dataeval.detectors.drift.DriftUncertainty¶
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class dataeval.detectors.drift.DriftUncertainty(x_ref, model, p_val=
0.05, x_ref_preprocessed=False, update_x_ref=None, preds_type='probs', batch_size=32, preprocess_batch_fn=None, device=None)¶ Test for a change in the number of instances falling into regions on which the model is uncertain.
Performs a K-S test on prediction entropies.
- Parameters:¶
- x_ref : ArrayLike¶
Data used as reference distribution.
- model : Callable¶
Classification model outputting class probabilities (or logits)
- p_val : float, default 0.05¶
P-Value used for the significance of the test.
- x_ref_preprocessed : bool, default False¶
Whether the given reference data
x_refhas been preprocessed yet. IfTrue, only the test dataxwill be preprocessed at prediction time. IfFalse, 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.
- preds_type : "probs" | "logits", default "logits"¶
Type of prediction output by the model. Options are ‘probs’ (in [0,1]) or ‘logits’ (in [-inf,inf]).
- batch_size : int, default 32¶
Batch size used to evaluate model. Only relevant when backend has been specified for batch prediction.
- preprocess_batch_fn : Callable | None, default None¶
Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the model.
- device : str | None, default None¶
Device type used. The default None tries to use the GPU and falls back on CPU if needed. Can be specified by passing either ‘cuda’, ‘gpu’ or ‘cpu’.