ZüNIS (Zürich Neural Importance Sampling) a work-in-progress Pytorch-based library for Monte-Carlo integration based on Neural imporance sampling [1], developed at ETH Zürich. In simple terms, we use artificial intelligence to compute integrals faster.
The goal is to provide a flexible library to integrate black-box functions with a level of automation comparable to the VEGAS Library [2], while using state-of-the-art methods that go around the limitations of existing tools.
pip
The library is available on PyPI:
pip install zunis
The latest version can be installed directly from GitHub:
pip install 'git+https://github.com/ndeutschmann/zunis#egg=zunis&subdirectory=zunis_lib'
If you would like to contribute to the library, run the benchmarks or try the examples, the easiest is to clone this repository directly and install the extended requirements:
# Clone the repository
git clone https://github.com/ndeutschmann/zunis.git ./zunis
# Create a virtual environment (recommended)
python3.7 -m venv zunis_venv
source ./zunis_venv/bin/activate
pip install --upgrade pip
# Install the requirements
cd ./zunis
pip install -r requirements.txt
# Run one benchmark (GPU recommended)
cd ./experiments/benchmarks
python benchmark_hypersphere.py
For basic applications, the integrator is provided with default choices and can be created and used as follows:
import torch
from zunis.integration import Integrator
device = torch.device("cuda")
d = 2
def f(x):
return x[:,0]**2 + x[:,1]**2
integrator = Integrator(d=d,f=f,device=device)
result, uncertainty, history = integrator.integrate()
The function f
is integrated over the d
-dimensional unit hypercube and
torch.Tensor
batched inputs with shape (N,d)
for arbitrary batch size N
on device
torch.Tensor
batched inputs with shape (N,)
for arbitrary batch size N
on device
A more systematic documentation is under construction here.