alibi_detect.cd.mmd

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.

has_keops

has_keops: 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.mmd (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.

MMDDrift

Inherits from: DriftConfigMixin

Constructor

MMDDrift(self, x_ref: Union[numpy.ndarray, list], backend: str = 'tensorflow', 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, kernel: Callable = None, sigma: Optional[numpy.ndarray] = None, configure_kernel_from_x_ref: bool = True, n_permutations: int = 100, batch_size_permutations: int = 1000000, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = 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.
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_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.
kernel	Callable	None	Kernel used for the MMD computation, defaults to Gaussian RBF kernel.
sigma	Optional[numpy.ndarray]	None	Optionally set the GaussianRBF 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.
batch_size_permutations	int	1000000	KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations. Only relevant for 'keops' backend.
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.

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]]]

score

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

Compute the p-value resulting from a permutation test using the maximum mean discrepancy

as a distance measure between the reference data and the data to be tested.

Name	Type	Default	Description
x	Union[numpy.ndarray, list]		Batch of instances.

Returns

• Type: Tuple[float, float, float]