DDM

class capymoa.drift.detectors.DDM

Bases: MOADriftDetector

Drift-Detection-Method (DDM) Drift Detector

Example:

>>> import numpy as np
>>> from capymoa.drift.detectors import DDM
>>> np.random.seed(0)
>>>
>>> detector = DDM()
>>>
>>> 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: 1005

Reference:

Gama, Joao, et al. “Learning with drift detection.” Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004.

__init__(

min_n_instances: int = 30,

warning_level: float = 2.0,

out_control_level: float = 3.0,

)

Create a DDM drift detector.

Parameters:

• min_n_instances – Minimum number of instances to observe before change detection is enabled. Defaults to 30.

• warning_level – Multiplier applied to the minimum error estimate for entering the warning zone. Defaults to 2.0.

• out_control_level – Multiplier applied to the minimum error estimate for reporting a concept change. Defaults to 3.0.

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.

classmethod from_params(

schema: Any = None,

params: dict[str, Any] | None = None,

random_seed: int = 1,

) → Any

Construct an instance from parameters produced by get_params.

get_params() → dict[str, Any]

Return the hyper-parameters captured from the constructor.

reset(clean_history: bool = False) → None

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().