module change_detection.tornado.fhddms

Stacking Fast Hoeffding Drift Detection Method.

The source code was adopted from tornado, please cite:

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

Original Paper: Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams URL: https://arxiv.org/pdf/1709.02457.pdf

Copyright (C) 2022 Johannes Haug.

class FHDDMS

FHDDMS change detector.

method FHDDMS.__init__

__init__(
    m: int = 4,
    n: int = 25,
    delta: float = 1e-06,
    reset_after_drift: bool = False
)

Inits the change detector.

Args:

  • m: Todo (left unspecified by the Tornado library).
  • 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 FHDDMS.detect_change

detect_change() → bool

Detects global concept drift.

Returns:

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

method FHDDMS.detect_partial_change

detect_partial_change() → Tuple[bool, list]

Detects partial concept drift.

Notes:

FHDDMS does not detect partial change.

method FHDDMS.detect_warning_zone

detect_warning_zone() → bool

Detects a warning zone.

Notes:

FHDDMS does not raise warnings.

method FHDDMS.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 FHDDMS.reset

reset()

Resets the change detector.

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