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