alibi 0.4.0

Algorithms for monitoring and explaining machine learning models

Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The initial focus on the library is on black-box, instance based model explanations.

• Documentation

If you're interested in outlier detection, concept drift or adversarial instance detection, check out our sister project alibi-detect.

Goals

• Provide high quality reference implementations of black-box ML model explanation and interpretation algorithms
• Define a consistent API for interpretable ML methods
• Support multiple use cases (e.g. tabular, text and image data classification, regression)

Installation

Alibi can be installed from PyPI:

pip install alibi

This will install alibi with all its dependencies:

  attrs
  beautifulsoup4
  numpy
  Pillow
  pandas
  prettyprinter
  requests
  scikit-learn
  scikit-image
  scipy
  shap
  spacy
  tensorflow

To run all the example notebooks, you may additionally run pip install alibi[examples] which will install the following:

  Keras
  seaborn
  xgboost

Supported algorithms

Model explanations

These algorithms provide instance-specific (sometimes also called local) explanations of ML model predictions. Given a single instance and a model prediction they aim to answer the question "Why did my model make this prediction?" The following algorithms all work with black-box models meaning that the only requirement is to have acces to a prediction function (which could be an API endpoint for a model in production).

The following table summarizes the capabilities of the current algorithms:

Explainer	Model types	Classification	Categorical data	Tabular	Text	Images	Need training set
Anchors	black-box	✔	✔	✔	✔	✔	For Tabular
CEM	black-box, TF/Keras	✔	✘	✔	✘	✔	Optional
Counterfactual Instances	black-box, TF/Keras	✔	✘	✔	✘	✔	No
Kernel SHAP	black-box	✔	✔	✔	✘	✘	✔
Prototype Counterfactuals	black-box, TF/Keras	✔	✔	✔	✘	✔	Optional

• Anchor explanations (Ribeiro et al., 2018)

  • Documentation
  • Examples: income prediction, Iris dataset, movie sentiment classification, ImageNet, fashion MNIST

• Contrastive Explanation Method (CEM, Dhurandhar et al., 2018)

  • Documentation
  • Examples: MNIST, Iris dataset

• Counterfactual Explanations (extension of Wachter et al., 2017)

  • Documentation
  • Examples: MNIST

• Kernel Shapley Additive Explanations (Lundberg et al., 2017)

  • Documentation
  • Examples: SVM with continuous data, multinomial logistic regression with continous data, handling categorical variables

• Counterfactual Explanations Guided by Prototypes (Van Looveren et al., 2019)

  • Documentation
  • Examples: MNIST, Boston housing dataset, Adult income (one-hot), Adult income (ordinal)

Model confidence metrics

These algorihtms provide instance-specific scores measuring the model confidence for making a particular prediction.

Algorithm	Model types	Classification	Regression	Categorical data	Tabular	Text	Images	Need training set
Trust Scores	black-box	✔	✘	✘	✔	✔(1)	✔(2)	Yes
Linearity Measure	black-box	✔	✔	✘	✔	✘	✔	Optional

(1) Depending on model

(2) May require dimensionality reduction

• Trust Scores (Jiang et al., 2018)
  • Documentation
  • Examples: MNIST, Iris dataset
• Linearity Measure
  • Examples: Iris dataset, fashion MNIST

Example outputs

Anchor method applied to the InceptionV3 model trained on ImageNet:

Prediction: Persian Cat	Anchor explanation

Contrastive Explanation method applied to a CNN trained on MNIST:

Prediction: 4	Pertinent Negative: 9	Pertinent Positive: 4

Trust scores applied to a softmax classifier trained on MNIST:

Citations

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

BibTeX entry:

@software{alibi,
  title = {Alibi: Algorithms for monitoring and explaining machine learning models},
  author = {Klaise, Janis and Van Looveren, Arnaud and Vacanti, Giovanni and Coca, Alexandru},
  url = {https://github.com/SeldonIO/alibi},
  version = {0.4.0},
  date = {2020-03-20},
}