signxai2 0.11.0

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.

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}
}

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

Install from PyPI

pip install signxai2

Note: This installs the complete package with both TensorFlow and PyTorch support. Ensure you're using Python 3.9 or 3.10 before installation.

Install from source

Option 1: Full installation (both frameworks)

git clone https://github.com/IRISlaboratory/signxai2.git
cd signxai2
pip install -e .

Option 2: Framework-specific installation

For users who want to install only specific framework support:

TensorFlow only:

git clone https://github.com/IRISlaboratory/signxai2.git
cd signxai2
pip install -r requirements/common.txt -r requirements/tensorflow.txt
pip install -e . --no-deps

PyTorch only:

git clone https://github.com/IRISlaboratory/signxai2.git
cd signxai2
pip install -r requirements/common.txt -r requirements/pytorch.txt
pip install -e . --no-deps

Note: Framework-specific installation is only available when installing from source. The PyPI package includes both frameworks for seamless compatibility.

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
• 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

Development version

To install with development dependencies for testing and documentation:

pip install signxai2[dev]

Or from source:

git clone https://github.com/IRISlaboratory/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 examples cover various use cases, including images and time series analysis.

Methods

Method	Base	Parameters
gradient	Gradient
input_t_gradient	Gradient x Input
gradient_x_input	Gradient x Input
gradient_x_sign	Gradient x SIGN	mu = 0
gradient_x_sign_mu	Gradient x SIGN	requires mu parameter
gradient_x_sign_mu_0	Gradient x SIGN	mu = 0
gradient_x_sign_mu_0_5	Gradient x SIGN	mu = 0.5
gradient_x_sign_mu_neg_0_5	Gradient x SIGN	mu = -0.5
guided_backprop	Guided Backpropagation
guided_backprop_x_sign	Guided Backpropagation x SIGN	mu = 0
guided_backprop_x_sign_mu	Guided Backpropagation x SIGN	requires mu parameter
guided_backprop_x_sign_mu_0	Guided Backpropagation x SIGN	mu = 0
guided_backprop_x_sign_mu_0_5	Guided Backpropagation x SIGN	mu = 0.5
guided_backprop_x_sign_mu_neg_0_5	Guided Backpropagation x SIGN	mu = -0.5
integrated_gradients	Integrated Gradients
smoothgrad	SmoothGrad
smoothgrad_x_sign	SmoothGrad x SIGN	mu = 0
smoothgrad_x_sign_mu	SmoothGrad x SIGN	requires mu parameter
smoothgrad_x_sign_mu_0	SmoothGrad x SIGN	mu = 0
smoothgrad_x_sign_mu_0_5	SmoothGrad x SIGN	mu = 0.5
smoothgrad_x_sign_mu_neg_0_5	SmoothGrad x SIGN	mu = -0.5
vargrad	VarGrad
deconvnet	DeconvNet
deconvnet_x_sign	DeconvNet x SIGN	mu = 0
deconvnet_x_sign_mu	DeconvNet x SIGN	requires mu parameter
deconvnet_x_sign_mu_0	DeconvNet x SIGN	mu = 0
deconvnet_x_sign_mu_0_5	DeconvNet x SIGN	mu = 0.5
deconvnet_x_sign_mu_neg_0_5	DeconvNet x SIGN	mu = -0.5
grad_cam	Grad-CAM	requires last_conv parameter
grad_cam_timeseries	Grad-CAM	(for time series data), requires last_conv parameter
grad_cam_VGG16ILSVRC		last_conv based on VGG16
guided_grad_cam_VGG16ILSVRC		last_conv based on VGG16
lrp_z	LRP-z
lrpsign_z	LRP-z / LRP-SIGN (Inputlayer-Rule)
zblrp_z_VGG16ILSVRC	LRP-z / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet
w2lrp_z	LRP-z / LRP-w² (Inputlayer-Rule)
flatlrp_z	LRP-z / LRP-flat (Inputlayer-Rule)
lrp_epsilon_0_001	LRP-epsilon	epsilon = 0.001
lrpsign_epsilon_0_001	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.001
zblrp_epsilon_0_001_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.001
lrpz_epsilon_0_001	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.001
lrp_epsilon_0_01	LRP-epsilon	epsilon = 0.01
lrpsign_epsilon_0_01	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.01
zblrp_epsilon_0_01_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.01
lrpz_epsilon_0_01	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.01
w2lrp_epsilon_0_01	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 0.01
flatlrp_epsilon_0_01	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 0.01
lrp_epsilon_0_1	LRP-epsilon	epsilon = 0.1
lrpsign_epsilon_0_1	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.1
zblrp_epsilon_0_1_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.1
lrpz_epsilon_0_1	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.1
w2lrp_epsilon_0_1	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 0.1
flatlrp_epsilon_0_1	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 0.1
lrp_epsilon_0_2	LRP-epsilon	epsilon = 0.2
lrpsign_epsilon_0_2	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.2
zblrp_epsilon_0_2_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.2
lrpz_epsilon_0_2	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.2
lrp_epsilon_0_5	LRP-epsilon	epsilon = 0.5
lrpsign_epsilon_0_5	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.5
zblrp_epsilon_0_5_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.5
lrpz_epsilon_0_5	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.5
lrp_epsilon_1	LRP-epsilon	epsilon = 1
lrpsign_epsilon_1	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 1
zblrp_epsilon_1_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 1
lrpz_epsilon_1	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 1
w2lrp_epsilon_1	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 1
flatlrp_epsilon_1	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 1
lrp_epsilon_5	LRP-epsilon	epsilon = 5
lrpsign_epsilon_5	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 5
zblrp_epsilon_5_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 5
lrpz_epsilon_5	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 5
lrp_epsilon_10	LRP-epsilon	epsilon = 10
lrpsign_epsilon_10	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 10
zblrp_epsilon_10_VGG106ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 10
lrpz_epsilon_10	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 10
w2lrp_epsilon_10	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 10
flatlrp_epsilon_10	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 10
lrp_epsilon_20	LRP-epsilon	epsilon = 20
lrpsign_epsilon_20	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 20
zblrp_epsilon_20_VGG206ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 20
lrpz_epsilon_20	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 20
w2lrp_epsilon_20	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 20
flatlrp_epsilon_20	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 20
lrp_epsilon_50	LRP-epsilon	epsilon = 50
lrpsign_epsilon_50	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 50
lrpz_epsilon_50	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 50
lrp_epsilon_75	LRP-epsilon	epsilon = 75
lrpsign_epsilon_75	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 75
lrpz_epsilon_75	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 75
lrp_epsilon_100	LRP-epsilon	epsilon = 100
lrpsign_epsilon_100	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 100, mu = 0
lrpsign_epsilon_100_mu_0	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 100, mu = 0
lrpsign_epsilon_100_mu_0_5	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 100, mu = 0.5
lrpsign_epsilon_100_mu_neg_0_5	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 100, mu = -0.5
lrpz_epsilon_100	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 100
zblrp_epsilon_100_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 100
w2lrp_epsilon_100	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 100
flatlrp_epsilon_100	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 100
lrp_epsilon_0_1_std_x	LRP-epsilon	epsilon = 0.1 * std(x)
lrpsign_epsilon_0_1_std_x	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.1 * std(x)
lrpz_epsilon_0_1_std_x	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.1 * std(x)
zblrp_epsilon_0_1_std_x_VGG16ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.1 * std(x)
w2lrp_epsilon_0_1_std_x	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 0.1 * std(x)
flatlrp_epsilon_0_1_std_x	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 0.1 * std(x)
lrp_epsilon_0_25_std_x	LRP-epsilon	epsilon = 0.25 * std(x)
lrpsign_epsilon_0_25_std_x	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.25 * std(x), mu = 0
lrpz_epsilon_0_25_std_x	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.25 * std(x)
zblrp_epsilon_0_25_std_x_VGG256ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.25 * std(x)
w2lrp_epsilon_0_25_std_x	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 0.25 * std(x)
flatlrp_epsilon_0_25_std_x	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 0.25 * std(x)
lrpsign_epsilon_0_25_std_x_mu_0	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.25 * std(x), mu = 0
lrpsign_epsilon_0_25_std_x_mu_0_5	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.25 * std(x), mu = 0.5
lrpsign_epsilon_0_25_std_x_mu_neg_0_5	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.25 * std(x), mu = -0.5
lrp_epsilon_0_5_std_x	LRP-epsilon	epsilon = 0.5 * std(x)
lrpsign_epsilon_0_5_std_x	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 0.5 * std(x)
lrpz_epsilon_0_5_std_x	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 0.5 * std(x)
zblrp_epsilon_0_5_std_x_VGG56ILSVRC	LRP-epsilon / LRP-ZB (Inputlayer-Rule)	bounds based on ImageNet, epsilon = 0.5 * std(x)
w2lrp_epsilon_0_5_std_x	LRP-epsilon / LRP-w² (Inputlayer-Rule)	epsilon = 0.5 * std(x)
flatlrp_epsilon_0_5_std_x	LRP-epsilon / LRP-flat (Inputlayer-Rule)	epsilon = 0.5 * std(x)
lrp_epsilon_1_std_x	LRP-epsilon	epsilon = 1 * std(x)
lrpsign_epsilon_1_std_x	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 1 * std(x), mu = 0
lrpz_epsilon_1_std_x	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 1 * std(x)
lrp_epsilon_2_std_x	LRP-epsilon	epsilon = 2 * std(x)
lrpsign_epsilon_2_std_x	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 2 * std(x), mu = 0
lrpz_epsilon_2_std_x	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 2 * std(x)
lrp_epsilon_3_std_x	LRP-epsilon	epsilon = 3 * std(x)
lrpsign_epsilon_3_std_x	LRP-epsilon / LRP-SIGN (Inputlayer-Rule)	epsilon = 3 * std(x), mu = 0
lrpz_epsilon_3_std_x	LRP-epsilon / LRP-z (Inputlayer-Rule)	epsilon = 3 * std(x)
lrp_alpha_1_beta_0	LRP-alpha-beta	alpha = 1, beta = 0
lrpsign_alpha_1_beta_0	LRP-alpha-beta / LRP-SIGN (Inputlayer-Rule)	alpha = 1, beta = 0, mu = 0
lrpz_alpha_1_beta_0	LRP-alpha-beta / LRP-z (Inputlayer-Rule)	alpha = 1, beta = 0
zblrp_alpha_1_beta_0_VGG16ILSVRC		bounds based on ImageNet, alpha = 1, beta = 0
w2lrp_alpha_1_beta_0	LRP-alpha-beta / LRP-ZB (Inputlayer-Rule)	alpha = 1, beta = 0
flatlrp_alpha_1_beta_0	LRP-alpha-beta / LRP-flat (Inputlayer-Rule)	alpha = 1, beta = 0
lrp_sequential_composite_a	LRP Comosite Variant A
lrpsign_sequential_composite_a	LRP Comosite Variant A / LRP-SIGN (Inputlayer-Rule)	mu = 0
lrpz_sequential_composite_a	LRP Comosite Variant A / LRP-z (Inputlayer-Rule)
zblrp_sequential_composite_a_VGG16ILSVRC		bounds based on ImageNet
w2lrp_sequential_composite_a	LRP Comosite Variant A / LRP-ZB (Inputlayer-Rule)
flatlrp_sequential_composite_a	LRP Comosite Variant A / LRP-flat (Inputlayer-Rule)
lrp_sequential_composite_b	LRP Comosite Variant B
lrpsign_sequential_composite_b	LRP Comosite Variant B / LRP-SIGN (Inputlayer-Rule)	mu = 0
lrpz_sequential_composite_b	LRP Comosite Variant B / LRP-z (Inputlayer-Rule)
zblrp_sequential_composite_b_VGG16ILSVRC		bounds based on ImageNet
w2lrp_sequential_composite_b	LRP Comosite Variant B / LRP-ZB (Inputlayer-Rule)
flatlrp_sequential_composite_b	LRP Comosite Variant B / LRP-flat (Inputlayer-Rule)