KNN

class capymoa.classifier.KNN

Bases: MOAClassifier

K-Nearest Neighbors.

K-Nearest Neighbors (KNN) [1] is a lazy classifier. KNN in the streaming setting [2] stores a window of the most recent instances and uses them to classify new instances based on the majority class among the k-nearest neighbors.

>>> from capymoa.classifier import KNN
>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.evaluation import prequential_evaluation
>>>
>>> stream = ElectricityTiny()
>>> classifier = KNN(stream.get_schema())
>>> results = prequential_evaluation(stream, classifier, max_instances=1000)
>>> print(f"{results['cumulative'].accuracy():.1f}")
80.7

See also

SAMkNN

Self Adjusted Memory k-Nearest Neighbor (SAMkNN) classifier.

[1]

Fix, E., & Hodges, J. L. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review / Revue Internationale de Statistique, 57(3), 238–247. https://doi.org/10.2307/1403797

[2]

Jesse Read, Albert Bifet, Bernhard Pfahringer, and Geoff Holmes. Batch-incremental versus instance-incremental learning in dynamic and evolving data. In Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25–27, 2012. Proceedings , pages 313–323, 2012.

__init__(schema=None, CLI=None, random_seed=1, k=3, window_size=1000)

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.