WeightedkNN

class capymoa.classifier.WeightedkNN

Bases: MOAClassifier

WeightedKNN Reference:

‘Effective Weighted k-Nearest Neighbors for Dynamic Data Streams’ Maroua Bahri IEEE International Conference on Big Data (Big Data), 2022 <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10020652> Example usages: >>> from capymoa.datasets import ElectricityTiny >>> from capymoa.classifier import WeightedkNN >>> from capymoa.evaluation import prequential_evaluation >>> stream = ElectricityTiny() >>> schema = stream.get_schema() >>> learner = WeightedkNN(schema) >>> results = prequential_evaluation(stream, learner, max_instances=1000) >>> results[“cumulative”].accuracy() 74.7

__init__(

schema: Schema,

k: int = 10,

limit: int = 1000,

)

Weighted KNN Classifier :param schema: The schema of the stream. :param k: The number of neighbors. :param w: The maximum number of instances to store.

CLI_help()

predict(instance)

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)

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)

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.