bdl_exam_project_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from io import BytesIO\n",
    "\n",
    "from docx import Document\n",
    "from docx.enum.style import WD_STYLE_TYPE\n",
    "from docx.shared import Pt, Cm\n",
    "from docx.enum.text import WD_PARAGRAPH_ALIGNMENT\n",
    "\n",
    "from loader_sas import LoaderSAS\n",
    "#from preprocessing import LazyDataStuart"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "font_common = {'family': 'Liberation Sans',\n",
    "               'weight': 'bold',\n",
    "               'size': 25}\n",
    "mpl.rc('font', **font_common)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Запланований pipeline після загального ознайомлення з даними та задачею:\n",
    "\n",
    "## Дослідження даних, опрацювання, виділення нових фіч\n",
    "1. Детальніший огляд даних.\n",
    "- Оглянути основний датасет з вітриною клієнта (Типи даних, наявність місінгів, очевидних викидів, тощо)\n",
    "- Додаткові датасети (що можна виділити або викорстати)\n",
    "2. Визначити стратегію заповнення місінгів (розділити фічі на групи для пакетного опрацювання)\n",
    "3. Виділити категоріальні фічі для подальшого перетворення\n",
    "4. Розглянути можливості оптимізації через: скейлінг(для окремих моделей), кореляцію (дубльовані дані), балансування датасету, опрацювання викидів\n",
    "5. Виділити нові фічі з датасету додатків (Apps). Кількість унікальних додатків близько 700 можна спробувати використати всі дані:\n",
    "- наявність додатку у абонента\n",
    "- статистичні показники - без обробки\n",
    "- абсолютні, спробувати у відношенні, наприклад, до лайфтайму клієнта\n",
    "6. Виділити нові фічі з датасету сервісів (bnum). Унікальних близько 16 000, можна спробувати виділити нові групи, яких немає в головному датасеті. Наприклад, магазини техніки, послуги для більш молодих користувачів, ознака маргінальності (мікрокредити, казино, колектори), ознака заможності (доставки їжі, приватні клініки, замовлення таксі, використання платіжних систем, тощо).\n",
    "7. Поєднання отриманих фіч з основним датасетом."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Load data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FILES SAS7DBAT IN DIR '.':\n",
      "\t 1 \t ./hash_school_device_model_t.sas7bdat \t 3.0078125  Mb\n",
      "\t 2 \t ./hash_school_device_model_t_fe.sas7bdat \t 1149.5078125  Mb\n",
      "\t 3 \t ./hash_school_device_test_dpi.sas7bdat \t 251.0078125  Mb\n",
      "\t 4 \t ./hash_school_device_model_fe.sas7bdat \t 1389.5078125  Mb\n",
      "\t 5 \t ./hash_school_device_b_num.sas7bdat \t 70.5078125  Mb\n",
      "\t 6 \t ./hash_school_device_dpi.sas7bdat \t 305.5078125  Mb\n",
      "\t 7 \t ./hash_school_device_b_num_test.sas7bdat \t 65.0078125  Mb\n"
     ]
    }
   ],
   "source": [
    "loader = LoaderSAS()\n",
    "loader.show_files(work_dir='.')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading hash_school_device_model_fe.sas7bdat (1389.5 Mb)...\n",
      "Loaded in 0:02:56 (7.9 Mb/sec), memory usage - 1.35 Gb\n"
     ]
    }
   ],
   "source": [
    "train_main_fe = loader.get_df('hash_school_device_model_fe.sas7bdat')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading hash_school_device_b_num.sas7bdat (70.5 Mb)...\n",
      "Loaded in 0:00:07 (9.2 Mb/sec), memory usage - 0.09 Gb\n"
     ]
    }
   ],
   "source": [
    "train_services = loader.get_df('hash_school_device_b_num.sas7bdat')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading hash_school_device_dpi.sas7bdat (305.5 Mb)...\n",
      "Loaded in 0:00:45 (6.7 Mb/sec), memory usage - 0.3 Gb\n"
     ]
    }
   ],
   "source": [
    "train_apps = loader.get_df('hash_school_device_dpi.sas7bdat')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading hash_school_device_model_t_fe.sas7bdat (1149.5 Mb)...\n",
      "Loaded in 0:02:25 (7.9 Mb/sec), memory usage - 1.11 Gb\n"
     ]
    }
   ],
   "source": [
    "test_main_fe = loader.get_df('hash_school_device_model_t_fe.sas7bdat')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading hash_school_device_b_num_test.sas7bdat (65.0 Mb)...\n",
      "Loaded in 0:00:07 (8.7 Mb/sec), memory usage - 0.09 Gb\n"
     ]
    }
   ],
   "source": [
    "test_services = loader.get_df('hash_school_device_b_num_test.sas7bdat')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading hash_school_device_test_dpi.sas7bdat (251.0 Mb)...\n",
      "Loaded in 0:00:37 (6.7 Mb/sec), memory usage - 0.24 Gb\n"
     ]
    }
   ],
   "source": [
    "test_apps = loader.get_df('hash_school_device_test_dpi.sas7bdat')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading hash_school_device_model_t.sas7bdat (3.0 Mb)...\n",
      "Loaded in 0:00:00 (5.9 Mb/sec), memory usage - 0.0 Gb\n"
     ]
    }
   ],
   "source": [
    "test_target = loader.get_df('hash_school_device_model_t.sas7bdat')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train main features df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Первинний огляд датасету"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_fe:\n",
      "\trows - 205625\n",
      "\tcols - 878\n"
     ]
    }
   ],
   "source": [
    "print(f\"model_fe:\\n\\trows - {train_main_fe.shape[0]}\\n\\tcols - {train_main_fe.shape[1]}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "0.0    155763\n1.0     49862\nName: target, dtype: int64"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Перевірка збалансованості датасету\n",
    "train_main_fe['target'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "205625"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Перевірка цілісності даних відносно ідентифікатора клієнта\n",
    "train_main_fe['abon_id'].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "train_main_fe['device_sim_count'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "                               0          1          2          3          4  \\\nabon_id                1215806.0  1215139.0  1236635.0  1205230.0  1019270.0   \ndevice_id               100001.0   100001.0   100001.0   100001.0   100001.0   \ntarget                       0.0        0.0        0.0        0.0        0.0   \nBalance_uah            -0.005008   0.129405   0.000212   3.929049   0.799395   \nTM_ID                  72.038647  72.038647  72.223109  72.223109  72.014152   \n...                          ...        ...        ...        ...        ...   \ndevice_type_tablet           0.0        0.0        0.0        0.0        0.0   \ndevice_type_nan              0.0        0.0        0.0        0.0        0.0   \ndevice_type_rus_other        1.0        1.0        1.0        1.0        1.0   \ndevice_sim_count             1.0        1.0        1.0        1.0        1.0   \ndevice_price                 NaN        NaN        NaN        NaN        NaN   \n\n                               5                6          7          8  \\\nabon_id                1016800.0        1514463.0  1406287.0  1303520.0   \ndevice_id               100001.0         100001.0   100001.0   100001.0   \ntarget                       1.0              0.0        1.0        1.0   \nBalance_uah             0.728297 -693596029.91064    2.04542   0.132472   \nTM_ID                  71.703516        71.834736  72.014152  72.014152   \n...                          ...              ...        ...        ...   \ndevice_type_tablet           0.0              0.0        0.0        0.0   \ndevice_type_nan              0.0              0.0        0.0        0.0   \ndevice_type_rus_other        1.0              1.0        1.0        1.0   \ndevice_sim_count             1.0              1.0        1.0        1.0   \ndevice_price                 NaN              NaN        NaN        NaN   \n\n                                9  \nabon_id                 1118461.0  \ndevice_id                100003.0  \ntarget                        0.0  \nBalance_uah            7112.46578  \nTM_ID                   71.721301  \n...                           ...  \ndevice_type_tablet            0.0  \ndevice_type_nan               0.0  \ndevice_type_rus_other         1.0  \ndevice_sim_count              1.0  \ndevice_price                  NaN  \n\n[878 rows x 10 columns]",
      "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>abon_id</th>\n      <td>1215806.0</td>\n      <td>1215139.0</td>\n      <td>1236635.0</td>\n      <td>1205230.0</td>\n      <td>1019270.0</td>\n      <td>1016800.0</td>\n      <td>1514463.0</td>\n      <td>1406287.0</td>\n      <td>1303520.0</td>\n      <td>1118461.0</td>\n    </tr>\n    <tr>\n      <th>device_id</th>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100001.0</td>\n      <td>100003.0</td>\n    </tr>\n    <tr>\n      <th>target</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Balance_uah</th>\n      <td>-0.005008</td>\n      <td>0.129405</td>\n      <td>0.000212</td>\n      <td>3.929049</td>\n      <td>0.799395</td>\n      <td>0.728297</td>\n      <td>-693596029.91064</td>\n      <td>2.04542</td>\n      <td>0.132472</td>\n      <td>7112.46578</td>\n    </tr>\n    <tr>\n      <th>TM_ID</th>\n      <td>72.038647</td>\n      <td>72.038647</td>\n      <td>72.223109</td>\n      <td>72.223109</td>\n      <td>72.014152</td>\n      <td>71.703516</td>\n      <td>71.834736</td>\n      <td>72.014152</td>\n      <td>72.014152</td>\n      <td>71.721301</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>device_type_tablet</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>device_type_nan</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>device_type_rus_other</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>device_sim_count</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>device_price</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>878 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_main_fe.head(10).transpose()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Виділення колонок з координатами та таргетом для створення фічі spatial в майбутньому"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "df_spatial = train_main_fe.loc[:][['target', 'loc_lat', 'loc_lon']].copy(deep=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "(205625, 3)"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "df_spatial.dropna(inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "(200406, 3)"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "# df_spatial.to_pickle('spatial_df_wonan.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# ten days later...\n",
    "*Тут було безліч спроб по різному відібрати колонки для опрацювання, визначити врахування змін на тестовий датасет, знайти узагальнені методи опрацювання. Вирішив винести усі напрацювання в окремий клас, інакше ноутбук ставав безкінечним....*"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "class LazyDataStuart:\n",
    "    def __init__(self,\n",
    "                 df_train_main: pd.DataFrame,\n",
    "                 target_name='target',\n",
    "                 protected_cols=None,\n",
    "                 feature_desc_file=None,\n",
    "                 useless_tresh=10,\n",
    "                 to_binary_tresh=0.005,\n",
    "                 quantile_border=(0.2, 0.8),\n",
    "                 outlier_quant_coef=2.0,\n",
    "                 outlier_strategy=None,\n",
    "                 high_corr_kill=0.98):\n",
    "\n",
    "        # Params assertion:\n",
    "        assert isinstance(df_train_main, pd.DataFrame), \"data should be Pandas DataFrame\"\n",
    "        assert isinstance(quantile_border, (list, tuple)) \\\n",
    "               and len(quantile_border) == 2 \\\n",
    "               and isinstance(quantile_border[0], (int, float)) \\\n",
    "               and isinstance(quantile_border[1], (int, float)) \\\n",
    "               and quantile_border[1] > quantile_border[0], f\"val '{quantile_border}'! Should be a tuple or a list (2 elements, numeric)\"\n",
    "        assert isinstance(outlier_quant_coef, (int, float)), f\"val '{outlier_quant_coef}' - Coef. should be numeric\"\n",
    "        assert outlier_strategy in [None, 'move', 'warn']\n",
    "        assert 0.5 < high_corr_kill < 1.0, 'Correlated duplicate coef. should be in range 0.5...1.0 '\n",
    "        assert 0 < to_binary_tresh < 1\n",
    "\n",
    "        # Defines\n",
    "        self.feature_desc = self._get_fea_desc(feature_desc_file) if feature_desc_file is not None else {}\n",
    "        self.protected_cols = ['abon_id', target_name, 'loc_lat', 'loc_lon'] if protected_cols is None else protected_cols\n",
    "        self.cols_useless = self._get_cols_useless(tresh=useless_tresh,\n",
    "                                                   input_df=df_train_main) - {protected_cols}\n",
    "        # Preprocessing params:\n",
    "        self.quantile_border = quantile_border\n",
    "        self.outlier_quant_coef = outlier_quant_coef\n",
    "        self.outlier_strategy = outlier_strategy\n",
    "\n",
    "        self.binary_tresh = to_binary_tresh\n",
    "        self.high_corr_kill = high_corr_kill\n",
    "\n",
    "        # DataFrame\n",
    "        self.df = df_train_main.copy(deep=True)\n",
    "        if len(self.cols_useless) > 0:\n",
    "            self.df.drop(columns=list(self.cols_useless), inplace=True)\n",
    "        self.target = df_train_main[target_name]\n",
    "        self.status_converted = False\n",
    "\n",
    "        # Metrics calc\n",
    "        self.df_info = self.calc_df_info_tab() # табличний загальний звіт (df)\n",
    "        self.pie_data = self._calc_pie_data()  # тільки метрики для pie\n",
    "        self.metrics = self._calc_metrics() # метрики груповані по фічам (dict)\n",
    "        self.corr_mtx = self.df.corr()\n",
    "\n",
    "        # Feature handling config:\n",
    "        self.config_df = None\n",
    "\n",
    "        # Missing handling\n",
    "        self.cols_miss_to_zero = set()\n",
    "        self.cols_miss_to_one = set()\n",
    "        self.cols_miss_to_median = set()\n",
    "        self.cols_miss_to_mode = set()\n",
    "        self.modemedian_missed_dict = dict()\n",
    "\n",
    "        # Feature convert\n",
    "        self.cols_OHE = {'CALCULATION_METHOD_ID',\n",
    "                         'device_android_version',\n",
    "                         'device_ios_version'}\n",
    "        self.cols_check_binary = set()\n",
    "        self.outliers_borders = dict()\n",
    "        self.cols_correlated = set()  # one of a pair\n",
    "\n",
    "    @staticmethod\n",
    "    def _get_fea_desc(file: str) -> dict:\n",
    "        \"\"\"\n",
    "        Формування словника з текстовим описом фіч (для зручного підпису у таблицях, прінтах, документах. Файл з описом формату MS Excel, у першому рядку заголовки 'name', 'desc'\n",
    "\n",
    "        :param file: назва файлу у робочій директорії або шлях\n",
    "        :return: словник {'фіча': 'текстовий опис'}\n",
    "        \"\"\"\n",
    "\n",
    "        features_desc = pd.read_excel(file)\n",
    "        features_desc.index = features_desc['name']\n",
    "        features_desc.drop(columns=['name'], inplace=True)\n",
    "        features_desc = features_desc.squeeze()\n",
    "        fea_names = features_desc.index.tolist()\n",
    "        fea_desc = features_desc['desc'].to_list()\n",
    "        return {x[0]: x[1] for x in list(zip(fea_names, fea_desc))}\n",
    "\n",
    "    def _calc_pie_data(self) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        Формування датафрейму з умовним розділенням значень у кожній фічі для відображення на круговій діаграмі\n",
    "\n",
    "        :return: датафрейм із кількістю значень у метриці (місінги, нулі, цілі числа, флоти)\n",
    "        \"\"\"\n",
    "        df = self.df\n",
    "        cnt_missing = df.isnull().sum()\n",
    "        cnt_integer = ((df.replace([0], np.nan) % 1) == 0).sum()\n",
    "        cnt_zero = (df == 0).sum()\n",
    "        cnt_other = df.shape[0] - (cnt_missing + cnt_integer + cnt_zero)\n",
    "        df_pie_data = pd.concat([cnt_missing, cnt_integer, cnt_zero, cnt_other], axis=1)\n",
    "        df_pie_data.columns = ['missed', 'integer', 'zero', 'float']\n",
    "        return df_pie_data\n",
    "\n",
    "    def _calc_metrics(self) -> dict:\n",
    "        source = self.df_info.copy(deep=True)\n",
    "        source.set_index('name', inplace=True)\n",
    "        m_names = source.columns.tolist()\n",
    "        m_dict = {}\n",
    "        for f_name in source.index:\n",
    "            cur_fea = {}\n",
    "            for m_name in m_names:\n",
    "                cur_fea.update({m_name: source.loc[f_name][m_name]})\n",
    "            m_dict.update({f_name: cur_fea})\n",
    "        return m_dict\n",
    "\n",
    "    @staticmethod\n",
    "    def _get_cols_useless(input_df: pd.DataFrame, tresh) -> set:\n",
    "        \"\"\"\n",
    "        Визначення неважливих фіч, які підлягають видаленню. Які мають кількість не пропущених значенень менше визначеної чутливості (замалу кількість даних відмінних від нуля або місінгу), мають тільки одне унікальне значення або не мають значень зовсім\n",
    "\n",
    "        :param tresh: чутливість до видалення у форматі 0...1 - у відсотковому представленні відносно розміру датасету або більше одиниці для абсолютної кількості записів.\n",
    "        :return: сет малозначних колонок\n",
    "        \"\"\"\n",
    "        cols = set()\n",
    "        if tresh < 1:\n",
    "            treshold = int(input_df.shape[0] * tresh)\n",
    "        elif tresh >= 1:\n",
    "            treshold = tresh\n",
    "        else:\n",
    "            treshold = 1\n",
    "\n",
    "        for col in input_df.columns:\n",
    "            count_notnull = input_df[col].value_counts()\n",
    "            count_not_zero = count_notnull.loc[count_notnull.index != 0].sum()\n",
    "            if count_not_zero <= treshold or input_df[col].nunique() <= 1:\n",
    "                cols.add(col)\n",
    "        return cols\n",
    "\n",
    "    def calc_df_info_tab(self):\n",
    "        \"\"\"\n",
    "        Формування датасету з загальними статистичними метриками фіч (використовувався для зручного групування фіч по ознакам, визначення значень заміни місінгів)\n",
    "            - назва фічі\n",
    "            - текстовий опис фічі\n",
    "            - первинно визначений тип\n",
    "            - кількість доступних значень (кількість без місінгів та нулів)\n",
    "            - кількість місінгів\n",
    "            - кількість нулів\n",
    "            - кількість цілих чисел (без врахування нулів)\n",
    "            - кількість унікальних значень\n",
    "            - максимум, мінімум, медіана, середнє, мода\n",
    "            - асиметрія, ексцес\n",
    "\n",
    "\n",
    "        :return: датасет з назвами фіч та метриками\n",
    "        \"\"\"\n",
    "        t_df = self.df\n",
    "        metrics_headers = ['name', 'desc', 'type',\n",
    "                           'available_cnt', 'available_pct',\n",
    "                           'missed_cnt', 'missed_pct',\n",
    "                           'zero_cnt', 'zero_pct',\n",
    "                           'int_cnt', 'int_pct',\n",
    "                           'unique',\n",
    "                           'max', 'min',\n",
    "                           'median', 'mean', 'mode',\n",
    "                           'skew', 'kurt']\n",
    "        metrics_data = []\n",
    "        rows_total = t_df.shape[0]\n",
    "        round_to = 5\n",
    "\n",
    "        def get_pct(val):\n",
    "            return round((float(val) / rows_total) * 100, 5)\n",
    "\n",
    "        for col in t_df:\n",
    "            null_vals = t_df[col].isnull().sum()\n",
    "            available = t_df[col].replace([0], np.nan).count()\n",
    "            zeros = t_df[col].count() - available\n",
    "            cnt_integer = ((t_df[col].replace([0], np.nan) % 1) == 0).sum()\n",
    "            metrics_data.append([col, self.feature_desc.get(col), str(t_df[col].dtype),\n",
    "                                 available, get_pct(available),\n",
    "                                 null_vals, get_pct(null_vals),\n",
    "                                 zeros, get_pct(zeros),\n",
    "                                 cnt_integer, round((cnt_integer / t_df[col].replace([0], np.nan).count()) * 100, 3),\n",
    "                                 t_df[col].nunique(),\n",
    "                                 round(t_df[col].max(), round_to), round(t_df[col].min(), round_to),\n",
    "                                 round(t_df[col].median(), round_to), round(t_df[col].mean(), round_to),\n",
    "                                 round(float(t_df[col].mode()[0]), round_to),\n",
    "                                 round(t_df[col].skew(), round_to), round(t_df[col].kurt(), round_to)])\n",
    "\n",
    "        metrics_df = pd.DataFrame(data=metrics_data, columns=metrics_headers)\n",
    "        return metrics_df\n",
    "\n",
    "    @staticmethod\n",
    "    def _create_doc(font='Liberation Sans'):\n",
    "        \"\"\"\n",
    "        Створення екземпляру порожнього документу з визначеними стилями для формування звітів у .docx\n",
    "\n",
    "        :param font: назва шрифту\n",
    "        :return: екзепляр python-docx.Document()\n",
    "        \"\"\"\n",
    "        document = Document()\n",
    "        styles = document.styles\n",
    "        # header style:\n",
    "        header_stl = styles.add_style('my_header', WD_STYLE_TYPE.PARAGRAPH)\n",
    "        header_stl.font.name = font\n",
    "        header_stl.font.size = Pt(12)\n",
    "        header_stl.font.bold = True\n",
    "        header_stl.paragraph_format.space_before = Pt(4)\n",
    "        header_stl.paragraph_format.space_after = Pt(4)\n",
    "        header_stl.paragraph_format.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER\n",
    "\n",
    "        # body text style:\n",
    "        body_stl = styles.add_style('my_text', WD_STYLE_TYPE.PARAGRAPH)\n",
    "        body_stl.font.name = font\n",
    "        body_stl.font.size = Pt(10)\n",
    "        body_stl.paragraph_format.space_before = Pt(0)\n",
    "        body_stl.paragraph_format.space_after = Pt(0)\n",
    "        body_stl.paragraph_format.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY\n",
    "        body_stl.paragraph_format.first_line_indent = Cm(1.25)\n",
    "        body_stl.paragraph_format.line_spacing = 1\n",
    "\n",
    "        # body text style:\n",
    "        list_stl = styles['List Bullet']\n",
    "        list_stl.font.name = font\n",
    "        list_stl.font.size = Pt(10)\n",
    "        list_stl.paragraph_format.space_before = Pt(0)\n",
    "        list_stl.paragraph_format.space_after = Pt(0)\n",
    "        list_stl.paragraph_format.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY\n",
    "        list_stl.paragraph_format.first_line_indent = Cm(1.25)\n",
    "        list_stl.paragraph_format.line_spacing = 1\n",
    "        sections = document.sections\n",
    "        for section in sections:\n",
    "            section.top_margin = Cm(2)\n",
    "            section.left_margin = Cm(3)\n",
    "            section.right_margin = Cm(1)\n",
    "            section.bottom_margin = Cm(2)\n",
    "        return document\n",
    "\n",
    "    def print_report(self,\n",
    "                     title: str,\n",
    "                     near_corr=10):\n",
    "        \"\"\"\n",
    "        Формування звіту про фічі у формат .docx. Але, нажаль, на такому великому датасеті виникає помилка з виводом розподілу, вирішив, замінити на вивід по одній фічі в ноутбук, а не у файл (інша функція)\n",
    "            - \"ящики з вусами\"\n",
    "            - розподіл\n",
    "            - кругова діаграми з описом числових значень (цілі, дрібні, нулі, місінги)\n",
    "            - статистичні метрики\n",
    "            - найближчі сусідні фічі по кореляції\n",
    "\n",
    "        :param title: Заголовок документу\n",
    "        :param near_corr: відображати кількість фіч, що найбільше корелюють з досліджуваною\n",
    "        :return: None, формується документ \"report_{title}.docx\"\n",
    "        \"\"\"\n",
    "        document = self._create_doc()\n",
    "        document.add_paragraph(f'REPORT: {title}', style='my_header')\n",
    "        document.add_paragraph(f'(rows: {self.df.shape[0]}, cols:{self.df.shape[1]})', style='my_header')\n",
    "\n",
    "        # Кореляція таргету\n",
    "        col_corr = self.corr_mtx['target'].reindex(self.corr_mtx['target'].abs().sort_values(ascending=False).index)[:15]\n",
    "        corr_labels = col_corr.index.tolist()\n",
    "        corr_vals = col_corr.tolist()\n",
    "        document.add_paragraph('Таргет корелюється з:', style='my_text')\n",
    "        for pos, corr_lbl in enumerate(corr_labels):\n",
    "            document.add_paragraph(f'{round(corr_vals[pos], 3)} - '\n",
    "                                   f'{corr_lbl} ({self.feature_desc.get(corr_lbl)})',\n",
    "                                   style='List Bullet')\n",
    "        document.add_page_break()\n",
    "\n",
    "        df = self.df\n",
    "        for col in df.columns:\n",
    "            document.add_paragraph(f'{col}', style='my_header')\n",
    "            document.add_paragraph(f'({self.feature_desc.get(col)})', style='my_header')\n",
    "            pic_io = BytesIO()\n",
    "            # Візуалізація\n",
    "            fig, ax = plt.subplots(2, 2, figsize=(17, 12))\n",
    "            sns.boxplot(x=df[col], y=self.target, ax=ax[0, 0], orient='h')\n",
    "            sns.boxplot(data=df[col], ax=ax[1, 0], orient='h')\n",
    "            try:\n",
    "                sns.histplot(data=df[col], ax=ax[0, 1])\n",
    "            except Exception as er:\n",
    "                print(f\"Skip '{col}' histplot, erorr - {er}\")\n",
    "            pie_list = self.pie_data.loc[self.pie_data.index == col].values.flatten().tolist()\n",
    "            pie_labels = self.pie_data.columns.tolist()\n",
    "            palette = ['red', 'magenta', 'gray', 'cyan']\n",
    "            pie_combine = list(zip(pie_list, pie_labels, palette))\n",
    "            pie_x = [x[0] for x in pie_combine if x[0] != 0]\n",
    "            pie_l = [x[1] for x in pie_combine if x[0] != 0]\n",
    "            pie_c = [x[2] for x in pie_combine if x[0] != 0]\n",
    "            ax[1, 1] = plt.pie(x=pie_x, labels=pie_l,\n",
    "                               autopct='%0.1F%%',\n",
    "                               shadow=True,\n",
    "                               wedgeprops={\"edgecolor\": \"k\", 'linewidth': 1},\n",
    "                               labeldistance=1.2,\n",
    "                               colors=pie_c,\n",
    "                               textprops={'color': \"b\"})\n",
    "            plt.savefig(pic_io)\n",
    "            document.add_picture(pic_io, width=Cm(17))\n",
    "            plt.clf()\n",
    "\n",
    "            # Друк метрик:\n",
    "            metr_dict = self.metrics.get(col)\n",
    "            if metr_dict is not None and type(metr_dict) == dict:\n",
    "                for metr_name in metr_dict.keys():\n",
    "                    document.add_paragraph(f'{metr_name} - {metr_dict.get(metr_name)}', style='List Bullet')\n",
    "\n",
    "            # Друк кореляції фічі:\n",
    "            col_corr = self.corr_mtx[col].reindex(self.corr_mtx[col].abs().sort_values(ascending=False).index)[\n",
    "                       :near_corr]\n",
    "            corr_labels = col_corr.index.tolist()\n",
    "            corr_vals = col_corr.tolist()\n",
    "            document.add_paragraph('Корелюється з:', style='my_text')\n",
    "            for pos, corr_lbl in enumerate(corr_labels):\n",
    "                document.add_paragraph(f'{round(corr_vals[pos], 3)} - '\n",
    "                                       f'{corr_lbl} ({self.feature_desc.get(corr_lbl)})',\n",
    "                                       style='List Bullet')\n",
    "            document.add_page_break()\n",
    "        document.save(f'report_{title}.docx')\n",
    "\n",
    "    def show_feature_description(self,\n",
    "                                feature_name: str,\n",
    "                                near_corr=10):\n",
    "        \"\"\"\n",
    "        Вивід у ноутбук звіту про фічу:\n",
    "            - \"ящики з вусами\"\n",
    "            - розподіл\n",
    "            - кругова діаграми з описом числових значень (цілі, дрібні, нулі, місінги)\n",
    "            - статистичні метрики\n",
    "            - найближчі сусідні фічі по кореляції\n",
    "\n",
    "        :param feature_name: Назва фічі (заголовок колонки)\n",
    "        :param near_corr: відображати кількість фіч, що найбільше корелюють з досліджуваною\n",
    "        :return: None\n",
    "        \"\"\"\n",
    "        df = self.df\n",
    "        print(feature_name)\n",
    "        print(f'({self.feature_desc.get(feature_name)})')\n",
    "\n",
    "        # Візуалізація\n",
    "        fig, ax = plt.subplots(2, 2, figsize=(17, 12))\n",
    "        sns.boxplot(x=df[feature_name], y=self.target, ax=ax[0, 0], orient='h')\n",
    "        sns.boxplot(data=df[feature_name], ax=ax[1, 0], orient='h')\n",
    "        try:\n",
    "            sns.histplot(data=df[feature_name], ax=ax[0, 1])\n",
    "        except Exception as er:\n",
    "            print(f\"Skip '{col}' histplot, erorr - {er}\")\n",
    "        pie_list = self.pie_data.loc[self.pie_data.index == feature_name].values.flatten().tolist()\n",
    "        pie_labels = self.pie_data.columns.tolist()\n",
    "        palette = ['red', 'magenta', 'gray', 'cyan']\n",
    "        pie_combine = list(zip(pie_list, pie_labels, palette))\n",
    "        pie_x = [x[0] for x in pie_combine if x[0] != 0]\n",
    "        pie_l = [x[1] for x in pie_combine if x[0] != 0]\n",
    "        pie_c = [x[2] for x in pie_combine if x[0] != 0]\n",
    "        ax[1, 1] = plt.pie(x=pie_x, labels=pie_l,\n",
    "                           autopct='%0.1F%%',\n",
    "                           shadow=True,\n",
    "                           wedgeprops={\"edgecolor\": \"k\", 'linewidth': 1},\n",
    "                           labeldistance=1.2,\n",
    "                           colors=pie_c,\n",
    "                           textprops={'color': \"b\"})\n",
    "        plt.show()\n",
    "        plt.clf()\n",
    "\n",
    "        # Друк метрик:\n",
    "        metr_dict = self.metrics.get(feature_name)\n",
    "        if metr_dict is not None and type(metr_dict) == dict:\n",
    "            for metr_name in metr_dict.keys():\n",
    "                print(f'{metr_name} - {metr_dict.get(metr_name)}')\n",
    "\n",
    "        # Друк кореляції фічі:\n",
    "        col_corr = self.corr_mtx[feature_name].reindex(self.corr_mtx[feature_name].abs().sort_values(ascending=False).index)[\n",
    "                   :near_corr]\n",
    "        corr_labels = col_corr.index.tolist()\n",
    "        corr_vals = col_corr.tolist()\n",
    "        print('Корелюється з:')\n",
    "        for pos, corr_lbl in enumerate(corr_labels):\n",
    "            print(f'\\t{round(corr_vals[pos], 3)} - '\n",
    "                  f'{corr_lbl} ({self.feature_desc.get(corr_lbl)})')\n",
    "\n",
    "    def get_preprocessing_config(self, config_file):\n",
    "        config_df = pd.read_excel(config_file)\n",
    "        cols_set = ['name', 'type_to', 'miss_to']\n",
    "        for col in cols_set:\n",
    "            if col not in config_df.columns:\n",
    "                print(f'Column \"{col}\" is absent in config. Need to reload...')\n",
    "                return\n",
    "\n",
    "        self.config_df = config_df[cols_set]\n",
    "        self.config_df.set_index('name', inplace=True)\n",
    "\n",
    "        def get_cols_setting(config_col, value):\n",
    "            return self.config_df.loc[self.config_df[config_col] == value].index.tolist()\n",
    "\n",
    "\n",
    "        self.cols_miss_to_zero = get_cols_setting('miss_to', 0)\n",
    "        self.cols_miss_to_one = get_cols_setting('miss_to', 1)\n",
    "        self.cols_miss_to_median = get_cols_setting('miss_to', 'median')\n",
    "        self.cols_miss_to_mode = get_cols_setting('miss_to', 'mode')\n",
    "\n",
    "        self.cols_integer_downcast = get_cols_setting('type_to', 'int')\n",
    "\n",
    "        print(f'Config load:'\n",
    "              f'\\n\\tmiss to \"0\":      {len(self.cols_miss_to_zero)}'\n",
    "              f'\\n\\tmiss to \"1\":      {len(self.cols_miss_to_one)}'\n",
    "              f'\\n\\tmiss to \"median\": {len(self.cols_miss_to_median)}'\n",
    "              f'\\n\\tmiss to \"mode\":   {len(self.cols_miss_to_mode)}')\n",
    "\n",
    "\n",
    "    def fill_missings(self,\n",
    "                      df_in=None):\n",
    "        df = self.df if df_in is None else df_in\n",
    "        for col_name in self.cols_miss_to_zero:\n",
    "            if col_name in df.columns:\n",
    "                df[col_name].fillna(0, inplace=True)\n",
    "\n",
    "        for col_name in self.cols_miss_to_one:\n",
    "            if col_name in df.columns:\n",
    "                df[col_name].fillna(1, inplace=True)\n",
    "\n",
    "        for col_name in self.cols_miss_to_median:\n",
    "            if col_name in df.columns:\n",
    "                cur_median = df[col_name].median()\n",
    "                df[col_name].fillna(cur_median, inplace=True)\n",
    "                self.modemedian_missed_dict.update({col_name: cur_median})\n",
    "\n",
    "        for col_name in self.cols_miss_to_mode:\n",
    "            if col_name in df.columns:\n",
    "                cur_mode = df[col_name].mode()[0]\n",
    "                df[col_name].fillna(cur_mode, inplace=True)\n",
    "                self.modemedian_missed_dict.update({col_name: cur_mode})\n",
    "\n",
    "        still_na = df.isnull().sum().sum()\n",
    "        if still_na > 0 :\n",
    "            print(f\"WARNING: After missing handling still present: {still_na} missed values\")\n",
    "        if df_in is None:\n",
    "            self.df = df\n",
    "        return df\n",
    "\n",
    "    def prepare_binary(self):\n",
    "        tab = self.df_info.set_index('name')\n",
    "        binary_cols = set(tab.loc[tab['available_pct'] < self.binary_tresh*100].index.tolist())\n",
    "        binary_cols2 = set(tab.loc[(tab['unique'] == 2) & (tab['min'] == 0) & (tab['max'] == 1)].index.tolist())\n",
    "        bin_total = binary_cols.union(binary_cols2)\n",
    "        self.cols_check_binary = bin_total\n",
    "\n",
    "        for col_name in self.cols_check_binary:\n",
    "            if col_name in self.df.columns:\n",
    "                self.df[col_name].loc[self.df[col_name] != 0] = 1\n",
    "\n",
    "\n",
    "    def _check_outliers(self,\n",
    "                       columns_to_check: list,\n",
    "                       upper_limit=0.8,\n",
    "                       lower_limit=0.2,\n",
    "                       cut_off=2,\n",
    "                        warn=True):\n",
    "        \"\"\"\n",
    "\n",
    "        :param df: Датафрейм для опрацювання\n",
    "        :param columns_to_check: Список попередньо відібраних колонок для перевірки (цифрові значення)\n",
    "        :param upper_limit: верхній ліміт (персентиль)\n",
    "        :param lower_limit: нижній ліміт (персентиль)\n",
    "        :param cut_off: коєфіцієнт меж відсічки\n",
    "        :return: опрацьований датафрейм\n",
    "        \"\"\"\n",
    "        assert upper_limit > lower_limit\n",
    "        assert cut_off > 0\n",
    "        assert 0 < upper_limit < 1\n",
    "        assert 0 < lower_limit < 1\n",
    "\n",
    "        df_size = self.df.shape[0]\n",
    "\n",
    "        for col in columns_to_check:\n",
    "            data = self.df.loc[self.df[col] > 0][col]\n",
    "            iqr = data.quantile(upper_limit) - data.quantile(lower_limit)\n",
    "            lower_bridge = data.quantile(lower_limit)-(iqr*cut_off)\n",
    "            upper_bridge = data.quantile(upper_limit)+(iqr*cut_off)\n",
    "\n",
    "            up_out_count = self.df[col].loc[self.df[col] > upper_bridge].count()\n",
    "            up_out_perc = round((up_out_count/df_size)*100, 1)\n",
    "            down_out_count = self.df[col].loc[self.df[col] < lower_bridge].count()\n",
    "            down_out_perc = round((down_out_count/df_size)*100, 1)\n",
    "\n",
    "            if warn == True and (up_out_count != 0 and down_out_count != 0):\n",
    "                print(f\"{col}:\"\n",
    "                      f\"\\n\\tБільше {upper_bridge} = {up_out_count} ({round(up_out_perc, 1)} %)\"\n",
    "                      f\"\\n\\tМенше  {lower_bridge} = {down_out_count} ({round(down_out_perc, 1)} %)\")\n",
    "\n",
    "\n",
    "            self.df.loc[(self.df[col] < lower_bridge) & (self.df[col] != 0), col] = lower_bridge\n",
    "            self.df.loc[(self.df[col] > upper_bridge) & (self.df[col] != 0), col] = upper_bridge\n",
    "            self.outliers_borders.update({col: (lower_bridge, upper_bridge)})\n",
    "\n",
    "    def integer_downcast(self, df_in=None):\n",
    "        df = self.df if df_in is None else df_in\n",
    "        for col_name in self.cols_integer_downcast:\n",
    "            if col_name in df.columns:\n",
    "                df[col_name] = pd.to_numeric(df[col_name], downcast='integer')\n",
    "        if df_in is None:\n",
    "            self.df = df\n",
    "        return df\n",
    "\n",
    "    def one_hot_encoding(self, df_in=None):\n",
    "        df = self.df if df_in is None else df_in\n",
    "        for col_name in self.cols_OHE:\n",
    "            if col_name in df.columns:\n",
    "                df[col_name] = round(df[col_name])\n",
    "                dummies_df = pd.get_dummies(df[col_name], prefix=col_name)\n",
    "                df = pd.merge(left=df, right=dummies_df, left_index=True, right_index=True)\n",
    "                df.drop(columns=[col_name], inplace=True)\n",
    "        if df_in is None:\n",
    "            self.df = df\n",
    "        return df\n",
    "\n",
    "    def create_sim_multiple_feature(self, df_in=None):\n",
    "        df = self.df if df_in is None else df_in\n",
    "        if 'device_sim_count' in df.columns:\n",
    "            df.loc[df['device_sim_count'] == 1, 'device_sim_count'] = 0\n",
    "            df.loc[df['device_sim_count'] > 1, 'device_sim_count'] = 1\n",
    "        if df_in is None:\n",
    "            self.df = df\n",
    "        return df\n",
    "\n",
    "    def convert_train(self, config_file):\n",
    "        self.get_preprocessing_config(config_file)\n",
    "        self.fill_missings()\n",
    "        self.prepare_binary()\n",
    "        self.integer_downcast()\n",
    "        self.one_hot_encoding()\n",
    "        self.create_sim_multiple_feature()\n",
    "        if self.outlier_strategy is not None:\n",
    "            self._check_outliers(columns_to_check=list({self.df.columns.tolist()} - {self.protected_cols}.union({'device_id'})))\n",
    "\n",
    "    def convert_test(self, input_df: pd.DataFrame):\n",
    "        \"\"\"\n",
    "        Тут бажано використати ті ж функції, що і для тесту, однак вони не в повній мірі універсальні (технічний борг))))\n",
    "        :param input_df: датафрейм отриманий з САС файлу\n",
    "        :return: опрацьований датафрейм з повним набором колонок необхідним для використання з моделями\n",
    "        \"\"\"\n",
    "        df = input_df.copy(deep=True)\n",
    "        for col in self.cols_miss_to_zero:\n",
    "            if col in df.columns:\n",
    "                df[col].fillna(0, inplace=True)\n",
    "\n",
    "        for col in self.cols_miss_to_one:\n",
    "            if col in df.columns:\n",
    "                df[col].fillna(1, inplace=True)\n",
    "\n",
    "        for col in self.modemedian_missed_dict.keys():\n",
    "            if col in df.columns:\n",
    "                cur_median = self.modemedian_missed_dict.get(col)\n",
    "                assert cur_median is not None, 'Проблема зі словником медіанних значень, отримано None'\n",
    "                df[col].fillna(cur_median, inplace=True)\n",
    "\n",
    "        for col in self.modemedian_missed_dict.keys():\n",
    "            if col in df.columns:\n",
    "                cur_median = self.modemedian_missed_dict.get(col)\n",
    "                assert cur_median is not None, 'Проблема зі словником медіанних та мод значень, отримано None'\n",
    "                df[col].fillna(cur_median, inplace=True)\n",
    "\n",
    "        for col in self.cols_check_binary:\n",
    "            if col in df.columns:\n",
    "                missed = df[col].isnull().sum()\n",
    "                assert missed == 0, f'У бінарній колонці {col} залишились місінги - {missed}'\n",
    "                df[col].loc[df[col] != 0] = 1\n",
    "\n",
    "        for col in self.cols_integer_downcast:\n",
    "            if col in df.columns:\n",
    "                df[col] = pd.to_numeric(df[col], downcast='integer')\n",
    "\n",
    "        for col in self.cols_OHE:\n",
    "            if col in df.columns:\n",
    "                df[col] = round(df[col])\n",
    "                dummies_df = pd.get_dummies(df[col], prefix=col)\n",
    "                df = pd.merge(left=df, right=dummies_df, left_index=True, right_index=True)\n",
    "                df.drop(columns=[col], inplace=True)\n",
    "\n",
    "        if 'device_sim_count' in df.columns:\n",
    "            df.loc[df['device_sim_count'] == 1, 'device_sim_count'] = 0\n",
    "            df.loc[df['device_sim_count'] > 1, 'device_sim_count'] = 1\n",
    "\n",
    "        for col in self.df.columns:\n",
    "            if col not in df.columns:\n",
    "                df[col] = 0\n",
    "                print(f'Увага - у тестовому фрагменті відсутня колонка {col}, додана зі значенням 0')\n",
    "\n",
    "        for col in df.columns:\n",
    "            if col not in self.df.columns:\n",
    "                df.drop(columns=[col], inplace=True)\n",
    "                print(f'Увага - у тестовому фрагменті зайва колонка {col}, видалено')\n",
    "\n",
    "        still_na = df.isnull().sum().sum()\n",
    "        if still_na > 0 :\n",
    "            print(f\"WARNING: After missing handling still present: {still_na} missed values\")\n",
    "\n",
    "        assert set(self.df.columns) == set(df.columns), 'У тестовому та тренувальному фрагменті різні набори колонок'\n",
    "        return df\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Огляд основної таблиці"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "time_start = datetime.now()\n",
    "eda = LazyDataStuart(df_train_main=train_main_fe,\n",
    "                     feature_desc_file='main_fe_desc.xlsx',\n",
    "                     target_name='target')\n",
    "print(f'Instance created {datetime.now()-time_start}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "cnt_sms_category_collect           -0.288675\ncnt_bank_sms_category_accordbank    0.273706\nentertainment                      -0.169031\ncnt_bank_sms_category_radabank     -0.167548\ndevice_android_version             -0.167284\ncnt_bank_sms_category_poltavaban    0.160256\ndevice_ios_version                 -0.157451\ncnt_bank_sms_category_bisbank       0.126865\ncnt_sms_category_ticket            -0.120925\ncnt_bank_sms_category_globusbank    0.109131\ndevice_sim_count                    0.107921\nFood                               -0.094499\nsms_in_cnt_std_mnt3                 0.084702\nMV_Traf_inc_s_Cnt                   0.084307\nName: target, dtype: float64"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Фічі з якими найбільше корелюється таргет:\n",
    "eda.corr_mtx['target'].reindex(eda.corr_mtx['target'].abs().sort_values(ascending=False).index)[:15].drop(index='target')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device_sim_count\n",
      "(Атрибут устройства - количество SIM-карт)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1224x864 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "desc - Атрибут устройства - количество SIM-карт\n",
      "type - float64\n",
      "available_cnt - 189125\n",
      "available_pct - 91.97568\n",
      "missed_cnt - 16500\n",
      "missed_pct - 8.02432\n",
      "zero_cnt - 0\n",
      "zero_pct - 0.0\n",
      "int_cnt - 189125\n",
      "int_pct - 100.0\n",
      "unique - 3\n",
      "max - 3.0\n",
      "min - 1.0\n",
      "median - 1.0\n",
      "mean - 1.49428\n",
      "mode - 1.0\n",
      "skew - 0.0325\n",
      "kurt - -1.97351\n",
      "Корелюється з:\n",
      "\t1.0 - device_sim_count (Атрибут устройства - количество SIM-карт)\n",
      "\t0.58 - device_ios_version (Атрибут устройства - операционная система)\n",
      "\t-0.532 - device_android_version (Атрибут устройства - операционная система)\n",
      "\t-0.408 - cnt_sms_category_collect (Количество SMS из категории \"Коллекторы\")\n",
      "\t-0.361 - cnt_bank_sms_category_poltavaban (Количество SMS из категории \"Банки\": Полтавабанк)\n",
      "\t0.35 - entertainment (Обмен бонусов на подарки категории Развлечения)\n",
      "\t-0.277 - device_brand_apple (Атрибут устройства - бренд)\n",
      "\t0.265 - Food (Обмен бонусов на подарки категории Рестораны и Кафе)\n",
      "\t0.259 - device_brand_samsung (Атрибут устройства - бренд)\n",
      "\t-0.214 - device_price (Атрибут устройства - цена)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Огляд метрик окремої фічі:\n",
    "eda.show_feature_description('device_sim_count')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device_sim_count\n",
      "(Атрибут устройства - количество SIM-карт)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1224x864 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "desc - Атрибут устройства - количество SIM-карт\n",
      "type - float64\n",
      "available_cnt - 189125\n",
      "available_pct - 91.97568\n",
      "missed_cnt - 16500\n",
      "missed_pct - 8.02432\n",
      "zero_cnt - 0\n",
      "zero_pct - 0.0\n",
      "int_cnt - 189125\n",
      "int_pct - 100.0\n",
      "unique - 3\n",
      "max - 3.0\n",
      "min - 1.0\n",
      "median - 1.0\n",
      "mean - 1.49428\n",
      "mode - 1.0\n",
      "skew - 0.0325\n",
      "kurt - -1.97351\n",
      "Корелюється з:\n",
      "\t1.0 - device_sim_count (Атрибут устройства - количество SIM-карт)\n",
      "\t0.58 - device_ios_version (Атрибут устройства - операционная система)\n",
      "\t-0.532 - device_android_version (Атрибут устройства - операционная система)\n",
      "\t-0.408 - cnt_sms_category_collect (Количество SMS из категории \"Коллекторы\")\n",
      "\t-0.361 - cnt_bank_sms_category_poltavaban (Количество SMS из категории \"Банки\": Полтавабанк)\n",
      "\t0.35 - entertainment (Обмен бонусов на подарки категории Развлечения)\n",
      "\t-0.277 - device_brand_apple (Атрибут устройства - бренд)\n",
      "\t0.265 - Food (Обмен бонусов на подарки категории Рестораны и Кафе)\n",
      "\t0.259 - device_brand_samsung (Атрибут устройства - бренд)\n",
      "\t-0.214 - device_price (Атрибут устройства - цена)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.show_feature_description('device_sim_count')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Підготовлені метрики: \n",
      "\t1. name\n",
      "\t2. desc\n",
      "\t3. type\n",
      "\t4. available_cnt\n",
      "\t5. available_pct\n",
      "\t6. missed_cnt\n",
      "\t7. missed_pct\n",
      "\t8. zero_cnt\n",
      "\t9. zero_pct\n",
      "\t10. int_cnt\n",
      "\t11. int_pct\n",
      "\t12. unique\n",
      "\t13. max\n",
      "\t14. min\n",
      "\t15. median\n",
      "\t16. mean\n",
      "\t17. mode\n",
      "\t18. skew\n",
      "\t19. kurt\n"
     ]
    }
   ],
   "source": [
    "# Огляд таблиці з метриками усіх фіч\n",
    "print('Підготовлені метрики: ')\n",
    "for pos, metr in enumerate(eda.df_info.columns):\n",
    "    print(f'\\t{pos+1}. {metr}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                           desc     type  \\\nname                                                                       \nabon_id                                  Идентификатор Абонента  float64   \ndevice_id                              Идентификатор устройства  float64   \ntarget                                                   Таргет  float64   \nBalance_uah                                              Баланс  float64   \nTM_ID                                                     Тариф  float64   \n...                                                         ...      ...   \ndevice_type_tablet          Атрибут устройства - тип устройства  float64   \ndevice_type_nan             Атрибут устройства - тип устройства  float64   \ndevice_type_rus_other       Атрибут устройства - тип устройства  float64   \ndevice_sim_count       Атрибут устройства - количество SIM-карт  float64   \ndevice_price                          Атрибут устройства - цена  float64   \n\n                       available_cnt  available_pct  missed_cnt  missed_pct  \\\nname                                                                          \nabon_id                       205625       100.0000           0      0.0000   \ndevice_id                     205625       100.0000           0      0.0000   \ntarget                         49862        24.2490           0      0.0000   \nBalance_uah                   204391        99.3999        1198      0.5826   \nTM_ID                         204427        99.4174        1198      0.5826   \n...                              ...            ...         ...         ...   \ndevice_type_tablet                75         0.0365        1299      0.6317   \ndevice_type_nan                15201         7.3926        1299      0.6317   \ndevice_type_rus_other         189125        91.9757        1299      0.6317   \ndevice_sim_count              189125        91.9757       16500      8.0243   \ndevice_price                  160161        77.8898       45464     22.1102   \n\n                       zero_cnt  zero_pct  int_cnt  int_pct  unique  \\\nname                                                                  \nabon_id                       0    0.0000   205625   100.00  205625   \ndevice_id                     0    0.0000   205625   100.00   12680   \ntarget                   155763   75.7510    49862   100.00       2   \nBalance_uah                  36    0.0175       21     0.01  196644   \nTM_ID                         0    0.0000        0     0.00      75   \n...                         ...       ...      ...      ...     ...   \ndevice_type_tablet       204251   99.3318       75   100.00       2   \ndevice_type_nan          189125   91.9757    15201   100.00       2   \ndevice_type_rus_other     15201    7.3926   189125   100.00       2   \ndevice_sim_count              0    0.0000   189125   100.00       3   \ndevice_price                  0    0.0000   160161   100.00      69   \n\n                                max           min       median          mean  \\\nname                                                                           \nabon_id                1.888225e+06  1.000001e+06  1414984.000  1.425224e+06   \ndevice_id              1.150570e+05  1.000010e+05   109998.000  1.090586e+05   \ntarget                 1.000000e+00  0.000000e+00        0.000  2.420000e-01   \nBalance_uah            1.672381e+15 -8.569925e+17        0.185 -1.742504e+13   \nTM_ID                  7.257000e+01  6.044200e+01       72.014  7.189900e+01   \n...                             ...           ...          ...           ...   \ndevice_type_tablet     1.000000e+00  0.000000e+00        0.000  0.000000e+00   \ndevice_type_nan        1.000000e+00  0.000000e+00        0.000  7.400000e-02   \ndevice_type_rus_other  1.000000e+00  0.000000e+00        1.000  9.260000e-01   \ndevice_sim_count       3.000000e+00  1.000000e+00        1.000  1.494000e+00   \ndevice_price           5.160000e+03  1.000000e+01      150.000  2.109120e+02   \n\n                              mode     skew       kurt  \nname                                                    \nabon_id                1000001.000    0.093     -1.146  \ndevice_id               109995.000   -0.417     -1.213  \ntarget                       0.000    1.202     -0.556  \nBalance_uah                  0.001 -225.880  51047.096  \nTM_ID                       72.206   -4.880     63.391  \n...                            ...      ...        ...  \ndevice_type_tablet           0.000   52.167   2719.414  \ndevice_type_nan              0.000    3.244      8.522  \ndevice_type_rus_other        1.000   -3.244      8.522  \ndevice_sim_count             1.000    0.032     -1.974  \ndevice_price               100.000    2.643     11.062  \n\n[855 rows x 18 columns]",
      "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>desc</th>\n      <th>type</th>\n      <th>available_cnt</th>\n      <th>available_pct</th>\n      <th>missed_cnt</th>\n      <th>missed_pct</th>\n      <th>zero_cnt</th>\n      <th>zero_pct</th>\n      <th>int_cnt</th>\n      <th>int_pct</th>\n      <th>unique</th>\n      <th>max</th>\n      <th>min</th>\n      <th>median</th>\n      <th>mean</th>\n      <th>mode</th>\n      <th>skew</th>\n      <th>kurt</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>abon_id</th>\n      <td>Идентификатор Абонента</td>\n      <td>float64</td>\n      <td>205625</td>\n      <td>100.0000</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>205625</td>\n      <td>100.00</td>\n      <td>205625</td>\n      <td>1.888225e+06</td>\n      <td>1.000001e+06</td>\n      <td>1414984.000</td>\n      <td>1.425224e+06</td>\n      <td>1000001.000</td>\n      <td>0.093</td>\n      <td>-1.146</td>\n    </tr>\n    <tr>\n      <th>device_id</th>\n      <td>Идентификатор устройства</td>\n      <td>float64</td>\n      <td>205625</td>\n      <td>100.0000</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>205625</td>\n      <td>100.00</td>\n      <td>12680</td>\n      <td>1.150570e+05</td>\n      <td>1.000010e+05</td>\n      <td>109998.000</td>\n      <td>1.090586e+05</td>\n      <td>109995.000</td>\n      <td>-0.417</td>\n      <td>-1.213</td>\n    </tr>\n    <tr>\n      <th>target</th>\n      <td>Таргет</td>\n      <td>float64</td>\n      <td>49862</td>\n      <td>24.2490</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>155763</td>\n      <td>75.7510</td>\n      <td>49862</td>\n      <td>100.00</td>\n      <td>2</td>\n      <td>1.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000</td>\n      <td>2.420000e-01</td>\n      <td>0.000</td>\n      <td>1.202</td>\n      <td>-0.556</td>\n    </tr>\n    <tr>\n      <th>Balance_uah</th>\n      <td>Баланс</td>\n      <td>float64</td>\n      <td>204391</td>\n      <td>99.3999</td>\n      <td>1198</td>\n      <td>0.5826</td>\n      <td>36</td>\n      <td>0.0175</td>\n      <td>21</td>\n      <td>0.01</td>\n      <td>196644</td>\n      <td>1.672381e+15</td>\n      <td>-8.569925e+17</td>\n      <td>0.185</td>\n      <td>-1.742504e+13</td>\n      <td>0.001</td>\n      <td>-225.880</td>\n      <td>51047.096</td>\n    </tr>\n    <tr>\n      <th>TM_ID</th>\n      <td>Тариф</td>\n      <td>float64</td>\n      <td>204427</td>\n      <td>99.4174</td>\n      <td>1198</td>\n      <td>0.5826</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>75</td>\n      <td>7.257000e+01</td>\n      <td>6.044200e+01</td>\n      <td>72.014</td>\n      <td>7.189900e+01</td>\n      <td>72.206</td>\n      <td>-4.880</td>\n      <td>63.391</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>device_type_tablet</th>\n      <td>Атрибут устройства - тип устройства</td>\n      <td>float64</td>\n      <td>75</td>\n      <td>0.0365</td>\n      <td>1299</td>\n      <td>0.6317</td>\n      <td>204251</td>\n      <td>99.3318</td>\n      <td>75</td>\n      <td>100.00</td>\n      <td>2</td>\n      <td>1.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000</td>\n      <td>0.000000e+00</td>\n      <td>0.000</td>\n      <td>52.167</td>\n      <td>2719.414</td>\n    </tr>\n    <tr>\n      <th>device_type_nan</th>\n      <td>Атрибут устройства - тип устройства</td>\n      <td>float64</td>\n      <td>15201</td>\n      <td>7.3926</td>\n      <td>1299</td>\n      <td>0.6317</td>\n      <td>189125</td>\n      <td>91.9757</td>\n      <td>15201</td>\n      <td>100.00</td>\n      <td>2</td>\n      <td>1.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000</td>\n      <td>7.400000e-02</td>\n      <td>0.000</td>\n      <td>3.244</td>\n      <td>8.522</td>\n    </tr>\n    <tr>\n      <th>device_type_rus_other</th>\n      <td>Атрибут устройства - тип устройства</td>\n      <td>float64</td>\n      <td>189125</td>\n      <td>91.9757</td>\n      <td>1299</td>\n      <td>0.6317</td>\n      <td>15201</td>\n      <td>7.3926</td>\n      <td>189125</td>\n      <td>100.00</td>\n      <td>2</td>\n      <td>1.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>1.000</td>\n      <td>9.260000e-01</td>\n      <td>1.000</td>\n      <td>-3.244</td>\n      <td>8.522</td>\n    </tr>\n    <tr>\n      <th>device_sim_count</th>\n      <td>Атрибут устройства - количество SIM-карт</td>\n      <td>float64</td>\n      <td>189125</td>\n      <td>91.9757</td>\n      <td>16500</td>\n      <td>8.0243</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>189125</td>\n      <td>100.00</td>\n      <td>3</td>\n      <td>3.000000e+00</td>\n      <td>1.000000e+00</td>\n      <td>1.000</td>\n      <td>1.494000e+00</td>\n      <td>1.000</td>\n      <td>0.032</td>\n      <td>-1.974</td>\n    </tr>\n    <tr>\n      <th>device_price</th>\n      <td>Атрибут устройства - цена</td>\n      <td>float64</td>\n      <td>160161</td>\n      <td>77.8898</td>\n      <td>45464</td>\n      <td>22.1102</td>\n      <td>0</td>\n      <td>0.0000</td>\n      <td>160161</td>\n      <td>100.00</td>\n      <td>69</td>\n      <td>5.160000e+03</td>\n      <td>1.000000e+01</td>\n      <td>150.000</td>\n      <td>2.109120e+02</td>\n      <td>100.000</td>\n      <td>2.643</td>\n      <td>11.062</td>\n    </tr>\n  </tbody>\n</table>\n<p>855 rows × 18 columns</p>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda.df_info.set_index('name', inplace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "raw",
   "source": [
    "Запис таблиці з метриками у окремий файл (він же в подальшому використовується як файл з налаштуваннями заміни місінгів):"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% raw\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "# eda.df_info.to_excel(metrics_df.xlsx)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Опрацювання основних таблиць train та test (без фічі координат)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "time_start = datetime.now()\n",
    "eda.convert_train(config_file='metrics_df.xlsx')\n",
    "print(f'{eda.df.shape[0]} records converted in {datetime.now()-time_start}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "eda.df.to_pickle('df_train_main_converted_wo_spatial.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df_test_main_wo_spatial = eda.convert_test(test_main_fe)\n",
    "df_test_main_wo_spatial.drop(columns=['target'], inplace=True)\n",
    "df_test_main_wo_spatial.to_pickle('df_test_main_converted_wo_spatial.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Огляд та опрацювання датасету з відомостями про використання сервісів (b_num)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Огляд"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "      abon_id        bnum  call_cnt_out  call_cnt_in  call_dur_out  \\\n0   1000006.0         311           1.0          0.0          13.0   \n1   1000006.0        7104           0.0          0.0           0.0   \n2   1000006.0     dzvinok           0.0          0.0           0.0   \n3   1000006.0    pensfond           0.0          0.0           0.0   \n4   1000006.0  sumyoenerg           0.0          0.0           0.0   \n5   1000009.0  ukrgasbank           0.0          0.0           0.0   \n6   1000012.0  novaposhta           0.0          0.0           0.0   \n7   1000012.0  oschadbank           0.0          0.0           0.0   \n8   1000012.0       vkcom           0.0          0.0           0.0   \n9   1000015.0       kocmo           0.0          0.0           0.0   \n10  1000020.0  getcontact           0.0          0.0           0.0   \n11  1000020.0  raiffeisen           0.0          0.0           0.0   \n12  1000021.0       33333           1.0          0.0          12.0   \n13  1000021.0   datagroup           0.0          0.0           0.0   \n14  1000021.0  raiffeisen           0.0          0.0           0.0   \n\n    call_dur_in  cnt_sms_out  cnt_sms_in  \n0           0.0          0.0         0.0  \n1           0.0          2.0         2.0  \n2           0.0          0.0         1.0  \n3           0.0          0.0         6.0  \n4           0.0          0.0         2.0  \n5           0.0          0.0        11.0  \n6           0.0          0.0         2.0  \n7           0.0          0.0         2.0  \n8           0.0          0.0         1.0  \n9           0.0          0.0         1.0  \n10          0.0          0.0         1.0  \n11          0.0          0.0         8.0  \n12          0.0          0.0         0.0  \n13          0.0          0.0         2.0  \n14          0.0          0.0         3.0  ",
      "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>abon_id</th>\n      <th>bnum</th>\n      <th>call_cnt_out</th>\n      <th>call_cnt_in</th>\n      <th>call_dur_out</th>\n      <th>call_dur_in</th>\n      <th>cnt_sms_out</th>\n      <th>cnt_sms_in</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000006.0</td>\n      <td>311</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000006.0</td>\n      <td>7104</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000006.0</td>\n      <td>dzvinok</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000006.0</td>\n      <td>pensfond</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000006.0</td>\n      <td>sumyoenerg</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1000009.0</td>\n      <td>ukrgasbank</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1000012.0</td>\n      <td>novaposhta</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1000012.0</td>\n      <td>oschadbank</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1000012.0</td>\n      <td>vkcom</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1000015.0</td>\n      <td>kocmo</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1000020.0</td>\n      <td>getcontact</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1000020.0</td>\n      <td>raiffeisen</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1000021.0</td>\n      <td>33333</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1000021.0</td>\n      <td>datagroup</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1000021.0</td>\n      <td>raiffeisen</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_services.head(15)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(836780, 8)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_services.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "16958"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_services['bnum'].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "169792"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_services['abon_id'].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "privatbank     46882\noschadbank     26470\nnovaposhta     24621\n111            18481\nfacebook       12402\n               ...  \npoliakovlab        1\n7ka                1\nsecretangel        1\nodessa-auto        1\nallmyphone         1\nName: bnum, Length: 16958, dtype: int64"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train_services['bnum'].value_counts(sort=True).to_excel('services.xlsx')  # для ручного формування нових фіч\n",
    "train_services['bnum'].value_counts(sort=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "abon_id          int32\nbnum            object\ncall_cnt_out     int16\ncall_cnt_in       int8\ncall_dur_out     int32\ncall_dur_in      int16\ncnt_sms_out      int16\ncnt_sms_in       int16\ndtype: object"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_services.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Клас опрацювання (bnum)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "class BnumFeatureHandler:\n",
    "    # За ключовими словами виділено декілька додаткових категорій (18)\n",
    "    bnum_cat = {'marginal':\n",
    "                [\"zvonmonet\", \"monetka\", \"7monet\", \"shvgroshi\", \"eurogroshi\", \"groshidozp\", \"egroshicom\", \"e groshi\", \"shv.groshi\", \"groshivsim\", \"daemogrosh\", \"groshman\", \"groshi doz\", \"groshiua\", \"groshik\", \"groshi dozp\", \"groshy\", \"megagroshi\", \"vashigrosh\", \"vashigroshi\", \"tvoigroshi\", \"groshi24\", \"groshi247\", \"egroshi\", \"daemogroshi\", \"tytgroshy\", \"svidkogrosh\", \"miloan.ua\", \"ccloan\", \"loanyua\", \"loan-credi\", \"megaloan\", \"loan-credit\", \"vloan\", \"moneygo\", \"moneyveo\", \"vip-money\", \"easy-money\", \"moneyboom\", \"money4me\", \"money4you\", \"sms2money\", \"money\", \"moneycredi\", \"moneyou\", \"gomoney\", \"stormoney\", \"moneycredit\", \"money24\", \"moneytop\", \"mrmoney\", \"globalmoney\", \"moneyu\", \"moneyexper\", \"moneyexpert\", \"moneypoint\", \"smartmoney\", \"automoney\", \"money_alg\", \"moneyto\", \"money club\", \"moneysend\", \"mr-money\", \"prostomoney\", \"moneymarke\", \"kredit-300\", \"kredobank\", \"kredit-3000\", \"newkrediti\", \"kredit\", \"kash kredi\", \"kredit vse\", \"kreditua\", \"kf-kredit\", \"kash kredit\", \"starkredit\", \"ukredit\", \"kredit vsem\", \"kredits24\", \"gokredit\", \"fastkredit\", \"kredytvamc\", \"kreditgo\", \"vashkredut\", \"debetkredi\", \"royalkredi\", \"kredit-24\", \"novikredit\", \"pvks-kredit\", \"kredytvamco\", \"kreditt\", \"kredit4u\", \"novikredyt\", \"novikrediti\", \"debetkredit\", \"kreditman\", \"royalkredit\", \"kredit 7\", \"kredit-0\", \"pvks-kredi\", \"kredit 365\", \"novikredyty\", \"kredityem\", \"kredit_m\", \"mykredit\", \"uakredit\", \"kreditka\", \"kredit_112\", \"kredenscafe\", \"kredit1\", \"hotkredit\", \"kreditonli\", \"kredytka\", \"kredo\", \"itkredobank\", \"kreditvsem\", \"kreditonlin\", \"creditplus\", \"creditkasa\", \"mycredit\", \"alexcredlt\", \"creditman\", \"creditexpr\", \"credit-0\", \"sloncredit\", \"credit7\", \"creditdnep\", \"alexcredit\", \"credit\", \"zecredit\", \"credit ua\", \"24credit\", \"creditcent\", \"soscredit\", \"credify\", \"creditcafe\", \"globalcred\", \"creditcentr\", \"credit.me\", \"globcredit\", \"creditup\", \"creditlite\", \"moneycredi\", \"litecredit\", \"microcredi\", \"credit24\", \"lime credi\", \"aiex credi\", \"goldcredit\", \"procredit\", \"forzacredi\", \"topcredit\", \"globalcredi\", \"top credit\", \"loan-credi\", \"credilo\", \"microcredit\", \"creditbot\", \"infocredit\", \"nicecredit\", \"creditnice\", \"moneycredit\", \"creditor\", \"forzacredit\", \"credit3000\", \"icredit\", \"creditdnepr\", \"ua-credit\", \"aiex credit\", \"lime credit\", \"credit-pro\", \"crediton\", \"credit2000\", \"creditbox\", \"bankcredit\", \"credit365\", \"loan-credit\", \"credy\", \"credit2u\", \"credit30000\", \"creditua\", \"mlcrocredi\", \"credit20000\", \"creditfree\", \"credit 0\", \"gutcreditu\", \"creditsmeu\", \"ua_credit\", \"o-credit\", \"supercredi\", \"creditik\", \"credit4you\", \"credmir\", \"credit10mi\", \"gutcreditua\", \"smartcredi\", \"aviracredi\", \"klt credit\", \"centrcredi\", \"supercredit\", \"creditlife\", \"creditcash\", \"sens credi\", \"oncredit\", \"credit25000\", \"catcredit\", \"credithelp\", \"creditpart\", \"megacredit\", \"creditsmeua\", \"credit10min\", \"creditorde\", \"cash credi\", \"credit deb\", \"credit2500\", \"monocredit\", \"aviracredit\", \"gogocredit\", \"centrcredit\", \"creditorder\", \"credit 2020\", \"micr0credi\", \"credit debt\", \"creditline\", \"credit clu\", \"credcenter\", \"cash credit\", \"cashcredit\", \"creditmone\", \"unicredit\", \"mlcrocredit\", \"credit fre\", \"uitracredit\", \"yescredit\", \"crediti\", \"1credit\", \"credit 202\", \"sens credit\", \"incredo\", \"cash-credi\", \"recredit\", \"exp credit\", \"credit0\", \"autocredo\", \"creditbank\", \"lombard 1\", \"lombard\", \"lombard zs\", \"bestlombar\", \"lombard gnp\", \"lomb.bazis\", \"lombard999\", \"lombard gn\", \"lombardspa\", \"lombsakura\", \"ilombard\", \"lombard ki\", \"bestlombard\", \"vashlombar\", \"lomb.parus\", \"lombgotivk\", \"evrolombar\", \"lomb-komod\", \"lombardreal\", \"lombardspas\", \"lombgotivka\", \"plomby gst\", \"lider lomb\", \"lombard zt\", \"lombardsbi\", \"pulombard\", \"lombard-ua\", \"garant lomb\", \"meb.lombar\", \"cash\", \"mistercash\", \"e-cash\", \"cashberry\", \"blago cash\", \"cashhelp\", \"verocash\", \"cash24\", \"cashup\", \"flashcash\", \"microcash\", \"casharing\", \"cashinsky\", \"cashme\", \"gdcashmere\", \"advcash\", \"cash247\", \"cashbox\", \"ultracashu\", \"creditcash\", \"cash credi\", \"cashyou\", \"cash2go\", \"ultracashua\", \"topcash\", \"allcash\", \"cashdeskmon\", \"hellocash\", \"cash credit\", \"cashcredit\", \"cashman\", \"cash4sms\", \"cash_games\", \"cashdeskmo\", \"yourcash\", \"fxcash\", \"cash-credi\", \"cashpro\", \"cashbelt.ua\", \"zaim\", \"zaim24-7\", \"zaim24\", \"zaimdozp\", \"fastzaim24\", \"bistrozaim\", \"leadszaim\", \"zaimpro\", \"izaime\", \"bystryjzaim\", \"shvidkozaim\", \"vashzaim\", \"vzaimi\", \"zaimvsem\", \"zaimi\", \"zaimiplus\", \"zaimo\", \"shvidk0zaim\", \"helpzaim\", \"ukrzaim\", \"ykr zaim\", \"cosmolot\", \"mikrozaym\", \"turbozaym\", \"zaymo\", \"zaymua\", \"zaymgrad\", \"ukrcollect\", \"collector\", \"fcollect\", \"dengi24/7\", \"dengi-24\", \"dengi_vdol\", \"denginakar\", \"dengi_vdolg\", \"dengi24\", \"goldengate\", \"denginakart\", \"dengi.com\", \"pozyka\", \"ukrpozyka\", \"biz-pozyka\", \"s.pozyka\", \"bizpozyka\", \"pozyka24\", \"pozykatut\", \"sim.pozyka\", \"tvojapozyk\", \"tvojapozyka\"],\n",
    "            'car_owner':\n",
    "                [\"wog\", \"socar\", \"dok\", \"brsm\", \"glusco\", \"club4x4\", \"upg\", \"okko\", \"exist.ua\", \"amic energy\", \"bmw\", \"bmw_idealm\", \"bmwfix\", \"bmwauto\", \"bmw lviv\", \"aria-bmw\", \"audi vipos\", \"ddaudio\", \"audi_odessa\", \"splaudio\", \"claudie\", \"mircaraudi\", \"claudia\", \"audicentre\", \"audi_odess\", \"caraudio\", \"audizp.com\", \"audiservice\", \"audi dnepr\", \"mircaraudio\", \"porsche\", \"volkswagen\", \"senseat\", \"hyundai\", \"carbot\", \"alfacars\", \"4mycar\", \"2car.com.ua\", \"winwincars\", \"mycar24\", \"2car.com.u\", \"caraudio\", \"carclip\", \"toyota\", \"toyota_cit\", \"toyotaluts\", \"toyota_city\", \"toyota st\", \"toyota nk\", \"toyotalutsk\", \"toyota lviv\", \"ford\", \"cto ford\", \"ford.pl.ua\", \"fordlvivua\", \"kia ukrain\", \"kia ukraine\", \"aptekiaks\", \"kiaparts\", \"kia kilceva\", \"kia kilcev\", \"benishgps\", \"ukrgps.net\", \"gps-plus\", \"vipgps.org\", \"benish gps\", \"lexus\", \"lexus kh\", \"lexus dnep\", \"mitsubishi\", \"nissan\", \"nissanpodo\", \"opel\", \"skoda auto\", \"skoda\", \"skodalutsk\", \"skodakreme\"],\n",
    "            'social':\n",
    "                [\"facebook\", \"google\", \"telegram\", \"tiktok\", \"microsoft\", \"whatsapp\", \"viber\", \"instagram\", \"ukrnet\", \"vkcom\", \"okru\", \"zoom\"],\n",
    "            'device_shop':\n",
    "                [\"eldorado\", \"foxtrot.ua\", \"comfy\", \"citrus.ua\", \"rozetka\", \"allo\", \"citrus\", \"mazilla ua\", \"hotline\", \"moyo.ua\"],\n",
    "            'clinic':\n",
    "                [\"e-health\", \"synevo\", \"dobrobut\", \"oxfordmed\", \"helsi\", \"dila\"],\n",
    "            'expens':\n",
    "                [\"dominos\", \"apple\", \"brocard\", \"glovo\", \"makeup\", \"metlife\", \"raketa\", \"sushi wok\", \"winetime\", \"luxoptica\", \"zolotiy vi\"],\n",
    "            'post':\n",
    "                [\"novaposhta\", \"ukrposhta\", \"meestua\", \"novapay\"],\n",
    "            'sport':\n",
    "                [\"megasport\", \"sportlife\", \"sportmaste\", \"sport-city\", \"sportmaster\", \"sportland\", \"sportbank\", \"megasportu\", \"sportlandia\", \"sportlandi\", \"urbansport\", \"intersport\", \"sport&spa\", \"sportfit\", \"sportstar\", \"deltasport\", \"zebrasport\", \"diromsport\", \"multisport\", \"terrasport\", \"sportclubx\", \"sportgoods\", \"instasport\", \"sport&moda\", \"delta sport\", \"sportshop\", \"sportmir\", \"svit sport\", \"sport havk\", \"sportstudi\", \"sportclub\", \"sofia-sport\", \"peak sport\", \"sport maga\", \"kid-sport\", \"sportlavka\", \"unisport\", \"sport-x\", \"sportpit_pl\", \"sport havka\", \"sportvida\", \"ligasporta\", \"sportcente\", \"sport_stil\", \"sport magaz\", \"mwsport\", \"sport city\", \"planetsport\", \"sportmarke\", \"sportobzor\", \"sportshots\", \"sportime\", \"sportextri\", \"kb-75sport\", \"sportextrim\", \"sport shop\", \"tviy sport\", \"ukrsportpit\", \"sportedu.o\", \"sportstudio\", \"sportbaza\", \"sport max\", \"semi sport\", \"sportpit_p\", \"sporthunte\", \"fansport\", \"sport star\", \"sportcar\", \"esportal\", \"sport cube\", \"rn-sport\", \"sportmax\", \"pro-sport\", \"sportinvent\", \"sportalm\", \"sportsale\", \"uasport\", \"waysport\", \"aktasport\", \"sport-atle\", \"rosport\", \"grand sport\", \"sporte\", \"arena sport\", \"sportmass\", \"sportactiv\", \"sport-stor\", \"volna-sport\", \"olimp sport\", \"osport.ua\", \"sportloto\", \"pir.sporta\", \"hop-sport\", \"sportplus\", \"sportwear\", \"ideal sport\", \"sportpit\", \"sportcenter\", \"energym\", \"i-gym\", \"pulse gym\", \"gym-rex\", \"everestgym\", \"ip gym\", \"gymbeam\", \"gymmaxx\", \"kotengym\", \"oxygym\", \"ladygym\", \"gymzelena20\", \"z gym\", \"eurogym\", \"atletgym\", \"militarygym\", \"megagym\", \"ekvator.gym\", \"olympia-gym\", \"gym oxford\", \"zeusgym&em\", \"gymfit\", \"top gym\", \"ultragym\", \"regym\"],\n",
    "            'house':\n",
    "                [\"volia\", \"contactgas\", \"triolan\", \"epicentrk\", \"vodokanal\", \"ukrtelecom\", \"gaz\", \"gazzbu\", \"viasat\", \"datagroup\", \"poltavagaz\", \"poltgazzbu\", \"gazpostavka\", \"poltgazzbut\", \"centrgazza\", \"centrgazzap\"],\n",
    "            'apteka':\n",
    "                [\"apteka911\", \"apteka tas\", \"apteka d.s.\", \"apteka d.s\", \"aptekar-\", \"aptekanetu\", \"apteka nc\", \"apteka\", \"aptekar\", \"apteka24.u\", \"nashaapteka\", \"aptekizpua\", \"apteka 36_\", \"geoapteka\", \"apteka_anc\", \"apteka24\", \"aptekazaka\", \"aptekanetua\", \"apteka24.ua\", \"aptekiaks\", \"apteka lif\", \"aptekazakaz\", \"apteka rua\", \"sapteka\", \"apteka 36.\", \"mega-aptek\", \"apteka.led\", \"apteka plu\", \"apteka plus\", \"fit0apteka\", \"fitoapteka\", \"avtoapteka\", \"apteka36i6\", \"lizalaptev\", \"apteka 7-y\", \"my apteka\", \"lizalapteva\", \"apteka life\"],\n",
    "            'mall':\n",
    "                [\"prostor\", \"varus\", \"chervonyi\", \"shop zakaz\", \"eva\", \"humana\", \"nova_linia\", \"intertop\", \"avon\", \"watsonsclu\", \"miraton\", \"jysk\", \"arber\", \"md fashion\", \"gloriajeans\", \"watsonsclub\", \"reserved\", \"adidas\", \"pazolini\", \"colins\", \"auchan\", \"columbia\", \"secunda\", \"yakaboo.ua\", \"auchan\", \"vitto rossi\", \"oodji\", \"fora\"],\n",
    "            'habits':\n",
    "                [\"morepiva\", \"more piva\", \"pivasov\", \"dom piva\", \"pivarium\", \"pivoplus\", \"pivobank\", \"pivobar\", \"nashe pivo\", \"pivoteka\", \"alcomag\", \"alcoparty\", \"alcopro\", \"alcotown\", \"alcofansho\", \"alcofanshop\", \"maxbeer\", \"beermarket\", \"vybeer\", \"vybeerai\", \"goldbeer\", \"beerline\", \"beerlander\", \"beerwine\", \"homebeer\", \"my-beer\", \"kriss beer\", \"beer.live\", \"thebeersho\", \"beerhouse\", \"varvar.beer\", \"beer&blues\"],\n",
    "            'young':\n",
    "                [\"tinder\", \"steam\", \"speak up\", \"wargaming\", \"game\", \"gaming\", \"snapchat\", \"discord\", \"badoo\", \"airbnb\", \"yakaboo\", \"spot-game\", \"game-shop\", \"egame\", \"4game\", \"x-game\", \"mygames\", \"game zone\", \"gamecore v\", \"upgames\", \"gamecore vr\", \"questgameu\", \"questgameua\", \"igame\", \"cbgames\", \"offgamers\", \"the game\", \"sharegameme\", \"sharegamem\", \"gamestore\", \"gamenet\", \"gamepad\"],\n",
    "            'security':\n",
    "                [\"ohrana.ua\", \"centrohrana\", \"liderohrana\", \"mun_ohrana\", \"vashaohrana\", \"vashaohran\", \"venbest\", \"a1 securit\", \"security\", \"a1 security\", \"secur\", \"secur.ua\", \"mccsecurit\", \"skysecurit\", \"pdp-secur\", \"bestsecur\", \"security-s\", \"mccsecurity\", \"ibmsecurit\", \"ajax\", \"sheriff\", \"sirius\"],\n",
    "            'pets':\n",
    "                [\"masterzoo\", \"zootovary\", \"zoocomplex\", \"e-zoo\", \"ezoo\", \"zoo-jungle\", \"eurozoo\", \"zooapteka\", \"zoolux\", \"zoolandia\", \"zoovet\", \"zoostar\", \"zoomark\", \"zoomagazin\", \"zoohelper\", \"zooera\", \"zooplus\", \"zoodom\", \"zoostyle\", \"zoogurman\", \"zoocool\", \"zoomir\", \"paintzoom\", \"ukrzoovetp\", \"zoomag\", \"zoopitomec\"],\n",
    "            'work_board':\n",
    "                [\"rabota.ua\", \"rabotalab\", \"estrabota\", \"rabota\", \"work.ua\", \"razem.work\", \"work in lc\", \"studwork\", \"norma-work\", \"smart work\"],\n",
    "            'baby':\n",
    "                [\"antoshka\", \"baby\", \"mother\", \"child\", \"kiddex\", \"justkids_u\", \"kids repub\", \"kiwi kids\", \"kidstaff\", \"evgakids\", \"bestkids\", \"klevokids\", \"kids club\", \"fashionkid\", \"justkids_ua\", \"kidsstore\", \"superskidk\", \"milkkids\", \"kid-sport\", \"kids republ\", \"kidauto\", \"kids\", \"kidr.com.u\", \"kidbutik.u\", \"fashionkids\", \"maris.kid\", \"oki-kids\", \"kiddy land\", \"kidtown\", \"kidslook\", \"kiditop\", \"kidbutik.ua\", \"ni-kids\", \"kiddyboom\", \"everykid\", \"a.kid\", \"happykids\", \"kmkids\", \"dg kids\"],\n",
    "            'pay_sys':\n",
    "                [\"paypal\", \"sharpay\", \"payoneer\", \"easypay\", \"papaya\", \"ipay.ua\", \"koronapay\", \"paysend.co\", \"paysend.com\", \"way for pay\", \"paycell\", \"alipay\", \"elpaysys\", \"dg payment\", \"paysera\", \"ua-pay\", \"epayments\", \"electropay\", \"payeer.com\", \"pay ukraine\", \"pay ukrain\", \"elupay\", \"pay vn ua\", \"paylink\", \"payukraine\", \"payalnik\", \"citypay\", \"vladpay\", \"wikipays\", \"b4bpayment\", \"paysend\", \"mpay\", \"payphone x\", \"skpay karta\", \"actionpay\", \"cardpay\", \"paytomat\"]}\n",
    "\n",
    "\n",
    "    def __init__(self):\n",
    "        self.cols_total = ['abon_id', 'unique_bnum', 'total_call_cnt_out', 'total_call_cnt_in', 'total_call_dur_out', 'total_call_dur_in', 'total_cnt_sms_out', 'total_cnt_sms_in']\n",
    "        self.cols_by_cat = ['_cat_present', '_unique_services', '_call_cnt_out', '_call_cnt_in', '_call_dur_out', '_call_dur_in', '_cnt_sms_out', '_cnt_sms_in']\n",
    "\n",
    "        for cat_name in self.bnum_cat.keys():\n",
    "            for suffix in self.cols_by_cat:\n",
    "                self.cols_total.append(cat_name+suffix)\n",
    "\n",
    "    def convert_services(self, input_df: pd.DataFrame):\n",
    "        services_df = input_df.copy(deep=True)\n",
    "        abons = services_df['abon_id'].unique().tolist()\n",
    "        service_feature_data = []\n",
    "        for abon in abons:\n",
    "            abon_df_part = services_df.loc[services_df['abon_id'] == abon].drop(columns=['abon_id'])\n",
    "            abon_data_row = []\n",
    "            abon_data_row.append(abon)\n",
    "            abon_data_row.append(abon_df_part['bnum'].nunique())\n",
    "            abon_data_row.append(abon_df_part['call_cnt_out'].sum())\n",
    "            abon_data_row.append(abon_df_part['call_cnt_in'].sum())\n",
    "            abon_data_row.append(abon_df_part['call_dur_out'].sum())\n",
    "            abon_data_row.append(abon_df_part['call_dur_in'].sum())\n",
    "            abon_data_row.append(abon_df_part['cnt_sms_out'].sum())\n",
    "            abon_data_row.append(abon_df_part['cnt_sms_in'].sum())\n",
    "            for cat in self.bnum_cat.keys():\n",
    "                cat_temp_df = abon_df_part.loc[abon_df_part['bnum'].isin(self.bnum_cat[cat])]\n",
    "                if cat_temp_df.shape[0] > 0:\n",
    "                    abon_data_row.append(1)\n",
    "                else:\n",
    "                    abon_data_row.append(0)\n",
    "                abon_data_row.append(cat_temp_df['bnum'].nunique())\n",
    "                abon_data_row.append(cat_temp_df['call_cnt_out'].sum())\n",
    "                abon_data_row.append(cat_temp_df['call_cnt_in'].sum())\n",
    "                abon_data_row.append(cat_temp_df['call_dur_out'].sum())\n",
    "                abon_data_row.append(cat_temp_df['call_dur_in'].sum())\n",
    "                abon_data_row.append(cat_temp_df['cnt_sms_out'].sum())\n",
    "                abon_data_row.append(cat_temp_df['cnt_sms_in'].sum())\n",
    "            service_feature_data.append(abon_data_row)\n",
    "\n",
    "        service_feature_df = pd.DataFrame(data=service_feature_data, columns=self.cols_total)\n",
    "        service_feature_df.set_index('abon_id', inplace=True, drop=True)\n",
    "        return service_feature_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [],
   "source": [
    "service_feature_handler = BnumFeatureHandler()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Опрацювання Train (bnum)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "df_converted_train_services = service_feature_handler.convert_services(train_services)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [],
   "source": [
    "# Виправлення деяких помилок класу (спрощення типів, визначення колонок, у яких відсутні дані)\n",
    "df_converted_train_services.index = pd.to_numeric(df_converted_train_services.index, downcast='integer')\n",
    "for col in df_converted_train_services.columns:\n",
    "    df_converted_train_services[col] = pd.to_numeric(df_converted_train_services[col], downcast='integer')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [],
   "source": [
    "serv_count = df_converted_train_services.replace([0], np.nan).count()\n",
    "zero_cols_serv = serv_count.loc[serv_count == 0].index.tolist()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "90"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zero_cols_serv)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "         unique_bnum  total_call_cnt_out  total_call_cnt_in  \\\nabon_id                                                       \n1000006            5                   1                  0   \n1000009            1                   0                  0   \n1000012            3                   0                  0   \n1000015            1                   0                  0   \n1000020            2                   0                  0   \n1000021            3                   1                  0   \n1000024            2                   0                  0   \n1000025            3                   0                  0   \n1000030            1                   0                  0   \n1000043            1                   0                  0   \n1000045            1                   0                  0   \n1000047            3                   1                  0   \n1000054            1                   0                  0   \n1000055            1                   0                  0   \n1000058            3                   0                  0   \n1000063            2                   0                  0   \n1000064            1                   0                  0   \n1000068            3                   0                  0   \n1000084            3                   0                  0   \n1000088            3                   0                  0   \n1000103            2                   0                  0   \n1000118            1                   0                  0   \n1000119            5                   0                  0   \n1000120            2                   0                  0   \n1000122            1                   0                  0   \n1000142           14                   0                  0   \n1000143            3                   1                  0   \n1000150            2                   0                  0   \n1000157            5                   0                  0   \n1000164            1                   0                  0   \n1000167            1                   0                  0   \n1000169            2                   0                  0   \n1000171            3                   0                  0   \n1000172            2                   0                  0   \n1000176            4                   0                  0   \n\n         total_call_dur_out  total_call_dur_in  total_cnt_sms_out  \\\nabon_id                                                             \n1000006                  13                  0                  2   \n1000009                   0                  0                  0   \n1000012                   0                  0                  0   \n1000015                   0                  0                  0   \n1000020                   0                  0                  0   \n1000021                  12                  0                  0   \n1000024                   0                  0                  0   \n1000025                   0                  0                  0   \n1000030                   0                  0                  0   \n1000043                   0                  0                  0   \n1000045                   0                  0                  1   \n1000047                   7                  0                  0   \n1000054                   0                  0                  0   \n1000055                   0                  0                  0   \n1000058                   0                  0                  0   \n1000063                   0                  0                  0   \n1000064                   0                  0                  0   \n1000068                   0                  0                  0   \n1000084                   0                  0                  0   \n1000088                   0                  0                  8   \n1000103                   0                  0                  0   \n1000118                   0                  0                  0   \n1000119                   0                  0                  0   \n1000120                   0                  0                  0   \n1000122                   0                  0                  0   \n1000142                   0                  0                  0   \n1000143                   4                  0                  0   \n1000150                   0                  0                  0   \n1000157                   0                  0                  0   \n1000164                   0                  0                  0   \n1000167                   0                  0                  0   \n1000169                   0                  0                  0   \n1000171                   0                  0                  0   \n1000172                   0                  0                  0   \n1000176                   0                  0                  0   \n\n         total_cnt_sms_in  marginal_cat_present  marginal_unique_services  \\\nabon_id                                                                     \n1000006                11                     0                         0   \n1000009                11                     0                         0   \n1000012                 5                     0                         0   \n1000015                 1                     0                         0   \n1000020                 9                     0                         0   \n1000021                 5                     0                         0   \n1000024                 7                     0                         0   \n1000025                31                     1                         1   \n1000030                 2                     0                         0   \n1000043                 5                     0                         0   \n1000045                 0                     0                         0   \n1000047                 4                     0                         0   \n1000054                22                     0                         0   \n1000055                 6                     0                         0   \n1000058                33                     1                         1   \n1000063                 2                     0                         0   \n1000064                 1                     0                         0   \n1000068                 5                     0                         0   \n1000084                 9                     0                         0   \n1000088                15                     0                         0   \n1000103                 2                     0                         0   \n1000118                 1                     0                         0   \n1000119                 9                     0                         0   \n1000120                16                     0                         0   \n1000122                 2                     0                         0   \n1000142                32                     0                         0   \n1000143                 3                     0                         0   \n1000150                 8                     0                         0   \n1000157                 7                     0                         0   \n1000164                 1                     0                         0   \n1000167                 4                     0                         0   \n1000169                24                     0                         0   \n1000171                 9                     0                         0   \n1000172                 3                     0                         0   \n1000176                16                     0                         0   \n\n         marginal_cnt_sms_in  ...  pets_cnt_sms_in  work_board_cat_present  \\\nabon_id                       ...                                            \n1000006                    0  ...                0                       0   \n1000009                    0  ...                0                       0   \n1000012                    0  ...                0                       0   \n1000015                    0  ...                0                       0   \n1000020                    0  ...                0                       0   \n1000021                    0  ...                0                       0   \n1000024                    0  ...                0                       0   \n1000025                   24  ...                0                       0   \n1000030                    0  ...                0                       0   \n1000043                    0  ...                0                       0   \n1000045                    0  ...                0                       0   \n1000047                    0  ...                0                       0   \n1000054                    0  ...                0                       0   \n1000055                    0  ...                0                       0   \n1000058                    2  ...                0                       0   \n1000063                    0  ...                0                       0   \n1000064                    0  ...                0                       0   \n1000068                    0  ...                0                       0   \n1000084                    0  ...                0                       0   \n1000088                    0  ...                0                       0   \n1000103                    0  ...                0                       0   \n1000118                    0  ...                0                       0   \n1000119                    0  ...                0                       0   \n1000120                    0  ...                0                       0   \n1000122                    0  ...                0                       0   \n1000142                    0  ...                0                       0   \n1000143                    0  ...                0                       0   \n1000150                    0  ...                0                       0   \n1000157                    0  ...                0                       0   \n1000164                    0  ...                0                       0   \n1000167                    0  ...                0                       0   \n1000169                    0  ...                0                       0   \n1000171                    0  ...                0                       0   \n1000172                    0  ...                0                       0   \n1000176                    0  ...                0                       0   \n\n         work_board_unique_services  work_board_cnt_sms_in  baby_cat_present  \\\nabon_id                                                                        \n1000006                           0                      0                 0   \n1000009                           0                      0                 0   \n1000012                           0                      0                 0   \n1000015                           0                      0                 0   \n1000020                           0                      0                 0   \n1000021                           0                      0                 0   \n1000024                           0                      0                 0   \n1000025                           0                      0                 0   \n1000030                           0                      0                 0   \n1000043                           0                      0                 0   \n1000045                           0                      0                 0   \n1000047                           0                      0                 0   \n1000054                           0                      0                 0   \n1000055                           0                      0                 0   \n1000058                           0                      0                 0   \n1000063                           0                      0                 0   \n1000064                           0                      0                 0   \n1000068                           0                      0                 0   \n1000084                           0                      0                 0   \n1000088                           0                      0                 0   \n1000103                           0                      0                 0   \n1000118                           0                      0                 0   \n1000119                           0                      0                 0   \n1000120                           0                      0                 0   \n1000122                           0                      0                 0   \n1000142                           0                      0                 0   \n1000143                           0                      0                 0   \n1000150                           0                      0                 0   \n1000157                           0                      0                 0   \n1000164                           0                      0                 0   \n1000167                           0                      0                 0   \n1000169                           0                      0                 0   \n1000171                           0                      0                 0   \n1000172                           0                      0                 0   \n1000176                           0                      0                 0   \n\n         baby_unique_services  baby_cnt_sms_in  pay_sys_cat_present  \\\nabon_id                                                               \n1000006                     0                0                    0   \n1000009                     0                0                    0   \n1000012                     0                0                    0   \n1000015                     0                0                    0   \n1000020                     0                0                    0   \n1000021                     0                0                    0   \n1000024                     0                0                    0   \n1000025                     0                0                    0   \n1000030                     0                0                    0   \n1000043                     0                0                    0   \n1000045                     0                0                    0   \n1000047                     0                0                    0   \n1000054                     0                0                    0   \n1000055                     0                0                    0   \n1000058                     0                0                    0   \n1000063                     0                0                    0   \n1000064                     0                0                    0   \n1000068                     0                0                    0   \n1000084                     0                0                    0   \n1000088                     0                0                    0   \n1000103                     0                0                    0   \n1000118                     0                0                    0   \n1000119                     0                0                    0   \n1000120                     0                0                    0   \n1000122                     0                0                    0   \n1000142                     0                0                    0   \n1000143                     0                0                    0   \n1000150                     0                0                    0   \n1000157                     0                0                    0   \n1000164                     0                0                    0   \n1000167                     0                0                    0   \n1000169                     0                0                    0   \n1000171                     0                0                    0   \n1000172                     0                0                    0   \n1000176                     0                0                    0   \n\n         pay_sys_unique_services  pay_sys_cnt_sms_in  \nabon_id                                               \n1000006                        0                   0  \n1000009                        0                   0  \n1000012                        0                   0  \n1000015                        0                   0  \n1000020                        0                   0  \n1000021                        0                   0  \n1000024                        0                   0  \n1000025                        0                   0  \n1000030                        0                   0  \n1000043                        0                   0  \n1000045                        0                   0  \n1000047                        0                   0  \n1000054                        0                   0  \n1000055                        0                   0  \n1000058                        0                   0  \n1000063                        0                   0  \n1000064                        0                   0  \n1000068                        0                   0  \n1000084                        0                   0  \n1000088                        0                   0  \n1000103                        0                   0  \n1000118                        0                   0  \n1000119                        0                   0  \n1000120                        0                   0  \n1000122                        0                   0  \n1000142                        0                   0  \n1000143                        0                   0  \n1000150                        0                   0  \n1000157                        0                   0  \n1000164                        0                   0  \n1000167                        0                   0  \n1000169                        0                   0  \n1000171                        0                   0  \n1000172                        0                   0  \n1000176                        0                   0  \n\n[35 rows x 61 columns]",
      "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>unique_bnum</th>\n      <th>total_call_cnt_out</th>\n      <th>total_call_cnt_in</th>\n      <th>total_call_dur_out</th>\n      <th>total_call_dur_in</th>\n      <th>total_cnt_sms_out</th>\n      <th>total_cnt_sms_in</th>\n      <th>marginal_cat_present</th>\n      <th>marginal_unique_services</th>\n      <th>marginal_cnt_sms_in</th>\n      <th>...</th>\n      <th>pets_cnt_sms_in</th>\n      <th>work_board_cat_present</th>\n      <th>work_board_unique_services</th>\n      <th>work_board_cnt_sms_in</th>\n      <th>baby_cat_present</th>\n      <th>baby_unique_services</th>\n      <th>baby_cnt_sms_in</th>\n      <th>pay_sys_cat_present</th>\n      <th>pay_sys_unique_services</th>\n      <th>pay_sys_cnt_sms_in</th>\n    </tr>\n    <tr>\n      <th>abon_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1000006</th>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>2</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000009</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000012</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000015</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000020</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000021</th>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>12</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000024</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000025</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>31</td>\n      <td>1</td>\n      <td>1</td>\n      <td>24</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000030</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000043</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000045</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000047</th>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000054</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>22</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000055</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000058</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>33</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000063</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000064</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000068</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000084</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000088</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>15</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000103</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000118</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000119</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000120</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000122</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000142</th>\n      <td>14</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>32</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000143</th>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000150</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000157</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000164</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000167</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000169</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000171</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000172</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000176</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>35 rows × 61 columns</p>\n</div>"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_converted_train_services.drop(columns=zero_cols_serv, inplace=True)\n",
    "df_converted_train_services.fillna(0, inplace=True)\n",
    "df_converted_train_services.head(35)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [],
   "source": [
    "df_converted_train_services.to_pickle('df_train_service_new_features.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Опрацювання Test (bnum)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [],
   "source": [
    "df_converted_test_services = service_feature_handler.convert_services(test_services)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [],
   "source": [
    "df_converted_test_services.index = pd.to_numeric(df_converted_test_services.index, downcast='integer')\n",
    "for col in df_converted_test_services.columns:\n",
    "    df_converted_test_services[col] = pd.to_numeric(df_converted_test_services[col], downcast='integer')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [
    {
     "data": {
      "text/plain": "         unique_bnum  total_call_cnt_out  total_call_cnt_in  \\\nabon_id                                                       \n1000003            2                   0                  0   \n1000007            1                   0                  0   \n1000023            2                   0                  0   \n1000026            2                   0                  0   \n1000027            2                   0                  0   \n1000032            6                   0                  0   \n1000035            3                   0                  0   \n1000036            1                   0                  0   \n1000037            2                   9                  0   \n1000040            2                   0                  0   \n1000057            1                   0                  0   \n1000060            2                   0                  0   \n1000065            1                   6                  0   \n1000070            4                   3                  0   \n1000071            1                   0                  0   \n1000082            4                   1                  0   \n1000085            1                   0                  0   \n1000087            2                   0                  0   \n1000098            9                   5                  0   \n1000099            7                   0                  0   \n1000100            2                   0                  0   \n1000102           10                   0                  0   \n1000106            4                   1                  0   \n1000107            3                   0                  0   \n1000109           16                   0                  0   \n1000110            7                   0                  0   \n1000114            2                   3                  0   \n1000115            1                   0                  0   \n1000116            4                   0                  0   \n1000117            3                   0                  0   \n1000136            1                   0                  0   \n1000139            3                   0                  0   \n1000141            5                   2                  0   \n1000145            4                   0                  0   \n1000146            4                   0                  0   \n\n         total_call_dur_out  total_call_dur_in  total_cnt_sms_out  \\\nabon_id                                                             \n1000003                   0                  0                  0   \n1000007                   0                  0                  0   \n1000023                   0                  0                  0   \n1000026                   0                  0                  0   \n1000027                   0                  0                  0   \n1000032                   0                  0                 25   \n1000035                   0                  0                  0   \n1000036                   0                  0                  0   \n1000037                  62                  0                  0   \n1000040                   0                  0                  0   \n1000057                   0                  0                  0   \n1000060                   0                  0                  0   \n1000065                  55                  0                  0   \n1000070                  26                  0                  0   \n1000071                   0                  0                  0   \n1000082                  52                  0                  0   \n1000085                   0                  0                  0   \n1000087                   0                  0                  0   \n1000098                 147                  0                  0   \n1000099                   0                  0                  2   \n1000100                   0                  0                  0   \n1000102                   0                  0                  0   \n1000106                 311                  0                  0   \n1000107                   0                  0                  3   \n1000109                   0                  0                  0   \n1000110                   0                  0                  0   \n1000114                1132                  0                  0   \n1000115                   0                  0                  0   \n1000116                   0                  0                  0   \n1000117                   0                  0                  0   \n1000136                   0                  0                  0   \n1000139                   0                  0                  0   \n1000141                  12                  0                  2   \n1000145                   0                  0                  0   \n1000146                   0                  0                  0   \n\n         total_cnt_sms_in  marginal_cat_present  marginal_unique_services  \\\nabon_id                                                                     \n1000003                 5                     0                         0   \n1000007                 1                     0                         0   \n1000023                13                     0                         0   \n1000026                 3                     0                         0   \n1000027                 4                     0                         0   \n1000032                19                     0                         0   \n1000035                 8                     0                         0   \n1000036                 6                     0                         0   \n1000037                 2                     0                         0   \n1000040                 4                     0                         0   \n1000057                17                     0                         0   \n1000060                 6                     0                         0   \n1000065                 0                     0                         0   \n1000070                16                     1                         1   \n1000071                 1                     0                         0   \n1000082                 4                     0                         0   \n1000085                21                     0                         0   \n1000087                 2                     0                         0   \n1000098                 9                     0                         0   \n1000099                34                     0                         0   \n1000100                 2                     0                         0   \n1000102                47                     0                         0   \n1000106                15                     0                         0   \n1000107                28                     0                         0   \n1000109                39                     0                         0   \n1000110                32                     0                         0   \n1000114                 5                     0                         0   \n1000115                 6                     0                         0   \n1000116                 8                     0                         0   \n1000117                 8                     0                         0   \n1000136                 2                     0                         0   \n1000139                14                     0                         0   \n1000141                25                     0                         0   \n1000145                 9                     0                         0   \n1000146                12                     0                         0   \n\n         marginal_cnt_sms_in  ...  pets_cnt_sms_in  work_board_cat_present  \\\nabon_id                       ...                                            \n1000003                    0  ...                0                       0   \n1000007                    0  ...                0                       0   \n1000023                    0  ...                0                       0   \n1000026                    0  ...                0                       0   \n1000027                    0  ...                0                       0   \n1000032                    0  ...                0                       0   \n1000035                    0  ...                0                       0   \n1000036                    0  ...                0                       0   \n1000037                    0  ...                0                       0   \n1000040                    0  ...                0                       0   \n1000057                    0  ...                0                       0   \n1000060                    0  ...                0                       0   \n1000065                    0  ...                0                       0   \n1000070                    2  ...                0                       0   \n1000071                    0  ...                0                       0   \n1000082                    0  ...                0                       0   \n1000085                    0  ...                0                       0   \n1000087                    0  ...                0                       0   \n1000098                    0  ...                0                       0   \n1000099                    0  ...                0                       0   \n1000100                    0  ...                0                       0   \n1000102                    0  ...                0                       0   \n1000106                    0  ...                0                       0   \n1000107                    0  ...                0                       0   \n1000109                    0  ...                0                       0   \n1000110                    0  ...                0                       0   \n1000114                    0  ...                0                       0   \n1000115                    0  ...                0                       0   \n1000116                    0  ...                0                       0   \n1000117                    0  ...                0                       0   \n1000136                    0  ...                0                       0   \n1000139                    0  ...                0                       0   \n1000141                    0  ...                0                       0   \n1000145                    0  ...                0                       0   \n1000146                    0  ...                0                       0   \n\n         work_board_unique_services  work_board_cnt_sms_in  baby_cat_present  \\\nabon_id                                                                        \n1000003                           0                      0                 0   \n1000007                           0                      0                 0   \n1000023                           0                      0                 0   \n1000026                           0                      0                 0   \n1000027                           0                      0                 0   \n1000032                           0                      0                 0   \n1000035                           0                      0                 0   \n1000036                           0                      0                 0   \n1000037                           0                      0                 0   \n1000040                           0                      0                 0   \n1000057                           0                      0                 0   \n1000060                           0                      0                 0   \n1000065                           0                      0                 0   \n1000070                           0                      0                 0   \n1000071                           0                      0                 0   \n1000082                           0                      0                 0   \n1000085                           0                      0                 0   \n1000087                           0                      0                 0   \n1000098                           0                      0                 0   \n1000099                           0                      0                 0   \n1000100                           0                      0                 0   \n1000102                           0                      0                 0   \n1000106                           0                      0                 0   \n1000107                           0                      0                 0   \n1000109                           0                      0                 0   \n1000110                           0                      0                 0   \n1000114                           0                      0                 0   \n1000115                           0                      0                 0   \n1000116                           0                      0                 0   \n1000117                           0                      0                 0   \n1000136                           0                      0                 0   \n1000139                           0                      0                 0   \n1000141                           0                      0                 0   \n1000145                           0                      0                 0   \n1000146                           0                      0                 0   \n\n         baby_unique_services  baby_cnt_sms_in  pay_sys_cat_present  \\\nabon_id                                                               \n1000003                     0                0                    0   \n1000007                     0                0                    0   \n1000023                     0                0                    0   \n1000026                     0                0                    0   \n1000027                     0                0                    0   \n1000032                     0                0                    0   \n1000035                     0                0                    0   \n1000036                     0                0                    0   \n1000037                     0                0                    0   \n1000040                     0                0                    0   \n1000057                     0                0                    0   \n1000060                     0                0                    0   \n1000065                     0                0                    0   \n1000070                     0                0                    0   \n1000071                     0                0                    0   \n1000082                     0                0                    0   \n1000085                     0                0                    0   \n1000087                     0                0                    0   \n1000098                     0                0                    0   \n1000099                     0                0                    0   \n1000100                     0                0                    0   \n1000102                     0                0                    0   \n1000106                     0                0                    0   \n1000107                     0                0                    0   \n1000109                     0                0                    0   \n1000110                     0                0                    0   \n1000114                     0                0                    0   \n1000115                     0                0                    0   \n1000116                     0                0                    0   \n1000117                     0                0                    0   \n1000136                     0                0                    0   \n1000139                     0                0                    0   \n1000141                     0                0                    0   \n1000145                     0                0                    0   \n1000146                     0                0                    0   \n\n         pay_sys_unique_services  pay_sys_cnt_sms_in  \nabon_id                                               \n1000003                        0                   0  \n1000007                        0                   0  \n1000023                        0                   0  \n1000026                        0                   0  \n1000027                        0                   0  \n1000032                        0                   0  \n1000035                        0                   0  \n1000036                        0                   0  \n1000037                        0                   0  \n1000040                        0                   0  \n1000057                        0                   0  \n1000060                        0                   0  \n1000065                        0                   0  \n1000070                        0                   0  \n1000071                        0                   0  \n1000082                        0                   0  \n1000085                        0                   0  \n1000087                        0                   0  \n1000098                        0                   0  \n1000099                        0                   0  \n1000100                        0                   0  \n1000102                        0                   0  \n1000106                        0                   0  \n1000107                        0                   0  \n1000109                        0                   0  \n1000110                        0                   0  \n1000114                        0                   0  \n1000115                        0                   0  \n1000116                        0                   0  \n1000117                        0                   0  \n1000136                        0                   0  \n1000139                        0                   0  \n1000141                        0                   0  \n1000145                        0                   0  \n1000146                        0                   0  \n\n[35 rows x 61 columns]",
      "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>unique_bnum</th>\n      <th>total_call_cnt_out</th>\n      <th>total_call_cnt_in</th>\n      <th>total_call_dur_out</th>\n      <th>total_call_dur_in</th>\n      <th>total_cnt_sms_out</th>\n      <th>total_cnt_sms_in</th>\n      <th>marginal_cat_present</th>\n      <th>marginal_unique_services</th>\n      <th>marginal_cnt_sms_in</th>\n      <th>...</th>\n      <th>pets_cnt_sms_in</th>\n      <th>work_board_cat_present</th>\n      <th>work_board_unique_services</th>\n      <th>work_board_cnt_sms_in</th>\n      <th>baby_cat_present</th>\n      <th>baby_unique_services</th>\n      <th>baby_cnt_sms_in</th>\n      <th>pay_sys_cat_present</th>\n      <th>pay_sys_unique_services</th>\n      <th>pay_sys_cnt_sms_in</th>\n    </tr>\n    <tr>\n      <th>abon_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1000003</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000007</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000023</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000026</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000027</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000032</th>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>25</td>\n      <td>19</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000035</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000036</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000037</th>\n      <td>2</td>\n      <td>9</td>\n      <td>0</td>\n      <td>62</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000040</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000057</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000060</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000065</th>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>55</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000070</th>\n      <td>4</td>\n      <td>3</td>\n      <td>0</td>\n      <td>26</td>\n      <td>0</td>\n      <td>0</td>\n      <td>16</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000071</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000082</th>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>52</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000085</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000087</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000098</th>\n      <td>9</td>\n      <td>5</td>\n      <td>0</td>\n      <td>147</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000099</th>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000100</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000102</th>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>47</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000106</th>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>311</td>\n      <td>0</td>\n      <td>0</td>\n      <td>15</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000107</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000109</th>\n      <td>16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>39</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000110</th>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>32</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000114</th>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1132</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000115</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000116</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000117</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000136</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000139</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>14</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000141</th>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>12</td>\n      <td>0</td>\n      <td>2</td>\n      <td>25</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000145</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000146</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>35 rows × 61 columns</p>\n</div>"
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_converted_test_services.drop(columns=zero_cols_serv, inplace=True)\n",
    "df_converted_test_services.fillna(0, inplace=True)\n",
    "df_converted_test_services.head(35)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [],
   "source": [
    "df_converted_test_services.to_pickle('df_test_service_new_features.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Огляд та опрацювання датафрейму з відомостіями про використані додатки (apps)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Огляд"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "     abon_id  Application  SUM_of_Duration_sec  SUM_of_Volume_kb  \\\n0  1000009.0         17.0                  2.0              2.41   \n1  1000009.0        232.0               1749.0           2190.77   \n2  1000009.0        246.0               2997.0           1397.89   \n3  1000009.0        249.0                694.0             52.67   \n4  1000009.0        250.0              12268.0          11305.78   \n5  1000009.0        373.0               4567.0         343726.29   \n6  1000009.0        538.0               1392.0            538.50   \n7  1000009.0        580.0                 28.0              5.72   \n8  1000009.0        590.0                113.0            405.25   \n9  1000009.0        596.0                  0.0              0.60   \n\n   SUM_of_Count_events  MAX_of_day_cnt  \n0                  8.0             3.0  \n1                146.0             5.0  \n2                102.0             4.0  \n3                 30.0             4.0  \n4                552.0             4.0  \n5               1665.0             5.0  \n6                125.0             5.0  \n7                  6.0             1.0  \n8               1898.0             8.0  \n9                  4.0             1.0  ",
      "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>abon_id</th>\n      <th>Application</th>\n      <th>SUM_of_Duration_sec</th>\n      <th>SUM_of_Volume_kb</th>\n      <th>SUM_of_Count_events</th>\n      <th>MAX_of_day_cnt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000009.0</td>\n      <td>17.0</td>\n      <td>2.0</td>\n      <td>2.41</td>\n      <td>8.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000009.0</td>\n      <td>232.0</td>\n      <td>1749.0</td>\n      <td>2190.77</td>\n      <td>146.0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000009.0</td>\n      <td>246.0</td>\n      <td>2997.0</td>\n      <td>1397.89</td>\n      <td>102.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000009.0</td>\n      <td>249.0</td>\n      <td>694.0</td>\n      <td>52.67</td>\n      <td>30.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000009.0</td>\n      <td>250.0</td>\n      <td>12268.0</td>\n      <td>11305.78</td>\n      <td>552.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1000009.0</td>\n      <td>373.0</td>\n      <td>4567.0</td>\n      <td>343726.29</td>\n      <td>1665.0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1000009.0</td>\n      <td>538.0</td>\n      <td>1392.0</td>\n      <td>538.50</td>\n      <td>125.0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1000009.0</td>\n      <td>580.0</td>\n      <td>28.0</td>\n      <td>5.72</td>\n      <td>6.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1000009.0</td>\n      <td>590.0</td>\n      <td>113.0</td>\n      <td>405.25</td>\n      <td>1898.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1000009.0</td>\n      <td>596.0</td>\n      <td>0.0</td>\n      <td>0.60</td>\n      <td>4.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_apps.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "(6647895, 6)"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_apps.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "140830"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_apps['abon_id'].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "719"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_apps['Application'].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "abon_id                float64\nApplication            float64\nSUM_of_Duration_sec    float64\nSUM_of_Volume_kb       float64\nSUM_of_Count_events    float64\nMAX_of_day_cnt         float64\ndtype: object"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_apps.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "abon_id                int32\nApplication            int16\nSUM_of_Duration_sec    int32\nSUM_of_Volume_kb       int32\nSUM_of_Count_events    int32\nMAX_of_day_cnt          int8\ndtype: object"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in train_apps.columns:\n",
    "    train_apps[col] = pd.to_numeric(round(train_apps[col]), downcast='integer')\n",
    "train_apps.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "140830"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abons = train_apps['abon_id'].unique().tolist()\n",
    "len(abons)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "719"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apps = train_apps['Application'].unique().tolist()\n",
    "len(apps)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Клас опрацювання Application"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "class AppsFeatureHandler:\n",
    "    res_features_list = {\"806_sum_cnt_events\", \"806_sum_duration_sec\", \"806_sum_vol_kb\", \"889_sum_duration_sec\", \"1023_max_day_cnt\", \"246_sum_vol_kb\", \"876_sum_duration_sec\", \"889_sum_vol_kb\", \"max_mean_day\", \"869_sum_cnt_events\", \"596_sum_cnt_events\", \"1345_sum_vol_kb\", \"249_sum_duration_sec\", \"807_max_day_cnt\", \"1357_max_day_cnt\", \"249_sum_cnt_events\", \"889_sum_cnt_events\", \"824_sum_vol_kb\", \"1347_sum_duration_sec\", \"669_max_day_cnt\", \"1345_sum_cnt_events\", \"232_sum_vol_kb\", \"670_sum_duration_sec\", \"249_sum_vol_kb\", \"580_sum_duration_sec\", \"670_sum_vol_kb\", \"232_sum_duration_sec\", \"1345_sum_duration_sec\", \"842_sum_duration_sec\", \"1411_max_day_cnt\", \"373_sum_duration_sec\", \"670_sum_cnt_events\", \"580_sum_cnt_events\", \"246_sum_duration_sec\", \"824_sum_duration_sec\", \"1863_sum_duration_sec\", \"232_sum_cnt_events\", \"869_sum_vol_kb\", \"682_sum_cnt_events\", \"708_sum_vol_kb\", \"824_sum_cnt_events\", \"580_sum_vol_kb\", \"669_sum_vol_kb\", \"sum_duration_apps\", \"689_sum_duration_sec\", \"590_sum_duration_sec\", \"1411_sum_cnt_events\", \"708_sum_duration_sec\", \"669_sum_duration_sec\", \"250_sum_duration_sec\", \"769_sum_duration_sec\", \"842_sum_vol_kb\", \"836_sum_vol_kb\", \"1367_max_day_cnt\", \"836_sum_cnt_events\", \"1365_sum_duration_sec\", \"266_max_day_cnt\", \"682_sum_vol_kb\", \"250_sum_vol_kb\", \"708_sum_cnt_events\", \"apps_unique\", \"682_sum_duration_sec\", \"876_max_day_cnt\", \"246_sum_cnt_events\", \"373_sum_cnt_events\", \"sum_vol_kb_apps\", \"1406_sum_duration_sec\", \"1406_sum_cnt_events\", \"842_sum_cnt_events\", \"669_sum_cnt_events\", \"538_sum_cnt_events\", \"250_sum_cnt_events\", \"538_sum_vol_kb\", \"1412_max_day_cnt\", \"869_sum_duration_sec\", \"249_max_day_cnt\", \"580_max_day_cnt\", \"1111_max_day_cnt\", \"538_sum_duration_sec\", \"800_max_day_cnt\", \"373_sum_vol_kb\", \"836_max_day_cnt\", \"sum_of_cnt_events_apps\", \"1365_sum_vol_kb\", \"839_max_day_cnt\", \"250_max_day_cnt\", \"1464_sum_cnt_events\", \"806_max_day_cnt\", \"590_sum_cnt_events\", \"826_max_day_cnt\", \"1365_sum_cnt_events\", \"590_sum_vol_kb\", \"889_max_day_cnt\", \"1464_max_day_cnt\", \"1023_app_present\", \"1406_max_day_cnt\", \"1457_max_day_cnt\", \"722_max_day_cnt\", \"15_max_day_cnt\", \"1127_max_day_cnt\", \"782_max_day_cnt\", \"940_max_day_cnt\", \"1476_max_day_cnt\", \"703_max_day_cnt\", \"1357_app_present\", \"1893_max_day_cnt\", \"1490_max_day_cnt\", \"803_max_day_cnt\", \"1412_app_present\", \"1111_app_present\", \"782_app_present\", \"675_max_day_cnt\", \"1459_max_day_cnt\", \"1127_app_present\", \"697_sum_cnt_events\", \"876_app_present\", \"812_max_day_cnt\", \"1476_app_present\", \"901_sum_cnt_events\", \"812_app_present\", \"307_app_present\", \"697_max_day_cnt\", \"1459_app_present\", \"1490_app_present\", \"889_app_present\", \"904_max_day_cnt\", \"803_app_present\"}\n",
    "    apps_to_convert = {15, 232, 246, 249, 250, 266, 307, 373, 538, 580, 590, 596, 669, 670, 675, 682, 689, 697, 703, 708, 722, 769, 782, 800, 803, 806, 807, 812, 824, 826, 836, 839, 842, 869, 876, 889, 901, 904, 940, 1023, 1111, 1127, 1345, 1347, 1357, 1365, 1367, 1406, 1411, 1412, 1457, 1459, 1464, 1476, 1490, 1863, 1893}\n",
    "\n",
    "    cols_total_apps = ['abon_id','apps_unique','sum_duration_apps','sum_vol_kb_apps','sum_of_cnt_events_apps', 'max_mean_day']\n",
    "    cols_by_app = ['_app_present', '_sum_duration_sec', '_sum_vol_kb', '_sum_cnt_events', '_max_day_cnt']\n",
    "\n",
    "\n",
    "    def __init__(self, feature_list=None):\n",
    "        \"\"\"\n",
    "        Якщо зміниться перелік фіч (наприклад їх планується більше), можна передати список\n",
    "\n",
    "        :param feature_list: список вихідних фіч(найменувань колонок, які очікуються у вихідному датафреймі) потрібно враховувати побудову назви фічі прийнятою у класі (prepare_apps_feaures())\n",
    "        \"\"\"\n",
    "        if feature_list is not None:\n",
    "            self.res_features_list = feature_list  # фічі, які мають бути у вихідному датафреймі\n",
    "            features_splited = []\n",
    "\n",
    "            # для більш швидкого опрацювання визначаються лише ті додатки, які потрібні для вихідного датафрейму\n",
    "            for f in feature_list:\n",
    "                features_splited += f.split('_')\n",
    "            numbers_in_features = [x for x in features_splited if x.isdigit()]\n",
    "            self.apps_to_convert = set(numbers_in_features)\n",
    "\n",
    "        self.headers_apps = []\n",
    "        self.headers_apps += self.cols_total_apps\n",
    "        for app in self.apps_to_convert:\n",
    "            for metr in self.cols_by_app:\n",
    "                self.headers_apps += [str(app)+metr]\n",
    "\n",
    "    def prepare_apps_feaures(self, df_apps: pd.DataFrame) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        Підготовка фіч використання додатків: з загальної таблиці фактів використання додатків абонентами форматує новий датасет з унікальністю запису відповідно ідентифікатора абонента (відомості про кожен окремий додаток формуються додатковими стовпчиками.\n",
    "        :param df_apps: датафрейм з відомостями про використання додатків\n",
    "        :return: датафрейм з показниками по абонентам (індекс - ід абонента для подальшого джоіну\n",
    "        \"\"\"\n",
    "        df_apps = df_apps.copy(deep=True)\n",
    "        for col in df_apps.columns:\n",
    "            df_apps[col] = pd.to_numeric(df_apps[col], downcast='integer')\n",
    "\n",
    "        apps_data = []\n",
    "        abons = df_apps['abon_id'].unique().tolist()\n",
    "\n",
    "        for pos, abon in enumerate(abons):\n",
    "            if pos%1000 == 0:\n",
    "                print(round((pos/len(abons))*100, 2), \" %\")\n",
    "            abon_df_part = df_apps.loc[df_apps['abon_id'] == abon].drop(columns=['abon_id'])\n",
    "            abon_data_row = []\n",
    "            abon_data_row.append(abon)\n",
    "            abon_data_row.append(abon_df_part['Application'].nunique())\n",
    "            abon_data_row.append(abon_df_part['SUM_of_Duration_sec'].sum())\n",
    "            abon_data_row.append(abon_df_part['SUM_of_Volume_kb'].sum())\n",
    "            abon_data_row.append(abon_df_part['SUM_of_Count_events'].sum())\n",
    "            abon_data_row.append(abon_df_part['MAX_of_day_cnt'].mean())\n",
    "\n",
    "            df_grouped_apps = abon_df_part.groupby(by='Application').sum()\n",
    "            for app in self.apps_to_convert:\n",
    "                if app in df_grouped_apps.index:\n",
    "                    abon_data_row.append(1)\n",
    "                    abon_data_row.append(df_grouped_apps.loc[app]['SUM_of_Duration_sec'])\n",
    "                    abon_data_row.append(df_grouped_apps.loc[app]['SUM_of_Volume_kb'])\n",
    "                    abon_data_row.append(df_grouped_apps.loc[app]['SUM_of_Count_events'])\n",
    "                    abon_data_row.append(df_grouped_apps.loc[app]['MAX_of_day_cnt'])\n",
    "                else:\n",
    "                    abon_data_row.append(0)\n",
    "                    abon_data_row.append(0)\n",
    "                    abon_data_row.append(0)\n",
    "                    abon_data_row.append(0)\n",
    "                    abon_data_row.append(0)\n",
    "\n",
    "            apps_data.append(abon_data_row)\n",
    "\n",
    "        apps_feature_df = pd.DataFrame(data=apps_data, columns=self.headers_apps)\n",
    "        return apps_feature_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Опрацювання Train (apps)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "apps_handler = AppsFeatureHandler()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "df_train_apps_features = apps_handler.prepare_apps_feaures(train_apps)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "df_train_apps_features.set_index('abon_id', inplace=True, drop=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "         apps_unique  sum_duration_apps  sum_vol_kb_apps  \\\nabon_id                                                    \n1000009           34              75176        468046.39   \n1000012           37             136466        255362.46   \n1000015           50             265303       1066408.44   \n1000016            4                165            58.64   \n1000020           54             440335       1225026.32   \n\n         sum_of_cnt_events_apps  max_mean_day  769_app_present  \\\nabon_id                                                          \n1000009                    8280      3.735294                1   \n1000012                   11808      7.243243                1   \n1000015                   16498      5.640000                1   \n1000016                     107      3.000000                0   \n1000020                   34825      4.537037                1   \n\n         769_sum_duration_sec  769_sum_vol_kb  769_sum_cnt_events  \\\nabon_id                                                             \n1000009                 217.0          254.49                26.0   \n1000012                  10.0          424.15                39.0   \n1000015                   0.0           12.42                 1.0   \n1000016                   0.0            0.00                 0.0   \n1000020                  82.0         2132.59                63.0   \n\n         769_max_day_cnt  ...  1406_app_present  1406_sum_duration_sec  \\\nabon_id                   ...                                            \n1000009              4.0  ...                 0                    0.0   \n1000012              7.0  ...                 0                    0.0   \n1000015              1.0  ...                 1                28385.0   \n1000016              0.0  ...                 0                    0.0   \n1000020              8.0  ...                 0                    0.0   \n\n         1406_sum_vol_kb  1406_sum_cnt_events  1406_max_day_cnt  \\\nabon_id                                                           \n1000009             0.00                  0.0               0.0   \n1000012             0.00                  0.0               0.0   \n1000015        152084.95                797.0               4.0   \n1000016             0.00                  0.0               0.0   \n1000020             0.00                  0.0               0.0   \n\n         1023_app_present  1023_sum_duration_sec  1023_sum_vol_kb  \\\nabon_id                                                             \n1000009                 1                    6.0           101.99   \n1000012                 0                    0.0             0.00   \n1000015                 1                    2.0            26.35   \n1000016                 0                    0.0             0.00   \n1000020                 0                    0.0             0.00   \n\n         1023_sum_cnt_events  1023_max_day_cnt  \nabon_id                                         \n1000009                  7.0               4.0  \n1000012                  0.0               0.0  \n1000015                  2.0               1.0  \n1000016                  0.0               0.0  \n1000020                  0.0               0.0  \n\n[5 rows x 290 columns]",
      "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>apps_unique</th>\n      <th>sum_duration_apps</th>\n      <th>sum_vol_kb_apps</th>\n      <th>sum_of_cnt_events_apps</th>\n      <th>max_mean_day</th>\n      <th>769_app_present</th>\n      <th>769_sum_duration_sec</th>\n      <th>769_sum_vol_kb</th>\n      <th>769_sum_cnt_events</th>\n      <th>769_max_day_cnt</th>\n      <th>...</th>\n      <th>1406_app_present</th>\n      <th>1406_sum_duration_sec</th>\n      <th>1406_sum_vol_kb</th>\n      <th>1406_sum_cnt_events</th>\n      <th>1406_max_day_cnt</th>\n      <th>1023_app_present</th>\n      <th>1023_sum_duration_sec</th>\n      <th>1023_sum_vol_kb</th>\n      <th>1023_sum_cnt_events</th>\n      <th>1023_max_day_cnt</th>\n    </tr>\n    <tr>\n      <th>abon_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1000009</th>\n      <td>34</td>\n      <td>75176</td>\n      <td>468046.39</td>\n      <td>8280</td>\n      <td>3.735294</td>\n      <td>1</td>\n      <td>217.0</td>\n      <td>254.49</td>\n      <td>26.0</td>\n      <td>4.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>101.99</td>\n      <td>7.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>1000012</th>\n      <td>37</td>\n      <td>136466</td>\n      <td>255362.46</td>\n      <td>11808</td>\n      <td>7.243243</td>\n      <td>1</td>\n      <td>10.0</td>\n      <td>424.15</td>\n      <td>39.0</td>\n      <td>7.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000015</th>\n      <td>50</td>\n      <td>265303</td>\n      <td>1066408.44</td>\n      <td>16498</td>\n      <td>5.640000</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>12.42</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>28385.0</td>\n      <td>152084.95</td>\n      <td>797.0</td>\n      <td>4.0</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>26.35</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1000016</th>\n      <td>4</td>\n      <td>165</td>\n      <td>58.64</td>\n      <td>107</td>\n      <td>3.000000</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000020</th>\n      <td>54</td>\n      <td>440335</td>\n      <td>1225026.32</td>\n      <td>34825</td>\n      <td>4.537037</td>\n      <td>1</td>\n      <td>82.0</td>\n      <td>2132.59</td>\n      <td>63.0</td>\n      <td>8.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 290 columns</p>\n</div>"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_apps_features.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "df_train_apps_features.to_pickle('df_train_apps_new_features.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Опрацювання Test (apps)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0  %\n",
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      "99.68  %\n"
     ]
    }
   ],
   "source": [
    "df_test_apps_features = apps_handler.prepare_apps_feaures(test_apps)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "df_test_apps_features.set_index('abon_id', inplace=True, drop=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "         apps_unique  sum_duration_apps  sum_vol_kb_apps  \\\nabon_id                                                    \n1000023           41             314014       2480347.73   \n1000026           73            2006861       4387456.83   \n1000027           47             497786       1190688.60   \n1000032           42             403237       3567840.39   \n1000036            5                 87            17.03   \n1000057           57             351517       2418892.78   \n1000060           57            2529465       4871598.21   \n1000070           65            3555429      36101233.39   \n1000071           38             558161        378475.94   \n1000087           38              51114        482814.72   \n1000098            1                 61             1.62   \n1000099           83            2791730      41746940.72   \n1000107           36             191512        222761.37   \n1000109           83             933467       5399480.42   \n1000114           53             460758       4849834.70   \n1000115            9               5341            72.54   \n1000146           20              14241          5240.91   \n1000147           58             669926        923640.98   \n1000153           51             778968       6711136.17   \n1000156           45             297646        936462.18   \n1000159           75            3626471      12832055.47   \n1000170           63            1892336       5800737.00   \n1000175           15              28511           296.47   \n1000195           64            2349490       1674315.69   \n1000196           43             265593        437090.29   \n1000199           64             486222       3327823.06   \n1000204           87            1672935      39633418.16   \n1000221            3               1772            58.29   \n1000224           51             471828       1170477.90   \n1000225           49             358253        535668.94   \n\n         sum_of_cnt_events_apps  max_mean_day  769_app_present  \\\nabon_id                                                          \n1000023                   21713      7.121951                1   \n1000026                  207578     15.136986                1   \n1000027                   60009     14.787234                1   \n1000032                   40043      7.190476                1   \n1000036                      42      1.600000                0   \n1000057                   38247      5.263158                1   \n1000060                  283759     16.842105                1   \n1000070                  288901     15.369231                1   \n1000071                   52107     19.078947                1   \n1000087                    5294      6.131579                1   \n1000098                      12      5.000000                0   \n1000099                  149806     12.240964                1   \n1000107                   26644     12.194444                1   \n1000109                  143386     14.590361                1   \n1000114                   58408     17.433962                1   \n1000115                     304      6.444444                0   \n1000146                    1618      1.700000                0   \n1000147                  119374     14.413793                1   \n1000153                   87119     11.470588                1   \n1000156                   38095      8.066667                1   \n1000159                  279202     15.773333                1   \n1000170                  204596     12.730159                1   \n1000175                    1276      1.000000                0   \n1000195                  279385     14.546875                1   \n1000196                   22563     10.953488                1   \n1000199                   80070     16.062500                1   \n1000204                  130928     12.172414                1   \n1000221                      60      1.000000                0   \n1000224                   48963      9.411765                1   \n1000225                   59816      6.714286                1   \n\n         769_sum_duration_sec  769_sum_vol_kb  769_sum_cnt_events  \\\nabon_id                                                             \n1000023                  59.0         3907.46                41.0   \n1000026                2922.0       159912.67              3148.0   \n1000027                 576.0        10050.23               417.0   \n1000032                1216.0         3837.10               106.0   \n1000036                   0.0            0.00                 0.0   \n1000057                 322.0        10543.90               215.0   \n1000060                 784.0        29716.37               440.0   \n1000070                3278.0        93506.06              1635.0   \n1000071                2532.0         2595.10               227.0   \n1000087                 298.0        19700.44               122.0   \n1000098                   0.0            0.00                 0.0   \n1000099                1304.0         5730.52                56.0   \n1000107                 105.0          424.50               169.0   \n1000109                 647.0        20650.61                67.0   \n1000114                 101.0          488.57                 4.0   \n1000115                   0.0            0.00                 0.0   \n1000146                   0.0            0.00                 0.0   \n1000147                 415.0        13180.55               419.0   \n1000153                 456.0         9397.29               152.0   \n1000156                 496.0          386.85                43.0   \n1000159                 291.0         1371.93                69.0   \n1000170                 498.0        21978.79               117.0   \n1000175                   0.0            0.00                 0.0   \n1000195                5059.0        31853.89              1438.0   \n1000196                 141.0         2415.76                49.0   \n1000199                 145.0          397.44                28.0   \n1000204                1849.0        23381.10               109.0   \n1000221                   0.0            0.00                 0.0   \n1000224                 214.0         6746.81               201.0   \n1000225                1524.0        50385.60               620.0   \n\n         769_max_day_cnt  ...  1406_app_present  1406_sum_duration_sec  \\\nabon_id                   ...                                            \n1000023              9.0  ...                 1                    0.0   \n1000026             30.0  ...                 0                    0.0   \n1000027             26.0  ...                 0                    0.0   \n1000032             12.0  ...                 0                    0.0   \n1000036              0.0  ...                 0                    0.0   \n1000057              9.0  ...                 0                    0.0   \n1000060             27.0  ...                 1                  694.0   \n1000070             31.0  ...                 1                   22.0   \n1000071             31.0  ...                 0                    0.0   \n1000087              7.0  ...                 0                    0.0   \n1000098              0.0  ...                 0                    0.0   \n1000099              6.0  ...                 0                    0.0   \n1000107             31.0  ...                 0                    0.0   \n1000109             11.0  ...                 1                    4.0   \n1000114              2.0  ...                 0                    0.0   \n1000115              0.0  ...                 0                    0.0   \n1000146              0.0  ...                 0                    0.0   \n1000147             31.0  ...                 1                 1534.0   \n1000153             18.0  ...                 0                    0.0   \n1000156              9.0  ...                 1                  116.0   \n1000159             21.0  ...                 1                  329.0   \n1000170             14.0  ...                 1               412744.0   \n1000175              0.0  ...                 0                    0.0   \n1000195             30.0  ...                 0                    0.0   \n1000196             10.0  ...                 0                    0.0   \n1000199              3.0  ...                 0                    0.0   \n1000204             12.0  ...                 0                    0.0   \n1000221              0.0  ...                 0                    0.0   \n1000224             11.0  ...                 1                   22.0   \n1000225              8.0  ...                 0                    0.0   \n\n         1406_sum_vol_kb  1406_sum_cnt_events  1406_max_day_cnt  \\\nabon_id                                                           \n1000023             3.62                  1.0               1.0   \n1000026             0.00                  0.0               0.0   \n1000027             0.00                  0.0               0.0   \n1000032             0.00                  0.0               0.0   \n1000036             0.00                  0.0               0.0   \n1000057             0.00                  0.0               0.0   \n1000060         18449.08                 21.0               1.0   \n1000070             7.87                  1.0               1.0   \n1000071             0.00                  0.0               0.0   \n1000087             0.00                  0.0               0.0   \n1000098             0.00                  0.0               0.0   \n1000099             0.00                  0.0               0.0   \n1000107             0.00                  0.0               0.0   \n1000109            51.96                  2.0               1.0   \n1000114             0.00                  0.0               0.0   \n1000115             0.00                  0.0               0.0   \n1000146             0.00                  0.0               0.0   \n1000147         15587.44                 75.0               3.0   \n1000153             0.00                  0.0               0.0   \n1000156            15.73                  3.0               2.0   \n1000159         10081.90                 15.0               2.0   \n1000170        124306.30               9591.0              21.0   \n1000175             0.00                  0.0               0.0   \n1000195             0.00                  0.0               0.0   \n1000196             0.00                  0.0               0.0   \n1000199             0.00                  0.0               0.0   \n1000204             0.00                  0.0               0.0   \n1000221             0.00                  0.0               0.0   \n1000224            32.81                  2.0               1.0   \n1000225             0.00                  0.0               0.0   \n\n         1023_app_present  1023_sum_duration_sec  1023_sum_vol_kb  \\\nabon_id                                                             \n1000023                 0                    0.0             0.00   \n1000026                 1                    1.0            15.73   \n1000027                 0                    0.0             0.00   \n1000032                 1                    2.0            13.62   \n1000036                 0                    0.0             0.00   \n1000057                 1                    1.0            13.37   \n1000060                 0                    0.0             0.00   \n1000070                 1                  196.0          1170.35   \n1000071                 0                    0.0             0.00   \n1000087                 1                  117.0          1369.97   \n1000098                 0                    0.0             0.00   \n1000099                 0                    0.0             0.00   \n1000107                 1                  119.0           317.99   \n1000109                 0                    0.0             0.00   \n1000114                 0                    0.0             0.00   \n1000115                 0                    0.0             0.00   \n1000146                 0                    0.0             0.00   \n1000147                 1                   52.0           638.82   \n1000153                 1                  164.0           456.45   \n1000156                 0                    0.0             0.00   \n1000159                 1                   14.0            84.88   \n1000170                 1                  111.0           323.75   \n1000175                 0                    0.0             0.00   \n1000195                 0                    0.0             0.00   \n1000196                 1                   14.0            31.08   \n1000199                 0                    0.0             0.00   \n1000204                 1                   65.0            23.92   \n1000221                 0                    0.0             0.00   \n1000224                 0                    0.0             0.00   \n1000225                 1                   70.0           275.71   \n\n         1023_sum_cnt_events  1023_max_day_cnt  \nabon_id                                         \n1000023                  0.0               0.0  \n1000026                  2.0               1.0  \n1000027                  0.0               0.0  \n1000032                  2.0               1.0  \n1000036                  0.0               0.0  \n1000057                  2.0               1.0  \n1000060                  0.0               0.0  \n1000070                102.0              28.0  \n1000071                  0.0               0.0  \n1000087                  8.0               1.0  \n1000098                  0.0               0.0  \n1000099                  0.0               0.0  \n1000107                 26.0               8.0  \n1000109                  0.0               0.0  \n1000114                  0.0               0.0  \n1000115                  0.0               0.0  \n1000146                  0.0               0.0  \n1000147                 40.0              30.0  \n1000153                 34.0              16.0  \n1000156                  0.0               0.0  \n1000159                  5.0               4.0  \n1000170                 26.0               9.0  \n1000175                  0.0               0.0  \n1000195                  0.0               0.0  \n1000196                  4.0               2.0  \n1000199                  0.0               0.0  \n1000204                  3.0               3.0  \n1000221                  0.0               0.0  \n1000224                  0.0               0.0  \n1000225                 20.0               9.0  \n\n[30 rows x 290 columns]",
      "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>apps_unique</th>\n      <th>sum_duration_apps</th>\n      <th>sum_vol_kb_apps</th>\n      <th>sum_of_cnt_events_apps</th>\n      <th>max_mean_day</th>\n      <th>769_app_present</th>\n      <th>769_sum_duration_sec</th>\n      <th>769_sum_vol_kb</th>\n      <th>769_sum_cnt_events</th>\n      <th>769_max_day_cnt</th>\n      <th>...</th>\n      <th>1406_app_present</th>\n      <th>1406_sum_duration_sec</th>\n      <th>1406_sum_vol_kb</th>\n      <th>1406_sum_cnt_events</th>\n      <th>1406_max_day_cnt</th>\n      <th>1023_app_present</th>\n      <th>1023_sum_duration_sec</th>\n      <th>1023_sum_vol_kb</th>\n      <th>1023_sum_cnt_events</th>\n      <th>1023_max_day_cnt</th>\n    </tr>\n    <tr>\n      <th>abon_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1000023</th>\n      <td>41</td>\n      <td>314014</td>\n      <td>2480347.73</td>\n      <td>21713</td>\n      <td>7.121951</td>\n      <td>1</td>\n      <td>59.0</td>\n      <td>3907.46</td>\n      <td>41.0</td>\n      <td>9.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>3.62</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000026</th>\n      <td>73</td>\n      <td>2006861</td>\n      <td>4387456.83</td>\n      <td>207578</td>\n      <td>15.136986</td>\n      <td>1</td>\n      <td>2922.0</td>\n      <td>159912.67</td>\n      <td>3148.0</td>\n      <td>30.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>15.73</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1000027</th>\n      <td>47</td>\n      <td>497786</td>\n      <td>1190688.60</td>\n      <td>60009</td>\n      <td>14.787234</td>\n      <td>1</td>\n      <td>576.0</td>\n      <td>10050.23</td>\n      <td>417.0</td>\n      <td>26.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000032</th>\n      <td>42</td>\n      <td>403237</td>\n      <td>3567840.39</td>\n      <td>40043</td>\n      <td>7.190476</td>\n      <td>1</td>\n      <td>1216.0</td>\n      <td>3837.10</td>\n      <td>106.0</td>\n      <td>12.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>13.62</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1000036</th>\n      <td>5</td>\n      <td>87</td>\n      <td>17.03</td>\n      <td>42</td>\n      <td>1.600000</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000057</th>\n      <td>57</td>\n      <td>351517</td>\n      <td>2418892.78</td>\n      <td>38247</td>\n      <td>5.263158</td>\n      <td>1</td>\n      <td>322.0</td>\n      <td>10543.90</td>\n      <td>215.0</td>\n      <td>9.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>13.37</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1000060</th>\n      <td>57</td>\n      <td>2529465</td>\n      <td>4871598.21</td>\n      <td>283759</td>\n      <td>16.842105</td>\n      <td>1</td>\n      <td>784.0</td>\n      <td>29716.37</td>\n      <td>440.0</td>\n      <td>27.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>694.0</td>\n      <td>18449.08</td>\n      <td>21.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000070</th>\n      <td>65</td>\n      <td>3555429</td>\n      <td>36101233.39</td>\n      <td>288901</td>\n      <td>15.369231</td>\n      <td>1</td>\n      <td>3278.0</td>\n      <td>93506.06</td>\n      <td>1635.0</td>\n      <td>31.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>22.0</td>\n      <td>7.87</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>196.0</td>\n      <td>1170.35</td>\n      <td>102.0</td>\n      <td>28.0</td>\n    </tr>\n    <tr>\n      <th>1000071</th>\n      <td>38</td>\n      <td>558161</td>\n      <td>378475.94</td>\n      <td>52107</td>\n      <td>19.078947</td>\n      <td>1</td>\n      <td>2532.0</td>\n      <td>2595.10</td>\n      <td>227.0</td>\n      <td>31.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000087</th>\n      <td>38</td>\n      <td>51114</td>\n      <td>482814.72</td>\n      <td>5294</td>\n      <td>6.131579</td>\n      <td>1</td>\n      <td>298.0</td>\n      <td>19700.44</td>\n      <td>122.0</td>\n      <td>7.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>117.0</td>\n      <td>1369.97</td>\n      <td>8.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1000098</th>\n      <td>1</td>\n      <td>61</td>\n      <td>1.62</td>\n      <td>12</td>\n      <td>5.000000</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000099</th>\n      <td>83</td>\n      <td>2791730</td>\n      <td>41746940.72</td>\n      <td>149806</td>\n      <td>12.240964</td>\n      <td>1</td>\n      <td>1304.0</td>\n      <td>5730.52</td>\n      <td>56.0</td>\n      <td>6.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000107</th>\n      <td>36</td>\n      <td>191512</td>\n      <td>222761.37</td>\n      <td>26644</td>\n      <td>12.194444</td>\n      <td>1</td>\n      <td>105.0</td>\n      <td>424.50</td>\n      <td>169.0</td>\n      <td>31.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>119.0</td>\n      <td>317.99</td>\n      <td>26.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>1000109</th>\n      <td>83</td>\n      <td>933467</td>\n      <td>5399480.42</td>\n      <td>143386</td>\n      <td>14.590361</td>\n      <td>1</td>\n      <td>647.0</td>\n      <td>20650.61</td>\n      <td>67.0</td>\n      <td>11.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>4.0</td>\n      <td>51.96</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000114</th>\n      <td>53</td>\n      <td>460758</td>\n      <td>4849834.70</td>\n      <td>58408</td>\n      <td>17.433962</td>\n      <td>1</td>\n      <td>101.0</td>\n      <td>488.57</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000115</th>\n      <td>9</td>\n      <td>5341</td>\n      <td>72.54</td>\n      <td>304</td>\n      <td>6.444444</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000146</th>\n      <td>20</td>\n      <td>14241</td>\n      <td>5240.91</td>\n      <td>1618</td>\n      <td>1.700000</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000147</th>\n      <td>58</td>\n      <td>669926</td>\n      <td>923640.98</td>\n      <td>119374</td>\n      <td>14.413793</td>\n      <td>1</td>\n      <td>415.0</td>\n      <td>13180.55</td>\n      <td>419.0</td>\n      <td>31.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1534.0</td>\n      <td>15587.44</td>\n      <td>75.0</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>52.0</td>\n      <td>638.82</td>\n      <td>40.0</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>1000153</th>\n      <td>51</td>\n      <td>778968</td>\n      <td>6711136.17</td>\n      <td>87119</td>\n      <td>11.470588</td>\n      <td>1</td>\n      <td>456.0</td>\n      <td>9397.29</td>\n      <td>152.0</td>\n      <td>18.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>164.0</td>\n      <td>456.45</td>\n      <td>34.0</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>1000156</th>\n      <td>45</td>\n      <td>297646</td>\n      <td>936462.18</td>\n      <td>38095</td>\n      <td>8.066667</td>\n      <td>1</td>\n      <td>496.0</td>\n      <td>386.85</td>\n      <td>43.0</td>\n      <td>9.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>116.0</td>\n      <td>15.73</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000159</th>\n      <td>75</td>\n      <td>3626471</td>\n      <td>12832055.47</td>\n      <td>279202</td>\n      <td>15.773333</td>\n      <td>1</td>\n      <td>291.0</td>\n      <td>1371.93</td>\n      <td>69.0</td>\n      <td>21.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>329.0</td>\n      <td>10081.90</td>\n      <td>15.0</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>84.88</td>\n      <td>5.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>1000170</th>\n      <td>63</td>\n      <td>1892336</td>\n      <td>5800737.00</td>\n      <td>204596</td>\n      <td>12.730159</td>\n      <td>1</td>\n      <td>498.0</td>\n      <td>21978.79</td>\n      <td>117.0</td>\n      <td>14.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>412744.0</td>\n      <td>124306.30</td>\n      <td>9591.0</td>\n      <td>21.0</td>\n      <td>1</td>\n      <td>111.0</td>\n      <td>323.75</td>\n      <td>26.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>1000175</th>\n      <td>15</td>\n      <td>28511</td>\n      <td>296.47</td>\n      <td>1276</td>\n      <td>1.000000</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000195</th>\n      <td>64</td>\n      <td>2349490</td>\n      <td>1674315.69</td>\n      <td>279385</td>\n      <td>14.546875</td>\n      <td>1</td>\n      <td>5059.0</td>\n      <td>31853.89</td>\n      <td>1438.0</td>\n      <td>30.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000196</th>\n      <td>43</td>\n      <td>265593</td>\n      <td>437090.29</td>\n      <td>22563</td>\n      <td>10.953488</td>\n      <td>1</td>\n      <td>141.0</td>\n      <td>2415.76</td>\n      <td>49.0</td>\n      <td>10.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>31.08</td>\n      <td>4.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1000199</th>\n      <td>64</td>\n      <td>486222</td>\n      <td>3327823.06</td>\n      <td>80070</td>\n      <td>16.062500</td>\n      <td>1</td>\n      <td>145.0</td>\n      <td>397.44</td>\n      <td>28.0</td>\n      <td>3.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000204</th>\n      <td>87</td>\n      <td>1672935</td>\n      <td>39633418.16</td>\n      <td>130928</td>\n      <td>12.172414</td>\n      <td>1</td>\n      <td>1849.0</td>\n      <td>23381.10</td>\n      <td>109.0</td>\n      <td>12.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>65.0</td>\n      <td>23.92</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1000221</th>\n      <td>3</td>\n      <td>1772</td>\n      <td>58.29</td>\n      <td>60</td>\n      <td>1.000000</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000224</th>\n      <td>51</td>\n      <td>471828</td>\n      <td>1170477.90</td>\n      <td>48963</td>\n      <td>9.411765</td>\n      <td>1</td>\n      <td>214.0</td>\n      <td>6746.81</td>\n      <td>201.0</td>\n      <td>11.0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>22.0</td>\n      <td>32.81</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1000225</th>\n      <td>49</td>\n      <td>358253</td>\n      <td>535668.94</td>\n      <td>59816</td>\n      <td>6.714286</td>\n      <td>1</td>\n      <td>1524.0</td>\n      <td>50385.60</td>\n      <td>620.0</td>\n      <td>8.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>70.0</td>\n      <td>275.71</td>\n      <td>20.0</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>30 rows × 290 columns</p>\n</div>"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_apps_features.head(30)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "df_test_apps_features.to_pickle('df_test_apps_new_features.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Попередні опрацювання - вибірка переліку додатків, які мають значення (кореляція та важливі фічі рандом форесту) - результат доданий до класу опрацювання"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [],
   "source": [
    "apps_feature_df = pd.read_pickle('new_apps_features.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "apps_feature_df = pd.read_pickle('new_apps_features.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "apps_feature_df.set_index('abon_id', drop=False, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "         abon_id  apps_unique  sum_duration_apps  sum_vol_kb_apps  \\\nabon_id                                                             \n1000009  1000009           34              75176           468047   \n1000012  1000012           37             136466           255361   \n1000015  1000015           50             265303          1066403   \n1000016  1000016            4                165               59   \n1000020  1000020           54             440335          1225030   \n\n         sum_of_cnt_events_apps  max_mean_day  17_app_present  \\\nabon_id                                                         \n1000009                    8280      3.735294               1   \n1000012                   11808      7.243243               1   \n1000015                   16498      5.640000               1   \n1000016                     107      3.000000               0   \n1000020                   34825      4.537037               1   \n\n         17_sum_duration_sec  17_sum_vol_kb  17_sum_cnt_events  ...  \\\nabon_id                                                         ...   \n1000009                    2              2                  8  ...   \n1000012                    3              3                  9  ...   \n1000015                    0              0                  1  ...   \n1000016                    0              0                  0  ...   \n1000020                    7              7                 11  ...   \n\n         894_app_present  894_sum_duration_sec  894_sum_vol_kb  \\\nabon_id                                                          \n1000009                0                     0               0   \n1000012                0                     0               0   \n1000015                0                     0               0   \n1000016                0                     0               0   \n1000020                0                     0               0   \n\n         894_sum_cnt_events  894_max_day_cnt  1132_app_present  \\\nabon_id                                                          \n1000009                   0                0                 0   \n1000012                   0                0                 0   \n1000015                   0                0                 0   \n1000016                   0                0                 0   \n1000020                   0                0                 0   \n\n         1132_sum_duration_sec  1132_sum_vol_kb  1132_sum_cnt_events  \\\nabon_id                                                                \n1000009                      0                0                    0   \n1000012                      0                0                    0   \n1000015                      0                0                    0   \n1000016                      0                0                    0   \n1000020                      0                0                    0   \n\n         1132_max_day_cnt  \nabon_id                    \n1000009                 0  \n1000012                 0  \n1000015                 0  \n1000016                 0  \n1000020                 0  \n\n[5 rows x 3601 columns]",
      "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>abon_id</th>\n      <th>apps_unique</th>\n      <th>sum_duration_apps</th>\n      <th>sum_vol_kb_apps</th>\n      <th>sum_of_cnt_events_apps</th>\n      <th>max_mean_day</th>\n      <th>17_app_present</th>\n      <th>17_sum_duration_sec</th>\n      <th>17_sum_vol_kb</th>\n      <th>17_sum_cnt_events</th>\n      <th>...</th>\n      <th>894_app_present</th>\n      <th>894_sum_duration_sec</th>\n      <th>894_sum_vol_kb</th>\n      <th>894_sum_cnt_events</th>\n      <th>894_max_day_cnt</th>\n      <th>1132_app_present</th>\n      <th>1132_sum_duration_sec</th>\n      <th>1132_sum_vol_kb</th>\n      <th>1132_sum_cnt_events</th>\n      <th>1132_max_day_cnt</th>\n    </tr>\n    <tr>\n      <th>abon_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1000009</th>\n      <td>1000009</td>\n      <td>34</td>\n      <td>75176</td>\n      <td>468047</td>\n      <td>8280</td>\n      <td>3.735294</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>8</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000012</th>\n      <td>1000012</td>\n      <td>37</td>\n      <td>136466</td>\n      <td>255361</td>\n      <td>11808</td>\n      <td>7.243243</td>\n      <td>1</td>\n      <td>3</td>\n      <td>3</td>\n      <td>9</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000015</th>\n      <td>1000015</td>\n      <td>50</td>\n      <td>265303</td>\n      <td>1066403</td>\n      <td>16498</td>\n      <td>5.640000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000016</th>\n      <td>1000016</td>\n      <td>4</td>\n      <td>165</td>\n      <td>59</td>\n      <td>107</td>\n      <td>3.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1000020</th>\n      <td>1000020</td>\n      <td>54</td>\n      <td>440335</td>\n      <td>1225030</td>\n      <td>34825</td>\n      <td>4.537037</td>\n      <td>1</td>\n      <td>7</td>\n      <td>7</td>\n      <td>11</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 3601 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apps_feature_df.head(5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "y_train_temp = train_main_fe.loc[:][['abon_id', 'target']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "         target\nabon_id        \n1215806       0\n1215139       0\n1236635       0\n1205230       0\n1019270       0\n...         ...\n1229807       0\n1441477       0\n1446209       0\n1322323       0\n1492899       0\n\n[205625 rows x 1 columns]",
      "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>target</th>\n    </tr>\n    <tr>\n      <th>abon_id</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1215806</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1215139</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1236635</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1205230</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1019270</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1229807</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1441477</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1446209</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1322323</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1492899</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>205625 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_temp['target'] = pd.to_numeric(y_train_temp['target'], downcast='integer')\n",
    "y_train_temp['abon_id'] = pd.to_numeric(y_train_temp['abon_id'], downcast='integer')\n",
    "y_train_temp.set_index('abon_id', drop=True, inplace=True)\n",
    "y_train_temp"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "apps_feature_df_w_target = apps_feature_df.join(y_train_temp, how='left')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "0"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apps_feature_df_w_target['target'].isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "RandomForestClassifier(n_estimators=500, n_jobs=30)",
      "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500, n_jobs=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500, n_jobs=30)</pre></div></div></div></div></div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=500, n_jobs=30)\n",
    "clf.fit(apps_feature_df_w_target.drop(columns=['target']), apps_feature_df_w_target['target'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "imp_vals = clf.feature_importances_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "abon_id                   1.542030e-02\napps_unique               4.924587e-03\nsum_duration_apps         8.161747e-03\nsum_vol_kb_apps           7.013348e-03\nsum_of_cnt_events_apps    7.191081e-03\n                              ...     \n1132_app_present          0.000000e+00\n1132_sum_duration_sec     0.000000e+00\n1132_sum_vol_kb           0.000000e+00\n1132_sum_cnt_events       3.744302e-08\n1132_max_day_cnt          4.992679e-08\nLength: 3601, dtype: float64"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_names = apps_feature_df_w_target.drop(columns=['target']).columns.tolist()\n",
    "impot_fea = pd.Series(index=list(imp_names), data=imp_vals)\n",
    "impot_fea"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "high_imp_list = impot_fea.loc[impot_fea > 0.0033].index.tolist()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "abon_id                   0.047744\napps_unique               0.006195\nsum_duration_apps         0.037300\nsum_vol_kb_apps           0.024853\nsum_of_cnt_events_apps    0.028926\n                            ...   \n1132_sum_duration_sec    -0.001559\n1132_sum_vol_kb          -0.001559\n1132_sum_cnt_events      -0.001559\n1132_max_day_cnt         -0.001559\ntarget                    1.000000\nLength: 3602, dtype: float64"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_corr_mtx = apps_feature_df_w_target.corrwith(apps_feature_df_w_target['target'], axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "abon_id                   0.047744\napps_unique               0.006195\nsum_duration_apps         0.037300\nsum_vol_kb_apps           0.024853\nsum_of_cnt_events_apps    0.028926\n                            ...   \n1132_sum_duration_sec     0.001559\n1132_sum_vol_kb           0.001559\n1132_sum_cnt_events       0.001559\n1132_max_day_cnt          0.001559\ntarget                    1.000000\nLength: 3602, dtype: float64"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_corr_mtx_abs = np.abs(temp_corr_mtx)\n",
    "temp_corr_mtx_abs"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "high_target_list = temp_corr_mtx_abs.loc[temp_corr_mtx_abs > 0.02].index.tolist()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "138"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apps_selected_colnames = set(high_target_list).union(set(high_imp_list))\n",
    "apps_selected_colnames = apps_selected_colnames - {'abon_id', 'target'}\n",
    "len(apps_selected_colnames)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "{'1023_app_present',\n '1023_max_day_cnt',\n '1111_app_present',\n '1111_max_day_cnt',\n '1127_app_present',\n '1127_max_day_cnt',\n '1280_app_present',\n '1345_sum_cnt_events',\n '1345_sum_duration_sec',\n '1345_sum_vol_kb',\n '1347_sum_duration_sec',\n '1357_app_present',\n '1357_max_day_cnt',\n '1365_sum_cnt_events',\n '1365_sum_duration_sec',\n '1365_sum_vol_kb',\n '1367_max_day_cnt',\n '1406_app_present',\n '1406_max_day_cnt',\n '1406_sum_cnt_events',\n '1406_sum_duration_sec',\n '1411_max_day_cnt',\n '1411_sum_cnt_events',\n '1412_app_present',\n '1412_max_day_cnt',\n '1413_app_present',\n '1457_max_day_cnt',\n '1459_app_present',\n '1459_max_day_cnt',\n '1464_max_day_cnt',\n '1464_sum_cnt_events',\n '1476_app_present',\n '1476_max_day_cnt',\n '1490_app_present',\n '1490_max_day_cnt',\n '15_max_day_cnt',\n '1863_sum_duration_sec',\n '1893_app_present',\n '1893_max_day_cnt',\n '232_sum_cnt_events',\n '232_sum_duration_sec',\n '232_sum_vol_kb',\n '246_sum_cnt_events',\n '246_sum_duration_sec',\n '246_sum_vol_kb',\n '249_max_day_cnt',\n '249_sum_cnt_events',\n '249_sum_duration_sec',\n '249_sum_vol_kb',\n '250_max_day_cnt',\n '250_sum_cnt_events',\n '250_sum_duration_sec',\n '250_sum_vol_kb',\n '266_max_day_cnt',\n '299_app_present',\n '307_app_present',\n '373_sum_cnt_events',\n '373_sum_duration_sec',\n '373_sum_vol_kb',\n '538_sum_cnt_events',\n '538_sum_duration_sec',\n '538_sum_vol_kb',\n '580_max_day_cnt',\n '580_sum_cnt_events',\n '580_sum_duration_sec',\n '580_sum_vol_kb',\n '590_app_present',\n '590_sum_cnt_events',\n '590_sum_duration_sec',\n '590_sum_vol_kb',\n '596_sum_cnt_events',\n '669_max_day_cnt',\n '669_sum_cnt_events',\n '669_sum_duration_sec',\n '669_sum_vol_kb',\n '670_sum_cnt_events',\n '670_sum_duration_sec',\n '670_sum_vol_kb',\n '675_app_present',\n '675_max_day_cnt',\n '682_sum_cnt_events',\n '682_sum_duration_sec',\n '682_sum_vol_kb',\n '689_sum_duration_sec',\n '697_max_day_cnt',\n '697_sum_cnt_events',\n '703_max_day_cnt',\n '708_sum_cnt_events',\n '708_sum_duration_sec',\n '708_sum_vol_kb',\n '722_app_present',\n '722_max_day_cnt',\n '769_sum_duration_sec',\n '782_app_present',\n '782_max_day_cnt',\n '800_max_day_cnt',\n '803_app_present',\n '803_max_day_cnt',\n '806_app_present',\n '806_max_day_cnt',\n '806_sum_cnt_events',\n '806_sum_duration_sec',\n '806_sum_vol_kb',\n '807_max_day_cnt',\n '812_app_present',\n '812_max_day_cnt',\n '822_app_present',\n '824_sum_cnt_events',\n '824_sum_duration_sec',\n '824_sum_vol_kb',\n '826_max_day_cnt',\n '836_max_day_cnt',\n '836_sum_cnt_events',\n '836_sum_vol_kb',\n '839_max_day_cnt',\n '842_sum_cnt_events',\n '842_sum_duration_sec',\n '842_sum_vol_kb',\n '869_sum_cnt_events',\n '869_sum_duration_sec',\n '869_sum_vol_kb',\n '876_app_present',\n '876_max_day_cnt',\n '876_sum_duration_sec',\n '889_app_present',\n '889_max_day_cnt',\n '889_sum_cnt_events',\n '889_sum_duration_sec',\n '889_sum_vol_kb',\n '901_sum_cnt_events',\n '904_app_present',\n '904_max_day_cnt',\n '940_max_day_cnt',\n 'apps_unique',\n 'max_mean_day',\n 'sum_duration_apps',\n 'sum_of_cnt_events_apps',\n 'sum_vol_kb_apps'}"
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apps_selected_colnames"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "apps_selected_colnames = {'1023_app_present',\n",
    " '1023_max_day_cnt',\n",
    " '1111_app_present',\n",
    " '1111_max_day_cnt',\n",
    " '1127_app_present',\n",
    " '1127_max_day_cnt',\n",
    " '1280_app_present',\n",
    " '1345_sum_cnt_events',\n",
    " '1345_sum_duration_sec',\n",
    " '1345_sum_vol_kb',\n",
    " '1347_sum_duration_sec',\n",
    " '1357_app_present',\n",
    " '1357_max_day_cnt',\n",
    " '1365_sum_cnt_events',\n",
    " '1365_sum_duration_sec',\n",
    " '1365_sum_vol_kb',\n",
    " '1367_max_day_cnt',\n",
    " '1406_app_present',\n",
    " '1406_max_day_cnt',\n",
    " '1406_sum_cnt_events',\n",
    " '1406_sum_duration_sec',\n",
    " '1411_max_day_cnt',\n",
    " '1411_sum_cnt_events',\n",
    " '1412_app_present',\n",
    " '1412_max_day_cnt',\n",
    " '1413_app_present',\n",
    " '1457_max_day_cnt',\n",
    " '1459_app_present',\n",
    " '1459_max_day_cnt',\n",
    " '1464_max_day_cnt',\n",
    " '1464_sum_cnt_events',\n",
    " '1476_app_present',\n",
    " '1476_max_day_cnt',\n",
    " '1490_app_present',\n",
    " '1490_max_day_cnt',\n",
    " '15_max_day_cnt',\n",
    " '1863_sum_duration_sec',\n",
    " '1893_app_present',\n",
    " '1893_max_day_cnt',\n",
    " '232_sum_cnt_events',\n",
    " '232_sum_duration_sec',\n",
    " '232_sum_vol_kb',\n",
    " '246_sum_cnt_events',\n",
    " '246_sum_duration_sec',\n",
    " '246_sum_vol_kb',\n",
    " '249_max_day_cnt',\n",
    " '249_sum_cnt_events',\n",
    " '249_sum_duration_sec',\n",
    " '249_sum_vol_kb',\n",
    " '250_max_day_cnt',\n",
    " '250_sum_cnt_events',\n",
    " '250_sum_duration_sec',\n",
    " '250_sum_vol_kb',\n",
    " '266_max_day_cnt',\n",
    " '299_app_present',\n",
    " '307_app_present',\n",
    " '373_sum_cnt_events',\n",
    " '373_sum_duration_sec',\n",
    " '373_sum_vol_kb',\n",
    " '538_sum_cnt_events',\n",
    " '538_sum_duration_sec',\n",
    " '538_sum_vol_kb',\n",
    " '580_max_day_cnt',\n",
    " '580_sum_cnt_events',\n",
    " '580_sum_duration_sec',\n",
    " '580_sum_vol_kb',\n",
    " '590_app_present',\n",
    " '590_sum_cnt_events',\n",
    " '590_sum_duration_sec',\n",
    " '590_sum_vol_kb',\n",
    " '596_sum_cnt_events',\n",
    " '669_max_day_cnt',\n",
    " '669_sum_cnt_events',\n",
    " '669_sum_duration_sec',\n",
    " '669_sum_vol_kb',\n",
    " '670_sum_cnt_events',\n",
    " '670_sum_duration_sec',\n",
    " '670_sum_vol_kb',\n",
    " '675_app_present',\n",
    " '675_max_day_cnt',\n",
    " '682_sum_cnt_events',\n",
    " '682_sum_duration_sec',\n",
    " '682_sum_vol_kb',\n",
    " '689_sum_duration_sec',\n",
    " '697_max_day_cnt',\n",
    " '697_sum_cnt_events',\n",
    " '703_max_day_cnt',\n",
    " '708_sum_cnt_events',\n",
    " '708_sum_duration_sec',\n",
    " '708_sum_vol_kb',\n",
    " '722_app_present',\n",
    " '722_max_day_cnt',\n",
    " '769_sum_duration_sec',\n",
    " '782_app_present',\n",
    " '782_max_day_cnt',\n",
    " '800_max_day_cnt',\n",
    " '803_app_present',\n",
    " '803_max_day_cnt',\n",
    " '806_app_present',\n",
    " '806_max_day_cnt',\n",
    " '806_sum_cnt_events',\n",
    " '806_sum_duration_sec',\n",
    " '806_sum_vol_kb',\n",
    " '807_max_day_cnt',\n",
    " '812_app_present',\n",
    " '812_max_day_cnt',\n",
    " '822_app_present',\n",
    " '824_sum_cnt_events',\n",
    " '824_sum_duration_sec',\n",
    " '824_sum_vol_kb',\n",
    " '826_max_day_cnt',\n",
    " '836_max_day_cnt',\n",
    " '836_sum_cnt_events',\n",
    " '836_sum_vol_kb',\n",
    " '839_max_day_cnt',\n",
    " '842_sum_cnt_events',\n",
    " '842_sum_duration_sec',\n",
    " '842_sum_vol_kb',\n",
    " '869_sum_cnt_events',\n",
    " '869_sum_duration_sec',\n",
    " '869_sum_vol_kb',\n",
    " '876_app_present',\n",
    " '876_max_day_cnt',\n",
    " '876_sum_duration_sec',\n",
    " '889_app_present',\n",
    " '889_max_day_cnt',\n",
    " '889_sum_cnt_events',\n",
    " '889_sum_duration_sec',\n",
    " '889_sum_vol_kb',\n",
    " '901_sum_cnt_events',\n",
    " '904_app_present',\n",
    " '904_max_day_cnt',\n",
    " '940_max_day_cnt',\n",
    " 'apps_unique',\n",
    " 'max_mean_day',\n",
    " 'sum_duration_apps',\n",
    " 'sum_of_cnt_events_apps',\n",
    " 'sum_vol_kb_apps'}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "         836_max_day_cnt  538_sum_duration_sec  1406_sum_duration_sec  \\\nabon_id                                                                 \n1000009                0                  1392                      0   \n1000012                0                   238                      0   \n1000015                0                  1561                  28385   \n1000016                0                     0                      0   \n1000020                0                  5304                      0   \n...                  ...                   ...                    ...   \n1888199                2                 15605                  23282   \n1888201                0                     0                      0   \n1888203                0                  1537                      0   \n1888218                8                     0                      0   \n1888225                7                   409                    102   \n\n         842_sum_vol_kb  803_max_day_cnt  250_max_day_cnt  889_sum_vol_kb  \\\nabon_id                                                                     \n1000009           18005                0                4               0   \n1000012           19885                0               14           13670   \n1000015            8106                0               14           27808   \n1000016               0                0                1               0   \n1000020           33082                0                8               0   \n...                 ...              ...              ...             ...   \n1888199           46862                0               26          952918   \n1888201               0                0                2               0   \n1888203            3078                0               18               0   \n1888218               2                0                1               0   \n1888225            1678                0                8              74   \n\n         901_sum_cnt_events  689_sum_duration_sec  806_sum_cnt_events  ...  \\\nabon_id                                                                ...   \n1000009                   0                    70                  60  ...   \n1000012                   0                   964                   7  ...   \n1000015                   0                    30                1010  ...   \n1000016                   0                     0                   0  ...   \n1000020                   0                   915                  31  ...   \n...                     ...                   ...                 ...  ...   \n1888199                   0                   749                1326  ...   \n1888201                   0                     0                   0  ...   \n1888203                   0                     4                   0  ...   \n1888218                   0                     0                   0  ...   \n1888225                   0                     0                   3  ...   \n\n         824_sum_duration_sec  1464_sum_cnt_events  876_max_day_cnt  \\\nabon_id                                                               \n1000009                   202                    0                0   \n1000012                    70                    0                0   \n1000015                  2120                   29                0   \n1000016                     0                    0                0   \n1000020                    62                    0                0   \n...                       ...                  ...              ...   \n1888199                   100                   64                0   \n1888201                     0                    0                0   \n1888203                  1718                    0                0   \n1888218                     0                    0                0   \n1888225                     0                    0                0   \n\n         307_app_present  1367_max_day_cnt  590_sum_vol_kb  1023_app_present  \\\nabon_id                                                                        \n1000009                0                 0             405                 1   \n1000012                0                 0             465                 0   \n1000015                0                 0            1125                 1   \n1000016                0                 0              57                 0   \n1000020                1                 0            1121                 0   \n...                  ...               ...             ...               ...   \n1888199                0                 2            3327                 1   \n1888201                0                 0               0                 0   \n1888203                0                 0             974                 0   \n1888218                0                 8            1451                 0   \n1888225                0                 3             252                 0   \n\n         538_sum_vol_kb  1357_max_day_cnt  250_sum_vol_kb  \nabon_id                                                    \n1000009             538                 0           11306  \n1000012             522                 8           80340  \n1000015             467                 0           21670  \n1000016               0                 0               1  \n1000020            1796                 0          216378  \n...                 ...               ...             ...  \n1888199            5140                21           18392  \n1888201               0                 0               0  \n1888203             342                 0           12606  \n1888218               0                 0              20  \n1888225             477                 0            1998  \n\n[140830 rows x 138 columns]",
      "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>836_max_day_cnt</th>\n      <th>538_sum_duration_sec</th>\n      <th>1406_sum_duration_sec</th>\n      <th>842_sum_vol_kb</th>\n      <th>803_max_day_cnt</th>\n      <th>250_max_day_cnt</th>\n      <th>889_sum_vol_kb</th>\n      <th>901_sum_cnt_events</th>\n      <th>689_sum_duration_sec</th>\n      <th>806_sum_cnt_events</th>\n      <th>...</th>\n      <th>824_sum_duration_sec</th>\n      <th>1464_sum_cnt_events</th>\n      <th>876_max_day_cnt</th>\n      <th>307_app_present</th>\n      <th>1367_max_day_cnt</th>\n      <th>590_sum_vol_kb</th>\n      <th>1023_app_present</th>\n      <th>538_sum_vol_kb</th>\n      <th>1357_max_day_cnt</th>\n      <th>250_sum_vol_kb</th>\n    </tr>\n    <tr>\n      <th>abon_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1000009</th>\n      <td>0</td>\n      <td>1392</td>\n      <td>0</td>\n      <td>18005</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>70</td>\n      <td>60</td>\n      <td>...</td>\n      <td>202</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>405</td>\n      <td>1</td>\n      <td>538</td>\n      <td>0</td>\n      <td>11306</td>\n    </tr>\n    <tr>\n      <th>1000012</th>\n      <td>0</td>\n      <td>238</td>\n      <td>0</td>\n      <td>19885</td>\n      <td>0</td>\n      <td>14</td>\n      <td>13670</td>\n      <td>0</td>\n      <td>964</td>\n      <td>7</td>\n      <td>...</td>\n      <td>70</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>465</td>\n      <td>0</td>\n      <td>522</td>\n      <td>8</td>\n      <td>80340</td>\n    </tr>\n    <tr>\n      <th>1000015</th>\n      <td>0</td>\n      <td>1561</td>\n      <td>28385</td>\n      <td>8106</td>\n      <td>0</td>\n      <td>14</td>\n      <td>27808</td>\n      <td>0</td>\n      <td>30</td>\n      <td>1010</td>\n      <td>...</td>\n      <td>2120</td>\n      <td>29</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1125</td>\n      <td>1</td>\n      <td>467</td>\n      <td>0</td>\n      <td>21670</td>\n    </tr>\n    <tr>\n      <th>1000016</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>57</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1000020</th>\n      <td>0</td>\n      <td>5304</td>\n      <td>0</td>\n      <td>33082</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>915</td>\n      <td>31</td>\n      <td>...</td>\n      <td>62</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1121</td>\n      <td>0</td>\n      <td>1796</td>\n      <td>0</td>\n      <td>216378</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1888199</th>\n      <td>2</td>\n      <td>15605</td>\n      <td>23282</td>\n      <td>46862</td>\n      <td>0</td>\n      <td>26</td>\n      <td>952918</td>\n      <td>0</td>\n      <td>749</td>\n      <td>1326</td>\n      <td>...</td>\n      <td>100</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3327</td>\n      <td>1</td>\n      <td>5140</td>\n      <td>21</td>\n      <td>18392</td>\n    </tr>\n    <tr>\n      <th>1888201</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1888203</th>\n      <td>0</td>\n      <td>1537</td>\n      <td>0</td>\n      <td>3078</td>\n      <td>0</td>\n      <td>18</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1718</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>974</td>\n      <td>0</td>\n      <td>342</td>\n      <td>0</td>\n      <td>12606</td>\n    </tr>\n    <tr>\n      <th>1888218</th>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1451</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>1888225</th>\n      <td>7</td>\n      <td>409</td>\n      <td>102</td>\n      <td>1678</td>\n      <td>0</td>\n      <td>8</td>\n      <td>74</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>252</td>\n      <td>0</td>\n      <td>477</td>\n      <td>0</td>\n      <td>1998</td>\n    </tr>\n  </tbody>\n</table>\n<p>140830 rows × 138 columns</p>\n</div>"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_apps_df_selected = apps_feature_df.loc[:][list(apps_selected_colnames)]\n",
    "train_apps_df_selected"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "#train_apps_df_selected.to_pickle('train_apps_df_selected.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Поєднання основного датафрейму фіч з підготовленими датафреймами додатків та сервісів"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "df_tr_main = pd.read_pickle('df_train_main_converted_with_spatial.pickle')\n",
    "df_tr_apps = pd.read_pickle('df_train_apps_new_features.pickle')\n",
    "df_tr_serv = pd.read_pickle('df_train_service_new_features.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "\n",
    "df_tr_comb = df_tr_main.join(df_tr_apps, how='left')\n",
    "df_tr_comb = df_tr_comb.join(df_tr_serv, how='left')\n",
    "df_tr_comb.fillna(0, inplace=True)\n",
    "df_tr_target = df_tr_comb['target']\n",
    "df_tr_comb.drop(columns=['target'], inplace=True)\n",
    "\n",
    "# for col in df_tr_comb.columns:\n",
    "#     if col not in selected_features:\n",
    "#         df_tr_comb.drop(columns=[col], inplace=True)\n",
    "#         print(f'For TRAIN DF column \"{col}\" - was dropped')\n",
    "columns_order = df_tr_comb.columns.tolist()\n",
    "df_tr_comb = df_tr_comb.join(df_tr_target, how='left')\n",
    "assert df_tr_comb.isnull().sum().sum() == 0\n",
    "df_tr_comb.reset_index(inplace=True, drop=True)\n",
    "X_train = df_tr_comb.drop(columns=['target'])\n",
    "y_train = df_tr_comb['target']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(205625, 1207)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "(205625,)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "df_te_main = pd.read_pickle('df_test_main_converted_with_spatial.pickle')\n",
    "df_te_main.set_index('abon_id', inplace=True, drop=True)\n",
    "df_te_apps = pd.read_pickle('df_test_apps_new_features.pickle')\n",
    "\n",
    "df_te_serv = pd.read_pickle('df_test_service_new_features.pickle')\n",
    "df_te_comb = df_te_main.join(df_te_apps, how='left')\n",
    "df_te_comb = df_te_comb.join(df_te_serv, how='left')\n",
    "df_te_comb.fillna(0, inplace=True)\n",
    "df_te_target = test_target.set_index('abon_id', drop=True)\n",
    "\n",
    "# for col in df_te_comb.columns:\n",
    "#     if col not in selected_features:\n",
    "#         df_te_comb.drop(columns=[col], inplace=True)\n",
    "#         print(f'For TEST DF column \"{col}\" - was dropped')\n",
    "\n",
    "df_te_comb = df_te_comb[columns_order]\n",
    "df_te_comb = df_te_comb.join(df_te_target, how='left')\n",
    "assert df_te_comb.isnull().sum().sum() == 0\n",
    "df_te_comb.reset_index(inplace=True, drop=True)\n",
    "X_test = df_te_comb.drop(columns=['target'])\n",
    "y_test = df_te_comb['target']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(170101, 1207)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "(170101,)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "X_train.to_pickle('X_train.pickle')\n",
    "y_train.to_pickle('y_train.pickle')\n",
    "X_test.to_pickle('X_test.pickle')\n",
    "y_test.to_pickle('y_test.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#  Балансування та скейлінг"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "X_train = pd.read_pickle('X_train.pickle')\n",
    "y_train = pd.read_pickle('y_train.pickle')\n",
    "\n",
    "X_test = pd.read_pickle('X_test.pickle')\n",
    "y_test = pd.read_pickle('y_test.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.metrics import roc_auc_score, precision_score, accuracy_score, recall_score, f1_score\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
    "from sklearn.preprocessing import RobustScaler, StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "ros = RandomOverSampler()\n",
    "X_ros, y_ros = ros.fit_resample(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "df_test = pd.read_pickle('combine_train_df.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "(311526, 1207)"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ros.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "(311526,)"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ros.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "0.0    155763\n1.0    155763\nName: target, dtype: int64"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ros.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "X_ros.to_pickle('x_ros.pickle')\n",
    "y_ros.to_pickle('y_ros.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "X_train = pd.read_pickle('X_train.pickle')\n",
    "y_train = pd.read_pickle('y_train.pickle')\n",
    "X_test = pd.read_pickle('x_test.pickle')\n",
    "y_test = pd.read_pickle('y_test.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "robuster = RobustScaler().fit(X_ros)\n",
    "X_train_scaled = robuster.transform(X_ros)\n",
    "X_test_scaled = robuster.transform(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 4.49153621e-01,  7.18054476e-03, -6.10185149e-01, ...,\n         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n       [-5.41555301e-01, -9.49246230e-03, -1.42484808e-02, ...,\n         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n       [ 4.14025710e-01, -8.92659424e-03, -6.31896092e-01, ...,\n         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n       ...,\n       [-1.16100293e+00,  5.13262139e-04,  0.00000000e+00, ...,\n         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n       [-5.44482627e-01,  5.13262139e-04,  0.00000000e+00, ...,\n         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n       [-6.02010946e-01,  5.13262139e-04,  0.00000000e+00, ...,\n         0.00000000e+00,  0.00000000e+00,  0.00000000e+00]])"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "X_test_scaled.to_pickle('x_test_scaled.pickle')\n",
    "X_train_scaled.to_pickle('x_train_ros_scaled.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# RF"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "X_train_t, X_test_t, y_train_t, y_test_t = train_test_split(X_ros, y_ros, train_size=0.8, random_state=22)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "rf_clf = RandomForestClassifier(n_jobs=30)\n",
    "param_grid = {\n",
    "    'n_estimators': [15, 25],\n",
    "    'max_depth': [18],\n",
    "    #'class_weight': ['balanced']\n",
    "    'min_samples_leaf': [1, 50, 190],\n",
    "    'min_samples_split': [2, 250, 605]\n",
    "}\n",
    "\n",
    "grid_clf = GridSearchCV(rf_clf, param_grid, cv=3, scoring='roc_auc', verbose=10)\n",
    "grid_clf.fit(X_train_t, y_train_t)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "# pd.DataFrame(grid_clf.cv_results_).to_excel('cv_all2.xlsx')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "{'max_depth': 19,\n 'max_features': 'sqrt',\n 'min_samples_leaf': 190,\n 'min_samples_split': 605,\n 'n_estimators': 15}"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "outputs": [],
   "source": [
    "pd.DataFrame(grid_clf.cv_results_).to_excel('cv_all.xlsx')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "outputs": [
    {
     "data": {
      "text/plain": "0.6817513426448942"
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train, grid_clf.predict(X_train))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "outputs": [
    {
     "data": {
      "text/plain": "0.6536458981191"
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, grid_clf.predict(X_test))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Light GBM"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "import lightgbm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import roc_auc_score, recall_score, precision_score, confusion_matrix, accuracy_score, roc_curve, plot_confusion_matrix, plot_roc_curve"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "font_common = {'family': 'Liberation Sans',\n",
    "               'weight': 'bold',\n",
    "               'size': 25}\n",
    "mpl.rc('font', **font_common)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "X_train = pd.read_pickle('X_train.pickle')\n",
    "y_train = pd.read_pickle('y_train.pickle')\n",
    "\n",
    "X_test = pd.read_pickle('X_test.pickle')\n",
    "y_test = pd.read_pickle('y_test.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "X_train_data, X_train_valid, y_train_data, y_train_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=222)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(205625, 1207)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "(164500, 1207)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(41125, 1207)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_valid.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "train_data = lightgbm.Dataset(X_train_data, label=y_train_data)\n",
    "test_data = lightgbm.Dataset(X_train_valid, label=y_train_valid)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matematik/.local/lib/python3.10/site-packages/lightgbm/engine.py:181: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's auc: 0.621352\n",
      "Training until validation scores don't improve for 25 rounds\n",
      "[2]\tvalid_0's auc: 0.634508\n",
      "[3]\tvalid_0's auc: 0.638629\n",
      "[4]\tvalid_0's auc: 0.640613\n",
      "[5]\tvalid_0's auc: 0.641569\n",
      "[6]\tvalid_0's auc: 0.644586\n",
      "[7]\tvalid_0's auc: 0.644582\n",
      "[8]\tvalid_0's auc: 0.644874\n",
      "[9]\tvalid_0's auc: 0.645986\n",
      "[10]\tvalid_0's auc: 0.646157\n",
      "[11]\tvalid_0's auc: 0.649447\n",
      "[12]\tvalid_0's auc: 0.651166\n",
      "[13]\tvalid_0's auc: 0.651734\n",
      "[14]\tvalid_0's auc: 0.652398\n",
      "[15]\tvalid_0's auc: 0.652841\n",
      "[16]\tvalid_0's auc: 0.653225\n",
      "[17]\tvalid_0's auc: 0.655038\n",
      "[18]\tvalid_0's auc: 0.656602\n",
      "[19]\tvalid_0's auc: 0.657684\n",
      "[20]\tvalid_0's auc: 0.658284\n",
      "[21]\tvalid_0's auc: 0.658952\n",
      "[22]\tvalid_0's auc: 0.659276\n",
      "[23]\tvalid_0's auc: 0.660009\n",
      "[24]\tvalid_0's auc: 0.660216\n",
      "[25]\tvalid_0's auc: 0.660252\n",
      "[26]\tvalid_0's auc: 0.660606\n",
      "[27]\tvalid_0's auc: 0.661081\n",
      "[28]\tvalid_0's auc: 0.661009\n",
      "[29]\tvalid_0's auc: 0.661178\n",
      "[30]\tvalid_0's auc: 0.661619\n",
      "[31]\tvalid_0's auc: 0.662889\n",
      "[32]\tvalid_0's auc: 0.663277\n",
      "[33]\tvalid_0's auc: 0.663526\n",
      "[34]\tvalid_0's auc: 0.664333\n",
      "[35]\tvalid_0's auc: 0.664686\n",
      "[36]\tvalid_0's auc: 0.664933\n",
      "[37]\tvalid_0's auc: 0.665201\n",
      "[38]\tvalid_0's auc: 0.665281\n",
      "[39]\tvalid_0's auc: 0.665607\n",
      "[40]\tvalid_0's auc: 0.665607\n",
      "[41]\tvalid_0's auc: 0.665828\n",
      "[42]\tvalid_0's auc: 0.666222\n",
      "[43]\tvalid_0's auc: 0.666374\n",
      "[44]\tvalid_0's auc: 0.666625\n",
      "[45]\tvalid_0's auc: 0.666663\n",
      "[46]\tvalid_0's auc: 0.666845\n",
      "[47]\tvalid_0's auc: 0.667105\n",
      "[48]\tvalid_0's auc: 0.667832\n",
      "[49]\tvalid_0's auc: 0.66793\n",
      "[50]\tvalid_0's auc: 0.668514\n",
      "[51]\tvalid_0's auc: 0.668794\n",
      "[52]\tvalid_0's auc: 0.669032\n",
      "[53]\tvalid_0's auc: 0.669258\n",
      "[54]\tvalid_0's auc: 0.669517\n",
      "[55]\tvalid_0's auc: 0.669895\n",
      "[56]\tvalid_0's auc: 0.670067\n",
      "[57]\tvalid_0's auc: 0.670367\n",
      "[58]\tvalid_0's auc: 0.67077\n",
      "[59]\tvalid_0's auc: 0.671014\n",
      "[60]\tvalid_0's auc: 0.67119\n",
      "[61]\tvalid_0's auc: 0.671266\n",
      "[62]\tvalid_0's auc: 0.671216\n",
      "[63]\tvalid_0's auc: 0.671174\n",
      "[64]\tvalid_0's auc: 0.671331\n",
      "[65]\tvalid_0's auc: 0.671215\n",
      "[66]\tvalid_0's auc: 0.671168\n",
      "[67]\tvalid_0's auc: 0.671294\n",
      "[68]\tvalid_0's auc: 0.671211\n",
      "[69]\tvalid_0's auc: 0.671414\n",
      "[70]\tvalid_0's auc: 0.671674\n",
      "[71]\tvalid_0's auc: 0.67199\n",
      "[72]\tvalid_0's auc: 0.672345\n",
      "[73]\tvalid_0's auc: 0.672435\n",
      "[74]\tvalid_0's auc: 0.672639\n",
      "[75]\tvalid_0's auc: 0.672809\n",
      "[76]\tvalid_0's auc: 0.673078\n",
      "[77]\tvalid_0's auc: 0.673325\n",
      "[78]\tvalid_0's auc: 0.673489\n",
      "[79]\tvalid_0's auc: 0.673665\n",
      "[80]\tvalid_0's auc: 0.673778\n",
      "[81]\tvalid_0's auc: 0.674148\n",
      "[82]\tvalid_0's auc: 0.674409\n",
      "[83]\tvalid_0's auc: 0.674759\n",
      "[84]\tvalid_0's auc: 0.674921\n",
      "[85]\tvalid_0's auc: 0.675174\n",
      "[86]\tvalid_0's auc: 0.675479\n",
      "[87]\tvalid_0's auc: 0.675764\n",
      "[88]\tvalid_0's auc: 0.675962\n",
      "[89]\tvalid_0's auc: 0.67627\n",
      "[90]\tvalid_0's auc: 0.676487\n",
      "[91]\tvalid_0's auc: 0.676486\n",
      "[92]\tvalid_0's auc: 0.676725\n",
      "[93]\tvalid_0's auc: 0.676984\n",
      "[94]\tvalid_0's auc: 0.677254\n",
      "[95]\tvalid_0's auc: 0.677544\n",
      "[96]\tvalid_0's auc: 0.67769\n",
      "[97]\tvalid_0's auc: 0.677877\n",
      "[98]\tvalid_0's auc: 0.678081\n",
      "[99]\tvalid_0's auc: 0.678202\n",
      "[100]\tvalid_0's auc: 0.678265\n",
      "[101]\tvalid_0's auc: 0.678368\n",
      "[102]\tvalid_0's auc: 0.678484\n",
      "[103]\tvalid_0's auc: 0.678584\n",
      "[104]\tvalid_0's auc: 0.678707\n",
      "[105]\tvalid_0's auc: 0.678868\n",
      "[106]\tvalid_0's auc: 0.679006\n",
      "[107]\tvalid_0's auc: 0.679045\n",
      "[108]\tvalid_0's auc: 0.679161\n",
      "[109]\tvalid_0's auc: 0.679331\n",
      "[110]\tvalid_0's auc: 0.679401\n",
      "[111]\tvalid_0's auc: 0.67962\n",
      "[112]\tvalid_0's auc: 0.679779\n",
      "[113]\tvalid_0's auc: 0.679862\n",
      "[114]\tvalid_0's auc: 0.679962\n",
      "[115]\tvalid_0's auc: 0.680072\n",
      "[116]\tvalid_0's auc: 0.680284\n",
      "[117]\tvalid_0's auc: 0.680414\n",
      "[118]\tvalid_0's auc: 0.680617\n",
      "[119]\tvalid_0's auc: 0.680744\n",
      "[120]\tvalid_0's auc: 0.680883\n",
      "[121]\tvalid_0's auc: 0.681039\n",
      "[122]\tvalid_0's auc: 0.681184\n",
      "[123]\tvalid_0's auc: 0.681286\n",
      "[124]\tvalid_0's auc: 0.681392\n",
      "[125]\tvalid_0's auc: 0.681474\n",
      "[126]\tvalid_0's auc: 0.681604\n",
      "[127]\tvalid_0's auc: 0.681699\n",
      "[128]\tvalid_0's auc: 0.681798\n",
      "[129]\tvalid_0's auc: 0.681888\n",
      "[130]\tvalid_0's auc: 0.682049\n",
      "[131]\tvalid_0's auc: 0.68222\n",
      "[132]\tvalid_0's auc: 0.682386\n",
      "[133]\tvalid_0's auc: 0.682512\n",
      "[134]\tvalid_0's auc: 0.682669\n",
      "[135]\tvalid_0's auc: 0.682871\n",
      "[136]\tvalid_0's auc: 0.682995\n",
      "[137]\tvalid_0's auc: 0.683081\n",
      "[138]\tvalid_0's auc: 0.683251\n",
      "[139]\tvalid_0's auc: 0.683375\n",
      "[140]\tvalid_0's auc: 0.683472\n",
      "[141]\tvalid_0's auc: 0.683554\n",
      "[142]\tvalid_0's auc: 0.683589\n",
      "[143]\tvalid_0's auc: 0.683653\n",
      "[144]\tvalid_0's auc: 0.683724\n",
      "[145]\tvalid_0's auc: 0.683786\n",
      "[146]\tvalid_0's auc: 0.683864\n",
      "[147]\tvalid_0's auc: 0.683987\n",
      "[148]\tvalid_0's auc: 0.684115\n",
      "[149]\tvalid_0's auc: 0.684154\n",
      "[150]\tvalid_0's auc: 0.684257\n",
      "[151]\tvalid_0's auc: 0.684353\n",
      "[152]\tvalid_0's auc: 0.684457\n",
      "[153]\tvalid_0's auc: 0.684586\n",
      "[154]\tvalid_0's auc: 0.684635\n",
      "[155]\tvalid_0's auc: 0.684759\n",
      "[156]\tvalid_0's auc: 0.684887\n",
      "[157]\tvalid_0's auc: 0.68495\n",
      "[158]\tvalid_0's auc: 0.685011\n",
      "[159]\tvalid_0's auc: 0.685064\n",
      "[160]\tvalid_0's auc: 0.685143\n",
      "[161]\tvalid_0's auc: 0.685196\n",
      "[162]\tvalid_0's auc: 0.685269\n",
      "[163]\tvalid_0's auc: 0.685277\n",
      "[164]\tvalid_0's auc: 0.685308\n",
      "[165]\tvalid_0's auc: 0.685403\n",
      "[166]\tvalid_0's auc: 0.685463\n",
      "[167]\tvalid_0's auc: 0.685523\n",
      "[168]\tvalid_0's auc: 0.685637\n",
      "[169]\tvalid_0's auc: 0.685705\n",
      "[170]\tvalid_0's auc: 0.685703\n",
      "[171]\tvalid_0's auc: 0.68585\n",
      "[172]\tvalid_0's auc: 0.686003\n",
      "[173]\tvalid_0's auc: 0.686111\n",
      "[174]\tvalid_0's auc: 0.686222\n",
      "[175]\tvalid_0's auc: 0.686336\n",
      "[176]\tvalid_0's auc: 0.686378\n",
      "[177]\tvalid_0's auc: 0.686468\n",
      "[178]\tvalid_0's auc: 0.68657\n",
      "[179]\tvalid_0's auc: 0.686721\n",
      "[180]\tvalid_0's auc: 0.686864\n",
      "[181]\tvalid_0's auc: 0.686937\n",
      "[182]\tvalid_0's auc: 0.686975\n",
      "[183]\tvalid_0's auc: 0.687042\n",
      "[184]\tvalid_0's auc: 0.687112\n",
      "[185]\tvalid_0's auc: 0.68717\n",
      "[186]\tvalid_0's auc: 0.687328\n",
      "[187]\tvalid_0's auc: 0.687417\n",
      "[188]\tvalid_0's auc: 0.687473\n",
      "[189]\tvalid_0's auc: 0.687523\n",
      "[190]\tvalid_0's auc: 0.687579\n",
      "[191]\tvalid_0's auc: 0.687653\n",
      "[192]\tvalid_0's auc: 0.687676\n",
      "[193]\tvalid_0's auc: 0.687744\n",
      "[194]\tvalid_0's auc: 0.687796\n",
      "[195]\tvalid_0's auc: 0.68781\n",
      "[196]\tvalid_0's auc: 0.687881\n",
      "[197]\tvalid_0's auc: 0.687945\n",
      "[198]\tvalid_0's auc: 0.688021\n",
      "[199]\tvalid_0's auc: 0.688093\n",
      "[200]\tvalid_0's auc: 0.688153\n",
      "[201]\tvalid_0's auc: 0.688242\n",
      "[202]\tvalid_0's auc: 0.688272\n",
      "[203]\tvalid_0's auc: 0.688326\n",
      "[204]\tvalid_0's auc: 0.688349\n",
      "[205]\tvalid_0's auc: 0.688356\n",
      "[206]\tvalid_0's auc: 0.688458\n",
      "[207]\tvalid_0's auc: 0.688519\n",
      "[208]\tvalid_0's auc: 0.688692\n",
      "[209]\tvalid_0's auc: 0.688729\n",
      "[210]\tvalid_0's auc: 0.688828\n",
      "[211]\tvalid_0's auc: 0.688885\n",
      "[212]\tvalid_0's auc: 0.688998\n",
      "[213]\tvalid_0's auc: 0.689015\n",
      "[214]\tvalid_0's auc: 0.689082\n",
      "[215]\tvalid_0's auc: 0.689143\n",
      "[216]\tvalid_0's auc: 0.689239\n",
      "[217]\tvalid_0's auc: 0.689316\n",
      "[218]\tvalid_0's auc: 0.689431\n",
      "[219]\tvalid_0's auc: 0.689473\n",
      "[220]\tvalid_0's auc: 0.689586\n",
      "[221]\tvalid_0's auc: 0.689624\n",
      "[222]\tvalid_0's auc: 0.689786\n",
      "[223]\tvalid_0's auc: 0.689801\n",
      "[224]\tvalid_0's auc: 0.689893\n",
      "[225]\tvalid_0's auc: 0.68998\n",
      "[226]\tvalid_0's auc: 0.690065\n",
      "[227]\tvalid_0's auc: 0.690167\n",
      "[228]\tvalid_0's auc: 0.690197\n",
      "[229]\tvalid_0's auc: 0.690274\n",
      "[230]\tvalid_0's auc: 0.690385\n",
      "[231]\tvalid_0's auc: 0.690461\n",
      "[232]\tvalid_0's auc: 0.690537\n",
      "[233]\tvalid_0's auc: 0.690605\n",
      "[234]\tvalid_0's auc: 0.690658\n",
      "[235]\tvalid_0's auc: 0.690706\n",
      "[236]\tvalid_0's auc: 0.690781\n",
      "[237]\tvalid_0's auc: 0.690828\n",
      "[238]\tvalid_0's auc: 0.69086\n",
      "[239]\tvalid_0's auc: 0.690917\n",
      "[240]\tvalid_0's auc: 0.690941\n",
      "[241]\tvalid_0's auc: 0.69101\n",
      "[242]\tvalid_0's auc: 0.691066\n",
      "[243]\tvalid_0's auc: 0.691116\n",
      "[244]\tvalid_0's auc: 0.691158\n",
      "[245]\tvalid_0's auc: 0.691209\n",
      "[246]\tvalid_0's auc: 0.691268\n",
      "[247]\tvalid_0's auc: 0.691375\n",
      "[248]\tvalid_0's auc: 0.691411\n",
      "[249]\tvalid_0's auc: 0.69149\n",
      "[250]\tvalid_0's auc: 0.691538\n",
      "[251]\tvalid_0's auc: 0.691598\n",
      "[252]\tvalid_0's auc: 0.691652\n",
      "[253]\tvalid_0's auc: 0.691705\n",
      "[254]\tvalid_0's auc: 0.691751\n",
      "[255]\tvalid_0's auc: 0.691792\n",
      "[256]\tvalid_0's auc: 0.691809\n",
      "[257]\tvalid_0's auc: 0.691831\n",
      "[258]\tvalid_0's auc: 0.69188\n",
      "[259]\tvalid_0's auc: 0.691921\n",
      "[260]\tvalid_0's auc: 0.69196\n",
      "[261]\tvalid_0's auc: 0.692045\n",
      "[262]\tvalid_0's auc: 0.692094\n",
      "[263]\tvalid_0's auc: 0.692155\n",
      "[264]\tvalid_0's auc: 0.692249\n",
      "[265]\tvalid_0's auc: 0.692305\n",
      "[266]\tvalid_0's auc: 0.69234\n",
      "[267]\tvalid_0's auc: 0.692373\n",
      "[268]\tvalid_0's auc: 0.692398\n",
      "[269]\tvalid_0's auc: 0.692407\n",
      "[270]\tvalid_0's auc: 0.692472\n",
      "[271]\tvalid_0's auc: 0.692535\n",
      "[272]\tvalid_0's auc: 0.692583\n",
      "[273]\tvalid_0's auc: 0.692606\n",
      "[274]\tvalid_0's auc: 0.692663\n",
      "[275]\tvalid_0's auc: 0.692694\n",
      "[276]\tvalid_0's auc: 0.692752\n",
      "[277]\tvalid_0's auc: 0.692792\n",
      "[278]\tvalid_0's auc: 0.692847\n",
      "[279]\tvalid_0's auc: 0.692866\n",
      "[280]\tvalid_0's auc: 0.692866\n",
      "[281]\tvalid_0's auc: 0.692879\n",
      "[282]\tvalid_0's auc: 0.692937\n",
      "[283]\tvalid_0's auc: 0.692995\n",
      "[284]\tvalid_0's auc: 0.693042\n",
      "[285]\tvalid_0's auc: 0.693048\n",
      "[286]\tvalid_0's auc: 0.693106\n",
      "[287]\tvalid_0's auc: 0.693132\n",
      "[288]\tvalid_0's auc: 0.693153\n",
      "[289]\tvalid_0's auc: 0.693198\n",
      "[290]\tvalid_0's auc: 0.69325\n",
      "[291]\tvalid_0's auc: 0.693291\n",
      "[292]\tvalid_0's auc: 0.69333\n",
      "[293]\tvalid_0's auc: 0.693363\n",
      "[294]\tvalid_0's auc: 0.69338\n",
      "[295]\tvalid_0's auc: 0.693392\n",
      "[296]\tvalid_0's auc: 0.693421\n",
      "[297]\tvalid_0's auc: 0.693478\n",
      "[298]\tvalid_0's auc: 0.693522\n",
      "[299]\tvalid_0's auc: 0.693551\n",
      "[300]\tvalid_0's auc: 0.6936\n",
      "[301]\tvalid_0's auc: 0.693635\n",
      "[302]\tvalid_0's auc: 0.693693\n",
      "[303]\tvalid_0's auc: 0.693731\n",
      "[304]\tvalid_0's auc: 0.693751\n",
      "[305]\tvalid_0's auc: 0.693783\n",
      "[306]\tvalid_0's auc: 0.693814\n",
      "[307]\tvalid_0's auc: 0.693847\n",
      "[308]\tvalid_0's auc: 0.693859\n",
      "[309]\tvalid_0's auc: 0.693888\n",
      "[310]\tvalid_0's auc: 0.693911\n",
      "[311]\tvalid_0's auc: 0.693926\n",
      "[312]\tvalid_0's auc: 0.694022\n",
      "[313]\tvalid_0's auc: 0.694055\n",
      "[314]\tvalid_0's auc: 0.694118\n",
      "[315]\tvalid_0's auc: 0.694195\n",
      "[316]\tvalid_0's auc: 0.694251\n",
      "[317]\tvalid_0's auc: 0.694353\n",
      "[318]\tvalid_0's auc: 0.694399\n",
      "[319]\tvalid_0's auc: 0.694473\n",
      "[320]\tvalid_0's auc: 0.694508\n",
      "[321]\tvalid_0's auc: 0.694528\n",
      "[322]\tvalid_0's auc: 0.694556\n",
      "[323]\tvalid_0's auc: 0.694565\n",
      "[324]\tvalid_0's auc: 0.69462\n",
      "[325]\tvalid_0's auc: 0.694633\n",
      "[326]\tvalid_0's auc: 0.694646\n",
      "[327]\tvalid_0's auc: 0.694722\n",
      "[328]\tvalid_0's auc: 0.694734\n",
      "[329]\tvalid_0's auc: 0.694753\n",
      "[330]\tvalid_0's auc: 0.694798\n",
      "[331]\tvalid_0's auc: 0.694821\n",
      "[332]\tvalid_0's auc: 0.694882\n",
      "[333]\tvalid_0's auc: 0.69492\n",
      "[334]\tvalid_0's auc: 0.694942\n",
      "[335]\tvalid_0's auc: 0.69497\n",
      "[336]\tvalid_0's auc: 0.695012\n",
      "[337]\tvalid_0's auc: 0.695077\n",
      "[338]\tvalid_0's auc: 0.695055\n",
      "[339]\tvalid_0's auc: 0.695089\n",
      "[340]\tvalid_0's auc: 0.695122\n",
      "[341]\tvalid_0's auc: 0.695133\n",
      "[342]\tvalid_0's auc: 0.695128\n",
      "[343]\tvalid_0's auc: 0.695139\n",
      "[344]\tvalid_0's auc: 0.695161\n",
      "[345]\tvalid_0's auc: 0.695188\n",
      "[346]\tvalid_0's auc: 0.69521\n",
      "[347]\tvalid_0's auc: 0.695223\n",
      "[348]\tvalid_0's auc: 0.695257\n",
      "[349]\tvalid_0's auc: 0.695262\n",
      "[350]\tvalid_0's auc: 0.695291\n",
      "[351]\tvalid_0's auc: 0.695379\n",
      "[352]\tvalid_0's auc: 0.695472\n",
      "[353]\tvalid_0's auc: 0.695569\n",
      "[354]\tvalid_0's auc: 0.695604\n",
      "[355]\tvalid_0's auc: 0.695626\n",
      "[356]\tvalid_0's auc: 0.695643\n",
      "[357]\tvalid_0's auc: 0.695729\n",
      "[358]\tvalid_0's auc: 0.695762\n",
      "[359]\tvalid_0's auc: 0.695799\n",
      "[360]\tvalid_0's auc: 0.695882\n",
      "[361]\tvalid_0's auc: 0.69593\n",
      "[362]\tvalid_0's auc: 0.695942\n",
      "[363]\tvalid_0's auc: 0.695981\n",
      "[364]\tvalid_0's auc: 0.695992\n",
      "[365]\tvalid_0's auc: 0.696026\n",
      "[366]\tvalid_0's auc: 0.696038\n",
      "[367]\tvalid_0's auc: 0.696051\n",
      "[368]\tvalid_0's auc: 0.696057\n",
      "[369]\tvalid_0's auc: 0.696056\n",
      "[370]\tvalid_0's auc: 0.696092\n",
      "[371]\tvalid_0's auc: 0.696123\n",
      "[372]\tvalid_0's auc: 0.696141\n",
      "[373]\tvalid_0's auc: 0.696187\n",
      "[374]\tvalid_0's auc: 0.696236\n",
      "[375]\tvalid_0's auc: 0.696278\n",
      "[376]\tvalid_0's auc: 0.696336\n",
      "[377]\tvalid_0's auc: 0.696374\n",
      "[378]\tvalid_0's auc: 0.696413\n",
      "[379]\tvalid_0's auc: 0.696447\n",
      "[380]\tvalid_0's auc: 0.696502\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[380]\tvalid_0's auc: 0.696502\n",
      "Train_fit : 0.7176\n",
      "Train_val : 0.6965\n",
      "Test      : 0.6711\n",
      "\n",
      "Diff 1    : 4.65\n",
      "Diff 2    : 2.11\n",
      "Diff-Diff : 2.54\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'objective': 'binary',\n",
    "    'metric': 'auc',\n",
    "    'is_unbalance': 'true',\n",
    "    'boosting': 'gbdt',\n",
    "\n",
    "    'num_leaves': 90,\n",
    "    'min_data_in_leaf': 1220,\n",
    "    'max_depth': -1,\n",
    "\n",
    "    'feature_fraction': 0.8,\n",
    "    'bagging_fraction': 0.45,\n",
    "    'bagging_freq': 10,\n",
    "\n",
    "    'learning_rate': 0.01,\n",
    "    'verbose': -1\n",
    "}\n",
    "model_lgbm = lightgbm.train(parameters,\n",
    "                            train_data,\n",
    "                            valid_sets=test_data,\n",
    "                            num_boost_round=380,\n",
    "                            early_stopping_rounds=25)\n",
    "\n",
    "real_ra = round(roc_auc_score(y_test, model_lgbm.predict(X_test)), 4)\n",
    "train_ra = round(roc_auc_score(y_train_data, model_lgbm.predict(X_train_data)), 4)\n",
    "train_val_ra = round(roc_auc_score(y_train_valid, model_lgbm.predict(X_train_valid)), 4)\n",
    "diff = round((train_ra - real_ra)*100, 2)\n",
    "diff2 = round((train_ra - train_val_ra)*100, 2)\n",
    "diff_diff = round((diff - diff2), 2)\n",
    "print(f'Train_fit : {train_ra}\\nTrain_val : {train_val_ra}\\nTest      : {real_ra}\\n'\n",
    "      f'\\nDiff 1    : {diff}'\n",
    "      f'\\nDiff 2    : {diff2}'\n",
    "      f'\\nDiff-Diff : {diff_diff}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "device_id                  442\nBalance_uah                101\nTM_ID                       83\nlt                         486\nloc_cnt_events              44\n                          ... \nbaby_unique_services         0\nbaby_cnt_sms_in              0\npay_sys_cat_present          0\npay_sys_unique_services      0\npay_sys_cnt_sms_in           0\nLength: 1207, dtype: int32"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_vals = model_lgbm.feature_importance()\n",
    "imp_names = X_train.columns.tolist()\n",
    "impot_fea = pd.Series(index=imp_names, data=imp_vals)\n",
    "impot_fea"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "pred = model_lgbm.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "cm = confusion_matrix(y_test, pred.round())\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot(cmap=plt.cm.Blues,values_format='g')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(np.array([[59.7, 40.3], [34.8, 65.2]]))\n",
    "disp.plot(cmap=plt.cm.cividis,values_format='g')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[92981, 62681],\n       [ 5018,  9421]])"
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 folds testing:\n",
      "\n",
      "ROC-AUC:\n",
      "\tMean :0.671\n",
      "\tMin  :0.663\n",
      "\tMax  :0.681\n",
      "\tDiff :0.018\n",
      "\n",
      "Precision:\n",
      "\tMean :0.131\n",
      "\tMin  :0.125\n",
      "\tMax  :0.136\n",
      "\tDiff :0.011\n",
      "\n",
      "Recall:\n",
      "\tMean :0.652\n",
      "\tMin  :0.64\n",
      "\tMax  :0.661\n",
      "\tDiff :0.021\n",
      "\n",
      "Accuracy:\n",
      "\tMean :0.602\n",
      "\tMin  :0.6\n",
      "\tMax  :0.606\n",
      "\tDiff :0.006\n",
      "\n"
     ]
    }
   ],
   "source": [
    "folds = 5\n",
    "\n",
    "kf = KFold(n_splits=folds, shuffle=True, random_state=22)\n",
    "roc_auc_kf = []\n",
    "precision_kf = []\n",
    "recall_kf = []\n",
    "accuracy_kf = []\n",
    "\n",
    "for bigger_part, smaller_part in kf.split(X_test):\n",
    "    cur_true = y_test.iloc[smaller_part]\n",
    "    cur_pred = model_lgbm.predict(X_test.iloc[smaller_part]).round()\n",
    "    roc_auc_kf.append(roc_auc_score(cur_true, model_lgbm.predict(X_test.iloc[smaller_part])))\n",
    "    precision_kf.append(precision_score(cur_true, cur_pred))\n",
    "    recall_kf.append(recall_score(cur_true, cur_pred))\n",
    "    accuracy_kf.append(accuracy_score(cur_true, cur_pred))\n",
    "\n",
    "print('5 folds testing:')\n",
    "print()\n",
    "print(\"ROC-AUC:\")\n",
    "print(f'\\tMean :{round(sum(roc_auc_kf)/folds, 3)}')\n",
    "print(f'\\tMin  :{round(min(roc_auc_kf), 3)}')\n",
    "print(f'\\tMax  :{round(max(roc_auc_kf), 3)}')\n",
    "print(f'\\tDiff :{round(max(roc_auc_kf) - min(roc_auc_kf), 3)}')\n",
    "print()\n",
    "print('Precision:')\n",
    "print(f'\\tMean :{round(sum(precision_kf)/folds, 3)}')\n",
    "print(f'\\tMin  :{round(min(precision_kf), 3)}')\n",
    "print(f'\\tMax  :{round(max(precision_kf), 3)}')\n",
    "print(f'\\tDiff :{round(max(precision_kf) - min(precision_kf), 3)}')\n",
    "print()\n",
    "print('Recall:')\n",
    "print(f'\\tMean :{round(sum(recall_kf)/folds, 3)}')\n",
    "print(f'\\tMin  :{round(min(recall_kf), 3)}')\n",
    "print(f'\\tMax  :{round(max(recall_kf), 3)}')\n",
    "print(f'\\tDiff :{round(max(recall_kf) - min(recall_kf), 3)}')\n",
    "print()\n",
    "print('Accuracy:')\n",
    "print(f'\\tMean :{round(sum(accuracy_kf)/folds, 3)}')\n",
    "print(f'\\tMin  :{round(min(accuracy_kf), 3)}')\n",
    "print(f'\\tMax  :{round(max(accuracy_kf), 3)}')\n",
    "print(f'\\tDiff :{round(max(accuracy_kf) - min(accuracy_kf), 3)}')\n",
    "print()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "1.5"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([1, 2])/2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[92981 62681]\n",
      "[5018 9421]\n"
     ]
    }
   ],
   "source": [
    "for i in cm:\n",
    "    print(i)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "def tune_weight(pred, true_border=0.5):\n",
    "    res = []\n",
    "    for num in pred:\n",
    "        int_num = 1 if num >= true_border else 0\n",
    "        res.append(int_num)\n",
    "    return res"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(pred[y_test==0], bins=50, label='Не придбали')\n",
    "plt.hist(pred[y_test==1], bins=50, label='Придбали', alpha=0.7, color='r')\n",
    "plt.xlabel('Вірогідність моделі (test)', fontsize=25)\n",
    "plt.ylabel('Кількість у кожному біні', fontsize=25)\n",
    "plt.legend(fontsize=15)\n",
    "plt.axvline(0.5, linewidth=5., color='r')\n",
    "plt.tick_params(axis='both', labelsize=25, pad=5)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.lines.Line2D at 0x7f4ff15a34f0>"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test.value_counts()\n",
    "plt.axvline(0.5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(model_lgbm.predict(X_train)[y_train==0], bins=50, label='Не придбали')\n",
    "plt.hist(model_lgbm.predict(X_train)[y_train==1], bins=50, label='Придбали', alpha=0.7, color='r')\n",
    "plt.xlabel('Вірогідність моделі (train)', fontsize=25)\n",
    "plt.ylabel('Кількість у кожному біні', fontsize=25)\n",
    "plt.legend(fontsize=15)\n",
    "plt.axvline(0.5, linewidth=5., color='r')\n",
    "plt.tick_params(axis='both', labelsize=25, pad=5)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "us = RandomUnderSampler()\n",
    "X_us_test, y_us_test = us.fit_resample(X_test, y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(model_lgbm.predict(X_us_test)[y_us_test==0], bins=50, label='Не придбали')\n",
    "plt.hist(model_lgbm.predict(X_us_test)[y_us_test==1], bins=50, label='Придбали', alpha=0.7, color='r')\n",
    "plt.xlabel('Вірогідність моделі (test - undersampled)', fontsize=25)\n",
    "plt.ylabel('Кількість у кожному біні', fontsize=25)\n",
    "plt.legend(fontsize=15)\n",
    "plt.axvline(0.5, linewidth=5., color='g')\n",
    "plt.tick_params(axis='both', labelsize=25, pad=5)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_us_train, y_us_train = us.fit_resample(X_train, y_train)\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(model_lgbm.predict(X_us_train)[y_us_train==0], bins=50, label='Не придбали')\n",
    "plt.hist(model_lgbm.predict(X_us_train)[y_us_train==1], bins=50, label='Придбали', alpha=0.7, color='r')\n",
    "plt.xlabel('Вірогідність моделі (train - undersampled)', fontsize=25)\n",
    "plt.ylabel('Кількість у кожному біні', fontsize=25)\n",
    "plt.axvline(0.5, linewidth=5., color='g')\n",
    "plt.legend(fontsize=15)\n",
    "plt.tick_params(axis='both', labelsize=25, pad=5)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "outputs": [],
   "source": [
    "pred_un = model_lgbm.predict(X_us_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[8667, 5772],\n       [5018, 9421]])"
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_us_test, pred_un.round())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [
    {
     "data": {
      "text/plain": "0.0    14439\n1.0    14439\nName: target, dtype: int64"
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_us_test.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "outputs": [],
   "source": [
    "values_total = 14439\n",
    "x_pos = []\n",
    "another_val = []\n",
    "target_val = []\n",
    "\n",
    "for tresh in np.arange(0, 1, 0.01):\n",
    "    cur_pred = tune_weight(pred_un, true_border=tresh)\n",
    "    another = confusion_matrix(y_us_test, cur_pred)[0][1]\n",
    "    target = confusion_matrix(y_us_test, cur_pred)[1][1]\n",
    "    x_pos.append(tresh)\n",
    "    another_val.append((another/values_total)*100)\n",
    "    target_val.append((target/values_total)*100)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8619710506267747"
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur_pred = tune_weight(pred_un, true_border=0.4)\n",
    "#confusion_matrix(y_us_test, cur_pred)[1][0]\n",
    "confusion_matrix(y_us_test, cur_pred)[1][1]/values_total"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "outputs": [
    {
     "data": {
      "text/plain": "(0.2, 0.8)"
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.plot(x_pos,target_val, color='r')\n",
    "plt.plot(x_pos,another_val, color='b')\n",
    "plt.axvline(0.5, color='g', linewidth=5.)\n",
    "plt.axvline(0.4, color='y', linestyle='dashed', linewidth=5.)\n",
    "plt.xlabel(\"Зміщення ваги передбачення до 0.4\")\n",
    "plt.ylabel(\"Відсоток від усіх осіб\")\n",
    "plt.xlim(0.2, 0.8)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}