HalfSpaceTrees

class capymoa.anomaly.HalfSpaceTrees

Bases: MOAAnomalyDetector

Half-Space Trees

This class implements the Half-Space Trees (HS-Trees) algorithm, which is an ensemble anomaly detector capable of adapting to concept drift.

HS-Trees is implemented in MOA (Massive Online Analysis) and provides several parameters for customization.

References:

Fast anomaly detection for streaming data. Swee Chuan Tan, Kai Ming Ting and Tony Fei Liu. International joint conference on artificial intelligence (IJCAI), 106, 1469-1495, 2017.

Example:

>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.anomaly import HalfSpaceTrees
>>> from capymoa.evaluation import AnomalyDetectionEvaluator
>>> stream = ElectricityTiny()
>>> schema = stream.get_schema()
>>> learner = HalfSpaceTrees(schema)
>>> evaluator = AnomalyDetectionEvaluator(schema)
>>> while stream.has_more_instances():
...     instance = stream.next_instance()
...     proba = learner.score_instance(instance)
...     evaluator.update(instance.y_index, proba)
...     learner.train(instance)
>>> auc = evaluator.auc()
>>> print(f"AUC: {auc:.2f}")
AUC: 0.54

__init__(

schema=None,

CLI=None,

random_seed=1,

window_size=100,

number_of_trees=25,

max_depth=15,

anomaly_threshold=0.5,

size_limit=0.1,

)

Construct a Half-Space Trees anomaly detector

Parameters:

• schema – The schema of the stream. If not provided, it will be inferred from the data.

• CLI – Command Line Interface (CLI) options for configuring the HS-Trees algorithm.

• random_seed – Random seed for reproducibility.

• window_size – The size of the window for each tree.

• number_of_trees – The number of trees in the ensemble.

• max_depth – The maximum depth of each tree.

CLI_help()

predict(instance)

score_instance(instance)

train(instance)