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]])