BOCD#

class frouros.detectors.concept_drift.streaming.change_detection.BOCD(config: BOCDConfig | None = None, callbacks: BaseCallbackStreaming | list[BaseCallbackStreaming] | None = None)#

BOCD (Bayesian Online Change Detection) [adams2007bayesian] detector.

Parameters:

config (Optional[BOCDConfig]) – configuration object of the detector, defaults to None. If None, the default configuration of BOCDConfig is used.
callbacks (Optional[Union[BaseCallbackStreaming, list[BaseCallbackStreaming]]]) – callbacks, defaults to None

Note:

Adapted from the implementation in gwgundersen/bocd.

References:

[adams2007bayesian]

Adams, Ryan Prescott, and David JC MacKay. “Bayesian online changepoint detection.” arXiv preprint arXiv:0710.3742 (2007).

Example:

>>> from frouros.detectors.concept_drift import BOCD
>>> import numpy as np
>>> np.random.seed(seed=31)
>>> dist_a = np.random.normal(loc=0.2, scale=0.01, size=1000)
>>> dist_b = np.random.normal(loc=0.8, scale=0.04, size=1000)
>>> stream = np.concatenate((dist_a, dist_b))
>>> detector = BOCD()
>>> for i, value in enumerate(stream):
...     _ = detector.update(value=value)
...     if detector.drift:
...         print(f"Change detected at step {i}")
...         break
Change detected at step 1031

config_type#: alias of BOCDConfig

property additional_vars: dict[str, Any] | None#

Additional variables property.

Returns:: additional variables
Return type:: Optional[dict[str, Any]]

property log_r: ndarray#

Log r getter.

Returns:: log r
Return type:: numpy.ndarray

property callbacks: list[BaseCallback] | None#

Callbacks property.

Returns:: callbacks
Return type:: Optional[list[BaseCallback]]

property config: BaseConceptDriftConfig#

Config property.

Returns:: configuration parameters of the estimator
Return type:: BaseConceptDriftConfig

property num_instances: int#

Number of instances counter property.

Returns:: Number of instances counter value
Return type:: int

property status: dict[str, bool]#

Status property.

Returns:: status dict
Return type:: dict[str, bool]

update(value: int | float, **kwargs: Any) → dict[str, Any]#

Update method.

Parameters:

value (Union[int, float]) – value to update detector
kwargs (Any) – additional keyword arguments

Returns:

callbacks logs

Return type:

dict[str, Any]]

property predicted_mean: float | None#

Predicted mean getter.

Returns:: predicted mean
Return type:: Optional[float]

property predicted_var: float | None#

Predicted variance getter.

Returns:: predicted variance
Return type:: Optional[float]

property log_message: ndarray#

Log message getter.

Returns:: log message
Return type:: numpy.ndarray

reset() → None#: Reset method.

BOCD

Contents

BOCD#