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Discard

Removes features.

This can be used in a pipeline when you want to remove certain features. The transform_one method is pure, and therefore returns a fresh new dictionary instead of removing the specified keys from the input.

Parameters

  • keys

    Type → tuple[base.typing.FeatureName]

    Key(s) to discard.

Examples

from river import compose

x = {'a': 42, 'b': 12, 'c': 13}
compose.Discard('a', 'b').transform_one(x)

{'c': 13}

You can chain a discarder with any estimator in order to apply said estimator to the desired features.

from river import feature_extraction as fx

x = {'sales': 10, 'shop': 'Ikea', 'country': 'Sweden'}

pipeline = (
    compose.Discard('shop', 'country') |
    fx.PolynomialExtender()
)
pipeline.transform_one(x)

{'sales': 10, 'sales*sales': 100}

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.