alibi_detect.cd.pytorch.learned_kernel

LearnedKernelDriftTorch

Inherits from: BaseLearnedKernelDrift, BaseDetector, ABC

Constructor

LearnedKernelDriftTorch(self, x_ref: Union[numpy.ndarray, list], kernel: Union[torch.nn.modules.module.Module, torch.nn.modules.container.Sequential], p_val: float = 0.05, x_ref_preprocessed: bool = False, preprocess_at_init: bool = True, update_x_ref: Optional[Dict[str, int]] = None, preprocess_fn: Optional[Callable] = None, n_permutations: int = 100, var_reg: float = 1e-05, reg_loss_fn: Callable = <function LearnedKernelDriftTorch.<lambda> at 0x28fde7b80>, train_size: Optional[float] = 0.75, retrain_from_scratch: bool = True, optimizer: torch.optim.optimizer.Optimizer = <class 'torch.optim.adam.Adam'>, learning_rate: float = 0.001, batch_size: int = 32, batch_size_predict: int = 32, preprocess_batch_fn: Optional[Callable] = None, epochs: int = 3, num_workers: int = 0, verbose: int = 0, train_kwargs: Optional[dict] = None, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None, dataset: Callable = <class 'alibi_detect.utils.pytorch.data.TorchDataset'>, dataloader: Callable = <class 'torch.utils.data.dataloader.DataLoader'>, input_shape: Optional[tuple] = None, data_type: Optional[str] = None) -> None

Name	Type	Default	Description
x_ref	Union[numpy.ndarray, list]		Data used as reference distribution.
kernel	Union[torch.nn.modules.module.Module, torch.nn.modules.container.Sequential]		Trainable PyTorch module that returns a similarity between two instances.
p_val	float	0.05	p-value used for the significance of the test.
x_ref_preprocessed	bool	False	Whether the given reference data x_ref has been preprocessed yet. If x_ref_preprocessed=True, only the test data x will be preprocessed at prediction time. If x_ref_preprocessed=False, the reference data will also be preprocessed.
preprocess_at_init	bool	True	Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference data will be preprocessed at prediction time. Only applies if x_ref_preprocessed=False.
update_x_ref	Optional[Dict[str, int]]	None	Reference data can optionally be updated to the last n instances seen by the detector or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while for reservoir sampling {'reservoir_sampling': n} is passed.
preprocess_fn	Optional[Callable]	None	Function to preprocess the data before applying the kernel.
n_permutations	int	100	The number of permutations to use in the permutation test once the MMD has been computed.
var_reg	float	1e-05	Constant added to the estimated variance of the MMD for stability.
reg_loss_fn	Callable	<function LearnedKernelDriftTorch.<lambda> at 0x28fde7b80>	The regularisation term reg_loss_fn(kernel) is added to the loss function being optimized.
train_size	Optional[float]	0.75	Optional fraction (float between 0 and 1) of the dataset used to train the kernel. The drift is detected on 1 - train_size.
retrain_from_scratch	bool	True	Whether the kernel should be retrained from scratch for each set of test data or whether it should instead continue training from where it left off on the previous set.
optimizer	torch.optim.optimizer.Optimizer	<class 'torch.optim.adam.Adam'>	Optimizer used during training of the kernel.
learning_rate	float	0.001	Learning rate used by optimizer.
batch_size	int	32	Batch size used during training of the kernel.
batch_size_predict	int	32	Batch size used for the trained drift detector predictions.
preprocess_batch_fn	Optional[Callable]	None	Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the kernel.
epochs	int	3	Number of training epochs for the kernel. Corresponds to the smaller of the reference and test sets.
num_workers	int	0	Number of workers for the dataloader. The default (num_workers=0) means multi-process data loading is disabled. Setting num_workers>0 may be unreliable on Windows.
verbose	int	0	Verbosity level during the training of the kernel. 0 is silent, 1 a progress bar.
train_kwargs	Optional[dict]	None	Optional additional kwargs when training the kernel.
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. Only relevant for 'pytorch' backend.
dataset	Callable	<class 'alibi_detect.utils.pytorch.data.TorchDataset'>	Dataset object used during training.
dataloader	Callable	<class 'torch.utils.data.dataloader.DataLoader'>	Dataloader object used during training. Only relevant for 'pytorch' backend.
input_shape	Optional[tuple]	None	Shape of input data.
data_type	Optional[str]	None	Optionally specify the data type (tabular, image or time-series). Added to metadata.

Methods

score

score(x: Union[numpy.ndarray, list]) -> Tuple[float, float, float]

Compute the p-value resulting from a permutation test using the maximum mean discrepancy

as a distance measure between the reference data and the data to be tested. The kernel used within the MMD is first trained to maximise an estimate of the resulting test power.

Name	Type	Default	Description
x	Union[numpy.ndarray, list]		Batch of instances.

Returns

• Type: Tuple[float, float, float]

trainer

trainer(j_hat: alibi_detect.cd.pytorch.learned_kernel.LearnedKernelDriftTorch.JHat, dataloaders: Tuple[torch.utils.data.dataloader.DataLoader, torch.utils.data.dataloader.DataLoader], device: torch.device, optimizer: Callable = <class 'torch.optim.adam.Adam'>, learning_rate: float = 0.001, preprocess_fn: Optional[Callable] = None, epochs: int = 20, reg_loss_fn: Callable = <function LearnedKernelDriftTorch.<lambda> at 0x28fde7e50>, verbose: int = 1) -> None

Train the kernel to maximise an estimate of test power using minibatch gradient descent.

Name	Type	Default	Description
j_hat	alibi_detect.cd.pytorch.learned_kernel.LearnedKernelDriftTorch.JHat
dataloaders	Tuple[torch.utils.data.dataloader.DataLoader, torch.utils.data.dataloader.DataLoader]
device	torch.device
optimizer	Callable	<class 'torch.optim.adam.Adam'>
learning_rate	float	0.001
preprocess_fn	Optional[Callable]	None
epochs	int	20
reg_loss_fn	Callable	<function LearnedKernelDriftTorch.<lambda> at 0x28fde7e50>
verbose	int	1

Returns

• Type: None