Book_Recommendation_System.ipynb

{
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  "metadata": {
    "colab": {
      "name": "FastAI V2 Book Recommendation System.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJBJT-z4DkZy",
        "colab_type": "text"
      },
      "source": [
        "# Book Recommendation System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4htqcGmKC44k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "da8486f5-65b1-416a-f8b0-79fb42b78754"
      },
      "source": [
        "!pip install fastai==2.0.10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fastai==2.0.10\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/cf/9140964d3544d904cc718e519e1632a8a42e1eaaf2fafdaaa14716c42336/fastai-2.0.10-py3-none-any.whl (354kB)\n",
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            "Collecting fastcore>=1.0.5\n",
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            "Installing collected packages: fastcore, fastai\n",
            "  Found existing installation: fastai 1.0.61\n",
            "    Uninstalling fastai-1.0.61:\n",
            "      Successfully uninstalled fastai-1.0.61\n",
            "Successfully installed fastai-2.0.10 fastcore-1.0.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbLprAv_Dwlv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.tabular.all import *\n",
        "from fastai.collab import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXfilFMoEAjw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "3a7e6b6e-a361-49cb-a51e-42d8d7fb067e"
      },
      "source": [
        "# Install Kaggle API\n",
        "!pip install -q kaggle\n",
        "!pip install -q kaggle-cli"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25h  Building wheel for kaggle-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4shUKWvcEDRh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# only for google colab\n",
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = \"<username>\" \n",
        "os.environ['KAGGLE_KEY'] = \"<key>\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpHOtSw8EEoS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = Path('goodbooks')\n",
        "path.mkdir(parents=True, exist_ok=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IhkLa1xEHsT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "71cceb4f-68cb-4966-9632-42dc6ff8a07e"
      },
      "source": [
        "! kaggle datasets download zygmunt/goodbooks-10k -p \"{path}\" --unzip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading goodbooks-10k.zip to goodbooks\n",
            " 78% 9.00M/11.6M [00:00<00:00, 14.3MB/s]\n",
            "100% 11.6M/11.6M [00:00<00:00, 17.0MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVvHFoX2EJUo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "581efd08-15a3-4ecd-ece6-e1328d730478"
      },
      "source": [
        "ratings = pd.read_csv(path/'ratings.csv')\n",
        "ratings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1169</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "      <td>4</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "   book_id  user_id  rating\n",
              "0        1      314       5\n",
              "1        1      439       3\n",
              "2        1      588       5\n",
              "3        1     1169       4\n",
              "4        1     1185       4"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDPvVcj3ELHE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "7ee2e273-154c-44b7-f65f-ff9af0d7b3bb"
      },
      "source": [
        "books = pd.read_csv(path/'books.csv')\n",
        "books.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "      <th>book_id</th>\n",
              "      <th>best_book_id</th>\n",
              "      <th>work_id</th>\n",
              "      <th>books_count</th>\n",
              "      <th>isbn</th>\n",
              "      <th>isbn13</th>\n",
              "      <th>authors</th>\n",
              "      <th>original_publication_year</th>\n",
              "      <th>original_title</th>\n",
              "      <th>title</th>\n",
              "      <th>language_code</th>\n",
              "      <th>average_rating</th>\n",
              "      <th>ratings_count</th>\n",
              "      <th>work_ratings_count</th>\n",
              "      <th>work_text_reviews_count</th>\n",
              "      <th>ratings_1</th>\n",
              "      <th>ratings_2</th>\n",
              "      <th>ratings_3</th>\n",
              "      <th>ratings_4</th>\n",
              "      <th>ratings_5</th>\n",
              "      <th>image_url</th>\n",
              "      <th>small_image_url</th>\n",
              "    </tr>\n",
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              "      <td>Suzanne Collins</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>The Hunger Games</td>\n",
              "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
              "      <td>eng</td>\n",
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              "      <td>9.780440e+12</td>\n",
              "      <td>J.K. Rowling, Mary GrandPré</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>Harry Potter and the Philosopher's Stone</td>\n",
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              "      <td>eng</td>\n",
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              "      <td>4800065</td>\n",
              "      <td>75867</td>\n",
              "      <td>75504</td>\n",
              "      <td>101676</td>\n",
              "      <td>455024</td>\n",
              "      <td>1156318</td>\n",
              "      <td>3011543</td>\n",
              "      <td>https://images.gr-assets.com/books/1474154022m/3.jpg</td>\n",
              "      <td>https://images.gr-assets.com/books/1474154022s/3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>41865</td>\n",
              "      <td>41865</td>\n",
              "      <td>3212258</td>\n",
              "      <td>226</td>\n",
              "      <td>316015849</td>\n",
              "      <td>9.780316e+12</td>\n",
              "      <td>Stephenie Meyer</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>Twilight</td>\n",
              "      <td>Twilight (Twilight, #1)</td>\n",
              "      <td>en-US</td>\n",
              "      <td>3.57</td>\n",
              "      <td>3866839</td>\n",
              "      <td>3916824</td>\n",
              "      <td>95009</td>\n",
              "      <td>456191</td>\n",
              "      <td>436802</td>\n",
              "      <td>793319</td>\n",
              "      <td>875073</td>\n",
              "      <td>1355439</td>\n",
              "      <td>https://images.gr-assets.com/books/1361039443m/41865.jpg</td>\n",
              "      <td>https://images.gr-assets.com/books/1361039443s/41865.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2657</td>\n",
              "      <td>2657</td>\n",
              "      <td>3275794</td>\n",
              "      <td>487</td>\n",
              "      <td>61120081</td>\n",
              "      <td>9.780061e+12</td>\n",
              "      <td>Harper Lee</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>To Kill a Mockingbird</td>\n",
              "      <td>To Kill a Mockingbird</td>\n",
              "      <td>eng</td>\n",
              "      <td>4.25</td>\n",
              "      <td>3198671</td>\n",
              "      <td>3340896</td>\n",
              "      <td>72586</td>\n",
              "      <td>60427</td>\n",
              "      <td>117415</td>\n",
              "      <td>446835</td>\n",
              "      <td>1001952</td>\n",
              "      <td>1714267</td>\n",
              "      <td>https://images.gr-assets.com/books/1361975680m/2657.jpg</td>\n",
              "      <td>https://images.gr-assets.com/books/1361975680s/2657.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4671</td>\n",
              "      <td>4671</td>\n",
              "      <td>245494</td>\n",
              "      <td>1356</td>\n",
              "      <td>743273567</td>\n",
              "      <td>9.780743e+12</td>\n",
              "      <td>F. Scott Fitzgerald</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>The Great Gatsby</td>\n",
              "      <td>The Great Gatsby</td>\n",
              "      <td>eng</td>\n",
              "      <td>3.89</td>\n",
              "      <td>2683664</td>\n",
              "      <td>2773745</td>\n",
              "      <td>51992</td>\n",
              "      <td>86236</td>\n",
              "      <td>197621</td>\n",
              "      <td>606158</td>\n",
              "      <td>936012</td>\n",
              "      <td>947718</td>\n",
              "      <td>https://images.gr-assets.com/books/1490528560m/4671.jpg</td>\n",
              "      <td>https://images.gr-assets.com/books/1490528560s/4671.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ...                                             small_image_url\n",
              "0   1  ...  https://images.gr-assets.com/books/1447303603s/2767052.jpg\n",
              "1   2  ...        https://images.gr-assets.com/books/1474154022s/3.jpg\n",
              "2   3  ...    https://images.gr-assets.com/books/1361039443s/41865.jpg\n",
              "3   4  ...     https://images.gr-assets.com/books/1361975680s/2657.jpg\n",
              "4   5  ...     https://images.gr-assets.com/books/1490528560s/4671.jpg\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLOCxGGjEMW4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "016c45ff-b3b1-44da-a3e7-1fab3a4c24e8"
      },
      "source": [
        "book_ratings = ratings.merge(books[['id', 'title']], left_on='book_id', right_on='id')\n",
        "book_ratings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>book_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>rating</th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>439</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>588</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1169</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1185</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   book_id  user_id  rating  id                                    title\n",
              "0        1      314       5   1  The Hunger Games (The Hunger Games, #1)\n",
              "1        1      439       3   1  The Hunger Games (The Hunger Games, #1)\n",
              "2        1      588       5   1  The Hunger Games (The Hunger Games, #1)\n",
              "3        1     1169       4   1  The Hunger Games (The Hunger Games, #1)\n",
              "4        1     1185       4   1  The Hunger Games (The Hunger Games, #1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X544pAMPEN5V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c29e2ff-bce0-4494-b428-9d202a57e1f1"
      },
      "source": [
        "book_ratings.rating.min(), book_ratings.rating.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDfWEAVDEPTt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dls = CollabDataLoaders.from_df(book_ratings, user_name='user_id', item_name='title', rating_name='rating', bs=64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15MA75F-Eg_B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "7b7d207f-645a-4ca6-fff0-b703b7e1207c"
      },
      "source": [
        "dls.show_batch()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29287</td>\n",
              "      <td>Ark Angel (Alex Rider, #6)</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30167</td>\n",
              "      <td>The Innovators: How a Group of  Hackers, Geniuses and Geeks Created the Digital Revolution</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26825</td>\n",
              "      <td>Financial Peace Revisited</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42695</td>\n",
              "      <td>The Battle for Skandia (Ranger's Apprentice, #4)</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>47408</td>\n",
              "      <td>Winter's Bone</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>30681</td>\n",
              "      <td>Dreamcatcher</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>37144</td>\n",
              "      <td>Grave Dance (Alex Craft, #2)</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4236</td>\n",
              "      <td>Blind Willow, Sleeping Woman</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7457</td>\n",
              "      <td>The Naked Face</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>34283</td>\n",
              "      <td>The Ice Twins</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cML8w0GEzZk",
        "colab_type": "text"
      },
      "source": [
        "## EmbeddingDotBias Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xl4tfdqE01f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1Dfe05fE5d4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "ed4ec6f0-fe69-49c0-e3ed-9692e944f4b7"
      },
      "source": [
        "learn.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "EmbeddingDotBias (Input shape: ['64 x 2'])\n",
              "================================================================\n",
              "Layer (type)         Output Shape         Param #    Trainable \n",
              "================================================================\n",
              "Embedding            64 x 50              2,651,700  True      \n",
              "________________________________________________________________\n",
              "Embedding            64 x 50              498,250    True      \n",
              "________________________________________________________________\n",
              "Embedding            64 x 1               53,034     True      \n",
              "________________________________________________________________\n",
              "Embedding            64 x 1               9,965      True      \n",
              "________________________________________________________________\n",
              "\n",
              "Total params: 3,212,949\n",
              "Total trainable params: 3,212,949\n",
              "Total non-trainable params: 0\n",
              "\n",
              "Optimizer used: <function Adam at 0x7f363ae7ec80>\n",
              "Loss function: FlattenedLoss of MSELoss()\n",
              "\n",
              "Callbacks:\n",
              "  - TrainEvalCallback\n",
              "  - Recorder\n",
              "  - ProgressCallback"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQOCAABnE7mh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a4f2f2c6-12d9-4fb9-a9af-a6a82571b675"
      },
      "source": [
        "lr_min, lr_steep = learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31LLL7dHFARX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "bf50c5f5-f492-4945-c05f-fa92dba281f0"
      },
      "source": [
        "learn.fit_one_cycle(5, lr_min, wd=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.836295</td>\n",
              "      <td>1.860142</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.911268</td>\n",
              "      <td>1.858519</td>\n",
              "      <td>02:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.715129</td>\n",
              "      <td>1.719781</td>\n",
              "      <td>02:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.333959</td>\n",
              "      <td>1.332779</td>\n",
              "      <td>02:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.063028</td>\n",
              "      <td>1.072152</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1xTf97WFDG4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dd98756c-0a47-4a79-e9f3-ea6c3c8e2353"
      },
      "source": [
        "learn.save('goodbooks-dot-1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('models/goodbooks-dot-1.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEhoZc0pFGLX",
        "colab_type": "text"
      },
      "source": [
        "## EmbeddingNN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-2jJgHRgs0H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = collab_learner(dls, use_nn=True, emb_szs={'user_id': 40, 'book_id':40}, layers=[256, 128], y_range=(0, 5.5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdGdDBFAg8P-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "4e46c102-6269-4c20-a4cb-8795914de4c5"
      },
      "source": [
        "lr_min, lr_steep = learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAGgs9UCg97s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ae14e27d-b405-4952-d958-c80f7549f5b8"
      },
      "source": [
        "learn.fit_one_cycle(5, lr_min, wd=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.881903</td>\n",
              "      <td>0.891226</td>\n",
              "      <td>02:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.883931</td>\n",
              "      <td>0.888753</td>\n",
              "      <td>02:58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.846806</td>\n",
              "      <td>0.856416</td>\n",
              "      <td>02:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.781652</td>\n",
              "      <td>0.791313</td>\n",
              "      <td>02:58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.724421</td>\n",
              "      <td>0.719955</td>\n",
              "      <td>02:56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXHrNz9jg_ur",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "04fe0178-cbd0-4072-de19-02a9271f2f7f"
      },
      "source": [
        "learn.save('goodbooks-nn-1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('models/goodbooks-nn-1.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAEBTynUhA7W",
        "colab_type": "text"
      },
      "source": [
        "## Interpretation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOS8vz5mhFst",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))\n",
        "learn.load('goodbooks-dot-1');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cyspXbshHv1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "d8b431e0-4bc8-4164-dcb6-01e0956a6a29"
      },
      "source": [
        "g = book_ratings.groupby('title')['rating'].count()\n",
        "top_books = g.sort_values(ascending=False).index.values[:1000]\n",
        "top_books[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Selected Poems', 'Stone Soup', 'Invisible', 'The List',\n",
              "       'Selected Stories', 'Between the Lines (Between the Lines, #1)',\n",
              "       'The Collected Poems', 'The Complete Stories and Poems', 'Bambi',\n",
              "       'Are You My Mother?'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sl-qgdaZhOaR",
        "colab_type": "text"
      },
      "source": [
        "## Book Bias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWDdOd19hPkw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "book_bias = learn.model.bias(top_books, is_item=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjALS_4-hSNp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean_ratings = book_ratings.groupby('title')['rating'].mean()\n",
        "book_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_books, book_bias)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21__f57yhUmi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "item0 = lambda o:o[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttStu-fVhV7S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "30c23852-ee06-40be-cd39-f635f464abb0"
      },
      "source": [
        "sorted(book_ratings, key=item0)[:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.0766), 'Left Behind (Left Behind, #1)', 2.92),\n",
              " (tensor(-0.0344), 'Message in a Bottle', 3.11),\n",
              " (tensor(-0.0135), 'Moby-Dick or, The Whale', 3.24),\n",
              " (tensor(-0.0097), 'Memnoch the Devil (The Vampire Chronicles, #5)', 3.36),\n",
              " (tensor(-0.0096), 'Men Are from Mars, Women Are from Venus', 3.25),\n",
              " (tensor(0.0116), 'Lost', 2.74),\n",
              " (tensor(0.0168), 'How to Be Popular', 3.27),\n",
              " (tensor(0.0191), 'How to Be Good', 2.99),\n",
              " (tensor(0.0201), 'Love Story (Love Story, #1)', 3.27),\n",
              " (tensor(0.0209), 'Mad About the Boy (Bridget Jones, #3)', 3.16),\n",
              " (tensor(0.0215), 'Halo (Halo, #1)', 3.26),\n",
              " (tensor(0.0224),\n",
              "  'MWF Seeking BFF: My Yearlong Search For A New Best Friend',\n",
              "  3.26),\n",
              " (tensor(0.0231), 'Labyrinth (Languedoc, #1)', 3.11),\n",
              " (tensor(0.0273), 'Last Night at Chateau Marmont', 3.07),\n",
              " (tensor(0.0368), 'Hausfrau', 2.98)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_XJhQDlhXsr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "713ff79b-4037-4da9-80fc-91eea63521c0"
      },
      "source": [
        "sorted(book_ratings, key=item0, reverse=True)[:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.5754), 'Miss Rumphius', 4.61),\n",
              " (tensor(0.5616),\n",
              "  \"It's a Magical World: A Calvin and Hobbes Collection\",\n",
              "  4.75),\n",
              " (tensor(0.5573), \"One Flew Over the Cuckoo's Nest\", 4.276923076923077),\n",
              " (tensor(0.5250), 'Harry Potter Collection (Harry Potter, #1-6)', 4.72),\n",
              " (tensor(0.5201), 'The Complete Stories', 4.32),\n",
              " (tensor(0.5132), 'Locke & Key, Vol. 5: Clockworks', 4.66),\n",
              " (tensor(0.5094), 'Jesus the Christ', 4.67),\n",
              " (tensor(0.5002),\n",
              "  'Harry Potter Boxed Set, Books 1-5 (Harry Potter, #1-5)',\n",
              "  4.77),\n",
              " (tensor(0.4970), 'The Complete Poems', 4.165),\n",
              " (tensor(0.4921), 'Anatomy for the Artist', 4.121387283236994),\n",
              " (tensor(0.4887), 'L.A. Requiem (Elvis Cole, #8)', 4.29),\n",
              " (tensor(0.4819), 'Selected Poems', 4.08),\n",
              " (tensor(0.4806), 'Jimmy Corrigan, the Smartest Kid on Earth', 4.19),\n",
              " (tensor(0.4724), 'Selected Stories', 4.145),\n",
              " (tensor(0.4697), 'The Collected Poems', 4.17)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtFS_rtyhqEI",
        "colab_type": "text"
      },
      "source": [
        "## Book Weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTiuPVO6hi7S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1cdb95c7-32d8-4fd9-ac68-36d4f2706932"
      },
      "source": [
        "book_w = learn.model.weight(top_books, is_item=True)\n",
        "book_w.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 50])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpNeTy4Dhvit",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e874c124-9ce1-416e-ddf8-ba4d987c43ea"
      },
      "source": [
        "book_pca = book_w.pca(3)\n",
        "book_pca.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oflgICpchv_E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fac0,fac1,fac2 = book_pca.t()\n",
        "book_comp = [(f, i) for f,i in zip(fac0, top_books)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khpl5KNthyBC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "e148e54e-696d-4df3-884f-391d2ebb5144"
      },
      "source": [
        "sorted(book_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.6268), 'The Complete Stories and Poems'),\n",
              " (tensor(0.5970), 'The Little Mermaid'),\n",
              " (tensor(0.5659), \"Little Bear's Friend\"),\n",
              " (tensor(0.5478), 'How the Grinch Stole Christmas!'),\n",
              " (tensor(0.5444), 'Lyle, Lyle, Crocodile'),\n",
              " (tensor(0.5200), 'Pinocchio'),\n",
              " (tensor(0.5130), \"Madeline's Rescue\"),\n",
              " (tensor(0.4886), 'Miss Nelson Is Back (Miss Nelson, #2)'),\n",
              " (tensor(0.4748), 'Me Talk Pretty One Day'),\n",
              " (tensor(0.4691), 'How Do Dinosaurs Say Good Night?')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6rJX3JnhzSq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "9da7abe2-ad8d-4aa6-91cf-4c6839860ac4"
      },
      "source": [
        "sorted(book_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.7042), 'Judgment in Death (In Death, #11)'),\n",
              " (tensor(-0.6298), 'Law Man (Dream Man, #3)'),\n",
              " (tensor(-0.5946), 'Lead (Stage Dive, #3)'),\n",
              " (tensor(-0.5822), 'Kindred in Death (In Death, #29)'),\n",
              " (tensor(-0.5599), 'Loyalty in Death (In Death, #9)'),\n",
              " (tensor(-0.5581), 'Leave Me Breathless (Ross Siblings, #3)'),\n",
              " (tensor(-0.5488), 'Imitation in Death (In Death, #17)'),\n",
              " (tensor(-0.5483), 'Lady Luck (Colorado Mountain, #3)'),\n",
              " (tensor(-0.4985), 'Jet (Marked Men, #2)'),\n",
              " (tensor(-0.4935), \"Mistral's Kiss (Merry Gentry, #5)\")]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47HtiQQ4h0ax",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "book_comp = [(f, i) for f,i in zip(fac1, top_books)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tw-AOi7rh4Hz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "bc741401-dbeb-452d-938a-da7b7151bc42"
      },
      "source": [
        "sorted(book_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.4463), 'Hey Nostradamus!'),\n",
              " (tensor(0.4206),\n",
              "  'Love Wins: A Book About Heaven, Hell, and the Fate of Every Person Who Ever Lived'),\n",
              " (tensor(0.4180), 'Hidden Bodies (You, #2)'),\n",
              " (tensor(0.4013), 'Lucky Us'),\n",
              " (tensor(0.3935), 'Juliet, Naked'),\n",
              " (tensor(0.3919), \"Love the One You're With\"),\n",
              " (tensor(0.3911), 'Little Altars Everywhere'),\n",
              " (tensor(0.3908), 'Memnoch the Devil (The Vampire Chronicles, #5)'),\n",
              " (tensor(0.3825), 'I Am Having So Much Fun Here Without You'),\n",
              " (tensor(0.3750), 'How to Talk to a Widower')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTeBhheQh6Zw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "eef89c32-2eaa-4e09-d60e-292f08a6f745"
      },
      "source": [
        "sorted(book_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.6480), 'Lover Mine (Black Dagger Brotherhood, #8)'),\n",
              " (tensor(-0.5672), 'Kiss of the Night (Dark-Hunter, #4)'),\n",
              " (tensor(-0.5285), 'Law Man (Dream Man, #3)'),\n",
              " (tensor(-0.5265), 'Magic Bleeds (Kate Daniels, #4)'),\n",
              " (tensor(-0.5262), 'Magic Slays (Kate Daniels, #5)'),\n",
              " (tensor(-0.5226), 'Kiss of Crimson (Midnight Breed, #2)'),\n",
              " (tensor(-0.4988), 'Imitation in Death (In Death, #17)'),\n",
              " (tensor(-0.4927), 'Loyalty in Death (In Death, #9)'),\n",
              " (tensor(-0.4888), 'Lothaire (Immortals After Dark #12)'),\n",
              " (tensor(-0.4800), 'Kiss of a Demon King (Immortals After Dark #7)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-qG38Wuh79l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        },
        "outputId": "09c07a81-2beb-4629-e957-9f76eb17168c"
      },
      "source": [
        "idxs = np.random.choice(len(top_books), 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_books[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": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}