signxai2 0.6.0

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}
}

🚀 Installation as Python Package via Pypi [Dual Framework Support]

pip install signxai2

Installation after cloning the repository [Dual Framework Support]

Navigate to the repository directory and run:

pip install -e .

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

PyTorch only

The library now includes a PyTorch implementation based on zennit. To use the PyTorch implementation only, you'll need to install:

pip install signxai2[pytorch]

TensorFlow only

To use the TensorFlow implementation only, you'll need to install:

pip install signxai2[tensorflow]

Development version can be installed with:

To reproduce all results from the paper, you can install the development version of SignXAI2 with:

pip install signxai2[dev]

After running the Dual Setup first.

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)