module change_detection.tornado.ewma

Exponentially Weigthed Moving Average 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: Ross, Gordon J., et al. "Exponentially weighted moving average charts for detecting concept drift." Published in: Pattern Recognition Letters 33.2 (2012): 191-198. URL: https://arxiv.org/pdf/1212.6018.pdf

Copyright (C) 2022 Johannes Haug.

class EWMA

EWMA change detector.

method EWMA.__init__

__init__(
    min_instance: int = 30,
    lambda_: float = 0.2,
    reset_after_drift: bool = False
)

Inits the change detector.

Args:

  • min_instance: Todo (left unspecified by the Tornado library).
  • lambda_: 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 EWMA.detect_change

detect_change() → bool

Detects global concept drift.

Returns:

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

method EWMA.detect_partial_change

detect_partial_change() → Tuple[bool, list]

Detects partial concept drift.

Notes:

EWMA does not detect partial change.

method EWMA.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 EWMA.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 EWMA.reset

reset()

Resets the change detector.

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