Getting Started

Installation

Alibi Detect can be installed from PyPI or conda-forge by following the instructions below.

Install via PyPI

• Alibi Detect can be installed from PyPI with pip:

Standard

Installation with default TensorFlow backend.

pip install alibi-detect

PyTorch

Installation with TensorFlow and PyTorch backends.

pip install alibi-detect[torch]

Note

If you wish to use the GPU version of PyTorch, or are installing on Windows, it is recommended to install and test PyTorch prior to installing alibi-detect.

Prophet

Installation with the Prophet time series outlier detector enabled.

pip install alibi-detect[prophet]

Install via conda-forge

• To install the conda-forge version it is recommended to use mamba, which can be installed to the base conda enviroment with:

conda install mamba -n base -c conda-forge

• mamba can then be used to install alibi-detect in a conda enviroment:

Standard

Installation with default TensorFlow backend.

mamba install -c conda-forge alibi-detect

PyTorch

Installation with TensorFlow and PyTorch backends.

mamba install -c conda-forge alibi-detect pytorch

Note

If you wish to use the GPU version of PyTorch, or are installing on Windows, it is recommended to install and test PyTorch prior to installing alibi-detect.

Features

Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. Alibi Detect does however not install PyTorch for you. Check the PyTorch docs how to do this.

To get a list of respectively the latest outlier, adversarial and drift detection algorithms, you can type:

import alibi_detect
alibi_detect.od.__all__

['OutlierAEGMM',
 'IForest',
 'Mahalanobis',
 'OutlierAE',
 'OutlierVAE',
 'OutlierVAEGMM',
 'OutlierProphet',  # requires prophet: pip install alibi-detect[prophet]
 'OutlierSeq2Seq',
 'SpectralResidual',
 'LLR']

alibi_detect.ad.__all__

['AdversarialAE',
'ModelDistillation']

alibi_detect.cd.__all__

['ChiSquareDrift',
 'ClassifierDrift',
 'ClassifierUncertaintyDrift',
 'ContextMMDDrift',
 'CVMDrift',
 'FETDrift',
 'KSDrift',
 'LearnedKernelDrift',
 'LSDDDrift',
 'LSDDDriftOnline',
 'MMDDrift',
 'MMDDriftOnline',
 'RegressorUncertaintyDrift',
 'SpotTheDiffDrift',
 'TabularDrift']

Summary tables highlighting the practical use cases for all the algorithms can be found here.

For detailed information on the outlier detectors:

• Isolation Forest

• Mahalanobis Distance

• Auto-Encoder (AE)

• Variational Auto-Encoder (VAE)

• Auto-Encoding Gaussian Mixture Model (AEGMM)

• Variational Auto-Encoding Gaussian Mixture Model (VAEGMM)

• Likelihood Ratios

• Prophet Detector

• Spectral Residual Detector

• Sequence-to-Sequence (Seq2Seq) Detector

Similar for adversarial detection:

• Adversarial AE Detector

• Model Distillation Detector

And data drift:

• Chi-Squared Drift Detector

• Classifier Drift Detector

• Classifier and Regressor Uncertainty Drift Detectors

• Context-aware Drift Detector

• Cramér-von Mises Drift Detector

• Fisher’s Exact Test Drift Detector

• Kolmogorov-Smirnov Drift Detector

• Learned Kernel MMD Drift Detector

• Least-Squares Density Difference Drift Detector

• Online Least-Squares Density Difference Drift Detector

• Maximum Mean Discrepancy (MMD) Drift Detector

• Online Maximum Mean Discrepancy Drift Detector

• Spot-the-diff Drift Detector

• Mixed-type Tabular Data Drift Detector

Basic Usage

We will use the VAE outlier detector to illustrate the usage of outlier and adversarial detectors in alibi-detect.

First, we import the detector:

from alibi_detect.od import OutlierVAE

Then we initialize it by passing it the necessary arguments:

od = OutlierVAE(
    threshold=0.1,
    encoder_net=encoder_net,
    decoder_net=decoder_net,
    latent_dim=1024
)

Some detectors require an additional .fit step using training data:

od.fit(X_train)

The detectors can be saved or loaded as described in Saving and loading. Finally, we can make predictions on test data and detect outliers or adversarial examples.

preds = od.predict(X_test)

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 (and other relevant values). It has either is_outlier, is_adversarial or is_drift (filled with 0’s and 1’s) as well as optional instance_score, feature_score or p_value as keys with numpy arrays as values.

The exact details will vary slightly from method to method, so we encourage the reader to become familiar with the types of algorithms supported in alibi-detect.