alibi_detect.cd.context_aware

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.context_aware (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.

ContextMMDDrift

Inherits from: DriftConfigMixin

Constructor

ContextMMDDrift(self, x_ref: Union[numpy.ndarray, list], c_ref: numpy.ndarray, backend: str = 'tensorflow', 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, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None, 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.
backend	str	'tensorflow'	Backend used for the MMD implementation.
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	Callable	None	Kernel defined on the input data, defaults to Gaussian RBF kernel.
c_kernel	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).
device	Union[Literal[cuda, gpu, cpu], ForwardRef('torch.device'), None]	None	Device type used. The default tries to use the GPU and falls back on CPU if needed. Can be specified by passing either 'cuda', 'gpu', 'cpu' or an instance of torch.device. Only relevant for 'pytorch' backend.
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 to print progress messages.

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]]]

score

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

Compute the MMD based conditional test statistic, and perform a conditional permutation test to obtain a

p-value representing the test statistic's extremity under the null hypothesis.

Name	Type	Default	Description
x	Union[numpy.ndarray, list]		Batch of instances.
c	numpy.ndarray		Context associated with batch of instances.

Returns

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