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This library contains some useful scikit-learn compatible classes for feature selection.

Features

• Recursive Feature Elimination with Cross Validation using Permutation Importance

Requirements

• Python 3.7+
• NumPy
• Scikit-learn
• Pandas

Installation

In a terminal shell run the following command

pip install felimination

Usage

from felimination.rfe import PermutationImportanceRFECV
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
import numpy as np


X, y = make_classification(
    n_samples=1000,
    n_features=20,
    n_informative=6,
    n_redundant=10,
    n_clusters_per_class=1,
    random_state=42,
)

selector = PermutationImportanceRFECV(LogisticRegression(), step=0.3)

selector.fit(X, y)

selector.support_
# array([False, False, False, False, False, False, False, False, False,
#        False, False,  True, False, False, False, False, False, False,
#        False, False])

selector.ranking_
# array([9, 3, 8, 9, 7, 8, 5, 6, 9, 6, 8, 1, 9, 7, 8, 9, 9, 2, 4, 7])
selector.plot()

It looks like 5 is a good number of features, we can set the number of features to select to 5 without need of retraining

selector.set_n_features_to_select(5)
selector.support_
# array([False,  True, False, False, False, False,  True, False, False,
#        False, False,  True, False, False, False, False, False,  True,
#         True, False])

License

This project is licensed under the BSD 3-Clause License - see the LICENSE.md file for details

Acknowledgments

• scikit-learn