Source code for

import numpy as np
from typing import Callable, Dict, Optional, Union
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

if has_pytorch:
    from import SpotTheDiffDriftTorch
    from import TorchDataset
    from import DataLoader

if has_tensorflow:
    from import SpotTheDiffDriftTF
    from import TFDataset

[docs]class SpotTheDiffDrift(DriftConfigMixin):
[docs] def __init__( self, x_ref: Union[np.ndarray, list], backend: str = 'tensorflow', p_val: float = .05, x_ref_preprocessed: bool = False, preprocess_fn: Optional[Callable] = None, kernel: Callable = None, n_diffs: int = 1, initial_diffs: Optional[np.ndarray] = None, l1_reg: float = 0.01, binarize_preds: bool = False, train_size: Optional[float] = .75, n_folds: Optional[int] = None, retrain_from_scratch: bool = True, seed: int = 0, optimizer: Optional[Callable] = None, learning_rate: float = 1e-3, batch_size: int = 32, preprocess_batch_fn: Optional[Callable] = None, epochs: int = 3, verbose: int = 0, train_kwargs: Optional[dict] = None, device: TorchDeviceType = None, dataset: Optional[Callable] = None, dataloader: Optional[Callable] = None, input_shape: Optional[tuple] = None, data_type: Optional[str] = None ) -> None: """ Classifier-based drift detector with a classifier of form y = a + b_1*k(x,w_1) + ... + b_J*k(x,w_J), where k is a kernel and w_1,...,w_J are learnable test locations. If drift has occured the test locations learn to be more/less (given by sign of b_i) similar to test instances than reference instances. The test locations are regularised to be close to the average reference instance such that the **difference** is then interpretable as the transformation required for each feature to make the average instance more/less like a test instance than a reference instance. 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 Data used as reference distribution. backend Backend used for the training loop implementation. p_val p-value used for the significance of the test. x_ref_preprocessed 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 Function to preprocess the data before computing the data drift metrics. kernel Kernel used to define similarity between instances, defaults to Gaussian RBF n_diffs The number of test locations to use, each corresponding to an interpretable difference. initial_diffs 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 Strength of l1 regularisation to apply to the differences. binarize_preds 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 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 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 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 Optional random seed for fold selection. optimizer Optimizer used during training of the classifier. learning_rate Learning rate used by optimizer. batch_size Batch size used during training of the classifier. preprocess_batch_fn Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the model. epochs Number of training epochs for the classifier for each (optional) fold. verbose Verbosity level during the training of the classifier. 0 is silent, 1 a progress bar. train_kwargs Optional additional kwargs when fitting the classifier. device 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``. Only relevant for 'pytorch' backend. dataset Dataset object used during training. dataloader Dataloader object used during training. Only relevant for 'pytorch' backend. input_shape Shape of input data. data_type Optionally specify the data type (tabular, image or time-series). Added to metadata. """ super().__init__() # Set config self._set_config(locals()) backend = backend.lower() BackendValidator( backend_options={Framework.TENSORFLOW: [Framework.TENSORFLOW], Framework.PYTORCH: [Framework.PYTORCH]}, construct_name=self.__class__.__name__ ).verify_backend(backend) kwargs = locals() args = [kwargs['x_ref']] pop_kwargs = ['self', 'x_ref', 'backend', '__class__'] if kwargs['optimizer'] is None: pop_kwargs += ['optimizer'] [kwargs.pop(k, None) for k in pop_kwargs] if backend == Framework.TENSORFLOW: pop_kwargs = ['device', 'dataloader'] [kwargs.pop(k, None) for k in pop_kwargs] if dataset is None: kwargs.update({'dataset': TFDataset}) self._detector = SpotTheDiffDriftTF(*args, **kwargs) else: if dataset is None: kwargs.update({'dataset': TorchDataset}) if dataloader is None: kwargs.update({'dataloader': DataLoader}) self._detector = SpotTheDiffDriftTorch(*args, **kwargs) # type: ignore self.meta = self._detector.meta
[docs] def predict( self, x: np.ndarray, return_p_val: bool = True, return_distance: bool = True, return_probs: bool = True, return_model: bool = True ) -> Dict[str, Dict[str, Union[int, str, float, Callable]]]: """ Predict whether a batch of data has drifted from the reference data. Parameters ---------- x Batch of instances. return_p_val Whether to return the p-value of the test. return_distance Whether to return a notion of strength of the drift. K-S test stat if binarize_preds=False, otherwise relative error reduction. return_probs Whether to return the instance level classifier probabilities for the reference and test data (0=reference data, 1=test data). return_model Whether to return the updated model trained to discriminate reference and test instances. Returns ------- Dictionary containing ``'meta'`` and ``'data'`` dictionaries. - ``'meta'`` has the detector's metadata. - ``'data'`` contains the drift prediction, the diffs used to distinguish reference from test instances, \ 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 as well as well as the associated reference and test instances of the out-of-fold predictions, \ and the trained model. """ return self._detector.predict(x, return_p_val, return_distance, return_probs, return_model)