ML-Fairness/fair_metrics_celeba.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAt8HaP0eCHB"
      },
      "source": [
        "# Applying fairnes metrics on the CelebA dataset\n",
        "\n",
        "<a href=\"https://colab.research.google.com/drive/1Gvyb40OXeq0RPJA5WYVyr0S7bQWBGYe9\" target=\"_blank\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\">\n",
        "</a>\n",
        "\n",
        "Return to the [Castle](https://github.com/Nkluge-correa/TeenyTinyCastle)\n",
        "\n",
        "Machine Learning Fairness is an area of research that focuses on ensuring that machine learning algorithms are fair and unbiased, while also trying to undestrand the many ways in wich such systems can discriminate. Addressing algorithmic biases is a crucial issue in ML, given that machine learning models are increasingly used in fields where their decisions can impact people's lives, such as finance, employment, and criminal justice.\n",
        "\n",
        "<img src=\"https://miro.medium.com/max/1060/1*cc8OWxqKFXje4d_1eYrQkg.jpeg\" width=\"600\"/>\n",
        "\n",
        "Source: [Moritz Hardt](https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb/).\n",
        "\n",
        "In this notebook, we will take a look at the [Celeb Faces Attributes](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset/) dataset (`CelebA`). `CelebA` contains 202,599 face images of size 178×218 from 10,177 celebrities, each annotated with 40 binary labels, indicating facial attributes like hair color, gender, and age. Here, we will train a CNN model based on the [LeNet5](https://en.wikipedia.org/wiki/LeNet) architecture.\n",
        "\n",
        "> **Note: `LeNet5` is a CNN introduced by Yann LeCun in 1989. It is one of the first Deep Learning models to recognize handwritten characters and digits.**\n",
        "\n",
        "We will train our `CNN` in a binary classification task using the `Smiling` attribute as a target, i.e., we are making a smile detector.\n",
        "\n",
        "After that, we will check if our model has a bias against the sensitive attribute `[age]`.\n",
        "\n",
        "> **Note: all datasets and models related to the course and repo are in the Hub🤗. Also, the dataset we will be using is only a subset of the original CelebA dataset, with 50K images (25K smiling, 25K not smiling).**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393,
          "referenced_widgets": [
            "27e2d7870c7e491298e810607dd3bc21",
            "d2f559ff12c7459e87be68b85e263c78",
            "7128e8a392904f759050e92e295b024b",
            "44823b93cd454974b2b200697e519cb9",
            "f22f73bd193245e08da7c1b7a97428d6",
            "4e285412e76d429bb98bda56f1d865cc",
            "4f742f07cc664ba09878faa182a89a25",
            "8e5cdbdaac1d4841b0f2aadad2ef0bec",
            "7260f7ca9ba84e08bc12995c14cb4fd5",
            "51b4f5c0a474466d912613bec074863d",
            "86a711a038d149348f2cfc40565123ec",
            "a5e891e60d6f4c0eb6eae1248dcc40a0",
            "29bb7d5459684e079763246001b0d4ad",
            "4518c77018c34a25b7da394ff84d6d6f",
            "758ec4002b7e4693a21ad9cf85441129",
            "479c957f5be34b589d9b326b64e39b22",
            "5c962cbe19bb434ca84859f875db5117",
            "cc36dddaa050494b82d6fd9fdcbb6e99",
            "d18cd9fb5ec5427498ca733f6376141a",
            "6db738031d204ef3a2876601b763e42a",
            "8c512af59ff34c99bcab877b887f0647",
            "44dedb39b6634627a76b0911bb7988ad",
            "1dab2e0787fa4f71969f5c737fd8b18a",
            "61c6e6390e4a49a78c7504da12491216",
            "6dc5909be57146fcad033a9bd202ea75",
            "9b33261598e84ddc8257f758d6297cbc",
            "b97417d751c94ce28e3a975e7cc4c95c",
            "c87f5a8db53840279a139aff7a24b0e6",
            "ddfc809d5ae743e898ae924c7813bf3d",
            "57ec4bd48e24481da0d2282eded95518",
            "79fc702743004e5eaaadc475de896fef",
            "2e0fc6940bb040df97e1c6c7187be702",
            "ff2fe76b13d5411a9292ff48cd84ccac"
          ]
        },
        "id": "JjC-kdnpICCM",
        "outputId": "6eb7e73c-2fe3-4019-cede-9b38fc69903e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "27e2d7870c7e491298e810607dd3bc21",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.85k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a5e891e60d6f4c0eb6eae1248dcc40a0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/350M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dab2e0787fa4f71969f5c737fd8b18a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install datasets -q\n",
        "from datasets import load_dataset\n",
        "\n",
        "# load the datasets from the hub\n",
        "dataset = load_dataset('AiresPucrs/CelebA-Smiles', split=\"train\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syhRGDArlAYe"
      },
      "source": [
        "Before starting our training, let us perform some pre-processing. CelebA images are 178 X 218 in size. To lower the computational cost of training our model, we will resize them to 100 X 100 pixels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "afe1adf66f724a1580439c11b1cb58ea",
            "613e3289b9de4b28802d100588ddeebb",
            "a740b741470a49ee8ba7b54e64011783",
            "b655422e8a944233a8b9c1c8a73ff1a5",
            "22dcd0b9bf18456e8aa3859ce1df9c50",
            "ff23881f93ff4966a3b389eebb34f379",
            "da4589280a544938b8b31740ac47c0a1",
            "00c403f538ea47e186038d7b8c9afe31",
            "3e0e366a6b6f4cc08e7ef6aa314779f7",
            "f8ef8cf86dab45eaa1a1c8a3ecc009b9",
            "d299b307bae448cdb082d514f790925f"
          ]
        },
        "id": "qoBxrvEclgg0",
        "outputId": "890445bd-8814-46fd-ef9c-f1623607c532"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "afe1adf66f724a1580439c11b1cb58ea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Resize images (100 x 100)\n",
        "def resize_image(examples):\n",
        "    examples[\"resized_image\"] = [image.convert(\"RGB\").resize((100,100)) for image in examples[\"image\"]]\n",
        "    return examples\n",
        "\n",
        "# Apply `resize_image` funtion to the dataset\n",
        "dataset = dataset.map(resize_image, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sv6uFjmg5NmL"
      },
      "source": [
        "Also, we will store our images in a local folder and create a new data frame where we will keep their respective paths (path to the pictures) and attributes of interest, i.e., our labels (`Smiling`) and sensitive characteristics of interest (`Young`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "kf-9sWTg2PeW",
        "outputId": "f44ea293-fa10-4d73-b39e-b2db76c29637"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 50000/50000 [03:06<00:00, 268.77it/s]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d4a2b044-1a89-4050-96db-a24f4d9b2b8e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_path</th>\n",
              "      <th>labels</th>\n",
              "      <th>sensitive_attribute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>./celebA_images/0.png</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>./celebA_images/1.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>./celebA_images/2.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>./celebA_images/3.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>./celebA_images/4.png</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>./celebA_images/49995.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>./celebA_images/49996.png</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>./celebA_images/49997.png</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>./celebA_images/49998.png</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>./celebA_images/49999.png</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4a2b044-1a89-4050-96db-a24f4d9b2b8e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d4a2b044-1a89-4050-96db-a24f4d9b2b8e 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-d4a2b044-1a89-4050-96db-a24f4d9b2b8e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-56361f79-19ca-46ea-aa7f-85f940faa25b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56361f79-19ca-46ea-aa7f-85f940faa25b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-56361f79-19ca-46ea-aa7f-85f940faa25b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                      image_path  labels  sensitive_attribute\n",
              "0          ./celebA_images/0.png       1                    1\n",
              "1          ./celebA_images/1.png       0                    1\n",
              "2          ./celebA_images/2.png       0                    1\n",
              "3          ./celebA_images/3.png       0                    1\n",
              "4          ./celebA_images/4.png       1                    1\n",
              "...                          ...     ...                  ...\n",
              "49995  ./celebA_images/49995.png       0                    1\n",
              "49996  ./celebA_images/49996.png       1                    1\n",
              "49997  ./celebA_images/49997.png       1                    1\n",
              "49998  ./celebA_images/49998.png       1                    1\n",
              "49999  ./celebA_images/49999.png       1                    1\n",
              "\n",
              "[50000 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tqdm\n",
        "import os\n",
        "\n",
        "# Create a folder to store the CelebA images\n",
        "os.makedirs('celebA_images', exist_ok=True)\n",
        "\n",
        "# Select the features we will use for this tutorial\n",
        "labels = [1 if x > 0 else 0 for x in dataset['Smiling']] # 1 = smiling\n",
        "sensitive_attribute = [1 if x > 0 else 0 for x in dataset['Young']] # 1 = young\n",
        "image_path = []\n",
        "\n",
        "# Iterate over the dataset, save images and their respective paths\n",
        "for i in tqdm.tqdm(range(len(dataset))):\n",
        "  dataset[i]['resized_image'].save(f\"./celebA_images/{i}.png\")\n",
        "  image_path.append(f\"./celebA_images/{i}.png\")\n",
        "\n",
        "# Create a new dataframe with all these features\n",
        "dataset_df = pd.DataFrame({\n",
        "    \"image_path\": image_path,\n",
        "    \"labels\": labels,\n",
        "    \"sensitive_attribute\": sensitive_attribute\n",
        "})\n",
        "\n",
        "display(dataset_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkHEFmaVeCHG"
      },
      "source": [
        "Using our new data frame, we can plot the distribution of our sensitive attribute. Then, we will see that the population of `young` samples dominates the dataset, regardless of the value of our label feature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "XxYUBRI4eCHG",
        "outputId": "8e01be7c-7738-44d2-c5b0-200d9e7a0118"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"7e2f9a3d-5747-48e8-a82d-52a69c1ebb64\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"7e2f9a3d-5747-48e8-a82d-52a69c1ebb64\")) {                    Plotly.newPlot(                        \"7e2f9a3d-5747-48e8-a82d-52a69c1ebb64\",                        [{\"name\":\"Smiling\",\"x\":[1,0],\"y\":[19086,5914],\"type\":\"bar\"},{\"name\":\"Not Smiling\",\"x\":[1,0],\"y\":[19613,5387],\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"tickmode\":\"array\",\"tickvals\":[0,1],\"ticktext\":[\"Not Young\",\"Young\"],\"title\":{\"text\":\"\\u003cb\\u003eAge\\u003c\\u002fb\\u003e\"}},\"barmode\":\"group\",\"yaxis\":{\"title\":{\"text\":\"\\u003cb\\u003eSmiling\\u003c\\u002fb\\u003e\"}},\"title\":{\"text\":\"Distribution of \\u003ci\\u003eSmiling People\\u003c\\u002fi\\u003e by \\\"Age\\\"\"},\"paper_bgcolor\":\"rgba(0, 0, 0, 0)\",\"plot_bgcolor\":\"rgba(0, 0, 0, 0)\"},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('7e2f9a3d-5747-48e8-a82d-52a69c1ebb64');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "not_smiling = []\n",
        "smiling = []\n",
        "\n",
        "for element in list(dataset_df['sensitive_attribute'].unique()):\n",
        "    temp_df = dataset_df[dataset_df['sensitive_attribute'] == element]['labels'].value_counts()\n",
        "    temp_df.sort_index(inplace=True)\n",
        "    not_smiling.append(temp_df.iloc[0])\n",
        "    smiling.append(temp_df.iloc[1])\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(name='Smiling', x=[1, 0], y=smiling),\n",
        "    go.Bar(name='Not Smiling', x=[1, 0], y=not_smiling)\n",
        "])\n",
        "\n",
        "fig.update_layout(\n",
        "    barmode='group',\n",
        "    template='plotly_dark',\n",
        "    xaxis=dict(\n",
        "        tickmode='array',\n",
        "        tickvals=[0, 1],\n",
        "        ticktext=['Not Young', 'Young']\n",
        "    ),\n",
        "    xaxis_title=\"<b>Age</b>\",\n",
        "    yaxis_title=\"<b>Smiling</b>\",\n",
        "    title='Distribution of <i>Smiling People</i> by \"Age\"',\n",
        "    paper_bgcolor='rgba(0, 0, 0, 0)',\n",
        "    plot_bgcolor='rgba(0, 0, 0, 0)',\n",
        ")\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOfJRpnIeCHH"
      },
      "source": [
        "Now, we need to split our dataset to train, validate, and verify the performance of our model. we will use `train_test_split` from `scikit-learn.`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h6NQTJseiz2u"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into train and test sets\n",
        "train_df, test_df = train_test_split(dataset_df, test_size=0.1, random_state=42, stratify=dataset_df['labels'])\n",
        "\n",
        "# Split the train set into train and validation sets\n",
        "train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=42, stratify=train_df['labels'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moK3-_-aeCHI"
      },
      "source": [
        "Now, we will create a wrapper around our three data frames to make them a generator that produces batches of images and labels. We are using the `torch.utils.data.Dataset` for this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4-jDVsoeCHI",
        "outputId": "6167aeb9-18c4-4d16-cdd3-5d48e5a7aa3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train dataset size: 40500\n",
            "Test dataset size: 5000\n",
            "Validation dataset size: 4500\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from PIL import Image\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "# Transforms image inputs into tensors\n",
        "transform = transforms.ToTensor()\n",
        "\n",
        "class Dataset(Dataset):\n",
        "    def __init__(self, dataset):\n",
        "\n",
        "        self.data = dataset\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "\n",
        "        # Retrive images\n",
        "        image_path = self.data['image_path'].iloc[index]\n",
        "        images = Image.open(image_path)\n",
        "        images = self.transform(images)\n",
        "\n",
        "        # Retrive labels\n",
        "        labels = self.data['labels'].iloc[index]\n",
        "\n",
        "        return images, labels\n",
        "\n",
        "train_data  = Dataset(train_df)\n",
        "val_data  = Dataset(val_df)\n",
        "test_data  = Dataset(test_df)\n",
        "\n",
        "\n",
        "print('Train dataset size:', len(train_data))\n",
        "print('Test dataset size:', len(test_data))\n",
        "print('Validation dataset size:', len(val_data))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ia68gnlIeCHJ"
      },
      "source": [
        "Now that we have a working dataset, let us look into some samples from our smiley version of the `CelebA` dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "c2UWU6u2eCHJ",
        "outputId": "194f971e-7b68-40ae-9791-97e21b50ba21"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1500x1500 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "plt.figure(figsize=(15,15))\n",
        "\n",
        "for i in range(4):\n",
        "\n",
        "    # Get a random sample\n",
        "    random_sample = random.randint(0, len(train_data)-1)\n",
        "\n",
        "    # Get the image and its corresponding label\n",
        "    image, label = train_data.__getitem__(random_sample)\n",
        "\n",
        "    # Plot the sample\n",
        "    plt.subplot(1,4,i+1)\n",
        "    plt.imshow(np.transpose(image.numpy(), (1, 2, 0)))\n",
        "    plt.axis('off')\n",
        "    plt.title('Smiling' if label == 1 else 'Not smiling')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KxeavIc0eCHJ"
      },
      "source": [
        "To train a model, we will use PyTorch to implement a simplified version of the `LeNet5` network (`LeNNon`). If you are still getting familiar with PyTorch, check out our [`Machine Learning Intro Course`](https://github.com/Nkluge-correa/TeenyTinyCastle/tree/master/ML-Intro-Course) or the [Pytorch tutorials](https://pytorch.org/tutorials/) section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "axNIDpuAeCHK",
        "outputId": "53712043-40a4-4a7e-8774-edf0ad4c1bcc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LeNNon(\n",
              "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (fc1): Linear(in_features=20000, out_features=128, bias=True)\n",
              "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class LeNNon(nn.Module):\n",
        "    def __init__(self):\n",
        "        \"\"\" Define a CNN architecture used for image classification.\n",
        "            This class defines the LeNNon architecture as a PyTorch module\n",
        "        \"\"\"\n",
        "        super(LeNNon, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)\n",
        "        self.fc1 = nn.Linear(32 * 25 * 25, 128)\n",
        "        self.fc2 = nn.Linear(128, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Perform a forward pass through the LeNNon architecture.\n",
        "\n",
        "        This method applies the convolutional layers, max pooling layers,\n",
        "        and fully connected layers to the input tensor x.\n",
        "\n",
        "        Parameters:\n",
        "        -----------\n",
        "        x (torch.Tensor): The input tensor.\n",
        "\n",
        "        Returns:\n",
        "        --------\n",
        "        torch.Tensor: The output tensor.\n",
        "        \"\"\"\n",
        "        x = self.pool(torch.relu(self.conv1(x)))\n",
        "        x = self.pool(torch.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 32 * 25 * 25)\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = LeNNon()\n",
        "\n",
        "model = model.to(device)\n",
        "\n",
        "display(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_YQDKZ8eCHK"
      },
      "source": [
        "Below, we will create dataloaders (`torch.utils.data.DataLoader`) for our datasets/generators, setting the batch size to 256. You should be able to run this training with almost any GPU with more than 2GB of VRAM.\n",
        "\n",
        "> **Note: there is no need to shuffle the data used in validation or testing!**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "qkBXqeLOeCHK"
      },
      "outputs": [],
      "source": [
        "batch_size = 256\n",
        "\n",
        "trainLoader = torch.utils.data.DataLoader(train_data,batch_size=batch_size, shuffle=True)\n",
        "testLoader  = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False)\n",
        "valLoader  = torch.utils.data.DataLoader(val_data, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NWQKyFzXeCHK"
      },
      "source": [
        "If everything was done accordingly, our CNN should train smoothly. If you do not wish to train this model, download our pre-trained model from the Hub! Ignore the cell below if you want to train your own model. 😎"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8FnS9SSgIG6",
        "outputId": "d8628a21-3a0b-4cb6-ee75-e7a301000864"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n",
            "Cloning into 'LeNNon-Smile-Detector'...\n",
            "remote: Enumerating objects: 18, done.\u001b[K\n",
            "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 18 (delta 6), reused 0 (delta 0), pack-reused 6\u001b[K\n",
            "Unpacking objects: 100% (18/18), 2.58 KiB | 879.00 KiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!git lfs install\n",
        "!git clone https://huggingface.co/AiresPucrs/LeNNon-Smile-Detector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iy9N6db6eCHK",
        "outputId": "68361e7e-4a7f-4750-8a51-a3847e2ff236"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 1/20: 100%|██████████| 159/159 [00:44<00:00,  3.60batch/s, Train Loss=0.458, Train Acc=0.773]\n",
            "Epoch 2/20: 100%|██████████| 159/159 [00:43<00:00,  3.68batch/s, Train Loss=0.252, Train Acc=0.894]\n",
            "Epoch 3/20: 100%|██████████| 159/159 [00:42<00:00,  3.73batch/s, Train Loss=0.227, Train Acc=0.906]\n",
            "Epoch 4/20: 100%|██████████| 159/159 [00:43<00:00,  3.69batch/s, Train Loss=0.213, Train Acc=0.912]\n",
            "Epoch 5/20: 100%|██████████| 159/159 [00:43<00:00,  3.68batch/s, Train Loss=0.208, Train Acc=0.915]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [5/20], Train Loss: 0.2076, Train Acc: 37051.0000, Val Loss: 0.2243, Val Acc: 0.9084\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 6/20: 100%|██████████| 159/159 [00:43<00:00,  3.69batch/s, Train Loss=0.198, Train Acc=0.917]\n",
            "Epoch 7/20: 100%|██████████| 159/159 [00:42<00:00,  3.70batch/s, Train Loss=0.196, Train Acc=0.919]\n",
            "Epoch 8/20: 100%|██████████| 159/159 [00:43<00:00,  3.69batch/s, Train Loss=0.183, Train Acc=0.925]\n",
            "Epoch 9/20: 100%|██████████| 159/159 [00:43<00:00,  3.63batch/s, Train Loss=0.175, Train Acc=0.928]\n",
            "Epoch 10/20: 100%|██████████| 159/159 [00:43<00:00,  3.63batch/s, Train Loss=0.171, Train Acc=0.93]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [10/20], Train Loss: 0.1708, Train Acc: 37667.0000, Val Loss: 0.2182, Val Acc: 0.9153\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 11/20: 100%|██████████| 159/159 [00:43<00:00,  3.68batch/s, Train Loss=0.16, Train Acc=0.935]\n",
            "Epoch 12/20: 100%|██████████| 159/159 [00:43<00:00,  3.66batch/s, Train Loss=0.149, Train Acc=0.939]\n",
            "Epoch 13/20: 100%|██████████| 159/159 [00:43<00:00,  3.66batch/s, Train Loss=0.145, Train Acc=0.94]\n",
            "Epoch 14/20: 100%|██████████| 159/159 [00:43<00:00,  3.67batch/s, Train Loss=0.126, Train Acc=0.95]\n",
            "Epoch 15/20: 100%|██████████| 159/159 [00:44<00:00,  3.61batch/s, Train Loss=0.117, Train Acc=0.953]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [15/20], Train Loss: 0.1170, Train Acc: 38605.0000, Val Loss: 0.2457, Val Acc: 0.9118\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 16/20: 100%|██████████| 159/159 [00:43<00:00,  3.61batch/s, Train Loss=0.0999, Train Acc=0.962]\n",
            "Epoch 17/20: 100%|██████████| 159/159 [00:43<00:00,  3.65batch/s, Train Loss=0.0911, Train Acc=0.964]\n",
            "Epoch 18/20: 100%|██████████| 159/159 [00:43<00:00,  3.65batch/s, Train Loss=0.0725, Train Acc=0.973]\n",
            "Epoch 19/20: 100%|██████████| 159/159 [00:43<00:00,  3.61batch/s, Train Loss=0.0576, Train Acc=0.979]\n",
            "Epoch 20/20: 100%|██████████| 159/159 [00:43<00:00,  3.64batch/s, Train Loss=0.0506, Train Acc=0.982]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [20/20], Train Loss: 0.0506, Train Acc: 39759.0000, Val Loss: 0.3589, Val Acc: 0.9082\n",
            "Final Validation Accuracy: 90.82% Final Validation Loss: 0.3589\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "num_epochs = 20\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    # Training\n",
        "    train_loss = 0\n",
        "    train_acc = 0\n",
        "    model.train()\n",
        "\n",
        "    with tqdm(total=len(trainLoader), desc=f'Epoch {epoch+1}/{num_epochs}', unit='batch') as pbar:\n",
        "        for images, labels in trainLoader:\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "\n",
        "            optimizer.step()\n",
        "\n",
        "            train_loss += loss.item()\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            train_acc += (predicted == labels).sum().item()\n",
        "\n",
        "            pbar.update(1)\n",
        "\n",
        "        pbar.set_postfix({'Train Loss': train_loss / len(trainLoader), 'Train Acc': train_acc / len(trainLoader.dataset)})\n",
        "\n",
        "    # Validation\n",
        "    val_loss = 0\n",
        "    val_acc = 0\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for images, labels in valLoader:\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "            val_loss += loss.item()\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            val_acc += (predicted == labels).sum().item()\n",
        "\n",
        "    val_loss /= len(valLoader)\n",
        "    val_acc /= len(valLoader.dataset)\n",
        "\n",
        "print(f'Final Validation Accuracy: ' + '{:.2f}' .format(100 * val_acc)+'%', 'Final Validation Loss: ' + '{:.4f}'.format(val_loss))\n",
        "torch.save(model, 'LeNNon-Smile-Detector.pt')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVK7vhtMeCHL"
      },
      "source": [
        "Now, let us load our trained model and evaluate it on the testing portion of our split.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "rdSgQ8zyeCHL",
        "outputId": "4a7b5405-81bb-4422-e06c-9e390475ef7c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 20/20 [00:04<00:00,  4.26it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy on the test images: 97 %\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"a564b726-6d58-44d5-821a-eab188dca46b\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a564b726-6d58-44d5-821a-eab188dca46b\")) {                    Plotly.newPlot(                        \"a564b726-6d58-44d5-821a-eab188dca46b\",                        [{\"coloraxis\":\"coloraxis\",\"name\":\"0\",\"texttemplate\":\"%{z}\",\"x\":[\"Smiling\",\"Not Smiling\"],\"y\":[\"Smiling\",\"Not Smiling\"],\"z\":[[2461,39],[71,2429]],\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"hovertemplate\":\"Predicted: %{x}\\u003cbr\\u003eTrue label: %{y}\\u003cbr\\u003ecolor: %{z}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"scaleanchor\":\"y\",\"constrain\":\"domain\",\"title\":{\"text\":\"Predicted\"},\"side\":\"top\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\",\"constrain\":\"domain\",\"title\":{\"text\":\"True label\"}},\"coloraxis\":{\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"showscale\":false},\"margin\":{\"t\":60},\"title\":{\"text\":\"Confusion Matrix\"},\"paper_bgcolor\":\"rgba(0, 0, 0, 0)\",\"plot_bgcolor\":\"rgba(0, 0, 0, 0)\"},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('a564b726-6d58-44d5-821a-eab188dca46b');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import plotly.express as px\n",
        "\n",
        "# If you cloned the model from the Hub and skipped training\n",
        "# use `./LeNNon-Smile-Detector/LeNNon-Smile-Detector.pt` as your path\n",
        "model = torch.load('./LeNNon-Smile-Detector.pt')\n",
        "model = model.to(device)\n",
        "model.eval()\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "predicted_labels = []\n",
        "true_labels = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for data in tqdm(testLoader, total=len(testLoader)):\n",
        "        images, labels = data\n",
        "        # if you are not using GPU, you can remove this next line\n",
        "        images, labels = images.cuda(), labels.cuda()\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "        predicted_labels.extend(predicted.tolist())\n",
        "        true_labels.extend(labels.tolist())\n",
        "\n",
        "print('\\nAccuracy on the test images: %d %%' % (100 * correct / total))\n",
        "\n",
        "# Create the confusion matrix\n",
        "matrix_test = confusion_matrix(true_labels, predicted_labels)\n",
        "\n",
        "fig = px.imshow(matrix_test,\n",
        "                labels=dict(x=\"Predicted\", y=\"True label\"),\n",
        "                x=['Smiling', 'Not Smiling'],\n",
        "                y=['Smiling', 'Not Smiling'],\n",
        "                text_auto=True\n",
        "                )\n",
        "fig.update_xaxes(side='top')\n",
        "fig.update_layout(template='plotly_dark',\n",
        "                  title='Confusion Matrix',\n",
        "                  coloraxis_showscale=False,\n",
        "                  paper_bgcolor='rgba(0, 0, 0, 0)',\n",
        "                  plot_bgcolor='rgba(0, 0, 0, 0)')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSkZgeVTeCHM"
      },
      "source": [
        "To further evaluate our model, let us plot the images together with their corresponding labels and predictions. By doing so, we can visually inspect the model's performance.\n",
        "\n",
        "> **Note: 1 = `smiling` and 0 = `not_smiling`.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "myNPklpeeCHM",
        "outputId": "ab40c181-89d3-4c07-9d53-b098e4c1d87e"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x2000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Select a random subset of images to plot\n",
        "subset_indices = random.sample(range(len(test_df)), 4)\n",
        "\n",
        "plt.figure(figsize=(20, 20))\n",
        "\n",
        "for i, idx in enumerate(subset_indices):\n",
        "    # Load and preprocess the image\n",
        "    image_path = test_df['image_path'].iloc[idx]\n",
        "    image = Image.open(image_path)\n",
        "    tensor = transform(image).unsqueeze(0)\n",
        "    # if you are not using GPU, you can remove this next line\n",
        "    tensor = tensor.cuda()\n",
        "\n",
        "    # Make a prediction\n",
        "    with torch.no_grad():\n",
        "        output = model(tensor)\n",
        "\n",
        "    # Apply softmax to get probabilities\n",
        "    probabilities = torch.softmax(output, dim=1)\n",
        "    percent_smiling = probabilities[0][1].item() * 100\n",
        "    percent_not_smiling = probabilities[0][0].item() * 100\n",
        "\n",
        "    # Extract the predicted class\n",
        "    _, predicted = torch.max(output.data, 1)\n",
        "\n",
        "    # Plot the samples\n",
        "    plt.subplot(1, 4, i + 1)\n",
        "    plt.imshow(image)\n",
        "    plt.axis('off')\n",
        "    plt.title('True Class: {}, Predicted Class: {}\\nSmiling: {:.2f}%, Not Smiling: {:.2f}%'.format(\n",
        "        'Smiling' if test_df.iloc[idx]['labels'] == 1 else 'Not smiling',\n",
        "        'Smiling' if predicted.item() == 1 else 'Not smiling',\n",
        "        percent_smiling,\n",
        "        percent_not_smiling\n",
        "    ))\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWAMW4G5eCHM"
      },
      "source": [
        "Now, let us evaluate our model's performance on two groups of people with different sensitive attributes: **_age_**. We want to measure any disparities in treatment or model output for these groups. To do this, we can use statistical metrics of fairness. These metrics can help us identify any unfair treatment of certain groups and provide insight into how to improve the fairness of our models. For more examples of how to work with fairness metrics, check these other tutorials:\n",
        "\n",
        "- [Creating Ceteris-paribus Profiles with the COMPAS dataset](https://github.com/Nkluge-correa/TeenyTinyCastle/blob/master/ML-Fairness/ceteris_paribus_profiles.ipynb).\n",
        "- [Removing Fairness Bias with the DisparateImpactRemover](https://github.com/Nkluge-correa/TeenyTinyCastle/blob/master/ML-Fairness/disparate_impact_remove_Hogwarts.ipynb).\n",
        "- [Measuring Fairness Metrics with the Credit Card Dataset](https://github.com/Nkluge-correa/TeenyTinyCastle/blob/master/ML-Fairness/fair_metrics_Credit_card_approval.ipynb).\n",
        "- [Measuring Fairness Metrics with the Adult Census Dataset](https://github.com/Nkluge-correa/TeenyTinyCastle/blob/master/ML-Fairness/fairness_income.ipynb).\n",
        "\n",
        "> **Note: For a full review of the most prominent definitions of fairness in algorithmic classification, we recommend [_Fairness Definitions Explained_](https://fairware.cs.umass.edu/papers/Verma.pdf).**\n",
        "\n",
        "In this example, we will first generate a confusion matrix for every sub-group (usually referred to as \"privileged\" and \"unprivileged\") we wish to measure.\n",
        "\n",
        "![confusion-matrix](https://upload.wikimedia.org/wikipedia/commons/3/32/Binary_confusion_matrix.jpg)\n",
        "\n",
        "- $TP$: True Positive - correctly predicted positives (predicted `smiling` and it was `smiling`).\n",
        "- $FP$: False Positive - correctly predicted negatives (predicted `not smiling` and it was `not smiling`).\n",
        "- $TN$: True Negative - incorrectly predicted positives (predicted `smiling` and it was `not smiling`).\n",
        "- $FN$: False Negative - incorrectly predicted negatives (predicted `not smiling` and it was `smiling`).\n",
        "\n",
        "We can calculate statistical fairness metrics with access to these values $(TP, FP, TN, FN)$. Let us apply these metrics to check for any age bias in our model. For this, we will split the test portion of our dataset into two groups: Group A: `young` and Group B: `not_young`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zme5w7nseCHN",
        "outputId": "63077b73-07f4-4e45-e582-e6d291902b35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Images that have the 'young' label:  3858\n",
            "Images that have the 'not_young' label:  1142\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# separate the test dataset into two datasets:\n",
        "# one with the \"young\" label and one with the \"not_young\" label\n",
        "young = test_df[test_df[\"sensitive_attribute\"] == 1]\n",
        "not_young = test_df[test_df[\"sensitive_attribute\"] != 1]\n",
        "\n",
        "# create a dataset for each of the two datasets\n",
        "test_young = Dataset(young)\n",
        "test_not_young = Dataset(not_young)\n",
        "\n",
        "# create a dataloader for each of the two datasets\n",
        "loader_young = DataLoader(test_young, batch_size=64)\n",
        "loader_not_young = DataLoader(test_not_young, batch_size=64)\n",
        "\n",
        "# how many images are in each dataset?\n",
        "print(\"Images that have the 'young' label: \", len(young))\n",
        "print(\"Images that have the 'not_young' label: \", len(not_young))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Bg9IoMNeCHN"
      },
      "source": [
        "By evaluating the performance of our trained model against the separated groups of `young` and `not_young`, we can construct a confusion matrix for each group."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nR1EJGo5eCHN",
        "outputId": "9538fb6b-c8b8-40f7-9be0-eddd460ff204"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"54e052d3-597d-423f-a03d-a76cdf5480de\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"54e052d3-597d-423f-a03d-a76cdf5480de\")) {                    Plotly.newPlot(                        \"54e052d3-597d-423f-a03d-a76cdf5480de\",                        [{\"coloraxis\":\"coloraxis\",\"name\":\"0\",\"texttemplate\":\"%{z}\",\"x\":[\"not_smiling\",\"smiling\"],\"y\":[\"not_smiling\",\"smiling\"],\"z\":[[1931,28],[54,1845]],\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"hovertemplate\":\"Predicted: %{x}\\u003cbr\\u003eTrue label: %{y}\\u003cbr\\u003ecolor: %{z}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"scaleanchor\":\"y\",\"constrain\":\"domain\",\"title\":{\"text\":\"Predicted\"},\"side\":\"top\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\",\"constrain\":\"domain\",\"title\":{\"text\":\"True label\"}},\"coloraxis\":{\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"showscale\":false},\"margin\":{\"t\":60},\"title\":{\"text\":\"Confusion Matrix for only the \\\"young\\\" label\"},\"paper_bgcolor\":\"rgba(0, 0, 0, 0)\",\"plot_bgcolor\":\"rgba(0, 0, 0, 0)\"},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('54e052d3-597d-423f-a03d-a76cdf5480de');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"1a9970ac-65b6-4faa-bfa0-9a23d73c6a6f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"1a9970ac-65b6-4faa-bfa0-9a23d73c6a6f\")) {                    Plotly.newPlot(                        \"1a9970ac-65b6-4faa-bfa0-9a23d73c6a6f\",                        [{\"coloraxis\":\"coloraxis\",\"name\":\"0\",\"texttemplate\":\"%{z}\",\"x\":[\"not_smiling\",\"smiling\"],\"y\":[\"not_smiling\",\"smiling\"],\"z\":[[530,11],[17,584]],\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"hovertemplate\":\"Predicted: %{x}\\u003cbr\\u003eTrue label: %{y}\\u003cbr\\u003ecolor: %{z}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"scaleanchor\":\"y\",\"constrain\":\"domain\",\"title\":{\"text\":\"Predicted\"},\"side\":\"top\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\",\"constrain\":\"domain\",\"title\":{\"text\":\"True label\"}},\"coloraxis\":{\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"showscale\":false},\"margin\":{\"t\":60},\"title\":{\"text\":\"Confusion Matrix for only the \\\"not_young\\\" label\"},\"paper_bgcolor\":\"rgba(0, 0, 0, 0)\",\"plot_bgcolor\":\"rgba(0, 0, 0, 0)\"},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('1a9970ac-65b6-4faa-bfa0-9a23d73c6a6f');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# Put the model into cuda\n",
        "model = model.to(device)\n",
        "\n",
        "# Define a function to make predictions on the test data\n",
        "def make_predictions(test_loader):\n",
        "    y_true = []\n",
        "    y_pred = []\n",
        "    with torch.no_grad():\n",
        "        for images, labels in test_loader:\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            y_true += labels.tolist()\n",
        "            y_pred += predicted.tolist()\n",
        "    return y_true, y_pred\n",
        "\n",
        "# Make predictions on the two test datasets\n",
        "y_true_young, y_pred_young = make_predictions(loader_young)\n",
        "y_true_not_young, y_pred_not_young = make_predictions(loader_not_young)\n",
        "\n",
        "# Compute the confusion matrices\n",
        "matrix_young = confusion_matrix(y_true_young, y_pred_young)\n",
        "matrix_not_young = confusion_matrix(y_true_not_young, y_pred_not_young)\n",
        "\n",
        "# Plot the confusion matrices\n",
        "class_names = ['not_smiling', 'smiling']\n",
        "\n",
        "fig = px.imshow(matrix_young,\n",
        "                labels=dict(x=\"Predicted\", y=\"True label\"),\n",
        "                x=class_names,\n",
        "                y=class_names,\n",
        "                text_auto=True\n",
        "                )\n",
        "fig.update_xaxes(side='top')\n",
        "fig.update_layout(template='plotly_dark',\n",
        "                  title='Confusion Matrix for only the \"young\" label',\n",
        "                  coloraxis_showscale=False,\n",
        "                  paper_bgcolor='rgba(0, 0, 0, 0)',\n",
        "                  plot_bgcolor='rgba(0, 0, 0, 0)')\n",
        "fig.show()\n",
        "\n",
        "fig = px.imshow(matrix_not_young,\n",
        "                labels=dict(x=\"Predicted\", y=\"True label\"),\n",
        "                x=class_names,\n",
        "                y=class_names,\n",
        "                text_auto=True\n",
        "                )\n",
        "fig.update_xaxes(side='top')\n",
        "fig.update_layout(template='plotly_dark',\n",
        "                  title='Confusion Matrix for only the \"not_young\" label',\n",
        "                  coloraxis_showscale=False,\n",
        "                  paper_bgcolor='rgba(0, 0, 0, 0)',\n",
        "                  plot_bgcolor='rgba(0, 0, 0, 0)')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOaiDFP8eCHN"
      },
      "source": [
        "Given that we have the confusion matrix values $(TP, FP, TN, FN)$, we can calculate several statistical fairness metrics."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "eznUX3BpeCHN",
        "outputId": "815f13d5-efb9-4f6a-c29f-64c32469d747"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "| Fairness Metrics                                      | Scores                               |\n",
              "|:------------------------------------------------------|:-------------------------------------|\n",
              "| Chance of receiving the positive class - privileged   | 0.49                                 |\n",
              "| Chance of receiving the positive class - unprivileged | 0.52                                 |\n",
              "| Statistical Parity Ratio (SPR)                        | 0.93                                 |\n",
              "| True Positive Rate - privileged                       | 0.97                                 |\n",
              "| True Positive Rate - unprivileged                     | 0.97                                 |\n",
              "| Equal Opportunity Ratio (EOR)                         | 1.0                                  |\n",
              "| Positive Predictive Value - privileged                | 0.99                                 |\n",
              "| Positive Predictive Value - unprivileged              | 0.98                                 |\n",
              "| Predictive Parity Ratio (PPR)                         | 1.0                                  |\n",
              "| False Positive Rate - privileged                      | 0.01                                 |\n",
              "| False Positive Rate - unprivileged                    | 0.02                                 |\n",
              "| Predictive Equality Ratio (PER)                       | 0.7                                  |\n",
              "| Accuracy - privileged                                 | 0.98                                 |\n",
              "| Accuracy - unprivileged                               | 0.98                                 |\n",
              "| Accuracy Equality Ratio (AER)                         | 1.0                                  |\n",
              "| Equalized Odds                                        | TPR: 0.97 vs 0.97. FPR: 0.01 vs 0.02 |"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def calc_fair(ground_truth_priv, predictions_priv, ground_truth_unpriv, predictions_unpriv):\n",
        "    \"\"\"\n",
        "    The function calc_fair computes several fairness metrics for a given machine\n",
        "    learning model on a test set DataFrame. The fairness metrics calculated include\n",
        "    statistical parity ratio, true positive rate, positive predictive value,\n",
        "    false positive rate, accuracy, equal opportunity ratio, predictive parity\n",
        "    ratio, predictive equality ratio, and accuracy equality ratio. The function\n",
        "    takes in the following arguments:\n",
        "\n",
        "    Args:\n",
        "    --------\n",
        "        - model: The trained machine learning model to evaluate fairness on.\n",
        "        - DataFrame: The test set data used to evaluate the model.\n",
        "        - protected_attribute: The name of the protected attribute in the DataFrame.\n",
        "        - group_priv: The value of the protected attribute for the privileged group.\n",
        "        - group_unpriv: The value of the protected attribute for the unprivileged group.\n",
        "        - label: The name of the column in the DataFrame that contains the ground truth labels.\n",
        "\n",
        "    Returns:\n",
        "    --------\n",
        "    The function returns a dictionary containing the fairness metric names and their\n",
        "    corresponding scores. The scores are rounded to two decimal places. Additionally,\n",
        "    the function returns the equalized odds as a string.\n",
        "    \"\"\"\n",
        "\n",
        "    TN_PV, FP_PV, FN_PV, TP_PV = confusion_matrix(ground_truth_priv, predictions_priv).ravel()\n",
        "    TN_UPV, FP_UPV, FN_UPV, TP_UPV = confusion_matrix(ground_truth_unpriv, predictions_unpriv).ravel()\n",
        "\n",
        "    statistical_parity_priv = (TP_PV + FP_PV)/(TP_PV + FP_PV + TN_PV + FN_PV)  # STATISTICAL PARITY RATIO\n",
        "    statistical_parity_unpriv = (TP_UPV + FP_UPV)/(TP_UPV + FP_UPV + TN_UPV + FN_UPV)  # STATISTICAL PARITY RATIO\n",
        "    equal_oportunity_priv = TP_PV / (TP_PV+FN_PV)  # TRUE POSITIVE RATIO\n",
        "    equal_oportunity_unpriv = TP_UPV / (TP_UPV+FN_UPV)  # TRUE POSITIVE RATIO\n",
        "    predictive_parity_priv = TP_PV/(TP_PV + FP_PV)  # POSITIVE PREDICTIVE VALUE\n",
        "    predictive_parity_unpriv = TP_UPV/(TP_UPV + FP_UPV)  # POSITIVE PREDICTIVE VALUE\n",
        "    predictive_equality_priv = FP_PV / (FP_PV+TN_PV)  # FALSE POSITIVE RATE\n",
        "    predictive_equality_unpriv = FP_UPV / (FP_UPV+TN_UPV)  # FALSE POSITIVE RATE\n",
        "    accuracy_equality_priv = (TP_PV + TN_PV)/(TP_PV + FP_PV + TN_PV + FN_PV)  # ACCURACY EQUALITY RATIO\n",
        "    accuracy_equality_unpriv = (TP_UPV + TN_UPV)/(TP_UPV + FP_UPV + TN_UPV + FN_UPV)  # ACCURACY EQUALITY RATIO\n",
        "\n",
        "    if statistical_parity_priv >= statistical_parity_unpriv:\n",
        "        statistical_parity_ratio = statistical_parity_unpriv/statistical_parity_priv\n",
        "    elif statistical_parity_priv < statistical_parity_unpriv:\n",
        "        statistical_parity_ratio = statistical_parity_priv/statistical_parity_unpriv\n",
        "\n",
        "    if equal_oportunity_priv >= equal_oportunity_unpriv:\n",
        "        equal_oportunity_ratio = equal_oportunity_unpriv/equal_oportunity_priv\n",
        "    elif equal_oportunity_priv < equal_oportunity_unpriv:\n",
        "        equal_oportunity_ratio = equal_oportunity_priv/equal_oportunity_unpriv\n",
        "\n",
        "    if predictive_parity_priv >= predictive_parity_unpriv:\n",
        "        predictive_parity_ratio = predictive_parity_unpriv/predictive_parity_priv\n",
        "    elif predictive_parity_priv < predictive_parity_unpriv:\n",
        "        predictive_parity_ratio = predictive_parity_priv/predictive_parity_unpriv\n",
        "\n",
        "    if predictive_equality_priv >= predictive_equality_unpriv:\n",
        "        predictive_equality_ratio = predictive_equality_unpriv/predictive_equality_priv\n",
        "    elif predictive_equality_priv < predictive_equality_unpriv:\n",
        "        predictive_equality_ratio = predictive_equality_priv/predictive_equality_unpriv\n",
        "\n",
        "    if accuracy_equality_priv >= accuracy_equality_unpriv:\n",
        "        accuracy_equality_ratio = accuracy_equality_unpriv/accuracy_equality_priv\n",
        "    elif accuracy_equality_priv < accuracy_equality_unpriv:\n",
        "        accuracy_equality_ratio = accuracy_equality_priv/accuracy_equality_unpriv\n",
        "\n",
        "    data = {'Fairness Metrics': ['Chance of receiving the positive class - privileged',\n",
        "                                'Chance of receiving the positive class - unprivileged',\n",
        "                                'Statistical Parity Ratio (SPR)',\n",
        "                                'True Positive Rate - privileged',\n",
        "                                'True Positive Rate - unprivileged',\n",
        "                                'Equal Opportunity Ratio (EOR)',\n",
        "                                'Positive Predictive Value - privileged',\n",
        "                                'Positive Predictive Value - unprivileged',\n",
        "                                'Predictive Parity Ratio (PPR)',\n",
        "                                'False Positive Rate - privileged',\n",
        "                                'False Positive Rate - unprivileged',\n",
        "                                'Predictive Equality Ratio (PER)',\n",
        "                                'Accuracy - privileged',\n",
        "                                'Accuracy - unprivileged',\n",
        "                                'Accuracy Equality Ratio (AER)',\n",
        "                                'Equalized Odds'],\n",
        "            'Scores': [round(statistical_parity_priv, 2),\n",
        "                        round(statistical_parity_unpriv, 2),\n",
        "                        round(statistical_parity_ratio,2),\n",
        "                        round(equal_oportunity_priv, 2),\n",
        "                        round(equal_oportunity_unpriv, 2),\n",
        "                        round(equal_oportunity_ratio, 2),\n",
        "                        round(predictive_parity_priv,2),\n",
        "                        round(predictive_parity_unpriv,2),\n",
        "                        round(predictive_parity_ratio,2),\n",
        "                        round(predictive_equality_priv,2),\n",
        "                        round(predictive_equality_unpriv,2),\n",
        "                        round(predictive_equality_ratio,2),\n",
        "                        round(accuracy_equality_priv,2),\n",
        "                        round(accuracy_equality_unpriv,2),\n",
        "                        round(accuracy_equality_ratio,2),\n",
        "                        f'TPR: {round(equal_oportunity_priv, 2)} vs {round(equal_oportunity_unpriv, 2)}. FPR: {round(predictive_equality_priv,2)} vs {round(predictive_equality_unpriv,2)}']\n",
        "            }\n",
        "    return pd.DataFrame(data).set_index('Fairness Metrics')\n",
        "\n",
        "from IPython.display import Markdown\n",
        "\n",
        "fairness_df = calc_fair(y_true_young, y_pred_young, y_true_not_young, y_pred_not_young)\n",
        "display(Markdown(fairness_df.to_markdown()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDklpiKheCHN"
      },
      "source": [
        "Fairness metrics can help us determine if our model discriminates according to any particular definition of fairness. However, as an [impossibility theorem](https://arxiv.org/abs/2007.06024), some Fairness metrics (_Statistical Parity_, _Equalized Odds_, _Predictive Parity_) are incompatible and cannot be completely satisfied simultaneously. Thus, the choice of which metric to use must be made according to the context of an application (i.e., benefit awarding, medical diagnosis, etc.).\n",
        "\n",
        "Overall, the model achieved a testing accuracy of `90%`. Meanwhile, the calculated fairness metrics seem balanced, except for the _Predictive Equality Ratio_. For example, this model has a False Positive Rate that makes `not_young` samples classified as `smiling`, even though they are not smiling, with more frequency. Given that our classifier was developed for an innocuous application, that may be a minor flaw.\n",
        "\n",
        "However, if our model was detecting **_police fugitives_**, and these rates remained, this would mean that older persons would have a more significant chance (double the chance) of being incorrectly classified as fugitives. Hence, we should remember that fairness and AI risks are contextual and should be interpreted under the specific domain in which an application will operate.\n",
        "\n",
        "---\n",
        "\n",
        "Return to the [Castle](https://github.com/Nkluge-correa/TeenyTinyCastle)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "orig_nbformat": 4,
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "00c403f538ea47e186038d7b8c9afe31": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1dab2e0787fa4f71969f5c737fd8b18a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_61c6e6390e4a49a78c7504da12491216",
              "IPY_MODEL_6dc5909be57146fcad033a9bd202ea75",
              "IPY_MODEL_9b33261598e84ddc8257f758d6297cbc"
            ],
            "layout": "IPY_MODEL_b97417d751c94ce28e3a975e7cc4c95c"
          }
        },
        "22dcd0b9bf18456e8aa3859ce1df9c50": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "27e2d7870c7e491298e810607dd3bc21": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d2f559ff12c7459e87be68b85e263c78",
              "IPY_MODEL_7128e8a392904f759050e92e295b024b",
              "IPY_MODEL_44823b93cd454974b2b200697e519cb9"
            ],
            "layout": "IPY_MODEL_f22f73bd193245e08da7c1b7a97428d6"
          }
        },
        "29bb7d5459684e079763246001b0d4ad": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5c962cbe19bb434ca84859f875db5117",
            "placeholder": "​",
            "style": "IPY_MODEL_cc36dddaa050494b82d6fd9fdcbb6e99",
            "value": "Downloading data: 100%"
          }
        },
        "2e0fc6940bb040df97e1c6c7187be702": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3e0e366a6b6f4cc08e7ef6aa314779f7": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "44823b93cd454974b2b200697e519cb9": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_51b4f5c0a474466d912613bec074863d",
            "placeholder": "​",
            "style": "IPY_MODEL_86a711a038d149348f2cfc40565123ec",
            "value": " 1.85k/1.85k [00:00&lt;00:00, 131kB/s]"
          }
        },
        "44dedb39b6634627a76b0911bb7988ad": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4518c77018c34a25b7da394ff84d6d6f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d18cd9fb5ec5427498ca733f6376141a",
            "max": 349853371,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_6db738031d204ef3a2876601b763e42a",
            "value": 349853371
          }
        },
        "479c957f5be34b589d9b326b64e39b22": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4e285412e76d429bb98bda56f1d865cc": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4f742f07cc664ba09878faa182a89a25": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "51b4f5c0a474466d912613bec074863d": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "57ec4bd48e24481da0d2282eded95518": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5c962cbe19bb434ca84859f875db5117": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "613e3289b9de4b28802d100588ddeebb": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ff23881f93ff4966a3b389eebb34f379",
            "placeholder": "​",
            "style": "IPY_MODEL_da4589280a544938b8b31740ac47c0a1",
            "value": "Map: 100%"
          }
        },
        "61c6e6390e4a49a78c7504da12491216": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c87f5a8db53840279a139aff7a24b0e6",
            "placeholder": "​",
            "style": "IPY_MODEL_ddfc809d5ae743e898ae924c7813bf3d",
            "value": "Generating train split: 100%"
          }
        },
        "6db738031d204ef3a2876601b763e42a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "6dc5909be57146fcad033a9bd202ea75": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_57ec4bd48e24481da0d2282eded95518",
            "max": 50000,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_79fc702743004e5eaaadc475de896fef",
            "value": 50000
          }
        },
        "7128e8a392904f759050e92e295b024b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8e5cdbdaac1d4841b0f2aadad2ef0bec",
            "max": 1849,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_7260f7ca9ba84e08bc12995c14cb4fd5",
            "value": 1849
          }
        },
        "7260f7ca9ba84e08bc12995c14cb4fd5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "758ec4002b7e4693a21ad9cf85441129": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8c512af59ff34c99bcab877b887f0647",
            "placeholder": "​",
            "style": "IPY_MODEL_44dedb39b6634627a76b0911bb7988ad",
            "value": " 350M/350M [01:52&lt;00:00, 3.62MB/s]"
          }
        },
        "79fc702743004e5eaaadc475de896fef": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "86a711a038d149348f2cfc40565123ec": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8c512af59ff34c99bcab877b887f0647": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8e5cdbdaac1d4841b0f2aadad2ef0bec": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9b33261598e84ddc8257f758d6297cbc": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2e0fc6940bb040df97e1c6c7187be702",
            "placeholder": "​",
            "style": "IPY_MODEL_ff2fe76b13d5411a9292ff48cd84ccac",
            "value": " 50000/50000 [00:01&lt;00:00, 43138.39 examples/s]"
          }
        },
        "a5e891e60d6f4c0eb6eae1248dcc40a0": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_29bb7d5459684e079763246001b0d4ad",
              "IPY_MODEL_4518c77018c34a25b7da394ff84d6d6f",
              "IPY_MODEL_758ec4002b7e4693a21ad9cf85441129"
            ],
            "layout": "IPY_MODEL_479c957f5be34b589d9b326b64e39b22"
          }
        },
        "a740b741470a49ee8ba7b54e64011783": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_00c403f538ea47e186038d7b8c9afe31",
            "max": 50000,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3e0e366a6b6f4cc08e7ef6aa314779f7",
            "value": 50000
          }
        },
        "afe1adf66f724a1580439c11b1cb58ea": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_613e3289b9de4b28802d100588ddeebb",
              "IPY_MODEL_a740b741470a49ee8ba7b54e64011783",
              "IPY_MODEL_b655422e8a944233a8b9c1c8a73ff1a5"
            ],
            "layout": "IPY_MODEL_22dcd0b9bf18456e8aa3859ce1df9c50"
          }
        },
        "b655422e8a944233a8b9c1c8a73ff1a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f8ef8cf86dab45eaa1a1c8a3ecc009b9",
            "placeholder": "​",
            "style": "IPY_MODEL_d299b307bae448cdb082d514f790925f",
            "value": " 50000/50000 [04:53&lt;00:00, 171.92 examples/s]"
          }
        },
        "b97417d751c94ce28e3a975e7cc4c95c": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c87f5a8db53840279a139aff7a24b0e6": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cc36dddaa050494b82d6fd9fdcbb6e99": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d18cd9fb5ec5427498ca733f6376141a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d299b307bae448cdb082d514f790925f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d2f559ff12c7459e87be68b85e263c78": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4e285412e76d429bb98bda56f1d865cc",
            "placeholder": "​",
            "style": "IPY_MODEL_4f742f07cc664ba09878faa182a89a25",
            "value": "Downloading readme: 100%"
          }
        },
        "da4589280a544938b8b31740ac47c0a1": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "ddfc809d5ae743e898ae924c7813bf3d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f22f73bd193245e08da7c1b7a97428d6": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f8ef8cf86dab45eaa1a1c8a3ecc009b9": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ff23881f93ff4966a3b389eebb34f379": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ff2fe76b13d5411a9292ff48cd84ccac": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}