diff --git a/hw_6.ipynb b/hw_6.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Импорты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.simplefilter('ignore')\n",
+    "\n",
+    "import dill\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "import shap\n",
+    "from lightfm import LightFM\n",
+    "from lightfm.data import Dataset\n",
+    "# from lightgbm import LGBMRanker, LGBMClassifier\n",
+    "from rectools.metrics import calc_metrics, NDCG, MAP, Precision, Recall, MeanInvUserFreq\n",
+    "from rectools import Columns\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from typing import Any, Dict, Tuple\n",
+    "from tqdm.auto import tqdm\n",
+    "from zipfile import ZipFile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Callable, Dict, Set, List, Optional\n",
+    "\n",
+    "import numpy as np\n",
+    "from lightfm import LightFM\n",
+    "from scipy.sparse import csr_matrix\n",
+    "\n",
+    "\n",
+    "def generate_lightfm_recs_mapper(\n",
+    "    model: LightFM, \n",
+    "    N: int, \n",
+    "    item_iids: List[int],  # iid - internal lfm id\n",
+    "    user_id_to_iid: Dict[int, int], \n",
+    "    item_iid_to_id: Dict[int, int], \n",
+    "    known_item_ids: Dict[int, Set[int]],\n",
+    "    user_features:  Optional[csr_matrix] = None, \n",
+    "    item_features: Optional[csr_matrix] = None, \n",
+    "    num_threads: int = 1,\n",
+    ") -> Callable:\n",
+    "    \"\"\"Возвращает функцию для генерации рекомендаций в формате item_ids, scores\"\"\"\n",
+    "    def _recs_mapper(user):\n",
+    "        # Предикт для одного юзера\n",
+    "        user_id = user_id_to_iid[user]\n",
+    "        # Получаем список скоров. index - соответствует внутренним \n",
+    "        # индексам lightfm для айтемов т.е. ключам из item_iid_to_id\n",
+    "        scores_vector = model.predict(user_id, item_iids, user_features=user_features,\n",
+    "                             item_features=item_features, num_threads=num_threads)\n",
+    "        # Оставляем запас для исключения уже просмотренного из рекомендаций\n",
+    "        additional_N = len(known_item_ids[user_id]) if user_id in known_item_ids else 0\n",
+    "        total_N = N + additional_N\n",
+    "        # Получаем список индексов топ-N айтемов\n",
+    "        top_iids = np.argpartition(scores_vector, -np.arange(total_N))[-total_N:][::-1]\n",
+    "        # Исключаем уже просмотренное из рекомендаций\n",
+    "        if additional_N > 0:\n",
+    "            filter_items = known_item_ids[user_id]\n",
+    "            top_iids = [item_index for item_index in top_iids if item_iid_to_id[item_index] not in filter_items]\n",
+    "        # Переводим индексы lightfm айтемов в их реальные id\n",
+    "        final_recs = [item_iid_to_id[item_index] for item_index in top_iids]\n",
+    "        # Сохраняем скоры\n",
+    "        final_scores = scores_vector[top_iids]\n",
+    "        return final_recs, final_scores\n",
+    "    return _recs_mapper\n",
+    "\n",
+    "\n",
+    "def avg_user_metric(\n",
+    "        y_true: np.ndarray,\n",
+    "        y_pred: np.ndarray,\n",
+    "        user_ids: np.ndarray,\n",
+    "        metric_function: Callable[[np.ndarray, np.ndarray], float],\n",
+    ") -> float:\n",
+    "    \"\"\"\n",
+    "    Вычисляем метрику, усредненную по всем значимым (есть разные таргеты) группам.\n",
+    "\n",
+    "    :param y_true: список таргетов\n",
+    "    :param y_pred: список предсказаний\n",
+    "    :param user_ids: список групп (обычно это список user_id той же размерности, что и предсказания и таргеты)\n",
+    "    :param metric_function: усредняемая метрика(y_true, y_pred) -> float\n",
+    "    :return: значение метрики metric_function, усредненное по всем значимым группам\n",
+    "    \"\"\"\n",
+    "    avg_score: float = 0.\n",
+    "\n",
+    "    if len(y_pred) == len(y_true) == len(user_ids):\n",
+    "        l_ind: int = 0\n",
+    "        cur_group_id: int = user_ids[0] if len(user_ids) else 0\n",
+    "        n_groups: int = 0\n",
+    "        for r_ind, group_id in enumerate(user_ids):\n",
+    "            if group_id != cur_group_id or r_ind == len(user_ids) - 1:\n",
+    "                if r_ind == len(user_ids) - 1:\n",
+    "                    r_ind += 1\n",
+    "                # Если группа не состоит из одного и того же таргета - добавляем ее\n",
+    "                group_true = y_true[l_ind: r_ind]\n",
+    "                if not np.all(group_true == group_true[0]):\n",
+    "                    avg_score += metric_function(group_true, y_pred[l_ind: r_ind])\n",
+    "                    n_groups += 1\n",
+    "                l_ind = r_ind\n",
+    "                cur_group_id = group_id\n",
+    "        avg_score /= max(1, n_groups)\n",
+    "    else:\n",
+    "        raise ValueError(f'Размерности не совпадают: '\n",
+    "                         f'y_pred - {len(y_pred)}, y_true - {len(y_true)}, user_ids - {len(user_ids)}')\n",
+    "    return avg_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Подготовка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interactions = pd.read_csv('data_original/interactions.csv')\n",
+    "users = pd.read_csv('data_original/users.csv')\n",
+    "items = pd.read_csv('data_original/items.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5476251, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>last_watch_dt</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>watched_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>176549</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>2021-05-11</td>\n",
+       "      <td>4250</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699317</td>\n",
+       "      <td>1659</td>\n",
+       "      <td>2021-05-29</td>\n",
+       "      <td>8317</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>656683</td>\n",
+       "      <td>7107</td>\n",
+       "      <td>2021-05-09</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id  item_id last_watch_dt  total_dur  watched_pct\n",
+       "0   176549     9506    2021-05-11       4250         72.0\n",
+       "1   699317     1659    2021-05-29       8317        100.0\n",
+       "2   656683     7107    2021-05-09         10          0.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(interactions.shape)\n",
+    "interactions.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>176549</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>2021-05-11</td>\n",
+       "      <td>4250</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699317</td>\n",
+       "      <td>1659</td>\n",
+       "      <td>2021-05-29</td>\n",
+       "      <td>8317</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>656683</td>\n",
+       "      <td>7107</td>\n",
+       "      <td>2021-05-09</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>864613</td>\n",
+       "      <td>7638</td>\n",
+       "      <td>2021-07-05</td>\n",
+       "      <td>14483</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>964868</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>2021-04-30</td>\n",
+       "      <td>6725</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1032142</td>\n",
+       "      <td>6686</td>\n",
+       "      <td>2021-05-13</td>\n",
+       "      <td>11286</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1016458</td>\n",
+       "      <td>354</td>\n",
+       "      <td>2021-08-14</td>\n",
+       "      <td>1672</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>884009</td>\n",
+       "      <td>693</td>\n",
+       "      <td>2021-08-04</td>\n",
+       "      <td>703</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>648682</td>\n",
+       "      <td>1449</td>\n",
+       "      <td>2021-06-13</td>\n",
+       "      <td>26246</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>203219</td>\n",
+       "      <td>13582</td>\n",
+       "      <td>2021-08-22</td>\n",
+       "      <td>6975</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id  item_id   datetime  total_dur  weight\n",
+       "0   176549     9506 2021-05-11       4250    72.0\n",
+       "1   699317     1659 2021-05-29       8317   100.0\n",
+       "2   656683     7107 2021-05-09         10     0.0\n",
+       "3   864613     7638 2021-07-05      14483   100.0\n",
+       "4   964868     9506 2021-04-30       6725   100.0\n",
+       "5  1032142     6686 2021-05-13      11286   100.0\n",
+       "6  1016458      354 2021-08-14       1672    25.0\n",
+       "7   884009      693 2021-08-04        703    14.0\n",
+       "8   648682     1449 2021-06-13      26246    75.0\n",
+       "9   203219    13582 2021-08-22       6975   100.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Меняем названия колонок для использования rectools\n",
+    "interactions.rename(\n",
+    "    columns={\n",
+    "        'last_watch_dt': Columns.Datetime,\n",
+    "        'watched_pct': Columns.Weight,\n",
+    "    }, \n",
+    "    inplace=True,\n",
+    ") \n",
+    "# Меняем тип данных\n",
+    "# interactions['datetime'] = interactions['datetime'].astype(np.datetime64)\n",
+    "interactions['datetime'] = pd.to_datetime(interactions['datetime'])\n",
+    "\n",
+    "# Заполняем пропуски\n",
+    "interactions_default_values: Dict[str, Any] = {\n",
+    "   Columns.Datetime: interactions[Columns.Datetime].median(),\n",
+    "    Columns.Weight: 0.,\n",
+    "    'total_dur': 0,\n",
+    "}\n",
+    "interactions.fillna(interactions_default_values, inplace=True)\n",
+    "\n",
+    "# Смотрим что получилось\n",
+    "interactions.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `users`: данные о пользователях\n",
+    "\n",
+    "- `age` бин по возрасту \n",
+    "- `income` бин по доходу \n",
+    "- `sex` пол \n",
+    "- `kids_flg` флаг наличия детей\n",
+    "\n",
+    "Все признаки - результат предсказания соцдем моделей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(840197, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>age</th>\n",
+       "      <th>income</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>kids_flg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>973171</td>\n",
+       "      <td>age_25_34</td>\n",
+       "      <td>income_60_90</td>\n",
+       "      <td>М</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>962099</td>\n",
+       "      <td>age_18_24</td>\n",
+       "      <td>income_20_40</td>\n",
+       "      <td>М</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1047345</td>\n",
+       "      <td>age_45_54</td>\n",
+       "      <td>income_40_60</td>\n",
+       "      <td>Ж</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id        age        income sex  kids_flg\n",
+       "0   973171  age_25_34  income_60_90   М         1\n",
+       "1   962099  age_18_24  income_20_40   М         0\n",
+       "2  1047345  age_45_54  income_40_60   Ж         0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(users.shape)\n",
+    "users.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Index(['income_0_20', 'income_150_inf', 'income_20_40', 'income_40_60',\n",
+       "        'income_60_90', 'income_90_150'],\n",
+       "       dtype='object'),\n",
+       " [4, 2, 3, 0, -1, 5, 1]\n",
+       " Categories (7, int8): [-1, 0, 1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def encode_cat_cols(df: pd.DataFrame, cat_cols) -> Tuple[pd.DataFrame, Dict]:\n",
+    "    cat_col_encoding = {}  # словарь с категориями\n",
+    "\n",
+    "    # Тут мы могли бы заполнять пропуски как еще одну категорию,\n",
+    "    # но они и так заполняются таким образом автоматически ниже\n",
+    "    # default_values = {col: 'None' for col in cat_cols}\n",
+    "    # df.fillna(default_values, inplace=True)\n",
+    "\n",
+    "    for col in cat_cols:\n",
+    "        cat_col = df[col].astype('category').cat\n",
+    "        cat_col_encoding[col] = cat_col.categories\n",
+    "        df[col] = cat_col.codes.astype('category')\n",
+    "    return df, cat_col_encoding\n",
+    "\n",
+    "users_cat_cols = [\n",
+    "    # 'user_id',\n",
+    "     'age', 'income', 'sex', 'kids_flg'\n",
+    "]\n",
+    "users, users_cat_col_encoding = encode_cat_cols(users, users_cat_cols)\n",
+    "\n",
+    "# None уже кодируется как -1\n",
+    "users_cat_col_encoding['income'], users['income'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>age</th>\n",
+       "      <th>income</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>kids_flg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>973171</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>962099</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1047345</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id age income sex kids_flg\n",
+       "0   973171   1      4   1        1\n",
+       "1   962099   0      2   1        0\n",
+       "2  1047345   3      3   0        0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "users.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>income</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>kids_flg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>840197</td>\n",
+       "      <td>840197</td>\n",
+       "      <td>840197</td>\n",
+       "      <td>840197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>233926</td>\n",
+       "      <td>471519</td>\n",
+       "      <td>425270</td>\n",
+       "      <td>587209</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           age  income     sex  kids_flg\n",
+       "count   840197  840197  840197    840197\n",
+       "unique       7       7       3         2\n",
+       "top          1       2       0         0\n",
+       "freq    233926  471519  425270    587209"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Наиболее популярны income - income_40_60\n",
+    "users.describe(include='category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `items`: данные об айтемах\n",
+    "\n",
+    "- `content_type` - тип контента\n",
+    "- `title` - название на русском\n",
+    "- `title_orig` - название оригинальное\n",
+    "- `release_year` - год выпуска\n",
+    "- `countries` - страны\n",
+    "- `for_kids` - флаг контент для детей\n",
+    "- `age_rating`- Возрастной рейтинг\n",
+    "- `studios` - студии\n",
+    "- `directors` - режиссеры\n",
+    "- `actors`- актеры\n",
+    "- `keywords` - ключевые слова \n",
+    "- `description` - описание"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(15963, 14)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>content_type</th>\n",
+       "      <th>title</th>\n",
+       "      <th>title_orig</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>genres</th>\n",
+       "      <th>countries</th>\n",
+       "      <th>for_kids</th>\n",
+       "      <th>age_rating</th>\n",
+       "      <th>studios</th>\n",
+       "      <th>directors</th>\n",
+       "      <th>actors</th>\n",
+       "      <th>description</th>\n",
+       "      <th>keywords</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10711</td>\n",
+       "      <td>film</td>\n",
+       "      <td>Поговори с ней</td>\n",
+       "      <td>Hable con ella</td>\n",
+       "      <td>2002.0</td>\n",
+       "      <td>драмы, зарубежные, детективы, мелодрамы</td>\n",
+       "      <td>Испания</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Педро Альмодовар</td>\n",
+       "      <td>Адольфо Фернандес, Ана Фернандес, Дарио Гранди...</td>\n",
+       "      <td>Мелодрама легендарного Педро Альмодовара «Пого...</td>\n",
+       "      <td>Поговори, ней, 2002, Испания, друзья, любовь, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2508</td>\n",
+       "      <td>film</td>\n",
+       "      <td>Голые перцы</td>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>зарубежные, приключения, комедии</td>\n",
+       "      <td>США</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Скот Армстронг</td>\n",
+       "      <td>Адам Палли, Брайан Хаски, Дж.Б. Смув, Джейсон ...</td>\n",
+       "      <td>Уморительная современная комедия на популярную...</td>\n",
+       "      <td>Голые, перцы, 2014, США, друзья, свадьбы, прео...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10716</td>\n",
+       "      <td>film</td>\n",
+       "      <td>Тактическая сила</td>\n",
+       "      <td>Tactical Force</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>криминал, зарубежные, триллеры, боевики, комедии</td>\n",
+       "      <td>Канада</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Адам П. Калтраро</td>\n",
+       "      <td>Адриан Холмс, Даррен Шалави, Джерри Вассерман,...</td>\n",
+       "      <td>Профессиональный рестлер Стив Остин («Все или ...</td>\n",
+       "      <td>Тактическая, сила, 2011, Канада, бандиты, ганг...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   item_id content_type             title      title_orig  release_year  \\\n",
+       "0    10711         film    Поговори с ней  Hable con ella        2002.0   \n",
+       "1     2508         film       Голые перцы    Search Party        2014.0   \n",
+       "2    10716         film  Тактическая сила  Tactical Force        2011.0   \n",
+       "\n",
+       "                                             genres countries  for_kids  \\\n",
+       "0           драмы, зарубежные, детективы, мелодрамы   Испания       NaN   \n",
+       "1                  зарубежные, приключения, комедии       США       NaN   \n",
+       "2  криминал, зарубежные, триллеры, боевики, комедии    Канада       NaN   \n",
+       "\n",
+       "   age_rating studios         directors  \\\n",
+       "0        16.0     NaN  Педро Альмодовар   \n",
+       "1        16.0     NaN    Скот Армстронг   \n",
+       "2        16.0     NaN  Адам П. Калтраро   \n",
+       "\n",
+       "                                              actors  \\\n",
+       "0  Адольфо Фернандес, Ана Фернандес, Дарио Гранди...   \n",
+       "1  Адам Палли, Брайан Хаски, Дж.Б. Смув, Джейсон ...   \n",
+       "2  Адриан Холмс, Даррен Шалави, Джерри Вассерман,...   \n",
+       "\n",
+       "                                         description  \\\n",
+       "0  Мелодрама легендарного Педро Альмодовара «Пого...   \n",
+       "1  Уморительная современная комедия на популярную...   \n",
+       "2  Профессиональный рестлер Стив Остин («Все или ...   \n",
+       "\n",
+       "                                            keywords  \n",
+       "0  Поговори, ней, 2002, Испания, друзья, любовь, ...  \n",
+       "1  Голые, перцы, 2014, США, друзья, свадьбы, прео...  \n",
+       "2  Тактическая, сила, 2011, Канада, бандиты, ганг...  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(items.shape)\n",
+    "items.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['ABC', 'Amediateka', 'BBC', 'CBS', 'CBS All Access', 'Channel 4',\n",
+       "       'Cinemax', 'DAZN', 'Disney', 'Endemol', 'FX', 'Fox', 'Fremantle', 'HBO',\n",
+       "       'HBO Max', 'HBO, BBC', 'Legendary', 'MGM', 'New Regency Productions',\n",
+       "       'Paramount', 'Showtime', 'Sky', 'Sky, Fremantle', 'Sony Pictures',\n",
+       "       'Sony Pictures Television', 'Sony Pictures, рентв', 'Sony Plus',\n",
+       "       'Sony Plus, рентв', 'Starz', 'Universal', 'Universal, рентв',\n",
+       "       'Warner Bros', 'Warner Bros. Television', 'Ленфильм', 'Ленфильм, рентв',\n",
+       "       'Мосфильм', 'Рок фильм', 'рентв'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Аналогичным образом кодируем категориальные колонки и пока удаляем текстовые\n",
+    "items_cat_cols = [ \n",
+    "    'content_type', 'for_kids', 'studios',\n",
+    "]\n",
+    "items_text_cols = [\n",
+    "    'title', 'title_orig', 'genres', 'countries', 'directors', 'actors', 'description', 'keywords',\n",
+    "]\n",
+    "items_num_cols = [\n",
+    "    'release_year', 'age_rating', \n",
+    "]\n",
+    "default_values_items = {\n",
+    "    'release_year': items['release_year'].median(),\n",
+    "    'age_rating': items['age_rating'].median(),\n",
+    "}\n",
+    "\n",
+    "items, items_cat_col_encoding = encode_cat_cols(items, items_cat_cols) \n",
+    "items = items.drop(items_text_cols, axis=1)\n",
+    "items.fillna(default_values_items, inplace=True)\n",
+    "\n",
+    "items_cat_col_encoding['studios']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Трейн-вал-тест сплит"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min дата в interactions: 2021-03-13 00:00:00\n",
+      "max дата в interactions: 2021-08-22 00:00:00\n",
+      "Продолжительность: 162 days 00:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_date = interactions[Columns.Datetime].max()\n",
+    "min_date = interactions[Columns.Datetime].min()\n",
+    "\n",
+    "print(f'min дата в interactions: {min_date}')\n",
+    "print(f'max дата в interactions: {max_date}')\n",
+    "print(f'Продолжительность: {max_date - min_date}')"
+   ]
+  },
+  {
+   "attachments": {
+    "validation.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Схема валидации\n",
+    "![validation.png](attachment:validation.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разделение на горячих и холодных пользователей. Пользователь считается горячим, если у него более 10 взаимодействий"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145410 816769\n"
+     ]
+    }
+   ],
+   "source": [
+    "interactions_count = interactions.groupby(\"user_id\")[[\"item_id\"]].count().reset_index()\n",
+    "\n",
+    "hot_users = interactions_count[interactions_count.item_id >= 10].user_id.values\n",
+    "cold_users = interactions_count[~(interactions_count.item_id >= 10)].user_id.values\n",
+    "\n",
+    "hot_interactions = interactions[interactions[\"user_id\"].isin(hot_users)].sort_values([\"user_id\", \"datetime\"])\n",
+    "user_type_dict = {i : 'hot' if i in hot_users else 'cold' for i in interactions_count.user_id.values}\n",
+    "        \n",
+    "print(len(hot_users), len(cold_users))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Модель первого уровня будет обучаться на горячих кандидатах. Холодным кандидатам будет предлагаться популярное"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Обучать ранжирование будем на последнем месяце (30 дней) не считая отложенной недели\n",
+    "ranker_days_count = 30\n",
+    "\n",
+    "ranker_data = interactions[\n",
+    "    (interactions[Columns.Datetime] >= max_date - pd.Timedelta(days=ranker_days_count))\n",
+    "]\n",
+    "\n",
+    "train_size = 0.7\n",
+    "val_size = 0.15\n",
+    "test_size = 0.15\n",
+    "\n",
+    "\n",
+    "train_val_users, test_users = train_test_split(\n",
+    "    ranker_data['user_id'].unique(), random_state=42, test_size=test_size\n",
+    ")\n",
+    "\n",
+    "train_users, val_users = train_test_split(\n",
+    "    train_val_users, random_state=42, test_size=val_size / (train_size + val_size)  # 15% от общего размера\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Подготовим данные для базовых моделей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3740952, 5)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# для LightFM\n",
+    "base_models_data = interactions[\n",
+    "    (interactions[Columns.Datetime] < max_date - pd.Timedelta(days=ranker_days_count))\n",
+    "]\n",
+    "\n",
+    "base_models_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2262613, 5)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# для UserKnn отделяем все оставшиеся взаимодействия горячих пользователей\n",
+    "base_models_data_knn = interactions[\n",
+    "    (interactions[Columns.Datetime] < max_date - pd.Timedelta(days=ranker_days_count)) &\n",
+    "    (interactions.user_id.isin(hot_users))\n",
+    "]\n",
+    "base_models_data_knn.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Обучаем модели первого уровня"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LightFM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lightfm_dataset = Dataset()\n",
+    "lightfm_user_ids = base_models_data['user_id'].unique()\n",
+    "lightfm_item_ids = base_models_data['item_id'].unique()\n",
+    "lightfm_dataset.fit(lightfm_user_ids, lightfm_item_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В качестве таргета возьмем процент досмотра - - watched_pct (Columns.Weight)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interactions_matrix, weights_matrix = lightfm_dataset.build_interactions(\n",
+    "    zip(*base_models_data[['user_id', 'item_id', Columns.Weight]].values.T)\n",
+    ")\n",
+    "weights_matrix = weights_matrix.tocsr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9592d4fdaab478e889b70f487c74762",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Обучаем модель\n",
+    "\n",
+    "lfm_model = LightFM(\n",
+    "    no_components=64, \n",
+    "    learning_rate=0.1, \n",
+    "    loss='warp', \n",
+    "    max_sampled=5, \n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "num_epochs = 20\n",
+    "\n",
+    "for _ in tqdm(range(num_epochs)):\n",
+    "    lfm_model.fit_partial(weights_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save model  \n",
+    "with open(f'lfm_model.dill', 'wb') as f:\n",
+    "    dill.dump(lfm_model, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Генерим кандидатов, которыми дополним датасет ранкера"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Маппинги обычных айдишников во внутренние индексы lightfm для юзеров и айтемов\n",
+    "lightfm_mapping = lightfm_dataset.mapping()\n",
+    "lightfm_mapping = {\n",
+    "    'user_id_to_iid': lightfm_mapping[0],  # iid - internal lfm id\n",
+    "    'item_id_to_iid': lightfm_mapping[2],\n",
+    "}\n",
+    "# Маппинги внутренние индексов lightfm в обычные айдишники для юзеров и айтемов\n",
+    "lightfm_mapping['user_iid_to_id'] = {v: k for k, v in lightfm_mapping['user_id_to_iid'].items()}\n",
+    "lightfm_mapping['item_iid_to_id'] = {v: k for k, v in lightfm_mapping['item_id_to_iid'].items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_N = 100\n",
+    "\n",
+    "# Внутренние индексы юзеров и айтемов lightfm (индексы матрицы user-item)\n",
+    "user_lfm_index = np.array(list(lightfm_mapping['user_id_to_iid'].values()))\n",
+    "item_lfm_index = np.array(list(lightfm_mapping['item_id_to_iid'].values()))\n",
+    "\n",
+    "mapper = generate_lightfm_recs_mapper(\n",
+    "    model=lfm_model, \n",
+    "    N=top_N,\n",
+    "    item_iids=item_lfm_index, \n",
+    "    user_id_to_iid=lightfm_mapping['user_id_to_iid'],\n",
+    "    item_iid_to_id=lightfm_mapping['item_iid_to_id'],\n",
+    "    known_item_ids=dict(),  # тут можно добавить уже просмотренный контент для его исключения\n",
+    "    num_threads=4,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Генерируем предсказания и получаем скоры и ранги lightfm\n",
+    "\n",
+    "candidates = pd.DataFrame({'user_id': lightfm_user_ids})\n",
+    "candidates['item_id'], candidates['lfm_score'] = zip(*candidates['user_id'].map(mapper))\n",
+    "candidates = candidates.explode(['item_id', 'lfm_score'], ignore_index=True)\n",
+    "candidates['lfm_rank'] = candidates.groupby('user_id').cumcount() + 1 \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>lfm_score</th>\n",
+       "      <th>lfm_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>176549</td>\n",
+       "      <td>11749</td>\n",
+       "      <td>1.012975</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>176549</td>\n",
+       "      <td>4475</td>\n",
+       "      <td>0.932427</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>176549</td>\n",
+       "      <td>13545</td>\n",
+       "      <td>0.875368</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id item_id lfm_score  lfm_rank\n",
+       "0   176549   11749  1.012975         1\n",
+       "1   176549    4475  0.932427         2\n",
+       "2   176549   13545  0.875368         3"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Делаем чекпоинт - сохраняем кандидатов\n",
+    "candidates.to_csv('data_original/candidates.csv', index=False)\n",
+    "candidates.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Precision@10': 0.020890826209077097,\n",
+       " 'recall@10': 0.07002662506974923,\n",
+       " 'ndcg@10': 0.02331144067713911,\n",
+       " 'map@10': 0.028402409873563787,\n",
+       " 'novelty@10': 4.428763914489864}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Считаем метрики\n",
+    "def calc_metrics_(candidates_df, rank_col: str) -> Dict[str, float]:\n",
+    "    metrics = {\n",
+    "        'ndcg@10': NDCG(k = 10),\n",
+    "        'map@10': MAP(k = 10),\n",
+    "        'Precision@10': Precision(k = 10),\n",
+    "        'recall@10': Recall(k = 10),\n",
+    "        'novelty@10': MeanInvUserFreq(k = 10),\n",
+    "    }\n",
+    "    return calc_metrics(\n",
+    "        metrics=metrics,\n",
+    "        reco=(\n",
+    "            candidates_df\n",
+    "            .rename(columns={rank_col: Columns.Rank})\n",
+    "            [[Columns.User, Columns.Item, Columns.Rank]]\n",
+    "            [candidates_df[Columns.User].isin(test_users)]\n",
+    "        ),\n",
+    "        interactions=(\n",
+    "            ranker_data\n",
+    "            [[Columns.User, Columns.Item, Columns.Datetime, Columns.Weight]]\n",
+    "            [ranker_data[Columns.User].isin(test_users)]\n",
+    "        ), \n",
+    "        prev_interactions=(\n",
+    "            base_models_data\n",
+    "            [[Columns.User, Columns.Item, Columns.Datetime, Columns.Weight]]\n",
+    "            [base_models_data[Columns.User].isin(test_users)]\n",
+    "        ),\n",
+    "        catalog=items['item_id'].unique()\n",
+    "    )\n",
+    "\n",
+    "models_metrics: Dict[str, Dict[str, float]] = dict()\n",
+    "models_metrics['lfm'] = calc_metrics_(candidates, 'lfm_rank')\n",
+    "models_metrics['lfm']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models_metrics: Dict[str, Dict[str, float]] = dict()\n",
+    "models_metrics['lfm'] = {'Precision@10': 0.020880082267892505,\n",
+    " 'recall@10': 0.07016524074728199,\n",
+    " 'ndcg@10': 0.0232367544621445,\n",
+    " 'map@10': 0.02830747440499919,\n",
+    " 'novelty@10': 4.424138616822346}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### UserKNN + Popular Recommender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "from pathlib import Path\n",
+    "from typing import Dict\n",
+    "import scipy as sp\n",
+    "from implicit.nearest_neighbours import CosineRecommender, ItemItemRecommender, BM25Recommender, TFIDFRecommender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class UserKnn:\n",
+    "    \"\"\"Class for fit-perdict UserKNN model\n",
+    "    based on ItemKNN model from implicit.nearest_neighbours\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, model: ItemItemRecommender, N_users: int = 50):\n",
+    "        self.N_users = N_users\n",
+    "        self.model = model\n",
+    "        self.is_fitted = False\n",
+    "\n",
+    "    def get_mappings(self, train):\n",
+    "        self.users_inv_mapping = dict(enumerate(train[\"user_id\"].unique()))\n",
+    "        self.users_mapping = {v: k for k, v in self.users_inv_mapping.items()}\n",
+    "\n",
+    "        self.items_inv_mapping = dict(enumerate(train[\"item_id\"].unique()))\n",
+    "        self.items_mapping = {v: k for k, v in self.items_inv_mapping.items()}\n",
+    "\n",
+    "    def get_matrix(\n",
+    "        self,\n",
+    "        df: pd.DataFrame,\n",
+    "        user_col: str = \"user_id\",\n",
+    "        item_col: str = \"item_id\",\n",
+    "        weight_col: str = \"weight\",\n",
+    "        users_mapping: Dict[int, int] = None,\n",
+    "        items_mapping: Dict[int, int] = None,\n",
+    "    ):\n",
+    "\n",
+    "        if weight_col:\n",
+    "            weights = df[weight_col].astype(np.float32)\n",
+    "        else:\n",
+    "            weights = np.ones(len(df), dtype=np.float32)\n",
+    "\n",
+    "        interaction_matrix = sp.sparse.coo_matrix(\n",
+    "            (\n",
+    "                weights,\n",
+    "                (\n",
+    "                    df[user_col].map(self.users_mapping.get),\n",
+    "                    df[item_col].map(self.items_mapping.get),\n",
+    "                ),\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "        return interaction_matrix\n",
+    "\n",
+    "    def idf(self, n: int, x: float):\n",
+    "        return np.log((1 + n) / (1 + x) + 1)\n",
+    "\n",
+    "    def _count_item_idf(self, df: pd.DataFrame):\n",
+    "        item_cnt = Counter(df[\"item_id\"].values)\n",
+    "        item_idf = pd.DataFrame.from_dict(\n",
+    "            item_cnt, orient=\"index\", columns=[\"doc_freq\"]\n",
+    "        ).reset_index()\n",
+    "        item_idf[\"idf\"] = item_idf[\"doc_freq\"].apply(\n",
+    "                            lambda x: self.idf(self.n, x)\n",
+    "        )\n",
+    "        self.item_idf = item_idf\n",
+    "\n",
+    "    def fit(self, train: pd.DataFrame):\n",
+    "        self.user_knn = self.model\n",
+    "        self.get_mappings(train)\n",
+    "        self.weights_matrix = self.get_matrix(\n",
+    "            train, users_mapping=self.users_mapping,\n",
+    "            items_mapping=self.items_mapping\n",
+    "        )\n",
+    "\n",
+    "        self.n = train.shape[0]\n",
+    "        self._count_item_idf(train)\n",
+    "\n",
+    "        self.user_knn.fit(self.weights_matrix)\n",
+    "        \n",
+    "        watched_items_df = (train.groupby(\"user_id\")\n",
+    "                                 .agg({\"item_id\": list})\n",
+    "                                 .reset_index())\n",
+    "        self.watched_items = {}\n",
+    "        for _, row in watched_items_df.iterrows():\n",
+    "            self.watched_items[row[\"user_id\"]] = row[\"item_id\"]\n",
+    "\n",
+    "        self.is_fitted = True\n",
+    "\n",
+    "    def _generate_recs_mapper(\n",
+    "        self,\n",
+    "        model: ItemItemRecommender,\n",
+    "        user_mapping: Dict[int, int],\n",
+    "        user_inv_mapping: Dict[int, int],\n",
+    "        N: int,\n",
+    "    ):\n",
+    "        def _recs_mapper(user):\n",
+    "            user_id = user_mapping[user]\n",
+    "            recs = model.similar_items(user_id, N=N)\n",
+    "            return [user_inv_mapping[user] for user, _ in recs], [\n",
+    "                sim for _, sim in recs\n",
+    "            ]\n",
+    "\n",
+    "        return _recs_mapper\n",
+    "\n",
+    "    def predict(self, test: pd.DataFrame, N_recs: int = 100) -> list:\n",
+    "        \n",
+    "        if not self.is_fitted:\n",
+    "            raise ValueError(\"Please call fit before predict\")\n",
+    "        \n",
+    "        mapper = self._generate_recs_mapper(\n",
+    "            model=self.user_knn,\n",
+    "            user_mapping=self.users_mapping,\n",
+    "            user_inv_mapping=self.users_inv_mapping,\n",
+    "            N=self.N_users,\n",
+    "        )\n",
+    "        recs = pd.DataFrame({\"user_id\": test[\"user_id\"].unique()})\n",
+    "        recs[\"sim_user_id\"], recs[\"sim\"] = zip(*recs[\"user_id\"].map(mapper))\n",
+    "        recs = recs.set_index(\"user_id\").apply(pd.Series.explode).reset_index()\n",
+    "        recs = recs[recs[\"user_id\"] != recs[\"sim_user_id\"]]\n",
+    "        recs[\"item_id\"] = recs[\"user_id\"].apply(\n",
+    "            lambda x: self.watched_items.get(x, [])\n",
+    "        )\n",
+    "        recs = recs.explode(\"item_id\")\n",
+    "        recs = recs.sort_values([\"user_id\", \"sim\"], ascending=False)\n",
+    "        recs = recs.drop_duplicates([\"user_id\", \"item_id\"], keep=\"first\")\n",
+    "        recs = recs.merge(\n",
+    "            self.item_idf, left_on=\"item_id\", right_on=\"index\", how=\"left\"\n",
+    "        )\n",
+    "        \n",
+    "        recs[\"score\"] = recs[\"sim\"] * recs[\"idf\"]\n",
+    "        recs = recs.sort_values([\"user_id\", \"score\"], ascending=False)\n",
+    "        recs[\"rank\"] = recs.groupby(\"user_id\").cumcount() + 1\n",
+    "        recs = recs[recs[\"rank\"] <= N_recs]\n",
+    "        \n",
+    "        return recs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def full_reco_items_list(\n",
+    "    arr_reco_after_model: np.array, pop_array: np.array, number: int\n",
+    ") -> list:\n",
+    "    \"\"\"\n",
+    "    Add number of items from pop_array to arr_reco_after_model.\n",
+    "    Return array of 10 unique items\n",
+    "    \"\"\"\n",
+    "\n",
+    "    CONST_K = 100\n",
+    "\n",
+    "    size_of_array = np.unique(arr_reco_after_model).size\n",
+    "    if size_of_array == CONST_K:\n",
+    "        return arr_reco_after_model\n",
+    "\n",
+    "    # all duplicates will be deleting and adding some items from pop_array\n",
+    "    def del_repeat_items(full_arr_mix_pop: np.array, k: int = CONST_K) -> np.array:\n",
+    "        \"\"\"\n",
+    "        Delete all duplicates items in array\n",
+    "        \"\"\"\n",
+    "\n",
+    "        size_of_array = np.unique(full_arr_mix_pop).size\n",
+    "        if size_of_array == CONST_K:\n",
+    "            return full_arr_mix_pop\n",
+    "        else:\n",
+    "            # delete duplicates and save the order of items\n",
+    "            full_arr_mix_pop = full_arr_mix_pop[\n",
+    "                np.sort(np.unique(full_arr_mix_pop, return_index=True)[1])\n",
+    "            ]\n",
+    "            # add new items from pop_array\n",
+    "            i = k - size_of_array\n",
+    "            full_arr_mix_pop = np.concatenate(\n",
+    "                (full_arr_mix_pop, np.random.choice(pop_array, i, replace=False))\n",
+    "            )\n",
+    "            return del_repeat_items(full_arr_mix_pop)\n",
+    "\n",
+    "    full_arr_mix_pop = np.array([])\n",
+    "    full_arr_mix_pop = np.concatenate((arr_reco_after_model, pop_array[:number]))\n",
+    "    full_arr_mix_pop = del_repeat_items(full_arr_mix_pop)\n",
+    "\n",
+    "    return list(full_arr_mix_pop)\n",
+    "\n",
+    "# Если модель генерит меньше нужных 100 рекомендаций, дополняем популярным\n",
+    "def knn_make_predict(model, data: pd.DataFrame, list_pop_items: list) -> list:\n",
+    "    \"\"\"\n",
+    "    Gets predict after bas-model and add lacking items(to 10)\n",
+    "    for each user if it needed\n",
+    "    \"\"\"\n",
+    "    try: \n",
+    "        predict = model.predict(data, 100)\n",
+    "        predict = predict[\"item_id\"].unique()\n",
+    "\n",
+    "        predict_len = len(predict)\n",
+    "\n",
+    "        if predict_len < 100:\n",
+    "            #print(predict)\n",
+    "            predict = list(predict) + list_pop_items[:(100-predict_len)]\n",
+    "\n",
+    "\n",
+    "        return list(predict)\n",
+    "    \n",
+    "    except:\n",
+    "        return list_pop_items"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f8d0ca3f1094c66ae6c00ea74434368",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/14517 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "userknn_hot_users = UserKnn(BM25Recommender(), 50)\n",
+    "userknn_hot_users.fit(base_models_data_knn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"userknn_hot_users.dill\", \"wb\") as f:\n",
+    "        dill.dump(userknn_hot_users, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recommend_popular(\n",
+    "    df: pd.DataFrame, k: int = 10, days: int = 7\n",
+    ") -> list:\n",
+    "    \"\"\"\n",
+    "    Returns most popular items for the last k days\n",
+    "    \"\"\"\n",
+    "\n",
+    "    min_date = df[\"datetime\"].max().normalize() - pd.DateOffset(days)\n",
+    "    result = list(df.loc[df[\"datetime\"] > min_date, \"item_id\"]\n",
+    "                   .value_counts()\n",
+    "                   .head(k)\n",
+    "                   .index.values)\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "popular_recs = recommend_popular(base_models_data_knn, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates_knn_cold = pd.DataFrame({'user_id': cold_users})\n",
+    "candidates_knn_cold['item_id'] = candidates_knn_cold['user_id'].apply(lambda x: popular_recs)\n",
+    "candidates_knn_cold['knn_rank'] = candidates_knn_cold['user_id'].apply(lambda x: [*range(1,101)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates_knn_cold = candidates_knn_cold.explode(['item_id', 'knn_rank'], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates_knn_hot = pd.DataFrame({'user_id': hot_users})\n",
+    "candidates_knn_hot['item_id'] = candidates_knn_hot['user_id'].apply(lambda x: knn_make_predict(userknn_hot_users, base_models_data_knn[base_models_data_knn.user_id == x][[\"user_id\", \"item_id\", \"weight\"]], popular_recs) )\n",
+    "candidates_knn_hot['knn_rank'] = candidates_knn_hot['user_id'].apply(lambda x: [*range(1,101)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates_knn_hot = candidates_knn_hot.explode(['item_id', 'knn_rank'], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates_knn = pd.concat([candidates_knn_cold, candidates_knn_hot], axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates_knn.reset_index(drop=True, inplace = True)\n",
+    "\n",
+    "candidates_knn['item_id'] = candidates_knn['item_id'].astype(int)\n",
+    "candidates_knn['knn_rank'] = candidates_knn['knn_rank'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates_knn.to_csv('candidates_knn.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Precision@10': 0.0695516706828542,\n",
+       " 'recall@10': 0.28085631319825966,\n",
+       " 'ndcg@10': 0.07254576539293645,\n",
+       " 'map@10': 0.09293199153983464,\n",
+       " 'novelty@10': 3.253271584821371}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_metrics['knn_popular'] = calc_metrics_(candidates_knn, 'knn_rank')\n",
+    "models_metrics['knn_popular']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models_metrics['knn_popular'] = {'Precision@10': 0.05839332033828066,\n",
+    " 'recall@10': 0.2573846305610062,\n",
+    " 'ndcg@10': 0.06019492573507385,\n",
+    " 'map@10': 0.08425860717082494,\n",
+    " 'novelty@10': 4.0829298648106525}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidates = pd.read_csv('candidates_knn.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Формируем датасет для ранкера"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Генерим фичи для ранкера"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Получаем длину истории юзера \n",
+    "base_models_data['user_hist'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_id'].transform('count')\n",
+    ")\n",
+    "# Получаем популярность контента\n",
+    "base_models_data['item_pop'] = (\n",
+    "    base_models_data.groupby('item_id')\n",
+    "    ['user_id'].transform('count')\n",
+    ")\n",
+    "# Получаем среднюю популярность контента, просматриваемого этим юзером\n",
+    "base_models_data['user_avg_pop'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_pop'].transform('mean')\n",
+    ")\n",
+    "# Получаем среднюю длину истории пользователя, которые смотрит этот контент\n",
+    "base_models_data['item_avg_hist'] = (\n",
+    "    base_models_data.groupby('item_id')\n",
+    "    ['user_hist'].transform('mean')\n",
+    ")\n",
+    "# Получаем популярность последнего просмотренного контента\n",
+    "base_models_data.sort_values(\n",
+    "    by=[Columns.User, Columns.Datetime], \n",
+    "    ascending=[True, False], \n",
+    "    ignore_index=True,\n",
+    "    inplace=True,\n",
+    ")\n",
+    "base_models_data['user_last_pop'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_pop'].transform('first')\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Добавим еще фичей \n",
+    "\n",
+    "# медианное время просмотра пользователем\n",
+    "base_models_data['user_median_duratiation'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['total_dur'].transform('median')\n",
+    ")\n",
+    "\n",
+    "# 0.25 квантиль времени просмотра пользователем\n",
+    "base_models_data['user_q025_duratiation'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['total_dur'].transform(lambda x: np.quantile(x, 0.25))\n",
+    ")\n",
+    "\n",
+    "# 0.75 квантиль времени просмотра пользователем\n",
+    "base_models_data['user_q075_duratiation'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['total_dur'].transform(lambda x: np.quantile(x, 0.75))\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# медианное время просмотра контента всеми пользователями\n",
+    "base_models_data['item_median_duratiation'] = (\n",
+    "    base_models_data.groupby('item_id')\n",
+    "    ['total_dur'].transform('median')\n",
+    ")\n",
+    "\n",
+    "# 0.25 кванитиль просмотра контента всеми пользователями\n",
+    "base_models_data['item_025_duratiation'] = (\n",
+    "    base_models_data.groupby('item_id')\n",
+    "    ['total_dur'].transform(lambda x: np.quantile(x, 0.25))\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# 0.75 кванитиль просмотра контента всеми пользователями\n",
+    "base_models_data['item_075_duratiation'] = (\n",
+    "    base_models_data.groupby('item_id')\n",
+    "    ['total_dur'].transform(lambda x: np.quantile(x, 0.75))\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Получаем медианную популярность контента, просматриваемого этим юзером\n",
+    "base_models_data['user_median_pop'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_pop'].transform('median')\n",
+    ")\n",
+    "\n",
+    "# 0.25 кванитиль популярности контента, просматриваемого этим юзером \n",
+    "base_models_data['user_025_quant_pop'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_pop'].transform(lambda x: np.quantile(x, 0.25))\n",
+    ")\n",
+    "\n",
+    "# 0.75 кванитиль популярности контента, просматриваемого этим юзером \n",
+    "base_models_data['user_075_quant_pop'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_pop'].transform(lambda x: np.quantile(x, 0.75))\n",
+    ")\n",
+    "\n",
+    "# Получаем максимальную популярность контента, просматриваемого этим юзером\n",
+    "base_models_data['user_max_pop'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_pop'].transform('max')\n",
+    ")\n",
+    "\n",
+    "# Получаем минимальную популярность контента, просматриваемого этим юзером\n",
+    "base_models_data['user_min_pop'] = (\n",
+    "    base_models_data.groupby('user_id')\n",
+    "    ['item_pop'].transform('min')\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# base_models_data = pd.read_csv('base_models_data_new.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['user_hist', 'user_avg_pop', 'user_last_pop', 'user_median_duratiation', 'user_q025_duratiation', 'user_q075_duratiation', 'user_median_pop', 'user_025_quant_pop', 'user_075_quant_pop', 'user_max_pop', 'user_min_pop']\n",
+      "['item_pop', 'item_avg_hist', 'item_median_duratiation', 'item_025_duratiation', 'item_075_duratiation']\n"
+     ]
+    }
+   ],
+   "source": [
+    "users_fts = [x for x in base_models_data.columns if 'user' in x][1:]\n",
+    "items_fts = [x for x in base_models_data.columns if 'item' in x][1:]\n",
+    "print(users_fts)\n",
+    "print(items_fts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>age</th>\n",
+       "      <th>income</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>kids_flg</th>\n",
+       "      <th>user_hist</th>\n",
+       "      <th>user_avg_pop</th>\n",
+       "      <th>user_last_pop</th>\n",
+       "      <th>user_median_duratiation</th>\n",
+       "      <th>user_q025_duratiation</th>\n",
+       "      <th>user_q075_duratiation</th>\n",
+       "      <th>user_median_pop</th>\n",
+       "      <th>user_025_quant_pop</th>\n",
+       "      <th>user_075_quant_pop</th>\n",
+       "      <th>user_max_pop</th>\n",
+       "      <th>user_min_pop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>973171</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>19550.800000</td>\n",
+       "      <td>93403.0</td>\n",
+       "      <td>7361.0</td>\n",
+       "      <td>6520.0</td>\n",
+       "      <td>91345.0</td>\n",
+       "      <td>743.0</td>\n",
+       "      <td>645.0</td>\n",
+       "      <td>2633.0</td>\n",
+       "      <td>93403.0</td>\n",
+       "      <td>330.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>962099</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1329.307692</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>4676.0</td>\n",
+       "      <td>3236.0</td>\n",
+       "      <td>8010.0</td>\n",
+       "      <td>901.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1384.0</td>\n",
+       "      <td>6112.0</td>\n",
+       "      <td>246.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1047345</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id age income sex kids_flg  user_hist  user_avg_pop  user_last_pop  \\\n",
+       "0   973171   1      4   1        1        5.0  19550.800000        93403.0   \n",
+       "1   962099   0      2   1        0       13.0   1329.307692          260.0   \n",
+       "2  1047345   3      3   0        0        NaN           NaN            NaN   \n",
+       "\n",
+       "   user_median_duratiation  user_q025_duratiation  user_q075_duratiation  \\\n",
+       "0                   7361.0                 6520.0                91345.0   \n",
+       "1                   4676.0                 3236.0                 8010.0   \n",
+       "2                      NaN                    NaN                    NaN   \n",
+       "\n",
+       "   user_median_pop  user_025_quant_pop  user_075_quant_pop  user_max_pop  \\\n",
+       "0            743.0               645.0              2633.0       93403.0   \n",
+       "1            901.0               330.0              1384.0        6112.0   \n",
+       "2              NaN                 NaN                 NaN           NaN   \n",
+       "\n",
+       "   user_min_pop  \n",
+       "0         330.0  \n",
+       "1         246.0  \n",
+       "2           NaN  "
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Добавляем новые фичи в соответствующие таблички\n",
+    "items = pd.merge(\n",
+    "    left=items, \n",
+    "    right=(\n",
+    "        base_models_data\n",
+    "        [['item_id'] + items_fts]\n",
+    "        .drop_duplicates()\n",
+    "    ),\n",
+    "    how='left',\n",
+    "    on='item_id',\n",
+    ")\n",
+    "\n",
+    "users = pd.merge(\n",
+    "    left=users, \n",
+    "    right=(\n",
+    "        base_models_data\n",
+    "        [['user_id'] + users_fts]\n",
+    "        .drop_duplicates()\n",
+    "    ),\n",
+    "    how='left',\n",
+    "    on='user_id',\n",
+    ")\n",
+    "users.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Обновляем дефолтные значения\n",
+    "# Прямо сейчас обновлять таблички users и items не обязательно, \n",
+    "# сделаем это при джойне с кандидатами\n",
+    "\n",
+    "# Для новых фичей айтемов\n",
+    "#default_values_items['item_pop'] = base_models_data['item_pop'].median()\n",
+    "#default_values_items['item_avg_hist'] = base_models_data['item_avg_hist'].median()\n",
+    "\n",
+    "for i in items_fts:\n",
+    "    default_values_items[i] = base_models_data[i].median()\n",
+    "\n",
+    "# Для новых фичей юзеров\n",
+    "default_values_users = {\n",
+    "    'user_hist': 0,\n",
+    "}\n",
+    "\n",
+    "for i in users_fts[1:]:\n",
+    "    default_values_users[i] = base_models_data[i].median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "({'user_hist': 0,\n",
+       "  'user_avg_pop': 11957.864864864863,\n",
+       "  'user_last_pop': 2858.0,\n",
+       "  'user_median_duratiation': 4146.0,\n",
+       "  'user_q025_duratiation': 897.0,\n",
+       "  'user_q075_duratiation': 6889.0,\n",
+       "  'user_median_pop': 2521.0,\n",
+       "  'user_025_quant_pop': 1068.5,\n",
+       "  'user_075_quant_pop': 7541.0,\n",
+       "  'user_max_pop': 93403.0,\n",
+       "  'user_min_pop': 246.0},\n",
+       " {'release_year': 2014.0,\n",
+       "  'age_rating': 16.0,\n",
+       "  'item_pop': 2846.0,\n",
+       "  'item_avg_hist': 22.67597765363129,\n",
+       "  'item_median_duratiation': 4268.0,\n",
+       "  'item_025_duratiation': 584.0,\n",
+       "  'item_075_duratiation': 7012.0})"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "default_values_users, default_values_items"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Джойним кандидатов и юзер/айтем фичи"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>knn_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>13865</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>9728</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3734</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id  item_id  knn_rank\n",
+       "0        0    13865         1\n",
+       "1        0     9728         2\n",
+       "2        0     3734         3"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Загружаем список айтемов-кандидатов. \n",
+    "\n",
+    "candidates = pd.read_csv('candidates_knn.csv')\n",
+    "candidates.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Вспоминаем про наши выборки интеракций для ранкера.\n",
+    "# Мы отобрали юзеров для обучения, валидации и теста.\n",
+    "# Оставляем среди них только тех, для кого есть и рекомы и таргеты\n",
+    "\n",
+    "def users_filter(\n",
+    "    user_list: np.ndarray,\n",
+    "    candidates_df: pd.DataFrame, \n",
+    "    df: pd.DataFrame,\n",
+    ") -> pd.DataFrame:\n",
+    "    # Джойним интеракции на наших кандидатов для юзеров из трейна, вал и теста\n",
+    "    df = pd.merge(\n",
+    "        df[df['user_id'].isin(user_list)], \n",
+    "        candidates_df[candidates_df['user_id'].isin(user_list)], \n",
+    "        how='outer',  \n",
+    "        on=['user_id', 'item_id']\n",
+    "    )\n",
+    "    # Проставляем дефолтные значения интеракций\n",
+    "    max_rank: int = df['knn_rank'].max() + 1  # 101\n",
+    "    \n",
+    "    default_values = {\n",
+    "        'knn_rank': max_rank,\n",
+    "        # Важно использовате те же дефолтные значения для интеракций, \n",
+    "        # чтобы не сделать утечку\n",
+    "        **interactions_default_values,\n",
+    "    }\n",
+    "    df.fillna(default_values, inplace=True)\n",
+    "        \n",
+    "    # Сортируем по user_id - это пригодится для вычисления рангов и групп для ранжирования\n",
+    "    df.sort_values(\n",
+    "        by=['user_id', 'item_id'],\n",
+    "        inplace=True,\n",
+    "    )\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ranker_train = users_filter(train_users, candidates, ranker_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>knn_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1180642</th>\n",
+       "      <td>3</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2021-08-16</td>\n",
+       "      <td>2179.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998971</th>\n",
+       "      <td>3</td>\n",
+       "      <td>142</td>\n",
+       "      <td>2021-08-13</td>\n",
+       "      <td>5892.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1067328</th>\n",
+       "      <td>3</td>\n",
+       "      <td>965</td>\n",
+       "      <td>2021-08-16</td>\n",
+       "      <td>5813.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         user_id  item_id   datetime  total_dur  weight  knn_rank\n",
+       "1180642        3       47 2021-08-16     2179.0    27.0     101.0\n",
+       "998971         3      142 2021-08-13     5892.0   100.0     101.0\n",
+       "1067328        3      965 2021-08-16     5813.0    96.0     101.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "ranker_val = users_filter(val_users, candidates, ranker_data)\n",
+    "ranker_test = users_filter(test_users, candidates, ranker_data)\n",
+    "\n",
+    "ranker_train.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Добавляем фичи\n",
+    "def add_features(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    df = pd.merge(\n",
+    "        df, \n",
+    "        users, \n",
+    "        how='left', \n",
+    "        on=['user_id']\n",
+    "    )\n",
+    "    df = pd.merge(\n",
+    "        df, \n",
+    "        items, \n",
+    "        how='left', \n",
+    "        on=['item_id']\n",
+    "    )\n",
+    "\n",
+    "    # При джойне могут получиться строки с несуществующими айтемами или юзерами.\n",
+    "    # Надо заполнить пропуски. Используем заготовленные дефолтные значения,\n",
+    "    # чтобы не сделать утечку\n",
+    "    df.fillna(default_values_items, inplace=True)\n",
+    "    df.fillna(default_values_users, inplace=True)\n",
+    "\n",
+    "    # Категориальные фичи закодированы пандасом так, что None === -1\n",
+    "    # Если изначально пропусков не было, то нужно добавить такое значение категории\n",
+    "    for col in df.columns:\n",
+    "        if isinstance(df[col].dtype, pd.CategoricalDtype):\n",
+    "            if -1 not in df[col].cat.categories:\n",
+    "                df[col] = df[col].cat.add_categories(-1)\n",
+    "            df.fillna({col: -1}, inplace=True)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ranker_train = add_features(ranker_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ranker_val = add_features(ranker_val)\n",
+    "ranker_test = add_features(ranker_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Датасеты готовы, остались только таргеты, \n",
+    "# которые можно посчитать на основе колонок total_dur и watched_pct\n",
+    "\n",
+    "# Делаем еще один чекпоинт.\n",
+    "\n",
+    "for name in ['train', 'val', 'test']:\n",
+    "    path: str = f'data/ranker_{name}.fth'\n",
+    "    locals()[f'ranker_{name}'].to_feather(path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Обучаем ранкер\n",
+    "### Pointwise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Загружаем данные\n",
+    "for name in ['train', 'val', 'test']:\n",
+    "    path: str = f'data/ranker_{name}.fth'\n",
+    "    locals()[f'ranker_{name}'] = pd.read_feather(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# таргет бинарный, так что - \n",
+    "# будем считать просмотр хорошим если доля досмотра больше половины\n",
+    "\n",
+    "def add_target(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    df['target'] = df[Columns.Weight] > 50  # 'watched_pct'\n",
+    "    df['target'] = df['target'].astype(int)\n",
+    "    return df\n",
+    "\n",
+    "ranker_train = add_target(ranker_train)\n",
+    "ranker_val = add_target(ranker_val)\n",
+    "ranker_test = add_target(ranker_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# В train и val можно удалить 'плохих' пользователей, \n",
+    "# Например тех у кого слишком много или мало просмотров или \n",
+    "# тех для которых нет достаточного количества рекомендаций от Knn\n",
+    "# Тестовую группу не меняем\n",
+    "\n",
+    "def filter_group(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    df.sort_values(\n",
+    "        by=['user_id', 'item_id'],\n",
+    "        inplace=True,\n",
+    "    )\n",
+    "    groups_df = (\n",
+    "        df[['user_id', 'item_id']]\n",
+    "        .groupby(by=['user_id']).count()\n",
+    "        .rename(columns={'item_id': 'group_size'})\n",
+    "    )\n",
+    "    df = pd.merge(\n",
+    "        df, \n",
+    "        groups_df, \n",
+    "        how='left', \n",
+    "        on=['user_id']\n",
+    "    )\n",
+    "    # Удаляем группы, без достаточного числа просмотров/кандидатов\n",
+    "    df = df[df['group_size'] >= 100]\n",
+    "\n",
+    "    # Колонка больше не нужна\n",
+    "    df.drop(columns=['group_size'], inplace=True)\n",
+    "    \n",
+    "    # Исправляем баг, с outher join в users_filter\n",
+    "    df = df[df['knn_rank'] <= 100]\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "ranker_train = filter_group(ranker_train)\n",
+    "ranker_val = filter_group(ranker_val)\n",
+    "# ranker_test = filter_group(ranker_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Убираем ненужные айдишники, временные метки и таргеты.\n",
+    "# Для обучения используются только cols:\n",
+    "cols = [\n",
+    "    'knn_rank', \n",
+    "    'age', 'income', 'sex', 'kids_flg',\n",
+    "    'user_hist', 'user_avg_pop', 'user_last_pop', 'user_median_duratiation', \n",
+    "    'user_q025_duratiation', 'user_q075_duratiation', 'user_median_pop', \n",
+    "    'user_025_quant_pop', 'user_075_quant_pop', 'user_max_pop', 'user_min_pop',\n",
+    "    'content_type', 'release_year', 'for_kids', 'age_rating', 'studios', \n",
+    "    'item_pop', 'item_avg_hist', 'item_median_duratiation', 'item_025_duratiation', 'item_075_duratiation'\n",
+    "]\n",
+    "# Из них категориальные:\n",
+    "cat_cols = [\n",
+    "    'age', 'income', 'sex', 'kids_flg',\n",
+    "    'content_type', 'for_kids', 'studios',\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    'objective': 'binary',\n",
+    "    'n_estimators': 10000,  # максимальное число деревьев\n",
+    "    'max_depth': 4,  # максимальная глубина дерева\n",
+    "    'num_leaves': 10,  # число листьев << 2^max_depth\n",
+    "    'min_child_samples': 100,  # число примеров в листе\n",
+    "    'learning_rate': 0.25,  # шаг обучения\n",
+    "    'reg_lambda': 1,  # L2 регуляризация\n",
+    "    'colsample_bytree': 0.9,  # доля колонок, которая используется в каждом дереве\n",
+    "    'random_state': 42,\n",
+    "}\n",
+    "early_stopping_rounds = 32\n",
+    "fit_params = {\n",
+    "    'X': ranker_train[cols],\n",
+    "    'y': ranker_train['target'],\n",
+    "    'eval_set': [(ranker_val[cols], ranker_val['target'])],\n",
+    "    'eval_metric': 'logloss',\n",
+    "    'early_stopping_rounds': early_stopping_rounds,\n",
+    "    'categorical_feature': cat_cols,\n",
+    "    'feature_name': cols,\n",
+    "    'verbose': early_stopping_rounds / 8,\n",
+    "}\n",
+    "pointwise_model = LGBMClassifier(**params)\n",
+    "pointwise_model.fit(**fit_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save model  \n",
+    "with open(f'models/pointwise_model.dill', 'wb') as f:\n",
+    "    dill.dump(pointwise_model, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0394652469075403"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Смотрим на логлосс на валидации\n",
+    "pointwise_model.best_score_['valid_0']['binary_logloss']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SHAP - values\n",
+    "explainer = shap.Explainer(pointwise_model)\n",
+    "shap_values = explainer(ranker_test[cols].iloc[:10_000])\n",
+    "\n",
+    "# lightGBM returns probabilities for both classes and I have to modify the SHAP values as\n",
+    "shap_values.values = shap_values.values[:,:,1]\n",
+    "shap_values.base_values = shap_values.base_values[:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1190 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# summarize the effects of all the features\n",
+    "shap.plots.beeswarm(shap_values, max_display=len(cols))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# mean shap-values\n",
+    "shap.plots.bar(shap_values, max_display=len(cols))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.00277117, 0.00052769, 0.00017571, ..., 0.00335048, 0.00063627,\n",
+       "       0.00053399])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Получаем предсказания для тестовых юзеров\n",
+    "y_pred: np.ndarray = pointwise_model.predict_proba(ranker_test[cols])[:, 1]\n",
+    "y_true: np.ndarray = np.array(ranker_test['target'])\n",
+    "\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_score_and_rank(df: pd.DataFrame, y_pred_scores: np.ndarray, name: str) -> pd.DataFrame:\n",
+    "    # Добавляем скор модели второго уровня\n",
+    "    df[f'{name}_score'] = y_pred_scores\n",
+    "    # Добавляем ранг модели второго уровня\n",
+    "    df.sort_values(\n",
+    "        by=['user_id', f'{name}_score'],\n",
+    "        ascending=[True, False],\n",
+    "        inplace=True,\n",
+    "    )\n",
+    "    df[f'{name}_rank'] = df.groupby('user_id').cumcount() + 1\n",
+    "\n",
+    "    # Исключаем айтемы, которые не были предсказаны на первом уровне\n",
+    "    mask = (df['knn_rank'] < 101).to_numpy()\n",
+    "    # Добавляем общий скор двух-этапной модели\n",
+    "    eps: float = 0.001\n",
+    "    min_score: float = min(y_pred_scores) - eps\n",
+    "    df[f'{name}_hybrid_score'] = df[f'{name}_score'] * mask\n",
+    "    df[f'{name}_hybrid_score'].replace(\n",
+    "        0,\n",
+    "        min_score,\n",
+    "        inplace=True,\n",
+    "    )\n",
+    "    # Добавляем общий ранг двух-этапной модели\n",
+    "    df[f'{name}_hybrid_rank'] = df[f'{name}_rank'] * mask\n",
+    "    max_rank: int = 101\n",
+    "    df[f'{name}_hybrid_rank'].replace(\n",
+    "        0,\n",
+    "        max_rank,\n",
+    "        inplace=True,\n",
+    "    )\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ranker_test = add_score_and_rank(ranker_test, y_pred, 'pointwise')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9384083165686217, 0.40131153714016216)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# усредненный рок аук по юзерам\n",
+    "# df должен быть отсортирован по user_id\n",
+    "(\n",
+    "    avg_user_metric(\n",
+    "        y_true=np.array(ranker_test['target']),\n",
+    "        y_pred=np.array(ranker_test['pointwise_score']),\n",
+    "        user_ids=np.array(ranker_test['user_id']),\n",
+    "        metric_function=roc_auc_score\n",
+    "    ), \n",
+    "    avg_user_metric(\n",
+    "        y_true=np.array(ranker_test['target']),\n",
+    "        y_pred=np.array(ranker_test['pointwise_hybrid_score']),\n",
+    "        user_ids=np.array(ranker_test['user_id']),\n",
+    "        metric_function=roc_auc_score\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lfm</th>\n",
+       "      <th>knn_popular</th>\n",
+       "      <th>pointwise</th>\n",
+       "      <th>pointwise_hybrid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Precision@10</th>\n",
+       "      <td>0.020880</td>\n",
+       "      <td>0.058393</td>\n",
+       "      <td>0.271447</td>\n",
+       "      <td>0.053135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall@10</th>\n",
+       "      <td>0.070165</td>\n",
+       "      <td>0.257385</td>\n",
+       "      <td>0.800389</td>\n",
+       "      <td>0.256736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ndcg@10</th>\n",
+       "      <td>0.023237</td>\n",
+       "      <td>0.060195</td>\n",
+       "      <td>0.362759</td>\n",
+       "      <td>0.060780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>map@10</th>\n",
+       "      <td>0.028307</td>\n",
+       "      <td>0.084259</td>\n",
+       "      <td>0.676406</td>\n",
+       "      <td>0.131028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>novelty@10</th>\n",
+       "      <td>4.424139</td>\n",
+       "      <td>4.082930</td>\n",
+       "      <td>4.507067</td>\n",
+       "      <td>2.990717</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   lfm  knn_popular  pointwise  pointwise_hybrid\n",
+       "Precision@10  0.020880     0.058393   0.271447          0.053135\n",
+       "recall@10     0.070165     0.257385   0.800389          0.256736\n",
+       "ndcg@10       0.023237     0.060195   0.362759          0.060780\n",
+       "map@10        0.028307     0.084259   0.676406          0.131028\n",
+       "novelty@10    4.424139     4.082930   4.507067          2.990717"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Получим значения метрик\n",
+    "models_metrics['pointwise'] = calc_metrics_(ranker_test, 'pointwise_rank')\n",
+    "models_metrics['pointwise_hybrid'] = calc_metrics_(ranker_test, 'pointwise_hybrid_rank')\n",
+    "\n",
+    "pd.DataFrame(models_metrics)[['lfm', 'knn_popular', 'pointwise', 'pointwise_hybrid']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pairwise/Listwise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Для обучения ранжированию нужно правильно сформировать группы. \n",
+    "# В нашем случае группа равна одному юзеру.\n",
+    "# Для LGBMRanker нужно задать отсортированный по юзерам (группам) датафрейм,\n",
+    "# для которого списком групп будет список из количества \n",
+    "# ранжируемых айтемов на каждого юзера (группу).\n",
+    "\n",
+    "# Официальная [дока|https://lightgbm.readthedocs.io/en/v3.3.2/pythonapi/lightgbm.LGBMRanker.html?highlight=ranker]:\n",
+    "# sum(group) = n_samples. \n",
+    "# For example, if you have a 100-document dataset with \n",
+    "# group = [10, 20, 40, 10, 10, 10], that means that you have 6 groups, \n",
+    "# where the first 10 records are in the first group\n",
+    "\n",
+    "# Важно! Если вы решите использовать CatBoostRanker или XGBoostRanker - там группы строятся по-другому.\n",
+    "\n",
+    "def get_group(df: pd.DataFrame) -> np.ndarray:\n",
+    "    return np.array(\n",
+    "        df[['user_id', 'item_id']]\n",
+    "        .groupby(by=['user_id']).count()\n",
+    "        ['item_id']\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>knn_rank</th>\n",
+       "      <th>age</th>\n",
+       "      <th>income</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>kids_flg</th>\n",
+       "      <th>...</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>for_kids</th>\n",
+       "      <th>age_rating</th>\n",
+       "      <th>studios</th>\n",
+       "      <th>item_pop</th>\n",
+       "      <th>item_avg_hist</th>\n",
+       "      <th>item_median_duratiation</th>\n",
+       "      <th>item_025_duratiation</th>\n",
+       "      <th>item_075_duratiation</th>\n",
+       "      <th>target_ranker</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2021-08-16</td>\n",
+       "      <td>2179.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2017.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1249.0</td>\n",
+       "      <td>38.180144</td>\n",
+       "      <td>7535.0</td>\n",
+       "      <td>1270.00</td>\n",
+       "      <td>8895.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>142</td>\n",
+       "      <td>2021-08-13</td>\n",
+       "      <td>5892.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>35862.0</td>\n",
+       "      <td>15.251464</td>\n",
+       "      <td>4953.0</td>\n",
+       "      <td>807.00</td>\n",
+       "      <td>6205.00</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>965</td>\n",
+       "      <td>2021-08-16</td>\n",
+       "      <td>5813.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2018.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>536.0</td>\n",
+       "      <td>29.130597</td>\n",
+       "      <td>3450.5</td>\n",
+       "      <td>665.75</td>\n",
+       "      <td>6774.25</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id  item_id   datetime  total_dur  weight  knn_rank age income sex  \\\n",
+       "0        3       47 2021-08-16     2179.0    27.0     101.0  -1     -1  -1   \n",
+       "1        3      142 2021-08-13     5892.0   100.0     101.0  -1     -1  -1   \n",
+       "2        3      965 2021-08-16     5813.0    96.0     101.0  -1     -1  -1   \n",
+       "\n",
+       "  kids_flg  ...  release_year  for_kids  age_rating  studios  item_pop  \\\n",
+       "0       -1  ...        2017.0        -1        18.0       -1    1249.0   \n",
+       "1       -1  ...        2020.0        -1        16.0       -1   35862.0   \n",
+       "2       -1  ...        2018.0        -1        12.0       -1     536.0   \n",
+       "\n",
+       "   item_avg_hist  item_median_duratiation  item_025_duratiation  \\\n",
+       "0      38.180144                   7535.0               1270.00   \n",
+       "1      15.251464                   4953.0                807.00   \n",
+       "2      29.130597                   3450.5                665.75   \n",
+       "\n",
+       "   item_075_duratiation  target_ranker  \n",
+       "0               8895.00              1  \n",
+       "1               6205.00              2  \n",
+       "2               6774.25              2  \n",
+       "\n",
+       "[3 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Добавим таргет посложнее\n",
+    "\n",
+    "def add_target(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    0 - доля досмотра < 0.15\n",
+    "    1 - 0.15 <= доля досмотра < 0.75\n",
+    "    2 - 0.75 <= доля досмотра\n",
+    "    \"\"\"\n",
+    "    df['target_ranker'] = (df[Columns.Weight] >= 15).astype(int)  # 'watched_pct'\n",
+    "    df['target_ranker'] += (df[Columns.Weight] >= 75).astype(int)\n",
+    "    return df\n",
+    "\n",
+    "ranker_train = add_target(ranker_train)\n",
+    "ranker_val = add_target(ranker_val)\n",
+    "ranker_test = add_target(ranker_test)\n",
+    "\n",
+    "ranker_train.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training until validation scores don't improve for 32 rounds\n",
+      "Early stopping, best iteration is:\n",
+      "[266]\tvalid_0's ndcg@3: 0.744172\tvalid_0's ndcg@5: 0.76713\tvalid_0's ndcg@10: 0.791184\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRanker(colsample_bytree=0.9, learning_rate=0.25, max_depth=4,\n",
+       "           min_child_samples=100, n_estimators=10000, num_leaves=10,\n",
+       "           objective=&#x27;lambdarank&#x27;, random_state=42, reg_lambda=1)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRanker</label><div class=\"sk-toggleable__content\"><pre>LGBMRanker(colsample_bytree=0.9, learning_rate=0.25, max_depth=4,\n",
+       "           min_child_samples=100, n_estimators=10000, num_leaves=10,\n",
+       "           objective=&#x27;lambdarank&#x27;, random_state=42, reg_lambda=1)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LGBMRanker(colsample_bytree=0.9, learning_rate=0.25, max_depth=4,\n",
+       "           min_child_samples=100, n_estimators=10000, num_leaves=10,\n",
+       "           objective='lambdarank', random_state=42, reg_lambda=1)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = {\n",
+    "    'objective': 'lambdarank',  # lambdarank, оптимизирующий ndcg \n",
+    "    'n_estimators': 10000,  # максимальное число деревьев\n",
+    "    'max_depth': 4,  # максимальная глубина дерева\n",
+    "    'num_leaves': 10,  # число листьев << 2^max_depth\n",
+    "    'min_child_samples': 100,  # число примеров в листе\n",
+    "    'learning_rate': 0.25,  # шаг обучения\n",
+    "    'reg_lambda': 1,  # L2 регуляризация\n",
+    "    'colsample_bytree': 0.9,  # доля колонок, которая используется в каждом дереве\n",
+    "    'random_state': 42,\n",
+    "}\n",
+    "early_stopping_rounds = 32\n",
+    "fit_params = {\n",
+    "    'X': ranker_train[cols],\n",
+    "    'y': ranker_train['target_ranker'],\n",
+    "    'group': get_group(ranker_train),\n",
+    "    'eval_set': [(ranker_val[cols], ranker_val['target_ranker'])],\n",
+    "    'eval_group': [get_group(ranker_val)],\n",
+    "    'eval_metric': 'ndcg',\n",
+    "    'eval_at': (3, 5, 10),\n",
+    "    'early_stopping_rounds': early_stopping_rounds,\n",
+    "    'categorical_feature': cat_cols,\n",
+    "    'feature_name': cols,\n",
+    "    'verbose': early_stopping_rounds / 8,\n",
+    "}\n",
+    "listwise_model = LGBMRanker(**params)\n",
+    "listwise_model.fit(**fit_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save model  \n",
+    "with open(f'models/listwise_model.dill', 'wb') as f:\n",
+    "    dill.dump(listwise_model, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('ndcg@3', 0.7441724318759053),\n",
+       "             ('ndcg@5', 0.7671298257412358),\n",
+       "             ('ndcg@10', 0.7911840632852781)])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "listwise_model.best_score_['valid_0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1450 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "explainer = shap.Explainer(listwise_model)\n",
+    "shap_values = explainer(ranker_test[cols].iloc[:10_000])\n",
+    "\n",
+    "shap.plots.waterfall(shap_values[0], max_display=len(cols))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1190 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# summarize the effects of all the features\n",
+    "shap.plots.beeswarm(shap_values, max_display=len(cols))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>knn_rank</th>\n",
+       "      <th>age</th>\n",
+       "      <th>income</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>kids_flg</th>\n",
+       "      <th>...</th>\n",
+       "      <th>item_pop</th>\n",
+       "      <th>item_avg_hist</th>\n",
+       "      <th>item_median_duratiation</th>\n",
+       "      <th>item_025_duratiation</th>\n",
+       "      <th>item_075_duratiation</th>\n",
+       "      <th>target_ranker</th>\n",
+       "      <th>listwise_score</th>\n",
+       "      <th>listwise_rank</th>\n",
+       "      <th>listwise_hybrid_score</th>\n",
+       "      <th>listwise_hybrid_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3669</td>\n",
+       "      <td>2021-08-16</td>\n",
+       "      <td>1593.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2846.0</td>\n",
+       "      <td>22.675978</td>\n",
+       "      <td>4268.0</td>\n",
+       "      <td>584.00</td>\n",
+       "      <td>7012.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.748616</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-19.168196</td>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>1</td>\n",
+       "      <td>15297</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>137128.0</td>\n",
+       "      <td>7.364295</td>\n",
+       "      <td>21818.5</td>\n",
+       "      <td>7137.75</td>\n",
+       "      <td>30687.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.542928</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.542928</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10440</td>\n",
+       "      <td>2021-08-13</td>\n",
+       "      <td>19579.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>141889.0</td>\n",
+       "      <td>8.068716</td>\n",
+       "      <td>24330.0</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>40322.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.504057</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.504057</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    user_id  item_id   datetime  total_dur  weight  knn_rank age income sex  \\\n",
+       "15        1     3669 2021-08-16     1593.0    26.0     101.0   1      2   0   \n",
+       "94        1    15297 2021-07-01        0.0     0.0       5.0   1      2   0   \n",
+       "60        1    10440 2021-08-13    19579.0    80.0       8.0   1      2   0   \n",
+       "\n",
+       "   kids_flg  ...  item_pop  item_avg_hist  item_median_duratiation  \\\n",
+       "15        1  ...    2846.0      22.675978                   4268.0   \n",
+       "94        1  ...  137128.0       7.364295                  21818.5   \n",
+       "60        1  ...  141889.0       8.068716                  24330.0   \n",
+       "\n",
+       "    item_025_duratiation  item_075_duratiation  target_ranker  listwise_score  \\\n",
+       "15                584.00                7012.0              1        2.748616   \n",
+       "94               7137.75               30687.0              0        0.542928   \n",
+       "60               3500.00               40322.0              2        0.504057   \n",
+       "\n",
+       "    listwise_rank  listwise_hybrid_score  listwise_hybrid_rank  \n",
+       "15              1             -19.168196                   101  \n",
+       "94              2               0.542928                     2  \n",
+       "60              3               0.504057                     3  \n",
+       "\n",
+       "[3 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred: np.ndarray = listwise_model.predict(ranker_test[cols])\n",
+    "ranker_test = add_score_and_rank(ranker_test, y_pred, 'listwise')\n",
+    "ranker_test.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>knn_rank</th>\n",
+       "      <th>age</th>\n",
+       "      <th>income</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>kids_flg</th>\n",
+       "      <th>...</th>\n",
+       "      <th>target</th>\n",
+       "      <th>pointwise_score</th>\n",
+       "      <th>pointwise_rank</th>\n",
+       "      <th>pointwise_hybrid_score</th>\n",
+       "      <th>pointwise_hybrid_rank</th>\n",
+       "      <th>target_ranker</th>\n",
+       "      <th>listwise_score</th>\n",
+       "      <th>listwise_rank</th>\n",
+       "      <th>listwise_hybrid_score</th>\n",
+       "      <th>listwise_hybrid_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>1</td>\n",
+       "      <td>15297</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.086521</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.086521</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.542928</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.542928</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10440</td>\n",
+       "      <td>2021-08-13</td>\n",
+       "      <td>19579.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.079407</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.079407</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.504057</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.504057</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9728</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.063357</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.063357</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.108057</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.108057</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3734</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.044763</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.044763</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.514344</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-0.514344</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13865</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.034935</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.034935</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.684751</td>\n",
+       "      <td>6</td>\n",
+       "      <td>-0.684751</td>\n",
+       "      <td>6</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>91</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14717</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000152</td>\n",
+       "      <td>98</td>\n",
+       "      <td>0.000152</td>\n",
+       "      <td>98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-5.791813</td>\n",
+       "      <td>98</td>\n",
+       "      <td>-5.791813</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5873</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000297</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0.000297</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-5.867301</td>\n",
+       "      <td>99</td>\n",
+       "      <td>-5.867301</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7460</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000123</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.000123</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-6.072857</td>\n",
+       "      <td>100</td>\n",
+       "      <td>-6.072857</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3669</td>\n",
+       "      <td>2021-08-16</td>\n",
+       "      <td>1593.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.428389</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.001000</td>\n",
+       "      <td>101</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.748616</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-19.168196</td>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13615</td>\n",
+       "      <td>2021-07-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000139</td>\n",
+       "      <td>99</td>\n",
+       "      <td>0.000139</td>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-6.574961</td>\n",
+       "      <td>101</td>\n",
+       "      <td>-6.574961</td>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>101 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    user_id  item_id   datetime  total_dur  weight  knn_rank age income sex  \\\n",
+       "94        1    15297 2021-07-01        0.0     0.0       5.0   1      2   0   \n",
+       "60        1    10440 2021-08-13    19579.0    80.0       8.0   1      2   0   \n",
+       "57        1     9728 2021-07-01        0.0     0.0       2.0   1      2   0   \n",
+       "16        1     3734 2021-07-01        0.0     0.0       3.0   1      2   0   \n",
+       "84        1    13865 2021-07-01        0.0     0.0       1.0   1      2   0   \n",
+       "..      ...      ...        ...        ...     ...       ...  ..    ...  ..   \n",
+       "91        1    14717 2021-07-01        0.0     0.0      50.0   1      2   0   \n",
+       "34        1     5873 2021-07-01        0.0     0.0     100.0   1      2   0   \n",
+       "45        1     7460 2021-07-01        0.0     0.0      29.0   1      2   0   \n",
+       "15        1     3669 2021-08-16     1593.0    26.0     101.0   1      2   0   \n",
+       "83        1    13615 2021-07-01        0.0     0.0      94.0   1      2   0   \n",
+       "\n",
+       "   kids_flg  ...  target  pointwise_score  pointwise_rank  \\\n",
+       "94        1  ...       0         0.086521               2   \n",
+       "60        1  ...       1         0.079407               3   \n",
+       "57        1  ...       0         0.063357               4   \n",
+       "16        1  ...       0         0.044763               5   \n",
+       "84        1  ...       0         0.034935               6   \n",
+       "..      ...  ...     ...              ...             ...   \n",
+       "91        1  ...       0         0.000152              98   \n",
+       "34        1  ...       0         0.000297              94   \n",
+       "45        1  ...       0         0.000123             100   \n",
+       "15        1  ...       0         0.428389               1   \n",
+       "83        1  ...       0         0.000139              99   \n",
+       "\n",
+       "    pointwise_hybrid_score  pointwise_hybrid_rank  target_ranker  \\\n",
+       "94                0.086521                      2              0   \n",
+       "60                0.079407                      3              2   \n",
+       "57                0.063357                      4              0   \n",
+       "16                0.044763                      5              0   \n",
+       "84                0.034935                      6              0   \n",
+       "..                     ...                    ...            ...   \n",
+       "91                0.000152                     98              0   \n",
+       "34                0.000297                     94              0   \n",
+       "45                0.000123                    100              0   \n",
+       "15               -0.001000                    101              1   \n",
+       "83                0.000139                     99              0   \n",
+       "\n",
+       "    listwise_score  listwise_rank  listwise_hybrid_score  listwise_hybrid_rank  \n",
+       "94        0.542928              2               0.542928                     2  \n",
+       "60        0.504057              3               0.504057                     3  \n",
+       "57        0.108057              4               0.108057                     4  \n",
+       "16       -0.514344              5              -0.514344                     5  \n",
+       "84       -0.684751              6              -0.684751                     6  \n",
+       "..             ...            ...                    ...                   ...  \n",
+       "91       -5.791813             98              -5.791813                    98  \n",
+       "34       -5.867301             99              -5.867301                    99  \n",
+       "45       -6.072857            100              -6.072857                   100  \n",
+       "15        2.748616              1             -19.168196                   101  \n",
+       "83       -6.574961            101              -6.574961                   101  \n",
+       "\n",
+       "[101 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranker_test[ranker_test.user_id == 1].sort_values(by = 'listwise_hybrid_rank')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.7486157 ,  0.54292794,  0.50405678, ..., -6.66454692,\n",
+       "       -6.062935  , -5.05943539])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>listwise</th>\n",
+       "      <th>listwise_hybrid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Precision@10</th>\n",
+       "      <td>0.276730</td>\n",
+       "      <td>0.056406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall@10</th>\n",
+       "      <td>0.824714</td>\n",
+       "      <td>0.278818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ndcg@10</th>\n",
+       "      <td>0.371164</td>\n",
+       "      <td>0.063102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>map@10</th>\n",
+       "      <td>0.704004</td>\n",
+       "      <td>0.136064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>novelty@10</th>\n",
+       "      <td>4.570098</td>\n",
+       "      <td>3.044439</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              listwise  listwise_hybrid\n",
+       "Precision@10  0.276730         0.056406\n",
+       "recall@10     0.824714         0.278818\n",
+       "ndcg@10       0.371164         0.063102\n",
+       "map@10        0.704004         0.136064\n",
+       "novelty@10    4.570098         3.044439"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models_metrics['listwise'] = calc_metrics_(ranker_test, 'listwise_rank')\n",
+    "models_metrics['listwise_hybrid'] = calc_metrics_(ranker_test, 'listwise_hybrid_rank')\n",
+    "pd.DataFrame(models_metrics)[['listwise', 'listwise_hybrid']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9378088980979155, 0.40079971118997665)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    avg_user_metric(\n",
+    "        y_true=np.array(ranker_test['target']),  # target_ranker\n",
+    "        y_pred=np.array(ranker_test['listwise_score']),\n",
+    "        user_ids=np.array(ranker_test['user_id']),\n",
+    "        metric_function=roc_auc_score,\n",
+    "    ),\n",
+    "    avg_user_metric(\n",
+    "        y_true=np.array(ranker_test['target']),\n",
+    "        y_pred=np.array(ranker_test['listwise_hybrid_score']),\n",
+    "        user_ids=np.array(ranker_test['user_id']),\n",
+    "        metric_function=roc_auc_score,\n",
+    "    ),\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "toc-autonumbering": false,
+  "toc-showmarkdowntxt": true,
+  "vscode": {
+   "interpreter": {
+    "hash": "0b9f80e97c28e0957cf0460331ba88da7f9dc1ecfdf50d1769bf45581762184a"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}