proportional_parity.py

import pandas as pd
from sklearn.metrics import accuracy_score


def prop_parity(data, outcome, group, base, predictions, col_name):
    """
    Metric: Proportional Parity
    Parameters:
    data: input dataframe with necessary columns
    outcome: column name that has outcome variable (target column)
    group: Dictionary corresponding to the feature you'd like to evaluate proportional parity on.
    base: Reference group for proportional parity.
    predictions: predicted outcome vector

    This function computes the Proportional parity metric with the following formula:
    (TP + FP) / (TP + FP + TN + FN)

    Proportional parity is calculated based on the proportion of all positively
    classified members in all subgroups of the data.

    The output will return the reference group with a 1, and the other groups will be
    assigned values corresponding to whether their positively predicted observations
    are greater or less than the reference groups. Lower proportions will be reflected
    in numbers lower than 1.

    """
    # first check if data is a dataframe object
    if not isinstance(data, pd.DataFrame):
        data = data.to_frame()
    # initialize accuracy array, where proportional parity and accuracy will be reported.
    accuracy_array = []

    # check lengths
    if len(outcome) != len(predictions):
        print("Prediction vector and outcome vector must be of the same length!")

    # get unique names for the specified group and create a dictionary for iteration
    group = pd.DataFrame(group.items())
    group_break = group[0].unique()
    dictionary = dict(list(enumerate(group_break)))
    dictionary = {v: k for k, v in dictionary.items()}
    # append test outcomes and predicted outcomes to our data for accuracy computation.
    data["y_test"] = outcome
    data["y_preds"] = predictions
    for x in group_break:
        final_df = data[data[col_name] == dictionary[x]]
        accuracy_array.append(
        {
            "Group": x,
            "Accuracy": accuracy_score(final_df["y_test"], final_df["y_preds"]),
         }
         )


    accuracy_array = pd.DataFrame(accuracy_array)
    baseline = base
    baseline_accuracy = accuracy_array[accuracy_array["Group"] == baseline][
        "Accuracy"
    ].item()
    accuracy_array["Proportional Parity"] = (
        accuracy_array["Accuracy"] / baseline_accuracy
    )
    print(accuracy_array)