alibi_detect.cd.lsdd

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.

LSDDDrift

Inherits from: DriftConfigMixin

Constructor

LSDDDrift(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, sigma: Optional[numpy.ndarray] = None, n_permutations: int = 100, n_kernel_centers: Optional[int] = None, lambda_rd_max: float = 0.2, 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 LSDD 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.
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.
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

get_config

get_config() -> dict

Returns

• Type: dict

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]]]

score

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

Compute the p-value resulting from a permutation test using the least-squares density

difference 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]