1-CandidateGenerators.ipynb

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        "!pip install -q tensorflow-recommenders\n",
        "!pip install -q --upgrade tensorflow-datasets\n",
        "!pip install -q scann\n",
        "!gdown https://drive.google.com/uc?id=1AzGFgJjNdlQ8LI5PXbeuxr21tyJinkv0\n",
        "!unzip hmdata.zip\n",
        "\n",
        "from IPython.display import clear_output\n",
        "import pandas as pd\n",
        "pd.options.display.max_columns = 999\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
        "import tensorflow_recommenders as tfrs\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from tqdm.notebook import tqdm\n",
        "from pathlib import Path\n",
        "from typing import Dict, Text\n",
        "import random\n",
        "from collections import Counter\n",
        "import gc\n",
        "from datetime import timedelta\n",
        "\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "Welcome to the week 1 project of the Personalized Recommendations at Scale course! In this week's project, we’ll look at one key component of multi-stage recommender system -- the candidate generator. As you can probably guess from the name, multi-stage recommender systems consist of multiple stages. To walk through how these systems work, let’s focus on a typical large scale recommendation system. The process starts with candidate generation, when the recommendation system takes a large corpus of content and cuts it down to a sample recommendation palette. The model for this stage optimizes for recall, because if there’s an option the user might like, we want it to be identified here. The goal of candidate generation is to cut down the number of content options from millions to hundreds.\n",
        "\n",
        "The candidates generated in this phase are then passed to the ranking phase. In the ranking phase, the recommendations are ranked based on how appropriate they are for the user. This stage often involves feeding additional information to the system, like user history and content features. It might also incorporate other candidates' sources, such as new content or time-specific recommendations. The best k-ranked are then returned and used to populate the recommendation list. A visualization of this process looks like is shown below:\n"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "![multi-stage system](https://miro.medium.com/max/1400/1*6LG9QN2XEtK6UCOZG4cavA.png)"
      ]
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "With this background, let's dive deep into the candidate generator topic!"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "# Candidate Generation\n",
        "\n",
        "When trying to recommend items to a user, we typically start with a huge catalog of options! As a result, our first step prior to ranking our recommendations is to generate a selection of candidates. In particular, we’ll use a mixture of various sources to pool a large catalog of millions of items to pick a smaller subset of options from. A heavy ranker cannot deal with and rank tens of millions of items due to latency requirements. A candidate generator comes in to help select a smaller pool of candidate items from a larger pool of millions of items which the ranker can then rank. As such, such candidate generators are often referred to as “light ranker”. An important property of the candidate generator is to fetch a sufficiently representative set of candidate items for the ranker to score – this means that the candidate generator is often evaluated on recall, and not precision. Precision of the selected candidates becomes ranker’s responsibility, and the candidate generator simply has to ensure it is selecting a representative set of candidate items the user might be interested in, and thus recall as an evaluation measure becomes an important metric for evaluating CGs.\n",
        "\n",
        "In this week's project, we work on H&M's fashion recommendation dataset, and implement a few candidate generators and evaluate their performance on recall metric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHXb2qnClxIH"
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      "source": [
        "# **H&M Personalized Fashion Recommendations**\n",
        "\n",
        "In this dataset, we are given the purchase history of customers across time, along with supporting metadata. The high level recommendation task is to predict what articles each customer will purchase in the test period immediately after the training data ends. Customer who did not make any purchase during that time are excluded from the scoring.\n",
        "\n",
        "**articles.csv** - detailed metadata for each article_id available for purchase\n",
        "\n",
        "**customers.csv** - metadata for each customer_id in dataset\n",
        "\n",
        "**sample_submission.csv** - a sample submission file in the correct format\n",
        "\n",
        "**transactions_train.csv** - the training data, consisting of the purchases each customer for each date, as well as additional information. Duplicate rows correspond to multiple purchases of the same item. Your task is to predict the article_ids each customer will purchase during the 7-day period immediately after the training data period.\n",
        "\n",
        "We will first load the datasets, and perform exploratory data analysis to better understand the dataset we are dealing with, before diving further into the candidate generators.\n",
        "\n",
        "Download the dataset hmdata.zip from https://drive.google.com/drive/folders/10LGZMgXRuz2qPr_QDbYdVVlKEcnQ25YL?usp=sharing"
      ]
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      "execution_count": 2,
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      "source": [
        "article_df = pd.read_csv(\"hmdata/articles.csv.zip\")\n",
        "customer_df = pd.read_csv(\"hmdata/customers.csv.zip\")\n",
        "train0 = pd.read_csv('hmdata/transactions_train.csv.zip')\n",
        "transaction_df = train0\n",
        "\n",
        "article_df.shape, customer_df.shape, train0.shape"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "# **Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(article_df.head(2))\n",
        "display(customer_df.head(2))\n",
        "display(train0.head(2))"
      ],
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              "0   108775015        108775  Strap top              253          Vest top   \n",
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              "   product_group_name  graphical_appearance_no graphical_appearance_name  \\\n",
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              "0  Ladieswear               1       Ladieswear          16   \n",
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              "      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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b32b6556-d0e5-4f4e-9ea5-52fb6061b5aa 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-b32b6556-d0e5-4f4e-9ea5-52fb6061b5aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        t_dat                                        customer_id  article_id  \\\n",
              "0  2018-09-20  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...   663713001   \n",
              "1  2018-09-20  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...   541518023   \n",
              "\n",
              "      price  sales_channel_id  \n",
              "0  0.050831                 2  \n",
              "1  0.030492                 2  "
            ],
            "text/html": [
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              "  <div id=\"df-3321edcd-4eda-45d1-8db5-51dccb5aec98\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t_dat</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>article_id</th>\n",
              "      <th>price</th>\n",
              "      <th>sales_channel_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
              "      <td>663713001</td>\n",
              "      <td>0.050831</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
              "      <td>541518023</td>\n",
              "      <td>0.030492</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3321edcd-4eda-45d1-8db5-51dccb5aec98')\"\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-3321edcd-4eda-45d1-8db5-51dccb5aec98 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-3321edcd-4eda-45d1-8db5-51dccb5aec98');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7bVbj3UqEB29",
        "outputId": "4a672fcb-c444-4484-cdf6-546843f60920"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1296 with 1 Axes>"
            ],
            "image/png": 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ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "f, ax = plt.subplots(figsize=(15, 7))\n",
        "ax = sns.histplot(data=article_df, y='index_name', color='blue')\n",
        "ax.set_xlabel('count by index name')\n",
        "ax.set_ylabel('index name')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "f, ax = plt.subplots(figsize=(15, 7))\n",
        "ax = sns.histplot(data=article_df, y='garment_group_name', color='orange', hue='index_group_name', multiple=\"stack\")\n",
        "ax.set_xlabel('count by garment group')\n",
        "ax.set_ylabel('garment group')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "sns.set_style(\"darkgrid\")\n",
        "f, ax = plt.subplots(figsize=(10,5))\n",
        "ax = sns.histplot(data=customer_df, x='age', bins=50, color='green')\n",
        "ax.set_xlabel('Distribution of the customers age')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "articles_for_merge = article_df[['article_id', 'prod_name', 'product_type_name', 'product_group_name', 'index_name']]\n",
        "articles_for_merge = transaction_df[['customer_id', 'article_id', 'price', 't_dat']]\\\n",
        "                        .merge(articles_for_merge, on='article_id', how='left')\n",
        "sns.set_style(\"darkgrid\")\n",
        "f, ax = plt.subplots(figsize=(25,18))\n",
        "ax = sns.boxplot(data=articles_for_merge, x='price', y='product_group_name')\n",
        "ax.set_xlabel('Price outliers', fontsize=22)\n",
        "ax.set_ylabel('Index names', fontsize=22)\n",
        "ax.xaxis.set_tick_params(labelsize=22)\n",
        "ax.yaxis.set_tick_params(labelsize=22)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bv75-bvqgPeg"
      },
      "source": [
        "# **Simple Feature Processing**\n",
        "\n",
        "Having looked at some initial analysis, we next perform simple feature processing for customer and article dataframes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "K2aa3cGhvuiN",
        "outputId": "97edc9e5-a43a-4b70-f65e-c9c83d5115e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         customer_id   FN  Active  \\\n",
              "0  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...  0.0     0.0   \n",
              "1  0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...  0.0     0.0   \n",
              "2  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...  0.0     0.0   \n",
              "3  00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...  0.0     0.0   \n",
              "4  00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...  1.0     1.0   \n",
              "\n",
              "  club_member_status fashion_news_frequency   age  \\\n",
              "0             ACTIVE                   NONE  49.0   \n",
              "1             ACTIVE                   NONE  25.0   \n",
              "2             ACTIVE                   NONE  24.0   \n",
              "3             ACTIVE                   NONE  54.0   \n",
              "4             ACTIVE              Regularly  52.0   \n",
              "\n",
              "                                         postal_code age_interval  \n",
              "0  52043ee2162cf5aa7ee79974281641c6f11a68d276429a...     [46, 55]  \n",
              "1  2973abc54daa8a5f8ccfe9362140c63247c5eee03f1d93...     [16, 25]  \n",
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              "      <th>customer_id</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "def create_age_interval(x):\n",
        "    if x <= 25:\n",
        "        return [16, 25]\n",
        "    elif x <= 35:\n",
        "        return [26, 35]\n",
        "    elif x <= 45:\n",
        "        return [36, 45]\n",
        "    elif x <= 55:\n",
        "        return [46, 55]\n",
        "    elif x <= 65:\n",
        "        return [56, 65]\n",
        "    else:\n",
        "        return [66, 99]\n",
        "\n",
        "customer_df[\"FN\"].fillna(0, inplace=True)\n",
        "customer_df[\"Active\"].fillna(0, inplace=True)\n",
        "\n",
        "# Set unknown the club member status & news frequency\n",
        "customer_df[\"club_member_status\"].fillna(\"UNKNOWN\", inplace=True)\n",
        "\n",
        "customer_df[\"fashion_news_frequency\"] = customer_df[\"fashion_news_frequency\"].replace({\"None\":\"NONE\"})\n",
        "customer_df[\"fashion_news_frequency\"].fillna(\"UNKNOWN\", inplace=True)\n",
        "\n",
        "# Set missing values in age with the median\n",
        "customer_df[\"age\"].fillna(customer_df[\"age\"].median(), inplace=True)\n",
        "customer_df[\"age_interval\"] = customer_df[\"age\"].apply(lambda x: create_age_interval(x))\n",
        "\n",
        "customer_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "jar-S0bPwJ5C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "572fd47f-e6f3-4dbf-ab14-3f8b5964da72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   article_id  product_code          prod_name  product_type_no  \\\n",
              "0  0108775015        108775          Strap top              253   \n",
              "1  0108775044        108775          Strap top              253   \n",
              "2  0108775051        108775      Strap top (1)              253   \n",
              "3  0110065001        110065  OP T-shirt (Idro)              306   \n",
              "4  0110065002        110065  OP T-shirt (Idro)              306   \n",
              "\n",
              "  product_type_name  product_group_name  graphical_appearance_no  \\\n",
              "0          Vest top  Garment Upper body                  1010016   \n",
              "1          Vest top  Garment Upper body                  1010016   \n",
              "2          Vest top  Garment Upper body                  1010017   \n",
              "3               Bra           Underwear                  1010016   \n",
              "4               Bra           Underwear                  1010016   \n",
              "\n",
              "  graphical_appearance_name  colour_group_code colour_group_name  \\\n",
              "0                     Solid                  9             Black   \n",
              "1                     Solid                 10             White   \n",
              "2                    Stripe                 11         Off White   \n",
              "3                     Solid                  9             Black   \n",
              "4                     Solid                 10             White   \n",
              "\n",
              "   perceived_colour_value_id perceived_colour_value_name  \\\n",
              "0                          4                        Dark   \n",
              "1                          3                       Light   \n",
              "2                          1                 Dusty Light   \n",
              "3                          4                        Dark   \n",
              "4                          3                       Light   \n",
              "\n",
              "   perceived_colour_master_id perceived_colour_master_name  department_no  \\\n",
              "0                           5                        Black           1676   \n",
              "1                           9                        White           1676   \n",
              "2                           9                        White           1676   \n",
              "3                           5                        Black           1339   \n",
              "4                           9                        White           1339   \n",
              "\n",
              "  department_name index_code        index_name  index_group_no  \\\n",
              "0    Jersey Basic          A        Ladieswear               1   \n",
              "1    Jersey Basic          A        Ladieswear               1   \n",
              "2    Jersey Basic          A        Ladieswear               1   \n",
              "3  Clean Lingerie          B  Lingeries/Tights               1   \n",
              "4  Clean Lingerie          B  Lingeries/Tights               1   \n",
              "\n",
              "  index_group_name  section_no            section_name  garment_group_no  \\\n",
              "0       Ladieswear          16  Womens Everyday Basics              1002   \n",
              "1       Ladieswear          16  Womens Everyday Basics              1002   \n",
              "2       Ladieswear          16  Womens Everyday Basics              1002   \n",
              "3       Ladieswear          61         Womens Lingerie              1017   \n",
              "4       Ladieswear          61         Womens Lingerie              1017   \n",
              "\n",
              "  garment_group_name                                        detail_desc  \n",
              "0       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
              "1       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
              "2       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
              "3  Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  \n",
              "4  Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  "
            ],
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              "      <td>1010016</td>\n",
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              "      <td>16</td>\n",
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              "      <td>Jersey top with narrow shoulder straps.</td>\n",
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              "      <td>0108775051</td>\n",
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              "      <td>Jersey top with narrow shoulder straps.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0110065001</td>\n",
              "      <td>110065</td>\n",
              "      <td>OP T-shirt (Idro)</td>\n",
              "      <td>306</td>\n",
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              "      <td>1010016</td>\n",
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              "      <td>9</td>\n",
              "      <td>Black</td>\n",
              "      <td>4</td>\n",
              "      <td>Dark</td>\n",
              "      <td>5</td>\n",
              "      <td>Black</td>\n",
              "      <td>1339</td>\n",
              "      <td>Clean Lingerie</td>\n",
              "      <td>B</td>\n",
              "      <td>Lingeries/Tights</td>\n",
              "      <td>1</td>\n",
              "      <td>Ladieswear</td>\n",
              "      <td>61</td>\n",
              "      <td>Womens Lingerie</td>\n",
              "      <td>1017</td>\n",
              "      <td>Under-, Nightwear</td>\n",
              "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0110065002</td>\n",
              "      <td>110065</td>\n",
              "      <td>OP T-shirt (Idro)</td>\n",
              "      <td>306</td>\n",
              "      <td>Bra</td>\n",
              "      <td>Underwear</td>\n",
              "      <td>1010016</td>\n",
              "      <td>Solid</td>\n",
              "      <td>10</td>\n",
              "      <td>White</td>\n",
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              "      <td>White</td>\n",
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              "      <td>Womens Lingerie</td>\n",
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              "      <td>Under-, Nightwear</td>\n",
              "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f67ac94c-ba36-4867-8546-90c3b37b9dcc')\"\n",
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              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-f67ac94c-ba36-4867-8546-90c3b37b9dcc 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-f67ac94c-ba36-4867-8546-90c3b37b9dcc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Replace missing values\n",
        "article_df.fillna(value=\"No Description\", inplace=True)\n",
        "\n",
        "article_df['article_id'] = article_df['article_id'].astype(str)\n",
        "article_df['article_id'] = article_df['article_id'].apply(lambda x: x.zfill(10))\n",
        "\n",
        "article_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PX_iGGy1_x0m"
      },
      "source": [
        "We subselect 1 month worth of data to use for all the exercises this week."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "0EyIFbK52-th",
        "outputId": "ba3587ef-4653-450f-9bf7-096fbde08c8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1155933, 5)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if __name__ == '__main__':\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               t_dat                                        customer_id  \\\n",
              "30632391  2020-08-23  001cb0cd244c58fa35f9eb21f995b9e2f3e0c67c4e89af...   \n",
              "30632392  2020-08-23  003a8444fce07c006258eb969943a4f1ff14c696af8c1f...   \n",
              "30632393  2020-08-23  003a8444fce07c006258eb969943a4f1ff14c696af8c1f...   \n",
              "30632394  2020-08-23  003e750f71b6b1a81b561d50b7c76620c8c1f6b6a91981...   \n",
              "30632395  2020-08-23  0050be3089108d675eaa2e173b9a045a33ee7c25acfafc...   \n",
              "\n",
              "          article_id     price  sales_channel_id  \n",
              "30632391  0464297031  0.016932                 1  \n",
              "30632392  0797988001  0.025407                 2  \n",
              "30632393  0832307003  0.038119                 2  \n",
              "30632394  0761696004  0.042356                 2  \n",
              "30632395  0743633001  0.050831                 1  "
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "N_DAYS_TRAIN = 30\n",
        "\n",
        "max_date = train0['t_dat'].max()\n",
        "train0 = train0[(train0['t_dat']>=((pd.to_datetime(max_date) - timedelta(days=N_DAYS_TRAIN)).date().strftime('%Y-%m-%d')))]\n",
        "gc.collect()\n",
        "\n",
        "# add 0 in article_id column (string)\n",
        "train0['article_id'] = train0['article_id'].astype(str)\n",
        "train0['article_id'] = train0['article_id'].apply(lambda x: x.zfill(10))\n",
        "\n",
        "print(train0.shape)\n",
        "train0.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNMmxdtE_x0n"
      },
      "source": [
        "Having selected this dataset, lets get to work. We will begin by describing the key metric we will use to evaluate the candidate generators, and then move on to discuss few candidate generator techniques. Finally, we will finish the project by comparing the recall metrics across the three candidate generators"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "khvwUVsb_x0n"
      },
      "source": [
        "# Key metric for candidate generation: _recall@k_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPCvH143_x0n"
      },
      "source": [
        "The goal of the candidate generator is to fetch enough reevant candidates for the ranker to rank. Indeed, the ranker works on top of the output of the candidate generator -- if the candidate generator fetches relevant candidates, the ranker can do a better job at ranking these candidates. However, if the candidate generator misses out on a large set of relevant items, the ranker cannot rank them since it doesn't have access to good candidate items.\n",
        "\n",
        "Given such a role of the candidate generator, recall becomes the key metric with which we evaluate different candidate generators. Intuitively, *Recall* = of all relevant items, how many made it into the selected list?\n",
        "\n",
        "_Recall@k_ is the proportion of relevant items found in the top-k selected candidates.\n",
        "\n",
        "Mathematically recall@k is defined as follows:\n",
        "\n",
        "_Recall@k = (# of recommended items @k that are relevant) / (total # of relevant items)_\n",
        "\n",
        "Suppose we have a user who will make 5 purchases next month, and we wish to make 10 recommendations to this user on the homepage from among the set of 1 million products. Further suppose we have two candidate generators (CG-A and CG-B), each returning 100 candidates that the ranker can consumer and select the 10 recommendations. If CG-A is able to retrieve 3 of these 5 products amongst its set of 100 candidates, and CG-B is able to retrieve only 2 of the 5 products amongst its set of 100 candidates, then CG-A would have a higher recall than CG-B.\n",
        "Recall for CG-A = 3/5\n",
        "Recall for CG-B = 2/5\n",
        "\n",
        "\n",
        "Lets implement this recall metric in a simple function and try it out."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "DBQYei4K_x0o",
        "outputId": "23fb1e3c-8ab0-4d36-d42d-0ece11d7eea8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for cg1:  0.4\n",
            "recall for cg1:  0.2\n"
          ]
        }
      ],
      "source": [
        "# Recall@k = (# of recommended items @k that are relevant) / (total # of relevant items)\n",
        "def intersection(lst1, lst2):\n",
        "    return list(set(lst1) & set(lst2))\n",
        "\n",
        "def estimateRecall(cg, purchase):\n",
        "    return len(intersection(cg,purchase))/len(purchase)\n",
        "\n",
        "\n",
        "purchases = [\"item45\",\"item97\",\"item71\",\"item125\",\"item5\"]\n",
        "cg1 = [\"item1\",\"item97\",\"item12\",\"item105\",\"item5\",\"item17\",\"item197\",\"item122\",\"item85\",\"item15\"]\n",
        "cg2 = [\"item13\",\"item94\",\"item14\",\"item15\",\"item5\",\"item18\",\"item197\",\"item132\",\"item86\",\"item65\"]\n",
        "print(\"recall for cg1: \",estimateRecall(cg1,purchases))\n",
        "print(\"recall for cg1: \",estimateRecall(cg2,purchases))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5Rgk8-agyiv"
      },
      "source": [
        "# **Traditional Candidate Generation**\n",
        "\n",
        "With the metric in place, now lets look into a few obvious candiate generators.\n",
        "\n",
        "> **1. Candidate Genertator 1: random products**\n",
        "The simplest candidate generator to start with is a random candidate selector -- from amongst the list of products, randomly select few products.\n",
        "\n",
        "\n",
        "> **2. Candidate Genertator 2: Top most popular products**\n",
        "Another simple candidate selection strategy is to select the top-k most popular products from historic interactions and use this list as the set of candidates for the downstream ranking goal.\n",
        "\n",
        "\n",
        "> **3. Candidate Genertator 3: Two-tower model CG**\n",
        "One of most popular, and industry-wide used CGs is a two-tower neural network. This is a learnt deep model with one tower for the user and another tower for the product, followed by a dot product. We describe this model in detail further down the notebook.\n",
        "\n",
        "\n",
        "Below we will implement each of these three candidate generators and compare their performace in terms of recall metric.\n",
        "\n",
        "In order to have a clean evaluation set-up, we will divide the transactions data we have into train and test split. We look at one year's of purchases and use the first 9 months as training data and the remaining three months as the test data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "PQR3Vlmng3kj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1346e0e6-7d72-40c6-9eff-2f987c434718"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((889569, 5), (266364, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "N_DAYS_TEST = 7\n",
        "\n",
        "train = train0[(train0['t_dat']>=((pd.to_datetime(max_date) - timedelta(days=N_DAYS_TRAIN)).date().strftime('%Y-%m-%d')))\n",
        "                  & (train0['t_dat']<((pd.to_datetime(max_date) - timedelta(days=N_DAYS_TEST)).date().strftime('%Y-%m-%d')))]\n",
        "test = train0[(train0['t_dat']>=((pd.to_datetime(max_date) - timedelta(days=N_DAYS_TEST)).date().strftime('%Y-%m-%d')))]\n",
        "\n",
        "train.shape, test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9TqihtY_x0o"
      },
      "source": [
        "For ease of evaluation, without any loss of generalization, we only consider customers that have made a purchase in both train and test time-periods. The methods and set-up remain the same for all other customers as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "BGWgKyUm_x0o",
        "outputId": "be22c159-3278-40d5-f791-4cf0e5ede68e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(205995, 75481, 30857)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "c1 = train['customer_id'].unique().tolist()\n",
        "c2 = test['customer_id'].unique().tolist()\n",
        "\n",
        "commonUsers = intersection(c1, c2)\n",
        "\n",
        "len(c1), len(c2), len(commonUsers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kY7SmMzj_x0p"
      },
      "source": [
        "Here commonUsers represent the set of users for whom we would be interested in generating candidates."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "scrolled": true,
        "tags": [],
        "id": "J-zUol_h_x0p"
      },
      "outputs": [],
      "source": [
        "# Candidate Generator 1: random products\n",
        "def getKCandidatesRandom(u,k):\n",
        "    return random.sample(train['article_id'].to_list(),k)\n",
        "\n",
        "# Candidate Generator 2: top-k popular products based on historic interactions\n",
        "def getTopKCandidatesPopular(u,k):\n",
        "    c = Counter(train['article_id'].to_list())\n",
        "    topPopular = [article for article, count in c.most_common(k)]\n",
        "    return topPopular\n",
        "\n",
        "# with the 2 CGs defined, we can now query these CGs for a set of users,\n",
        "# and compute the recall metrics to see how well they perform.\n",
        "def runCandidateGeneration(method,k):\n",
        "    overallRecall = 0\n",
        "    recallList = []\n",
        "    i=0\n",
        "    userSet = commonUsers[0:10]\n",
        "    for u in tqdm(userSet):\n",
        "        purchaseList = test[test['customer_id']==u]['article_id'].to_list()\n",
        "        cg = method(u,k)\n",
        "        r = estimateRecall(cg,purchaseList)\n",
        "        overallRecall+=r\n",
        "        recallList.append(r)\n",
        "    overallRecall = overallRecall/len(userSet)\n",
        "    return (overallRecall,recallList)\n",
        "\n",
        "def runCG(method):\n",
        "    for K in [100, 1000]:\n",
        "        r, rlist = runCandidateGeneration(method, k=K)\n",
        "        print(f\"recall for candidate generation at k={K} candidates fetched: {r}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "JlLbEOpi_x0p",
        "outputId": "b751b03a-5ead-4ceb-f16a-d4496ceff07f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
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            "9ef3780d9ad44bcda6d9f64f13025d0f",
            "dea5681296d94c99a4baff259331fc24",
            "236d72e893844b8cbe8f0f54c6329b0b",
            "54f93728b0b449409ae79a688ab4add9",
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            "431db61b03f54e178db1fceb229c1c3c",
            "50e0dbca3c404eeabcb23877059ec044",
            "214a4cdab1a74980b5585d243a46cdab",
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            "ae9815fd2a7847a39355d3ac5cb9dd85",
            "4e35df7257a14bafb920aa9de76b6309",
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            "24abfa76437646599d7955ae3cb63317",
            "e7affeec84774111ba1e015f38ad9b30"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0db37604faa47bbba54f2a51e807986"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=100 candidates fetched: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd906f97dac94630bf714fb8f9c3870c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=1000 candidates fetched: 0.15409090909090908\n"
          ]
        }
      ],
      "source": [
        "# Running metric calculations for the CG 1: random CG, to fetch 100 and 1000 candidates.\n",
        "runCG(getKCandidatesRandom)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "uPnrQA1j_x0p",
        "outputId": "4af6a846-38eb-47a6-bf29-d3d1fd7d5c89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "511aa0e64abe47b4a31edb1a349d3594",
            "666959ca38d444dcaa52379164ed4d43",
            "78c1ae0b56a94942947e113136f7d290",
            "d76dc423ef0c48dd988fe1bf90535391",
            "43031654eb824eed81d881bf1ac9c38c",
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            "5d4ea0099d1a49f69a91f557402c8e3c",
            "7dd4f00269054b13adf247489972903a",
            "a97a6624bf1d40f0bc90ef536bc7a92d",
            "99de128b037a46b69dccf37475d865a2",
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            "65abb5c4fe7847e28843ba8690446aa4",
            "92375809974b4005865d3a137afeff00"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "511aa0e64abe47b4a31edb1a349d3594"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=100 candidates fetched: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67a0f03ee4894c12bb6c13aece9893ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=1000 candidates fetched: 0.2590909090909091\n"
          ]
        }
      ],
      "source": [
        "# Running metric calculations for CG 2: top popular products CG, to fetch 100 and 1000 candidates.\n",
        "runCG(getTopKCandidatesPopular)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGoeKOhv_x0q"
      },
      "source": [
        "We notice that the recall@k metric is significantly higher for popular CG than for random CG, which is as per our expectations.\n",
        "\n",
        "Both these CGs are non-learnt, and generic across all users. We now switch to discuss a learnt CG model -- the two-tower model which we covered in the Monday lecture."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nijeGmCYgU0d"
      },
      "source": [
        "# **Two Tower Model: Candidate Generator**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10OghI3zkkq_"
      },
      "source": [
        "![Screenshot 2022-05-16 at 21.33.58.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0Yn8awB_x0s"
      },
      "source": [
        "A two tower model is a neural network that is made up of two separate sub-models, one that learns representations for the users, and one that learns representations for candidate objects. This information is then combined in a single layer to produce a prediction of whether a user will like an item. This is currently one of the best-performing models for candidate generation, as it learns information from both the user and the items. Above, you can see a  visualization of how this process works.\n",
        "\n",
        "This approach is useful for candidate generation across a wide variety of industries, from online retail to social media! With one tower representing the users and the other representing the items, the system can learn a lot about whether a user might like a particular item. These models are trained using implicit signals – YouTube, for example, might consider how many seconds a user streamed a video, or how they interacted with it. The learned information can then be combined to produce the final prediction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "kOYUvr6qwjo7"
      },
      "outputs": [],
      "source": [
        "embedding_dimension = 64\n",
        "\n",
        "unique_customer_ids = customer_df.customer_id.unique()\n",
        "unique_article_ids = article_df.article_id.unique()\n",
        "\n",
        "customer_model = tf.keras.Sequential([\n",
        "  tf.keras.layers.StringLookup(\n",
        "      vocabulary=unique_customer_ids, mask_token=None),  \n",
        "  tf.keras.layers.Embedding(len(unique_customer_ids) + 1, embedding_dimension),\n",
        "  tf.keras.layers.Dense(embedding_dimension, activation='relu')\n",
        "])\n",
        "\n",
        "article_model = tf.keras.Sequential([\n",
        "  tf.keras.layers.StringLookup(\n",
        "      vocabulary=unique_article_ids, mask_token=None),\n",
        "  tf.keras.layers.Embedding(len(unique_article_ids) + 1, embedding_dimension),\n",
        "  tf.keras.layers.Dense(embedding_dimension, activation='relu')\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASlwXtJkhoS-"
      },
      "source": [
        "### **Metric: FactorizedTopK**\n",
        "To figure out how good our model is, we need to compare the affinity score that the model calculates for this pair to the scores of all the other possible candidates: if the score for the positive pair is higher than for all other candidates, our model is highly accurate.\n",
        "\n",
        "To do this, we can use the tfrs.metrics.FactorizedTopK metric. The metric has one required argument: the dataset of candidates that are used as implicit negatives for evaluation.\n",
        "\n",
        " The default metric is top K categorical accuracy: how often the true candidate is in the top K candidates for a given query."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "6dWNaLjZwxM0"
      },
      "outputs": [],
      "source": [
        "article_ds = tf.data.Dataset.from_tensor_slices(dict(article_df[['article_id']]))\n",
        "articles = article_ds.map(lambda x: x['article_id'])\n",
        "\n",
        "class HandMModel(tfrs.Model):\n",
        "    \n",
        "    def __init__(self, customer_model, article_model):\n",
        "        super().__init__()\n",
        "        self.article_model: tf.keras.Model = article_model\n",
        "        self.customer_model: tf.keras.Model = customer_model\n",
        "        self.task = tfrs.tasks.Retrieval(\n",
        "        metrics=tfrs.metrics.FactorizedTopK(\n",
        "            candidates=articles.batch(128).map(self.article_model),            \n",
        "            ),\n",
        "        )        \n",
        "\n",
        "    def compute_loss(self, features: Dict[str, tf.Tensor], training=False) -> tf.Tensor:\n",
        "    \n",
        "        customer_embeddings = self.customer_model(features[\"customer_id\"])    \n",
        "        article_embeddings = self.article_model(features[\"article_id\"])\n",
        "\n",
        "        # The task computes the loss and the metrics.\n",
        "        return self.task(customer_embeddings, article_embeddings,compute_metrics=not training)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvG8v6GZmQxm"
      },
      "source": [
        "TFRS exposes a base model class (tfrs.models.Model) which streamlines bulding models: all we need to do is to set up the components in the __init__ method, and implement the compute_loss method, taking in the raw features and returning a loss value.\n",
        "\n",
        "The base model will then take care of creating the appropriate training loop to fit our model.\n",
        "\n",
        "\n",
        "The tfrs.Model base class is a simply convenience class: it allows us to compute both training and test losses using the same method.\n",
        "\n",
        "Under the hood, it's still a plain Keras model. You could achieve the same functionality by inheriting from tf.keras.Model and overriding the train_step and test_step functions (see the guide for details):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "wVwaz47Zi8eq"
      },
      "outputs": [],
      "source": [
        "class NoBaseClassHandMModel(tf.keras.Model):\n",
        "\n",
        "  def __init__(self, user_model, movie_model):\n",
        "    super().__init__()\n",
        "    self.movie_model: tf.keras.Model = movie_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",
        "    # Set up a gradient tape to record gradients.\n",
        "    with tf.GradientTape() as tape:\n",
        "\n",
        "      # Loss computation.\n",
        "      customer_embeddings = self.customer_model(features[\"customer_id\"])\n",
        "      positive_article_embeddings = self.article_model(features[\"article_id\"])\n",
        "      loss = self.task(customer_embeddings, positive_article_embeddings)\n",
        "\n",
        "      # Handle regularization losses as well.\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",
        "    # Loss computation.\n",
        "    customer_embeddings = self.customer_model(features[\"user_id\"])\n",
        "    positive_article_embeddings = self.article_model(features[\"movie_title\"])\n",
        "    loss = self.task(customer_embeddings, positive_article_embeddings)\n",
        "\n",
        "    # Handle regularization losses as well.\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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gv9ZuKHR_x0s"
      },
      "source": [
        "After defining the model, we can use standard Keras fitting and evaluation routines to fit and evaluate the model.\n",
        "\n",
        "Let's first instantiate the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "-VmUOnE9xChq"
      },
      "outputs": [],
      "source": [
        "model = HandMModel(customer_model, article_model)\n",
        "model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DetEIfIb_X72"
      },
      "source": [
        "Then lets shuffle, batch, and cache the training and evaluation data, and train the model on the training data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu-_dT1xxdub",
        "outputId": "e8e151d1-09af-4ba9-9b3b-28a3ed2c1659"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "3475/3475 [==============================] - 20s 5ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 1322.2602 - regularization_loss: 0.0000e+00 - total_loss: 1322.2602\n",
            "Epoch 2/4\n",
            "3475/3475 [==============================] - 17s 5ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 1023.1233 - regularization_loss: 0.0000e+00 - total_loss: 1023.1233\n",
            "Epoch 3/4\n",
            "3475/3475 [==============================] - 16s 5ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 744.6005 - regularization_loss: 0.0000e+00 - total_loss: 744.6005\n",
            "Epoch 4/4\n",
            "3475/3475 [==============================] - 16s 5ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 567.1854 - regularization_loss: 0.0000e+00 - total_loss: 567.1854\n"
          ]
        }
      ],
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices(dict(train[['customer_id','article_id']])).shuffle(100_000).batch(256).cache()\n",
        "test_ds = tf.data.Dataset.from_tensor_slices(dict(test[['customer_id','article_id']])).batch(256).cache()\n",
        "\n",
        "num_epochs = 4\n",
        "history = model.fit(train_ds, epochs=num_epochs, verbose=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLF3oKiSmoW8"
      },
      "source": [
        "As the model trains, the loss is falling and a set of top-k retrieval metrics is updated. These tell us whether the true positive is in the top-k retrieved items from the entire candidate set. For example, a top-5 categorical accuracy metric of 0.2 would tell us that, on average, the true positive is in the top 5 retrieved items 20% of the time.\n",
        "\n",
        "Finally, we can evaluate our model on the test set:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_sample = test.sample(2000).copy()\n",
        "test_ds = tf.data.Dataset.from_tensor_slices(dict(test_sample[['customer_id','article_id']])).batch(500).cache()\n",
        "model.evaluate(test_ds, return_dict=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EDnfg8hd7mnO",
        "outputId": "19697f0c-c7f5-4e96-9520-b53d7f8c32e8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 19s 5s/step - factorized_top_k/top_1_categorical_accuracy: 5.0000e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0035 - factorized_top_k/top_10_categorical_accuracy: 0.0085 - factorized_top_k/top_50_categorical_accuracy: 0.0235 - factorized_top_k/top_100_categorical_accuracy: 0.0305 - loss: 3861.4837 - regularization_loss: 0.0000e+00 - total_loss: 3861.4837\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'factorized_top_k/top_100_categorical_accuracy': 0.030500000342726707,\n",
              " 'factorized_top_k/top_10_categorical_accuracy': 0.008500000461935997,\n",
              " 'factorized_top_k/top_1_categorical_accuracy': 0.0005000000237487257,\n",
              " 'factorized_top_k/top_50_categorical_accuracy': 0.023499999195337296,\n",
              " 'factorized_top_k/top_5_categorical_accuracy': 0.0035000001080334187,\n",
              " 'loss': 3917.21923828125,\n",
              " 'regularization_loss': 0,\n",
              " 'total_loss': 3917.21923828125}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkCuBilpm4vq"
      },
      "source": [
        "### **Making predictions**\n",
        "Now that we have a model, we would like to be able to make predictions. We can use the tfrs.layers.factorized_top_k.BruteForce layer to do this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "h-3TqFv57CrM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d31073f-41d5-4d77-f63a-50c4312ca091"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations for customer: [b'0568601043' b'0858856005' b'0859805007' b'0843555003' b'0762856001'\n",
            " b'0872378001' b'0728156001' b'0816083007' b'0716670009' b'0909552002'\n",
            " b'0859808003' b'0568601026' b'0612800020' b'0912075007' b'0855769001'\n",
            " b'0790791002' b'0507909030' b'0884174001' b'0728156024' b'0813060005'\n",
            " b'0857440002' b'0771970006' b'0568601030' b'0537116012' b'0929042001'\n",
            " b'0801768001' b'0826508001' b'0826508011' b'0872924001' b'0751471034'\n",
            " b'0818030002' b'0934053001' b'0751788002' b'0841793003' b'0858565001'\n",
            " b'0874054002' b'0813538002' b'0728156018' b'0859805001' b'0842605015'\n",
            " b'0892093002' b'0912075001' b'0770315026' b'0898662001' b'0612800009'\n",
            " b'0851400007' b'0695632111' b'0429313002' b'0858856001' b'0806529003']\n"
          ]
        }
      ],
      "source": [
        "article_ds = tf.data.Dataset.from_tensor_slices(dict(article_df[['article_id']]))\n",
        "articles = article_ds.map(lambda x: x['article_id'])\n",
        "\n",
        "# Create a model that takes in raw customer features, and\n",
        "index = tfrs.layers.factorized_top_k.BruteForce(model.customer_model,k=50)\n",
        "# recommends articles out of the entire dataset.\n",
        "index.index_from_dataset(\n",
        "  tf.data.Dataset.zip((articles.batch(1000), articles.batch(1000).map(model.article_model)))\n",
        ")\n",
        "\n",
        "# Get recommendations.\n",
        "_, titles = index(tf.constant([\"00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d25f88aa139fdfc657\"]))\n",
        "print(f\"Recommendations for customer: {titles[0, :100]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHGN_3K3_x0t"
      },
      "source": [
        "Now that we have the ability to make predictions, we would like to use this model and fetch the top-k candidates for the set of customers we have used for the previous two candidate generators.\n",
        "\n",
        "Similar to the CG1 and CG2 functions, we define the two-tower CG function that takes in a customer ID and value k, and returns the list of top-k products as the list of candidate products."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "J-GLbiwr_x0t"
      },
      "outputs": [],
      "source": [
        "def getTopKCandidates2Tower(u,k):\n",
        "    # Create a model that takes in raw customer features, and\n",
        "    index = tfrs.layers.factorized_top_k.BruteForce(model.customer_model,k)\n",
        "    # recommends articles out of the entire dataset.\n",
        "    index.index_from_dataset(\n",
        "        tf.data.Dataset.zip((articles.batch(1000), articles.batch(1000).map(model.article_model)))\n",
        "    )\n",
        "    _, candidates = index(tf.constant([u]))\n",
        "    candidates = candidates.numpy().tolist()[0]\n",
        "    cg = []\n",
        "    for c in candidates:\n",
        "        cg.append(c.decode(\"utf-8\") )\n",
        "    return (cg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ls28y2uO_x0t"
      },
      "source": [
        "We can now use this function to estimate the recall metrics for this new CG, and compare with the other 2 CGs we implemented before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "skeVGLEz_x0t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "a7e31e5b18144c2085e6fd1517399db3",
            "45c56c1b6659411dbe506064951c66a4",
            "e12f10cb9c364e4e977232b73133ae2c",
            "e261a30b3e1e4cf08399c22c07011c56",
            "9bc191b94fb046b3abbdc395168e2971",
            "60e676fb9e3b4dd6a3d9e3ea851e5ef4",
            "d78e07316afa48c9b6c493cab49d12b9",
            "1863d8fad9774cfa93f4e2195531f42e",
            "a9aed8d5e6944f10b3eb0f49d315df55",
            "c821d954c26c46af81c5a435a189f20f",
            "abfb0358ecf34da0b411322f58006423",
            "269c3b24681041fa9eb650f90bc12559",
            "3e6423ee8de54b11973c1bccc4da6d2c",
            "cc75bee9a12c45e08539a3e3ba6fe9c9",
            "b4fd94e7a90c47cab8d7519732d702d4",
            "d2725f6eca454e4386d8f09781460457",
            "ea2f876211e64d11967f30721d1d2226",
            "fea6233cb59647d19d648837a23d7ea8",
            "796a113be987431db996973b19bebf86",
            "3453c37ca9bb4437ad2d6869b6e596f4",
            "cf4a192730964d3b80e04303c3b7e058",
            "63a9e9c070464b4886e15d7e7238ce9f"
          ]
        },
        "outputId": "078fb30c-899e-46ac-8267-73fbe2dac5da"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7e31e5b18144c2085e6fd1517399db3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=100 candidates fetched: 0.025\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "269c3b24681041fa9eb650f90bc12559"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=1000 candidates fetched: 0.15409090909090908\n"
          ]
        }
      ],
      "source": [
        "runCG(getTopKCandidates2Tower)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EjpHPm4m_x0t"
      },
      "source": [
        "Now lets compile the results from the three different Candidate Generators we implemented:\n",
        "\n",
        "**Recall at 100**\n",
        "- CG1:Random 0.0\n",
        "- CG2:Popular 0.0203\n",
        "- CG3: Two-tower 0.0013\n",
        "\n",
        "**Recall at 1000**\n",
        "- CG1:Random 0.057\n",
        "- CG2:Popular 0.0678\n",
        "- CG3: Two-tower 0.15\n",
        "\n",
        "We notice that the random CG performs the worst, and with just 4 epochs of training, the two-tower CG is able to give a significant improvement in recall metric for k=1000 candidates.\n",
        "\n",
        "This completes the week 1 project, where we looked at the basic implementation of the two-tower model for candidate generator. The retrieved set of candidates from the CG output forms the dataset on which sophiticated rankers operate. Due to low-latency requirements, high-capacity rankers do not operate on top of the large candidate pool of millions of items, so CGs help them by sub-selecting a smaller set of candidates for the ranker to operate upon.  In week 2 we will implement a few ranking methods that will work directly on top of the candidates that these CG generate.\n",
        "\n",
        "Important to note – the embeddings from the pen-ultimate layer of these models are typically stored for real-time retrieval. Indeed, these embeddings represent the user and items in a shared embedding space and can be consumed by various models powering downstream tasks. To store embeddings for 400 million users and over 100 million items and querying them in real time is a challenging task. This is where approximate nearest neighbor approaches step in to help. In week 3 project, we will briefly try a few such approximate NN approaches when dealing with user representations."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Goal for this week:**\n",
        "Implement two personalized non-learnt candidate generators:\n",
        "\n",
        "1.   CG personalized to user’s interest\n",
        "2.   Personalized to user’s price sensitivity\n",
        "\n",
        "Once implemented, compare and report recall-k metrics together with the results of the 2 non-personalized CG and two-tower model. Please use k=100, and k=1000 to report the metrics.\n",
        "\n",
        "**[optional] Adding features to two-tower model**\n",
        "The two-tower we implemented just uses the customer-ID and article-ID as features. It'd be good to add additional features about customer and articles as input features and see how the recall@k performance changes.\n",
        "\n"
      ],
      "metadata": {
        "id": "oVW8xloEDr0F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Candidate Generator 3: Personalized based on user's interest\n",
        "    # Recommend the K most popular items in the user's most purchased product_group_name\n",
        "\n",
        "\n",
        "# Add product_group_name to train\n",
        "articleid2productgroupname = dict(zip(article_df['article_id'], article_df['product_group_name']))\n",
        "train['product_group_name'] = train['article_id'].map(articleid2productgroupname)\n",
        "\n",
        "# Create lookup between product_group_name and ranked article_id by popularity\n",
        "productgroupname2popularitems = dict(train.groupby('product_group_name')['article_id'].apply(lambda x: x.value_counts().index))\n",
        "\n",
        "def getTopKCandidatesPopular_ProductGroupName(u,k):\n",
        "    articles = train[train['customer_id']==u]['article_id'].unique()\n",
        "    most_common_product_group_name = article_df[article_df['article_id'].isin(articles)]\\\n",
        "                                        ['product_group_name'].value_counts().index[0]\n",
        "    return productgroupname2popularitems[most_common_product_group_name][:k]\n",
        "\n",
        "\n",
        "runCG(getTopKCandidatesPopular_ProductGroupName)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "94544e75f781486483487ad909377ff0",
            "d8ad48f24c5847778de23700fa6a0053",
            "0c8f634c2c854d6296a5de81733645a2",
            "aa9585f5a27e46cba1a57bcb3e22511c",
            "20f217ccb9a24eba8630461afe417c41",
            "e6a588bba9cb4eea969dbcca8a4a3d1a",
            "19efa586d1c34910a574d00a359bd11f",
            "b1b507a770714934998bcf3f993e3ae5",
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            "cf8045b0c96c4e52b3c9c7f345b84970",
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        },
        "id": "2M8IX4AABuPm",
        "outputId": "db2c4a4f-8061-45f9-95ca-fee0eceeffab"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94544e75f781486483487ad909377ff0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=100 candidates fetched: 0.025\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "992f618da626425f8cccc792502ea87b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall for candidate generation at k=1000 candidates fetched: 0.08409090909090909\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Candidate Generator 4: Personalized based on user's price sensitivity\n",
        "     # Recommend the K most popular items in the user's bucketed mean purchase price\n",
        "\n",
        "\n",
        "def convert_mean_to_decile_bucket(x):\n",
        "    for n, mp_dec in enumerate(mean_prices_deciles):\n",
        "        if x <= mp_dec:\n",
        "            return n\n",
        "\n",
        "# Add decile price bucket to train\n",
        "mean_prices = train.groupby('customer_id')['price'].mean()\n",
        "mean_prices_deciles = np.percentile(mean_prices, np.arange(10,110,10))\n",
        "customerid2pricebucket = dict(mean_prices.apply(convert_mean_to_decile_bucket))\n",
        "train['price_bucket'] = train['price'].apply(convert_mean_to_decile_bucket)\n",
        "\n",
        "# Create lookup between decile price bucket and ranked article_id by popularity\n",
        "pricebucket2popularitems = dict(train.groupby('price_bucket')['article_id'].apply(lambda x: x.value_counts().index))\n",
        "\n",
        "def getTopKCandidatesPopular_PriceBucket(u,k):\n",
        "    user_price_bucket = customerid2pricebucket[u]\n",
        "    return pricebucket2popularitems[user_price_bucket][:k]\n",
        "\n",
        "\n",
        "runCG(getTopKCandidatesPopular_PriceBucket)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
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      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
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          "text": [
            "recall for candidate generation at k=100 candidates fetched: 0.1\n"
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            "recall for candidate generation at k=1000 candidates fetched: 0.12727272727272726\n"
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    {
      "cell_type": "code",
      "source": [
        "# OPTIONAL: Add features to the two-tower model\n",
        "# https://www.tensorflow.org/recommenders/examples/context_features\n",
        "\n",
        "\n",
        "# Create the customer model with customer_id and bucketed age\n",
        "customer_df['age_interval_str'] = customer_df['age_interval'].map(str)\n",
        "unique_age_intervals = customer_df.age_interval_str.unique()\n",
        "unique_customer_ids = customer_df.customer_id.unique()\n",
        "class CustomerModel(tf.keras.Model):\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "    self.id_embedding = tf.keras.Sequential([\n",
        "        tf.keras.layers.StringLookup(\n",
        "            vocabulary=unique_customer_ids, mask_token=None),  \n",
        "        tf.keras.layers.Embedding(len(unique_customer_ids) + 1, 64)\n",
        "    ])\n",
        "\n",
        "    self.age_interval_embedding = tf.keras.Sequential([\n",
        "        tf.keras.layers.StringLookup(\n",
        "            vocabulary=unique_age_intervals, mask_token=None),  \n",
        "        tf.keras.layers.Embedding(len(unique_age_intervals) + 1, 64)\n",
        "    ])\n",
        "\n",
        "  def call(self, features):\n",
        "    return tf.concat([\n",
        "        self.id_embedding(features['customer_id']),\n",
        "        self.age_interval_embedding(features['age_interval_str'])\n",
        "    ], axis=1)\n",
        "\n",
        "\n",
        "\n",
        "# Create the article model with article_id only\n",
        "unique_article_ids = article_df.article_id.unique()\n",
        "class ArticleModel(tf.keras.Model):\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "    self.id_embedding = tf.keras.Sequential([\n",
        "        tf.keras.layers.StringLookup(\n",
        "            vocabulary=unique_article_ids, mask_token=None),  \n",
        "        tf.keras.layers.Embedding(len(unique_article_ids) + 1, 64)\n",
        "    ])\n",
        "\n",
        "  def call(self, article_id):\n",
        "    return self.id_embedding(article_id)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Create combined model\n",
        "article_ds = tf.data.Dataset.from_tensor_slices(dict(article_df[['article_id']]))\n",
        "articles = article_ds.map(lambda x: x['article_id'])\n",
        "class HandMModel2(tfrs.models.Model):\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.customer_model = tf.keras.Sequential([\n",
        "      CustomerModel(),\n",
        "      tf.keras.layers.Dense(64, activation='relu')\n",
        "    ])\n",
        "    self.article_model = tf.keras.Sequential([\n",
        "      ArticleModel(),\n",
        "      tf.keras.layers.Dense(64, activation='relu')\n",
        "    ])\n",
        "    self.task = tfrs.tasks.Retrieval(\n",
        "        metrics=tfrs.metrics.FactorizedTopK(\n",
        "            candidates=articles.batch(128).map(self.article_model),\n",
        "        ),\n",
        "    )\n",
        "\n",
        "  def compute_loss(self, features, training=False):\n",
        "    customer_embeddings = self.customer_model(features)    \n",
        "    article_embeddings = self.article_model(features['article_id'])\n",
        "\n",
        "    return self.task(customer_embeddings, article_embeddings, compute_metrics=not training)\n",
        "\n",
        "\n",
        "\n",
        "model2 = HandMModel2()\n",
        "model2.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))"
      ],
      "metadata": {
        "id": "RVUu0sXQGrnZ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train combined model\n",
        "train = train.merge(customer_df[['customer_id', 'age_interval_str']], \n",
        "                    how='left', on='customer_id')\n",
        "test = test.merge(customer_df[['customer_id', 'age_interval_str']], \n",
        "                  how='left', on='customer_id')\n",
        "\n",
        "cols = ['customer_id','article_id', 'age_interval_str']\n",
        "train_ds2 = tf.data.Dataset.from_tensor_slices(dict(train[cols])).shuffle(100_000).batch(256).cache()\n",
        "test_ds2 = tf.data.Dataset.from_tensor_slices(dict(test[cols])).batch(256).cache()\n",
        "\n",
        "num_epochs = 4\n",
        "history = model2.fit(train_ds2, epochs=num_epochs, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqjmILZtExVJ",
        "outputId": "d036fa49-e87a-4983-f443-59d829fabc07"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "3475/3475 [==============================] - 23s 6ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 1326.2390 - regularization_loss: 0.0000e+00 - total_loss: 1326.2390\n",
            "Epoch 2/4\n",
            "3475/3475 [==============================] - 18s 5ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 1058.4692 - regularization_loss: 0.0000e+00 - total_loss: 1058.4692\n",
            "Epoch 3/4\n",
            "3475/3475 [==============================] - 21s 6ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 809.5230 - regularization_loss: 0.0000e+00 - total_loss: 809.5230\n",
            "Epoch 4/4\n",
            "3475/3475 [==============================] - 18s 5ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 635.0809 - regularization_loss: 0.0000e+00 - total_loss: 635.0809\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate new model with customer context on same test dataset\n",
        "test_sample = test_sample.merge(customer_df[['customer_id', 'age_interval_str']], \n",
        "                                how='left', on='customer_id')\n",
        "\n",
        "test_ds2 = tf.data.Dataset.from_tensor_slices(dict(test_sample[cols])).batch(500).cache()\n",
        "model2.evaluate(test_ds2, return_dict=True)"
      ],
      "metadata": {
        "id": "zkg7D-EwExYI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41d4e418-a320-4fb7-a143-11dfd1f6e179"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 25s 6s/step - factorized_top_k/top_1_categorical_accuracy: 0.0010 - factorized_top_k/top_5_categorical_accuracy: 0.0045 - factorized_top_k/top_10_categorical_accuracy: 0.0080 - factorized_top_k/top_50_categorical_accuracy: 0.0190 - factorized_top_k/top_100_categorical_accuracy: 0.0330 - loss: 3792.0612 - regularization_loss: 0.0000e+00 - total_loss: 3792.0612\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'factorized_top_k/top_100_categorical_accuracy': 0.032999999821186066,\n",
              " 'factorized_top_k/top_10_categorical_accuracy': 0.00800000037997961,\n",
              " 'factorized_top_k/top_1_categorical_accuracy': 0.0010000000474974513,\n",
              " 'factorized_top_k/top_50_categorical_accuracy': 0.01899999938905239,\n",
              " 'factorized_top_k/top_5_categorical_accuracy': 0.0044999998062849045,\n",
              " 'loss': 3854.351806640625,\n",
              " 'regularization_loss': 0,\n",
              " 'total_loss': 3854.351806640625}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run new combined model on same 10 users as other CGs\n",
        "cid2agestr = dict(zip(customer_df['customer_id'], customer_df['age_interval_str']))\n",
        "\n",
        "def getTopKCandidates2Tower2(u,k):\n",
        "    # Create a model that takes in raw customer features, and\n",
        "    index = tfrs.layers.factorized_top_k.BruteForce(model2.customer_model, k)\n",
        "    # recommends articles out of the entire dataset.\n",
        "    index.index_from_dataset(\n",
        "        tf.data.Dataset.zip((articles.batch(1000), articles.batch(1000).map(model2.article_model)))\n",
        "    )\n",
        "    _, candidates = index({'customer_id':tf.constant([u]), 'age_interval_str':tf.constant([cid2agestr[u]])})\n",
        "    candidates = candidates.numpy().tolist()[0]\n",
        "    cg = []\n",
        "    for c in candidates:\n",
        "        cg.append(c.decode(\"utf-8\") )\n",
        "    return (cg)\n",
        "\n",
        "runCG(getTopKCandidates2Tower2)"
      ],
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            "recall for candidate generation at k=100 candidates fetched: 0.1340909090909091\n"
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