alibi_detect.od.pytorch.lof

LOFTorch

Inherits from: TorchOutlierDetector, Module, FitMixinTorch, ABC

Constructor

LOFTorch(self, k: Union[numpy.ndarray, List, Tuple, int], kernel: Optional[torch.nn.modules.module.Module] = None, ensembler: Optional[alibi_detect.od.pytorch.ensemble.Ensembler] = None, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None)

Name	Type	Default	Description
k	Union[numpy.ndarray, List[Any], Tuple, int]		Number of nearest neighbors used to compute the local outlier factor. k can be a single value or an array of integers. If k is a single value the score method uses the distance/kernel similarity to the k-th nearest neighbor. If k is a list then it uses the distance/kernel similarity to each of the specified k neighbors.
kernel	Optional[torch.nn.modules.module.Module]	None	If a kernel is specified then instead of using torch.cdist the kernel defines the k nearest neighbor distance.
ensembler	Optional[alibi_detect.od.pytorch.ensemble.Ensembler]	None	If k is an array of integers then the ensembler must not be None. Should be an instance of :py:obj:alibi_detect.od.pytorch.ensemble.ensembler. Responsible for combining multiple scores into a single score.
device	Union[Literal[cuda, gpu, cpu], 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.

Methods

fit

fit(x_ref: torch.Tensor)

Fits the detector

Name	Type	Default	Description
x_ref	torch.Tensor		The Dataset tensor.

forward

forward(x: torch.Tensor) -> torch.Tensor

Detect if x is an outlier.

Name	Type	Default	Description
x	torch.Tensor		torch.Tensor with leading batch dimension.

Returns

• Type: torch.Tensor

score

score(x: torch.Tensor) -> torch.Tensor

Computes the score of x

Name	Type	Default	Description
x	torch.Tensor		The tensor of instances. First dimension corresponds to batch.

Returns

• Type: torch.Tensor