JS

JS#

class frouros.detectors.data_drift.batch.distance_based.JS(num_bins: int = 10, callbacks: BaseCallbackBatch | list[BaseCallbackBatch] | None = None, **kwargs: Any)#

JS (Jensen-Shannon distance) [lin1991divergence] detector.

Parameters:

  • num_bins (int) – number of bins in which to divide probabilities, defaults to 10

  • callbacks (Optional[Union[BaseCallbackBatch, list[BaseCallbackBatch]]]) – callbacks, defaults to None

  • kwargs (dict[str, Any]) – additional keyword arguments to pass to scipy.spatial.distance.jensenshannon

References:

[lin1991divergence]

Lin, Jianhua. “Divergence measures based on the Shannon entropy.” IEEE Transactions on Information theory 37.1 (1991): 145-151.

Example:

>>> from frouros.detectors.data_drift import JS
>>> import numpy as np
>>> np.random.seed(seed=31)
>>> X = np.random.normal(loc=0, scale=1, size=100)
>>> Y = np.random.normal(loc=1, scale=1, size=100)
>>> detector = JS(num_bins=20)
>>> _ = detector.fit(X=X)
>>> detector.compare(X=Y)[0]
DistanceResult(distance=0.41702877367162156)
property num_bins: int#

Number of bins property.

Returns:

number of bins in which to divide probabilities

Return type:

int

property X_ref: ndarray | None#

Reference data property.

Returns:

reference data

Return type:

Optional[numpy.ndarray]

property callbacks: list[BaseCallback] | None#

Callbacks property.

Returns:

callbacks

Return type:

Optional[list[BaseCallback]]

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

Compare values.

Parameters:

X (numpy.ndarray) – test 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: Any) → dict[str, Any]#

Fit detector.

Parameters:

  • X (numpy.ndarray) – feature data

  • kwargs (Any) – additional fit parameters

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

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