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xai4hep

XAI toolbox for interpreting state-of-the-art ML algorithms for high energy physics.

https://github.com/farakiko/xai4hep

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 3 DOI reference(s) in README
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    Academic publication links
    Links to: arxiv.org, zenodo.org
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    Scientific vocabulary similarity
    Low similarity (12.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

XAI toolbox for interpreting state-of-the-art ML algorithms for high energy physics.

Basic Info
  • Host: GitHub
  • Owner: farakiko
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 72.2 MB
Statistics
  • Stars: 10
  • Watchers: 1
  • Forks: 4
  • Open Issues: 0
  • Releases: 1
Created almost 4 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

DOI

xai4hep

Code for:

[1] Farouk Mokhtar et. al., Do graph neural networks learn traditional jet substructure?, ML4PS @ NeurIPS 2022 arXiv:2211.09912 \ [2] Farouk Mokhtar et. al., Explaining machine‑learned particle‑flow reconstruction, ML4PS @ NeurIPS 2021 arXiv:2111.12840

Overview

XAI toolbox for interpreting state-of-the-art ML algorithms for high energy physics.

xai4hep provides necessary implementation of explainable AI (XAI) techniques for state-of-the-art graph neural networks (GNNs) developed for various tasks at the CERN LHC. Current models include: machine-learned particle flow (MLPF), and ParticleNet. The layerwise-relevance propagation (LRP) technique is implemented for such models, and additional XAI techniques are under development.

Explaining ParticleNet using LRP will produce the following edge-R-graphs.

Trulli
Fig.1 - The jet constituents are represented as nodes in (eta, phi) space with interconnections as edges, whose intensities correspond to the connection's edge R score. Each node's intensity corresponds to the relative pT of the corresponding particle. Constituents belonging to the three different CA subjets are shown in blue, red, and green in descending pT order. We observe that by the last EdgeConv block the model learns to rely more on edge connections between the different subjets.


Explaining MLPF using LRP will produce the following R-maps.

Trulli
Fig.2 - This figure constitutes averaged R-maps for elements associated to charged hadrons (top), and neutral hadrons (bottom). We see that charged hadrons use more neighbor information than neutral hadrons.

Setup

We recommend using the requirements.txt file then installing xai4hep as a module by running pip install .

Other ways to setup,

  1. If you have access to the kubernetes PRP Nautlius cluster, then refer to this gitlab repo for the setup https://gitlab.nrp-nautilus.io/fmokhtar/xai4hep

  2. Using docker bash docker build docker/

Owner

  • Name: Farouk Mokhtar
  • Login: farakiko
  • Kind: user
  • Location: Geneva
  • Company: CERN

Particle physicist @CERN/UCSD interested in machine learning and statistics

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Mokhtar"
  given-names: "Farouk"
  orcid: "https://orcid.org/0000-0003-2533-3402"
- family-names: "Kansal"
  given-names: "Raghav"
  orcid: "https://orcid.org/0000-0003-2445-1060"
- family-names: "Duarte"
  given-names: "Javier"
  orcid: "https://orcid.org/0000-0002-5076-7096"
title: "xai4hep toolbox"
version: 1.0.0
doi: 10.5281/zenodo.7266537
date-released: 2022-10-31
url: "https://github.com/farakiko/xai4hep"

GitHub Events

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  • Fork event: 1
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  • Fork event: 1

Issues and Pull Requests

Last synced: 10 months ago

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  • Total issues: 0
  • Total pull requests: 42
  • Average time to close issues: N/A
  • Average time to close pull requests: about 8 hours
  • Total issue authors: 0
  • Total pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 42
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
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Top Authors
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  • farakiko (23)
  • jmduarte (1)
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Dependencies

docker/Dockerfile docker
  • gitlab-registry.nrp-nautilus.io/prp/jupyter-stack/prp latest build
docker/requirements.txt pypi
  • awkward *
  • boost_histogram *
  • click *
  • comet-ml *
  • fastjet *
  • keras *
  • keras-tuner *
  • matplotlib *
  • mplhep *
  • networkx *
  • nevergrad *
  • notebook *
  • onnxruntime *
  • pandas *
  • papermill *
  • pre-commit *
  • pyarrow *
  • ray ==1.6.0
  • scikit-optimize *
  • scipy *
  • seaborn *
  • setGPU *
  • sklearn *
  • tensorflow ==2.9
  • tensorflow-addons *
  • tensorflow-datasets *
  • tensorflow-estimator *
  • tensorflow-probability *
  • tensorflow-text *
  • tf-models-official *
  • tf2onnx *
  • tqdm *
  • uproot *
  • vector *
  • zenodo_get *
requirements.txt pypi
  • captum *
  • fastjet *
  • matplotlib *
  • mplhep *
  • numpy >=1.21.0
  • pandas *
  • torch >=1.8.0
  • torch-cluster *
  • torch_geometric *
  • tqdm *
setup.py pypi
  • captum *
  • fastjet *
  • matplotlib *
  • mplhep *
  • numpy >=1.21
  • pandas *
  • torch >=1.8
  • torch-cluster *
  • torch_geometric *
  • tqdm *