def __init__(self, step_name, step, df, sensitive_att, target_col, input_score=True, clf_threshold=0.5):
"""
:param step_name: str, name of the current input step.
:param step: object of the initialized class.
:param df: pandas dataframe, stores the data.
:param sensitive_att: str, the name of a sensitive attribute.
:param target_col: str, the name of the target attribute.
:param input_score: boolean, represent whether the post-processor takes predicted score as input. Default is True.
:param clf_threshold: float in [0, 1], represents the threshold to categorize class labels from predicted scores.
"""
if "pred_"+target_col not in df.columns:
print("Require the predictions for ",target_col, " existing in the data!")
raise ValueError
super().__init__(step_name=step_name, df=df, sensitive_att=sensitive_att, target_col=target_col)
# assume the data set has been encoded to numerical values,
# intitialize a BinaryLabelDataset from AIF 360
aif_true_df = BinaryLabelDataset(df=df.drop(columns=["pred_"+target_col]), label_names=[target_col], protected_attribute_names=[sensitive_att])
aif_pred_df = aif_true_df.copy()
if input_score:
aif_pred_df.scores = df["pred_"+target_col]
else:
aif_pred_df.labels = np.array([int(x >= clf_threshold) for x in df["pred_"+target_col]])
self.input_score = input_score
self.step = step.fit(aif_true_df, aif_pred_df)
self.clf_threshold = clf_threshold
def fairness_IBM(y_pred, Ztr, ytr, verbose=0):
from aif360.datasets import BinaryLabelDataset
from aif360.metrics import ClassificationMetric
assert np.array_equal(np.unique(Ztr),
np.array([0, 1])), "Z must contain either 0 or 1"
# if len(ytr.shape) == 1:
# ytr = np.expand_dims(ytr, -1)
Ztr = np.squeeze(Ztr)
if verbose:
print(ytr.shape)
print(Ztr.shape)
unprivileged_groups = [{"zs": [0]}]
privileged_groups = [{"zs": [1]}]
metric_arrs = defaultdict(list)
dict_ = {"y_true": ytr, "zs": Ztr}
df = pd.DataFrame(dict_)
dataset = BinaryLabelDataset(df=df,
label_names=["y_true"],
protected_attribute_names=["zs"],
unprivileged_protected_attributes=[[0]],
privileged_protected_attributes=[[1]])
dataset_pred = dataset.copy()
dataset_pred.labels = y_pred
metric = ClassificationMetric(dataset,
dataset_pred,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
# metric_arrs['bal_acc'].append((metric.true_positive_rate()
# + metric.true_negative_rate()) / 2)
metric_arrs["EA"].append(
metric.accuracy(privileged=False) - metric.accuracy(privileged=True))
# ASSUMING ALL OTHER METRICS RETURN U - P
metric_arrs['EO'].append(metric.average_odds_difference())
# The ideal value of this metric is 1.0
# A value < 1 implies higher benefit for the privileged group
# and a value >1 implies a higher
metric_arrs['DI'].append(metric.disparate_impact() - 1)
metric_arrs['DP'].append(metric.statistical_parity_difference())
metric_arrs['EQ'].append(metric.equal_opportunity_difference())
metric_arrs['TH'].append(metric.between_group_theil_index() * 10)
results = pd.DataFrame(metric_arrs)
return results