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Search Space

The sklearn_genetic.space module provides three dimension types for defining the hyperparameter search space.

Integer

Samples integer values from a range [lower, upper].

from sklearn_genetic.space import Integer

Integer(lower, upper, distribution="uniform")

Parameter	Type	Default	Description
lower	int	—	Minimum value (inclusive)
upper	int	—	Maximum value (inclusive)
distribution	str	"uniform"	Sampling distribution: "uniform"

Example:

"n_estimators": Integer(50, 500),
"max_depth": Integer(1, 20),

Continuous

Samples floating-point values from a range [lower, upper].

from sklearn_genetic.space import Continuous

Continuous(lower, upper, distribution="uniform")

Parameter	Type	Default	Description
lower	float	—	Minimum value
upper	float	—	Maximum value
distribution	str	"uniform"	Sampling distribution: "uniform" or "log-uniform"

Use distribution="log-uniform" for parameters that span orders of magnitude (learning rates, regularization strengths):

"learning_rate": Continuous(1e-4, 1e-1, distribution="log-uniform"),
"alpha": Continuous(1e-6, 1.0, distribution="log-uniform"),

Categorical

Samples from a fixed list of choices.

from sklearn_genetic.space import Categorical

Categorical(choices)

Parameter	Type	Default	Description
choices	list	—	List of valid values. Can include None

Example:

"max_features": Categorical(["sqrt", "log2", None]),
"solver": Categorical(["lbfgs", "sgd", "adam"]),
"activation": Categorical(["relu", "tanh", "logistic"]),

Complete Example

from sklearn_genetic.space import Categorical, Continuous, Integer

param_grid = {
    "n_estimators": Integer(50, 300),
    "max_depth": Integer(2, 15),
    "learning_rate": Continuous(0.01, 0.3, distribution="log-uniform"),
    "subsample": Continuous(0.5, 1.0),
    "max_features": Categorical(["sqrt", "log2"]),
    "min_samples_leaf": Integer(1, 20),
}

Convert sklearn/scipy-style spaces

from_sklearn_space converts common RandomizedSearchCV-style dictionaries into native sklearn-genetic-opt dimensions:

from scipy import stats

from sklearn_genetic.space import from_sklearn_space

param_grid = from_sklearn_space({
    "n_estimators": stats.randint(50, 300),
    "learning_rate": stats.loguniform(1e-3, 1e-1),
    "max_features": stats.uniform(0.2, 0.8),
    "criterion": ["gini", "entropy"],
})

Conversion rules:

Input value	Output dimension
list, tuple, set, range, numpy array	Categorical([...])
scipy.stats.randint(low, high)	Integer(low, high - 1)
scipy.stats.uniform(loc, scale)	Continuous(loc, loc + scale)
scipy.stats.loguniform(a, b) / reciprocal(a, b)	Continuous(a, b, distribution="log-uniform")
Existing Integer, Continuous, Categorical	returned unchanged

Unsupported scipy distributions raise an actionable ValueError that names the distribution and suggests defining the corresponding Integer, Continuous, or Categorical dimension manually.

See Also

• Basic Usage — tutorial using all three dimension types
• Presets — starter spaces for common scikit-learn estimators
• GASearchCV — the search estimator that consumes param_grid