module change_detection.tornado.mddm_a

McDiarmid Drift Detection Method (Arithmetic Scheme).

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, et al. "McDiarmid Drift Detection Method for Evolving Data Streams." Published in: International Joint Conference on Neural Network (IJCNN 2018) URL: https://arxiv.org/abs/1710.02030

Copyright (C) 2022 Johannes Haug.

class MDDMA

MDDMA change detector.

method MDDMA.__init__

__init__(
    n: int = 100,
    difference: float = 0.01,
    delta: float = 1e-06,
    reset_after_drift: bool = False
)

Inits the change detector.

Args:

  • n: Todo (left unspecified by the Tornado library).
  • difference: 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 MDDMA.detect_change

detect_change() → bool

Detects global concept drift.

Returns:

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

method MDDMA.detect_partial_change

detect_partial_change() → Tuple[bool, list]

Detects partial concept drift.

Notes:

MDDMA does not detect partial change.

method MDDMA.detect_warning_zone

detect_warning_zone() → bool

Detects a warning zone.

Notes:

MDDMA does not raise warnings.

method MDDMA.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 MDDMA.reset

reset()

Resets the change detector.

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