def setUp(self):
super(RegistryTest, self).setUp()
self.test_directory = os.path.join(
os.path.dirname(os.path.abspath(__file__)).split("test")[0],
"test")
self.custom_folder = os.path.join(self.test_directory,
"base_tests/custom_elements/")
self.registry = PhotonRegistry(self.custom_folder)
def test_PhotonRegistry_load_json(): # too many args
r = PhotonRegistry()
assert (len(
r.load_json(
PhotonRegistry.PHOTON_REGISTRIES[PhotonRegistry.PhotonCoreID]))
== T_PhotonCoreLen and len(
r.load_json(PhotonRegistry.PHOTON_REGISTRIES[
PhotonRegistry.PhotonClusterID])) == T_PhotonClusterLen
and len(
r.load_json(PhotonRegistry.PHOTON_REGISTRIES[
PhotonRegistry.PhotonNeuroID])) == T_PhotonNeuroLen)
def test_PhotonRegistry_3arg(): # too many args
name = "CustomElements"
element = PhotonRegistry(name)
assert ((element.custom_elements == {})
& (element.custom_elements_folder == name)
& (element.custom_elements_file == name + "/" + name + ".json"))
def test_PhotonRegistry_0arg(): # too many args
assert ((PhotonRegistry().custom_elements == None)
& (PhotonRegistry().custom_elements_folder == None)
& (PhotonRegistry().custom_elements_file == None))
def test_PhotonRegistry_pos_arg_erroe(): # too many args
name = "myhype"
with pytest.raises(TypeError):
assert PhotonRegistry(name, 1, 2, 3)
import os
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import KFold
from photonai.base import Hyperpipe, PipelineElement, OutputSettings, PhotonRegistry
from photonai.optimization import IntegerRange
# REGISTER ELEMENT
base_folder = os.path.dirname(os.path.abspath(__file__))
custom_elements_folder = os.path.join(base_folder, "custom_elements")
registry = PhotonRegistry(custom_elements_folder=custom_elements_folder)
registry.register(
photon_name="MyCustomEstimator",
class_str="custom_estimator.CustomEstimator",
element_type="Estimator",
)
registry.register(
photon_name="MyCustomTransformer",
class_str="custom_transformer.CustomTransformer",
element_type="Transformer",
)
registry.activate()
# WE USE THE BREAST CANCER SET FROM SKLEARN
X, y = load_breast_cancer(True)
settings = OutputSettings(project_folder="./tmp/")
import os
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import KFold
from photonai.base import Hyperpipe, PipelineElement, OutputSettings, PhotonRegistry
from photonai.optimization import IntegerRange
# REGISTER ELEMENT
base_folder = os.path.dirname(os.path.abspath(__file__))
custom_elements_folder = os.path.join(base_folder, 'custom_elements')
registry = PhotonRegistry(custom_elements_folder=custom_elements_folder)
registry.register(photon_name='MyCustomEstimator',
class_str='custom_estimator.CustomEstimator',
element_type='Estimator')
registry.register(photon_name='MyCustomTransformer',
class_str='custom_transformer.CustomTransformer',
element_type='Transformer')
registry.activate()
# WE USE THE BREAST CANCER SET FROM SKLEARN
X, y = load_breast_cancer(return_X_y=True)
settings = OutputSettings(project_folder='./tmp/')
# DESIGN YOUR PIPELINE
my_pipe = Hyperpipe(
'custom_estimator_pipe',
def register_photonai_neuro():
current_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"photonai_neuro.json")
reg = PhotonRegistry()
reg.add_module(current_path)
def test_PhotonRegistry_get_package_info(): # too many args
r = PhotonRegistry()
assert (len(r.get_package_info()) == sum(PHOTON_pkgs_len))
def test_PhotonRegistry_get_element_metadata_SmoothImages(): # too many args
r = PhotonRegistry()
md = r.load_json(
PhotonRegistry.PHOTON_REGISTRIES[PhotonRegistry.PhotonNeuroID])
assert r.get_element_metadata('SmoothImages', md)[3] == SM_MD[2]
def test_PhotonRegistry_get_element_metadata_PCA(): # too many args
r = PhotonRegistry()
md = r.load_json(
PhotonRegistry.PHOTON_REGISTRIES[PhotonRegistry.PhotonCoreID])
assert r.get_element_metadata('PCA', md)[0:2] == PCA_MD[0:2]
def test_PhotonRegistry_get_element_metadata_KMEANS_ERROR(): # too many args
r = PhotonRegistry()
md = r.load_json(
PhotonRegistry.PHOTON_REGISTRIES[PhotonRegistry.PhotonCoreID])
with pytest.raises(PhotonaiError):
assert r.get_element_metadata('FRED', md)[0:2] == GM_MD[0:2]
def test_PhotonRegistry_get_element_metadata_KMEANS(): # too many args
r = PhotonRegistry()
md = r.load_json(PHOTON_pkgs[1])
assert r.get_element_metadata('KMeans', md)[0] == KMEANS_MD[0]
def test_PhotonRegistry_PHOTON_REGISTRIES(): # too many args
PhotonRegistry().reset()
assert PhotonRegistry.PHOTON_REGISTRIES[0:2] == [
"PhotonCore", "PhotonNeuro"
]
def test_PhotonRegistry_get_package_info_error(): # too many args
r = PhotonRegistry()
assert (len(r.get_package_info('fred')) == 0)
class RegistryTest(PhotonBaseTest):
@classmethod
def setUpClass(cls) -> None:
cls.file = __file__
super(RegistryTest, cls).setUpClass()
def setUp(self):
super(RegistryTest, self).setUp()
self.test_directory = os.path.join(os.path.dirname(os.path.abspath(__file__)).split('test')[0], 'test')
self.custom_folder = os.path.join(self.test_directory, 'base_tests/custom_elements/')
self.registry = PhotonRegistry(self.custom_folder)
def tearDown(self):
super(RegistryTest, self).tearDown()
if os.path.isfile(os.path.join(self.custom_folder, 'CustomElements.json')):
os.remove(os.path.join(self.custom_folder, 'CustomElements.json'))
def test_register_without_custom_folder(self):
registry = PhotonRegistry()
with self.assertRaises(ValueError):
registry.register('SomeName', 'not_existing_file.SomeName', 'Estimator')
def test_python_file_not_in_custom_folder(self):
with self.assertRaises(FileNotFoundError):
self.registry.register('SomeName', 'not_existing_file.SomeName', 'Estimator')
def test_list_available_elements(self):
self.registry.list_available_elements()
self.registry.info('PCA')
self.registry.info('NotExistingEstimator')
def test_register_element(self):
with self.assertRaises(ValueError):
self.registry.register('MyCustomEstimator', 'custom_estimator.CustomEstimator', 'WrongType')
self.registry.register('MyCustomEstimator', 'custom_estimator.CustomEstimator', 'Estimator')
self.registry.activate()
settings = OutputSettings(save_output=False, project_folder='./tmp/')
# DESIGN YOUR PIPELINE
pipe = Hyperpipe('custom_estimator_pipe',
optimizer='random_grid_search',
optimizer_params={'n_configurations': 2},
metrics=['accuracy', 'precision', 'recall', 'balanced_accuracy'],
best_config_metric='accuracy',
outer_cv=KFold(n_splits=2),
inner_cv=KFold(n_splits=2),
verbosity=1,
output_settings=settings)
pipe += PipelineElement('MyCustomEstimator')
pipe.fit(np.random.randn(30, 30), np.random.randint(0, 2, 30))
self.registry.delete('MyCustomEstimator')
os.remove(os.path.join(self.custom_folder, 'CustomElements.json'))
def test_estimator_check_during_register(self):
with self.assertRaises(NotImplementedError):
self.registry.register('MyCustomEstimatorNoFit', 'custom_estimator.CustomEstimatorNoFit', 'Estimator')
with self.assertRaises(NotImplementedError):
self.registry.register('MyCustomEstimatorNoPredict', 'custom_estimator.CustomEstimatorNoPredict',
'Estimator')
with self.assertRaises(NotImplementedError):
self.registry.register('MyCustomEstimatorNoEstimatorType',
'custom_estimator.CustomEstimatorNoEstimatorType',
'Estimator')
with self.assertRaises(NotImplementedError):
self.registry.register('MyCustomEstimatorNotReturningSelf',
'custom_estimator.CustomEstimatorNotReturningSelf',
'Estimator')
e = self.registry.register('MyCustomEstimatorReturningFalsePredictions',
'custom_estimator.CustomEstimatorReturningFalsePredictions',
'Estimator')
self.assertIsInstance(e, ValueError)
e = self.registry.register('MyCustomEstimatorNotWorking', 'custom_estimator.CustomEstimatorNotWorking',
'Estimator')
self.assertIsInstance(e, ValueError)
os.remove(os.path.join(self.custom_folder, 'CustomElements.json'))
def test_transformer_needs_covariates(self):
self.registry.register('MyCustomTransformerNeedsCovariates',
'custom_transformer.CustomTransformerNeedsCovariates',
'Transformer')
with self.assertRaises(ValueError):
self.registry.register('MyCustomTransformerNeedsCovariatesWrongInterface',
'custom_transformer.CustomTransformerNeedsCovariatesWrongInterface',
'Transformer')
def test_transformer_needs_y(self):
self.registry.register('MyCustomTransformerNeedsY',
'custom_transformer.CustomTransformerNeedsY',
'Transformer')
with self.assertRaises(ValueError):
self.registry.register('MyCustomTransformerNeedsYWrongInterface',
'custom_transformer.CustomTransformerNeedsYWrongInterface',
'Transformer')
def test_add_module(self):
json_module_file = os.path.join(self.custom_folder, "fake_module.json")
# first add module
self.registry.add_module(json_module_file)
self.assertTrue("fake_module" in self.registry.PHOTON_REGISTRIES)
# check if registered items are available
self.assertTrue("FakeElement1" in self.registry.ELEMENT_DICTIONARY)
fe = PipelineElement("FakeElement1")
# then delete the module
self.registry.delete_module("fake_module")
self.assertFalse(os.path.isfile(os.path.join(self.registry.module_path, "fake_module.json")))
self.assertFalse("FakeElement1" in self.registry.ELEMENT_DICTIONARY)
with self.assertRaises(NameError):
fe = PipelineElement("FakeElement1")
def test_add_failing_module(self):
json_module_file = os.path.join(self.custom_folder, "not_working_fake_module.json")
# try to add module
self.registry.add_module(json_module_file)
# that should not have worked because we cant import first element
self.assertFalse("FakeElement2001" in self.registry.ELEMENT_DICTIONARY)
self.assertFalse(os.path.isfile(os.path.join(self.registry.module_path, "not_working_fake_module.json")))
def test_PhotonRegistry_PHOTON_REGISTRIES_reset(): # too many args
PhotonRegistry().reset()
assert PhotonRegistry.PHOTON_REGISTRIES[0:] == PHOTON_pkgs[:-1]
def delete_photonai_neuro():
reg = PhotonRegistry()
if 'photonai_neuro' in reg.PHOTON_REGISTRIES:
reg.delete_module('photonai_neuro')
def test_PhotonRegistry_PHOTON_REGISTRIES_folder():
f = '/photonai/base/registry'
r = PhotonRegistry()
r._load_custom_folder(f)
assert r.custom_elements_folder == f
class RegistryTest(PhotonBaseTest):
def setUp(self):
super(RegistryTest, self).setUp()
self.test_directory = os.path.join(
os.path.dirname(os.path.abspath(__file__)).split("test")[0],
"test")
self.custom_folder = os.path.join(self.test_directory,
"base_tests/custom_elements/")
self.registry = PhotonRegistry(self.custom_folder)
def tearDown(self):
super(RegistryTest, self).tearDown()
if os.path.isfile(
os.path.join(self.custom_folder, "CustomElements.json")):
os.remove(os.path.join(self.custom_folder, "CustomElements.json"))
def test_register_without_custom_folder(self):
registry = PhotonRegistry()
with self.assertRaises(ValueError):
registry.register("SomeName", "not_existing_file.SomeName",
"Estimator")
def test_python_file_not_in_custom_folder(self):
with self.assertRaises(FileNotFoundError):
self.registry.register("SomeName", "not_existing_file.SomeName",
"Estimator")
def test_list_available_elements(self):
self.registry.list_available_elements()
self.registry.list_available_elements("PhotonNeuro")
self.registry.info("PCA")
self.registry.info("NotExistingEstimator")
def test_register_element(self):
with self.assertRaises(ValueError):
self.registry.register("MyCustomEstimator",
"custom_estimator.CustomEstimator",
"WrongType")
self.registry.register("MyCustomEstimator",
"custom_estimator.CustomEstimator", "Estimator")
self.registry.activate()
settings = OutputSettings(save_output=False, project_folder="./tmp/")
# DESIGN YOUR PIPELINE
pipe = Hyperpipe(
"custom_estimator_pipe",
optimizer="random_grid_search",
optimizer_params={"n_configurations": 2},
metrics=["accuracy", "precision", "recall", "balanced_accuracy"],
best_config_metric="accuracy",
outer_cv=KFold(n_splits=2),
inner_cv=KFold(n_splits=2),
verbosity=1,
output_settings=settings,
)
pipe += PipelineElement("MyCustomEstimator")
pipe.fit(np.random.randn(30, 30), np.random.randint(0, 2, 30))
self.registry.delete("MyCustomEstimator")
os.remove(os.path.join(self.custom_folder, "CustomElements.json"))
def test_estimator_check_during_register(self):
with self.assertRaises(NotImplementedError):
self.registry.register(
"MyCustomEstimatorNoFit",
"custom_estimator.CustomEstimatorNoFit",
"Estimator",
)
with self.assertRaises(NotImplementedError):
self.registry.register(
"MyCustomEstimatorNoPredict",
"custom_estimator.CustomEstimatorNoPredict",
"Estimator",
)
with self.assertRaises(NotImplementedError):
self.registry.register(
"MyCustomEstimatorNoEstimatorType",
"custom_estimator.CustomEstimatorNoEstimatorType",
"Estimator",
)
with self.assertRaises(NotImplementedError):
self.registry.register(
"MyCustomEstimatorNotReturningSelf",
"custom_estimator.CustomEstimatorNotReturningSelf",
"Estimator",
)
e = self.registry.register(
"MyCustomEstimatorReturningFalsePredictions",
"custom_estimator.CustomEstimatorReturningFalsePredictions",
"Estimator",
)
self.assertIsInstance(e, ValueError)
e = self.registry.register(
"MyCustomEstimatorNotWorking",
"custom_estimator.CustomEstimatorNotWorking",
"Estimator",
)
self.assertIsInstance(e, ValueError)
os.remove(os.path.join(self.custom_folder, "CustomElements.json"))
def test_transformer_needs_covariates(self):
self.registry.register(
"MyCustomTransformerNeedsCovariates",
"custom_transformer.CustomTransformerNeedsCovariates",
"Transformer",
)
with self.assertRaises(ValueError):
self.registry.register(
"MyCustomTransformerNeedsCovariatesWrongInterface",
"custom_transformer.CustomTransformerNeedsCovariatesWrongInterface",
"Transformer",
)
def test_transformer_needs_y(self):
self.registry.register(
"MyCustomTransformerNeedsY",
"custom_transformer.CustomTransformerNeedsY",
"Transformer",
)
with self.assertRaises(ValueError):
self.registry.register(
"MyCustomTransformerNeedsYWrongInterface",
"custom_transformer.CustomTransformerNeedsYWrongInterface",
"Transformer",
)
def test_PhotonRegistry_PHOTON_REGISTRIES_contents(): # too many args
f = '/photonai/base/registry'
r = PhotonRegistry()
r._load_custom_folder(f)
assert r.activate() == None
def test_register_without_custom_folder(self):
registry = PhotonRegistry()
with self.assertRaises(ValueError):
registry.register("SomeName", "not_existing_file.SomeName",
"Estimator")
class RegistryTest(PhotonBaseTest):
def setUp(self):
super(RegistryTest, self).setUp()
self.test_directory = os.path.join(
os.path.dirname(os.path.abspath(__file__)).split('test')[0],
'test')
self.custom_folder = os.path.join(self.test_directory,
'base_tests/custom_elements/')
self.registry = PhotonRegistry(self.custom_folder)
def tearDown(self):
super(RegistryTest, self).tearDown()
if os.path.isfile(
os.path.join(self.custom_folder, 'CustomElements.json')):
os.remove(os.path.join(self.custom_folder, 'CustomElements.json'))
def test_register_without_custom_folder(self):
registry = PhotonRegistry()
with self.assertRaises(ValueError):
registry.register('SomeName', 'not_existing_file.SomeName',
'Estimator')
def test_python_file_not_in_custom_folder(self):
with self.assertRaises(FileNotFoundError):
self.registry.register('SomeName', 'not_existing_file.SomeName',
'Estimator')
def test_list_available_elements(self):
self.registry.list_available_elements()
self.registry.list_available_elements('PhotonNeuro')
self.registry.info('PCA')
self.registry.info('NotExistingEstimator')
def test_register_element(self):
with self.assertRaises(ValueError):
self.registry.register('MyCustomEstimator',
'custom_estimator.CustomEstimator',
'WrongType')
self.registry.register('MyCustomEstimator',
'custom_estimator.CustomEstimator', 'Estimator')
self.registry.activate()
settings = OutputSettings(save_output=False, project_folder='./tmp/')
# DESIGN YOUR PIPELINE
pipe = Hyperpipe(
'custom_estimator_pipe',
optimizer='random_grid_search',
optimizer_params={'n_configurations': 2},
metrics=['accuracy', 'precision', 'recall', 'balanced_accuracy'],
best_config_metric='accuracy',
outer_cv=KFold(n_splits=2),
inner_cv=KFold(n_splits=2),
verbosity=1,
output_settings=settings)
pipe += PipelineElement('MyCustomEstimator')
pipe.fit(np.random.randn(30, 30), np.random.randint(0, 2, 30))
self.registry.delete('MyCustomEstimator')
os.remove(os.path.join(self.custom_folder, 'CustomElements.json'))
def test_estimator_check_during_register(self):
with self.assertRaises(NotImplementedError):
self.registry.register('MyCustomEstimatorNoFit',
'custom_estimator.CustomEstimatorNoFit',
'Estimator')
with self.assertRaises(NotImplementedError):
self.registry.register(
'MyCustomEstimatorNoPredict',
'custom_estimator.CustomEstimatorNoPredict', 'Estimator')
with self.assertRaises(NotImplementedError):
self.registry.register(
'MyCustomEstimatorNoEstimatorType',
'custom_estimator.CustomEstimatorNoEstimatorType', 'Estimator')
with self.assertRaises(NotImplementedError):
self.registry.register(
'MyCustomEstimatorNotReturningSelf',
'custom_estimator.CustomEstimatorNotReturningSelf',
'Estimator')
e = self.registry.register(
'MyCustomEstimatorReturningFalsePredictions',
'custom_estimator.CustomEstimatorReturningFalsePredictions',
'Estimator')
self.assertIsInstance(e, ValueError)
e = self.registry.register(
'MyCustomEstimatorNotWorking',
'custom_estimator.CustomEstimatorNotWorking', 'Estimator')
self.assertIsInstance(e, ValueError)
os.remove(os.path.join(self.custom_folder, 'CustomElements.json'))
def test_transformer_needs_covariates(self):
self.registry.register(
'MyCustomTransformerNeedsCovariates',
'custom_transformer.CustomTransformerNeedsCovariates',
'Transformer')
with self.assertRaises(ValueError):
self.registry.register(
'MyCustomTransformerNeedsCovariatesWrongInterface',
'custom_transformer.CustomTransformerNeedsCovariatesWrongInterface',
'Transformer')
def test_transformer_needs_y(self):
self.registry.register('MyCustomTransformerNeedsY',
'custom_transformer.CustomTransformerNeedsY',
'Transformer')
with self.assertRaises(ValueError):
self.registry.register(
'MyCustomTransformerNeedsYWrongInterface',
'custom_transformer.CustomTransformerNeedsYWrongInterface',
'Transformer')
