Binarizer

Binarizes the data to 0 or 1 according to a threshold.

Parameters

• threshold

  Default → 0.0

  Values above this are replaced by 1 and the others by 0.

• dtype

  Default → <class 'bool'>

  The desired data type to apply.

Examples

import river
import numpy as np

rng = np.random.RandomState(42)
X = [{'x1': v, 'x2': int(v)} for v in rng.uniform(low=-4, high=4, size=6)]

binarizer = river.preprocessing.Binarizer()
for x in X:
    print(binarizer.learn_one(x).transform_one(x))

{'x1': False, 'x2': False}
{'x1': True, 'x2': True}
{'x1': True, 'x2': True}
{'x1': True, 'x2': False}
{'x1': False, 'x2': False}
{'x1': False, 'x2': False}

Methods

learn_one

Update with a set of features x.

A lot of transformers don't actually have to do anything during the learn_one step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one can override this method.

Parameters

• x — 'dict'

Returns

Transformer: self

transform_one

Transform a set of features x.

Parameters

• x — 'dict'

Returns

dict: The transformed values.