lecture_6/ods_mts_recsys_dssm_code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ODS_MTS_RECSYS.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRMeplevHf9W",
        "outputId": "d65bf7ea-1822-4370-dc82-79d42c8148f3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/gdrive\", force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcmzJ-AOL3yE"
      },
      "source": [
        "# **YOUR <font color='red'>SECOND</font> RECSYS**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkHJvZQTFHGe"
      },
      "source": [
        "<div>\n",
        "<img 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\" width=\"300\"/>\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAOIhEE1EXmq"
      },
      "source": [
        "# Intro\n",
        "\n",
        "Всем привет!\n",
        "В этой лекции мы разберемся как обучить простую DSSM-нейронку на данных KION, а еще поскорим ее в оффлайне. Погнали!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCx_oM9WHkX-"
      },
      "source": [
        "import ast\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras.backend as K\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from collections import Counter\n",
        "from random import randint, random\n",
        "from scipy.sparse import coo_matrix, hstack\n",
        "from sklearn.metrics.pairwise import euclidean_distances, cosine_distances, cosine_similarity\n",
        "from tensorflow import keras\n",
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFErvu92--ni"
      },
      "source": [
        "Загрузим данные Киона"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5nE2I1SHrpR"
      },
      "source": [
        "users_df = pd.read_csv(\"/gdrive/MyDrive/work/mts/data_kion/users.csv\")\n",
        "items_df = pd.read_csv(\"/gdrive/MyDrive/work/mts/data_kion/items.csv\")\n",
        "interactions_df = pd.read_csv(\"/gdrive/MyDrive/work/mts/data_kion/interactions.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2SVmJrw-cIX"
      },
      "source": [
        "# Подготовка данных"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwUJ2-GNlpUR"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAb5NAmRMv-r"
      },
      "source": [
        "Для начала посмотрим на данные:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "ZpJuOThXH8KV",
        "outputId": "b2c9534b-40c9-4fc5-ec5d-924eec869ece"
      },
      "source": [
        "users_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>age</th>\n",
              "      <th>income</th>\n",
              "      <th>sex</th>\n",
              "      <th>kids_flg</th>\n",
              "      <th>business_dt</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",
              "      <td>2021-09-10</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",
              "      <td>2021-09-10</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",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>721985</td>\n",
              "      <td>age_45_54</td>\n",
              "      <td>income_20_40</td>\n",
              "      <td>Ж</td>\n",
              "      <td>0</td>\n",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>704055</td>\n",
              "      <td>age_35_44</td>\n",
              "      <td>income_60_90</td>\n",
              "      <td>Ж</td>\n",
              "      <td>0</td>\n",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id        age        income sex  kids_flg business_dt\n",
              "0   973171  age_25_34  income_60_90   М         1  2021-09-10\n",
              "1   962099  age_18_24  income_20_40   М         0  2021-09-10\n",
              "2  1047345  age_45_54  income_40_60   Ж         0  2021-09-10\n",
              "3   721985  age_45_54  income_20_40   Ж         0  2021-09-10\n",
              "4   704055  age_35_44  income_60_90   Ж         0  2021-09-10"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "W6fSB9K3IQ1e",
        "outputId": "a3b13077-fab5-47cd-de4f-0a1290f0ebfb"
      },
      "source": [
        "items_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>keywords</th>\n",
              "      <th>description</th>\n",
              "      <th>business_dt</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>Поговори, ней, 2002, Испания, друзья, любовь, ...</td>\n",
              "      <td>Мелодрама легендарного Педро Альмодовара «Пого...</td>\n",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8250</td>\n",
              "      <td>film</td>\n",
              "      <td>Объезд</td>\n",
              "      <td>Detour</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>зарубежные, триллеры</td>\n",
              "      <td>США</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Уильям Дикерсон</td>\n",
              "      <td>Бриа Грант, Джон Форест, Дэб Снайдер, Нил Хопк...</td>\n",
              "      <td>Объезд, 2013, США, выживание, диких, условиях,...</td>\n",
              "      <td>История среднестатистического американца, чью ...</td>\n",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</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>Голые, перцы, 2014, США, друзья, свадьбы, прео...</td>\n",
              "      <td>Уморительная современная комедия на популярную...</td>\n",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</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>Тактическая, сила, 2011, Канада, бандиты, ганг...</td>\n",
              "      <td>Профессиональный рестлер Стив Остин («Все или ...</td>\n",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7868</td>\n",
              "      <td>film</td>\n",
              "      <td>45 лет</td>\n",
              "      <td>45 Years</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>драмы, зарубежные, мелодрамы</td>\n",
              "      <td>Великобритания</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Эндрю Хэй</td>\n",
              "      <td>Александра Риддлстон-Барретт, Джеральдин Джейм...</td>\n",
              "      <td>45, лет, 2015, Великобритания, брак, жизнь, лю...</td>\n",
              "      <td>Шарлотта Рэмплинг, Том Кортни, Джеральдин Джей...</td>\n",
              "      <td>2021-09-10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   item_id  ... business_dt\n",
              "0    10711  ...  2021-09-10\n",
              "1     8250  ...  2021-09-10\n",
              "2     2508  ...  2021-09-10\n",
              "3    10716  ...  2021-09-10\n",
              "4     7868  ...  2021-09-10\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "rIX0BuQ_IR4k",
        "outputId": "478a2d04-9d11-4119-cce2-555d26a05f95"
      },
      "source": [
        "interactions_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <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",
              "  </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\n",
              "3   864613     7638    2021-07-05      14483        100.0\n",
              "4   964868     9506    2021-04-30       6725        100.0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVm0hOuaZSM1"
      },
      "source": [
        "## Готовим фичи пользователей\n",
        "\n",
        "Посмотрим, какие фичи в датасете фильмов являются категориальными и закодируем их с помощью one-hot encoding."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "RmiOZstSYFWB",
        "outputId": "a94880b1-605c-46b5-d825-fbfcfbd6922f"
      },
      "source": [
        "user_cat_feats = [\"age\", \"income\", \"sex\", \"kids_flg\"]\n",
        "\n",
        "# из исходного датафрейма оставим только item_id - этот признак нам понадобится позже\n",
        "# для того, чтобы маппить айтемы из датафрейма с фильмами с айтемами \n",
        "# из датафрейма с взаимодействиями\n",
        "users_ohe_df = users_df.user_id\n",
        "for feat in user_cat_feats:\n",
        "  # получаем датафрейм с one-hot encoding для каждой категориальной фичи\n",
        "  ohe_feat_df = pd.get_dummies(users_df[feat], prefix=feat)\n",
        "  # конкатенируем ohe-hot датафрейм с датафреймом, \n",
        "  # который мы получили на предыдущем шаге\n",
        "  users_ohe_df = pd.concat([users_ohe_df, ohe_feat_df], axis=1)\n",
        "\n",
        "users_ohe_df.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>age_age_18_24</th>\n",
              "      <th>age_age_25_34</th>\n",
              "      <th>age_age_35_44</th>\n",
              "      <th>age_age_45_54</th>\n",
              "      <th>age_age_55_64</th>\n",
              "      <th>age_age_65_inf</th>\n",
              "      <th>income_income_0_20</th>\n",
              "      <th>income_income_150_inf</th>\n",
              "      <th>income_income_20_40</th>\n",
              "      <th>income_income_40_60</th>\n",
              "      <th>income_income_60_90</th>\n",
              "      <th>income_income_90_150</th>\n",
              "      <th>sex_Ж</th>\n",
              "      <th>sex_М</th>\n",
              "      <th>kids_flg_0</th>\n",
              "      <th>kids_flg_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>973171</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>962099</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1047345</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>721985</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>704055</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id  age_age_18_24  age_age_25_34  ...  sex_М  kids_flg_0  kids_flg_1\n",
              "0   973171              0              1  ...      1           0           1\n",
              "1   962099              1              0  ...      1           1           0\n",
              "2  1047345              0              0  ...      0           1           0\n",
              "3   721985              0              0  ...      0           1           0\n",
              "4   704055              0              0  ...      0           1           0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3jmBkQeZNp-"
      },
      "source": [
        "## Готовим фичи айтемов\n",
        "\n",
        "Кодируем их точно так же - one-hot'ом."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z5p5ASbITUx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "5c134717-c3f2-4386-f4aa-538bc866eb83"
      },
      "source": [
        "item_cat_feats = ['content_type', 'release_year',\n",
        "                  'for_kids', 'age_rating', \n",
        "                  'studios', 'countries', 'directors']\n",
        "\n",
        "items_ohe_df = items_df.item_id\n",
        "\n",
        "for feat in item_cat_feats:\n",
        "  ohe_feat_df = pd.get_dummies(items_df[feat], prefix=feat)\n",
        "  items_ohe_df = pd.concat([items_ohe_df, ohe_feat_df], axis=1) \n",
        "\n",
        "items_ohe_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<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_film</th>\n",
              "      <th>content_type_series</th>\n",
              "      <th>release_year_1897.0</th>\n",
              "      <th>release_year_1916.0</th>\n",
              "      <th>release_year_1917.0</th>\n",
              "      <th>release_year_1918.0</th>\n",
              "      <th>release_year_1920.0</th>\n",
              "      <th>release_year_1921.0</th>\n",
              "      <th>release_year_1922.0</th>\n",
              "      <th>release_year_1924.0</th>\n",
              "      <th>release_year_1925.0</th>\n",
              "      <th>release_year_1926.0</th>\n",
              "      <th>release_year_1927.0</th>\n",
              "      <th>release_year_1928.0</th>\n",
              "      <th>release_year_1929.0</th>\n",
              "      <th>release_year_1930.0</th>\n",
              "      <th>release_year_1931.0</th>\n",
              "      <th>release_year_1932.0</th>\n",
              "      <th>release_year_1933.0</th>\n",
              "      <th>release_year_1934.0</th>\n",
              "      <th>release_year_1935.0</th>\n",
              "      <th>release_year_1936.0</th>\n",
              "      <th>release_year_1937.0</th>\n",
              "      <th>release_year_1938.0</th>\n",
              "      <th>release_year_1939.0</th>\n",
              "      <th>release_year_1940.0</th>\n",
              "      <th>release_year_1941.0</th>\n",
              "      <th>release_year_1942.0</th>\n",
              "      <th>release_year_1943.0</th>\n",
              "      <th>release_year_1944.0</th>\n",
              "      <th>release_year_1945.0</th>\n",
              "      <th>release_year_1946.0</th>\n",
              "      <th>release_year_1947.0</th>\n",
              "      <th>release_year_1948.0</th>\n",
              "      <th>release_year_1949.0</th>\n",
              "      <th>release_year_1950.0</th>\n",
              "      <th>release_year_1951.0</th>\n",
              "      <th>release_year_1952.0</th>\n",
              "      <th>release_year_1953.0</th>\n",
              "      <th>...</th>\n",
              "      <th>directors_Якоб Вербрюгген, Тим Милантс</th>\n",
              "      <th>directors_Яков Базелян</th>\n",
              "      <th>directors_Яков Протазанов</th>\n",
              "      <th>directors_Яков Сегель</th>\n",
              "      <th>directors_Яков Уринов</th>\n",
              "      <th>directors_Якуб Кронер</th>\n",
              "      <th>directors_Ялмари Хеландер</th>\n",
              "      <th>directors_Ям Ларанас</th>\n",
              "      <th>directors_Ян Гэ</th>\n",
              "      <th>directors_Ян Деманж</th>\n",
              "      <th>directors_Ян Комаса</th>\n",
              "      <th>directors_Ян Кунен</th>\n",
              "      <th>directors_Ян Лануетт Тургеон</th>\n",
              "      <th>directors_Ян Ларин</th>\n",
              "      <th>directors_Ян Лахойер, Джейкоб Шух</th>\n",
              "      <th>directors_Ян Леноре</th>\n",
              "      <th>directors_Ян Самюэль</th>\n",
              "      <th>directors_Ян Ситта</th>\n",
              "      <th>directors_Ян Сысоев</th>\n",
              "      <th>directors_Ян Фрид</th>\n",
              "      <th>directors_Ян Элен</th>\n",
              "      <th>directors_Ян де Бонт</th>\n",
              "      <th>directors_Яна Гладких</th>\n",
              "      <th>directors_Яна Друзь</th>\n",
              "      <th>directors_Яна Ерлашова</th>\n",
              "      <th>directors_Яна Поляруш, Тамара Цоцория, Константин Кутуев</th>\n",
              "      <th>directors_Яна Титова</th>\n",
              "      <th>directors_Янгджун Л</th>\n",
              "      <th>directors_Яне Магнуссон</th>\n",
              "      <th>directors_Янн Артюс-Бертран, Анастасия Микова</th>\n",
              "      <th>directors_Яннике Систад Якобсен</th>\n",
              "      <th>directors_Янус Мец</th>\n",
              "      <th>directors_Ярив Хоровиц</th>\n",
              "      <th>directors_Ярон Зильберман</th>\n",
              "      <th>directors_Ярополк Лапшин</th>\n",
              "      <th>directors_Ярослав Лупий</th>\n",
              "      <th>directors_Ярроу Чейни, Скотт Моужер</th>\n",
              "      <th>directors_Ясина Сезар</th>\n",
              "      <th>directors_Ясуоми Умэцу</th>\n",
              "      <th>directors_сения Завьялова</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8250</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2508</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10716</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7868</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 8830 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   item_id  ...  directors_сения Завьялова\n",
              "0    10711  ...                          0\n",
              "1     8250  ...                          0\n",
              "2     2508  ...                          0\n",
              "3    10716  ...                          0\n",
              "4     7868  ...                          0\n",
              "\n",
              "[5 rows x 8830 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbTV6mXwZUxK"
      },
      "source": [
        "## Собираем матрицу взаимодействий\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "zlEFl6QEZb0a",
        "outputId": "6201bd5c-51aa-44be-e9d1-7dc669fe9abc"
      },
      "source": [
        "interactions_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th></th>\n",
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              "      <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",
              "  </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\n",
              "3   864613     7638    2021-07-05      14483        100.0\n",
              "4   964868     9506    2021-04-30       6725        100.0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vt0wHr8GnNL2"
      },
      "source": [
        "В датасете взаимодействий есть непопулярные фильмы и малоактивные пользователи. Кроме того, в таблице взаимодействий есть события с низким качеством взаимодействия - когда юзер начал смотреть фильм, но вскоре после начала просмотра выключил. \n",
        "\n",
        "Отфильтруем такие события*, малоактивных юзеров и непопулярные фильмы.\n",
        "\n",
        "_Можете не фильтровать такие события, тогда у вас будет больше негативных примеров._"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50OMb_Kz_pEF",
        "outputId": "098d5bf3-efc8-4c4e-d8e7-034934616820"
      },
      "source": [
        "interactions_df.item_id.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10440    202457\n",
              "15297    193123\n",
              "9728     132865\n",
              "13865    122119\n",
              "4151      91167\n",
              "          ...  \n",
              "486           1\n",
              "6290          1\n",
              "10388         1\n",
              "14486         1\n",
              "10218         1\n",
              "Name: item_id, Length: 15715, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HlKKIU26_abo",
        "outputId": "3160564f-efe3-44c0-e501-d8e76b0069d6"
      },
      "source": [
        "interactions_df.user_id.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "416206     1341\n",
              "1010539     764\n",
              "555233      685\n",
              "11526       676\n",
              "409259      625\n",
              "           ... \n",
              "388806        1\n",
              "539650        1\n",
              "194790        1\n",
              "392900        1\n",
              "828514        1\n",
              "Name: user_id, Length: 962198, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mA9Ormx-aokt",
        "outputId": "1e2e7f72-8db4-4b65-a2e9-19c83ed9d1f5"
      },
      "source": [
        "print(f\"N users before: {interactions_df.user_id.nunique()}\")\n",
        "print(f\"N items before: {interactions_df.item_id.nunique()}\\n\")\n",
        "\n",
        "# отфильтруем все события взаимодействий, в которых пользователь посмотрел\n",
        "# фильм менее чем на 35 процентов\n",
        "interactions_df = interactions_df[interactions_df.watched_pct > 35]\n",
        "\n",
        "# соберем всех пользователей, которые посмотрели \n",
        "# больше 10 фильмов (можете выбрать другой порог)\n",
        "valid_users = []\n",
        "\n",
        "c = Counter(interactions_df.user_id)\n",
        "for user_id, entries in c.most_common():\n",
        "  if entries > 10:\n",
        "    valid_users.append(user_id)\n",
        "\n",
        "# и соберем все фильмы, которые посмотрели больше 10 пользователей\n",
        "valid_items = []\n",
        "\n",
        "c = Counter(interactions_df.item_id)\n",
        "for item_id, entries in c.most_common():\n",
        "  if entries > 10:\n",
        "    valid_items.append(item_id)\n",
        "\n",
        "# отбросим непопулярные фильмы и неактивных юзеров\n",
        "interactions_df = interactions_df[interactions_df.user_id.isin(valid_users)]\n",
        "interactions_df = interactions_df[interactions_df.item_id.isin(valid_items)]\n",
        "\n",
        "print(f\"N users after: {interactions_df.user_id.nunique()}\")\n",
        "print(f\"N items after: {interactions_df.item_id.nunique()}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "N users before: 962198\n",
            "N items before: 15715\n",
            "\n",
            "N users after: 54217\n",
            "N items after: 6145\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EsmpxFJ1oiP4"
      },
      "source": [
        "После фильтрации может получиться так, что некоторые айтемы/юзеры есть в датасете взаимодействий, но при этом они отсутствуют в датасетах айтемов/юзеров или наоборот. Поэтому найдем id айтемов и id юзеров, которые есть во всех датасетах и оставим только их."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xG4Gw713BSXk",
        "outputId": "6009078f-3c73-4743-8b3e-8ca3cf855dbb"
      },
      "source": [
        "common_users = set(interactions_df.user_id.unique()).intersection(set(users_ohe_df.user_id.unique()))\n",
        "common_items = set(interactions_df.item_id.unique()).intersection(set(items_ohe_df.item_id.unique()))\n",
        "\n",
        "print(len(common_users))\n",
        "print(len(common_items))\n",
        "\n",
        "interactions_df = interactions_df[interactions_df.item_id.isin(common_items)]\n",
        "interactions_df = interactions_df[interactions_df.user_id.isin(common_users)]\n",
        "\n",
        "items_ohe_df = items_ohe_df[items_ohe_df.item_id.isin(common_items)]\n",
        "users_ohe_df = users_ohe_df[users_ohe_df.user_id.isin(common_users)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44963\n",
            "6141\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euPsAK8enRPV"
      },
      "source": [
        "Соберем взаимодействия в матрицу user*item так, чтобы в строках этой матрицы были user_id, в столбцах - item_id, а на пересечениях строк и столбцов - единица, если пользователь взаимодействовал с айтемом и ноль, если нет.\n",
        "\n",
        "Такую матрицу удобно собирать в numpy array, однако нужно помнить, что numpy array индексируется порядковыми индексами, а нам же удобнее использовать item_id и user_id.\n",
        "\n",
        "Создадим некие внутренние индексы для user_id и item_id - uid и iid. Для этого просто соберем все user_id и item_id и пронумеруем их по порядку."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "hfGrVE6_maft",
        "outputId": "9be85942-2dec-4425-8e0d-9ac6f7edf507"
      },
      "source": [
        "interactions_df[\"uid\"] = interactions_df[\"user_id\"].astype(\"category\")\n",
        "interactions_df[\"uid\"] = interactions_df[\"uid\"].cat.codes\n",
        "\n",
        "interactions_df[\"iid\"] = interactions_df[\"item_id\"].astype(\"category\")\n",
        "interactions_df[\"iid\"] = interactions_df[\"iid\"].cat.codes\n",
        "\n",
        "print(sorted(interactions_df.iid.unique())[:5])\n",
        "print(sorted(interactions_df.uid.unique())[:5])\n",
        "interactions_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2, 3, 4]\n",
            "[0, 1, 2, 3, 4]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>uid</th>\n",
              "      <th>iid</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",
              "      <td>7234</td>\n",
              "      <td>3504</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",
              "      <td>28661</td>\n",
              "      <td>594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5324</td>\n",
              "      <td>8437</td>\n",
              "      <td>2021-04-18</td>\n",
              "      <td>6598</td>\n",
              "      <td>92.0</td>\n",
              "      <td>208</td>\n",
              "      <td>3087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>927973</td>\n",
              "      <td>9617</td>\n",
              "      <td>2021-06-19</td>\n",
              "      <td>8422</td>\n",
              "      <td>100.0</td>\n",
              "      <td>37994</td>\n",
              "      <td>3540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>896751</td>\n",
              "      <td>8081</td>\n",
              "      <td>2021-05-17</td>\n",
              "      <td>6358</td>\n",
              "      <td>100.0</td>\n",
              "      <td>36739</td>\n",
              "      <td>2956</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    user_id  item_id last_watch_dt  total_dur  watched_pct    uid   iid\n",
              "0    176549     9506    2021-05-11       4250         72.0   7234  3504\n",
              "1    699317     1659    2021-05-29       8317        100.0  28661   594\n",
              "14     5324     8437    2021-04-18       6598         92.0    208  3087\n",
              "18   927973     9617    2021-06-19       8422        100.0  37994  3540\n",
              "20   896751     8081    2021-05-17       6358        100.0  36739  2956"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igZza5QMr7YS"
      },
      "source": [
        "Наконец соберем и отнормируем матрицу взаимодействий:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46tyZwPMYqcL"
      },
      "source": [
        "interactions_vec = np.zeros((interactions_df.uid.nunique(), \n",
        "                             interactions_df.iid.nunique())) \n",
        "\n",
        "for user_id, item_id in zip(interactions_df.uid, interactions_df.iid):\n",
        "    interactions_vec[user_id, item_id] += 1\n",
        "\n",
        "\n",
        "res = interactions_vec.sum(axis=1)\n",
        "for i in range(len(interactions_vec)):\n",
        "    interactions_vec[i] /= res[i]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kq1tvfT8GOX9",
        "outputId": "e915eeeb-5dbd-494d-acaf-6f5dfca083bb"
      },
      "source": [
        "print(interactions_df.item_id.nunique())\n",
        "print(items_ohe_df.item_id.nunique())\n",
        "print(interactions_df.user_id.nunique())\n",
        "print(users_ohe_df.user_id.nunique())\n",
        "\n",
        "set(items_ohe_df.item_id.unique()) - set(interactions_df.item_id.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6141\n",
            "6141\n",
            "44963\n",
            "44963\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUaLBoUrsEge"
      },
      "source": [
        "Для того, чтобы можно было удобно превратить iid/uid в item_id/user_id и наоборот соберем словари \n",
        "\n",
        "{iid: item_id}, {uid: user_id} и {item_id: iid}, {user_id: uid}."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLpM_WZoahwl"
      },
      "source": [
        "iid_to_item_id = interactions_df[[\"iid\", \"item_id\"]].drop_duplicates().set_index(\"iid\").to_dict()[\"item_id\"]\n",
        "item_id_to_iid = interactions_df[[\"iid\", \"item_id\"]].drop_duplicates().set_index(\"item_id\").to_dict()[\"iid\"]\n",
        "\n",
        "uid_to_user_id = interactions_df[[\"uid\", \"user_id\"]].drop_duplicates().set_index(\"uid\").to_dict()[\"user_id\"]\n",
        "user_id_to_uid = interactions_df[[\"uid\", \"user_id\"]].drop_duplicates().set_index(\"user_id\").to_dict()[\"uid\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6xsMB9Hs0Xg"
      },
      "source": [
        "И проиндексируем датасеты users_ohe_df и items_ohe_df по внутренним айди:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnwnPYDsApKo"
      },
      "source": [
        "items_ohe_df[\"iid\"] = items_ohe_df[\"item_id\"].apply(lambda x: item_id_to_iid[x])\n",
        "items_ohe_df = items_ohe_df.set_index(\"iid\")\n",
        "\n",
        "users_ohe_df[\"uid\"] = users_ohe_df[\"user_id\"].apply(lambda x: user_id_to_uid[x])\n",
        "users_ohe_df = users_ohe_df.set_index(\"uid\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdf6VSSP-ljv"
      },
      "source": [
        "# DSSM starter's pack"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hd5Op9Dv-_5p"
      },
      "source": [
        "Что нужно для того, чтобы собрать плюс-минус любую нейронку?\n",
        "- функция потерь\n",
        "- генератор\n",
        "- понимание архитектуры модели\n",
        "\n",
        "Начнем с функции потерь:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRxMxpy_mzYB"
      },
      "source": [
        "## Triplet loss\n",
        "\n",
        "Triplet loss работает очень просто. Нужно всего лишь..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJWyGGhcUAbZ"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ub4KK0PQ_0Hh"
      },
      "source": [
        "Возьмем вектор юзера (anchor) и векторы двух айтемов - \"хорошего\" и \"плохого\" (positive и negative). Хороший айтем - это тот, который пользователь уже посмотрел, а в качестве плохого возьмем любой случайный айтем из датасета. Затем посчитаем расстояния:\n",
        "1. между вектором юзера и вектором \"хорошего\" айтема\n",
        "2. между вектором юзера и вектором \"плохого\" айтема\n",
        "\n",
        "_Значением функции потерь будет разность между первым и вторым расстоянием._"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcsYLEwqdlht"
      },
      "source": [
        "def triplet_loss(y_true, y_pred, n_dims=128, alpha=0.4):\n",
        "    # будем ожидать, что на вход функции прилетит три сконкатенированных \n",
        "    # вектора - вектор юзера и два вектора айтема\n",
        "    anchor = y_pred[:, 0:n_dims]\n",
        "    positive = y_pred[:, n_dims:n_dims*2]\n",
        "    negative = y_pred[:, n_dims*2:n_dims*3]\n",
        "\n",
        "    # считаем расстояния от вектора юзера до вектора хорошего айтема\n",
        "    pos_dist = K.sum(K.square(anchor - positive), axis=1)\n",
        "    # и до плохого\n",
        "    neg_dist = K.sum(K.square(anchor - negative), axis=1)\n",
        "\n",
        "    # считаем лосс\n",
        "    basic_loss = pos_dist - neg_dist + alpha\n",
        "    loss = K.maximum(basic_loss, 0.0) # возвращаем ноль, если лосс отрицательный\n",
        " \n",
        "    return loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKUyXS_qCGwe"
      },
      "source": [
        "*- \"а что такое __n_dims__?\"*\n",
        "\n",
        "Это параметр n_dims это размерность внутреннего пространства модели\n",
        "\n",
        "\n",
        "*- \"а что за __alpha__?\"*\n",
        "\n",
        "alpha это гиперпараметр. Если бы его не было, то basic_loss обращался бы в ноль, когда neg_dist = pos_dist и обучения бы на этом примере не происходило. \n",
        "\n",
        "\n",
        "*- \"как выбрать __alpha__?\"*\n",
        "\n",
        "Как известно, в подборе гиперпараметров нет каких-то железных правил. Но если подумать, то можно установить некоторые зависимости alpha от n_dims и регуляризации в слоях нейронки. Например, чем сильнее регуляризация, тем меньше будут расстояния между векторами, и тем меньше нужно установить alpha. И так же, чем больше внутренняя размерность модели, тем меньше расстояния между векторами. Опять же, получается тем меньше нужно установить alpha.\n",
        "\n",
        "Если вы хотите больше узнать про триплет лосс - рекомендуем прочитать вот эти две статьи: [[1](https://towardsdatascience.com/lossless-triplet-loss-7e932f990b24)]\n",
        "и [[2](https://medium.com/@enoshshr/triplet-loss-and-siamese-neural-networks-5d363fdeba9b)]\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QfbI-hCIm5pv"
      },
      "source": [
        "## Генератор и семплирование\n",
        "\n",
        "Сделаем простой генератор. Он будет брать рандромного юзера, и два разных айтема - хороший пример и плохой:\n",
        "- хорошим примером будет тот айтем, который был взят из датасета взаимодействий в соответствии с распределением просмотренных айтемов для этого юзера;\n",
        "- а плохим айтемом будет просто любой другой _случайный айтем_*\n",
        "\n",
        "\n",
        "\\* _подумайте, как можно использовать информацию о качестве взаимодействия юзеров с айтемами для более репрезентативного сэмплирования?_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5AV42Q2m16m"
      },
      "source": [
        "def generator(items, users, interactions, batch_size=1024):\n",
        "    while True:\n",
        "        uid_meta = []\n",
        "        uid_interaction = []\n",
        "        pos = []\n",
        "        neg = []\n",
        "        for _ in range(batch_size):\n",
        "            # берем рандомный uid\n",
        "            uid_i = randint(0, interactions.shape[0]-1)\n",
        "            # id хорошего айтема\n",
        "            pos_i = np.random.choice(range(interactions.shape[1]), p=interactions[uid_i])\n",
        "            # id плохого айтема\n",
        "            neg_i = np.random.choice(range(interactions.shape[1]))\n",
        "            # фичи юзера\n",
        "            uid_meta.append(users.iloc[uid_i])\n",
        "            # вектор айтемов, с которыми юзер взаимодействовал\n",
        "            uid_interaction.append(interactions_vec[uid_i])\n",
        "            # фичи хорошего айтема\n",
        "            pos.append(items.iloc[pos_i])\n",
        "            # фичи плохого айтема\n",
        "            neg.append(items.iloc[neg_i])\n",
        "            \n",
        "        yield [np.array(uid_meta), np.array(uid_interaction), np.array(pos), np.array(neg)], [np.array(uid_meta), np.array(uid_interaction)]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rg5p_s-gacgo"
      },
      "source": [
        "Посмотрим, что получается:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vngi_esIFYFU",
        "outputId": "17ef4575-1a7a-47f3-e3ae-ebdee0cff69c"
      },
      "source": [
        "# инициализируем генератор\n",
        "gen = generator(items=items_ohe_df.drop([\"item_id\"], axis=1), \n",
        "                users=users_ohe_df.drop([\"user_id\"], axis=1), \n",
        "                interactions=interactions_vec)\n",
        "\n",
        "ret = next(gen)\n",
        "\n",
        "\n",
        "print(f\"вектор фичей юзера: {ret[0][0].shape}\")\n",
        "print(f\"вектор взаимодействий юзера с айтемами: {ret[0][1].shape}\")\n",
        "print(f\"вектор 'хорошего' айтема: {ret[0][2].shape}\")\n",
        "print(f\"вектор 'плохого' айтема: {ret[0][3].shape}\")\n",
        "print()\n",
        "print(f\"вектор фичей юзера: {ret[1][0].shape}\")\n",
        "print(f\"вектор взаимодействий юзера с айтемами: {ret[1][1].shape}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "вектор фичей юзера: (1024, 16)\n",
            "вектор взаимодействий юзера с айтемами: (1024, 6141)\n",
            "вектор 'хорошего' айтема: (1024, 8829)\n",
            "вектор 'плохого' айтема: (1024, 8829)\n",
            "\n",
            "вектор фичей юзера: (1024, 16)\n",
            "вектор взаимодействий юзера с айтемами: (1024, 6141)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "di5PdRcjAYzs"
      },
      "source": [
        "## Собираем модель"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CguhBapzx3YI"
      },
      "source": [
        "Для того, чтобы обучить модель используя триплет лосс, нам нужно получить три вектора - вектор юзера, вектор \"хорошего\" айтема и вектор \"плохого\" айтема. Для этого нам нужно две модели (\"хороший\" и \"плохой\" айтем будут семплироваться одной и той же моделью).  \n",
        "\n",
        "Модель юзера будет иметь два входа:\n",
        "- вход для фичей юзера (фичи из users_ohe_df)\n",
        "- вход для вектора айтемов, которые посмотрел юзер (строка interactions_vec, которая соответствует uid конкретного юзера)\n",
        "\n",
        "Выход модели юзера будет размерностью N_FACTORS.\n",
        "\n",
        "У модели айтема будет один вход для фичей айтема (из items_ohe_df) и один выход также размерностью N_FACTORS.\n",
        "\n",
        "Общая архитектура будет вот такой: \n",
        "- есть модель юзера и модель айтема\n",
        "- обе модели семплируют юзер и айтем-фичи во внутреннее пространство размерностью N_FACTORS\n",
        "- модель айтема семплирует два айтема - \"хороший\" и \"плохой\"\n",
        "- в итоге получается три вектора размерностью N_FACTORS (вектор юзера, вектор \"хорошего\" айтема и вектор \"плохого\" айтема)\n",
        "- затем полученные векторы конкатенируются, по ним считается triplet loss\n",
        "- profit\n",
        "\n",
        "Для того, чтобы собрать модель, помимо размерности внутреннего пространства, нам нужно знать еще размерность вектора юзера и вектора айтема. Зададим их сразу."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wo88C_skVsyf",
        "outputId": "2243b8fa-0308-4aa2-eb79-73bd2f39ce5d"
      },
      "source": [
        "N_FACTORS = 128\n",
        "\n",
        "# в датасетах есть столбец user_id/item_id, помним, что он не является фичей для обучения!\n",
        "ITEM_MODEL_SHAPE = (items_ohe_df.drop([\"item_id\"], axis=1).shape[1], ) \n",
        "USER_META_MODEL_SHAPE = (users_ohe_df.drop([\"user_id\"], axis=1).shape[1], )\n",
        "\n",
        "USER_INTERACTION_MODEL_SHAPE = (interactions_vec.shape[1], )\n",
        "\n",
        "print(f\"N_FACTORS: {N_FACTORS}\")\n",
        "print(f\"ITEM_MODEL_SHAPE: {ITEM_MODEL_SHAPE}\")\n",
        "print(f\"USER_META_MODEL_SHAPE: {USER_META_MODEL_SHAPE}\")\n",
        "print(f\"USER_INTERACTION_MODEL_SHAPE: {USER_INTERACTION_MODEL_SHAPE}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "N_FACTORS: 128\n",
            "ITEM_MODEL_SHAPE: (8829,)\n",
            "USER_META_MODEL_SHAPE: (16,)\n",
            "USER_INTERACTION_MODEL_SHAPE: (6141,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bf1W16ICnCKE"
      },
      "source": [
        "def item_model(n_factors=N_FACTORS):\n",
        "    # входной слой\n",
        "    inp = keras.layers.Input(shape=ITEM_MODEL_SHAPE)\n",
        "    \n",
        "    # полносвязный слой\n",
        "    layer_1 = keras.layers.Dense(N_FACTORS, activation='elu', use_bias=False,\n",
        "                               kernel_regularizer=keras.regularizers.l2(1e-6),\n",
        "                               activity_regularizer=keras.regularizers.l2(l2=1e-6))(inp)\n",
        "\n",
        "    # делаем residual connection - складываем два слоя, \n",
        "    # чтобы градиенты не затухали во время обучения\n",
        "    layer_2 = keras.layers.Dense(N_FACTORS, activation='elu', use_bias=False,\n",
        "                             kernel_regularizer=keras.regularizers.l2(1e-6),\n",
        "                             activity_regularizer=keras.regularizers.l2(l2=1e-6))(layer_1)\n",
        "    \n",
        "    add = keras.layers.Add()([layer_1, layer_2])\n",
        "    \n",
        "    # выходной слой\n",
        "    out = keras.layers.Dense(N_FACTORS, activation='linear', use_bias=False,\n",
        "                             kernel_regularizer=keras.regularizers.l2(1e-6),\n",
        "                             activity_regularizer=keras.regularizers.l2(l2=1e-6))(add)\n",
        "    \n",
        "    return keras.models.Model(inp, out)\n",
        "\n",
        "\n",
        "def user_model(n_factors=N_FACTORS):\n",
        "    # входной слой для вектора фичей юзера (из users_ohe_df)\n",
        "    inp_meta = keras.layers.Input(shape=USER_META_MODEL_SHAPE)\n",
        "    # входной слой для вектора просмотров (из iteractions_vec)\n",
        "    inp_interaction = keras.layers.Input(shape=USER_INTERACTION_MODEL_SHAPE)\n",
        "\n",
        "    # полносвязный слой\n",
        "    layer_1_meta = keras.layers.Dense(N_FACTORS, activation='elu', use_bias=False,\n",
        "                                 kernel_regularizer=keras.regularizers.l2(1e-6),\n",
        "                                 activity_regularizer=keras.regularizers.l2(l2=1e-6))(inp_meta)\n",
        "\n",
        "    layer_1_interaction = keras.layers.Dense(N_FACTORS, activation='elu', use_bias=False,\n",
        "                                 kernel_regularizer=keras.regularizers.l2(1e-6),\n",
        "                                 activity_regularizer=keras.regularizers.l2(l2=1e-6))(inp_interaction)\n",
        "\n",
        "    # делаем residual connection - складываем два слоя,\n",
        "    # чтобы градиенты не затухали во время обучения\n",
        "    layer_2_meta = keras.layers.Dense(N_FACTORS, activation='elu', use_bias=False,\n",
        "                                 kernel_regularizer=keras.regularizers.l2(1e-6),\n",
        "                                 activity_regularizer=keras.regularizers.l2(l2=1e-6))(layer_1_meta)\n",
        "    \n",
        "\n",
        "    add = keras.layers.Add()([layer_1_meta, layer_2_meta])\n",
        "    \n",
        "    # конкатенируем вектор фичей с вектором просмотров\n",
        "    concat_meta_interaction = keras.layers.Concatenate()([add, layer_1_interaction])\n",
        "    \n",
        "    # выходной слой\n",
        "    out = keras.layers.Dense(N_FACTORS, activation='linear', use_bias=False,\n",
        "                             kernel_regularizer=keras.regularizers.l2(1e-6),\n",
        "                             activity_regularizer=keras.regularizers.l2(l2=1e-6))(concat_meta_interaction)\n",
        "    \n",
        "    return keras.models.Model([inp_meta, inp_interaction], out)\n",
        "\n",
        "# инициализируем модели юзера и айтема\n",
        "i2v = item_model()\n",
        "u2v = user_model()\n",
        "\n",
        "# вход для вектора фичей юзера (из users_ohe_df)\n",
        "ancor_meta_in = keras.layers.Input(shape=USER_META_MODEL_SHAPE)\n",
        "# вход для вектора просмотра юзера (из interactions_vec)\n",
        "ancor_interaction_in = keras.layers.Input(shape=USER_INTERACTION_MODEL_SHAPE)\n",
        "\n",
        "# вход для вектора \"хорошего\" айтема\n",
        "pos_in = keras.layers.Input(shape=ITEM_MODEL_SHAPE)\n",
        "# вход для вектора \"плохого\" айтема\n",
        "neg_in = keras.layers.Input(shape=ITEM_MODEL_SHAPE)\n",
        "\n",
        "# получаем вектор юзера\n",
        "ancor = u2v([ancor_meta_in, ancor_interaction_in])\n",
        "# получаем вектор \"хорошего\" айтема\n",
        "pos = i2v(pos_in)\n",
        "# получаем вектор \"плохого\" айтема\n",
        "neg = i2v(neg_in)\n",
        "\n",
        "# конкатенируем полученные векторы\n",
        "res = keras.layers.Concatenate(name=\"concat_ancor_pos_neg\")([ancor, pos, neg])\n",
        "\n",
        "# собираем модель\n",
        "model = keras.models.Model([ancor_meta_in, ancor_interaction_in, pos_in, neg_in], res)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCYWk9DnBl4X"
      },
      "source": [
        "*- а почему не Pytorch?*\n",
        "\n",
        "> В нашей команде звезды сошлись так, что мы пишем нейронки на TF. Какой-то особенной причины нет, если вы привыкли писать на торче - вэлком! Модель, которая реализована ниже, имеет очень простую архитектуру, и вы с легкостью сможете воспроизвести ее на любом другом фреймворке.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xV-7rtBanFYd"
      },
      "source": [
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrlCzs1R3FhM"
      },
      "source": [
        "Определяем традиционные коллбэки для обучения.\n",
        "Коллбэк - это какая-то функция, которая отрабатывает в начале или в конце эпохи."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpCgG446opA0"
      },
      "source": [
        "model_name = 'recsys_resnet_linear'\n",
        "\n",
        "# логируем процесс обучения в тензорборд\n",
        "t_board = keras.callbacks.TensorBoard(log_dir=f'runs/{model_name}')\n",
        "\n",
        "# уменьшаем learning_rate, если лосс долго не уменьшается (в течение двух эпох)\n",
        "decay = keras.callbacks.ReduceLROnPlateau(monitor='loss', patience=2, factor=0.8, verbose=1)\n",
        "\n",
        "# сохраняем модель после каждой эпохи, если лосс уменьшился\n",
        "check = keras.callbacks.ModelCheckpoint(filepath=model_name + '/epoch{epoch}-{loss:.2f}.h5', monitor=\"loss\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Or6ov-Z3txf"
      },
      "source": [
        "# компилируем модель, используем оптимайзер Adam и triplet loss\n",
        "opt = keras.optimizers.Adam(lr=0.001)\n",
        "model.compile(loss=triplet_loss, optimizer=opt)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBuMcGp-ZKxt"
      },
      "source": [
        "Посмотрим, что получилось:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAQiR8yIdfCG",
        "outputId": "f8592763-6eec-46aa-8dc2-1f8a0d38009a"
      },
      "source": [
        "# модель айтема\n",
        "item_model().summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_20\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_37 (InputLayer)           [(None, 8829)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_58 (Dense)                (None, 128)          1130112     input_37[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_59 (Dense)                (None, 128)          16384       dense_58[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_17 (Add)                    (None, 128)          0           dense_58[0][0]                   \n",
            "                                                                 dense_59[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_60 (Dense)                (None, 128)          16384       add_17[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 1,162,880\n",
            "Trainable params: 1,162,880\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BhphMaOidoMC",
        "outputId": "b4742635-be53-4062-d2eb-cae3f6ed18f1"
      },
      "source": [
        "# модель юзера\n",
        "user_model().summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_21\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_38 (InputLayer)           [(None, 16)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_61 (Dense)                (None, 128)          2048        input_38[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_63 (Dense)                (None, 128)          16384       dense_61[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "input_39 (InputLayer)           [(None, 6141)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "add_18 (Add)                    (None, 128)          0           dense_61[0][0]                   \n",
            "                                                                 dense_63[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_62 (Dense)                (None, 128)          786048      input_39[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_7 (Concatenate)     (None, 256)          0           add_18[0][0]                     \n",
            "                                                                 dense_62[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_64 (Dense)                (None, 128)          32768       concatenate_7[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 837,248\n",
            "Trainable params: 837,248\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TG7nnDa731rt",
        "outputId": "066ad6ea-90fc-4499-8d13-8c232d51d4e6"
      },
      "source": [
        "# общая модель\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_19\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_29 (InputLayer)           [(None, 16)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_30 (InputLayer)           [(None, 6141)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_31 (InputLayer)           [(None, 8829)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_32 (InputLayer)           [(None, 8829)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_16 (Functional)           (None, 128)          837248      input_29[0][0]                   \n",
            "                                                                 input_30[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "model_17 (Functional)           (None, 128)          1162880     input_31[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "model_18 (Functional)           (None, 128)          1162880     input_32[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concat_ancor_pos_neg (Concatena (None, 384)          0           model_16[0][0]                   \n",
            "                                                                 model_17[0][0]                   \n",
            "                                                                 model_18[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 3,163,008\n",
            "Trainable params: 3,163,008\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgvkgmYyowBM"
      },
      "source": [
        "# начинаем обучение, не забывая дропнуть столбцы item_id и user_id \n",
        "# из датафреймов при инициализации генератора.\n",
        "\n",
        "# batch_size можно (и лучше) поставить побольше, если вы не органичены в ресурсах\n",
        "\n",
        "model.fit(generator(items=items_ohe_df.drop([\"item_id\"], axis=1), \n",
        "                    users=users_ohe_df.drop([\"user_id\"], axis=1), \n",
        "                    interactions=interactions_vec,\n",
        "                    batch_size=64), \n",
        "          steps_per_epoch=100, \n",
        "          epochs=30, \n",
        "          initial_epoch=0,\n",
        "          callbacks=[decay, t_board, check]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wvfbqwj7o-cj"
      },
      "source": [
        "# Inference\n",
        "\n",
        "Отлично! Мы подготовили данные, собрали модель по архитектуре DSSM и обучили ее.\n",
        "Теперь возьмем случайного юзера и случайный айтем. Как понять, насколько этот айтем релевантен юзеру?\n",
        "\n",
        "Нужно:\n",
        "- получить вектор айтема;\n",
        "- получить вектор юзера;\n",
        "- посчитать расстояние между ними.\n",
        "\n",
        "Это расстояние и есть мера релевантности.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSgreYCUWgjP",
        "outputId": "36ced8d0-34e1-4f18-ea90-799dc3316bfb"
      },
      "source": [
        "# берем рандомного юзера\n",
        "rand_uid = np.random.choice(list(users_ohe_df.index))\n",
        "\n",
        "# получаем фичи юзера и вектор его просмотров айтемов\n",
        "user_meta_feats = users_ohe_df.drop([\"user_id\"], axis=1).iloc[rand_uid]\n",
        "user_interaction_vec = interactions_vec[rand_uid]\n",
        "\n",
        "# берем рандомный айтем\n",
        "rand_iid = np.random.choice(list(items_ohe_df.index))\n",
        "# получаем фичи айтема\n",
        "item_feats = items_ohe_df.drop([\"item_id\"], axis=1).iloc[rand_iid]\n",
        "\n",
        "# получаем вектор юзера\n",
        "user_vec = u2v.predict([np.array(user_meta_feats).reshape(1, -1), \n",
        "                        np.array(user_interaction_vec).reshape(1, -1)])\n",
        "\n",
        "# и вектор айтема\n",
        "item_vec = i2v.predict(np.array(item_feats).reshape(1, -1))\n",
        "\n",
        "# считаем расстояние между вектором юзера и вектором айтема\n",
        "from sklearn.metrics.pairwise import euclidean_distances as ED\n",
        "\n",
        "ED(user_vec, item_vec)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.5728612]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97V41Y9TzR10"
      },
      "source": [
        "Окей, получилось примерно два с половиной папугая: \n",
        "<div>\n",
        "<img 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\" width=\"200\"/>\n",
        "</div>\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwwF59aL48fG"
      },
      "source": [
        "Что дальше? Как понять, 2.5 это релевантно или нет?\n",
        "Очень просто: возьмем все айтемы, которые у нас есть, получим векторы для всех из них, а потом посчитаем расстояния от вектора юзера до векторов всех айтемов:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgWX7_rPYETl"
      },
      "source": [
        "# получаем фичи всех айтемов\n",
        "items_feats = items_ohe_df.drop([\"item_id\"], axis=1).to_numpy()\n",
        "# получаем векторы всех айтемов\n",
        "items_vecs = i2v.predict(items_feats)\n",
        "\n",
        "# считаем расстояния\n",
        "dists = ED(user_vec, items_vecs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzPfBgtUs2wx"
      },
      "source": [
        "Теперь мы знаем расстояния от юзера до всех айтемов. Значит, мы легко сможем найти для юзера например топ 5 релевантных айтемов:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JeWj30vw6uop",
        "outputId": "dda75f52-abbb-40d5-9707-53d91d46565f"
      },
      "source": [
        "top5_iids = np.argsort(dists, axis=1)[0][:5]\n",
        "top5_iids"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3191, 5380, 3267,  828, 4935])"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvtbfsbW7yef"
      },
      "source": [
        "Осталось конвертировать внутренние iid в ~~id здорового человека~~ item_id:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQDiYBlW7kTo",
        "outputId": "6e60fb7a-8285-40ec-ebdb-462980db07d3"
      },
      "source": [
        "top5_item_ids = [iid_to_item_id[iid] for iid in top5_iids]\n",
        "top5_item_ids"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[8693, 14500, 8880, 2323, 13337]"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0WQZGcq8Pdk",
        "outputId": "dae3abe6-607d-433f-f075-3be1613ef730"
      },
      "source": [
        "recommended_titles = items_df.loc[items_df.item_id.isin(top5_item_ids)].title\n",
        "recommended_titles"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "666      Форсаж: Хоббс и Шоу\n",
              "2916            Цена страсти\n",
              "4249                  Бараны\n",
              "7564                  Глотай\n",
              "11471    Смертоносная жертва\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30tkomWw8oDN",
        "outputId": "1c8098c2-948a-45ee-8c00-c76f8bd4a6f1"
      },
      "source": [
        "iids_viewed = np.where(interactions_vec[rand_uid] > 0)\n",
        "viewed_titles = items_df.iloc[iids_viewed].title\n",
        "viewed_titles"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "541                                                 Черта\n",
              "571                                 Искусство соблазнения\n",
              "928                                                 Кровь\n",
              "1008                                     Маленькая смерть\n",
              "1327                                        Рыцарь кубков\n",
              "1349                                              Наживка\n",
              "1629                                   Покажите мне героя\n",
              "1716                                     Как назло Сибирь\n",
              "1763                                      Бокс в хиджабах\n",
              "2100                                               Байкер\n",
              "2677                                             Чудо-Юдо\n",
              "2825                             Купание слона. Индонезия\n",
              "3165                                       Норвежский лес\n",
              "3577                                             Спасение\n",
              "3836                                    Берега моей мечты\n",
              "4182                                     Умный фитнес 2.0\n",
              "4343                                  Ворон: Ночной шепот\n",
              "4541                 Между нот, или Тантрическая симфония\n",
              "5545    Отель Мумбаи: Противостояние (с тифлокомментар...\n",
              "5696                                              Эликсир\n",
              "5984                                                 Игра\n",
              "6123                 Со дна вершины (с тифлокомментарием)\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kucanX-m8-e9"
      },
      "source": [
        "# Бонус\n",
        "\n",
        "1. Часто бывает так, что история просмотров айтемов известна далеко не для всех юзеров. Юзеры, которые пока еще никак не взаимодействовали с айтемами, называются холодными. Есть очень простой способ, как эффективно использовать модель, которую мы собрали выше, для таких холодных пользаков.\n",
        "Для этого нужно переписать генератор так, чтобы он иногда занулял вектор взаимодействий пользователя. В этом случае модель будет использовать только фичи юзера и фичи айтема, что поможет ей работать для холодных пользователей.\n",
        "\n",
        "2. Также, помимо персональных рекомендаций, можно использовать модель для item to item рекомендаций. Другими словами, можно находить айтемы, которые похожи на конкретный айтем. С помощью нашей модели, это можно сделать, используя модель айтема - похожие друг на друга фильмы будут иметь похожие вектора. Таким образом, если посчитать расстояния от вектора нужного айтема до векторов всех остальных, а потом отранжировать этот список по расстояниям, то можно легко сформировать топ N айтемов похожих на рассматриваемый."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZeG27gQUKL4"
      },
      "source": [
        "Если у вас остались вопросы, задавайте их здесь!\n",
        "Telegram: @vsgranovskiy"
      ]
    }
  ]
}