SKL2RiverClassifier

Compatibility layer from scikit-learn to River for classification.

Parameters

• estimator ('sklearn_base.ClassifierMixin')

  A scikit-learn regressor which has a partial_fit method.

• classes ('list')

Examples

>>> from river import compat
>>> from river import evaluate
>>> from river import metrics
>>> from river import preprocessing
>>> from river import stream
>>> from sklearn import linear_model
>>> from sklearn import datasets

>>> dataset = stream.iter_sklearn_dataset(
...     dataset=datasets.load_breast_cancer(),
...     shuffle=True,
...     seed=42
... )

>>> model = preprocessing.StandardScaler()
>>> model |= compat.convert_sklearn_to_river(
...     estimator=linear_model.SGDClassifier(
...         loss='log_loss',
...         eta0=0.01,
...         learning_rate='constant'
...     ),
...     classes=[False, True]
... )

>>> metric = metrics.LogLoss()

>>> evaluate.progressive_val_score(dataset, model, metric)
LogLoss: 0.199554

Methods

learn_many

learn_one

Update the model with a set of features x and a label y.

Parameters

• x
• y

Returns

self

predict_many

predict_one

Predict the label of a set of features x.

Parameters

• x

Returns

The predicted label.

predict_proba_many

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

• x

Returns

A dictionary that associates a probability which each label.