alibi_detect.cd.tabular

TabularDrift

Inherits from: BaseUnivariateDrift, BaseDetector, ABC, DriftConfigMixin

Constructor

TabularDrift(self, x_ref: Union[numpy.ndarray, list], p_val: float = 0.05, categories_per_feature: Dict[int, Optional[int]] = None, x_ref_preprocessed: bool = False, preprocess_at_init: bool = True, update_x_ref: Optional[Dict[str, int]] = None, preprocess_fn: Optional[Callable] = None, correction: str = 'bonferroni', alternative: str = 'two-sided', n_features: Optional[int] = None, input_shape: Optional[tuple] = None, data_type: Optional[str] = None) -> None

Name	Type	Default	Description
x_ref	Union[numpy.ndarray, list]		Data used as reference distribution.
p_val	float	0.05	p-value used for significance of the K-S and Chi2 test for each feature. If the FDR correction method is used, this corresponds to the acceptable q-value.
categories_per_feature	Dict[int, Optional[int]]	None	Dictionary with as keys the column indices of the categorical features and optionally as values the number of possible categorical values for that feature or a list with the possible values. If you know which features are categorical and simply want to infer the possible values of the categorical feature from the reference data you can pass a Dict[int, NoneType] such as {0: None, 3: None} if features 0 and 3 are categorical. If you also know how many categories are present for a given feature you could pass this in the categories_per_feature dict in the Dict[int, int] format, e.g. {0: 3, 3: 2}. If you pass N categories this will assume the possible values for the feature are [0, ..., N-1]. You can also explicitly pass the possible categories in the Dict[int, List[int]] format, e.g. {0: [0, 1, 2], 3: [0, 55]}. Note that the categories can be arbitrary int values.
x_ref_preprocessed	bool	False	Whether the given reference data x_ref has been preprocessed yet. If x_ref_preprocessed=True, only the test data x will be preprocessed at prediction time. If x_ref_preprocessed=False, the reference data will also be preprocessed.
preprocess_at_init	bool	True	Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference data will be preprocessed at prediction time. Only applies if x_ref_preprocessed=False.
update_x_ref	Optional[Dict[str, int]]	None	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]	None	Function to preprocess the data before computing the data drift metrics. Typically a dimensionality reduction technique.
correction	str	'bonferroni'	Correction type for multivariate data. Either 'bonferroni' or 'fdr' (False Discovery Rate).
alternative	str	'two-sided'	Defines the alternative hypothesis for the K-S tests. Options are 'two-sided', 'less' or 'greater'.
n_features	Optional[int]	None	Number of features used in the combined K-S/Chi-Squared tests. 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.
input_shape	Optional[tuple]	None	Shape of input data.
data_type	Optional[str]	None	Optionally specify the data type (tabular, image or time-series). Added to metadata.

Methods

feature_score

feature_score(x_ref: numpy.ndarray, x: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray]

Compute K-S or Chi-Squared test statistics and p-values per feature.

Name	Type	Default	Description
x_ref	numpy.ndarray		Reference instances to compare distribution with.
x	numpy.ndarray		Batch of instances.

Returns

• Type: Tuple[numpy.ndarray, numpy.ndarray]