GASearchCV

Genetic algorithm hyperparameter search for scikit-learn estimators.

GASearchCV implements the scikit-learn estimator interface and follows the same patterns as GridSearchCV and RandomizedSearchCV. After calling fit, it exposes best_params_, best_score_, cv_results_, predict, predict_proba, and score.

Class Signature

python

from sklearn_genetic import GASearchCV

GASearchCV(
    estimator,
    param_grid,
    *,
    scoring=None,
    cv=5,
    refit=True,
    verbose=0,
    error_score=np.nan,
    return_train_score=False,
    evolution_config=None,
    population_config=None,
    runtime_config=None,
    optimization_config=None,
)

Parameters

Parameter	Type	Default	Description
`estimator`	estimator	—	A scikit-learn estimator with a `fit` method
`param_grid`	dict	—	Mapping from parameter name to `Integer`, `Continuous`, or `Categorical`
`scoring`	str or dict	`None`	Metric(s) to evaluate. `None` uses the estimator's default scorer
`cv`	int or CV splitter	`5`	Cross-validation strategy
`refit`	bool or str	`True`	Metric to use when refitting the best model. If `scoring` is a dict, pass the metric name
`verbose`	int	`0`	Verbosity level. `1` = generation log
`error_score`	float or `"raise"`	`np.nan`	Score to use when a candidate raises an exception
`return_train_score`	bool	`False`	Include training scores in `cv_results_`
`evolution_config`	`EvolutionConfig`	`None`	Controls population size, generations, crossover/mutation rates, elitism
`population_config`	`PopulationConfig`	`None`	Controls initialization strategy, warm starts, diversity
`runtime_config`	`RuntimeConfig`	`None`	Controls parallelism, caching, verbosity
`optimization_config`	`OptimizationConfig`	`None`	Controls local search, fitness sharing

Attributes After `fit`

Attribute	Description
`best_params_`	Parameter setting that gave the best mean cross-validated score
`best_score_`	Mean cross-validated score of the best estimator
`best_estimator_`	Estimator fitted with `best_params_` on the full training data (if `refit=True`)
`cv_results_`	Dict with per-candidate results, compatible with `pd.DataFrame`
`history`	List of per-generation dicts with fitness and diversity telemetry
`logbook`	DEAP logbook — same data as `history` in DEAP's format
`fit_stats_`	Dict with evaluation counters (cache hits, skipped candidates, etc.)
`support_`	(Not applicable for GASearchCV — see GAFeatureSelectionCV)

Methods

Method	Description
`fit(X, y, callbacks=None)`	Run the genetic search
`predict(X)`	Predict using `best_estimator_`
`predict_proba(X)`	Predict class probabilities (if estimator supports it)
`score(X, y)`	Score using `best_estimator_`
`get_params()` / `set_params()`	Standard sklearn estimator interface

Example

python

from sklearn_genetic import EvolutionConfig, GASearchCV, PopulationConfig, RuntimeConfig
from sklearn_genetic.space import Categorical, Continuous, Integer

search = GASearchCV(
    estimator=your_estimator,
    param_grid={
        "param1": Integer(1, 100),
        "param2": Continuous(0.01, 1.0, distribution="log-uniform"),
        "param3": Categorical(["a", "b", "c"]),
    },
    cv=5,
    scoring="roc_auc",
    evolution_config=EvolutionConfig(population_size=20, generations=15),
    population_config=PopulationConfig(initializer="smart"),
    runtime_config=RuntimeConfig(n_jobs=-1, use_cache=True),
)

search.fit(X_train, y_train)
print(search.best_params_)
print(search.best_score_)

GASearchCV ​

Class Signature ​

Parameters ​

Attributes After fit ​

Methods ​

Example ​

See Also ​