alibi_detect.cd.pytorch.classifier

ClassifierDriftTorch

Inherits from: BaseClassifierDrift, BaseDetector, ABC

Constructor

ClassifierDriftTorch(self, x_ref: Union[numpy.ndarray, list], model: 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, preds_type: str = 'probs', binarize_preds: bool = False, reg_loss_fn: Callable = <function ClassifierDriftTorch.<lambda> at 0x28fe6ed30>, 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	Union[numpy.ndarray, list]		Data used as reference distribution.
model	Union[torch.nn.modules.module.Module, torch.nn.modules.container.Sequential]		PyTorch classification model used for drift detection.
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 computing the data drift metrics.
preds_type	str	'probs'	Whether the model outputs 'probs' or 'logits'
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.
reg_loss_fn	Callable	<function ClassifierDriftTorch.<lambda> at 0x28fe6ed30>	The regularisation term reg_loss_fn(model) 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 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 predictions. 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

score

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

Compute the out-of-fold drift metric such as the accuracy from a classifier

trained to distinguish the reference data from the data to be tested.

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

Returns

• Type: Tuple[float, float, numpy.ndarray, numpy.ndarray, Union[numpy.ndarray, list], Union[numpy.ndarray, list]]