README.md

Felimination

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

Features

• Recursive Feature Elimination with Cross Validation using Permutation Importance
• Hybrid Genetic Algorithms x Feature Importance selection

Requirements

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

Installation

In a terminal shell run the following command

pip install felimination

Usage

Recursive Feature Elimination

In this section it will be illustrated how to use the PermutationImportanceRFECV class.

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])

Genetic Algorithms

In this section it will be illustrated how to use the HybridImportanceGACVFeatureSelector class.

from felimination.ga import HybridImportanceGACVFeatureSelector
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
import numpy as np

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

# Initialize selector
selector = HybridImportanceGACVFeatureSelector(
    LogisticRegression(random_state=42),
    random_state=42,
    pool_size=5,
    patience=5
)

# Run optimisation
selector.fit(X, y)

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

# Show best solution
selector.best_solution_
# {'features': [1, 12, 13, 8, 17, 15, 18, 4, 3, 14],
#  'train_scores_per_fold': [0.88625, 0.89, 0.8825, 0.8925, 0.88625],
#  'test_scores_per_fold': [0.895, 0.885, 0.885, 0.89, 0.89],
#  'cv_importances': [array([[ 1.09135972,  1.13502636,  1.12100231,  0.38285736,  0.28944072,
#            0.04688614,  0.44259813,  0.09832365,  0.10190421, -0.48101593]]),
#   array([[ 1.17345812,  1.29375208,  1.2065342 ,  0.40418709,  0.41839714,
#            0.00447802,  0.466717  ,  0.21733829, -0.00842075, -0.50078996]]),
#   array([[ 1.15416104,  1.18458564,  1.18083266,  0.37071253,  0.22842685,
#            0.1087814 ,  0.44446793,  0.12740545,  0.00621562, -0.54064287]]),
#   array([[ 1.26011643,  1.36996058,  1.30481424,  0.48183549,  0.40589887,
#           -0.01849671,  0.45606913,  0.18330816,  0.03667055, -0.50869557]]),
#   array([[ 1.18227123,  1.28988253,  1.2496398 ,  0.50754295,  0.38942303,
#           -0.01725074,  0.4481891 ,  0.19472963,  0.10034316, -0.50131192]])],
#  'mean_train_score': 0.8875,
#  'mean_test_score': 0.889,
#  'mean_cv_importances': array([ 1.17227331,  1.25464144,  1.21256464,  0.42942709,  0.34631732,
#          0.02487962,  0.45160826,  0.16422104,  0.04734256, -0.50649125])}

# Show progress as a plot
selector.plot()

Looks like that the optimisation process converged after 2 steps, since the best score did not improve for 5(=patience) consecutive steps, the optimisation process stopped early.

License

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

Acknowledgments

• scikit-learn