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alibi_detect.cd.base_online

Constants

TYPE_CHECKING

TYPE_CHECKING: bool = False

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.

logger

logger: logging.Logger = <Logger alibi_detect.cd.base_online (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

BaseMultiDriftOnline

Inherits from: BaseDetector, StateMixin, ABC

Constructor

BaseMultiDriftOnline(self, x_ref: Union[numpy.ndarray, list], ert: float, window_size: int, preprocess_fn: Optional[Callable] = None, x_ref_preprocessed: bool = False, n_bootstraps: int = 1000, 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.

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

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.

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

get_threshold

get_threshold(t: int) -> float

Return the threshold for timestep t.

Name
Type
Default
Description

t

int

The timestep to return a threshold for.

Returns

  • Type: float

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 and threshold.

Returns

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

reset

reset() -> None

Deprecated reset method. This method will be repurposed or removed in the future. To reset the detector to

its initial state (t=0) use :meth:reset_state.

Returns

  • Type: None

reset_state

reset_state() -> None

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

Returns

  • Type: None

BaseUniDriftOnline

Inherits from: BaseDetector, StateMixin, ABC

Constructor

BaseUniDriftOnline(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 = 1000, 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)-1.

window_sizes

List[int]

The sizes of 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

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_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

get_threshold

get_threshold(t: int) -> numpy.ndarray

Return the threshold for timestep t.

Name
Type
Default
Description

t

int

The timestep to return a threshold for.

Returns

  • Type: numpy.ndarray

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(s) of data have 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(s).

return_test_stat

bool

True

Whether to return the test statistic and threshold.

Returns

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

reset

reset() -> None

Deprecated reset method. This method will be repurposed or removed in the future. To reset the detector to

its initial state (t=0) use :meth:reset_state.

Returns

  • Type: None

reset_state

reset_state() -> None

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

Returns

  • Type: None

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