我想使用群体公平性指标通过AIF360进行计算。这是一个示例数据集和模型，其中性别是受保护属性，收入是目标变量。
import pandas as pdfrom sklearn.svm import SVCfrom aif360.sklearn import metricsdf = 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)它抛出了以下错误：
---------------------------------------------------------------------------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'对于disparate_impact_ratio也出现了类似的错误。看起来数据的输入方式需要不同，但我还没能弄清楚该怎么做。
回答：
可以通过将数据转换为StandardDataset，然后调用下面的fair_metrics函数来实现：
from aif360.datasets import StandardDatasetfrom aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetricdataset = 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 resultfair_metrics(dataset, y_pred)这将返回正确的结果（图像参考）：
{'statistical_parity_difference': -0.6666666666666667, 'disparate_impact': 0.3333333333333333, 'equal_opportunity_difference': 0.0}
