> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-explain/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/alibi-explain/api-reference/datasets/default.md). # alibi.datasets.default ## Constants ### `logger` ```python logger: logging.Logger = ``` 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` ```python 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` ```python 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` ```python 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` ```python 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: | 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` ```python 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` ```python 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` ```python 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` ```python 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]]` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.seldon.ai/alibi-explain/api-reference/datasets/default.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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