Module-7-  Project.ipynb

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      "source": [
        "#Module 7 Project: Movie Recommendation Engine"
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "In this module, we will try to create a Movie Recommendation System using different unsupervised learning techniques."
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      "source": [
        "# dataset download\n",
        "\n",
        "# !wget https://files.grouplens.org/datasets/movielens/ml-100k.zip\n",
        "!wget https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\n",
        "!unzip ml-latest-small.zip"
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            "--2023-04-16 13:34:54--  https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\n",
            "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n",
            "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 978202 (955K) [application/zip]\n",
            "Saving to: ‘ml-latest-small.zip’\n",
            "\n",
            "ml-latest-small.zip 100%[===================>] 955.28K  3.05MB/s    in 0.3s    \n",
            "\n",
            "2023-04-16 13:34:55 (3.05 MB/s) - ‘ml-latest-small.zip’ saved [978202/978202]\n",
            "\n",
            "Archive:  ml-latest-small.zip\n",
            "   creating: ml-latest-small/\n",
            "  inflating: ml-latest-small/links.csv  \n",
            "  inflating: ml-latest-small/tags.csv  \n",
            "  inflating: ml-latest-small/ratings.csv  \n",
            "  inflating: ml-latest-small/README.txt  \n",
            "  inflating: ml-latest-small/movies.csv  \n"
          ]
        }
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      "source": [
        "## Clustering based recommendation"
      ]
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    {
      "cell_type": "code",
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      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from scipy.sparse import csr_matrix\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import itertools\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score"
      ],
      "execution_count": 2,
      "outputs": []
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      "source": [
        "# Import the Movies dataset\n",
        "movies = pd.read_csv('ml-latest-small/movies.csv')\n",
        "movies.head()"
      ],
      "execution_count": 3,
      "outputs": [
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          "data": {
            "text/plain": [
              "   movieId                               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",
              "\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  "
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        "# Import the ratings dataset\n",
        "ratings = pd.read_csv('ml-latest-small/ratings.csv')\n",
        "ratings.head()"
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      "execution_count": 4,
      "outputs": [
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              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
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      "source": [
        "# Print the number of records and the total number of movies\n",
        "print('The dataset contains: ', len(ratings), ' ratings of ', len(movies), ' movies.')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dataset contains:  100836  ratings of  9742  movies.\n"
          ]
        }
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      "source": [
        "### Romance versus Science Fiction\n",
        "\n",
        "We will start by considering a subset of users and discovering what are their favourite genre. We will do this by defining a function that will calculate each user’s average rating for all science fiction and romance movies."
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      "source": [
        "# Function to get the genre ratings\n",
        "def get_genre_ratings(ratings, movies, genres, column_names):\n",
        "    genre_ratings = pd.DataFrame()\n",
        "    for genre in genres:        \n",
        "        genre_movies = movies[movies['genres'].str.contains(genre) ]\n",
        "        avg_genre_votes_per_user = ratings[ratings['movieId'].isin(genre_movies['movieId'])].loc[:, ['userId', 'rating']].groupby(['userId'])['rating'].mean().round(2)\n",
        "        \n",
        "        genre_ratings = pd.concat([genre_ratings, avg_genre_votes_per_user], axis=1)\n",
        "        \n",
        "    genre_ratings.columns = column_names\n",
        "    return genre_ratings# Calculate the average rating of romance and scifi movies\n",
        "genre_ratings = get_genre_ratings(ratings, movies, ['Romance', 'Sci-Fi'], ['avg_romance_rating', 'avg_scifi_rating'])\n",
        "genre_ratings.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   avg_romance_rating  avg_scifi_rating\n",
              "1                4.31              4.22\n",
              "2                4.50              3.88\n",
              "3                0.50              4.20\n",
              "4                3.38              2.83\n",
              "5                3.09              2.50"
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>avg_romance_rating</th>\n",
              "      <th>avg_scifi_rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.31</td>\n",
              "      <td>4.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.50</td>\n",
              "      <td>3.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.50</td>\n",
              "      <td>4.20</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.38</td>\n",
              "      <td>2.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.09</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79b5Uz3KcmSu"
      },
      "source": [
        "In order to have a more delimited subset of people to study, we are going to bias our grouping to only get ratings from those users that like either romance or science fiction movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "AdiswAdbceds",
        "outputId": "75bcb669-c934-4b87-dc1d-6ddb55613472"
      },
      "source": [
        "# Function to get the biased dataset\n",
        "def bias_genre_rating_dataset(genre_ratings, score_limit_1, score_limit_2):\n",
        "    biased_dataset =    genre_ratings[((genre_ratings['avg_romance_rating'] < score_limit_1 - 0.2) & (genre_ratings['avg_scifi_rating'] > score_limit_2)) | ((genre_ratings['avg_scifi_rating'] < score_limit_1) & (genre_ratings['avg_romance_rating'] > score_limit_2))]\n",
        "    biased_dataset = pd.concat([biased_dataset[:300], genre_ratings[:2]])\n",
        "    biased_dataset = pd.DataFrame(biased_dataset.to_records())\n",
        "    return biased_dataset\n",
        "\n",
        "# Bias the dataset\n",
        "biased_dataset = bias_genre_rating_dataset(genre_ratings, 3.2, 2.5)\n",
        "\n",
        "# Printing the resulting number of records & the head of the dataset\n",
        "print( \"Number of records: \", len(biased_dataset))\n",
        "biased_dataset.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records:  162\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  avg_romance_rating  avg_scifi_rating\n",
              "0      3                0.50              4.20\n",
              "1      4                3.38              2.83\n",
              "2      5                3.09              2.50\n",
              "3      7                2.65              3.15\n",
              "4      9                3.17              3.00"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "MLCoVvz0ctAh",
        "outputId": "572c47ec-6501-47f1-ec2f-127d3addce8f"
      },
      "source": [
        "# Defining the scatterplot drawing function\n",
        "def draw_scatterplot(x_data, x_label, y_data, y_label):\n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    ax = fig.add_subplot(111)    \n",
        "    plt.xlim(0, 5)\n",
        "    plt.ylim(0, 5)\n",
        "    ax.set_xlabel(x_label)\n",
        "    ax.set_ylabel(y_label)\n",
        "    ax.scatter(x_data, y_data, s=30)\n",
        "    \n",
        "# Plot the scatterplot\n",
        "draw_scatterplot(biased_dataset['avg_scifi_rating'],'Avg scifi rating', biased_dataset['avg_romance_rating'], 'Avg romance rating')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "LRfknsM1c1Ur",
        "outputId": "c5d70860-88de-41e0-94e2-81edb81ae99f"
      },
      "source": [
        "# Let's turn our dataset into a list\n",
        "X = biased_dataset[['avg_scifi_rating','avg_romance_rating']].values\n",
        "\n",
        "# Import KMeans\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Create an instance of KMeans to find two clusters\n",
        "kmeans_1 = KMeans(n_clusters=2)\n",
        "\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions = kmeans_1.fit_predict(X)\n",
        "\n",
        "# Defining the cluster plotting function\n",
        "def draw_clusters(biased_dataset, predictions, cmap='viridis'):\n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    ax = fig.add_subplot(111)\n",
        "    plt.xlim(0, 5)\n",
        "    plt.ylim(0, 5)\n",
        "    ax.set_xlabel('Avg scifi rating')\n",
        "    ax.set_ylabel('Avg romance rating')\n",
        "    clustered = pd.concat([biased_dataset.reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
        "    plt.scatter(clustered['avg_scifi_rating'], clustered['avg_romance_rating'], c=clustered['group'], s=20, cmap=cmap)\n",
        "    \n",
        "# Plot\n",
        "draw_clusters(biased_dataset, predictions)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "4tVnggzlc-rg",
        "outputId": "8c35c55a-6fa8-4ddc-97c2-69cc4d202976"
      },
      "source": [
        "# Create an instance of KMeans to find three clusters\n",
        "kmeans_2 = KMeans(n_clusters=3)\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions_2 = kmeans_2.fit_predict(X)\n",
        "# Plot\n",
        "draw_clusters(biased_dataset, predictions_2)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xi5ObyC0dLle"
      },
      "source": [
        "It is evident now that the science-fiction rating has started to come into play:\n",
        "\n",
        "    People who like sci-fi and romance modeately belong to the green group.\n",
        "    People who like scifi but not romance belong to the yello group.\n",
        "    People who like romance but not sci-fi belong to the purple group."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5w_-jzld_rM"
      },
      "source": [
        "Finding optimal clusters using silhouette score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyzcralUdFxj",
        "outputId": "432447e0-6675-43ac-9182-5772f72a98cb"
      },
      "source": [
        "# Selecting our dataset to study\n",
        "df = biased_dataset[['avg_scifi_rating','avg_romance_rating']]\n",
        "\n",
        "# Choose the range of k values to test.\n",
        "# We added a stride of 5 to improve performance. We don't need to calculate the error for every k value\n",
        "possible_k_values = range(2, len(X)+1, 5)\n",
        "\n",
        "# Define function to calculate the clustering errors\n",
        "def clustering_errors(k, data):\n",
        "    kmeans = KMeans(n_clusters=k).fit(data)\n",
        "    predictions = kmeans.predict(data)\n",
        "    #cluster_centers = kmeans.cluster_centers_\n",
        "    # errors = [mean_squared_error(row, cluster_centers[cluster]) for row, cluster in zip(data.values, predictions)]\n",
        "    # return sum(errors)\n",
        "    silhouette_avg = silhouette_score(data, predictions)\n",
        "    return silhouette_avg\n",
        "\n",
        "# Calculate error values for all k values we're interested in\n",
        "errors_per_k = [clustering_errors(k, X) for k in possible_k_values]\n",
        "\n",
        "# Plot the each value of K vs. the silhouette score at that value\n",
        "fig, ax = plt.subplots(figsize=(16, 6))\n",
        "plt.plot(possible_k_values, errors_per_k)\n",
        "\n",
        "# Ticks and grid\n",
        "xticks = np.arange(min(possible_k_values), max(possible_k_values)+1, 5.0)\n",
        "ax.set_xticks(xticks, minor=False)\n",
        "ax.set_xticks(xticks, minor=True)\n",
        "ax.xaxis.grid(True, which='both')\n",
        "yticks = np.arange(round(min(errors_per_k), 2), max(errors_per_k), .05)\n",
        "ax.set_yticks(yticks, minor=False)\n",
        "ax.set_yticks(yticks, minor=True)\n",
        "ax.yaxis.grid(True, which='both')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j02Q87YjepyP"
      },
      "source": [
        "We will chose the K = 7 as it is the one that yields the best score and will be easier to visualize.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lV1yJyAbeWHC"
      },
      "source": [
        "# Create an instance of KMeans to find seven clusters\n",
        "kmeans_4 = KMeans(n_clusters=7)\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions_4 = kmeans_4.fit_predict(X)\n",
        "# Plot\n",
        "draw_clusters(biased_dataset, predictions_4, cmap='Accent')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bqqi2h3FfaXs"
      },
      "source": [
        "Up to now, we have only analyzed romance and science-fiction movies. Let us see what happens when adding other genre to our analysis by adding Action movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv4ykXxweyrf"
      },
      "source": [
        "# Select our biased dataset and add action genre\n",
        "biased_dataset_3_genres = get_genre_ratings(ratings, movies, ['Romance','Sci-Fi', 'Action'],                                          \n",
        "['avg_romance_rating', 'avg_scifi_rating', 'avg_action_rating'])\n",
        "# Drop null values\n",
        "biased_dataset_3_genres = bias_genre_rating_dataset(biased_dataset_3_genres, 3.2, 2.5).dropna()\n",
        "# Print the number of records and the head of our dataset\n",
        "print( \"Number of records: \", len(biased_dataset_3_genres))\n",
        "biased_dataset_3_genres.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmxE7OEbfjgH"
      },
      "source": [
        "# Turn dataset into a list\n",
        "X_with_action = biased_dataset_3_genres[['avg_scifi_rating','avg_romance_rating','avg_action_rating']].values# Create an instance of KMeans to find seven clusters\n",
        "kmeans_5 = KMeans(n_clusters=7)\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions_5 = kmeans_5.fit_predict(X_with_action)\n",
        "# Define 3d plotting function\n",
        "def draw_clusters_3d(biased_dataset_3, predictions):\n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    ax = fig.add_subplot(111)\n",
        "    plt.xlim(0, 5)\n",
        "    plt.ylim(0, 5)\n",
        "    ax.set_xlabel('Avg scifi rating')\n",
        "    ax.set_ylabel('Avg romance rating')\n",
        "    clustered = pd.concat([biased_dataset_3.reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
        "    colors = itertools.cycle(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])\n",
        "    for g in clustered.group.unique():\n",
        "        color = next(colors)\n",
        "        for index, point in clustered[clustered.group == g].iterrows():\n",
        "            if point['avg_action_rating'].astype(float) > 3: \n",
        "                size = 50\n",
        "            else:\n",
        "                size = 15\n",
        "            plt.scatter(point['avg_scifi_rating'], \n",
        "                        point['avg_romance_rating'], \n",
        "                        s=size, \n",
        "                        color=color)# Plot\n",
        "draw_clusters_3d(biased_dataset_3_genres, predictions_5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Upy7G0Ta5MH"
      },
      "source": [
        "The size of the dots represent the ratings of the action movies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzfFJTMJbGCk"
      },
      "source": [
        "### Taking users into consideration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JahmtB_0f5fd"
      },
      "source": [
        "Once we have seen and understood how the K-Means algorithm group the users by their movie genre preferences, we are going to take a bigger picture of the dataset and explore how users rate individual movies.\n",
        "\n",
        "To do so, we will subset the dataset by ‘userid’ vs ‘user rating’ as follows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oy6yk-kxfuxC"
      },
      "source": [
        "# Merge the two tables then pivot so we have Users X Movies dataframe\n",
        "ratings_title = pd.merge(ratings, movies[['movieId', 'title']], on='movieId' )\n",
        "user_movie_ratings = pd.pivot_table(ratings_title, index='userId', columns= 'title', values='rating')\n",
        "\n",
        "# Print he number of dimensions and a subset of the dataset\n",
        "print('dataset dimensions: ', user_movie_ratings.shape, '\\n\\nSubset example:')\n",
        "user_movie_ratings.iloc[:6, :10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSB8eHPqf-Xc"
      },
      "source": [
        "# Define Function to get the most rated movies\n",
        "def get_most_rated_movies(user_movie_ratings, max_number_of_movies):\n",
        "    # 1- Count\n",
        "    user_movie_ratings = user_movie_ratings.append(user_movie_ratings.count(), ignore_index=True)\n",
        "    # 2- sort\n",
        "    user_movie_ratings_sorted = user_movie_ratings.sort_values(len(user_movie_ratings)-1, axis=1, ascending=False)\n",
        "    user_movie_ratings_sorted = user_movie_ratings_sorted.drop(user_movie_ratings_sorted.tail(1).index)\n",
        "    # 3- slice\n",
        "    most_rated_movies = user_movie_ratings_sorted.iloc[:, :max_number_of_movies]\n",
        "    return most_rated_movies\n",
        "\n",
        "# Define function to get the user who rate a movie the most\n",
        "def get_users_who_rate_the_most(most_rated_movies, n_users):\n",
        "    most_rated_movies['num_ratings'] = - most_rated_movies.isnull().sum(axis=1)\n",
        "    most_rated_movies = most_rated_movies.sort_values(by=['num_ratings'], ascending=False)\n",
        "    most_rated_movies = most_rated_movies.iloc[:n_users, :]\n",
        "    most_rated_movies = most_rated_movies.drop('num_ratings', axis=1)\n",
        "    return most_rated_movies\n",
        "\n",
        "# Define the sorting by rating function\n",
        "def sort_by_rating_density(user_movie_ratings, n_movies, n_users):\n",
        "    most_rated_movies = get_most_rated_movies(user_movie_ratings, n_movies)\n",
        "    most_rated_movies = get_users_who_rate_the_most(most_rated_movies, n_users)\n",
        "    return most_rated_movies\n",
        "    \n",
        "# choose the number of movies and users and sort\n",
        "n_movies = 30\n",
        "n_users = 18\n",
        "most_rated_movies_users_selection = sort_by_rating_density(user_movie_ratings, n_movies, n_users)\n",
        "\n",
        "# Print the result\n",
        "# print(most_rated_movies_users_selection)\n",
        "print('dataset dimensions: ', most_rated_movies_users_selection.shape)\n",
        "print(most_rated_movies_users_selection.head())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMmuIqVxgGAN"
      },
      "source": [
        "# Define the plotting heatmap function\n",
        "def draw_movies_heatmap(most_rated_movies_users_selection, axis_labels=True):\n",
        "    \n",
        "    fig = plt.figure(figsize=(15,4))\n",
        "    ax = plt.gca()\n",
        "    \n",
        "    # Draw heatmap\n",
        "    heatmap = ax.imshow(most_rated_movies_users_selection,  interpolation='nearest', vmin=0, vmax=5, aspect='auto')\n",
        "    if axis_labels:\n",
        "        ax.set_yticks(np.arange(most_rated_movies_users_selection.shape[0]) , minor=False)\n",
        "        ax.set_xticks(np.arange(most_rated_movies_users_selection.shape[1]) , minor=False)\n",
        "        ax.invert_yaxis()\n",
        "        ax.xaxis.tick_top()\n",
        "        labels = most_rated_movies_users_selection.columns.str[:40]\n",
        "        ax.set_xticklabels(labels, minor=False)\n",
        "        ax.set_yticklabels(most_rated_movies_users_selection.index, minor=False)\n",
        "        plt.setp(ax.get_xticklabels(), rotation=90)\n",
        "    else:\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "    \n",
        "    ax.grid(False)\n",
        "    ax.set_ylabel('User id')# Separate heatmap from color bar\n",
        "    divider = make_axes_locatable(ax)\n",
        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)# Color bar\n",
        "    cbar = fig.colorbar(heatmap, ticks=[5, 4, 3, 2, 1, 0], cax=cax)\n",
        "    cbar.ax.set_yticklabels(['5 stars', '4 stars','3 stars','2 stars','1 stars','0 stars'])\n",
        "    plt.show()# Print the heatmap\n",
        "draw_movies_heatmap(most_rated_movies_users_selection)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTdEC1i5hS-M"
      },
      "source": [
        "# Pivot the dataset and choose the first 1000 movies\n",
        "user_movie_ratings =  pd.pivot_table(ratings_title, index='userId', columns= 'title', values='rating')\n",
        "most_rated_movies_1k = get_most_rated_movies(user_movie_ratings, 1000).replace(np.nan, 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMGpCM8SikyB"
      },
      "source": [
        "from scipy import sparse\n",
        "\n",
        "# Conversion to sparse csr matrix\n",
        "sparse_ratings = sparse.csr_matrix(most_rated_movies_1k.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5j6M6TL7j5B7"
      },
      "source": [
        "### Large Scale Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcvHmGnZipva"
      },
      "source": [
        "# 20 clusters\n",
        "predictions = KMeans(n_clusters=20, algorithm='full').fit_predict(sparse_ratings)\n",
        "# Select the mas number of users and movies heatmap cluster\n",
        "# Cluster and print some of them\n",
        "clustered = pd.concat([get_most_rated_movies(user_movie_ratings, 1000).reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
        "# draw_movie_clusters(clustered, max_users, max_movies)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wr82U3hYo7gg"
      },
      "source": [
        "### Predictions and Recommendation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSx-kAa2j9rJ"
      },
      "source": [
        "# Pick a cluster ID from the clusters above\n",
        "cluster_number = 11\n",
        "# Let's filter to only see the region of the dataset with the most number of values \n",
        "n_users = 75\n",
        "n_movies = 300\n",
        "cluster = clustered[clustered.group == cluster_number].drop(['index', 'group'], axis=1)\n",
        "print(cluster)\n",
        "# Sort and print the cluster\n",
        "cluster = sort_by_rating_density(cluster, n_movies, n_users)\n",
        "draw_movies_heatmap(cluster, axis_labels=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1H3vAespxSf"
      },
      "source": [
        "# Fill in the name of the column/movie. e.g. 'Forrest Gump (1994)'\n",
        "movie_name = \"Matrix, The (1999)\"\n",
        "cluster[movie_name].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eaBbILNpsWA"
      },
      "source": [
        "# The average rating of 20 movies as rated by the users in the cluster\n",
        "cluster.mean().head(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VKa3_ZiyeGg"
      },
      "source": [
        "# Predict for a specific user\n",
        "\n",
        "# Pick a user ID from the dataset\n",
        "user_id = 19\n",
        "# Get all this user's ratings\n",
        "user_2_ratings  = cluster.loc[user_id, :]\n",
        "# Which movies did they not rate? \n",
        "user_2_unrated_movies =  user_2_ratings[user_2_ratings.isnull()]\n",
        "# What are the ratings of these movies the user did not rate?\n",
        "avg_ratings = pd.concat([user_2_unrated_movies, cluster.mean()], axis=1, join='inner').loc[:,0]\n",
        "# Let's sort by rating so the highest rated movies are presented first\n",
        "avg_ratings.sort_values(ascending=False)[:20]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4SISKyey5rb"
      },
      "source": [
        "## Using Collaborative Filtering (With fast.ai)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eh_wLcmh4nFi"
      },
      "source": [
        "! [ -e /content ] && pip install -Uqq fastai"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GQhuh1QzBO-"
      },
      "source": [
        "from fastai.tabular.all import *\n",
        "from fastai.collab import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_C6VqNfy5KGG"
      },
      "source": [
        "### Training a model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVnoQsA25KGH"
      },
      "source": [
        "For this, we will use the [Movielens 100k data dataset](https://grouplens.org/datasets/movielens/100k/). We can download it easily and decompress it with the following function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSpPNWVo5KGI"
      },
      "source": [
        "path = untar_data(URLs.ML_100k)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ryXnRG55KGJ"
      },
      "source": [
        "The main table is in `u.data`. Since it's not a proper csv, we have to specify a few things while opening it: the tab delimiter, the columns we want to keep and their names."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aIeuyeJ5KGL"
      },
      "source": [
        "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
        "                      usecols=(0,1,2), names=['user','movie','rating'])\n",
        "ratings.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0FSMvM05KGP"
      },
      "source": [
        "Movie ids are not ideal to look at things, so we load the corresponding movie id to the title that is in the table `u.item`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3y_V-da5KGQ"
      },
      "source": [
        "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1',\n",
        "                     usecols=(0,1), names=('movie','title'), header=None)\n",
        "movies.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g11IQqmd5KGR"
      },
      "source": [
        "Next we merge it to our ratings table:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZmPTq285KGT"
      },
      "source": [
        "ratings = ratings.merge(movies)\n",
        "ratings.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PksImdkw5KGV"
      },
      "source": [
        "We can then build a `DataLoaders` object from this table. By default, it takes the first column for user, the second column for the item (here our movies) and the third column for the ratings. We need to change the value of `item_name` in our case, to use the titles instead of the ids:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OV-H74uh5KGV"
      },
      "source": [
        "dls = CollabDataLoaders.from_df(ratings, item_name='title', bs=64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyK3Sy9L5KGW"
      },
      "source": [
        "In all applications, when the data has been assembled in a `DataLoaders`, you can have a look at it with the `show_batch` method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U07N7Tue5KGW"
      },
      "source": [
        "dls.show_batch()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cMiAsrvW5KGY"
      },
      "source": [
        "fastai can create and train a collaborative filtering model by using `collab_learner`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rXePHqS5KGY"
      },
      "source": [
        "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58blYToq5KGY"
      },
      "source": [
        "It uses a simple dot product model with 50 latent factors. To train it using the 1cycle policy, we just run this command:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ze2sG7Pd5KGZ"
      },
      "source": [
        "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXpc4wrh5KGZ"
      },
      "source": [
        "Here's [some benchmarks](https://www.librec.net/release/v1.3/example.html) on the same dataset for the popular Librec system for collaborative filtering. They show best results based on RMSE of 0.91 (scroll down to the 100k dataset), which corresponds to an MSE of `0.91**2 = 0.83`. So in less than a minute, we got pretty good results!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNlR8PdV5KGa"
      },
      "source": [
        "### Interpretation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Kp8v39y5KGa"
      },
      "source": [
        "Let's analyze the results of our previous model. We will keep the 1000 most rated movies for this:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOBM1Dmn5KGa"
      },
      "source": [
        "g = ratings.groupby('title')['rating'].count()\n",
        "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
        "top_movies[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iT0TUZeV5KGa"
      },
      "source": [
        "### Movie bias"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTfd24Ce5KGb"
      },
      "source": [
        "Our model has learned one bias per movie, a unique number independent of users that can be interpreted as the intrinsic \"value\" of the movie. We can grab the bias of each movie in our `top_movies` list with the following command:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6_3vygf5KGb"
      },
      "source": [
        "movie_bias = learn.model.bias(top_movies, is_item=True)\n",
        "movie_bias.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LWSBSXB5KGb"
      },
      "source": [
        "Let's compare those biases with the average ratings:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-cmUxrz5KGc"
      },
      "source": [
        "mean_ratings = ratings.groupby('title')['rating'].mean()\n",
        "movie_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_movies,movie_bias)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQ9NpRIY5KGd"
      },
      "source": [
        "Now let's have a look at the movies with the worst bias:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPK_3uoL5KGe"
      },
      "source": [
        "item0 = lambda o:o[0]\n",
        "sorted(movie_ratings, key=item0)[:15]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyZduCwf5KGf"
      },
      "source": [
        "Or the ones with the best bias:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "la5TBDHx5KGg"
      },
      "source": [
        "sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6evHscZk5KGh"
      },
      "source": [
        "There is certainly a strong correlation!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eVUbKjL5KGi"
      },
      "source": [
        "### Movie weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfpKAgSC5KGi"
      },
      "source": [
        "Now let's try to analyze the latent factors our model has learned. We can grab the weights for each movie in `top_movies` the same way as we did for the bias before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deDn2gcH5KGj"
      },
      "source": [
        "movie_w = learn.model.weight(top_movies, is_item=True)\n",
        "movie_w.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMKR-0Um5KGj"
      },
      "source": [
        "Let's try a PCA to reduce the dimensions and see if we can see what the model learned:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eaYCuVt5KGk"
      },
      "source": [
        "movie_pca = movie_w.pca(3)\n",
        "movie_pca.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gP8YQMM55KGl"
      },
      "source": [
        "fac0,fac1,fac2 = movie_pca.t()\n",
        "movie_comp = [(f, i) for f,i in zip(fac0, top_movies)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRscjh3K5KGl"
      },
      "source": [
        "Here are the highest score on the first dimension:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L74WWxjA5KGl"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMS1ASOm5KGm"
      },
      "source": [
        "And the worst:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDxgTWUW5KGm"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXZyCaJL5KGn"
      },
      "source": [
        "Same thing for our second dimension:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JR6BnTn5KGn"
      },
      "source": [
        "movie_comp = [(f, i) for f,i in zip(fac1, top_movies)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xj4_P5dS5KGn"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28K9s9Rp5KGo"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZAynsWP5KGo"
      },
      "source": [
        "And we can even plot the movies according to their scores on those dimensions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHtORCAp5KGp"
      },
      "source": [
        "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
        "idxs = list(range(50))\n",
        "X = fac0[idxs]\n",
        "Y = fac2[idxs]\n",
        "plt.figure(figsize=(15,15))\n",
        "plt.scatter(X, Y)\n",
        "for i, x, y in zip(top_movies[idxs], X, Y):\n",
        "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# END\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "PnXNRzkZlkTw"
      }
    }
  ]
}