alibi_detect.saving.loading

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.

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.

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.

supported_models_tf

supported_models_tf: tuple = (<class 'keras.src.models.model.Model'>,)

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.

If the argument is a tuple, the return value is the same object.

supported_models_torch

supported_models_torch: tuple = (<class 'torch.nn.modules.module.Module'>,)

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.

If the argument is a tuple, the return value is the same object.

STATE_PATH

STATE_PATH: str = 'state/'

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to 'strict'.

logger

logger: logging.Logger = <Logger alibi_detect.saving.loading (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.

FIELDS_TO_RESOLVE

FIELDS_TO_RESOLVE: list = [['preprocess_fn', 'src'], ['preprocess_fn', 'model'], ['preprocess_fn', 'emb...

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

FIELDS_TO_DTYPE

FIELDS_TO_DTYPE: list = [['preprocess_fn', 'dtype']]

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

Functions

load_detector

load_detector(filepath: Union[str, os.PathLike], enable_unsafe_loading: bool = False, kwargs) -> Union[alibi_detect.base.Detector, alibi_detect.base.ConfigurableDetector]

Load outlier, drift or adversarial detector.

Name	Type	Default	Description
filepath	Union[str, os.PathLike]		Load directory.
enable_unsafe_loading	bool	False	Sets allow_pickle=True when a np.ndarray is loaded from a .npy file referenced in the detector config. Needed if you have to load objects. Only applied if the filepath is config.toml or a directory containing a config.toml. It has security implications: https://nvd.nist.gov/vuln/detail/cve-2019-6446.

Returns

• Type: Union[alibi_detect.base.Detector, alibi_detect.base.ConfigurableDetector]

read_config

read_config(filepath: Union[os.PathLike, str]) -> dict

This function reads a detector toml config file and returns a dict specifying the detector.

Name	Type	Default	Description
filepath	Union[os.PathLike, str]		The filepath to the config.toml file.

Returns

• Type: dict

resolve_config

resolve_config(cfg: dict, config_dir: Optional[pathlib.Path], enable_unsafe_loading: bool = False) -> dict

Resolves artefacts in a config dict. For example x_ref='x_ref.npy' is resolved by loading the np.ndarray from

the .npy file. For a list of fields that are resolved, see https://docs.seldon.io/projects/alibi-detect/en/stable/overview/config_file.html.

Name	Type	Default	Description
cfg	dict		The unresolved config dict.
config_dir	Optional[pathlib.Path]		Filepath to directory the config.toml is located in. Only required if different from the runtime directory, and artefacts are specified with filepaths relative to the config.toml file.
enable_unsafe_loading	bool	False	If set to true, allow_pickle=True is set in np.load(). Needed if you have to load objects. It has security implications: https://nvd.nist.gov/vuln/detail/cve-2019-6446

Returns

• Type: dict