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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
- Forward Feature Selection with Minimum Redundancy Maximum Relevance (MRMR)
- Hybrid Genetic Algorithms x Feature Importance selection
Requirements
- Python 3.10+
- NumPy
- Scikit-learn
- Pandas
Installation
In a terminal shell run the following command
Usage
Recursive Feature Elimination
In this section it will be illustrated how to use the PermutationImportanceRFECV class.
import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold, cross_validate
from felimination.callbacks import plot_progress_callback
from felimination.rfe import PermutationImportanceRFECV
X, y = make_classification(
n_samples=1000,
n_features=200,
n_informative=6,
n_redundant=10,
n_clusters_per_class=1,
random_state=42,
shuffle=False
)
model = LogisticRegression(random_state=42)
selector = PermutationImportanceRFECV(
model,
step=0.2,
callbacks=[plot_progress_callback],
scoring="roc_auc",
cv=StratifiedKFold(random_state=42, shuffle=True),
best_iteration_selection_criteria="mean_test_score"
)
selector.fit(X, y)
selector.support_
# array([False, True, True, True, False, True, True, True, True,
# True, True, False, False, False, False, True, False, True,
# False, True, False, False, False, False, False, False, False,
# True, False, False, False, False, False, False, False, False,
# True, False, False, False, False, False, True, False, False,
# True, False, True, False, False, False, False, False, False,
# False, False, False, False, False, False, False, False, False,
# False, False, True, True, False, False, True, False, True,
# False, True, False, False, True, False, False, False, True,
# False, True, False, False, False, True, False, True, False,
# False, False, False, True, False, True, False, False, False,
# False, False, False, True, False, False, True, True, True,
# False, False, False, False, False, True, False, False, False,
# False, False, False, False, False, False, False, False, False,
# False, False, False, False, False, False, False, False, False,
# False, False, False, False, False, False, False, False, False,
# False, True, False, False, False, False, False, False, False,
# False, False, True, True, False, False, False, True, False,
# False, False, False, False, False, False, True, False, False,
# False, False, False, False, False, True, False, False, False,
# True, False, True, False, False, False, True, False, False,
# False, False, False, False, False, False, False, False, True,
# False, False])
selector.ranking_
# array([23, 5, 12, 2, 24, 15, 16, 1, 3, 6, 4, 24, 23, 19, 24, 14, 19,
# 17, 21, 16, 24, 20, 24, 21, 24, 18, 22, 16, 22, 23, 24, 21, 22, 22,
# 21, 22, 16, 20, 23, 23, 24, 20, 13, 24, 23, 13, 23, 14, 23, 22, 22,
# 24, 19, 19, 23, 19, 23, 20, 23, 23, 22, 23, 23, 23, 24, 17, 11, 20,
# 23, 10, 22, 14, 18, 13, 24, 21, 12, 23, 24, 18, 9, 21, 13, 21, 24,
# 21, 16, 18, 15, 21, 24, 22, 20, 17, 20, 17, 22, 21, 24, 19, 19, 24,
# 16, 20, 24, 15, 17, 17, 24, 24, 24, 22, 21, 14, 21, 22, 23, 24, 21,
# 21, 22, 20, 23, 23, 24, 20, 23, 23, 24, 24, 18, 19, 20, 22, 23, 24,
# 22, 18, 21, 24, 24, 23, 22, 24, 22, 15, 20, 21, 23, 23, 22, 19, 22,
# 20, 22, 8, 12, 20, 23, 22, 17, 18, 23, 24, 24, 22, 21, 24, 11, 19,
# 20, 24, 21, 24, 18, 21, 16, 21, 19, 24, 17, 18, 15, 24, 22, 24, 10,
# 19, 22, 24, 23, 24, 23, 20, 24, 23, 19, 7, 18, 23])
selector.plot()
Forward Feature Selection with MRMR
In this section it will be illustrated how to use the MRMRCV class, which performs forward feature selection using the Minimum Redundancy Maximum Relevance (MRMR) criterion.
MRMR scores candidate features by combining two quantities:
- Relevance: how much information a feature shares with the target (mutual information by default).
- Redundancy: how much information a feature shares with already-selected features.
This actively avoids picking correlated features: once a relevant feature is selected, correlated copies are penalised and deprioritised.
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from felimination.callbacks import plot_progress_callback
from felimination.mrmr import MRMRCV
X, y = make_classification(
n_samples=1000,
n_features=200,
n_informative=6,
n_redundant=10,
n_clusters_per_class=1,
random_state=42,
shuffle=False
)
model = LogisticRegression(random_state=42)
selector = MRMRCV(
model,
step=0.2,
max_features_to_select=50,
callbacks=[plot_progress_callback],
scoring="roc_auc",
cv=StratifiedKFold(random_state=42, shuffle=True),
best_iteration_selection_criteria="mean_test_score",
random_state=42,
min_relevance=0.1,
max_redundancy=0.3,
)
selector.fit(X, y)
selector.support_
# array([False, False, True, False, True, False, False, False, True,
# False, True, False, True, False, True, False, False, False, ...])
selector.plot()
Genetic Algorithms
In this section it will be illustrated how to use the HybridImportanceGACVFeatureSelector class.
import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from felimination.ga import HybridImportanceGACVFeatureSelector
# 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