lecture_04_content_based_recommenders.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "literary-toyota",
   "metadata": {},
   "source": [
    "# Content-based recommenders\n",
    "\n",
    "Content-based recommenders in their recommendations rely purely on the features of users and items. Conceptually it can be expressed as a model of the form (personalized):\n",
    "\n",
    "<center>\n",
    "$$\n",
    "    score \\sim (user\\_feature\\_1, user\\_feature\\_2, ..., user\\_feature\\_k, item\\_feature_1, item\\_feature_2, ..., item\\_feature_n)\n",
    "$$\n",
    "</center>\n",
    "\n",
    "or (not personalized)\n",
    "\n",
    "<center>\n",
    "$$\n",
    "    score \\sim (item\\_feature_1, item\\_feature_2, ..., item\\_feature_n)\n",
    "$$\n",
    "</center>\n",
    "\n",
    "\n",
    "    + Content-based recommenders do not suffer from the cold-start problem for new items.\n",
    "    - They do not use information about complex patterns of user-item interactions - what other similar users have already\n",
    "    discovered and liked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "exciting-specific",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import Markdown, display, HTML\n",
    "from collections import defaultdict\n",
    "from sklearn.model_selection import KFold\n",
    "import time\n",
    "\n",
    "pd.set_option('display.max_columns', 50)\n",
    "\n",
    "from evaluation_and_testing.testing import evaluate_train_test_split_explicit\n",
    "from evaluation_and_testing.testing import evaluate_leave_one_out_explicit\n",
    "from evaluation_and_testing.testing import evaluate_train_test_split_implicit\n",
    "from evaluation_and_testing.testing import evaluate_leave_one_out_implicit\n",
    "\n",
    "# Fix the dying kernel problem (only a problem in some installations - you can remove it, if it works without it)\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "administrative-charleston",
   "metadata": {},
   "source": [
    "# Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "architectural-andrews",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "5        6                         Heat (1995)   \n",
       "6        7                      Sabrina (1995)   \n",
       "7        8                 Tom and Huck (1995)   \n",
       "8        9                 Sudden Death (1995)   \n",
       "9       10                    GoldenEye (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  \n",
       "5                        Action|Crime|Thriller  \n",
       "6                               Comedy|Romance  \n",
       "7                           Adventure|Children  \n",
       "8                                       Action  \n",
       "9                    Action|Adventure|Thriller  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of chosen interactions: 9692\n"
     ]
    }
   ],
   "source": [
    "ml_ratings_df = pd.read_csv(os.path.join(\"data\", \"movielens_small\", \"ratings.csv\")).rename(columns={'userId': 'user_id', 'movieId': 'item_id'})\n",
    "ml_movies_df = pd.read_csv(os.path.join(\"data\", \"movielens_small\", \"movies.csv\")).rename(columns={'movieId': 'item_id'})\n",
    "ml_df = pd.merge(ml_ratings_df, ml_movies_df, on='item_id')\n",
    "\n",
    "display(ml_movies_df.head(10))\n",
    "\n",
    "# Filter the data to reduce the number of movies\n",
    "seed = 6789\n",
    "rng = np.random.RandomState(seed=seed)\n",
    "left_ids = rng.choice(ml_movies_df['item_id'], size=1000, replace=False)\n",
    "\n",
    "ml_ratings_df = ml_ratings_df.loc[ml_ratings_df['item_id'].isin(left_ids)]\n",
    "ml_movies_df = ml_movies_df.loc[ml_movies_df['item_id'].isin(left_ids)]\n",
    "ml_df = ml_df.loc[ml_df['item_id'].isin(left_ids)]\n",
    "\n",
    "print(\"Number of chosen interactions: {}\".format(len(ml_ratings_df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optional-chain",
   "metadata": {},
   "source": [
    "# Linear Regression Recommender\n",
    "\n",
    "For every movie we transform its genres into one-hot encoded features and we normalize them, for every user we count percentages for all genres how often do they appear among films watched by the user, we multiply both vectors (for the item and the user) to obtain explanaytory variables, and then we fit a linear regression model to those features and actual ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "sonic-horror",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "from recommenders.recommender import Recommender\n",
    "\n",
    "class LinearRegressionRecommender(Recommender):\n",
    "    \"\"\"\n",
    "    Linear regression recommender class.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        Initialize recommender params and variables.\n",
    "        \"\"\"\n",
    "        self.model = None\n",
    "        self.mlb = None\n",
    "        self.users_dict = None\n",
    "        self.user_features = None\n",
    "        \n",
    "        self.uses_dot_product = True\n",
    "    \n",
    "    def fit(self, interactions_df, users_df, items_df):\n",
    "        \"\"\"\n",
    "        Training of the recommender.\n",
    "        \n",
    "        :param pd.DataFrame interactions_df: DataFrame with recorded interactions between users and items \n",
    "            defined by user_id, item_id and features of the interaction.\n",
    "        :param pd.DataFrame users_df: DataFrame with users and their features defined by user_id and the user feature columns.\n",
    "        :param pd.DataFrame items_df: DataFrame with items and their features defined by item_id and the item feature columns.\n",
    "        \"\"\"\n",
    "        \n",
    "        # Transform genres to a more code-friendly form\n",
    "        \n",
    "        interactions_df = pd.merge(interactions_df, items_df, on='item_id')\n",
    "        interactions_df = self._transform_genres(interactions_df)\n",
    "        \n",
    "        # Prepare user features\n",
    "        \n",
    "        users_df = interactions_df[['user_id', 'genres']].copy()\n",
    "        users_df = users_df.explode('genres')\n",
    "        users_df['val'] = 1\n",
    "        users_df = users_df.pivot_table(index='user_id', columns='genres', values='val', aggfunc='count')\n",
    "        users_df = users_df / users_df.sum(axis=1).values.reshape(-1, 1)\n",
    "        users_df = users_df.rename_axis(None, axis=1).fillna(0)\n",
    "        users_df = users_df.add_prefix('user_')\n",
    "#         display(users_df.head(10))\n",
    "        \n",
    "        self.users_dict = users_df.to_dict('index')\n",
    "        \n",
    "        self.user_features = users_df.columns.tolist()\n",
    "        \n",
    "        interactions_df = interactions_df.merge(users_df, on='user_id')\n",
    "#         display(interactions_df.head(10))\n",
    "                \n",
    "        # Prepare item features\n",
    "        \n",
    "        # Transform genres into binary values\n",
    "        \n",
    "        self.mlb = MultiLabelBinarizer()\n",
    "        interactions_df = interactions_df.join(\n",
    "            pd.DataFrame(self.mlb.fit_transform(interactions_df.pop('genres')),\n",
    "                         columns=self.mlb.classes_,\n",
    "                         index=interactions_df.index))\n",
    "        \n",
    "        # Normalize the values so that each movie's genres sum up to 1\n",
    "        \n",
    "        interactions_df[self.mlb.classes_] = interactions_df[self.mlb.classes_] \\\n",
    "            / interactions_df[self.mlb.classes_].sum(axis=1).values.reshape(-1, 1)\n",
    "        \n",
    "#         display(interactions_df.loc[:, self.mlb.classes_].head(10))\n",
    "\n",
    "        # Prepare input data and fit the model\n",
    "    \n",
    "        # Dot product for personalization\n",
    "        if self.uses_dot_product:\n",
    "            interactions_df[self.mlb.classes_] = interactions_df[self.mlb.classes_] \\\n",
    "                * interactions_df[self.user_features].values\n",
    "    \n",
    "#         display(interactions_df.head(10))\n",
    "        \n",
    "        if self.uses_dot_product:  # Personalized\n",
    "            x = interactions_df.loc[:, self.mlb.classes_].values\n",
    "        else:  # Non-personalized\n",
    "            x = interactions_df.loc[:, list(self.mlb.classes_) + self.user_features].values\n",
    "        \n",
    "        y = interactions_df['rating'].values\n",
    "    \n",
    "        self.model = LinearRegression().fit(x, y)\n",
    "    \n",
    "    def recommend(self, users_df, items_df, n_recommendations=1):\n",
    "        \"\"\"\n",
    "        Serving of recommendations. Scores items in items_df for each user in users_df and returns \n",
    "        top n_recommendations for each user.\n",
    "        \n",
    "        :param pd.DataFrame users_df: DataFrame with users and their features for which recommendations should be generated.\n",
    "        :param pd.DataFrame items_df: DataFrame with items and their features which should be scored.\n",
    "        :param int n_recommendations: Number of recommendations to be returned for each user.\n",
    "        :return: DataFrame with user_id, item_id and score as columns returning n_recommendations top recommendations \n",
    "            for each user.\n",
    "        :rtype: pd.DataFrame\n",
    "        \"\"\"\n",
    "        \n",
    "        # Transform the item to be scored into proper features\n",
    "        \n",
    "        items_df = items_df.copy()\n",
    "        items_df = self._transform_genres(items_df)\n",
    "        \n",
    "        items_df = items_df.join(\n",
    "            pd.DataFrame(self.mlb.transform(items_df.pop('genres')),\n",
    "                         columns=self.mlb.classes_,\n",
    "                         index=items_df.index))\n",
    "        \n",
    "        items_df[self.mlb.classes_] = items_df[self.mlb.classes_] \\\n",
    "            / items_df[self.mlb.classes_].sum(axis=1).values.reshape(-1, 1)\n",
    "\n",
    "        # Score the item\n",
    "    \n",
    "        recommendations = pd.DataFrame(columns=['user_id', 'item_id', 'score'])\n",
    "        \n",
    "        for ix, user in users_df.iterrows():\n",
    "            if user['user_id'] in self.users_dict:\n",
    "                user_df = pd.DataFrame.from_dict({user['user_id']: self.users_dict[user['user_id']]}, orient='index')\n",
    "            else:\n",
    "                user_df = pd.DataFrame.from_dict(\n",
    "                    {user['user_id']: [1 / len(self.user_features)]*len(self.user_features)}, orient='index')\n",
    "                user_df.columns = self.user_features\n",
    "#             display(user_df)\n",
    "#             display(items_df)\n",
    "            input_df = items_df.copy()\n",
    "            \n",
    "            if self.uses_dot_product:\n",
    "                input_df[self.mlb.classes_] = items_df[self.mlb.classes_] * user_df.values\n",
    "#                 display(input_df)\n",
    "                scores = self.model.predict(input_df.loc[:, self.mlb.classes_].values)\n",
    "            else:\n",
    "                input_df = input_df.merge(user_df, how='cross')\n",
    "#                 display(input_df)\n",
    "                scores = self.model.predict(input_df.loc[:, list(self.mlb.classes_) + self.user_features].values)\n",
    "    \n",
    "            chosen_pos = np.argsort(-scores)[:n_recommendations]\n",
    "        \n",
    "            user_recommendations = []\n",
    "            for item_pos in chosen_pos:\n",
    "                user_recommendations.append(\n",
    "                    {\n",
    "                        'user_id': user['user_id'],\n",
    "                        'item_id': input_df.iloc[item_pos]['item_id'],\n",
    "                        'score': scores[item_pos]\n",
    "                    }\n",
    "                )\n",
    "                \n",
    "            user_recommendations = pd.DataFrame(user_recommendations)\n",
    "\n",
    "            recommendations = pd.concat([recommendations, user_recommendations])\n",
    "\n",
    "        return recommendations\n",
    "    \n",
    "    def _transform_genres(self, df):\n",
    "        \"\"\"\n",
    "        Transforms a string with genres into a list of cleaned genre names.\n",
    "        \n",
    "        :param pd.DataFrame df: A DataFrame with 'genres' column.\n",
    "        \"\"\"\n",
    "        df.loc[:, 'genres'] = df['genres'].str.replace(\"-\", \"_\", regex=False)\n",
    "        df.loc[:, 'genres'] = df['genres'].str.replace(\" \", \"_\", regex=False)\n",
    "        df.loc[:, 'genres'] = df['genres'].str.replace(\"(\", \"\", regex=False)\n",
    "        df.loc[:, 'genres'] = df['genres'].str.replace(\")\", \"\", regex=False)\n",
    "        df.loc[:, 'genres'] = df['genres'].str.lower()\n",
    "        df.loc[:, 'genres'] = df['genres'].str.split(\"|\")\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "designed-square",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active users history\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18810</th>\n",
       "      <td>3</td>\n",
       "      <td>5181</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1306463718</td>\n",
       "      <td>Hangar 18 (1980)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18827</th>\n",
       "      <td>3</td>\n",
       "      <td>7991</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1306463684</td>\n",
       "      <td>Death Race 2000 (1975)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18832</th>\n",
       "      <td>3</td>\n",
       "      <td>70946</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1306463815</td>\n",
       "      <td>Troll 2 (1990)</td>\n",
       "      <td>Fantasy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847434881</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19321</th>\n",
       "      <td>5</td>\n",
       "      <td>232</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847435292</td>\n",
       "      <td>Eat Drink Man Woman (Yin shi nan nu) (1994)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25824</th>\n",
       "      <td>5</td>\n",
       "      <td>266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>847435311</td>\n",
       "      <td>Legends of the Fall (1994)</td>\n",
       "      <td>Drama|Romance|War|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19722</th>\n",
       "      <td>5</td>\n",
       "      <td>475</td>\n",
       "      <td>5.0</td>\n",
       "      <td>847435311</td>\n",
       "      <td>In the Name of the Father (1993)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20123</th>\n",
       "      <td>5</td>\n",
       "      <td>595</td>\n",
       "      <td>5.0</td>\n",
       "      <td>847434832</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical|Romance|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>39</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974788030</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45366</th>\n",
       "      <td>39</td>\n",
       "      <td>858</td>\n",
       "      <td>4.0</td>\n",
       "      <td>974789111</td>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7762</th>\n",
       "      <td>39</td>\n",
       "      <td>1213</td>\n",
       "      <td>4.0</td>\n",
       "      <td>974789540</td>\n",
       "      <td>Goodfellas (1990)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9403</th>\n",
       "      <td>39</td>\n",
       "      <td>1500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>974789727</td>\n",
       "      <td>Grosse Pointe Blank (1997)</td>\n",
       "      <td>Comedy|Crime|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60382</th>\n",
       "      <td>39</td>\n",
       "      <td>1862</td>\n",
       "      <td>2.0</td>\n",
       "      <td>974787293</td>\n",
       "      <td>Species II (1998)</td>\n",
       "      <td>Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11108</th>\n",
       "      <td>39</td>\n",
       "      <td>2078</td>\n",
       "      <td>3.0</td>\n",
       "      <td>974788976</td>\n",
       "      <td>Jungle Book, The (1967)</td>\n",
       "      <td>Animation|Children|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13425</th>\n",
       "      <td>39</td>\n",
       "      <td>2692</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974789111</td>\n",
       "      <td>Run Lola Run (Lola rennt) (1998)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14203</th>\n",
       "      <td>39</td>\n",
       "      <td>2947</td>\n",
       "      <td>3.0</td>\n",
       "      <td>974789327</td>\n",
       "      <td>Goldfinger (1964)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49571</th>\n",
       "      <td>39</td>\n",
       "      <td>3000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974789174</td>\n",
       "      <td>Princess Mononoke (Mononoke-hime) (1997)</td>\n",
       "      <td>Action|Adventure|Animation|Drama|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63222</th>\n",
       "      <td>39</td>\n",
       "      <td>3213</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974788976</td>\n",
       "      <td>Batman: Mask of the Phantasm (1993)</td>\n",
       "      <td>Animation|Children</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  item_id  rating   timestamp  \\\n",
       "18810        3     5181     5.0  1306463718   \n",
       "18827        3     7991     5.0  1306463684   \n",
       "18832        3    70946     5.0  1306463815   \n",
       "573          5       50     4.0   847434881   \n",
       "19321        5      232     4.0   847435292   \n",
       "25824        5      266     1.0   847435311   \n",
       "19722        5      475     5.0   847435311   \n",
       "20123        5      595     5.0   847434832   \n",
       "588         39       50     5.0   974788030   \n",
       "45366       39      858     4.0   974789111   \n",
       "7762        39     1213     4.0   974789540   \n",
       "9403        39     1500     4.0   974789727   \n",
       "60382       39     1862     2.0   974787293   \n",
       "11108       39     2078     3.0   974788976   \n",
       "13425       39     2692     5.0   974789111   \n",
       "14203       39     2947     3.0   974789327   \n",
       "49571       39     3000     5.0   974789174   \n",
       "63222       39     3213     5.0   974788976   \n",
       "\n",
       "                                             title  \\\n",
       "18810                             Hangar 18 (1980)   \n",
       "18827                       Death Race 2000 (1975)   \n",
       "18832                               Troll 2 (1990)   \n",
       "573                     Usual Suspects, The (1995)   \n",
       "19321  Eat Drink Man Woman (Yin shi nan nu) (1994)   \n",
       "25824                   Legends of the Fall (1994)   \n",
       "19722             In the Name of the Father (1993)   \n",
       "20123                  Beauty and the Beast (1991)   \n",
       "588                     Usual Suspects, The (1995)   \n",
       "45366                        Godfather, The (1972)   \n",
       "7762                             Goodfellas (1990)   \n",
       "9403                    Grosse Pointe Blank (1997)   \n",
       "60382                            Species II (1998)   \n",
       "11108                      Jungle Book, The (1967)   \n",
       "13425             Run Lola Run (Lola rennt) (1998)   \n",
       "14203                            Goldfinger (1964)   \n",
       "49571     Princess Mononoke (Mononoke-hime) (1997)   \n",
       "63222          Batman: Mask of the Phantasm (1993)   \n",
       "\n",
       "                                                genres  \n",
       "18810                           Action|Sci-Fi|Thriller  \n",
       "18827                                    Action|Sci-Fi  \n",
       "18832                                   Fantasy|Horror  \n",
       "573                             Crime|Mystery|Thriller  \n",
       "19321                             Comedy|Drama|Romance  \n",
       "25824                        Drama|Romance|War|Western  \n",
       "19722                                            Drama  \n",
       "20123  Animation|Children|Fantasy|Musical|Romance|IMAX  \n",
       "588                             Crime|Mystery|Thriller  \n",
       "45366                                      Crime|Drama  \n",
       "7762                                       Crime|Drama  \n",
       "9403                              Comedy|Crime|Romance  \n",
       "60382                                    Horror|Sci-Fi  \n",
       "11108                Animation|Children|Comedy|Musical  \n",
       "13425                                     Action|Crime  \n",
       "14203                        Action|Adventure|Thriller  \n",
       "49571         Action|Adventure|Animation|Drama|Fantasy  \n",
       "63222                               Animation|Children  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print movies watched by user 3, 5, 39\n",
    "\n",
    "active_user_movies = ml_df.loc[\n",
    "    (ml_df['user_id'] == 3) | (ml_df['user_id'] == 5) | (ml_df['user_id'] == 39)].sort_values(['user_id', 'item_id'])\n",
    "print(\"Active users history\")\n",
    "display(active_user_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "colored-favorite",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>score</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>51562</td>\n",
       "      <td>3.501376</td>\n",
       "      <td>Babylon 5: The Gathering (1993)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>5468</td>\n",
       "      <td>3.501376</td>\n",
       "      <td>20 Million Miles to Earth (1957)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>176371</td>\n",
       "      <td>3.501376</td>\n",
       "      <td>Blade Runner 2049 (2017)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>147384</td>\n",
       "      <td>3.501376</td>\n",
       "      <td>Doctor Who: The Runaway Bride (2007)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2698</td>\n",
       "      <td>3.501376</td>\n",
       "      <td>Zone 39 (1997)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2661</td>\n",
       "      <td>3.501376</td>\n",
       "      <td>It Came from Outer Space (1953)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2311</td>\n",
       "      <td>3.501376</td>\n",
       "      <td>2010: The Year We Make Contact (1984)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>3926</td>\n",
       "      <td>3.425357</td>\n",
       "      <td>Voyage to the Bottom of the Sea (1961)</td>\n",
       "      <td>Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>4953</td>\n",
       "      <td>3.425357</td>\n",
       "      <td>People That Time Forgot, The (1977)</td>\n",
       "      <td>Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>3994</td>\n",
       "      <td>3.425357</td>\n",
       "      <td>Unbreakable (2000)</td>\n",
       "      <td>Drama|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>66335</td>\n",
       "      <td>4.402264</td>\n",
       "      <td>Afro Samurai: Resurrection (2009)</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>160848</td>\n",
       "      <td>4.402264</td>\n",
       "      <td>The Red Turtle (2016)</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>151763</td>\n",
       "      <td>4.214813</td>\n",
       "      <td>Death Note Rewrite: Genshisuru Kami (2007)</td>\n",
       "      <td>Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>32657</td>\n",
       "      <td>4.075275</td>\n",
       "      <td>Man Who Planted Trees, The (Homme qui plantait...</td>\n",
       "      <td>Animation|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>90746</td>\n",
       "      <td>4.033668</td>\n",
       "      <td>Adventures of Tintin, The (2011)</td>\n",
       "      <td>Action|Animation|Mystery|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>146028</td>\n",
       "      <td>4.007686</td>\n",
       "      <td>The Adventures of Sherlock Holmes and Dr. Wats...</td>\n",
       "      <td>Crime|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>147286</td>\n",
       "      <td>4.007686</td>\n",
       "      <td>The Adventures of Sherlock Holmes and Doctor W...</td>\n",
       "      <td>Crime|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>3801</td>\n",
       "      <td>3.981549</td>\n",
       "      <td>Anatomy of a Murder (1959)</td>\n",
       "      <td>Drama|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>64575</td>\n",
       "      <td>3.981549</td>\n",
       "      <td>Doubt (2008)</td>\n",
       "      <td>Drama|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>1964</td>\n",
       "      <td>3.981549</td>\n",
       "      <td>Klute (1971)</td>\n",
       "      <td>Drama|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>39</td>\n",
       "      <td>66335</td>\n",
       "      <td>5.267168</td>\n",
       "      <td>Afro Samurai: Resurrection (2009)</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>39</td>\n",
       "      <td>160848</td>\n",
       "      <td>5.267168</td>\n",
       "      <td>The Red Turtle (2016)</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>39</td>\n",
       "      <td>32657</td>\n",
       "      <td>4.429362</td>\n",
       "      <td>Man Who Planted Trees, The (Homme qui plantait...</td>\n",
       "      <td>Animation|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39</td>\n",
       "      <td>146028</td>\n",
       "      <td>4.296961</td>\n",
       "      <td>The Adventures of Sherlock Holmes and Dr. Wats...</td>\n",
       "      <td>Crime|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>39</td>\n",
       "      <td>147286</td>\n",
       "      <td>4.296961</td>\n",
       "      <td>The Adventures of Sherlock Holmes and Doctor W...</td>\n",
       "      <td>Crime|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>39</td>\n",
       "      <td>95004</td>\n",
       "      <td>4.288561</td>\n",
       "      <td>Superman/Doomsday (2007)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39</td>\n",
       "      <td>158872</td>\n",
       "      <td>4.261897</td>\n",
       "      <td>Sausage Party (2016)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>39</td>\n",
       "      <td>175431</td>\n",
       "      <td>4.261897</td>\n",
       "      <td>Bobik Visiting Barbos (1977)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>48982</td>\n",
       "      <td>4.261897</td>\n",
       "      <td>Flushed Away (2006)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39</td>\n",
       "      <td>163925</td>\n",
       "      <td>4.261897</td>\n",
       "      <td>Wings, Legs and Tails (1986)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id item_id     score  \\\n",
       "0        3   51562  3.501376   \n",
       "1        3    5468  3.501376   \n",
       "2        3  176371  3.501376   \n",
       "3        3  147384  3.501376   \n",
       "4        3    2698  3.501376   \n",
       "5        3    2661  3.501376   \n",
       "6        3    2311  3.501376   \n",
       "7        3    3926  3.425357   \n",
       "8        3    4953  3.425357   \n",
       "9        3    3994  3.425357   \n",
       "10       5   66335  4.402264   \n",
       "11       5  160848  4.402264   \n",
       "12       5  151763  4.214813   \n",
       "13       5   32657  4.075275   \n",
       "14       5   90746  4.033668   \n",
       "15       5  146028  4.007686   \n",
       "16       5  147286  4.007686   \n",
       "17       5    3801  3.981549   \n",
       "18       5   64575  3.981549   \n",
       "19       5    1964  3.981549   \n",
       "20      39   66335  5.267168   \n",
       "21      39  160848  5.267168   \n",
       "22      39   32657  4.429362   \n",
       "23      39  146028  4.296961   \n",
       "24      39  147286  4.296961   \n",
       "25      39   95004  4.288561   \n",
       "26      39  158872  4.261897   \n",
       "27      39  175431  4.261897   \n",
       "28      39   48982  4.261897   \n",
       "29      39  163925  4.261897   \n",
       "\n",
       "                                                title  \\\n",
       "0                     Babylon 5: The Gathering (1993)   \n",
       "1                    20 Million Miles to Earth (1957)   \n",
       "2                            Blade Runner 2049 (2017)   \n",
       "3                Doctor Who: The Runaway Bride (2007)   \n",
       "4                                      Zone 39 (1997)   \n",
       "5                     It Came from Outer Space (1953)   \n",
       "6               2010: The Year We Make Contact (1984)   \n",
       "7              Voyage to the Bottom of the Sea (1961)   \n",
       "8                 People That Time Forgot, The (1977)   \n",
       "9                                  Unbreakable (2000)   \n",
       "10                  Afro Samurai: Resurrection (2009)   \n",
       "11                              The Red Turtle (2016)   \n",
       "12         Death Note Rewrite: Genshisuru Kami (2007)   \n",
       "13  Man Who Planted Trees, The (Homme qui plantait...   \n",
       "14                   Adventures of Tintin, The (2011)   \n",
       "15  The Adventures of Sherlock Holmes and Dr. Wats...   \n",
       "16  The Adventures of Sherlock Holmes and Doctor W...   \n",
       "17                         Anatomy of a Murder (1959)   \n",
       "18                                       Doubt (2008)   \n",
       "19                                       Klute (1971)   \n",
       "20                  Afro Samurai: Resurrection (2009)   \n",
       "21                              The Red Turtle (2016)   \n",
       "22  Man Who Planted Trees, The (Homme qui plantait...   \n",
       "23  The Adventures of Sherlock Holmes and Dr. Wats...   \n",
       "24  The Adventures of Sherlock Holmes and Doctor W...   \n",
       "25                          Superman/Doomsday (2007)    \n",
       "26                               Sausage Party (2016)   \n",
       "27                       Bobik Visiting Barbos (1977)   \n",
       "28                                Flushed Away (2006)   \n",
       "29                       Wings, Legs and Tails (1986)   \n",
       "\n",
       "                           genres  \n",
       "0                          Sci-Fi  \n",
       "1                          Sci-Fi  \n",
       "2                          Sci-Fi  \n",
       "3                          Sci-Fi  \n",
       "4                          Sci-Fi  \n",
       "5                          Sci-Fi  \n",
       "6                          Sci-Fi  \n",
       "7                Adventure|Sci-Fi  \n",
       "8                Adventure|Sci-Fi  \n",
       "9                    Drama|Sci-Fi  \n",
       "10                      Animation  \n",
       "11                      Animation  \n",
       "12                        Mystery  \n",
       "13                Animation|Drama  \n",
       "14  Action|Animation|Mystery|IMAX  \n",
       "15                  Crime|Mystery  \n",
       "16                  Crime|Mystery  \n",
       "17                  Drama|Mystery  \n",
       "18                  Drama|Mystery  \n",
       "19                  Drama|Mystery  \n",
       "20                      Animation  \n",
       "21                      Animation  \n",
       "22                Animation|Drama  \n",
       "23                  Crime|Mystery  \n",
       "24                  Crime|Mystery  \n",
       "25               Action|Animation  \n",
       "26               Animation|Comedy  \n",
       "27               Animation|Comedy  \n",
       "28               Animation|Comedy  \n",
       "29               Animation|Comedy  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick test of the recommender\n",
    "\n",
    "lr_recommender = LinearRegressionRecommender()\n",
    "lr_recommender.uses_dot_product = True\n",
    "lr_recommender.fit(ml_ratings_df, None, ml_movies_df)\n",
    "recommendations = lr_recommender.recommend(pd.DataFrame([[3], [5], [39]], columns=['user_id']), ml_movies_df, 10)\n",
    "\n",
    "recommendations = pd.merge(recommendations, ml_movies_df, on='item_id', how='left')\n",
    "display(recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "usual-archives",
   "metadata": {},
   "source": [
    "### Train-test split test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bound-offer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recommender</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MRE</th>\n",
       "      <th>TRE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegressionRecommender</td>\n",
       "      <td>1.016594</td>\n",
       "      <td>0.348461</td>\n",
       "      <td>0.230529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Recommender      RMSE       MRE       TRE\n",
       "0  LinearRegressionRecommender  1.016594  0.348461  0.230529"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total evaluation time: 47.25206780433655\n"
     ]
    }
   ],
   "source": [
    "lr_recommender = LinearRegressionRecommender()\n",
    "\n",
    "t0 = time.time()\n",
    "\n",
    "results = [['LinearRegressionRecommender'] + list(evaluate_train_test_split_explicit(\n",
    "    lr_recommender, ml_ratings_df.loc[:, ['user_id', 'item_id', 'rating']], ml_movies_df, seed=seed))]\n",
    "\n",
    "results = pd.DataFrame(results, \n",
    "                       columns=['Recommender', 'RMSE', 'MRE', 'TRE'])\n",
    "\n",
    "display(results)\n",
    "\n",
    "print('Total evaluation time: {}'.format(time.time() - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immediate-warehouse",
   "metadata": {},
   "source": [
    "### Leave-one-out test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "national-sight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recommender</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MRE</th>\n",
       "      <th>TRE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegressionRecommender</td>\n",
       "      <td>1.045415</td>\n",
       "      <td>0.401399</td>\n",
       "      <td>0.242301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Recommender      RMSE       MRE       TRE\n",
       "0  LinearRegressionRecommender  1.045415  0.401399  0.242301"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total evaluation time: 54.93246841430664\n"
     ]
    }
   ],
   "source": [
    "lr_recommender = LinearRegressionRecommender()\n",
    "\n",
    "t0 = time.time()\n",
    "\n",
    "results = [['LinearRegressionRecommender'] + list(evaluate_leave_one_out_explicit(\n",
    "    lr_recommender, ml_ratings_df.loc[:, ['user_id', 'item_id', 'rating']], ml_movies_df, seed=seed))]\n",
    "\n",
    "results = pd.DataFrame(results, \n",
    "                       columns=['Recommender', 'RMSE', 'MRE', 'TRE'])\n",
    "\n",
    "display(results)\n",
    "\n",
    "print('Total evaluation time: {}'.format(time.time() - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "static-mozambique",
   "metadata": {},
   "source": [
    "# TF-IDF Recommender\n",
    "TF-IDF stands for term frequency–inverse document frequency. Typically Tf-IDF method is used to assign keywords (words describing the gist of a document) to documents in a corpus of documents.\n",
    "\n",
    "In our case we will treat users as documents and genres as words.\n",
    "\n",
    "Term-frequency is given by the following formula:\n",
    "<center>\n",
    "$$\n",
    "    \\text{tf}(g, u) = f_{g, u}\n",
    "$$\n",
    "</center>\n",
    "where $f_{g, i}$ is the number of times genre $g$ appear for movies watched by user $u$.\n",
    "\n",
    "Inverse document frequency is defined as follows:\n",
    "<center>\n",
    "$$\n",
    "    \\text{idf}(g) = \\log \\frac{N}{n_g}\n",
    "$$\n",
    "</center>\n",
    "where $N$ is the number of users and $n_g$ is the number of users with $g$ in their genres list.\n",
    "\n",
    "Finally, tf-idf is defined as follows:\n",
    "<center>\n",
    "$$\n",
    "    \\text{tfidf}(g, u) = \\text{tf}(g, u) \\cdot \\text{idf}(g)\n",
    "$$\n",
    "</center>\n",
    "\n",
    "In our case we will measure how often a given genre appears for movies watched by a given user vs how often it appears for all users. To obtain a movie score we will take the average of its genres' scores for this user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "infrared-southwest",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "class TFIDFRecommender(Recommender):\n",
    "    \"\"\"\n",
    "    Recommender based on the TF-IDF method.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        Initialize base recommender params and variables.\n",
    "        \"\"\"\n",
    "        self.tfidf_scores = None\n",
    "    \n",
    "    def fit(self, interactions_df, users_df, items_df):\n",
    "        \"\"\"\n",
    "        Training of the recommender.\n",
    "        \n",
    "        :param pd.DataFrame interactions_df: DataFrame with recorded interactions between users and items \n",
    "            defined by user_id, item_id and features of the interaction.\n",
    "        :param pd.DataFrame users_df: DataFrame with users and their features defined by user_id and the user feature columns.\n",
    "        :param pd.DataFrame items_df: DataFrame with items and their features defined by item_id and the item feature columns.\n",
    "        \"\"\"\n",
    "        \n",
    "        self.tfidf_scores = defaultdict(lambda: 0.0)\n",
    "\n",
    "        # Prepare the corpus for tfidf calculation\n",
    "        \n",
    "        interactions_df = pd.merge(interactions_df, items_df, on='item_id')\n",
    "#         print(\"Training set\")\n",
    "#         display(interactions_df.head(10))\n",
    "#         print()\n",
    "        user_genres = interactions_df.loc[:, ['user_id', 'genres']]\n",
    "        user_genres.loc[:, 'genres'] = user_genres['genres'].str.replace(\"-\", \"_\", regex=False)\n",
    "        user_genres.loc[:, 'genres'] = user_genres['genres'].str.replace(\" \", \"_\", regex=False)\n",
    "        user_genres = user_genres.groupby('user_id').aggregate(lambda x: \"|\".join(x))\n",
    "        user_genres.loc[:, 'genres'] = user_genres['genres'].str.replace(\"|\", \" \", regex=False)\n",
    "#         print(\"User genres\")\n",
    "#         display(user_genres.head(10))\n",
    "#         print()\n",
    "        user_ids = user_genres.index.tolist()\n",
    "        genres_corpus = user_genres['genres'].tolist()\n",
    "#         print(\"Genres corpus\")\n",
    "#         print(genres_corpus)\n",
    "#         print()\n",
    "        \n",
    "        # Calculate tf-idf scores\n",
    "        \n",
    "        vectorizer = TfidfVectorizer()\n",
    "        tfidf_scores = vectorizer.fit_transform(genres_corpus)\n",
    "        \n",
    "        # Transform results into a dict {(user_id, genre): score}\n",
    "        \n",
    "        for u in range(tfidf_scores.shape[0]):\n",
    "            for g in range(tfidf_scores.shape[1]):\n",
    "                self.tfidf_scores[(user_ids[u], vectorizer.get_feature_names()[g])] = tfidf_scores[u, g]\n",
    "\n",
    "#         print(\"TF-IDF scores\")\n",
    "#         print(self.tfidf_scores)\n",
    "#         print()\n",
    "    \n",
    "    def recommend(self, users_df, items_df, n_recommendations=1):\n",
    "        \"\"\"\n",
    "        Serving of recommendations. Scores items in items_df for each user in users_df and returns \n",
    "        top n_recommendations for each user.\n",
    "        \n",
    "        :param pd.DataFrame users_df: DataFrame with users and their features for which recommendations should be generated.\n",
    "        :param pd.DataFrame items_df: DataFrame with items and their features which should be scored.\n",
    "        :param int n_recommendations: Number of recommendations to be returned for each user.\n",
    "        :return: DataFrame with user_id, item_id and score as columns returning n_recommendations top recommendations \n",
    "            for each user.\n",
    "        :rtype: pd.DataFrame\n",
    "        \"\"\"\n",
    "        \n",
    "        recommendations = pd.DataFrame(columns=['user_id', 'item_id', 'score'])\n",
    "        \n",
    "        # Transform genres to a unified form used by the vectorizer\n",
    "        \n",
    "        items_df = items_df.copy()\n",
    "        items_df.loc[:, 'genres'] = items_df['genres'].str.replace(\"-\", \"_\", regex=False)\n",
    "        items_df.loc[:, 'genres'] = items_df['genres'].str.replace(\" \", \"_\", regex=False)\n",
    "        items_df.loc[:, 'genres'] = items_df['genres'].str.lower()\n",
    "        items_df.loc[:, 'genres'] = items_df['genres'].str.split(\"|\")\n",
    "                \n",
    "        # Score items    \n",
    "        \n",
    "        for uix, user in users_df.iterrows():\n",
    "            items = []\n",
    "            for iix, item in items_df.iterrows():\n",
    "                score = 0.0\n",
    "                for genre in item['genres']:\n",
    "                    score += self.tfidf_scores[(user['user_id'], genre)]\n",
    "                score /= len(item['genres'])\n",
    "                items.append((item['item_id'], score))\n",
    "                \n",
    "            items = sorted(items, key=lambda x: x[1], reverse=True)\n",
    "            user_recommendations = pd.DataFrame({'user_id': user['user_id'],\n",
    "                                                 'item_id': [item[0] for item in items][:n_recommendations],\n",
    "                                                 'score': [item[1] for item in items][:n_recommendations]})\n",
    "\n",
    "            recommendations = pd.concat([recommendations, user_recommendations])\n",
    "\n",
    "        return recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "sorted-alcohol",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active users history\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18810</th>\n",
       "      <td>3</td>\n",
       "      <td>5181</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1306463718</td>\n",
       "      <td>Hangar 18 (1980)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18827</th>\n",
       "      <td>3</td>\n",
       "      <td>7991</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1306463684</td>\n",
       "      <td>Death Race 2000 (1975)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18832</th>\n",
       "      <td>3</td>\n",
       "      <td>70946</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1306463815</td>\n",
       "      <td>Troll 2 (1990)</td>\n",
       "      <td>Fantasy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847434881</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19321</th>\n",
       "      <td>5</td>\n",
       "      <td>232</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847435292</td>\n",
       "      <td>Eat Drink Man Woman (Yin shi nan nu) (1994)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25824</th>\n",
       "      <td>5</td>\n",
       "      <td>266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>847435311</td>\n",
       "      <td>Legends of the Fall (1994)</td>\n",
       "      <td>Drama|Romance|War|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19722</th>\n",
       "      <td>5</td>\n",
       "      <td>475</td>\n",
       "      <td>5.0</td>\n",
       "      <td>847435311</td>\n",
       "      <td>In the Name of the Father (1993)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20123</th>\n",
       "      <td>5</td>\n",
       "      <td>595</td>\n",
       "      <td>5.0</td>\n",
       "      <td>847434832</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical|Romance|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>39</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974788030</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45366</th>\n",
       "      <td>39</td>\n",
       "      <td>858</td>\n",
       "      <td>4.0</td>\n",
       "      <td>974789111</td>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7762</th>\n",
       "      <td>39</td>\n",
       "      <td>1213</td>\n",
       "      <td>4.0</td>\n",
       "      <td>974789540</td>\n",
       "      <td>Goodfellas (1990)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9403</th>\n",
       "      <td>39</td>\n",
       "      <td>1500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>974789727</td>\n",
       "      <td>Grosse Pointe Blank (1997)</td>\n",
       "      <td>Comedy|Crime|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60382</th>\n",
       "      <td>39</td>\n",
       "      <td>1862</td>\n",
       "      <td>2.0</td>\n",
       "      <td>974787293</td>\n",
       "      <td>Species II (1998)</td>\n",
       "      <td>Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11108</th>\n",
       "      <td>39</td>\n",
       "      <td>2078</td>\n",
       "      <td>3.0</td>\n",
       "      <td>974788976</td>\n",
       "      <td>Jungle Book, The (1967)</td>\n",
       "      <td>Animation|Children|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13425</th>\n",
       "      <td>39</td>\n",
       "      <td>2692</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974789111</td>\n",
       "      <td>Run Lola Run (Lola rennt) (1998)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14203</th>\n",
       "      <td>39</td>\n",
       "      <td>2947</td>\n",
       "      <td>3.0</td>\n",
       "      <td>974789327</td>\n",
       "      <td>Goldfinger (1964)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49571</th>\n",
       "      <td>39</td>\n",
       "      <td>3000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974789174</td>\n",
       "      <td>Princess Mononoke (Mononoke-hime) (1997)</td>\n",
       "      <td>Action|Adventure|Animation|Drama|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63222</th>\n",
       "      <td>39</td>\n",
       "      <td>3213</td>\n",
       "      <td>5.0</td>\n",
       "      <td>974788976</td>\n",
       "      <td>Batman: Mask of the Phantasm (1993)</td>\n",
       "      <td>Animation|Children</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  item_id  rating   timestamp  \\\n",
       "18810        3     5181     5.0  1306463718   \n",
       "18827        3     7991     5.0  1306463684   \n",
       "18832        3    70946     5.0  1306463815   \n",
       "573          5       50     4.0   847434881   \n",
       "19321        5      232     4.0   847435292   \n",
       "25824        5      266     1.0   847435311   \n",
       "19722        5      475     5.0   847435311   \n",
       "20123        5      595     5.0   847434832   \n",
       "588         39       50     5.0   974788030   \n",
       "45366       39      858     4.0   974789111   \n",
       "7762        39     1213     4.0   974789540   \n",
       "9403        39     1500     4.0   974789727   \n",
       "60382       39     1862     2.0   974787293   \n",
       "11108       39     2078     3.0   974788976   \n",
       "13425       39     2692     5.0   974789111   \n",
       "14203       39     2947     3.0   974789327   \n",
       "49571       39     3000     5.0   974789174   \n",
       "63222       39     3213     5.0   974788976   \n",
       "\n",
       "                                             title  \\\n",
       "18810                             Hangar 18 (1980)   \n",
       "18827                       Death Race 2000 (1975)   \n",
       "18832                               Troll 2 (1990)   \n",
       "573                     Usual Suspects, The (1995)   \n",
       "19321  Eat Drink Man Woman (Yin shi nan nu) (1994)   \n",
       "25824                   Legends of the Fall (1994)   \n",
       "19722             In the Name of the Father (1993)   \n",
       "20123                  Beauty and the Beast (1991)   \n",
       "588                     Usual Suspects, The (1995)   \n",
       "45366                        Godfather, The (1972)   \n",
       "7762                             Goodfellas (1990)   \n",
       "9403                    Grosse Pointe Blank (1997)   \n",
       "60382                            Species II (1998)   \n",
       "11108                      Jungle Book, The (1967)   \n",
       "13425             Run Lola Run (Lola rennt) (1998)   \n",
       "14203                            Goldfinger (1964)   \n",
       "49571     Princess Mononoke (Mononoke-hime) (1997)   \n",
       "63222          Batman: Mask of the Phantasm (1993)   \n",
       "\n",
       "                                                genres  \n",
       "18810                           Action|Sci-Fi|Thriller  \n",
       "18827                                    Action|Sci-Fi  \n",
       "18832                                   Fantasy|Horror  \n",
       "573                             Crime|Mystery|Thriller  \n",
       "19321                             Comedy|Drama|Romance  \n",
       "25824                        Drama|Romance|War|Western  \n",
       "19722                                            Drama  \n",
       "20123  Animation|Children|Fantasy|Musical|Romance|IMAX  \n",
       "588                             Crime|Mystery|Thriller  \n",
       "45366                                      Crime|Drama  \n",
       "7762                                       Crime|Drama  \n",
       "9403                              Comedy|Crime|Romance  \n",
       "60382                                    Horror|Sci-Fi  \n",
       "11108                Animation|Children|Comedy|Musical  \n",
       "13425                                     Action|Crime  \n",
       "14203                        Action|Adventure|Thriller  \n",
       "49571         Action|Adventure|Animation|Drama|Fantasy  \n",
       "63222                               Animation|Children  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print movies watched by user 3, 5, 39\n",
    "\n",
    "active_user_movies = ml_df.loc[\n",
    "    (ml_df['user_id'] == 3) | (ml_df['user_id'] == 5) | (ml_df['user_id'] == 39)].sort_values(['user_id', 'item_id'])\n",
    "print(\"Active users history\")\n",
    "display(active_user_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "oriented-service",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>score</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2311</td>\n",
       "      <td>0.571887</td>\n",
       "      <td>2010: The Year We Make Contact (1984)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2661</td>\n",
       "      <td>0.571887</td>\n",
       "      <td>It Came from Outer Space (1953)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2698</td>\n",
       "      <td>0.571887</td>\n",
       "      <td>Zone 39 (1997)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5468</td>\n",
       "      <td>0.571887</td>\n",
       "      <td>20 Million Miles to Earth (1957)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>51562</td>\n",
       "      <td>0.571887</td>\n",
       "      <td>Babylon 5: The Gathering (1993)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>147384</td>\n",
       "      <td>0.571887</td>\n",
       "      <td>Doctor Who: The Runaway Bride (2007)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>176371</td>\n",
       "      <td>0.571887</td>\n",
       "      <td>Blade Runner 2049 (2017)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>7991</td>\n",
       "      <td>0.550571</td>\n",
       "      <td>Death Race 2000 (1975)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>74668</td>\n",
       "      <td>0.550571</td>\n",
       "      <td>District 13: Ultimatum (Banlieue 13 - Ultimatu...</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>168252</td>\n",
       "      <td>0.550571</td>\n",
       "      <td>Logan (2017)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>6192</td>\n",
       "      <td>0.505298</td>\n",
       "      <td>Open Hearts (Elsker dig for evigt) (2002)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>139747</td>\n",
       "      <td>0.505298</td>\n",
       "      <td>Before We Go (2014)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>74</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>Bed of Roses (1996)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>Walk in the Clouds, A (1995)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>779</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>'Til There Was You (1997)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>902</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>Breakfast at Tiffany's (1961)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>1496</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>Anna Karenina (1997)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>1704</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>Good Will Hunting (1997)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>3330</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>Splendor in the Grass (1961)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>3668</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>Romeo and Juliet (1968)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>39</td>\n",
       "      <td>66335</td>\n",
       "      <td>0.456156</td>\n",
       "      <td>Afro Samurai: Resurrection (2009)</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>39</td>\n",
       "      <td>160848</td>\n",
       "      <td>0.456156</td>\n",
       "      <td>The Red Turtle (2016)</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>39</td>\n",
       "      <td>2307</td>\n",
       "      <td>0.429189</td>\n",
       "      <td>One Tough Cop (1998)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39</td>\n",
       "      <td>2692</td>\n",
       "      <td>0.429189</td>\n",
       "      <td>Run Lola Run (Lola rennt) (1998)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>39</td>\n",
       "      <td>7482</td>\n",
       "      <td>0.429189</td>\n",
       "      <td>Enter the Dragon (1973)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>39</td>\n",
       "      <td>74851</td>\n",
       "      <td>0.429189</td>\n",
       "      <td>From Paris with Love (2010)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39</td>\n",
       "      <td>104760</td>\n",
       "      <td>0.429189</td>\n",
       "      <td>Getaway (2013)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>39</td>\n",
       "      <td>104837</td>\n",
       "      <td>0.429189</td>\n",
       "      <td>Rage of Honor (1987)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>858</td>\n",
       "      <td>0.423262</td>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39</td>\n",
       "      <td>1213</td>\n",
       "      <td>0.423262</td>\n",
       "      <td>Goodfellas (1990)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id item_id     score  \\\n",
       "0        3    2311  0.571887   \n",
       "1        3    2661  0.571887   \n",
       "2        3    2698  0.571887   \n",
       "3        3    5468  0.571887   \n",
       "4        3   51562  0.571887   \n",
       "5        3  147384  0.571887   \n",
       "6        3  176371  0.571887   \n",
       "7        3    7991  0.550571   \n",
       "8        3   74668  0.550571   \n",
       "9        3  168252  0.550571   \n",
       "10       5    6192  0.505298   \n",
       "11       5  139747  0.505298   \n",
       "12       5      74  0.479087   \n",
       "13       5     207  0.479087   \n",
       "14       5     779  0.479087   \n",
       "15       5     902  0.479087   \n",
       "16       5    1496  0.479087   \n",
       "17       5    1704  0.479087   \n",
       "18       5    3330  0.479087   \n",
       "19       5    3668  0.479087   \n",
       "20      39   66335  0.456156   \n",
       "21      39  160848  0.456156   \n",
       "22      39    2307  0.429189   \n",
       "23      39    2692  0.429189   \n",
       "24      39    7482  0.429189   \n",
       "25      39   74851  0.429189   \n",
       "26      39  104760  0.429189   \n",
       "27      39  104837  0.429189   \n",
       "28      39     858  0.423262   \n",
       "29      39    1213  0.423262   \n",
       "\n",
       "                                                title         genres  \n",
       "0               2010: The Year We Make Contact (1984)         Sci-Fi  \n",
       "1                     It Came from Outer Space (1953)         Sci-Fi  \n",
       "2                                      Zone 39 (1997)         Sci-Fi  \n",
       "3                    20 Million Miles to Earth (1957)         Sci-Fi  \n",
       "4                     Babylon 5: The Gathering (1993)         Sci-Fi  \n",
       "5                Doctor Who: The Runaway Bride (2007)         Sci-Fi  \n",
       "6                            Blade Runner 2049 (2017)         Sci-Fi  \n",
       "7                              Death Race 2000 (1975)  Action|Sci-Fi  \n",
       "8   District 13: Ultimatum (Banlieue 13 - Ultimatu...  Action|Sci-Fi  \n",
       "9                                        Logan (2017)  Action|Sci-Fi  \n",
       "10          Open Hearts (Elsker dig for evigt) (2002)        Romance  \n",
       "11                                Before We Go (2014)        Romance  \n",
       "12                                Bed of Roses (1996)  Drama|Romance  \n",
       "13                       Walk in the Clouds, A (1995)  Drama|Romance  \n",
       "14                          'Til There Was You (1997)  Drama|Romance  \n",
       "15                      Breakfast at Tiffany's (1961)  Drama|Romance  \n",
       "16                               Anna Karenina (1997)  Drama|Romance  \n",
       "17                           Good Will Hunting (1997)  Drama|Romance  \n",
       "18                       Splendor in the Grass (1961)  Drama|Romance  \n",
       "19                            Romeo and Juliet (1968)  Drama|Romance  \n",
       "20                  Afro Samurai: Resurrection (2009)      Animation  \n",
       "21                              The Red Turtle (2016)      Animation  \n",
       "22                               One Tough Cop (1998)   Action|Crime  \n",
       "23                   Run Lola Run (Lola rennt) (1998)   Action|Crime  \n",
       "24                            Enter the Dragon (1973)   Action|Crime  \n",
       "25                        From Paris with Love (2010)   Action|Crime  \n",
       "26                                     Getaway (2013)   Action|Crime  \n",
       "27                               Rage of Honor (1987)   Action|Crime  \n",
       "28                              Godfather, The (1972)    Crime|Drama  \n",
       "29                                  Goodfellas (1990)    Crime|Drama  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick test of the recommender\n",
    "\n",
    "tfidf_recommender = TFIDFRecommender()\n",
    "tfidf_recommender.fit(ml_ratings_df, None, ml_movies_df)\n",
    "recommendations = tfidf_recommender.recommend(pd.DataFrame([[3], [5], [39]], columns=['user_id']), ml_movies_df, 10)\n",
    "\n",
    "recommendations = pd.merge(recommendations, ml_movies_df, on='item_id', how='left')\n",
    "print(\"Recommendations\")\n",
    "display(recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sealed-liberty",
   "metadata": {},
   "source": [
    "### Train-test split test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "identified-destination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recommender</th>\n",
       "      <th>HR@1</th>\n",
       "      <th>HR@3</th>\n",
       "      <th>HR@5</th>\n",
       "      <th>HR@10</th>\n",
       "      <th>NDCG@1</th>\n",
       "      <th>NDCG@3</th>\n",
       "      <th>NDCG@5</th>\n",
       "      <th>NDCG@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TFIDFRecommender</td>\n",
       "      <td>0.017897</td>\n",
       "      <td>0.044743</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>0.09396</td>\n",
       "      <td>0.017897</td>\n",
       "      <td>0.033077</td>\n",
       "      <td>0.038368</td>\n",
       "      <td>0.049966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Recommender      HR@1      HR@3      HR@5    HR@10    NDCG@1  \\\n",
       "0  TFIDFRecommender  0.017897  0.044743  0.058166  0.09396  0.017897   \n",
       "\n",
       "     NDCG@3    NDCG@5   NDCG@10  \n",
       "0  0.033077  0.038368  0.049966  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total evaluation time: 67.2819995880127\n"
     ]
    }
   ],
   "source": [
    "tfidf_recommender = TFIDFRecommender()\n",
    "\n",
    "t0 = time.time()\n",
    "\n",
    "results = [['TFIDFRecommender'] + list(evaluate_train_test_split_implicit(\n",
    "    tfidf_recommender, ml_ratings_df.loc[:, ['user_id', 'item_id']], ml_movies_df))]\n",
    "\n",
    "results = pd.DataFrame(results, \n",
    "                       columns=['Recommender', 'HR@1', 'HR@3', 'HR@5', 'HR@10', 'NDCG@1', 'NDCG@3', 'NDCG@5', 'NDCG@10'])\n",
    "\n",
    "display(results)\n",
    "\n",
    "print('Total evaluation time: {}'.format(time.time() - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "creative-beast",
   "metadata": {},
   "source": [
    "### Leave-one-out test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "qualified-westminster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recommender</th>\n",
       "      <th>HR@1</th>\n",
       "      <th>HR@3</th>\n",
       "      <th>HR@5</th>\n",
       "      <th>HR@10</th>\n",
       "      <th>NDCG@1</th>\n",
       "      <th>NDCG@3</th>\n",
       "      <th>NDCG@5</th>\n",
       "      <th>NDCG@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TFIDFRecommender</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.005436</td>\n",
       "      <td>0.005436</td>\n",
       "      <td>0.009773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Recommender      HR@1      HR@3      HR@5  HR@10    NDCG@1    NDCG@3  \\\n",
       "0  TFIDFRecommender  0.003333  0.006667  0.006667   0.02  0.003333  0.005436   \n",
       "\n",
       "     NDCG@5   NDCG@10  \n",
       "0  0.005436  0.009773  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total evaluation time: 198.83639669418335\n"
     ]
    }
   ],
   "source": [
    "tfidf_recommender = TFIDFRecommender()\n",
    "\n",
    "t0 = time.time()\n",
    "\n",
    "results = [['TFIDFRecommender'] + list(evaluate_leave_one_out_implicit(\n",
    "    tfidf_recommender, ml_ratings_df.loc[:, ['user_id', 'item_id']], ml_movies_df, max_evals=300, seed=6789))]\n",
    "\n",
    "results = pd.DataFrame(results, \n",
    "                       columns=['Recommender', 'HR@1', 'HR@3', 'HR@5', 'HR@10', 'NDCG@1', 'NDCG@3', 'NDCG@5', 'NDCG@10'])\n",
    "\n",
    "display(results)\n",
    "\n",
    "print('Total evaluation time: {}'.format(time.time() - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "split-insertion",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}