Space
- class sklearn_genetic.space.Categorical(choices: list | None = None, priors: list | None = None, distribution: str = 'choice', random_state=None)[source]
class for hyperparameters search space of categorical values
- Parameters:
- choices: list, default=None
List with all the possible values of the hyperparameter.
- priors: int, default=None
List with the probability of sampling each element of the “choices”, if not set gives equals probability.
- distribution: str, default=’choice’
Distribution to sample initial population and mutation values, currently only supports “choice”.
- random_stateint or None, RandomState instance, default=None
Pseudo random number generator state used for random dimension sampling.
- class sklearn_genetic.space.Continuous(lower: float | None = None, upper: float | None = None, distribution: str = 'uniform', random_state=None)[source]
class for hyperparameters search space of real values
- Parameters:
- lowerint, default=None
Lower bound of the possible values of the hyperparameter.
- upperint, default=None
Upper bound of the possible values of the hyperparameter.
- distribution{‘uniform’, ‘log-uniform’}, default=’uniform’
Distribution to sample initial population and mutation values.
- random_stateint or None, RandomState instance, default=None
Pseudo random number generator state used for random dimension sampling.
- class sklearn_genetic.space.Integer(lower: int | None = None, upper: int | None = None, distribution: str = 'uniform', random_state=None)[source]
class for hyperparameters search space of integer values
- Parameters:
- lowerint, default=None
Lower bound of the possible values of the hyperparameter.
- upperint, default=None
Upper bound of the possible values of the hyperparameter.
- distributionstr, default=”uniform”
Distribution to sample initial population and mutation values, currently only supports ‘uniform’.
- random_stateint or None, RandomState instance, default=None
Pseudo random number generator state used for random dimension sampling.
- class sklearn_genetic.space.Space(param_grid: dict | None = None)[source]
Search space for all the models hyperparameters
- Parameters:
- param_grid: dict, default=None
Grid with the parameters to tune, expects keys a valid name of hyperparameter based on the estimator selected and as values one of
Integer
,Categorical
Continuous
classes
- property dimensions
- Returns:
- The number of hyperparameters defined in the param_grid
- property parameters
- Returns:
- A list with all the names of the hyperparametes in the param_Grid