from sklearn.feature_selection import f_classif
from sklearn.feature_selection import chi2
from sklearn.model_selection import cross_val_score
from fastsklearnfeature.interactiveAutoML.fair_measure import true_positive_rate_score
from fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.robust_measure import robust_score
from fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.robust_measure import robust_score_test
import fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.my_global_variable as my_global_variable
from fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.bench_utils import get_data
n_estimators = 5
X_train, X_test, y_train, y_test, names, sensitive_ids = get_data(
data_path='/heart/dataset_53_heart-statlog.csv',
continuous_columns=[0, 3, 4, 7, 9, 10, 11],
sensitive_attribute="sex",
limit=250)
start_time = time.time()
auc_scorer = make_scorer(roc_auc_score,
greater_is_better=True,
needs_threshold=True)
fair_train = make_scorer(true_positive_rate_score,
greater_is_better=True,
sensitive_data=X_train[:, sensitive_ids[0]])
fair_test = make_scorer(true_positive_rate_score,
greater_is_better=True,
sensitive_data=X_test[:, sensitive_ids[0]])
from fastsklearnfeature.interactiveAutoML.fair_measure import true_positive_rate_score
from fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.robust_measure import robust_score
import diffprivlib.models as models
from fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.bench_utils import get_data
n_estimators = 5
'''
X_train, X_test, y_train, y_test, names, sensitive_ids = get_data(data_path='/heart/dataset_53_heart-statlog.csv',
continuous_columns = [0,3,4,7,9,10,11],
sensitive_attribute = "sex",
limit=250)
'''
X_train, X_test, y_train, y_test, names, sensitive_ids = get_data()
start_time = time.time()
## run hyperparameter weighting
auc_scorer = make_scorer(roc_auc_score,
greater_is_better=True,
needs_threshold=True)
fair_train = make_scorer(true_positive_rate_score,
greater_is_better=True,
sensitive_data=X_train[:, sensitive_ids[0]])
fair_test = make_scorer(true_positive_rate_score,
greater_is_better=True,
sensitive_data=X_test[:, sensitive_ids[0]])