sklearn-genetic's Introduction

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

pip install git+https://github.com/manuel-calzolari/sklearn-genetic.git

Requirements

Python >= 2.7
scikit-learn >= 0.17.1
DEAP >= 1.0.2

Example

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()

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