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| from typing import Union | |
| from aif360.detectors import bias_scan | |
| from aif360.detectors.mdss.ScoringFunctions import ScoringFunction | |
| import pandas as pd | |
| def bias_scan( | |
| X: pd.DataFrame, | |
| y_true: pd.Series, | |
| y_pred: Union[pd.Series, pd.DataFrame] = None, | |
| pos_label: Union[str, float] = None, | |
| overpredicted: bool = True, | |
| scoring: Union[str, ScoringFunction] = "Bernoulli", | |
| num_iters: int = 10, | |
| penalty: float = 1e-17, | |
| mode: str = "binary", | |
| **kwargs, | |
| ): | |
| """Scan to find the highest scoring subset of records. | |
| Args: | |
| X (pandas.DataFrame): the dataset (containing the features) the model was trained on | |
| y_true (pandas.Series): ground truth (correct) target values | |
| y_pred (pandas.Series, pandas.DataFrame, optional): pandas series estimated targets | |
| as returned by a model for binary, continuous and ordinal modes. | |
| If mode is nominal, this is a dataframe with columns containing expectations/predictions for each nominal class. | |
| If None, model is assumed to be a dumb model that predicts the mean of the targets or 1/(num of categories) for nominal mode. | |
| pos_label (str, float, optional): Should be high or low or float if the mode in [binary, ordinal, or continuous]. | |
| If float, value has to be minimum or maximum in the y_true column. Defaults to high if None for these modes. | |
| Support for float left in to keep the intuition clear in binary classification tasks. | |
| If mode is nominal, favorable values should be one of the unique categories in the y_true column. | |
| Defaults to a one-vs-all scan if None for nominal mode. | |
| overpredicted (bool, optional): flag for group to scan for. | |
| True means we scan for a group whose expectations/predictions are systematically higher than observed. | |
| In other words, True means we scan for a group whose observeed is systematically lower than the expectations. | |
| False means we scan for a group whose expectations/predictions are systematically lower than observed. | |
| In other words, False means we scan for a group whose observed is systematically higher than the expectations. | |
| scoring (str or class): One of 'Bernoulli', 'Gaussian', 'Poisson', or 'BerkJones' or subclass of | |
| :class:`aif360.detectors.mdss.ScoringFunctions.ScoringFunction`. | |
| num_iters (int, optional): number of iterations (random restarts). Should be positive. | |
| penalty (float,optional): penalty term. Should be positive. The penalty term as with any regularization parameter may need to be | |
| tuned for ones use case. The higher the penalty, the less complex (number of features and feature values) the | |
| highest scoring subset that gets returned is. | |
| mode(str): one of ['binary', 'continuous', 'nominal', 'ordinal']. Defaults to binary. | |
| In nominal mode, up to 10 categories are supported by default. | |
| To increase this, pass in keyword argument max_nominal = integer value. | |
| Returns: | |
| tuple: The highest scoring subset and the score or dict of the highest scoring subset and the score for each category in nominal mode | |
| """ | |
| return bias_scan( | |
| data=X, | |
| observations=y_true, | |
| expectations=y_pred, | |
| favorable_value=pos_label, | |
| overpredicted=overpredicted, | |
| scoring=scoring, | |
| num_iters=num_iters, | |
| penalty=penalty, | |
| mode=mode, | |
| kwargs=kwargs | |
| ) | |