MamMates_Food_Recommendation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP0JCX49ckXYwZmiLthlXQ1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MamMates/ml-food-recommendation/blob/%233-recommender-model/MamMates_Food_Recommendation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YFM6rA9WjU7T"
      },
      "outputs": [],
      "source": [
        "!pip install -q tensorflow-recommenders"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_recommenders as tfrs"
      ],
      "metadata": {
        "id": "C9BSA2Z2jjiy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_link = \"https://docs.google.com/spreadsheets/d/1o0f663wcmMfta_PAILOJzkvn09_Mbjw5zBxgcDk1Au0\"\n",
        "\n",
        "df_dataset = pd.read_csv(f'{dataset_link}/export?gid=0&format=csv')\n",
        "df_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7kknJDXajsut",
        "outputId": "7a078d83-b752-48bd-e3f4-85dec11e5333"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     id_user  id_food  rating\n",
              "0         63       15       1\n",
              "1         66       20       2\n",
              "2         37        1       2\n",
              "3         39       13       3\n",
              "4         52       18       1\n",
              "..       ...      ...     ...\n",
              "995       35       17       3\n",
              "996       79       20       1\n",
              "997       92        1       1\n",
              "998        1       14       3\n",
              "999       29       15       2\n",
              "\n",
              "[1000 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5504fecd-f8c5-479b-9e4a-25ef90f5592e\" 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>id_user</th>\n",
              "      <th>id_food</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>66</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>35</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>79</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>29</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5504fecd-f8c5-479b-9e4a-25ef90f5592e')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5504fecd-f8c5-479b-9e4a-25ef90f5592e 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-5504fecd-f8c5-479b-9e4a-25ef90f5592e');\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",
              "\n",
              "\n",
              "<div id=\"df-816fde35-193a-4f32-b15c-b6c087baefb8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-816fde35-193a-4f32-b15c-b6c087baefb8')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-816fde35-193a-4f32-b15c-b6c087baefb8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_food_info = pd.read_csv(f'{dataset_link}/export?gid=1905501804&format=csv')\n",
        "df_food_info.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qbOMD77hsuHF",
        "outputId": "23bc3cdd-8ffa-4ce7-ae82-f614a82fdb50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id_food         food_name\n",
              "0        1   Donat Ubi Mawar\n",
              "1        2   Donat Ubi Mawar\n",
              "2        3  Kue Cubit Maniez\n",
              "3        4  Kue Cubit Maniez\n",
              "4        5   Kue Lapis Legit"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-94a60cbc-7707-4f95-93f7-6cbab9dd3ac7\" 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>id_food</th>\n",
              "      <th>food_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Donat Ubi Mawar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Donat Ubi Mawar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Kue Cubit Maniez</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Kue Cubit Maniez</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Kue Lapis Legit</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94a60cbc-7707-4f95-93f7-6cbab9dd3ac7')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-94a60cbc-7707-4f95-93f7-6cbab9dd3ac7 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-94a60cbc-7707-4f95-93f7-6cbab9dd3ac7');\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",
              "\n",
              "\n",
              "<div id=\"df-92cbbd7f-94b7-4318-9ec8-3b39434bca93\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-92cbbd7f-94b7-4318-9ec8-3b39434bca93')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-92cbbd7f-94b7-4318-9ec8-3b39434bca93 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_dataset.id_user.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KX6diJUZqX1U",
        "outputId": "bc0d8e21-8bc4-4ed1-a5ca-c63f15165db8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_dataset.id_food.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kMQeQv4qeXV",
        "outputId": "e115df20-5317-4344-ae5e-6fb9c5ff7fe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings = tf.data.Dataset.from_tensor_slices(\n",
        "    {\"user_id\": df_dataset.id_user.astype(str),\n",
        "     \"food_id\": df_dataset.id_food.astype(str)}\n",
        ")"
      ],
      "metadata": {
        "id": "8xw6PBsCp0Ka"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x in ratings.take(2).as_numpy_iterator():\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_RTUa3It83W",
        "outputId": "925b2f7a-d7c8-43c9-8322-64d3f1282fc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'user_id': b'63', 'food_id': b'15'}\n",
            "{'user_id': b'66', 'food_id': b'20'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "foods = tf.data.Dataset.from_tensor_slices(\n",
        "    df_food_info.id_food.astype(str)\n",
        ")"
      ],
      "metadata": {
        "id": "W9Itp9hsuDT4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x in foods.take(2).as_numpy_iterator():\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGAjf97yuJl2",
        "outputId": "e95d0796-7740-4cf8-b752-854cdfa560ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b'1'\n",
            "b'2'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "shuffled = ratings.shuffle(1000, seed=42, reshuffle_each_iteration=False)\n",
        "train = shuffled.take(800)\n",
        "test = shuffled.skip(800).take(200)"
      ],
      "metadata": {
        "id": "Xl6EzRnludse"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "food_ids = foods.batch(32)\n",
        "user_ids = ratings.batch(32).map(lambda x: x[\"user_id\"])\n",
        "\n",
        "unique_food_ids = np.unique(np.concatenate(list(food_ids)))\n",
        "unique_user_ids = np.unique(np.concatenate(list(user_ids)))\n",
        "\n",
        "unique_food_ids[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLBIUlGSumyR",
        "outputId": "8f5cc627-b4cc-4708-927b-765e5b694c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([b'1', b'10', b'11', b'12', b'13', b'14', b'15', b'16', b'17',\n",
              "       b'18'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_dimension = 32"
      ],
      "metadata": {
        "id": "wllc3TqVlLRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_model = tf.keras.Sequential([\n",
        "  tf.keras.layers.StringLookup(\n",
        "      vocabulary=unique_user_ids, mask_token=None),\n",
        "  tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension)\n",
        "])"
      ],
      "metadata": {
        "id": "oP1EClIRlzaE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "food_model = tf.keras.Sequential([\n",
        "  tf.keras.layers.StringLookup(\n",
        "      vocabulary=unique_food_ids, mask_token=None),\n",
        "  tf.keras.layers.Embedding(len(unique_food_ids) + 1, embedding_dimension)\n",
        "])"
      ],
      "metadata": {
        "id": "cMOy7PcSl1mx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = tfrs.metrics.FactorizedTopK(\n",
        "  candidates=foods.batch(32).map(food_model)\n",
        ")"
      ],
      "metadata": {
        "id": "pP5WvcaLl6Z7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "task = tfrs.tasks.Retrieval(\n",
        "  metrics=metrics\n",
        ")"
      ],
      "metadata": {
        "id": "86P6Q0oZl78d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Dict, Text\n",
        "\n",
        "class MamMatesModel(tfrs.Model):\n",
        "\n",
        "  def __init__(self, user_model, food_model):\n",
        "    super().__init__()\n",
        "    self.food_model: tf.keras.Model = food_model\n",
        "    self.user_model: tf.keras.Model = user_model\n",
        "    self.task: tf.keras.layers.Layer = task\n",
        "\n",
        "  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n",
        "    user_embeddings = self.user_model(features[\"user_id\"])\n",
        "    positive_food_embeddings = self.food_model(features[\"food_id\"])\n",
        "\n",
        "    return self.task(user_embeddings, positive_food_embeddings)"
      ],
      "metadata": {
        "id": "eyvpoKQKvZib"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class NoBaseClassMammatesModel(tf.keras.Model):\n",
        "\n",
        "  def __init__(self, user_model, food_model):\n",
        "    super().__init__()\n",
        "    self.food_model: tf.keras.Model = food_model\n",
        "    self.user_model: tf.keras.Model = user_model\n",
        "    self.task: tf.keras.layers.Layer = task\n",
        "\n",
        "  def train_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n",
        "\n",
        "    with tf.GradientTape() as tape:\n",
        "\n",
        "      user_embeddings = self.user_model(features[\"user_id\"])\n",
        "      positive_food_embeddings = self.food_model(features[\"food_id\"])\n",
        "      loss = self.task(user_embeddings, positive_food_embeddings)\n",
        "\n",
        "      regularization_loss = sum(self.losses)\n",
        "\n",
        "      total_loss = loss + regularization_loss\n",
        "\n",
        "    gradients = tape.gradient(total_loss, self.trainable_variables)\n",
        "    self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n",
        "\n",
        "    metrics = {metric.name: metric.result() for metric in self.metrics}\n",
        "    metrics[\"loss\"] = loss\n",
        "    metrics[\"regularization_loss\"] = regularization_loss\n",
        "    metrics[\"total_loss\"] = total_loss\n",
        "\n",
        "    return metrics\n",
        "\n",
        "  def test_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n",
        "\n",
        "    user_embeddings = self.user_model(features[\"user_id\"])\n",
        "    positive_food_embeddings = self.food_model(features[\"food_id\"])\n",
        "    loss = self.task(user_embeddings, positive_food_embeddings)\n",
        "\n",
        "    regularization_loss = sum(self.losses)\n",
        "\n",
        "    total_loss = loss + regularization_loss\n",
        "\n",
        "    metrics = {metric.name: metric.result() for metric in self.metrics}\n",
        "    metrics[\"loss\"] = loss\n",
        "    metrics[\"regularization_loss\"] = regularization_loss\n",
        "    metrics[\"total_loss\"] = total_loss\n",
        "\n",
        "    return metrics"
      ],
      "metadata": {
        "id": "0VojqHclvu1K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = MamMatesModel(user_model, food_model)\n",
        "model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))"
      ],
      "metadata": {
        "id": "FfU1Jjgmv6kG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cached_train = train.shuffle(1000).batch(32).cache()\n",
        "cached_test = test.batch(32).cache()"
      ],
      "metadata": {
        "id": "a8QxhPIJwNP3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(cached_train, epochs=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NBy9ts6NwQJx",
        "outputId": "ead9bd3c-1cae-491e-f206-3bba7de320b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "25/25 [==============================] - 4s 49ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.2025 - factorized_top_k/top_10_categorical_accuracy: 0.4787 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 110.9281 - regularization_loss: 0.0000e+00 - total_loss: 110.9281\n",
            "Epoch 2/3\n",
            "25/25 [==============================] - 3s 102ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0012 - factorized_top_k/top_5_categorical_accuracy: 0.3237 - factorized_top_k/top_10_categorical_accuracy: 0.6488 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 109.5470 - regularization_loss: 0.0000e+00 - total_loss: 109.5470\n",
            "Epoch 3/3\n",
            "25/25 [==============================] - 4s 149ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0088 - factorized_top_k/top_5_categorical_accuracy: 0.4137 - factorized_top_k/top_10_categorical_accuracy: 0.7500 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 105.0930 - regularization_loss: 0.0000e+00 - total_loss: 105.0930\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7af684199b10>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(cached_test, return_dict=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3k7U2ERXwQ_q",
        "outputId": "ee35229a-5ed3-41c4-9516-40bdbdc7ade8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 1s 94ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0250 - factorized_top_k/top_5_categorical_accuracy: 0.2400 - factorized_top_k/top_10_categorical_accuracy: 0.4800 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 90.3494 - regularization_loss: 0.0000e+00 - total_loss: 90.3494\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'factorized_top_k/top_1_categorical_accuracy': 0.02500000037252903,\n",
              " 'factorized_top_k/top_5_categorical_accuracy': 0.23999999463558197,\n",
              " 'factorized_top_k/top_10_categorical_accuracy': 0.47999998927116394,\n",
              " 'factorized_top_k/top_50_categorical_accuracy': 1.0,\n",
              " 'factorized_top_k/top_100_categorical_accuracy': 1.0,\n",
              " 'loss': 16.14521026611328,\n",
              " 'regularization_loss': 0,\n",
              " 'total_loss': 16.14521026611328}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)\n",
        "index.index_from_dataset(\n",
        "  tf.data.Dataset.zip((foods.batch(32), foods.batch(32).map(model.food_model)))\n",
        ")\n",
        "\n",
        "_, titles = index(tf.constant([\"14\"]))\n",
        "print(f\"Recommendations for user with ID 14: {titles[0, :3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6F3hGUB9wUvW",
        "outputId": "9e0ba438-b825-4b5b-acb4-455447008c92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations for user with ID 14: [b'13' b'14' b'12']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list_titles = titles.numpy().astype(int).tolist()\n",
        "list_titles[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXxgMglnwiMR",
        "outputId": "50ac6086-4244-4c69-d9d7-6cce9bcaaa9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[13, 14, 12, 2, 18, 20, 11, 10, 7, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_foods = df_food_info[df_food_info['id_food'].isin(list_titles[0])]\n",
        "\n",
        "id_to_food = dict(zip(filtered_foods['id_food'], filtered_foods['food_name']))\n",
        "\n",
        "food_names = [id_to_food.get(id) for id in list_titles[0]]\n",
        "print(f\"Recommendations for user with ID 14: {food_names[:3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4UbSuDbw9Jz",
        "outputId": "0bf494a4-ad22-42c8-d32a-e0fd73fd340f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations for user with ID 14: ['Roti Bakar Cokelat Keju', 'Roti Bakar Niqmat', 'Donat Ubi Rasa Cinta']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tempfile\n",
        "import os\n",
        "\n",
        "MODEL_DIR = tempfile.gettempdir()\n",
        "version = 1\n",
        "export_path = os.path.join(MODEL_DIR, str(version))\n",
        "print('export_path = {}\\n'.format(export_path))\n",
        "\n",
        "tf.saved_model.save(index, export_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHQo33U_xTHh",
        "outputId": "c77e8768-b53c-4e57-bbf2-1d9fa7337e0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "export_path = /tmp/1\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
            "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r model.zip /tmp/1/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4Am38VZzEYW",
        "outputId": "8292736f-8e9e-4422-b46a-5d63cc20c391"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "updating: tmp/1/ (stored 0%)\n",
            "updating: tmp/1/fingerprint.pb (stored 0%)\n",
            "updating: tmp/1/variables/ (stored 0%)\n",
            "updating: tmp/1/variables/variables.index (deflated 34%)\n",
            "updating: tmp/1/variables/variables.data-00000-of-00001 (deflated 15%)\n",
            "updating: tmp/1/assets/ (stored 0%)\n",
            "updating: tmp/1/saved_model.pb (deflated 84%)\n"
          ]
        }
      ]
    }
  ]
}