CVMTest

CVMTest#

class frouros.detectors.data_drift.batch.statistical_test.CVMTest(callbacks: BaseCallbackBatch | list[BaseCallbackBatch] | None = None)#

CVMTest (Cramér-von Mises test) [cramer1928composition] detector.

Parameters:

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

Note:

References:

[cramer1928composition]

Cramér, Harald. “On the composition of elementary errors: First paper: Mathematical deductions.” Scandinavian Actuarial Journal 1928.1 (1928): 13-74.

Example:

>>> from frouros.detectors.data_drift import CVMTest
>>> 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 = CVMTest()
>>> _ = detector.fit(X=X)
>>> detector.compare(X=Y)[0]
StatisticalResult(statistic=5.331699999999998, p_value=1.7705426014202885e-10)
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_type: BaseStatisticalType#

Statistical type property.

Returns:

statistical type

Return type:

BaseStatisticalType

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