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_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:])
