Datasets

source

Overview

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)

Sequential Data and Time Series

Genome Dataset: fetch_genome

• Bacteria genomics dataset for out-of-distribution detection, released as part of Likelihood Ratios for Out-of-Distribution Detection. From the original TL;DR: The dataset contains genomic sequences of 250 base pairs from 10 in-distribution bacteria classes for training, 60 OOD bacteria classes for validation, and another 60 different OOD bacteria classes for test. There are respectively 1, 7 and again 7 million sequences in the training, validation and test sets. For detailed info on the dataset check the README.

from alibi_detect.datasets import fetch_genome

(X_train, y_train), (X_val, y_val), (X_test, y_test) = fetch_genome(return_X_y=True)

ECG 5000: fetch_ecg

• 5000 ECG's, originally obtained from Physionet.

NAB: fetch_nab

• Any univariate time series in a DataFrame from the Numenta Anomaly Benchmark. A list with the available time series can be retrieved using alibi_detect.datasets.get_list_nab().

Images

CIFAR-10-C: fetch_cifar10c

• 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_cifar10c allows 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)

Adversarial CIFAR-10: fetch_attack

• Load adversarial instances on a ResNet-56 classifier trained on CIFAR-10. Available attacks: Carlini-Wagner ('cw') and SLIDE ('slide'). Example:

from alibi_detect.datasets import fetch_attack

(X_train, y_train), (X_test, y_test) = fetch_attack('cifar10', 'resnet56', 'cw', return_X_y=True)

Tabular

KDD Cup '99: fetch_kdd

• Dataset with different types of computer network intrusions. fetch_kdd allows you to select a subset of network intrusions as targets or pick only specified features. The original data can be found here.