OneVsOneClassifier

One-vs-One (OvO) multiclass strategy.

This strategy consists in fitting one binary classifier for each pair of classes. 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.

The number of classifiers is k * (k - 1) / 2, where k is the number of classes. However, each call to learn_one only requires training k - 1 models. Indeed, only the models that pertain to the given label have to be trained. Meanwhile, making a prediction requires going through each and every model.

Parameters

• classifier

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

Attributes

• classifiers (dict)

  A mapping between pairs of classes and classifiers. The keys are tuples which contain a pair of classes. Each pair is sorted in lexicographical order.

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()
>>> ovo = multiclass.OneVsOneClassifier(linear_model.LogisticRegression())
>>> model = scaler | ovo

>>> metric = metrics.MacroF1()

>>> evaluate.progressive_val_score(dataset, model, metric)
MacroF1: 80.76%

Methods

learn_one

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

Parameters

• x
• y
• kwargs

Returns

self

predict_one

Predict the label of a set of features x.

Parameters

• x
• kwargs

Returns

The predicted label.

predict_proba_one

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

Parameters

• x (dict)

Returns

typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.