MaxAbsScaler¶

Scales the data to a [-1, 1] range based on absolute maximum.

Under the hood a running absolute max is maintained. This scaler is meant for data that is already centered at zero or sparse data. It does not shift/center the data, and thus does not destroy any sparsity.

Attributes¶

• abs_max (dict)

Mapping between features and instances of stats.AbsMax.

Examples¶

>>> import random
>>> from river import preprocessing

>>> random.seed(42)
>>> X = [{'x': random.uniform(8, 12)} for _ in range(5)]
>>> for x in X:
...     print(x)
{'x': 10.557707}
{'x': 8.100043}
{'x': 9.100117}
{'x': 8.892842}
{'x': 10.945884}

>>> scaler = preprocessing.MaxAbsScaler()

>>> for x in X:
...     print(scaler.learn_one(x).transform_one(x))
{'x': 1.0}
{'x': 0.767216}
{'x': 0.861940}
{'x': 0.842308}
{'x': 1.0}


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.