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Alibi Detect is a 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.

Documentation

For more background on the importance of monitoring outliers and distributions in a production setting, check out this talk from the Challenges in Deploying and Monitoring Machine Learning Systems ICML 2020 workshop, based on the paper Monitoring and explainability of models in production and referencing Alibi Detect.

For a thorough introduction to drift detection, check out Protecting Your Machine Learning Against Drift: An Introduction. The talk covers what drift is and why it pays to detect it, the different types of drift, how it can be detected in a principled manner and also describes the anatomy of a drift detector.

Installation and Usage
Supported Algorithms
Datasets
Models
Integrations
Citations

Installation and Usage

The package, alibi-detect can be installed from:

PyPI or GitHub source (with pip)
Anaconda (with conda/mamba)

With pip

alibi-detect can be installed from PyPI:
```
pip install alibi-detect
```

Alternatively, the development version can be installed:

pip install git+https://github.com/SeldonIO/alibi-detect.git

To install with the TensorFlow backend:
```
pip install alibi-detect[tensorflow]
```
To install with the PyTorch backend:
```
pip install alibi-detect[torch]
```
To install with the KeOps backend:
```
pip install alibi-detect[keops]
```
To use the Prophet time series outlier detector:
```
pip install alibi-detect[prophet]
```

With conda

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

conda install mamba -n base -c conda-forge

To install alibi-detect:

mamba install -c conda-forge alibi-detect

Usage

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

from alibi_detect.od import OutlierVAE
from alibi_detect.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 and thresholds 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.

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	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	✔	✔
Model distillation	✔	✔	✔	✔	✔

Drift Detection

Detector	Tabular	Image	Time Series	Text	Categorical Features	Online	Feature Level
Kolmogorov-Smirnov	✔	✔		✔	✔		✔
Cramér-von Mises	✔	✔				✔	✔
Fisher's Exact Test	✔				✔	✔	✔
Maximum Mean Discrepancy (MMD)	✔	✔		✔	✔	✔
Learned Kernel MMD	✔	✔		✔	✔
Context-aware MMD	✔	✔	✔	✔	✔
Least-Squares Density Difference	✔	✔		✔	✔	✔
Chi-Squared	✔				✔		✔
Mixed-type tabular data	✔				✔		✔
Classifier	✔	✔	✔	✔	✔
Spot-the-diff	✔	✔	✔	✔	✔		✔
Classifier Uncertainty	✔	✔	✔	✔	✔
Regressor Uncertainty	✔	✔	✔	✔	✔

TensorFlow and PyTorch support

The drift detectors support TensorFlow, PyTorch and (where applicable) KeOps backends. However, Alibi Detect does not install these by default. See the installation options for more details.

from alibi_detect.cd import MMDDrift

cd = MMDDrift(x_ref, backend='tensorflow', p_val=.05)
preds = cd.predict(x)

The same detector in PyTorch:

cd = MMDDrift(x_ref, backend='pytorch', p_val=.05)
preds = cd.predict(x)

Or in KeOps:

cd = MMDDrift(x_ref, backend='keops', p_val=.05)
preds = cd.predict(x)

Built-in preprocessing steps

Alibi Detect also comes with various preprocessing steps such as randomly initialized encoders, pretrained text embeddings to detect drift on using the transformers library and extraction of hidden layers from machine learning models. This allows to detect different types of drift such as covariate and predicted distribution shift. The preprocessing steps are again supported in TensorFlow and PyTorch.

from alibi_detect.cd.tensorflow import HiddenOutput, preprocess_drift

model = # TensorFlow model; tf.keras.Model or tf.keras.Sequential
preprocess_fn = partial(preprocess_drift, model=HiddenOutput(model, layer=-1), batch_size=128)
cd = MMDDrift(x_ref, backend='tensorflow', p_val=.05, preprocess_fn=preprocess_fn)
preds = cd.predict(x)

Check the example notebooks (e.g. CIFAR10, movie reviews) for more details.

Reference List

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

Alibi-detect is integrated in the machine learning model deployment platform Seldon Core and model serving framework KFServing.

Seldon Core: outlier and drift detection worked examples.
KFServing: outlier and drift detection examples.

Citations

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

BibTeX entry:

@software{alibi-detect,
  title = {Alibi Detect: Algorithms for outlier, adversarial and drift detection},
  author = {Van Looveren, Arnaud and Klaise, Janis and Vacanti, Giovanni and Cobb, Oliver and Scillitoe, Ashley and Samoilescu, Robert and Athorne, Alex},
  url = {https://github.com/SeldonIO/alibi-detect},
  version = {0.12.1.dev0},
  date = {2024-04-17},
  year = {2019}
}

Awesome

Table of Contents

Installation and Usage

With pip

With conda

Usage

Supported Algorithms

Outlier Detection

Adversarial Detection

Drift Detection

TensorFlow and PyTorch support

Built-in preprocessing steps

Reference List

Outlier Detection

Adversarial Detection

Drift Detection

Datasets

Sequential Data and Time Series

Images

Tabular

Models

Integrations

Citations