alibi_detect.cd.lsdd_online module
- class alibi_detect.cd.lsdd_online.LSDDDriftOnline(x_ref, ert, window_size, backend='tensorflow', preprocess_fn=None, x_ref_preprocessed=False, sigma=None, n_bootstraps=1000, n_kernel_centers=None, lambda_rd_max=0.2, device=None, verbose=True, input_shape=None, data_type=None)[source]
Bases:
DriftConfigMixin- __init__(x_ref, ert, window_size, backend='tensorflow', preprocess_fn=None, x_ref_preprocessed=False, sigma=None, n_bootstraps=1000, n_kernel_centers=None, lambda_rd_max=0.2, device=None, verbose=True, input_shape=None, data_type=None)[source]
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 (
Union[ndarray,list]) – Data used as reference distribution.ert (
float) – The expected run-time (ERT) in the absence of drift. For the multivariate detectors, the ERT is defined as the expected run-time from t=0.window_size (
int) – 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 (
str) – Backend used for the LSDD implementation and configuration.preprocess_fn (
Optional[Callable]) – Function to preprocess the data before computing the data drift metrics.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.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_bootstraps (
int) – 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 (
Optional[int]) – 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 (
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.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 oftorch.device. Only relevant for ‘pytorch’ backend.verbose (
bool) – Whether or not to print progress during configuration.data_type (
Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.
- get_config()[source]
Get the detector’s configuration dictionary.
- Return type:
- Returns:
The detector’s configuration dictionary.
- load_state(filepath)[source]
Load the detector’s state from disk, in order to restart from a checkpoint previously generated with
save_state().
- predict(x_t, return_test_stat=True)[source]
Predict whether the most recent window of data has drifted from the reference data.
- Parameters:
- Return type:
- 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.
- reset_state()[source]
Resets the detector to its initial state (t=0). This does not include reconfiguring thresholds.
- property t
- property test_stats
- property thresholds