DynamicWeightedMajority#

class capymoa.classifier.DynamicWeightedMajority[source]#

Bases: MOAClassifier

Dynamic Weighted Majority.

Dynamic Weighted Majority [1] is a meta-strategy.

>>> from capymoa.classifier import DynamicWeightedMajority
>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.evaluation import prequential_evaluation
>>>
>>> stream = ElectricityTiny()
>>> classifier = DynamicWeightedMajority(stream.get_schema())
>>> results = prequential_evaluation(stream, classifier, max_instances=1000)
>>> print(f"{results['cumulative'].accuracy():.1f}")
85.7
__init__(
schema: Schema,
random_seed: int = 1,
base_learner='bayes.NaiveBayes',
period: int = 50,
beta: float = 0.5,
theta: float = 0.01,
max_experts: int = 10000,
)[source]#

Construct Dynamic Weighted Majority.

Parameters:

  • schema – Describes the data stream.

  • random_seed – random seed for reproducibility.

  • base_learner – the base learner to be used, defaults to “NaiveBayes”

  • period – period between expert removal, creation, and weight update.

  • beta – factor to punish mistakes of experts.

  • theta – minimum fraction of weight per model.

  • max_experts – maximum number of allowed experts.

cli_help()[source]#
predict(instance)[source]#

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.

predict_proba(instance)[source]#

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)[source]#

Train the classifier with a labeled instance.

Parameters:

instance – The labeled instance to train the classifier with.

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.