def test_rfe():
"""Ensure that the TPOT RFE outputs the input dataframe when no. of training features is 0"""
tpot_obj = TPOT()
assert np.array_equal(
tpot_obj._rfe(training_testing_data.ix[:, -3:], 0, 0.1),
training_testing_data.ix[:, -3:])
def test_rfe_2():
"""Ensure that the TPOT RFE outputs the same result as the sklearn rfe when num_features>no. of features in the dataframe """
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)
estimator = LinearSVC()
rfe = RFE(estimator, 100, step=0.1)
rfe.fit(training_features, training_classes)
mask = rfe.get_support(True)
mask_cols = list(training_features.iloc[:, mask].columns) + non_feature_columns
assert np.array_equal(training_testing_data[mask_cols], tpot_obj._rfe(training_testing_data, 64, 0.1))
def test_rfe_2():
"""Ensure that the TPOT RFE outputs the same result as the sklearn rfe when num_features>no. of features in the dataframe """
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)
estimator = LinearSVC()
rfe = RFE(estimator, 100, step=0.1)
rfe.fit(training_features, training_classes)
mask = rfe.get_support(True)
mask_cols = list(training_features.iloc[:, mask].columns) + non_feature_columns
assert np.array_equal(training_testing_data[mask_cols], tpot_obj._rfe(training_testing_data, 64, 0.1))
def test_rfe():
"""Ensure that the TPOT RFE outputs the input dataframe when no. of training features is 0"""
tpot_obj = TPOT()
assert np.array_equal(tpot_obj._rfe(training_testing_data.ix[:, -3:], 0, 0.1), training_testing_data.ix[:, -3:])
