module change_detection.tornado.ddm

Drift Detection Method.

The source code was adopted from tornado, please cite:

The Tornado Framework By Ali Pesaranghader University of Ottawa, Ontario, Canada E-mail: apesaran -at- uottawa -dot- ca / alipsgh -at- gmail -dot- com

Original Paper: Gama, Joao, et al. "Learning with drift detection." Published in: Brazilian Symposium on Artificial Intelligence. Springer, Berlin, Heidelberg, 2004. URL: https://link.springer.com/chapter/10.1007/978-3-540-28645-5_29

Copyright (C) 2022 Johannes Haug.

class DDM

DDM change detector.

method DDM.__init__

__init__(min_instance: int = 30, reset_after_drift: bool = False)

Inits the change detector.

Args:

  • min_instance: Todo (left unspecified by the Tornado library).
  • reset_after_drift: A boolean indicating if the change detector will be reset after a drift was detected.

method DDM.detect_change

detect_change() → bool

Detects global concept drift.

Returns:

  • bool: True, if a concept drift was detected, False otherwise.

method DDM.detect_partial_change

detect_partial_change() → Tuple[bool, list]

Detects partial concept drift.

Notes:

DDM does not detect partial change.

method DDM.detect_warning_zone

detect_warning_zone() → bool

Detects a warning zone.

Returns:

  • bool: True, if the change detector has detected a warning zone, False otherwise.

method DDM.partial_fit

partial_fit(pr_scores: List[bool])

Updates the change detector.

Args:

  • pr_scores: A boolean vector indicating correct predictions. 'True' values indicate that the prediction by the online learner was correct, otherwise the vector contains 'False'.

method DDM.reset

reset()

Resets the change detector.

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