BatchClassifierSSL

class capymoa.base.BatchClassifierSSL

Bases: BatchClassifier, ClassifierSSL

Base class for semi-supervised learning batch classifiers.

train_on_unlabeled(instance: Instance)

__init__(

schema: Schema,

batch_size: int,

random_seed: int = 1,

) → None

Initialize the batch classifier.

Parameters:

• schema – A schema used to allocate memory for the batch.

• batch_size – The size of the batch.

• random_seed – The random seed for reproducibility.

abstract batch_train(

x: ndarray[Any, dtype[number]],

y: ndarray[Any, dtype[integer]],

) → None

Train the classifier with a batch of instances.

Parameters:

• x – A real valued matrix of shape (batch_size, num_attributes) containing a batch of feature vectors.

• y – An integer array of shape (batch_size,) containing the label index. Missing labels are coded as -1 in the semi-supervised setting.

predict(instance: Instance) → int | None

Predict the label of an instance.

The base implementation calls predict_proba() and returns the label with the highest probability.

Parameters:

instance – The instance to predict the label for.

Returns:

The predicted label or None if the classifier is unable to make a prediction.

abstract predict_proba(

instance: Instance,

) → ndarray[Any, dtype[float64]] | None

Return probability estimates for each label.

Parameters:

instance – The instance to estimate the probabilities for.

Returns:

An array of probabilities for each label or None if the classifier is unable to make a prediction.

train(instance: LabeledInstance) → None

Collate instances into a batch and call batch_train().

random_seed: int

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

schema: Schema

The schema representing the instances.