alibi_detect.od.pytorch.pca

KernelPCATorch

Inherits from: PCATorch, TorchOutlierDetector, Module, FitMixinTorch, ABC

Constructor

KernelPCATorch(self, n_components: int, kernel: Optional[Callable], device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None)

Name	Type	Default	Description
n_components	int		The number of dimensions in the principal subspace.
kernel	Optional[Callable]		Kernel function to use for outlier detection.
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

compute_kernel_mat

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

Computes the centered kernel matrix.

Name	Type	Default	Description
x	torch.Tensor		The reference data.

Returns

• Type: torch.Tensor

LinearPCATorch

Inherits from: PCATorch, TorchOutlierDetector, Module, FitMixinTorch, ABC

Constructor

LinearPCATorch(self, n_components: int, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None)

Name	Type	Default	Description
n_components	int		The number of dimensions in the principal subspace.
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.

PCATorch

Inherits from: TorchOutlierDetector, Module, FitMixinTorch, ABC

Constructor

PCATorch(self, n_components: int, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None)

Name	Type	Default	Description
n_components	int		The number of dimensions in the principal subspace. For linear PCA should have 1 <= n_components < dim(data). For kernel pca should have 1 <= n_components < len(data).
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) -> None

Fits the PCA detector.

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

Returns

• Type: None

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