How to one hot encode variant length features?

Question

Given a list of variant length features:

features = [
    [\'f1\', \'f2\', \'f3\'],
    [\'f2\', \'f4\', \'f5\', \'f6\'],
    [\'f1\', \'f2\']
]

Answer 1

You can use MultiLabelBinarizer present in scikit which is specifically used for doing this.

Code for your example:

features = [
            ['f1', 'f2', 'f3'],
            ['f2', 'f4', 'f5', 'f6'],
            ['f1', 'f2']
           ]
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
new_features = mlb.fit_transform(features)

Output:

array([[1, 1, 1, 0, 0, 0],
       [0, 1, 0, 1, 1, 1],
       [1, 1, 0, 0, 0, 0]])

This can also be used in a pipeline, along with other feature_selection utilities.