hep-ml-lab 0.4.3

An end-to-end framework used for research combining high-energy physics phenomenology with machine learning.

HEP ML Lab (HML)

Introduction

HEP-ML-Lab is an end-to-end framework used for research combining high-energy physics phenomenology with machine learning. It covers three main parts: the generation of simulated data, the conversion of data representation, and the application of analysis approaches.

With HML, researchers can easily compare the performance between traditional methods and modern machine learning algorithms, and obtain robust and reproducible results.

To get started, please check out the documents.

Installation

pip install hep-ml-lab

Check out the install via pip for more details of prerequisites and post-installation steps or install via Docker for a hassle-free experience.

Module overview

• hml.generators: API of Madgraph5 for simulating colliding events;
• hml.physics_objects: Physics objects classfied by their counts;
• hml.observables: General observables in jet physics;
• hml.representations: Different data structure used to represent an event;
• hml.datasets: Existing datasets and helper classes for creating new datasets;
• hml.approaches: Cuts, trees and networks for classification;
• hml.metrics: Metrics used in classical signal vs background analysis;

Updates

v0.4.3

• Fix the thresholds issue in hml.metrics.MaxSignificance by making it similar to the one returned by sklearn.metrics.roc_curve.
• Change hml.datasets.SetDataset.show to display figures using seaborn.
• Drop the requirement of executable in hml.generators.Madgraph5.from_output.

v0.4.2

• Fix parsing cuts like "muon0.charge != muon1.charge".
• Fix inconsistent model layers in hml.approaches.networks.SimpleCNN.
• Fix registering and saving custom observables.
• Add cross sections, luminosity and weights as parameters in hml.metrics.MaxSignificance.
• Improve the figure ratio in hml.datasets.SetDataset.show.

v0.4.1

• Fix module overview image in README.
• Fix GradientBoostedDecisionTree to be compatible with different sklearn versions.
• Fix hml.datasets.SetDataset.show to display the correct rows and columns.
• Rename the parse and register functions to parse_physics_object, parse_observable, and register_observable.
• Update the installation document.

v0.4.0

This version refactors most of the codebase to make it compatible with the array (from awkward and uproot) representation of the data.

v0.3.0.1

• Fix a bug that Madgraph5 may run into an infinite loop caused by HML keeping removing py.py file during initialization.
• Fix nan value not implemented in Fileter.
• Fix the wrong order of runs when using hml.generators.Madgraph5.runs and hml.generators.Madgraph5.summary.
• Fix the typo "g1" in quickstart.

v0.3.0

• New Madgraph5 API now is closer to the original Madgraph5 CLI.
• New Observable parsing system makes it easier to use and define new observables.
• New CutAndCout and BoostedDecisionTree in Keras style.

v0.2.2

• Change output structure of hml.generators.Madgraph5 to ensure reproducibility.
• Refactor hml.generators.Madgraph5 and hml.generators.MG5Run to make them more robust.

v0.2.1

• Add summary to hml.generators.Madgraph5 to print a summary of all run.
• Add remove to hml.generators.Madgraph5 to remove a run.
• Add clean to hml.generators.Madgraph5 to remove the output directory.