ABCpy
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 likelihoodfree inference schemes.
Content
ABCpy presently includes the following ABC algorithms:
 RejectionABC
 PMCABC (Population Monte Carlo ABC)
 SMCABC (Sequential Monte Carlo ABC)
 RSMCABC (Replenishment SMCABC)
 APMCABC (Adaptive Population Monte Carlo ABC)
 SABC (Simulated Annealing ABC)
 ABCsubsim (ABC using subset simulation)
The above can be used with the following distances:
 Euclidean Distance
 Logistic Regression and Penalised Logistic Regression (classification accuracy)
 Divergences between datasets:
Moreover, we provide the following methods for directly approximating the likelihood functions:
 Bayesian Synthetic Likelihood
 Semiparametric Bayesian Synthetic Likelihood
 Penalised Logistic Regression for Ratio Estimation
The above likelihood approximation methods can be used with the following samplers:
 PMC (Population Monte Carlo)
 MetropolisHastings MCMC (Markov Chain Monte Carlo)
Additional features are:
 plotting utilities for the obtained posterior
 several methods for summary selection:
 Random Forest Model Selection Scheme
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 nonintrusive, userfriendly 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
Documentation
For more information, check out the
 Documentation
 Examples directory and
 Reference
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
:
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:

torch
is needed in order to use neural networks to learn summary statistics. It can be installed by runningpip install r requirements/neural_networks_requirements.txt
 In order to use Apache Spark for parallelization,
findspark
andpyspark
are required; install them bypip install r requirements/backendspark.txt
mpi4py
installation
Troubleshooting mpi4py
requires a working MPI implementation to be installed; check the official docs for more info. On Ubuntu, that can be installed with:
sudo aptget install libopenmpidev
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
(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 python3mpi4py
which however does not work with virtual environments.
Author
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. JukkaPekka Onnela, Harvard University for helpful contributions and advice; Avinash Ummadisinghu and Nicole Widmern respectively for developing dynamicMPI backend and making ABCpy suitable for hierarchical models; and finally CSCS (Swiss National Super Computing Center) for their generous support.
Citation
There is a paper in the proceedings of the 2017 PASC conference. In case you use ABCpy for your publication, we would appreciate a citation. You can use this BibTex reference.
Other References
Publications in which ABCpy was applied:

L. Pacchiardi, R. Dutta. "Generalized Bayesian LikelihoodFree Inference Using Scoring Rules Estimators", 2021, arXiv:2104.03889.

L. Pacchiardi, R. Dutta. "Score Matched Conditional Exponential Families for LikelihoodFree Inference", 2020, arXiv:2012.10903.

R. Dutta, K. ZouaouiBoudjeltia, C. Kotsalos, A. Rousseau, D. Ribeiro de Sousa, J. M. Desmet, A. Van Meerhaeghe, A. Mira, and B. Chopard. "Interpretable pathological test for Cardiovascular 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 COVID19 pandemic in England.", 2020, arXiv:2006.16059.

L. Pacchiardi, P. Künzli, M. Schöngens, B. Chopard, R. Dutta, "DistanceLearning for Approximate Bayesian Computation to Model a Volcanic Eruption", 2020, Sankhya B, 130.

R. Dutta, J. P. Onnela, A. Mira, "Bayesian Inference of Spreading Processes on Networks", 2018, Proceedings of Royal Society A, 474(2215), 20180129.

R. Dutta, Z. Faidon Brotzakis and A. Mira, "Bayesian Calibration of Forcefields 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, "LikelihoodFree Parameter Estimation for Dynamic Queueing Networks", 2018, arXiv:1804.02526, To apear in Jouranl of Royal Statistical Scoiety: Series C.

R. Dutta, M. Schoengens, L. Pacchiardi, A. Ummadisingu, N. Widerman, P. Künzli, J. P. Onnela, A. Mira, "ABCpy: A HighPerformance Computing Perspective to Approximate Bayesian Computation", 2018, arXiv:1711.04694, To appear in Journal of Statistical Software.
License
ABCpy is published under the BSD 3clause license, see here.
Contribute
You are very welcome to contribute to ABCpy.
If you want to contribute code, there are a few things to consider:
 a good start is to fork the repository
 know our branching strategy
 use GitHub pull requests to merge your contribution
 consider documenting your code according to the NumPy documentation style guide
 consider writing reasonable unit tests