`MOADriftDetector`#

class capymoa.drift.base_detector.MOADriftDetector[source]#

A MOA (Massive Online Analysis) drift detector for CapyMOA.

__init__( cli: str = '', moa_detector_type: type[AbstractChangeDetector] | None = None, )[source]#: Initialize the wrapped MOA drift detector.

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?

classmethod from_cli(cli: str) → MOADriftDetector[source]#

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.

classmethod from_params( schema: Any = None, params: dict[str, Any] | None = None, random_seed: int = 1, ) → Any[source]#: Construct an instance from parameters produced by get_params.

get_params() → dict[str, Any][source]#: Return the hyper-parameters captured from the constructor.

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

Reset the drift detector.

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

REQUIRES_FIT: bool = False#: If True, this detector needs a reference distribution before it can detect change. Concept drift detectors leave this as False and only use add_element().

MOADriftDetector#