module change_detection.tornado.hddm_w
Hoeffding's Bound based Drift Detection Method (W_test 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: Frías-Blanco, Isvani, et al. "Online and non-parametric drift detection methods based on Hoeffding’s bounds." Published in: IEEE Transactions on Knowledge and Data Engineering 27.3 (2015): 810-823. URL: http://ieeexplore.ieee.org/abstract/document/6871418/
Copyright (C) 2022 Johannes Haug.
class HDDMW
HDDMW change detector.
method HDDMW.__init__
__init__(
drift_confidence: float = 0.001,
warning_confidence: float = 0.005,
lambda_: float = 0.05,
test_type: str = 'one-sided',
reset_after_drift: bool = False
)
Inits the change detector.
Args:
drift_confidence
: Todo (left unspecified by the Tornado library).warning_confidence
: Todo (left unspecified by the Tornado library).lambda_
: Todo (left unspecified by the Tornado library).test_type
: 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 HDDMW.detect_change
detect_change() → bool
Detects global concept drift.
Returns:
bool
: True, if a concept drift was detected, False otherwise.
method HDDMW.detect_partial_change
detect_partial_change() → Tuple[bool, list]
Detects partial concept drift.
Notes:
HDDMW does not detect partial change.
method HDDMW.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 HDDMW.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 HDDMW.reset
reset()
Resets the change detector.
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