python-data-analytics-feature-selection

Python project on feature selection

Features

The project tries to simplify the feature selection provided by scikit-learn to a single line for the following feature selection technique:

• Remove low-variance features
• Uni-variate feature selection technique
  • regression: f_regression (default, which is anova),
  • classification: chi2 (default),
• L1-based feature selection technique
  • regression: lasso
  • classification: linearSVC (default),

Usage

For unsupervised learning

The following shows how to do feature selection with data for unsupervised learning:

from ml_feature_selection.library.feature_selector import FeatureSelector
import numpy as np
X = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]])
print(X.shape)
s = FeatureSelector(X).should_remove_low_variance_features()
X2 = s.apply()
print(X2.shape)

After the above steps, X2 has two features

For classification

The following shows how to do feature selection with data for classification:

from ml_feature_selection.library.feature_selector import FeatureSelector
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target
print(X.shape)
s = FeatureSelector(samples=X, categorical_targets=y) \
    .should_apply_univariate_feature_selection(k=3) \
    .should_apply_L1_feature_selection(k=2) 
X2 = s.apply()
print(X2.shape)

For regression

The following shows how to do feature selection with data for regression:

from ml_feature_selection.library.feature_selector import FeatureSelector
from sklearn.datasets import load_boston

boston = load_boston()
X, y = boston['data'], boston['target']
print(X.shape)
s = FeatureSelector(samples=X, numerical_targets=y) \
    .should_apply_univariate_feature_selection(k=3) \
    .should_apply_L1_feature_selection(k=2) 
X2 = s.apply()
print(X2.shape)