goit_python_ds_hw_07.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lexxai/goit_python_ds_hw_07/blob/main/goit_python_ds_hw_07.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTvBpLm5Jqyj"
      },
      "source": [
        "# Модуль 7. Рекомендаційні системи.\n",
        "\n",
        "*З циклу [домашніх завдань Python Data Science](https://github.com/lexxai/goit_python_data_sciense_homework).*\n",
        "\n",
        "# Домашнє завдання"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLW2WK4cK08b"
      },
      "source": [
        "## LOAD DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQlrrC64Ei_D",
        "outputId": "6c836d7b-a418-4857-f20d-eb1f835ef759"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-surprise\n",
            "  Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m772.0/772.0 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.3.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.11.4)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp310-cp310-linux_x86_64.whl size=3163002 sha256=6077747674f48e2f38f3fe6d531fb529b7f5e775b20c9013bf1124cac961757e\n",
            "  Stored in directory: /root/.cache/pip/wheels/a5/ca/a8/4e28def53797fdc4363ca4af740db15a9c2f1595ebc51fb445\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise\n",
            "Successfully installed scikit-surprise-1.1.3\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-surprise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "m63KlyHSprCX"
      },
      "outputs": [],
      "source": [
        "from surprise import accuracy, Dataset, Reader, SVD\n",
        "from surprise.model_selection import cross_validate, train_test_split\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from pathlib import Path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "8_JhmJld6eFR"
      },
      "outputs": [],
      "source": [
        "laod_mode = \"load_from_df\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "6SaBHI8tEE-8"
      },
      "outputs": [],
      "source": [
        "if laod_mode == \"load_builtin\":\n",
        "  # Load the movielens-100k dataset (download it if needed).\n",
        "  data = Dataset.load_builtin('ml-100k', prompt=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "T0DErstIgFzd"
      },
      "outputs": [],
      "source": [
        "if laod_mode == \"load_builtin\":\n",
        "  !ls -1  /root/.surprise_data/ml-100k/ml-100k/u* | sed  's/\\/root\\/\\.surprise_data\\/ml-100k\\/ml-100k\\///g' | sort"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "z-K7JzgVjES4"
      },
      "outputs": [],
      "source": [
        "if laod_mode == \"load_builtin\":\n",
        "  base_path = Path(\"/root/.surprise_data/ml-100k/ml-100k\")\n",
        "  filelist =  list(map(lambda x: x.name, sorted(base_path.glob(\"u*\"))))\n",
        "  types = {\n",
        "      \"test\": \"Test_files\",\n",
        "      \"base\": \"Train_files\"\n",
        "  }\n",
        "  for f, d in types.items():\n",
        "    print(f\"\\n{d}:\")\n",
        "    print(list(filter(lambda x : x.endswith(f\".{f}\") ,filelist)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "oVTS5xZsmyJS"
      },
      "outputs": [],
      "source": [
        "  latest_url = \"https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\"\n",
        "  latest_zip_file = Path(\"ml-latest-small.zip\")\n",
        "  if not latest_zip_file.exists():\n",
        "    !wget -q $latest_url\n",
        "    !unzip -q $latest_zip_file\n",
        "  base_path = Path(\"/content/ml-latest-small/\")\n",
        "  rating_csv = base_path.joinpath(\"ratings.csv\")\n",
        "  tags_csv = base_path.joinpath(\"tags.csv\")\n",
        "  movies_csv = base_path.joinpath(\"movies.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "dNYxkIYrpWZE"
      },
      "outputs": [],
      "source": [
        "if laod_mode == \"load_from_file\":\n",
        "  reader = Reader(line_format=u'user item rating timestamp', sep=',', rating_scale=(1, 5), skip_lines=1)\n",
        "  data = Dataset.load_from_file(rating_csv, reader=reader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "NEEvbfqBquBr"
      },
      "outputs": [],
      "source": [
        "if laod_mode == \"load_from_df\":\n",
        "  df = pd.read_csv(rating_csv)\n",
        "\n",
        "  # A reader is still needed but only the rating_scale param is requiered.\n",
        "  reader = Reader(rating_scale=(1, 5))\n",
        "\n",
        "  # The columns must correspond to user id, item id and ratings (in that order).\n",
        "  data = Dataset.load_from_df(df[[\"userId\", \"movieId\",  \"rating\"]], reader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "id": "zk4mZ2b0cx7F",
        "outputId": "4e86c800-c489-4d49-8478-9eb1f6c87b14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "surprise.dataset.DatasetAutoFolds"
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>surprise.dataset.DatasetAutoFolds</b><br/>def __init__(ratings_file=None, reader=None, df=None)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/surprise/dataset.py</a>A derived class from :class:`Dataset` for which folds (for\n",
              "cross-validation) are not predefined. (Or for when there are no folds at\n",
              "all).</pre>\n",
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          "metadata": {},
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      "source": [
        "type(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VumP61raGOTE"
      },
      "source": [
        "### Аналіз записів"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DjEBc7AqBy9O"
      },
      "source": [
        "Неважлива інофромаційна частина для пошуку реальних значень з датасету: uid, iid, ur. А також для уявлення змісту даних датасету."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPNxTcHLbyWK",
        "outputId": "2a42f51f-7515-4237-d7cb-970a48c1b0f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of ratings: 100836\n",
            "\n",
            "Top 5 samples of raw ratings:\n",
            "UserID:     1, ItemID:     1, UserRateForItem:   4.0, date: None\n",
            "UserID:     1, ItemID:     3, UserRateForItem:   4.0, date: None\n",
            "UserID:     1, ItemID:     6, UserRateForItem:   4.0, date: None\n",
            "UserID:     1, ItemID:    47, UserRateForItem:   5.0, date: None\n",
            "UserID:     1, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "\n",
            "Top 5 ratings of user: 1\n",
            "UserID:     1, ItemID:     1, UserRateForItem:   4.0, date: None\n",
            "UserID:     1, ItemID:     3, UserRateForItem:   4.0, date: None\n",
            "UserID:     1, ItemID:     6, UserRateForItem:   4.0, date: None\n",
            "UserID:     1, ItemID:    47, UserRateForItem:   5.0, date: None\n",
            "UserID:     1, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "\n",
            "Top 5 ratings of item: 50\n",
            "UserID:     1, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "UserID:     5, ItemID:    50, UserRateForItem:   4.0, date: None\n",
            "UserID:     6, ItemID:    50, UserRateForItem:   1.0, date: None\n",
            "UserID:     7, ItemID:    50, UserRateForItem:   4.5, date: None\n",
            "UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n"
          ]
        }
      ],
      "source": [
        "from datetime import date\n",
        "\n",
        "dater = None\n",
        "\n",
        "# Access dataset attributes\n",
        "raw_ratings = data.raw_ratings\n",
        "print(\"Number of ratings:\", len(raw_ratings))\n",
        "\n",
        "# Print a sample of raw ratings\n",
        "print(\"\\nTop 5 samples of raw ratings:\")\n",
        "for rating in raw_ratings[:5]:\n",
        "  uid, iid, ur, timestamp = rating\n",
        "  if timestamp:\n",
        "    dater = date.fromtimestamp(int(timestamp))\n",
        "  print(f\"UserID: {uid:5}, ItemID: {iid:5}, UserRateForItem: {ur:5}, date: {dater}\")\n",
        "\n",
        "search_uid = uid\n",
        "count_uid = 5\n",
        "print(f\"\\nTop {count_uid} ratings of user: {search_uid}\")\n",
        "\n",
        "for rating in raw_ratings:\n",
        "  uid, iid, ur, timestamp = rating\n",
        "  if uid == search_uid:\n",
        "    if timestamp:\n",
        "      dater = date.fromtimestamp(int(timestamp))\n",
        "    count_uid -= 1;\n",
        "    print(f\"UserID: {uid:5}, ItemID: {iid:5}, UserRateForItem: {ur:5}, date: {dater}\")\n",
        "    if count_uid <= 0:\n",
        "      break\n",
        "\n",
        "search_iid = iid\n",
        "count_iid = 5\n",
        "print(f\"\\nTop {count_iid} ratings of item: {search_iid}\")\n",
        "\n",
        "for rating in raw_ratings:\n",
        "  uid, iid, ur, timestamp = rating\n",
        "  if iid == search_iid:\n",
        "    if timestamp:\n",
        "      dater = date.fromtimestamp(int(timestamp))\n",
        "    count_iid -= 1;\n",
        "    print(f\"UserID: {uid:5}, ItemID: {iid:5}, UserRateForItem: {ur:5}, date: {dater}\")\n",
        "    if count_iid <= 0:\n",
        "      break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UGbdKhLZGOTI"
      },
      "source": [
        "Використовуєтьс прямий відгук (Explicit feedback) - це оцінка, яку користувач поставив, щоб висловити свою задоволеність товаром."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vd_a5Z0QGOTI"
      },
      "source": [
        "Для оцінки що базується на спільній інформації про користувачів  та їхню взаємодію з об'єктами (Collaborative Filtering) вибранно модель матричної факторизації (Matrix Factorization)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzuccKchc2ji"
      },
      "source": [
        "Ми використовуємо об'єкт ***DatasetAutoFolds*** у Surprise.\n",
        "\n",
        "\n",
        "Якщо використаний метод **load_builtin** то  Ми можемо працювати з набором даних, який вже було розбито на файли (folds) для перехресної перевірки. Мета цього методу - охопити загальний випадок використання, коли набір даних вже розбито на попередньо визначені складові, наприклад, набір даних movielens-100k ('ml-100k'), який визначає файли u1.base, u1.test, u2.base, u2.test і т.д... Його також можна використовувати, коли ми не хочемо виконувати перехресну перевірку, але все одно хочемо вказати наші навчальні та тестові дані (що в будь-якому випадку зводиться до 1-кратної перехресної перевірки)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYbeZAPjtK1_"
      },
      "source": [
        "## Mоделі матричної факторизації"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueUxG3LaLG4I"
      },
      "source": [
        "### Algorithm SVD.\n",
        "\n",
        "Метод SVD може бути адаптований до наборів даних з неявним зворотним зв'язком (implicit feedback)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyQ5s_mFwxP5"
      },
      "source": [
        "\n",
        "- RMSE - root-mean-square error\n",
        "- MAE - mean absolute error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "xtGVOa9MvqW-"
      },
      "outputs": [],
      "source": [
        "SEED = 42\n",
        "algo_cv = {}\n",
        "algo_split = {}\n",
        "# Use the SVD algorithm.\n",
        "algo = SVD(random_state=SEED)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXSz9le_xsT4"
      },
      "source": [
        "#### Прогноз"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCXw_cYz2z45"
      },
      "source": [
        "#### Full_trainset та він же і testset. Accuracy RMSE (root-mean-square error).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "Vqp3Qyoivlrg"
      },
      "outputs": [],
      "source": [
        "def data_full_trainset(algo, data, name_opt=None):\n",
        "  algorithm_name = algo.__class__.__name__\n",
        "  if name_opt:\n",
        "    algorithm_name = f\"{algorithm_name}_{name_opt}\"\n",
        "  print(f\"Algo: {algorithm_name}, UserID: {uid:5}, ItemID: {iid:5}, UserRateForItem: {ur:5}, date: {dater}\")\n",
        "\n",
        "  #trainset, testset = train_test_split(data, test_size=0.25)\n",
        "  trainset = data.build_full_trainset()\n",
        "\n",
        "  print(\"Number of train ratings:\", len(list(trainset.all_ratings())))\n",
        "  algo.fit(trainset)\n",
        "  pred = algo.predict(uid, iid, r_ui=ur, verbose=True)\n",
        "\n",
        "  testset = trainset.build_testset()\n",
        "  print(\"Number of test ratings:\", len(testset))\n",
        "\n",
        "  predictions = algo.test(testset)\n",
        "  # RMSE має бути низьким, оскільки ми є упередженими\n",
        "  accuracy.rmse(predictions, verbose=True)  # ~ 0.68 (що є низьким показником)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IA0MEHsC_pac",
        "outputId": "6e7c9cb9-1c94-4222-e9ed-97b700b950d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVD, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 100836\n",
            "user: 8          item: 50         r_ui = 5.00   est = 4.39   {'was_impossible': False}\n",
            "Number of test ratings: 100836\n",
            "RMSE: 0.6390\n"
          ]
        }
      ],
      "source": [
        "data_full_trainset(algo, data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9A4q5cF3OF0"
      },
      "source": [
        "#### Train_test_split 25%. Accuracy RMSE (root-mean-square error).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "LdeuubKe2U_G"
      },
      "outputs": [],
      "source": [
        "def data_train_test_split(algo, data, name_opt=None, test_size=0.25):\n",
        "  algorithm_name = algo.__class__.__name__\n",
        "  if name_opt:\n",
        "    algorithm_name = f\"{algorithm_name}_{name_opt}\"\n",
        "  print(f\"Algo: {algorithm_name}, UserID: {uid:5}, ItemID: {iid:5}, UserRateForItem: {ur:5}, date: {dater}\")\n",
        "  trainset, testset = train_test_split(data, test_size=test_size)\n",
        "\n",
        "  print(\"Number of train ratings:\", len(list(trainset.all_ratings())))\n",
        "  algo.fit(trainset)\n",
        "  pred = algo.predict(uid, iid, r_ui=ur, verbose=True)\n",
        "\n",
        "  print(\"Number of test ratings:\", len(testset))\n",
        "\n",
        "  predictions = algo.test(testset)\n",
        "  # RMSE має бути більшим\n",
        "  rmse = accuracy.rmse(predictions, verbose=True)  # ~ 0.94 (що є високим показником)\n",
        "  return {algorithm_name: rmse}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glUST0_nAXnR",
        "outputId": "82ba9531-a319-44cc-c332-f5992bf3f91d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVD, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 75627\n",
            "user: 8          item: 50         r_ui = 5.00   est = 4.14   {'was_impossible': False}\n",
            "Number of test ratings: 25209\n",
            "RMSE: 0.8752\n"
          ]
        }
      ],
      "source": [
        "algo_split.update(data_train_test_split(algo, data))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_uWjxHB2OVX"
      },
      "source": [
        "#### Cross Validate, cv = 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "s2Kyk9JeGBJy"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def data_cross_validate(algo, data, name_opt=None, cv=5):\n",
        "  algorithm_name = algo.__class__.__name__\n",
        "  if name_opt:\n",
        "    algorithm_name = f\"{algorithm_name}_{name_opt}\"\n",
        "  print(f\"Algo: {algorithm_name}. Cross Validate: {cv}\")\n",
        "  # Run 5-fold cross-validation and print results.\n",
        "  cv_result = cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=cv, verbose=True)\n",
        "  mean_rmse = np.array(cv_result.get('test_rmse')).mean()\n",
        "  mean_fit = np.array(cv_result.get('fit_time')).mean()\n",
        "  mean_test = np.array(cv_result.get('test_time')).mean()\n",
        "\n",
        "  print(f\"{mean_rmse=}\")\n",
        "  return {algorithm_name: (mean_rmse, mean_fit, mean_test)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxWiH8bUWrk3",
        "outputId": "67dff561-b367-468e-c648-472d7948fe71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVD. Cross Validate: 5\n",
            "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
            "\n",
            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
            "RMSE (testset)    0.8734  0.8753  0.8740  0.8749  0.8719  0.8739  0.0012  \n",
            "MAE (testset)     0.6708  0.6697  0.6724  0.6729  0.6689  0.6710  0.0015  \n",
            "Fit time          1.47    2.76    3.27    3.22    3.80    2.91    0.79    \n",
            "Test time         0.11    0.26    1.02    0.22    0.61    0.44    0.33    \n",
            "mean_rmse=0.8738940553015198\n"
          ]
        }
      ],
      "source": [
        "algo_cv.update(data_cross_validate(algo, data))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUP_-6eYM_1T"
      },
      "source": [
        "### Algorithm SVD++ (SVDpp)\n",
        "\n",
        "Алгоритм SVD++ можна використовувати, коли ми маємо доступ як до явного (explicit), так і до неявного (implicit) зворотного зв'язку. Це може бути дуже корисно, тому що зазвичай користувачі взаємодіють з багатьма елементами (= неявний зворотній зв'язок), але оцінюють лише невелику підмножину з них (= явний зворотній зв'язок).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "QxLJcJhUNA2o"
      },
      "outputs": [],
      "source": [
        " # surprise.prediction_algorithms.matrix_factorization.SVDpp\n",
        "from surprise import SVDpp\n",
        "\n",
        "# Use the SVDpp algorithm.\n",
        "algo = SVDpp(cache_ratings=True, random_state=SEED)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IS0gaLu--bMW",
        "outputId": "4a8aa5de-e789-4b9c-9723-3c68c5741778"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVDpp, UserID:     4, ItemID:  1276, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 100836\n",
            "user: 4          item: 1276       r_ui = 5.00   est = 4.12   {'was_impossible': False}\n",
            "Number of test ratings: 100836\n",
            "RMSE: 0.6811\n"
          ]
        }
      ],
      "source": [
        "data_full_trainset(algo, data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9aQtziO-fTA",
        "outputId": "c1673608-92c6-402d-c9d8-8d371e2adcb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVDpp, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 75627\n",
            "user: 8          item: 50         r_ui = 5.00   est = 4.47   {'was_impossible': False}\n",
            "Number of test ratings: 25209\n",
            "RMSE: 0.8596\n"
          ]
        }
      ],
      "source": [
        "algo_split.update(data_train_test_split(algo, data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1mlY6C_-WWh",
        "outputId": "567e327a-3a78-4574-976a-b169c8c1d662"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVDpp. Cross Validate: 5\n",
            "Evaluating RMSE, MAE of algorithm SVDpp on 5 split(s).\n",
            "\n",
            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
            "RMSE (testset)    0.8687  0.8542  0.8614  0.8658  0.8623  0.8625  0.0049  \n",
            "MAE (testset)     0.6665  0.6540  0.6612  0.6619  0.6598  0.6607  0.0040  \n",
            "Fit time          94.98   87.57   95.62   95.75   96.35   94.05   3.27    \n",
            "Test time         12.22   12.17   12.03   12.22   12.07   12.14   0.08    \n",
            "mean_rmse=0.8624868395853745\n"
          ]
        }
      ],
      "source": [
        "algo_cv.update(data_cross_validate(algo, data))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7yAcmVTsMtDD"
      },
      "source": [
        "### Algorithm NMF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "fsbcpEK5MuMz"
      },
      "outputs": [],
      "source": [
        "# surprise.prediction_algorithms.matrix_factorization.NMF\n",
        "from surprise import NMF\n",
        "\n",
        "# Use the NMF algorithm.\n",
        "algo = NMF()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hnz2KCaC_EDw",
        "outputId": "a83749ca-dc28-4d38-d777-fe09c7d7f546"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: NMF, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 100836\n",
            "user: 8          item: 50         r_ui = 5.00   est = 4.45   {'was_impossible': False}\n",
            "Number of test ratings: 100836\n",
            "RMSE: 0.6365\n"
          ]
        }
      ],
      "source": [
        "data_full_trainset(algo, data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u92zY9Yi_ILp",
        "outputId": "551fed11-052e-4a39-8c22-7094a4912df1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: NMF, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 75627\n",
            "user: 8          item: 50         r_ui = 5.00   est = 4.26   {'was_impossible': False}\n",
            "Number of test ratings: 25209\n",
            "RMSE: 0.9269\n"
          ]
        }
      ],
      "source": [
        "algo_split.update(data_train_test_split(algo, data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnuu_byH_Hiw",
        "outputId": "967f3c39-64ba-46d0-d28a-1a91fadf3ec5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: NMF. Cross Validate: 5\n",
            "Evaluating RMSE, MAE of algorithm NMF on 5 split(s).\n",
            "\n",
            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
            "RMSE (testset)    0.9192  0.9268  0.9227  0.9227  0.9202  0.9223  0.0026  \n",
            "MAE (testset)     0.7036  0.7105  0.7080  0.7078  0.7046  0.7069  0.0025  \n",
            "Fit time          2.61    3.35    2.63    2.62    2.64    2.77    0.29    \n",
            "Test time         0.09    0.22    0.10    0.21    0.16    0.16    0.05    \n",
            "mean_rmse=0.9223173555082889\n"
          ]
        }
      ],
      "source": [
        "algo_cv.update(data_cross_validate(algo, data))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GiwVM6d_pBf"
      },
      "source": [
        "## Сімейство алгоритмів k-NN\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sa_CWfroAVwO"
      },
      "source": [
        "\n",
        "Ці алгоритми безпосередньо випливають з базового методу найближчих сусідів.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TrQTNYlL-zX"
      },
      "source": [
        "### Algorithm KNNBasic (User based ON)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Whether similarities will be computed between users."
      ],
      "metadata": {
        "id": "uOjGDVxuY0Es"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "vr_csvEwI1f0"
      },
      "outputs": [],
      "source": [
        "from surprise import KNNBasic\n",
        "\n",
        "# Build an algorithm, and train it.\n",
        "algo = KNNBasic(sim_options={'user_based': True})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QwLukw-JGdP",
        "outputId": "7c7a84c8-ef02-4db2-a317-9aa71c7caa92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 100836\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 8          item: 50         r_ui = 5.00   est = 4.64   {'actual_k': 40, 'was_impossible': False}\n",
            "Number of test ratings: 100836\n",
            "RMSE: 0.7265\n"
          ]
        }
      ],
      "source": [
        "data_full_trainset(algo, data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNmfqM4I97wW",
        "outputId": "044b0457-3501-4b7e-e97e-9551e254b5c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic_user_on, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 75627\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 8          item: 50         r_ui = 5.00   est = 4.43   {'actual_k': 40, 'was_impossible': False}\n",
            "Number of test ratings: 25209\n",
            "RMSE: 0.9532\n"
          ]
        }
      ],
      "source": [
        "algo_split.update(data_train_test_split(algo, data, name_opt=\"user_on\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrM3m5dh-MTt",
        "outputId": "3b232ea1-b9ac-4ea1-995b-a177ab96ee88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic_user_on. Cross Validate: 5\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Evaluating RMSE, MAE of algorithm KNNBasic on 5 split(s).\n",
            "\n",
            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
            "RMSE (testset)    0.9449  0.9477  0.9421  0.9464  0.9480  0.9458  0.0022  \n",
            "MAE (testset)     0.7243  0.7240  0.7243  0.7246  0.7268  0.7248  0.0010  \n",
            "Fit time          0.16    0.19    0.16    0.14    0.14    0.16    0.02    \n",
            "Test time         1.57    2.04    1.36    1.45    1.36    1.56    0.25    \n",
            "mean_rmse=0.9458186051560442\n"
          ]
        }
      ],
      "source": [
        "algo_cv.update(data_cross_validate(algo, data, name_opt=\"user_on\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6WUPYir3QpX"
      },
      "source": [
        "### Algorithm KNNBasic (User based OFF)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Whether similarities will be computed between movies"
      ],
      "metadata": {
        "id": "kzrf_pydY47p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "IBr2u-613QpZ"
      },
      "outputs": [],
      "source": [
        "from surprise import KNNBasic\n",
        "\n",
        "# Build an algorithm, and train it.\n",
        "algo = KNNBasic(sim_options={'user_based': False})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffSwqQWZ3Qpc",
        "outputId": "8628e331-e98e-4992-b06f-656fc407bcd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 100836\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 8          item: 50         r_ui = 5.00   est = 3.77   {'actual_k': 40, 'was_impossible': False}\n",
            "Number of test ratings: 100836\n",
            "RMSE: 0.4572\n"
          ]
        }
      ],
      "source": [
        "data_full_trainset(algo, data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pM_rSiKr3Qpd",
        "outputId": "7f55e64a-1f1f-4b06-8d05-f2a07831372c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic_user_off, UserID:     8, ItemID:    50, UserRateForItem:   5.0, date: None\n",
            "Number of train ratings: 75627\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 8          item: 50         r_ui = 5.00   est = 3.77   {'actual_k': 40, 'was_impossible': False}\n",
            "Number of test ratings: 25209\n",
            "RMSE: 0.9079\n"
          ]
        }
      ],
      "source": [
        "algo_split.update(data_train_test_split(algo, data, name_opt=\"user_off\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dy3xJyDY3Qpe",
        "outputId": "4f2297af-21d8-4095-b4e0-2cd957325e68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic_user_off. Cross Validate: 5\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Evaluating RMSE, MAE of algorithm KNNBasic on 5 split(s).\n",
            "\n",
            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
            "RMSE (testset)    0.9021  0.9077  0.9129  0.9097  0.9083  0.9082  0.0035  \n",
            "MAE (testset)     0.6961  0.7017  0.7018  0.6983  0.6996  0.6995  0.0021  \n",
            "Fit time          4.54    5.61    6.63    4.77    4.55    5.22    0.81    \n",
            "Test time         8.94    7.85    8.24    8.85    8.81    8.54    0.42    \n",
            "mean_rmse=0.908158259405757\n"
          ]
        }
      ],
      "source": [
        "algo_cv.update(data_cross_validate(algo, data, name_opt=\"user_off\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEf1LzGQH1qZ"
      },
      "source": [
        "## Результати accuracy для різних алгоритмів"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "SojB-No_IrpS"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_algo(input_data: dict, title_type: str=\"\", ylabel_type: str=\"RMSE\", column=None):\n",
        "  x = list(input_data.keys())\n",
        "  if column is None:\n",
        "    y = list(input_data.values())\n",
        "  else:\n",
        "    y = [v[column] for v in input_data.values()]\n",
        "\n",
        "  y = np.array(y)\n",
        "\n",
        "  plt.figure(figsize=(16, 5))\n",
        "  h_y = y[-1] -  y[0]\n",
        "  max_y = y[-1] + h_y * 0.16\n",
        "  min_y = y[0] - h_y * 0.16\n",
        "  min_y = 0 if min_y < 0 else min_y\n",
        "  plt.ylim(min_y, max_y)\n",
        "  plt.title(f\"Результати {title_type} для різних алгоритмів. [зміна у % від поперед.] (зміна у % від найкр.)\")\n",
        "\n",
        "  cmap = plt.get_cmap('Paired')\n",
        "  num_colors = len(x)\n",
        "  colors_map = [cmap(i / num_colors) for i in range(num_colors)]\n",
        "  custom_colors = {key: colors_map[i] for i, key in enumerate(sorted(x))}\n",
        "  colors = [custom_colors.get(x_v, 'blue') for x_v in x]\n",
        "\n",
        "  bar_w = 0.6\n",
        "\n",
        "  plt.bar(x, height=y, width=bar_w, label=x, color=colors)\n",
        "  plt.plot(x, y, \"o-\")\n",
        "  plt.xlabel(\"Назва алгоритму\")\n",
        "  plt.ylabel(ylabel_type)\n",
        "  plt.vlines(x[0], min_y, max_y, color=\"red\", linestyle=\"-.\", label=\"Best Algo, lower value\")\n",
        "  plt.hlines(y[0], -bar_w / 2, len(x) - 1 + bar_w / 2, color=\"red\", linestyle=\"-.\")\n",
        "  diff_y = np.diff(y) / y[:-1] * 100\n",
        "  diff_y_best = y / y[0] * 100 - 100\n",
        "\n",
        "  for i, val in enumerate(y):\n",
        "      text = str(round(val, 4))\n",
        "      if i > 0:\n",
        "        text += f\" [{round(diff_y[i-1], 1):+,}%]\"\n",
        "      if i > 1:\n",
        "        text += f\" ({round(diff_y_best[i], 1):+,}%)\"\n",
        "\n",
        "      plt.text(x[i], val + h_y * 0.05 , text, ha='center',  va='bottom', bbox=dict(facecolor='white', alpha=0.95))\n",
        "  plt.legend(loc=\"best\")\n",
        "  plt.show()\n",
        "  print(f\"Best algo: {x[0]}, with value: {y[0]:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0s7f5PcQH_lH",
        "outputId": "42fd1908-6072-4eba-8599-de2d9b5b6f87"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'SVD': 0.8752241498320181,\n",
              " 'SVDpp': 0.8596436124244224,\n",
              " 'NMF': 0.9269388053103035,\n",
              " 'KNNBasic_user_on': 0.9532127552729714,\n",
              " 'KNNBasic_user_off': 0.9078954061347988}"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "algo_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "QslxVHDiIj8S"
      },
      "outputs": [],
      "source": [
        "algo_split_s = dict(sorted(algo_split.items(), key=lambda x: x[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "orw3cpgESMTs",
        "outputId": "4a505f5f-b320-4314-bf1b-cfd41e15b6eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_algo(algo_split_s, \"predict accuracy\", \" Значення RMSE\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9oZbFr2xZnyM"
      },
      "source": [
        "## Результати кросвалідації для різних алгоритмів"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5l5ekHeqZnba",
        "outputId": "5ee11222-2014-49e4-848d-6292f8b0151c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best algo: SVDpp, with value: 0.8596\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'SVD': (0.8738940553015198, 2.905899429321289, 0.44420251846313474),\n",
              " 'SVDpp': (0.8624868395853745, 94.05305962562561, 12.141802740097045),\n",
              " 'NMF': (0.9223173555082889, 2.768272542953491, 0.1576249122619629),\n",
              " 'KNNBasic_user_on': (0.9458186051560442,\n",
              "  0.15866055488586425,\n",
              "  1.5575635433197021),\n",
              " 'KNNBasic_user_off': (0.908158259405757,\n",
              "  5.221751022338867,\n",
              "  8.539007520675659)}"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "algo_cv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "yUon2b_nbUFS",
        "outputId": "584e3d6b-38ab-4df4-85d1-d4f11965996e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best algo: SVDpp, with value: 0.8625\n"
          ]
        }
      ],
      "source": [
        "column = 0\n",
        "algo_cv_s_rmse = dict(sorted(algo_cv.items(), key=lambda x: x[1][column]))\n",
        "plot_algo(algo_cv_s_rmse, \"кросвалідації\", \"Середнє значення RMSE\", column=column)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "He1I-qal5Oh8",
        "outputId": "be15e977-f31f-4c3e-d398-fc6eaee23031"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best algo: KNNBasic_user_on, with value: 0.1587\n"
          ]
        }
      ],
      "source": [
        "column = 1\n",
        "algo_cv_s_fit = dict(sorted(algo_cv.items(), key=lambda x: x[1][column]))\n",
        "plot_algo(algo_cv_s_fit, \"часу кросвалідації\", \"Середнє значення часу навчання (fit), сек\", column=column)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "4rRRGBOf5jis",
        "outputId": "d1b445df-31f7-4378-8f6e-ca403c5a46e7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best algo: NMF, with value: 0.1576\n"
          ]
        }
      ],
      "source": [
        "column = 2\n",
        "algo_cv_s_test = dict(sorted(algo_cv.items(), key=lambda x: x[1][column]))\n",
        "plot_algo(algo_cv_s_test, \"часу кросвалідації\", \"Середнє значення часу тестування (test), сек\",  column=column)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UfO5MUvEABAK"
      },
      "source": [
        "## Найкращій алгорим"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nav4ol5M-YkZ"
      },
      "source": [
        "- за точністю предбачення: SVD++, SVD\n",
        "- за часом навчання моделі: KNNBasic (user_based: on), NMF\n",
        "- за часом тестування моделі: NMF, SVD\n",
        "\n",
        "Тому вибираємо для подальшіх розрахунків за критерієм точності: SVD++, але за критерієм швидкісті та точності вибриаємо: SVD."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "qodh0GyF6zry"
      },
      "outputs": [],
      "source": [
        "best_algo = SVDpp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9xezE86PB4A"
      },
      "source": [
        "## Пошук оптимальних параметрів використовуючи GridSearchCV для SVD++ (SVDpp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "3xG17GmmPClu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "515f0f06-0865-4e9e-ad76-5f94a95b84d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 13.7 s, sys: 2.52 s, total: 16.2 s\n",
            "Wall time: 21min 7s\n",
            "0.8608948378550695\n",
            "best_params={'random_state': 42, 'cache_ratings': True, 'n_epochs': 15, 'lr_all': 0.007, 'reg_all': 0.02}\n"
          ]
        }
      ],
      "source": [
        "from surprise.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\"random_state\": [SEED], \"cache_ratings\": [True],\n",
        "              \"n_epochs\": [15, 20], \"lr_all\": [0.007], \"reg_all\": [0.02, 0.2]\n",
        "              }\n",
        "gs = GridSearchCV(best_algo, param_grid, measures=[\"rmse\"], cv=5, n_jobs=-1)\n",
        "\n",
        "%time gs.fit(data)\n",
        "\n",
        "# best RMSE score\n",
        "print(gs.best_score[\"rmse\"])\n",
        "\n",
        "# combination of parameters that gave the best RMSE score\n",
        "best_params = gs.best_params[\"rmse\"]\n",
        "print(f\"{best_params=}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hEdiA9J8XbC"
      },
      "source": [
        "#### Звіт роботи GridSearchCV для всіх комбінацій параметрів"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "RE51ObkqRAua",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "73aa857c-c1e9-42b1-d947-f5d7da25b7d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   split0_test_rmse  split1_test_rmse  split2_test_rmse  split3_test_rmse  \\\n",
              "0          0.854265          0.860528          0.861643          0.863861   \n",
              "1          0.867642          0.871681          0.874627          0.877937   \n",
              "2          0.853864          0.861881          0.862578          0.864652   \n",
              "3          0.865542          0.869611          0.872774          0.876097   \n",
              "\n",
              "   split4_test_rmse  mean_test_rmse  std_test_rmse  rank_test_rmse  \\\n",
              "0          0.864177        0.860895       0.003584               1   \n",
              "1          0.878120        0.874001       0.003966               4   \n",
              "2          0.863841        0.861363       0.003871               2   \n",
              "3          0.875772        0.871959       0.003975               3   \n",
              "\n",
              "   mean_fit_time  std_fit_time  mean_test_time  std_test_time  \\\n",
              "0      94.624936      2.106076       17.173924       0.504958   \n",
              "1      93.221780      0.570155       17.170962       0.583233   \n",
              "2     124.811340      0.632849       16.885598       0.221311   \n",
              "3     124.474253      0.689725       16.738724       0.324934   \n",
              "\n",
              "                                              params  param_random_state  \\\n",
              "0  {'random_state': 42, 'cache_ratings': True, 'n...                  42   \n",
              "1  {'random_state': 42, 'cache_ratings': True, 'n...                  42   \n",
              "2  {'random_state': 42, 'cache_ratings': True, 'n...                  42   \n",
              "3  {'random_state': 42, 'cache_ratings': True, 'n...                  42   \n",
              "\n",
              "   param_cache_ratings  param_n_epochs  param_lr_all  param_reg_all  \n",
              "0                 True              15         0.007           0.02  \n",
              "1                 True              15         0.007           0.20  \n",
              "2                 True              20         0.007           0.02  \n",
              "3                 True              20         0.007           0.20  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eea0279e-0764-4432-a473-9487a8b48bb6\" 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>split0_test_rmse</th>\n",
              "      <th>split1_test_rmse</th>\n",
              "      <th>split2_test_rmse</th>\n",
              "      <th>split3_test_rmse</th>\n",
              "      <th>split4_test_rmse</th>\n",
              "      <th>mean_test_rmse</th>\n",
              "      <th>std_test_rmse</th>\n",
              "      <th>rank_test_rmse</th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_test_time</th>\n",
              "      <th>std_test_time</th>\n",
              "      <th>params</th>\n",
              "      <th>param_random_state</th>\n",
              "      <th>param_cache_ratings</th>\n",
              "      <th>param_n_epochs</th>\n",
              "      <th>param_lr_all</th>\n",
              "      <th>param_reg_all</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.854265</td>\n",
              "      <td>0.860528</td>\n",
              "      <td>0.861643</td>\n",
              "      <td>0.863861</td>\n",
              "      <td>0.864177</td>\n",
              "      <td>0.860895</td>\n",
              "      <td>0.003584</td>\n",
              "      <td>1</td>\n",
              "      <td>94.624936</td>\n",
              "      <td>2.106076</td>\n",
              "      <td>17.173924</td>\n",
              "      <td>0.504958</td>\n",
              "      <td>{'random_state': 42, 'cache_ratings': True, 'n...</td>\n",
              "      <td>42</td>\n",
              "      <td>True</td>\n",
              "      <td>15</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.867642</td>\n",
              "      <td>0.871681</td>\n",
              "      <td>0.874627</td>\n",
              "      <td>0.877937</td>\n",
              "      <td>0.878120</td>\n",
              "      <td>0.874001</td>\n",
              "      <td>0.003966</td>\n",
              "      <td>4</td>\n",
              "      <td>93.221780</td>\n",
              "      <td>0.570155</td>\n",
              "      <td>17.170962</td>\n",
              "      <td>0.583233</td>\n",
              "      <td>{'random_state': 42, 'cache_ratings': True, 'n...</td>\n",
              "      <td>42</td>\n",
              "      <td>True</td>\n",
              "      <td>15</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.853864</td>\n",
              "      <td>0.861881</td>\n",
              "      <td>0.862578</td>\n",
              "      <td>0.864652</td>\n",
              "      <td>0.863841</td>\n",
              "      <td>0.861363</td>\n",
              "      <td>0.003871</td>\n",
              "      <td>2</td>\n",
              "      <td>124.811340</td>\n",
              "      <td>0.632849</td>\n",
              "      <td>16.885598</td>\n",
              "      <td>0.221311</td>\n",
              "      <td>{'random_state': 42, 'cache_ratings': True, 'n...</td>\n",
              "      <td>42</td>\n",
              "      <td>True</td>\n",
              "      <td>20</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.865542</td>\n",
              "      <td>0.869611</td>\n",
              "      <td>0.872774</td>\n",
              "      <td>0.876097</td>\n",
              "      <td>0.875772</td>\n",
              "      <td>0.871959</td>\n",
              "      <td>0.003975</td>\n",
              "      <td>3</td>\n",
              "      <td>124.474253</td>\n",
              "      <td>0.689725</td>\n",
              "      <td>16.738724</td>\n",
              "      <td>0.324934</td>\n",
              "      <td>{'random_state': 42, 'cache_ratings': True, 'n...</td>\n",
              "      <td>42</td>\n",
              "      <td>True</td>\n",
              "      <td>20</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.20</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-eea0279e-0764-4432-a473-9487a8b48bb6')\"\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-eea0279e-0764-4432-a473-9487a8b48bb6 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-eea0279e-0764-4432-a473-9487a8b48bb6');\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-79a102a7-369a-4703-9cb5-3214b9c8664b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79a102a7-369a-4703-9cb5-3214b9c8664b')\"\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-79a102a7-369a-4703-9cb5-3214b9c8664b 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": "results_df",
              "repr_error": "unhashable type: 'dict'"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "results_df = pd.DataFrame.from_dict(gs.cv_results)\n",
        "results_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bF8ur3t79PC"
      },
      "source": [
        "#### навчання на full_trainset для найкращого алгоритму"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "rwvpU1k9Qtgf"
      },
      "outputs": [],
      "source": [
        "# We can now use the algorithm that yields the best rmse:\n",
        "algo = gs.best_estimator[\"rmse\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ym6EZVw9y9YZ",
        "outputId": "1143dd2e-5e44-408a-9a40-3081aebac417"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<surprise.model_selection.search.GridSearchCV at 0x7aa89b066b30>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_cv[algo.__class__.__name__]=(gs.best_score[\"rmse\"],0,0)"
      ],
      "metadata": {
        "id": "aW1SUM7DyCK_"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column = 0\n",
        "algo_cv_s_rmse = dict(sorted(algo_cv.items(), key=lambda x: x[1][column]))\n",
        "plot_algo(algo_cv_s_rmse, \"уточненні кросвалідації\", \"Середнє значення RMSE\", column=column)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "_v4THRMMxb4t",
        "outputId": "2d8e9b6e-e60b-4941-84fb-0a78347efa88"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best algo: SVDpp, with value: 0.8609\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmFGtg1lSWng"
      },
      "source": [
        "## Найкращі рекомендації для користувачів (SVDpp):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "sUtJHoN-SXaz"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "\n",
        "def get_top_n(predictions, n=10):\n",
        "    \"\"\"Return the top-N recommendation for each user from a set of predictions.\n",
        "\n",
        "    Args:\n",
        "        predictions(list of Prediction objects): The list of predictions, as\n",
        "            returned by the test method of an algorithm.\n",
        "        n(int): The number of recommendation to output for each user. Default\n",
        "            is 10.\n",
        "\n",
        "    Returns:\n",
        "    A dict where keys are user (raw) ids and values are lists of tuples:\n",
        "        [(raw item id, rating estimation), ...] of size n.\n",
        "    \"\"\"\n",
        "\n",
        "    # First map the predictions to each user.\n",
        "    top_n = defaultdict(list)\n",
        "    for uid, iid, true_r, est, _ in predictions:\n",
        "        top_n[uid].append((iid, est))\n",
        "\n",
        "    # Then sort the predictions for each user and retrieve the k highest ones.\n",
        "    for uid, user_ratings in top_n.items():\n",
        "        user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
        "        top_n[uid] = user_ratings[:n]\n",
        "\n",
        "    return top_n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "Qc14fUnHGuVi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9558b89d-0db6-4829-fa5e-6bc2ef0ed644"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVDpp params: {'random_state': 42, 'cache_ratings': True, 'n_epochs': 15, 'lr_all': 0.007, 'reg_all': 0.02}\n",
            "CPU times: user 1min 17s, sys: 147 ms, total: 1min 18s\n",
            "Wall time: 1min 18s\n",
            "CPU times: user 18min 40s, sys: 29.5 s, total: 19min 9s\n",
            "Wall time: 18min 57s\n"
          ]
        }
      ],
      "source": [
        "# First train an SVD algorithm on the movielens dataset.\n",
        "trainset = data.build_full_trainset()\n",
        "\n",
        "# # best value from GridSearchCV\n",
        "# algo_param = {'random_state': SEED, 'n_epochs': 20, 'lr_all': 0.005, 'reg_all': 0.02}\n",
        "print(algo.__class__.__name__, \"params:\", best_params)\n",
        "# algo = SVDpp(**algo_param)\n",
        "%time algo.fit(trainset)\n",
        "\n",
        "# Than predict ratings for all pairs (u, i) that are NOT in the training set.\n",
        "testset = trainset.build_anti_testset()\n",
        "%time predictions = algo.test(testset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "2wgCVlumNEit"
      },
      "outputs": [],
      "source": [
        "recomendations = 5\n",
        "top_n = get_top_n(predictions, n=recomendations)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "_692eM54DUzj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb300deb-f4d0-42a1-ece4-666e8cd9f2e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Найкращі рекомендації для перших 15 користувачів:\n",
            "\n",
            "Для користувача:  1, найкращі 5 рекомендацій: ['   318: ***** (5.00)', ' 58559: ***** (5.00)', '   720: ***** (5.00)', '   914: ***** (5.00)', '   898: ***** (5.00)']\n",
            "Для користувача:  2, найкращі 5 рекомендацій: ['  1204: ****  (4.41)', '   750: ****  (4.41)', '  3451: ****  (4.35)', '  2324: ****  (4.34)', '   933: ****  (4.34)']\n",
            "Для користувача:  3, найкращі 5 рекомендацій: ['   356: ****  (3.64)', '   318: ****  (3.56)', '  1204: ****  (3.54)', '   457: ****  (3.50)', '  1223: ***   (3.50)']\n",
            "Для користувача:  4, найкращі 5 рекомендацій: [' 56782: ****  (4.34)', '   922: ****  (4.28)', '  6016: ****  (4.23)', '  1228: ****  (4.22)', '  1276: ****  (4.20)']\n",
            "Для користувача:  5, найкращі 5 рекомендацій: ['   933: ****  (4.24)', '  1204: ****  (4.20)', '  3451: ****  (4.19)', '   898: ****  (4.19)', '  1276: ****  (4.18)']\n",
            "Для користувача:  6, найкращі 5 рекомендацій: ['  1197: ****  (4.49)', '  1203: ****  (4.35)', '  1223: ****  (4.35)', '   922: ****  (4.34)', '   910: ****  (4.34)']\n",
            "Для користувача:  7, найкращі 5 рекомендацій: ['   296: ****  (4.29)', '  1203: ****  (4.22)', '  2959: ****  (4.11)', '   318: ****  (4.09)', '  1291: ****  (4.09)']\n",
            "Для користувача:  8, найкращі 5 рекомендацій: ['   750: ****  (4.49)', '  1204: ****  (4.44)', '  2959: ****  (4.42)', '  7361: ****  (4.37)', '  2858: ****  (4.36)']\n",
            "Для користувача:  9, найкращі 5 рекомендацій: ['   296: ****  (4.35)', '   858: ****  (4.33)', '  3030: ****  (4.20)', '  4973: ****  (4.19)', '   898: ****  (4.18)']\n",
            "Для користувача: 10, найкращі 5 рекомендацій: ['  2572: ****  (4.05)', '  3451: ****  (4.03)', '  1584: ****  (3.96)', '    58: ****  (3.96)', '  2580: ****  (3.91)']\n",
            "Для користувача: 11, найкращі 5 рекомендацій: ['   898: ***** (4.60)', '  2571: ***** (4.58)', '  1262: ***** (4.58)', '   904: ***** (4.56)', '  1248: ***** (4.56)']\n",
            "Для користувача: 12, найкращі 5 рекомендацій: ['    50: ***** (5.00)', '   260: ***** (5.00)', '   954: ***** (5.00)', '  1197: ***** (5.00)', '  1219: ***** (5.00)']\n",
            "Для користувача: 13, найкращі 5 рекомендацій: ['   318: ****  (4.50)', '   898: ****  (4.45)', '   922: ****  (4.45)', '  1204: ****  (4.45)', '   246: ****  (4.41)']\n",
            "Для користувача: 14, найкращі 5 рекомендацій: ['  1204: ****  (4.31)', '  3451: ****  (4.30)', '105504: ****  (4.21)', '  1104: ****  (4.19)', '   908: ****  (4.18)']\n",
            "Для користувача: 15, найкращі 5 рекомендацій: ['  1204: ****  (4.04)', '   922: ****  (4.00)', '  1704: ****  (3.99)', '   750: ****  (3.96)', '  1203: ****  (3.95)']\n"
          ]
        }
      ],
      "source": [
        "counter=15\n",
        "# Print the recommended items for each user\n",
        "print(f\"Найкращі рекомендації для перших {counter} користувачів:\\n\")\n",
        "\n",
        "for uid, user_ratings in top_n.items():\n",
        "    recomend = [f\"{iid:6}: {'*'*round(est):5} ({est:.2f})\" for (iid, est) in user_ratings]\n",
        "    print(f\"Для користувача: {uid:2}, найкращі {recomendations} рекомендацій: {recomend}\")\n",
        "    counter -= 1\n",
        "    if counter <= 0:\n",
        "      break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "st-VpfsOHfV7"
      },
      "source": [
        "### Decode name of movies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "2P4cU4v11CpR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "7d4fe6f4-c4fd-4fbf-9e8b-1289207f0587"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    title                            genres\n",
              "movieId                                                    \n",
              "6517     Babe, The (1992)                             Drama\n",
              "1029         Dumbo (1941)  Animation|Children|Drama|Musical\n",
              "7714       Camelot (1967)             Drama|Musical|Romance"
            ],
            "text/html": [
              "\n",
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              "      <td>Babe, The (1992)</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"movie_list\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Babe, The (1992)\",\n          \"Dumbo (1941)\",\n          \"Camelot (1967)\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Drama\",\n          \"Animation|Children|Drama|Musical\",\n          \"Drama|Musical|Romance\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "if laod_mode == \"load_builtin\":\n",
        "  movie_list = pd.read_csv(movies_csv ) # index_col=\"movieId\"\n",
        "else:\n",
        "  movie_list = pd.read_csv(movies_csv, index_col=\"movieId\")\n",
        "movie_list.sample(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "WYBFE850AlC8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88c93e6a-f296-4656-eda4-208357482784"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Найкращі рекомендації для перших 4 користувачів:\n",
            "\n",
            "Для користувача: 1, найкращі 5 рекомендацій:\n",
            " - ***** (5.00): Shawshank Redemption, The (1994), [Crime], ID:318\n",
            " - ***** (5.00): Dark Knight, The (2008), [Action], ID:58559\n",
            " - ***** (5.00): Wallace & Gromit: The Best of Aardman Animation (1996), [Adventure], ID:720\n",
            " - ***** (5.00): My Fair Lady (1964), [Comedy], ID:914\n",
            " - ***** (5.00): Philadelphia Story, The (1940), [Comedy], ID:898\n",
            "Для користувача: 2, найкращі 5 рекомендацій:\n",
            " - ****  (4.41): Lawrence of Arabia (1962), [Adventure], ID:1204\n",
            " - ****  (4.41): Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964), [Comedy], ID:750\n",
            " - ****  (4.35): Guess Who's Coming to Dinner (1967), [Drama], ID:3451\n",
            " - ****  (4.34): Life Is Beautiful (La Vita è bella) (1997), [Comedy], ID:2324\n",
            " - ****  (4.34): To Catch a Thief (1955), [Crime], ID:933\n",
            "Для користувача: 3, найкращі 5 рекомендацій:\n",
            " - ****  (3.64): Forrest Gump (1994), [Comedy], ID:356\n",
            " - ****  (3.56): Shawshank Redemption, The (1994), [Crime], ID:318\n",
            " - ****  (3.54): Lawrence of Arabia (1962), [Adventure], ID:1204\n",
            " - ****  (3.50): Fugitive, The (1993), [Thriller], ID:457\n",
            " - ***   (3.50): Grand Day Out with Wallace and Gromit, A (1989), [Adventure], ID:1223\n",
            "Для користувача: 4, найкращі 5 рекомендацій:\n",
            " - ****  (4.34): There Will Be Blood (2007), [Drama], ID:56782\n",
            " - ****  (4.28): Sunset Blvd. (a.k.a. Sunset Boulevard) (1950), [Drama], ID:922\n",
            " - ****  (4.23): City of God (Cidade de Deus) (2002), [Action], ID:6016\n",
            " - ****  (4.22): Raging Bull (1980), [Drama], ID:1228\n",
            " - ****  (4.20): Cool Hand Luke (1967), [Drama], ID:1276\n"
          ]
        }
      ],
      "source": [
        "recomendations = 5\n",
        "# top_n = get_top_n(predictions, n=recomendations)\n",
        "\n",
        "counter=4\n",
        "# Print the recommended items for each user\n",
        "print(f\"Найкращі рекомендації для перших {counter} користувачів:\\n\")\n",
        "\n",
        "for uid, user_ratings in top_n.items():\n",
        "    print(f\"Для користувача: {uid}, найкращі {recomendations} рекомендацій:\")\n",
        "    for (iid, est) in user_ratings:\n",
        "      title = movie_list['title'].get(int(iid), f\"ID:{iid}\")\n",
        "      genre = movie_list['genres'].get(int(iid))\n",
        "      link = \"\" if laod_mode == \"load_builtin\" else f\", ID:{iid}\"\n",
        "      if genre:\n",
        "        genre = genre.split('|')[0]\n",
        "      print(f\" - {'*'*round(est):5} ({est:.2f}): {title}, [{genre}]{link}\")\n",
        "    counter -= 1\n",
        "    if counter <= 0:\n",
        "      break"
      ]
    }
  ],
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