CSMOTE

class capymoa.classifier.CSMOTE

Bases: MOAClassifier

CSMOTE

This strategy saves all the minority samples in a window managed by ADWIN. Meanwhile, a model is trained with the input data. When the minority sample ratio falls below a certain threshold, an online version of SMOTE is applied. A random minority sample is chosen from the window, and a new synthetic sample is generated until the minority sample ratio is greater than or equal to the threshold. The model is then trained with the newly generated samples.

Reference: Alessio Bernardo, Heitor Murilo Gomes, Jacob Montiel, Bernhard Pfahringer, Albert Bifet, Emanuele Della Valle. C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams. In BigData, IEEE, 2020.

Example usages:

>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.classifier import CSMOTE
>>> from capymoa.evaluation import prequential_evaluation
>>> stream = ElectricityTiny()
>>> schema = stream.get_schema()
>>> learner = CSMOTE(schema)
>>> results = prequential_evaluation(stream, learner, max_instances=1000)
>>> results["cumulative"].accuracy()
83.1

__init__(

schema: Schema = None,

random_seed: int = 0,

base_learner='trees.HoeffdingTree',

neighbors: int = 10,

threshold: float = 0.5,

min_size_allowed: int = 100,

disable_drift_detection: bool = False,

)

Continuous Synthetic Minority Oversampling (C-SMOTE) by Bernardo et al.

Parameters:

• schema – The schema of the stream.

• random_seed – The random seed passed to the MOA learner.

• base_learner – The base learner to be trained. Default AdaptiveRandomForestClassifier.

• neighbors – Number of neighbors for SMOTE.

• threshold – Minority class samples threshold.

• min_size_allowed – Minimum number of samples in the minority class for applying SMOTE.

• disable_drift_detection – If set, disables ADWIN drift detector

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.