ADWIN

class capymoa.drift.detectors.ADWIN

Bases: MOADriftDetector

ADWIN Drift Detector

Example:

>>> import numpy as np
>>> from capymoa.drift.detectors import ADWIN
>>> np.random.seed(0)
>>> detector = ADWIN(delta=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: 4 - at index: 1023
Change detected in data: 5 - at index: 1055

Reference:

Bifet, Albert, and Ricard Gavalda. “Learning from time-changing data with adaptive windowing.” Proceedings of the 2007 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, 2007.

__init__(delta: float = 0.002)

Create an ADWIN drift detector.

Parameters:

delta – Confidence parameter used by ADWIN’s change test. Smaller values make the detector more conservative, while larger values make it more sensitive to distribution changes. Defaults to 0.002.

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.

cli_help() → str

detected_change() → bool

Is the detector currently detecting a concept drift?

detected_warning() → bool

Is the detector currently warning of an upcoming concept drift?

classmethod from_cli(cli: str) → MOADriftDetector

Create a detector instance configured from a MOA CLI string.

Parameters:

cli – Command-line style options string for MOA detector hyper-parameters.

Returns:

A new detector instance initialized with cli.

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