Baseline конкурса.ipynb

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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Импорт библиотек"
      ],
      "metadata": {
        "id": "f5iBcowrvgTT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install implicit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqShdtHevczV",
        "outputId": "9b72d174-08cc-41e7-ff24-deece7666807"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting implicit\n",
            "  Downloading implicit-0.6.0-cp37-cp37m-manylinux2014_x86_64.whl (18.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 18.6 MB 648 kB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from implicit) (4.64.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from implicit) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.7/dist-packages (from implicit) (1.7.3)\n",
            "Installing collected packages: implicit\n",
            "Successfully installed implicit-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.sparse import csr_matrix\n",
        "import scipy.sparse as sparse\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "# Сохранение и загрузка моделей\n",
        "import pickle\n",
        "\n",
        "# Графики\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Библиотеки для рекомендаций\n",
        "from implicit.als import AlternatingLeastSquares\n",
        "from implicit.bpr import BayesianPersonalizedRanking"
      ],
      "metadata": {
        "id": "xCPgrUJEEgaP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "793fc96b-2b4b-4805-ddb1-427fa1eef475"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/implicit/gpu/__init__.py:14: UserWarning: CUDA extension is built, but disabling GPU support because of 'Cuda Error: no CUDA-capable device is detected (/project/./implicit/gpu/utils.h:71)'\n",
            "  f\"CUDA extension is built, but disabling GPU support because of '{e}'\",\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def contest_metric(df_solution, df_grd):\n",
        "\n",
        "  \"\"\"Recall, Precision и F1-score по правилам конкурса\"\"\"\n",
        "\n",
        "  pred = set(df_solution['chb'] + '_' + df_solution['sys_numb'].values)\n",
        "  true = set(df_grd['chb'] + '_' + df_grd['sys_numb'].values)\n",
        "\n",
        "  recall = len(pred.intersection(true)) / len(true)\n",
        "  precision = len(pred.intersection(true)) / (20 * len(df_grd['chb'].unique()))\n",
        "  \n",
        "  f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "  print(f\"Recall: {round(recall, 5)}\")\n",
        "  print(f\"Precision: {round(precision, 5)}\")\n",
        "  print(f\"F1-score: {round(f1_score, 5)}\")"
      ],
      "metadata": {
        "id": "Xr1uX1EL6ap0"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Загрузка файлов"
      ],
      "metadata": {
        "id": "w33Da8I_TMCc"
      }
    },
    {
      "metadata": {
        "id": "XTFHRtl68d40",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b0a8f6a-bbe7-4dd3-e95c-4e8779afea5d"
      },
      "cell_type": "code",
      "source": [
        "# Доступ к файлам на colab\n",
        "\n",
        "import glob\n",
        "import os\n",
        "\n",
        "# Importing drive method from colab for accessing google drive\n",
        "from google.colab import drive\n",
        "\n",
        "# Mounting drive\n",
        "# This will require authentication : Follow the steps as guided\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Viewing the data in the folder present from the drive\n",
        "!ls \"/content/drive/My Drive/Colab Notebooks\""
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "baseline.ipynb\t\t\t\t  solution_full_train_high_features.csv\n",
            "items.csv\t\t\t\t  solution_full_train_no_duplicated.csv\n",
            "savefile.pickle\t\t\t\t  solution_full_train_top_10.csv\n",
            "solution.csv\t\t\t\t  solution_full_train_top_15.csv\n",
            "solution_full_train_confident.csv\t  solution_train_high_features.csv\n",
            "solution_full_train_confident_True_2.csv  train_transactions_extended.csv\n",
            "solution_full_train_confident_True.csv\t  Untitled0.ipynb\n",
            "solution_full_train.csv\t\t\t  users.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Меняем домашнюю директорию\n",
        "# для доступа к датасетам\n",
        "os.chdir(\"/content/drive/My Drive/Colab Notebooks\")"
      ],
      "metadata": {
        "id": "7lfTaeUtQncv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cчитывание данных\n",
        "users = pd.read_csv('./users.csv', sep=';', index_col=None, dtype={'age': str, 'chb': str, 'chit_type': str, 'gender': str})\n",
        "items = pd.read_csv('items.csv', sep=';', index_col=None, dtype={'author': str, 'bbk': str, 'izd': str, 'sys_numb': str, 'title': str, 'year_izd': str})\n",
        "transactions = pd.read_csv('train_transactions_extended.csv', sep=';', index_col=None, dtype={'chb': str, 'date_1': str, 'is_printed': str, 'is_real': str, 'source': str, 'sys_numb': str, 'type': str})"
      ],
      "metadata": {
        "id": "8mfp-NaFD6KW"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ЭТАП 1. ALS - Базовая модель для Colloborative Filtering\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mkwb9bcnSznP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Вводные**"
      ],
      "metadata": {
        "id": "oeFGsy1ILy-G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Результаты первых испытаний\n",
        "\n",
        "1. В целом результаты ALS-модели оказались достаточно низкими \n",
        "  - подтвердилась наша гипотеза о новых пользователях\n",
        "\n",
        "2.  На тесте платформы мы получили F1-Score = 0.000690, а на кросс-валидации 0.00514\n",
        "\n",
        "  - возможно, стоит изменить подход к разбиению выборки и включить всех читателей в train и test\n",
        "\n",
        "3. Общее время обучения модели ~2-3 минуты, подбор рекомендаций очень долгий ~ 6 минут.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Bih0ZsylLLvR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### План работы\n",
        "\n",
        "- Подготовить функции для экспериментов с моделями и рекомендаций\n",
        "\n",
        "- Попробовать улучшить качество базовой модели\n"
      ],
      "metadata": {
        "id": "v63N16xGLqQY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Подготовка данных"
      ],
      "metadata": {
        "id": "eaHQcRcFTCGI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Словари для маппинга ID"
      ],
      "metadata": {
        "id": "uXyCXNuNmQGd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Информация о датасетах библиотеки:\\n\")\n",
        "print(f\"Кол-во пользователей: {len(transactions['chb'].unique())}\")\n",
        "print(f\"Кол-во документов в истории пользователей: {len(transactions['sys_numb'].unique())}\")\n",
        "print(f\"Общее кол-во документов: {len(items['sys_numb'].unique())}\")"
      ],
      "metadata": {
        "id": "C6L-vQgH5Sbe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea7fc42c-8a5d-4e62-f3c6-bf1d4ad4bb00"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Информация о датасетах библиотеки:\n",
            "\n",
            "Кол-во пользователей: 16753\n",
            "Кол-во документов в истории пользователей: 194666\n",
            "Общее кол-во документов: 354355\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Предварительно удаляем дубликаты\n",
        "df_ML = transactions[['chb', 'sys_numb']].drop_duplicates()"
      ],
      "metadata": {
        "id": "ixLm1GPOotdI"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "# Словари для отображения индексов и идентификаторов \n",
        "# для матриц с users и books\n",
        "user_find_index = {chb_number: index for index, chb_number in enumerate(transactions['chb'].unique())}\n",
        "book_find_index = {sys_number: index for index, sys_number in enumerate(items['sys_numb'].unique())}\n",
        "\n",
        "user_find_chb = {index: chb_number for index, chb_number in enumerate(transactions['chb'].unique())}\n",
        "book_find_numb = {index: sys_number for index, sys_number in enumerate(items['sys_numb'].unique())}\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Словари для получения идексов user и book (для модели)\n",
        "find_user_index = {user_name: index for index, user_name \n",
        "                   in enumerate(df_ML['chb'].unique())}\n",
        "\n",
        "find_book_index = {item_name: index for index, item_name \n",
        "                     in enumerate(items['sys_numb'].unique())}\n",
        "\n",
        "# Словари для поиска user и book по индексу (для обратного маппинга)\n",
        "find_user_chb = {index: user_name for index, user_name \n",
        "                    in enumerate(df_ML['chb'].unique())}\n",
        "\n",
        "find_book_numb = {index: book_name for index, book_name \n",
        "                    in enumerate(items['sys_numb'].unique())}\n",
        "\n",
        "model_dicts = {'user_index': find_user_index ,\n",
        "               'book_index': find_book_index,\n",
        "               'user_chb': find_user_chb ,\n",
        "               'book_numb': find_book_numb,\n",
        "               }"
      ],
      "metadata": {
        "id": "o9n3aWsU4Qp_"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Теперь все 4 словаря собраны в одном, так мы их не потеряем и сможем быстро передавать в функции"
      ],
      "metadata": {
        "id": "agtZqIORpECj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверяем, как работают словари\n",
        "\n",
        "print(\"Пример поиска со словарем\\n\")\n",
        "print(\"Индекс пользователя '300001020830':\",\n",
        "      f\"{model_dicts['user_index']['300001020830']}\")\n",
        "\n",
        "print(\"Идентификатор пользователя с индексом 5:\",\n",
        "      f\"{model_dicts['user_chb'][5]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0gaq-SoobnU",
        "outputId": "3ffc8aaa-68e0-4748-d5a4-6cc38774316e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Пример поиска со словарем\n",
            "\n",
            "Индекс пользователя '300001020830': 12581\n",
            "Идентификатор пользователя с индексом 5: 100000681262\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Разделение на выборки"
      ],
      "metadata": {
        "id": "EdeW1IFgnGWo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Для равномерного распределения пользователей по датасетам попробуем добавить параметр `stratify`"
      ],
      "metadata": {
        "id": "3DPq2GZjr79D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Делим данные на тренировочный и тестовый наборы\n",
        "train_data, test_data = train_test_split(\n",
        "    df_ML,\n",
        "    test_size=0.3, \n",
        "    stratify=df_ML['chb'])"
      ],
      "metadata": {
        "id": "hLBb6KL_8H8U"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Проверим количество пользователей в выборках"
      ],
      "metadata": {
        "id": "f5odsUUqpsPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Кол-во уникальных пользователей: {len(df_ML['chb'].unique())}\")\n",
        "print(f\"Кол-во уникальных пользвоателей в выборке для обучения: {len(train_data['chb'].unique())}\")\n",
        "print(f\"Кол-во уникальных пользвоателей в выборке для тестирования: {len(test_data['chb'].unique())}\")"
      ],
      "metadata": {
        "id": "dSyFiABD8PQX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "246239e5-9a0a-43e1-b12f-4366e49ed09a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Кол-во уникальных пользователей: 16753\n",
            "Кол-во уникальных пользвоателей в выборке для обучения: 16753\n",
            "Кол-во уникальных пользвоателей в выборке для тестирования: 16753\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Отлично! Такой вариант подойдет для первых экспериментов\n",
        "\n",
        "- Далее можно будет воспользоваться подходом \"leave-one-out\""
      ],
      "metadata": {
        "id": "iswA3JklqAs6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Sparse матрица (users, books)"
      ],
      "metadata": {
        "id": "To_FpEmvB5GB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Добавим в параметры функции словари, чтобы не использовать внешние переменные"
      ],
      "metadata": {
        "id": "XH9RhUfZqy98"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Создаем sparse матрицу\n",
        "# используем датафреймы с маппингом\n",
        "def df_to_sparse(df, user_index_dict, item_index_dict):\n",
        "  row = []\n",
        "  col = []\n",
        "  data = []\n",
        "  model_user_list = []\n",
        "  for line in df.itertuples():\n",
        "    row.append(user_index_dict[line.chb])\n",
        "    col.append(item_index_dict[line.sys_numb])\n",
        "    data.append(1)\n",
        "  \n",
        "  return csr_matrix((data, (row, col)))"
      ],
      "metadata": {
        "id": "uBpR_DBkB5GB"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Попробуем добавить к матрицам параметр alpha"
      ],
      "metadata": {
        "id": "ZxowgKssraeM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Создаем sparse матрицу для обучающей выборки\n",
        "train_data_sparse = df_to_sparse(train_data, \n",
        "                                 model_dicts['user_index'], \n",
        "                                 model_dicts['book_index'])\n",
        "\n",
        "# для полного train датасета\n",
        "# full_train_sparse = df_to_sparse(transactions)\n",
        "\n",
        "# Добавляем alpha\n",
        "alpha = 40\n",
        "train_data_sparse = (train_data_sparse * alpha).astype('double')\n",
        "# full_train_sparse_data = (full_train_sparse * alpha).astype('double')\n"
      ],
      "metadata": {
        "id": "sIioYN2Pnw1e"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Обучаем модель"
      ],
      "metadata": {
        "id": "Wr5ukLLVT2dD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AlternatingLeastSquares(factors=200, random_state=1234)\n",
        "model.fit(train_data_sparse)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "c1524afbfaec422386c275ecff371f63",
            "c0811415c4994b17bbb161c374a9d365",
            "c5bbe83ae7924d588e64e6b9fdd8c853",
            "6424bfe89d8a4976a5261d66b5a3e674",
            "9a196ec04f0c48b5974ad91030d36b5f",
            "2bc20c1a1ae043a7bcab1c1f69625d8a",
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            "0e75103951cb42feb50f3d004392bfaa",
            "709c2f3703b745ab8268dcc68a0127eb",
            "e0b3774178ec40c1aaceadb9b02f8921"
          ]
        },
        "id": "X62dRVnjo5RH",
        "outputId": "44a77187-ecee-439d-a132-f2055df0ec50"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/implicit/utils.py:29: UserWarning: OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading\n",
            "  \"OpenBLAS detected. Its highly recommend to set the environment variable \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1524afbfaec422386c275ecff371f63"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Можно сохранить модель, чтобы сэкномить время и ресурсы"
      ],
      "metadata": {
        "id": "Me6qdQc-smgS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('savefile.pickle', 'wb') as fle:\n",
        "    pickle.dump(model, fle, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "metadata": {
        "id": "hY_ZEBJpG3Do"
      },
      "execution_count": 306,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Формируем рекомендации\n"
      ],
      "metadata": {
        "id": "g5TqAsshTts8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Базовый подход\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "c6fINF4UHn2I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Получим рекомендации для конкретного пользователя \n",
        "userid = 5000\n",
        "ids, scores = model.recommend(userid, train_data_sparse[userid], N=30, filter_already_liked_items=False)\n",
        "\n",
        "# Отобразим рекомендации в DataFrame\n",
        "top20recom_df = pd.DataFrame({\"sys_numb\": [user_find_chb[id] for id in ids],\n",
        "                              \"score\": scores, \n",
        "                              \"already_liked\": np.in1d(ids, train_data_sparse[userid].indices)})\n",
        "\n",
        "top20recom_df"
      ],
      "metadata": {
        "id": "qsjyPGJE9iqL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_recom(userid):\n",
        "  ids, scores = model.recommend(userid, train_data_sparse[userid], \n",
        "                                N=20, filter_already_liked_items=True) \n",
        "\n",
        "  top20recom_df = pd.DataFrame({\n",
        "      \"sys_numb\": [model_dicts['book_numb'][id] for id in ids], \n",
        "      \"score\": scores,\n",
        "      \"already_liked\": np.in1d(ids, train_data_sparse[userid].indices)})\n",
        "  \n",
        "  return top20recom_df['sys_numb'].values"
      ],
      "metadata": {
        "id": "H49XugRXoZsm"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#подбор рекомендаций для всех пользователей из train\n",
        "\n",
        "def get_all_recommendations_baseline (user_item_sparse_data):\n",
        "  all_rec = []\n",
        "\n",
        "  for userid in tqdm(range(user_item_sparse_data.shape[0])):    \n",
        "    user_chb = model_dicts['user_chb'][userid]\n",
        "    user_rec = get_recom(userid)\n",
        "    for rec in user_rec:\n",
        "      all_rec.append([user_chb, rec])\n",
        "\n",
        "  return all_rec"
      ],
      "metadata": {
        "id": "pIDlJb2joZsy"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ускоряем генерацию рекомендаций"
      ],
      "metadata": {
        "id": "x2QsyDwJ3if_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Первая имплементация функции обучения работает очень медленно\n",
        "\n",
        "  - будем использовать встроенную функцию  `recommend` со списком пользователей\n",
        "  - для получения массива рекомендаций [user1_id, [item1_id, item2_id, ..]] \n",
        "\n",
        "2. Для оценки качества модели нам нужен список пар [user_id, item_id]. \n",
        "\n",
        "  - Переведем 2D-массив в 1D с помощью ravel (без циклов и создания копий)\n",
        "  - Добавим столбец с идентификаторами пользователей\n",
        "\n",
        "3. Раскодируем идентификаторы и сравним результат с набором предсказаний из исходной функции."
      ],
      "metadata": {
        "id": "z84lhTxN3tGD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Генерируем набор из ТОП-20 рекомендаций\n",
        "# для всех пользователей (на входе подаем список индексов)\n",
        "ids, scores = model.recommend(list(range(0, len(model_dicts['user_index']))), \n",
        "                              train_data_sparse, \n",
        "                              N=20, filter_already_liked_items=True)"
      ],
      "metadata": {
        "id": "6M0e_WZGvv3d"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Получили рекоменадации примерно за минуту, в 10 раз быстрее!"
      ],
      "metadata": {
        "id": "fSppHCDcu9Gj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Переводим 2D набор из рекомендованных книг в 1D список\n",
        "# Важен порядок - 20 книг для 1-го пользователя, 20 книг для 2-го пользователя и т.д.\n",
        "user_item_predictions = pd.DataFrame(ids.ravel(), columns=['book_id'])\n",
        "user_item_predictions.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "AP_oP0ED1rFG",
        "outputId": "4d94b6fe-007b-4668-97f7-a692fa5b70ca"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   book_id\n",
              "0   207072\n",
              "1     4910\n",
              "2    13484"
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>207072</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Добавляем индексы пользователей (в порядке, заданном исходной матрицей user-book)\n",
        "# первые 20 книг - 0, следующие 20 книг - 1 и т.д.\n",
        "user_item_predictions['user_id'] = user_item_predictions.index  // 20\n",
        "\n",
        "print(\"Уникальные индексы пользователей\")\n",
        "print(user_item_predictions.user_id.unique())\n",
        "print()\n",
        "\n",
        "user_item_predictions.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "MF8IQLIGUL4Q",
        "outputId": "097c5a5f-2483-489a-ebfb-175f78785da9"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Уникальные индексы пользователей\n",
            "[    0     1     2 ... 16750 16751 16752]\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   book_id  user_id        sys_numb           chb\n",
              "0   207072        0  RSL01010562870  100000641403\n",
              "1     4910        0  RSL07000372451  100000641403\n",
              "2    13484        0  RSL01002452173  100000641403"
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              "      <th></th>\n",
              "      <th>book_id</th>\n",
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              "      <th>chb</th>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-748319f2-d471-4cf1-b2f0-3da62aeb7242 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-748319f2-d471-4cf1-b2f0-3da62aeb7242');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Добавляем столбцы с исходными идентификаторами книг и пользователей \n",
        "# используем маппинг по индексам\n",
        "user_item_predictions['sys_numb'] = user_item_predictions['book_id'].map(model_dicts['book_numb'])\n",
        "user_item_predictions['chb'] = user_item_predictions['user_id'].map(model_dicts['user_chb'])"
      ],
      "metadata": {
        "id": "7YLOLEdSC0NV"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Прежде чем использовать новый вариант функции, убедимся, что результаты полностью совпадают)"
      ],
      "metadata": {
        "id": "IpRidTLHDUnl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Для сравнения сгенерируем результаты с исходной функции\n",
        "baseline_recommendations = pd.DataFrame(\n",
        "    get_all_recommendations_baseline(train_data_sparse),\n",
        "    columns=[\"chb\", \"sys_numb\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500,
          "referenced_widgets": [
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            "0ab83a23fe6c419bb274f3fbdf12fb93"
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        "id": "g5R57KiAoSW7",
        "outputId": "3d9006cc-9ca8-44da-d0b2-d0bd94abc88c"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16753 [00:00<?, ?it/s]"
            ],
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              "model_id": "081656f067bb43378c33e4a10fd77c6a"
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        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
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          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-70-b6120d4dd2e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Для сравнения сгенерируем результаты с исходной функции\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m baseline_recommendations = pd.DataFrame(\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mget_all_recommendations_baseline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data_sparse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     columns=[\"chb\", \"sys_numb\"])\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-52-be985f6de5f5>\u001b[0m in \u001b[0;36mget_all_recommendations_baseline\u001b[0;34m(user_item_sparse_data)\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0muserid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_item_sparse_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0muser_chb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_dicts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_chb'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0muserid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0muser_rec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_recom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muserid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0muser_rec\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m       \u001b[0mall_rec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0muser_chb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrec\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-51-4ebc6ec24d9c>\u001b[0m in \u001b[0;36mget_recom\u001b[0;34m(userid)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_recom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muserid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   ids, scores = model.recommend(userid, train_data_sparse[userid], \n\u001b[0;32m----> 3\u001b[0;31m                                 N=20, filter_already_liked_items=True) \n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   top20recom_df = pd.DataFrame({\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/implicit/cpu/matrix_factorization_base.py\u001b[0m in \u001b[0;36mrecommend\u001b[0;34m(self, userid, user_items, N, filter_already_liked_items, filter_items, recalculate_user, items)\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0mfilter_query_items\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilter_query_items\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m             \u001b[0mfilter_items\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilter_items\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m             \u001b[0mnum_threads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_threads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m         )\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mtopk.pyx\u001b[0m in \u001b[0;36mimplicit.cpu.topk.topk\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mtopk.pyx\u001b[0m in \u001b[0;36mimplicit.cpu.topk._topk_batch\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/getlimits.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, dtype)\u001b[0m\n\u001b[1;32m    380\u001b[0m     \u001b[0m_finfo_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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      "source": [
        "baseline_recommendations = als_model_experiment(df_ML, model_dicts)"
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      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "for data in [user_item_predictions, baseline_recommendations]:\n",
        "  display(data.sort_values(by='user_id').head(7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "wViI8EETPDVU",
        "outputId": "08faea21-ff0c-4e29-a805-ba4eea0758c3"
      },
      "execution_count": 123,
      "outputs": [
        {
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          "data": {
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              "    book_id  user_id        sys_numb           chb\n",
              "0    207072        0  RSL01010562870  100000641403\n",
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              "    item_id  user_id           chb        sys_numb\n",
              "0    207072        0  100000641403  RSL01010562870\n",
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              "      <th>sys_numb</th>\n",
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              "      <th>0</th>\n",
              "      <td>207072</td>\n",
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              "      <td>100000641403</td>\n",
              "      <td>RSL01010562870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>198834</td>\n",
              "      <td>0</td>\n",
              "      <td>100000641403</td>\n",
              "      <td>RSL01006680743</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
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              "      <td>100000641403</td>\n",
              "      <td>RSL01010250541</td>\n",
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              "      <th>17</th>\n",
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              "      <td>100000641403</td>\n",
              "      <td>RSL01007914010</td>\n",
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              "      <th>16</th>\n",
              "      <td>21963</td>\n",
              "      <td>0</td>\n",
              "      <td>100000641403</td>\n",
              "      <td>RSL01004252523</td>\n",
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              "      <th>15</th>\n",
              "      <td>81244</td>\n",
              "      <td>0</td>\n",
              "      <td>100000641403</td>\n",
              "      <td>RSL01003187450</td>\n",
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              "      <td>15056</td>\n",
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              "      <td>100000641403</td>\n",
              "      <td>RSL01003016669</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "results = pd.concat([baseline_recommendations, user_item_predictions], axis=1)\n",
        "results.columns = ['book_id', 'user_id', 'chb', 'sys_numb', 'book_id2', 'user_id2', 'chb2', 'sys_numb2']\n",
        "results.head(5)"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "dEB_wQLVUTFr",
        "outputId": "111fa9b8-8f8a-47e2-f109-eef8530f9b28"
      },
      "execution_count": 124,
      "outputs": [
        {
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          "data": {
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              "   book_id  user_id           chb        sys_numb  book_id2  user_id2  \\\n",
              "0   207072        0  100000641403  RSL01010562870    207072         0   \n",
              "1     4910        0  100000641403  RSL07000372451      4910         0   \n",
              "2    13484        0  100000641403  RSL01002452173     13484         0   \n",
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              "4   166523        0  100000641403  RSL01008275241    166523         0   \n",
              "\n",
              "             chb2     sys_numb2  \n",
              "0  RSL01010562870  100000641403  \n",
              "1  RSL07000372451  100000641403  \n",
              "2  RSL01002452173  100000641403  \n",
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        "results['index_check'] = (results['book_id'] == results['book_id2']) & ((results['user_id'] == results['user_id2']))\n",
        "results[results['index_check'] == False]"
      ],
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      },
      "execution_count": 125,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        book_id  user_id           chb        sys_numb  book_id2  user_id2  \\\n",
              "20         3279        1  100000644359  RSL01003557352      1411         1   \n",
              "21         1411        1  100000644359  RSL01010647947       310         1   \n",
              "22        21982        1  100000644359  RSL01010586376      4186         1   \n",
              "23        17256        1  100000644359  RSL01010586255     10133         1   \n",
              "24        17591        1  100000644359  RSL01010587317      5445         1   \n",
              "...         ...      ...           ...             ...       ...       ...   \n",
              "335015    38997    16750  300001172795  RSL01003786334     21141     16750   \n",
              "335016    42631    16750  300001172795  RSL01003581372     64507     16750   \n",
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              "335019    32231    16750  300001172795  RSL01003720203     40186     16750   \n",
              "\n",
              "                  chb2     sys_numb2  index_check  \n",
              "20      RSL01010647947  100000644359        False  \n",
              "21      RSL01004230805  100000644359        False  \n",
              "22      RSL07000474962  100000644359        False  \n",
              "23      RSL01010705965  100000644359        False  \n",
              "24      RSL01010559974  100000644359        False  \n",
              "...                ...           ...          ...  \n",
              "335015  RSL01005783636  300001172795        False  \n",
              "335016  RSL01000237009  300001172795        False  \n",
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              "335018  RSL01000340818  300001172795        False  \n",
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335015</th>\n",
              "      <td>38997</td>\n",
              "      <td>16750</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003786334</td>\n",
              "      <td>21141</td>\n",
              "      <td>16750</td>\n",
              "      <td>RSL01005783636</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335016</th>\n",
              "      <td>42631</td>\n",
              "      <td>16750</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003581372</td>\n",
              "      <td>64507</td>\n",
              "      <td>16750</td>\n",
              "      <td>RSL01000237009</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335017</th>\n",
              "      <td>52616</td>\n",
              "      <td>16750</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003694145</td>\n",
              "      <td>64527</td>\n",
              "      <td>16750</td>\n",
              "      <td>RSL01003552541</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335018</th>\n",
              "      <td>33993</td>\n",
              "      <td>16750</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003908477</td>\n",
              "      <td>51360</td>\n",
              "      <td>16750</td>\n",
              "      <td>RSL01000340818</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335019</th>\n",
              "      <td>32231</td>\n",
              "      <td>16750</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003720203</td>\n",
              "      <td>40186</td>\n",
              "      <td>16750</td>\n",
              "      <td>RSL01004194671</td>\n",
              "      <td>300001172795</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244675 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17610e69-ff50-4705-9b3f-5e2c811b85a7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-17610e69-ff50-4705-9b3f-5e2c811b85a7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-17610e69-ff50-4705-9b3f-5e2c811b85a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Создаем множества пар для сравнения\n",
        "\"\"\"\n",
        "fast = set(user_item_predictions['chb'] + '_' + user_item_predictions['sys_numb'].values)\n",
        "baseline = set(baseline_recommendations['chb'] + '_' + baseline_recommendations['sys_numb'].values)\n",
        "\"\"\"\n",
        "\n",
        "fast = set(str(user_item_predictions['user_id']) + '_' + str(user_item_predictions['book_id']))\n",
        "baseline = set(str(baseline_recommendations['user_id']) + '_' + str(baseline_recommendations['item_id']))\n",
        "# Находим пересечение множеств\n",
        "print(\"Совпадений со старой версией:\", len(baseline.intersection(fast)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCwODog0Feon",
        "outputId": "8eebe2f8-f379-4747-a047-5a710cdd46c8"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Совпадений со старой версией: 32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Оценка качества и сохранение результатов"
      ],
      "metadata": {
        "id": "DwGxhLm8EOwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Считаем метрики (f1-score, precision и recall)\n",
        "contest_metric(user_item_predictions, test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ighjaDXHEZN1",
        "outputId": "8728b29e-fec6-4024-de27-24b48fd4c4ef"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall: 0.0083\n",
            "Precision: 0.0017\n",
            "F1-score: 0.00282\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DataFrame для отправки решения с рекомендациями\n",
        "solution = user_item_predictions[['chb', 'sys_numb']]\n",
        "len(solution)"
      ],
      "metadata": {
        "id": "MrQNq8mBtRyQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2117aa40-0879-4525-bb9a-156dcf809280"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "335060"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Формирование csv файла для отправки на платформу\n",
        "solution.to_csv(\"solution_train_high_features.csv\", index=False, sep=';')"
      ],
      "metadata": {
        "id": "5wgnbzQs-4LH"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Набор функций для следующих этапов"
      ],
      "metadata": {
        "id": "N36hCqlUBb97"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Запишем все шаги, которые мы протестировали, в итоговый набор функций для дальнейших экспериментов."
      ],
      "metadata": {
        "id": "qW516uRAFTNG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# Функции для работы с библиотекой Implicit и методами Colloborative Filtering\n",
        "##############################################################################\n",
        "\n",
        "\n",
        "def train_test_stratified(df, user_col):\n",
        "  \"\"\"\n",
        "  Возвращает train и test, разделенные по пользователям\n",
        "  \"\"\"\n",
        "\n",
        "  # Делим данные на тренировочный и тестовый наборы\n",
        "  train, test = train_test_split(df, \n",
        "                                 test_size=0.3,\n",
        "                                 stratify=df[user_col],\n",
        "                                 random_state=777)\n",
        "  return train, test\n",
        "\n",
        "\n",
        "def df_to_sparse(df, user_index_dict, item_index_dict):\n",
        "  row = []\n",
        "  col = []\n",
        "  data = []\n",
        "  model_user_list = []\n",
        "  for line in df.itertuples():\n",
        "    row.append(user_index_dict[line.chb])\n",
        "    col.append(item_index_dict[line.sys_numb])\n",
        "    data.append(1)\n",
        "  \n",
        "  return csr_matrix((data, (row, col)))\n",
        "\n",
        "def get_item_recommendations(implicit_model, \n",
        "                             user_name_dict, item_name_dict,                               \n",
        "                             user_item_matrix,\n",
        "                             user_col='user', item_col='item',                            \n",
        "                             top_n=20):  \n",
        "  \"\"\"\n",
        "  Возвращает ТОП-N рекомендаций, используя обученную модель implicit\n",
        "  \"\"\"\n",
        "  \n",
        "  # Список идентификаторов всех пользователей\n",
        "  user_ids_list = list(range(0, len(user_name_dict)))\n",
        " \n",
        "  # Получаем набор рекомендаций для пользователей в user_ids_list\n",
        "  # Пары (user-item = 1) из матрицы user_item_matrix исключаются\n",
        "  ids, scores = implicit_model.recommend(user_ids_list, \n",
        "                                         user_item_matrix, N=top_n, \n",
        "                                         filter_already_liked_items=False)\n",
        "\n",
        "  # Переводим 2D набор items в 1D список\n",
        "  recommendations = pd.DataFrame(ids.ravel(), columns=['item_id'])\n",
        "\n",
        "  # Добавляем индексы пользователей по порядку\n",
        "  # одинаковый индекс для каждых top_n строк\n",
        "  recommendations['user_id'] = recommendations.index  // top_n\n",
        "\n",
        "  # Выполняем обратное кодирование user_id -> user, book_id -> book\n",
        "  # Сохраняем в отдельных столбцах\n",
        "  recommendations[user_col] = recommendations['user_id'].map(user_name_dict)\n",
        "  recommendations[item_col] = recommendations['item_id'].map(item_name_dict)\n",
        "  \n",
        "\n",
        "  return recommendations\n",
        "\n",
        "\n",
        "def als_model_experiment(data, \n",
        "                         mapping_dicts, \n",
        "                         alpha_value=40, factors_count=200):\n",
        "  \n",
        "  \"\"\"\n",
        "  Обучает ALS модель и выводит результат на тестовой выборке  \n",
        "  \"\"\"\n",
        "\n",
        "  # Разделяем выборки \n",
        "  train, test = train_test_stratified(data, 'chb')\n",
        "  \n",
        "  # Создаем sparse матрицу для обучающей выборки\n",
        "  train_sparse_matrix = df_to_sparse(train, \n",
        "                                     mapping_dicts['user_index'],\n",
        "                                     mapping_dicts['book_index'])\n",
        "  \n",
        "  # Умножаем матрицу на alpha_value\n",
        "  train_sparse_matrix = (train_sparse_matrix * alpha_value).astype('double')\n",
        "\n",
        "  # Обучаем модель\n",
        "  als_model = AlternatingLeastSquares(factors_count, random_state=1234)\n",
        "  als_model.fit(train_sparse_matrix)\n",
        "  \n",
        "  # Генерируем рекомендации с помощью модели\n",
        "  # Получаем набор пар {user-chb, book-sys_numb}\n",
        "  predicted_books = get_item_recommendations(als_model, \n",
        "                                             mapping_dicts['user_chb'],\n",
        "                                             mapping_dicts['book_numb'],                                              \n",
        "                                             train_sparse_matrix,\n",
        "                                             'chb', 'sys_numb')      \n",
        "                                             \n",
        "  # Считаем качество модели\n",
        "  # и выводим результат\n",
        "  # contest_metric(predicted_books, test)\n",
        "\n",
        "  return predicted_books               "
      ],
      "metadata": {
        "id": "D5OFhfIMDY9y"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Проверим работу функции для экспериментов с ALS моделью"
      ],
      "metadata": {
        "id": "_vIcTR13CIRr"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355,
          "referenced_widgets": [
            "ff8984bbee624cf2b8b20fe678547244",
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            "9a75f772e998443ab86ee3469ab342ce",
            "c12fdbd656d243d5a3fcc73f88ddbdd9",
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        "id": "p7ksbZpNCHMi",
        "outputId": "3c544fe3-e251-4223-c491-0d02c013d0d1"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff8984bbee624cf2b8b20fe678547244"
            }
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          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ZeroDivisionError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-85-2a7b4a828841>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mals_model_experiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_ML\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_dicts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-82-7bf08800b7d5>\u001b[0m in \u001b[0;36mals_model_experiment\u001b[0;34m(data, mapping_dicts, alpha_value, factors_count)\u001b[0m\n\u001b[1;32m     95\u001b[0m   \u001b[0;31m# Считаем качество модели\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m   \u001b[0;31m# и выводим результат\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m   \u001b[0mcontest_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicted_books\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mpredicted_books\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-76e01da719b7>\u001b[0m in \u001b[0;36mcontest_metric\u001b[0;34m(df_solution, df_grd)\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0mprecision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_grd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'chb'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m   \u001b[0mf1_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mprecision\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrecall\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mprecision\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrecall\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Recall: {round(recall, 5)}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Precision: {round(precision, 5)}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Получили хороший набор функций для будущего класса!"
      ],
      "metadata": {
        "id": "_BIOObLJKdrk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Общий вывод\n",
        "\n",
        "ALS модель:\n",
        "- Добавлили stratify, чтобы оценивать всех пользователей\n",
        "- Дольше всего проходит подготовка рекомендаций. Ускорили рекомендации\n",
        "- Повысили качество, изменив подход - дубликаты и filter included\n",
        "- Убедились в возможностях тюнинга модели (features_count и добавление параметра alpha). \n",
        "\n",
        "Сформировали набор функций.\n",
        "\n",
        "Общий вывод: Подтвердилась наша гипотеза. Collaborative Filtering не может дать высоких результатов, т.к. большинство пользователей брали книгу только 1 раз.\n",
        "\n",
        "Также это означает, что будеь много книг без информации о чтении (только content-based подходит)\n",
        "Попробовать выделить группы активных пользователей и сделать предсказания только для них, а для остальных использовать другой подход.\n",
        "\n",
        "**\n",
        "Попробуем другой подход MF - bpr и warp."
      ],
      "metadata": {
        "id": "MkhqQFKoGs_R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ЭТАП 2. Группы читателей. Возможности Colloborative Filtering"
      ],
      "metadata": {
        "id": "_GgFB5ueMdb6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "После проведенного исследования и создания baseline модели появилось несколько гипотез:\n",
        "\n",
        "1. Если взять читателей и книги из \"активного\" пула, то CF должен показать значительно более высокие результаты.\n",
        "\n",
        "2. В тех редких рекомендациях, которые попали в точку, находятся именно те самые \"редкие пользователи\".\n",
        "\n",
        "3. Если мы сделаем предсказания только для тех, кто находится в активном пуле (а остальных пользователей оставим без рекомендаций):\n",
        "\n",
        "  - Качество модели не ухудшится\n",
        "\n",
        "  - Возможно, станет немного выше, за счет более \"фокусного\" использования CF.\n",
        "\n",
        "Если наши гипотезы подтвердятся, тогда для остальных пользователей мы подберем другой подход с рекомендациями:)"
      ],
      "metadata": {
        "id": "AJdGnduDMi-0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Рекомендации для активного пула читателей и книг"
      ],
      "metadata": {
        "id": "qX9GAiF-kF5c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Поиск условий для фильтрации"
      ],
      "metadata": {
        "id": "hrMeH5bDkOQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Добавляем столбцы с рандомными идентификаторами книг и читателей\n",
        "# для ускорения построения графика\n",
        "transactions['user_id'] = transactions['chb'].astype(\"category\").cat.codes\n",
        "transactions['book_id'] = transactions['sys_numb'].astype(\"category\").cat.codes\n",
        "\n",
        "# Удаляем дубликаты транзакций\n",
        "unique_transactions = transactions[['user_id', 'book_id']].drop_duplicates()"
      ],
      "metadata": {
        "id": "7m2zACcE1ZVR"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Создаем новый датафрейм с активностью пользователей\n",
        "user_books_count = (\n",
        "    unique_transactions.groupby('chb')\n",
        "    .count().rename(columns={'sys_numb':'books_count'}))\n",
        "\n",
        "print(\"Количество уникальных книг, взятых каждым пользователем:\")\n",
        "user_books_count.head(7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "tEgDVJuklTTs",
        "outputId": "4c2abf81-f18a-4cdc-e30d-776d51bd1cd8"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Количество уникальных книг, взятых каждым пользователем:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              books_count\n",
              "chb                      \n",
              "100000641403            3\n",
              "100000644359           11\n",
              "100000665127           17\n",
              "100000676191            3\n",
              "100000679200            6\n",
              "100000681262           34\n",
              "100000683677            2"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Вспомним, какая активность у большинства наших *читателей*"
      ],
      "metadata": {
        "id": "XKQ_qaACn4NQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_books_count.describe()"
      ],
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        },
        "id": "w14kjUlelTqs",
        "outputId": "e0d2f924-57bd-43ab-8887-b850f79a28d7"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        books_count\n",
              "count  16753.000000\n",
              "mean      13.669313\n",
              "std       70.121205\n",
              "min        2.000000\n",
              "25%        3.000000\n",
              "50%        6.000000\n",
              "75%       12.000000\n",
              "max     6671.000000"
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              "      <td>70.121205</td>\n",
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              "      <td>2.000000</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-69a20599-3af4-4ea1-8429-8ca9aed7fa0a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-69a20599-3af4-4ea1-8429-8ca9aed7fa0a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. 50% пользователей прочитали меньше 6 книг (среди них могут быть и новички, и те, кто в целом редко читает книги)\n",
        "\n",
        "  - за нижнюю границу возьмем 6 книг\n",
        "\n",
        "2. 25% пользователей взяли больше 12 книг, но среди них есть и рекордсмены с тысячами\n",
        "\n",
        "  - попробуем найти границу для рекордсменов, которые скачивают все подряд)"
      ],
      "metadata": {
        "id": "2_7ttWalqDGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"99% пользователей прочитали < \",\n",
        "      f\"{int(user_books_count.books_count.quantile(0.99))} книг\")\n",
        "\n",
        "print(\"90% пользователей прочитали < \",\n",
        "      f\"{int(user_books_count.books_count.quantile(0.90))} книг\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3SFyhUolT5B",
        "outputId": "9e26fc91-6eb8-4731-ae80-e46ec58396b2"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99% пользователей прочитали <  111 книг\n",
            "90% пользователей прочитали <  25 книг\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Попробуем взять верхнюю границу в `111 книг`"
      ],
      "metadata": {
        "id": "ZIFDxkf1smlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filter_111 = 'books_count > 6 and books_count < 111'\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "user_books_count.query(filter_111).hist(ax=plt.gca())\n",
        "plt.title(\n",
        "    label=\"Пул активных пользователей - \"\n",
        "    + f\"{user_books_count.query(filter).shape[0]} читателей\", \n",
        "    size=16)\n",
        "plt.xlabel(\"Количество взятых книг\", fontsize=14)\n",
        "plt.ylabel(\"Количество читателей\", fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "YFH58vKVlUGa",
        "outputId": "d65b1995-fb06-4870-86e7-4e896cffcdb6"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "book_users_count = transactions.groupby('book_id').user_id.count().rename(columns={'chb':'users_count'})\n",
        "\n",
        "book_users_count = (\n",
        "    unique_transactions.groupby('book_id')\n",
        "    .count().rename(columns={'chb':'users_count'}))\n"
      ],
      "metadata": {
        "id": "xh0pXGlizs-W"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://anchormen.nl/blog/data-science-ai/putting-a-hybrid-recommendation-system-into-production/\n",
        "\n"
      ],
      "metadata": {
        "id": "jjbzaXM7v-DJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Вывод**\n",
        "\n",
        "1. Мы отобрали пользователей, которые прочитали от 6 до 111 книг (~половина)\n",
        "\n",
        "  - попробуем обучить модель с рекомендациями на этом пуле\n",
        "\n",
        "2. Мало книг не значит, что читатель неактивный, возможно, он только недавно пришел в библиотеку\n",
        "\n",
        "  - пока нам это неважно, важно количество книг для качественных предсказаний\n",
        "\n",
        "  - во время сегментации пользователей учтем этот нюанс!)"
      ],
      "metadata": {
        "id": "ATnYsplYs-pL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Обучение модели и оценка результатов\n"
      ],
      "metadata": {
        "id": "nc041PzJkYUi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unique_transactions = transactions[['chb', 'sys_numb']].drop_duplicates()\n",
        "\n",
        "# Делаем фильтрацию пользователей\n",
        "df_ML = unique_transactions[\n",
        "    unique_transactions.chb.isin(user_books_count.query(filter_111).index)\n",
        "]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "4B8oaZ827nWJ",
        "outputId": "8d8b118d-5049-403b-d1ee-f8cf3cc1b7bb"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-207-9c5f43c1e050>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m train_data, test_data = train_test_split(df_ML, \n\u001b[1;32m     10\u001b[0m                                  \u001b[0mstratify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransactions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'chb'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m                                  random_state=777)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCVClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2441\u001b[0;31m         \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstratify\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2443\u001b[0m     return list(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36msplit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m   1597\u001b[0m         \u001b[0mto\u001b[0m \u001b[0man\u001b[0m \u001b[0minteger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m         \"\"\"\n\u001b[0;32m-> 1599\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iter_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_make_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterables\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    379\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    332\u001b[0m         raise ValueError(\n\u001b[1;32m    333\u001b[0m             \u001b[0;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m             \u001b[0;34m%\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m         )\n\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [142229, 259566]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "train_data, test_data = get_train_test(df_ML)"
      ],
      "metadata": {
        "id": "4XJXU7P85O1P"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Sparse матрица (user, book)"
      ],
      "metadata": {
        "id": "3Jsry65K5O1Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Общий вывод"
      ],
      "metadata": {
        "id": "1M7UWhynlBvg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LightFM - Matrix Factorization (Baseline)"
      ],
      "metadata": {
        "id": "9qZwEPsMVp04"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Вводные"
      ],
      "metadata": {
        "id": "A9Bt4pUmA8Cx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Мы протестируем работу LightFM без добавления признаков (т.е. классический подход с Matrix Factorization для Collaborative Filtering).\n",
        "\n",
        "1. Испытаем две loss функции:\n",
        "\n",
        "  - WARP (Weighted Approximate Rank Pairwise Loss) \n",
        "  - (BPR) Bayesian Personalized Ranking  mode\n",
        "\n",
        "2. Попробуем разбить датасет с помощью built-in функции библиотеки LightFM.\n",
        "\n",
        "3. Сравним итоговые результаты с ALS и посмотрим также на значения других метрик (ROC-AUC и precision@k)\n",
        "\n",
        "Тюнингом параметров пока заниматься не будем - это baseline модель, чтобы оценить возможности нового подхода."
      ],
      "metadata": {
        "id": "oMKOF7_0_0Qh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lightfm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOcxJqr-BgWN",
        "outputId": "c59e7f4f-0b48-443f-dc4a-e1d7c3cd1dbb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting lightfm\n",
            "  Downloading lightfm-1.16.tar.gz (310 kB)\n",
            "\u001b[K     |████████████████████████████████| 310 kB 4.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lightfm) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from lightfm) (1.7.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from lightfm) (2.23.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from lightfm) (1.0.2)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (1.24.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightfm) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightfm) (3.1.0)\n",
            "Building wheels for collected packages: lightfm\n",
            "  Building wheel for lightfm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lightfm: filename=lightfm-1.16-cp37-cp37m-linux_x86_64.whl size=705367 sha256=d28afe92db23d645eb28ef3d4cc3a89059b449bde88ff24135fd6b6fb6f6a6ac\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/56/28/5772a3bd3413d65f03aa452190b00898b680b10028a1021914\n",
            "Successfully built lightfm\n",
            "Installing collected packages: lightfm\n",
            "Successfully installed lightfm-1.16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lightfm import LightFM\n",
        "from lightfm.data import Dataset\n",
        "from lightfm.cross_validation import random_train_test_split\n",
        "\n",
        "# Встроенные функции lightfm для метрик\n",
        "from lightfm.evaluation import precision_at_k\n",
        "from lightfm.evaluation import auc_score\n"
      ],
      "metadata": {
        "id": "pdW0EBxpB8JV"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Подготовка данных"
      ],
      "metadata": {
        "id": "5JMlAGzABCPw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_actions = transactions = pd.read_csv('transactions_extended.csv', sep=';', index_col=None, dtype={'chb': str, 'date_1': str, 'is_printed': str, 'is_real': str, 'source': str, 'sys_numb': str, 'type': str})\n",
        "book_actions.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pfz_vlOuFjgr",
        "outputId": "18ef2fd2-9a9a-4afa-84cf-03b54eaae523"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['chb', 'sys_numb', 'date_1', 'is_real', 'type', 'source', 'is_printed'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Создаем идентификаторы для читателей и книг\n",
        "# Внутренний датасет для lightfm\n",
        "light_dataset = Dataset()\n",
        "light_dataset.fit(book_actions['chb'], book_actions['sys_numb'])\n",
        "\n",
        "num_users, num_books = light_dataset.interactions_shape()\n",
        "print(f'Количество читатателей: {num_users}') \n",
        "print(f'Количество книг: {num_books}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DbVdPNqwCBmP",
        "outputId": "d8e904de-8464-4bde-bfbb-578cc303a0d9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Количество читатателей: 16753\n",
            "Количество книг: 194666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Отлично, осталось передать все данные о выдачах книг модели\n",
        "\n",
        "- Перед этим проверим, нет ли у нас дубликатов (кто-то повторно брал книги)"
      ],
      "metadata": {
        "id": "FPU009OFJmgq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_actions[book_actions[['chb', 'sys_numb']].duplicated()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "3wyhNWHSIIkY",
        "outputId": "bc301ed8-9d48-45e4-e10a-6c66e8c57e44"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 chb        sys_numb      date_1 is_real         type  \\\n",
              "12      100000644359  RSL01009833577  2021-03-16     yes  книговыдача   \n",
              "79      100000689674  RSL01003474844  2021-02-11     yes   скачивание   \n",
              "115     100000689674  RSL01004840469  2021-04-06     yes   скачивание   \n",
              "116     100000689674  RSL01004840469  2021-04-30     yes   скачивание   \n",
              "122     100000689674  RSL01002974250  2021-03-06     yes   скачивание   \n",
              "...              ...             ...         ...     ...          ...   \n",
              "259538  300001172795  RSL01003702190  2021-03-03     yes   скачивание   \n",
              "259541  300001172795  RSL01003794908  2021-02-17     yes  книговыдача   \n",
              "259545  300001172795  RSL01003581372  2021-03-04     yes   скачивание   \n",
              "259558  300001173062  RSL01002899944  2022-03-10     yes       скачка   \n",
              "259562  300001173062  RSL01002975109  2022-03-10     yes  книговыдача   \n",
              "\n",
              "                     source is_printed  \n",
              "12      единый просмоторщик      False  \n",
              "79              dlib.rsl.ru      False  \n",
              "115             dlib.rsl.ru      False  \n",
              "116             dlib.rsl.ru      False  \n",
              "122             dlib.rsl.ru      False  \n",
              "...                     ...        ...  \n",
              "259538          dlib.rsl.ru      False  \n",
              "259541           2DL.Viewer      False  \n",
              "259545          dlib.rsl.ru      False  \n",
              "259558  единый просмоторщик      False  \n",
              "259562  единый просмоторщик      False  \n",
              "\n",
              "[30564 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2acf7eab-3f7c-4ecf-bb86-12f7205be9ee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chb</th>\n",
              "      <th>sys_numb</th>\n",
              "      <th>date_1</th>\n",
              "      <th>is_real</th>\n",
              "      <th>type</th>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>100000644359</td>\n",
              "      <td>RSL01009833577</td>\n",
              "      <td>2021-03-16</td>\n",
              "      <td>yes</td>\n",
              "      <td>книговыдача</td>\n",
              "      <td>единый просмоторщик</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>100000689674</td>\n",
              "      <td>RSL01003474844</td>\n",
              "      <td>2021-02-11</td>\n",
              "      <td>yes</td>\n",
              "      <td>скачивание</td>\n",
              "      <td>dlib.rsl.ru</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>100000689674</td>\n",
              "      <td>RSL01004840469</td>\n",
              "      <td>2021-04-06</td>\n",
              "      <td>yes</td>\n",
              "      <td>скачивание</td>\n",
              "      <td>dlib.rsl.ru</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>100000689674</td>\n",
              "      <td>RSL01004840469</td>\n",
              "      <td>2021-04-30</td>\n",
              "      <td>yes</td>\n",
              "      <td>скачивание</td>\n",
              "      <td>dlib.rsl.ru</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>100000689674</td>\n",
              "      <td>RSL01002974250</td>\n",
              "      <td>2021-03-06</td>\n",
              "      <td>yes</td>\n",
              "      <td>скачивание</td>\n",
              "      <td>dlib.rsl.ru</td>\n",
              "      <td>False</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259538</th>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003702190</td>\n",
              "      <td>2021-03-03</td>\n",
              "      <td>yes</td>\n",
              "      <td>скачивание</td>\n",
              "      <td>dlib.rsl.ru</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259541</th>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003794908</td>\n",
              "      <td>2021-02-17</td>\n",
              "      <td>yes</td>\n",
              "      <td>книговыдача</td>\n",
              "      <td>2DL.Viewer</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259545</th>\n",
              "      <td>300001172795</td>\n",
              "      <td>RSL01003581372</td>\n",
              "      <td>2021-03-04</td>\n",
              "      <td>yes</td>\n",
              "      <td>скачивание</td>\n",
              "      <td>dlib.rsl.ru</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259558</th>\n",
              "      <td>300001173062</td>\n",
              "      <td>RSL01002899944</td>\n",
              "      <td>2022-03-10</td>\n",
              "      <td>yes</td>\n",
              "      <td>скачка</td>\n",
              "      <td>единый просмоторщик</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259562</th>\n",
              "      <td>300001173062</td>\n",
              "      <td>RSL01002975109</td>\n",
              "      <td>2022-03-10</td>\n",
              "      <td>yes</td>\n",
              "      <td>книговыдача</td>\n",
              "      <td>единый просмоторщик</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30564 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2acf7eab-3f7c-4ecf-bb86-12f7205be9ee')\"\n",
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              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-2acf7eab-3f7c-4ecf-bb86-12f7205be9ee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2acf7eab-3f7c-4ecf-bb86-12f7205be9ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 30 000 дубликатов! Уберем их из обучения, но позже лучше проверить, с чем это связано"
      ],
      "metadata": {
        "id": "XxojjyKmKHBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_user_unique = book_actions[['chb', 'sys_numb']].drop_duplicates()"
      ],
      "metadata": {
        "id": "KCdiYxNtPZau"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Добавляем в lightfm данные для рекомендаций - пары {читатель, книга}\n",
        "# на входе д.б тип iterables\n",
        "(interactions, weights) = light_dataset.build_interactions(\n",
        "    list(zip(book_user_unique.chb, book_user_unique.sys_numb)))\n",
        "\n",
        "print(repr(interactions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lch6VEIzHrbd",
        "outputId": "1bff57fe-c3f6-4b7e-d146-a2d23ecd55a6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<16753x194666 sparse matrix of type '<class 'numpy.int32'>'\n",
            "\twith 229002 stored elements in COOrdinate format>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Sparse матрица (users, books) для обучения модели готовы!"
      ],
      "metadata": {
        "id": "QhPfizesQjR7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверяем, корректно ли сработал zip\n",
        "\n",
        "# Исходный датасет\n",
        "display(book_actions[['chb', 'sys_numb']].head(3))\n",
        "print()\n",
        "\n",
        "# Первые пары элементов в списке\n",
        "list(zip(book_user_unique.chb, book_user_unique.sys_numb))[0:3]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "d4bMUpNYOitQ",
        "outputId": "0343aa78-e1a3-4c43-f967-1c626f8f0cbe"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            chb        sys_numb\n",
              "0  100000641403  RSL01004206702\n",
              "1  100000641403  RSL01000769304\n",
              "2  100000641403  RSL01004211574"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1bcd2083-cd19-4b59-b0f5-0dfeebf082b9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>chb</th>\n",
              "      <th>sys_numb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100000641403</td>\n",
              "      <td>RSL01004206702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100000641403</td>\n",
              "      <td>RSL01000769304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100000641403</td>\n",
              "      <td>RSL01004211574</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1bcd2083-cd19-4b59-b0f5-0dfeebf082b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1bcd2083-cd19-4b59-b0f5-0dfeebf082b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('100000641403', 'RSL01004206702'),\n",
              " ('100000641403', 'RSL01000769304'),\n",
              " ('100000641403', 'RSL01004211574')]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Все верно! Можем двигаться дальше)"
      ],
      "metadata": {
        "id": "mINRUFE1O6Nh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Разделение выборок (кросс-валидация)"
      ],
      "metadata": {
        "id": "EkRMY1MOLUzK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Разделяем на выборки с помощью метода lightfm\n",
        "# На выходе получаем две одинаковые sparse матрицы\n",
        "train_fm, test_fm = random_train_test_split(\n",
        "    interactions, \n",
        "    test_percentage=0.1, \n",
        "    random_state=777)\n",
        "\n",
        "print(f\"Обучающая выборка lightfm: {train_fm.get_shape()}\")\n",
        "print(f\"Тестовая выборка lightfm: {test_fm.get_shape()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmlHtySC9f8z",
        "outputId": "2a19ce18-29ef-4490-bdd3-37cd698e3c3b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Обучающая выборка lightfm: (16753, 194666)\n",
            "Тестовая выборка lightfm: (16753, 194666)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Главная особенность модели lightfm:\n",
        "- одинаковое количество users и items в обучающей и тестовой матрицах"
      ],
      "metadata": {
        "id": "egrQtmtVUiqc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Обучаем модель"
      ],
      "metadata": {
        "id": "mn7jVqCdVD7p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"LightFM matrix factorization, loss функция = bpr\")\n",
        "model = LightFM(learning_rate=0.05, loss='bpr')\n",
        "model.fit(train_fm, epochs=10)\n",
        "\n",
        "train_precision = precision_at_k(model, train_fm, k=20).mean()\n",
        "test_precision = precision_at_k(model, test_fm, k=20).mean()\n",
        "\n",
        "train_auc = auc_score(model, train_fm).mean()\n",
        "test_auc = auc_score(model, test_fm).mean()\n",
        "\n",
        "print('Precision: train %.2f, test %.2f.' % (train_precision, test_precision))\n",
        "print('AUC: train %.2f, test %.2f.' % (train_auc, test_auc))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3hrk4tQWEbO",
        "outputId": "f024163a-3b84-41e4-e5a3-23739c1f41f0"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LightFM matrix factorization, loss функция = bpr\n",
            "Precision: train 0.00, test 0.00.\n",
            "AUC: train 0.57, test 0.49.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('savefile.pickle', 'wb') as fle:\n",
        "    pickle.dump(model, fle, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "metadata": {
        "id": "XQ5wSb_1ccUm"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs=5, num_threads=4, verbose=True "
      ],
      "metadata": {
        "id": "e78fffGGX29k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load the model from disk\n",
        "model = pickle.load(open('savefile.pickle', 'rb'))"
      ],
      "metadata": {
        "id": "q4vQtC_9gELD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Выгружаем результаты"
      ],
      "metadata": {
        "id": "YkrG2K42Vjt2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_item_representations(features=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6uRLb-V9hQO",
        "outputId": "ec6a9efd-7838-4115-eff0-fc72fbaf20bb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.0344176 , -0.01944616,  0.01806399, ...,  0.01152962,\n",
              "       -0.00060117, -0.01445462], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "light_dataset.mapping()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gw-0Gg8Ujy_p",
        "outputId": "9c5397ba-6bab-4f90-c119-ed3d8492a0c6"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  'RSL01001339547': 960,\n",
              "  'RSL01005497333': 961,\n",
              "  'RSL01010427921': 962,\n",
              "  'RSL01003758413': 963,\n",
              "  'RSL01003863418': 964,\n",
              "  'RSL01003611460': 965,\n",
              "  'RSL01010417546': 966,\n",
              "  'RSL01008875604': 967,\n",
              "  'RSL01003370366': 968,\n",
              "  'RSL01008875527': 969,\n",
              "  'RSL01002571946': 970,\n",
              "  'RSL01004986125': 971,\n",
              "  'RSL01010588138': 972,\n",
              "  'RSL01002907727': 973,\n",
              "  'RSL01001834976': 974,\n",
              "  'RSL01004156776': 975,\n",
              "  'RSL01002888865': 976,\n",
              "  'RSL01008768294': 977,\n",
              "  'RSL01007944324': 978,\n",
              "  'RSL01006764850': 979,\n",
              "  'RSL01002962675': 980,\n",
              "  'RSL01008146898': 981,\n",
              "  'RSL01002594411': 982,\n",
              "  'RSL01003390846': 983,\n",
              "  'RSL01002425390': 984,\n",
              "  'RSL01002483658': 985,\n",
              "  'RSL01002598667': 986,\n",
              "  'RSL01006898459': 987,\n",
              "  'RSL01001658312': 988,\n",
              "  'RSL01006898458': 989,\n",
              "  'RSL01007147869': 990,\n",
              "  'RSL01005573082': 991,\n",
              "  'RSL01005293071': 992,\n",
              "  'RSL01005208384': 993,\n",
              "  'RSL01004353863': 994,\n",
              "  'RSL01006201286': 995,\n",
              "  'RSL01007126670': 996,\n",
              "  'RSL01004856007': 997,\n",
              "  'RSL01004846963': 998,\n",
              "  'RSL01010622660': 999,\n",
              "  ...})"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Matrix Factorization - RankFM"
      ],
      "metadata": {
        "id": "RyfqWw3D3qYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# model = BayesianPersonalizedRanking(factors=200, verify_negative_samples=False, random_state=777)\n",
        "# model.fit(full_train_sparse_data)"
      ],
      "metadata": {
        "id": "GG1wlpv-3ycR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "fdRP7Wkk6_iM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0FuwS4p-kjW8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}