SelectType

Selects features based on their type.

This is practical when you want to apply different preprocessing steps to different kinds of features. For instance, a common usecase is to apply a preprocessing.StandardScaler to numeric features and a preprocessing.OneHotEncoder to categorical features.

Parameters

• types (Tuple[type])

  Python types which you want to select. Under the hood, the isinstance method will be used to check if a value is of a given type.

Examples

>>> import numbers
>>> from river import compose
>>> from river import linear_model
>>> from river import preprocessing

>>> num = compose.SelectType(numbers.Number) | preprocessing.StandardScaler()
>>> cat = compose.SelectType(str) | preprocessing.OneHotEncoder()
>>> model = (num + cat) | linear_model.LogisticRegression()

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.