def entropy_index(random_data, predicted_data, target_variable, protected_variable, unprivileged_input):
random_data['Pred'] = np.random.binomial(1, .5, 1000)
dataset = BinaryLabelDataset(df=random_data, label_names=[target_variable], protected_attribute_names=[protected_variable])
classified_dataset = BinaryLabelDataset(df=predicted_data, label_names=[target_variable], protected_attribute_names=[protected_variable])
privileged_group = []
for v in predicted_data[protected_variable].unique()[predicted_data[protected_variable].unique() != unprivileged_input]:
privileged_group.append({protected_variable: v})
unprivileged_group = [{protected_variable: unprivileged_input}] #female=0
metric = ClassificationMetric(dataset, classified_dataset, unprivileged_group, privileged_group)
print(metric.between_all_groups_generalized_entropy_index(alpha=2))
if abs(metric.between_all_groups_generalized_entropy_index(alpha=2).round(3)) < 0.2:
print('The algorithm can be considered to be not biased')
else:
print('There is a potential bias')
def test_between_all_groups():
data = np.array([[0, 1], [0, 0], [1, 0], [1, 1], [1, 0], [1, 0], [2, 1],
[2, 0], [2, 1], [2, 1]])
pred = data.copy()
pred[[3, 9], -1] = 0
pred[[4, 5], -1] = 1
df = pd.DataFrame(data, columns=['feat', 'label'])
df2 = pd.DataFrame(pred, columns=['feat', 'label'])
bld = BinaryLabelDataset(df=df,
label_names=['label'],
protected_attribute_names=['feat'])
bld2 = BinaryLabelDataset(df=df2,
label_names=['label'],
protected_attribute_names=['feat'])
cm = ClassificationMetric(bld, bld2)
b = np.array([1, 1, 1.25, 1.25, 1.25, 1.25, 0.75, 0.75, 0.75, 0.75])
assert cm.between_all_groups_generalized_entropy_index(
) == 1 / 20 * np.sum(b**2 - 1)
# (3) think about a way to visualize these values
# Statistical Parity difference (SPD)
spd_pre_race = fairness_metrics.statistical_parity_difference()
# Disparate Impact Ratio
dir_pre_race = fairness_metrics.disparate_impact()
# Average Odds Difference and Average absolute odds difference
aod_pre_race = fairness_metrics.average_odds_difference()
aaod_pre_race = fairness_metrics.average_abs_odds_difference()
# Equal Opportunity Difference aka true positive rate difference
eod_pre_race = fairness_metrics.equal_opportunity_difference()
# Generealized entropy index with various alpha's
fairness_metrics.between_all_groups_generalized_entropy_index(alpha=2)
ClassificationMetric(dataset=bld_true,
classified_dataset=bld_pred,
unprivileged_groups=None,
privileged_groups=None).false_positive_rate()
df_fm.head()
# TO DELETE
# =============================================================================
# bld_pred.align_datasets
# bld_true.temporarily_ignore('score_cat')
# =============================================================================
# TO DELETE: NOT YET NECESSARY
# =============================================================================
