AdaptiveStandardScaler

Scales data using exponentially weighted moving average and variance.

Under the hood, a exponentially weighted running mean and variance are maintained for each feature. This can potentially provide better results for drifting data in comparison to preprocessing.StandardScaler. Indeed, the latter computes a global mean and variance for each feature, whereas this scaler weights data in proportion to their recency.

Parameters

• fading_factor

  Default → 0.3

  This parameter is passed to stats.EWVar. It is expected to be in [0, 1]. More weight is assigned to recent samples the closer fading_factor is to 1.

Examples

Consider the following series which contains a positive trend.

import random

random.seed(42)
X = [
    {'x': random.uniform(4 + i, 6 + i)}
    for i in range(8)
]
for x in X:
    print(x)

{'x': 5.278}
{'x': 5.050}
{'x': 6.550}
{'x': 7.446}
{'x': 9.472}
{'x': 10.353}
{'x': 11.784}
{'x': 11.173}

This scaler works well with this kind of data because it uses statistics that assign higher weight to more recent data.

from river import preprocessing

scaler = preprocessing.AdaptiveStandardScaler(fading_factor=.6)

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

{'x': 0.0}
{'x': -0.816}
{'x': 0.812}
{'x': 0.695}
{'x': 0.754}
{'x': 0.598}
{'x': 0.651}
{'x': 0.124}

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.