JNotebooks/tutorial_bias_mitigation_fairness.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Installations and Imports"
      ],
      "metadata": {
        "id": "-apdFHhOiJjE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ucimlrepo\n",
        "!pip install aif360"
      ],
      "metadata": {
        "id": "cJT91wHfE99D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47a9dd1a-5da5-4f66-a86a-22a59415d5f6"
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      "execution_count": 1,
      "outputs": [
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          "text": [
            "Collecting ucimlrepo\n",
            "  Downloading ucimlrepo-0.0.3-py3-none-any.whl (7.0 kB)\n",
            "Installing collected packages: ucimlrepo\n",
            "Successfully installed ucimlrepo-0.0.3\n",
            "Collecting aif360\n",
            "  Downloading aif360-0.6.0-py3-none-any.whl (229 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m229.8/229.8 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.11.4)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from aif360) (3.7.1)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2023.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (1.3.2)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (4.49.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (3.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->aif360) (1.16.0)\n",
            "Installing collected packages: aif360\n",
            "Successfully installed aif360-0.6.0\n"
          ]
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import ucimlrepo\n",
        "\n",
        "from aif360.datasets import AdultDataset, GermanDataset, CompasDataset, BinaryLabelDataset\n",
        "\n",
        "from aif360.metrics import BinaryLabelDatasetMetric\n",
        "from aif360.metrics import ClassificationMetric\n",
        "from aif360.metrics.utils import compute_boolean_conditioning_vector\n",
        "\n",
        "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions\\\n",
        "                import load_preproc_data_adult, load_preproc_data_compas\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "\n",
        "from IPython.display import Markdown, display\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from tqdm import tqdm\n"
      ],
      "metadata": {
        "id": "I3vGeFYMr62K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03926b61-6eb7-42e9-bd13-34780834c317"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:No module named 'fairlearn': ExponentiatedGradientReduction will be unavailable. To install, run:\n",
            "pip install 'aif360[Reductions]'\n",
            "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
            "pip install 'aif360[Reductions]'\n",
            "WARNING:root:No module named 'inFairness': SenSeI and SenSR will be unavailable. To install, run:\n",
            "pip install 'aif360[inFairness]'\n",
            "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
            "pip install 'aif360[Reductions]'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Exploration and Preprocessing"
      ],
      "metadata": {
        "id": "C5Lfta2Px6zz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ba58907f-c793-4fed-8923-7012dd471c34"
      },
      "outputs": [
        {
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              "   age         workclass  fnlwgt  education  education-num  \\\n",
              "0   39         State-gov   77516  Bachelors             13   \n",
              "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
              "2   38           Private  215646    HS-grad              9   \n",
              "3   53           Private  234721       11th              7   \n",
              "4   28           Private  338409  Bachelors             13   \n",
              "\n",
              "       marital-status         occupation   relationship   race     sex  \\\n",
              "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
              "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
              "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
              "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
              "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
              "\n",
              "   capital-gain  capital-loss  hours-per-week native-country income  \n",
              "0          2174             0              40  United-States  <=50K  \n",
              "1             0             0              13  United-States  <=50K  \n",
              "2             0             0              40  United-States  <=50K  \n",
              "3             0             0              40  United-States  <=50K  \n",
              "4             0             0              40           Cuba  <=50K  "
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              "      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-8f8009d1-7c1e-4214-baed-d4b20a4f14c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48842,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 17,\n        \"max\": 90,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          28,\n          73,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Without-pay\",\n          \"Self-emp-not-inc\",\n          \"?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105604,\n        \"min\": 12285,\n        \"max\": 1490400,\n        \"num_unique_values\": 28523,\n        \"samples\": [\n          159077,\n          199450,\n          181773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Bachelors\",\n          \"HS-grad\",\n          \"Some-college\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education-num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 16,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          13,\n          9,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital-status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Never-married\",\n          \"Married-civ-spouse\",\n          \"Married-AF-spouse\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Machine-op-inspct\",\n          \"?\",\n          \"Adm-clerical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Not-in-family\",\n          \"Husband\",\n          \"Other-relative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Black\",\n          \"Other\",\n          \"Asian-Pac-Islander\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7452,\n        \"min\": 0,\n        \"max\": 99999,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          2176,\n          10520\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 403,\n        \"min\": 0,\n        \"max\": 4356,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          1974,\n          419\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours-per-week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          97,\n          88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"native-country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 42,\n        \"samples\": [\n          \"El-Salvador\",\n          \"Philippines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \">50K\",\n          \">50K.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# fetch dataset\n",
        "adult = ucimlrepo.fetch_ucirepo(id=2)\n",
        "\n",
        "df = adult.data.features\n",
        "y = adult.data.targets\n",
        "df = df.join(y)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAb1o_dpGqxE",
        "outputId": "23bfecb9-eb76-43ff-d809-aa8a75a996ba"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 48842 entries, 0 to 48841\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             48842 non-null  int64 \n",
            " 1   workclass       47879 non-null  object\n",
            " 2   fnlwgt          48842 non-null  int64 \n",
            " 3   education       48842 non-null  object\n",
            " 4   education-num   48842 non-null  int64 \n",
            " 5   marital-status  48842 non-null  object\n",
            " 6   occupation      47876 non-null  object\n",
            " 7   relationship    48842 non-null  object\n",
            " 8   race            48842 non-null  object\n",
            " 9   sex             48842 non-null  object\n",
            " 10  capital-gain    48842 non-null  int64 \n",
            " 11  capital-loss    48842 non-null  int64 \n",
            " 12  hours-per-week  48842 non-null  int64 \n",
            " 13  native-country  48568 non-null  object\n",
            " 14  income          48842 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 5.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling null values in the data. Some null values are identified by `'?'` which we need to change."
      ],
      "metadata": {
        "id": "VU8txwYSizxt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df == '?'] = np.nan\n",
        "df = df.dropna()\n",
        "df.info()"
      ],
      "metadata": {
        "id": "2aOxnHKCM0JL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32d70e92-b4b9-4036-ea71-ab30965a9402"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 45222 entries, 0 to 48841\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             45222 non-null  int64 \n",
            " 1   workclass       45222 non-null  object\n",
            " 2   fnlwgt          45222 non-null  int64 \n",
            " 3   education       45222 non-null  object\n",
            " 4   education-num   45222 non-null  int64 \n",
            " 5   marital-status  45222 non-null  object\n",
            " 6   occupation      45222 non-null  object\n",
            " 7   relationship    45222 non-null  object\n",
            " 8   race            45222 non-null  object\n",
            " 9   sex             45222 non-null  object\n",
            " 10  capital-gain    45222 non-null  int64 \n",
            " 11  capital-loss    45222 non-null  int64 \n",
            " 12  hours-per-week  45222 non-null  int64 \n",
            " 13  native-country  45222 non-null  object\n",
            " 14  income          45222 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 5.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['race'].replace(['Black', 'Asian-Pac-Islander', 'Amer-Indian-Eskimo', 'Other'],'Non-White', inplace = True)\n",
        "\n",
        "df = df.replace({\"<=50K.\":\"<=50K\", \">50K.\":\">50K\"})\n",
        "df['income']=df['income'].map({'<=50K': 0, '>50K': 1})\n",
        "df.drop(['education'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "n5GGpSpmMMz7"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "import matplotlib.pyplot as plt\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "# Plot distribution of sex by income class\n",
        "sns.countplot(x='sex', hue='income', data=df, ax=axes[0])\n",
        "axes[0].set_title('Distribution of Sex by Income Class')\n",
        "\n",
        "# Plot distribution of race by income class\n",
        "sns.countplot(x='race', hue='income', data=df, ax=axes[1])\n",
        "axes[1].set_title('Distribution of Race by Income Class')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "cellView": "form",
        "id": "2EKovA30jr1I",
        "outputId": "17fc6056-cc69-42cd-9bca-9d8a5ac36b05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Number of columns in the dataframe excluding the 'income' column\n",
        "num_columns = len(df.columns) - 1\n",
        "\n",
        "# Number of rows and columns for the subplots\n",
        "num_rows = (num_columns + 2) // 3  # Add 2 to ensure that there are enough rows for all subplots\n",
        "num_cols = 3\n",
        "\n",
        "# Create subplots\n",
        "fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, num_rows * 3))\n",
        "\n",
        "# Flatten the axes array for easier indexing\n",
        "axes = axes.flatten()\n",
        "\n",
        "# Iterate over the columns and plot each one in a separate subplot\n",
        "for i, column in enumerate(df.columns[:-1]):  # Exclude the 'income' column\n",
        "    ax = axes[i]\n",
        "    if df[column].dtype == 'object':\n",
        "        sns.countplot(x=column, hue='income', data=df, ax=ax)\n",
        "    else:\n",
        "        sns.histplot(df, x=column, hue='income', bins=20, kde=True, stat='density', ax=ax)\n",
        "    ax.set_title(f\"Distribution of {column} by Income Class\")\n",
        "    ax.tick_params(axis='x', rotation=45)\n",
        "\n",
        "# Hide any unused subplots\n",
        "for j in range(i + 1, num_rows * num_cols):\n",
        "    axes[j].axis('off')\n",
        "\n",
        "# Adjust the layout and show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "8RPUvrwpKPU8",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'] = df['sex'].map({'Male': 1, 'Female': 0})\n",
        "df['race'] = df['race'].map({'White': 1, 'Non-White': 0})"
      ],
      "metadata": {
        "id": "ktRZ5ldxzjFd"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "one-hot encoding the categorical variables"
      ],
      "metadata": {
        "id": "qjn41CULkW3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_encoded = pd.get_dummies(df, columns=df.select_dtypes(include=['object']).columns)\n",
        "df_encoded.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "f0sXE_SRtsQ9",
        "outputId": "9688b06a-1fc9-41bb-ee87-0baa7f894140"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  fnlwgt  education-num  race  sex  capital-gain  capital-loss  \\\n",
              "0   39   77516             13     1    1          2174             0   \n",
              "1   50   83311             13     1    1             0             0   \n",
              "2   38  215646              9     1    1             0             0   \n",
              "3   53  234721              7     0    1             0             0   \n",
              "4   28  338409             13     0    0             0             0   \n",
              "\n",
              "   hours-per-week  income  workclass_Federal-gov  ...  \\\n",
              "0              40       0                      0  ...   \n",
              "1              13       0                      0  ...   \n",
              "2              40       0                      0  ...   \n",
              "3              40       0                      0  ...   \n",
              "4              40       0                      0  ...   \n",
              "\n",
              "   native-country_Portugal  native-country_Puerto-Rico  \\\n",
              "0                        0                           0   \n",
              "1                        0                           0   \n",
              "2                        0                           0   \n",
              "3                        0                           0   \n",
              "4                        0                           0   \n",
              "\n",
              "   native-country_Scotland  native-country_South  native-country_Taiwan  \\\n",
              "0                        0                     0                      0   \n",
              "1                        0                     0                      0   \n",
              "2                        0                     0                      0   \n",
              "3                        0                     0                      0   \n",
              "4                        0                     0                      0   \n",
              "\n",
              "   native-country_Thailand  native-country_Trinadad&Tobago  \\\n",
              "0                        0                               0   \n",
              "1                        0                               0   \n",
              "2                        0                               0   \n",
              "3                        0                               0   \n",
              "4                        0                               0   \n",
              "\n",
              "   native-country_United-States  native-country_Vietnam  \\\n",
              "0                             1                       0   \n",
              "1                             1                       0   \n",
              "2                             1                       0   \n",
              "3                             1                       0   \n",
              "4                             0                       0   \n",
              "\n",
              "   native-country_Yugoslavia  \n",
              "0                          0  \n",
              "1                          0  \n",
              "2                          0  \n",
              "3                          0  \n",
              "4                          0  \n",
              "\n",
              "[5 rows x 84 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22cc0ae6-549e-494a-bdcb-392c2de133a0\" 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>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education-num</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>income</th>\n",
              "      <th>workclass_Federal-gov</th>\n",
              "      <th>...</th>\n",
              "      <th>native-country_Portugal</th>\n",
              "      <th>native-country_Puerto-Rico</th>\n",
              "      <th>native-country_Scotland</th>\n",
              "      <th>native-country_South</th>\n",
              "      <th>native-country_Taiwan</th>\n",
              "      <th>native-country_Thailand</th>\n",
              "      <th>native-country_Trinadad&amp;Tobago</th>\n",
              "      <th>native-country_United-States</th>\n",
              "      <th>native-country_Vietnam</th>\n",
              "      <th>native-country_Yugoslavia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
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              "      <td>1</td>\n",
              "      <td>2174</td>\n",
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              "      <td>40</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>83311</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>215646</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>234721</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>40</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>338409</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
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              "      <td>40</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 84 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_encoded"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Development Set Splits and Scaling**\n",
        "\n",
        "Split Data into Training (70%), Validation (15%), Test (15%) stratified based on **sex, race, and income** to maintain the proportions in the original dataset."
      ],
      "metadata": {
        "id": "HXoKGfhTygJK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the combined DataFrame into train and test sets, stratifying on sex and race\n",
        "df_train, df_test = train_test_split(df_encoded, test_size=0.3, stratify=df_encoded[['sex', 'race','income']], random_state=42)\n",
        "\n",
        "# Split the train set into train and validation sets, stratifying on sex and race\n",
        "df_test, df_val = train_test_split(df_test, test_size=0.5, stratify=df_test[['sex', 'race','income']], random_state=42)\n"
      ],
      "metadata": {
        "id": "oNmKsEoj8fdd"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploring Biases in the Data\n",
        "\n",
        "The sensitive attributes in this dataset are race and sex. **For this tutorial, we focus on the biases against sex.**\n",
        "\n",
        "According to the distribution of incomes for females and males:\n",
        "*   Privileged group: Males\n",
        "*   Unprivileged group: Females\n",
        "\n"
      ],
      "metadata": {
        "id": "o-zCeplrzmQf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are using **AIF360** library for exploring biases.\n",
        "\n",
        "We need to convert dataframes to it's acceptable format `BinaryLabelDataset`."
      ],
      "metadata": {
        "id": "Nh0YJrmAmWhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the name of the target label column\n",
        "label_name = 'income'  # Change this to the name of your label column\n",
        "\n",
        "# Define the names of the sensitive attribute columns\n",
        "sensitive_attributes = ['race', 'sex']  # Change this to your sensitive attribute columns\n",
        "\n",
        "train_bld = BinaryLabelDataset(df=df_train, label_names=[label_name],protected_attribute_names=sensitive_attributes)\n",
        "valid_bld = BinaryLabelDataset(df=df_val, label_names=[label_name],protected_attribute_names=sensitive_attributes)\n",
        "test_bld = BinaryLabelDataset(df=df_test, label_names=[label_name],protected_attribute_names=sensitive_attributes)"
      ],
      "metadata": {
        "id": "DOYc93Acrtzl"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fairness can be evaluated in different stages of machine learning pipeline, either on the training data or on the learned model.\n",
        "\n",
        "Here we look at the difference in mean outcomes difference/ Statistical Parity Difference between unprivileged and privileged groups (females vs males).\n",
        "\n",
        "Statistical Parity Difference (SPD) is defined as:\n",
        "\n",
        "$$\n",
        "\\text{SPD} = P(\\hat{Y} = 1 | D = \\text{unprivileged}) - P(\\hat{Y} = 1 | D = \\text{privileged})\n",
        "$$\n",
        "where:\n",
        "\n",
        "- $\\hat{Y} = 1$ indicates a positive outcome (e.g., having >50k income).\n",
        "- $D = \\text{unprivileged}$ indicates membership in the unprivileged group.\n",
        "- $D = \\text{privileged}$ indicates membership in the privileged group.\n",
        "\n",
        "Ideal value for $SPD$ is zero. $SPD < 0$ indicates that unprivileged group has a lower rate of positive outcomes.\n",
        "\n",
        "For metrics on Data we use `BinaryLabelDatasetMetric`."
      ],
      "metadata": {
        "id": "RQ6CHrdc3Vmp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "privileged_groups = [{'sex': 1}]\n",
        "unprivileged_groups = [{'sex': 0}]\n",
        "\n",
        "DM_train_org = BinaryLabelDatasetMetric(train_bld,\n",
        "                             unprivileged_groups=unprivileged_groups,\n",
        "                             privileged_groups=privileged_groups)\n",
        "print(\"Train Set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % DM_train_org.mean_difference())\n",
        "\n",
        "\n",
        "DM_valid_org = BinaryLabelDatasetMetric(valid_bld,\n",
        "                             unprivileged_groups=unprivileged_groups,\n",
        "                             privileged_groups=privileged_groups)\n",
        "print(\"Valid Set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % DM_valid_org.mean_difference())\n",
        "\n",
        "\n",
        "DM_test_org = BinaryLabelDatasetMetric(test_bld,\n",
        "                             unprivileged_groups=unprivileged_groups,\n",
        "                             privileged_groups=privileged_groups)\n",
        "print(\"Test Set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % DM_test_org.mean_difference())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "un-W0bSAtKml",
        "outputId": "40fb9443-5ae4-425e-e80c-270f23f380fd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Set: Difference in mean outcomes between unprivileged and privileged groups = -0.198935\n",
            "Valid Set: Difference in mean outcomes between unprivileged and privileged groups = -0.199015\n",
            "Test Set: Difference in mean outcomes between unprivileged and privileged groups = -0.198630\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The $SPD$ results suggest that the unprivileged group has consistently lower rate of positive incomes in all sets."
      ],
      "metadata": {
        "id": "PAITLIjCq9K-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fairness Evaluation\n",
        "### Choose Appropriate Fairness Metrics:\n",
        "Define what fairness means in the context of predicting income levels.\n",
        "\n",
        "* Does it mean that men and women should be classified as earning more than $50K at equal rates when they actually do (equality of opportunity)?\n",
        "* Should the overall rates of prediction be equal regardless of the base rates (demographic parity)?\n",
        "\n",
        "> *Other consideration in real-world*: Legal and ethical considerations, different stakeholders perspectives and needs, how bias impacts both unprivileged and privileged groups?\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "***If a woman and a man have the same qualifications and contribute similarly to their roles, it is fair for a model to predict their income levels similarly.***\n",
        "\n",
        "#### **Equality of Opportunity:**\n",
        " ideal value is zero. A value of < 0 implies higher benefit for the privileged group and a value > 0 implies higher benefit for the unprivileged group. Fairness range for this metric is $[-0.1, 0.1]$.\n",
        "\n",
        "\n",
        "$${TPR}_{D = unprivileged} - {TPR}_{D = privileged}$$\n",
        "<!-- $$ TPR = \\frac{TP}{P} $$ -->\n",
        "\n",
        "\n",
        "#### **Average Odds Difference**:\n",
        " ideally zero. A value of < 0 implies higher benefit for the privileged group and a value > 0 implies higher benefit for the unprivileged group. Fairness range for this metric is $[-0.1, 0.1]$.\n",
        "\n",
        "$$ \\frac{1}{2}[({FPR}_{D = unprivileged} - {FPR}_{D = privileged}) + ({TPR}_{D = unprivileged} - {TPR}_{D = privileged})]$$\n",
        "\n",
        "#### **Balanced Accuracy**:\n",
        "\n",
        "$$\n",
        "\\frac{{TPR} + {TNR}}{2}\n",
        "$$"
      ],
      "metadata": {
        "id": "WvkUX5TmDBov"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Train initial model with original data\n",
        "\n"
      ],
      "metadata": {
        "id": "eVP_ySgS48l-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "import torch # pytorch main library\n",
        "import torchvision # computer vision utilities\n",
        "import torchvision.transforms as transforms # transforms used in the pre-processing of the data\n",
        "\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "_QN99EIswr7i"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Using MinMaxScaler to trasnform data into [0,1] range.\n",
        "\n"
      ],
      "metadata": {
        "id": "xxpZzsXp4s4g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(train_bld.features)\n",
        "y_train = train_bld.labels.ravel()\n",
        "\n",
        "X_val = scaler.transform(valid_bld.features)\n",
        "y_val = valid_bld.labels.ravel()\n",
        "\n",
        "X_test = scaler.transform(test_bld.features)\n",
        "y_test = test_bld.labels.ravel()"
      ],
      "metadata": {
        "id": "p1cI746NzQ9s"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the shapes of the train, validation, and test sets\n",
        "print(f\"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}\")\n",
        "print(f\"X_val shape: {X_val.shape}, y_val shape: {y_val.shape}\")\n",
        "print(f\"X_test shape: {X_test.shape}, y_test shape: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "554SX7n6rtN1",
        "outputId": "f06791af-d233-41df-9668-f772a7ccf4b4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (31655, 83), y_train shape: (31655,)\n",
            "X_val shape: (6784, 83), y_val shape: (6784,)\n",
            "X_test shape: (6783, 83), y_test shape: (6783,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class IncomeDataset(Dataset):\n",
        "    def __init__(self, features, labels, weights = None):\n",
        "        self.features = torch.tensor(features, dtype=torch.float32)\n",
        "        self.labels = torch.tensor(labels, dtype=torch.float32)\n",
        "        self.weights = weights\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if self.weights is not None:\n",
        "            return self.features[idx], self.labels[idx], self.weights[idx]\n",
        "        return self.features[idx], self.labels[idx]\n",
        "\n",
        "\n",
        "def load_data(X_train, y_train, X_val, y_val, X_test, y_test, w_train = None, batch = 500):\n",
        "    # Create dataset instances\n",
        "    train_dataset = IncomeDataset(X_train, y_train, w_train)\n",
        "    val_dataset = IncomeDataset(X_val, y_val)\n",
        "    test_dataset = IncomeDataset(X_test, y_test)\n",
        "\n",
        "    # Create DataLoader instances\n",
        "    train_loader = DataLoader(train_dataset, batch_size=batch, shuffle=True)\n",
        "    val_loader = DataLoader(val_dataset, batch_size=batch, shuffle=False)\n",
        "    test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)\n",
        "\n",
        "    return train_loader, val_loader, test_loader"
      ],
      "metadata": {
        "id": "ku_uDWlrQlMm"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Define Fully Connected Network."
      ],
      "metadata": {
        "id": "JemBAllF5zQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self, input_size):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_size, 64)\n",
        "        self.fc2 = nn.Linear(64, 64)\n",
        "        self.fc3 = nn.Linear(64, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = torch.sigmoid(self.fc3(x))\n",
        "        return x"
      ],
      "metadata": {
        "id": "MwzlV4sUTHnL"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Training Function"
      ],
      "metadata": {
        "id": "Agh4nqysEve-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def training(model, train_loader, val_loader, epoch, optimizer, scheduler, model_path, reweigh = False):\n",
        "\n",
        "    criterion = nn.BCELoss(reduction='none')\n",
        "\n",
        "    best_val_loss = 100000000\n",
        "    train_losses = []\n",
        "    val_losses = []\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        train_loss = 0\n",
        "        for data in train_loader:\n",
        "            inputs, labels = data[:2]\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "\n",
        "            if reweigh:\n",
        "                weights = data[2]\n",
        "                loss = (criterion(outputs.squeeze(), labels.float()) * weights).mean()\n",
        "            else:\n",
        "                loss = criterion(outputs.squeeze(), labels.float()).mean()\n",
        "\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            train_loss += loss.item()\n",
        "\n",
        "        # Validation loop\n",
        "        model.eval()\n",
        "        val_loss = 0\n",
        "        with torch.no_grad():\n",
        "            for inputs, labels in val_loader:\n",
        "                outputs = model(inputs)\n",
        "                loss = criterion(outputs.squeeze(), labels.float()).mean()\n",
        "                val_loss += loss.item()\n",
        "\n",
        "\n",
        "        train_loss /= len(train_loader.dataset)\n",
        "        val_loss /= len(val_loader.dataset)\n",
        "\n",
        "        train_losses.append(train_loss)\n",
        "        val_losses.append(val_loss)\n",
        "\n",
        "        if val_loss < best_val_loss:\n",
        "            torch.save(model.state_dict(), model_path)\n",
        "\n",
        "        scheduler.step(val_loss)\n",
        "        print(f\"Epoch {epoch+1}/{num_epochs} - Train Loss: {train_loss:.5f}, Val Loss: {val_loss:.5f}, LR: {optimizer.param_groups[0]['lr']}\")\n",
        "\n",
        "    return train_losses, val_losses"
      ],
      "metadata": {
        "id": "XtoDnldYT3Yj"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train Baseline Model using original data (no bias mitigation)"
      ],
      "metadata": {
        "id": "PygrRapMzNu6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_model_path = 'baseline_model_path.pth'\n",
        "\n",
        "input_size = X_train.shape[1]\n",
        "model_baseline = Net(input_size)\n",
        "\n",
        "# Set up optimizer and learning rate scheduler\n",
        "optimizer = torch.optim.Adam(model_baseline.parameters(), lr=0.001)\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=2)\n",
        "num_epochs = 10\n",
        "\n",
        "train_loader_org, val_loader_org, test_loader_org = load_data(X_train, y_train, X_val, y_val, X_test, y_test)\n",
        "\n",
        "train_losses_org, val_losses_org = training(model_baseline, train_loader_org, val_loader_org, num_epochs, optimizer, scheduler, baseline_model_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd7U0WShERbo",
        "outputId": "e5a09075-601a-42cb-8cb1-cd4388d4dabd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10 - Train Loss: 0.00115, Val Loss: 0.00090, LR: 0.001\n",
            "Epoch 2/10 - Train Loss: 0.00081, Val Loss: 0.00078, LR: 0.001\n",
            "Epoch 3/10 - Train Loss: 0.00075, Val Loss: 0.00074, LR: 0.001\n",
            "Epoch 4/10 - Train Loss: 0.00072, Val Loss: 0.00072, LR: 0.001\n",
            "Epoch 5/10 - Train Loss: 0.00069, Val Loss: 0.00070, LR: 0.001\n",
            "Epoch 6/10 - Train Loss: 0.00067, Val Loss: 0.00068, LR: 0.001\n",
            "Epoch 7/10 - Train Loss: 0.00066, Val Loss: 0.00068, LR: 0.001\n",
            "Epoch 8/10 - Train Loss: 0.00066, Val Loss: 0.00068, LR: 0.001\n",
            "Epoch 9/10 - Train Loss: 0.00065, Val Loss: 0.00067, LR: 0.001\n",
            "Epoch 10/10 - Train Loss: 0.00065, Val Loss: 0.00067, LR: 0.001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Create the plot\n",
        "plt.figure(figsize=(5,3))\n",
        "plt.plot(train_losses_org, label='Train Loss')\n",
        "plt.plot(val_losses_org, label='Validation Loss')\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss - baseline model')\n",
        "\n",
        "# Add legend and show the plot\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "eswDp-rgWeY0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "fad5a48f-8234-46b4-cf7e-00769211e8f3",
        "cellView": "form"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### predict_prob function to get Valid and Test probabilities with the optimized model and return `BinaryLabelDataset` objects."
      ],
      "metadata": {
        "id": "8P0l-O-f_wGE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_prob(model, val_loader, val_data, test_loader, test_data, model_path):\n",
        "    model.load_state_dict(torch.load(model_path))\n",
        "    model.eval()\n",
        "\n",
        "    val_probs = []\n",
        "    test_probs = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, label in val_loader_org:\n",
        "            outputs = model(inputs)\n",
        "            val_probs.extend(outputs.squeeze().cpu().numpy())\n",
        "\n",
        "        for inputs, label in test_loader_org:\n",
        "            outputs = model(inputs)\n",
        "            test_probs.append(outputs.squeeze().cpu().numpy())\n",
        "\n",
        "\n",
        "    valid_pred = val_data.copy(deepcopy=True)\n",
        "    test_pred = test_data.copy(deepcopy=True)\n",
        "\n",
        "    valid_pred.scores = np.array(val_probs).reshape(-1, 1)\n",
        "    test_pred.scores = np.array(test_probs).reshape(-1, 1)\n",
        "\n",
        "    return valid_pred, test_pred"
      ],
      "metadata": {
        "id": "a19x3H21TPQo"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_bld_pred, test_bld_pred = predict_prob(model_baseline, val_loader_org, valid_bld, test_loader_org, test_bld, baseline_model_path)"
      ],
      "metadata": {
        "id": "KeNTsN2IUqD5"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Find the optimal classification threshold for baseline model from the validation set"
      ],
      "metadata": {
        "id": "OvJhEG_WFH2w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_thresh = 100\n",
        "ba_arr = np.zeros(num_thresh)\n",
        "class_thresh_arr = np.linspace(0.01, 0.99, num_thresh)\n",
        "\n",
        "for idx, class_thresh in enumerate(class_thresh_arr):\n",
        "\n",
        "    valid_bld_pred.labels = (valid_bld_pred.scores >= class_thresh).astype(int)\n",
        "\n",
        "    classified_metric_orig_valid = ClassificationMetric(valid_bld,\n",
        "                                             valid_bld_pred,\n",
        "                                             unprivileged_groups=unprivileged_groups,\n",
        "                                             privileged_groups=privileged_groups)\n",
        "\n",
        "    ba_arr[idx] = 0.5*(classified_metric_orig_valid.true_positive_rate()\\\n",
        "                       +classified_metric_orig_valid.true_negative_rate())\n",
        "\n",
        "best_ind = np.where(ba_arr == np.max(ba_arr))[0][0]\n",
        "best_class_thresh = class_thresh_arr[best_ind]\n",
        "\n",
        "print(\"Best balanced accuracy (no reweighing) = %.4f\" % np.max(ba_arr))\n",
        "print(\"Optimal classification threshold (no reweighing) = %.4f\" % best_class_thresh)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSZ3TTzuF73j",
        "outputId": "86905737-3c93-41f0-bfa2-df72a2f08518"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best balanced accuracy (no reweighing) = 0.8258\n",
            "Optimal classification threshold (no reweighing) = 0.2872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluate accuracy and fairness on test set for each threshold."
      ],
      "metadata": {
        "id": "CbaaB2xe54zb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_metrics = {\n",
        "    'balanced accuracy': [],\n",
        "    'equal opportunity diff': [],\n",
        "    'avg odds diff':[]\n",
        "}\n",
        "\n",
        "print(\"Classification threshold used = %.4f\" % best_class_thresh)\n",
        "\n",
        "for thresh in tqdm(class_thresh_arr):\n",
        "    test_bld_pred.labels = (test_bld_pred.scores >= thresh).astype(int)\n",
        "\n",
        "    metric_test_org = ClassificationMetric(test_bld, test_bld_pred,\n",
        "                             unprivileged_groups=unprivileged_groups,\n",
        "                             privileged_groups=privileged_groups)\n",
        "\n",
        "    bal_acc = 0.5*(metric_test_org.true_positive_rate()\\\n",
        "                       +metric_test_org.true_negative_rate())\n",
        "    eq_opp_diff = metric_test_org.equal_opportunity_difference()\n",
        "    avg_odd_diff = metric_test_org.average_odds_difference()\n",
        "\n",
        "    if thresh == best_class_thresh:\n",
        "        print(\"\\nMetrics using the optimal classification threshold on Test set\")\n",
        "        print(\"Balanced Accuracy:\", bal_acc)\n",
        "        print(\"Equal Opportunity difference:\", eq_opp_diff)\n",
        "        print(\"Average Odds difference:\", avg_odd_diff)\n",
        "\n",
        "    test_metrics['balanced accuracy'].append(bal_acc)\n",
        "    test_metrics['equal opportunity diff'].append(metric_test_org.equal_opportunity_difference())\n",
        "    test_metrics['avg odds diff'].append(metric_test_org.average_odds_difference())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzQJLBzIG3Wp",
        "outputId": "c6761e12-9714-43ea-9c50-3c34db9762ed"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification threshold used = 0.2872\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 42%|████▏     | 42/100 [00:00<00:00, 100.95it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Metrics using the optimal classification threshold on Test set\n",
            "Balanced Accuracy: 0.8153585911787598\n",
            "Equal Opportunity difference: -0.1951801459431317\n",
            "Average Odds difference: -0.2047689578331576\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:01<00:00, 99.54it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Display results for all thresholds\n",
        "fig, ax1 = plt.subplots(figsize=(6,4))\n",
        "ax1.plot(class_thresh_arr, test_metrics['balanced accuracy'])\n",
        "ax1.set_xlabel('Classification Thresholds', fontsize=14)\n",
        "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=14)\n",
        "ax1.xaxis.set_tick_params(labelsize=12)\n",
        "ax1.yaxis.set_tick_params(labelsize=12)\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(class_thresh_arr, test_metrics['equal opportunity diff'] , color='r')\n",
        "ax2.set_ylabel('Equal Opportunity diff', color='r', fontsize=14)\n",
        "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
        "ax2.yaxis.set_tick_params(labelsize=12)\n",
        "ax2.grid(True)\n",
        "plt.title(\"Test Set Evaluation\", fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "cellView": "form",
        "id": "iJ08mykiQZtp",
        "outputId": "496755d2-9bac-469b-b3e2-5edae96b511a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Set Evaluation')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Display results for all thresholds\n",
        "fig, ax1 = plt.subplots(figsize=(6,4))\n",
        "ax1.plot(class_thresh_arr, test_metrics['balanced accuracy'])\n",
        "ax1.set_xlabel('Classification Thresholds', fontsize=14)\n",
        "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=14)\n",
        "ax1.xaxis.set_tick_params(labelsize=12)\n",
        "ax1.yaxis.set_tick_params(labelsize=12)\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(class_thresh_arr, test_metrics['avg odds diff'] , color='r')\n",
        "ax2.set_ylabel('Average odds diff', color='r', fontsize=14)\n",
        "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
        "ax2.yaxis.set_tick_params(labelsize=12)\n",
        "ax2.grid(True)\n",
        "plt.title(\"Test Set Evaluation\", fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "cellView": "form",
        "id": "1yIxqOx7R1DK",
        "outputId": "6f29749f-7e5e-49fa-e628-79d625a85bae"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Set Evaluation')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both metrics showed that the model is biased against unprivileged group at the optimized threshold since they're not in the fairness range."
      ],
      "metadata": {
        "id": "VUWIp89MV846"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Choose Bias Mitigation Technique:\n",
        "* Pre-processing: Modify the training data to remove biases before model\n",
        "training (e.g., reweighing, sampling).\n",
        "* In-processing: Modify the learning algorithm to reduce bias during model training (e.g., adversarial debiasing).\n",
        "* Post-processing: Adjust the model’s predictions to reduce bias after training (e.g., equalized odds post-processing).\n"
      ],
      "metadata": {
        "id": "LW11bdE-QBYL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Preprocessing: Reweighing\n",
        "\n",
        "Weights the examples in each (group, label) combination differently to ensure fairness before classification.\n",
        "\n",
        "- Overall positive class probability: $(P(Y=1))$.\n",
        "- Positive class probability for each group: $(P(Y=1|G=\\text{group}))$.\n",
        "\n",
        "- For each sample in a group:\n",
        "    - Positive class weight: $(\\frac{P(Y=1)}{P(Y=1|G=\\text{group})})$.\n",
        "\n",
        "    - Negative class weight: $(\\frac{1 - P(Y=1)}{1 - P(Y=1|G=\\text{group})})$.\n",
        "\n"
      ],
      "metadata": {
        "id": "deUGDhg_z5dC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from aif360.algorithms.preprocessing.reweighing import Reweighing\n",
        "\n",
        "# Reweigh trian samples\n",
        "RW = Reweighing(unprivileged_groups=unprivileged_groups,\n",
        "               privileged_groups=privileged_groups)\n",
        "RW.fit(train_bld)\n",
        "train_bld_rw = RW.transform(train_bld)\n",
        "\n",
        "# Fit scaler to reweighted train set\n",
        "scale_rw = MinMaxScaler()\n",
        "X_train_rw = scale_rw.fit_transform(train_bld_rw.features)\n",
        "y_train_rw = train_bld_rw.labels.ravel()\n",
        "w_train_rw = train_bld_rw.instance_weights.ravel()\n",
        "\n",
        "# Scale validation and test set using new scaler\n",
        "X_val_rw = scale_rw.transform(valid_bld.features)\n",
        "\n",
        "X_test_rw = scale_rw.transform(test_bld.features)"
      ],
      "metadata": {
        "id": "h2_4WpF60DQG"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train model using reweighed data."
      ],
      "metadata": {
        "id": "PzOuGszy3dAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RW_model_path = 'rw_model_path.pth'\n",
        "# Set up the loss function and optimizer\n",
        "model_RW = Net(input_size)\n",
        "optimizer = torch.optim.Adam(model_RW.parameters(), lr=0.001)\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=2)\n",
        "num_epochs = 10\n",
        "\n",
        "train_loader_rw, val_loader_rw, test_loader_rw = load_data(X_train_rw, y_train_rw, X_val_rw, y_val, X_test_rw, y_test, w_train_rw)\n",
        "\n",
        "train_losses_rw, val_losses_rw = training(model_RW, train_loader_rw, val_loader_rw, num_epochs, optimizer, scheduler, RW_model_path, reweigh=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSuRL-v7cEKg",
        "outputId": "ca43831b-1d9b-4d9e-cbbb-1260aeb84835"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10 - Train Loss: 0.00114, Val Loss: 0.00090, LR: 0.001\n",
            "Epoch 2/10 - Train Loss: 0.00081, Val Loss: 0.00079, LR: 0.001\n",
            "Epoch 3/10 - Train Loss: 0.00075, Val Loss: 0.00076, LR: 0.001\n",
            "Epoch 4/10 - Train Loss: 0.00072, Val Loss: 0.00075, LR: 0.001\n",
            "Epoch 5/10 - Train Loss: 0.00070, Val Loss: 0.00073, LR: 0.001\n",
            "Epoch 6/10 - Train Loss: 0.00068, Val Loss: 0.00072, LR: 0.001\n",
            "Epoch 7/10 - Train Loss: 0.00068, Val Loss: 0.00071, LR: 0.001\n",
            "Epoch 8/10 - Train Loss: 0.00067, Val Loss: 0.00070, LR: 0.001\n",
            "Epoch 9/10 - Train Loss: 0.00066, Val Loss: 0.00069, LR: 0.001\n",
            "Epoch 10/10 - Train Loss: 0.00065, Val Loss: 0.00069, LR: 0.001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Create the plot\n",
        "plt.figure(figsize=(5,3))\n",
        "plt.plot(train_losses_rw, label='Train Loss')\n",
        "plt.plot(val_losses_rw, label='Validation Loss')\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss (after reweighing)')\n",
        "\n",
        "# Add legend and show the plot\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "cellView": "form",
        "id": "KvUN7wVNaZ6w",
        "outputId": "25f8dedc-9dd3-4884-8d5d-b8d0bdcc35dc"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting Validation and Test probabilities using the reweighed model."
      ],
      "metadata": {
        "id": "VrQGIiYs35ma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "valid_pred_rw, test_pred_rw = predict_prob(model_RW, val_loader_rw, valid_bld, test_loader_rw, test_bld, RW_model_path)"
      ],
      "metadata": {
        "id": "6JysZxNga0QE"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find new optimal threshold using validation set for reweigh model."
      ],
      "metadata": {
        "id": "O0cunIqu3_t6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_thresh = 100\n",
        "ba_arr_rw = np.zeros(num_thresh)\n",
        "class_thresh_arr = np.linspace(0.01, 0.99, num_thresh)\n",
        "\n",
        "for idx, class_thresh in enumerate(class_thresh_arr):\n",
        "\n",
        "    valid_pred_rw.labels = (valid_pred_rw.scores >= class_thresh).astype(int)\n",
        "\n",
        "    classified_metric_valid_rw = ClassificationMetric(valid_bld,\n",
        "                                             valid_pred_rw,\n",
        "                                             unprivileged_groups=unprivileged_groups,\n",
        "                                             privileged_groups=privileged_groups)\n",
        "\n",
        "    ba_arr_rw[idx] = 0.5*(classified_metric_valid_rw.true_positive_rate()\\\n",
        "                       +classified_metric_valid_rw.true_negative_rate())\n",
        "\n",
        "best_ind_rw = np.where(ba_arr_rw == np.max(ba_arr_rw))[0][0]\n",
        "best_class_thresh_rw = class_thresh_arr[best_ind_rw]\n",
        "\n",
        "print(\"Best balanced accuracy (reweighing) = %.4f\" % np.max(ba_arr_rw))\n",
        "print(\"Optimal classification threshold (reweighing) = %.4f\" % best_class_thresh_rw)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1605ed5-23b4-46b6-b894-24ac8548db06",
        "id": "QCjPdtaoa0QQ"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best balanced accuracy (reweighing) = 0.8168\n",
            "Optimal classification threshold (reweighing) = 0.2278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluation results of test set using reweigh model on different threshold."
      ],
      "metadata": {
        "id": "2iiwRReg4Jwc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_metrics_rw = {\n",
        "    'balanced accuracy': [],\n",
        "    'equal opportunity diff': [],\n",
        "    'avg odds diff':[]\n",
        "}\n",
        "\n",
        "print(\"Classification threshold used = %.4f\" % best_class_thresh_rw)\n",
        "\n",
        "for thresh in tqdm(class_thresh_arr):\n",
        "    test_pred_rw.labels = (test_pred_rw.scores >= thresh).astype(int)\n",
        "\n",
        "    metric_test_rw = ClassificationMetric(test_bld, test_pred_rw,\n",
        "                             unprivileged_groups=unprivileged_groups,\n",
        "                             privileged_groups=privileged_groups)\n",
        "\n",
        "    bal_acc = 0.5*(metric_test_rw.true_positive_rate()\\\n",
        "                       +metric_test_rw.true_negative_rate())\n",
        "    eq_opp_diff = metric_test_rw.equal_opportunity_difference()\n",
        "    avg_odd_diff = metric_test_rw.average_odds_difference()\n",
        "\n",
        "    if thresh == best_class_thresh_rw:\n",
        "        print(f\"\\nMetrics using the new optimal classification threshold = {best_class_thresh_rw:.4f} on Test set\")\n",
        "        print(\"Balanced Accuracy:\", bal_acc)\n",
        "        print(\"Equal Opportunity difference:\", eq_opp_diff)\n",
        "        print(\"Average Odds difference:\", avg_odd_diff)\n",
        "\n",
        "    if thresh == best_class_thresh:\n",
        "        print(f\"\\nMetrics using the initial optimal classification threshold = {best_class_thresh:.4f} on Test set\")\n",
        "        print(\"Balanced Accuracy:\", bal_acc)\n",
        "        print(\"Equal Opportunity difference:\", eq_opp_diff)\n",
        "        print(\"Average Odds difference:\", avg_odd_diff)\n",
        "\n",
        "    test_metrics_rw['balanced accuracy'].append(bal_acc)\n",
        "    test_metrics_rw['equal opportunity diff'].append(metric_test_rw.equal_opportunity_difference())\n",
        "    test_metrics_rw['avg odds diff'].append(metric_test_rw.average_odds_difference())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24c45949-1392-44c9-93da-1facffe9b81e",
        "id": "9LnPdh-wa0QQ"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification threshold used = 0.2278\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 27%|██▋       | 27/100 [00:00<00:02, 30.67it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Metrics using the new optimal classification threshold = 0.2278 on Test set\n",
            "Balanced Accuracy: 0.8069334170330082\n",
            "Equal Opportunity difference: 0.0259674091892389\n",
            "Average Odds difference: -0.0315799810263683\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 34%|███▍      | 34/100 [00:01<00:02, 29.12it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Metrics using the initial optimal classification threshold = 0.2872 on Test set\n",
            "Balanced Accuracy: 0.8065693677372253\n",
            "Equal Opportunity difference: 0.05118867646117142\n",
            "Average Odds difference: -0.0007364752769298544\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:01<00:00, 50.03it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Display results for all thresholds\n",
        "fig, ax1 = plt.subplots(figsize=(6,4))\n",
        "ax1.plot(class_thresh_arr, test_metrics_rw['balanced accuracy'])\n",
        "ax1.set_xlabel('Classification Thresholds', fontsize=14)\n",
        "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=14)\n",
        "ax1.xaxis.set_tick_params(labelsize=12)\n",
        "ax1.yaxis.set_tick_params(labelsize=12)\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(class_thresh_arr, test_metrics_rw['equal opportunity diff'] , color='r')\n",
        "ax2.set_ylabel('Equal Opportunity diff', color='r', fontsize=14)\n",
        "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
        "ax2.yaxis.set_tick_params(labelsize=12)\n",
        "ax2.grid(True)\n",
        "plt.title(\"Test Set Evaluation (Reweighing)\",fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "34715353-7c3a-4eb3-e5c9-f88ece5d6a43",
        "id": "gpw22dUFa0QQ",
        "cellView": "form"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Set Evaluation (Reweighing)')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Display results for all thresholds\n",
        "fig, ax1 = plt.subplots(figsize=(6,4))\n",
        "ax1.plot(class_thresh_arr, test_metrics_rw['balanced accuracy'])\n",
        "ax1.set_xlabel('Classification Thresholds', fontsize=14)\n",
        "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=14)\n",
        "ax1.xaxis.set_tick_params(labelsize=12)\n",
        "ax1.yaxis.set_tick_params(labelsize=12)\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(class_thresh_arr, test_metrics_rw['avg odds diff'] , color='r')\n",
        "ax2.set_ylabel('Average odds diff', color='r', fontsize=14)\n",
        "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
        "ax2.yaxis.set_tick_params(labelsize=12)\n",
        "ax2.grid(True)\n",
        "plt.title(\"Test Set Evaluation (Reweighing)\",fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "811cff25-41e8-41b2-80cd-476f43b73a5f",
        "id": "3rvrkO0aa0QQ",
        "cellView": "form"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Set Evaluation (Reweighing)')"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The reweighing approach was successful to mitigate bias according to both metrics. The value of both metrics is now in fairness range at new optimal classification threshold."
      ],
      "metadata": {
        "id": "zLXc-alqZPNz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Post-processing: Calibrated Equal Odds Prediction\n",
        "\n",
        "This technique has to aims:\n",
        "1. **Equal Odds**: The model should have similar accuracy for both positive and negative classes across privileged and unprivileged groups. This means equalizing the false positive rates (FPR) and/or true positive rates (TPR) across these groups.\n",
        "\n",
        "2. **Calibration**: The predicted probabilities should accurately reflect the true likelihood of the positive class. For example, if a model predicts a probability of 0.7 for that event, then approximately 70% of those predictions should be correct."
      ],
      "metadata": {
        "id": "VQHKQOj-zw_t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Odds equalizing post-processing algorithm\n",
        "from aif360.algorithms.postprocessing.calibrated_eq_odds_postprocessing import CalibratedEqOddsPostprocessing\n",
        "cost_constraint = \"fnr\" # \"fnr\", \"fpr\", \"weighted\"  FNR = 1 - TPR\n",
        "#random seed for calibrated equal odds prediction\n",
        "randseed = 42\n",
        "# Learn parameters to equalize odds and apply to create a new dataset\n",
        "CEO = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,\n",
        "                                     unprivileged_groups = unprivileged_groups,\n",
        "                                     cost_constraint=cost_constraint,\n",
        "                                     seed=randseed)\n",
        "CEO = CEO.fit(valid_bld, valid_bld_pred)"
      ],
      "metadata": {
        "id": "XCOESP5cIUTN"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_pred_ceo = CEO.predict(valid_bld_pred)\n",
        "test_pred_ceo = CEO.predict(test_bld_pred)"
      ],
      "metadata": {
        "id": "zI4jL96mIpmK"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_thresh = 100\n",
        "ba_arr_ceo = np.zeros(num_thresh)\n",
        "class_thresh_arr = np.linspace(0.01, 0.99, num_thresh)\n",
        "\n",
        "for idx, class_thresh in enumerate(class_thresh_arr):\n",
        "\n",
        "    valid_pred_ceo.labels = (valid_pred_ceo.scores >= class_thresh).astype(int)\n",
        "\n",
        "    classified_metric_valid_ceo = ClassificationMetric(valid_bld,\n",
        "                                             valid_pred_ceo,\n",
        "                                             unprivileged_groups=unprivileged_groups,\n",
        "                                             privileged_groups=privileged_groups)\n",
        "\n",
        "    ba_arr_ceo[idx] = 0.5*(classified_metric_valid_ceo.true_positive_rate()\\\n",
        "                       +classified_metric_valid_ceo.true_negative_rate())\n",
        "\n",
        "best_ind_ceo = np.where(ba_arr_ceo == np.max(ba_arr_ceo))[0][0]\n",
        "best_class_thresh_ceo = class_thresh_arr[best_ind_ceo]\n",
        "\n",
        "print(\"Best balanced accuracy (Calibrated Equalized Odds) = %.4f\" % np.max(ba_arr_ceo))\n",
        "print(\"Optimal classification threshold (Calibrated Equalized Odds) = %.4f\" % best_class_thresh_ceo)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQXKmgDIczZq",
        "outputId": "08869d76-651b-4897-8858-3fc31b9c314d"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best balanced accuracy (Calibrated Equalized Odds) = 0.7595\n",
            "Optimal classification threshold (Calibrated Equalized Odds) = 0.2674\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_metrics_ceo = {\n",
        "    'balanced accuracy': [],\n",
        "    'equal opportunity diff': [],\n",
        "    'avg odds diff':[]\n",
        "}\n",
        "\n",
        "print(\"Classification threshold used = %.4f\" % best_class_thresh)\n",
        "\n",
        "for thresh in tqdm(class_thresh_arr):\n",
        "    test_pred_ceo.labels = (test_pred_ceo.scores >= thresh).astype(int)\n",
        "\n",
        "    metric_test_ceo = ClassificationMetric(test_bld, test_pred_ceo,\n",
        "                             unprivileged_groups=unprivileged_groups,\n",
        "                             privileged_groups=privileged_groups)\n",
        "\n",
        "    bal_acc = 0.5*(metric_test_ceo.true_positive_rate()\\\n",
        "                       +metric_test_ceo.true_negative_rate())\n",
        "    eq_opp_diff = metric_test_ceo.equal_opportunity_difference()\n",
        "    avg_odd_diff = metric_test_ceo.average_odds_difference()\n",
        "\n",
        "    if thresh == best_class_thresh_ceo:\n",
        "        print(f\"\\nMetrics using the new optimal classification threshold = {best_class_thresh_ceo:.4f} on Test set\")\n",
        "        print(\"Balanced Accuracy:\", bal_acc)\n",
        "        print(\"Equal Opportunity difference:\", eq_opp_diff)\n",
        "        print(\"Average Odds difference:\", avg_odd_diff)\n",
        "\n",
        "    if thresh == best_class_thresh:\n",
        "        print(f\"\\nMetrics using the initial optimal classification threshold = {best_class_thresh:.4f} on Test set\")\n",
        "        print(\"Balanced Accuracy:\", bal_acc)\n",
        "        print(\"Equal Opportunity difference:\", eq_opp_diff)\n",
        "        print(\"Average Odds difference:\", avg_odd_diff)\n",
        "\n",
        "    test_metrics_ceo['balanced accuracy'].append(bal_acc)\n",
        "    test_metrics_ceo['equal opportunity diff'].append(metric_test_ceo.equal_opportunity_difference())\n",
        "    test_metrics_ceo['avg odds diff'].append(metric_test_ceo.average_odds_difference())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbvrahGHbUCS",
        "outputId": "ebe58303-0403-4e28-d8f0-17e7e4215899"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification threshold used = 0.2872\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 17%|█▋        | 17/100 [00:00<00:00, 166.29it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Metrics using the new optimal classification threshold = 0.2674 on Test set\n",
            "Balanced Accuracy: 0.743981618861425\n",
            "Equal Opportunity difference: -0.24122935233211118\n",
            "Average Odds difference: -0.3823883394644965\n",
            "\n",
            "Metrics using the initial optimal classification threshold = 0.2872 on Test set\n",
            "Balanced Accuracy: 0.7439239257602843\n",
            "Equal Opportunity difference: -0.2426993632736698\n",
            "Average Odds difference: -0.37703555506259434\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:02<00:00, 49.53it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Display results for all thresholds\n",
        "fig, ax1 = plt.subplots(figsize=(6,4))\n",
        "ax1.plot(class_thresh_arr, test_metrics_ceo['balanced accuracy'])\n",
        "ax1.set_xlabel('Classification Thresholds', fontsize=14)\n",
        "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=14)\n",
        "ax1.xaxis.set_tick_params(labelsize=12)\n",
        "ax1.yaxis.set_tick_params(labelsize=12)\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(class_thresh_arr, test_metrics_ceo['equal opportunity diff'] , color='r')\n",
        "ax2.set_ylabel('Equal Opportunity diff', color='r', fontsize=14)\n",
        "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
        "# ax2.axvline(best_class_thresh_ceo, color='green', linestyle='-.')\n",
        "ax2.yaxis.set_tick_params(labelsize=12)\n",
        "ax2.grid(True)\n",
        "plt.title(\"Test Set Evaluation (Calibrated Eq Odds)\", fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "cellView": "form",
        "id": "F15fBoASd26T",
        "outputId": "6895c53b-c65c-4921-cee5-17ac1021cceb"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Set Evaluation (Calibrated Eq Odds)')"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Display results for all thresholds\n",
        "fig, ax1 = plt.subplots(figsize=(6,4))\n",
        "ax1.plot(class_thresh_arr, test_metrics_ceo['balanced accuracy'])\n",
        "ax1.set_xlabel('Classification Thresholds', fontsize=14)\n",
        "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=14)\n",
        "ax1.xaxis.set_tick_params(labelsize=12)\n",
        "ax1.yaxis.set_tick_params(labelsize=12)\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(class_thresh_arr, test_metrics_ceo['avg odds diff'] , color='r')\n",
        "ax2.set_ylabel('Average odds diff', color='r', fontsize=14)\n",
        "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
        "ax2.yaxis.set_tick_params(labelsize=12)\n",
        "ax2.grid(True)\n",
        "plt.title(\"Test Set Evaluation  (Calibrated Eq Odds)\", fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "cellView": "form",
        "id": "nWwtjurLe1_T",
        "outputId": "0d7f896d-6892-452b-c92d-aed1434aeecc"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Set Evaluation  (Calibrated Eq Odds)')"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Troubleshooting\n",
        "ideas to understand why Calibrated Equalized Odds wasn't effective."
      ],
      "metadata": {
        "id": "KcAZ1IJ8YD5J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "from sklearn.calibration import calibration_curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Split the data into privileged and unprivileged groups\n",
        "privileged_indices = test_bld.protected_attributes[:, 0] == 1  # Adjust the index based on your protected attribute\n",
        "unprivileged_indices = test_bld.protected_attributes[:, 0] == 0\n",
        "\n",
        "y_true_priv = test_bld.labels[privileged_indices]\n",
        "y_pred_priv = test_bld_pred.scores[privileged_indices]\n",
        "\n",
        "y_true_unpriv = test_bld.labels[unprivileged_indices]\n",
        "y_pred_unpriv = test_bld_pred.scores[unprivileged_indices]\n",
        "\n",
        "# Compute calibration curves for each group\n",
        "prob_true_priv, prob_pred_priv = calibration_curve(y_true_priv, y_pred_priv, n_bins=10)\n",
        "prob_true_unpriv, prob_pred_unpriv = calibration_curve(y_true_unpriv, y_pred_unpriv, n_bins=10)\n",
        "plt.figure(figsize=(6, 4))\n",
        "\n",
        "# Plot calibration curves\n",
        "plt.plot(prob_pred_priv, prob_true_priv, marker='o', linewidth=1, label='Privileged Group')\n",
        "plt.plot(prob_pred_unpriv, prob_true_unpriv, marker='x', linewidth=1, label='Unprivileged Group')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', label='Perfectly calibrated')\n",
        "\n",
        "plt.xlabel('Mean predicted probability')\n",
        "plt.ylabel('Fraction of positives')\n",
        "plt.legend()\n",
        "plt.title('Calibration Plot by Group')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "gXf7xgJWQZIJ",
        "outputId": "da394237-670d-4e94-8751-47530e917348",
        "cellView": "form"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "y_pred_priv = y_pred_priv.flatten()\n",
        "y_pred_unpriv = y_pred_unpriv.flatten()\n",
        "\n",
        "# Now concatenate them and create the DataFrame\n",
        "df_scores = pd.DataFrame({\n",
        "    'Score': np.concatenate([y_pred_priv, y_pred_unpriv]),\n",
        "    'Group': ['Privileged']*len(y_pred_priv) + ['Unprivileged']*len(y_pred_unpriv)\n",
        "})\n",
        "\n",
        "# Plot the distribution of scores\n",
        "plt.figure(figsize=(6,4))\n",
        "sns.kdeplot(data=df_scores, x='Score', hue='Group', fill=True)\n",
        "plt.title('Distribution of Predicted Scores by Group')\n",
        "plt.xlabel('Predicted Probability')\n",
        "plt.ylabel('Density')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "cellView": "form",
        "id": "KTtq6EphVN-g",
        "outputId": "6fcda012-6474-4dac-b669-e53ea2f86111"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Summary"
      ],
      "metadata": {
        "id": "FJL2XmshY3n4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "data = {\n",
        "    'Model': ['Initial', 'Aft. Reweighing', 'Aft. Calibrate Equalized Odds'],\n",
        "    'Balanced Accuracy': [0.815, 0.806, 0.743],\n",
        "    'Equal Opportunity Difference': [-0.195, 0.0511, -0.242],\n",
        "    'Average Odds Difference': [-0.204, -0.0007, -0.377]\n",
        "}\n",
        "\n",
        "results_df = pd.DataFrame(data)\n",
        "\n",
        "results_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "Y-ttnChhh6RD",
        "outputId": "9f002426-bb42-4f27-f6ba-6bdfcfa87231"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Model  Balanced Accuracy  \\\n",
              "0                        Initial              0.815   \n",
              "1                Aft. Reweighing              0.806   \n",
              "2  Aft. Calibrate Equalized Odds              0.743   \n",
              "\n",
              "   Equal Opportunity Difference  Average Odds Difference  \n",
              "0                       -0.1950                  -0.2040  \n",
              "1                        0.0511                  -0.0007  \n",
              "2                       -0.2420                  -0.3770  "
            ],
            "text/html": [
              "\n",
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              "    <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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>Equal Opportunity Difference</th>\n",
              "      <th>Average Odds Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Initial</td>\n",
              "      <td>0.815</td>\n",
              "      <td>-0.1950</td>\n",
              "      <td>-0.2040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aft. Reweighing</td>\n",
              "      <td>0.806</td>\n",
              "      <td>0.0511</td>\n",
              "      <td>-0.0007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aft. Calibrate Equalized Odds</td>\n",
              "      <td>0.743</td>\n",
              "      <td>-0.2420</td>\n",
              "      <td>-0.3770</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf844ad6-3d4a-429b-916b-1b05c479a11e')\"\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",
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              "  </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-cf844ad6-3d4a-429b-916b-1b05c479a11e 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-cf844ad6-3d4a-429b-916b-1b05c479a11e');\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-c19e5daf-5d51-4f7f-b8b6-594a311c286a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c19e5daf-5d51-4f7f-b8b6-594a311c286a')\"\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-c19e5daf-5d51-4f7f-b8b6-594a311c286a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Initial\",\n          \"Aft. Reweighing\",\n          \"Aft. Calibrate Equalized Odds\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balanced Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.039230090491866064,\n        \"min\": 0.743,\n        \"max\": 0.815,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.815,\n          0.806,\n          0.743\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equal Opportunity Difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15741760807906252,\n        \"min\": -0.242,\n        \"max\": 0.0511,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.195,\n          0.0511,\n          -0.242\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average Odds Difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18835320544126666,\n        \"min\": -0.377,\n        \"max\": -0.0007,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.204,\n          -0.0007,\n          -0.377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we can notice that Reweighing preprocessing algorithm was successful in mitigating the bias and satisfying the fairness constraints we chose. However, Calibrated Equalized Odds not only reduced the balanced accuracy but also made the predictions more biased according to the fairness metrics."
      ],
      "metadata": {
        "id": "3jIBePTgF-Pw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If possible, all algorithms from all permissible categories should be tested because the ultimate performance depends on dataset characteristics: **there is no one best algorithm independent of dataset **!"
      ],
      "metadata": {
        "id": "GJDPe3jdF4BJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercise\n",
        "Try Adversial Debiasing (in-processing) algorithm and compare its effectiveness to these two models."
      ],
      "metadata": {
        "id": "qnfZwdcqAb8Y"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jv3Pi0MpZOpm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}