alibi_detect.cd.learned_kernel

Constants

has_pytorch

has_pytorch: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_tensorflow

has_tensorflow: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_keops

has_keops: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

LearnedKernelDrift

Inherits from: DriftConfigMixin

Constructor

LearnedKernelDrift(self, x_ref: Union[numpy.ndarray, list], kernel: Callable, backend: str = 'tensorflow', 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, batch_size_permutations: int = 1000000, var_reg: float = 1e-05, reg_loss_fn: Callable = <function LearnedKernelDrift.<lambda> at 0x28fe7ea60>, train_size: Optional[float] = 0.75, retrain_from_scratch: bool = True, optimizer: Optional[Callable] = None, 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: Optional[Callable] = None, dataloader: Optional[Callable] = None, 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	Callable		Trainable PyTorch or TensorFlow module that returns a similarity between two instances.
backend	str	'tensorflow'	Backend used by the kernel and training loop.
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.
batch_size_permutations	int	1000000	KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations. Only relevant for 'keops' backend.
var_reg	float	1e-05	Constant added to the estimated variance of the MMD for stability.
reg_loss_fn	Callable	<function LearnedKernelDrift.<lambda> at 0x28fe7ea60>	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	Optional[Callable]	None	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], ForwardRef('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. Relevant for 'pytorch' and 'keops' backends.
dataset	Optional[Callable]	None	Dataset object used during training.
dataloader	Optional[Callable]	None	Dataloader object used during training. Relevant for 'pytorch' and 'keops' backends.
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

predict

predict(x: Union[numpy.ndarray, list], return_p_val: bool = True, return_distance: bool = True, return_kernel: bool = True) -> Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]]

Predict whether a batch of data has drifted from the reference data.

Name	Type	Default	Description
x	Union[numpy.ndarray, list]		Batch of instances.
return_p_val	bool	True	Whether to return the p-value of the permutation test.
return_distance	bool	True	Whether to return the MMD metric between the new batch and reference data.
return_kernel	bool	True	Whether to return the updated kernel trained to discriminate reference and test instances.

Returns

• Type: Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]]