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One-hot encoding.

This transformer will encode every feature it is provided it with. You can apply it to a subset of features by composing it with compose.Select or compose.SelectType.


  • sparse – defaults to False

    Whether or not 0s should be made explicit or not.


Let us first create an example dataset.

>>> from pprint import pprint
>>> import random
>>> import string

>>> random.seed(42)
>>> alphabet = list(string.ascii_lowercase)
>>> X = [
...     {
...         'c1': random.choice(alphabet),
...         'c2': random.choice(alphabet),
...     }
...     for _ in range(4)
... ]
>>> pprint(X)
[{'c1': 'u', 'c2': 'd'},
    {'c1': 'a', 'c2': 'x'},
    {'c1': 'i', 'c2': 'h'},
    {'c1': 'h', 'c2': 'e'}]

We can now apply one-hot encoding. All the provided are one-hot encoded, there is therefore no need to specify which features to encode.

>>> import river.preprocessing

>>> oh = river.preprocessing.OneHotEncoder(sparse=True)
>>> for x in X:
...     oh = oh.learn_one(x)
...     pprint(oh.transform_one(x))
{'c1_u': 1, 'c2_d': 1}
{'c1_a': 1, 'c2_x': 1}
{'c1_i': 1, 'c2_h': 1}
{'c1_h': 1, 'c2_e': 1}

The sparse parameter can be set to False in order to include the values that are not present in the output.

>>> oh = river.preprocessing.OneHotEncoder(sparse=False)
>>> for x in X[:2]:
...     oh = oh.learn_one(x)
...     pprint(oh.transform_one(x))
{'c1_u': 1, 'c2_d': 1}
{'c1_a': 1, 'c1_u': 0, 'c2_d': 0, 'c2_x': 1}

A subset of the features can be one-hot encoded by using an instance of compose.Select.

>>> from river import compose

>>> pp = compose.Select('c1') | river.preprocessing.OneHotEncoder()

>>> for x in X:
...     pp = pp.learn_one(x)
...     pprint(pp.transform_one(x))
{'c1_u': 1}
{'c1_a': 1, 'c1_u': 0}
{'c1_a': 0, 'c1_i': 1, 'c1_u': 0}
{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0}

You can preserve the c2 feature by using a union:

>>> pp = compose.Select('c1') | river.preprocessing.OneHotEncoder()
>>> pp += compose.Select('c2')

>>> for x in X:
...     pp = pp.learn_one(x)
...     pprint(pp.transform_one(x))
{'c1_u': 1, 'c2': 'd'}
{'c1_a': 1, 'c1_u': 0, 'c2': 'x'}
{'c1_a': 0, 'c1_i': 1, 'c1_u': 0, 'c2': 'h'}
{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0, 'c2': 'e'}



Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.


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.


  • x (dict)


Transformer: self


Transform a set of features x.


  • x (dict)
  • y – defaults to None


dict: The transformed values.