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. When window_size is set, the scaler tracks the min and max over the last window_size observations via stats.RollingMin and stats.RollingMax instead.

Parameters¶

• window_size

  Type → int | None

  Default → None

  Size of the rolling window used to compute the min and max. If None, the running min and max over the entire stream are used.

Attributes¶

• min (dict)

  Mapping between features and instances of stats.Min (or stats.RollingMin when window_size is set).

• max (dict)

  Mapping between features and instances of stats.Max (or stats.RollingMax when window_size is set).

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:
    scaler.learn_one(x)
    print(scaler.transform_one(x))

{'x': 0.0}
{'x': 0.0}
{'x': 0.406920}
{'x': 0.322582}
{'x': 1.0}

A rolling window can be used to scale relative to the most recent observations only:

scaler = preprocessing.MinMaxScaler(window_size=3)
for x in X:
    scaler.learn_one(x)
    print(scaler.transform_one(x))

{'x': 0.0}
{'x': 0.0}
{'x': 0.406920}
{'x': 0.792741}
{'x': 1.0}

A scaler can also be warm-started from previously computed statistics, e.g. to resume from a checkpoint or to seed the stream with an offline estimate:

scaler = preprocessing.MinMaxScaler._from_state(min={'x': 8.0}, max={'x': 12.0})
scaler.transform_one({'x': 10.0})

{'x': 0.5}

Methods¶