def test_select_fwe(): """Ensure that the TPOT select fwe outputs the input dataframe when no. of training features is 0""" tpot_obj = TPOT() assert np.array_equal( tpot_obj._select_fwe(training_testing_data.ix[:, -3:], 0.005), training_testing_data.ix[:, -3:])
def test_select_fwe_4(): """Ensure that the TPOT select fwe outputs the same result as sklearn fwe when 0.001 < alpha < 0.05""" tpot_obj = TPOT() non_feature_columns = ['class', 'group', 'guess'] training_features = training_testing_data.loc[training_testing_data['group'] == 'training'].drop(non_feature_columns, axis=1) training_class_vals = training_testing_data.loc[training_testing_data['group'] == 'training', 'class'].values with warnings.catch_warnings(): warnings.simplefilter('ignore', category=UserWarning) selector = SelectFwe(f_classif, alpha=0.042) selector.fit(training_features, training_class_vals) mask = selector.get_support(True) mask_cols = list(training_features.iloc[:, mask].columns) + non_feature_columns assert np.array_equal(tpot_obj._select_fwe(training_testing_data, 0.042), training_testing_data[mask_cols])
def test_select_fwe(): """Ensure that the TPOT select fwe outputs the input dataframe when no. of training features is 0""" tpot_obj = TPOT() assert np.array_equal(tpot_obj._select_fwe(training_testing_data.ix[:, -3:], 0.005), training_testing_data.ix[:, -3:])