alibi_detect.cd.fet_online

FETDriftOnline

Inherits from: BaseUniDriftOnline, BaseDetector, StateMixin, ABC, DriftConfigMixin

Constructor

FETDriftOnline(self, x_ref: Union[numpy.ndarray, list], ert: float, window_sizes: List[int], preprocess_fn: Optional[Callable] = None, x_ref_preprocessed: bool = False, n_bootstraps: int = 10000, t_max: Optional[int] = None, alternative: str = 'greater', lam: float = 0.99, n_features: Optional[int] = 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 univariate detectors, the ERT is defined as the expected run-time after the smallest window is full i.e. the run-time from t=min(windows_sizes).
window_sizes	List[int]		window sizes for the sliding test-windows used to compute the test-statistic. Smaller windows focus on responding quickly to severe drift, larger windows focus on ability to detect slight drift.
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.
n_bootstraps	int	10000	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.
t_max	Optional[int]	None	Length of the streams to simulate when configuring thresholds. If None, this is set to 2 * max(window_sizes) - 1.
alternative	str	'greater'	Defines the alternative hypothesis. Options are 'greater' or 'less', which correspond to an increase or decrease in the mean of the Bernoulli stream.
lam	float	0.99	Smoothing coefficient used for exponential moving average.
n_features	Optional[int]	None	Number of features used in the statistical test. No need to pass it if no preprocessing takes place. In case of a preprocessing step, this can also be inferred automatically but could be more expensive to compute.
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.

Methods

score

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

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

If a given test-window is not yet full then a test-statistic of np.nan is returned for that window.

Name	Type	Default	Description
x_t	Union[numpy.ndarray, typing.Any]		A single instance.

Returns

• Type: numpy.ndarray