alibi_detect.cd.classifier module

class alibi_detect.cd.classifier.ClassifierDrift(x_ref, model, backend='tensorflow', p_val=0.05, preprocess_x_ref=True, update_x_ref=None, preprocess_fn=None, preds_type='probs', binarize_preds=False, reg_loss_fn=<function ClassifierDrift.<lambda>>, train_size=0.75, n_folds=None, retrain_from_scratch=True, seed=0, optimizer=None, learning_rate=0.001, batch_size=32, preprocess_batch_fn=None, epochs=3, verbose=0, train_kwargs=None, device=None, dataset=None, dataloader=None, data_type=None)[source]

Bases: object

__init__(x_ref, model, backend='tensorflow', p_val=0.05, preprocess_x_ref=True, update_x_ref=None, preprocess_fn=None, preds_type='probs', binarize_preds=False, reg_loss_fn=<function ClassifierDrift.<lambda>>, train_size=0.75, n_folds=None, retrain_from_scratch=True, seed=0, optimizer=None, learning_rate=0.001, batch_size=32, preprocess_batch_fn=None, epochs=3, verbose=0, train_kwargs=None, device=None, dataset=None, dataloader=None, data_type=None)[source]

Classifier-based drift detector. The classifier is trained on a fraction of the combined reference and test data and drift is detected on the remaining data. To use all the data to detect drift, a stratified cross-validation scheme can be chosen.

Parameters
  • x_ref (Union[ndarray, list]) – Data used as reference distribution.

  • model (Callable) – PyTorch or TensorFlow classification model used for drift detection.

  • backend (str) – Backend used for the training loop implementation.

  • p_val (float) – p-value used for the significance of the test.

  • preprocess_x_ref (bool) – Whether to already preprocess and store the reference data.

  • update_x_ref (Optional[Dict[str, int]]) – 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]) – Function to preprocess the data before computing the data drift metrics.

  • preds_type (str) – Whether the model outputs ‘probs’ or ‘logits’

  • binarize_preds (bool) – 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) – The regularisation term reg_loss_fn(model) is added to the loss function being optimized.

  • train_size (Optional[float]) – 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]) – 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) – 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) – Optional random seed for fold selection.

  • optimizer (Optional[Callable]) – Optimizer used during training of the classifier.

  • learning_rate (float) – Learning rate used by optimizer.

  • batch_size (int) – Batch size used during training of the classifier.

  • preprocess_batch_fn (Optional[Callable]) – Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the model.

  • epochs (int) – Number of training epochs for the classifier for each (optional) fold.

  • verbose (int) – Verbosity level during the training of the classifier. 0 is silent, 1 a progress bar.

  • train_kwargs (Optional[dict]) – Optional additional kwargs when fitting the classifier.

  • device (Optional[str]) – Device type used. The default None tries to use the GPU and falls back on CPU if needed. Can be specified by passing either ‘cuda’, ‘gpu’ or ‘cpu’. Only relevant for ‘pytorch’ backend.

  • dataset (Optional[Callable]) – Dataset object used during training.

  • dataloader (Optional[Callable]) – Dataloader object used during training. Only relevant for ‘pytorch’ backend.

  • data_type (Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.

Return type

None

predict(x, return_p_val=True, return_distance=True, return_probs=True, return_model=True)[source]

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

Parameters
  • x (Union[ndarray, list]) – Batch of instances.

  • return_p_val (bool) – Whether to return the p-value of the test.

  • return_distance (bool) – 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) – Whether to return the instance level classifier probabilities for the reference and test data (0=reference data, 1=test data).

  • return_model (bool) – Whether to return the updated model trained to discriminate reference and test instances.

Return type

Dict[str, Dict[str, Union[str, int, float, Callable]]]

Returns

  • Dictionary containing ‘meta’ and ‘data’ dictionaries.

  • ’meta’ has the model’s metadata.

  • ’data’ contains the drift prediction and optionally the p-value, performance of the classifier

  • relative to its expectation under the no-change null, the out-of-fold classifier model

  • prediction probabilities on the reference and test data, and the trained model.