MF_RR.ipynb

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    "colab": {
      "provenance": [],
      "mount_file_id": "19a7gcYkjbEHd2YPWPi3OYM_KqcL3gjK8",
      "authorship_tag": "ABX9TyMHW+TwJnB4l/VOG/h3C9A7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sarvesh1814/US-Book-Recommendation-System-/blob/main/MF_RR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mathematics Behind Matrix Factorization Model"
      ],
      "metadata": {
        "id": "Z7uKes15v17v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "* **Latent factor SVD**\n",
        "---\n",
        "\\begin{equation}\n",
        "\\hat{r}_{ui} = \\mu + b_u + b_i + \\sum{f=1}^{k} (q_{if} + p_{uf})\n",
        "\\end{equation}\n",
        "\n",
        "---\n",
        "* **Optimization Problem**\n",
        "---\n",
        "\\begin{equation}\n",
        "\\min_{b_u, b_i, q_i, p_u} \\sum_{(u,i) \\in R_{train}} (r_{ui} - \\hat{r}_{ui})^2 + \\lambda (||q_i||^2 + ||p_u||^2 + ||b_u||^2 + ||b_i||^2)\n",
        "\\end{equation}\n",
        "\n",
        "---\n",
        "* **Gradient Descent**\n",
        "---\n",
        "\n",
        "\\begin{equation}\n",
        "\\frac{\\partial}{\\partial b_u} = -2 (r_{ui} - \\hat{r}_{ui}) + 2 \\lambda b_u\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "\\frac{\\partial}{\\partial b_i} = -2 (r_{ui} - \\hat{r}_{ui}) + 2 \\lambda b_i\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "\\frac{\\partial}{\\partial p_{uf}} = -2 (r_{ui} - \\hat{r}{ui}) q{if} + 2 \\lambda p_{uf}\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "\\frac{\\partial}{\\partial q_{if}} = -2 (r_{ui} - \\hat{r}{ui}) p{uf} + 2 \\lambda q_{if}\n",
        "\\end{equation}\n",
        "\n",
        "---\n",
        "* **Update Rule**\n",
        "---\n",
        "\n",
        "\n",
        "\\begin{aligned}\n",
        "b_{u}^{(k+1)} &= b_{u}^{(k)} + \\gamma \\cdot \\left( \\sum_{i \\in I_u} (r_{ui} - \\mu - b_u^{(k)} - b_i^{(k)}) - \\lambda b_u^{(k)} \\right) \\\\\n",
        "b_{i}^{(k+1)} &= b_{i}^{(k)} + \\gamma \\cdot \\left( \\sum_{u \\in U_i} (r_{ui} - \\mu - b_u^{(k)} - b_i^{(k)}) - \\lambda b_i^{(k)} \\right)\n",
        "\\end{aligned}\n",
        "\n",
        "\\begin{equation}\n",
        "p_{uk}^{(t+1)} = p_{uk}^{(t)} - \\gamma \\cdot \\frac{\\partial}{\\partial p_{uk}} J(p_{uk}^{(t)}, q_{ik}^{(t)}, b_u^{(t)}, b_i^{(t)})\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "q_{ik}^{(t+1)} = q_{ik}^{(t)} - \\gamma \\cdot \\frac{\\partial}{\\partial q_{ik}} J(p_{uk}^{(t)}, q_{ik}^{(t)}, b_u^{(t)}, b_i^{(t)})\n",
        "\\end{equation}"
      ],
      "metadata": {
        "id": "HEUEelwPv871"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "9j6-khx70OZ9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xNMewDoz3pp",
        "outputId": "e177ea04-b7ac-40f0-d078-ee1af457dfc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting surprise\n",
            "  Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n",
            "Collecting scikit-surprise\n",
            "  Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m772.0/772.0 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.10.1)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp310-cp310-linux_x86_64.whl size=3095390 sha256=0c05d6e8cfbbac10bb9f651c22ca1f1903f0ea08dfe8f219f7502e97aad85857\n",
            "  Stored in directory: /root/.cache/pip/wheels/a5/ca/a8/4e28def53797fdc4363ca4af740db15a9c2f1595ebc51fb445\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise, surprise\n",
            "Successfully installed scikit-surprise-1.1.3 surprise-0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install surprise\n",
        "from surprise import Dataset, Reader\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from surprise.model_selection import GridSearchCV\n",
        "from surprise import SVD\n",
        "from surprise.model_selection import cross_validate, train_test_split\n",
        "from surprise import accuracy\n",
        "from surprise import dump\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data "
      ],
      "metadata": {
        "id": "6fXwXgoa1UZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Base = \"/content/drive/MyDrive/RS Data/Assignment 3/Processed_data/\"\n",
        "train=pd.read_csv(Base+\"Train_df_final.csv\")\n",
        "test = pd.read_csv(Base+\"Test_df_final.csv\")"
      ],
      "metadata": {
        "id": "O8pngzCi0Vep"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = train[[\"User-ID\"\t,\"ISBN\",\t\"Book-Rating\"\t,\"book_name\"]]\n",
        "test = test[[\"User-ID\"\t,\"ISBN\",\t\"Book-Rating\"\t,\"book_name\"]]"
      ],
      "metadata": {
        "id": "EEkYF7du0bxR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reader = Reader(rating_scale=(1, 10))\n",
        "data = Dataset.load_from_df(train[['User-ID', 'ISBN', 'Book-Rating']], reader)\n",
        "trainset = data.build_full_trainset()\n"
      ],
      "metadata": {
        "id": "ecQ-shft0faW"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "users= pd.read_csv(\"/content/drive/MyDrive/RS Data/Assignment 3/Processed_data/users_features.csv\")"
      ],
      "metadata": {
        "id": "nJRsrO6H1GeQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "books=pd.read_csv(\"/content/drive/MyDrive/RS Data/Assignment 3/Processed_data/book_features.csv\")\n",
        "books.head()"
      ],
      "metadata": {
        "id": "4_cXSIPh9DAl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "89ef36e0-4d9a-406e-c17f-e2e3f09c3cc6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Book-Title        ISBN  page_count  categories  Year-Of-Publication  \\\n",
              "0    Footfall  0345323440       706.0         330                 1996   \n",
              "1    Footfall  0345323475       706.0         330                 1985   \n",
              "2  The Reader  0679442790       224.0         330                 1997   \n",
              "3  The Reader  0375707972       224.0         330                 1999   \n",
              "4  The Reader  0679781307       224.0         330                 1998   \n",
              "\n",
              "   feature_1  feature_2  feature_3  feature_4  feature_5  ...  feature_91  \\\n",
              "0  -0.324090   0.178090   0.085679   0.051699  -0.233513  ...    0.382992   \n",
              "1  -0.324090   0.178090   0.085679   0.051699  -0.233513  ...    0.382992   \n",
              "2  -0.265439   0.180581  -0.044998   0.521798   0.064341  ...    0.527207   \n",
              "3  -0.265439   0.180581  -0.044998   0.521798   0.064341  ...    0.527207   \n",
              "4  -0.265439   0.180581  -0.044998   0.521798   0.064341  ...    0.527207   \n",
              "\n",
              "   feature_92  feature_93  feature_94  feature_95  feature_96  feature_97  \\\n",
              "0   -0.090160   -0.040599   -0.159770    0.468369    0.346564    0.053775   \n",
              "1   -0.090160   -0.040599   -0.159770    0.468369    0.346564    0.053775   \n",
              "2   -0.152366   -0.067668    0.264588    0.671669    0.287058    0.089154   \n",
              "3   -0.152366   -0.067668    0.264588    0.671669    0.287058    0.089154   \n",
              "4   -0.152366   -0.067668    0.264588    0.671669    0.287058    0.089154   \n",
              "\n",
              "   feature_98  feature_99  feature_100  \n",
              "0   -0.575963    0.202055     0.013086  \n",
              "1   -0.575963    0.202055     0.013086  \n",
              "2   -0.696850   -0.135784     0.029446  \n",
              "3   -0.696850   -0.135784     0.029446  \n",
              "4   -0.696850   -0.135784     0.029446  \n",
              "\n",
              "[5 rows x 105 columns]"
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              "      <th></th>\n",
              "      <th>Book-Title</th>\n",
              "      <th>ISBN</th>\n",
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              "      <th>1</th>\n",
              "      <td>Footfall</td>\n",
              "      <td>0345323475</td>\n",
              "      <td>706.0</td>\n",
              "      <td>330</td>\n",
              "      <td>1985</td>\n",
              "      <td>-0.324090</td>\n",
              "      <td>0.178090</td>\n",
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              "      <td>0679442790</td>\n",
              "      <td>224.0</td>\n",
              "      <td>330</td>\n",
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              "          const element = document.querySelector('#df-13614535-7628-48ae-9b4f-002ae6050095');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Models"
      ],
      "metadata": {
        "id": "avFOMZpJDdbz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recommendation Algorithm"
      ],
      "metadata": {
        "id": "3Qa-50PQDj5n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "algs, algo = dump.load('/content/drive/MyDrive/RS Data/Assignment 3/Processed_data/Matrix_Factorisation.pkl')\n"
      ],
      "metadata": {
        "id": "iBuFjCR50rvg"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reranking Algorithm"
      ],
      "metadata": {
        "id": "oE6tDhioDoxG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/RS Data/Assignment 3/Processed_data/XGB_Ranker.pkl', 'rb') as file:\n",
        "    model = pickle.load(file)\n",
        "\n"
      ],
      "metadata": {
        "id": "l00oTHVoUVuH"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recommendation System with Reranking (MF + XGB Ranker) "
      ],
      "metadata": {
        "id": "nb8ZlLJDDw4S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def MF_RS():\n",
        "  k = int(input(\"Value of K for top-K Recommendations: \"))\n",
        "  user_id = int(input(\"Enter User-ID: \"))  \n",
        "  uid = trainset.to_inner_uid(user_id)\n",
        "  all_items = trainset.all_items()\n",
        "  x = trainset.ur[int(uid)]\n",
        "  a = [x[i][0] for i in range(len(x))]\n",
        "  not_rated_items = [item for item in all_items if item not in a]\n",
        "  predictions = []\n",
        "  for iid in not_rated_items:\n",
        "      pred = algo.predict(uid, trainset.to_raw_iid(iid))\n",
        "      predictions.append((iid, pred.est))\n",
        "  predictions_sorted = sorted(predictions, key=lambda x: x[1], reverse=True)[:k]\n",
        "  print(\"\\n\")\n",
        "  print(f\"Top {k} Book recommendations for User {user_id} are: \")\n",
        "  lst =()\n",
        "  for item, rating in predictions_sorted:\n",
        "      lst+= ((trainset.to_raw_iid(item),rating),)\n",
        "  dx = pd.DataFrame(lst,columns=[\"ISBN\",\"Predicted Rating\"])\n",
        "  uf =users.loc[users['User-ID'] ==user_id ].drop(columns=['User-ID'])\n",
        "  temp = dx.merge(books,on=\"ISBN\",how=\"inner\").drop(columns=['page_count',\"Predicted Rating\",\"Book-Title\"])\n",
        "  temp[\"Age\"] = users[\"Age\"].iloc[0]\n",
        "  temp[\"State\"] =users[\"State\"].iloc[0]\n",
        "  X_test = temp.iloc[:,1:]\n",
        "  y_pred = model.predict(X_test)\n",
        "  temp['predicted_score'] = y_pred\n",
        "  temp = temp.sort_values(by='predicted_score', ascending=False)\n",
        "  temp = temp[[\"ISBN\"]].merge(books[[\"Book-Title\",\"ISBN\"]],on = 'ISBN',how = 'inner')\n",
        "  \n",
        "  display(temp.head(k))\n",
        "  data_dict = temp.iloc[0:5].to_dict(orient='records')\n",
        "  "
      ],
      "metadata": {
        "id": "DwDCbpg2021Q"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MF_RS()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "9zSLWp2c1ES_",
        "outputId": "8d23cf04-32c0-4b2c-c802-ab4fe15d5a5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value of K for top-K Recommendations: 5\n",
            "Enter User-ID: 151824\n",
            "\n",
            "\n",
            "Top 5 Book recommendations for User 151824 are: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         ISBN                                         Book-Title\n",
              "0  0439139597       Harry Potter and the Goblet of Fire (Book 4)\n",
              "1  059035342X  Harry Potter and the Sorcerer's Stone (Harry P...\n",
              "2  0446310786                              To Kill a Mockingbird\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0439136350</td>\n",
              "      <td>Harry Potter and the Prisoner of Azkaban (Book 3)</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>043935806X</td>\n",
              "      <td>Harry Potter and the Order of the Phoenix (Boo...</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4723092-ea2a-4361-8848-71e95dc96c3e')\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\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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    {
      "cell_type": "code",
      "source": [
        "MF_RS()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "HSXP54-5qSCM",
        "outputId": "423f57e3-489e-4ade-91d1-af89fc42bd3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value of K for top-K Recommendations: 5\n",
            "Enter User-ID: 2033\n",
            "\n",
            "\n",
            "Top 5 Book recommendations for User 2033 are: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         ISBN                                         Book-Title\n",
              "0  0439139597       Harry Potter and the Goblet of Fire (Book 4)\n",
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              "2  0446310786                              To Kill a Mockingbird\n",
              "3  0439064872   Harry Potter and the Chamber of Secrets (Book 2)\n",
              "4  0385504209                                  The Da Vinci Code"
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              "      <th>4</th>\n",
              "      <td>0385504209</td>\n",
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              "  </tbody>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "          document.querySelector('#df-9b7ee615-9eb7-4caf-a938-508182ba1ec7 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-9b7ee615-9eb7-4caf-a938-508182ba1ec7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "MF_RS()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "1RHTi_WR2jIe",
        "outputId": "b417bd4f-01fa-4090-e7eb-dcc35d688a21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value of K for top-K Recommendations: 5\n",
            "Enter User-ID: 254\n",
            "\n",
            "\n",
            "Top 5 Book recommendations for User 254 are: \n"
          ]
        },
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              "         ISBN                                         Book-Title\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0439064872</td>\n",
              "      <td>Harry Potter and the Chamber of Secrets (Book 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0439136350</td>\n",
              "      <td>Harry Potter and the Prisoner of Azkaban (Book 3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>043935806X</td>\n",
              "      <td>Harry Potter and the Order of the Phoenix (Boo...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0f63b35-7efe-4cec-a3f5-c29423a85da4')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c0f63b35-7efe-4cec-a3f5-c29423a85da4 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-c0f63b35-7efe-4cec-a3f5-c29423a85da4');\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",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deployed On Postman "
      ],
      "metadata": {
        "id": "8ck8D2Ylup9s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Without Reranking "
      ],
      "metadata": {
        "id": "bEouOGjPv3e8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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      ],
      "metadata": {
        "id": "na852D7Qv1gy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## With Reranking"
      ],
      "metadata": {
        "id": "0iFb4KSZv8fc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "9A5O6ypgusws"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**From the image it can be seen Footfall was at 5th position which is now at 1st position**"
      ],
      "metadata": {
        "id": "ZDe7RoO5wKti"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Time Comparision"
      ],
      "metadata": {
        "id": "KFh3J41Ew1jp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|Method| Response Time (Average)|\n",
        "|--|--|\n",
        "|Without Reranking|69ms|\n",
        "|With Reranking|111ms|"
      ],
      "metadata": {
        "id": "F74TASfmwYQ6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Coverage"
      ],
      "metadata": {
        "id": "71Cll1FPkgX6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def MF_RT(user_id,trainset, algo, model, books, users,k):\n",
        "   \n",
        "    uid = trainset.to_inner_uid(user_id)\n",
        "    all_items = trainset.all_items()\n",
        "    x = trainset.ur[int(uid)]\n",
        "    a = [x[i][0] for i in range(len(x))]\n",
        "    not_rated_items = [item for item in all_items if item not in a]\n",
        "    predictions = []\n",
        "    for iid in not_rated_items:\n",
        "        pred = algo.predict(uid, trainset.to_raw_iid(iid))\n",
        "        predictions.append((iid, pred.est))\n",
        "    predictions_sorted = sorted(predictions, key=lambda x: x[1], reverse=True)[:k]\n",
        "    \n",
        "    lst =()\n",
        "    for item, rating in predictions_sorted:\n",
        "        lst+= ((trainset.to_raw_iid(item),rating),)\n",
        "    dx = pd.DataFrame(lst,columns=[\"ISBN\",\"Predicted Rating\"])\n",
        "    uf =users.loc[users['User-ID'] ==user_id ].drop(columns=['User-ID'])\n",
        "    temp = dx.merge(books,on=\"ISBN\",how=\"inner\").drop(columns=['page_count',\"Predicted Rating\",\"Book-Title\"])\n",
        "    temp[\"Age\"] = users[\"Age\"].iloc[0]\n",
        "    temp[\"State\"] =users[\"State\"].iloc[0]\n",
        "    X_test = temp.iloc[:,1:]\n",
        "    y_pred = model.predict(X_test)\n",
        "    temp['predicted_score'] = y_pred\n",
        "    temp = temp.sort_values(by='predicted_score', ascending=False)\n",
        "    temp = temp[[\"ISBN\"]].merge(books[[\"Book-Title\",\"ISBN\"]],on = 'ISBN',how = 'inner')\n",
        "    data_dict = temp.iloc[0:k].to_dict(orient='records')\n",
        "    return [x['ISBN'] for x in data_dict]\n"
      ],
      "metadata": {
        "id": "Cse3Vqz5ke3h"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "true = test[test['Book-Rating'] == 10]\n",
        "true = true[true['User-ID'].isin(list(train[\"User-ID\"]))]\n",
        "true   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Jtpg1LC5mo3B",
        "outputId": "f5951963-c400-456f-e015-bcbc72fd56cc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       User-ID        ISBN  Book-Rating  \\\n",
              "9        31315  051513452X           10   \n",
              "15       16634  0452261341           10   \n",
              "18       46003  1583224890           10   \n",
              "29       83287  0553563521           10   \n",
              "30       51883  0425077047           10   \n",
              "...        ...         ...          ...   \n",
              "23403   116122  0345434684           10   \n",
              "23409   198781  0060961325           10   \n",
              "23410   219259  0767915054           10   \n",
              "23416   145449  0316107255           10   \n",
              "23425   153927  0842329293           10   \n",
              "\n",
              "                                               book_name  \n",
              "9      The Lunatic Cafe (Anita Blake Vampire Hunter (...  \n",
              "15                 Gunslinger Tower 1 (Dark Tower, No 1)  \n",
              "18                                                  9-11  \n",
              "29      Devil's Waltz (Alex Delaware Novels (Paperback))  \n",
              "30                                And Ladies of the Club  \n",
              "...                                                  ...  \n",
              "23403                                  The Skies Of Pern  \n",
              "23409  The Celluloid Closet: Homosexuality in the Movies  \n",
              "23410  A Girl Named Zippy: Growing Up Small in Moorel...  \n",
              "23416  Penguin Dreams and Stranger Things (A Bloom Co...  \n",
              "23425  The Indwelling: The Beast Takes Possession (Le...  \n",
              "\n",
              "[3875 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb7b8dc9-5e44-45eb-8fee-b5c7431581c6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User-ID</th>\n",
              "      <th>ISBN</th>\n",
              "      <th>Book-Rating</th>\n",
              "      <th>book_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>31315</td>\n",
              "      <td>051513452X</td>\n",
              "      <td>10</td>\n",
              "      <td>The Lunatic Cafe (Anita Blake Vampire Hunter (...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16634</td>\n",
              "      <td>0452261341</td>\n",
              "      <td>10</td>\n",
              "      <td>Gunslinger Tower 1 (Dark Tower, No 1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>46003</td>\n",
              "      <td>1583224890</td>\n",
              "      <td>10</td>\n",
              "      <td>9-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>83287</td>\n",
              "      <td>0553563521</td>\n",
              "      <td>10</td>\n",
              "      <td>Devil's Waltz (Alex Delaware Novels (Paperback))</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>51883</td>\n",
              "      <td>0425077047</td>\n",
              "      <td>10</td>\n",
              "      <td>And Ladies of the Club</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23403</th>\n",
              "      <td>116122</td>\n",
              "      <td>0345434684</td>\n",
              "      <td>10</td>\n",
              "      <td>The Skies Of Pern</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23409</th>\n",
              "      <td>198781</td>\n",
              "      <td>0060961325</td>\n",
              "      <td>10</td>\n",
              "      <td>The Celluloid Closet: Homosexuality in the Movies</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23410</th>\n",
              "      <td>219259</td>\n",
              "      <td>0767915054</td>\n",
              "      <td>10</td>\n",
              "      <td>A Girl Named Zippy: Growing Up Small in Moorel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23416</th>\n",
              "      <td>145449</td>\n",
              "      <td>0316107255</td>\n",
              "      <td>10</td>\n",
              "      <td>Penguin Dreams and Stranger Things (A Bloom Co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23425</th>\n",
              "      <td>153927</td>\n",
              "      <td>0842329293</td>\n",
              "      <td>10</td>\n",
              "      <td>The Indwelling: The Beast Takes Possession (Le...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3875 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb7b8dc9-5e44-45eb-8fee-b5c7431581c6')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fb7b8dc9-5e44-45eb-8fee-b5c7431581c6 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-fb7b8dc9-5e44-45eb-8fee-b5c7431581c6');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Coverage @5 "
      ],
      "metadata": {
        "id": "fojA4h-Gk0n_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_recommendations = []\n",
        "for user_id in true[\"User-ID\"]:\n",
        "    recommendations = MF_RT(user_id, trainset, algo, model, books, users,5)\n",
        "    all_recommendations.extend(recommendations)\n",
        "intersection = list(set(all_recommendations).intersection(set(books[\"ISBN\"])))\n",
        "coverage = len(intersection)/len(set(books[\"ISBN\"]))\n",
        "coverage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQUxKWzQklzn",
        "outputId": "041c801c-5cd5-4bf0-9c41-e61cb1d784da"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0543291059965555"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Coverage @10 "
      ],
      "metadata": {
        "id": "DKQ6s97fk4d7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_recommendations = []\n",
        "for user_id in true[\"User-ID\"]:\n",
        "    recommendations = MF_RT(user_id, trainset, algo, model, books, users,10)\n",
        "    all_recommendations.extend(recommendations)\n",
        "intersection = list(set(all_recommendations).intersection(set(books[\"ISBN\"])))\n",
        "coverage = len(intersection)/len(set(books[\"ISBN\"]))\n",
        "coverage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KClAhKl3k5K9",
        "outputId": "8a03227e-d516-4cfb-e9ab-205ea3104448"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10560513543134492"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Coverage @100"
      ],
      "metadata": {
        "id": "xFeU27_Bk53q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_recommendations = []\n",
        "for user_id in true[\"User-ID\"]:\n",
        "    recommendations = MF_RT(user_id, trainset, algo, model, books, users,100)\n",
        "    all_recommendations.extend(recommendations)\n",
        "intersection = list(set(all_recommendations).intersection(set(books[\"ISBN\"])))\n",
        "coverage = len(intersection)/len(set(books[\"ISBN\"]))\n",
        "coverage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CivOiveXk6rl",
        "outputId": "402765fe-4ec3-4876-c41a-2cd2392dcccf"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4984343197119148"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Coverage @1000 "
      ],
      "metadata": {
        "id": "FUnFQ7pok7-_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_recommendations = []\n",
        "for user_id in true[\"User-ID\"]:\n",
        "    recommendations = MF_RT(user_id, trainset, algo, model, books, users,1000)\n",
        "    all_recommendations.extend(recommendations)\n",
        "intersection = list(set(all_recommendations).intersection(set(books[\"ISBN\"])))\n",
        "coverage = len(intersection)/len(set(books[\"ISBN\"]))\n",
        "coverage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMH28yaMk7RY",
        "outputId": "d663eff9-7896-4b75-af73-5b8c87c34eec"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5599655550336621"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fgCRwnhivbt6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}