Source code for autosklearn.pipeline.components.regression

from collections import OrderedDict
from typing import Type
import os

from ..base import AutoSklearnRegressionAlgorithm, find_components, \
    ThirdPartyComponents, AutoSklearnChoice, _addons
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter

regressor_directory = os.path.split(__file__)[0]
_regressors = find_components(__package__,
additional_components = ThirdPartyComponents(AutoSklearnRegressionAlgorithm)
_addons['regression'] = additional_components

[docs]def add_regressor(regressor: Type[AutoSklearnRegressionAlgorithm]) -> None: additional_components.add_component(regressor)
class RegressorChoice(AutoSklearnChoice): @classmethod def get_components(cls): components = OrderedDict() components.update(_regressors) components.update(additional_components.components) return components @classmethod def get_available_components(cls, dataset_properties=None, include=None, exclude=None): available_comp = cls.get_components() components_dict = OrderedDict() if dataset_properties is None: dataset_properties = {} if include is not None and exclude is not None: raise ValueError( "The argument include and exclude cannot be used together.") if include is not None: for incl in include: if incl not in available_comp: raise ValueError("Trying to include unknown component: " "%s" % incl) for name in available_comp: if include is not None and name not in include: continue elif exclude is not None and name in exclude: continue entry = available_comp[name] # Avoid infinite loop if entry == RegressorChoice: continue if entry.get_properties()['handles_regression'] is False: continue if dataset_properties.get('multioutput') is True and \ entry.get_properties()['handles_multioutput'] is False: continue components_dict[name] = entry return components_dict def get_hyperparameter_search_space(self, dataset_properties=None, default=None, include=None, exclude=None): if include is not None and exclude is not None: raise ValueError("The argument include and exclude cannot be used together.") cs = ConfigurationSpace() # Compile a list of all estimator objects for this problem available_estimators = self.get_available_components( dataset_properties=dataset_properties, include=include, exclude=exclude) if len(available_estimators) == 0: raise ValueError("No regressors found") if default is None: defaults = ['random_forest', 'support_vector_regression'] + \ list(available_estimators.keys()) for default_ in defaults: if default_ in available_estimators: if include is not None and default_ not in include: continue if exclude is not None and default_ in exclude: continue default = default_ break estimator = CategoricalHyperparameter('__choice__', list(available_estimators.keys()), default_value=default) cs.add_hyperparameter(estimator) for estimator_name in available_estimators.keys(): estimator_configuration_space = available_estimators[estimator_name].\ get_hyperparameter_search_space(dataset_properties) parent_hyperparameter = {'parent': estimator, 'value': estimator_name} cs.add_configuration_space(estimator_name, estimator_configuration_space, parent_hyperparameter=parent_hyperparameter) return cs def estimator_supports_iterative_fit(self): return hasattr(self.choice, 'iterative_fit') def get_max_iter(self): if self.estimator_supports_iterative_fit(): return self.choice.get_max_iter() else: raise NotImplementedError() def get_current_iter(self): if self.estimator_supports_iterative_fit(): return self.choice.get_current_iter() else: raise NotImplementedError() def iterative_fit(self, X, y, n_iter=1, **fit_params): # Allows to use check_is_fitted on the choice object self.fitted_ = True if fit_params is None: fit_params = {} return self.choice.iterative_fit(X, y, n_iter=n_iter, **fit_params) def configuration_fully_fitted(self): return self.choice.configuration_fully_fitted()