def test_get_hyperparameter_search_space(self):
cs = SimpleClassificationPipeline().get_hyperparameter_search_space()
self.assertIsInstance(cs, ConfigurationSpace)
conditions = cs.get_conditions()
forbiddens = cs.get_forbiddens()
self.assertEqual(
len(
cs.get_hyperparameter(
'data_preprocessing:numerical_transformer:rescaling:__choice__'
).choices), 7)
self.assertEqual(
len(cs.get_hyperparameter('classifier:__choice__').choices), 16)
self.assertEqual(
len(
cs.get_hyperparameter(
'feature_preprocessor:__choice__').choices), 13)
hyperparameters = cs.get_hyperparameters()
self.assertEqual(167, len(hyperparameters))
# for hp in sorted([str(h) for h in hyperparameters]):
# print hp
# The four components which are always active are classifier,
# feature preprocessor, balancing and data preprocessing pipeline.
self.assertEqual(len(hyperparameters) - 7, len(conditions))
self.assertEqual(len(forbiddens), 53)
def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(
self):
cs = SimpleClassificationPipeline(
include={'preprocessor': ['densifier']}, dataset_properties={'sparse': True}).\
get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('classifier:__choice__').default, 'qda')
cs = SimpleClassificationPipeline(include={'preprocessor': ['nystroem_sampler']}).\
get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('classifier:__choice__').default, 'sgd')
def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(self):
cs = SimpleClassificationPipeline(
include={'preprocessor': ['densifier']}, dataset_properties={'sparse': True}).\
get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter(
'classifier:__choice__').default_value,
'qda'
)
cs = SimpleClassificationPipeline(include={'preprocessor': ['nystroem_sampler']}).\
get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter(
'classifier:__choice__').default_value,
'sgd'
)
def test_get_hyperparameter_search_space_include_exclude_models(self):
cs = SimpleClassificationPipeline(include={'classifier': ['libsvm_svc']})\
.get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter('classifier:__choice__'),
CategoricalHyperparameter('classifier:__choice__', ['libsvm_svc']))
cs = SimpleClassificationPipeline(exclude={'classifier': ['libsvm_svc']}).\
get_hyperparameter_search_space()
self.assertNotIn('libsvm_svc', str(cs))
cs = SimpleClassificationPipeline(
include={'preprocessor': ['select_percentile_classification']}).\
get_hyperparameter_search_space()
self.assertEqual(cs.get_hyperparameter('preprocessor:__choice__'),
CategoricalHyperparameter('preprocessor:__choice__',
['select_percentile_classification']))
cs = SimpleClassificationPipeline(
exclude={'preprocessor': ['select_percentile_classification']}
).get_hyperparameter_search_space()
self.assertNotIn('select_percentile_classification', str(cs))
def test_get_hyperparameter_search_space(self):
cs = SimpleClassificationPipeline().get_hyperparameter_search_space()
self.assertIsInstance(cs, ConfigurationSpace)
conditions = cs.get_conditions()
self.assertEqual(len(cs.get_hyperparameter(
'rescaling:__choice__').choices), 6)
self.assertEqual(len(cs.get_hyperparameter(
'classifier:__choice__').choices), 16)
self.assertEqual(len(cs.get_hyperparameter(
'preprocessor:__choice__').choices), 13)
hyperparameters = cs.get_hyperparameters()
self.assertEqual(172, len(hyperparameters))
#for hp in sorted([str(h) for h in hyperparameters]):
# print hp
# The four parameters which are always active are classifier,
# preprocessor, imputation strategy and scaling strategy
self.assertEqual(len(hyperparameters) - 6, len(conditions))
def test_get_hyperparameter_search_space(self):
cs = SimpleClassificationPipeline().get_hyperparameter_search_space()
self.assertIsInstance(cs, ConfigurationSpace)
conditions = cs.get_conditions()
self.assertEqual(
len(cs.get_hyperparameter('rescaling:__choice__').choices), 6)
self.assertEqual(
len(cs.get_hyperparameter('classifier:__choice__').choices), 15)
self.assertEqual(
len(cs.get_hyperparameter('preprocessor:__choice__').choices), 13)
hyperparameters = cs.get_hyperparameters()
self.assertEqual(156, len(hyperparameters))
#for hp in sorted([str(h) for h in hyperparameters]):
# print hp
# The four parameters which are always active are classifier,
# preprocessor, imputation strategy and scaling strategy
self.assertEqual(len(hyperparameters) - 6, len(conditions))
def test_get_hyperparameter_search_space_include_exclude_models(self):
cs = SimpleClassificationPipeline(include={'classifier': ['libsvm_svc']})\
.get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('classifier:__choice__'),
CategoricalHyperparameter('classifier:__choice__', ['libsvm_svc']))
cs = SimpleClassificationPipeline(exclude={'classifier': ['libsvm_svc']}).\
get_hyperparameter_search_space()
self.assertNotIn('libsvm_svc', str(cs))
cs = SimpleClassificationPipeline(
include={'preprocessor': ['select_percentile_classification']}).\
get_hyperparameter_search_space()
self.assertEqual(
cs.get_hyperparameter('preprocessor:__choice__'),
CategoricalHyperparameter('preprocessor:__choice__',
['select_percentile_classification']))
cs = SimpleClassificationPipeline(exclude={
'preprocessor': ['select_percentile_classification']
}).get_hyperparameter_search_space()
self.assertNotIn('select_percentile_classification', str(cs))
