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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. Use it when migrating an existing param_distributions mapping; when defining a new space, prefer the native dimensions directly:
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
- Migrating from RandomizedSearchCV — copyable conversion recipe and bound semantics
- Basic Usage — tutorial using all three dimension types
- Presets — starter spaces for common scikit-learn estimators
- GASearchCV — the search estimator that consumes
param_grid