alibi_detect.cd.tensorflow.context_aware

Constants

logger

logger: logging.Logger = <Logger alibi_detect.cd.tensorflow.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.

ContextMMDDriftTF

Inherits from: BaseContextMMDDrift, BaseDetector, ABC

Constructor

ContextMMDDriftTF(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 = <class 'alibi_detect.utils.tensorflow.kernels.GaussianRBF'>, c_kernel: Callable = <class 'alibi_detect.utils.tensorflow.kernels.GaussianRBF'>, 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	Callable	<class 'alibi_detect.utils.tensorflow.kernels.GaussianRBF'>	Kernel defined on the input data, defaults to Gaussian RBF kernel.
c_kernel	Callable	<class 'alibi_detect.utils.tensorflow.kernels.GaussianRBF'>	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

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]