Source code for

import numpy as np
from typing import Callable, Dict, Optional, Union
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow

if has_pytorch:
    from import MMDDriftOnlineTorch

if has_tensorflow:
    from import MMDDriftOnlineTF

[docs]class MMDDriftOnline:
[docs] def __init__( self, x_ref: Union[np.ndarray, list], ert: float, window_size: int, backend: str = 'tensorflow', preprocess_fn: Optional[Callable] = None, kernel: Callable = None, sigma: Optional[np.ndarray] = None, n_bootstraps: int = 1000, device: Optional[str] = None, verbose: bool = True, input_shape: Optional[tuple] = None, data_type: Optional[str] = None ) -> None: """ Online maximum Mean Discrepancy (MMD) data drift detector using preconfigured thresholds. Parameters ---------- x_ref Data used as reference distribution. ert The expected run-time (ERT) in the absence of drift. window_size The size of the sliding test-window used to compute the test-statistic. Smaller windows focus on responding quickly to severe drift, larger windows focus on ability to detect slight drift. backend Backend used for the MMD implementation and configuration. 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. If `sigma` is not specified, the 'median heuristic' is adopted whereby `sigma` is set as the median pairwise distance between reference samples. n_bootstraps The number of bootstrap simulations used to configure the thresholds. The larger this is the more accurately the desired ERT will be targeted. Should ideally be at least an order of magnitude larger than the ERT. device 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. verbose Whether or not to print progress during configuration. input_shape Shape of input data. data_type Optionally specify the data type (tabular, image or time-series). Added to metadata. """ super().__init__() backend = backend.lower() if backend == 'tensorflow' and not has_tensorflow or backend == 'pytorch' and not has_pytorch: raise ImportError(f'{backend} not installed. Cannot initialize and run the ' f'MMDDrift detector with {backend} backend.') elif backend not in ['tensorflow', 'pytorch']: raise NotImplementedError(f'{backend} not implemented. Use tensorflow or pytorch instead.') kwargs = locals() args = [kwargs['x_ref'], kwargs['ert'], kwargs['window_size']] pop_kwargs = ['self', 'x_ref', 'ert', 'window_size', 'backend', '__class__'] [kwargs.pop(k, None) for k in pop_kwargs] if kernel is None: if backend == 'tensorflow': from alibi_detect.utils.tensorflow.kernels import GaussianRBF else: from alibi_detect.utils.pytorch.kernels import GaussianRBF # type: ignore kwargs.update({'kernel': GaussianRBF}) if backend == 'tensorflow' and has_tensorflow: kwargs.pop('device', None) self._detector = MMDDriftOnlineTF(*args, **kwargs) # type: ignore else: self._detector = MMDDriftOnlineTorch(*args, **kwargs) # type: ignore self.meta = self._detector.meta
@property def t(self): return self._detector.t @property def test_stats(self): return self._detector.test_stats @property def thresholds(self): return [self._detector.thresholds[min(s, self._detector.window_size-1)] for s in range(self.t)]
[docs] def reset(self): "Resets the detector but does not reconfigure thresholds." self._detector.reset()
[docs] def predict(self, x_t: Union[np.ndarray, list], return_test_stat: bool = True) \ -> Dict[Dict[str, str], Dict[str, Union[int, float]]]: """ Predict whether the most recent window of data has drifted from the reference data. Parameters ---------- x_t A single instance to be added to the test-window. return_test_stat Whether to return the test statistic (squared MMD) and threshold. Returns ------- Dictionary containing 'meta' and 'data' dictionaries. 'meta' has the model's metadata. 'data' contains the drift prediction and optionally the test-statistic and threshold. """ return self._detector.predict(x_t, return_test_stat)