alibi_detect.cd.ks module
- class alibi_detect.cd.ks.KSDrift(x_ref, p_val=0.05, preprocess_x_ref=True, update_x_ref=None, preprocess_fn=None, correction='bonferroni', alternative='two-sided', n_features=None, input_shape=None, data_type=None)[source]
Bases:
alibi_detect.cd.base.BaseUnivariateDrift- __init__(x_ref, p_val=0.05, preprocess_x_ref=True, update_x_ref=None, preprocess_fn=None, correction='bonferroni', alternative='two-sided', n_features=None, input_shape=None, data_type=None)[source]
Kolmogorov-Smirnov (K-S) data drift detector with Bonferroni or False Discovery Rate (FDR) correction for multivariate data.
- Parameters
x_ref (
Union[ndarray,list]) – Data used as reference distribution.p_val (
float) – p-value used for significance of the K-S test for each feature. If the FDR correction method is used, this corresponds to the acceptable q-value.preprocess_x_ref (
bool) – Whether to already preprocess and store the reference data.update_x_ref (
Optional[Dict[str,int]]) – Reference data can optionally be updated to the last n instances seen by the detector or via reservoir sampling with size n. For the former, the parameter equals {‘last’: n} while for reservoir sampling {‘reservoir_sampling’: n} is passed.preprocess_fn (
Optional[Callable]) – Function to preprocess the data before computing the data drift metrics. Typically a dimensionality reduction technique.correction (
str) – Correction type for multivariate data. Either ‘bonferroni’ or ‘fdr’ (False Discovery Rate).alternative (
str) – Defines the alternative hypothesis. Options are ‘two-sided’, ‘less’ or ‘greater’.n_features (
Optional[int]) – Number of features used in the K-S 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.data_type (
Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.