzASMfD22yHTLJov\nKAFW73/1quTowSEiLuwLCqzxPDXlw7FjMqqqWDbPgQMyWlqcRRQn02v+jDdsYOP5zTet9ubl5d6+\nO06lg3bvH1EoyGbmG4uxjnCqCrS2aqbvBA8oIxEDZ88GoCjAyy9b5ts8ABOvWXwP7EFkOm15ANXU\nBB0zIJ2QJKv0KRrNLHWKRg0YBuswxsUxt2OI2TjxOPu/7m7VLLUQTVe9BAFRpLCbkE9P+8xd/qYm\nzVEAsyNmUojClfj7cy1Xcvr+iCV2zc3sunPpPiTOexMTEo4dk9HWpmLLlnmz21yu5UX8GkSR7vXX\nw+a1v/32wu9lLserqtLR3q6ipoY9Qz5P8nbmiy1ntmdpWN5TYYTDsxnjOpe1jqIA4+MSotE0jhyR\n8ZnPzKG8PI25uTyMj7P5x6sdPRd9vTYq+Hnnek7iWOKZNQAz7a6pyTwf0bPNqVyJjzW7yTSfB7IJ\nTfbzisWYb11rK5u3xsctA2hxnsn2TbYL4PzbIJbBr16tmSVKXutX+5zGfY/Ky3WcP8/K/aamfPju\nd9lacNUqzbOUPhplGZHd3Sp6exWzy9/Ro2w9yTKd2bMJhw3s2OH9XMfHfTh4UMb69RKi0aR5ziUl\nTJgTy8d6epQMEc4J/k7W1DhnQ1p/r2PtWsuY2el5FhWxMmTDAM6cYaXN69alzA2lcBg5tba/n9xP\ngWWpm/8PKssptidxZwkspwFg7+pgNyLL9vETA4OpKZ9Z5gJYO4IHDwbx9NNRyDJw6ZLf09hQrFW3\nSo1YLf7goLPRpBti2U1FhdXmElgo0uRizvfOO7JZW71//8LdBbeOQdkQy6D4ztSKFTouX/bjscdS\nyM838MYb7iUWw8MSXnwxgq4uFc3NYbPkRczA4dkmo6MS3nqL7Qh3dzubVI6MsLTe6mrd9PA4f54Z\n58myge5uxfyolpToCzIzPgkkiQWHL74YQWWl7th1J9tHTyzREDNFWBcJFeXlOg4cYAul/v4Uiooy\ny6RE7EF2JAKsWaMgmbSyg3iGQWGhju3bmceB23gWvUZ4INvTo2akvXPEdyYb9l1zu8llcbGB6mqr\n3MopK+DSJQkFBaysgLcbTyQs00wmRFqL84WmmNb8OTrKsmO2bw+jpUVHd7e2IPB3KolyEyZ5YJBO\ns3eUm3ty75Fbt3ym2JYtDV8sjRsZCWDXLtYyuqNDw7p1Clat0rBmjea6eBQRF0F8N3DdutSdVq4s\n0ygeZ+ddWsrawa9bp2Rtwy2WOoktXNNpJhBt2JBCRUXac2fdqUW4LANNTayNeFFRZvmDmEHidG5i\nsJFOG7h+3Yf2dtYiXgxCeKZHMulzFcy56MFbKff2qigvNzICSi42t7dr6OtTFtxPJyGPC+J1dew7\nVFvLxhO/Tkny4+WXfTn5bdmvnYs0sRgLPvnzziZWiGIB/+7y/88zebgok61bFhcp7Cbk0Wimb4j4\nDLxMU3/xC9beurVVM++p+M3k55ntO2oXJ5xEFz5+sh1LnPfEzD9uzGyfP7x8RvizE0W6dJpl6X30\nkR+bNi1cs2TbSOB/z78XO3ZYAhZvZ76YYIyPWdE/iM8rDQ1+hEJWVkSu2av834fDBi5c8GNgQMUT\nTyimSbnbHO50nWJprn18BIOWSW9Rkbf459RtrrmZ+aiJIm+2c3MTwpYaBPN3e8cO650RM8PsG30n\nTwZQV+dcAuwmgFdVaVDVPGzezMyO+dyeLVNvaIgZOVdVaQgGYZaB83K/ZJJl9Dz66Dyqq7NnivGM\np/JyA6tWsWvg68mWFg1/+EMYIyN+rF6tZs3Uu3XLh/PnA1i3zsrcAaz1V2srK6flHcuyZZzxMVFf\nz9YL9p/lfy9mYjqdm66zrOwf/rAAK1cygZt33OLPZTFj5X5ZStxPgWUxc83DwHKK7UncWQLLaQAU\nFLDgvKsrhbNnmcu/phn4/veZEVlJifdCfnLShxMnAigpScPvN/Dee6xWt6ZGN9396+o0VFSk8bnP\nzaG52b2Nrh0+aUkS8/jo61NQU6OZ9evZSoLEspuZGasrAheh/H5LpBE79TiV3YyOMuPSmRlWW80z\nKObmrHNYjNAhfgg0jX00169P4eJFZgrZ0aFhzRoVHR0sO8ArjZbvjKxeraKqKmy2meYp/m1tmnmv\namqstpFOniqKYqX1lpWlzftz+TK7/ps3mU8ALzUSOxF9kh8Wt/P0KgOxIwae3OOFm/pJEszdw/Z2\nDdeusYUiFzDsiAt9UTSqqGC7P/E4y1rjPjTPPZePDRucS58UhY0HFugbWLGCiVednapZVmHPtPNa\nuLplsLndK6+dWjEroK1NNVsOV1VZiyFFsTx5nBZr4vwZiRior2diGvd1spfriOnw770newqTXOyw\nm3tynErx3OY2sTQuP99AV5eKeFw3O/GI2QvZFnaikW9BAQtKw2Er04iPue3b2Xwnmrh6IS7s0mmY\n1yZJzG+mt1fFqlWa5866aIruNI4kKbO9OTfczEUg4y3gueDPRRweFPGSU8A9HZ+LFWLWlbjpwHfJ\n16xRoKoL76fbON6+PYyiIgP79lneTPz/iorCOHZM9yxX4ti78vBAjF9bNsNzjpdY4GTm6tUtiwdn\nxcWZIq74O0Qh1Kvsd3zchwMHWDtg3m5cnCdEk+1snjv28jOxhIg/Fz5GuOdKLkGLOEbTaas0Tpw/\nFtM9hx+vro5lT3ExJVv2Lse+sSAGQXYRJNegyMk/iL9Hra1hTE8ncxKxRPh5SZLV8rmkhI0dnhmb\na4mX+G1oamJjgs8TqZQPv/tdGGfOBMwSGrdjiFna3Bg+kdCQSmWKvPwac3lnFmsyne1+ie+2kxDw\n+ushPP10FBUVaXR0LPR7sb+HPNtveDiA3/yGNWDQNJgbp/ZMOfs94xskRUXMgJzPzfzfLLarqdO9\nKipifmytrVaZXFsb+w56jbVgkHX66uwM4sMPUwveCS6g55pdL35L7Znloh9btqyi0VEJf/gD8zR7\n4ol5rFqlZXQSFMdhLsKguEl7NztxZmOpAstSrTNEsgncDxvLKbYncWcJLKcBwAOY2VkffvrTAkxO\nsjrbN94IY8WKNPLz056tsKNRA8HgwraBN2+yrIqmJuZUH40yk78LFwIoK3NfKDhN0l1dLFOkvV3D\n4cPWBHrtmndJkFh2MzNjBXUAC4J6ehSsWMHKnU6fDji2AOb4/YZZW809PKJRI6P0ye+HKQDwUrTK\nSuedGzHLZ9Uqln4rVem5mwAAIABJREFUSTB9Fnp7VXPHNlur61u3fDh7NoCysjRUNYjvfU9CfT3b\nWeKmqh98EMC3vsVSaisrdTP11mnXg6f1rlljpcjyLkObN6eQTsM04RQzoj6J3QoeRM/NwcyqEc/T\nrQzE6b4dOcJElnDYMFspi6agFRWsHOTMmQD8flb64RXs8bEr7ubx8qIbN3z4yU/yMT3N2qI3N7uX\nPomL5NpaHQcPBvHKK+x+nzgRwHPP5aOuzsq0y0WQ4QsV0SDU7V557RKKWQGSBOzdG4Ki5EGWrYXU\niRNsrIXDBo4dCy4opxTnT0kCysqYTwEfP/bFG1+8NDWxd8RNmOTXyssxnBbdYqevbItJsTTu0KGQ\nmUnjdG+yZe3xzIu8PPu9WNj6127i6oX477mXE4A7fhLs/bx0iQmFboswcYHmFgCJGUJTU85+V3YU\nhZlvszIeHTt2WCIO31UVjWv5+8cDXnuW2eysc7Bvz+DgJsnc9FScN+3vaFOTZna3EsudqqsjqKiY\nztmLg5vKtrRYop/4Ht2NrAEuwsZi+oJ75XROdhHX78/03wHYMxUN0J3mcL7z3tHBvJy4kbWTyXZX\nl/dOPg80eWYTb9VcXZ3pqcTKYnP37+HZQ5EIG591dRpUlZ07v75g0DvY4yLd+DgzKLcLY2KZTzZv\nELtIIYqRfH4/fjyAYJD9vbhJ5PYddcr24MJhNBrB0FBq0eIFf/enp304d46Zj4vlw17fFfu9m5z0\noa2NlZtVVLAsiOFhqyy2tpZ9Nyoq3LMiRC+W6uqFrcqz+X2J2A3c7QJbLhsj4vWJwpn4bjt9Ryor\n2Ybm1q3O7cbtx+SGymIHqGzeOOI94xskAwOpDI86y8zYew3pdEw+j/JnJWYT8k2JmhrvTE6xk1ck\nYvlBip6B/DxzKdMFMjvkcbFWbHP/5pu5ZRWx+6Zj40bFXPOLhtmRiGF2ApubQ1bj+akp4PRpPwYG\nUpidzb2jpBOLEVjs4zzX44nfCNGf89Ilb8Ntt0YTuVZBPMgsp9iexJ0lsJwGAN+Ve/TRefT2qti0\nKYXKSh3Dw34EAuzjv2+fu9fI3BwTiLq7FaxcqZkfkDNnrNphSYJpLPjcc6y0p6XFuSxraEjCM8/k\nIxRirRuZuSFL1ywrs7JcNm1KmSZojzwyj4KChccSMyhqa3UzqItEmOhTUGCYddyhkOHYAlg8Fq+t\nFsvP/H4Dg4N+PPpoCoHAwlI0t50bnjG1bl3KNKSsqNBhGGyR19hoebSIooPTBC4KbJrmx65dzEeh\nsNAwz2dqyocDB4JobtZw4EAIIyN+JBLOu+R8Z5UvYsbGJDNwnJ/3IZmEmaElCki57HB/3Emf7460\ntLA0Wl5vzT9ummZgaMiPVauUjLRmp99XUKBjdpZ5C3DTWNEUNJ0GfvQjtiPW2amipUU3/T7szMwA\nv/1tCDt2hDLEg8JCdtze3hTm5/Owe3cILS061q1TXEufIhHLGDgcNvD++8E7Xbc088+bNqUyOkRk\nE2T4grmuzlqQiiKUWFbhtVMrZgVUVOi4ft2HV16JmAarTU2sW8eBA0E89hhbaNq7mfj9ETO7zOm5\n2IUQcVEpZpC4Ldi8Ft3iglLM3HJagIulcfb7ZP/d4vvsloZvL20aHmat6bkZJT+3xfhAiEG/GCTw\noE9sN51LS+/xcasLlfj8xYWgOJc6LeZ5VsLUFCthqK7W0dbGxBvuVwEgw8S4uJgtqMWSObvQ5Bac\n2YUgvgtsN2t28uDgY6q8PFMsETu6ZYOfg730jT+bkREJg4MBR98Wr2fhthjnJtvZMmTs7wGw0H+H\nZ8YePy5nLREFmKcR/3ZwISwatQRf3uHN676J2Q+8dT3PRhHFzWz+PcDCroliRtabb7JrLShg7100\naqC3V/E0Av/FLyI4cMAyOy4uNjI8h/j59vQongKb/X23e/Ww87WMo8+eDWQt6RbnLvtufSQSATCb\n0w6+PdOMz+n8nebZMk1NWsY3xumYdj+mzk6WJTg2Zm0ScU87noGSzT9GnOOnpnw4fjyA0tK0+e9z\nzUqyzx9OIrYoMJaXu2d2OR1LFFt5aTcXT8VyplzOj2+usECbHZv7YYlZsW6CKd8gCYedzbdFz6Fs\nWaZiaZxTlqBdiPHK5BRtAD7zGRnFxdML3hl+nnv3htDRkT1b1SnjUCyrb2rKbS6SJKsMUzSa58Jt\nRYWOsjIde/YEEY2mceZMAN3dqmN8ALC15sWLflRVpc25J9fmDXZEu4tg0LkxivizL7zAmq+4vafi\nPMk3nuvqWCZmU1Pmpnk4bJiVAG5VDHxMTE6yRhOBANvoS6dZTJhr6fuDxnKK7UncWQLLZQAoCstK\nGBuT0N/P0vfn5313Pq5pDAwoppmvm9eI2LGipcX68Il+FXyXsLeX+cdcvOh3XUhyE9P+fgV1dTrO\nnvXD7wd+9jMmCq1dq6Kri/kInDwp45VXWAqr04JIzADQddzJbmGlIrzWt7lZu2OgqZvHdQsaeeqs\nqrJ66tLSNAoLmSN/by/7CPNStKoqFhx97nNzjsITz5hqamJ+Qs88k28upgYHA2YJAm+36dV+WBda\nCnd3hzEzM4fNm5nJa2Ojtcvb08PEO/7xc1rkuu2slpdbfw4GDRw/LmPtWgWBAMyyvGyLSrH7kNsH\nMhtTU8DgoB8rV2o4fVpeYLB48GAQzz5bgHg8jVDIyNLyU8LgoIx166zggi8SuAgYChno72f+HVwI\ndBq3hw7JePZZ9qA3blRMYY4HTo2NOgYGFFOQ8iqtkCRrZ6y1lfkr9PWxdPmaGh19fUyMEBer4p/t\n2BfM9l1f8d/kkrouBpnc8LW83Lo/zPSWCVh1dQvFmGvXMtv2ApnBLPe7cPJ48RI+xEUp4L0TqCgs\ndf7nP89HY6NzGjXfESsudg8o+HnPzPhw4oR7eaLTM7CLLksVP8VAhXuN8N1x3oHIa7dbzIb79a+t\n4F2876J46PUMxKwzLjBzUVGSrHIZ7j8lLtBTKeDAAWtR6ebFIgZnfAx5CUGiOGL3e+HPxt5aPBKJ\n4NatZE7Pgy+qnUrfRkfZIn142G+alHplONrbQIulROIzyObB4dSJp6lpof9OVRXrFCN5ZO7w+y6K\nOOJ1ikFSroIkP55YNrNYcZOX5vLskNJSw/FaEwkNPh9w7JjsGTxy0YWbHfN3iD+L5mbmDdLerpkb\nB24Cm/19F6+F3y9uHB2L6ZidBUpL067rLDt8buJd9mKxMJLJpLmZ4bR7Lo6t55+PYOfOIBIJ9i0W\n3y1xU4zfUzdxgb93dXVaRtMHrzLCbGUk4rmIG1dcJHMqjXO6XnHNJnYj1HVLxOaNEtauVVBQYLg+\nT7f5ZNu2MPLyYF7nYjoyisfkY543IgkEmIF0e7uWkUm1GKFAFFaam1UcPChjeFjCpUsS/u3f3IVE\ncS7l6yHRq3J83Idf/Spyx5fQcO1cyq8RYNm27e1+KMq8eY/592Vy0mdmrLe3q66bXhxx/uD3T2zA\nwEvnc/HRFDciROE2Lw84fFi+U0EgoaGBWRMAQEOD8zwpbgaJ3d2WktEu+uANDQVM4SUe1xeM/akp\nH955R8bYmHMDBnGeXLlSxRtvhPHMMwUIBFgFgywzj89Ll9imeSDASrrdPPrETbG5OXaefX3s/dm1\nK4gVK9IYHfXnVNL8oLFcYnuAxJ0lcb8HwN1yRxdLcBIJzTQrDQQsz4e5OZ8pQji9qGJXAH7MSMTA\nlSuWRwn3u0kkdKxfr7qWHCgKCzQqK1nJiiyzSWbzZmYKevGiH2VlVkvHykrvjgTcGFgsS2L1sBqu\nX/ehtVXFxYsB7N0byshocLqvYgDQ2MgEoUOHZHR1qejrY4vdS5csI+LLlwMYHfW7drsRP4hXr/Jd\nZlaK1dWlIp1mu6RsgmQZEomE5uhXJNbiX7oUxk9+4jcDDd7q9623mBlsNMpK4xIJ910lLoqJXQyC\nQQOvvRbG+vXsOb/9dgirVzNB0L74sj9TPibEbK6l1iTz2vGBAcU09ywo0HHjhu9OVxJLVEulvNOa\n+QKUZ5tMTfmgqiz9VpJYe+hDh0KmKCN2wbGPD95l6jOfmUdNjVUSIrYMFjsvZFvg2uvR7cHtYuYA\nt2DPLcsn206tKJrwayoqshbSpaVMVAScs4CKiyO4fTtpmmuKu2bl5SxjraZGQyAA1NRoOHbM20SZ\nIy5K+bvKMzfs92t0VMKOHSHk5RnYutU5W89J6JqZYdljySTrKsfnTP5uegmc4vNz8pFZakc//sxS\nKWvOSKV8GR2IvAJvXlLW3q5hwwbFDN7F88nWrlk8FzGLg3vY2AUJHsyI/216mpX/rF7t7qchllXx\nssnr130ZJqtuYqcYcALOnii8DCEWC+P8+dzLXNzE1UjEuPMOp3HunHs5IcduXnz5stWpSXwG9uDe\nLd1ezFiQZSvrTvQt4oGRW8chHgDZy5SccMoKsf+9m1DrNKd5zXPi+qWzU8t47gBM35hwmK0pysq8\nS9m46MLNjvn455mcXBDj9ztXo2G3+ZqPuyNHZLz5ZhiTkxLa23N790Ufsn37Qmho8OP69VTG/Gf3\nGTp9WsJ3vhNFZ6cCv5+JB6tWLfwW278BXoI//1mAdafjY8g+17mJtE7Yx4hdJOPviOiZ6FQ+Jq7Z\n+H3iP/vSSxGzdIeb+c7MuG8G8HO2m0SPj1uNPnj5eq5BvdN94EF9X59i+jpmG2du9y8WYz8/NBTA\nzZs+/Ou/FuDAgSD6+xU0N2uuWabipujEhJVFy0uTiovTuHzZCt7FrC+n+8afX2trGLo+CyDzu55I\nMGFwaCiAQIBZE7h1xuUbEQUFlp8fv2/2Z5dto8q+ESEKt5WV7LhnzgSwebOCvj4Ft2758OKL+WYW\nlB1xM2h+3ipbW0rmTjBowO9na4y8PLZ2fvTReSSTll/o7t0hXL3KvBg7OiwDdKd5cufOEEpK0igr\nYxugjzySwtq1KezfH8S1axL6+lQMDKjm5pRXN7V335Xxwx8WoKHB6rxWX8++xYcOsSYOPK6JxR7s\nTmN27ndsfzchcWcJ3M8BIKZaFhTo+OMfg6is1MyPcCRi5FS/DWQudi9dYoa5AwPMw4Rne6gqzLbG\nbipsXh77SJ0/b2VmSBLLwmlqUnHyZABXr0pYvVrNagz38ssR3LghmW2ou7pYOUxbGwuSR0Yks6Vj\nPM5acfPSDTui0fD0NCtL2rBBwc2bktmK+PTpgGnQms2oki9SVBXYti0MVc3D+vVWByP+YS4qSuPt\nt4NYv5511nG6Vv5BrKhgpWZdXSoqK3W89VbYDD74R+f8+QD+3/8rwNSUD6tWaZ67Sj5fBENDLENn\nepp9CJqaVNNb6Nix7B29xC4GiQTr/pROsw/dxo2s/K6xkRmY8g+hW6mM+IFtabH+3VInfG7+Wl+v\nmyUoY2M+/Pu/56OhQUdhITA0JJsfJ6+PFP8Y83KQ48cDd8wu/bhyhZVY8IAdYJlfe/c6iwyyzHbv\n5+fZ8d58M4zCQgN1dZkBgbiw91rgiotJp2CJ7xoqCntHnYI8p2dgTym3G4NmOy+7qacY+PHn4ZZN\nwblxI4KXX2bzir09M989lSSW8SRJrI20V4c9jrhTy71ceAmPvf2yvd7eS2ATx/WhQyx9eWREMneq\nBgZY56xsmQvijjMvU5ifz2x/vRRjRP7Mbt2yBOPFeGGJhsSlpVbwny2ocAtYxSwOcTzZvQHsmVYT\nEz7TpNPtnO2lSS+9xMpohocDC8xC7ecnnou9nIf/HQ/oGxr8KCnJrczF677wLm08SyPbvUyl2HvR\n0aFiYkLC4cMyensV5OcbC95/MbgX3zEvgdbpncz287lkI4hZIV5Zpvbf72T2zgWJbN3xxPWLW8cc\ncc7zyrRxwklgF4/rJXjaRS4vI/vdu0NYv17Bxo3uJWPiPRaFJV7C1toahiRZ49Vu6l1VpePYsQBe\nfZWJVZ2dLNN448aFAb79G+A1PvjP8qzXVIr5HIljNds3xY5XORVgbYz192cXQLhQ0dFhdc+rqNAX\nGAI7lXFmO7dk0ofDh4Po6WHvpxjUl5QwzyGv0icn0ZKbDzc2MgNpu3m8F/yY4+PsHYzHdRQWGne6\nqzERb8MG5k94/HgQfr+zgCJmbvF7duCAjMuXJdy+7cOGDQrWrrWEhGyeL9YYieD8+RT27QtlfNc7\nO5mRcXk5K0EqKDBcNwv5RkQoZODo0aDjRg4nlw00vhHBnye/Bi5K1dTopuDj8xm4eDGz+6yIU5nd\nUrtI8VK3c+cCiMfTmJryobeXeZ4dOCBj/foUNC0Pr7wSQWOjjs5OLeP87dc5Ps7WpKWlTJj7i7+Y\nQzQKfPgh68jb2KjhwgX3JhgiySTw0UfsPpSUGOaz55szXV0qBgcDeP55JoRl6xx8P1u52yFx52NA\n4s7HY3iYBXXr1ysYGfHju99lvi6SZOCZZ5iwcu5cICfXdrtJ5unTAWzYoKCqyjAnzNZWZu7rVafO\nJ7TLlyUzM4MrulevSvjZz/Lx2GMprF+/sDOQiDjZApkdL/gHYn7e8tz58EPvOvXr1304epR1gEgk\nmCjS358CwNJo165N4dYtVifLP2Jehm78fqkq62rwuc+lsGqVFXDyD/OKFTpOnw6gocFdWJibY2KB\nogC7d7OsGr4D1tTErpnvhqRSwMWLEv70T+fR1bUwwBWfY11dBPX1t82uYKdOBVBensbJk7LpieKV\n7cSfA1/U8oBdHAdc0OHps16LDzHg5hkdH0fJFxd6/DyTyTy8+y4T7rifR1OTllF/7gQfu83N2h3x\nMo2zZwPYsiWFRx5RMmq23Uo63I7Hy1FEo9DFZGRkC5a4p8OuXUGcPh1AKOTdbcgppZwHh+JOWbZU\nZruXxwsvsOC6pcUqtbF8IJwXV+XlEZSUTDu2Z+YeDzxo4eM1lw57YgAnGiqLXhn82em6tbPvdlyn\nRSsXQjZtYiWmQ0Nsziwvz15W5RSQ80VgLJZpfLwYU0LRNDORYOc3Pu5s4OwE3x0fH88sB0omfZ6L\nvFxK+Nx+PhJhPgJ79oTMrB2+m273AnMKanlAJ5bR8LnWXtrkdH6Tk8zLo6goDV23sjl4MNjdHYRh\nJBcVlHrdl1xE09dft+ZZgI2Rvj72XbAbAfN3y6mkSvxdbp28xHfS69zEb7JbNzXx/JubNbS1qYjF\n0qiqYpsJYoArfg/sogcvAeHGx4C3sOt03k6m9vyc3dpWu+F0fHuGl9e7LopiYrBnN7KvrmbtqrN1\nHeKCPu/sFo0ykZiZqUeQSiXNuYOLprwspqlJQ0ODhvj/Z+/bn6O4sjQ/VdYzSyUJvSX0Qg8khACJ\nN+bltr398m5PR2zsTsTGxv4zGxszMdGz3dER3Rv2uDvG40e7u/1oPzDY3RgMBowNNtjmZbBsIV7C\nIJARQlWVWbk/HE7myVs3M0tidn8xN2Ji3FBUZd68ee853/nO97WX0N9fxB/+kMXMTAwrVkQDdpW4\nb/He++KLnrD4Ylsy1DWi/i4Xxrq7vcJJ0DUyUCHd8zo6bCQS8AkCT0+TCDd/f9C9qgUEdlJ8911/\nW+y1awaeeqqy1icJclKLDYEfYYLdusHfmc06mJiIo7Gx5LZ2tbXRnPX2Ess2SA+K71EyLFtbbVy/\nTuDAY4/lsWWLx6yUxeYg5jbvPy0tHgApz3V+TqUSsHdvBmNjVBTVvad8/m7c6AlHq8L1suCkK6zx\nsG3SsQSA2dnys0J9/9ktLEy6QT3Lw1oaw8b0dMyNRdevL7haptPTMZw4kcS6dUWsWlVEJuNgbCyP\nzz8PfgaGAXR1WejqstHZaeHiRWq54ha24WFPc4c1HoOYUwCxierqyIWXxZf5s6yxeuwYaUOOjOTx\n7rvBxjKAV2jJ5WwcPkxkBS7I/f+2lX8I7jzAeAjuPNiYmorh6NEktm3L3+8LJ0X+8XGv5WXJEhun\nTxONr7U1vArBwcLAAG1aAHxBYJSooTzsli0j3Z21awvuYd/VZbmtUPX14UmLIaq+QZUY3mDb2sgK\nfWjICmSC3L4dc3tHLSuGTz9NoaoKeOedDLZsKaBUiuHo0RS6uiw0NJRc1kxQUCIDcLYpB7zDZHaW\nDuahIXJKCKo+AP5WiI0bC27fMCdkTMM3DLrn4WELIyP6BNf/HFMwjLuuy9bAgF8QVrYGBQ2Z1Mp1\nIA/KSltIZMK90HaTsCGr/kuXEkOntZUS0sOH/U4KYWuX7625mQCGjg59oB1WJdZ9n64dhRMC1bUm\nCigaGSlqdS7Yqpd1JVjUUccS4io4H5ZSm4Laz+imLCtcqJVBG9ZzSSbpOaxYUcT8vAcGqK1gcqRS\nJm7cmC8TEpZVwLo62gcWIi4sn4FMoqRWBl/L+LiB554LFiGUQWtzs2f13NDgoK/PRlMTtZSxxXcl\nbVVyzpkZyfepavAsBBCUbBYpli1doCr5jjfeyMA0SXQW8K8DHXAVVRmNejb79vnFdINaGoLYEnxe\ncBuNmmzoWuUkEGaawP79KZdBxsydgwfT6OqK4/LlfMWOJ0EA1ELXLZ+3OsBTB8iEtVTx3Ml1pWOb\nhIGS8kxmkFu3LuX1M3vh+vUYnnmm2ldkUs8DqekhtfCGhqz7TpuebXrYXKqAntQTk+9HlCV9FEDL\n865aJaufVUEx3nODhOwruSb1nZZ7RGNjxhWpl/vQjRuea2lXF8URd+8Sw3jlyiJGR8s1oCoBR3WD\n50wVFg+6nyA7+aiYQZ4XUm9Md97LPSdIs4pZO7W1Dg4fDgdU5LV5TmUeS4oLkv39lru+a2v1bAqp\nB8QMtZ4e63685zGNKnVc4nvt77fK4j51fsPY1vKMZ5bp0BC5yKoOn1FFL7n/rFgRRzY757aIqs9Q\ntj8GaeYw2CTZJep6kE6j0o1PxyKUbVlhbdVh8QwPyQRnkDOddrBrVybUZUs3cjnHBV5kkVIWrPN5\nYjybJu0J3IGge5/pGZK+FrPZ5TOYmwO++srAypXE+t2/P4WuLv01T0wYroMdQB0aDQ0lHDiQxr17\nVVi5ktbK+vVF7N2bDjWWkXpAt27F8C//Uo14HJibq3I7NP7/2so/BHcWPR6COw822JZ0zZoiOjoc\n9+XPZGwUi1XYvn0epRLpkqg0dXVw9Z8FL/N5uLbnra1ORRVkedi1t1MgNjsbc+2kuYeTK4+VJi26\nhBTwb6CsI9PcrP8ebuFhYKKx0catWzF8841nzwlQT3Jfn18QMGi+VMBrfJzcvc6dS7htPGpyr5s3\n2QrBQIJpUhL13nspt52nksqZfI69vV7vPQcfC02QZcuCbKOTf66yNILG7Cy39RXQ0GCXPc+FUjJl\n4LlrFwXXUqy1r49Esi9fpuQRCK5AqJUZ/t+6Na9+VjfkZ3T/HeRaEwYEdHbabsuTGuRJ9gm/hxzM\nq8GEatXN1wQQoMNU6Eq0Y2SyNDxsuS2U/DxaWz1gRRd0X75s4umnYzAMlIFRC2WDhD0D9X2VVb1i\nkeYkSIRQBq2plINnnsne3ye8tgrpThVFxZbBoU6MVU0GFwIQ6AAtppOH7UHqd5RKpJ01PFzOwloM\nG0Xeu7qPmaaDpUu9yu3EBAl9HjiQwrJl/rW7EBBJPreVK8tb5eTaHRqyXHBUClBTZT+DZ5+NVSyO\nKpPMKMZTUDubbi9S9xJ1RDm+6bSddAlY1DunMmKkM5DaOsLPK5cruSKdso1BPs9Ll7xEgdccv6/s\nglgJwKAmiEGMnaiWdd1cBAGbYewHFRST380OcUFJu2o1LB2P5Dst94ibNz2RerkPSZ27xkYbH32U\nRHe3hWyWBGPVd003l1EsJXnP7e16YXFd+51lAW++qU98de+8Ts+JP5fP6x16ws5kFWxjtmjUPhOk\nISSfB7+LKuNOXpfUA2KGWk0NAUySadTY6BUXwoAevg7pLhm0b0Tt3SrLtLvbdtv6VSZeWNFLnmtD\nQxnk817epO4paiGZnXNVAw4V0JMgoRR952sPAp7ktUkWmK74JguoQfEkO1wNDhbR1FTClSsGurps\nXLwYr8hwRA7bhiv0LNuY2QBndJTu79SpBIaHC/j2W8N1fQ1rheX2KXUd1dZSserzz5P39+VgQWRm\nvTY0UFtjfz8Bieycyi1uTU02amstXL9u4Gc/m8OSJfo5e/PNDB59NI+dO+fdPb+uzsb583Hs2DGP\nxkZnQTnCg4yH4M4DjO8LuLNQwdNKPyvFy2Sgcvq05xw1MGC5rk1hDA1DQfBlINDebvv6d8Ns7aQA\n78svExI+OFh0wRJZeZQ2m0GW0nxPrA/D7UoyMJBBXNhhx/+GE7EPPqD+9poaAi0YhAH8goBB3yfp\n7qyRcOFCwm3T4OuULRa665OtEHw4sUsQaxxxBSAK9TcMAotiMWonSCbvBlLHowQvAb/Ys/zdqamY\n62Yje23ld6vrWLqpffut4QIMra32oqqDUnC0ro7Av+ZmCu6yWQcdHRY++iiFU6cS6O62cO5cIpC5\nEDQXDwow6OZCChEzSy7MwUiuNRnMcJAnq32yiptKOTh1KlFmYymFz1VWBL3zBSxZQv35Ue0BKm2b\nA1uZ7HCCITUfeC7v3jWxf3/p/tr0088XygYJm3c1uJWVujVrili3rhgoQiiD1jt3PIr08LBV1lah\nzoUOHFTXlC5BkLasUVVCeb9BYtlAuBaCHLZw3FOZBvG44wqEy7manibHMZVyrROvDgOGJie99j4A\n2LChGJigRY1Kkg35vHjOVQe5tjZ/62ClABuvd13i8SD7i7pX8Ro7dSqB3/+exC11FU4GES5cCNYL\ni2qDkdfM86Nze5O/WVfnuIwbVUNJPk951tTVOWUJTdR+EJYgsjtjZyfFQ7kc7ZlhrdxBLaxS34zB\nLD5zg2IZFaTh/R8IZ0fK/f706QT++ldqOR4etnx7mpzH5mYTtbV3ythA8biNb7818IMf3MPZs3Tv\ntL7tQC1FnoOWFmrtPncu4Yp6R63XoHdVp0dSVeXg44+TWntp3ffo9J/4c1JvrFKGhAq2MYvSMMLt\nwoPuUf3zqLXYcTnrAAAgAElEQVQr9YCWLqXCKLVa+mNlGVfrgOag/KGSvCLoM5Jlyteje3ei9mZ5\nruVyfgfCMNdOwHPOlQYcMo4C4IJj3FpO2j0e+7+pKZj5x3sjF87Cim/EYPLEs3XvAWturl5NshR9\nfdSxcPduDGfPBv873VAZSNPT9NuffJLE9u1kgHPrVgwNDSXMzcXw2msmHnkkj/Xry6Uv1OKPBKn4\nHMnlvFyoq4vA36D8MZVy8N13dE/d3V4BmeIAmqvf/Y7WyfnzCfz5z1l0d+uZO7ITZelSB/X1JQwN\n0ZwdPpzG2BiZy0h9vP+X4yG48wDj+wLuqPoCYZtvVIIblLDIavzwcAHptIONG/O4dSucqi2/98IF\nAxMTBopF6hXmtirupayri7a1Y5reihVFrFhB/fbZrONaI0squKT06b5P3tPoKAkMNjba2L2bNiQ+\nbKLaxXTPob2d/o97pCXNn5k9ldJg+XuXLbPQ1lZCc7ONP/+ZqJjV1Q527w5nZsj2gVjMS3A7O20X\nvJuZ8dyemMUTdFBzO0Ffn0d9lUloPO64dsdRgcLMTMytZsjfkqwxnT2nLmmprrbdddnU5P331au0\nvnp7i/eDZUdLX1YHJwNr1hRdIT52FpuYiGNujqoHjzySx49/PO+6oAUlLLoERbpF8OeCKORy3sIS\nWgZRhoYsN/io1DpYB55mMn49Cq4aGoaDzz4j2q4MmJNJrx1PBq4MvAIeuBm0X/F9ShFcuS8xUNzY\naAstiPIAt63NRHv7d1i9uuhzE1MTlrDgNOjvgirv4+MExK5YQfos3d12qPMPJ5bc3rh8ue0mVzpw\nS17v1FQ5eyqo2huWSC+kJYs/K1sqdeypMM0UVZRSVm8PH06jr8/CzEwMU1Mx3L4dw/vvp/CrX+XQ\n3FzCyIjl+y4ZQKqaCPw+cXW0pYXm8do1A+vWFbF2bbBdeBQ4XUmyoft79fnkciYymbsVvaPyneD1\nziAP4O3t/F7V1NBvhLFEdeyN11/P+ETIJeCrsmNUpoSudYLnknUn+IwNajOS8yOr3sx6kkD25KSB\nq1fJMrm31yq7Nn6GbW2eTpQOMIoSa9W1XPHvT0wYePfdDFpbbRw/noLjUCLd0xOsOSffewnKmKaD\n/ftTOHEigZkZajfnNrKwljide59kxcn3kZmF+Tw5vxWLwIsvmigWq/DYY3ktQ9lrM8wgk7lbVmg5\neDCFf/u3LJYuLWHHjrwbK967pz/jeQ44fnjxRRPffBPH1q169kOlhUkJIDKIcudODOfPe+54QUMC\neEFFkaoqYoyvXKlnsAVdU1ABQmW6LvR+gei9SNUD2rXLs1XndmduU2QdK50LV1CuobYJ665dFZbn\nobZHG4YXy42NUfxWScuqv13VxLlzeV88HhZzNzbaZQYc8pzi94hby1UWnWFEM/90oGNvb3nx7coV\nA8eOpbB+ffB7wHFzW5unuXPvHslBBP27oCH312KROiomJw3s3FnAxo0FTE0Zrkbdpk1k6KC2zPG4\nd48Kxvk80NZGe85776XQ2Gjj4MEU3n03jaYmG19/TeYyp08n3W4O3ZzJuejttdy98sYNA59+SgLj\nIyMWRkcLaGvz3Gurq8uv7fZt0np1HABw8Mor5PYpTVtmZxcO3C52PAR3HmB8X8AdmSAGuTDxxtLV\nFd4SFEQJlNX4W7foxWKRsUqq35OTVNl6440MLl82sGaNhdWr6RoYhd64seBafQd9l0y402kSqlu+\nnNpj1I07iBUC0AZ57x4dbiMjBVy/To40HR3ehsRtVlGaJTyCWAZqcKW2WERVR/j5xuMO3n47gyVL\nSujqIlHpri4bGzcWfCKf6pDC2GvXFrQ9+NxW1tJih7qGyPtcvtyjvsp756qLanesXpsuyeMhWWO6\nthG55nkdX7pEiUVfn+3qHrW30zx9/XUcra0lfPCBX/cibMhr4Lli28gNGwpYtaqIZctIcO/69XD2\nlNoKw5+RbhGcRAHhAodqgKVacz4II0UGeay/wwwLDgQ5kGTBPRXc4VEoENvi2WfJyYBpvKoGQRC4\noCYsOleTKAttmTiHUcfDQI6gv5PzzO82i1KfPp3A+vWeu13U0CWOQLBgLbeiZrNOGSCuJo3y96W9\n69TUwoRfVdaFjlUkk5fpaT0bUDdvartCqURWuEePJnH+PAWCX30Vx9q1BXdOubJqmg6OHaNNQOqH\nScDorbcyOHUqgf5+As5ME5F24TpAVj6DMNc4IDg5U59PLld5kCfXYkMDsbKY6cTOkTIOKBaBt95K\nl4EegLcW7t2Dr5V5/36/oxK/r7QnlIt8ymtirZbRUdLCk+v1pZdMt4AQizl48cUMMhnHbd0Mmh+V\n9STjE16D331Xhb170xgZIQ0G3TMcGPDrRKlJVRTQGcS02bUrg9WrCxgdLWLz5jwSCQLDe3psN8ap\n5Hlycjg2VsDq1UXYNvDaa6bbIhW2p/O1NTbaPuabTm+Ef4sZbO3tVCA7f55c4IIAT/73PT1xxGLl\nrIilSy3cvm3g0Ufv4euvE7AsYuhxEhXU0s7X39lpY9UqKqRU2samG7pEWwfI6Bivsi2tr09f1Dt+\nPInnniPHzCh3Hv4dCciqIEMqRUYNW7fO+5LSStv2okBoyUDh91lt85NtxWHW2kHMDKntFuTYxu58\najyuu8+pKcoxEglyiz18mFr87t4N1jb0gycZAJ6jW1TbpSxI6e6VCy1TU8Z9TalytnpY3KU+A9nO\nJotv/DlyHQt+DzhuZrty7mCoq3MwOlqoGHQE/GLP+Ty1S/+H/5DHihUF/PWvafT1FTEwYLt7a5hR\niXT4HBmxkM06LqDy0ktZALQ+Xnghi3TawZdfJkKNQ+JxB1VVBJRLIWeON7q7qcg3NWXg3r0Yzp1L\nIpXSFw1yOWLdvfYaEQm++SaONWuKWLrUcZ/9YoDbxY6H4M4DjO8LuCMTxJoa2+2L5ypcdTXpv2Sz\nxFp4550Menr0bBa1v1ui8/F4OWBRqcaKaTro7qaEcceOvC9AlJbTYdVLlekhBXgZLJEU9dnZ4IrR\n5CRV2jZuLCCfj/kCwGzWcQ+huTlKUE6dSmh7TOXQVU/kn+mQe91BoAqwspBlLkdtMNeuGdi+vYDW\n1hJOnky6tMwwQOzUKbIWb2sLpo1yshzl1MSflUmJrHo2NXkaP21twYyFsMOQf4PbRpihwayWGzfI\nUUGysvg+OWDnZPPAARI8Xbcuj2QS6OykRCUs6A4StbtyxcChQ2msXEmuEF1ddF3PPx/u3GEY5boI\ncg5kUK4yTHTzxgHizEzM5/RWiRhf2JBBHrNNgt7x27fDq6GTkwaOHiUng23b8lqHHR1Ip14Laz6o\nQbEMmNSKvvcdlR2gYWsxn3dw7lwcq1YVfMG37t0OEqUOG2rgF/RvJicNdy9iUcb+fst1twhKuGUl\n9fTpBJ56yrN3rUT4Vd6vDJB5D2PHjnweKJWICaCyAYNan1RQS4LinZ02Vq8m9tO9e9TmOjcXw/Cw\n5WOojY0V3LZJTlbU1sSeHr/gbFSbC6AHZOUzSKf97m+6M6YSwLC9PVNxkCf3C3Zn4/c/n4+5cQCL\nB9+5QywZFfQA9EL7LK7MQu+ybU/nCKiuXSm6fOdOzCciyv/X3U1W2a++auL2bdJ0WAigqmvJNM0S\nLlxI4NFHPWMHvrbBQc/hTLZnqMl7FCiuO9/53xBjlfZ4vq4owFTdvzyQjvbenh5KIjlZq4QlFmaz\nre6njgN8+mkC335rYGysiNbWUijgKYs6Fy54rAiOOTdtymPz5iJu3jTwi1/kcORICkuX2rh1ywis\nhEuNrDB2o3r96hoM0sfhz+oAGR3jNSj2kb/R3m7BNIkVXEkCqAKyKshw4oQncyDBojAwUWUxB7Ut\n8t/LNWEYfokEtTW3kriM51jqaBoGys4GWcAJcsMNAvtbWqhdbMmSEq5dM9DUVMKhQ8FFL/k9VVUm\nDh60XCfToN/heEHXGidjUNn1INtFdQWnIOAp6L1c6OfU95jPqFIJZTFxJUO2Kre1lXDzpoGtWws4\nciSF//2/c2hqKqGnp+SenWGMsvp6eu79/eSYx6y9kREqhD7++Px9DVcbS5eS+UQQiAV4ua1hAB9+\nmIRplrB2LcVhuthLOseqa8S2AcNgFzLPyUvO1UKB2wcZD8GdBxjfF3BHJh+nTiXxzDPV7ubDlnOH\nDqUxMRFHczMhlkGiW3Ljb2z0WByxmL/XtNI2D/m9TU2Oa48oqcgTE+GsBx66Q0oeNI2NxNJ4/326\nXnaTCgKxdBski8r29np94IODwXoZUSPI2SQMFJuejuHQoSTOnYu7rAneMAcHLddakPtWo0QIJQNF\n9/fqNYbpSPjn0FubMuhobvaQ8Ch3FDkHus/K6vvBg2lYFlXha2vLW/j4OQIeHTuXc2BZDHICBw8S\nc2dwMPyQZcG6MNaBTAoMI9q5Qx0SiJma8rdSBTEveN44QFSFKCvRrVKvQTIRuMWpEmAoal1xcrx+\nfXAVRALT6mEs14cKLKjtZ7rWPQCIx03X2SXs3VUDOdka99e/pvH009Vobw92YeAkTdeCVYltudzX\nghxeuLrNCTJ/9ssvE2hqKq866hxSVq8mGrO0d40SspdDggu8VqamKLn48MMkvv464YrrS5H5ShI2\neR1c0WR3qro60jN44ol59Pb6GWotLTYch/r2GeSToIdsTeTfUMFhtTVJTThl0sFA0cCAnqLvrT0P\nuGRWQH097U8yqb9500QsNlcxwCYZa1IPTgKg7KbY2Gjj6lUDO3fmy9wsmY07MkIFjlyO/l62JkWx\nJdS1G3SutrXZyOdjboLQ3k6ukZxIBwG76tmmJqZ8jt69S+YFLS1eXMPXtnKlV/xQv1e+Ywtxk+J1\nIvfvMAah7ns++iiJAwfSGB623OKUjFd0bIKooZ5PQW32hkHJzvnzcbS12a5rXS7naNtx/OwTE5IV\nceIEJUS9vcRUamykdbVlSwHbtuUxOGgHgu8LaQsNSp514Ib6WZ0OnK79Lyj2kb9hmsDHHyeRyejd\nD6Oeifq/ZTu5PCf5s1HskCBWcNDvq3OpPoMwkEJ9HvJdDIpv5T6tmjWoxSgJ9hsGMQrXrStg3bpi\noCOXbr7On8/g179O+DQXWWSfi97796ddNtXhw8Q4YWkGuVdLR0TZIbBYI4KwGFyKyIfFf/I95rNt\nMVpQfG0cv27eTHNNBjCk57lqVRG7d2dgGIhkWyeTQCwGHDlCgAznYG1tDnp6yPmT13iUY7Cct95e\nC9euxfCXv5CI8rVrRpkba5S5zOSkgd27M1i6lIoXXBSTcQcXpoNaaf89x0Nw5wHG9wXc2bPHSz56\neixMTBj3K+UOTp+OY+fOeSxdWsKGDQX09UWLbs3MAIcOpVBVRVo269YVMDpaKAtkw4TgwoZs/bIs\nch5gMa8wS+mowOXOnRgOHKDrHR4mWnhQZTZog+T/LQPoujonFF0OGoUCcPhwEn/5C22E0r45bORy\nFCRfvUpgQV2d4/Yky8RRvc6gAEneq+6ZqZWlSg52wL82g5K0Sm3M5XVI3Q4+QLldg9Z0AmvXFtDe\nXvIJsekAAMMgYcozZxJYv77gOgtEgTAsvqYKMOoCHz70dM4dcqgVxiCxXMMI7k/35t7vzsTflc06\nmJgI161S55yD1kymvB0sDJgI0qjQORbp5qLSYEa9Z15jKtVcFzxdv+45u+jWX5B+EeCB2WNjebS3\nl/DYY/M+Sr9sOZHsKd0ch7lfqOwlFdBkavuVK4bLqOLvOHAgiX/6p5yrn8VDvguAp5m0ahVpR0l7\n18UkWColv7ubWDarV3u6KFHOe/L7ooCEqSkDn3yScpkR6v6ngnxBIvj8G+p7EtYCKP+dZHxI7Scd\nm+D4cU9P4MsvE/jNb6qRTDpIpx13vczNxbB7dxaNjfORcy+H+v7zfEjQt6+PXEXOnqWWZxXcYfBA\nAhPqvYdVu/k6JKNOJnW2cNhT28UzGaCmxsHRox6wqwMiJiZof2ppsd2WDKB838nlHKRS1FKgtprI\neEEFY4DwFlh1BK2TeJwSwpGRPGZm/CDh7Kxzv1BQdJl/KkskKlGsVH9FPZ/C9p5UysGXX8bx1lsZ\nmKaDPXsyuHLFwPLl0UyzfH7O/R0GToaHSZ+jrs7BwIDtgrDT08FCyUFJb1hsqZ6jDCIGubRJe+Yw\n8CQM3JMASibj4IMPkvj00wSWL/faHXUAse571WfELUjs9qqbd46JdHuqYfjFesPAAN0IegbT08Dr\nr6dh2w7q66PP+KDfCYtT1furqSGgPpn0BJ8ZFOKCXdj65+9bvz6O/v47GB31ND15r5ycJHdTydCa\nmQHOnIlj+/Y8Tp9O4H/9LzpTUylAOiLKDoEop8KgZxBWxAxiBYUNTwvLE7qv1NKer43j17Y2D4Br\nanKwZg3tTdLtM0qKQ4p3cwFJ/RyfG/k8McCDrlfqMSUSDi5eNFBbW8If/pB1Naru3fMKXGEMR5Vp\nxkCVLIgyG///NbBD1/MQ3Fn0+L6AO62tFlpaKPlobWXaGbFOzpxJoKurhC++SKKuzkEshlDRrUIB\neP55E089VY3eXguPP553PycdTgwjWAgu+p68l2xgwGtjAPwWx1GHlBq4SNZJlAChes+6QzmX8+zC\n29rs0H5f9Xs4WXn99Qzu3q3C0qUlHDgQTOFX71OCBVEbfVhrizp0zyyqqhB0n42NnuaODJSkaHel\nmkzyOqRuB4NNHCCRXpTtVgXU56u7F9n219kZDcIA5S1Huv58+b+jAiiu1EqhUjk3agAYphfF8y0/\nz/fd32+5oqGVMDJk0KprBwtLuIMAqErAgslJA6++msG338awenXRFb2WTKmgOebKG1e9VRafHNLZ\nRTcH6rXKShGD2W1tNgyjCkuX+gFR2XIS5KYndWFk4imHZC+ZJjllZLMORka84CjI7n183MCRI+Rw\nw+COCpz19vo1k+Q9m2a5ZlMlQw2U+vpsl2UTZk2ve67yO4P2CrUdJ+id5yRZVyWWn5XvCe9d6neo\n1XGdIGfQuuPkfc2aArJZaknOZBxcvBjH4KCfbTMwkEF9/d1A8F035Lsg51KCPnNzFLSOjyewbh25\nwOjOOm6BKJWAlhY/2MrJcxAr0LYpsNa5Y0W1IavW6nrtDXJNbGgouS0Rc3MxvP56Bo7jnaW2XR6f\nqM9G7ldSILVSzSl1rUk3zpdfNvG731WjqqoKFy8mXPDnjTcy+NvfUnj++WrU1DhYv77ofo9019GJ\npUtW0UKSPcC/BwSxywgUTePaNcNtfWSXPvWMk+8Et2Pz39fV0fkqATzTdNxWFlqDcWzbVh5zqs+I\nv/OzzxL41a9ySKUcLF/u/zcqU4cLc3fv6jVV1DhxZsZz3rtxo3IdH/6d2loHd+9W4ejRNFat8tyV\nJFDx5ZeJQN1C+XwnJgzcugXk81UYHi5nuOpiIhUI5Vhzoa54Qc+Ax65dafzzP+cwORkva50M+70g\ntz2duLt6f4YBHDhA7OqBgYUDHfx9K1dm0NJyx6fpybGUaVKBQDK07tyJ4eJFAvs//jiJo0dT9zW0\nij42l3q9QWdc2AgrYgaxu9ShithLttNCHGF58BqYmKA4rLXVK0bX1xObj7sXohiOUrw7qOWbzw3W\nwgvSwJRzZRhwXUelUD3nNG1tXj6j+03D8Bdj+F6lkU+Umcy/53gI7jzA+L6AO5kMMDxMVQlZTWeH\ngE2bpCaBhWKxChs35rWK4pOTBk6cSGLDhgJ+8pN7sCyPZcAVwbm52ANR2ORLJrU9JJjS1RXeVqJL\nmnjjZo2WSoO2oGpcQ4Pjsj4aGsL7faUYn6wcdnXZWLeuiMHBcAq/bo5UWmtQ9TSstUUdapWNK0sq\n/Tds3vk+OzriuHIl7zrazMzQ3Jmmg7GxQkUHge6ew6qYvL45CVE/o0t6VDp6JdfDmh/832qAsdBg\nipM9KVQaNjdRLU/q4PvSCWiGBSAyaNVdT1CQUSiQOOL4eLyMBRW1Xvkz166R21hXl41YzMF775H7\nATMMguZ4ctLA3r1pdHRQwMF/poKzhkGCysnkXTdZidJmkPPIe9TlywaeeqocEGVQLEwrbHzcwEsv\nmfcFuPUAkGwVOX48iTffzGByMo6hIS84Cgoqm5stVFVVYefOeR8rQFYBAT/TQd5zmEaHOlRmRRBr\nhb9/dpacN7q7/WK+Qc81iAkGlAdmQQki/3dQQiCfr65qL1tt5LqKEuRUky0+H+/cIa2gZcuoPVF1\nXzQMAiAvXKDWwUOH9EysoKECrOq50dNjYXS0iPXrC2XAHgte9/TYSCQQqCfBZyKJ7sZh20BPjwcU\nBumUSEBMrbqz2PqLL2axbBn9pnwP+LncuUOsj0zGuc8KKSGdpjPixImk21bB91VT4zG71OcyM+MB\n5s3NwaBK2Bko1+ilS54b5+hoATU1Dv7Lf7mL/n4PJCyVgIsXDTQ12fgf/2MWS5Z46zBoPanMPZ7D\nSgFYVRh4aMjStm/THkaFwCeemMfKlZ5LnxrPSEYvt7pyEYf3IgkSsuA7a5CtWFFETU00K5jvva7O\nxtmzcRQKVRgYKAdzde5pHDeo96laSx84QFoizc0lbNxYjHQuVdeFbQOJhINVq7z3SrbmAsT26Oiw\nMDRUdOMs/m75fHftymDfvhQ++iiN/n46X3QMNlVzTt3fFlKgq5QF1tBgIRYDNm3KlzHXw35PBd94\n/zAM4OTJpC9OVWM+nXtUJfEEDy8+9AqPanuw3CM5JigWgTVryNr79Okktmwp4Oc/v+fTdtFdrwTd\nGNCMmtewIqb6O0HfI2M7uTcspFWMh7pHnjrlz3c4PqxUiqOS5yXZU8uW2dpWUP4ultv44AMq5rW1\nOWht9TQ4CwXcb7+2XWOAoJhc7t98r9LIZ6Eg+oOMh+DOA4zvC7gjh9QK6e6mALGtzdMkuH7dwAsv\nBAtGmSYltTt2kCtWmBuK1FZZ6FA3WKZ4lkp6RX11cD/92FgBw8Ne0qQmNgvdjNTNkQ+Gri7PiSRI\nwV4GuZwkMHhVqYgnj6BeeV1iVCnKH4+ThSAzmxYDVsj7bGpK4dlnqbJ64QI9s5oaAtuYUbGYoR5s\nQVWKsOf7IJUsvgadeKzaEhSkJaAO0/QLlUaty0pBqLARVPVbyNCBZcycOXo0icFBCytXFvHllwbe\nfz+F9narTOyOq6b19Z7zHFV7PcHQ8fEE/vY3ukau1Aata9Okezh3jsQ/JSA7PR3ziZFL0W816ORr\nDJpn/ruaGk8Tge9JFQANYv9xALF6teecFcRIYjFzCQBK/RcGwNvaiM784osmJifjOHiQHOIk8BSW\nCKgAQKXBoFoBD0qMAXo/Pv00gYMH/dVtuS6DWqaC1mkY+KOOsPvia5UMQ7VNIKpFSZfQqHMs24TZ\nwUpH45etg9evx3DgADGx+vujwZ0whh/PV02Nt07lPUjBa2mZrX6O11x/P9nT/+EP5H7HgEx7OyVl\nly75gVPDIOZNEMC3Z08ac3NVePxxYhzL94DnklsjzpxJoLGxhC++SODcOTIHGB72tMYaGz2XS1kY\nkuAXx0Mqq1EVTA1L1ORzzueBI0eSWLOmiGXLiJVTU+MHGuvrbZw7F8dHH6WwYoUfsAtig0n2IDvH\nhbV9yjXNZySf0XLdTU0ZPsZTMgnMz8dw8iQ5g3I1m1tNDx70vkNqJH76qYk9e6owMEAMND5fmDkg\nCzzd3TSXR46k8PHHqcgCFN87gf9xPPZYHo2Nlq+tzTD87mlqS7m63125YuDw4RSy2RJWrSrg229j\nuHzZwE9/eg+dneUCx7r5VGMl1u5gIWGOR9k2G6DzKZEob/uTz5eBwTVriq4Nt24fDItHVMAibKig\nXRggcfWqgffeS+PmzfJWvbDfY/CN2+S4ENfSYuHmTY+pJwd/n+oepf5WFGOI7623N450eq7s36uD\nY4LTpxOu2DPnP7rit3q9lTp/8vXpzvJKWtJ1c8yxndwbFhM3yjnt7KRnlck4rjHCQmPQSp4Xs6dy\nOZL+CNoXdOeo7A4AgA8/TGPjRtpromIZ9V6XLfNa0VIpx+c0+CCxdyXjIbjzAOP7CO4wBXFwsOjS\n6i5dourxyAgxSMLYNjKA1gmkLQYZjhrqC1eJ+C8zc2QVFAgOmHSjkr5h/t/SBUS3Cckgd2pKnzgs\nhGEjkfnp6Zivv1yd/6gNXa0E6oKDSpM8vk+qBpqor7+DNWvIjYRFnpubK5t/HpUIzqogY00NsRzU\n9gV1fTzIOpWVZJmYyUAuSMhXHUGAlSqaW+kImrOgqt+DvrNq0ktihBY+/zyB2VlyxXn1VRMtLSWM\njHiiw6bptdPEYhT4M7giweHGRgo0L12Ku+8Hr0t1fgwjWMhxZiaGDz9M4quvDHzzTRwjIwk4jqcL\nJYUnKxUSvnSJRNp7e+n7dULbQaAABxByPwsLeCQAyCCaDMSZVXLnTgyHDycxN1eFnTvz6OmxUFvr\n7WFhwKRcd3Lvi3oP5bMMa8Fhl6SpKQM//OE8duzI+8TVUylPH8K2PTZVS4vts9NVrykseFbfp7D7\n4mvV6fIEAf2S0RNUYVTfMblXqpozcsjWQf6dwUHLTZaDhuoeqQOcwsDMVMpBOk1FH14zEphR2VDJ\nJGDbDs6eJW0KFZDh35LaeWfPJrBnD7l1tbfb7jOamYlhyZISZmdjWLcumPnHRZGbN2OYmjKwbVsB\nGzaQnp7U8uno8LtcSgdFydbhdTQ9HXNbwFmknwGJJUts7NmTxocfemCE7lwhADOJsbFimc08P5/j\nx5M4cCCJqSlyuJTgjm1TIevNNzPo6rLdlgBeu8wukwmHbCmS+l9yn2hvt92Wr9lZ/z7gOKS/xawy\nqXl2/LjXNsxtuup3UKKVxerVd7FypeVLUiVbiQs8uZyDXbvS+POfTTzySB7r1xcD17TURKq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IV9h2rjztesExleaBeEOg+6eHkx96QrupMxDc3vxYtxGMbi5Ah081RpW5vU9FLZRzom/WLjeN17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\nHBR13pYssfJkBtL5493Lg0E3HzyorRVOLj8yhVIUshcxlcmoR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7ZtaUZGwvzmNwaf+cwk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DMi5eNMFiCaClRUJ8PHD5sjlYDE6LeBcUiC+R2SxSLgsLfSgrG1nMa3Trb7tdxb59Mt5/X2yl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\nAAAAAAX7f20b1E7a2abyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": "markdown",
      "metadata": {
        "id": "TYdF9Mh00i4a",
        "colab_type": "text"
      },
      "source": [
        "-- -- -- -- -- -- -- -- -- Предсказание -- -- -- -- -- -- -- -- --"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQ7txJwCTi6Z",
        "colab_type": "code",
        "outputId": "5432771f-c21a-4f67-befd-4793bd75015a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        }
      },
      "source": [
        "#Предсказание из 4х значений:\n",
        "  nn_0 = keras.models.load_model (DIR_SAVE + '0HANDLY_SAVED3best.MODEL')\n",
        "  nn_1 = keras.models.load_model (DIR_SAVE + '1HANDLY_SAVED3best.MODEL')\n",
        "  nn_2 = keras.models.load_model (DIR_SAVE + '2HANDLY_SAVED3best.MODEL')\n",
        "  nn_3 = keras.models.load_model (DIR_SAVE + '3HANDLY_SAVED3best.MODEL')\n",
        "\n",
        "  # score = model.evaluate(X, Y, verbose=0)\n",
        "  # print(\"%s: %.2f%%\" % (model.metrics_names[1], score[1]*100))\n",
        "  gc.collect()\n",
        "\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()\n",
        "\n",
        "  df['NN_ERR'] = np.abs(df['NN_PRED']-df['meter_reading'])\n",
        "  df['k_NN_ERR'] = (df['NN_PRED']+0.33)/(df['meter_reading']+0.33)\n",
        "\n",
        "  print( 'Ошибка общая: ', np.sqrt( np.mean( np.power(np.log(df['meter_reading']+1) - np.log(df['NN_PRED']+1),2.00000)))\n",
        "      , 'Ошибка (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 0 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 0 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 0 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 1 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 1 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 1 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 2 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==2) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==2) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 2 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 2 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 3 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==3) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==3) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 3 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 3 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+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_PRED']+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_PRED']+1),2.00000)))        )\n",
        "\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1702: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Ошибка общая:  1.2174089570459088 Ошибка (VAL):  1.2307203885453104 Ошибка (VAL_чист):  0.5406913735004337 Ошибка (VAL_чист2):  0.4416252597532206\n",
            "Ошибка 0:  1.0854703420517136 Ошибка 0 (VAL):  1.078260498883952 Ошибка 0 (VAL_чист):  0.3096183002113901 Ошибка 0 (VAL_чист2):  0.2856832716823828\n",
            "Ошибка 1:  1.3786985710716286 Ошибка 1 (VAL):  1.4146374427353003 Ошибка 1 (VAL_чист):  0.7336364189822585 Ошибка 1 (VAL_чист2):  0.6205787113997202\n",
            "Ошибка 2:  1.412089120404965 Ошибка 2 (VAL):  1.4307337011199783 Ошибка 2 (VAL_чист):  0.7681072077180416 Ошибка 2 (VAL_чист2):  0.5899942163118871\n",
            "Ошибка 3:  1.377944923928185 Ошибка 3 (VAL):  1.4566577965681673 Ошибка 3 (VAL_чист):  1.0939884892538 Ошибка 3 (VAL_чист2):  0.7449018059731893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ON0qwgtpSld6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Ошибки:\n",
        "  Ошибка  :  1.2019422970227465 Ошибка (VAL):   1.2157846256813984 Ошибка (VAL_чист):   0.5428777256494879 Ошибка (VAL_чист2)  :  0.4439034380986012\n",
        "  Ошибка 0:  1.0561841191435806 Ошибка 0 (VAL): 1.0494709359899506 Ошибка 0 (VAL_чист): 0.3156408787681845 Ошибка 0 (VAL_чист2):  0.2912135296204619\n",
        "  Ошибка 1:  1.3786985710716286 Ошибка 1 (VAL): 1.4146374427353003 Ошибка 1 (VAL_чист): 0.7336364189822585 Ошибка 1 (VAL_чист2):  0.6205787113997202\n",
        "  Ошибка 2:  1.412089120404965 Ошибка 2 (VAL):  1.4307337011199783 Ошибка 2 (VAL_чист): 0.7681072077180416 Ошибка 2 (VAL_чист2):  0.5899942163118871\n",
        "  Ошибка 3:  1.377944923928185 Ошибка 3 (VAL):  1.4566577965681673 Ошибка 3 (VAL_чист): 1.0939884892538    Ошибка 3 (VAL_чист2):  0.7449018059731893\n",
        "  0\n",
        "  Общая:     1.2940425445438188 Ошибка (VAL):   1.3035619563953205 Ошибка (VAL_чист):   0.6154425269032927 Ошибка (VAL_чист2):    0.5166720488706185\n",
        "  Ошибка 0:  1.1108272200417615 Ошибка 0 (VAL): 1.1039979542354519 Ошибка 0 (VAL_чист): 0.4199684887318391 Ошибка 0 (VAL_чист2):  0.40071672645920714\n",
        "  Ошибка 1:  1.4674246744239359 Ошибка 1 (VAL): 1.4998371692364791 Ошибка 1 (VAL_чист): 0.7936248109822508 Ошибка 1 (VAL_чист2):  0.6763456453516548\n",
        "  Ошибка 2:  1.584895359471045 Ошибка 2 (VAL):  1.595279682390636 Ошибка 2 (VAL_чист):  0.8156651059087789 Ошибка 2 (VAL_чист2):  0.6075341541971445\n",
        "  Ошибка 3:  1.5811641937262615 Ошибка 3 (VAL):  1.6275882770258792 Ошибка 3 (VAL_чист):1.1791615642131834 Ошибка 3 (VAL_чист2):  0.8287557439810707\n",
        "  0\n",
        "  Общая  :   1.2383488513622485 Ошибка (VAL):   1.2497646476956246 Ошибка (VAL_чист):    0.5613541671314798 Ошибка (VAL_чист2):    0.4601055341524471\n",
        "  Ошибка 0:  1.089517078219136 Ошибка 0 (VAL):  1.0830665788767133 Ошибка 0 (VAL_чист):  0.32307141312090654 Ошибка 0 (VAL_чист2): 0.2984998363948374\n",
        "  Ошибка 1:  1.4093559595246432 Ошибка 1 (VAL): 1.4441371298998904 Ошибка 1 (VAL_чист):  0.7674735069465627 Ошибка 1 (VAL_чист2):  0.6515384132612695\n",
        "  Ошибка 2:  1.434270799339943 Ошибка 2 (VAL):  1.4459949706379165 Ошибка 2 (VAL_чист):  0.7751574071879366 Ошибка 2 (VAL_чист2):  0.59349123776128\n",
        "  Ошибка 3:  1.4481746111510552 Ошибка 3 (VAL): 1.5064615354375264 Ошибка 3 (VAL_чист):  1.0977130741767314 Ошибка 3 (VAL_чист2):  0.7436043422326556\n",
        "  0\n",
        "  Общая:     1.288451580726024 Ошибка (VAL):     1.2967080663610595 Ошибка (VAL_чист):    0.5559658155345275 Ошибка (VAL_чист2):   0.45027898146764\n",
        "  Ошибка 0:  1.1135345769523262 Ошибка 0 (VAL):  1.1073602983520954 Ошибка 0 (VAL_чист):  0.30211474219652695 Ошибка 0 (VAL_чист2):0.2760929212947043\n",
        "  Ошибка 1:  1.4447116762681158 Ошибка 1 (VAL):  1.4773486254929924 Ошибка 1 (VAL_чист):  0.778543934083951 Ошибка 1 (VAL_чист2):  0.6485716599103746\n",
        "  Ошибка 2:  1.4827505038656164 Ошибка 2 (VAL):  1.4859210115055426 Ошибка 2 (VAL_чист):  0.776992332783179 Ошибка 2 (VAL_чист2):  0.5982294414393752\n",
        "  Ошибка 3:  1.6852442156559078 Ошибка 3 (VAL):  1.7187446259260037 Ошибка 3 (VAL_чист):  1.0748931586686294 Ошибка 3 (VAL_чист2): 0.726536756006331\n",
        "  0\n",
        "  Общая:     1.3473874674637107 Ошибка (VAL):    1.3483415928656848 Ошибка (VAL_чист):    0.5756767405084868 Ошибка (VAL_чист2):   0.4623615327006715\n",
        "  Ошибка 0:  1.097150680567493 Ошибка 0 (VAL):   1.0891511641164369 Ошибка 0 (VAL_чист):  0.33891800740903766 Ошибка 0 (VAL_чист2):0.3149225188104313\n",
        "  Ошибка 1:  1.5319581086991891 Ошибка 1 (VAL):  1.5591904765901698 Ошибка 1 (VAL_чист):  0.7038461305727779 Ошибка 1 (VAL_чист2): 0.5850605730802025\n",
        "  Ошибка 2:  1.6638633608261502 Ошибка 2 (VAL):  1.6575565750506658 Ошибка 2 (VAL_чист):  0.9184081840472923 Ошибка 2 (VAL_чист2): 0.6847327356541326\n",
        "  Ошибка 3:  1.9625404814033556 Ошибка 3 (VAL):  1.955545484766667 Ошибка 3 (VAL_чист):   1.1170540079900018 Ошибка 3 (VAL_чист2): 0.7521327934769413"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxFd8mjwt7KQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#тмп\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",
        "  df['k_NN_ERR'] = (df['NN_PRED']+0.33)/(df['meter_reading']+0.33)\n",
        "  print(len(df), len(df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50) & (df['IS_BAD_PRCNT']<0.25)]))\n",
        "  print(df[(df['hour']==14) & (df['meter']==0) & (df['building_id']==1) & (df['IS_BAD_PRCNT']<0.25)][['timestamp','hour','weekday','meter_reading','NN_PRED','NN_ERR','k_NN_ERR']].sort_values('timestamp').head(400))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ti8V0jrzZ1C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50)  & (df['IS_BAD_PRCNT']<0.25)].describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Uz_DyKXziWJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print( np.sqrt( np.mean( np.power(np.log(df['meter_reading']+1) - np.log(df['NN_PRED']+1),2.00000)))     )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EBMy61yg06BW",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "**-- -- -- -- -- -- -- -- --РАСЧЁТ НЕЙРОНОК после очистки -- -- -- -- -- -- -- -- --**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Q9aG2Cg_01Ci",
        "colab": {}
      },
      "source": [
        "#0: electricity, 1: chilledwater, 2: steam, 3: hotwater\n",
        "# РАСЧЁТ НЕЙРОНОК:\n",
        "  reg = 0.00001\n",
        "  batch_size = 2048\n",
        "  epochs = 30\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 = 0.1)\n",
        "  \n",
        "  if 0==0:\n",
        "    meter = 0\n",
        "    opt = Adam(lr = 0.001)\n",
        "\n",
        "    nn_0 = Sequential()\n",
        "    nn_0.add(Dense(100, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_initializer = 'normal', kernel_regularizer=l2(reg), bias_initializer=bias))\n",
        "    nn_0.add(Dense(100, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "    nn_0.add(Dense(100, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias, kernel_regularizer=l1(reg)))\n",
        "    nn_0.add(Dense(100, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "    nn_0.add(Dense(1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "    nn_0.compile(optimizer = opt, loss = MALE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "    \n",
        "    val = df[(df['meter'] == meter) & (df['day']%6 == 0) ]# & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100 & (df['IS_BAD_PRCNT'] < 0.25)]\n",
        "    #val_2 = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100) & (df['IS_BAD_PRCNT'] < 0.25)]\n",
        "    df_cleared = df[(df['day']%6 != 0) & (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['meter'] == meter) & (df['IS_BAD_PRCNT'] < 0.45)][In_Columns + [Out_Columns]]\n",
        "\n",
        "    hist = nn_0.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                    , epochs = epochs, shuffle = True, callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                    , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                    ) \n",
        "    nn_0.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "    gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "f9h2QsKV01Cn",
        "colab": {}
      },
      "source": [
        "for lr in [0.001]:\n",
        "  opt = Adam(lr = lr)\n",
        "  epochs = 20\n",
        "  batch_size = 2048\n",
        "  meter = 0\n",
        "  nn_0 = keras.models.load_model (DIR + '0HANDLY_SAVED_3best.MODEL')\n",
        "  val = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100) & (df['IS_BAD_PRCNT'] < 0.25)]\n",
        "  df_cleared = df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50) & (df['day']%6 != 0) & (df['meter']==meter) & (df['IS_BAD_PRCNT']<0.25)][In_Columns + [Out_Columns]]\n",
        "  print('*'*9, lr, '*'*33)\n",
        "  nn_0.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  hist = nn_0.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[ MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_0.save(DIR + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZExPUYLIfQy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#История ошибок:\n",
        "    # 0 - 0.001 |  TST_loss: 0.4634 TST_MAE_: 0.2970 TST_RMSLE: 0.7195 TST_VAL_: 0.9428 TST_mean_squared_error: 95502.1200 TST_MAPE_: 0.5170 ///  TRN_loss: 0.2670 TRN_MAE_: 0.3262 TRN_RMSLE: 0.4111 TRN_VAL_: 0.7641 TRN_mean_squared_error: 60868.2692 TRN_MAPE_: 0.2894\n",
        "    # Ошибка 0:  1.090040142661055  Ошибка 0 (VAL):  1.0821874168451948 Ошибка 0 (VAL_чист):  0.4200194527624282 Ошибка 0 (VAL_чист):  0.3982010565789388\n",
        "    #-Ошибка 0:  1.0561841191435806 Ошибка 0 (VAL):  1.0494709359899506 Ошибка 0 (VAL_чист):  0.3156408787681845 Ошибка 0 (VAL_чист):  0.2912135296204619\n",
        "\n",
        "    # 1 - \n",
        "    #Ошибка 1:  1.432671362118033 Ошибка 1 (VAL):  1.4735626469840335 Ошибка 1 (VAL_чист):  0.6584706237724794 Ошибка 1 (VAL_чист):  0.521774231818768\n",
        "    #Ошибка 1:  1.434113723728525 Ошибка 1 (VAL):  1.4742295518711512 Ошибка 1 (VAL_чист):  0.665647741234266  Ошибка 1 (VAL_чист):  0.5298978788312768\n",
        "      #Ошибка 1:  1.431368815234858  Ошибка 1 (VAL):  1.4686131222512504 Ошибка 1 (VAL_чист):  0.6759214072760591 Ошибка 1 (VAL_чист):  0.5449250862446658\n",
        "      #Ошибка 1:  1.4691654987946658 Ошибка 1 (VAL):  1.5049969920282047 Ошибка 1 (VAL_чист):  0.6922470701866289 Ошибка 1 (VAL_чист):  0.5644578193999968\n",
        "      #Ошибка 1:  1.485687076453129  Ошибка 1 (VAL):  1.5214040444045644 Ошибка 1 (VAL_чист):  0.6952623800744953 Ошибка 1 (VAL_чист):  0.5632174649208512\n",
        "      #Ошибка 1:  1.4856709523394191 Ошибка 1 (VAL):  1.5205201826214512 Ошибка 1 (VAL_чист):  0.7050914102031174 Ошибка 1 (VAL_чист):  0.5759439648595509\n",
        "      #Ошибка 1:  1.4859038202056085 Ошибка 1 (VAL):  1.519788443345392 Ошибка 1 (VAL_чист):  0.7140834358696859 Ошибка 1 (VAL_чист):  0.5869849079847015\n",
        "    #-Ошибка 1:  1.3667812429174853 Ошибка 1 (VAL):  1.4029945499659757 Ошибка 1 (VAL_чист):  0.7281895570502563 Ошибка 1 (VAL_чист):  0.6149901419154421\n",
        "\n",
        "    # 2 - \n",
        "    #Ошибка 2:  1.468362770102303  Ошибка 2 (VAL):  1.4851731624800186 Ошибка 2 (VAL_чист):  0.8590320764392491 Ошибка 2 (VAL_чист):  0.6062495965271513\n",
        "      #Ошибка 2:  1.4611062782548563 Ошибка 2 (VAL):  1.4752267752422543 Ошибка 2 (VAL_чист):  0.8709034145221702 Ошибка 2 (VAL_чист):  0.6277950102050106\n",
        "      #Ошибка 2:  1.5130303989486868 Ошибка 2 (VAL):  1.5254828603596124 Ошибка 2 (VAL_чист):  0.8949163247727348 Ошибка 2 (VAL_чист):  0.6479195418249479\n",
        "    #-Ошибка 2:  1.412089120404965 Ошибка 2 (VAL):  1.4307337011199783 Ошибка 2 (VAL_чист):  0.7681072077180416 Ошибка 2 (VAL_чист):  0.5899942163118871\n",
        "\n",
        "    #3 - \n",
        "    #Ошибка 3:  1.42819615426734 Ошибка 3 (VAL):  1.512136946020539  Ошибка 3 (VAL_чист):  1.191953830076372  Ошибка 3 (VAL_чист):  0.7587751529627325\n",
        "    #Ошибка 3:  1.42971650236124 Ошибка 3 (VAL):  1.5122676972866884 Ошибка 3 (VAL_чист):  1.1904067684130355 Ошибка 3 (VAL_чист):  0.7592933381085122\n",
        "      #Ошибка 3:  1.418658013012662  Ошибка 3 (VAL):  1.489236064645708  Ошибка 3 (VAL_чист):  1.196464556606421 Ошибка 3 (VAL_чист):  0.7835597310779343\n",
        "      #Ошибка 3:  1.4462524866377109 Ошибка 3 (VAL):  1.5214038274048738 Ошибка 3 (VAL_чист):  1.192125464837195 Ошибка 3 (VAL_чист):  0.7625560621553749\n",
        "      #Ошибка 3:  1.4495662773326137 Ошибка 3 (VAL):  1.5192560445361858 Ошибка 3 (VAL_чист):  1.196845847794619 Ошибка 3 (VAL_чист):  0.7829511528840523\n",
        "    #-Ошибка 3:  1.377944923928185  Ошибка 3 (VAL):  1.4566577965681673 Ошибка 3 (VAL_чист):  1.0939884892538    Ошибка 3 (VAL_чист):  0.7449018059731893\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SssjG5U44j38",
        "colab_type": "code",
        "outputId": "35799417-0633-4b74-bbb6-ad0edaaecad7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#Предсказание из 4х значений = 0:\n",
        "  meter = 3\n",
        "  nn_tmp = keras.models.load_model (DIR_SAVE + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  \n",
        "  gc.collect()\n",
        "  col_name_tmp = 'NN_PRED_'+str(meter) \n",
        "  df['NN_PRED'] = 0.0000\n",
        "  bad_pcnt = 0.45\n",
        "  k_err = 50\n",
        "  df[col_name_tmp] = nn_tmp.predict(df[In_Columns], batch_size = 400000) \n",
        "  df['NN_PRED'] = np.where(df['meter'] == meter, df[col_name_tmp], df['NN_PRED'])\n",
        "  df.drop(columns = [col_name_tmp], inplace = True) #, 'NN_PRED_1', 'NN_PRED_2', 'NN_PRED_3'\n",
        "  #df.drop(columns = ['Value_x', 'Value_y'])\n",
        "  gc.collect()\n",
        "\n",
        "  df['NN_ERR'] = np.abs(df['NN_PRED']-df['meter_reading'])\n",
        "  #df['k_NN_ERR'] = (df['NN_PRED']+0.33)/(df['meter_reading']+0.33)\n",
        "\n",
        "  #print( np.sqrt( np.mean( np.power(np.log(df['meter_reading']+1) - np.log(df['NN_PRED']+1),2.00000)))     )\n",
        "  print('Ошибка ' + str(meter) + ': '  , np.sqrt( np.mean( np.power(np.log(df[df['meter']==meter]['meter_reading']+1) - np.log(df[df['meter']==meter]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка ' + str(meter) + ' (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==meter) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==meter) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка ' + str(meter) + ' (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['IS_BAD_PRCNT'] < bad_pcnt) & (df['meter']==meter) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < bad_pcnt) & (df['meter']==meter) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка ' + str(meter) + ' (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < k_err) & (df['k_NN_ERR'] > 1/k_err) & (df['IS_BAD_PRCNT'] < bad_pcnt) & (df['meter']==meter) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < k_err) & (df['k_NN_ERR'] > 1/k_err) & (df['IS_BAD_PRCNT'] < bad_pcnt) & (df['meter']==meter) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        )\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ошибка 3:  1.8326811620613366 Ошибка 3 (VAL):  1.8223275268585466 Ошибка 3 (VAL_чист):  1.3900923334944844 Ошибка 3 (VAL_чист):  1.0931338321657924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAcxnR-6GGQp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nn_tmp.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED_3best(MALE).MODEL')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XhZvGSgQm9T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Дообучение!\n",
        "  meter = 3\n",
        "  nn_tmp = keras.models.load_model (DIR_SAVE + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  \n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.5, patience=10, verbose=1, min_delta=1e-4, 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",
        "  bad_pcnt = 0.87\n",
        "  k_err = 32\n",
        "  epochs = 20\n",
        "  batch_size = 2048\n",
        "\n",
        "  val = df[ ( df['meter'] == meter ) & ( df['day']%6 == 0 ) & ( df['IS_BAD_PRCNT'] < bad_pcnt ) ]\n",
        "  tar = df[ ( df['meter'] == meter ) & ( df['day']%6 != 0 ) & ( df['IS_BAD_PRCNT'] < bad_pcnt ) & (df['k_NN_ERR'] < k_err) & (df['k_NN_ERR'] > 1/k_err) & (df['meter'] == meter) ]\n",
        "\n",
        "  print('Длина трейн ',len(tar), 'длина валидации ',len(val), 'сфильтровано:'\n",
        "  , len(df[ ( df['meter'] == meter ) & ( df['day']%6 != 0 ) & ( df['IS_BAD_PRCNT'] < bad_pcnt ) & (df['k_NN_ERR'] > k_err)  ]) + len(df[ ( df['meter'] == meter ) & ( df['day']%6 != 0 ) & ( df['IS_BAD_PRCNT'] < bad_pcnt ) & (df['k_NN_ERR'] < 1/k_err)  ]))  \n",
        "  \n",
        "  for lr in [0.0003]: #,0.001]:\n",
        "    for los in [ mse, MAE_, RMSLE ]: # mse, MALE, MSLE, RMSLE ]: # mse, MALE, RMSLE, MAE_, RMSLE ]:\n",
        "      print(los, lr)\n",
        "      opt = Adam(lr = lr)\n",
        "\n",
        "      print('*'*9, lr, '*'*33)\n",
        "      nn_tmp.compile(optimizer = opt, loss = los, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "      hist = nn_tmp.fit( tar[In_Columns], tar[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                      , epochs = epochs, shuffle = True, callbacks=[ MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                      , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                    )\n",
        "      \n",
        "      nn_tmp.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  del val, tar\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NyBVLwrQvDf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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_PRED']+1),2.00000)))        \n",
        "  , 'Ошибка 0 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  , 'Ошибка 0 (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        )\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2V4xV9ON01Co",
        "colab": {}
      },
      "source": [
        "if 1==1:\n",
        "  reg = 0.00001\n",
        "  meter = 1\n",
        "  epochs = 190\n",
        "  batch_size = 1024\n",
        "  opt = Adam(lr = 0.001)\n",
        "  bias = Constant(value = 0.1)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=15, 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(100, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_initializer = 'normal', kernel_regularizer=l2(reg), bias_initializer=bias))\n",
        "  nn_1.add(Dense(100, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(100, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias, kernel_regularizer=l1(reg)))\n",
        "  nn_1.add(Dense(100, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias))\n",
        "  nn_1.add(Dense(1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "  nn_1.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  \n",
        "  val = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100) & (df['IS_BAD_PRCNT'] < 0.25)]\n",
        "  df_cleared = df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50) & (df['day']%6 != 0) & (df['meter']==meter) & (df['IS_BAD_PRCNT']<0.25)][In_Columns + [Out_Columns]]\n",
        "\n",
        "  hist = nn_1.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_1.save(DIR + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qy1YnwWo01Cq",
        "colab": {}
      },
      "source": [
        "for lr in [0.007]:\n",
        "  opt = Adam(lr = lr)\n",
        "  epochs = 70\n",
        "  meter = 1\n",
        "  batch_size = 720\n",
        "\n",
        "  val = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100) & (df['IS_BAD_PRCNT'] < 0.45)]\n",
        "  df_cleared = df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50) & (df['day']%6 != 0) & (df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][In_Columns + [Out_Columns]]\n",
        "\n",
        "  print('*'*9, lr, '*'*33)\n",
        "  nn_1.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  hist = nn_1.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[ MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_1.save(DIR + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfcTV2-g1JNG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Предсказание из 4х значений = 0:\n",
        "  #nn_0 = keras.models.load_model (DIR + '0HANDLY_SAVED_3.MODEL')\n",
        "  nn_1 = keras.models.load_model (DIR + '1HANDLY_SAVED_3.MODEL')\n",
        "  #nn_2 = keras.models.load_model (DIR + '2HANDLY_SAVED_3.MODEL')\n",
        "  #nn_3 = keras.models.load_model (DIR + '3HANDLY_SAVED_3.MODEL')\n",
        "\n",
        "  # score = model.evaluate(X, Y, verbose=0)\n",
        "  # print(\"%s: %.2f%%\" % (model.metrics_names[1], score[1]*100))\n",
        "  gc.collect()\n",
        "\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'], inplace = True) #, 'NN_PRED_1', 'NN_PRED_2', 'NN_PRED_3'\n",
        "  #df.drop(columns = ['Value_x', 'Value_y'])\n",
        "  gc.collect()\n",
        "\n",
        "  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_PRED']+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_PRED']+1),2.00000)))        \n",
        "  # , 'Ошибка 0 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  # , 'Ошибка 0 (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  # , 'Ошибка 0 (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        )\n",
        "  \n",
        "  print('Ошибка 0: ', np.sqrt( np.mean( np.power(np.log(df[df['meter']==1]['meter_reading']+1) - np.log(df[df['meter']==1]['NN_PRED']+1),2.00000)))        \n",
        "  , 'Ошибка 0 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  , 'Ошибка 0 (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  , 'Ошибка 0 (VAL_чист): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        )\n",
        "  \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_PRED']+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_PRED']+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_PRED']+1),2.00000)))        )\n",
        "  #df['k_NN_ERR'] = (df['NN_PRED']+0.33)/(df['meter_reading']+0.33)\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW6m54J1Ixjn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Ошибка 0:  1.4622222874555681 Ошибка 0 (VAL):  1.4974377063554198 Ошибка 0 (VAL_чист):  0.6416758062352609 Ошибка 0 (VAL_чист):  0.5087780382284447\n",
        "Ошибка 0:  1.4513305175601738 Ошибка 0 (VAL):  1.4857607748364132 Ошибка 0 (VAL_чист):  0.6544772099268412 Ошибка 0 (VAL_чист):  0.5241878970208895\n",
        "Ошибка 0:  1.559959814457174 Ошибка 0 (VAL):  1.5880652782130562 Ошибка 0 (VAL_чист):  0.6931055197830647 Ошибка 0 (VAL_чист):  0.5696662087687516\n",
        "Ошибка 1:  1.5319581160458586 Ошибка 1 (VAL):  1.5591904792676097 Ошибка 1 (VAL_чист):  0.7038461338617784 Ошибка 1 (VAL_чист2):  0.5850605758832433"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ur0NQSVo01Cs",
        "colab": {}
      },
      "source": [
        "if 2==2:\n",
        "  reg = 0.00002\n",
        "  meter = 2\n",
        "  epochs = 30\n",
        "  batch_size = 2400\n",
        "  opt = Adam(lr = 0.003)\n",
        "  bias = Constant(value = 5.7)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=30, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=12, 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(100, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_initializer = 'uniform', kernel_regularizer=l2(reg), bias_initializer=bias))\n",
        "  nn_2.add(Dense(100, activation = 'tanh',  kernel_initializer = 'uniform', bias_initializer=bias))\n",
        "  nn_2.add(Dense(100, activation = 'tanh',  kernel_initializer = 'uniform', bias_initializer=bias, kernel_regularizer=l1(reg)))\n",
        "  nn_2.add(Dense(100, activation = 'tanh',  kernel_initializer = 'uniform', bias_initializer=bias))\n",
        "  nn_2.add(Dense( 1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "  nn_2.compile(optimizer = opt, loss = MAE_RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100) & (df['IS_BAD_PRCNT'] < 0.75)]\n",
        "  df_cleared = df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50) & (df['meter']==meter) & (df['IS_BAD_PRCNT']<0.75)][In_Columns + [Out_Columns]]\n",
        "  print (len(df_cleared), len(val))\n",
        "\n",
        "  hist = nn_2.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss,model_checkpoint]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_2.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBMyJFG7Ugac",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for lr in [0.03]:\n",
        "  nn_2 = keras.models.load_model (DIR + '2HANDLY_SAVED_3best.MODEL')\n",
        "  opt = Adam(lr = lr)\n",
        "  epochs = 70\n",
        "  batch_size = 700\n",
        "  meter = 2\n",
        "  bias = Constant(value = 0.1)\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=100, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=15, 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",
        "  val = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100) & (df['IS_BAD_PRCNT'] < 0.45)]\n",
        "  df_cleared = df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50) & (df['day']%6 != 0) & (df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][In_Columns + [Out_Columns]]\n",
        "  print('*'*9, lr, '*'*33)\n",
        "  nn_0.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  hist = nn_2.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[ MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_2.save(DIR + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DQZRFeEH01Cw",
        "outputId": "09344292-e574-4acb-a902-c765c0a76420",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "if 3==3:\n",
        "  meter = 3\n",
        "  epochs = 3\n",
        "  batch_size = 6400\n",
        "  opt = Adam(lr = 0.001)\n",
        "  reg = 0.00000001\n",
        "  bias = Constant(value = 5.7)\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=2e-4, 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(300, input_shape = df[In_Columns].shape[1:], activation = 'tanh', kernel_initializer = 'normal', kernel_regularizer=l1_l2(reg,reg), bias_initializer=bias))\n",
        "  nn_3.add(Dense(350, activation = 'tanh',  kernel_initializer = 'normal', kernel_regularizer=l1_l2(reg,reg), bias_initializer=bias))\n",
        "  nn_3.add(Dropout(0.000001, noise_shape=None, seed=None) )\n",
        "  nn_3.add(Dense(350, activation = 'tanh',  kernel_initializer = 'normal', kernel_regularizer=l1_l2(reg,reg), bias_initializer=bias))\n",
        "  nn_3.add(Dense(350, activation = 'tanh',  kernel_initializer = 'normal', kernel_regularizer=l1_l2(reg,reg), bias_initializer=bias))\n",
        "  nn_3.add(Dense(360, activation = 'tanh',  kernel_initializer = 'normal', bias_initializer=bias, kernel_regularizer=l1(reg)))\n",
        "  nn_3.add(Dense(1, activation = 'relu', kernel_initializer = 'normal'))\n",
        "\n",
        "  nn_3.compile(optimizer = opt, loss = mse, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  \n",
        "  val = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.75)]\n",
        "  df_cleared = df[(df['k_NN_ERR']<50) & (df['k_NN_ERR']>1/50)  & (df['day']%6!=0) & (df['meter']==meter) & (df['IS_BAD_PRCNT']<0.75)][In_Columns + [Out_Columns]]\n",
        "\n",
        "  hist = nn_3.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_3.save(DIR_SAVE + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# 0001 |  TST_loss: 8484542.9442 TST_MAE_: 0.9330 TST_RMSLE: 2.1452 TST_VAL_: 0.4088 TST_mean_squared_error: 8484542.9442 TST_MAPE_: 1.5700 ///  TRN_loss: 8284613.8233 TRN_MAE_: 0.9701 TRN_RMSLE: 2.5801 TRN_VAL_: 0.0537 TRN_mean_squared_error: 8284613.8233 TRN_MAPE_: 1.4861\n",
            "# 0002 |  TST_loss: 8448871.2414 TST_MAE_: 1.0609 TST_RMSLE: 2.1194 TST_VAL_: 0.7402 TST_mean_squared_error: 8448871.2414 TST_MAPE_: 1.7832 ///  TRN_loss: 8244989.5977 TRN_MAE_: 0.9457 TRN_RMSLE: 2.2151 TRN_VAL_: 0.1371 TRN_mean_squared_error: 8244989.5977 TRN_MAPE_: 1.7155\n",
            "# 0003 |  TST_loss: 8417879.9826 TST_MAE_: 1.2403 TST_RMSLE: 2.1705 TST_VAL_: 1.0564 TST_mean_squared_error: 8417879.9826 TST_MAPE_: 1.9729 ///  TRN_loss: 8211290.0781 TRN_MAE_: 0.9410 TRN_RMSLE: 2.2376 TRN_VAL_: 0.2134 TRN_mean_squared_error: 8211290.0781 TRN_MAPE_: 1.9087\n",
            ".......\n",
            "# 0003 |  TST_loss: 8417879.9826 TST_MAE_: 1.2403 TST_RMSLE: 2.1705 TST_VAL_: 1.0564 TST_mean_squared_error: 8417879.9826 TST_MAPE_: 1.9729 ///  TRN_loss: 8211290.0781 TRN_MAE_: 0.9410 TRN_RMSLE: 2.2376 TRN_VAL_: 0.2134 TRN_mean_squared_error: 8211290.0781 TRN_MAPE_: 1.9087\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_Ihng3E901Cy",
        "colab": {}
      },
      "source": [
        "for lr in [0.007]:\n",
        "  opt = Adam(lr = lr)\n",
        "  epochs = 200\n",
        "  batch_size = 700\n",
        "  meter = 3\n",
        "  nn_3 = keras.models.load_model (DIR + str(meter) + 'HANDLY_SAVED_3best.MODEL')\n",
        "\n",
        "  earlyStopping = EarlyStopping(monitor='loss', patience=50, verbose=1, mode='min')\n",
        "  reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=12, 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",
        "  val = df[(df['meter'] == meter) & (df['day']%6 == 0) & (df['k_NN_ERR'] < 100) & (df['k_NN_ERR'] > 1/100) & (df['IS_BAD_PRCNT'] < 0.45)]\n",
        "  df_cleared = df[(df['k_NN_ERR']<50) & (df['day']%6 != 0) & (df['k_NN_ERR']>1/50) & (df['meter']==meter) & (df['IS_BAD_PRCNT']<0.45)][In_Columns + [Out_Columns]]\n",
        "  print('*'*9, lr, '*'*33)\n",
        "  nn_3.compile(optimizer = opt, loss = RMSLE, metrics=[ MAE_, RMSLE, VAL_, 'mse', MAPE_ ])\n",
        "  hist = nn_3.fit( df_cleared[In_Columns], df_cleared[Out_Columns], batch_size = batch_size, verbose = 0\n",
        "                  , epochs = epochs, shuffle = True, callbacks=[ MyCustomCallback(epochs, 1), earlyStopping, reduce_lr_loss]\n",
        "                  , validation_data=(val[In_Columns], val[Out_Columns])\n",
        "                  ) \n",
        "  nn_3.save(DIR + str(meter) + 'HANDLY_SAVED_3.MODEL')\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Q-4iZ-RLtI2v",
        "colab": {}
      },
      "source": [
        "#nn_0.save(DIR + '0HANDLY_SAVED_3best.MODEL')\n",
        "  # nn_1.save(DIR + '1HANDLY_SAVED_3best.MODEL')\n",
        "  # nn_2.save(DIR + '2HANDLY_SAVED_3best.MODEL')\n",
        "  # nn_3.save(DIR + '3HANDLY_SAVED_3best.MODEL')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_knQD6Zw0yFy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Предсказание из 4х значений:\n",
        "  nn_0 = keras.models.load_model (DIR_SAVE + '0HANDLY_SAVED_3best.MODEL')\n",
        "  nn_1 = keras.models.load_model (DIR_SAVE + '1HANDLY_SAVED_3best.MODEL')\n",
        "  nn_2 = keras.models.load_model (DIR_SAVE + '2HANDLY_SAVED_3best.MODEL')\n",
        "  nn_3 = keras.models.load_model (DIR_SAVE + '3HANDLY_SAVED_3best.MODEL')\n",
        "\n",
        "  # score = model.evaluate(X, Y, verbose=0)\n",
        "  # print(\"%s: %.2f%%\" % (model.metrics_names[1], score[1]*100))\n",
        "  gc.collect()\n",
        "\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()\n",
        "\n",
        "  df['NN_ERR'] = np.abs(df['NN_PRED']-df['meter_reading'])\n",
        "  df['k_NN_ERR'] = (df['NN_PRED']+0.33)/(df['meter_reading']+0.33)\n",
        "\n",
        "  print( 'Ошибка общая: ', np.sqrt( np.mean( np.power(np.log(df['meter_reading']+1) - np.log(df['NN_PRED']+1),2.00000)))\n",
        "      , 'Ошибка (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 0 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 0 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 0 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==0) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 1 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 1 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 1 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==1) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 2 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==2) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==2) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 2 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 2 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==2) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 3 (VAL): ', np.sqrt( np.mean( np.power(np.log(df[(df['meter']==3) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[(df['meter']==3) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 3 (VAL_чист): ' , np.sqrt( np.mean( np.power(np.log(df[  (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "      , 'Ошибка 3 (VAL_чист2): ', np.sqrt( np.mean( np.power(np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['meter_reading']+1) - np.log(df[ (df['k_NN_ERR'] < 50) & (df['k_NN_ERR'] > 1/50) & (df['IS_BAD_PRCNT'] < 0.25) & (df['meter']==3) & (df['day']%6 == 0)]['NN_PRED']+1),2.00000)))        \n",
        "  )\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_PRED']+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_PRED']+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_PRED']+1),2.00000)))        )\n",
        "\n",
        "  gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWGPRQypQAuh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Ошибки:\n",
        "  Ошибка общая:  1.3332699709812266 Ошибка (VAL):  1.337540194067479 Ошибка (VAL_чист):  0.5418592287241801 Ошибка (VAL_чист2):  0.40073096858305257\n",
        "  Ошибка 0:  1.098206407152078 Ошибка 0 (VAL):  1.0905813857988798 Ошибка 0 (VAL_чист):  0.28240200416498673 Ошибка 0 (VAL_чист2):  0.2562246375809472\n",
        "  Ошибка 1:  1.4462519064009887 Ошибка 1 (VAL):  1.4819992377016542 Ошибка 1 (VAL_чист):  0.628029160200588 Ошибка 1 (VAL_чист2):  0.47946068818401355\n",
        "  Ошибка 2:  1.6966445281141582 Ошибка 2 (VAL):  1.6978240187666451 Ошибка 2 (VAL_чист):  0.926254557141954 Ошибка 2 (VAL_чист2):  0.6472006361307439\n",
        "  Ошибка 3:  1.9577298686058777 Ошибка 3 (VAL):  1.9537862377808086 Ошибка 3 (VAL_чист):  1.1455655044777464 Ошибка 3 (VAL_чист2):  0.6972188121146171\n",
        "\n",
        "  Ошибка общая:  1.3473873510158345 Ошибка (VAL):  1.3483414756663568 Ошибка (VAL_чист):  0.5756767186919248 Ошибка (VAL_чист2):  0.462361504102313\n",
        "  Ошибка 0:  1.0971504229404558 Ошибка 0 (VAL):  1.0891509074774033 Ошибка 0 (VAL_чист):  0.3389179667173395 Ошибка 0 (VAL_чист2):  0.3149224768996143\n",
        "  Ошибка 1:  1.5319581160458586 Ошибка 1 (VAL):  1.5591904792676097 Ошибка 1 (VAL_чист):  0.7038461338617784 Ошибка 1 (VAL_чист2):  0.5850605758832433\n",
        "  Ошибка 2:  1.6638634087676607 Ошибка 2 (VAL):  1.6575566177087862 Ошибка 2 (VAL_чист):  0.918408148629441 Ошибка 2 (VAL_чист2):  0.6847326774214916\n",
        "  Ошибка 3:  1.9625404709477796 Ошибка 3 (VAL):  1.9555454713909437 Ошибка 3 (VAL_чист):  1.117054007408267 Ошибка 3 (VAL_чист2):  0.7521327942946583"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": "o8qHP3Icaw2D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Важность показателей 0\n",
        "  from sklearn.utils import shuffle\n",
        "  nn = keras.models.load_model (DIR_SAVE + '0HANDLY_SAVED3best.MODEL')\n",
        "  meter = 0\n",
        "  val = df[(df['meter']==meter) & (df['day']%6==0)  & (df['IS_BAD_PRCNT']<0.45)]\n",
        "  #Подставить нужную нейронку в зависимости от meter\n",
        "  #print(nn.metrics_names)\n",
        "  k = pd.DataFrame( [nn.evaluate(val[In_Columns], val[Out_Columns], batch_size = 800000, verbose = 0)],columns=nn.metrics_names)\n",
        "  k['name'] = '---INITIAL---'\n",
        "  tmp_out=k.copy()\n",
        "  for i in range(len(In_Columns)):\n",
        "      X_val_2 = val[In_Columns].copy()\n",
        "      s = shuffle(X_val_2.iloc[:,i])\n",
        "      s.index = X_val_2.index\n",
        "      X_val_2.iloc[:, i] = s\n",
        "      tmp = pd.DataFrame([nn.evaluate(X_val_2, val[Out_Columns], batch_size = 800000, verbose = 0)], columns = nn.metrics_names)-k[nn.metrics_names] \n",
        "      tmp['name'] = In_Columns[i]\n",
        "      tmp_out = pd.concat([tmp_out, tmp])\n",
        "      X_val_2 = None\n",
        "      gc.collect()\n",
        "  tmp_out.set_index('name', inplace = True)\n",
        "  pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
        "  print (tmp_out[nn.metrics_names].sort_values('RMSLE', ascending=False))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCDCxcqhKHO1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Важность показателей 1\n",
        "  from sklearn.utils import shuffle\n",
        "  nn = keras.models.load_model (DIR_SAVE + '1HANDLY_SAVED3.MODEL')\n",
        "  meter = 1\n",
        "  val = df[(df['meter']==meter) & (df['day']%6==0)  & (df['IS_BAD_PRCNT']<0.25)]\n",
        "  #Подставить нужную нейронку в зависимости от meter\n",
        "  #print(nn.metrics_names)\n",
        "  k = pd.DataFrame( [nn.evaluate(val[In_Columns], val[Out_Columns], batch_size = 800000, verbose = 0)],columns=nn.metrics_names)\n",
        "  k['name'] = '---INITIAL---'\n",
        "  tmp_out=k.copy()\n",
        "  for i in range(len(In_Columns)):\n",
        "      X_val_2 = val[In_Columns].copy()\n",
        "      s = shuffle(X_val_2.iloc[:,i])\n",
        "      s.index = X_val_2.index\n",
        "      X_val_2.iloc[:, i] = s\n",
        "      tmp = pd.DataFrame([nn.evaluate(X_val_2, val[Out_Columns], batch_size = 800000, verbose = 0)], columns = nn.metrics_names)-k[nn.metrics_names] \n",
        "      tmp['name'] = In_Columns[i]\n",
        "      tmp_out = pd.concat([tmp_out, tmp])\n",
        "      X_val_2 = None\n",
        "      gc.collect()\n",
        "  tmp_out.set_index('name', inplace = True)\n",
        "  pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
        "  print (tmp_out[nn.metrics_names].sort_values('RMSLE', ascending=False))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIfQd2t9KN04",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Важность показателей 2\n",
        "  from sklearn.utils import shuffle\n",
        "  nn = keras.models.load_model (DIR_SAVE + '2HANDLY_SAVED3.MODEL')\n",
        "  meter = 2\n",
        "  val = df[(df['meter']==meter) & (df['day']%6==0)  & (df['IS_BAD_PRCNT']<0.25)]\n",
        "  #Подставить нужную нейронку в зависимости от meter\n",
        "  #print(nn.metrics_names)\n",
        "  k = pd.DataFrame( [nn.evaluate(val[In_Columns], val[Out_Columns], batch_size = 800000, verbose = 0)],columns=nn.metrics_names)\n",
        "  k['name'] = '---INITIAL---'\n",
        "  tmp_out=k.copy()\n",
        "  for i in range(len(In_Columns)):\n",
        "      X_val_2 = val[In_Columns].copy()\n",
        "      s = shuffle(X_val_2.iloc[:,i])\n",
        "      s.index = X_val_2.index\n",
        "      X_val_2.iloc[:, i] = s\n",
        "      tmp = pd.DataFrame([nn.evaluate(X_val_2, val[Out_Columns], batch_size = 800000, verbose = 0)], columns = nn.metrics_names)-k[nn.metrics_names] \n",
        "      tmp['name'] = In_Columns[i]\n",
        "      tmp_out = pd.concat([tmp_out, tmp])\n",
        "      X_val_2 = None\n",
        "      gc.collect()\n",
        "  tmp_out.set_index('name', inplace = True)\n",
        "  pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
        "  print (tmp_out[nn.metrics_names].sort_values('RMSLE', ascending=False))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wxn_T9DhKRzX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Важность показателей 3\n",
        "  from sklearn.utils import shuffle\n",
        "  nn = keras.models.load_model (DIR_SAVE + '3HANDLY_SAVED03.MODEL')\n",
        "  meter = 3\n",
        "  val = df[(df['meter']==meter) & (df['day']%6==0)  & (df['IS_BAD_PRCNT']<0.25)]\n",
        "  #Подставить нужную нейронку в зависимости от meter\n",
        "  #print(nn.metrics_names)\n",
        "  k = pd.DataFrame( [nn.evaluate(val[In_Columns], val[Out_Columns], batch_size = 800000, verbose = 0)],columns=nn.metrics_names)\n",
        "  k['name'] = '---INITIAL---'\n",
        "  tmp_out=k.copy()\n",
        "  for i in range(len(In_Columns)):\n",
        "      X_val_2 = val[In_Columns].copy()\n",
        "      s = shuffle(X_val_2.iloc[:,i])\n",
        "      s.index = X_val_2.index\n",
        "      X_val_2.iloc[:, i] = s\n",
        "      tmp = pd.DataFrame([nn.evaluate(X_val_2, val[Out_Columns], batch_size = 800000, verbose = 0)], columns = nn.metrics_names)-k[nn.metrics_names] \n",
        "      tmp['name'] = In_Columns[i]\n",
        "      tmp_out = pd.concat([tmp_out, tmp])\n",
        "      X_val_2 = None\n",
        "      gc.collect()\n",
        "  tmp_out.set_index('name', inplace = True)\n",
        "  pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
        "  print (tmp_out[nn.metrics_names].sort_values('RMSLE', ascending=False))"
      ],
      "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) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLLVe_MMYIz-",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6AezgKyEBi6",
        "colab_type": "code",
        "outputId": "26eb9e7a-6504-4dac-b822-27b0e467fb7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#reduce_mem_usage(df_test)\n",
        "gc.collect()\n",
        "#df_test.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWDYNM3xOBZx",
        "colab_type": "code",
        "outputId": "cd7750df-9ded-4797-bccf-e5aaafc1de46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Забрали с диска\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "91"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "6d9925cb-d229-45fd-97fc-cc15d533037b",
        "id": "z2TmthILE1wp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# Добавление медиан:\n",
        "  # Добавление медианы по метрике постройки\n",
        "    df_median = df.groupby(by=['building_id','meter'])['building_meter_median'].median()\n",
        "    df_test = pd.merge(df_test, df_median, how = 'left', 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'])['building_meter_hour_median'].median()\n",
        "    df_test = pd.merge(df_test, df_median, how = 'left', on = ['building_id','hour','meter'])\n",
        "    #print(len(df_test))\n",
        "    del df_median #, df_mean, df_median2, df_mean2\n",
        "    gc.collect()\n",
        "    \n",
        "    df_test['weekday'] = df_test['timestamp'].dt.weekday\n",
        "    df_median = df.groupby(by=['building_id','weekday','meter'])['building_meter_weekday_median'].median()\n",
        "    #df_median.name = 'building_meter_weekday_median'\n",
        "    df_test = pd.merge(df_test, df_median, how = 'left', on = ['building_id','weekday','meter'])\n",
        "    del df_median \n",
        "    #print(len(df_test))\n",
        "\n",
        "    print('Построены медианы по сооружению/часу')\n",
        "    holidays = [\"2016-01-01\", \"2016-01-18\", \"2016-02-15\", \"2016-05-30\", \"2016-07-04\",\n",
        "                      \"2016-09-05\", \"2016-10-10\", \"2016-11-11\", \"2016-11-24\", \"2016-12-26\",\n",
        "                      \"2017-01-02\", \"2017-01-16\", \"2017-02-20\", \"2017-05-29\", \"2017-07-04\",\n",
        "                      \"2017-09-04\", \"2017-10-09\", \"2017-11-10\", \"2017-11-23\", \"2017-12-25\",\n",
        "                      \"2018-01-01\", \"2018-01-15\", \"2018-02-19\", \"2018-05-28\", \"2018-07-04\",\n",
        "                      \"2018-09-03\", \"2018-10-08\", \"2018-11-12\", \"2018-11-22\", \"2018-12-25\",\n",
        "                      \"2019-01-01\"]\n",
        "    df_test[\"is_holiday\"] = (df_test.timestamp.isin(holidays)).astype(int)\n",
        "    del holidays\n",
        "    gc.collect()\n",
        "    #print(len(df_test))\n",
        "  # Подстановка параметров сооружения\n",
        "    df_median = df.groupby(by=['building_id'])['primary_use_ID','site_id','square_feet'].median()\n",
        "    df_test = pd.merge(df_test, df_median, how = 'left', on = ['building_id'])\n",
        "    del df_median \n",
        "\n",
        "    print('Подставлены данные по сооружению')\n",
        "    gc.collect()\n",
        "    #print(len(df_test))"
      ],
      "execution_count": 0,
      "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": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i34pNJGAWNLV",
        "colab_type": "code",
        "outputId": "0ff2362c-681b-4c23-cab5-72fa812270fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "del df\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "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",
        "  df_weather.drop(columns = ['precip_depth_1_hr'], inplace = True)\n",
        "  gc.collect()\n",
        "\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.sort_values(by = ['site_id','timestamp'], inplace = True)\n",
        "  gc.collect()\n",
        "\n",
        "  df_times = df_times.groupby('site_id'  ).apply(lambda group: group.interpolate(limit_direction='both'))\n",
        "  df_times = df_times.groupby('timestamp').apply(lambda group: group.interpolate(limit_direction='both'))\n",
        "  \n",
        "  df_test = pd.merge(df_test, df_times, how = 'left', on = ['site_id','timestamp'])\n",
        "  del df_weather, df_times\n",
        "\n",
        "  #del df_weather"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jblybbA1X8IM",
        "colab_type": "code",
        "outputId": "6ed91f04-db3f-4715-d4a4-5ad69dafcc20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#reduce_mem_usage(df_test)\n",
        "df_test.to_feather(DIR_SAVE + 'DF_TEST_REDUCED3.FTHR')\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjgCdq3PZh81",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Загрузка данных:\n",
        "  #df_test = pd.read_feather(DIR_SAVE + 'DF_TEST_REDUCED3.FTHR')\n",
        "  #gc.collect()\n",
        "  import pickle\n",
        "  with open(DIR_SAVE+'scaler31.pickle', 'rb') as handle:\n",
        "      scaler = pickle.load(handle)\n",
        "      In_Columns = pickle.load(handle)\n",
        "      Out_Columns = pickle.load(handle)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTDcNmss8mK5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Нормализация\n",
        "  i=0\n",
        "  #print(scaler.data_max_)\n",
        "  for c in In_Columns:\n",
        "    if c in ['building_meter_median','building_meter_hour_median','building_meter_weekday_median','primary_use_ID','site_id','square_feet']:\n",
        "      print(i, c, 'Уже нормализовано:', scaler.scale_[i],scaler.min_[i], '     ',scaler.data_min_[i], scaler.data_max_[i])\n",
        "    else:\n",
        "      print(i, c, scaler.scale_[i],scaler.min_[i], '     ', scaler.data_min_[i], scaler.data_max_[i])\n",
        "      df_test[c] = df_test[c] * scaler.scale_[i] + scaler.min_[i]\n",
        "    i+=1\n",
        "    gc.collect()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cG1CZk1i-CHl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Выбор нейронок и предсказание:\n",
        "  nn_0 = keras.models.load_model (DIR_SAVE + '0HANDLY_SAVED3best.MODEL')\n",
        "  nn_1 = keras.models.load_model (DIR_SAVE + '1HANDLY_SAVED3best.MODEL')\n",
        "  nn_2 = keras.models.load_model (DIR_SAVE + '2HANDLY_SAVED3best.MODEL')\n",
        "  nn_3 = keras.models.load_model (DIR_SAVE + '3HANDLY_SAVED3best.MODEL')\n",
        "  # score = model.evaluate(X, Y, verbose=0)\n",
        "  # print(\"%s: %.2f%%\" % (model.metrics_names[1], score[1]*100))\n",
        "  gc.collect()\n",
        "  #Предсказание из 4х значений:\n",
        "  if 1==1:\n",
        "    df_test['NN_PRED'] = 0 \n",
        "    df_test['NN_PRED']  = df_test['NN_PRED'].astype('float32')\n",
        "    l = len(df_test)\n",
        "    print( len(df_test) )\n",
        "    step = 1600000\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', 'meter']].copy()\n",
        "      else:\n",
        "        a = pd.concat([a, tmp[['row_id','NN_PRED', 'meter']].copy()], sort=False)\n",
        "      #del tmp\n",
        "      #gc.collect() "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtxkAc9E00Yn",
        "colab_type": "code",
        "outputId": "b24d653f-2b43-4e9e-f552-1b83bd137906",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(df_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "41697600\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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_SAVE+'OUT_Ver0.31 no clean_6.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pSaG8s-5_yL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a.head(-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZW2CAz9u1yg0",
        "colab_type": "code",
        "outputId": "51c0f5a2-3df5-4a2e-a88a-95bc3ebc395a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "a.groupby(['meter'])['NN_PRED'].agg(['mean','median', 'min', 'max'])"
      ],
      "execution_count": 0,
      "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>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>159.116</td>\n",
              "      <td>68.534</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1355.983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>352.780</td>\n",
              "      <td>116.053</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2035.047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12714.100</td>\n",
              "      <td>237.592</td>\n",
              "      <td>0.000</td>\n",
              "      <td>17835566.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>219.173</td>\n",
              "      <td>32.681</td>\n",
              "      <td>0.000</td>\n",
              "      <td>32506.373</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           mean  median   min          max\n",
              "meter                                     \n",
              "0       159.116  68.534 0.000     1355.983\n",
              "1       352.780 116.053 0.000     2035.047\n",
              "2     12714.100 237.592 0.000 17835566.000\n",
              "3       219.173  32.681 0.000    32506.373"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAlQJtbx36Az",
        "colab_type": "code",
        "outputId": "a90a7cd1-7a6e-440b-aa7f-8688118dc5cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.groupby(['meter'])['meter_reading'].agg(['mean','median', 'min', 'max'])"
      ],
      "execution_count": 0,
      "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>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>162.707</td>\n",
              "      <td>68.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>79769.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>633.801</td>\n",
              "      <td>120.698</td>\n",
              "      <td>0.000</td>\n",
              "      <td>880374.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14402.644</td>\n",
              "      <td>253.150</td>\n",
              "      <td>0.000</td>\n",
              "      <td>21904700.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>374.154</td>\n",
              "      <td>38.099</td>\n",
              "      <td>0.000</td>\n",
              "      <td>160187.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           mean  median   min          max\n",
              "meter                                     \n",
              "0       162.707  68.000 0.000    79769.000\n",
              "1       633.801 120.698 0.000   880374.000\n",
              "2     14402.644 253.150 0.000 21904700.000\n",
              "3       374.154  38.099 0.000   160187.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NFKqWrr7nz7",
        "colab_type": "code",
        "outputId": "24454c2c-8344-4adf-cb1c-47dd0452049c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.groupby(['meter'])['NN_PRED'].agg(['mean','median', 'min', 'max'])"
      ],
      "execution_count": 0,
      "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>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>158.798</td>\n",
              "      <td>74.755</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1289.670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>378.482</td>\n",
              "      <td>138.774</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2033.057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11507.809</td>\n",
              "      <td>244.696</td>\n",
              "      <td>0.000</td>\n",
              "      <td>17275218.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>269.547</td>\n",
              "      <td>40.498</td>\n",
              "      <td>0.000</td>\n",
              "      <td>45745.234</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           mean  median   min          max\n",
              "meter                                     \n",
              "0       158.798  74.755 0.000     1289.670\n",
              "1       378.482 138.774 0.000     2033.057\n",
              "2     11507.809 244.696 0.000 17275218.000\n",
              "3       269.547  40.498 0.000    45745.234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZATVfSA78tqg",
        "colab_type": "code",
        "outputId": "14e02d7f-0af5-4383-a3ae-1a0270317f59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "df.describe()\n",
        "air_temperature\n",
        "is_holiday\n",
        "cloud_coverage\n",
        "dew_temperature\n",
        "sea_level_pressure\t\n",
        "wind_direction\t\n",
        "wind_speed"
      ],
      "execution_count": 0,
      "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>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</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>meter_reading_prev</th>\n",
              "      <th>is_equal_prev</th>\n",
              "      <th>day</th>\n",
              "      <th>IS_BAD_PRCNT</th>\n",
              "      <th>building_meter_median</th>\n",
              "      <th>building_meter_mean</th>\n",
              "      <th>building_meter_mean_45</th>\n",
              "      <th>hour</th>\n",
              "      <th>building_meter_hour_median</th>\n",
              "      <th>building_meter_hour_mean</th>\n",
              "      <th>k_fact_med_hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>building_meter_weekday_median</th>\n",
              "      <th>is_holiday</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_ID</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948183.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18293583.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>6974756.000</td>\n",
              "      <td>2358476.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18078918.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "      <td>18948184.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>801.974</td>\n",
              "      <td>0.676</td>\n",
              "      <td>2232.996</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.472</td>\n",
              "      <td>0.498</td>\n",
              "      <td>0.488</td>\n",
              "      <td>0.493</td>\n",
              "      <td>2232.996</td>\n",
              "      <td>0.161</td>\n",
              "      <td>185.601</td>\n",
              "      <td>0.161</td>\n",
              "      <td>0.001</td>\n",
              "      <td>2232.996</td>\n",
              "      <td>2575.451</td>\n",
              "      <td>11.502</td>\n",
              "      <td>0.001</td>\n",
              "      <td>2232.996</td>\n",
              "      <td>23368.884</td>\n",
              "      <td>3.007</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.541</td>\n",
              "      <td>0.121</td>\n",
              "      <td>1969.248</td>\n",
              "      <td>4.052</td>\n",
              "      <td>0.220</td>\n",
              "      <td>0.595</td>\n",
              "      <td>0.316</td>\n",
              "      <td>0.685</td>\n",
              "      <td>0.984</td>\n",
              "      <td>0.620</td>\n",
              "      <td>0.470</td>\n",
              "      <td>0.175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>440.083</td>\n",
              "      <td>0.934</td>\n",
              "      <td>158278.578</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.372</td>\n",
              "      <td>0.363</td>\n",
              "      <td>0.352</td>\n",
              "      <td>0.355</td>\n",
              "      <td>158278.582</td>\n",
              "      <td>0.367</td>\n",
              "      <td>105.237</td>\n",
              "      <td>0.314</td>\n",
              "      <td>0.022</td>\n",
              "      <td>82117.057</td>\n",
              "      <td>85395.660</td>\n",
              "      <td>6.922</td>\n",
              "      <td>0.024</td>\n",
              "      <td>82265.539</td>\n",
              "      <td>1023290.661</td>\n",
              "      <td>1.997</td>\n",
              "      <td>0.039</td>\n",
              "      <td>0.034</td>\n",
              "      <td>0.348</td>\n",
              "      <td>0.127</td>\n",
              "      <td>31.279</td>\n",
              "      <td>3.071</td>\n",
              "      <td>0.231</td>\n",
              "      <td>0.144</td>\n",
              "      <td>0.306</td>\n",
              "      <td>0.178</td>\n",
              "      <td>7.683</td>\n",
              "      <td>0.091</td>\n",
              "      <td>0.314</td>\n",
              "      <td>0.118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1900.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>357.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>20.707</td>\n",
              "      <td>0.146</td>\n",
              "      <td>0.146</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.099</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.131</td>\n",
              "      <td>20.707</td>\n",
              "      <td>0.000</td>\n",
              "      <td>97.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>38.634</td>\n",
              "      <td>47.025</td>\n",
              "      <td>6.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>35.973</td>\n",
              "      <td>1.083</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.038</td>\n",
              "      <td>1951.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.497</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.548</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.563</td>\n",
              "      <td>0.194</td>\n",
              "      <td>0.111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>918.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>83.000</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.357</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.455</td>\n",
              "      <td>0.470</td>\n",
              "      <td>83.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>186.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>109.848</td>\n",
              "      <td>127.470</td>\n",
              "      <td>12.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>107.921</td>\n",
              "      <td>1.274</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.083</td>\n",
              "      <td>1970.000</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.606</td>\n",
              "      <td>0.222</td>\n",
              "      <td>0.702</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.620</td>\n",
              "      <td>0.472</td>\n",
              "      <td>0.163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1200.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>272.860</td>\n",
              "      <td>0.854</td>\n",
              "      <td>0.854</td>\n",
              "      <td>0.802</td>\n",
              "      <td>0.901</td>\n",
              "      <td>0.837</td>\n",
              "      <td>0.848</td>\n",
              "      <td>272.860</td>\n",
              "      <td>0.000</td>\n",
              "      <td>277.000</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.001</td>\n",
              "      <td>307.502</td>\n",
              "      <td>358.264</td>\n",
              "      <td>18.000</td>\n",
              "      <td>0.001</td>\n",
              "      <td>305.962</td>\n",
              "      <td>1.978</td>\n",
              "      <td>5.000</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.867</td>\n",
              "      <td>0.157</td>\n",
              "      <td>1999.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.827</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.750</td>\n",
              "      <td>0.242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1448.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>21904700.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>21904700.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>366.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>3813882.148</td>\n",
              "      <td>3866686.396</td>\n",
              "      <td>23.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>4138801.770</td>\n",
              "      <td>57329462.143</td>\n",
              "      <td>6.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2017.000</td>\n",
              "      <td>16.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>343.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       building_id        meter  meter_reading     hour_cos     hour_sin  weekday_cos  \\\n",
              "count 18948184.000 18948184.000   18948184.000 18948184.000 18948184.000 18948184.000   \n",
              "mean       801.974        0.676       2232.996        0.500        0.500        0.472   \n",
              "std        440.083        0.934     158278.578        0.354        0.354        0.372   \n",
              "min          0.000        0.000          0.000        0.000        0.000        0.000   \n",
              "25%        357.000        0.000         20.707        0.146        0.146        0.000   \n",
              "50%        918.000        0.000         83.000        0.500        0.500        0.357   \n",
              "75%       1200.000        1.000        272.860        0.854        0.854        0.802   \n",
              "max       1448.000        3.000   21904700.000        1.000        1.000        1.000   \n",
              "\n",
              "       weekday_sin     week_cos     week_sin  meter_reading_prev  is_equal_prev          day  \\\n",
              "count 18948184.000 18948184.000 18948184.000        18948183.000   18948184.000 18948184.000   \n",
              "mean         0.498        0.488        0.493            2232.996          0.161      185.601   \n",
              "std          0.363        0.352        0.355          158278.582          0.367      105.237   \n",
              "min          0.000        0.000        0.000               0.000          0.000        1.000   \n",
              "25%          0.099        0.140        0.131              20.707          0.000       97.000   \n",
              "50%          0.500        0.455        0.470              83.000          0.000      186.000   \n",
              "75%          0.901        0.837        0.848             272.860          0.000      277.000   \n",
              "max          1.000        1.000        1.000        21904700.000          1.000      366.000   \n",
              "\n",
              "       IS_BAD_PRCNT  building_meter_median  building_meter_mean  building_meter_mean_45  \\\n",
              "count  18948184.000           18948184.000         18948184.000            18293583.000   \n",
              "mean          0.161                  0.001             2232.996                2575.451   \n",
              "std           0.314                  0.022            82117.057               85395.660   \n",
              "min           0.000                  0.000                0.000                   0.000   \n",
              "25%           0.000                  0.000               38.634                  47.025   \n",
              "50%           0.000                  0.000              109.848                 127.470   \n",
              "75%           0.125                  0.001              307.502                 358.264   \n",
              "max           1.000                  1.000          3813882.148             3866686.396   \n",
              "\n",
              "              hour  building_meter_hour_median  building_meter_hour_mean  k_fact_med_hour  \\\n",
              "count 18948184.000                18948184.000              18948184.000     18948184.000   \n",
              "mean        11.502                       0.001                  2232.996        23368.884   \n",
              "std          6.922                       0.024                 82265.539      1023290.661   \n",
              "min          0.000                       0.000                     0.000            1.000   \n",
              "25%          6.000                       0.000                    35.973            1.083   \n",
              "50%         12.000                       0.000                   107.921            1.274   \n",
              "75%         18.000                       0.001                   305.962            1.978   \n",
              "max         23.000                       1.000               4138801.770     57329462.143   \n",
              "\n",
              "           weekday  building_meter_weekday_median   is_holiday      site_id  square_feet  \\\n",
              "count 18948184.000                   18948184.000 18948184.000 18948184.000 18948184.000   \n",
              "mean         3.007                          0.009        0.001        0.541        0.121   \n",
              "std          1.997                          0.039        0.034        0.348        0.127   \n",
              "min          0.000                          0.000        0.000        0.000        0.000   \n",
              "25%          1.000                          0.001        0.000        0.200        0.038   \n",
              "50%          3.000                          0.002        0.000        0.600        0.083   \n",
              "75%          5.000                          0.006        0.000        0.867        0.157   \n",
              "max          6.000                          1.000        1.000        1.000        1.000   \n",
              "\n",
              "       year_built  floor_count  primary_use_ID  air_temperature  cloud_coverage  dew_temperature  \\\n",
              "count 6974756.000  2358476.000    18948184.000     18948184.000    18948184.000     18948184.000   \n",
              "mean     1969.248        4.052           0.220            0.595           0.316            0.685   \n",
              "std        31.279        3.071           0.231            0.144           0.306            0.178   \n",
              "min      1900.000        1.000           0.000            0.000           0.000            0.000   \n",
              "25%      1951.000        1.000           0.000            0.497           0.000            0.548   \n",
              "50%      1970.000        4.000           0.200            0.606           0.222            0.702   \n",
              "75%      1999.000        6.000           0.400            0.700           0.500            0.827   \n",
              "max      2017.000       16.000           1.000            1.000           1.000            1.000   \n",
              "\n",
              "       precip_depth_1_hr  sea_level_pressure  wind_direction   wind_speed  \n",
              "count       18078918.000        18948184.000    18948184.000 18948184.000  \n",
              "mean               0.984               0.620           0.470        0.175  \n",
              "std                7.683               0.091           0.314        0.118  \n",
              "min               -1.000               0.000           0.000        0.000  \n",
              "25%                0.000               0.563           0.194        0.111  \n",
              "50%                0.000               0.620           0.472        0.163  \n",
              "75%                0.000               0.675           0.750        0.242  \n",
              "max              343.000               1.000           1.000        1.000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ij-PYo9p8t0g",
        "colab_type": "code",
        "outputId": "c0e63b96-44f5-44a8-ee2a-25f5362b27bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "df_test.describe()"
      ],
      "execution_count": 0,
      "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>building_id</th>\n",
              "      <th>meter</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>building_meter_median</th>\n",
              "      <th>hour</th>\n",
              "      <th>building_meter_hour_median</th>\n",
              "      <th>weekday</th>\n",
              "      <th>building_meter_weekday_median</th>\n",
              "      <th>is_holiday</th>\n",
              "      <th>primary_use_ID</th>\n",
              "      <th>site_id</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>cloud_coverage</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>sea_level_pressure</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>NN_PRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "      <td>41697600.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20848799.5000</td>\n",
              "      <td>807.5824</td>\n",
              "      <td>0.6643</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.4751</td>\n",
              "      <td>0.4995</td>\n",
              "      <td>0.4913</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0013</td>\n",
              "      <td>11.5000</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>0.0088</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.2105</td>\n",
              "      <td>0.5391</td>\n",
              "      <td>0.1219</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.1318</td>\n",
              "      <td>12.2029</td>\n",
              "      <td>13.7401</td>\n",
              "      <td>19.5723</td>\n",
              "      <td>0.6340</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12037060.4366</td>\n",
              "      <td>429.7680</td>\n",
              "      <td>0.9278</td>\n",
              "      <td>0.3536</td>\n",
              "      <td>0.3536</td>\n",
              "      <td>0.3722</td>\n",
              "      <td>0.3625</td>\n",
              "      <td>0.3508</td>\n",
              "      <td>0.3566</td>\n",
              "      <td>0.0215</td>\n",
              "      <td>6.9222</td>\n",
              "      <td>0.0236</td>\n",
              "      <td>2.0034</td>\n",
              "      <td>0.0375</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>0.2301</td>\n",
              "      <td>0.3423</td>\n",
              "      <td>0.1327</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1338</td>\n",
              "      <td>6.3944</td>\n",
              "      <td>0.0915</td>\n",
              "      <td>9.9943</td>\n",
              "      <td>0.0308</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-10.6000</td>\n",
              "      <td>13.1524</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.5728</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10424399.7500</td>\n",
              "      <td>404.7500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1464</td>\n",
              "      <td>0.1464</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0990</td>\n",
              "      <td>0.1405</td>\n",
              "      <td>0.1516</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>5.7500</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>0.0365</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.0178</td>\n",
              "      <td>7.6000</td>\n",
              "      <td>13.6999</td>\n",
              "      <td>11.2593</td>\n",
              "      <td>0.6131</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20848799.5000</td>\n",
              "      <td>900.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.3569</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.5144</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>11.5000</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>0.0020</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0667</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.0823</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.0978</td>\n",
              "      <td>12.4000</td>\n",
              "      <td>13.7472</td>\n",
              "      <td>20.2222</td>\n",
              "      <td>0.6307</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>31273199.2500</td>\n",
              "      <td>1194.2500</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.8536</td>\n",
              "      <td>0.8536</td>\n",
              "      <td>0.8019</td>\n",
              "      <td>0.9010</td>\n",
              "      <td>0.8373</td>\n",
              "      <td>0.8484</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>17.2500</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>0.0061</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.8667</td>\n",
              "      <td>0.1579</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.2133</td>\n",
              "      <td>17.0667</td>\n",
              "      <td>13.7922</td>\n",
              "      <td>27.4074</td>\n",
              "      <td>0.6514</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>41697599.0000</td>\n",
              "      <td>1448.0000</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9965</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>23.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.0667</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>26.7000</td>\n",
              "      <td>14.0407</td>\n",
              "      <td>40.0000</td>\n",
              "      <td>0.9689</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             row_id   building_id         meter      hour_cos      hour_sin   weekday_cos  \\\n",
              "count 41697600.0000 41697600.0000 41697600.0000 41697600.0000 41697600.0000 41697600.0000   \n",
              "mean  20848799.5000      807.5824        0.6643        0.5000        0.5000        0.4751   \n",
              "std   12037060.4366      429.7680        0.9278        0.3536        0.3536        0.3722   \n",
              "min          0.0000        0.0000        0.0000        0.0000        0.0000        0.0000   \n",
              "25%   10424399.7500      404.7500        0.0000        0.1464        0.1464        0.0000   \n",
              "50%   20848799.5000      900.0000        0.0000        0.5000        0.5000        0.3569   \n",
              "75%   31273199.2500     1194.2500        1.0000        0.8536        0.8536        0.8019   \n",
              "max   41697599.0000     1448.0000        3.0000        1.0000        1.0000        1.0000   \n",
              "\n",
              "        weekday_sin      week_cos      week_sin  building_meter_median          hour  \\\n",
              "count 41697600.0000 41697600.0000 41697600.0000          41697600.0000 41697600.0000   \n",
              "mean         0.4995        0.4913        0.5000                 0.0013       11.5000   \n",
              "std          0.3625        0.3508        0.3566                 0.0215        6.9222   \n",
              "min          0.0000        0.0000        0.0000                 0.0000        0.0000   \n",
              "25%          0.0990        0.1405        0.1516                 0.0001        5.7500   \n",
              "50%          0.5000        0.5144        0.5000                 0.0002       11.5000   \n",
              "75%          0.9010        0.8373        0.8484                 0.0006       17.2500   \n",
              "max          1.0000        0.9965        1.0000                 1.0000       23.0000   \n",
              "\n",
              "       building_meter_hour_median       weekday  building_meter_weekday_median    is_holiday  \\\n",
              "count               41697600.0000 41697600.0000                  41697600.0000 41697600.0000   \n",
              "mean                       0.0014        3.0000                         0.0088        0.0001   \n",
              "std                        0.0236        2.0034                         0.0375        0.0023   \n",
              "min                        0.0000        0.0000                         0.0000        0.0000   \n",
              "25%                        0.0001        1.0000                         0.0005        0.0000   \n",
              "50%                        0.0002        3.0000                         0.0020        0.0000   \n",
              "75%                        0.0006        5.0000                         0.0061        0.0000   \n",
              "max                        1.0000        6.0000                         1.0000        0.0667   \n",
              "\n",
              "       primary_use_ID       site_id   square_feet  air_temperature  cloud_coverage  \\\n",
              "count   41697600.0000 41697600.0000 41697600.0000    41697600.0000   41697600.0000   \n",
              "mean           0.2105        0.5391        0.1219          -0.0003          0.1318   \n",
              "std            0.2301        0.3423        0.1327           0.0000          0.1338   \n",
              "min            0.0000        0.0000        0.0000          -0.0003          0.0000   \n",
              "25%            0.0000        0.2000        0.0365          -0.0003          0.0178   \n",
              "50%            0.0667        0.6000        0.0823          -0.0003          0.0978   \n",
              "75%            0.4000        0.8667        0.1579          -0.0003          0.2133   \n",
              "max            1.0000        1.0000        1.0000          -0.0003          0.6000   \n",
              "\n",
              "       dew_temperature  sea_level_pressure  wind_direction    wind_speed       NN_PRED  \n",
              "count    41697600.0000       41697600.0000   41697600.0000 41697600.0000 41697600.0000  \n",
              "mean           12.2029             13.7401         19.5723        0.6340        0.0000  \n",
              "std             6.3944              0.0915          9.9943        0.0308        0.0000  \n",
              "min           -10.6000             13.1524          0.0000        0.5728        0.0000  \n",
              "25%             7.6000             13.6999         11.2593        0.6131        0.0000  \n",
              "50%            12.4000             13.7472         20.2222        0.6307        0.0000  \n",
              "75%            17.0667             13.7922         27.4074        0.6514        0.0000  \n",
              "max            26.7000             14.0407         40.0000        0.9689        0.0000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HcOED4IDv0k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "show_plot(hist) "
      ],
      "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": []
    }
  ]
}