This page was generated from doc/source/datasets/overview.ipynb.
The package also contains functionality in
alibi_detect.datasets to easily fetch a number of datasets for different modalities. For each dataset either the data and labels or a Bunch object with the data, labels and optional metadata are returned. Example:
from alibi_detect.datasets import fetch_ecg (X_train, y_train), (X_test, y_test) = fetch_ecg(return_X_y=True)
5000 ECG’s, originally obtained from Physionet.
Any univariate time series in a DataFrame from the Numenta Anomaly Benchmark. A list with the available time series can be retrieved using
CIFAR-10-C (Hendrycks & Dietterich, 2019) contains the test set of CIFAR-10, but corrupted and perturbed by various types of noise, blur, brightness etc. at different levels of severity, leading to a gradual decline in a classification model’s performance trained on CIFAR-10.
fetch_cifar10callows you to pick any severity level or corruption type. The list with available corruption types can be retrieved with
alibi_detect.datasets.corruption_types_cifar10c(). The dataset can be used in research on robustness and drift. The original data can be found here. Example:
from alibi_detect.datasets import fetch_cifar10c corruption = ['gaussian_noise', 'motion_blur', 'brightness', 'pixelate'] X, y = fetch_cifar10c(corruption=corruption, severity=5, return_X_y=True)
from alibi_detect.datasets import fetch_attack (X_train, y_train), (X_test, y_test) = fetch_attack('cifar10', 'resnet56', 'cw', return_X_y=True)