CUSUM#

class capymoa.drift.detectors.CUSUM[source]#

Bases: MOADriftDetector

CUSUM Drift Detector

Example usages:

>>> import numpy as np
>>> np.random.seed(0)
>>> from capymoa.drift.detectors import CUSUM
>>>
>>> detector = CUSUM(delta=0.005, lambda_=60)
>>>
>>> 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: 1011
Change detected in data: 7 - at index: 1556

__init__( min_n_instances: int = 30, delta: float = 0.005, lambda_: float = 50, CLI: str | None = None, )[source]#

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

CLI_help()[source]#

add_element(element: float) → None[source]#

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.

detected_change() → bool[source]#: Is the detector currently detecting a concept drift?

detected_warning() → bool[source]#: Is the detector currently warning of an upcoming concept drift?

get_params() → Dict[str, Any][source]#: Get the hyper-parameters of the drift detector.

reset(clean_history: bool = False) → None[source]#

Reset the drift detector.

Parameters:: clean_history – Whether to reset detection history, defaults to False