Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

DOI

On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics

This repo is designed to support the paper "On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics", Strong 2020 Mach. Learn.: Sci. Technol.https://doi.org/10.1088/2632-2153/ab983a (Preprint: arXiv:2002.01427 [physics.data-an]). It contains code to rerun the experiments performed in the paper to reproduce the results, allow users to better understand how each method is used, and provide a baseline for future comparisons.

Installation

Get code & data

  1. git clone https://github.com/GilesStrong/HiggsML_Lumin.git
  2. cd HiggsML_Lumin
  3. mkdir data
  4. wget -O data/atlas-higgs-challenge-2014-v2.csv.gz http://opendata.cern.ch/record/328/files/atlas-higgs-challenge-2014-v2.csv.gz
  5. gunzip data/atlas-higgs-challenge-2014-v2.csv.gz

Install requirements

Via PIP

  • pip install -r requirements.txt

Via Conda environment

  1. conda env create -f environment.yml
  2. conda activate higgsml_lumin

Running

The experiments are run using Jupyter Notebooks, which can be accessed by running:

  • jupyter notebook

In the browser window which should have opened, navigate to the notebooks directory. Here there are several directories and four notebooks. Each directory is associated with a different experiment and contains all the notebooks relevant to that particular experiment.

Running experiments

Each directory contains a single notebook which can be duplicated to run multiple times and save the results. Experiment 13 (13_swish_ensemble_embed_aug_onecycle_dense), which was the final model used for the paper, contains an example of this where the same experiment was run six different times on six different computing setups.

Rerunning of the experiments also uses different random seeds for splitting of the validation data, as described in the paper. This is achieved by configuring the third cell in the notebooks, which contains experiment = Experiment(NAME, 'mbp', RESULTS_PATH). Where NAME is the basic of the experiment, e.g. '13_swish_ensemble_embed_aug_onecycle_dense', and 'mbp' is the name of the computing setup. These names are used to lookup particular settings in the Experiment class, defined in ./modules/basics.py, where each machine is assigned its own random seed, as well as a description which is used later for annotating plots and results. When Experiment.save() is called, the results are written to ./results/{experiment name}_{machine name}.json'.

Users should edit Experiment in ./modules/basics.py to include their own machines and names. Each notebook is designed to be run top-to-bottom, except for those in 17_hyperparam_search which will be discussed later.

Comparing results

notebooks/Results_Viewing.ipynb takes experiment results from ./results and compares average performance between configurations. The variable BLIND determines whether the private AMS results should be shown to the user. By default this is True to attempt to preserve challenge conditions. It is recommended to only set this to False once you are happy with your model configuration.

Results are loaded using the Result class located in ./modules/basics.py`, which loads up the results and computes mean values for the metrics, and also has functions for comparing configurations and producing plots.

The git repo currently ships with single results for each experiment, except for the final model (experiment 13) where six example results are available. In order to reproduce the results of the paper, one should run each experiment several more times, to get average results.

Hyper-parameter scan

The last experiment (17) is the hyper-parameter scan used to try to find a better architecture. The notebook included when run will sample parameters and train an ensemble of three networks. This repeats 30 times. Results are saved between each iteration and past results are automatically loaded. This notebook should ideally be run on several machines simultaneously, which may need restarting from time to time according to memory requirements.

Once sufficient results have been collected, notebooks/17_hyperparam_search/Analysis.ipynb can be used to analyse them, fit the Gaussian processes, and discover promising new architectures. It is left to the user to configure new experiments to run these new architectures, if they so wish.

Misc. notebooks

  • notebooks/Results_Viewing.ipynb contains code to plot out and view features from the data
  • notebooks/Feature_Selection.ipynb runs the feature selection tests used for the paper
  • notebooks/Feature_Selection-Full.ipynb runs a more advanced set of feature selection tests which were not used for the paper, but might of interest to the reader

Paper results

The code in this repo is a refactoring of the original experimental framework, since the software library, LUMIN, evolved alongside this study, not all of the original versions of the experiment are fully compatible with the latest version. The notebooks included, however, have been updated to run with v0.5.1 of LUMIIN. The actual results presented in the paper, however, are included in the directory ./paper_results/results and are viewable using ./paper_results/Results_Viewing.ipynb.

Citation

Please cite as:

@misc{strong2020impact,
  author = {Giles Chatham Strong},
  title = {On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics},
  year = 2020,
eprint = {2002.01427},
archivePrefix = {arXiv},
primaryClass = {physics.data-an}
}

You can’t perform that action at this time.