assets/Lab_2_Fairlearn_Student_DS_UA_202_Spring'25.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **Lab 2 - Detecting and Mitigating Biases using Fairlearn**"
      ],
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        "**Following Labs:**\n",
        "\n",
        "*   Lab2: Detecting bias and mitigating bias via pre-processing interventions\n",
        "*   Lab3: Mitigating bias via in-processing and postprocessing interventions"
      ],
      "metadata": {
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      "source": [
        "#Detecting Bias Using Fairlearn"
      ],
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        "id": "3LrSVlCSDA9Z"
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        "## Bias in ML\n",
        "\n",
        "A machine learning algorithm will attempt to find patterns, or generalizations, in the training dataset to use when a prediction for a new instance is needed. For example, the model may discover a pattern whereby a person with a salary over \\$40,000 and an outstanding debt of less than $5 is very likely to repay a loan.\n",
        "\n",
        "However, sometimes the patterns that are found and replicated by a model may not be desirable or, even worse, might be illegal. For example, a loan repayment model may determine that age plays a significant role in the prediction of repayment because the training dataset happened to have better repayment for one age group compared to another. This raises two problems: 1) the training dataset may not be representative of the true population of loan applications for all age groups, and 2) even if it is representative, it is illegal (with limited exceptions) to base loan decisions on an applicant's age, regardless of whether this is an accurate basis for prediction based on historical data.\n",
        "\n",
        "The loan scenario describes an intuitive example of illegal bias. However, not all undesirable biases in machine learning are illegal; it may also exist in more subtle ways. For example, a loan company may want a diverse portfolio of customers across all income levels, and thus, will deem it undesirable if they are making more loans to high income levels over low income levels. Although this is not illegal or unethical, it is undesirable for the company's strategy.\n",
        "\n",
        "## The `Fairlearn` toolkit\n",
        "\n",
        "Fairlearn is a toolkit designed to help address this problem with fairness metrics and bias mitigators. Fairness metrics can be used to check for bias in machine learning workflows. Bias mitigators can be used to overcome bias in the workflow to produce a more fair outcome.\n",
        "\n",
        "As these two examples illustrate, a bias detection and/or mitigation toolkit needs to be tailored to the particular bias of interest. More specifically, we need to define the attribute(s), called protected (or sensitive) attributes of interest: the attribute whose skewness/bias we are trying to detect and mitigate. The term suggests that the system designer should be sensitive to these features when assessing and mitigating group fairness.\n",
        "\n",
        "Several stages of the machine learning pipeline are susceptible to bias. One useful way to categorize these stages are, intuitively, 'before,' 'during,' and 'after' training a model. These stages are commonly referred to as *pre-processing*, *in-processing*, and *post-processing* (in Fairlearn, in-processing techniques are available in the *reductions* package)."
      ],
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      "source": [
        "## Fairlearn\n",
        "\n",
        "In Labs 2 and 3 we will use Fairlearn to detect and mitigate bias in a classifier. We will use the [ACS PUMS files](https://www.census.gov/programs-surveys/acs/microdata.html), particularly a fraction of the ACS Income dataset, and train a classifier to predict whether an individual has a salary greater than $50K. The protected attribute will be the sex of the individual.\n",
        "\n",
        "In this Lab 2 notebook, we will:\n",
        "\n",
        "1. Load the dataset and conduct basic data exploration and preprocessing\n",
        "2. Explore possible fairness metrics\n",
        "3. Train a logistic regression classifier and evaluate the fairness of this classifier\n",
        "4. Train other logistic regression classifiers with pre-processing interventions and re-evaluate fairness\n",
        "5. Compare the results obtained in 3 and 4"
      ],
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      "source": [
        "#1. Import Statements\n",
        "First, we install Fairlearn and import the necessary packages\n",
        "\n",
        "**BEWARE**: On Colab, you will probably have to restart the runtime (in the `Runtime` dropdown menu at the top of the screen) to import these successfully."
      ],
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      "source": [
        "%%capture\n",
        "#@markdown\n",
        "\n",
        "!pip install fairlearn\n",
        "!pip install folktables\n",
        "# !pip install matplotlib==3.4.1 --force-reinstall\n",
        "# !git clone https://github.com/lurosenb/superquail"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mtick\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import sklearn.metrics as skm\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "from folktables import ACSDataSource, ACSEmployment, ACSIncome, ACSPublicCoverage, ACSTravelTime\n",
        "# from superquail.data.acs_helper import ACSData\n",
        "\n",
        "from fairlearn.metrics import MetricFrame\n",
        "from fairlearn.metrics import demographic_parity_difference, demographic_parity_ratio, selection_rate\n",
        "from fairlearn.metrics import false_negative_rate, false_positive_rate\n",
        "\n",
        "from IPython.display import clear_output"
      ],
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      "source": [
        "#2. Load Data, Conduct Exploratory Analysis, and Preprocess Data\n",
        "Next, we will load the Folktables dataset.  The Folktables dataset is taken from US Census Data and is built to solve a few simple prediction tasks. The sample we pull is data from 2018 in California. The column names are described in the table below. Note that certain categorical variables have been mapped to integer values, which we will keep as-is for the following analyses.\n",
        "\n",
        "For more information on the this dataset, please see the following paper (namely page 18): https://eaamo2021.eaamo.org/accepted/acceptednonarchival/EAMO21_paper_16.pdf"
      ],
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        "| Column Name | Feature | Description/Notes |\n",
        "| --- | ----------- | --- |\n",
        "| PINCP | Total person’s income | (Target) 1 if >= $50k, 0 if less |\n",
        "| SEX | Sex | (Sensitive Attribute) Male=1, Female=2 |\n",
        "| RAC1P | Race | (Sensitive Attribute) White=1, Black=2, Other races are between 3 and 9 |\n",
        "| AGEP | Age | Ranges from 0-99 |\n",
        "| COW | Class of Worker | Ranges 1-9, see paper for description |\n",
        "| SCHL | Education Level | Ranges 1-24, see paper for description |\n",
        "| MAR | Marital Status | Ranges 1-5, see paper for description |\n",
        "| OCCP | Occupation | Codes taken from Public Use Microdata Sample (PUMS) from the US Census, see paper |\n",
        "| POBP | Place of Birth | Codes taken from Public Use Microdata Sample (PUMS) from the US Census, see paper |\n",
        "| RELP | Relationship | Relationship of individual to person who responded to the Census taker. Ranges 0-17, see paper for description |\n",
        "| WKHP | Hours worked per week | Ranges from 0-99, averaged over previous year |"
      ],
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      "source": [
        "Download the data from this [link](https://drive.google.com/file/d/1pWRwWc3y6bSNBhjIw0dIqlMjyoO-CxVF/view?usp=sharing) and upload it to Colab."
      ],
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      "cell_type": "code",
      "source": [
        "#@markdown Read in the folktables dataset\n",
        "import gdown\n",
        "# Replace with your actual file ID from the Google Drive link\n",
        "file_id = '1PNFAJjJN0Wh0H_IGPCFNaEg8iOF8AmrP'\n",
        "# Construct the downloadable URL\n",
        "url = f'https://drive.google.com/uc?id={file_id}'\n",
        "# Download the file\n",
        "gdown.download(url, 'downloaded_file.csv', quiet=False)\n",
        "# Read the CSV file\n",
        "acs_data = pd.read_csv('downloaded_file.csv')\n",
        "\n",
        "features, label, group = ACSIncome.df_to_numpy(acs_data)\n",
        "full_df = pd.DataFrame(features, columns=ACSIncome.features)\n",
        "full_df[ACSIncome.target] = label\n",
        "\n",
        "# full_df, features_df, target_df, groups_df = ACSData().return_acs_data_scenario(scenario=\"ACSIncome\", subsample=70000)\n",
        "\n",
        "print(full_df.shape)\n",
        "full_df.head()"
      ],
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          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1PNFAJjJN0Wh0H_IGPCFNaEg8iOF8AmrP\n",
            "From (redirected): https://drive.google.com/uc?id=1PNFAJjJN0Wh0H_IGPCFNaEg8iOF8AmrP&confirm=t&uuid=33eb049f-4558-4a86-bdb3-bf74588abde7\n",
            "To: /content/downloaded_file.csv\n",
            "100%|██████████| 138M/138M [00:01<00:00, 81.4MB/s]\n"
          ]
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          "text": [
            "(103021, 11)\n"
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              "   AGEP  COW  SCHL  MAR    OCCP   POBP  RELP  WKHP  SEX  RAC1P  PINCP\n",
              "0  26.0  2.0  21.0  5.0  5110.0  109.0  17.0  50.0  2.0    1.0  False\n",
              "1  21.0  1.0  20.0  5.0  4110.0   36.0  17.0   2.0  1.0    1.0  False\n",
              "2  19.0  1.0  19.0  5.0  2723.0   36.0  17.0   4.0  2.0    1.0  False\n",
              "3  22.0  1.0  21.0  5.0  5120.0  207.0  17.0  50.0  2.0    6.0  False\n",
              "4  19.0  2.0  16.0  5.0  4622.0   22.0  17.0  20.0  1.0    9.0  False"
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Check missing values and data types\n",
        "full_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9D-hmorRFKia",
        "outputId": "cce3c313-c325-4788-92ac-5588ebe18fd6",
        "cellView": "form"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 103021 entries, 0 to 103020\n",
            "Data columns (total 11 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   AGEP    103021 non-null  float64\n",
            " 1   COW     103021 non-null  float64\n",
            " 2   SCHL    103021 non-null  float64\n",
            " 3   MAR     103021 non-null  float64\n",
            " 4   OCCP    103021 non-null  float64\n",
            " 5   POBP    103021 non-null  float64\n",
            " 6   RELP    103021 non-null  float64\n",
            " 7   WKHP    103021 non-null  float64\n",
            " 8   SEX     103021 non-null  float64\n",
            " 9   RAC1P   103021 non-null  float64\n",
            " 10  PINCP   103021 non-null  bool   \n",
            "dtypes: bool(1), float64(10)\n",
            "memory usage: 8.0 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Change data types of categorical features\n",
        "numerical_cols = ['AGEP','WKHP']\n",
        "categorical_cols = ['COW','SCHL','MAR','OCCP','POBP','RELP','RAC1P','SEX']\n",
        "\n",
        "for col in categorical_cols:\n",
        "  full_df[col] = full_df[col].astype('int')\n",
        "  full_df[col] = full_df[col].astype('str')\n",
        "\n",
        "full_df.info()\n",
        "full_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "j1x2mdUaID9G",
        "outputId": "97e263bd-cd09-41b0-8e0b-2a6d4d33baa2",
        "cellView": "form"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 103021 entries, 0 to 103020\n",
            "Data columns (total 11 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   AGEP    103021 non-null  float64\n",
            " 1   COW     103021 non-null  object \n",
            " 2   SCHL    103021 non-null  object \n",
            " 3   MAR     103021 non-null  object \n",
            " 4   OCCP    103021 non-null  object \n",
            " 5   POBP    103021 non-null  object \n",
            " 6   RELP    103021 non-null  object \n",
            " 7   WKHP    103021 non-null  float64\n",
            " 8   SEX     103021 non-null  object \n",
            " 9   RAC1P   103021 non-null  object \n",
            " 10  PINCP   103021 non-null  bool   \n",
            "dtypes: bool(1), float64(2), object(8)\n",
            "memory usage: 8.0+ MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AGEP COW SCHL MAR  OCCP POBP RELP  WKHP SEX RAC1P  PINCP\n",
              "0  26.0   2   21   5  5110  109   17  50.0   2     1  False\n",
              "1  21.0   1   20   5  4110   36   17   2.0   1     1  False\n",
              "2  19.0   1   19   5  2723   36   17   4.0   2     1  False\n",
              "3  22.0   1   21   5  5120  207   17  50.0   2     6  False\n",
              "4  19.0   2   16   5  4622   22   17  20.0   1     9  False"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will conduct some basic exploratory analysis of the data beginning with plotting the distributions of our features."
      ],
      "metadata": {
        "id": "pHRpGsboZ_wa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Plot distribution of categorical columns\n",
        "fig, ax = plt.subplots(2,4,figsize=(12,8))\n",
        "ax[0,0].barh(full_df['COW'].value_counts().index[::-1], full_df['COW'].value_counts()[::-1])\n",
        "ax[0,0].set_title('COW')\n",
        "\n",
        "ax[0,1].barh(full_df['SCHL'].value_counts().index[:10][::-1], full_df['SCHL'].value_counts()[:10][::-1])\n",
        "ax[0,1].set_title('SCHL (top-10)')\n",
        "\n",
        "ax[0,2].barh(full_df['MAR'].value_counts().index[::-1], full_df['MAR'].value_counts()[::-1])\n",
        "ax[0,2].set_title('MAR')\n",
        "\n",
        "ax[0,3].barh(full_df['OCCP'].value_counts().index[:10][::-1], full_df['OCCP'].value_counts()[:10][::-1])\n",
        "ax[0,3].set_title('OCCP (top-10)')\n",
        "\n",
        "ax[1,0].barh(full_df['POBP'].value_counts().index[:10][::-1], full_df['POBP'].value_counts()[:10][::-1])\n",
        "ax[1,0].set_title('POBP (top-10)')\n",
        "\n",
        "ax[1,1].barh(full_df['RELP'].value_counts().index[:10][::-1], full_df['RELP'].value_counts()[:10][::-1])\n",
        "ax[1,1].set_title('RELP (top-10)')\n",
        "\n",
        "ax[1,2].barh(full_df['SEX'].value_counts().index[::-1], full_df['SEX'].value_counts()[::-1])\n",
        "ax[1,2].set_title('SEX')\n",
        "labels = ('Female = 2', 'Male = 1')\n",
        "ax[1,2].set_yticklabels(labels)\n",
        "\n",
        "ax[1,3].barh(full_df['RAC1P'].value_counts().index[::-1], full_df['RAC1P'].value_counts()[::-1])\n",
        "ax[1,3].set_title('RAC1P')\n",
        "\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "umsO2aZ3J3WT",
        "outputId": "eb377f0d-fba6-4220-c526-2e5af9bdb409",
        "cellView": "form"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-e594a9aa6ce7>:24: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
            "  ax[1,2].set_yticklabels(labels)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 8 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Plot distribution of numeric features\n",
        "fig, ax = plt.subplots(1,2,figsize=(12,8))\n",
        "\n",
        "#histogram for AGEP (age)\n",
        "num_of_bins_agep = 10\n",
        "y_vals_agep, x_vals_agep, e_agep = ax[0].hist(full_df['AGEP'], bins=num_of_bins_agep, edgecolor='black')\n",
        "ax[0].set_title(\"Histogram of AGEP\")\n",
        "ax[0].set_xlabel(\"age\")\n",
        "ax[0].set_ylabel(\"Percentage\")\n",
        "y_max_agep = round((max(y_vals_agep) / len(full_df)) + 0.02, 2)\n",
        "ax[0].set_yticks(ticks=np.arange(0.0, y_max_agep * len(full_df), 0.01 * len(full_df)))\n",
        "ax[0].set_ylim(ax[0].get_yticks()[0], ax[0].get_yticks()[-1])\n",
        "ax[0].yaxis.set_major_formatter(ticker.PercentFormatter(xmax=len(full_df)))\n",
        "\n",
        "#histogram for WKHP (Hours worked per week)\n",
        "num_of_bins_wkhp = 10\n",
        "y_vals_wkhp, x_vals_wkhp, e_wkhp = ax[1].hist(full_df['WKHP'], bins=num_of_bins_wkhp, edgecolor='black')\n",
        "ax[1].set_title(\"Histogram of WKHP\")\n",
        "ax[1].set_xlabel(\"hours worked per week\")\n",
        "ax[1].set_ylabel(\"Percentage\")\n",
        "y_max_wkhp = round((max(y_vals_wkhp) / len(full_df)) + 0.05, 2)\n",
        "ax[1].set_yticks(ticks=np.arange(0.0, y_max_wkhp * len(full_df), 0.05 * len(full_df)))\n",
        "ax[1].set_ylim(ax[1].get_yticks()[0], ax[1].get_yticks()[-1])\n",
        "ax[1].yaxis.set_major_formatter(ticker.PercentFormatter(xmax=len(full_df)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "fIN5VbbVQT4_",
        "outputId": "da263e31-bc0b-4f83-9324-867692665e5e",
        "cellView": "form"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, there is nothing very unusual about the distritubtion of the features in this dataset. Also, we note that the proportion of men and women is rather balanced.\n",
        "\n",
        "We can also examine the pairwise correlations between the numeric features and our target variable."
      ],
      "metadata": {
        "id": "uhrMJoM1sOrj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Plot pairwise correlations between numeric features\n",
        "\n",
        "sn.heatmap(full_df.corr(), mask=np.identity(len(full_df.corr())), annot=True, cmap='Blues')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "KXO7X2tMyTI6",
        "outputId": "4368f959-41fa-4e68-b504-d84f181ea060",
        "cellView": "form"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we can see that there are only relatively low correlations between our target variable (PINCP) and our numeric features of age and hours worked per week."
      ],
      "metadata": {
        "id": "5kyJCKQ3yxhQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we can look at the distribution of our target variable as well as the joint distribution of our protected and target attributes."
      ],
      "metadata": {
        "id": "iL6p5-Axxbvh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Examine distribution of target variable\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "full_df['PINCP'].value_counts().divide(full_df.shape[0]).plot(kind='bar')\n",
        "ax.set_xlabel('Income')\n",
        "ax.set_ylabel('Frequency')\n",
        "plt.setp(ax.get_xticklabels(), rotation=0, ha='center')\n",
        "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
        "labels_target = ('0 (<$50k)', '1 (>=$50k)')\n",
        "ax.set_xticklabels(labels_target)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "7JCRb5o76oiH",
        "outputId": "dfb669e5-98f9-4114-8759-2f2081422ba4",
        "cellView": "form"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph above we note that there is a considerable imbalance of the target variable. Let's see how is this distribution looks by gender."
      ],
      "metadata": {
        "id": "Kq9_1ZSXduNs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Plot distribution of target variable among males and plot distribution of target variable among females\n",
        "hist_df = full_df.groupby(['SEX','PINCP']).size().to_frame('count').reset_index()\n",
        "new_col = full_df.groupby(['SEX']).PINCP.value_counts(normalize=True).values\n",
        "hist_df['frac'] = new_col\n",
        "hist_df.replace({'SEX': {'1': 'Male', '2': 'Female'}}, inplace=True)\n",
        "hist_df.replace({'PINCP': {0.0: '<$50k', 1.0: '>=$50k'}}, inplace=True)\n",
        "sn.barplot(x='PINCP', y='frac', hue='SEX', data=hist_df)\n",
        "plt.ylabel('Percent of Group')\n",
        "plt.xlabel('Income')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "rWqWUOfamsAm",
        "outputId": "3db16b85-8eea-4de4-ef5b-7e1062094f5c",
        "cellView": "form"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Showing the number of Males and Females per Income group\n",
        "full_df.groupby(['SEX', 'PINCP'])['PINCP'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "mb4qHH0dQYAB",
        "outputId": "656939a0-9a3a-4ccf-e666-34ab092ed484",
        "cellView": "form"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEX  PINCP\n",
              "1    False    27598\n",
              "     True     24580\n",
              "2    False    32696\n",
              "     True     18147\n",
              "Name: PINCP, dtype: int64"
            ],
            "text/html": [
              "<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></th>\n",
              "      <th>PINCP</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEX</th>\n",
              "      <th>PINCP</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>False</th>\n",
              "      <td>27598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>24580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>False</th>\n",
              "      <td>32696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>18147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we can see that the proportion of males who make at least \\$50k is higher than the proportion of females who make at least $50k in this dataset. Thus, the initial finding about imbalance of the target variable is more accentuated for females."
      ],
      "metadata": {
        "id": "3lB_TQJGE1tV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **TODO**: Given the graphs above, what results might we expect from our classifier when it comes to labelling males and females as high or low income?"
      ],
      "metadata": {
        "id": "c1ffXieBGVGx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer**:"
      ],
      "metadata": {
        "id": "vpjRuRyZgbHi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **TODO**: Why might this data be biased? What type of bias is this?\n"
      ],
      "metadata": {
        "id": "Vccos4UWJieU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer**:"
      ],
      "metadata": {
        "id": "7oVti2pfgfqD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown #**TODO**: write some code in this cell that replicates the above histogram plot for black and white individuals in the data.\n",
        "# TODO: write code here"
      ],
      "metadata": {
        "id": "nQP7PQb0BGWv",
        "cellView": "form"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing\n",
        "Next, we will do some preprocessing on our data to prepare it for use in our model."
      ],
      "metadata": {
        "id": "v7mYHwvvrlZn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Standardize numerical features\n",
        "scaler = StandardScaler()\n",
        "full_df[numerical_cols] = scaler.fit_transform(full_df[numerical_cols])\n",
        "display(full_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "UJmRpooUU6cC",
        "outputId": "e85e6829-7103-4bad-b569-d8871f9bc89d",
        "cellView": "form"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            AGEP COW SCHL MAR  OCCP POBP RELP      WKHP SEX RAC1P  PINCP\n",
              "0      -1.139728   2   21   5  5110  109   17  0.947328   2     1  False\n",
              "1      -1.467170   1   20   5  4110   36   17 -2.746395   1     1  False\n",
              "2      -1.598147   1   19   5  2723   36   17 -2.592490   2     1  False\n",
              "3      -1.401682   1   21   5  5120  207   17  0.947328   2     6  False\n",
              "4      -1.598147   2   16   5  4622   22   17 -1.361249   1     9  False\n",
              "...          ...  ..  ...  ..   ...  ...  ...       ...  ..   ...    ...\n",
              "103016  1.414322   1   11   1  4510  329    6  0.177803   2     1  False\n",
              "103017 -0.484843   1   22   1  1021   36    0  0.177803   2     1   True\n",
              "103018 -0.353866   1   22   1  1021   53    1  0.177803   1     6   True\n",
              "103019 -1.074239   1   21   5  2205   36    0  0.562566   1     1  False\n",
              "103020  0.824926   1   21   1  9142  414    6  0.562566   1     1  False\n",
              "\n",
              "[103021 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7f58493-c3a5-474a-bd66-e5a35fd7047c\" class=\"colab-df-container\">\n",
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    {
      "cell_type": "code",
      "source": [
        "#@markdown One-hot encode categorical features\n",
        "full_df = pd.get_dummies(full_df, columns=categorical_cols)\n",
        "display(full_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "ttOb_ULRsGUB",
        "outputId": "e2e3e030-2586-4e12-9e23-e7860cb2ed47",
        "cellView": "form"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            AGEP      WKHP  PINCP  COW_1  COW_2  COW_3  COW_4  COW_5  COW_6  \\\n",
              "0      -1.139728  0.947328  False  False   True  False  False  False  False   \n",
              "1      -1.467170 -2.746395  False   True  False  False  False  False  False   \n",
              "2      -1.598147 -2.592490  False   True  False  False  False  False  False   \n",
              "3      -1.401682  0.947328  False   True  False  False  False  False  False   \n",
              "4      -1.598147 -1.361249  False  False   True  False  False  False  False   \n",
              "...          ...       ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "103016  1.414322  0.177803  False   True  False  False  False  False  False   \n",
              "103017 -0.484843  0.177803   True   True  False  False  False  False  False   \n",
              "103018 -0.353866  0.177803   True   True  False  False  False  False  False   \n",
              "103019 -1.074239  0.562566  False   True  False  False  False  False  False   \n",
              "103020  0.824926  0.562566  False   True  False  False  False  False  False   \n",
              "\n",
              "        COW_7  ...  RAC1P_2  RAC1P_3  RAC1P_4  RAC1P_5  RAC1P_6  RAC1P_7  \\\n",
              "0       False  ...    False    False    False    False    False    False   \n",
              "1       False  ...    False    False    False    False    False    False   \n",
              "2       False  ...    False    False    False    False    False    False   \n",
              "3       False  ...    False    False    False    False     True    False   \n",
              "4       False  ...    False    False    False    False    False    False   \n",
              "...       ...  ...      ...      ...      ...      ...      ...      ...   \n",
              "103016  False  ...    False    False    False    False    False    False   \n",
              "103017  False  ...    False    False    False    False    False    False   \n",
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      "source": [
        "#@markdown Since the sex attribute is already binary we can drop one of the redundant dummy columns\n",
        "#note: males are now labeled as 1 and females are labeled as 0\n",
        "full_df.drop(columns=['SEX_2'], inplace=True)\n",
        "full_df.rename(columns={'SEX_1':'SEX'}, inplace=True)\n",
        "\n",
        "full_df.head()"
      ],
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      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       AGEP      WKHP  PINCP  COW_1  COW_2  COW_3  COW_4  COW_5  COW_6  COW_7  \\\n",
              "0 -1.139728  0.947328  False  False   True  False  False  False  False  False   \n",
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              "4 -1.598147 -1.361249  False  False   True  False  False  False  False  False   \n",
              "\n",
              "   ...  RAC1P_1  RAC1P_2  RAC1P_3  RAC1P_4  RAC1P_5  RAC1P_6  RAC1P_7  \\\n",
              "0  ...     True    False    False    False    False    False    False   \n",
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              "3  ...    False    False    False    False    False     True    False   \n",
              "4  ...    False    False    False    False    False    False    False   \n",
              "\n",
              "   RAC1P_8  RAC1P_9    SEX  \n",
              "0    False    False  False  \n",
              "1    False    False   True  \n",
              "2    False    False  False  \n",
              "3    False    False  False  \n",
              "4    False     True   True  \n",
              "\n",
              "[5 rows x 811 columns]"
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              "      <th></th>\n",
              "      <th>AGEP</th>\n",
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              "      <td>True</td>\n",
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              "<p>5 rows × 811 columns</p>\n",
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          "metadata": {},
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    {
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      "source": [
        "#3. Train Logistic Regression Classifier\n",
        "Next, we will split our data into training and test sets randomly.  Then, we will train a logistic regression classifier and evaluate possible biases within that classifier.\n",
        "\n",
        "### Aside: Accuracy as a Metric\n",
        "\n",
        "Traditional machine learning (i.e. without a focus on fairness) often measures the quality of a classifier by it's **accuracy**, or what fraction of samples were labeled correctly:\n",
        "$$\n",
        "\\text{Accuracy} = \\frac{\\text{Number of Correctly Labeled People}}{\\text{Total Number of People}}\n",
        "$$\n",
        "This can also be expressed using the terms of the [\"Confusion Matrix\"](https://en.wikipedia.org/wiki/Confusion_matrix), where we let\n",
        "- $\\text{TP} = $ \"True Positives\" $ = \\text{Num. people Correctly labeled as Positive}$\n",
        "- $\\text{FP} = $ \"False Positives\" $ = \\text{Num. people Wrongly labeled as Positive}$\n",
        "- $\\text{TN} = $ \"True Negatives\" $ = \\text{Num. people Correctly labeled as Negative}$\n",
        "- $\\text{FN} = $ \"False Negatives\" $ = \\text{Num. people Wrongly labeled as Negative}$\n",
        "\n",
        "Which lets us express the Accuacy as\n",
        "$$\n",
        "\\text{Accuracy} = \\frac{TP + TN}{TP + FP + TN + FN}\n",
        "$$"
      ],
      "metadata": {
        "id": "lldcQFkCx1Y5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Split data into training and test sets\n",
        "target = full_df['PINCP']\n",
        "full_df.drop(columns='PINCP', inplace=True)\n",
        "\n",
        "#note: here we are setting a value for the random_state (seed) parameter so that the results of this lab will remain consistent\n",
        "X_train, X_test, y_train, y_test = train_test_split(full_df, target, test_size=0.2, random_state=4)\n",
        "\n",
        "print(f'X_train shape: {X_train.shape}')\n",
        "print(f'X_test shape: {X_test.shape}')\n",
        "print(f'y_train shape: {y_train.shape}')\n",
        "print(f'y_test shape: {y_test.shape}')"
      ],
      "metadata": {
        "id": "ugcIGIK_XDxC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4ae95cb-32e4-45d9-965f-85e6c3e4f071",
        "cellView": "form"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (82416, 810)\n",
            "X_test shape: (20605, 810)\n",
            "y_train shape: (82416,)\n",
            "y_test shape: (20605,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown #**TODO**: What would be the accuracy ($\\frac{(TP + TN)}{(TP + FP + TN + FN)}$) of a classifier that always predicts the majority label (baseline classifier)?\n",
        "#@markdown Since the majority label is 0, the accuracy of this majority label classifier would be the number of 0's over the total number of records.\n",
        "\n",
        "# TODO: write code here\n",
        "\n",
        "# baseline_accuracy = ...\n",
        "# print(f'General baseline accuracy: {baseline_accuracy:.4f}')\n",
        "\n",
        "y_test.value_counts()"
      ],
      "metadata": {
        "id": "NlbkMZZEoFxG",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "e18b1e0a-2350-4bf7-d8b9-ddd8d157094e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "PINCP\n",
              "False    12111\n",
              "True      8494\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# @markdown #**TODO**: What would be the accuracy ($\\frac{(TP + TN)}{(TP + FP + TN + FN)}$) for the Male and Female groups considering a classifier that always predicts the majority label (baseline classifier) for each of these groups?\n",
        "\n",
        "# TODO: write code here\n",
        "\n",
        "# male_baseline_accuracy = ...\n",
        "# female_baseline_accuracy = ...\n",
        "# print(f'Male baseline accuracy: {male_baseline_accuracy:.4f}')\n",
        "# print(f'Female baseline accuracy: {female_baseline_accuracy:.4f}')"
      ],
      "metadata": {
        "id": "FHt5VXJefZ8L",
        "cellView": "form"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Implement logistic regression\n",
        "clf = LogisticRegression()\n",
        "clf.fit(X_train, y_train)\n",
        "clf_accuracy = clf.score(X_test, y_test)\n",
        "\n",
        "clear_output()\n",
        "\n",
        "print(f'Logistic Regression test accuracy: {clf_accuracy:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6f1ZdiyXSE3",
        "outputId": "bf38d6c7-ffc0-43d6-b6e1-39bc91e195e3",
        "cellView": "form"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression test accuracy: 0.8045\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate fairness"
      ],
      "metadata": {
        "id": "d9vugXFztlG8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will evaluate the fairness of our classifier on the test set. We first define the **selection rate** of the classifier on a group:\n",
        "$$\n",
        "\\text{Selection Rate} = \\frac{\\text{Number of People Classified Positive}}{\\text{Total Number of People}}\n",
        "$$\n",
        "We will compute the selection rate amongst men and amongst women, and compare them. The difference of their selection rates is called the **Demographic parity difference**, and the ratio of their rates is called the **Demographic parity ratio**.\n",
        "\n",
        "In general, if we have more than 2 classes,\n",
        "- The demographic parity difference is the difference of the largest and smallest selection rates, so it is always positive. A demographic parity difference of 0 means that all groups have the same selection rate.\n",
        "\n",
        "- The demographic parity ratio is the ratio of the smallest to largest selection rates, so it is always between 0 and 1, where a ratio of 1 means that all groups have the same selection rate.\n",
        "\n",
        "<!-- Next, we will evaluate the fairness of our classifier on the test set.  We will first focus on two metrics - demographic parity difference and demographic parity ratio.  **Demographic parity difference** is defined as the difference between the largest and the smallest group-level selection rate across all values of the sensitive feature(s).  A demographic parity difference of 0 means that all groups have the same selection rate.  **Demographic parity ratio** is defined as the ratio between the smallest and the largest group-level selection rate across all values of the sensitive feature(s).  A demographic parity ratio of 1 means that all groups have the same selection rate. -->\n",
        "\n",
        "More formally: let $X$ denote a feature vector used for predictions, $A$ be a single sensitive feature (such as age or race), $Y$ be the true label, and $h$ a classifier or predictor resulted from a Machine Learning algorithm. Then:\n",
        "\n",
        "*Demographic Parity Difference* is defined as $(max_a\\mathbb{E}[h(X)~|~  A = a])~ - ~ (min_a\\mathbb{E}[h(X)~|~  A = a]) $\n",
        "\n",
        "\n",
        "*Demographic Parity Ratio* is defined as $\\frac{max_a\\mathbb{E}[h(X)~|~  A = a]}{min_a\\mathbb{E}[h(X)~|~  A = a]} $"
      ],
      "metadata": {
        "id": "GlGbK5w4x7q-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Evaluate the fairness of the classifier using `demographic_parity_difference` and `demographic_parity_ratio`\n",
        "#note: we are conducting this analysis over the test set\n",
        "\n",
        "#compute test predictions\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "#compute demographic parity difference and demographic parity ratio\n",
        "demo_parity_diff = demographic_parity_difference(y_test, y_pred, sensitive_features=X_test['SEX'])\n",
        "demo_parity_ratio = demographic_parity_ratio(y_test, y_pred, sensitive_features=X_test['SEX'])\n",
        "\n",
        "print(f'Demographic parity difference: {demo_parity_diff:.4f}')\n",
        "print(f'Demographic parity ratio: {demo_parity_ratio:.4f}')\n",
        "\n",
        "#compute selection rate for males and females\n",
        "male_selection_rate = selection_rate(y_test[X_test['SEX']==1], y_pred[X_test['SEX']==1])\n",
        "female_selection_rate = selection_rate(y_test[X_test['SEX']==0], y_pred[X_test['SEX']==0])\n",
        "\n",
        "print(f'Male selection rate: {male_selection_rate:.4f}')\n",
        "print(f'Female selection rate: {female_selection_rate:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9-CPsT9bhhy",
        "outputId": "3b5cfe3e-4990-42dc-b88d-bd9a37e3bfb0",
        "cellView": "form"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Demographic parity difference: 0.1377\n",
            "Demographic parity ratio: 0.7091\n",
            "Male selection rate: 0.4733\n",
            "Female selection rate: 0.3356\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we can see that there are substantial differences in selection rates between males and females with males being significantly more likely to be classified as high income."
      ],
      "metadata": {
        "id": "JbwDQ2nQ8c_J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fairlearn also provides the fairlearn.metrics.MetricFrame class to evaluate disparities in treatment between different sub-populations.\n",
        "\n",
        "The **fairlearn.metrics.MetricFrame** object requires a minimum of four arguments:\n",
        "\n",
        "*   The underlying metric function(s) to be evaluated\n",
        "*   The true values\n",
        "*   The predicted values\n",
        "*   The sensitive feature values\n",
        "\n",
        "Metric functions must have a signature ''fn(y_true, y_pred)'', i.e., require only two arguments.  Here we will again look at selection rate, but we will also examine a few other metrics.  We will use accuracy, selection rate, false negative rate, and false positive rate."
      ],
      "metadata": {
        "id": "DxWyzIlKyHIH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Evaluate the fairness of the classifier using the MetricFrame class for the `SEX` variable\n",
        "\n",
        "#changing sensitive feature inputs to be 'male' and 'female' instead of 1 and 0\n",
        "sensitive_feature_sex = X_test['SEX'].replace({0:'female', 1:'male'})\n",
        "\n",
        "#evaluation metrics\n",
        "metrics = {'accuracy': skm.accuracy_score,\n",
        "           'selection_rate': selection_rate,  # i.e., the percentage of the population which have ‘1’ as their predicted label\n",
        "           'FNR': false_negative_rate,\n",
        "           'FPR': false_positive_rate\n",
        "           }\n",
        "\n",
        "grouped_on_sex = MetricFrame(metrics=metrics,\n",
        "                             y_true=y_test,\n",
        "                             y_pred=y_pred,\n",
        "                             sensitive_features=sensitive_feature_sex)"
      ],
      "metadata": {
        "id": "0lhzxtUcmay1",
        "cellView": "form"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **fairlearn.metrics.MetricFrame** has the **overall** property, which evaluates the metrics on the entire dataset."
      ],
      "metadata": {
        "id": "P-AAN3eYyOrh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "grouped_on_sex.overall"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "JPuojgxHm4EG",
        "outputId": "4629f28c-29ac-4786-cb7c-a9c19779ea7a",
        "cellView": "form"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "accuracy          0.804513\n",
              "selection_rate    0.404562\n",
              "FNR               0.246409\n",
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          "metadata": {},
          "execution_count": 22
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    {
      "cell_type": "code",
      "source": [
        "#@markdown # **TODO**: Evaluate fairness for the `RAC1P` variable (for black and white individuals at minimum), and show `grouped_on_race.overall`\n",
        "\n",
        "# TODO: write code here\n"
      ],
      "metadata": {
        "id": "OG4i1QxPYDm9",
        "cellView": "form"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown\n",
        "grouped_on_race.overall"
      ],
      "metadata": {
        "id": "HD4OsxeWbXjW",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **fairlearn.metrics.MetricFrame** object also has the **by_group** functionality. This displays the selected metrics evaluated on each subgroup defined by the categories in the sensitive_features (sex in our case)."
      ],
      "metadata": {
        "id": "yr-EFAs8yQBd"
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      "source": [
        "#@title\n",
        "grouped_on_sex.by_group"
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              "       accuracy  selection_rate       FNR       FPR\n",
              "SEX                                                \n",
              "False  0.809445        0.335633  0.296094  0.132278\n",
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              "            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-b5335992-545f-481b-897b-76c2907bb9d8 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",
              "summary": "{\n  \"name\": \"grouped_on_sex\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"SEX\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006966670383203811,\n        \"min\": 0.7995927865037813,\n        \"max\": 0.8094451462442911,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7995927865037813,\n          0.8094451462442911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09737152702716458,\n        \"min\": 0.3356330774463123,\n        \"max\": 0.47333721155710684,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.47333721155710684,\n          0.3356330774463123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FNR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06174523204442098,\n        \"min\": 0.20877301882888474,\n        \"max\": 0.2960939633979787,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.20877301882888474,\n          0.2960939633979787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FPR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04295881761692275,\n        \"min\": 0.13227752639517346,\n        \"max\": 0.19303046889253786,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.19303046889253786,\n          0.13227752639517346\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown # **TODO**: Display for `RAC1P` as well\n",
        "\n",
        "# TODO: write code here"
      ],
      "metadata": {
        "id": "LaYBo98cbbJK",
        "cellView": "form"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reminder: Females are labeled 0 and males are labeled 1."
      ],
      "metadata": {
        "id": "LNr0r1yIIE3Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Plot the metric values\n",
        "\n",
        "metrics_1 = {'accuracy': skm.accuracy_score,\n",
        "           'selection_rate': selection_rate,  # i.e., the percentage of the population which have ‘1’ as their predicted label\n",
        "           }\n",
        "\n",
        "metrics_2 = {\n",
        "           'FNR': false_negative_rate,\n",
        "           'FPR': false_positive_rate\n",
        "           }\n",
        "\n",
        "grouped_on_sex_accuracy_selection = MetricFrame(metrics=metrics_1,\n",
        "                             y_true=y_test,\n",
        "                             y_pred=y_pred,\n",
        "                             sensitive_features=sensitive_feature_sex)\n",
        "\n",
        "grouped_on_sex_fpr_fnr = MetricFrame(metrics=metrics_2,\n",
        "                             y_true=y_test,\n",
        "                             y_pred=y_pred,\n",
        "                             sensitive_features=sensitive_feature_sex)\n",
        "\n",
        "grouped_on_sex_accuracy_selection.by_group.plot.bar(\n",
        "    subplots=False,\n",
        "    figsize=(10, 7),\n",
        "    ylim=[0,1],\n",
        "    title=\"Accuracy and selection rate by sex\",\n",
        "    )\n",
        "\n",
        "grouped_on_sex_fpr_fnr.by_group.plot.bar(\n",
        "    subplots=False,\n",
        "    figsize=(10, 7),\n",
        "    ylim=[0,1],\n",
        "    title=\"FNR and FPR by sex\",\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5B9z7HUdm_up",
        "outputId": "e80a51d1-55c3-4f81-f2da-4598242c3443",
        "cellView": "form"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'FNR and FPR by sex'}, xlabel='SEX'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown #**TODO**: Replicate the above plots for at `RAC1P` and at least black/white individuals in the data.\n",
        "\n",
        "# TODO: write code here"
      ],
      "metadata": {
        "id": "PK3tfGI-bjAy",
        "cellView": "form"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **TODO**: Observe that accuracy for male and female groups is comparable, and yet we see disparities in FPR and FNR.  Which group benefits from the discrepancies in FPR and FNR shown above?  If you were deploying this system how would you measure performance (eg. accuracy, FNR, FPR)? (Reminder: females are labelled 0, males are labelled 1)\n"
      ],
      "metadata": {
        "id": "LH7SZrv2N8ot"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer**:"
      ],
      "metadata": {
        "id": "5cX_eE4QhmLO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Train \"Blind\" Logistic Regression Classifier (Fairness through Blindness)"
      ],
      "metadata": {
        "id": "PFqCzXyXyhcO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will remove the protected attribute of \"sex\" from our data and see what effect this has on the performance of our classifier."
      ],
      "metadata": {
        "id": "7cJPVQ7byn53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Remove sensitive attribute from data\n",
        "X_train_blind = X_train.drop(columns='SEX')\n",
        "X_test_blind = X_test.drop(columns='SEX')"
      ],
      "metadata": {
        "id": "BGZewKk9tBbZ",
        "cellView": "form"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Implement logistic regression\n",
        "clf_blind = LogisticRegression()\n",
        "clf_blind.fit(X_train_blind, y_train)\n",
        "clf_blind_accuracy = clf_blind.score(X_test_blind, y_test)\n",
        "\n",
        "clear_output()\n",
        "\n",
        "print(f'Logistic Regression test accuracy (without sensitive attribute): {clf_blind_accuracy:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-0MCOFjtL9l",
        "outputId": "5b625833-fe53-4395-d186-71b2b1d1e360",
        "cellView": "form"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression test accuracy (without sensitive attribute): 0.8027\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Recall the accuracy fairness of the original classifier\n",
        "print('Recall the original classifier:')\n",
        "print(f'Logistic Regression test accuracy: {clf_accuracy:.4f}')\n",
        "print(f'Demographic parity difference: {demo_parity_diff:.4f}')\n",
        "print(f'Demographic parity ratio: {demo_parity_ratio:.4f}')\n",
        "print(f'Male selection rate: {male_selection_rate:.4f}')\n",
        "print(f'Female selection rate: {female_selection_rate:.4f}')"
      ],
      "metadata": {
        "id": "5Svx5AUycMC5",
        "outputId": "11af436b-212b-48b8-f118-cb316e35ba8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall the original classifier:\n",
            "Logistic Regression test accuracy: 0.8045\n",
            "Demographic parity difference: 0.1377\n",
            "Demographic parity ratio: 0.7091\n",
            "Male selection rate: 0.4733\n",
            "Female selection rate: 0.3356\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Evaluate fairness of the blind classifier\n",
        "\n",
        "#compute test predictions\n",
        "y_pred_blind = clf_blind.predict(X_test_blind)\n",
        "\n",
        "#compute demographic parity difference and demographic parity ratio\n",
        "demo_parity_diff_blind = demographic_parity_difference(y_test, y_pred_blind, sensitive_features=X_test['SEX'])\n",
        "demo_parity_ratio_blind = demographic_parity_ratio(y_test, y_pred_blind, sensitive_features=X_test['SEX'])\n",
        "\n",
        "print(f'Demographic parity difference (without sensitive attribute): {demo_parity_diff_blind:.4f}')\n",
        "print(f'Demographic parity ratio (without sensitive attribute): {demo_parity_ratio_blind:.4f}')\n",
        "\n",
        "#compute selection rate for males and females\n",
        "male_selection_rate_blind = selection_rate(y_test[X_test['SEX']==1], y_pred_blind[X_test['SEX']==1])\n",
        "female_selection_rate_blind = selection_rate(y_test[X_test['SEX']==0], y_pred_blind[X_test['SEX']==0])\n",
        "\n",
        "print(f'Male selection rate (without sensitive attribute): {male_selection_rate_blind:.4f}')\n",
        "print(f'Female selection rate (without sensitive attribute): {female_selection_rate_blind:.4f}')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEg0YU9Ltbzh",
        "outputId": "b271802e-1a85-4f0d-b01c-d1bb336f977a",
        "cellView": "form"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Demographic parity difference (without sensitive attribute): 0.0826\n",
            "Demographic parity ratio (without sensitive attribute): 0.8153\n",
            "Male selection rate (without sensitive attribute): 0.4471\n",
            "Female selection rate (without sensitive attribute): 0.3645\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **TODO:** describe the differences of both models in terms of accuracy and fairness among male and female groups?\n"
      ],
      "metadata": {
        "id": "Yueobvhd78gp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer**:"
      ],
      "metadata": {
        "id": "lh0IiGaahqs9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Nevertheless**, we see that removing the protected feature did not eliminate the biases within our classifier, as exhibited below."
      ],
      "metadata": {
        "id": "PRVVozeK0IYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Evaluate the biases of the classifier using the MetricFrame class\n",
        "grouped_on_sex_blind = MetricFrame(metrics=metrics,\n",
        "                                     y_true=y_test,\n",
        "                                     y_pred=y_pred_blind,\n",
        "                                     sensitive_features=sensitive_feature_sex)"
      ],
      "metadata": {
        "id": "6FP75qKmuC6_",
        "cellView": "form"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown\n",
        "grouped_on_sex_blind.overall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "Z0l6FUGluMWh",
        "outputId": "3036e3d2-c346-4404-d850-caed66978532",
        "cellView": "form"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "accuracy          0.802669\n",
              "selection_rate    0.405824\n",
              "FNR               0.247116\n",
              "FPR               0.162414\n",
              "dtype: float64"
            ],
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.802669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>selection_rate</th>\n",
              "      <td>0.405824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FNR</th>\n",
              "      <td>0.247116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FPR</th>\n",
              "      <td>0.162414</td>\n",
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              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown\n",
        "grouped_on_sex_blind.by_group"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
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        "id": "DwAXmgKcuR0M",
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        "cellView": "form"
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      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       accuracy  selection_rate       FNR       FPR\n",
              "SEX                                                \n",
              "False  0.807793        0.364493  0.257853  0.155958\n",
              "True   0.797557        0.447062  0.238982  0.170224"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"grouped_on_sex_blind\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"SEX\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007238297400104105,\n        \"min\": 0.7975567190226877,\n        \"max\": 0.8077932173744048,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7975567190226877,\n          0.8077932173744048\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05838509908427176,\n        \"min\": 0.3644932465260908,\n        \"max\": 0.44706224549156487,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.44706224549156487,\n          0.3644932465260908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FNR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013343845200968532,\n        \"min\": 0.23898199875853507,\n        \"max\": 0.2578530456159519,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.23898199875853507,\n          0.2578530456159519\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FPR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010088040633204273,\n        \"min\": 0.1559577677224736,\n        \"max\": 0.17022441160372195,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.17022441160372195,\n          0.1559577677224736\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Showing all the metrics for the full data and blinded data.**"
      ],
      "metadata": {
        "id": "zENn3IQzsNjR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Comparing the results: full-data vs. blinded data\n",
        "\n",
        "#demographic parity difference\n",
        "demo_parity_diff = demographic_parity_difference(y_test, y_pred, sensitive_features=X_test['SEX'])\n",
        "demo_parity_diff_blind = demographic_parity_difference(y_test, y_pred_blind, sensitive_features=X_test['SEX'])\n",
        "\n",
        "#demographic parity ratio\n",
        "demo_parity_ratio = demographic_parity_ratio(y_test, y_pred, sensitive_features=X_test['SEX'])\n",
        "demo_parity_ratio_blind = demographic_parity_ratio(y_test, y_pred_blind, sensitive_features=X_test['SEX'])\n",
        "\n",
        "#selection rate\n",
        "male_selection_rate = selection_rate(y_test[X_test['SEX']==1], y_pred[X_test['SEX']==1])\n",
        "male_selection_rate_blind = selection_rate(y_test[X_test['SEX']==1], y_pred_blind[X_test['SEX']==1])\n",
        "\n",
        "female_selection_rate = selection_rate(y_test[X_test['SEX']==0], y_pred[X_test['SEX']==0])\n",
        "female_selection_rate_blind = selection_rate(y_test[X_test['SEX']==0], y_pred_blind[X_test['SEX']==0])\n",
        "\n",
        "#fnr\n",
        "male_fnr = false_negative_rate(y_test[X_test['SEX']==1], y_pred[X_test['SEX']==1])\n",
        "male_fnr_blind = false_negative_rate(y_test[X_test['SEX']==1], y_pred_blind[X_test['SEX']==1])\n",
        "\n",
        "female_fnr = false_negative_rate(y_test[X_test['SEX']==0], y_pred[X_test['SEX']==0])\n",
        "female_fnr_blind = false_negative_rate(y_test[X_test['SEX']==0], y_pred_blind[X_test['SEX']==0])\n",
        "\n",
        "#fpr\n",
        "male_fpr = false_positive_rate(y_test[X_test['SEX']==1], y_pred[X_test['SEX']==1])\n",
        "male_fpr_blind = false_positive_rate(y_test[X_test['SEX']==1], y_pred_blind[X_test['SEX']==1])\n",
        "\n",
        "female_fpr = false_positive_rate(y_test[X_test['SEX']==0], y_pred[X_test['SEX']==0])\n",
        "female_fpr_blind = false_positive_rate(y_test[X_test['SEX']==0], y_pred_blind[X_test['SEX']==0])\n",
        "\n",
        "#plot\n",
        "labels = ['Demo Parity Diff','Demo Parity Ratio','Sel Rate (Male)',\n",
        "          'Sel Rate (Female)', 'FNR (Male)', 'FNR (Female)', 'FPR(Male)',\n",
        "          'FPR(Female)']\n",
        "\n",
        "Y_full = [demo_parity_diff, demo_parity_ratio, male_selection_rate,\n",
        "          female_selection_rate, male_fnr, female_fnr, male_fpr, female_fpr]\n",
        "\n",
        "Y_blind = [demo_parity_diff_blind, demo_parity_ratio_blind,\n",
        "           male_selection_rate_blind, female_selection_rate_blind, male_fnr_blind,\n",
        "           female_fnr_blind, male_fpr_blind, female_fpr_blind]\n",
        "\n",
        "x = np.arange(len(labels))\n",
        "width = 0.35\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 7))\n",
        "rects1 = ax.bar(x - width/2, Y_full, width, label='Full Model')\n",
        "rects2 = ax.bar(x + width/2, Y_blind, width, label='Blind Model')\n",
        "\n",
        "ax.set_title('Metrics Comparison', size=20)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend(fontsize='x-large')\n",
        "ax.bar_label(rects1, padding=3, fmt='%.3f')\n",
        "ax.bar_label(rects2, padding=3, fmt='%.3f')\n",
        "ax.set_ylim([0, 1])\n",
        "fig.tight_layout()\n",
        "plt.rcParams[\"figure.figsize\"] = (18,8)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "5TOyiVARo16T",
        "outputId": "27bceccb-48e3-4666-f8e6-03d2a7c21119",
        "cellView": "form"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **TODO:** What are the key takeaways from this Lab?\n"
      ],
      "metadata": {
        "id": "wz-7mcA60DyU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer**:"
      ],
      "metadata": {
        "id": "13fHBQTGhvCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown #**TODO:** For 10 different train/test splits, train a classifier on the full dataset as well as a classifier on a \"blind\" dataset (i.e. one without the sensitive attribute).  For each of these classifiers, calculate the accuracy, selection rate, false negative rate, and false positive rate by gender.  Plot your results as boxplots.  You should have 8 total subplots (i.e. a plot for each dataset (full and blind) as well as a plot for each metric).  Each plot should contain a 2 boxplots (one for males and one for females).\n",
        "\n",
        "n = 10\n",
        "\n",
        "for i in range(n):\n",
        "  # write code here\n",
        "  pass\n",
        "\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "g10rl5fO758W"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## @markdown #**TODO:** Create boxplots for each metric, as shown in lab.\n",
        "# write code here\n",
        "\n",
        "\n",
        "#plt.tight_layout()"
      ],
      "metadata": {
        "id": "wpK3FtCl7-fe"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we can see our previous results replicated on multiple train/test splits. The accuracy between the classsifier trained on the full dataset and the \"blind\" classifier are comparable.  We also see a reduction in disparity between the selection rates, false negative rates, and false positive rates between males and females.  However, we can see that the disparities were not eliminated by removing the sensitive attribute."
      ],
      "metadata": {
        "id": "7arsPlHD8AAE"
      }
    }
  ]
}