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| from typing import Union | |
| from aif360.datasets import BinaryLabelDataset | |
| from aif360.metrics import ClassificationMetric | |
| from aif360.detectors.mdss.ScoringFunctions import Bernoulli, BerkJones, ScoringFunction | |
| from aif360.detectors.mdss.MDSS import MDSS | |
| import pandas as pd | |
| from sklearn.utils.deprecation import deprecated | |
| class MDSSClassificationMetric(ClassificationMetric): | |
| """Bias subset scanning is proposed as a technique to identify bias in | |
| predictive models using subset scanning [#zhang16]_. | |
| This class is a wrapper for the bias scan scoring and scanning methods that | |
| uses the ClassificationMetric abstraction. | |
| References: | |
| .. [#zhang16] `Zhang, Z. and Neill, D. B., "Identifying significant | |
| predictive bias in classifiers," arXiv preprint, 2016. | |
| <https://arxiv.org/abs/1611.08292>`_ | |
| """ | |
| def __init__( | |
| self, | |
| dataset: BinaryLabelDataset, | |
| classified_dataset: BinaryLabelDataset, | |
| scoring: Union[str, ScoringFunction] = 'Bernoulli', | |
| unprivileged_groups: dict = None, | |
| privileged_groups: dict = None, | |
| **kwargs | |
| ): | |
| """ | |
| Args: | |
| dataset (BinaryLabelDataset): Dataset containing ground-truth | |
| labels. | |
| classified_dataset (BinaryLabelDataset): Dataset containing | |
| predictions. | |
| scoring (str or ScoringFunction): One of 'Bernoulli' (parametric), or 'BerkJones' (non-parametric) | |
| or subclass of :class:`aif360.metrics.mdss.ScoringFunctions.ScoringFunction`. | |
| Defaults to Bernoulli. | |
| privileged_groups (list(dict)): Privileged groups. Format is a list | |
| of `dicts` where the keys are `protected_attribute_names` and | |
| the values are values in `protected_attributes`. Each `dict` | |
| element describes a single group. See examples for more details. | |
| unprivileged_groups (list(dict)): Unprivileged groups in the same | |
| format as `privileged_groups`. | |
| """ | |
| super(MDSSClassificationMetric, self).__init__( | |
| dataset, | |
| classified_dataset, | |
| unprivileged_groups=unprivileged_groups, | |
| privileged_groups=privileged_groups, | |
| ) | |
| self.scoring = scoring | |
| self.kwargs = kwargs | |
| def score_groups(self, privileged=True, penalty=1e-17): | |
| """Compute the bias score for a prespecified group of records. | |
| Args: | |
| privileged (bool): Flag for which direction to scan: privileged | |
| (``True``) implies negative (observed worse than predicted | |
| outcomes) while unprivileged (``False``) implies positive | |
| (observed better than predicted outcomes). | |
| Returns: | |
| float: Bias score for the given group. | |
| The higher the score, the evidence for bias. | |
| """ | |
| groups = self.privileged_groups if privileged else self.unprivileged_groups | |
| subset = dict() | |
| for g in groups: | |
| for k, v in g.items(): | |
| if k in subset.keys(): | |
| subset[k].append(v) | |
| else: | |
| subset[k] = [v] | |
| coordinates = pd.DataFrame( | |
| self.dataset.features, columns=self.dataset.feature_names | |
| ) | |
| expected = pd.Series(self.classified_dataset.scores.flatten()) | |
| outcomes = pd.Series(self.dataset.labels.flatten() == self.dataset.favorable_label, dtype=int) | |
| # In MDSS, we look for subset whose observations systematically deviates from expectations. | |
| # Positive direction means observations are systematically higher than expectations | |
| # (or expectations are systematically higher than observations) while | |
| # Negative direction means observatons are systematically lower than expectations | |
| # (or expectations are systematically higher than observations) | |
| # For a privileged group, we are looking for a subset whose expectations | |
| # (where expectations is obtained from a model) is systematically higher than the observations. | |
| # This means we scan in the negative direction. | |
| # For an uprivileged group, we are looking for a subset whose expectations | |
| # (where expectations is obtained from a model) is systematically lower the observations. | |
| # This means we scan in the position direction. | |
| self.kwargs['direction'] = "negative" if privileged else "positive" | |
| if self.scoring == "Bernoulli": | |
| scoring_function = Bernoulli(**self.kwargs) | |
| elif self.scoring == "BerkJones": | |
| scoring_function = BerkJones(**self.kwargs) | |
| else: | |
| scoring_function = self.scoring(**self.kwargs) | |
| scanner = MDSS(scoring_function) | |
| return scanner.score_current_subset( | |
| coordinates, expected, outcomes, dict(subset), penalty | |
| ) | |
| def bias_scan(self, privileged=True, num_iters=10, penalty=1e-17): | |
| """ | |
| scan to find the highest scoring subset of records | |
| :param privileged: flag for group to scan for - privileged group (True) or unprivileged group (False). | |
| This abstract the need to explicitly specify the direction of bias to scan for which depends on what the favourable label is. | |
| :param num_iters: number of iterations (random restarts) | |
| :param penalty: 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. | |
| :returns: the highest scoring subset and the score | |
| """ | |
| coordinates = pd.DataFrame( | |
| self.classified_dataset.features, | |
| columns=self.classified_dataset.feature_names, | |
| ) | |
| expected = pd.Series(self.classified_dataset.scores.flatten()) | |
| outcomes = pd.Series(self.dataset.labels.flatten() == self.dataset.favorable_label, dtype=int) | |
| # In MDSS, we look for subset whose observations systematically deviates from expectations. | |
| # Positive direction means observations are systematically higher than expectations | |
| # (or expectations are systematically lower than observations) while | |
| # Negative direction means observatons are systematically lower than expectations | |
| # (or expectations are systematically higher than observations) | |
| # For a privileged group, we are looking for a subset whose expectations | |
| # (where expectations is obtained from a model) is systematically higher than the observations. | |
| # This means we scan in the negative direction. | |
| # For an uprivileged group, we are looking for a subset whose expectations | |
| # (where expectations is obtained from a model) is systematically lower the observations. | |
| # This means we scan in the position direction. | |
| self.kwargs['direction'] = "negative" if privileged else "positive" | |
| if self.scoring == "Bernoulli": | |
| scoring_function = Bernoulli(**self.kwargs) | |
| elif self.scoring == "BerkJones": | |
| scoring_function = BerkJones(**self.kwargs) | |
| else: | |
| scoring_function = self.scoring(**self.kwargs) | |
| scanner = MDSS(scoring_function) | |
| return scanner.scan(coordinates, expected, outcomes, penalty, num_iters) | |