signxai2 0.14.1

A comprehensive explainable AI library supporting both TensorFlow and PyTorch with unified API and advanced XAI methods including SIGN, LRP, and Grad-CAM. Authored by Nils Gumpfer, Jana Fischer and Alexander Paul.

SignXAI2

Project Description

SIGNed explanations: Unveiling relevant features by reducing bias

This repository and python package is an extended version of the published python package of the following journal article: https://doi.org/10.1016/j.inffus.2023.101883

If you use the code from this repository in your work, please cite:

@article{Gumpfer2023SIGN,
    title = {SIGNed explanations: Unveiling relevant features by reducing bias},
    author = {Nils Gumpfer and Joshua Prim and Till Keller and Bernhard Seeger and Michael Guckert and Jennifer Hannig},
    journal = {Information Fusion},
    pages = {101883},
    year = {2023},
    issn = {1566-2535},
    doi = {https://doi.org/10.1016/j.inffus.2023.101883},
    url = {https://www.sciencedirect.com/science/article/pii/S1566253523001999}
}

Documentation

The documentation for SignXAI2 is available at: https://TimeXAIgroup.github.io/signxai2/index.html

Requirements

• Python 3.9 or 3.10 (Python 3.11+ is not supported)
• TensorFlow >=2.8.0,<=2.12.1
• PyTorch >=1.10.0
• NumPy, Matplotlib, SciPy

🚀 Installation

SignXAI2 requires you to explicitly choose which deep learning framework(s) to install. This ensures you only install what you need.

Install from PyPI

For TensorFlow users:

pip install signxai2[tensorflow]

For PyTorch users:

pip install signxai2[pytorch]

For both frameworks:

pip install signxai2[all]

For development (includes all frameworks + dev tools):

pip install signxai2[dev]

Note: Installing pip install signxai2 alone is not supported. You must specify at least one framework.

Install from source

git clone https://github.com/TimeXAIgroup/signxai2.git
cd signxai2

# Choose your installation:
pip install -e .[tensorflow]    # TensorFlow only
pip install -e .[pytorch]       # PyTorch only  
pip install -e .[all]           # Both frameworks
pip install -e .[dev]           # Development (all frameworks + tools)

Setup of Git LFS

Before you get started please set up Git LFS to download the large files in this repository. This is required to access the pre-trained models and example data.

git lfs install

📦 Load Data and Documentation

After installation, run the setup script to download documentation, examples, and sample data:

bash ./prepare.sh

This will download:

• 📚 Full documentation (viewable at docs/index.html)
• 📝 Example scripts and notebooks (examples/)
• 📊 Sample ECG data and images (examples/data/)

Examples

To get started with SignXAI2 Methods, please follow the example tutorials ('examples/tutorials/').

Features

• Support for TensorFlow and PyTorch models
• Consistent API across frameworks with dynamic method parsing
• Wide range of explanation methods:
  • Gradient-based: Vanilla gradient, Integrated gradients, SmoothGrad
  • Class activation maps: Grad-CAM
  • Guided backpropagation
  • Layer-wise Relevance Propagation (LRP)
  • Sign-based thresholding for binary relevance maps
• No wrapper classes - direct method calls with parameters embedded in method names

Development version

To install with development dependencies for testing and documentation:

pip install signxai2[dev]

Or from source:

git clone https://github.com/TimeXAIgroup/signxai2.git
cd signxai2
pip install -e ".[dev]"

Project Structure

• signxai/: Main package with unified API and framework detection
• signxai/tf_signxai/: TensorFlow implementation using modified iNNvestigate
• signxai/torch_signxai/: PyTorch implementation using zennit with custom hooks
• examples/tutorials/: Tutorials for both frameworks covering images and time series
• examples/comparison/: Implementation for reproducing results from the paper
• utils/: Helper scripts for model conversion (tf -> torch) and data preprocessing

Usage

Please follow the example tutorials in the examples/tutorials/ directory to get started with SignXAI2 methods. The new API uses dynamic method parsing where parameters are embedded directly in method names:

from signxai import explain

# Basic gradient
explanation = explain(model, x, method_name="gradient")

# Complex method with parameter chaining
explanation = explain(model, x, method_name="gradient_x_input_x_sign_mu_neg_0_5")

License

BSD 3-Clause License - See LICENSE file for details.