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Showing content from https://github.com/SeldonIO/alibi below:

SeldonIO/alibi: Algorithms for explaining machine learning models

Alibi is a source-available Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.

Documentation

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

Alibi can be installed from:

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

Alibi can be installed from PyPI:
Alternatively, the development version can be installed:
```
pip install git+https://github.com/SeldonIO/alibi.git 
```
To take advantage of distributed computation of explanations, install alibi with ray:
For SHAP support, install alibi as follows:

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

For the standard Alibi install:
```
mamba install -c conda-forge alibi
```
For distributed computing support:
```
mamba install -c conda-forge alibi ray
```
For SHAP support:
```
mamba install -c conda-forge alibi shap
```

The alibi explanation API takes inspiration from scikit-learn, consisting of distinct initialize, fit and explain steps. We will use the AnchorTabular explainer to illustrate the API:

from alibi.explainers import AnchorTabular

# initialize and fit explainer by passing a prediction function and any other required arguments
explainer = AnchorTabular(predict_fn, feature_names=feature_names, category_map=category_map)
explainer.fit(X_train)

# explain an instance
explanation = explainer.explain(x)

The explanation returned is an Explanation object with attributes meta and data. meta is a dictionary containing the explainer metadata and any hyperparameters and data is a dictionary containing everything related to the computed explanation. For example, for the Anchor algorithm the explanation can be accessed via explanation.data['anchor'] (or explanation.anchor). The exact details of available fields varies from method to method so we encourage the reader to become familiar with the types of methods supported.

The following tables summarize the possible use cases for each method.

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

Method Models Classification Regression Tabular Text Images Categorical Features Train set required Trust Scores BB ✔ ✔ ✔(1) ✔(2) Yes Linearity Measure BB ✔ ✔ ✔ ✔ Optional

Key:

BB - black-box (only require a prediction function)
BB* - black-box but assume model is differentiable
WB - requires white-box model access. There may be limitations on models supported
TF/Keras - TensorFlow models via the Keras API
Local - instance specific explanation, why was this prediction made?
Global - explains the model with respect to a set of instances
(1) - depending on model
(2) - may require dimensionality reduction

These algorithms provide a distilled view of the dataset and help construct a 1-KNN interpretable classifier.

Method Classification Regression Tabular Text Images Categorical Features Train set labels ProtoSelect ✔ ✔ ✔ ✔ ✔ Optional

Accumulated Local Effects (ALE, Apley and Zhu, 2016)
- Documentation
- Examples: California housing dataset, Iris dataset
Partial Dependence (J.H. Friedman, 2001)
- Documentation
- Examples: Bike rental
Partial Dependence Variance(Greenwell et al., 2018)
- Documentation
- Examples: Friedman’s regression problem
Permutation Importance(Breiman, 2001; Fisher et al., 2018)
- Documentation
- Examples: Who's Going to Leave Next?
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
Counterfactual Explanations Guided by Prototypes (Van Looveren and Klaise, 2019)
- Documentation
- Examples: MNIST, California housing dataset, Adult income (one-hot), Adult income (ordinal)
Model-agnostic Counterfactual Explanations via RL(Samoilescu et al., 2021)
- Documentation
- Examples: MNIST, Adult income
Integrated Gradients (Sundararajan et al., 2017)
- Documentation,
- Examples: MNIST example, Imagenet example, IMDB example.
Kernel Shapley Additive Explanations (Lundberg et al., 2017)
- Documentation
- Examples: SVM with continuous data, multinomial logistic regression with continous data, handling categorical variables
Tree Shapley Additive Explanations (Lundberg et al., 2020)
- Documentation
- Examples: Interventional (adult income, xgboost), Path-dependent (adult income, xgboost)
Trust Scores (Jiang et al., 2018)
- Documentation
- Examples: MNIST, Iris dataset
Linearity Measure
- Documentation
- Examples: Iris dataset, fashion MNIST
ProtoSelect
- Documentation
- Examples: Adult Census & CIFAR10
Similarity explanations
- Documentation
- Examples: 20 news groups dataset, ImageNet dataset, MNIST dataset

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

BibTeX entry:

@article{JMLR:v22:21-0017,
  author  = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
  title   = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {181},
  pages   = {1-7},
  url     = {http://jmlr.org/papers/v22/21-0017.html}
}

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