dataeval.detectors.drift.DriftMMD#

class dataeval.detectors.drift.DriftMMD(x_ref, p_val=0.05, x_ref_preprocessed=False, update_x_ref=None, preprocess_fn=None, sigma=None, configure_kernel_from_x_ref=True, n_permutations=100, device=None)#

Maximum Mean Discrepancy (MMD) Drift Detection algorithm using a permutation test.

Parameters:

  • x_ref (ArrayLike) – Data used as reference distribution.

  • p_val (float | None, default 0.05) – P-value used for significance of the statistical test for each feature. If the FDR correction method is used, this corresponds to the acceptable q-value.

  • x_ref_preprocessed (bool, default False) – Whether the given reference data x_ref has been preprocessed yet. If True, only the test data x will be preprocessed at prediction time. If False, the reference data will also be preprocessed.

  • update_x_ref (UpdateStrategy | None, default None) – Reference data can optionally be updated using an UpdateStrategy class. Update using the last n instances seen by the detector with LastSeenUpdateStrategy or via reservoir sampling with ReservoirSamplingUpdateStrategy.

  • preprocess_fn (Callable | None, default None) – Function to preprocess the data before computing the data drift metrics. Typically a dimensionality reduction technique.

  • sigma (ArrayLike | None, default None) – Optionally set the internal GaussianRBF kernel bandwidth. Can also pass multiple bandwidth values as an array. The kernel evaluation is then averaged over those bandwidths.

  • configure_kernel_from_x_ref (bool, default True) – Whether to already configure the kernel bandwidth from the reference data.

  • n_permutations (int, default 100) – Number of permutations used in the permutation test.

  • device (str | None, default None) – Device type used. The default None uses the GPU and falls back on CPU. Can be specified by passing either ‘cuda’, ‘gpu’ or ‘cpu’.

predict(x)#

Predict whether a batch of data has drifted from the reference data and then updates reference data using specified strategy.

Parameters:

x (ArrayLike) – Batch of instances.

Returns:

Output class containing the drift prediction, p-value, threshold and MMD metric.

Return type:

DriftMMDOutput

score(x)#

Compute the p-value resulting from a permutation test using the maximum mean discrepancy as a distance measure between the reference data and the data to be tested.

Parameters:

x (ArrayLike) – Batch of instances.

Returns:

p-value obtained from the permutation test, MMD^2 between the reference and test set, and MMD^2 threshold above which drift is flagged

Return type:

tuple(float, float, float)