dataeval.detectors.drift.DriftKS
================================

.. py:class:: dataeval.detectors.drift.DriftKS(x_ref, p_val = 0.05, x_ref_preprocessed = False, update_x_ref = None, preprocess_fn = None, correction = 'bonferroni', alternative = 'two-sided', n_features = None)



   :term:`Drift` detector employing the Kolmogorov-Smirnov (KS) distribution test.

   The KS test detects changes in the maximum distance between two data
   distributions with Bonferroni or :term:`False Discovery Rate (FDR)` correction
   for multivariate data.

   :param x_ref: Data used as reference distribution.
   :type x_ref: ArrayLike
   :param p_val: :term:`p-value<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.
   :type p_val: float | None, default 0.05
   :param x_ref_preprocessed: 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.
   :type x_ref_preprocessed: bool, default False
   :param update_x_ref: 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.
   :type update_x_ref: UpdateStrategy | None, default None
   :param preprocess_fn: Function to preprocess the data before computing the data :term:`drift<Drift>` metrics.
                         Typically a :term:`dimensionality reduction<Dimensionality Reduction>` technique.
   :type preprocess_fn: Callable | None, default None
   :param correction: Correction type for multivariate data. Either 'bonferroni' or 'fdr' (False
                      Discovery Rate).
   :type correction: "bonferroni" | "fdr", default "bonferroni"
   :param alternative: Defines the alternative hypothesis. Options are 'two-sided', 'less' or
                       'greater'.
   :type alternative: "two-sided" | "less" | "greater", default "two-sided"
   :param n_features: Number of features used in the statistical test. No need to pass it if no
                      preprocessing takes place. In case of a preprocessing step, this can also
                      be inferred automatically but could be more expensive to compute.
   :type n_features: int | None, default None


   .. py:method:: score(x)

      Compute KS scores and :term:Statistics` per feature.

      :param x: Batch of instances.
      :type x: ArrayLike

      :returns: Feature level :term:p-values and KS statistic
      :rtype: tuple[NDArray, NDArray]