I'm trying to use the aif360 library of ibm for debiasing. I'm working on a linear regression model and want to try out a metric to calculate the difference between the priviliged and unpriviliged groups. However when this code is run I get the following error:
TypeError: difference() missing 1 required positional argument: 'metric_fun'
I've looked into the class for this function but they are referring to a metric_fun, also read the docs but didn't get any further. The function is missing an argument, but I don't know which argument it expects.
A short snippit of the code is:
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())
The stack trace that was given is:
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'
After creating a function:
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))
still get a similar error traceback:
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
Could someone please point me in the right direction? There are no examples available of similar code online.
Regards,
Sef