Metadata-Version: 2.1
Name: sklearn-genetic
Version: 0.3.0
Summary: Genetic feature selection module for scikit-learn
Home-page: https://github.com/manuel-calzolari/sklearn-genetic
Author: Manuel Calzolari
License: UNKNOWN
Download-URL: https://github.com/manuel-calzolari/sklearn-genetic/archive/0.3.0.tar.gz
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
        
        ```bash
        pip install sklearn-genetic
        ```
        
        ## Requirements
        
        * Python >= 2.7
        * scikit-learn >= 0.20.3
        * 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(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. (2019, April 21). manuel-calzolari/sklearn-genetic: sklearn-genetic 0.2 (Version 0.2). Zenodo. http://doi.org/10.5281/zenodo.3348077
        
        BibTeX entry:
        ```
        @misc{manuel_calzolari_2019_3348077,
          author       = {Manuel Calzolari},
          title        = {{manuel-calzolari/sklearn-genetic: sklearn-genetic 
                           0.2}},
          month        = apr,
          year         = 2019,
          doi          = {10.5281/zenodo.3348077},
          url          = {https://doi.org/10.5281/zenodo.3348077}
        }
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=2.7
Description-Content-Type: text/markdown
