def test_get_hyperparameter_search_space_include_exclude_models(self):
cs = SimpleRegressionPipeline(include={
'regressor': ['random_forest']
}).get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('regressor:__choice__'),
CategoricalHyperparameter('regressor:__choice__',
['random_forest']),
)
# TODO add this test when more than one regressor is present
cs = SimpleRegressionPipeline(exclude={'regressor': ['random_forest']}).\
get_hyperparameter_search_space()
self.assertNotIn('random_forest', str(cs))
cs = SimpleRegressionPipeline(include={'feature_preprocessor': ['pca']}).\
get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('feature_preprocessor:__choice__'),
CategoricalHyperparameter('feature_preprocessor:__choice__',
['pca']))
cs = SimpleRegressionPipeline(
exclude={'feature_preprocessor': ['no_preprocessing']}).\
get_hyperparameter_search_space()
self.assertNotIn('no_preprocessing', str(cs))
def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(
self):
cs = SimpleRegressionPipeline(include={'preprocessor': ['densifier']},
dataset_properties={'sparse': True}).\
get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter('regressor:__choice__').default,
'gradient_boosting')
cs = SimpleRegressionPipeline(include={'preprocessor': ['nystroem_sampler']}).\
get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter('regressor:__choice__').default,
'sgd')
def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(
self):
cs = SimpleRegressionPipeline(include={'preprocessor': ['densifier']},
dataset_properties={'sparse': True}).\
get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('regressor:__choice__').default_value,
'gradient_boosting'
)
cs = SimpleRegressionPipeline(include={'preprocessor': ['nystroem_sampler']}).\
get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('regressor:__choice__').default_value,
'sgd'
)
def test_get_hyperparameter_search_space_include_exclude_models(self):
cs = SimpleRegressionPipeline(
include={'regressor': ['random_forest']}).get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter('regressor:__choice__'),
CategoricalHyperparameter('regressor:__choice__', ['random_forest']))
# TODO add this test when more than one regressor is present
cs = SimpleRegressionPipeline(exclude={'regressor': ['random_forest']}).\
get_hyperparameter_search_space()
self.assertNotIn('random_forest', str(cs))
cs = SimpleRegressionPipeline(include={'preprocessor': ['pca']}).\
get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter('preprocessor:__choice__'),
CategoricalHyperparameter('preprocessor:__choice__', ['pca']))
cs = SimpleRegressionPipeline(exclude={'preprocessor': ['no_preprocessing']}).\
get_hyperparameter_search_space()
self.assertNotIn('no_preprocessing', str(cs))
