alibi_detect.cd.fet

FETDrift

Inherits from: BaseUnivariateDrift, BaseDetector, ABC, DriftConfigMixin

Constructor

FETDrift(self, x_ref: Union[numpy.ndarray, list], p_val: float = 0.05, 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 = 'greater', 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. Data must consist of either [True, False]'s, or [0, 1]'s.
p_val	float	0.05	p-value used for significance of the FET test. If the FDR correction method is used, this corresponds to the acceptable q-value.
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.
correction	str	'bonferroni'	Correction type for multivariate data. Either 'bonferroni' or 'fdr' (False Discovery Rate).
alternative	str	'greater'	Defines the alternative hypothesis. Options are 'greater', 'less' or two-sided. These correspond to an increase, decrease, or any change in the mean of the Bernoulli data.
n_features	Optional[int]	None	Number of features used in the FET 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.
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]

Performs Fisher exact test(s), computing the p-value per feature.

Name	Type	Default	Description
x_ref	numpy.ndarray		Reference instances to compare distribution with. Data must consist of either [True, False]'s, or [0, 1]'s.
x	numpy.ndarray		Batch of instances. Data must consist of either [True, False]'s, or [0, 1]'s.

Returns

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