sklearn-genetic-opt

sklearn-genetic-opt adds evolutionary optimization tools to the scikit-learn workflow. It can tune hyperparameters with GASearchCV and select feature subsets with GAFeatureSelectionCV using algorithms powered by DEAP.

Development version

You are reading the latest (development) docs. This version tracks the master branch and may contain unreleased features or breaking changes. For stable documentation, see 0.13.

Highlights

• GASearchCV for hyperparameter search across classification, regression, and supported outlier-detection estimators.
• GAFeatureSelectionCV for wrapper-based feature selection with cross-validation.
• Search spaces for integer, continuous, and categorical parameters.
• Grouped configuration objects: EvolutionConfig, PopulationConfig, RuntimeConfig, and OptimizationConfig.
• Smart initial populations with PopulationConfig(initializer="smart").
• Adaptive mutation and crossover schedules.
• Optional local search, diversity control, random immigrants, and fitness sharing.
• Parallel candidate evaluation with n_jobs and parallel_backend.
• Evaluation caching, optimizer telemetry through history, and fit-cost counters through fit_stats_.
• Callbacks for early stopping, progress reporting, checkpoints, TensorBoard, and custom logic.
• Plotting helpers plus MLflow 3 logging support.

Installation (dev)

Install from the master branch:

pip install git+https://github.com/rodrigo-arenas/Sklearn-genetic-opt.git@master

Or install the released version:

pip install sklearn-genetic-opt

Quick Start

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold, train_test_split
from sklearn.metrics import roc_auc_score

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

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y, random_state=42
)

param_grid = {
    "n_estimators": Integer(50, 250),
    "max_depth": Integer(2, 14),
    "min_samples_split": Integer(2, 12),
    "min_samples_leaf": Integer(1, 8),
    "max_features": Categorical(["sqrt", "log2", None]),
    "ccp_alpha": Continuous(0.0, 0.03),
}

search = GASearchCV(
    estimator=RandomForestClassifier(random_state=42),
    param_grid=param_grid,
    cv=StratifiedKFold(n_splits=3, shuffle=True, random_state=42),
    scoring="roc_auc",
    evolution_config=EvolutionConfig(population_size=20, generations=12),
    population_config=PopulationConfig(initializer="smart"),
    runtime_config=RuntimeConfig(n_jobs=-1, parallel_backend="auto", use_cache=True),
)

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

Next Steps

• When to Use
• Basic Usage
• Troubleshooting