Source code for alibi_detect.cd.lsdd_online

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 alibi_detect.cd.pytorch.lsdd_online import LSDDDriftOnlineTorch

if has_tensorflow:
    from alibi_detect.cd.tensorflow.lsdd_online import LSDDDriftOnlineTF


[docs]class LSDDDriftOnline:
[docs] def __init__( self, x_ref: Union[np.ndarray, list], ert: float, window_size: int, backend: str = 'tensorflow', preprocess_fn: Optional[Callable] = None, sigma: Optional[np.ndarray] = None, n_bootstraps: int = 1000, n_kernel_centers: Optional[int] = None, lambda_rd_max: float = 0.2, device: Optional[str] = None, verbose: bool = True, input_shape: Optional[tuple] = None, data_type: Optional[str] = None ) -> None: """ Online least squares density difference (LSDD) data drift detector using preconfigured thresholds. Motivated by Bu et al. (2017): https://ieeexplore.ieee.org/abstract/document/7890493 We have made modifications such that a desired ERT can be accurately targeted however. 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 LSDD implementation and configuration. preprocess_fn Function to preprocess the data before computing the data drift metrics.s sigma Optionally set the bandwidth of the Gaussian kernel used in estimating the LSDD. 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. n_kernel_centers The number of reference samples to use as centers in the Gaussian kernel model used to estimate LSDD. Defaults to 2*window_size. lambda_rd_max The maximum relative difference between two estimates of LSDD that the regularization parameter lambda is allowed to cause. Defaults to 0.2 as in the paper. 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 backend == 'tensorflow' and has_tensorflow: kwargs.pop('device', None) self._detector = LSDDDriftOnlineTF(*args, **kwargs) # type: ignore else: self._detector = LSDDDriftOnlineTorch(*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 (LSDD) 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)