OneVsRestClassifier

One-vs-the-rest (OvR) multiclass strategy.

This strategy consists in fitting one binary classifier per class. Because we are in a streaming context, the number of classes isn't known from the start. Hence, new classifiers are instantiated on the fly. Likewise, the predicted probabilities will only include the classes seen up to a given point in time.

Note that this classifier supports mini-batches as well as single instances.

The computational complexity for both learning and predicting grows linearly with the number of classes. If you have a very large number of classes, then you might want to consider using an multiclass.OutputCodeClassifier instead.

Parameters

• classifier (base.Classifier)

  A binary classifier, although a multi-class classifier will work too.

Attributes

• classifiers (dict)

  A mapping between classes and classifiers.

Examples

>>> from river import datasets
>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import multiclass
>>> from river import preprocessing

>>> dataset = datasets.ImageSegments()

>>> scaler = preprocessing.StandardScaler()
>>> ovr = multiclass.OneVsRestClassifier(linear_model.LogisticRegression())
>>> model = scaler | ovr

>>> metric = metrics.MacroF1()

>>> evaluate.progressive_val_score(dataset, model, metric)
MacroF1: 0.774573

This estimator also also supports mini-batching.

>>> for X in pd.read_csv(dataset.path, chunksize=64):
...     y = X.pop('category')
...     y_pred = model.predict_many(X)
...     model = model.learn_many(X, y)

Methods

clone

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.

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 (dict)

Returns

typing.Union[bool, str, int]: 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.