facet.selection.MultiEstimatorParameterSpace#

class facet.selection.MultiEstimatorParameterSpace(*spaces)[source]#

A collection of parameter spaces, each representing a competing estimator from which to select the best-performing candidate with optimal hyperparameters.

See ParameterSpace for details on setting up and using parameter spaces.

Bases: BaseParameterSpace [+T_Estimator_co]
Generic types: +T_Estimator_co(bound= EstimatorDF, __covariant__=True)
Metaclasses: ABCMeta
Parameters: spaces (ParameterSpace[TypeVar(T_Estimator_co, bound= EstimatorDF, covariant=True)]) – the parameter spaces from which to select the best estimator

Method summary

`get_parameters`	Generate a list of dictionaries of parameter distributions, compatible with scikit-learn's CV search API (e.g., `GridSearchCV` or `RandomizedSearchCV`).
`to_expression`	Render this object as an expression.

Attribute summary

`estimator`	The estimator associated with this parameter space.
`parameters`	The parameter choices (as lists) or distributions (from `scipy.stats`) that constitute this parameter space.
`spaces`	The parameter spaces constituting this multi-estimator parameter space.

Definitions

get_parameters(prefix=None)[source]#

Generate a list of dictionaries of parameter distributions, compatible with scikit-learn’s CV search API (e.g., GridSearchCV or RandomizedSearchCV).

Parameters: prefix (Optional[str]) – an optional prefix to prepend to all parameter names in the resulting dictionary, separated by two underscore characters (__) as per scikit-learn’s convention for hierarchical parameter names
Return type: List[Dict[str, Union[List[Any], rv_continuous, rv_discrete]]]
Returns: a list of dictionaries, each mapping parameter names to parameter choices (as lists) or distributions (from scipy.stats)

to_expression()[source]#

Render this object as an expression.

Return type: Expression
Returns: the expression representing this object

property estimator: base.T_Estimator#

The estimator associated with this parameter space.

Return type: TypeVar(T_Estimator, bound= EstimatorDF, covariant=True)

property parameters: Union[List[Dict[str, Union[List[Any], scipy.stats.rv_continuous, scipy.stats.rv_discrete]]], Dict[str, Union[List[Any], scipy.stats.rv_continuous, scipy.stats.rv_discrete]]]#

The parameter choices (as lists) or distributions (from scipy.stats) that constitute this parameter space.

This is a shortcut for calling method get_parameters() with no arguments.

Return type: Union[List[Dict[str, Union[List[Any], rv_continuous, rv_discrete]]], Dict[str, Union[List[Any], rv_continuous, rv_discrete]]]

spaces: Tuple[ParameterSpace[T_Estimator_co], ...]#: The parameter spaces constituting this multi-estimator parameter space.

facet.selection.LearnerSelector

facet.selection.ParameterSpace