overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Импорты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandasql\n",
      "  Downloading pandasql-0.7.3.tar.gz (26 kB)\n",
      "Requirement already satisfied: numpy in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandasql) (1.18.1)\n",
      "Requirement already satisfied: pandas in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandasql) (1.0.3)\n",
      "Requirement already satisfied: sqlalchemy in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandasql) (1.3.16)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandas->pandasql) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandas->pandasql) (2019.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from python-dateutil>=2.6.1->pandas->pandasql) (1.14.0)\n",
      "Building wheels for collected packages: pandasql\n",
      "  Building wheel for pandasql (setup.py): started\n",
      "  Building wheel for pandasql (setup.py): finished with status 'done'\n",
      "  Created wheel for pandasql: filename=pandasql-0.7.3-py3-none-any.whl size=26822 sha256=3827474085b14c924ae709601f468ae0d5e52f7be3335983151a05808107f289\n",
      "  Stored in directory: c:\\users\\anastasia\\appdata\\local\\pip\\cache\\wheels\\5c\\4b\\ec\\41f4e116c8053c3654e2c2a47c62b4fca34cc67ef7b55deb7f\n",
      "Successfully built pandasql\n",
      "Installing collected packages: pandasql\n",
      "Successfully installed pandasql-0.7.3\n",
      "Processing c:\\users\\anastasia\\appdata\\local\\pip\\cache\\wheels\\5c\\4b\\ec\\41f4e116c8053c3654e2c2a47c62b4fca34cc67ef7b55deb7f\\pandasql-0.7.3-py3-none-any.whl\n",
      "Requirement already satisfied: pandas in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandasql) (1.0.3)\n",
      "Requirement already satisfied: sqlalchemy in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandasql) (1.3.16)\n",
      "Requirement already satisfied: numpy in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandasql) (1.18.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandas->pandasql) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from pandas->pandasql) (2019.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\anastasia\\anaconda3\\lib\\site-packages (from python-dateutil>=2.6.1->pandas->pandasql) (1.14.0)\n",
      "Installing collected packages: pandasql\n",
      "  Attempting uninstall: pandasql\n",
      "    Found existing installation: pandasql 0.7.3\n",
      "    Uninstalling pandasql-0.7.3:\n",
      "      Successfully uninstalled pandasql-0.7.3\n",
      "Successfully installed pandasql-0.7.3\n"
     ]
    }
   ],
   "source": [
    "!pip install pandasql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:24.227080Z",
     "start_time": "2020-10-18T00:12:23.551484Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from os.path import join\n",
    "from glob import glob\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import f1_score\n",
    "import pandasql as ps\n",
    "from datetime import datetime, date, timedelta\n",
    "from tqdm.notebook import tqdm as tqdm_notebook\n",
    "import catboost\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:24.230779Z",
     "start_time": "2020-10-18T00:12:24.228527Z"
    }
   },
   "outputs": [],
   "source": [
    "DATADIR = '/Users/Anastasia/Desktop/python/Hackaton/DATA'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Посмотрим на таблицы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(join(DATADIR, 'train', 'train.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:27.144568Z",
     "start_time": "2020-10-18T00:12:24.317934Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1159576, 3)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phone_id</th>\n",
       "      <th>order_completed_at</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19843</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19843</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19843</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19843</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19843</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   phone_id  order_completed_at  target\n",
       "0     19843                   1     1.0\n",
       "1     19843                   2     0.0\n",
       "2     19843                   3     0.0\n",
       "3     19843                   4     0.0\n",
       "4     19843                   5     0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(join(DATADIR, 'train', 'train.csv'))\n",
    "train_df['order_completed_at'] = train_df['order_completed_at'].str.extract('\\d{4}-(\\d{2})')[0].astype(int)\n",
    "print(train_df.shape)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тут заказы с января по июль 2020 года. Надо сделать предсказание для людей из файла sample_submission на сентябрь 2020.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:28.296483Z",
     "start_time": "2020-10-18T00:12:28.263569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01     59652\n",
       "2020-02     75985\n",
       "2020-03    106791\n",
       "2020-04    159845\n",
       "2020-05    218569\n",
       "2020-06    254285\n",
       "2020-07    284449\n",
       "Name: order_completed_at, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['order_completed_at'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:29.230042Z",
     "start_time": "2020-10-18T00:12:28.589628Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>phone_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8531</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2982657</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3271801</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3648206</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        id  phone_id\n",
       "0       41         1\n",
       "1     8531         2\n",
       "2  2982657         2\n",
       "3  3271801         2\n",
       "4  3648206         2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addr_df = pd.read_csv(join(DATADIR, 'misc', 'addresses.csv'))\n",
    "addr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shipments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:40.923454Z",
     "start_time": "2020-10-18T00:12:29.231858Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2260686, 20)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>ship_address_id</th>\n",
       "      <th>shipment_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_created_at</th>\n",
       "      <th>order_completed_at</th>\n",
       "      <th>shipment_starts_at</th>\n",
       "      <th>retailer</th>\n",
       "      <th>s.order_state</th>\n",
       "      <th>shipment_state</th>\n",
       "      <th>s.city_name</th>\n",
       "      <th>s.store_id</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>rate</th>\n",
       "      <th>dw_kind</th>\n",
       "      <th>promo_total</th>\n",
       "      <th>total_weight</th>\n",
       "      <th>platform</th>\n",
       "      <th>os</th>\n",
       "      <th>shipped_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11019</td>\n",
       "      <td>171906</td>\n",
       "      <td>322307</td>\n",
       "      <td>2156687</td>\n",
       "      <td>2017-08-03 21:25:23</td>\n",
       "      <td>2020-02-18 14:07:00</td>\n",
       "      <td>2020-02-20 07:00:00</td>\n",
       "      <td>METRO</td>\n",
       "      <td>complete</td>\n",
       "      <td>shipped</td>\n",
       "      <td>Москва</td>\n",
       "      <td>21</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "      <td>courier</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30170</td>\n",
       "      <td>web</td>\n",
       "      <td>windows</td>\n",
       "      <td>2020-02-20 08:08:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62278</td>\n",
       "      <td>278832</td>\n",
       "      <td>387023</td>\n",
       "      <td>3021953</td>\n",
       "      <td>2018-03-02 17:22:04</td>\n",
       "      <td>2020-01-03 13:09:26</td>\n",
       "      <td>2020-01-03 17:00:00</td>\n",
       "      <td>METRO</td>\n",
       "      <td>complete</td>\n",
       "      <td>shipped</td>\n",
       "      <td>Москва</td>\n",
       "      <td>10</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>courier</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>11305</td>\n",
       "      <td>web</td>\n",
       "      <td>windows</td>\n",
       "      <td>2020-01-03 18:10:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>905126</td>\n",
       "      <td>468407</td>\n",
       "      <td>388943</td>\n",
       "      <td>3018198</td>\n",
       "      <td>2018-02-28 11:32:47</td>\n",
       "      <td>2020-02-12 12:39:28</td>\n",
       "      <td>2020-02-13 11:00:00</td>\n",
       "      <td>METRO</td>\n",
       "      <td>complete</td>\n",
       "      <td>shipped</td>\n",
       "      <td>Москва</td>\n",
       "      <td>21</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5</td>\n",
       "      <td>courier</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13589</td>\n",
       "      <td>app</td>\n",
       "      <td>ios</td>\n",
       "      <td>2020-02-13 12:33:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21412</td>\n",
       "      <td>61962</td>\n",
       "      <td>421048</td>\n",
       "      <td>3030227</td>\n",
       "      <td>2018-03-07 20:37:27</td>\n",
       "      <td>2020-01-25 11:58:56</td>\n",
       "      <td>2020-01-25 18:00:00</td>\n",
       "      <td>METRO</td>\n",
       "      <td>complete</td>\n",
       "      <td>shipped</td>\n",
       "      <td>Москва</td>\n",
       "      <td>8</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0</td>\n",
       "      <td>courier</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9726</td>\n",
       "      <td>web</td>\n",
       "      <td>mac</td>\n",
       "      <td>2020-01-25 19:55:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42110</td>\n",
       "      <td>378297</td>\n",
       "      <td>442659</td>\n",
       "      <td>2923996</td>\n",
       "      <td>2017-12-24 11:19:04</td>\n",
       "      <td>2020-01-07 14:30:44</td>\n",
       "      <td>2020-01-07 19:00:00</td>\n",
       "      <td>METRO</td>\n",
       "      <td>complete</td>\n",
       "      <td>shipped</td>\n",
       "      <td>Москва</td>\n",
       "      <td>2</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0</td>\n",
       "      <td>courier</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30323</td>\n",
       "      <td>web</td>\n",
       "      <td>windows</td>\n",
       "      <td>2020-01-07 19:51:37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  ship_address_id  shipment_id  order_id    order_created_at  \\\n",
       "0    11019           171906       322307   2156687 2017-08-03 21:25:23   \n",
       "1    62278           278832       387023   3021953 2018-03-02 17:22:04   \n",
       "2   905126           468407       388943   3018198 2018-02-28 11:32:47   \n",
       "3    21412            61962       421048   3030227 2018-03-07 20:37:27   \n",
       "4    42110           378297       442659   2923996 2017-12-24 11:19:04   \n",
       "\n",
       "   order_completed_at  shipment_starts_at retailer s.order_state  \\\n",
       "0 2020-02-18 14:07:00 2020-02-20 07:00:00    METRO      complete   \n",
       "1 2020-01-03 13:09:26 2020-01-03 17:00:00    METRO      complete   \n",
       "2 2020-02-12 12:39:28 2020-02-13 11:00:00    METRO      complete   \n",
       "3 2020-01-25 11:58:56 2020-01-25 18:00:00    METRO      complete   \n",
       "4 2020-01-07 14:30:44 2020-01-07 19:00:00    METRO      complete   \n",
       "\n",
       "  shipment_state s.city_name  s.store_id  total_cost  rate  dw_kind  \\\n",
       "0        shipped      Москва          21       168.0     0  courier   \n",
       "1        shipped      Москва          10        98.0     0  courier   \n",
       "2        shipped      Москва          21        98.0     5  courier   \n",
       "3        shipped      Москва           8       158.0     0  courier   \n",
       "4        shipped      Москва           2       163.0     0  courier   \n",
       "\n",
       "   promo_total  total_weight platform       os          shipped_at  \n",
       "0          0.0         30170      web  windows 2020-02-20 08:08:54  \n",
       "1       -150.0         11305      web  windows 2020-01-03 18:10:40  \n",
       "2          0.0         13589      app      ios 2020-02-13 12:33:53  \n",
       "3          0.0          9726      web      mac 2020-01-25 19:55:32  \n",
       "4          0.0         30323      web  windows 2020-01-07 19:51:37  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ship_df = pd.concat([pd.read_csv(file) for file in glob(join(DATADIR, 'shipments', '*'))], axis=0, ignore_index=False)\n",
    "\n",
    "for col in ['order_created_at', 'order_completed_at', 'shipment_starts_at', 'shipped_at']:\n",
    "    ship_df[col] = pd.to_datetime(ship_df[col], format='%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "print(ship_df.shape)\n",
    "ship_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:41.297084Z",
     "start_time": "2020-10-18T00:12:40.926040Z"
    }
   },
   "outputs": [],
   "source": [
    "ship_df['order_created_at_month'] = ship_df['order_created_at'].dt.month\n",
    "ship_df['order_completed_at_month'] = ship_df['order_completed_at'].dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:41.467434Z",
     "start_time": "2020-10-18T00:12:41.298311Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48329, 3)\n"
     ]
    }
   ],
   "source": [
    "users_df = pd.read_csv(join(DATADIR, 'misc', 'user_profiles.csv')).dropna(subset=['gender', 'bdate'], how='all')\n",
    "users_df = users_df.drop_duplicates()\n",
    "print(users_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:41.702649Z",
     "start_time": "2020-10-18T00:12:41.468916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>bdate</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1684318</td>\n",
       "      <td>male</td>\n",
       "      <td>1987-10-11</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1290242</td>\n",
       "      <td>male</td>\n",
       "      <td>1982-09-24</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>632900</td>\n",
       "      <td>male</td>\n",
       "      <td>1989-03-16</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2107573</td>\n",
       "      <td>male</td>\n",
       "      <td>1978-12-04</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1781771</td>\n",
       "      <td>female</td>\n",
       "      <td>1948-01-10</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  gender       bdate   age\n",
       "3    1684318    male  1987-10-11  33.0\n",
       "22   1290242    male  1982-09-24  38.0\n",
       "55    632900    male  1989-03-16  31.0\n",
       "103  2107573    male  1978-12-04  41.0\n",
       "116  1781771  female  1948-01-10  72.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_age(bdate):\n",
    "    if not isinstance(bdate, str):\n",
    "        return None\n",
    "    if '.' in bdate:\n",
    "        sep = '.'\n",
    "    elif '-' in bdate:\n",
    "        sep = '-'\n",
    "    else:\n",
    "        sep = '/'\n",
    "    vals = bdate.split(sep)\n",
    "    if len(vals) < 3:\n",
    "        return None\n",
    "    vals = list(map(int, vals))\n",
    "    if len(str(vals[0])) == 4:\n",
    "        born = date(year=vals[0], month=vals[1], day=vals[2])\n",
    "    else:\n",
    "        born = date(year=vals[2], month=vals[1], day=vals[0])\n",
    "    today = date.today()\n",
    "    return today.year - born.year - ((today.month, today.day) < (born.month, born.day))\n",
    "\n",
    "users_df['age'] = users_df['bdate'].apply(get_age)\n",
    "\n",
    "users_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sample_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:41.739645Z",
     "start_time": "2020-10-18T00:12:41.704150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19843</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>471287</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>342522</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>457799</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>233778</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id  Predicted\n",
       "0   19843        NaN\n",
       "1  471287        NaN\n",
       "2  342522        NaN\n",
       "3  457799        NaN\n",
       "4  233778        NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(join(DATADIR, 'sample_submission.csv'), sep=';')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обработка"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Соответствие phone_id-addr_id-user_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:42.761535Z",
     "start_time": "2020-10-18T00:12:41.741440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>address_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11019</td>\n",
       "      <td>171906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62278</td>\n",
       "      <td>278832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>905126</td>\n",
       "      <td>468407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21412</td>\n",
       "      <td>61962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42110</td>\n",
       "      <td>378297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  address_id\n",
       "0    11019      171906\n",
       "1    62278      278832\n",
       "2   905126      468407\n",
       "3    21412       61962\n",
       "4    42110      378297"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids_df = ship_df[['user_id', 'ship_address_id']].drop_duplicates().rename(columns={'ship_address_id':'address_id'})\n",
    "ids_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:43.465278Z",
     "start_time": "2020-10-18T00:12:42.764250Z"
    }
   },
   "outputs": [],
   "source": [
    "addr_df = addr_df.rename(columns={'id':'address_id'}).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:45.075606Z",
     "start_time": "2020-10-18T00:12:43.466950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>address_id</th>\n",
       "      <th>phone_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11019.0</td>\n",
       "      <td>171906</td>\n",
       "      <td>78177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62278.0</td>\n",
       "      <td>278832</td>\n",
       "      <td>96681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>905126.0</td>\n",
       "      <td>468407</td>\n",
       "      <td>140317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21412.0</td>\n",
       "      <td>61962</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42110.0</td>\n",
       "      <td>378297</td>\n",
       "      <td>60075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  address_id  phone_id\n",
       "0   11019.0      171906     78177\n",
       "1   62278.0      278832     96681\n",
       "2  905126.0      468407    140317\n",
       "3   21412.0       61962        16\n",
       "4   42110.0      378297     60075"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids_df = pd.merge(ids_df, addr_df, on='address_id', how='outer')\n",
    "ids_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:50.425212Z",
     "start_time": "2020-10-18T00:12:45.077831Z"
    }
   },
   "outputs": [],
   "source": [
    "ids_df.to_csv('/Users/Anastasia/Desktop/python/Hackaton/DATA/ids.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Генерация признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:50.536061Z",
     "start_time": "2020-10-18T00:12:50.426302Z"
    }
   },
   "outputs": [],
   "source": [
    "ship_df['time_to_complete'] = (ship_df['order_completed_at'] - ship_df['order_created_at']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:12:50.548924Z",
     "start_time": "2020-10-18T00:12:50.537427Z"
    }
   },
   "outputs": [],
   "source": [
    "def calculate_features(curr_ships, month, dt):\n",
    "    features_df = pd.concat([\n",
    "        curr_ships.groupby('phone_id').size().to_frame('n_orders'),\n",
    "        curr_ships.groupby('phone_id')['order_id'].nunique().to_frame('n_total_orders'),\n",
    "        curr_ships.groupby('phone_id')['time_to_complete'].agg(['min', 'mean', 'max', 'std']).add_prefix('time_to_complete_'),\n",
    "        curr_ships.groupby('phone_id')['total_weight'].agg(['min', 'mean', 'max', 'std']).add_prefix('total_weight_'),\n",
    "        curr_ships.groupby('phone_id')['total_cost'].agg(['min', 'mean', 'max', 'std']).add_prefix('total_cost_'),\n",
    "        curr_ships.groupby('phone_id')['rate'].agg(['min', 'mean', 'max', 'std']).add_prefix('rate_'),\n",
    "        pd.crosstab(index=curr_ships['phone_id'], columns=curr_ships['s.order_state']),\n",
    "        (curr_ships.groupby('phone_id')['order_completed_at_month'].nunique() / (month-1)).to_frame('n_unique_months'),\n",
    "        (dt - curr_ships.groupby('phone_id')['order_completed_at'].max()).dt.days.to_frame('last_order_days'),\n",
    "        (dt - curr_ships.groupby('phone_id')['order_completed_at'].min()).dt.days.to_frame('first_order_days'),\n",
    "        (curr_ships.groupby('phone_id')['order_completed_at'].max() - curr_ships.groupby('phone_id')['order_completed_at'].min()).dt.days.to_frame('client_days')\n",
    "    ], axis=1).reset_index()\n",
    "\n",
    "    \n",
    "    return features_df\n",
    "\n",
    "def get_completed_retro_features(month, n_days=None):\n",
    "    \n",
    "    dt = datetime(year=2020, month=month, day=1, hour=0, minute=0)\n",
    "    \n",
    "    if isinstance(n_days, int):\n",
    "        curr_ships = ship_df[(ship_df['order_completed_at'] < dt) & (ship_df['order_completed_at'] > dt-timedelta(days=n_days))]\n",
    "    else:\n",
    "        curr_ships = ship_df[ship_df['order_completed_at'] < dt]\n",
    "        \n",
    "    curr_ships = curr_ships.merge(addr_df, left_on='ship_address_id', right_on='address_id')\n",
    "\n",
    "    features_df = calculate_features(curr_ships, month=month, dt=dt)\n",
    "    \n",
    "    features_df['order_completed_at'] = month\n",
    "    \n",
    "    if isinstance(n_days, int):\n",
    "        features_df = features_df.set_index(['phone_id', 'order_completed_at']).add_prefix(f'last_{n_days}_days_').reset_index()\n",
    "    else:\n",
    "        features_df = features_df.set_index(['phone_id', 'order_completed_at']).add_prefix('overall_').reset_index()\n",
    "    \n",
    "    return features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:25.403594Z",
     "start_time": "2020-10-18T00:12:50.550645Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3442563d84644b76be9ce348395ee7b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=6.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a23a86a166da4cc78125c4eeb929d07b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=6.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "features_df_overall = pd.concat([get_completed_retro_features(month) for month in tqdm_notebook(range(2, 8))], axis=0)\n",
    "features_df_last_month = pd.concat([get_completed_retro_features(month, n_days=30) for month in tqdm_notebook(range(2, 8))], axis=0)\n",
    "#features_df_last_week = pd.concat([get_completed_retro_features(month, n_days=7) for month in tqdm_notebook(range(2, 8))], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:30.946028Z",
     "start_time": "2020-10-18T00:13:25.405589Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df = train_df.merge(features_df_overall, on=['phone_id', 'order_completed_at'], how='left')\n",
    "final_df = final_df.merge(features_df_last_month, on=['phone_id', 'order_completed_at'], how='left')\n",
    "#final_df = final_df.merge(features_df_last_week, on=['phone_id', 'order_completed_at'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:30.962463Z",
     "start_time": "2020-10-18T00:13:30.947648Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df['last_30_total_ratio'] = final_df['last_30_days_n_orders'] / final_df['overall_n_orders']\n",
    "final_df['mean_days_per_order'] = (final_df['overall_client_days'] / final_df['overall_n_total_orders'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### По возрасту"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:31.884957Z",
     "start_time": "2020-10-18T00:13:30.964129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phone_id</th>\n",
       "      <th>age</th>\n",
       "      <th>is_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>182858</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180591</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78475</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49534</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>446979</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    phone_id   age  is_male\n",
       "0     182858  17.0      1.0\n",
       "1     180591  17.0      1.0\n",
       "4      78475  25.0      1.0\n",
       "6      49534  36.0      0.0\n",
       "12    446979  36.0      0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phone_age = ids_df.merge(users_df, on='user_id')[['phone_id', 'gender', 'age']].drop_duplicates()\n",
    "phone_age['is_male'] = (phone_age['gender'] == 'male').astype(float)\n",
    "phone_age = phone_age[['phone_id', 'age', 'is_male']].drop_duplicates('phone_id')\n",
    "phone_age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:32.300649Z",
     "start_time": "2020-10-18T00:13:31.886533Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df = final_df.merge(phone_age, on='phone_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Статистика по заказам"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таргет 2, 3, 4 месяца назад:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:32.341256Z",
     "start_time": "2020-10-18T00:13:32.301969Z"
    }
   },
   "outputs": [],
   "source": [
    "two_years_ago = train_df.copy()\n",
    "two_years_ago['order_completed_at'] += 2\n",
    "two_years_ago.rename(columns={'target':'target_2m_ago'}, inplace=True)\n",
    "\n",
    "three_years_ago = train_df.copy()\n",
    "three_years_ago['order_completed_at'] += 3\n",
    "three_years_ago.rename(columns={'target':'target_3m_ago'}, inplace=True)\n",
    "\n",
    "four_years_ago = train_df.copy()\n",
    "four_years_ago['order_completed_at'] += 4\n",
    "four_years_ago.rename(columns={'target':'target_4m_ago'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:34.171875Z",
     "start_time": "2020-10-18T00:13:32.342787Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df = final_df.merge(two_years_ago, on=['phone_id', 'order_completed_at'], how='left')\n",
    "final_df = final_df.merge(three_years_ago, on=['phone_id', 'order_completed_at'], how='left')\n",
    "final_df = final_df.merge(four_years_ago, on=['phone_id', 'order_completed_at'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:35.780487Z",
     "start_time": "2020-10-18T00:13:34.173138Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df = final_df[final_df['order_completed_at'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:35.916513Z",
     "start_time": "2020-10-18T00:13:35.781825Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df = final_df.drop(['last_30_days_cart', 'overall_cart', 'last_30_days_resumed', 'overall_resumed', 'last_30_days_last_order_days', 'last_30_days_n_unique_months', 'last_30_days_first_order_days', 'last_30_days_client_days'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:13:36.215460Z",
     "start_time": "2020-10-18T00:13:35.918366Z"
    }
   },
   "outputs": [],
   "source": [
    "train = final_df[final_df['order_completed_at'] < 7]\n",
    "test = final_df[final_df['order_completed_at'] == 7]\n",
    "\n",
    "X_train, y_train = train.drop(['target', 'phone_id', 'order_completed_at'], axis=1), train['target']\n",
    "X_test, y_test = test.drop(['target', 'phone_id', 'order_completed_at'], axis=1), test['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:17:24.904377Z",
     "start_time": "2020-10-18T00:13:36.218200Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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    {
     "name": "stdout",
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      "999:\tlearn: 0.6169754\ttotal: 3m 46s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "model = catboost.CatBoostClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:21:20.083243Z",
     "start_time": "2020-10-18T00:21:19.928182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6629112850004714"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:17:27.851141Z",
     "start_time": "2020-10-18T00:17:25.130057Z"
    }
   },
   "outputs": [],
   "source": [
    "final_df = final_df.drop(['last_30_days_total_cost_std', \n",
    "                'last_30_days_total_cost_max',\n",
    "                'last_30_days_total_cost_min',\n",
    "                'last_30_days_rate_max',\n",
    "                'overall_rate_max',\n",
    "                'last_30_days_rate_min',\n",
    "                'overall_rate_min'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:17:27.983457Z",
     "start_time": "2020-10-18T00:17:27.852689Z"
    }
   },
   "outputs": [],
   "source": [
    "X, y = final_df.drop(['target', 'phone_id', 'order_completed_at'], axis=1), final_df['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:21:16.948429Z",
     "start_time": "2020-10-18T00:17:27.985487Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.121571\n",
      "0:\tlearn: 0.6768900\ttotal: 401ms\tremaining: 6m 40s\n",
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    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-083e740b4ec1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcatboost\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCatBoostClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/catboost/core.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, cat_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)\u001b[0m\n\u001b[1;32m   3668\u001b[0m         self._fit(X, y, cat_features, None, sample_weight, None, None, None, None, baseline, use_best_model,\n\u001b[1;32m   3669\u001b[0m                   \u001b[0meval_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogging_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_description\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric_period\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3670\u001b[0;31m                   silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)\n\u001b[0m\u001b[1;32m   3671\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3672\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/catboost/core.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, cat_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)\u001b[0m\n\u001b[1;32m   1594\u001b[0m                 \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1595\u001b[0m                 \u001b[0mallow_clear_pool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1596\u001b[0;31m                 \u001b[0mtrain_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"init_model\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1597\u001b[0m             )\n\u001b[1;32m   1598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/catboost/core.py\u001b[0m in \u001b[0;36m_train\u001b[0;34m(self, train_pool, test_pool, params, allow_clear_pool, init_model)\u001b[0m\n\u001b[1;32m   1164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1165\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1166\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minit_model\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1167\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_trained_model_attributes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = catboost.CatBoostClassifier()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:01:05.556392Z",
     "start_time": "2020-10-18T00:01:05.518473Z"
    }
   },
   "outputs": [],
   "source": [
    "model.save_model('model.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Накатим на тест"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:06:41.466977Z",
     "start_time": "2020-10-18T00:06:41.452482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>471287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>342522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>457799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>233778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214604</th>\n",
       "      <td>16841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214605</th>\n",
       "      <td>69348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214606</th>\n",
       "      <td>594974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214607</th>\n",
       "      <td>320024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214608</th>\n",
       "      <td>23853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214609 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id\n",
       "0        19843\n",
       "1       471287\n",
       "2       342522\n",
       "3       457799\n",
       "4       233778\n",
       "...        ...\n",
       "214604   16841\n",
       "214605   69348\n",
       "214606  594974\n",
       "214607  320024\n",
       "214608   23853\n",
       "\n",
       "[214609 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = test_df[['Id']]\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:06:56.754152Z",
     "start_time": "2020-10-18T00:06:42.688679Z"
    }
   },
   "outputs": [],
   "source": [
    "test_features_df_overall = get_completed_retro_features(9)\n",
    "test_features_df_last_month = get_completed_retro_features(9, n_days=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:06:57.260579Z",
     "start_time": "2020-10-18T00:06:56.755795Z"
    }
   },
   "outputs": [],
   "source": [
    "test_df = test_df.merge(test_features_df_overall, left_on='Id', right_on='phone_id', how='left').merge(test_features_df_last_month, left_on='Id', right_on='phone_id', how='left').drop(['phone_id_x', 'order_completed_at_x', 'phone_id_y', 'order_completed_at_y'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:06:57.272716Z",
     "start_time": "2020-10-18T00:06:57.262920Z"
    }
   },
   "outputs": [],
   "source": [
    "test_df['last_30_total_ratio'] = test_df['last_30_days_n_orders'] / test_df['overall_n_orders']\n",
    "test_df['mean_days_per_order'] = (test_df['overall_client_days'] / test_df['overall_n_total_orders'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:07:12.096726Z",
     "start_time": "2020-10-18T00:07:11.818157Z"
    }
   },
   "outputs": [],
   "source": [
    "test_df = test_df.merge(phone_age, left_on='Id', right_on='phone_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:08:23.772582Z",
     "start_time": "2020-10-18T00:08:23.760945Z"
    }
   },
   "outputs": [],
   "source": [
    "model = catboost.CatBoostClassifier()\n",
    "model = model.load_model('./model.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:08:24.182640Z",
     "start_time": "2020-10-18T00:08:24.177873Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(model.feature_names_) - set(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:08:41.971009Z",
     "start_time": "2020-10-18T00:08:41.838180Z"
    }
   },
   "outputs": [],
   "source": [
    "test_df['pred'] = model.predict(test_df[model.feature_names_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:10:39.967210Z",
     "start_time": "2020-10-18T00:10:39.946719Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>342522</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>457799</td>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "       Id  Predicted\n",
       "0   19843        0.0\n",
       "1  471287        1.0\n",
       "2  342522        0.0\n",
       "3  457799        1.0\n",
       "4  233778        0.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = test_df[['Id', 'pred']].rename(columns={'pred':'Predicted'})\n",
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:10:42.204546Z",
     "start_time": "2020-10-18T00:10:42.149275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>4</th>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id  Predicted\n",
       "0   19843          1\n",
       "1  471287          1\n",
       "2  342522          1\n",
       "3  457799          1\n",
       "4  233778          1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = pd.read_csv(join(DATADIR, 'sample_submission_from_kaggle_change_sep.csv'))\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:10:43.459512Z",
     "start_time": "2020-10-18T00:10:43.120563Z"
    }
   },
   "outputs": [],
   "source": [
    "f.to_csv('./sub1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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