alibi_detect.cd.pytorch.spot_the_diff

Constants

logger

logger: logging.Logger = <Logger alibi_detect.cd.pytorch.spot_the_diff (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

SpotTheDiffDriftTorch

Constructor

SpotTheDiffDriftTorch(self, x_ref: numpy.ndarray, p_val: float = 0.05, x_ref_preprocessed: bool = False, preprocess_fn: Optional[Callable] = None, kernel: Optional[torch.nn.modules.module.Module] = None, n_diffs: int = 1, initial_diffs: Optional[numpy.ndarray] = None, l1_reg: float = 0.01, binarize_preds: bool = False, train_size: Optional[float] = 0.75, n_folds: Optional[int] = None, retrain_from_scratch: bool = True, seed: int = 0, optimizer: Callable = <class 'torch.optim.adam.Adam'>, learning_rate: float = 0.001, batch_size: int = 32, preprocess_batch_fn: Optional[Callable] = None, epochs: int = 3, 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	numpy.ndarray		Data used as reference distribution.
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_fn	Optional[Callable]	None	Function to preprocess the data before computing the data drift metrics.
kernel	Optional[torch.nn.modules.module.Module]	None	Differentiable Pytorch model used to define similarity between instances, defaults to Gaussian RBF.
n_diffs	int	1	The number of test locations to use, each corresponding to an interpretable difference.
initial_diffs	Optional[numpy.ndarray]	None	Array used to initialise the diffs that will be learned. Defaults to Gaussian for each feature with equal variance to that of reference data.
l1_reg	float	0.01	Strength of l1 regularisation to apply to the differences.
binarize_preds	bool	False	Whether to test for discrepency on soft (e.g. probs/logits) model predictions directly with a K-S test or binarise to 0-1 prediction errors and apply a binomial test.
train_size	Optional[float]	0.75	Optional fraction (float between 0 and 1) of the dataset used to train the classifier. The drift is detected on 1 - train_size. Cannot be used in combination with n_folds.
n_folds	Optional[int]	None	Optional number of stratified folds used for training. The model preds are then calculated on all the out-of-fold instances. This allows to leverage all the reference and test data for drift detection at the expense of longer computation. If both train_size and n_folds are specified, n_folds is prioritized.
retrain_from_scratch	bool	True	Whether the classifier 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.
seed	int	0	Optional random seed for fold selection.
optimizer	Callable	<class 'torch.optim.adam.Adam'>	Optimizer used during training of the classifier.
learning_rate	float	0.001	Learning rate used by optimizer.
batch_size	int	32	Batch size used during training of the classifier.
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 model.
epochs	int	3	Number of training epochs for the classifier for each (optional) fold.
verbose	int	0	Verbosity level during the training of the classifier. 0 is silent, 1 a progress bar.
train_kwargs	Optional[dict]	None	Optional additional kwargs when fitting the classifier.
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.
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.
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: numpy.ndarray, return_p_val: bool = True, return_distance: bool = True, return_probs: bool = True, return_model: bool = False) -> Dict[str, Dict[str, Union[str, int, float, Callable]]]

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

Name	Type	Default	Description
x	numpy.ndarray		Batch of instances.
return_p_val	bool	True	Whether to return the p-value of the test.
return_distance	bool	True	Whether to return a notion of strength of the drift. K-S test stat if binarize_preds=False, otherwise relative error reduction.
return_probs	bool	True	Whether to return the instance level classifier probabilities for the reference and test data (0=reference data, 1=test data).
return_model	bool	False	Whether to return the updated model trained to discriminate reference and test instances.

Returns

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