使用 AIF360 计算群体公平性指标

Question

我要计算group fairness metrics using AIF360。这是一个示例数据集和模型，其中性别是受保护的属性，收入是目标。

import pandas as pd
from sklearn.svm import SVC
from aif360.sklearn import metrics

df = pd.DataFrame({'gender': [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
                  'experience': [0, 0.1, 0.2, 0.4, 0.5, 0.6, 0, 0.1, 0.2, 0.4, 0.5, 0.6],
                  'income': [0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1]})

clf = SVC(random_state=0).fit(df[['gender', 'experience']], df['income'])

y_pred = clf.predict(df[['gender', 'experience']])

metrics.statistical_parity_difference(y_true=df['income'], y_pred=y_pred, prot_attr='gender', priv_group=1, pos_label=1)

它抛出：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-609692e52b2a> in <module>
     11 y_pred = clf.predict(X)
     12 
---> 13 metrics.statistical_parity_difference(y_true=df['income'], y_pred=y_pred, prot_attr='gender', priv_group=1, pos_label=1)

TypeError: statistical_parity_difference() got an unexpected keyword argument 'y_true'

disparate_impact_ratio 的类似错误。似乎需要输入不同的数据，但我一直无法弄清楚如何。

Answer 1

删除函数调用中的 y_true= 和 y_pred= 字符，然后重试。正如 documentation, *y within the function prototype stands for arbitrary number of arguments (see this post) 中所见。所以这是最合乎逻辑的猜测。

换句话说，y_true 和 y_pred 不是关键字参数。所以他们不能用他们的名字传递。关键字参数在函数原型中表示为 **kwargs。

Answer 2

这可以通过将数据转换为 StandardDataset 然后调用下面的 fair_metrics 函数来完成：

from aif360.datasets import StandardDataset
from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric

dataset = StandardDataset(df, 
                          label_name='income', 
                          favorable_classes=[1], 
                          protected_attribute_names=['gender'], 
                          privileged_classes=[[1]])

def fair_metrics(dataset, y_pred):
    dataset_pred = dataset.copy()
    dataset_pred.labels = y_pred
        
    attr = dataset_pred.protected_attribute_names[0]
    
    idx = dataset_pred.protected_attribute_names.index(attr)
    privileged_groups =  [{attr:dataset_pred.privileged_protected_attributes[idx][0]}] 
    unprivileged_groups = [{attr:dataset_pred.unprivileged_protected_attributes[idx][0]}] 

    classified_metric = ClassificationMetric(dataset, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

    metric_pred = BinaryLabelDatasetMetric(dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

    result = {'statistical_parity_difference': metric_pred.statistical_parity_difference(),
             'disparate_impact': metric_pred.disparate_impact(),
             'equal_opportunity_difference': classified_metric.equal_opportunity_difference()}
        
    return result


fair_metrics(dataset, y_pred)

哪个returns正确的结果（image ref）：

{'statistical_parity_difference': -0.6666666666666667,
 'disparate_impact': 0.3333333333333333,
 'equal_opportunity_difference': 0.0}

使用 AIF360 计算群体公平性指标

Calculate group fairness metrics with AIF360

python

machine-learning

pandas

aif360