Alibi Detect
  • Alibi Detect
  • Getting Started
  • Algorithm Overview
  • Saving and Loading
  • Detector Configuration Files
  • Roadmap
  • Outlier Detection
    • Methods
      • Mahalanobis Distance
      • Isolation Forest
      • Variational Auto-Encoder
      • Auto-Encoder
      • Variational Auto-Encoding Gaussian Mixture Model
      • Auto-Encoding Gaussian Mixture Model
      • Likelihood Ratios for Outlier Detection
      • Prophet Detector
      • Spectral Residual
      • Sequence-to-Sequence (Seq2Seq)
    • Examples
      • AE outlier detection on CIFAR10
      • AEGMM and VAEGMM outlier detection on KDD Cup ‘99 dataset
      • Isolation Forest outlier detection on KDD Cup ‘99 dataset
      • Likelihood Ratio Outlier Detection on Genomic Sequences
      • Likelihood Ratio Outlier Detection with PixelCNN++
      • Mahalanobis outlier detection on KDD Cup ‘99 dataset
      • Time-series outlier detection using Prophet on weather data
      • Seq2Seq time series outlier detection on ECG data
      • Time series outlier detection with Seq2Seq models on synthetic data
      • Time series outlier detection with Spectral Residuals on synthetic data
      • VAE outlier detection for income prediction
      • VAE outlier detection on CIFAR10
      • VAE outlier detection on KDD Cup ‘99 dataset
  • Drift Detection
    • Methods
      • Offline
        • Chi-Squared
        • Kolmogorov-Smirnov
        • Cramér-von Mises
        • Fisher’s Exact Test
        • Maximum Mean Discrepancy
        • Least-Squares Density Difference
        • Learned Kernel
        • Classifier
        • Spot-the-diff
        • Model Uncertainty
        • Mixed-type tabular data
        • Context-Aware Maximum Mean Discrepancy
      • Online
        • Online Maximum Mean Discrepancy
        • Online Least-Squares Density Difference
        • Online Cramér-von Mises
        • Online Fisher’s Exact Test
    • Examples
      • Categorical and mixed type data drift detection on income prediction
      • Learned drift detectors on Adult Census
      • Learned drift detectors on CIFAR-10
      • Context-aware drift detection on news articles
      • Context-aware drift detection on ECGs
      • Model Distillation drift detector on CIFAR-10
      • Kolmogorov-Smirnov data drift detector on CIFAR-10
      • Maximum Mean Discrepancy drift detector on CIFAR-10
      • Scaling up drift detection with KeOps
      • Model uncertainty based drift detection on CIFAR-10 and Wine-Quality datasets
      • Drift detection on molecular graphs
      • Online drift detection for Camelyon17 medical imaging dataset
      • Online Drift Detection on the Wine Quality Dataset
      • Interpretable drift detection with the spot-the-diff detector on MNIST and Wine-Quality datasets
      • Supervised drift detection on the penguins dataset
      • Drift detection on Amazon reviews
      • Text drift detection on IMDB movie reviews
  • Adversarial Detection
    • Methods
      • Adversarial Auto-Encoder
      • Model Distillation
    • Examples
      • Adversarial AE detection and correction on CIFAR-10
  • Deployment
  • Datasets
  • Models
  • Bibliography
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Datasets

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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.

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Last updated 5 months ago

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