我正在尝试使用ibm的aif360库进行去偏。我正在研究一个线性回归模型，并想尝试一种指标来计算特权组和非特权组之间的差异。然而，当运行此代码时，我会得到以下错误：
TypeError:difference((缺少1个必需的位置参数："metric_fun">
我已经研究了这个函数的类，但它们引用了metric_fun，也阅读了文档，但没有得到任何进一步的信息。函数缺少一个参数，但我不知道它需要哪个参数。
代码的一个简短片段是：
train_pp_bld = StructuredDataset(df=pd.concat((x_train, y_train),
axis=1),
label_names=['decile_score'],
protected_attribute_names=['sex_Male'],
privileged_protected_attributes=1,
unprivileged_protected_attributes=0)
privileged_groups = [{'sex_Male': 1}]
unprivileged_groups = [{'sex_Male': 0}]
# Create the metric object
metric_train_bld = DatasetMetric(train_pp_bld,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
# Metric for the original dataset
metric_orig_train = DatasetMetric(train_pp_bld,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
display(Markdown("#### Original training dataset"))
print("Difference in mean outcomes between unprivileged and privileged groups = %f" % metric_orig_train.difference())
给出的堆栈跟踪是：
Traceback (most recent call last):
File "/Users/sef/Desktop/Thesis/Python Projects/Stats/COMPAS_Debias_AIF360_Continuous_Variable.py", line 116, in <module>
print("Difference in mean outcomes between unprivileged and privileged groups = %f" % metric_orig_train.difference())
File "/Users/sef/opt/anaconda3/envs/AI/lib/python3.8/site-packages/aif360/metrics/metric.py", line 37, in wrapper
result = func(*args, **kwargs)
TypeError: difference() missing 1 required positional argument: 'metric_fun'
创建函数后：
def privileged_value(self, privileged=False):
if privileged:
return unprivileged_groups['sex_Male']
else:
return privileged_groups['sex_Male']
display(Markdown("#### Original training dataset"))
print("Difference in mean outcomes between unprivileged and privileged groups = %f" % metric_orig_train.difference(privileged_value))
仍然得到类似的错误追溯：
Traceback (most recent call last):
File "/Users/sef/Desktop/Thesis/Python Projects/Stats/COMPAS_Debias_AIF360_Continuous_Variable.py", line 123, in <module>
print("Difference in mean outcomes between unprivileged and privileged groups = %f" % metric_orig_train.difference(privileged_value))
File "/Users/sef/opt/anaconda3/envs/AI/lib/python3.8/site-packages/aif360/metrics/metric.py", line 37, in wrapper
result = func(*args, **kwargs)
File "/Users/sef/opt/anaconda3/envs/AI/lib/python3.8/site-packages/aif360/metrics/dataset_metric.py", line 77, in difference
return metric_fun(privileged=False) - metric_fun(privileged=True)
File "/Users/youssefennali/Desktop/Thesis/Python Projects/Stats/COMPAS_Debias_AIF360_Continuous_Variable.py", line 120, in privileged_value
return privileged_groups['sex_Male']
TypeError: list indices must be integers or slices, not str
有人能告诉我正确的方向吗？网上没有类似代码的例子。
问候，
Sef
查看GitHub上库的源代码，需要将对函数的引用传递到difference(self, metric_fun)中。所有的区别就是用privileged=False作为输入减去函数的输出，用privileged=True作为输入。
def difference(self, metric_fun):
"""Compute difference of the metric for unprivileged and privileged
groups.
"""
return metric_fun(privileged=False) - metric_fun(privileged=True)
创建这样的函数并将其传递给difference。
def privilege_value(privileged=False) -> int:
if privileged:
return unprivileged_groups[0]['sex_male']
else:
return privileged_groups[0]['sex_male']
metric_orig_train.difference(privilege_value)
在不了解您正在使用的库的情况下，错误消息似乎仍然很清楚，尤其是因为您只调用difference一次，如下所示：
metric_orig_train.difference()
错误消息告诉您应该在此调用中传递一个参数。参数的名称是metric_fun，这向我建议您应该向它传递一个函数引用。
注意：difference((可能是在代码外部调用的。当您提供错误消息时，请始终提交随附的堆栈跟踪(如果有(。然后我们可以确切地看到问题发生在代码中的哪个位置。