alibi_detect.cd.mmd module

class alibi_detect.cd.mmd.MMDDrift(x_ref, backend='tensorflow', p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, kernel=None, sigma=None, configure_kernel_from_x_ref=True, n_permutations=100, batch_size_permutations=1000000, device=None, input_shape=None, data_type=None)[source]

Bases: DriftConfigMixin

__init__(x_ref, backend='tensorflow', p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, kernel=None, sigma=None, configure_kernel_from_x_ref=True, n_permutations=100, batch_size_permutations=1000000, 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.

  • backend (str) – Backend used for the MMD implementation.

  • 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.

  • batch_size_permutations (int) – KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations. Only relevant for ‘keops’ backend.

  • device (Union[Literal['cuda', 'gpu', 'cpu'], torch.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. Only relevant for ‘pytorch’ backend.

  • 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.

predict(x, return_p_val=True, return_distance=True)[source]

Predict whether a batch of data has drifted from the reference data.

Parameters:

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

  • return_p_val (bool) – Whether to return the p-value of the permutation test.

  • return_distance (bool) – Whether to return the MMD metric between the new batch and reference data.

Return type:

Dict[Dict[str, str], Dict[str, Union[int, float]]]

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.

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.