module change_detection.tornado.fhddm

Fast Hoeffding 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: Pesaranghader, Ali, and Herna L. Viktor. "Fast hoeffding drift detection method for evolving data streams." Published in: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2016. URL: https://link.springer.com/chapter/10.1007/978-3-319-46227-1_7

Copyright (C) 2022 Johannes Haug.

class FHDDM

FHDDM change detector.

method FHDDM.__init__

__init__(n: int = 100, delta: float = 1e-06, reset_after_drift: bool = False)

Inits the change detector.

Args:

  • n: Todo (left unspecified by the Tornado library).
  • delta: 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 FHDDM.detect_change

detect_change() → bool

Detects global concept drift.

Returns:

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

method FHDDM.detect_partial_change

detect_partial_change() → Tuple[bool, list]

Detects partial concept drift.

Notes:

FHDDM does not detect partial change.

method FHDDM.detect_warning_zone

detect_warning_zone() → bool

Detects a warning zone.

Notes:

FHDDM does not raise warnings.

method FHDDM.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 FHDDM.reset

reset()

Resets the change detector.

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