def test_column_transformer_get_set_params():
ct = ColumnTransformer([('trans1', StandardScaler(), [0]),
('trans2', StandardScaler(), [1])])
exp = {
'n_jobs': None,
'remainder': 'drop',
'sparse_threshold': 0.3,
'trans1': ct.transformers[0][1],
'trans1__copy': True,
'trans1__with_mean': True,
'trans1__with_std': True,
'trans2': ct.transformers[1][1],
'trans2__copy': True,
'trans2__with_mean': True,
'trans2__with_std': True,
'transformers': ct.transformers,
'transformer_weights': None,
'verbose': False
}
assert ct.get_params() == exp
ct.set_params(trans1__with_mean=False)
assert not ct.get_params()['trans1__with_mean']
ct.set_params(trans1='passthrough')
exp = {
'n_jobs': None,
'remainder': 'drop',
'sparse_threshold': 0.3,
'trans1': 'passthrough',
'trans2': ct.transformers[1][1],
'trans2__copy': True,
'trans2__with_mean': True,
'trans2__with_std': True,
'transformers': ct.transformers,
'transformer_weights': None,
'verbose': False
}
assert ct.get_params() == exp
def test_column_transformer_no_estimators():
X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).astype('float').T
ct = ColumnTransformer([], remainder=StandardScaler())
params = ct.get_params()
assert params['remainder__with_mean']
X_trans = ct.fit_transform(X_array)
assert X_trans.shape == X_array.shape
assert len(ct.transformers_) == 1
assert ct.transformers_[-1][0] == 'remainder'
assert ct.transformers_[-1][2] == [0, 1, 2]
