KL

class frouros.detectors.data_drift.batch.distance_based.KL(num_bins: int = 10, callbacks: Optional[Union[BaseCallbackBatch, List[BaseCallbackBatch]]] = None, **kwargs)

KL (Kullback-Leibler divergence) [kullback1951information] detector.

References:

[kullback1951information]

Kullback, Solomon, and Richard A. Leibler. “On information and sufficiency.” The annals of mathematical statistics 22.1 (1951): 79-86.

property num_bins: int

Number of bins property.

Returns:

number of bins in which to divide probabilities

Return type:

int

property X_ref: Optional[ndarray]

Reference data property.

Returns:

reference data

Return type:

Optional[numpy.ndarray]

property callbacks: Optional[List[BaseCallback]]

Callbacks property.

Returns:

callbacks

Return type:

Optional[List[BaseCallback]]

compare(X: ndarray, **kwargs) → Tuple[ndarray, Dict[str, Any]]

Compare values.

Parameters:

X (numpy.ndarray) – feature data

Returns:

compare result and callbacks logs

Return type:

Tuple[numpy.ndarray, Dict[str, Any]]

property data_type: BaseDataType

Data type property.

Returns:

data type

Return type:

BaseDataType

fit(X: ndarray, **kwargs) → Dict[str, Any]

Fit detector.

Parameters:

X (numpy.ndarray) – feature data

Returns:

callbacks logs

Return type:

Dict[str, Any]

reset() → None

Reset method.

property statistical_kwargs: Dict[str, Any]

Statistical kwargs property.

Returns:

statistical kwargs

Return type:

Dict[str, Any]

property statistical_method: Callable

Statistical method property.

Returns:

statistical method

Return type:

Callable

property statistical_type: BaseStatisticalType

Statistical type property.

Returns:

statistical type

Return type:

BaseStatisticalType