omnifold 0.1.23

OmniFold, a library to perform unbinned and high-dimensional unfolding for HEP.

OmniFold: A Method to Simultaneously Unfold All Observables

This repository contains the implementation and examples of the OmniFold algorithm originally described in Phys. Rev. Lett. 124 (2020) 182001, 1911.09107 [hep-ph]. The code for the original paper can be found at this repository, which includes a binder demo. This repository was created to maintain the pip installable version of OmniFold with additional functionality compared to the original package:

Installation

pip install omnifold

Getting Started

Examples for tabular data and for Point Cloud-like inputs are provided in the notebooks OmniFold_example.ipynb and OmniFold_example_pc.ipynb, respectively. To unfold your own data you can follow these steps:

Creating a DataLoader to hold your data

from omnifold import DataLoader

# Load your own dataset, possibly with weights.
mock_dataset = np.zeros((100,3,4))
mock_dataloader = DataLoader(
		  reco = mock_dataset,
		  gen = mock_dataset,
		  normalize=True)

The DataLoader class will automatically normalize the weights if normalize is true. To estimate the statistical uncertainty using the Bootstrap method, you can use the optional flag bootstrap = True.

Creating your own Keras model to be used for Unfolding

In the MultiFold class, we provide simple neural network models that you can use. For a Multilayer Perceptron you can load

from omnifold import MLP
ndim = 3 #The number of features present in your dataset
reco_model = MLP(ndim)
gen_model = MLP(ndim)

to create the models to be used at both reconstruction and generator level trainings of OmniFold. In case your data is better described by a point cloud, we also provide the implementation of the Point-Edge Transformer (PET) model that can be used similarly to the MLP implementation:

from omnifold import PET
ndim = 3 #The number of features present in your dataset
npart = 5 #Maximum number of particles present in the dataset

reco_model = PET(ndim,num_part = npart)
gen_model = PET(ndim,num_part = npart)