ABCpy is a scientific library written in Python for Bayesian uncertainty quantification in absence of likelihood function, which parallelizes existing approximate Bayesian computation (ABC) algorithms and other likelihood-free inference schemes. It presently includes:
- PMCABC (Population Monte Carlo ABC)
- SMCABC (Sequential Monte Carlo ABC)
- RSMCABC (Replenishment SMC-ABC)
- APMCABC (Adaptive Population Monte Carlo ABC)
- SABC (Simulated Annealing ABC)
- ABCsubsim (ABC using subset simulation)
- PMC (Population Monte Carlo) using approximations of likelihood functions
- Random Forest Model Selection Scheme
- Semi-automatic summary selection (with Neural networks)
- summary selection using distance learning (with Neural networks)
ABCpy addresses the needs of domain scientists and data scientists by providing
- a fully modularized framework that is easy to use and easy to extend,
- a quick way to integrate your generative model into the framework (from C++, R etc.) and
- a non-intrusive, user-friendly way to parallelize inference computations (for your laptop to clusters, supercomputers and AWS)
- an intuitive way to perform inference on hierarchical models or more generally on Bayesian networks
For more information, check out the
Further, we provide a collection of models for which ABCpy has been applied successfully. This is a good place to look at more complicated inference setups.
Quick installation and requirements
ABCpy can be installed from
pip install abcpy
Check here for more details.
Basic requirements are listed in
requirements.txt. That also includes packages required for MPI parallelization there, which is very often used. However, we also provide support for parallelization with Apache Spark (see below).
Additional packages are required for additional features:
torchis needed in order to use neural networks to learn summary statistics. It can be installed by running
pip install -r requirements/neural_networks_requirements.txt
- In order to use Apache Spark for parallelization,
pysparkare required; install them by
pip install -r requirements/backend-spark.txt
mpi4py requires a working MPI implementation to be installed; check the official docs for more info. On Ubuntu, that can be installed with:
sudo apt-get install libopenmpi-dev
Even when that is present, running
pip install mpi4py can sometimes lead to errors. In fact, as specified in the official docs, the
mpicc compiler needs to be in the search path. If that is not the case, a workaround is:
env MPICC=/path/to/mpicc pip install mpi4py
In some cases, even the above may not be enough. A possibility is using
conda install mpi4py) which usually handles package dependencies better than
pip. Alternatively, you can try by installing directly
mpi4py from the package manager; in Ubuntu, you can do:
sudo apt install python3-mpi4py
which however does not work with virtual environments.
ABCpy was written by Ritabrata Dutta, Warwick University and Marcel Schoengens, CSCS, ETH Zurich, and presently actively maintained by Lorenzo Pacchiardi, Oxford University and Ritabrata Dutta, Warwick University. Please feel free to submit any bugs or feature requests. We'd also love to hear about your experiences with ABCpy in general. Drop us an email!
We want to thank Prof. Antonietta Mira, Università della svizzera italiana, and Prof. Jukka-Pekka Onnela, Harvard University for helpful contributions and advice; Avinash Ummadisinghu and Nicole Widmern respectively for developing dynamic-MPI backend and making ABCpy suitable for hierarchical models; and finally CSCS (Swiss National Super Computing Center) for their generous support.
Publications in which ABCpy was applied:
L. Pacchiardi, R. Dutta. "Score Matched Conditional Exponential Families for Likelihood-Free Inference", 2020, arXiv:2012.10903.
R. Dutta, K. Zouaoui-Boudjeltia, C. Kotsalos, A. Rousseau, D. Ribeiro de Sousa, J. M. Desmet, A. Van Meerhaeghe, A. Mira, and B. Chopard. "Interpretable pathological test for Cardio-vascular disease: Approximate Bayesian computation with distance learning.", 2020, arXiv:2010.06465.
R. Dutta, S. Gomes, D. Kalise, L. Pacchiardi. "Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic in England.", 2020, arXiv:2006.16059.
L. Pacchiardi, P. Künzli, M. Schöngens, B. Chopard, R. Dutta, "Distance-Learning for Approximate Bayesian Computation to Model a Volcanic Eruption", 2020, Sankhya B, ISSN 0976-8394, DOI: 10.1007/s13571-019-00208-8.
R. Dutta, J. P. Onnela, A. Mira, "Bayesian Inference of Spreading Processes on Networks", 2018, Proc. R. Soc. A, 474(2215), 20180129.
R. Dutta, Z. Faidon Brotzakis and A. Mira, "Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water", 2018, Journal of Chemical Physics 149, 154110.
R. Dutta, B. Chopard, J. Lätt, F. Dubois, K. Zouaoui Boudjeltia and A. Mira, "Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation with High Performance Computing", 2018, Frontiers in physiology, 9.
A. Ebert, R. Dutta, P. Wu, K. Mengersen and A. Mira, "Likelihood-Free Parameter Estimation for Dynamic Queueing Networks", 2018, arXiv:1804.02526.
R. Dutta, M. Schoengens, L. Pacchiardi, A. Ummadisingu, N. Widerman, J. P. Onnela, A. Mira, "ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation", 2020, arXiv:1711.04694.
ABCpy is published under the BSD 3-clause license, see here.
You are very welcome to contribute to ABCpy.
If you want to contribute code, there are a few things to consider: