SelectKBest

Removes all but the \(k\) highest scoring features.

Parameters

• similarity (river.stats.base.Bivariate)

• k – defaults to 10

  The number of features to keep.

Attributes

• similarities (dict)

  The similarity instances used for each feature.

• leaderboard (dict)

  The actual similarity measures.

Examples

>>> from pprint import pprint
>>> from river import feature_selection
>>> from river import stats
>>> from river import stream
>>> from sklearn import datasets

>>> X, y = datasets.make_regression(
...     n_samples=100,
...     n_features=10,
...     n_informative=2,
...     random_state=42
... )

>>> selector = feature_selection.SelectKBest(
...     similarity=stats.PearsonCorr(),
...     k=2
... )

>>> for xi, yi, in stream.iter_array(X, y):
...     selector = selector.learn_one(xi, yi)

>>> pprint(selector.leaderboard)
Counter({9: 0.7898,
        7: 0.5444,
        8: 0.1062,
        2: 0.0638,
        4: 0.0538,
        5: 0.0271,
        1: -0.0312,
        6: -0.0657,
        3: -0.1501,
        0: -0.1895})

>>> selector.transform_one(xi)
{7: -1.2795, 9: -1.8408}

Methods

learn_one

Update with a set of features x and a target y.

Parameters

• x (dict)
• y (Union[bool, str, int, numbers.Number])

Returns

SupervisedTransformer: self

transform_one

Transform a set of features x.

Parameters

• x (dict)

Returns

dict: The transformed values.