alibi_detect.cd.pytorch.mmd module

class alibi_detect.cd.pytorch.mmd.MMDDriftTorch(x_ref, p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, kernel=<class 'alibi_detect.utils.pytorch.kernels.GaussianRBF'>, sigma=None, configure_kernel_from_x_ref=True, n_permutations=100, device=None, input_shape=None, data_type=None)[source]

Bases: BaseMMDDrift

__init__(x_ref, p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, kernel=<class 'alibi_detect.utils.pytorch.kernels.GaussianRBF'>, sigma=None, configure_kernel_from_x_ref=True, n_permutations=100, device=None, input_shape=None, data_type=None)[source]

Maximum Mean Discrepancy (MMD) 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.

  • x_ref_preprocessed (bool) – 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 (bool) – 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 (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.

  • kernel (Callable) – Kernel used for the MMD computation, defaults to Gaussian RBF kernel.

  • sigma (Optional[ndarray]) – 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 (bool) – Whether to already configure the kernel bandwidth from the reference data.

  • n_permutations (int) – Number of permutations used in the permutation test.

  • device (Union[Literal[‘cuda’, ‘gpu’, ‘cpu’], device, None]) – 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.

  • 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.

kernel_matrix(x, y)[source]

Compute and return full kernel matrix between arrays x and y.

Return type:

Tensor

score(x)[source]

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

Return type:

Tuple[float, float, float]

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.