HDDMWeighted

class capymoa.drift.detectors.HDDMWeighted

Bases: MOADriftDetector

Weighted Hoeffding’s bounds Drift Detector

Example usages:

>>> import numpy as np
>>> from capymoa.drift.detectors import HDDMWeighted
>>> np.random.seed(0)
>>>
>>> detector = HDDMWeighted(lambda_=0.001)
>>>
>>> data_stream = np.random.randint(2, size=2000)
>>> for i in range(999, 2000):
...     data_stream[i] = np.random.randint(4, high=8)
>>>
>>> for i in range(2000):
...     detector.add_element(data_stream[i])
...     if detector.detected_change():
...         print('Change detected in data: ' + str(data_stream[i]) + ' - at index: ' + str(i))
Change detected in data: 6 - at index: 1234

Reference:

Frias-Blanco, Isvani, et al. “Online and non-parametric drift detection methods based on Hoeffding’s bounds.” IEEE Transactions on Knowledge and Data Engineering 27.3 (2014): 810-823.

TEST_TYPES = ['Two-sided', 'One-sided']

__init__(

drift_confidence: float = 0.001,

warning_confidence: float = 0.005,

lambda_: float = 0.05,

test_type: str = 'Two-sided',

CLI: str | None = None,

)

Parameters:

• moa_detector – The MOA detector object or class identifier.

• CLI – The command-line interface (CLI) configuration for the MOA drift detector, defaults to None

add_element(element: float)

Update the drift detector with a new input value.

Parameters:

element – A value to update the drift detector with. Usually, this is the prediction error of a model.

CLI_help()

detected_change() → bool

Is the detector currently detecting a concept drift?

detected_warning() → bool

Is the detector currently warning of an upcoming concept drift?

get_params() → Dict[str, Any]

Get the hyper-parameters of the drift detector.

reset(clean_history: bool = False) → None

Reset the drift detector.

Parameters:

clean_history – Whether to reset detection history, defaults to False