Book_recommendation_modelling.ipynb

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          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
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      "source": [
        "url = '/content/drive/MyDrive/AlmaBetter /Capstone Project/Book Recommendation System/Books_data.csv' \n",
        "books_url = '/content/drive/MyDrive/AlmaBetter /Capstone Project/Book Recommendation System/Book RCD Dataset /Copy of Books.csv' "
      ]
    },
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      "source": [
        "import numpy as np\n",
        "import scipy\n",
        "import pandas as pd\n",
        "import math\n",
        "import random\n",
        "import sklearn\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from scipy.sparse.linalg import svds\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
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          "text": [
            "<ipython-input-4-b2330a23dfaf>:2: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  books = pd.read_csv(books_url)\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv(url , dtype = {'ISBN': str, 'Rating': int, 'Uid': str} )\n",
        "books = pd.read_csv(books_url)"
      ]
    },
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              "   Unnamed: 0     Uid        ISBN  Rating  \\\n",
              "0           3  278418  0446520802       0   \n",
              "1           4  278418  0671537458       0   \n",
              "2           5  278418  0786817070       0   \n",
              "3           6  278418  0446364193       0   \n",
              "4           7  278418  0449217264       0   \n",
              "\n",
              "                                     Title             Author  Pub_Year  \\\n",
              "0                             the notebook    nicholas sparks      1996   \n",
              "1                        waiting to exhale     terry mcmillan      1995   \n",
              "2      artemis fowl (artemis fowl, book 1)        eoin colfer      2002   \n",
              "3  along came a spider (alex cross novels)    james patterson      1993   \n",
              "4                                   alaska  james a. michener      1994   \n",
              "\n",
              "       Publisher                                          Image_Url  \\\n",
              "0   warner books  http://images.amazon.com/images/p/0446520802.0...   \n",
              "1         pocket  http://images.amazon.com/images/p/0671537458.0...   \n",
              "2   miramax kids  http://images.amazon.com/images/p/0786817070.0...   \n",
              "3   warner books  http://images.amazon.com/images/p/0446364193.0...   \n",
              "4  fawcett books  http://images.amazon.com/images/p/0449217264.0...   \n",
              "\n",
              "               Location        Age  \n",
              "0  omaha, nebraska, usa  37.394772  \n",
              "1  omaha, nebraska, usa  37.394772  \n",
              "2  omaha, nebraska, usa  37.394772  \n",
              "3  omaha, nebraska, usa  37.394772  \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 89451 entries, 0 to 89450\n",
            "Data columns (total 11 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  89451 non-null  int64  \n",
            " 1   Uid         89451 non-null  object \n",
            " 2   ISBN        89451 non-null  object \n",
            " 3   Rating      89451 non-null  int64  \n",
            " 4   Title       89451 non-null  object \n",
            " 5   Author      89451 non-null  object \n",
            " 6   Pub_Year    89451 non-null  int64  \n",
            " 7   Publisher   89451 non-null  object \n",
            " 8   Image_Url   89451 non-null  object \n",
            " 9   Location    89451 non-null  object \n",
            " 10  Age         89451 non-null  float64\n",
            "dtypes: float64(1), int64(3), object(7)\n",
            "memory usage: 7.5+ MB\n"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3K0l4XkjfORL"
      },
      "source": [
        "## Creating Memory Based Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46ZoHdXTwRKC"
      },
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      "source": [
        "# drop columns which are not really required\n",
        "df.drop(['Image_Url', 'Location','Publisher','Unnamed: 0' ],axis = 1, inplace = True)"
      ]
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              "          document.querySelector('#df-c4de80a6-fa1f-411d-8acd-91eefd669192 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c4de80a6-fa1f-411d-8acd-91eefd669192');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Uid        ISBN  Rating                                    Title  \\\n",
              "0  278418  0446520802       0                             the notebook   \n",
              "1  278418  0671537458       0                        waiting to exhale   \n",
              "2  278418  0786817070       0      artemis fowl (artemis fowl, book 1)   \n",
              "3  278418  0446364193       0  along came a spider (alex cross novels)   \n",
              "4  278418  0449217264       0                                   alaska   \n",
              "\n",
              "              Author  Pub_Year        Age  \n",
              "0    nicholas sparks      1996  37.394772  \n",
              "1     terry mcmillan      1995  37.394772  \n",
              "2        eoin colfer      2002  37.394772  \n",
              "3    james patterson      1993  37.394772  \n",
              "4  james a. michener      1994  37.394772  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mHWC3JinfMFF",
        "outputId": "a2a2bc04-69a5-45a6-b6d1-f161cf12031d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(89451, 7)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10ecGWQqgJJq"
      },
      "source": [
        "# **KNN based Recommendation system**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "-GDBFx7dfMHv",
        "outputId": "d9d1f89b-701f-4fc5-88f8-ab994413ccfe"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Uid</th>\n",
              "      <th>100088</th>\n",
              "      <th>100264</th>\n",
              "      <th>100459</th>\n",
              "      <th>100644</th>\n",
              "      <th>100846</th>\n",
              "      <th>100906</th>\n",
              "      <th>101209</th>\n",
              "      <th>101305</th>\n",
              "      <th>101606</th>\n",
              "      <th>101876</th>\n",
              "      <th>...</th>\n",
              "      <th>9747</th>\n",
              "      <th>97754</th>\n",
              "      <th>98391</th>\n",
              "      <th>9856</th>\n",
              "      <th>98723</th>\n",
              "      <th>98741</th>\n",
              "      <th>98758</th>\n",
              "      <th>98787</th>\n",
              "      <th>98904</th>\n",
              "      <th>99955</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1984</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1st to die: a novel</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010: odyssey two</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24 hours</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2nd chance</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1148 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e50a479a-d9a5-41d1-bc5a-3523822a9655')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e50a479a-d9a5-41d1-bc5a-3523822a9655 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e50a479a-d9a5-41d1-bc5a-3523822a9655');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Uid                  100088  100264  100459  100644  100846  100906  101209  \\\n",
              "Title                                                                         \n",
              "1984                    0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "1st to die: a novel     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2010: odyssey two       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "24 hours                0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2nd chance              0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "Uid                  101305  101606  101876  ...  9747  97754  98391  9856  \\\n",
              "Title                                        ...                             \n",
              "1984                    0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "1st to die: a novel     0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "2010: odyssey two       0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "24 hours                0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "2nd chance              0.0     0.0     0.0  ...   0.0    0.0    9.0   0.0   \n",
              "\n",
              "Uid                  98723  98741  98758  98787  98904  99955  \n",
              "Title                                                          \n",
              "1984                   0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "1st to die: a novel    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "2010: odyssey two      0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "24 hours               0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "2nd chance             0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "\n",
              "[5 rows x 1148 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a pivot table\n",
        "df_pivot = df.pivot_table(index = 'Title',columns = 'Uid', values = 'Rating',).fillna(0)\n",
        "df_pivot.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfBQjLJ8AjgP",
        "outputId": "9393ef47-530d-43ce-cefa-7af0a462bbcd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1144, 1148)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_pivot.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BvIe16drfMKT"
      },
      "outputs": [],
      "source": [
        "# create a sparse matrix from df_pivot\n",
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "df_matrix = csr_matrix(df_pivot.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTq8xInqWgeu",
        "outputId": "97ca2ff2-dd0d-4022-bd29-3bbaa37ff11d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1144, 1148)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_matrix.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "j7qoMXQOfMNc",
        "outputId": "84b6d13e-e5b7-4838-b90b-49c9a0d4f22e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "NearestNeighbors(algorithm='brute', metric='cosine')"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# create a Nearest neighbors model with cosine similarity  \n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "\n",
        "model_knn = NearestNeighbors(metric = 'cosine', algorithm = 'brute') \n",
        "model_knn.fit(df_matrix) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q031Ca99fMPk",
        "outputId": "051c8e65-8b1c-4a9c-edf5-ee2c45ff0895"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1144, 1148)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_pivot.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "onOE-QgrWot4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ls67tMkzfMTY",
        "outputId": "55dd1b52-7898-429c-a565-459e44ebfbba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "78\n"
          ]
        }
      ],
      "source": [
        "#select a random book index and then find its distance and indices\n",
        "query_index = np.random.choice(df_pivot.shape[0])\n",
        "print(query_index)\n",
        "distances, indices = model_knn.kneighbors(df_pivot.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6A3PKqcsfMVc"
      },
      "outputs": [],
      "source": [
        "# function to find out the top 5 similar books\n",
        "def top_5_books(query_index):\n",
        "  distances, indices = model_knn.kneighbors(df_pivot.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 6)\n",
        "  for i in range(0, len(distances.flatten())):\n",
        "      if i == 0:\n",
        "          print('Recommendations for {0}:\\n'.format(df_pivot.index[query_index]))\n",
        "      else:\n",
        "          print('{0}: {1}, with distance of {2}:'.format(i, df_pivot.index[indices.flatten()[i]], distances.flatten()[i]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6J-eWpzfMX_",
        "outputId": "3dccfb3e-5708-419b-b627-5574f144e49c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "enter the name of book you like: the notebook\n",
            "Recommendations for the notebook:\n",
            "\n",
            "1: a walk to remember, with distance of 0.7326657538383645:\n",
            "2: nights in rodanthe, with distance of 0.7453577801080011:\n",
            "3: the rescue, with distance of 0.7670361381874238:\n",
            "4: message in a bottle, with distance of 0.7812529772709633:\n",
            "5: toxin, with distance of 0.7818726608333926:\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# get a name of book from the user and recommend him 5 books using top_5_books function\n",
        "book = input('enter the name of book you like: ')\n",
        "try:\n",
        "  n = df_pivot.index == book\n",
        "  query_index = int(np.where(n == True)[0])\n",
        "  top_5_books(query_index)\n",
        "except:\n",
        "  print(f'try for another book, Book not Found')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FenbH8s7gZoU"
      },
      "source": [
        "# **Correlation based Recommendation system**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "X9VyenkQfMZ-",
        "outputId": "2edf6252-c023-426c-b3f4-c04ff8c6bb7c"
      },
      "outputs": [
        {
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ],
            "text/plain": [
              "Uid                  100088  100264  100459  100644  100846  100906  101209  \\\n",
              "Title                                                                         \n",
              "1984                    0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "1st to die: a novel     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2010: odyssey two       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "24 hours                0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2nd chance              0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "Uid                  101305  101606  101876  ...  9747  97754  98391  9856  \\\n",
              "Title                                        ...                             \n",
              "1984                    0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "1st to die: a novel     0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "2010: odyssey two       0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "24 hours                0.0     0.0     0.0  ...   0.0    0.0    0.0   0.0   \n",
              "2nd chance              0.0     0.0     0.0  ...   0.0    0.0    9.0   0.0   \n",
              "\n",
              "Uid                  98723  98741  98758  98787  98904  99955  \n",
              "Title                                                          \n",
              "1984                   0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "1st to die: a novel    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "2010: odyssey two      0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "24 hours               0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "2nd chance             0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "\n",
              "[5 rows x 1148 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_pivot.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aDF14EZKgiID"
      },
      "outputs": [],
      "source": [
        "# Transpose the matrix\n",
        "ndf_pivot = df_pivot.T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2PY14R8kgiLB"
      },
      "outputs": [],
      "source": [
        "def corr_recomendation(book):\n",
        "  cor_vector = ndf_pivot.corrwith( ndf_pivot[book], )\n",
        "  cor_vector.sort_values(ascending= False, inplace= True)\n",
        "  print(f\"top movies similar to {cor_vector.iloc[:1].index } are\")\n",
        "  print(f'{cor_vector.iloc[2:7] }', end = '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sW7uwrUugiNf",
        "outputId": "89e47773-ecf9-476b-deeb-f73f8e54c607"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "enter the name of book you like: the notebook\n",
            "top movies similar to Index(['the notebook'], dtype='object', name='Title') are\n",
            "Title\n",
            "nights in rodanthe                    0.230055\n",
            "the rescue                            0.201472\n",
            "toxin                                 0.197444\n",
            "message in a bottle                   0.176519\n",
            "the five people you meet in heaven    0.154547\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "book = input('enter the name of book you like: ')\n",
        "try:\n",
        "  corr_recomendation(book)\n",
        "except:\n",
        "  print('your book is not in list, Please Retry....')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oElwH_efgsD5"
      },
      "source": [
        "### Issues\n",
        "extremely Time consuming memory consuming"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EwLyw-MifMjM"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqFrzIKGgsHp"
      },
      "source": [
        "# Model Based recommendation engine\n",
        "using collaborative filtering\n",
        "\n",
        "---\n",
        "Latent filter SVD model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dPAg9nXJfMly"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XsYnc15phdw1"
      },
      "outputs": [],
      "source": [
        "# df.drop('Unnamed: 0', axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9gspfU_AvxZ5"
      },
      "outputs": [],
      "source": [
        "# rating df, to work on the useful features\n",
        "rating_df = df[['Uid',\t'ISBN',\t'Rating',\t'Title']].reset_index().drop('index', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HPb2HVFs2XIj",
        "outputId": "a4ddbd18-755a-4a56-ca98-9e46232cd681"
      },
      "outputs": [
        {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Uid</th>\n",
              "      <th>ISBN</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
              "      <td>waiting to exhale</td>\n",
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              "      <td>artemis fowl (artemis fowl, book 1)</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "      Uid        ISBN  Rating                                    Title\n",
              "0  278418  0446520802       0                             the notebook\n",
              "1  278418  0671537458       0                        waiting to exhale\n",
              "2  278418  0786817070       0      artemis fowl (artemis fowl, book 1)\n",
              "3  278418  0446364193       0  along came a spider (alex cross novels)\n",
              "4  278418  0449217264       0                                   alaska"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rating_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNArjVU2yqmS",
        "outputId": "fd9896ee-7fa3-4a3d-f9c7-fc77e3ff8b50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "11676     1340\n",
              "35859      573\n",
              "76352      514\n",
              "16795      474\n",
              "153662     446\n",
              "          ... \n",
              "11224       30\n",
              "139387      30\n",
              "216099      30\n",
              "32721       30\n",
              "57725       30\n",
              "Name: Uid, Length: 1148, dtype: int64"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rating_df['Uid'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pt9wn3MGCoWN"
      },
      "source": [
        "## Stratified Train test Split "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xK6Ahor34tM9",
        "outputId": "8cdfc6f5-a8fe-402a-ee0d-4b3ce0201351"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train shape (71560, 4)\n",
            "test shape (17891, 4)\n"
          ]
        }
      ],
      "source": [
        " # train test split\n",
        "rating_train_df, rating_test_df = train_test_split(rating_df, \n",
        "                                                   test_size = 0.2, \n",
        "                                                   random_state = 10,\n",
        "                                                   stratify= rating_df['Uid'])\n",
        "print(f\"train shape {rating_train_df.shape}\")\n",
        "print(f\"test shape {rating_test_df.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pdEME3nO7nDz"
      },
      "outputs": [],
      "source": [
        "# create a pivot of train set\n",
        "ratings_train_pivot_df = rating_train_df.pivot_table(values = 'Rating', columns = 'ISBN', index = 'Uid').fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uVwBFZ0e9Bx3"
      },
      "outputs": [],
      "source": [
        "# keep a list of user id's for an instace to use them further\n",
        "user_ids = list(ratings_train_pivot_df.index) \n",
        "# keep values of ratings train df as a matrix\n",
        "ratings_train_values = ratings_train_pivot_df.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "bs1I5HBS6Y3-",
        "outputId": "63e66b91-b954-436f-e973-700302325c87"
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      "outputs": [
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "ISBN    0001047973  0006177379  0007110928  0020412401  0027000303  \\\n",
              "Uid                                                                  \n",
              "100088         0.0         0.0         0.0         0.0         0.0   \n",
              "\n",
              "ISBN    0048231398  0060008024  0060008032  0060008296  0060008776  ...  \\\n",
              "Uid                                                                 ...   \n",
              "100088         0.0         0.0         0.0         0.0         0.0  ...   \n",
              "\n",
              "ISBN    2290303348  3190025355  3257208626  3426616904  3895084573  \\\n",
              "Uid                                                                  \n",
              "100088         0.0         0.0         0.0         0.0         0.0   \n",
              "\n",
              "ISBN    5551154718  5552027574  8186939008  8401426715  8445072773  \n",
              "Uid                                                                 \n",
              "100088         0.0         0.0         0.0         0.0         0.0  \n",
              "\n",
              "[1 rows x 3043 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ratings_train_pivot_df.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49g1m5ePCxRe"
      },
      "source": [
        "## Svd Model to create factor matrix of original matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fW0g4C_vCeNq"
      },
      "outputs": [],
      "source": [
        "\n",
        "# The number of factors to factor the user-item matrix.\n",
        "NUMBER_OF_FACTORS_MF = 15\n",
        "\n",
        "#Performs matrix factorization of the original user item matrix\n",
        "U, sigma, Vt = svds(ratings_train_values, k = NUMBER_OF_FACTORS_MF)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJh86cNQDTtN",
        "outputId": "a3eef46f-a95b-4d65-fc77-3c68f5ba7ea6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " shape of original matrix: (1148, 3043)\n",
            " shape of U factor matrix: (1148, 15)\n",
            " shape of V-transpose matrix: (15, 3043)\n",
            " shape of sigma matrix: (15, 15)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# see the shapes of original matrix and all other matrix\n",
        "print(f' shape of original matrix: {ratings_train_pivot_df.shape}')\n",
        "print(f' shape of U factor matrix: {U.shape}')\n",
        "print(f' shape of V-transpose matrix: {Vt.shape}')\n",
        "sigma = np.diag(sigma) # To Create a diagnol matrix of the sigma\n",
        "print(f' shape of sigma matrix: {sigma.shape}') \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtO33ZihFfdg"
      },
      "source": [
        "## Predict Rating for all users"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_rQXhBUEb3M",
        "outputId": "3ab97e64-44de-4d3b-9488-3b2cd0dc48b5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1148, 3043)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# multiply all small matrix to create a predicted final matrix for user ratings \n",
        "predicted_rating_for_users = np.dot(np.dot(U,sigma),Vt) \n",
        "# predicted rating matrix shape \n",
        "predicted_rating_for_users.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "cQA7Qt57HEkO",
        "outputId": "b71d4080-310e-43ef-f0c3-55b2921fd66e"
      },
      "outputs": [
        {
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              "                  100088        100264        100459        100644  \\\n",
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              "0027000303  2.247965e-16  6.962626e-17  9.116683e-17  6.899426e-17   \n",
              "\n",
              "                  100846        100906        101209        101305  \\\n",
              "0001047973  1.792534e-02  2.273521e-02  7.896953e-03  2.273502e-02   \n",
              "0006177379  8.221062e-16 -5.377836e-16  4.795093e-17  4.049230e-17   \n",
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              "0027000303  3.330450e-17 -5.385809e-17  3.695490e-17 -3.335207e-16   \n",
              "\n",
              "                  101606        101876  ...          9747         97754  \\\n",
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              "\n",
              "                   98391          9856         98723         98741  \\\n",
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              "0027000303 -4.025893e-16 -3.046626e-17  7.940344e-17 -4.989790e-18   \n",
              "\n",
              "                   98758         98787         98904         99955  \n",
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              "0027000303  2.343566e-16 -2.839541e-16 -1.695526e-16 -3.127632e-17  \n",
              "\n",
              "[5 rows x 1148 columns]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Converting the reconstructed matrix back to a Pandas dataframe  \n",
        "user_ids = [str(s) for s in user_ids]\n",
        "col = [str(c) for c in  ratings_train_pivot_df.columns]\n",
        "pred_df = pd.DataFrame(predicted_rating_for_users, columns = col, index=user_ids).transpose()  \n",
        "pred_df.head() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pb3vKA0Wi9Nr"
      },
      "outputs": [],
      "source": [
        "# try:\n",
        "#   books.set_index('ISBN', inplace = True)\n",
        "# except:\n",
        "#   pass\n",
        "# books.reset_index( inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g4WpGemvkPtq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y0c1nimZJKPC"
      },
      "outputs": [],
      "source": [
        "# class for recommendation model \n",
        "class CFRecommender:\n",
        "    \n",
        "    MODEL_NAME = 'Collaborative Filtering'\n",
        "    \n",
        "    def __init__(self, predictions_df, items_df=None):\n",
        "        self.predictions_df = predictions_df\n",
        "        self.items_df = items_df\n",
        "        \n",
        "    def get_model_name(self):\n",
        "        return self.MODEL_NAME\n",
        "        \n",
        "    def recommend_items_by_user(self, user_id, items_to_ignore=[], topn=10, verbose=True):\n",
        "        # Get and sort the user's predictions\n",
        "        sorted_user_predictions = self.predictions_df[user_id].sort_values(ascending=False).reset_index().rename(columns={user_id: 'recStrength'})\n",
        "\n",
        "        # Recommend the highest predicted rating content that the user hasn't seen yet.\n",
        "        recommendations_df = sorted_user_predictions[~sorted_user_predictions.index.isin(items_to_ignore)].sort_values('recStrength', ascending = False).head(topn)\n",
        "\n",
        "        if verbose:\n",
        "            if self.items_df is None:\n",
        "                raise Exception('\"items_df\" is required in verbose mode')\n",
        "            \n",
        "            recommendations_df = recommendations_df.merge(self.items_df, how = 'left', \n",
        "                                                          left_on = 'index', \n",
        "                                                          right_on = 'ISBN')[['recStrength', 'ISBN', 'Book-Title', \n",
        "                                                                              'Year-Of-Publication','Publisher',\n",
        "                                                                              'Book-Author']]\n",
        "\n",
        "        return recommendations_df\n",
        "\n",
        "    def recommend_items_by_item(self, book_id,topn=10, verbose=True):\n",
        "      pdt = self.predictions_df.transpose()\n",
        "      cosine_similarities = cosine_similarity(np.array(pdt[book_id]).reshape(1,-1),pred_df )\n",
        "      recommended_df = pd.DataFrame( {'recStrength': cosine_similarities[0] }, index = pred_df.index ).sort_values(by = 'recStrength',ascending = False)\n",
        "      recommended_df = recommended_df.iloc[:topn,] \n",
        "      # print(recommended_df)\n",
        "      if verbose:\n",
        "            if self.items_df is None:\n",
        "                raise Exception('\"items_df\" is required in verbose mode')\n",
        "            # print(recommended_df)\n",
        "            # lower case the Book-Title of books DataFrame if not to avoid errors\n",
        "            self.items_df['Book-Title'] = self.items_df['Book-Title'].str.lower()\n",
        "            recommended_df = recommended_df.merge(self.items_df, how = 'left', \n",
        "                                                          # left_on = 'index', \n",
        "                                                          left_index = True,\n",
        "                                                          right_on = 'ISBN')[['recStrength', 'ISBN', 'Book-Title', \n",
        "                                                                              'Year-Of-Publication','Publisher',\n",
        "                                                                              'Book-Author']]\n",
        "      return recommended_df\n",
        "\n",
        "cf_recommender_model = CFRecommender(pred_df, books)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tb2ej3wf6EAB",
        "outputId": "b2bcf689-903d-490b-8c3b-50ccce214b53"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<function __main__.get_readen_books(u_id)>"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# This Function is useful only when we need to find \n",
        "# function to get all readen books by a user\n",
        "def get_readen_books(u_id):\n",
        "  read_notread = ratings_train_pivot_df.loc[u_id ,]\n",
        "  readen = list(read_notread.iloc[read_notread.values!= 0].index) \n",
        "  return readen\n",
        "get_readen_books"
      ]
    },
    {
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        "id": "DoSjPhfDHZHh",
        "outputId": "9d837a99-c13d-4b5d-bec5-560527cc3e6b"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>recStrength</th>\n",
              "      <th>ISBN</th>\n",
              "      <th>Book-Title</th>\n",
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              "      <td>0440214041</td>\n",
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              "    <tr>\n",
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              "      <td>0345337662</td>\n",
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              "      <td>1993</td>\n",
              "      <td>Ballantine Books</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.241084</td>\n",
              "      <td>0440213525</td>\n",
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              "      <td>1994</td>\n",
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              "      <td>2.151656</td>\n",
              "      <td>0446672211</td>\n",
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              "      <td>Warner Books</td>\n",
              "      <td>Billie Letts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.059044</td>\n",
              "      <td>0312195516</td>\n",
              "      <td>The Red Tent (Bestselling Backlist)</td>\n",
              "      <td>1998</td>\n",
              "      <td>Picador USA</td>\n",
              "      <td>Anita Diamant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.052845</td>\n",
              "      <td>0345313860</td>\n",
              "      <td>The Vampire Lestat (Vampire Chronicles, Book II)</td>\n",
              "      <td>1986</td>\n",
              "      <td>Ballantine Books</td>\n",
              "      <td>ANNE RICE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.852674</td>\n",
              "      <td>0452282152</td>\n",
              "      <td>Girl with a Pearl Earring</td>\n",
              "      <td>2001</td>\n",
              "      <td>Plume Books</td>\n",
              "      <td>Tracy Chevalier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.765132</td>\n",
              "      <td>0440220602</td>\n",
              "      <td>The Chamber</td>\n",
              "      <td>1995</td>\n",
              "      <td>Dell Publishing Company</td>\n",
              "      <td>John Grisham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.729783</td>\n",
              "      <td>0440226430</td>\n",
              "      <td>Summer Sisters</td>\n",
              "      <td>1999</td>\n",
              "      <td>Dell Publishing Company</td>\n",
              "      <td>Judy Blume</td>\n",
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            "text/plain": [
              "   recStrength        ISBN                                         Book-Title  \\\n",
              "0     2.927338  0440211727                                     A Time to Kill   \n",
              "1     2.672418  0440214041                                  The Pelican Brief   \n",
              "2     2.595368  0345337662                         Interview with the Vampire   \n",
              "3     2.241084  0440213525                                         The Client   \n",
              "4     2.151656  0446672211  Where the Heart Is (Oprah's Book Club (Paperba...   \n",
              "5     2.059044  0312195516                The Red Tent (Bestselling Backlist)   \n",
              "6     2.052845  0345313860   The Vampire Lestat (Vampire Chronicles, Book II)   \n",
              "7     1.852674  0452282152                          Girl with a Pearl Earring   \n",
              "8     1.765132  0440220602                                        The Chamber   \n",
              "9     1.729783  0440226430                                     Summer Sisters   \n",
              "\n",
              "  Year-Of-Publication                Publisher      Book-Author  \n",
              "0                1992                     Dell     JOHN GRISHAM  \n",
              "1                1993                     Dell     John Grisham  \n",
              "2                1993         Ballantine Books        Anne Rice  \n",
              "3                1994  Dell Publishing Company     John Grisham  \n",
              "4                1998             Warner Books     Billie Letts  \n",
              "5                1998              Picador USA    Anita Diamant  \n",
              "6                1986         Ballantine Books        ANNE RICE  \n",
              "7                2001              Plume Books  Tracy Chevalier  \n",
              "8                1995  Dell Publishing Company     John Grisham  \n",
              "9                1999  Dell Publishing Company       Judy Blume  "
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": []
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "wMXFug4xrsxP",
        "outputId": "f63635a0-da79-44a4-d13d-fa7e0c03b2eb"
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        {
          "data": {
            "text/plain": [
              "['0060175400',\n",
              " '0060199652',\n",
              " '0060392452',\n",
              " '0064400557',\n",
              " '0142000205',\n",
              " '0156005891']"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_readen_books('100459')[:6]\n",
        "# get_readen_books('100459')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8yhvUlxZ95zP"
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      "source": [
        "def get_items_interacted(id, rating_df = rating_df):\n",
        "  res = rating_df[rating_df['Uid'] == id].sort_values( by = 'Rating', ascending=False)['ISBN']\n",
        "  return res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJMM4bqbAXGv",
        "outputId": "233243aa-753d-459f-e26d-27f60b804031"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "set(read).intersection(get_items_interacted(100459))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ibYWXwrzFBA"
      },
      "source": [
        "# **Evaluation**\n",
        "\n",
        "### In Recommender Systems, there are a set metrics commonly used for evaluation. We choose to work with **Top-N accuracy metrics**, which evaluates the accuracy of the top recommendations provided to a user, comparing to the items the user has actually interacted in test set.\n",
        "\n",
        "### This evaluation method works as follows:\n",
        "\n",
        "* ### For each user\n",
        "    * ### For each item the user has interacted in test set\n",
        "        * ### Sample 100 other items the user has never interacted.   \n",
        "        * ### Ask the recommender model to produce a ranked list of recommended items, from a set composed of one interacted item and the 100 non-interacted items\n",
        "        * ### Compute the Top-N accuracy metrics for this user and interacted item from the recommendations ranked list\n",
        "* ### Aggregate the global Top-N accuracy metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lsZDNuTRosSR"
      },
      "outputs": [],
      "source": [
        "#Indexing by personId to speed up the searches during evaluation\n",
        "rating_indexed_df = rating_df.set_index('Uid')\n",
        "rating_train_indexed_df = rating_train_df.set_index('Uid')\n",
        "rating_test_indexed_df = rating_test_df.set_index('Uid')"
      ]
    },
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              "      <th></th>\n",
              "      <th>Uid</th>\n",
              "      <th>ISBN</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Title</th>\n",
              "      <th>Author</th>\n",
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              "\n",
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              "          const element = document.querySelector('#df-eb3746db-5568-488d-9f9b-87f84cea3e2a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Uid        ISBN  Rating         Title           Author  Pub_Year  \\\n",
              "0  278418  0446520802       0  the notebook  nicholas sparks      1996   \n",
              "\n",
              "         Age  \n",
              "0  37.394772  "
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mTdxKA8i2pph"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(action = 'ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U3K4lXdpzguV"
      },
      "outputs": [],
      "source": [
        "#Top-N accuracy metrics consts\n",
        "EVAL_RANDOM_SAMPLE_NON_INTERACTED_ITEMS = 100\n",
        "\n",
        "class ModelEvaluator:\n",
        "\n",
        "    # Function for getting the set of items which a user has not interacted with\n",
        "    def get_not_interacted_items_sample(self, Uid, sample_size, seed=42):\n",
        "        interacted_items = get_items_interacted(str(Uid))\n",
        "        all_items = set(df['ISBN'])\n",
        "        # print(type(all_items), type(interacted_items))\n",
        "        non_interacted_items = all_items - set(interacted_items)\n",
        "\n",
        "        random.seed(seed)\n",
        "        non_interacted_items_sample = random.sample(non_interacted_items, sample_size)\n",
        "        return set(non_interacted_items_sample)\n",
        "\n",
        "    # Function to verify whether a particular item_id was present in the set of top N recommended items\n",
        "    def _verify_hit_top_n(self, item_id, recommended_items, topn):        \n",
        "            try:\n",
        "                index = next(i for i, c in enumerate(recommended_items) if c == item_id)\n",
        "            except:\n",
        "                index = -1\n",
        "            hit = int(index in range(0, topn))\n",
        "            return hit, index\n",
        "    \n",
        "    # Function to evaluate the performance of model for each user\n",
        "    def evaluate_model_for_user(self, model, Uid):\n",
        "        \n",
        "        # Getting the items in test set\n",
        "        interacted_values_testset = rating_test_indexed_df.loc[Uid]\n",
        "        \n",
        "        if type(interacted_values_testset['ISBN']) == pd.Series:\n",
        "            person_interacted_items_testset = set(interacted_values_testset['ISBN'])\n",
        "        else:\n",
        "            person_interacted_items_testset = set([int(interacted_values_testset['ISBN'])])\n",
        "            \n",
        "        interacted_items_count_testset = len(person_interacted_items_testset) \n",
        "\n",
        "        # Getting a ranked recommendation list from the model for a given user\n",
        "        person_recs_df = model.recommend_items_by_user(Uid, items_to_ignore=get_items_interacted(str(Uid)),topn=10000000000)\n",
        "\n",
        "        hits_at_5_count = 0\n",
        "        hits_at_10_count = 0\n",
        "        \n",
        "        # For each item the user has interacted in test set\n",
        "        for item_id in person_interacted_items_testset:\n",
        "            \n",
        "            # Getting a random sample of 100 items the user has not interacted with\n",
        "            non_interacted_items_sample = self.get_not_interacted_items_sample(Uid, sample_size=EVAL_RANDOM_SAMPLE_NON_INTERACTED_ITEMS, seed=int(item_id)%(2**32))\n",
        "\n",
        "            # Combining the current interacted item with the 100 random items\n",
        "            items_to_filter_recs = non_interacted_items_sample.union(set([item_id]))\n",
        "\n",
        "            # Filtering only recommendations that are either the interacted item or from a random sample of 100 non-interacted items\n",
        "            valid_recs_df = person_recs_df[person_recs_df['ISBN'].isin(items_to_filter_recs)]                    \n",
        "            valid_recs = valid_recs_df['ISBN'].values\n",
        "            \n",
        "            # Verifying if the current interacted item is among the Top-N recommended items\n",
        "            hit_at_5, index_at_5 = self._verify_hit_top_n(item_id, valid_recs, 5)\n",
        "            hits_at_5_count += hit_at_5\n",
        "            hit_at_10, index_at_10 = self._verify_hit_top_n(item_id, valid_recs, 10)\n",
        "            hits_at_10_count += hit_at_10\n",
        "\n",
        "        # Recall is the rate of the interacted items that are ranked among the Top-N recommended items\n",
        "        recall_at_5 = hits_at_5_count / float(interacted_items_count_testset)\n",
        "        recall_at_10 = hits_at_10_count / float(interacted_items_count_testset)\n",
        "\n",
        "        person_metrics = {'hits@5_count':hits_at_5_count, \n",
        "                          'hits@10_count':hits_at_10_count, \n",
        "                          'interacted_count': interacted_items_count_testset,\n",
        "                          'recall@5': recall_at_5,\n",
        "                          'recall@10': recall_at_10}\n",
        "        return person_metrics\n",
        "\n",
        "    \n",
        "    # Function to evaluate the performance of model at overall level\n",
        "    def evaluate_model(self, model):\n",
        "        \n",
        "        people_metrics = []\n",
        "        \n",
        "        for idx, Uid in enumerate(list(rating_test_indexed_df.index.unique().values)):    \n",
        "            person_metrics = self.evaluate_model_for_user(model, Uid)  \n",
        "            person_metrics['_person_id'] = Uid\n",
        "            people_metrics.append(person_metrics)\n",
        "            \n",
        "        print('%d users processed' % idx)\n",
        "\n",
        "        detailed_results_df = pd.DataFrame(people_metrics).sort_values('interacted_count', ascending=False)\n",
        "        \n",
        "        global_recall_at_5 = detailed_results_df['hits@5_count'].sum() / float(detailed_results_df['interacted_count'].sum())\n",
        "        global_recall_at_10 = detailed_results_df['hits@10_count'].sum() / float(detailed_results_df['interacted_count'].sum())\n",
        "        \n",
        "        global_metrics = {'modelName': model.get_model_name(),\n",
        "                          'recall@5': global_recall_at_5,\n",
        "                          'recall@10': global_recall_at_10}    \n",
        "        return global_metrics, detailed_results_df\n",
        "    \n",
        "model_evaluator = ModelEvaluator()    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "QhgsULWNSZkg",
        "outputId": "84a4cd16-6bfc-4239-ba60-ac1cac3db978"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluating Collaborative Filtering (SVD Matrix Factorization) model...\n",
            "1147 users processed\n",
            "\n",
            "Global metrics:\n",
            "{'modelName': 'Collaborative Filtering', 'recall@5': 0.24794589458386898, 'recall@10': 0.3776200324185345}\n"
          ]
        },
        {
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              "          const element = document.querySelector('#df-c4c40eb3-c5a1-4878-b546-cae47d36624c');\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "     hits@5_count  hits@10_count  interacted_count  recall@5  recall@10  \\\n",
              "120            53             88               268  0.197761   0.328358   \n",
              "126            22             39               115  0.191304   0.339130   \n",
              "75             11             31               103  0.106796   0.300971   \n",
              "103            13             29                95  0.136842   0.305263   \n",
              "91             18             24                89  0.202247   0.269663   \n",
              "62             10             17                84  0.119048   0.202381   \n",
              "9              13             23                77  0.168831   0.298701   \n",
              "18             11             20                76  0.144737   0.263158   \n",
              "265             9             16                75  0.120000   0.213333   \n",
              "12             14             24                75  0.186667   0.320000   \n",
              "\n",
              "    _person_id  \n",
              "120      11676  \n",
              "126      35859  \n",
              "75       76352  \n",
              "103      16795  \n",
              "91      153662  \n",
              "62      102967  \n",
              "9       230522  \n",
              "18       78783  \n",
              "265      55492  \n",
              "12      185233  "
            ]
          },
          "execution_count": 113,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Evaluating Collaborative Filtering (SVD Matrix Factorization) model...')\n",
        "cf_global_metrics, cf_detailed_results_df = model_evaluator.evaluate_model(cf_recommender_model)\n",
        "\n",
        "print('\\nGlobal metrics:\\n%s' % cf_global_metrics)\n",
        "cf_detailed_results_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPXA_ii15p7m"
      },
      "source": [
        "Evaluating the Collaborative Filtering model (SVD matrix factorization), we observe that we got **Recall@5 (24%)** and **Recall@10 (38%)** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C9QgM35BcNaL"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
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