alibi_detect.utils.pytorch.misc

Constants

logger

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

get_device

get_device(device: Union[Literal[cuda, gpu, cpu], torch.device, None] = None) -> torch.device

Instantiates a PyTorch device object.

Name	Type	Default	Description
device	Union[Literal[cuda, gpu, cpu], torch.device, None]	None	Either None, a str ('gpu', 'cuda' or 'cpu') indicating the device to choose, or an already instantiated device object. If None, the GPU is selected if it is detected, otherwise the CPU is used as a fallback.

Returns

• Type: torch.device

get_optimizer

get_optimizer(name: str = 'Adam') -> type[torch.optim.optimizer.Optimizer]

Get an optimizer class from its name.

Name	Type	Default	Description
name	str	'Adam'	Name of the optimizer.

Returns

• Type: type[torch.optim.optimizer.Optimizer]

quantile

quantile(sample: torch.Tensor, p: float, type: int = 7, sorted: bool = False) -> float

Estimate a desired quantile of a univariate distribution from a vector of samples

Name	Type	Default	Description
sample	torch.Tensor		A 1D vector of values
p	float		The desired quantile in (0,1)
type	int	7	The method for computing the quantile. See https://wikipedia.org/wiki/Quantile#Estimating_quantiles_from_a_sample
sorted	bool	False	Whether or not the vector is already sorted into ascending order

Returns

• Type: float

zero_diag

zero_diag(mat: torch.Tensor) -> torch.Tensor

Set the diagonal of a matrix to 0

Name	Type	Default	Description
mat	torch.Tensor		A 2D square matrix

Returns

• Type: torch.Tensor