alibi-detect 0.4.3

Algorithms for outlier detection, concept drift and metrics.

alibi-detect is an open source Python library focused on outlier, adversarial and concept drift detection. The package aims to cover both online and offline detectors for tabular data, images and time series. The outlier detection methods should allow the user to identify global, contextual and collective outliers.

• Documentation

Table of Contents

• Installation and Usage
• Supported Algorithms
  • Outlier Detection
  • Adversarial Detection
  • Drift Detection
  • Reference List
• Datasets
  • Sequential Data and Time Series
  • Images
  • Tabular
• Models
• Integrations
• Dependencies
• Citations

Installation and Usage

alibi-detect can be installed from PyPI:

pip install alibi-detect

We will use the VAE outlier detector to illustrate the API.

from alibi_detect.od import OutlierVAE
from alibi_detect.utils.saving import save_detector, load_detector

# initialize and fit detector
od = OutlierVAE(threshold=0.1, encoder_net=encoder_net, decoder_net=decoder_net, latent_dim=1024)
od.fit(X_train)

# make predictions
preds = od.predict(X_test)

# save and load detectors
filepath = './my_detector/'
save_detector(od, filepath)
od = load_detector(filepath)

The predictions are returned in a dictionary with as keys meta and data. meta contains the detector's metadata while data is in itself a dictionary with the actual predictions. It contains the outlier, adversarial or drift scores as well as the predictions whether instances are e.g. outliers or not. The exact details can vary slightly from method to method, so we encourage the reader to become familiar with the types of algorithms supported.

The save and load functionality for the Prophet time series outlier detector is currently experiencing issues in Python 3.6 but works in Python 3.7.

Supported Algorithms

The following tables show the advised use cases for each algorithm. The column Feature Level indicates whether the detection can be done at the feature level, e.g. per pixel for an image. Check the algorithm reference list for more information with links to the documentation and original papers as well as examples for each of the detectors.

Outlier Detection

Detector	Tabular	Image	Time Series	Text	Categorical Features	Online	Feature Level
Isolation Forest	✔	✘	✘	✘	✔	✘	✘
Mahalanobis Distance	✔	✘	✘	✘	✔	✔	✘
AE	✔	✔	✘	✘	✘	✘	✔
VAE	✔	✔	✘	✘	✘	✘	✔
AEGMM	✔	✔	✘	✘	✘	✘	✘
VAEGMM	✔	✔	✘	✘	✘	✘	✘
Likelihood Ratios	✔	✔	✔	✘	✔	✘	✔
Prophet	✘	✘	✔	✘	✘	✘	✘
Spectral Residual	✘	✘	✔	✘	✘	✔	✔
Seq2Seq	✘	✘	✔	✘	✘	✘	✔

Adversarial Detection

Detector	Tabular	Image	Time Series	Text	Categorical Features	Online	Feature Level
Adversarial AE	✔	✔	✘	✘	✘	✘	✘

Drift Detection

Detector	Tabular	Image	Time Series	Text	Categorical Features	Online	Feature Level
Kolmogorov-Smirnov	✔	✔	✘	✔	✔	✔	✔
Maximum Mean Discrepancy	✔	✔	✘	✔	✔	✘	✘

Reference List

Outlier Detection

• Isolation Forest (FT Liu et al., 2008)

  • Example: Network Intrusion

• Mahalanobis Distance (Mahalanobis, 1936)

  • Example: Network Intrusion

• Auto-Encoder (AE)

  • Example: CIFAR10

• Variational Auto-Encoder (VAE) (Kingma et al., 2013)

  • Examples: Network Intrusion, CIFAR10

• Auto-Encoding Gaussian Mixture Model (AEGMM) (Zong et al., 2018)

  • Example: Network Intrusion

• Variational Auto-Encoding Gaussian Mixture Model (VAEGMM)

  • Example: Network Intrusion

• Likelihood Ratios (Ren et al., 2019)

  • Examples: Genome, Fashion-MNIST vs. MNIST

• Prophet Time Series Outlier Detector (Taylor et al., 2018)

  • Example: Weather Forecast

• Spectral Residual Time Series Outlier Detector (Ren et al., 2019)

  • Example: Synthetic Dataset

• Sequence-to-Sequence (Seq2Seq) Outlier Detector (Sutskever et al., 2014; Park et al., 2017)

  • Examples: ECG, Synthetic Dataset

Adversarial Detection

• Adversarial Auto-Encoder (Vacanti and Van Looveren, 2020)
  • Example: CIFAR10

Drift Detection

• Kolmogorov-Smirnov

  • Example: CIFAR10, movie reviews

• Maximum Mean Discrepancy (Gretton et al, 2012)

  • Example: CIFAR10, movie reviews

Datasets

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.

Models

Models and/or building blocks that can be useful outside of outlier, adversarial or drift detection can be found under alibi_detect.models. Main implementations:

• PixelCNN++: alibi_detect.models.pixelcnn.PixelCNN

• Variational Autoencoder: alibi_detect.models.autoencoder.VAE

• Sequence-to-sequence model: alibi_detect.models.autoencoder.Seq2Seq

• ResNet: alibi_detect.models.resnet

  • Pre-trained ResNet-20/32/44 models on CIFAR-10 can be found on our Google Cloud Bucket and can be fetched as follows:

  from alibi_detect.utils.fetching import fetch_tf_model
  
  model = fetch_tf_model('cifar10', 'resnet32')
  

Integrations

The integrations folder contains various wrapper tools to allow the alibi-detect algorithms to be used in production machine learning systems with examples on how to deploy outlier and adversarial detectors with KFServing.

Dependencies

creme
dask[array]
matplotlib
numpy
pandas
opencv-python
Pillow
scipy
scikit-image
scikit-learn
tensorflow>=2.0.0
tensorflow_probability>=0.8
transformers>=2.10.0

Extra dependencies for OutlierProphet (install via pip install alibi-detect[prophet]):

fbprophet>=0.5,<0.7
holidays==0.9.11

Citations

If you use alibi-detect in your research, please consider citing it.

BibTeX entry:

@software{alibi-detect,
  title = {{Alibi-Detect}: Algorithms for outlier and adversarial instance detection, concept drift and metrics.},
  author = {Van Looveren, Arnaud and Vacanti, Giovanni and Klaise, Janis and Coca, Alexandru},
  url = {https://github.com/SeldonIO/alibi-detect},
  version = {0.4.3},
  date = {2020-10-08},
  year = {2019}
}