Recommender_Systems_with_DL.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/selimunal/Pytorch/blob/main/Recommender_Systems_with_DL.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": "aUCmV_jnRcYz"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7F6brFC7R3kQ",
        "outputId": "c0281904-a6f1-4af2-c384-c9e10b012ab0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-02-15 09:39:20--  https://files.grouplens.org/datasets/movielens/ml-20m.zip\n",
            "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n",
            "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 198702078 (189M) [application/zip]\n",
            "Saving to: ‘ml-20m.zip’\n",
            "\n",
            "ml-20m.zip          100%[===================>] 189.50M  49.1MB/s    in 4.1s    \n",
            "\n",
            "2023-02-15 09:39:25 (46.0 MB/s) - ‘ml-20m.zip’ saved [198702078/198702078]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -nc https://files.grouplens.org/datasets/movielens/ml-20m.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMvLQHPNSCkP",
        "outputId": "c0daabaa-19d2-475a-ada6-0b1be45ad4d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  ml-20m.zip\n",
            "   creating: ml-20m/\n",
            "  inflating: ml-20m/genome-scores.csv  \n",
            "  inflating: ml-20m/genome-tags.csv  \n",
            "  inflating: ml-20m/links.csv        \n",
            "  inflating: ml-20m/movies.csv       \n",
            "  inflating: ml-20m/ratings.csv      \n",
            "  inflating: ml-20m/README.txt       \n",
            "  inflating: ml-20m/tags.csv         \n"
          ]
        }
      ],
      "source": [
        "!unzip -n ml-20m.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_mSUgehSVJ_",
        "outputId": "933b979a-19d4-4978-d39f-548488cd188d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ml-20m\tml-20m.zip  sample_data\n"
          ]
        }
      ],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SAL-H3v5TN-Z",
        "outputId": "3559ecc5-c110-459a-abae-94e1036f2969"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-234fce6f-900a-40dd-9efa-96a5865512bf\">\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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112486027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-234fce6f-900a-40dd-9efa-96a5865512bf')\"\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-234fce6f-900a-40dd-9efa-96a5865512bf 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-234fce6f-900a-40dd-9efa-96a5865512bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1        2     3.5  1112486027\n",
              "1       1       29     3.5  1112484676\n",
              "2       1       32     3.5  1112484819\n",
              "3       1       47     3.5  1112484727\n",
              "4       1       50     3.5  1112484580"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=pd.read_csv(\"ml-20m/ratings.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EaeVoyeCT06p",
        "outputId": "0a85d1b4-598b-41ab-bde4-791120377bd9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c1c9c789-1ab0-4e50-9002-8ba38802d932\">\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>movieId</th>\n",
              "      <th>tagId</th>\n",
              "      <th>relevance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.02500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.02500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.05775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.09675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.14675</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1c9c789-1ab0-4e50-9002-8ba38802d932')\"\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-c1c9c789-1ab0-4e50-9002-8ba38802d932 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-c1c9c789-1ab0-4e50-9002-8ba38802d932');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   movieId  tagId  relevance\n",
              "0        1      1    0.02500\n",
              "1        1      2    0.02500\n",
              "2        1      3    0.05775\n",
              "3        1      4    0.09675\n",
              "4        1      5    0.14675"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2=pd.read_csv(\"ml-20m/genome-scores.csv\")\n",
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YuLLzeGwUCFq",
        "outputId": "e1ed1d30-98bd-4185-903a-ecb66195d83b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-caf1fe31-b6b4-40dc-bd1d-cf584e734f24\">\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>movieId</th>\n",
              "      <th>imdbId</th>\n",
              "      <th>tmdbId</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>114709</td>\n",
              "      <td>862.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>113497</td>\n",
              "      <td>8844.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>113228</td>\n",
              "      <td>15602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>114885</td>\n",
              "      <td>31357.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>113041</td>\n",
              "      <td>11862.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-caf1fe31-b6b4-40dc-bd1d-cf584e734f24')\"\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-caf1fe31-b6b4-40dc-bd1d-cf584e734f24 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-caf1fe31-b6b4-40dc-bd1d-cf584e734f24');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   movieId  imdbId   tmdbId\n",
              "0        1  114709    862.0\n",
              "1        2  113497   8844.0\n",
              "2        3  113228  15602.0\n",
              "3        4  114885  31357.0\n",
              "4        5  113041  11862.0"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df3=pd.read_csv(\"ml-20m/links.csv\")\n",
        "df3.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Up4lSOr6Uk80"
      },
      "outputs": [],
      "source": [
        "df.userId=pd.Categorical(df.userId)\n",
        "df[\"new_user_id\"]=df.userId.cat.codes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxHRVCArU61T",
        "outputId": "a5936a52-a38e-4cf5-ce8f-2f2998b94b66"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(  userId  movieId  rating   timestamp  new_user_id\n",
              " 0      1        2     3.5  1112486027            0\n",
              " 1      1       29     3.5  1112484676            0\n",
              " 2      1       32     3.5  1112484819            0\n",
              " 3      1       47     3.5  1112484727            0\n",
              " 4      1       50     3.5  1112484580            0,\n",
              "           userId  movieId  rating   timestamp  new_user_id\n",
              " 20000258  138493    68954     4.5  1258126920       138492\n",
              " 20000259  138493    69526     4.5  1259865108       138492\n",
              " 20000260  138493    69644     3.0  1260209457       138492\n",
              " 20000261  138493    70286     5.0  1258126944       138492\n",
              " 20000262  138493    71619     2.5  1255811136       138492)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(), df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aNN-82p1U9h0",
        "outputId": "52552a23-af3d-461c-d2e8-0c3fac4b2918"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3731831a-d1f1-4716-bdf7-c3ad08ceb5bd\">\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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>new_user_id</th>\n",
              "      <th>new_movie_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112486027</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484676</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484819</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484727</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1112484580</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3731831a-d1f1-4716-bdf7-c3ad08ceb5bd')\"\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-3731831a-d1f1-4716-bdf7-c3ad08ceb5bd 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-3731831a-d1f1-4716-bdf7-c3ad08ceb5bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  userId movieId  rating   timestamp  new_user_id  new_movie_id\n",
              "0      1       2     3.5  1112486027            0             1\n",
              "1      1      29     3.5  1112484676            0            28\n",
              "2      1      32     3.5  1112484819            0            31\n",
              "3      1      47     3.5  1112484727            0            46\n",
              "4      1      50     3.5  1112484580            0            49"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.movieId=pd.Categorical(df.movieId)\n",
        "df[\"new_movie_id\"]=df.movieId.cat.codes\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FNyGO_j4VXSs"
      },
      "outputs": [],
      "source": [
        "#get user Ids movie IDs and ratings as seperate arrays\n",
        "user_ids=df[\"new_user_id\"].values\n",
        "movie_ids=df[\"new_movie_id\"].values\n",
        "ratings=df[\"rating\"].values-2.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asY4j3EAVxSe",
        "outputId": "589750c8-6e09-4d8a-c83e-fe0b8e412c50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(138493, 26744)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#get number of users and number of movies\n",
        "N=len(set(user_ids))\n",
        "M=len(set(movie_ids))\n",
        "N , M"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gdFDvrkBV9Ls"
      },
      "outputs": [],
      "source": [
        "#set embedding dimension\n",
        "D=10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KgHmL-CDWCUE"
      },
      "outputs": [],
      "source": [
        "#make a NN\n",
        "class Model(nn.Module):\n",
        "  def __init__(self,n_users,n_items,embed_dim,n_hidden=1024):\n",
        "    super(Model, self).__init__()\n",
        "    self.N=n_users\n",
        "    self.M=n_items\n",
        "    self.D=embed_dim\n",
        "\n",
        "    self.u_emb=nn.Embedding(self.N,self.D)\n",
        "    self.m_emb=nn.Embedding(self.M,self.D)\n",
        "    self.fc1=nn.Linear(2*self.D,n_hidden)\n",
        "    self.fc2=nn.Linear(n_hidden,1)\n",
        "\n",
        "  def forward(self, u, m):\n",
        "    u=self.u_emb(u) #output is (num_samples,D)\n",
        "    m=self.m_emb(m) #output is (num_samples, D)\n",
        "\n",
        "    #merge\n",
        "    out=torch.cat((u,m),1) # output is (num_samples,2D)\n",
        "\n",
        "    #ANN\n",
        "    out=self.fc1(out)\n",
        "    out=F.relu(out)\n",
        "    out=self.fc2(out)\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWkwHEdBXdX_",
        "outputId": "4b979475-1e1a-4850-e745-a5f63e7ed574"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61MuEi9eXome",
        "outputId": "b4006bc3-cdef-456f-b473-dcdd66b21446"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Model(\n",
              "  (u_emb): Embedding(138493, 10)\n",
              "  (m_emb): Embedding(26744, 10)\n",
              "  (fc1): Linear(in_features=20, out_features=1024, bias=True)\n",
              "  (fc2): Linear(in_features=1024, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model=Model(N,M,D)\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xyXbeYZkX3_W"
      },
      "outputs": [],
      "source": [
        "#loss and optimizer\n",
        "criterion=nn.MSELoss()\n",
        "optimizer=torch.optim.Adam(model.parameters())\n",
        "#optimizer=torch.optim.SGD(model.paratemeters(),lr=0.8,momentum=0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o8HpjounYMQo"
      },
      "outputs": [],
      "source": [
        "#shuffke the data in corresponding order\n",
        "user_ids, movie_ids, ratings=shuffle(user_ids,movie_ids, ratings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RxcpQsz2YbU_"
      },
      "outputs": [],
      "source": [
        "#convert to tensors\n",
        "user_ids_t=torch.from_numpy(user_ids).long()\n",
        "movie_ids_t=torch.from_numpy(movie_ids).long()\n",
        "ratings_t=torch.from_numpy(ratings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K6hUGI-wYtqf"
      },
      "outputs": [],
      "source": [
        "#make datasets\n",
        "Ntrain=int(0.8*len(ratings))\n",
        "train_dataset=torch.utils.data.TensorDataset(\n",
        "    user_ids_t[:Ntrain],\n",
        "    movie_ids_t[:Ntrain],\n",
        "    ratings_t[:Ntrain],\n",
        ")\n",
        "\n",
        "test_dataset=torch.utils.data.TensorDataset(\n",
        "    user_ids_t[Ntrain:],\n",
        "    movie_ids_t[Ntrain:],\n",
        "    ratings_t[Ntrain:],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jYzUERetZFvJ"
      },
      "outputs": [],
      "source": [
        "#data loadders\n",
        "batch_size=512\n",
        "train_loader=torch.utils.data.DataLoader(\n",
        "    dataset=train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True\n",
        ")\n",
        "test_loader=torch.utils.data.DataLoader(\n",
        "    dataset=train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MRKep7LsZxgV"
      },
      "outputs": [],
      "source": [
        "#an function to encap the training loop\n",
        "def batch_gd(model, criterion, optimizer, train_iter, test_iter,epochs):\n",
        "  train_losses=np.zeros(epochs)\n",
        "  test_losses=np.zeros(epochs)\n",
        "\n",
        "  for it in range(epochs):\n",
        "    t0=datetime.now()\n",
        "    train_loss=[]\n",
        "    for users, movies, targets in train_loader:\n",
        "      targets=targets.view(-1,1).float()\n",
        "\n",
        "      #move data to GPU\n",
        "      users, movies, targets=users.to(device), movies.to(device), targets.to(device)\n",
        "\n",
        "      #zro the optimizer\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      #forward pass\n",
        "      outputs=model(users,movies)\n",
        "      loss=criterion(outputs, targets)\n",
        "\n",
        "      #backward and optimize\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      train_loss.append(loss.item())\n",
        "    \n",
        "    #get train loss and test loss\n",
        "    train_loss=np.mean(train_loss)\n",
        "\n",
        "    test_loss=[]\n",
        "\n",
        "    for users, movies, targets in test_loader:\n",
        "      #move data to GPU\n",
        "      users, movies, targets=users.to(device), movies.to(device), targets.to(device)\n",
        "      targets=targets.view(-1,1).float()\n",
        "    \n",
        "      #forward pass\n",
        "      outputs=model(users,movies)\n",
        "      loss=criterion(outputs, targets)\n",
        "\n",
        "      #backward and optimize\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      test_loss.append(loss.item())\n",
        "    \n",
        "    #get train loss and test loss\n",
        "    test_loss=np.mean(test_loss)\n",
        "\n",
        "    #save losses\n",
        "    train_losses[it]=train_loss\n",
        "    test_losses[it]=test_loss\n",
        "\n",
        "\n",
        "\n",
        "    print(f\"epoch {it+1}/{epochs}, train loss:{train_loss:.4f}\",\n",
        "          f\"test loss {test_loss:.4f}\")\n",
        "    \n",
        "  return train_losses, test_losses\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OzMhL-scOV9",
        "outputId": "acadd842-79ca-4b89-f866-4c6c6f883f2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 1/25, train loss:0.8170 test loss 93651.2708\n",
            "epoch 2/25, train loss:246455.5133 test loss 290998.5064\n",
            "epoch 3/25, train loss:1056107.5950 test loss 3858220.0657\n",
            "epoch 4/25, train loss:23277776.6880 test loss 26919828.6035\n",
            "epoch 5/25, train loss:54016129.7740 test loss 26626846.4449\n",
            "epoch 6/25, train loss:20415075.1703 test loss 14865865.5611\n",
            "epoch 7/25, train loss:10040242.4726 test loss 8624763.6201\n",
            "epoch 8/25, train loss:99349166.6248 test loss 96538816.2645\n",
            "epoch 9/25, train loss:1617145.0249 test loss 1450841.4101\n",
            "epoch 10/25, train loss:14.7294 test loss 10.6264\n",
            "epoch 11/25, train loss:1.1100 test loss 1.1601\n",
            "epoch 12/25, train loss:1.1278 test loss 1.1606\n",
            "epoch 13/25, train loss:1.1265 test loss 1.1602\n",
            "epoch 14/25, train loss:1.1280 test loss 1.1602\n",
            "epoch 15/25, train loss:1.1280 test loss 1.1603\n",
            "epoch 16/25, train loss:1.1257 test loss 1.1602\n",
            "epoch 17/25, train loss:1.1278 test loss 1.1604\n",
            "epoch 18/25, train loss:1.1276 test loss 1.1602\n",
            "epoch 19/25, train loss:1.1276 test loss 1.1602\n",
            "epoch 20/25, train loss:1.1279 test loss 1.1601\n",
            "epoch 21/25, train loss:1.1280 test loss 1.1613\n",
            "epoch 22/25, train loss:1.1168 test loss 1.1601\n",
            "epoch 23/25, train loss:1.1280 test loss 1.1600\n",
            "epoch 24/25, train loss:1.1226 test loss 1.1601\n",
            "epoch 25/25, train loss:1.1281 test loss 1.1601\n",
            " "
          ]
        }
      ],
      "source": [
        "%prun train_losses, test_losses=batch_gd( \\\n",
        "    model, criterion, optimizer, train_loader, test_loader, 25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "jcTgw1GHcfXk",
        "outputId": "b6bd69bf-9fc3-4408-8304-95de507cbe62"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-99e92534a80b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"test loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'ply' is not defined"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot loss per iteration\n",
        "plt.plot(train_losses, label=\"train loss\")\n",
        "plt.plot(test_losses, label=\"test loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HPbty6B9LdJJ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpfhUkm9LlKU"
      },
      "source": [
        "# Modified version of Recommenders code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "myHCDbjBLrFP"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GlLj4ewsMMkS"
      },
      "outputs": [],
      "source": [
        "#make a NN\n",
        "class Model(nn.Module):\n",
        "  def __init__(self,n_users,n_items,embed_dim,n_hidden=1024):\n",
        "    super(Model, self).__init__()\n",
        "    self.N=n_users\n",
        "    self.M=n_items\n",
        "    self.D=embed_dim\n",
        "\n",
        "    self.u_emb=nn.Embedding(self.N,self.D)\n",
        "    self.m_emb=nn.Embedding(self.M,self.D)\n",
        "    self.fc1=nn.Linear(2*self.D,n_hidden)\n",
        "    self.fc2=nn.Linear(n_hidden,1)\n",
        "\n",
        "    #set weights since N(0,1) leads to poor results\n",
        "\n",
        "    self.u_emb.weight.data=nn.Parameter(\n",
        "        torch.Tensor(np.random.randn(self.N,self.D)*0.01)\n",
        "    )\n",
        "    self.m_emb.weight.data=nn.Parameter(\n",
        "        torch.Tensor(np.random.randn(self.M,self.D)*0.01)\n",
        "    )\n",
        "\n",
        "  def forward(self, u, m):\n",
        "    u=self.u_emb(u) #output is (num_samples,D)\n",
        "    m=self.m_emb(m) #output is (num_samples, D)\n",
        "\n",
        "    #merge\n",
        "    out=torch.cat((u,m),1) # output is (num_samples,2D)\n",
        "\n",
        "    #ANN\n",
        "    out=self.fc1(out)\n",
        "    out=F.relu(out)\n",
        "    out=self.fc2(out)\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kdxrijdn17LP"
      },
      "outputs": [],
      "source": [
        "#an function to encap the training loop\n",
        "def batch_gd2(model, criterion, optimizer, train_data, test_data,epochs, bs=512):\n",
        "  train_users, train_movies, train_ratings=train_data\n",
        "  test_users, test_movies, test_ratings=test_data\n",
        "\n",
        "  train_losses=np.zeros(epochs)\n",
        "  test_losses=np.zeros(epochs)\n",
        "  #batches per epoch\n",
        "  Ntrain=len(train_users)\n",
        "  batches_per_epoch=int(np.ceil(Ntrain/bs))\n",
        "\n",
        "  for it in range(epochs):\n",
        "    t0=datetime.now()\n",
        "    train_loss=[]\n",
        "\n",
        "    #shuffle each batch\n",
        "    train_users, train_movies, train_ratings= shuffle(\n",
        "        train_users, train_movies,train_ratings\n",
        "    )\n",
        "\n",
        "\n",
        "\n",
        "    for j in range(batches_per_epoch):\n",
        "      users=train_users[j*bs:(j+1)*bs]\n",
        "      movies=train_movies[j*bs:(j+1)*bs]\n",
        "      targets=train_ratings[j*bs:(j+1)*bs]\n",
        "\n",
        "      #convert to tensor\n",
        "      users=torch.from_numpy(users).long()\n",
        "      movies=torch.from_numpy(movies).long()\n",
        "      targets=torch.from_numpy(targets)\n",
        "     \n",
        "      targets=targets.view(-1,1).float()\n",
        "\n",
        "      #move data to GPU\n",
        "      users, movies, targets=users.to(device), movies.to(device), targets.to(device)\n",
        "\n",
        "      #zro the optimizer\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      #forward pass\n",
        "      outputs=model(users,movies)\n",
        "      loss=criterion(outputs, targets)\n",
        "\n",
        "      #backward and optimize\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      train_loss.append(loss.item())\n",
        "    \n",
        "    #get train loss and test loss\n",
        "    train_loss=np.mean(train_loss)\n",
        "\n",
        "    test_loss=[]\n",
        "\n",
        "    for j in range(int(np.ceil(len(test_users)/bs))):\n",
        "\n",
        "      users=train_users[j*bs:(j+1)*bs]\n",
        "      movies=train_movies[j*bs:(j+1)*bs]\n",
        "      targets=train_ratings[j*bs:(j+1)*bs]\n",
        "\n",
        "      #convert to tensor\n",
        "      users=torch.from_numpy(users).long()\n",
        "      movies=torch.from_numpy(movies).long()\n",
        "      targets=torch.from_numpy(targets)\n",
        "\n",
        "      #move data to GPU\n",
        "      users, movies, targets=users.to(device), movies.to(device), targets.to(device)\n",
        "      targets=targets.view(-1,1).float()\n",
        "    \n",
        "      #forward pass\n",
        "      outputs=model(users,movies)\n",
        "      loss=criterion(outputs, targets)\n",
        "\n",
        "      #backward and optimize\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      test_loss.append(loss.item())\n",
        "    \n",
        "    #get train loss and test loss\n",
        "    test_loss=np.mean(test_loss)\n",
        "\n",
        "    #save losses\n",
        "    train_losses[it]=train_loss\n",
        "    test_losses[it]=test_loss\n",
        "\n",
        "\n",
        "\n",
        "    print(f\"epoch {it+1}/{epochs}, train loss:{train_loss:.4f}\",\n",
        "          f\"test loss {test_loss:.4f}\")\n",
        "    \n",
        "  return train_losses, test_losses\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VTJU9U4G4F7-"
      },
      "outputs": [],
      "source": [
        "#shuffke the data in corresponding order\n",
        "user_ids, movie_ids, ratings=shuffle(user_ids, movie_ids,ratings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMtnHhwE4ko5",
        "outputId": "b6dfb847-7af1-4862-f8ba-b4a46308204f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 1/10, train loss:0.7271 test loss 1715.2385\n",
            "epoch 2/10, train loss:1495.9709 test loss 8966.0168\n",
            "epoch 3/10, train loss:4745.8130 test loss 49135.3399\n",
            "epoch 4/10, train loss:7310.5154 test loss 9797.6144\n",
            "epoch 5/10, train loss:6223.0243 test loss 8066.9924\n"
          ]
        }
      ],
      "source": [
        "#make datasets\n",
        "Ntrain=int(0.8*len(ratings))\n",
        "train_users=user_ids[:Ntrain]\n",
        "train_movies=movie_ids[:Ntrain]\n",
        "train_ratings=ratings[:Ntrain]\n",
        "\n",
        "test_users=user_ids[Ntrain:]\n",
        "test_movies=movie_ids[Ntrain:]\n",
        "test_ratings=ratings[Ntrain:]\n",
        "\n",
        "train_losses, test_losses=batch_gd2(\n",
        "    model,\n",
        "    criterion,\n",
        "    optimizer,\n",
        "    (train_users, train_movies, train_ratings),\n",
        "    (test_users, test_movies, test_ratings),\n",
        "    10\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D8fGZPtn54Gx"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOXP6OEZX6EIeJXq83SSgu4",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}