alibi_detect.utils.pytorch.distance

Constants

logger

logger: logging.Logger = <Logger alibi_detect.utils.pytorch.distance (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.

Functions

batch_compute_kernel_matrix

batch_compute_kernel_matrix(x: Union[list, numpy.ndarray, torch.Tensor], y: Union[list, numpy.ndarray, torch.Tensor], kernel: Union[torch.nn.modules.module.Module, torch.nn.modules.container.Sequential], device: Optional[torch.device] = None, batch_size: int = 10000000000, preprocess_fn: Optional[Callable[[...], torch.Tensor]] = None) -> torch.Tensor

Compute the kernel matrix between x and y by filling in blocks of size

batch_size x batch_size at a time.

Name	Type	Default	Description
x	Union[list, numpy.ndarray, torch.Tensor]		Reference set.
y	Union[list, numpy.ndarray, torch.Tensor]		Test set.
kernel	Union[torch.nn.modules.module.Module, torch.nn.modules.container.Sequential]		PyTorch module.
device	Optional[torch.device]	None	Device type used. The default None tries to use the GPU and falls back on CPU if needed. Can be specified by passing either torch.device('cuda') or torch.device('cpu').
batch_size	int	10000000000	Batch size used during prediction.
preprocess_fn	Optional[Callable[[...], torch.Tensor]]	None	Optional preprocessing function for each batch.

Returns

• Type: torch.Tensor

mmd2

mmd2(x: torch.Tensor, y: torch.Tensor, kernel: Callable) -> float

Compute MMD^2 between 2 samples.

Name	Type	Default	Description
x	torch.Tensor		Batch of instances of shape [Nx, features].
y	torch.Tensor		Batch of instances of shape [Ny, features].
kernel	Callable		Kernel function.

Returns

• Type: float

mmd2_from_kernel_matrix

mmd2_from_kernel_matrix(kernel_mat: torch.Tensor, m: int, permute: bool = False, zero_diag: bool = True) -> torch.Tensor

Compute maximum mean discrepancy (MMD^2) between 2 samples x and y from the

full kernel matrix between the samples.

Name	Type	Default	Description
kernel_mat	torch.Tensor		Kernel matrix between samples x and y.
m	int		Number of instances in y.
permute	bool	False	Whether to permute the row indices. Used for permutation tests.
zero_diag	bool	True	Whether to zero out the diagonal of the kernel matrix.

Returns

• Type: torch.Tensor

permed_lsdds

permed_lsdds(k_all_c: torch.Tensor, x_perms: List[torch.Tensor], y_perms: List[torch.Tensor], H: torch.Tensor, H_lam_inv: Optional[torch.Tensor] = None, lam_rd_max: float = 0.2, return_unpermed: bool = False) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]

Compute LSDD estimates from kernel matrix across various ref and test window samples

Name	Type	Default	Description
k_all_c	torch.Tensor		Kernel matrix of similarities between all samples and the kernel centers.
x_perms	List[torch.Tensor]		List of B reference window index vectors
y_perms	List[torch.Tensor]		List of B test window index vectors
H	torch.Tensor		Special (scaled) kernel matrix of similarities between kernel centers
H_lam_inv	Optional[torch.Tensor]	None	Function of H corresponding to a particular regulariation parameter lambda. See Eqn 11 of Bu et al. (2017)
lam_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. Only relavent if H_lam_inv is not supplied.
return_unpermed	bool	False	Whether or not to return value corresponding to unpermed order defined by k_all_c

Returns

• Type: Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]