File size: 7,206 Bytes
1273a08 c430c1e 1273a08 c430c1e de109df c430c1e de109df c430c1e ed3eb55 c430c1e 1273a08 209034b 1273a08 60307cd 1273a08 a87b5c5 217f149 a87b5c5 4903673 a87b5c5 1273a08 a87b5c5 1273a08 77a7a3e 1273a08 77a7a3e 1273a08 77a7a3e 1273a08 a87b5c5 77a7a3e a87b5c5 77a7a3e a87b5c5 1273a08 60307cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
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 np.asarray(outputs).shape[-1] - 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 np.asarray(outputs).shape[-1] - 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 np.asarray(outputs).shape[-1] - 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'][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
|