alibi_detect.cd.utils

Constants

logger

logger: logging.Logger = <Logger alibi_detect.cd.utils (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.

Functions

encompass_batching

encompass_batching(model: Callable, backend: str, batch_size: int, device: Union[Literal[cuda, gpu, cpu], ForwardRef('torch.device'), None] = None, preprocess_batch_fn: Optional[Callable] = None, tokenizer: Optional[Callable] = None, max_len: Optional[int] = None) -> Callable

Takes a function that must be batch evaluated (on tokenized input) and returns a function

that handles batching (and tokenization).

Name	Type	Default	Description
model	Callable
backend	str
batch_size	int
device	Union[Literal[cuda, gpu, cpu], ForwardRef('torch.device'), None]	None
preprocess_batch_fn	Optional[Callable]	None
tokenizer	Optional[Callable]	None
max_len	Optional[int]	None

Returns

• Type: Callable

encompass_shuffling_and_batch_filling

encompass_shuffling_and_batch_filling(model_fn: Callable, batch_size: int) -> Callable

Takes a function that already handles batching but additionally performing shuffling

and ensures instances are evaluated as part of full batches.

Name	Type	Default	Description
model_fn	Callable
batch_size	int

Returns

• Type: Callable

get_input_shape

get_input_shape(shape: Optional[Tuple], x_ref: Union[numpy.ndarray, list]) -> Optional[Tuple]

Optionally infer shape from reference data.

Name	Type	Default	Description
shape	Optional[Tuple]
x_ref	Union[numpy.ndarray, list]

Returns

• Type: Optional[Tuple]

update_reference

update_reference(X_ref: numpy.ndarray, X: numpy.ndarray, n: int, update_method: Optional[Dict[str, int]] = None) -> numpy.ndarray

Update reference dataset for drift detectors.

Name	Type	Default	Description
X_ref	numpy.ndarray		Current reference dataset.
X	numpy.ndarray		New data.
n	int		Count of the total number of instances that have been used so far.
update_method	Optional[Dict[str, int]]	None	Dict with as key reservoir_sampling or last and as value n. reservoir_sampling will apply reservoir sampling with reservoir of size n while last will return (at most) the last n instances.

Returns

• Type: numpy.ndarray