# sklearn-genetic **Repository Path**: initialdream1659/sklearn-genetic ## Basic Information - **Project Name**: sklearn-genetic - **Description**: Genetic feature selection module for scikit-learn - **Primary Language**: Python - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2018-10-18 - **Last Updated**: 2024-07-01 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # sklearn-genetic Genetic feature selection module for scikit-learn Genetic algorithms mimic the process of natural selection to search for optimal values of a function. ## Installation ```bash pip install git+https://github.com/manuel-calzolari/sklearn-genetic.git ``` ## Requirements * Python >= 2.7 * scikit-learn >= 0.18 * DEAP >= 1.0.2 ## Example ```python from __future__ import print_function import numpy as np from sklearn import datasets, linear_model from genetic_selection import GeneticSelectionCV def main(): iris = datasets.load_iris() # Some noisy data not correlated E = np.random.uniform(0, 0.1, size=(len(iris.data), 20)) X = np.hstack((iris.data, E)) y = iris.target estimator = linear_model.LogisticRegression() selector = GeneticSelectionCV(estimator, cv=5, verbose=1, scoring="accuracy", n_population=50, crossover_proba=0.5, mutation_proba=0.2, n_generations=40, crossover_independent_proba=0.5, mutation_independent_proba=0.05, tournament_size=3, caching=True, n_jobs=-1) selector = selector.fit(X, y) print(selector.support_) if __name__ == "__main__": main() ```