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MinMaxScaler

Scales the data to a fixed range from 0 to 1.

Under the hood a running min and a running peak to peak (max - min) are maintained.

Attributes

  • min (dict)

    Mapping between features and instances of stats.Min.

  • max (dict)

    Mapping between features and instances of stats.Max.

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.MinMaxScaler()

for x in X:
    print(scaler.learn_one(x).transform_one(x))

{'x': 0.0}
{'x': 0.0}
{'x': 0.406920}
{'x': 0.322582}
{'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.