alibi_detect.cd.lsdd_online

Constants

has_pytorch

has_pytorch: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_tensorflow

has_tensorflow: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

LSDDDriftOnline

Inherits from: DriftConfigMixin

Constructor

LSDDDriftOnline(self, x_ref: Union[numpy.ndarray, list], ert: float, window_size: int, backend: str = 'tensorflow', preprocess_fn: Optional[Callable] = None, x_ref_preprocessed: bool = False, sigma: Optional[numpy.ndarray] = None, n_bootstraps: int = 1000, n_kernel_centers: Optional[int] = None, lambda_rd_max: float = 0.2, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None, verbose: bool = True, input_shape: Optional[tuple] = None, data_type: Optional[str] = None) -> None

Name	Type	Default	Description
x_ref	Union[numpy.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	'tensorflow'	Backend used for the LSDD implementation and configuration.
preprocess_fn	Optional[Callable]	None	Function to preprocess the data before computing the data drift metrics.
x_ref_preprocessed	bool	False	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[numpy.ndarray]	None	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	1000	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]	None	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	0.2	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], ForwardRef('torch.device'), None]	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.
verbose	bool	True	Whether or not to print progress during configuration.
input_shape	Optional[tuple]	None	Shape of input data.
data_type	Optional[str]	None	Optionally specify the data type (tabular, image or time-series). Added to metadata.

Properties

Property	Type	Description
t	``
test_stats	``
thresholds	``

Methods

get_config

get_config() -> dict

Returns

• Type: dict

load_state

load_state(filepath: Union[str, os.PathLike])

Load the detector's state from disk, in order to restart from a checkpoint previously generated with

:meth:~save_state.

Name	Type	Default	Description
filepath	Union[str, os.PathLike]		The directory to load state from.

predict

predict(x_t: Union[numpy.ndarray, typing.Any], 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.

Name	Type	Default	Description
x_t	Union[numpy.ndarray, typing.Any]		A single instance to be added to the test-window.
return_test_stat	bool	True	Whether to return the test statistic (LSDD) and threshold.

Returns

• Type: Dict[Dict[str, str], Dict[str, Union[int, float]]]

reset_state

reset_state()

Resets the detector to its initial state (t=0). This does not include reconfiguring thresholds.

save_state

save_state(filepath: Union[str, os.PathLike])

Save a detector's state to disk in order to generate a checkpoint.

Name	Type	Default	Description
filepath	Union[str, os.PathLike]		The directory to save state to.

score

score(x_t: Union[numpy.ndarray, typing.Any]) -> float

Compute the test-statistic (LSDD) between the reference window and test window.

Name	Type	Default	Description
x_t	Union[numpy.ndarray, typing.Any]		A single instance to be added to the test-window.

Returns

• Type: float