alibi.datasets.default

Constants

logger

logger: logging.Logger = <Logger alibi.datasets.default (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.

ADULT_URLS

ADULT_URLS: list = ['https://storage.googleapis.com/seldon-datasets/adult/adult.data', 'https://...

Built-in mutable sequence.

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

MOVIESENTIMENT_URLS

MOVIESENTIMENT_URLS: list = ['https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-pola...

Built-in mutable sequence.

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

IMAGENET_URLS

IMAGENET_URLS: list = ['https://storage.googleapis.com/seldon-datasets/imagenet10/imagenet10.tar.gz']

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

fetch_adult

fetch_adult(features_drop: Optional[list] = None, return_X_y: bool = False, url_id: int = 0) -> Union[alibi.utils.data.Bunch, Tuple[numpy.ndarray, numpy.ndarray]]

Downloads and pre-processes 'adult' dataset. More info: http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/

Name	Type	Default	Description
features_drop	Optional[list]	None	List of features to be dropped from dataset, by default drops ["fnlwgt", "Education-Num"].
return_X_y	bool	False	If True, return features X and labels y as numpy arrays. If False return a Bunch object.
url_id	int	0	Index specifying which URL to use for downloading.

Returns

• Type: Union[alibi.utils.data.Bunch, Tuple[numpy.ndarray, numpy.ndarray]]

fetch_imagenet

fetch_imagenet(category: str = 'Persian cat', nb_images: int = 10, target_size: tuple = (299, 299), min_std: float = 10.0, seed: int = 42, return_X_y: bool = False) -> None

Name	Type	Default	Description
category	str	'Persian cat'
nb_images	int	10
target_size	tuple	(299, 299)
min_std	float	10.0
seed	int	42
return_X_y	bool	False

Returns

• Type: None

fetch_imagenet_10

fetch_imagenet_10(url_id: int = 0) -> Dict

Sample dataset extracted from imagenet in a dictionary format. The train set contains 1000 random samples, 100 for each of the following 10 selected classes:

• stingray

• trilobite

• centipede

• slug

• snail

• Rhodesian ridgeback

• beagle

• golden retriever

• sea lion

• espresso

The test set contains 50 random samples, 5 for each of the classes above.

Name	Type	Default	Description
url_id	int	0	Index specifying which URL to use for downloading.

Returns

• Type: Dict

fetch_movie_sentiment

fetch_movie_sentiment(return_X_y: bool = False, url_id: int = 0) -> Union[alibi.utils.data.Bunch, Tuple[list, list]]

The movie review dataset, equally split between negative and positive reviews.

Name	Type	Default	Description
return_X_y	bool	False	If True, return features X and labels y as Python lists. If False return a Bunch object.
url_id	int	0	Index specifying which URL to use for downloading

Returns

• Type: Union[alibi.utils.data.Bunch, Tuple[list, list]]

load_cats

load_cats(target_size: tuple = (299, 299), return_X_y: bool = False) -> Union[alibi.utils.data.Bunch, Tuple[numpy.ndarray, numpy.ndarray]]

A small sample of Imagenet-like public domain images of cats used primarily for examples. The images were hand-collected using flickr.com by searching for various cat types, filtered by images in the public domain.

Name	Type	Default	Description
target_size	tuple	(299, 299)	Size of the returned images, used to crop images for a specified model input size.
return_X_y	bool	False	If True, return features X and labels y as numpy arrays. If False return a Bunch object

Returns

• Type: Union[alibi.utils.data.Bunch, Tuple[numpy.ndarray, numpy.ndarray]]