Edit

alibi_detect.cd.base

Constants

has_pytorch

has_pytorch: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_tensorflow

has_tensorflow: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

logger

logger: logging.Logger = <Logger alibi_detect.cd.base (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

BaseClassifierDrift

Inherits from: BaseDetector, ABC

Constructor

BaseClassifierDrift(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, preds_type: str = 'probs', binarize_preds: bool = False, train_size: Optional[float] = 0.75, n_folds: Optional[int] = None, retrain_from_scratch: bool = True, seed: int = 0, 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 the significance of the test.

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.

preds_type

str

'probs'

Whether the model outputs probabilities or logits

binarize_preds

bool

False

Whether to test for discrepency on soft (e.g. probs/logits) model predictions directly with a K-S test or binarise to 0-1 prediction errors and apply a binomial test.

train_size

Optional[float]

0.75

Optional fraction (float between 0 and 1) of the dataset used to train the classifier. The drift is detected on 1 - train_size. Cannot be used in combination with n_folds.

n_folds

Optional[int]

None

Optional number of stratified folds used for training. The model preds are then calculated on all the out-of-fold predictions. This allows to leverage all the reference and test data for drift detection at the expense of longer computation. If both train_size and n_folds are specified, n_folds is prioritized.

retrain_from_scratch

bool

True

Whether the classifier should be retrained from scratch for each set of test data or whether it should instead continue training from where it left off on the previous set.

seed

int

0

Optional random seed for fold selection.

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

get_splits

get_splits(x_ref: Union[numpy.ndarray, list], x: Union[numpy.ndarray, list], return_splits: bool = True) -> Union[Tuple[Union[numpy.ndarray, list], numpy.ndarray], Tuple[Union[numpy.ndarray, list], numpy.ndarray, Optional[List[Tuple[numpy.ndarray, numpy.ndarray]]]]]

Split reference and test data in train and test folds used by the classifier.

Name
Type
Default
Description

x_ref

Union[numpy.ndarray, list]

Data used as reference distribution.

x

Union[numpy.ndarray, list]

Batch of instances.

return_splits

bool

True

Whether to return the splits.

Returns

  • Type: Union[Tuple[Union[numpy.ndarray, list], numpy.ndarray], Tuple[Union[numpy.ndarray, list], numpy.ndarray, Optional[List[Tuple[numpy.ndarray, numpy.ndarray]]]]]

predict

predict(x: Union[numpy.ndarray, list], return_p_val: bool = True, return_distance: bool = True, return_probs: bool = True, return_model: bool = True) -> Dict[str, Dict[str, Union[str, int, float, Callable]]]

Predict whether a batch of data has drifted from the reference data.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

return_p_val

bool

True

Whether to return the p-value of the test.

return_distance

bool

True

Whether to return a notion of strength of the drift. K-S test stat if binarize_preds=False, otherwise relative error reduction.

return_probs

bool

True

Whether to return the instance level classifier probabilities for the reference and test data (0=reference data, 1=test data). The reference and test instances of the associated probabilities are also returned.

return_model

bool

True

Whether to return the updated model trained to discriminate reference and test instances.

Returns

  • Type: Dict[str, Dict[str, Union[str, int, float, Callable]]]

preprocess

preprocess(x: Union[numpy.ndarray, list]) -> Tuple[Union[numpy.ndarray, list], Union[numpy.ndarray, list]]

Data preprocessing before computing the drift scores.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

score

score(x: Union[numpy.ndarray, list]) -> Tuple[float, float, numpy.ndarray, numpy.ndarray, Union[numpy.ndarray, list], Union[numpy.ndarray, list]]
Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Returns

  • Type: Tuple[float, float, numpy.ndarray, numpy.ndarray, Union[numpy.ndarray, list], Union[numpy.ndarray, list]]

test_probs

test_probs(y_oof: numpy.ndarray, probs_oof: numpy.ndarray, n_ref: int, n_cur: int) -> Tuple[float, float]

Perform a statistical test of the probabilities predicted by the model against

what we'd expect under the no-change null.

Name
Type
Default
Description

y_oof

numpy.ndarray

Out of fold targets (0 ref, 1 cur)

probs_oof

numpy.ndarray

Probabilities predicted by the model

n_ref

int

Size of reference window used in training model

n_cur

int

Size of current window used in training model

Returns

  • Type: Tuple[float, float]

BaseContextMMDDrift

Inherits from: BaseDetector, ABC

Constructor

BaseContextMMDDrift(self, x_ref: Union[numpy.ndarray, list], c_ref: numpy.ndarray, p_val: float = 0.05, x_ref_preprocessed: bool = False, preprocess_at_init: bool = True, update_ref: Optional[Dict[str, int]] = None, preprocess_fn: Optional[Callable] = None, x_kernel: Callable = None, c_kernel: Callable = None, n_permutations: int = 1000, prop_c_held: float = 0.25, n_folds: int = 5, batch_size: Optional[int] = 256, input_shape: Optional[tuple] = None, data_type: Optional[str] = None, verbose: bool = False) -> None
Name
Type
Default
Description

x_ref

Union[numpy.ndarray, list]

Data used as reference distribution.

c_ref

numpy.ndarray

Context for the reference distribution.

p_val

float

0.05

p-value used for the significance of the permutation test.

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_ref

Optional[Dict[str, int]]

None

Reference data can optionally be updated to the last N instances seen by the detector. The parameter should be passed as a dictionary {'last': N}.

preprocess_fn

Optional[Callable]

None

Function to preprocess the data before computing the data drift metrics.

x_kernel

Optional[Callable]

None

Kernel defined on the input data, defaults to Gaussian RBF kernel.

c_kernel

Optional[Callable]

None

Kernel defined on the context data, defaults to Gaussian RBF kernel.

n_permutations

int

1000

Number of permutations used in the permutation test.

prop_c_held

float

0.25

Proportion of contexts held out to condition on.

n_folds

int

5

Number of cross-validation folds used when tuning the regularisation parameters.

batch_size

Optional[int]

256

If not None, then compute batches of MMDs at a time (rather than all at once).

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.

verbose

bool

False

Whether or not to print progress during configuration.

Methods

predict

predict(x: Union[numpy.ndarray, list], c: numpy.ndarray, return_p_val: bool = True, return_distance: bool = True, return_coupling: bool = False) -> Dict[Dict[str, str], Dict[str, Union[int, float]]]

Predict whether a batch of data has drifted from the reference data, given the provided context.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

c

numpy.ndarray

Context associated with batch of instances.

return_p_val

bool

True

Whether to return the p-value of the permutation test.

return_distance

bool

True

Whether to return the conditional MMD test statistic between the new batch and reference data.

return_coupling

bool

False

Whether to return the coupling matrices.

Returns

  • Type: Dict[Dict[str, str], Dict[str, Union[int, float]]]

preprocess

preprocess(x: Union[numpy.ndarray, list]) -> Tuple[numpy.ndarray, numpy.ndarray]

Data preprocessing before computing the drift scores.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

score

score(x: Union[numpy.ndarray, list], c: numpy.ndarray) -> Tuple[float, float, float, Tuple]
Name
Type
Default
Description

x

Union[numpy.ndarray, list]

c

numpy.ndarray

Returns

  • Type: Tuple[float, float, float, Tuple]

BaseLSDDDrift

Inherits from: BaseDetector, ABC

Constructor

BaseLSDDDrift(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, sigma: Optional[numpy.ndarray] = None, n_permutations: int = 100, n_kernel_centers: Optional[int] = None, lambda_rd_max: float = 0.2, 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 the significance of the permutation test.

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.

sigma

Optional[numpy.ndarray]

None

Optionally set the bandwidth of the Gaussian kernel used in estimating the LSDD. Can also pass multiple bandwidth values as an array. The kernel evaluation is then averaged over those bandwidths. If sigma is not specified, the 'median heuristic' is adopted whereby sigma is set as the median pairwise distance between reference samples.

n_permutations

int

100

Number of permutations used in the permutation test.

n_kernel_centers

Optional[int]

None

The number of reference samples to use as centers in the Gaussian kernel model used to estimate LSDD. Defaults to 1/20th of the reference data.

lambda_rd_max

float

0.2

The maximum relative difference between two estimates of LSDD that the regularization parameter lambda is allowed to cause. Defaults to 0.2 as in the paper.

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

predict

predict(x: Union[numpy.ndarray, list], return_p_val: bool = True, return_distance: bool = True) -> Dict[Dict[str, str], Dict[str, Union[int, float]]]

Predict whether a batch of data has drifted from the reference data.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

return_p_val

bool

True

Whether to return the p-value of the permutation test.

return_distance

bool

True

Whether to return the LSDD metric between the new batch and reference data.

Returns

  • Type: Dict[Dict[str, str], Dict[str, Union[int, float]]]

preprocess

preprocess(x: Union[numpy.ndarray, list]) -> Tuple[numpy.ndarray, numpy.ndarray]

Data preprocessing before computing the drift scores.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

score

score(x: Union[numpy.ndarray, list]) -> Tuple[float, float, float]
Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Returns

  • Type: Tuple[float, float, float]

BaseLearnedKernelDrift

Inherits from: BaseDetector, ABC

Constructor

BaseLearnedKernelDrift(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, n_permutations: int = 100, train_size: Optional[float] = 0.75, retrain_from_scratch: bool = True, 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 the significance of the test.

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.

n_permutations

int

100

The number of permutations to use in the permutation test once the MMD has been computed.

train_size

Optional[float]

0.75

Optional fraction (float between 0 and 1) of the dataset used to train the kernel. The drift is detected on 1 - train_size. Cannot be used in combination with n_folds.

retrain_from_scratch

bool

True

Whether the kernel should be retrained from scratch for each set of test data or whether it should instead continue training from where it left off on the previous set.

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

get_splits

get_splits(x_ref: Union[numpy.ndarray, list], x: Union[numpy.ndarray, list]) -> Tuple[Tuple[Union[numpy.ndarray, list], Union[numpy.ndarray, list]], Tuple[Union[numpy.ndarray, list], Union[numpy.ndarray, list]]]

Split reference and test data into two splits -- one of which to learn test locations

and parameters and one to use for tests.

Name
Type
Default
Description

x_ref

Union[numpy.ndarray, list]

Data used as reference distribution.

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

predict

predict(x: Union[numpy.ndarray, list], return_p_val: bool = True, return_distance: bool = True, return_kernel: bool = True) -> Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]]

Predict whether a batch of data has drifted from the reference data.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

return_p_val

bool

True

Whether to return the p-value of the permutation test.

return_distance

bool

True

Whether to return the MMD metric between the new batch and reference data.

return_kernel

bool

True

Whether to return the updated kernel trained to discriminate reference and test instances.

Returns

  • Type: Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]]

preprocess

preprocess(x: Union[numpy.ndarray, list]) -> Tuple[Union[numpy.ndarray, list], Union[numpy.ndarray, list]]

Data preprocessing before computing the drift scores.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

score

score(x: Union[numpy.ndarray, list]) -> Tuple[float, float, float]
Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Returns

  • Type: Tuple[float, float, float]

BaseMMDDrift

Inherits from: BaseDetector, ABC

Constructor

BaseMMDDrift(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, sigma: Optional[numpy.ndarray] = None, configure_kernel_from_x_ref: bool = True, n_permutations: int = 100, 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 the significance of the permutation test.

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.

sigma

Optional[numpy.ndarray]

None

Optionally set the Gaussian RBF kernel bandwidth. Can also pass multiple bandwidth values as an array. The kernel evaluation is then averaged over those bandwidths.

configure_kernel_from_x_ref

bool

True

Whether to already configure the kernel bandwidth from the reference data.

n_permutations

int

100

Number of permutations used in the permutation test.

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

predict

predict(x: Union[numpy.ndarray, list], return_p_val: bool = True, return_distance: bool = True) -> Dict[Dict[str, str], Dict[str, Union[int, float]]]

Predict whether a batch of data has drifted from the reference data.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

return_p_val

bool

True

Whether to return the p-value of the permutation test.

return_distance

bool

True

Whether to return the MMD metric between the new batch and reference data.

Returns

  • Type: Dict[Dict[str, str], Dict[str, Union[int, float]]]

preprocess

preprocess(x: Union[numpy.ndarray, list]) -> Tuple[numpy.ndarray, numpy.ndarray]

Data preprocessing before computing the drift scores.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

score

score(x: Union[numpy.ndarray, list]) -> Tuple[float, float, float]
Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Returns

  • Type: Tuple[float, float, float]

BaseUnivariateDrift

Inherits from: BaseDetector, ABC, DriftConfigMixin

Constructor

BaseUnivariateDrift(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', 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 statistical test for each feature. 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. 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 statistical 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. Needs to be provided for text 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]
Name
Type
Default
Description

x_ref

numpy.ndarray

x

numpy.ndarray

Returns

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

predict

predict(x: Union[numpy.ndarray, list], drift_type: str = 'batch', return_p_val: bool = True, return_distance: bool = True) -> Dict[Dict[str, str], Dict[str, Union[numpy.ndarray, int, float]]]

Predict whether a batch of data has drifted from the reference data.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

drift_type

str

'batch'

Predict drift at the 'feature' or 'batch' level. For 'batch', the test statistics for each feature are aggregated using the Bonferroni or False Discovery Rate correction (if n_features>1).

return_p_val

bool

True

Whether to return feature level p-values.

return_distance

bool

True

Whether to return the test statistic between the features of the new batch and reference data.

Returns

  • Type: Dict[Dict[str, str], Dict[str, Union[numpy.ndarray, int, float]]]

preprocess

preprocess(x: Union[numpy.ndarray, list]) -> Tuple[numpy.ndarray, numpy.ndarray]

Data preprocessing before computing the drift scores.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

score

score(x: Union[numpy.ndarray, list]) -> Tuple[numpy.ndarray, numpy.ndarray]

Compute the feature-wise drift score which is the p-value of the

statistical test and the test statistic.

Name
Type
Default
Description

x

Union[numpy.ndarray, list]

Batch of instances.

Returns

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

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