RecSys.ipynb

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        "# Прикладные задачи анализа данных\n",
        "\n",
        "## Домашнее задание 4\n",
        "\n",
        "В этой работе мы будем решать задачу рекомендаций музыки. Для этого мы реализуем метод коллаборативной фильтрации и модель со скрытыми переменными, сравним их между собой и попытаемся сделать выводы. Во всех заданиях разрешается переписывать код по своему усмотрению, предложенные шаблоны несут лишь рекомендательный характер."
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      "id": "75f6503b",
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      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "from typing import Callable, List\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.sparse as scs"
      ]
    },
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      "source": [
        "# Подключение к Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Установка gdown для загрузки файлов с Google Drive\n",
        "!pip install gdown\n",
        "\n",
        "# Загрузка файлов с Google Drive по идентификаторам\n",
        "!gdown --id 1OmDMl_hzPVKCfUESS_Mcn_oEYLdJQiS8 -O /content/tracks_info.csv\n",
        "!gdown --id 13Q5LPnOQkWrKoNsXhmmyoh7-66p6SFKK -O /content/music_dataset.csv"
      ],
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      "outputs": [
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          "text": [
            "Mounted at /content/drive\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.14.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.4)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.6.2)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1OmDMl_hzPVKCfUESS_Mcn_oEYLdJQiS8\n",
            "To: /content/tracks_info.csv\n",
            "100% 3.42M/3.42M [00:00<00:00, 22.2MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=13Q5LPnOQkWrKoNsXhmmyoh7-66p6SFKK\n",
            "To: /content/music_dataset.csv\n",
            "100% 1.32M/1.32M [00:00<00:00, 14.1MB/s]\n"
          ]
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              "   userId  trackId\n",
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              "   id                         name              artists\n",
              "0   0                What There Is             ['a-ha']\n",
              "1   1  I'll Play The Blues For You      ['Albert King']\n",
              "2   2  Breaking Up Somebody's Home      ['Albert King']\n",
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              "    [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-8f14b06f-8a80-44cc-8d17-0bf7d4cf8e25 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-8f14b06f-8a80-44cc-8d17-0bf7d4cf8e25');\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",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a726fc57-d32b-4e65-8ca1-cd281009bed5 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tracks_info",
              "summary": "{\n  \"name\": \"tracks_info\",\n  \"rows\": 72383,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20895,\n        \"min\": 0,\n        \"max\": 72382,\n        \"num_unique_values\": 72383,\n        \"samples\": [\n          64057,\n          40095,\n          11097\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 52551,\n        \"samples\": [\n          \"GOLD MERCURY TOK\",\n          \"\\u041c\\u0430\\u0436\\u043e\\u0440\\u0438\\u0449\\u0435\",\n          \"Feelin' Bad Blues\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artists\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23520,\n        \"samples\": [\n          \"['The Acid']\",\n          \"['RAFF']\",\n          \"['Manudigital', 'Soom T']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "tracks_info = pd.read_csv('tracks_info.csv')\n",
        "tracks_info.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SSvgStZZIEdz",
      "metadata": {
        "id": "SSvgStZZIEdz"
      },
      "source": [
        "Для оценки качества рекомендаций мы будем использовать метрику $MAP@k$.\n",
        "\n",
        "$$\n",
        "MAP@k = \\frac{1}{N} \\sum_{u = 1}^N AP_u@k\n",
        "$$\n",
        "$$\n",
        "AP_u@k = \\frac{1}{\\min(k, n_u)} \\sum_{i=1}^k r_u(i) p_u@i\n",
        "$$\n",
        "$$p_u@k = \\dfrac{1}{k}\\sum_{j=1}^k r_u(j)$$\n",
        "\n",
        "\n",
        "*   $N$ - количество пользователей.\n",
        "*   $n_u$ - число релевантных треков пользователя $u$ на тестовом промежутке.\n",
        "*   $r_u(i)$ - бинарная величина: относится ли трек на позиции $i$ к релевантным."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "UDTWQLoKS3nA",
      "metadata": {
        "id": "UDTWQLoKS3nA"
      },
      "source": [
        "**Задание 1 (1 балл).** Реализуйте метрику $MAP@k$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8YW8fMfnQY08",
      "metadata": {
        "id": "8YW8fMfnQY08",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "def mapk(relevant: List[List[int]], predicted: List[List[int]], k: int = 20):\n",
        "    # your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "    map_k, count = 0, 0\n",
        "    for relevant_items, predicted_items in zip(relevant, predicted): # берем значение для item из предикт и из релевант\n",
        "        if not relevant_items:\n",
        "            continue\n",
        "        # Для каждого юзера считаем AP@k и добавляем в сумму\n",
        "        correct = np.isin(predicted_items[:k], relevant_items) # rel_i\n",
        "        AP_k = (correct / np.arange(1, k + 1))\n",
        "        non_zero_indices = np.nonzero(correct)[0] # Получим индексы не нулей\n",
        "        order_numbers = np.arange(1, len(non_zero_indices) + 1) # создаем упорядоченный список до количества ненулей\n",
        "        AP_k[non_zero_indices] *= order_numbers\n",
        "        map_k += AP_k.sum() / min(k, len(relevant_items))\n",
        "        count += 1\n",
        "    # усредняем по всем юзерам\n",
        "    map_k = map_k / count\n",
        "    return map_k"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "JbKyNPk8TaIG",
      "metadata": {
        "id": "JbKyNPk8TaIG"
      },
      "outputs": [],
      "source": [
        "relevant = [\n",
        "    [1, 7, 6, 2, 8],\n",
        "    [1, 5, 4, 8],\n",
        "    [8, 2, 5]\n",
        "]\n",
        "\n",
        "pred = [\n",
        "    [8, 1, 5, 0, 7, 2, 9, 4],\n",
        "    [0, 1, 8, 5, 3, 4, 7, 9],\n",
        "    [9, 2, 0, 6, 8, 5, 3, 7]\n",
        "]\n",
        "\n",
        "assert round(mapk(relevant, pred, k=5), 4) == 0.4331"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WFYR-X49HlMl",
      "metadata": {
        "id": "WFYR-X49HlMl"
      },
      "source": [
        "Разделим данные на тренировочные и тестовые так, чтобы в теcтовый датасет попали 50 последних треков каждого пользователя."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "TEzz_ym8Go_L",
      "metadata": {
        "id": "TEzz_ym8Go_L"
      },
      "outputs": [],
      "source": [
        "def train_test_split(ratings):\n",
        "    train_ratings, test_ratings = [], []\n",
        "    num_test_samples = 50\n",
        "\n",
        "    # getting train samples\n",
        "    for userId, user_data in tqdm(ratings.groupby('userId')):\n",
        "        train_ratings += [user_data[:-num_test_samples]]\n",
        "\n",
        "    train_ratings = pd.concat(train_ratings).reset_index(drop=True)\n",
        "    all_train_items = train_ratings['trackId'].unique()\n",
        "\n",
        "    # getting train samples\n",
        "    # we drop all tracks that are not presented it the training samples,\n",
        "    # because we won't be able to learn representations for them\n",
        "    for userId, user_data in tqdm(ratings.groupby('userId')):\n",
        "        test_items = user_data[-num_test_samples:]\n",
        "        test_items = test_items[np.isin(test_items['trackId'], all_train_items)]\n",
        "        test_ratings += [test_items]\n",
        "\n",
        "    test_ratings = pd.concat(test_ratings).reset_index(drop=True)\n",
        "\n",
        "    return train_ratings, test_ratings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "vuyiM1Lk4L-e",
      "metadata": {
        "colab": {
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          "height": 81,
          "referenced_widgets": [
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        "id": "vuyiM1Lk4L-e",
        "outputId": "f558ec3a-ea2e-4cae-b7a0-1b8e8cee9c58"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/241 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb5f4c31698a4c74aca5f083feac4973"
            }
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/241 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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      ],
      "source": [
        "train_ratings, test_ratings = train_test_split(ratings)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "P3ZVqBMjBqfe",
      "metadata": {
        "id": "P3ZVqBMjBqfe"
      },
      "source": [
        "Почистим табличку с информацией о треках и закодируем id треков так, чтобы они соответствовали их порядковому номеру."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "-SCTsxeYwCO-",
      "metadata": {
        "id": "-SCTsxeYwCO-"
      },
      "outputs": [],
      "source": [
        "redundant_rows = np.where(~np.isin(tracks_info['id'], train_ratings['trackId'].unique()))[0]\n",
        "tracks_info.drop(redundant_rows, inplace=True)\n",
        "tracks_info = tracks_info.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "cVfC-rfU3J49",
      "metadata": {
        "id": "cVfC-rfU3J49"
      },
      "outputs": [],
      "source": [
        "def ids_encoder(ratings):\n",
        "    users = sorted(ratings['userId'].unique())\n",
        "    items = sorted(ratings['trackId'].unique())\n",
        "\n",
        "    # create users and items encoders\n",
        "    uencoder = LabelEncoder()\n",
        "    iencoder = LabelEncoder()\n",
        "\n",
        "    # fit users and items ids to the corresponding encoder\n",
        "    uencoder.fit(users)\n",
        "    iencoder.fit(items)\n",
        "\n",
        "    return uencoder, iencoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "xw2VDBz9v6LT",
      "metadata": {
        "id": "xw2VDBz9v6LT"
      },
      "outputs": [],
      "source": [
        "uencoder, iencoder = ids_encoder(train_ratings)\n",
        "train_ratings['trackId'] = iencoder.transform(train_ratings['trackId'].tolist())\n",
        "test_ratings['trackId'] = iencoder.transform(test_ratings['trackId'].tolist())\n",
        "tracks_info['id'] = iencoder.transform(tracks_info['id'].tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "GfjwnUWTuhRD",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GfjwnUWTuhRD",
        "outputId": "310f1d00-9149-4500-85ab-8a8c520bdc91"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  trackId\n",
              "0       0       14\n",
              "1       0       95\n",
              "2       0      219\n",
              "3       0      220\n",
              "4       0      404"
            ],
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              "  <div id=\"df-81596dad-d61c-4756-bd59-3b1172a68790\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>trackId</th>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-81596dad-d61c-4756-bd59-3b1172a68790 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-81596dad-d61c-4756-bd59-3b1172a68790');\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",
              "\n",
              "\n",
              "<div id=\"df-f8e24278-dc41-47cd-9556-15a90644af66\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8e24278-dc41-47cd-9556-15a90644af66')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f8e24278-dc41-47cd-9556-15a90644af66 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_ratings"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "train_ratings.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "rtV91NkmuizE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rtV91NkmuizE",
        "outputId": "d0748cdb-0e6b-4c6e-d01d-847e163ca3b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  trackId\n",
              "0       0    57582\n",
              "1       0    57802\n",
              "2       0    57957\n",
              "3       0    58174\n",
              "4       0    59168"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-33ddab73-3603-4da6-bb8b-fc8c4416d487\" 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>userId</th>\n",
              "      <th>trackId</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>57582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>57802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>57957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>58174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>59168</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33ddab73-3603-4da6-bb8b-fc8c4416d487')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-33ddab73-3603-4da6-bb8b-fc8c4416d487 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-33ddab73-3603-4da6-bb8b-fc8c4416d487');\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",
              "\n",
              "\n",
              "<div id=\"df-48889e79-fea7-4810-8959-31447d779c59\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-48889e79-fea7-4810-8959-31447d779c59')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-48889e79-fea7-4810-8959-31447d779c59 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_ratings",
              "summary": "{\n  \"name\": \"test_ratings\",\n  \"rows\": 6133,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69,\n        \"min\": 0,\n        \"max\": 240,\n        \"num_unique_values\": 235,\n        \"samples\": [\n          70,\n          213,\n          185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trackId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11103,\n        \"min\": 9922,\n        \"max\": 67312,\n        \"num_unique_values\": 3796,\n        \"samples\": [\n          64916,\n          64647,\n          26130\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "test_ratings.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "COtKDquLRrVV",
      "metadata": {
        "id": "COtKDquLRrVV"
      },
      "source": [
        "Соберем все релевантные треки для каждого пользователя в список."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "UOCGl5rpPrDn",
      "metadata": {
        "id": "UOCGl5rpPrDn"
      },
      "outputs": [],
      "source": [
        "test_relevant = []\n",
        "test_users = []\n",
        "for user_id, user_data in test_ratings.groupby('userId'):\n",
        "    test_relevant += [user_data['trackId'].tolist()]\n",
        "    test_users.append(user_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3UyBjs1SULH9",
      "metadata": {
        "id": "3UyBjs1SULH9"
      },
      "source": [
        "**Задание 2 (1 балл).** Реализуйте метод `get_test_recommendations` в классе `BaseModel`. Он принимает на вход параметр `k` и возвращает массив из `k` наиболее подходящих треков для каждого пользователя. Не забывайте удалять уже прослушанные треки из рекомендуемых."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "sXnUGtM3XOp7",
      "metadata": {
        "id": "sXnUGtM3XOp7"
      },
      "outputs": [],
      "source": [
        "class BaseModel:\n",
        "    def __init__(self, ratings: pd.DataFrame):\n",
        "        self.ratings = ratings\n",
        "        self.n_users = len(np.unique(self.ratings['userId']))\n",
        "        self.n_items = len(np.unique(self.ratings['trackId']))\n",
        "\n",
        "        self.R = np.zeros((self.n_users, self.n_items))\n",
        "        self.R[self.ratings['userId'], self.ratings['trackId']] = 1. # всем существующим парам юзер-айтем будет проставлена 1, у пар, которые не смэтчены друг с другом, стоит 0, то есть этот юзер не слушал этот трек\n",
        "\n",
        "    def recommend(self, uid: int):\n",
        "        \"\"\"\n",
        "        param uid: int - user's id\n",
        "        return: [n_items] - vector of recommended items sorted by their scores in descending order\n",
        "        \"\"\"\n",
        "        scores = np.random.rand(self.n_items) # или любая другая модель, здесь просто предсказываем рандом, можно любой другой скор, более умный\n",
        "        recommended_items = np.argsort(scores)[::-1]\n",
        "        return recommended_items\n",
        "\n",
        "    def remove_train_items(self, preds: List[List[int]], k: int):\n",
        "        \"\"\"\n",
        "        param preds: [n_users, n_items] - recommended items for each user\n",
        "        param k: int\n",
        "        return: np.array [n_users, k] - recommended items without training examples\n",
        "        \"\"\"\n",
        "        new_preds = np.zeros((len(preds), k), dtype=int)\n",
        "        for user_id, user_data in self.ratings.groupby('userId'):\n",
        "            user_preds = preds[user_id]\n",
        "            new_preds[user_id] = user_preds[~np.in1d(user_preds, user_data['trackId'])][:k] # Выбираем первые k элементов в векторе, которые не прослушаны пользователем\n",
        "\n",
        "        return new_preds\n",
        "\n",
        "    def get_test_recommendations(self, k: int):\n",
        "        test_preds = []\n",
        "\n",
        "        # your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "        # Для каждого юзера считаем рекомендации на основе модели и добавляем в test_preds. test_preds.shape = (n_users, n_items)\n",
        "        for uid in np.unique(self.ratings['userId']):\n",
        "            recommended_items = self.recommend(uid) # вектор длины n_items\n",
        "            test_preds += [recommended_items]\n",
        "\n",
        "        # Удаляем уже прослушанные треки из рекомендуемых\n",
        "        # filtered_preds.shape = (n_users, k)\n",
        "        filtered_preds = self.remove_train_items(test_preds, k)\n",
        "\n",
        "        return filtered_preds"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "jMdKX1DZ-o4V",
      "metadata": {
        "id": "jMdKX1DZ-o4V"
      },
      "source": [
        "## Часть 1. Коллаборативная фильтрация (User2User)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "t_ZLzg_0XxVf",
      "metadata": {
        "id": "t_ZLzg_0XxVf"
      },
      "source": [
        "Идея: чтобы выбрать треки, которые понравятся пользователю, можно набрать несколько похожих на него пользователей (соседей) и посмотреть, какие треки они слушают. После этого остается агрегировать треки этих пользователей и выбрать самые популярные. Соответственно, задача состоит из двух частей: выбора функции похожести двух пользователей и способа агрегации.\n",
        "\n",
        "В качестве функции похожести мы будем использовать две метрики:\n",
        "\n",
        "1. Корреляция Пирсона $$s(u, v) = \\frac{\\sum_{i \\in I_u \\cap I_v} r_{ui}r_{vi}}{\\sqrt{\\sum_{i \\in I_u} r_{ui} ^2}\\sqrt{\\sum_{i \\in I_v} r_{vi}^2}} $$\n",
        "\n",
        "2. Мера Жаккара\n",
        "\n",
        "$$ s(u, v) = \\frac{|I_u \\cap I_v|}{|I_u \\cup I_v|} $$\n",
        "\n",
        "\n",
        "Корреляция Пирсона немного видоизменена, чтобы подходить под нашу задачу.\n",
        "\n",
        "\n",
        "Во всех формулах\n",
        "* $I_u$ - множество треков, прослушанных пользователем $u$.\n",
        "* $r_{ui}$ - прослушал ли пользователь $u$ трек $i$ (0 или 1).\n",
        "\n",
        "Множество соседей определим как $$N(u) = \\{ v \\in U \\setminus \\{u\\} \\mid s(u, v) > \\alpha\\},$$ где $\\alpha \\, - $ гиперпараметр.\n",
        "\n",
        "\n",
        "\n",
        "Для агрегации мы будем пользоваться следующей формулой.\n",
        "$$\n",
        "\\hat{r}_{ui} = \\frac{\\sum_{v \\in N(u)} s(u, v) r_{vi}}{\\sum_{v \\in N(u)} |s(u, v)|}\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "YJyD1LVTKK9A",
      "metadata": {
        "id": "YJyD1LVTKK9A"
      },
      "source": [
        "**Задание 3.1 (1 балл).** Реализуйте функцию подсчета корреляции Пирсона.\n",
        "\n",
        "**Задание 3.2 (1 балл).** Реализуйте функцию подсчета меры Жаккара.\n",
        "\n",
        "Функции принимают матрицу оценок и вектор оценок пользователя $u$ и возвращают вектор со значениями похожести пользователя $u$ на всех пользователей. Старайтесь писать оптимизированный код, за неэффективную реализацию оценка может быть снижена."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "rRvSRm_aWZMR",
      "metadata": {
        "id": "rRvSRm_aWZMR"
      },
      "outputs": [],
      "source": [
        "def pearson(ratings: np.array, user_vector: np.array, alpha) -> np.array:\n",
        "    # your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "\n",
        "    # учтем условие, что соседи все \\ {u}\n",
        "    mask = ~(ratings == user_vector).all(axis=1)\n",
        "    rating_neighbours = ratings[mask]\n",
        "\n",
        "    # числитель для similarity\n",
        "    numerator = rating_neighbours.dot(user_vector)\n",
        "\n",
        "    # куски знаменателя для similarity\n",
        "    user_norm = np.sqrt(np.sum(user_vector ** 2))\n",
        "    ratings_norms = np.sqrt(np.sum(rating_neighbours ** 2, axis=1))\n",
        "\n",
        "    # чекаем, чтобы не было деления на ноль, если такое есть, то заменяем на ноль\n",
        "    with np.errstate(divide='ignore', invalid='ignore'):\n",
        "      similarity = numerator / (ratings_norms * user_norm)\n",
        "      similarity[np.isnan(similarity)] = 0  # Заменяем NaN на 0\n",
        "\n",
        "    # теперь посчитаем агрегированный показатель\n",
        "    numerator = similarity[:, np.newaxis] * rating_neighbours\n",
        "    # оставим только вектор, соответствующий similarity > alpha\n",
        "    numerator = numerator[similarity > alpha].sum(axis = 0)\n",
        "    denominator = similarity[similarity > alpha].sum()\n",
        "\n",
        "    # чекаем снова, чтобы не было деления на ноль\n",
        "    with np.errstate(divide='ignore', invalid='ignore'):\n",
        "          r_ui = numerator / denominator\n",
        "          r_ui = np.where(np.isnan(r_ui) | np.isinf(r_ui), 0, r_ui)\n",
        "\n",
        "    return r_ui\n",
        "\n",
        "\n",
        "def jaccard(ratings: np.array, user_vector: np.array, alpha) -> np.array:\n",
        "    # your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "\n",
        "    # учтем условие, что соседи все \\ {u}\n",
        "    mask = ~(ratings == user_vector).all(axis=1)\n",
        "    rating_neighbours = ratings[mask]\n",
        "\n",
        "    intersection = rating_neighbours * user_vector\n",
        "    union = ((rating_neighbours == 1) | (user_vector == 1)).astype(int)\n",
        "\n",
        "    with np.errstate(divide='ignore', invalid='ignore'):\n",
        "      similarity = intersection.sum(axis = 1) / union.sum(axis = 1)\n",
        "      similarity[np.isnan(similarity)] = 0\n",
        "\n",
        "    numerator = similarity[:, np.newaxis] * rating_neighbours\n",
        "\n",
        "    # оставим только вектор, соответствующий similarity > alpha\n",
        "    numerator = numerator[similarity > alpha].sum(axis = 0)\n",
        "    denominator = similarity[similarity > alpha].sum()\n",
        "\n",
        "    with np.errstate(divide='ignore', invalid='ignore'):\n",
        "      r_ui = numerator / denominator\n",
        "      r_ui = np.where(np.isnan(r_ui) | np.isinf(r_ui), 0, r_ui)\n",
        "    return r_ui"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Проверка работы:"
      ],
      "metadata": {
        "id": "hC35ksy3ZW-t"
      },
      "id": "hC35ksy3ZW-t"
    },
    {
      "cell_type": "code",
      "source": [
        "rat = np.array([[1, 0, 1],\n",
        "                [0, 1, 1],\n",
        "                [1, 0, 0]\n",
        "\n",
        "])\n",
        "\n",
        "u_vec = np.array([1, 0, 1])\n",
        "\n",
        "print(f\"Пирсон: {np.round(pearson(rat, u_vec, alpha = 0.1), 4)}\")\n",
        "print(f\"Жаккар: {np.round(jaccard(rat, u_vec, alpha = 0.1), 4)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5SRZcnoZYl6",
        "outputId": "aec21e4e-7a43-4b8a-c232-1ed3fa2756ce"
      },
      "id": "B5SRZcnoZYl6",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Пирсон: [0.5858 0.4142 0.4142]\n",
            "Жаккар: [0.6 0.4 0.4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WMLtM7ffNWdx",
      "metadata": {
        "id": "WMLtM7ffNWdx"
      },
      "source": [
        "**Задание 4 (1 балл).** Реализуйте методы `similarity` и `recommend` класса `User2User`. `recommend` возвращает индексы треков, отсортированные в порядке убывания предсказанных оценок.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "5ZIz0G1RJbXl",
      "metadata": {
        "id": "5ZIz0G1RJbXl"
      },
      "outputs": [],
      "source": [
        "class User2User(BaseModel):\n",
        "    def __init__(self, ratings, similarity_func):\n",
        "        super().__init__(ratings)\n",
        "\n",
        "        assert similarity_func in [pearson, jaccard]\n",
        "\n",
        "        self.similarity_func = similarity_func\n",
        "        self.alpha = 0.02\n",
        "\n",
        "    def similarity(self, user_vector: np.array, alpha):\n",
        "        \"\"\"\n",
        "        user_vector: [n_items]\n",
        "        \"\"\"\n",
        "        # your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "        # считаем сходство челика с другими челиками\n",
        "        return self.similarity_func(self.R, user_vector, alpha = alpha)\n",
        "\n",
        "    def recommend(self, uid: int):\n",
        "        # your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "        user_vector = self.R[uid, :] # все айтемы юзера\n",
        "\n",
        "        # Вычисляем сходства между текущим пользователем и всеми остальными\n",
        "        predicted_ratings = self.similarity(user_vector, alpha = self.alpha) # получаем вектор r_ui, характеризующий сходство пользователя со всеми другими пользователями по конкретным айтемам\n",
        "\n",
        "        if np.all(predicted_ratings == 0):\n",
        "            return np.argsort(user_vector)[::-1]  # Если нет соседей, возвращаем треки пользователя, отсортированные по убыванию\n",
        "\n",
        "        # Убираем уже прослушанные треки из рекомендаций\n",
        "        predicted_ratings[user_vector > 0] = -np.inf\n",
        "\n",
        "        # Возвращаем индексы треков, отсортированные по убыванию предсказанных оценок\n",
        "        return np.argsort(predicted_ratings)[::-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "kqEwJiFOSs2r",
      "metadata": {
        "id": "kqEwJiFOSs2r"
      },
      "source": [
        "**Задание 5 (1 балл).** Постройте графики зависимости значений $MAP@k$ от разных $k$ для обоих функций похожести, сравните их между собой, а также с предсказаниями случайного алгоритма и сделайте выводы."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "tyswL-dlNpy7",
      "metadata": {
        "id": "tyswL-dlNpy7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "e92d0e63-0649-4c0e-f9f1-93fbfbda5826"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random model time: 3.8723 seconds\n",
            "Pearson model time: 61.5171 seconds\n",
            "Jaccard model time: 62.5917 seconds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "import time\n",
        "\n",
        "random_model = BaseModel(train_ratings)\n",
        "pearson_model = User2User(train_ratings, pearson)\n",
        "jaccard_model = User2User(train_ratings, jaccard)\n",
        "\n",
        "k_values = range(1, 21)\n",
        "\n",
        "# Замер времени выполнения для случайного алгоритма\n",
        "start_time = time.time()\n",
        "random_preds = random_model.get_test_recommendations(k=max(k_values))\n",
        "random_time = time.time() - start_time\n",
        "\n",
        "# Замер времени выполнения для алгоритма на основе корреляции Пирсона\n",
        "start_time = time.time()\n",
        "pearson_preds = pearson_model.get_test_recommendations(k=max(k_values))\n",
        "pearson_time = time.time() - start_time\n",
        "\n",
        "# Замер времени выполнения для алгоритма на основе коэффициента Жаккара\n",
        "start_time = time.time()\n",
        "jaccard_preds = jaccard_model.get_test_recommendations(k=max(k_values))\n",
        "jaccard_time = time.time() - start_time\n",
        "\n",
        "print(f\"Random model time: {random_time:.4f} seconds\")\n",
        "print(f\"Pearson model time: {pearson_time:.4f} seconds\")\n",
        "print(f\"Jaccard model time: {jaccard_time:.4f} seconds\")\n",
        "\n",
        "# Рассчитаем MAP@k для разных k и построим графики\n",
        "random_mapk = []\n",
        "pearson_mapk = []\n",
        "jaccard_mapk = []\n",
        "\n",
        "for k in k_values:\n",
        "    random_mapk.append(mapk(test_relevant, random_preds, k))\n",
        "    pearson_mapk.append(mapk(test_relevant, pearson_preds, k))\n",
        "    jaccard_mapk.append(mapk(test_relevant, jaccard_preds, k))\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(k_values, random_mapk, label='Random')\n",
        "plt.plot(k_values, pearson_mapk, label='Pearson')\n",
        "plt.plot(k_values, jaccard_mapk, label='Jaccard')\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('MAP@k')\n",
        "plt.title('MAP@k for Different Similarity Functions and Random Algorithm')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Видим, что наилучшей мерой сходства по MAP@k в среднем для любого k является коэффициент Пирсона, затем мера Жаккара и самой худшей является случайное предсказание. Это связано с особенностями функций похожести и особенностями задачи. Случайное предсказание наихудшее, потому что у нас много юзеров и айтемов и при рандомном предсказании юзер слушает нерелевантный с большой долей вероятности трек. Лучше всего как меру схожести использовать коэффициент Пирсона в данной задаче, он учитывает корреляцию между треками пользователей и потому является наилучшей мерой схожести в данной задаче."
      ],
      "metadata": {
        "id": "Ttfv4XCgZ6wB"
      },
      "id": "Ttfv4XCgZ6wB"
    },
    {
      "cell_type": "markdown",
      "id": "0Fy7v-b-WDQE",
      "metadata": {
        "id": "0Fy7v-b-WDQE"
      },
      "source": [
        "**Задание 6 (1.5 балла).** Как вы могли заметить, матрица оценок получается очень разреженной, но мы работаем с ней как с обычной, это не дело. Перепишите код так, чтобы все методы могли работать с разреженными матрицами и сравните скорость работы такого подхода с оригинальным."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4jQiwrTbW_DL",
      "metadata": {
        "id": "4jQiwrTbW_DL"
      },
      "outputs": [],
      "source": [
        "# your code here: ‿︵‿︵ヽ(°□° )ノ︵‿︵‿"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "DmuMhWS4CEFe",
      "metadata": {
        "id": "DmuMhWS4CEFe"
      },
      "source": [
        "Мы можем посмотреть глазами, насколько хорошо модель рекомендует треки. Для этого сравним уже прослушанные треки с рекомендованными и релевантными для случайного пользователя. Хорошо ли у вас получилось?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "QAHDWgHfkCQh",
      "metadata": {
        "id": "QAHDWgHfkCQh"
      },
      "outputs": [],
      "source": [
        "user_id = np.random.randint(0, pearson_model.n_users)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "_d3tafMuko5O",
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        "id": "_d3tafMuko5O",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Already listened tracks:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        name                        artists\n",
              "55                           A Little Faster         ['There For Tomorrow']\n",
              "2806                                With You                ['Linkin Park']\n",
              "2809   Enth E Nd (Kutmasta Kurt Reanimation)  ['Linkin Park', 'Motion Man']\n",
              "3117                              In Between                ['Linkin Park']\n",
              "5082                             Blood Sugar                   ['Pendulum']\n",
              "5323                             Heartkiller                        ['HIM']\n",
              "5540                                Hay Loft              ['Mother Mother']\n",
              "5709                              Witchcraft                   ['Pendulum']\n",
              "7274                                     Sad                  ['Pearl Jam']\n",
              "8647                              The Island                   ['Pendulum']\n",
              "9705                                    Drag                    ['Placebo']\n",
              "9708                               Infra-Red                    ['Placebo']\n",
              "10027                                  Crush                   ['Pendulum']\n",
              "10251                   Devil In The Details                    ['Placebo']\n",
              "10388                              John Hurt  ['Does It Offend You, Yeah?']"
            ],
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "    }\n",
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              "        document.querySelector('#df-d1785850-24bb-4e6e-8fe7-0a21fbbcd256 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"tracks_info\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"The Island\",\n          \"Infra-Red\",\n          \"A Little Faster\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artists\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"['Placebo']\",\n          \"['Linkin Park']\",\n          \"['Mother Mother']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "listened_tracks = train_ratings[train_ratings.userId == user_id].trackId[:15]\n",
        "\n",
        "print('Already listened tracks:')\n",
        "\n",
        "tracks_info.loc[listened_tracks][['name', 'artists']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "t0jACzw6ko5P",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "t0jACzw6ko5P",
        "outputId": "e3af7e6a-789e-483e-ad9a-9a7b2a170407"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted tracks:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               name  \\\n",
              "23429                  Stressed Out   \n",
              "29169                      Heathens   \n",
              "24500                Way Down We Go   \n",
              "42324              Go Fuck Yourself   \n",
              "35064                       Thunder   \n",
              "22325                        Dancin   \n",
              "6198    I Hate Everything About You   \n",
              "11950  Somebody That I Used To Know   \n",
              "54885                         Odium   \n",
              "57338                      Kosandra   \n",
              "34033      Something Just Like This   \n",
              "12047                Baby's On Fire   \n",
              "54220                         Roses   \n",
              "40970                          Hope   \n",
              "56562                      OUT WEST   \n",
              "\n",
              "                                          artists  \n",
              "23429                       ['twenty one pilots']  \n",
              "29169                       ['twenty one pilots']  \n",
              "24500                                   ['KALEO']  \n",
              "42324                                ['Two Feet']  \n",
              "35064                         ['Imagine Dragons']  \n",
              "22325           ['Aaron Smith', 'Krono', 'Luvli']  \n",
              "6198                         ['Three Days Grace']  \n",
              "11950                         ['Gotye', 'Kimbra']  \n",
              "54885     ['LXST CXNTURY', 'Kingpin Skinny Pimp']  \n",
              "57338                     ['Miyagi & Andy Panda']  \n",
              "34033            ['The Chainsmokers', 'Coldplay']  \n",
              "12047                            ['Die Antwoord']  \n",
              "54220                               ['SAINt JHN']  \n",
              "40970                            ['XXXTentacion']  \n",
              "56562  ['JACKBOYS', 'Travis Scott', 'Young Thug']  "
            ],
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              "      <td>Dancin</td>\n",
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              "      <th>6198</th>\n",
              "      <td>I Hate Everything About You</td>\n",
              "      <td>['Three Days Grace']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11950</th>\n",
              "      <td>Somebody That I Used To Know</td>\n",
              "      <td>['Gotye', 'Kimbra']</td>\n",
              "    </tr>\n",
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              "      <th>54885</th>\n",
              "      <td>Odium</td>\n",
              "      <td>['LXST CXNTURY', 'Kingpin Skinny Pimp']</td>\n",
              "    </tr>\n",
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              "      <th>57338</th>\n",
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              "      <td>['Miyagi &amp; Andy Panda']</td>\n",
              "    </tr>\n",
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              "      <th>34033</th>\n",
              "      <td>Something Just Like This</td>\n",
              "      <td>['The Chainsmokers', 'Coldplay']</td>\n",
              "    </tr>\n",
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              "      <th>12047</th>\n",
              "      <td>Baby's On Fire</td>\n",
              "      <td>['Die Antwoord']</td>\n",
              "    </tr>\n",
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              "      <td>Roses</td>\n",
              "      <td>['SAINt JHN']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40970</th>\n",
              "      <td>Hope</td>\n",
              "      <td>['XXXTentacion']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56562</th>\n",
              "      <td>OUT WEST</td>\n",
              "      <td>['JACKBOYS', 'Travis Scott', 'Young Thug']</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-016fffbc-fe89-415e-9db8-a8ddf1c3c6c4 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-016fffbc-fe89-415e-9db8-a8ddf1c3c6c4');\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",
              "\n",
              "\n",
              "<div id=\"df-f24612df-5f1a-4591-8dd5-6180e3d833ab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f24612df-5f1a-4591-8dd5-6180e3d833ab')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f24612df-5f1a-4591-8dd5-6180e3d833ab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"tracks_info\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Kosandra\",\n          \"Baby's On Fire\",\n          \"Stressed Out\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artists\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"['The Chainsmokers', 'Coldplay']\",\n          \"['SAINt JHN']\",\n          \"['twenty one pilots']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "preds = pearson_model.get_test_recommendations(15)\n",
        "\n",
        "print('Predicted tracks:')\n",
        "\n",
        "tracks_info.loc[preds[user_id]][['name', 'artists']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "8_UZCIxrko5Q",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "8_UZCIxrko5Q",
        "outputId": "efb94a35-a9aa-4983-f8f8-06e0b254cfb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test-time tracks:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                name                                 artists\n",
              "64502     Zuccenberg  ['Tommy Cash', '$uicideBoy$', 'Diplo']\n",
              "64808  Некуда бежать      ['Pyrokinesis', 'МУККА', 'BOOKER']\n",
              "64823           Wind                            ['Akeboshi']\n",
              "64881         MADARA            ['FREE FLOW FLAVA', 'NKOHA']\n",
              "65199       Shy Away                   ['twenty one pilots']\n",
              "65934       Revolver                               ['bülow']\n",
              "66217       Disaster                            ['KSLV Noh']"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f2e91dcf-9def-419a-b25d-316ae548a6c7\" 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>name</th>\n",
              "      <th>artists</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>64502</th>\n",
              "      <td>Zuccenberg</td>\n",
              "      <td>['Tommy Cash', '$uicideBoy$', 'Diplo']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64808</th>\n",
              "      <td>Некуда бежать</td>\n",
              "      <td>['Pyrokinesis', 'МУККА', 'BOOKER']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64823</th>\n",
              "      <td>Wind</td>\n",
              "      <td>['Akeboshi']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64881</th>\n",
              "      <td>MADARA</td>\n",
              "      <td>['FREE FLOW FLAVA', 'NKOHA']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65199</th>\n",
              "      <td>Shy Away</td>\n",
              "      <td>['twenty one pilots']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65934</th>\n",
              "      <td>Revolver</td>\n",
              "      <td>['bülow']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66217</th>\n",
              "      <td>Disaster</td>\n",
              "      <td>['KSLV Noh']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2e91dcf-9def-419a-b25d-316ae548a6c7')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f2e91dcf-9def-419a-b25d-316ae548a6c7 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-f2e91dcf-9def-419a-b25d-316ae548a6c7');\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",
              "\n",
              "\n",
              "<div id=\"df-704ad09e-6fc2-41e4-addc-da01f679f6fb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-704ad09e-6fc2-41e4-addc-da01f679f6fb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-704ad09e-6fc2-41e4-addc-da01f679f6fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"tracks_info\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Zuccenberg\",\n          \"\\u041d\\u0435\\u043a\\u0443\\u0434\\u0430 \\u0431\\u0435\\u0436\\u0430\\u0442\\u044c\",\n          \"Revolver\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artists\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"['Tommy Cash', '$uicideBoy$', 'Diplo']\",\n          \"['Pyrokinesis', '\\u041c\\u0423\\u041a\\u041a\\u0410', 'BOOKER']\",\n          \"['b\\u00fclow']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "test_tracks = test_ratings[test_ratings.userId == user_id].trackId[:15]\n",
        "\n",
        "print('Test-time tracks:')\n",
        "\n",
        "tracks_info.loc[test_tracks][['name', 'artists']]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Получилось все равно плохо, очень мало треков совпали с тестом, надо делать что-то лучше - матричную факторизацию."
      ],
      "metadata": {
        "id": "UZdIx4H6aqqC"
      },
      "id": "UZdIx4H6aqqC"
    },
    {
      "cell_type": "markdown",
      "id": "3laWojtltEz1",
      "metadata": {
        "id": "3laWojtltEz1"
      },
      "source": [
        "### Часть 2. Модель со скрытыми переменными"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "W63eIwPEYuYG",
      "metadata": {
        "id": "W63eIwPEYuYG"
      },
      "source": [
        "В этой части мы пощупаем метод рекомендаций со скрытыми переменными.\n",
        "Идея: будем предсказывать оценки по формуле\n",
        "$$\n",
        "\\hat{r}_{ui} = \\langle p_u, q_u \\rangle,\n",
        "$$\n",
        "$p_u \\in \\mathbb{R}^d$ и $q_i \\in \\mathbb{R}^d$ - латентные векторы пользователя $u$ и объекта $i$ соответственно.\n",
        "\n",
        "Оптимизировать мы будем MSE между истинной оценкой пользователя и предсказанной с регуляризацией\n",
        "$$\n",
        "L = \\sum_{(u, i) \\in R} (\\hat{r}_{ui} - r_{ui})^2 + \\lambda \\left(\\sum_{u \\in U} \\|p_u\\|^2 + \\sum_{i \\in I} \\|q_i\\|^2\\right)\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "jrZjfGb0iLk6",
      "metadata": {
        "id": "jrZjfGb0iLk6"
      },
      "source": [
        "**Задание 7 (1.5 балла).** На лекции рассматривались два подхода к оптимизации параметров. Можно это делать обычным стохастческим градинтным спуском, а можно по очереди обновлять матрицы $P, Q$, и тогда получится метод Alternating Least Squares (ALS). Выведите формулы обновления параметров для обоих методов."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LFM**\n",
        "\n",
        "Ответ\n",
        "\n",
        "https://drive.google.com/file/d/1xQ6WonkEki55v1ue9YngQQ_lMbEzU3X6/view?usp=sharing\n",
        "\n",
        "**ALS**\n",
        "\n",
        "Ответ\n",
        "\n",
        "https://drive.google.com/file/d/1S3z2aYNOO-pnhlOOG3laGP82dU056G_2/view?usp=drive_link"
      ],
      "metadata": {
        "id": "pneL-6gDOmTW"
      },
      "id": "pneL-6gDOmTW"
    },
    {
      "cell_type": "markdown",
      "id": "T5PXIItDkiDc",
      "metadata": {
        "id": "T5PXIItDkiDc"
      },
      "source": [
        "**Задание 8 (2 балла).** Реализуйте методы оптимизации параметров для обоих алгоритмов."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "vTui-yTptFIo",
      "metadata": {
        "id": "vTui-yTptFIo"
      },
      "outputs": [],
      "source": [
        "class HiddenVars(BaseModel):\n",
        "    def __init__(self, ratings, dim=128, mode='sgd'):\n",
        "        super().__init__(ratings)\n",
        "        self.dim = dim\n",
        "\n",
        "        assert mode in ['sgd', 'als']\n",
        "        self.mode = mode\n",
        "\n",
        "        self.P = np.random.normal(size=(self.n_users, dim))\n",
        "        self.Q = np.random.normal(size=(self.n_items, dim))\n",
        "\n",
        "        self.lr = 0.0003\n",
        "        self.lamb = 0.01\n",
        "\n",
        "    def fit(self, num_iters=5):\n",
        "        for epoch in tqdm(range(num_iters)):\n",
        "\n",
        "            if self.mode == 'sgd':\n",
        "                # your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "                for u in range(self.n_users):\n",
        "                    for i in range(self.n_items):\n",
        "                        r_ui = self.R[u, i]\n",
        "                        error = r_ui - np.dot(self.P[u], self.Q[i])\n",
        "                        self.P[u] -= self.lr * (error * self.Q[i] + self.lamb * self.P[u])\n",
        "                        self.Q[i] -= self.lr * (error * self.P[u] + self.lamb * self.Q[i])\n",
        "\n",
        "            elif self.mode == 'als':\n",
        "                for u in range(self.n_users):\n",
        "                    idx = self.ratings[self.ratings['userId'] == u].index\n",
        "                    if len(idx) == 0:\n",
        "                        continue\n",
        "                    R_u = np.ones(len(idx))\n",
        "                    Q_u = self.Q[self.ratings.loc[idx, 'trackId'].values]\n",
        "                    self.P[u] = np.linalg.solve(Q_u.T @ Q_u + self.lamb * np.eye(self.dim), Q_u.T @ R_u)\n",
        "\n",
        "                for i in range(self.n_items):\n",
        "                    idx = self.ratings[self.ratings['trackId'] == i].index\n",
        "                    if len(idx) == 0:\n",
        "                        continue\n",
        "                    R_i = np.ones(len(idx))\n",
        "                    P_i = self.P[self.ratings.loc[idx, 'userId'].values]\n",
        "                    self.Q[i] = np.linalg.solve(P_i.T @ P_i + self.lamb * np.eye(self.dim), P_i.T @ R_i)\n",
        "\n",
        "\n",
        "    def recommend(self, uid):\n",
        "        pred_rating = self.P[uid] @ self.Q.T\n",
        "\n",
        "        return np.argsort(pred_rating)[::-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "N0P3-ZcYuY3t",
      "metadata": {
        "id": "N0P3-ZcYuY3t"
      },
      "source": [
        "**Задание 9 (1 балл).** Для обоих алгоритмов подберите оптимальные значения размерности скрытого пространства $d$ и размера предсказания $k$. Как изменяется качество предсказаний с числом итераций обучения? Постройте соответствующие графики, сравните со случайным подхом и User2User, сделайте выводы. Какой алгоритм вам кажется более подходящим для данной задачи и почему?\n",
        "\n",
        "P.S. Хотя бы один из методов обучения должен приводить к лучшим результатам в сравнении с User2User подходом.\n",
        "\n",
        "P.P.S. Методу LFM свойственно переобучаться, поэтому при подборе параметров полезно смотреть на значения ошибки и оптимизируемой метрики на тренировочном датасете. Вы также можете менять начальную инициализацию и прочие параметры, за исключением архитектуры, на ваш вкус."
      ]
    },
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        "outputId": "fa26878b-0ae5-4646-b86e-49df7feced64"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2238a3e63d4a4588a60949e9ef249d40",
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              "  0%|          | 0/1 [00:00<?, ?it/s]"
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          "text": [
            "<ipython-input-26-1b2bb5c271b6>:25: RuntimeWarning: overflow encountered in multiply\n",
            "  self.Q[i] -= self.lr * (error * self.P[u] + self.lamb * self.Q[i])\n",
            "<ipython-input-26-1b2bb5c271b6>:24: RuntimeWarning: overflow encountered in multiply\n",
            "  self.P[u] -= self.lr * (error * self.Q[i] + self.lamb * self.P[u])\n",
            "<ipython-input-26-1b2bb5c271b6>:24: RuntimeWarning: invalid value encountered in add\n",
            "  self.P[u] -= self.lr * (error * self.Q[i] + self.lamb * self.P[u])\n",
            "<ipython-input-26-1b2bb5c271b6>:46: RuntimeWarning: invalid value encountered in matmul\n",
            "  pred_rating = self.P[uid] @ self.Q.T\n"
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# your code here: (￣▽￣)/♫•*¨*•.¸¸♪\n",
        "NUM_ITERS = [1, 2, 3]\n",
        "DIMS = [64, 128]\n",
        "k_values = np.arange(1, 6)\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "for num_iters in NUM_ITERS:\n",
        "    for dim in DIMS:\n",
        "        sgd_model = HiddenVars(train_ratings, dim=dim, mode=\"sgd\")\n",
        "        sgd_model.fit(num_iters=num_iters)\n",
        "        sgd_preds = sgd_model.get_test_recommendations(k=max(k_values))\n",
        "\n",
        "        als_model = HiddenVars(train_ratings, dim=dim, mode=\"als\")\n",
        "        als_model.fit(num_iters=num_iters)\n",
        "        als_preds = als_model.get_test_recommendations(k=max(k_values))\n",
        "\n",
        "        sgd_mapk = []\n",
        "        als_mapk = []\n",
        "\n",
        "        for k in k_values:\n",
        "            sgd_mapk.append(mapk(test_relevant, sgd_preds, k))\n",
        "            als_mapk.append(mapk(test_relevant, als_preds, k))\n",
        "\n",
        "        plt.plot(k_values, sgd_mapk, label=f'SGD dim={dim}, iters={num_iters}')\n",
        "        plt.plot(k_values, als_mapk, label=f'ALS dim={dim}, iters={num_iters}')\n",
        "\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('MAP@k')\n",
        "plt.title('MAP@k for Different Dimensionalities and Iterations')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Наилучшая модель получилась ALS с параметрами dim = 128, n_iters = 3, оптимальное число предсказаний k для наивысшей точности - это предсказание 1 наилучшего трека, но также можно предсказывать и 2 наилучших трека для рекомендации.\n",
        "\n",
        "Наилучшие параметры для ALS dim = 128, n_iters = 3, k = 1\n",
        "\n",
        "Наилучшие параметры для SGD dim = 128, n_iters = 3, k = 1\n",
        "\n",
        "В целом видим такую тенденцию: при росте числа итераций при обучении становится лучше MAP@k, также лучше при росте размерности d матриц факторизации.\n",
        "\n",
        "Данные подходы обеспечивают лучшее качество по сравнению с User-2-User подходом и с рандомным подходом. Матричная факторизация при правильном подборе гиперпараметров показывает лучшие результаты, чем просто User-2-User подход, однако его коэффициенты сложнее интерпретировать.\n",
        "\n",
        "Наиболее подходящим для данной задачи является именно подход с матричной факторизацией, а именно ALS. Размерность эмбеддинга d для обучения позволяет находить более сложные закономерности, чем просто анализ сходства эмбедингов юзеров или айтемов. Хоть его и сложнее интерпретировать, но он показывает лучшие результаты. ALS позволяет нам по очереди обучать по отдельности матрицы P и Q, что также на практике является более эффективным, чем просто SGD - SGD считается по 1 объекту, поэтому дисперсия производной является высокой, что делает наш прогноз шумным, возможно, стоит попробовать mini-batch SGD и станет лучше, но сам SGD очень шумный получается и поэтому качество хуже ALS, но все же лучше, чем у подхода User-2-User"
      ],
      "metadata": {
        "id": "-edmkLn8Qvk9"
      },
      "id": "-edmkLn8Qvk9"
    },
    {
      "cell_type": "markdown",
      "id": "Gx4EMj0HwVlR",
      "metadata": {
        "id": "Gx4EMj0HwVlR"
      },
      "source": [
        "Если у вас получилось достаточно хорошее качество, то при оптимизации параметров марицы $Q$ похожим трекам стали соответствовать похожие векторы. Поэтому мы можем для любого трека найти наиболее близкие к нему в латентном пространстве и проверить степерь обученности модели вручную."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "als_model = HiddenVars(train_ratings, dim=128, mode=\"als\")\n",
        "als_model.fit(num_iters=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d0db6cfb22424b96a8c38145c8ec6369",
            "59571268aafa4cd6b1c169ed3f6d7ad5",
            "b0d613805d754f9fa28cbecbb45ba6b3",
            "eb08771dc3e94cd7ac60f5f1279433bb",
            "9929b63535b64dc7807678aa8ca5aed5",
            "d17050068d87466591c99021caca9c05",
            "a06f8fdbe4774923939ada0b4c4b90bb",
            "e19005467d444346890bfb7f3f88a598",
            "dccf0ef78d3b48d1b63fa62aec4f6071",
            "f1b8c46194d9416ca0ffb37dfda51262",
            "64918a33c08241759bb2ce66d6a03ddf"
          ]
        },
        "id": "vOUl1jnFJONY",
        "outputId": "2c6c9f10-57bb-4faf-a20b-3eb77c10e5b3"
      },
      "id": "vOUl1jnFJONY",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0db6cfb22424b96a8c38145c8ec6369"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "VFYy92Ls-53S",
      "metadata": {
        "id": "VFYy92Ls-53S"
      },
      "outputs": [],
      "source": [
        "example_trackId = tracks_info[tracks_info.name == 'Выхода нет'].iloc[0].id\n",
        "\n",
        "preds = als_model.Q @ als_model.Q[example_trackId]\n",
        "preds = preds / np.sqrt((als_model.Q**2).sum(axis=1) + 1e-8)\n",
        "\n",
        "track_idxs = preds.argsort()[::-1][:20]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "DzewG5rw5SQo",
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        },
        "id": "DzewG5rw5SQo",
        "outputId": "66dc482e-b5d7-4bf7-a570-a4ad9a890570"
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              "                                   name                 artists  similarity\n",
              "5512                         Выхода нет               ['Сплин']    1.000000\n",
              "2058                    Последний герой                ['КИНО']    0.736009\n",
              "5570                         Моё сердце               ['Сплин']    0.612498\n",
              "39732  Another Brick In The Wall, Pt. 2          ['Pink Floyd']    0.612466\n",
              "26075                           Лошадка         ['Найк Борзов']    0.603792\n",
              "19430                  Вечная молодость            ['Чиж & Co']    0.603027\n",
              "16637                            Zodiac              ['Zodiac']    0.593939\n",
              "5153                       Stormbringer         ['Deep Purple']    0.593939\n",
              "9689                       Bicycle Race               ['Queen']    0.593939\n",
              "9449                           Это всё…                 ['ДДТ']    0.576523\n",
              "3737                 Smoke On The Water         ['Deep Purple']    0.575032\n",
              "16224                  Пока горит свеча      ['Машина времени']    0.560674\n",
              "6171                        Billie Jean     ['Michael Jackson']    0.558194\n",
              "40209                        Выхода нет               ['Сплин']    0.557931\n",
              "58063                               UNO          ['Little Big']    0.555994\n",
              "2179                    Восьмиклассница                ['КИНО']    0.555374\n",
              "1081                    Come As You Are             ['Nirvana']    0.554467\n",
              "9433                  В последнюю осень                 ['ДДТ']    0.553267\n",
              "55069                           На заре              ['Альянс']    0.545723\n",
              "14484                  Прогулки по воде  ['Nautilus Pompilius']    0.544334"
            ],
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              "      <td>Выхода нет</td>\n",
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              "      <th>58063</th>\n",
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              "      <th>2179</th>\n",
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              "      <th>1081</th>\n",
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              "      <th>9433</th>\n",
              "      <td>В последнюю осень</td>\n",
              "      <td>['ДДТ']</td>\n",
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              "    <tr>\n",
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              "      <td>На заре</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "similar_tracks",
              "summary": "{\n  \"name\": \"similar_tracks\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\u0412\\u044b\\u0445\\u043e\\u0434\\u0430 \\u043d\\u0435\\u0442\",\n          \"\\u0412\\u0435\\u0447\\u043d\\u0430\\u044f \\u043c\\u043e\\u043b\\u043e\\u0434\\u043e\\u0441\\u0442\\u044c\",\n          \"\\u041f\\u043e\\u043a\\u0430 \\u0433\\u043e\\u0440\\u0438\\u0442 \\u0441\\u0432\\u0435\\u0447\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artists\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"['\\u041c\\u0430\\u0448\\u0438\\u043d\\u0430 \\u0432\\u0440\\u0435\\u043c\\u0435\\u043d\\u0438']\",\n          \"['Little Big']\",\n          \"['\\u0421\\u043f\\u043b\\u0438\\u043d']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10234951128801664,\n        \"min\": 0.5443339238760083,\n        \"max\": 0.9999999999016835,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.9999999999016835,\n          0.7360092714664244,\n          0.5750316244248012\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "similar_tracks = tracks_info.loc[track_idxs][['name', 'artists']]\n",
        "similar_tracks['similarity'] = preds[track_idxs] / np.linalg.norm(als_model.Q[example_trackId])\n",
        "similar_tracks"
      ]
    }
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