Computing Shapley values using VAEAC

Overview

Shapley values and the VAEAC method

In this GitHub repository, we present the implementation of the VAEAC approach from our paper "Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features", see Olsen et al. (2021).

The variational autoencoder with arbitrary condiditioning (VAEAC) approach is based on the work of (Ivanov et al., 2019). The VAEAC is an extension of the regular variational autoencoder (Kingma and Welling, 2019). Instead of giving a probabilistic representation for the distribution equation it gives a representation for the conditional distribution equation, for all possible feature subsets equation simultaneously, where equation is the set of all features.

To make the VAEAC methodology work in the Shapley value framework, established in the R-package Shapr (Sellereite and Jullum, 2019), we have made alterations to the original implementation of Ivanov.

The VAEAC model is implemented in Pytorch, hence, that portion of the repository is written in Python. To compute the Shapley values, we have written the necessary R-code to make the VAEAC approach run on top of the R-package shapr.

Setup

In addition to the prerequisites required by Ivanov, we also need several R-packages. All prerequisites are specified in requirements.txt.

This code was tested on Linux and macOS (should also work on Windows), Python 3.6.4, PyTorch 1.0. and R 4.0.2.

To user has to specify the system path to the Python environment and the system path of the downloaded repository in Source_Shapr_VAEAC.R.

Example

The following example shows how a random forest model is trained on the Abalone data set from the UCI machine learning repository, and how shapr explains the individual predictions.

Note that we only use Diameter (continuous), ShuckedWeight (continuous), and Sex (categorical) as features and let the response be Rings, that is, the age of the abalone.

# Import libraries
library(shapr)
library(ranger)
library(data.table)

# Load the R files needed for computing Shapley values using VAEAC.
source("/Users/larsolsen/Desktop/PhD/R_Codes/Source_Shapr_VAEAC.R")

# Set the working directory to be the root folder of the GitHub repository. 
setwd("~/PhD/Paper1/Code_for_GitHub")

# Read in the Abalone data set.
abalone = readRDS("data/Abalone.data")
str(abalone)

# Predict rings based on Diameter, ShuckedWeight, and Sex (categorical), using a random forrest model.
model = ranger(Rings ~ Diameter + ShuckedWeight + Sex, data = abalone[abalone$test_instance == FALSE,])

# Specifying the phi_0, i.e. the expected prediction without any features.
phi_0 <- mean(abalone$Rings[abalone$test_instance == FALSE])

# Prepare the data for explanation. Diameter, ShuckedWeight, and Sex correspond to 3,6,9.
explainer <- shapr(abalone[abalone$test_instance == FALSE, c(3,6,9)], model)
#> The specified model provides feature classes that are NA. The classes of data are taken as the truth.

# Train the VAEAC model with specified parameters and add it to the explainer
explainer_added_vaeac = add_vaeac_to_explainer(
  explainer, 
  epochs = 30L,
  width = 32L,
  depth = 3L,
  latent_dim = 8L,
  lr = 0.002,
  num_different_vaeac_initiate = 2L,
  epochs_initiation_phase = 2L,
  validation_iwae_num_samples = 25L,
  verbose_summary = TRUE)

# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using
# the VAEAC distribution approach with parameters defined above
explanation = explain.vaeac(abalone[abalone$test_instance == TRUE][1:8,c(3,6,9)],
                            approach = "vaeac",
                            explainer = explainer_added_vaeac,
                            prediction_zero = phi_0,
                            which_vaeac_model = "best")

# Printing the Shapley values for the test data.
# For more information about the interpretation of the values in the table, see ?shapr::explain.
print(explanation$dt)
#>        none   Diameter  ShuckedWeight        Sex
#> 1: 9.927152  0.63282471     0.4175608  0.4499676
#> 2: 9.927152 -0.79836795    -0.6419839  1.5737014
#> 3: 9.927152 -0.93500891    -1.1925897 -0.9140548
#> 4: 9.927152  0.57225851     0.5306906 -1.3036202
#> 5: 9.927152 -1.24280895    -1.1766845  1.2437640
#> 6: 9.927152 -0.77290507    -0.5976597  1.5194251
#> 7: 9.927152 -0.05275627     0.1306941 -1.1755597
#> 8: 9.927153  0.44593977     0.1788577  0.6895557

# Finally, we plot the resulting explanations.
plot(explanation, plot_phi0 = FALSE)

Citation

If you find this code useful in your research, please consider citing our paper:

@misc{Olsen2021Shapley,
      title={Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features}, 
      author={Lars Henry Berge Olsen and Ingrid Kristine Glad and Martin Jullum and Kjersti Aas},
      year={2021},
      eprint={2111.13507},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/2111.13507}
}

References

Ivanov, O., Figurnov, M., and Vetrov, D. (2019). “Variational Autoencoder with ArbitraryConditioning”. In:International Conference on Learning Representations.

Kingma, D. P. and Welling, M. (2014). "Auto-Encoding Variational Bayes". In: 2nd International Conference on Learning Representations, ICLR 2014.

Olsen, L. H. B., Glad, I. K., Jullum, M. and Aas, K. (2021). "Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features".

Sellereite, N. and Jullum, M. (2019). “shapr: An R-package for explaining machine learningmodels with dependence-aware Shapley values”. In:Journal of Open Source Softwarevol. 5,no. 46, p. 2027.

Turn based roguelike in python

pyTB Turn based roguelike in python Documentation can be found here: http://mcgillij.github.io/pyTB/index.html Screenshot Dependencies Written in Pyth

Jason McGillivray 4 Sep 29, 2022
This repository collects project-relevant Isabelle/HOL formalizations.

Isabelle/HOL formalizations related to the AuReLeE project Formalization of Abstract Argumentation Frameworks See AbstractArgumentation folder for the

AuReLeE project 1 Sep 10, 2022
Using LSTM to detect spoofing attacks in an Air-Ground network

Using LSTM to detect spoofing attacks in an Air-Ground network Specifications IDE: Spider Packages: Tensorflow 2.1.0 Keras NumPy Scikit-learn Matplotl

Tiep M. H. 1 Nov 20, 2021
Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV) Title FLAME (Fire Luminosity Airborne-b

79 Jan 06, 2023
Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face Manipulation" published in CVPR 2020.

FFD Source Code Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face M

88 Nov 22, 2022
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

NNAISENSE 56 Jan 01, 2023
TraSw for FairMOT - A Single-Target Attack example (Attack ID: 19; Screener ID: 24):

TraSw for FairMOT A Single-Target Attack example (Attack ID: 19; Screener ID: 24): Fig.1 Original Fig.2 Attacked By perturbing only two frames in this

Derry Lin 21 Dec 21, 2022
📖 Deep Attentional Guided Image Filtering

📖 Deep Attentional Guided Image Filtering [Paper] Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao ,Xiangyang Ji Harbin Institute of Technology,

9 Dec 23, 2022
Flask101 - FullStack Web Development with Python & JS - From TAQWA

Task: Create a CLI Calculator Step 0: Creating Virtual Environment $ python -m

Hossain Foysal 1 May 31, 2022
Pytorch implementation for "Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter".

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter This is a pytorch-based implementation for paper Implicit Feature Alignme

wangtianwei 61 Nov 12, 2022
Dynamic hair modeling from monocular videos using deep neural networks

Dynamic Hair Modeling The source code of the networks for our paper "Dynamic hair modeling from monocular videos using deep neural networks" (SIGGRAPH

53 Oct 18, 2022
Multi-label classification of retinal disorders

Multi-label classification of retinal disorders This is a deep learning course project. The goal is to develop a solution, using computer vision techn

Sundeep Bhimireddy 1 Jan 29, 2022
A medical imaging framework for Pytorch

Welcome to MedicalTorch MedicalTorch is an open-source framework for PyTorch, implementing an extensive set of loaders, pre-processors and datasets fo

Christian S. Perone 799 Jan 03, 2023
Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes

Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes [Paper] Method overview 4DMatch Benchmark 4DMatch is a benchmark for matc

103 Jan 06, 2023
NeRF visualization library under construction

NeRF visualization library using PlenOctrees, under construction pip install nerfvis Docs will be at: https://nerfvis.readthedocs.org import nerfvis s

Alex Yu 196 Jan 04, 2023
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

Benedek Rozemberczki 303 Dec 09, 2022
Weakly Supervised Text-to-SQL Parsing through Question Decomposition

Weakly Supervised Text-to-SQL Parsing through Question Decomposition The official repository for the paper "Weakly Supervised Text-to-SQL Parsing thro

14 Dec 19, 2022
Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Sidd Karamcheti 50 Nov 16, 2022
Convert ONNX model graph to Keras model format.

Convert ONNX model graph to Keras model format.

Grigory Malivenko 175 Dec 28, 2022
This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

TransUNet This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Usage

1.4k Jan 04, 2023