A statistical learning toolkit for high-dimensional Hawkes processes in Python
Project description
Toolkit for Hawkes Processes in Python
Goal
The purpose of Sparklen
package is to provide the Python
community with
a complete suite of cutting-edge tools specifically tailored for
the study of exponential Hawkes processes, with a particular focus
on high-dimensional framework. It notably features:
-
A efficient cluster-based simulation method for generating events.
-
A highly versatile and flexible framework for performing inference of multivariate Hawkes process.
-
Novel approaches to address the challenge of multiclass classification within the supervised learning framework.
Installation
This section describes how to install the necessary dependencies to set up the package.
1. Install SWIG
Sparklen
uses a C++
core code for computationally intensive
components, ensuring both efficiency and performance. The binding between C++
and Python
is handled through SWIG
wrapper code.
So first, you need to install SWIG
. Below are the instructions for various platforms.
Anaconda/Miniconda
If you're using Anaconda or Miniconda, install SWIG
from the conda-forge
channel:
conda install -c conda-forge swig
Linux (Ubuntu/Debian)
On Ubuntu or Debian-based systems, you can install SWIG
using apt
:
sudo apt update
sudo apt install swig
macOS (Homebrew)
On macOS, you can install SWIG
using Homebrew
:
brew install swig
Windows
For Windows, follow these steps:
- Download the latest
SWIG
release from the SWIG website - Add the
SWIG
folder to your system's PATH environment variable
If you are using Chocolatey you can also install SWIG
by running:
choco install swig
2. Get the Source Code
Clone the repository to get the latest version of the source code:
git clone https://github.com/romain-e-lacoste/sparklen.git
cd sparklen
3. Install the Package
It's recommended to set up a dedicated Python environment (e.g., using venv
or conda
).
Once your environment is ready, install the package by running:
pip install .
Citing this work
If you found this package useful, please consider citing it in your work:
@article{lacoste2025sparkle,
title={Sparkle: A Statistical Learning Toolkit for High-Dimensional Hawkes Processes in Python},
author={Lacoste, Romain E.},
year={2025},
eprint={2502.18979},
archivePrefix={arXiv},
primaryClass={stat.ME},
url={https://arxiv.org/abs/2502.18979},
}
Acknowledgement
This work has been supported by the Chaire “Modélisation Mathématique et Biodiversité” of Veolia-École polytechnique-Museum national d’Histoire naturelle-Fondation X
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