from sklearn.svm import SVC as BaseSVC, LinearSVC as BaseLinearSVC
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, SMALLEST_NUMBER_ABOVE_ZERO
class _OptimizableSVC(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 SVC-based estimator."""
parameters = [
sk.FloatParameter('C', minimum=0.2, maximum=5.0, distribution=Distribution.LOGUNIFORM),
sk.FloatParameter('tol', minimum=SMALLEST_NUMBER_ABOVE_ZERO, maximum=1),
sk.ConstantParameter('class_weight', value='balanced', optional=True),
]
return parameters, [], []
[docs]class SVC(BaseSVC, _OptimizableSVC):
[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 an :class:`.SVC` estimator."""
parameters, constraints, prior_means = super().make_parameters_constraints_and_prior_means(sk, **kwargs)
gamma_categories = ["scale"]
if sk.defaults.get("gamma") == "auto_deprecated":
sk.defaults["gamma"] = "scale"
parameters.extend([
sk.FloatOrCategorical('gamma', minimum=1.0e-5, maximum=1, categories=gamma_categories, distribution=Distribution.LOGUNIFORM),
sk.CategoricalParameter('kernel', values=['linear', 'poly', 'rbf', 'sigmoid']),
sk.IntParameter('degree', minimum=2, maximum=10, distribution=Distribution.LOGUNIFORM),
sk.FloatParameter('coef0', minimum=0, maximum=1),
sk.BoolParameter('shrinking')
])
return parameters, constraints, prior_means
[docs]class LinearSVC(BaseLinearSVC, _OptimizableSVC):
pass