goit_python_ds_hw_07_load_builtin.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/lexxai/goit_python_ds_hw_07/blob/main/goit_python_ds_hw_07_load_builtin.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "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"
      ],
      "metadata": {
        "id": "VTvBpLm5Jqyj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LOAD DATASET"
      ],
      "metadata": {
        "id": "OLW2WK4cK08b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-surprise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQlrrC64Ei_D",
        "outputId": "8b3c5a02-997a-438a-d6fa-9647f259b046"
      },
      "execution_count": 1,
      "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[31m6.2 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=3163009 sha256=e6ced9278176b02d481e5b319505209453c57ff1a40b4de3d66ef63959ca699b\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "m63KlyHSprCX"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "laod_mode = \"load_builtin\""
      ],
      "metadata": {
        "id": "8_JhmJld6eFR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "6SaBHI8tEE-8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1577ce64-ac23-40ee-8290-d0ecb074973f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trying to download dataset from https://files.grouplens.org/datasets/movielens/ml-100k.zip...\n",
            "Done! Dataset ml-100k has been saved to /root/.surprise_data/ml-100k\n"
          ]
        }
      ],
      "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",
      "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"
      ],
      "metadata": {
        "id": "T0DErstIgFzd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2eecf64-536e-4cba-bc64-6df2170ef0d8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "u1.base\n",
            "u1.test\n",
            "u2.base\n",
            "u2.test\n",
            "u3.base\n",
            "u3.test\n",
            "u4.base\n",
            "u4.test\n",
            "u5.base\n",
            "u5.test\n",
            "ua.base\n",
            "ua.test\n",
            "ub.base\n",
            "ub.test\n",
            "u.data\n",
            "u.genre\n",
            "u.info\n",
            "u.item\n",
            "u.occupation\n",
            "u.user\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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)))"
      ],
      "metadata": {
        "id": "z-K7JzgVjES4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd6b337c-5825-4c9e-f349-09d71acba857"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test_files:\n",
            "['u1.test', 'u2.test', 'u3.test', 'u4.test', 'u5.test', 'ua.test', 'ub.test']\n",
            "\n",
            "Train_files:\n",
            "['u1.base', 'u2.base', 'u3.base', 'u4.base', 'u5.base', 'ua.base', 'ub.base']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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\")"
      ],
      "metadata": {
        "id": "oVTS5xZsmyJS"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "dNYxkIYrpWZE"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "NEEvbfqBquBr"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "id": "zk4mZ2b0cx7F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "14ea597a-a281-40d7-ef65-9e78947e8cdb"
      },
      "execution_count": 10,
      "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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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Аналіз записів"
      ],
      "metadata": {
        "id": "VumP61raGOTE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Неважлива інофромаційна частина для пошуку реальних значень з датасету: uid, iid, ur. А також для уявлення змісту даних датасету."
      ],
      "metadata": {
        "id": "DjEBc7AqBy9O"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "HPNxTcHLbyWK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc4159ee-a910-4ea3-d66a-058017751b8c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of ratings: 100000\n",
            "\n",
            "Top 5 samples of raw ratings:\n",
            "UserID: 196  , ItemID: 242  , UserRateForItem:   3.0, date: 1997-12-04\n",
            "UserID: 186  , ItemID: 302  , UserRateForItem:   3.0, date: 1998-04-04\n",
            "UserID: 22   , ItemID: 377  , UserRateForItem:   1.0, date: 1997-11-07\n",
            "UserID: 244  , ItemID: 51   , UserRateForItem:   2.0, date: 1997-11-27\n",
            "UserID: 166  , ItemID: 346  , UserRateForItem:   1.0, date: 1998-02-02\n",
            "\n",
            "Top 5 ratings of user: 166\n",
            "UserID: 166  , ItemID: 346  , UserRateForItem:   1.0, date: 1998-02-02\n",
            "UserID: 166  , ItemID: 328  , UserRateForItem:   5.0, date: 1998-02-02\n",
            "UserID: 166  , ItemID: 322  , UserRateForItem:   5.0, date: 1998-02-02\n",
            "UserID: 166  , ItemID: 288  , UserRateForItem:   3.0, date: 1998-02-02\n",
            "UserID: 166  , ItemID: 258  , UserRateForItem:   4.0, date: 1998-02-02\n",
            "\n",
            "Top 5 ratings of item: 258\n",
            "UserID: 26   , ItemID: 258  , UserRateForItem:   3.0, date: 1998-03-31\n",
            "UserID: 66   , ItemID: 258  , UserRateForItem:   4.0, date: 1997-12-31\n",
            "UserID: 207  , ItemID: 258  , UserRateForItem:   4.0, date: 1997-10-26\n",
            "UserID: 90   , ItemID: 258  , UserRateForItem:   3.0, date: 1998-03-31\n",
            "UserID: 6    , ItemID: 258  , UserRateForItem:   2.0, date: 1997-12-28\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Використовуєтьс прямий відгук (Explicit feedback) - це оцінка, яку користувач поставив, щоб висловити свою задоволеність товаром."
      ],
      "metadata": {
        "id": "UGbdKhLZGOTI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Для оцінки що базується на спільній інформації про користувачів  та їхню взаємодію з об'єктами (Collaborative Filtering) вибранно модель матричної факторизації (Matrix Factorization)."
      ],
      "metadata": {
        "id": "vd_a5Z0QGOTI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# uid = 6\n",
        "# iid = 258\n",
        "# ur = 2.0\n",
        "# dater = \"1997-12-28\""
      ],
      "metadata": {
        "id": "djCv_xDbDPFc"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ми використовуємо об'єкт ***DatasetAutoFolds*** у Surprise, це означає, що ми працюємо з набором даних, який вже було розбито на файли (folds) для перехресної перевірки.\n",
        "\n",
        "\n",
        "\n",
        "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)."
      ],
      "metadata": {
        "id": "yzuccKchc2ji"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mоделі матричної факторизації"
      ],
      "metadata": {
        "id": "PYbeZAPjtK1_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Algorithm SVD.\n",
        "\n",
        "Метод SVD може бути адаптований до наборів даних з неявним зворотним зв'язком (implicit feedback)."
      ],
      "metadata": {
        "id": "ueUxG3LaLG4I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "- RMSE - root-mean-square error\n",
        "- MAE - mean absolute error"
      ],
      "metadata": {
        "id": "uyQ5s_mFwxP5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 42\n",
        "algo_cv = {}\n",
        "algo_split = {}\n",
        "# Use the SVD algorithm.\n",
        "algo = SVD(random_state=SEED)"
      ],
      "metadata": {
        "id": "xtGVOa9MvqW-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Прогноз"
      ],
      "metadata": {
        "id": "AXSz9le_xsT4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Full_trainset та він же і testset. Accuracy RMSE (root-mean-square error).\n"
      ],
      "metadata": {
        "id": "eCXw_cYz2z45"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def data_full_trainset(algo, data):\n",
        "  algorithm_name = algo.__class__.__name__\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)"
      ],
      "metadata": {
        "id": "Vqp3Qyoivlrg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_full_trainset(algo, data)"
      ],
      "metadata": {
        "id": "IA0MEHsC_pac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9166d8ef-4a6e-4276-e1b8-5e92243bf093"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVD, UserID: 6    , ItemID: 258  , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 100000\n",
            "user: 6          item: 258        r_ui = 2.00   est = 2.60   {'was_impossible': False}\n",
            "Number of test ratings: 100000\n",
            "RMSE: 0.6752\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Train_test_split 25%. Accuracy RMSE (root-mean-square error).\n"
      ],
      "metadata": {
        "id": "Q9A4q5cF3OF0"
      }
    },
    {
      "cell_type": "code",
      "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}"
      ],
      "metadata": {
        "id": "LdeuubKe2U_G"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "algo_split.update(data_train_test_split(algo, data))"
      ],
      "metadata": {
        "id": "glUST0_nAXnR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d92330b-deb0-4190-e938-0b0cb7aa9484"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVD, UserID: 6    , ItemID: 258  , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 75000\n",
            "user: 6          item: 258        r_ui = 2.00   est = 2.91   {'was_impossible': False}\n",
            "Number of test ratings: 25000\n",
            "RMSE: 0.9390\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cross Validate, cv = 5"
      ],
      "metadata": {
        "id": "O_uWjxHB2OVX"
      }
    },
    {
      "cell_type": "code",
      "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)}"
      ],
      "metadata": {
        "id": "s2Kyk9JeGBJy"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "algo_cv.update(data_cross_validate(algo, data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxWiH8bUWrk3",
        "outputId": "599bb7d4-732d-4931-f735-ce7840fd2221"
      },
      "execution_count": 19,
      "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)    0.9370  0.9348  0.9389  0.9312  0.9372  0.9358  0.0027  \n",
            "MAE (testset)     0.7381  0.7399  0.7378  0.7327  0.7399  0.7377  0.0026  \n",
            "Fit time          3.54    3.03    2.71    3.13    1.55    2.79    0.68    \n",
            "Test time         0.32    0.23    0.25    0.19    0.12    0.22    0.07    \n",
            "mean_rmse=0.9358139953699286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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"
      ],
      "metadata": {
        "id": "xUP_-6eYM_1T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # surprise.prediction_algorithms.matrix_factorization.SVDpp\n",
        "from surprise import SVDpp\n",
        "\n",
        "# Use the SVDpp algorithm.\n",
        "algo = SVDpp()"
      ],
      "metadata": {
        "id": "QxLJcJhUNA2o"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_full_trainset(algo, data)"
      ],
      "metadata": {
        "id": "IS0gaLu--bMW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58de80e1-5c04-46e2-bc9e-490068f0ed80"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVDpp, UserID: 6    , ItemID: 258  , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 100000\n",
            "user: 6          item: 258        r_ui = 2.00   est = 3.11   {'was_impossible': False}\n",
            "Number of test ratings: 100000\n",
            "RMSE: 0.7559\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_split.update(data_train_test_split(algo, data))"
      ],
      "metadata": {
        "id": "F9aQtziO-fTA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e840739-b721-4575-fcb5-9ade71a4ce80"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: SVDpp, UserID: 6    , ItemID: 258  , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 75000\n",
            "user: 6          item: 258        r_ui = 2.00   est = 3.29   {'was_impossible': False}\n",
            "Number of test ratings: 25000\n",
            "RMSE: 0.9253\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_cv.update(data_cross_validate(algo, data))"
      ],
      "metadata": {
        "id": "D1mlY6C_-WWh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30d8c38b-3fc7-4e76-b5cb-41d8475a2253"
      },
      "execution_count": 23,
      "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)    0.9167  0.9189  0.9179  0.9202  0.9183  0.9184  0.0012  \n",
            "MAE (testset)     0.7186  0.7196  0.7207  0.7222  0.7202  0.7203  0.0012  \n",
            "Fit time          28.43   26.93   26.68   26.93   38.13   29.42   4.40    \n",
            "Test time         4.39    5.32    4.47    4.92    4.44    4.71    0.36    \n",
            "mean_rmse=0.9183848633406491\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Algorithm NMF"
      ],
      "metadata": {
        "id": "7yAcmVTsMtDD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# surprise.prediction_algorithms.matrix_factorization.NMF\n",
        "from surprise import NMF\n",
        "\n",
        "# Use the NMF algorithm.\n",
        "algo = NMF()"
      ],
      "metadata": {
        "id": "fsbcpEK5MuMz"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_full_trainset(algo, data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hnz2KCaC_EDw",
        "outputId": "07371ca5-cefe-46e8-e989-e756aca7829d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: NMF, UserID: 6    , ItemID: 258  , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 100000\n",
            "user: 6          item: 258        r_ui = 2.00   est = 3.82   {'was_impossible': False}\n",
            "Number of test ratings: 100000\n",
            "RMSE: 0.8199\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_split.update(data_train_test_split(algo, data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u92zY9Yi_ILp",
        "outputId": "60148fc1-2a58-4ec0-b371-2a77154d13ba"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: NMF, UserID: 6    , ItemID: 258  , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 75000\n",
            "user: 6          item: 258        r_ui = 2.00   est = 3.65   {'was_impossible': False}\n",
            "Number of test ratings: 25000\n",
            "RMSE: 0.9689\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_cv.update(data_cross_validate(algo, data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnuu_byH_Hiw",
        "outputId": "eb0d5e75-fbdd-43fd-abda-d9184c35f0fb"
      },
      "execution_count": 27,
      "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)    0.9529  0.9664  0.9648  0.9655  0.9555  0.9610  0.0057  \n",
            "MAE (testset)     0.7476  0.7615  0.7563  0.7593  0.7519  0.7553  0.0050  \n",
            "Fit time          2.04    2.02    2.19    2.41    2.00    2.13    0.15    \n",
            "Test time         0.11    0.10    0.28    0.12    0.19    0.16    0.07    \n",
            "mean_rmse=0.9610232683090464\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Сімейство алгоритмів k-NN\n"
      ],
      "metadata": {
        "id": "7GiwVM6d_pBf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Ці алгоритми безпосередньо випливають з базового методу найближчих сусідів.\n"
      ],
      "metadata": {
        "id": "sa_CWfroAVwO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Algorithm KNNBasic."
      ],
      "metadata": {
        "id": "4TrQTNYlL-zX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "sim_options={'user_based': True}"
      ],
      "metadata": {
        "id": "7R243CJgBPdY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from surprise import KNNBasic\n",
        "\n",
        "# Build an algorithm, and train it.\n",
        "algo = KNNBasic(sim_options={'user_based': True})"
      ],
      "metadata": {
        "id": "vr_csvEwI1f0"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_full_trainset(algo, data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QwLukw-JGdP",
        "outputId": "43b348a2-435b-4222-c2fd-7b5b01c72060"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic, UserID: 244  , ItemID: 1449 , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 100000\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 244        item: 1449       r_ui = 2.00   est = 4.67   {'actual_k': 8, 'was_impossible': False}\n",
            "Number of test ratings: 100000\n",
            "RMSE: 0.7781\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_split.update(data_train_test_split(algo, data, name_opt=\"user_on\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNmfqM4I97wW",
        "outputId": "f5f3bdc3-805d-49c2-8c52-c33f41725e3b"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic_user_on, UserID: 244  , ItemID: 1449 , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 75000\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 244        item: 1449       r_ui = 2.00   est = 4.67   {'actual_k': 8, 'was_impossible': False}\n",
            "Number of test ratings: 25000\n",
            "RMSE: 0.9835\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_cv.update(data_cross_validate(algo, data, name_opt=\"user_on\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrM3m5dh-MTt",
        "outputId": "178a8d5c-5870-4f68-b125-02ed90d34f53"
      },
      "execution_count": 61,
      "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)    0.9842  0.9743  0.9774  0.9771  0.9799  0.9786  0.0033  \n",
            "MAE (testset)     0.7785  0.7681  0.7719  0.7723  0.7741  0.7730  0.0034  \n",
            "Fit time          0.51    0.37    0.36    0.50    0.36    0.42    0.07    \n",
            "Test time         4.01    3.09    3.13    3.70    3.11    3.41    0.38    \n",
            "mean_rmse=0.9785791598707767\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Algorithm KNNBasic."
      ],
      "metadata": {
        "id": "IfuCUaXnwFSz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "sim_options={'user_based': False}"
      ],
      "metadata": {
        "id": "SACrcBQ0wFS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from surprise import KNNBasic\n",
        "\n",
        "# Build an algorithm, and train it.\n",
        "algo = KNNBasic(sim_options={'user_based': False})"
      ],
      "metadata": {
        "id": "Lu12nFsRwFS3"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_full_trainset(algo, data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcb71a7d-80b9-4efa-fea2-19e48b4675bd",
        "id": "AOko3LB3wFS4"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic, UserID: 244  , ItemID: 1449 , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 100000\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 244        item: 1449       r_ui = 2.00   est = 3.95   {'actual_k': 40, 'was_impossible': False}\n",
            "Number of test ratings: 100000\n",
            "RMSE: 0.7166\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_split.update(data_train_test_split(algo, data, name_opt=\"user_off\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f32673da-dd52-4534-ac26-0c0bbc104f11",
        "id": "BY2xVXMCwFS6"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algo: KNNBasic_user_off, UserID: 244  , ItemID: 1449 , UserRateForItem:   2.0, date: 1997-12-28\n",
            "Number of train ratings: 75000\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "user: 244        item: 1449       r_ui = 2.00   est = 3.91   {'actual_k': 40, 'was_impossible': False}\n",
            "Number of test ratings: 25000\n",
            "RMSE: 0.9732\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_cv.update(data_cross_validate(algo, data, name_opt=\"user_off\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6c17c55-eb15-4764-8338-425a73f049e9",
        "id": "usGWn8g3wFS6"
      },
      "execution_count": 65,
      "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)    0.9783  0.9718  0.9740  0.9779  0.9704  0.9745  0.0032  \n",
            "MAE (testset)     0.7716  0.7676  0.7696  0.7735  0.7665  0.7697  0.0026  \n",
            "Fit time          0.55    0.53    0.52    0.74    0.55    0.58    0.08    \n",
            "Test time         4.61    3.53    3.55    4.18    3.57    3.89    0.44    \n",
            "mean_rmse=0.9744575309985397\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Результати accuracy для різних алгоритмів"
      ],
      "metadata": {
        "id": "ZEf1LzGQH1qZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "algo_split"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0s7f5PcQH_lH",
        "outputId": "60da1b8b-670b-4102-d327-a952c83b9bf8"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'SVD': 0.9389789510116466,\n",
              " 'SVDpp': 0.9253395909276804,\n",
              " 'NMF': 0.9689142969809889,\n",
              " 'KNNBasic_user_on': 0.9835228263710808,\n",
              " 'KNNBasic_user_off': 0.9732485565027006}"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "algo_split_s = dict(sorted(algo_split.items(), key=lambda x: x[1]))"
      ],
      "metadata": {
        "id": "QslxVHDiIj8S"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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}\")"
      ],
      "metadata": {
        "id": "SojB-No_IrpS"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_algo(algo_split_s, \"predict accuracy\", \" Значення RMSE\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "orw3cpgESMTs",
        "outputId": "3082dac1-39ab-4c55-f3b7-c4904a1ee610"
      },
      "execution_count": 71,
      "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: 0.9253\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Результати кросвалідації для різних алгоритмів"
      ],
      "metadata": {
        "id": "9oZbFr2xZnyM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "algo_cv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5l5ekHeqZnba",
        "outputId": "ca0e622d-4c7c-4060-9aa0-b916367e1692"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'SVD': (0.9358139953699286, 2.792861223220825, 0.2228849411010742),\n",
              " 'SVDpp': (0.9183848633406491, 29.419613695144655, 4.708288049697876),\n",
              " 'NMF': (0.9610232683090464, 2.133493185043335, 0.15938348770141603),\n",
              " 'KNNBasic_user_on': (0.9785791598707767,\n",
              "  0.420868444442749,\n",
              "  3.4068908214569094),\n",
              " 'KNNBasic_user_off': (0.9744575309985397,\n",
              "  0.5808469295501709,\n",
              "  3.8856775760650635)}"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "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, \"кросвалідації\", \"Середнє значення RMSE\", column=column)"
      ],
      "metadata": {
        "id": "yUon2b_nbUFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "20141f7c-40db-40d0-ee10-d7b70ccc815e"
      },
      "execution_count": 76,
      "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: 0.9184\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "He1I-qal5Oh8",
        "outputId": "e32e7b4b-dd24-40b9-f0b7-d6f85b3719d2"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best algo: KNNBasic_user_on, with value: 0.4209\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "4rRRGBOf5jis",
        "outputId": "26069d6a-afa5-482f-c1e0-0a67585f6893"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best algo: NMF, with value: 0.1594\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Найкращій алгорим"
      ],
      "metadata": {
        "id": "UfO5MUvEABAK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- за точністю предбачення: SVD++, SVD\n",
        "- за часом навчання моделі: KNNBasic, SVD\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."
      ],
      "metadata": {
        "id": "nav4ol5M-YkZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_algo = SVDpp"
      ],
      "metadata": {
        "id": "qodh0GyF6zry"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Пошук оптимальних параметрів використовуючи GridSearchCV для SVD++ (SVDpp)"
      ],
      "metadata": {
        "id": "t9xezE86PB4A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from surprise.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\"random_state\": [SEED],\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=}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xG17GmmPClu",
        "outputId": "488d8d8e-f4ff-4b69-fec1-7a8fdaa9a1be"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 7.21 s, sys: 1.12 s, total: 8.33 s\n",
            "Wall time: 7min 53s\n",
            "0.9175445602961035\n",
            "best_params={'random_state': 42, 'n_epochs': 15, 'lr_all': 0.007, 'reg_all': 0.02}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Звіт роботи GridSearchCV для всіх комбінацій параметрів"
      ],
      "metadata": {
        "id": "-hEdiA9J8XbC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results_df = pd.DataFrame.from_dict(gs.cv_results)\n",
        "results_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "RE51ObkqRAua",
        "outputId": "af6f40c7-8168-42b5-93d6-00c8fe12e237"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   split0_test_rmse  split1_test_rmse  split2_test_rmse  split3_test_rmse  \\\n",
              "0          0.923154          0.913083          0.910013          0.922126   \n",
              "1          0.949519          0.944492          0.941126          0.952146   \n",
              "2          0.923251          0.913915          0.910829          0.923096   \n",
              "3          0.948422          0.943261          0.939979          0.951171   \n",
              "\n",
              "   split4_test_rmse  mean_test_rmse  std_test_rmse  rank_test_rmse  \\\n",
              "0          0.919347        0.917545       0.005145               1   \n",
              "1          0.946538        0.946764       0.003836               4   \n",
              "2          0.921542        0.918527       0.005154               2   \n",
              "3          0.945285        0.945624       0.003903               3   \n",
              "\n",
              "   mean_fit_time  std_fit_time  mean_test_time  std_test_time  \\\n",
              "0      34.786697      0.311591        5.851484       0.038042   \n",
              "1      34.546105      0.215875        6.286263       0.562962   \n",
              "2      47.465124      2.111579        6.904918       0.813040   \n",
              "3      45.592980      0.441380        6.522812       0.361262   \n",
              "\n",
              "                                              params  param_random_state  \\\n",
              "0  {'random_state': 42, 'n_epochs': 15, 'lr_all':...                  42   \n",
              "1  {'random_state': 42, 'n_epochs': 15, 'lr_all':...                  42   \n",
              "2  {'random_state': 42, 'n_epochs': 20, 'lr_all':...                  42   \n",
              "3  {'random_state': 42, 'n_epochs': 20, 'lr_all':...                  42   \n",
              "\n",
              "   param_n_epochs  param_lr_all  param_reg_all  \n",
              "0              15         0.007           0.02  \n",
              "1              15         0.007           0.20  \n",
              "2              20         0.007           0.02  \n",
              "3              20         0.007           0.20  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c914d403-c395-4289-94c8-65d7f14f4225\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "  <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_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>0.923154</td>\n",
              "      <td>0.913083</td>\n",
              "      <td>0.910013</td>\n",
              "      <td>0.922126</td>\n",
              "      <td>0.919347</td>\n",
              "      <td>0.917545</td>\n",
              "      <td>0.005145</td>\n",
              "      <td>1</td>\n",
              "      <td>34.786697</td>\n",
              "      <td>0.311591</td>\n",
              "      <td>5.851484</td>\n",
              "      <td>0.038042</td>\n",
              "      <td>{'random_state': 42, 'n_epochs': 15, 'lr_all':...</td>\n",
              "      <td>42</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>0.949519</td>\n",
              "      <td>0.944492</td>\n",
              "      <td>0.941126</td>\n",
              "      <td>0.952146</td>\n",
              "      <td>0.946538</td>\n",
              "      <td>0.946764</td>\n",
              "      <td>0.003836</td>\n",
              "      <td>4</td>\n",
              "      <td>34.546105</td>\n",
              "      <td>0.215875</td>\n",
              "      <td>6.286263</td>\n",
              "      <td>0.562962</td>\n",
              "      <td>{'random_state': 42, 'n_epochs': 15, 'lr_all':...</td>\n",
              "      <td>42</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>0.923251</td>\n",
              "      <td>0.913915</td>\n",
              "      <td>0.910829</td>\n",
              "      <td>0.923096</td>\n",
              "      <td>0.921542</td>\n",
              "      <td>0.918527</td>\n",
              "      <td>0.005154</td>\n",
              "      <td>2</td>\n",
              "      <td>47.465124</td>\n",
              "      <td>2.111579</td>\n",
              "      <td>6.904918</td>\n",
              "      <td>0.813040</td>\n",
              "      <td>{'random_state': 42, 'n_epochs': 20, 'lr_all':...</td>\n",
              "      <td>42</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>0.948422</td>\n",
              "      <td>0.943261</td>\n",
              "      <td>0.939979</td>\n",
              "      <td>0.951171</td>\n",
              "      <td>0.945285</td>\n",
              "      <td>0.945624</td>\n",
              "      <td>0.003903</td>\n",
              "      <td>3</td>\n",
              "      <td>45.592980</td>\n",
              "      <td>0.441380</td>\n",
              "      <td>6.522812</td>\n",
              "      <td>0.361262</td>\n",
              "      <td>{'random_state': 42, 'n_epochs': 20, 'lr_all':...</td>\n",
              "      <td>42</td>\n",
              "      <td>20</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.20</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  <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",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-c914d403-c395-4289-94c8-65d7f14f4225 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-c914d403-c395-4289-94c8-65d7f14f4225');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fed927c-0bc7-461d-a179-b114ca48a487')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<style>\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    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",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\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-8fed927c-0bc7-461d-a179-b114ca48a487 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</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": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### навчання на full_trainset для найкращого алгоритму"
      ],
      "metadata": {
        "id": "7bF8ur3t79PC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We can now use the algorithm that yields the best rmse:\n",
        "algo = gs.best_estimator[\"rmse\"]"
      ],
      "metadata": {
        "id": "rwvpU1k9Qtgf"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Найкращі рекомендації для користувачів (SVDpp):"
      ],
      "metadata": {
        "id": "NmFGtg1lSWng"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "sUtJHoN-SXaz"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qc14fUnHGuVi",
        "outputId": "c51e0743-9e39-4fd5-936f-a1d8b6a31f2e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVDpp params: {'random_state': 42, 'n_epochs': 15, 'lr_all': 0.007, 'reg_all': 0.02}\n",
            "CPU times: user 32 s, sys: 69.3 ms, total: 32.1 s\n",
            "Wall time: 34.8 s\n",
            "CPU times: user 3min 26s, sys: 7.3 s, total: 3min 33s\n",
            "Wall time: 3min 32s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recomendations = 5\n",
        "top_n = get_top_n(predictions, n=recomendations)"
      ],
      "metadata": {
        "id": "2wgCVlumNEit"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_692eM54DUzj",
        "outputId": "806e2688-38ce-47e9-cb50-57693ac51025"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Найкращі рекомендації для перших 15 користувачів:\n",
            "\n",
            "Для користувача: 196, найкращі 5 рекомендацій: ['318   : ****  (4.44)', '169   : ****  (4.43)', '408   : ****  (4.37)', '483   : ****  (4.33)', '515   : ****  (4.29)']\n",
            "Для користувача: 186, найкращі 5 рекомендацій: ['318   : ****  (4.49)', '174   : ****  (4.38)', '64    : ****  (4.37)', '143   : ****  (4.34)', '169   : ****  (4.31)']\n",
            "Для користувача: 22, найкращі 5 рекомендацій: ['169   : ***** (4.94)', '64    : ***** (4.85)', '408   : ***** (4.77)', '199   : ***** (4.75)', '313   : ***** (4.74)']\n",
            "Для користувача: 244, найкращі 5 рекомендацій: ['127   : ***** (5.00)', '408   : ***** (4.95)', '187   : ***** (4.92)', '12    : ***** (4.89)', '1449  : ***** (4.87)']\n",
            "Для користувача: 166, найкращі 5 рекомендацій: ['169   : ***** (4.66)', '318   : ***** (4.64)', '174   : ***** (4.63)', '408   : ***** (4.58)', '22    : ***** (4.57)']\n",
            "Для користувача: 298, найкращі 5 рекомендацій: ['408   : ***** (4.68)', '169   : ***** (4.67)', '64    : ***** (4.65)', '114   : ***** (4.52)', '515   : ***** (4.51)']\n",
            "Для користувача: 115, найкращі 5 рекомендацій: ['408   : ***** (5.00)', '179   : ***** (4.96)', '285   : ***** (4.85)', '134   : ***** (4.84)', '474   : ***** (4.81)']\n",
            "Для користувача: 253, найкращі 5 рекомендацій: ['174   : ***** (4.69)', '272   : ***** (4.60)', '313   : ***** (4.59)', '603   : ***** (4.58)', '169   : ***** (4.56)']\n",
            "Для користувача: 305, найкращі 5 рекомендацій: ['657   : ****  (4.19)', '515   : ****  (4.15)', '114   : ****  (4.13)', '603   : ****  (4.13)', '513   : ****  (4.07)']\n",
            "Для користувача: 6 , найкращі 5 рекомендацій: ['603   : ****  (4.46)', '657   : ****  (4.32)', '923   : ****  (4.31)', '251   : ****  (4.29)', '48    : ****  (4.28)']\n",
            "Для користувача: 62, найкращі 5 рекомендацій: ['169   : ****  (4.39)', '654   : ****  (4.32)', '963   : ****  (4.30)', '408   : ****  (4.29)', '187   : ****  (4.28)']\n",
            "Для користувача: 286, найкращі 5 рекомендацій: ['318   : ***** (4.80)', '480   : ***** (4.66)', '114   : ***** (4.64)', '515   : ***** (4.62)', '134   : ***** (4.61)']\n",
            "Для користувача: 200, найкращі 5 рекомендацій: ['181   : ***** (5.00)', '64    : ***** (5.00)', '12    : ***** (4.92)', '272   : ***** (4.88)', '316   : ***** (4.79)']\n",
            "Для користувача: 210, найкращі 5 рекомендацій: ['64    : ***** (4.85)', '603   : ***** (4.80)', '318   : ***** (4.79)', '169   : ***** (4.77)', '12    : ***** (4.77)']\n",
            "Для користувача: 224, найкращі 5 рекомендацій: ['174   : ****  (4.31)', '121   : ****  (4.29)', '96    : ****  (4.16)', '210   : ****  (4.12)', '117   : ****  (4.08)']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decode name of movies"
      ],
      "metadata": {
        "id": "st-VpfsOHfV7"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "2P4cU4v11CpR",
        "outputId": "8981c24a-da3a-460a-f29c-617e9d609dc5"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      movieId                            title                      genres\n",
              "767      1009  Escape to Witch Mountain (1975)  Adventure|Children|Fantasy\n",
              "3224     4354            Unlawful Entry (1992)              Crime|Thriller\n",
              "2051     2730              Barry Lyndon (1975)           Drama|Romance|War"
            ],
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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\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1672,\n        \"min\": 1009,\n        \"max\": 4354,\n        \"samples\": [\n          1009,\n          4354,\n          2730\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Escape to Witch Mountain (1975)\",\n          \"Unlawful Entry (1992)\",\n          \"Barry Lyndon (1975)\"\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          \"Adventure|Children|Fantasy\",\n          \"Crime|Thriller\",\n          \"Drama|Romance|War\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "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\"https://www.themoviedb.org/movie/{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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYBFE850AlC8",
        "outputId": "9d78c852-3da8-4d12-875f-c2019fde9518"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Найкращі рекомендації для перших 4 користувачів:\n",
            "\n",
            "Для користувача: 196, найкращі 5 рекомендацій:\n",
            " - ****  (4.44): I Love Trouble (1994), [Action] \n",
            " - ****  (4.43): Three Wishes (1995), [Drama] \n",
            " - ****  (4.37): House Party 3 (1994), [Comedy] \n",
            " - ****  (4.33): Nightmare Before Christmas, The (1993), [Animation] \n",
            " - ****  (4.29): Wild Bunch, The (1969), [Adventure] \n",
            "Для користувача: 186, найкращі 5 рекомендацій:\n",
            " - ****  (4.49): I Love Trouble (1994), [Action] \n",
            " - ****  (4.38): Unzipped (1995), [Documentary] \n",
            " - ****  (4.37): Kicking and Screaming (1995), [Comedy] \n",
            " - ****  (4.34): Jeffrey (1995), [Comedy] \n",
            " - ****  (4.31): Three Wishes (1995), [Drama] \n",
            "Для користувача: 22, найкращі 5 рекомендацій:\n",
            " - ***** (4.94): Three Wishes (1995), [Drama] \n",
            " - ***** (4.85): Kicking and Screaming (1995), [Comedy] \n",
            " - ***** (4.77): House Party 3 (1994), [Comedy] \n",
            " - ***** (4.75): Exotica (1994), [Drama] \n",
            " - ***** (4.74): Flintstones, The (1994), [Children] \n",
            "Для користувача: 244, найкращі 5 рекомендацій:\n",
            " - ***** (5.00): Beauty of the Day (Belle de jour) (1967), [Drama] \n",
            " - ***** (4.95): House Party 3 (1994), [Comedy] \n",
            " - ***** (4.92): Cure, The (1995), [Drama] \n",
            " - ***** (4.89): Balto (1995), [Adventure] \n",
            " - ***** (4.87): Nightmare on Elm Street 5: The Dream Child, A (1989), [Horror] \n"
          ]
        }
      ]
    }
  ]
}