Genetic feature selection module for scikit-learn
Project description
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
The easiest way to install sklearn-genetic is using pip
pip install sklearn-genetic
or conda
conda install -c conda-forge sklearn-genetic
Requirements
- Python >= 2.7
- scikit-learn >= 0.20.3
- DEAP >= 1.0.2
Example
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(solver="liblinear", multi_class="ovr")
selector = GeneticSelectionCV(estimator,
cv=5,
verbose=1,
scoring="accuracy",
max_features=5,
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,
n_gen_no_change=10,
caching=True,
n_jobs=-1)
selector = selector.fit(X, y)
print(selector.support_)
if __name__ == "__main__":
main()
Citing sklearn-genetic
Manuel Calzolari. (2021, April 3). manuel-calzolari/sklearn-genetic: sklearn-genetic 0.4.0 (Version 0.4.0). Zenodo. http://doi.org/10.5281/zenodo.4661178
BibTeX entry:
@software{manuel_calzolari_2021_4661178,
author = {Manuel Calzolari},
title = {{manuel-calzolari/sklearn-genetic: sklearn-genetic
0.4.0}},
month = apr,
year = 2021,
publisher = {Zenodo},
version = {0.4.0},
doi = {10.5281/zenodo.4661178},
url = {https://doi.org/10.5281/zenodo.4661178}
}
See also
- shapicant, a feature selection package based on SHAP and target permutation, for pandas and Spark
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