def explain(self, request: Dict) -> Dict:
inputs = request["instances"]
predictions = np.array(request["outputs"])
dataframe_predicted = pd.DataFrame(inputs, columns=self.feature_names)
dataframe_predicted[self.label_names[0]] = predictions
dataset_predicted = BinaryLabelDataset(
favorable_label=self.favorable_label,
unfavorable_label=self.unfavorable_label,
df=dataframe_predicted,
label_names=self.label_names,
protected_attribute_names=['age'])
metrics = BinaryLabelDatasetMetric(
dataset_predicted,
unprivileged_groups=self.unprivileged_groups,
privileged_groups=self.privileged_groups)
return {
"predictions": predictions.tolist(),
"metrics": {
"base_rate":
metrics.base_rate(),
"consistency":
metrics.consistency().tolist(),
"disparate_impact":
metrics.disparate_impact(),
"num_instances":
metrics.num_instances(),
"num_negatives":
metrics.num_negatives(),
"num_positives":
metrics.num_positives(),
"statistical_parity_difference":
metrics.statistical_parity_difference(),
}
}
def checkClassifierFairnessAndReweighData(frame,
dpoints,
mname,
x_columns,
verbose=True,
pre=True):
''' Measure fairness according to the metric using the value of A and the classification outcome.
Results get added to a dictionary used to pass them to a function to generate graphs of the results.
If we have not performed intervention, perform intervention and return post intervention data.'''
xay_columns = copy.deepcopy(x_columns)
xay_columns.extend(["A", "Y"])
ycols = copy.deepcopy(frame["Y"])
tempframe = copy.deepcopy(frame)
tempframe.drop(["Y"], axis=1, inplace=True)
aifdf = BinaryLabelDataset(favorable_label=1.0,
unfavorable_label=0.0,
df=tempframe,
label_names=['Ya'],
protected_attribute_names=['A'])
privileged_groups = [{'A': 1}]
unprivileged_groups = [{'A': 0}]
RW = Reweighing(unprivileged_groups=[{
'A': 0
}],
privileged_groups=[{
'A': 1
}])
metric_aifdf_train = BinaryLabelDatasetMetric(
aifdf,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
if pre:
if verbose:
print("\n\tINTERVENTION: {}\n".format(type(RW).__name__))
print("\t######### PRE {} ###########".format(type(RW).__name__))
print(
"\tDifference in mean outcomes between unprivileged and privileged groups = {}\n"
.format(metric_aifdf_train.mean_difference()))
dpoints[mname]['PRE'][type(
RW).__name__]['FAIR'] = metric_aifdf_train.mean_difference()
print("PRE CLASSIFICATION MATRIX")
print("----------------")
print(" |Y'=0 | Y'=1 |")
print("----------------")
print("A=0| {0} | {1} |".format(
metric_aifdf_train.num_negatives(False),
metric_aifdf_train.num_positives(False)))
print("A=1| {0} | {1} |".format(
metric_aifdf_train.num_negatives(True),
metric_aifdf_train.num_positives(True)))
print("----------------")
dataset_transf_train = RW.fit_transform(aifdf)
fairdf = dataset_transf_train.convert_to_dataframe()[0]
fairdf.drop(['Ya'], axis=1, inplace=True)
ycols.reset_index(drop=True, inplace=True)
fairdf.reset_index(drop=True, inplace=True)
fairdf.insert(0, "Y", ycols)
fairdf[xay_columns] = fairdf[xay_columns].astype(int)
fairdf.insert(loc=len(fairdf.columns),
column="weights",
value=dataset_transf_train.instance_weights)
return fairdf
else:
if verbose:
print(
"\tDifference in mean outcomes between unprivileged and privileged groups = {}\n"
.format(metric_aifdf_train.mean_difference()))
dpoints[mname]['POST'][type(
RW).__name__]['FAIR'] = metric_aifdf_train.mean_difference()
print("POST CLASSIFICATION MATRIX")
print("----------------")
print(" |Y'=0 | Y'=1 |")
print("----------------")
print("A=0| {0} | {1} |".format(
metric_aifdf_train.num_negatives(False),
metric_aifdf_train.num_positives(False)))
print("A=1| {0} | {1} |".format(
metric_aifdf_train.num_negatives(True),
metric_aifdf_train.num_positives(True)))
print("----------------")
return frame
