Using a Custom NiaPy Algorithm

If you do not want to use one of the predefined algorithm configurations, you can use any algorithm from the NiaPy collection. This gives you more control over the algorithm behavior. Refer to the NiaPy documentation and examples for usage details.

Note

Use NiaPy version 2.7.0 or later.

pip install 'niapy>=2.7.0'

Example

Usage is almost the same as with a predefined algorithm. Instead of passing a shorthand value, pass the NiaPy algorithm object via the algorithm parameter.

from sklearn_nature_inspired_algorithms.model_selection import NatureInspiredSearchCV
from sklearn.ensemble import RandomForestClassifier
from niapy.algorithms.basic import GeneticAlgorithm

param_grid = {
    'n_estimators': range(20, 100, 20),
    'max_depth': range(2, 40, 2),
    'min_samples_split': range(2, 20, 2),
    'max_features': ["auto", "sqrt", "log2"],
}

clf = RandomForestClassifier(random_state=42)

algorithm = GeneticAlgorithm() # When a custom algorithm is provided, random_state is ignored.
algorithm.set_parameters(NP=50, Ts=5, Mr=0.25)

nia_search = NatureInspiredSearchCV(
    clf,
    param_grid,
    algorithm=algorithm,
    population_size=50,
    max_n_gen=100,
    max_stagnating_gen=20,
    runs=3,
)

nia_search.fit(X_train, y_train)

# The best parameters are stored in:
# nia_search.best_params_