## Table of Contents
* [Installation and Usage](#installation-and-usage)
* [Supported Methods](#supported-methods)
* [Model Explanations](#model-explanations)
* [Model Confidence](#model-confidence)
* [Prototypes](#prototypes)
* [References and Examples](#references-and-examples)
* [Citations](#citations)
## Installation and Usage
Alibi can be installed from:
- PyPI or GitHub source (with `pip`)
- Anaconda (with `conda`/`mamba`)
### With pip
- Alibi can be installed from [PyPI](https://pypi.org/project/alibi):
```bash
pip install alibi
```
- Alternatively, the development version can be installed:
```bash
pip install git+https://github.com/SeldonIO/alibi.git
```
- To take advantage of distributed computation of explanations, install `alibi` with `ray`:
```bash
pip install alibi[ray]
```
- For SHAP support, install `alibi` as follows:
```bash
pip install alibi[shap]
```
### With conda
To install from [conda-forge](https://conda-forge.org/) it is recommended to use [mamba](https://mamba.readthedocs.io/en/stable/),
which can be installed to the *base* conda enviroment with:
```bash
conda install mamba -n base -c conda-forge
```
- For the standard Alibi install:
```bash
mamba install -c conda-forge alibi
```
- For distributed computing support:
```bash
mamba install -c conda-forge alibi ray
```
- For SHAP support:
```bash
mamba install -c conda-forge alibi shap
```
### Usage
The alibi explanation API takes inspiration from `scikit-learn`, consisting of distinct initialize,
fit and explain steps. We will use the [AnchorTabular](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)
explainer to illustrate the API:
```python
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](https://docs.seldon.io/projects/alibi/en/stable/overview/algorithms.html).
## Supported Methods
The following tables summarize the possible use cases for each method.
### Model Explanations
| Method | Models | Explanations | Classification | Regression | Tabular | Text | Images | Categorical features | Train set required | Distributed |
|:-------------------------------------------------------------------------------------------------------------|:------------:|:---------------------:|:--------------:|:----------:|:-------:|:----:|:------:|:--------------------:|:------------------:|:-----------:|
| [ALE](https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html) | BB | global | ✔ | ✔ | ✔ | | | | | |
| [Partial Dependence](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependence.html) | BB WB | global | ✔ | ✔ | ✔ | | | ✔ | | |
| [PD Variance](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependenceVariance.html) | BB WB | global | ✔ | ✔ | ✔ | | | ✔ | | |
| [Permutation Importance](https://docs.seldon.io/projects/alibi/en/stable/methods/PermutationImportance.html) | BB | global | ✔ | ✔ | ✔ | | | ✔ | | |
| [Anchors](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html) | BB | local | ✔ | | ✔ | ✔ | ✔ | ✔ | For Tabular | |
| [CEM](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html) | BB* TF/Keras | local | ✔ | | ✔ | | ✔ | | Optional | |
| [Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html) | BB* TF/Keras | local | ✔ | | ✔ | | ✔ | | No | |
| [Prototype Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html) | BB* TF/Keras | local | ✔ | | ✔ | | ✔ | ✔ | Optional | |
| [Counterfactuals with RL](https://docs.seldon.io/projects/alibi/en/stable/methods/CFRL.html) | BB | local | ✔ | | ✔ | | ✔ | ✔ | ✔ | |
| [Integrated Gradients](https://docs.seldon.io/projects/alibi/en/stable/methods/IntegratedGradients.html) | TF/Keras | local | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | Optional | |
| [Kernel SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html) | BB | local 
