alibi_detect.od.pytorch.svm

BgdSVMTorch

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

Constructor

BgdSVMTorch(self, nu: float, kernel: 'torch.nn.Module' = None, n_components: Optional[int] = None, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None)

Name	Type	Default	Description
nu	float		The proportion of the training data that should be considered outliers. Note that this does not necessarily correspond to the false positive rate on test data, which is still defined when calling the infer_threshold method.
kernel	Optional[torch.nn.modules.module.Module]	None	Kernel function to use for outlier detection.
n_components	Optional[int]	None	Number of components in the Nystroem approximation, by default uses all of them.
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, step_size_range: Tuple[float, float] = (1e-08, 1.0), n_step_sizes: int = 16, tol: float = 1e-06, n_iter_no_change: int = 25, max_iter: int = 1000, verbose: int = 0) -> Dict

Fit the Nystroem approximation and python SVM model.

Name	Type	Default	Description
x_ref	torch.Tensor		Training data.
step_size_range	Tuple[float, float]	(1e-08, 1.0)	The range of values to be considered for the gradient descent step size at each iteration. This is specified as a tuple of the form (min_eta, max_eta).
n_step_sizes	int	16	The number of step sizes in the defined range to be tested for loss reduction. This many points are spaced equidistantly along the range in log space.
tol	float	1e-06	The decrease in loss required over the previous n_iter_no_change iterations in order to continue optimizing.
n_iter_no_change	int	25	The number of iterations over which the loss must decrease by tol in order for optimization to continue.
max_iter	int	1000	The maximum number of optimization steps.
verbose	int	0	Verbosity level during training. 0 is silent, 1 a progress bar.

Returns

• Type: Dict

format_fit_kwargs

format_fit_kwargs(fit_kwargs: Dict) -> Dict

Format kwargs for fit method.

Name	Type	Default	Description
fit_kwargs	Dict		dictionary of Kwargs to format. See fit method for details.

Returns

• Type: Dict

score

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

Computes the score of x

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

Returns

• Type: torch.Tensor

SVMTorch

Inherits from: TorchOutlierDetector, Module, FitMixinTorch, ABC

Constructor

SVMTorch(self, nu: float, kernel: 'torch.nn.Module' = None, n_components: Optional[int] = None, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None)

Name	Type	Default	Description
nu	float		The proportion of the training data that should be considered outliers. Note that this does not necessarily correspond to the false positive rate on test data, which is still defined when calling the infer_threshold method.
kernel	Optional[torch.nn.modules.module.Module]	None	Kernel function to use for outlier detection.
n_components	Optional[int]	None	Number of components in the Nystroem approximation, by default uses all of them.
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

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

SgdSVMTorch

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

Constructor

SgdSVMTorch(self, nu: float, kernel: 'torch.nn.Module' = None, n_components: Optional[int] = None, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None)

Name	Type	Default	Description
nu	float		The proportion of the training data that should be considered outliers. Note that this does not necessarily correspond to the false positive rate on test data, which is still defined when calling the infer_threshold method.
kernel	Optional[torch.nn.modules.module.Module]	None	Kernel function to use for outlier detection.
n_components	Optional[int]	None	Number of components in the Nystroem approximation, by default uses all of them.
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, tol: float = 1e-06, max_iter: int = 1000, verbose: int = 0) -> Dict

Fit the Nystroem approximation and Sklearn SGDOneClassSVM SVM model.

Name	Type	Default	Description
x_ref	torch.Tensor		Training data.
tol	float	1e-06	The decrease in loss required over the previous n_iter_no_change iterations in order to continue optimizing.
max_iter	int	1000	The maximum number of optimization steps.
verbose	int	0	Verbosity level during training. 0 is silent, 1 a progress bar.

Returns

• Type: Dict

format_fit_kwargs

format_fit_kwargs(fit_kwargs: Dict) -> Dict

Format kwargs for fit method.

Name	Type	Default	Description
fit_kwargs	Dict		dictionary of Kwargs to format. See fit method for details.

Returns

• Type: Dict

score

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

Computes the score of x

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

Returns

• Type: torch.Tensor