import evaluate import datasets from datasets.features import Sequence, Value, ClassLabel from sklearn.metrics import roc_auc_score import numpy as np _DESCRIPTION = """\ Suite of threshold-agnostic metrics that provide a nuanced view of this unintended bias, by considering the various ways that a classifier’s score distribution can vary across designated groups. The following are computed: - BNSP (Background Negative, Subgroup Positive); and - BPSN (Background Positive, Subgroup Negative) AUC """ _CITATION = """\ @inproceedings{borkan2019nuanced, title={Nuanced metrics for measuring unintended bias with real data for text classification}, author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy}, booktitle={Companion proceedings of the 2019 world wide web conference}, pages={491--500}, year={2019} } """ _KWARGS_DESCRIPTION = """\ Args: target list[list[str]]: list containing list of group targeted for each item label list[int]: list containing label index for each item output list[list[float]]: list of model output values for each subgroup list[str] (optional): list of subgroups that appear in target to compute metric over Returns (for each subgroup in target): 'Subgroup' : Subgroup AUC score, 'BPSN' : BPSN (Background Positive, Subgroup Negative) AUC, 'BNSP' : BNSP (Background Negative, Subgroup Positive) AUC score, Example: >>> from evaluate import load >>> target = [['Islam'], ... ['Sexuality'], ... ['Sexuality'], ... ['Islam']] >>> label = [0, 0, 1, 1] >>> output = [[0.44452348351478577, 0.5554765462875366], ... [0.4341845214366913, 0.5658154487609863], ... [0.400595098733902, 0.5994048714637756], ... [0.3840397894382477, 0.6159601807594299]] >>> metric = load('Intel/bias_auc') >>> metric.add_batch(target=target, label=label, output=output) >>> metric.compute(subgroups = None) """ class BiasAUC(evaluate.Metric): def _info(self): return evaluate.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features( { 'target': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'label': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), } ), reference_urls=["https://arxiv.org/abs/1903.04561"], ) def _genreate_subgroup(self, targets, labels, outputs, subgroup, target_class=None): """Returns label and output score from `targets` and `labels` if `subgroup` is in list of targeted groups found in `targets` """ target_class = target_class if target_class is not None else 1 for target, label, result in zip(targets, labels, outputs): if subgroup in target: yield label, result[target_class] def _genreate_bpsn(self, targets, labels, outputs, subgroup, target_class=None): """Returns label and output score from `targets` and `labels` if (1) `subgroup` is in list of targeted groups found in `targets` and label is not the same as `target_class`; or (2) `subgroup` is not in list of targeted groups found in `targets` and label is the same as `target_class` """ target_class = target_class if target_class is not None else 1 for target, label, result in zip(targets, labels, outputs): if not target: continue # background positive if subgroup not in target and label == target_class: yield label, result[target_class] # subgroup negative elif subgroup in target and label != target_class: yield label, result[target_class] def _genreate_bnsp(self, targets, labels, outputs, subgroup, target_class=None): """Returns label and output score from `targets` and `labels` if (1) `subgroup` is not in list of targeted groups found in `targets` and label is the same as `target_class`; or (2) `subgroup` is in list of targeted groups found in `targets` and label is not the same as `target_class` """ # get the index from class target_class = target_class if target_class is not None else 1 for target, label, result in zip(targets, labels, outputs): if not target: continue # background negative if subgroup not in target and label != target_class: yield label, result[target_class] # subgroup positive elif subgroup in target and label == target_class: yield label, result[target_class] def _get_auc_score(self, gen_func, *args, **kwargs): try: y_trues, y_preds = zip(*gen_func(*args, **kwargs)) score = roc_auc_score(y_trues, y_preds) except ValueError: print(f"Sample not sufficient: need negative and positive examples for both target subgroup '{args[-1]}' and background set") score = np.nan return score def _auc_by_group(self, target, label, output, subgroup): """ Compute bias AUC metrics """ return { 'Subgroup' : self._get_auc_score(self._genreate_subgroup, target, label, output, subgroup), 'BPSN' : self._get_auc_score(self._genreate_bpsn, target, label, output, subgroup), 'BNSP' : self._get_auc_score(self._genreate_bnsp, target, label, output, subgroup) } def _update_overall(self, result, labels, outputs, power_value=-5): """Compute the generalized mean of Bias AUCs""" result['Overall generalized mean'] = {} for metric in ['Subgroup', 'BPSN', 'BNSP']: metric_values = np.array([result[community][metric] for community in result if community != 'Overall generalized mean']) metric_values **= power_value mean_value = np.power(np.sum(metric_values)/(len(result) - 1), 1/power_value) result['Overall generalized mean'][f"{metric}"] = mean_value y_preds = [output[1] for output in outputs] # try: # result['Overall generalized mean']["Overall AUC"] = roc_auc_score(labels, y_preds) # except ValueError: # result['Overall generalized mean']["Overall AUC"] = np.nan return result def _compute(self, target, label, output, subgroups=None): if subgroups is None: subgroups = set(group for group_list in target for group in group_list) result = {subgroup : self._auc_by_group(target, label, output, subgroup) for subgroup in subgroups} result = self._update_overall(result, label, output) return result