alibi_detect.cd.chisquare module
- class alibi_detect.cd.chisquare.ChiSquareDrift(x_ref, p_val=0.05, categories_per_feature=None, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, correction='bonferroni', n_features=None, input_shape=None, data_type=None)[source]
Bases:
BaseUnivariateDrift
- __init__(x_ref, p_val=0.05, categories_per_feature=None, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, correction='bonferroni', n_features=None, input_shape=None, data_type=None)[source]
Chi-Squared 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 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
]]) – 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
) – 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
) – 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
]]) – 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).n_features (
Optional
[int
]) – 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.data_type (
Optional
[str
]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.
- feature_score(x_ref, x)[source]
Compute Chi-Squared test statistic and p-values per feature.
- Parameters:
x_ref (
ndarray
) – Reference instances to compare distribution with.x (
ndarray
) – Batch of instances.
- Return type:
Tuple
[ndarray
,ndarray
]- Returns:
Feature level p-values and Chi-Squared statistics.