alibi_detect.cd.chisquare

ChiSquareDrift

Inherits from: BaseUnivariateDrift, BaseDetector, ABC, DriftConfigMixin

Constructor

ChiSquareDrift(self, x_ref: Union[numpy.ndarray, list], p_val: float = 0.05, categories_per_feature: Optional[Dict[int, 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', 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 Chi-Squared test for each feature. If the FDR correction method is used, this corresponds to the acceptable q-value.
categories_per_feature	Optional[Dict[int, int]]	None	Optional dictionary with as keys the feature column index and as values the number of possible categorical values for that feature or a list with the possible values. If you 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. If it is not specified, categories_per_feature is inferred from x_ref.
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).
n_features	Optional[int]	None	Number of features used in the Chi-Squared 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]

Compute Chi-Squared test statistic 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]