Quick Start

The provided feature selection methods implement the SelectorMixin base class from the sklearn.feature_selection module and can by used in the same way as other feature selection methods from sklearn.

Below you can find a simple example for the VIP selection method:

import numpy as np
from auswahl import VIP

rs = np.random.RandomState(1337) # Sample seed for reproducibility
x = rs.randn(100, 10)            # 100 samples and 10 features
y = 5 * x[:,0] - 2 * x[:,5]      # y depends only on two features

selector = VIP(n_features_to_select=2)
selector.fit(x,y)

selector.get_support()
>>> array([True, False, False, False, False, True, False, False, False, False])

If you want to use another feature selection method, you can simply replace VIP(...) with any other method.

The VIP method also allows to select the number of features after the model has been fitted. An example is given below:

vip = VIP(n_features_to_select=x.shape[1]-1)
vip.fit(x,y)
vip.get_support_for_threshold(threshold=1)
>>> array([True, False, False, False, False, True, False, False, False, False])