def test_get_extension_by_model(self):
self.assertIsNone(get_extension_by_model(DummyModel()))
with self.assertRaisesRegex(ValueError, 'No extension registered which can handle model:'):
get_extension_by_model(DummyModel(), raise_if_no_extension=True)
register_extension(DummyExtension1)
self.assertIsInstance(get_extension_by_model(DummyModel()), DummyExtension1)
register_extension(DummyExtension2)
self.assertIsInstance(get_extension_by_model(DummyModel()), DummyExtension1)
register_extension(DummyExtension1)
with self.assertRaisesRegex(
ValueError,
'Multiple extensions registered which can handle model:',
):
get_extension_by_model(DummyModel())
def test_get_extension_by_flow(self):
self.assertIsNone(get_extension_by_flow(DummyFlow()))
with self.assertRaisesRegex(
ValueError, "No extension registered which can handle flow:"):
get_extension_by_flow(DummyFlow(), raise_if_no_extension=True)
register_extension(DummyExtension1)
self.assertIsInstance(get_extension_by_flow(DummyFlow()),
DummyExtension1)
register_extension(DummyExtension2)
self.assertIsInstance(get_extension_by_flow(DummyFlow()),
DummyExtension1)
register_extension(DummyExtension1)
with self.assertRaisesRegex(
ValueError,
"Multiple extensions registered which can handle flow:",
):
get_extension_by_flow(DummyFlow())
'oml:value': value,
'oml:component': flow.flow_id
})
return parameter_settings
def instantiate_model_from_hpo_class(
self,
model: Any,
trace_iteration: OpenMLTraceIteration,
) -> Any:
"""Instantiate a ``base_estimator`` which can be searched over by the hyperparameter
optimization model (UNUSED)
Parameters
----------
model : Any
A hyperparameter optimization model which defines the model to be instantiated.
trace_iteration : OpenMLTraceIteration
Describing the hyperparameter settings to instantiate.
Returns
-------
Any
"""
return model
register_extension(OnnxExtension)
flow_structure = flow.get_structure('name')
if openml_parameter.flow_name not in flow_structure:
raise ValueError('Obtained OpenMLParameter and OpenMLFlow do not correspond. ')
name = openml_parameter.flow_name # for PEP8
return '__'.join(flow_structure[name] + [openml_parameter.parameter_name])
def instantiate_model_from_hpo_class(
self,
model: Any,
trace_iteration: OpenMLTraceIteration,
) -> Any:
"""Instantiate a ``base_estimator`` which can be searched over by the hyperparameter
optimization model (UNUSED)
Parameters
----------
model : Any
A hyperparameter optimization model which defines the model to be instantiated.
trace_iteration : OpenMLTraceIteration
Describing the hyperparameter settings to instantiate.
Returns
-------
Any
"""
return model
register_extension(KerasExtension)
# License: BSD 3-Clause from .extension import SklearnExtension from openml.extensions import register_extension __all__ = ['SklearnExtension'] register_extension(SklearnExtension)
from .extension import PytorchExtension from . import config from . import layers from openml.extensions import register_extension __all__ = ['PytorchExtension', 'config', 'layers'] register_extension(PytorchExtension)
from .extension import MXNetExtension from .config import Config from openml.extensions import register_extension __all__ = ['MXNetExtension', 'Config'] register_extension(MXNetExtension)
import os from .extension import TensorflowExtension from openml.extensions import register_extension from . import config __all__ = ['TensorflowExtension', 'config'] register_extension(TensorflowExtension)
import os from .extension import TFExtension from openml.extensions import register_extension __all__ = ['TFExtension'] register_extension(TFExtension)