Normalizer

Scales a set of features so that it has unit norm.

This is particularly useful when used after a feature_extraction.TFIDF.

Parameters

• order – defaults to 2

  Order of the norm (e.g. 2 corresponds to the \(L^2\) norm).

Examples

>>> from river import preprocessing
>>> from river import stream

>>> scaler = preprocessing.Normalizer(order=2)

>>> X = [[4, 1, 2, 2],
...      [1, 3, 9, 3],
...      [5, 7, 5, 1]]

>>> for x, _ in stream.iter_array(X):
...     print(scaler.transform_one(x))
{0: 0.8, 1: 0.2, 2: 0.4, 3: 0.4}
{0: 0.1, 1: 0.3, 2: 0.9, 3: 0.3}
{0: 0.5, 1: 0.7, 2: 0.5, 3: 0.1}

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.