alibi_detect.cd.tensorflow.lsdd module

class alibi_detect.cd.tensorflow.lsdd.LSDDDriftTF(x_ref, p_val=0.05, preprocess_x_ref=True, update_x_ref=None, preprocess_fn=None, sigma=None, n_permutations=100, n_kernel_centers=None, lambda_rd_max=0.2, input_shape=None, data_type=None)[source]

Bases: alibi_detect.cd.base.BaseLSDDDrift

__init__(x_ref, p_val=0.05, preprocess_x_ref=True, update_x_ref=None, preprocess_fn=None, sigma=None, n_permutations=100, n_kernel_centers=None, lambda_rd_max=0.2, input_shape=None, data_type=None)[source]

Least-squares density difference (LSDD) data drift detector using a permutation test.

Parameters
  • x_ref (Union[ndarray, list]) – Data used as reference distribution.

  • p_val (float) – p-value used for the significance of the permutation test.

  • preprocess_x_ref (bool) – Whether to already preprocess and store the reference data.

  • update_x_ref (Optional[Dict[str, int]]) – 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 (Optional[Callable]) – Function to preprocess the data before computing the data drift metrics.

  • sigma (Optional[ndarray]) – 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_permutations (int) – Number of permutations used in the permutation test.

  • n_kernel_centers (Optional[int]) – The number of reference samples to use as centers in the Gaussian kernel model used to estimate LSDD. Defaults to 1/20th of the reference data.

  • lambda_rd_max (float) – 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.

  • input_shape (Optional[tuple]) – Shape of input data.

  • data_type (Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.

Return type

None

score(x)[source]

Compute the p-value resulting from a permutation test using the least-squares density difference as a distance measure between the reference data and the data to be tested.

Parameters

x (Union[ndarray, list]) – Batch of instances.

Return type

Tuple[float, float, ndarray]

Returns

  • p-value obtained from the permutation test, the LSDD between the reference and test set

  • and the LSDD values from the permutation test.