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

  Type → 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: 77.46%

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.learn_many(X, y)

Methods

learn_many

learn_one

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

Parameters

• x
• y
• kwargs

predict_many

predict_one

Predict the label of a set of features x.

Parameters

• x — 'dict'
• kwargs

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_many

predict_proba_one

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

Parameters

• x
• kwargs

Returns

A dictionary that associates a probability which each label.