from sklearn.ensemble import AdaBoostClassifier as BaseAdaBoostClassifier, AdaBoostRegressor as BaseAdaBoostRegressor
from mindfoundry.optaas.client.parameter import Distribution
from mindfoundry.optaas.client.sklearn_pipelines.mixin import OptimizableBaseEstimator
from mindfoundry.optaas.client.sklearn_pipelines.parameter_maker import SklearnParameterMaker
from mindfoundry.optaas.client.sklearn_pipelines.utils import ParametersConstraintsAndPriorMeans
class _OptimizableAdaBoost(OptimizableBaseEstimator):
def make_parameters_constraints_and_prior_means(self, sk: SklearnParameterMaker, **kwargs) \
-> ParametersConstraintsAndPriorMeans:
"""Generates :class:`Parameters <.Parameter>`, :class:`Constraints <.Constraint>`,
and :class:`PriorMeans <.PriorMeans>` to optimize an AdaBoost estimator."""
return [
sk.IntParameter('n_estimators', minimum=10, maximum=500),
sk.FloatParameter('learning_rate', minimum=0.01, maximum=2.5, distribution=Distribution.LOGUNIFORM),
], [], []
[docs]class AdaBoostClassifier(BaseAdaBoostClassifier, _OptimizableAdaBoost):
[docs] def make_parameters_constraints_and_prior_means(self, sk: SklearnParameterMaker, **kwargs) \
-> ParametersConstraintsAndPriorMeans:
"""Generates :class:`Parameters <.Parameter>`, :class:`Constraints <.Constraint>`,
and :class:`PriorMeans <.PriorMeans>` to optimize a :class:`.AdaBoostClassifier`."""
parameters, constraints, prior_means = super().make_parameters_constraints_and_prior_means(sk, **kwargs)
parameters.append(sk.CategoricalParameter('algorithm', values=['SAMME', 'SAMME.R']))
return parameters, constraints, prior_means
[docs]class AdaBoostRegressor(BaseAdaBoostRegressor, _OptimizableAdaBoost):
[docs] def make_parameters_constraints_and_prior_means(self, sk: SklearnParameterMaker, **kwargs) \
-> ParametersConstraintsAndPriorMeans:
"""Generates :class:`Parameters <.Parameter>`, :class:`Constraints <.Constraint>`,
and :class:`PriorMeans <.PriorMeans>` to optimize a :class:`.AdaBoostRegressor`."""
parameters, constraints, prior_means = super().make_parameters_constraints_and_prior_means(sk, **kwargs)
parameters.append(sk.CategoricalParameter('loss', values=['linear', 'square', 'exponential']))
return parameters, constraints, prior_means