EWMAChart

class capymoa.drift.detectors.EWMAChart

Bases: MOADriftDetector

EWMA Charts Drift Detector

Example:

>>> import numpy as np
>>> from capymoa.drift.detectors import EWMAChart
>>> np.random.seed(0)
>>>
>>> detector = EWMAChart()
>>>
>>> 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: 5 - at index: 999

Reference:

Ross, Gordon J., et al. “Exponentially weighted moving average charts for detecting concept drift.” Pattern recognition letters 33.2 (2012): 191-198.

__init__(

min_n_instances: int = 30,

lambda_: float = 0.2,

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

CLI_help()

add_element(element: float) → None

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

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