def german_dataset_age(name_prot=['age']):
dataset_orig = GermanDataset(protected_attribute_names=name_prot,
privileged_classes=[lambda x: x >= 25],
features_to_drop=['personal_status', 'sex'])
data, _ = dataset_orig.convert_to_dataframe()
data.rename(columns={'credit': 'labels'}, inplace=True)
data.to_csv("dataset/German_age.csv")
def german_dataset(name_prot=['sex']):
dataset_orig = GermanDataset(protected_attribute_names=name_prot,
features_to_drop=['personal_status', 'age'])
privileged_groups = [{'sex': 1}]
unprivileged_groups = [{'sex': 0}]
data, _ = dataset_orig.convert_to_dataframe()
data.rename(columns={'credit': 'labels'}, inplace=True)
sensitive = data[name_prot]
output = data['labels']
output.replace((1, 2), (0, 1), inplace=True)
atribute = data.drop('labels', axis=1, inplace=False)
atribute.drop(name_prot, axis=1, inplace=True)
return data, atribute, sensitive, output, privileged_groups, unprivileged_groups
def german_dataset_sex(name_prot=['sex']):
dataset_orig = GermanDataset(protected_attribute_names=name_prot,
features_to_drop=['personal_status', 'age'])
data, _ = dataset_orig.convert_to_dataframe()
data.rename(columns={'credit': 'labels'}, inplace=True)
data.to_csv("dataset/German_sex.csv")