alibi_detect.cd.sklearn.classifier module

class alibi_detect.cd.sklearn.classifier.ClassifierDriftSklearn(x_ref, model, p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, preds_type='probs', binarize_preds=False, train_size=0.75, n_folds=None, retrain_from_scratch=True, seed=0, use_calibration=False, calibration_kwargs=None, use_oob=False, input_shape=None, data_type=None)[source]

Bases: BaseClassifierDrift

__init__(x_ref, model, p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, preds_type='probs', binarize_preds=False, train_size=0.75, n_folds=None, retrain_from_scratch=True, seed=0, use_calibration=False, calibration_kwargs=None, use_oob=False, input_shape=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 (ndarray) – Data used as reference distribution.

  • model (ClassifierMixin) – Sklearn classification model used for drift detection.

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

  • x_ref_preprocessed (bool) – 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) – 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]]) – 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 ‘scores’.

  • binarize_preds (bool) – Whether to test for discrepancy on soft (e.g. probs/scores) model predictions directly with a K-S test or binarise to 0-1 prediction errors and apply a binomial test.

  • 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 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) – 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.

  • use_calibration (bool) – Whether to use calibration. Whether to use calibration. Calibration can be used on top of any model.

  • calibration_kwargs (Optional[dict]) – Optional additional kwargs for calibration. See https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html for more details.

  • use_oob (bool) – Whether to use out-of-bag(OOB) predictions. Supported only for RandomForestClassifier.

  • input_shape (Optional[tuple]) – Shape of input data.

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

score(x)[source]

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.

Parameters:

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

Return type:

Tuple[float, float, ndarray, ndarray, Union[ndarray, list], Union[ndarray, list]]

Returns:

p-value, a notion of distance between the trained classifier’s out-of-fold performance and that which we’d expect under the null assumption of no drift, and the out-of-fold classifier model prediction probabilities on the reference and test data as well as the associated reference and test instances of the out-of-fold predictions.