DynamicEnsembleMemberSelection#

class capymoa.classifier.DynamicEnsembleMemberSelection[source]#

Bases: MOAClassifier

Dynamic Ensemble Member Selection (DEMS).

Dynamic Ensemble Member Selection (DEMS) [1] dynamically selects a subset of ensemble members based on their estimated performance and tree-level information. Only SRP and ARF are included here because of the performance significance.

>>> from capymoa.classifier import DynamicEnsembleMemberSelection
>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.evaluation import prequential_evaluation
>>>
>>> stream = ElectricityTiny()
>>> classifier = DynamicEnsembleMemberSelection(stream.get_schema())
>>> results = prequential_evaluation(stream, classifier, max_instances=1000)
>>> print(f"{results.accuracy():.1f}")
90.6
__init__(
schema: Schema | None = None,
random_seed: int = 0,
ensemble_class: str = 'StreamingRandomPatches',
base_learner: str = 'trees.HoeffdingTree -g 50 -c 0.01',
tree_learner: str = 'ARFHoeffdingTree -e 2000000 -g 50 -c 0.01',
ensemble_size: int = 100,
max_features=0.6,
training_method: str = 'RandomPatches',
lambda_param: float = 6.0,
number_of_jobs: int = 1,
drift_detection_method: str = 'ADWINChangeDetector -a 1.0E-5',
warning_detection_method: str = 'ADWINChangeDetector -a 1.0E-4',
disable_weighted_vote: bool = False,
disable_drift_detection: bool = False,
disable_background_learner: bool = False,
k_value: int = 5,
disable_self_optimising: bool = False,
)[source]#

Dynamic Ensemble Member Selection (DEMS) Classifier.

Parameters:

  • ensemble_class – which ensemble to use (“StreamingRandomPatches” or “AdaptiveRandomForest”).

  • base_learner – base classifier (used by SRP).

  • tree_learner – ARF tree learner (only used by ARF, cannot be changed).

  • ensemble_size – number of ensemble members.

  • max_features – subspace size configuration, similar to SRP: float in [0, 1]: percentage of features (e.g. 0.6 = 60%). int: exact number of features. “sqrt”: use sqrt(M)+1. None: default (60%).

  • training_method – “RandomSubspaces”, “Resampling”, or “RandomPatches” (SRP).

  • lambda_param – Poisson lambda for bagging.

  • number_of_jobs – number of parallel jobs for ARF (-1 = as many as possible).

  • drift_detection_method – MOA CLI string for drift detector.

  • warning_detection_method – MOA CLI string for warning detector.

  • disable_weighted_vote – if True, disables accuracy-weighted voting.

  • disable_drift_detection – if True, turns off drift detectors (and bkg learners).

  • disable_background_learner – if True, turns off background learners.

  • k_value – fixed K for DEMS when self-optimising is disabled.

  • disable_self_optimising – if True, use the fixed k_value instead of self-optimising.

cli_help()[source]#
predict(
instance: Instance,
) → int | None[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,
) → ndarray[tuple[Any, ...], dtype[float64]] | None[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.