def load_german_dataset():
"""
Collect the aif360 preprocessed German Credit Data Set.
Assigns 'age' as the protected attribute with age >= 25 considered privileged.
Sex-related attributes are removed (the other option for privileged attribute)
:return: The German Credit Data Set
"""
dataset = GermanDataset(protected_attribute_names=['age'],
privileged_classes=[lambda x: x >= 25],
features_to_drop=['personal_status', 'sex'])
dataset_orig_train, dataset_orig_test = dataset.split([0.7], shuffle=True)
return dataset_orig_train, dataset_orig_test
def main():
import sys
sys.path.insert(1, "../")
import numpy as np
np.random.seed(0)
#pip install numba==0.43.0
#pip install --ignore-installed llvmlite==0.32.1
from aif360.datasets import GermanDataset
from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric as CM
from aif360.algorithms.preprocessing import Reweighing
from IPython.display import Markdown, display
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.datasets import make_classification as mc
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
#Step 2 Load dataset, specifying protected attribute, and split dataset into train and test
dataset_orig = GermanDataset(
protected_attribute_names=[
'age'
], # this dataset also contains protected attribute for "sex"
# which we do not consider in this evaluation
privileged_classes=[lambda x: x >= 25
], # age >=25 is considered privileged
features_to_drop=['personal_status',
'sex'] # ignore sex-related attributes
)
dataset_orig_train, dataset_orig_test = dataset_orig.split([0.7],
shuffle=True)
dataset_orig_test_pred = dataset_orig_test.copy(deepcopy=True)
privileged_groups = [{'age': 1}]
unprivileged_groups = [{'age': 0}]
#Step 3 Compute fairness metric on original training dataset
metric_orig_train = BinaryLabelDatasetMetric(
dataset_orig_train, #mean difference
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
display(Markdown("#### Original training dataset"))
print(
"Difference in mean outcomes between unprivileged and privileged groups = %f. AKA the privileged group is getting .17 more positive outcomes in the training dataset."
% metric_orig_train.mean_difference()) #
print()
#metrics
clf = RF()
clf.fit(dataset_orig_train.features, dataset_orig_train.labels)
predictions = clf.predict(dataset_orig_test.features)
proba_predictions = clf.predict_proba(dataset_orig_test.features)
dataset_orig_test_pred.scores = proba_predictions[:, 0].reshape(-1, 1)
dataset_orig_test_pred.labels = predictions.reshape(-1, 1)
cm_pred_valid = CM(dataset_orig_test,
dataset_orig_test_pred,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
cm = ["precision", "recall", "accuracy"]
metrics = {}
for c in cm:
metric = eval("cm_pred_valid." + c + "()")
metrics[c] = metric
metrics["recall"], metrics["accuracy"], metrics["precision"]
print("AIF360 metrics")
for key in ["recall", "accuracy", "precision"]:
print("{} score is: {}".format(key, metrics[key]))
#Step 4 Mitigate bias by transforming the original dataset
RW = Reweighing(
unprivileged_groups=
unprivileged_groups, #pre-processing mitigation algorithm
privileged_groups=privileged_groups)
dataset_transf_train = RW.fit_transform(dataset_orig_train)
#Step 5 Compute fairness metric on transformed dataset
metric_transf_train = BinaryLabelDatasetMetric(
dataset_transf_train,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
display(Markdown("#### Transformed training dataset"))
print(
"Difference in mean outcomes between unprivileged and privileged groups = %f"
% metric_transf_train.mean_difference()) #
#metrics
#split
dataset_transf_train, dataset_transf_test = dataset_transf_train.split(
[0.7], shuffle=True)
dataset_transf_test_pred = dataset_transf_test.copy(deepcopy=True)
clf = RF()
clf.fit(dataset_transf_train.features, dataset_transf_train.labels)
predictions = clf.predict(dataset_transf_test.features)
proba_predictions = clf.predict_proba(dataset_transf_test.features)
dataset_transf_test_pred.scores = proba_predictions[:, 0].reshape(-1, 1)
dataset_transf_test_pred.labels = predictions.reshape(-1, 1)
cm_pred_valid = CM(dataset_transf_test,
dataset_transf_test_pred,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
cm = ["precision", "recall", "accuracy"]
metrics = {}
for c in cm:
metric = eval("cm_pred_valid." + c + "()")
metrics[c] = metric
metrics["recall"], metrics["accuracy"], metrics["precision"]
print("AIF360 metrics")
for key in ["recall", "accuracy", "precision"]:
print("{} score is: {}".format(key, metrics[key]))
