Source code for

import logging
import numpy as np
from typing import Callable, Dict, Optional, Union, Tuple
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 MMDDriftTorch

if has_tensorflow:
    from import MMDDriftTF

if has_keops and has_pytorch:
    from import MMDDriftKeops

logger = logging.getLogger(__name__)

[docs]class MMDDrift(DriftConfigMixin):
[docs] @deprecated_alias(preprocess_x_ref='preprocess_at_init') def __init__( self, x_ref: Union[np.ndarray, list], 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, kernel: Callable = None, sigma: Optional[np.ndarray] = None, configure_kernel_from_x_ref: bool = True, n_permutations: int = 100, batch_size_permutations: int = 1000000, device: TorchDeviceType = None, input_shape: Optional[tuple] = None, data_type: Optional[str] = None ) -> None: """ Maximum Mean Discrepancy (MMD) data drift detector using a permutation test. Parameters ---------- x_ref Data used as reference distribution. backend Backend used for the MMD implementation. p_val p-value used for the significance of the permutation 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 computing the data drift metrics. kernel Kernel used for the MMD computation, defaults to Gaussian RBF kernel. sigma Optionally set the GaussianRBF kernel bandwidth. Can also pass multiple bandwidth values as an array. The kernel evaluation is then averaged over those bandwidths. configure_kernel_from_x_ref Whether to already configure the kernel bandwidth from the reference data. n_permutations Number of permutations used in the permutation test. batch_size_permutations KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations. Only relevant for 'keops' backend. 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. 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']] pop_kwargs = ['self', 'x_ref', 'backend', '__class__'] if backend == Framework.TENSORFLOW: pop_kwargs += ['device', 'batch_size_permutations'] detector = MMDDriftTF elif backend == Framework.PYTORCH: pop_kwargs += ['batch_size_permutations'] detector = MMDDriftTorch else: detector = MMDDriftKeops [kwargs.pop(k, None) for k in pop_kwargs] if kernel is None: if backend == Framework.TENSORFLOW: from alibi_detect.utils.tensorflow.kernels import GaussianRBF elif backend == Framework.PYTORCH: from alibi_detect.utils.pytorch.kernels import GaussianRBF # type: ignore else: from alibi_detect.utils.keops.kernels import GaussianRBF # type: ignore kwargs.update({'kernel': GaussianRBF}) 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) \ -> Dict[Dict[str, str], Dict[str, Union[int, float]]]: """ 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. Returns ------- Dictionary containing ``'meta'`` and ``'data'`` dictionaries. - ``'meta'`` has the model's metadata. - ``'data'`` contains the drift prediction and optionally the p-value, threshold and MMD metric. """ return self._detector.predict(x, return_p_val, return_distance)
[docs] def score(self, x: Union[np.ndarray, list]) -> Tuple[float, float, float]: """ Compute the p-value resulting from a permutation test using the maximum mean discrepancy as a distance measure between the reference data and the data to be tested. Parameters ---------- x Batch of instances. Returns ------- p-value obtained from the permutation test, the MMD^2 between the reference and test set, \ and the MMD^2 threshold above which drift is flagged. """ return self._detector.score(x)