Getting Started

Installation

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

PyPI

Alibi Detect can be installed from PyPI with pip. We provide optional dependency buckets for several modules that are large or sometimes tricky to install. Many detectors are supported out of the box with the default install but some detectors require a specific optional dependency installation to use. For instance, the OutlierProphet detector requires the prophet installation. Other detectors have a choice of backend. For instance, the LSDDDrift detector has a choice of tensorflow or pytorch backends. The tabs below list the full set of detector functionality provided by each optional dependency.

pip install alibi-detect

pip install alibi-detect[tensorflow,prophet]

pip install alibi-detect[torch,prophet]

pip install alibi-detect[all]

pip install alibi-detect[torch]

pip install alibi-detect[tensorflow]

pip install alibi-detect[keops]

pip install alibi-detect[prophet]

conda-forge

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

conda install mamba -n base -c conda-forge

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

mamba install -c conda-forge 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. TensorFlow, PyTorch and (where applicable) KeOps backends are supported for drift detection. Alibi-Detect does not install these as default. See installation options for more details.

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

import alibi_detect
# View all the Outlier Detection (od) algorithms available
alibi_detect.od.__all__

['OutlierAEGMM',
 'IForest',
 'Mahalanobis',
 'OutlierAE',
 'OutlierVAE',
 'OutlierVAEGMM',
 'OutlierProphet',
 'OutlierSeq2Seq',
 'SpectralResidual',
 'LLR']

# View all the Adversarial Detection (ad) algorithms available
alibi_detect.ad.__all__

['AdversarialAE',
'ModelDistillation']

# View all the Concept Drift (cd) detection algorithms available
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.