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https://github.com/Telefonica/XAIoGraphs

XAIoGraphs (eXplainability Articicial Intelligence over Graphs) is an Explicability and Fairness Python library for classification problems with tabulated and discretized data.
https://github.com/Telefonica/XAIoGraphs

artificial-intelligence explainable-artificial-intelligence fairness-ai machine-learning

Last synced: 2 months ago
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XAIoGraphs (eXplainability Articicial Intelligence over Graphs) is an Explicability and Fairness Python library for classification problems with tabulated and discretized data.

Host: GitHub
URL: https://github.com/Telefonica/XAIoGraphs
Owner: Telefonica
License: agpl-3.0
Created: 2022-07-21T12:21:25.000Z (over 2 years ago)
Default Branch: master
Last Pushed: 2024-10-09T10:05:35.000Z (4 months ago)
Last Synced: 2024-10-09T12:44:13.628Z (4 months ago)
Topics: artificial-intelligence, explainable-artificial-intelligence, fairness-ai, machine-learning
Language: Python
Homepage: https://xaiographs.readthedocs.io/
Size: 35.6 MB
Stars: 2
Watchers: 8
Forks: 1
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

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AwesomeResponsibleAI - XAIoGraphs

README

        #   XAIoGraphs

![python versions](https://img.shields.io/badge/python-3.7%2C%203.8%2C%203.9-blue.svg)

[![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)

XAIoGraphs (e**X**plainability **A**rticicial **I**ntelligence **o**ver **Graphs**) is an Explicability and Fairness 

Python library for classification problems with tabulated and discretized data.

The explainability methods in this library don't make any hypotheses about the data, so it does not require the AI model.

Simply need data and predictions (or decisions), being able to explain AI models, rule models, and reality.



    



This library includes the following functionalities:

+ **Global Explainability**: Explains predictions or decisions as a whole, focusing on the variables that have the most influence.

+ **Local Explainability**: Explains the prediction of a single element.

+ **Reliability Measure** of local explainability.

+ **Reason Why:** *explanation in natural language* of the classification of each element.

+ **Fairness Scoring**: highlights potential discriminations in classifications based on sensitive features.

To understand or ***interpret the explanations*** uses ***XAIoWeb***, a ***web interface*** running in local mode (127.0.0.1:8080). 

It displays the explanations' outcomes in three sections: Global, Local and Fairness.



    



# 🚀 Quickstart 

## 🔨 Installation XAIoGraphs

Create a virtual environment using conda for easier management of dependencies and packages. 

For installing conda, follow the instructions on the [official conda website](https://docs.conda.io/projects/conda/en/latest/user-guide/install/)

```python

>> conda create --name xaio_env python=3.7

>> conda activate xaio_env

```

```{warning} 

Use a python version 3.7 or higher

```

**From PyPI repository** 

```python

>> pip install xaiographs

```

## 📝 Start with your first example

Use the following entry point to view an example run with the virtual environment enabled:

```python

>> titanic_example

```

Alternatively, you may run the code below to view a full implementation of all XAIoGraphs functionalities:

```python

from xaiographs import Explainer

from xaiographs import Why

from xaiographs import Fairness

from xaiographs.datasets import load_titanic_discretized, load_titanic_why

LANG = 'en'

# LOAD DATASETS & SEMANTICS

df_titanic, feature_cols, target_cols, y_true, y_predict = load_titanic_discretized()

df_values_semantics, df_target_values_semantics = load_titanic_why(language=LANG)

# EXPLAINER

explainer = Explainer(importance_engine='LIDE', verbose=1)

explainer.fit(df=df_titanic, feature_cols=feature_cols, target_cols=target_cols)

# WHY

why = Why(language=LANG,

          explainer=explainer,

          why_values_semantics=df_values_semantics,

          why_target_values_semantics=df_target_values_semantics,

          verbose=1)

why.fit()

# FAIRNESS

f = Fairness(verbose=1)

f.fit(df=df_titanic[feature_cols + [y_true] + [y_predict]],

      sensitive_cols=['gender', 'class', 'age'],

      target_col=y_true,

      predict_col=y_predict)

```

Following execution, a folder called "xaioweb files" is created, which contains a set of '.json' files that will 

be used to present the results in the XAIoWeb graphical interface.

## 📊 Launching XAIoWeb

XAIoWeb is a local web interface that displays the outcomes of the explanations in three sections: Global, Local, 

and Fairness. To launch the web (with the virtual environment enabled), run the following entry point:

```python

>> xaioweb -d xaioweb_files -o

```

This entry point takes the following parameters:

- `-d` o `--data` [REQUIRED]: JSON files path

- `-p` o `--port` [OPTIONAL]: Web server port. 8080 by default

- `-o` o `--open` [OPTIONAL]: Open web in browser

- `-f` o `--force` [OPTIONAL]: Force building the web from scratch, overwriting the existing one

# 🤝 Contributors

XAIoGraphs has been developed by ***Applied AI & Privacy*** team (Telefónica Innovación Digital - Chief Data Officer)

* [Ricardo Moya](https://www.linkedin.com/in/phdricardomoya/)

* [Matteo Salvatori](https://github.com/matteo-salvatori)

* [Enrique Fernandez](https://github.com/QuiqueFdez)

* [Alejandro Manuel Arranz](https://github.com/cx02747)

* [Manuel Martín](https://github.com/mmarmar)

* [Mario Villaizan](https://github.com/mvvmvv)

* [Cesar García](https://github.com/cesarggtid)

* [David Cadenas](https://github.com/davidcadi)

* [Alejandra Maria Alonso](https://www.linkedin.com/in/alejandraalonsodiaz/)

* [Miguel Angel Martín](https://github.com/mamj-telefonica)

* [Oriol Arnau](https://github.com/oarnau)

# 📥  Contact

Contact with [@Ricardo Moya](https://www.linkedin.com/in/phdricardomoya/)