Source code for

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

if has_pytorch:
    from import DataLoader
    from import LearnedKernelDriftTorch
    from import TorchDataset

if has_tensorflow:
    from import LearnedKernelDriftTF
    from import TFDataset

if has_keops:
    from import LearnedKernelDriftKeops

[docs] class LearnedKernelDrift(DriftConfigMixin):
[docs] @deprecated_alias(preprocess_x_ref='preprocess_at_init') def __init__( self, x_ref: Union[np.ndarray, list], kernel: Callable, backend: str = 'tensorflow', p_val: float = .05, x_ref_preprocessed: bool = False, preprocess_at_init: bool = True, update_x_ref: Optional[Dict[str, int]] = None, preprocess_fn: Optional[Callable] = None, n_permutations: int = 100, batch_size_permutations: int = 1000000, var_reg: float = 1e-5, reg_loss_fn: Callable = (lambda kernel: 0), train_size: Optional[float] = .75, retrain_from_scratch: bool = True, optimizer: Optional[Callable] = None, learning_rate: float = 1e-3, batch_size: int = 32, batch_size_predict: int = 32, preprocess_batch_fn: Optional[Callable] = None, epochs: int = 3, num_workers: int = 0, 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: """ Maximum Mean Discrepancy (MMD) data drift detector where the kernel is trained to maximise an estimate of the test power. The kernel is trained on a split of the reference and test instances and then the MMD is evaluated on held out instances and a permutation test is performed. For details see Liu et al (2020): Learning Deep Kernels for Non-Parametric Two-Sample Tests ( Parameters ---------- x_ref Data used as reference distribution. kernel Trainable PyTorch or TensorFlow module that returns a similarity between two instances. backend Backend used by the kernel and training loop. 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_at_init 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 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 Function to preprocess the data before applying the kernel. n_permutations The number of permutations to use in the permutation test once the MMD has been computed. batch_size_permutations KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations. Only relevant for 'keops' backend. var_reg Constant added to the estimated variance of the MMD for stability. reg_loss_fn The regularisation term reg_loss_fn(kernel) is added to the loss function being optimized. train_size Optional fraction (float between 0 and 1) of the dataset used to train the kernel. The drift is detected on `1 - train_size`. retrain_from_scratch Whether the kernel 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. optimizer Optimizer used during training of the kernel. learning_rate Learning rate used by optimizer. batch_size Batch size used during training of the kernel. batch_size_predict Batch size used for the trained drift detector predictions. preprocess_batch_fn Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the kernel. epochs Number of training epochs for the kernel. Corresponds to the smaller of the reference and test sets. num_workers Number of workers for the dataloader. The default (`num_workers=0`) means multi-process data loading is disabled. Setting `num_workers>0` may be unreliable on Windows. verbose Verbosity level during the training of the kernel. 0 is silent, 1 a progress bar. train_kwargs Optional additional kwargs when training the kernel. 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``. Relevant for 'pytorch' and 'keops' backends. dataset Dataset object used during training. dataloader Dataloader object used during training. Relevant for 'pytorch' and 'keops' backends. 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], Framework.KEOPS: [Framework.KEOPS]}, construct_name=self.__class__.__name__ ).verify_backend(backend) kwargs = locals() args = [kwargs['x_ref'], kwargs['kernel']] pop_kwargs = ['self', 'x_ref', 'kernel', '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', 'batch_size_permutations', 'num_workers'] [kwargs.pop(k, None) for k in pop_kwargs] if dataset is None: kwargs.update({'dataset': TFDataset}) detector = LearnedKernelDriftTF else: if dataset is None: kwargs.update({'dataset': TorchDataset}) if dataloader is None: kwargs.update({'dataloader': DataLoader}) if backend == Framework.PYTORCH: pop_kwargs = ['batch_size_permutations'] [kwargs.pop(k, None) for k in pop_kwargs] detector = LearnedKernelDriftTorch else: detector = LearnedKernelDriftKeops self._detector = detector(*args, **kwargs) self.meta = self._detector.meta
[docs] def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True, return_distance: bool = True, return_kernel: bool = True) \ -> Dict[Dict[str, str], Dict[str, Union[int, 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 permutation test. return_distance Whether to return the MMD metric between the new batch and reference data. return_kernel Whether to return the updated kernel 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 and optionally the p-value, threshold, MMD metric and \ trained kernel. """ return self._detector.predict(x, return_p_val, return_distance, return_kernel)