ABCD

class capymoa.drift.detectors.ABCD

Bases: BaseDriftDetector

__init__(

delta_drift: float = 0.002,

delta_warn: float = 0.01,

model_id: str = 'ae',

split_type: str = 'ed',

encoding_factor: float = 0.5,

update_epochs: int = 50,

num_splits: int = 20,

max_size: int = inf,

subspace_threshold: float = 2.5,

n_min: int = 100,

maximum_absolute_value: float = 1.0,

bonferroni: bool = False,

)

Parameters:

• delta_drift – The desired confidence level at which a drift is detected

• delta_warn – The desired confidence level at which a warning is detected

• model_id – The name of the model to use

• update_epochs – The number of epochs to train the AE after a change occurred

• split_type – Investigation of different split types

• subspace_threshold – Called tau in the paper

• bonferroni – Use bonferroni correction to account for multiple testing?

• encoding_factor – The relative size of the bottleneck

• maximum_absolute_value – The maximum absolute value that one can expect (e.g. 1.0 for normalized data). Smaller values can increase false alarms but speed up change detection

• num_splits – The number of time point to evaluate

get_params() → Dict[str, Any]

Get the hyper-parameters of the drift detector.

pre_train(data)

add_element(element: float | ndarray | Instance) → None

Add the new element and also perform change detection :param element: The new observation :return:

loss()

get_dims_p_values() → ndarray

get_drift_dims() → ndarray

get_severity()

detected_change() → bool

Is the detector currently detecting a concept drift?

detected_warning() → bool

Is the detector currently warning of an upcoming concept drift?

reset(clean_history: bool = False) → None

Reset the drift detector.

Parameters:

clean_history – Whether to reset detection history, defaults to False