mafR {mafR} | R Documentation |
Interface for masked autoregressive flows
Description
This wraps Python procedures to train Masked Autoregressive Flows (MAFs, Paramakarios et al. 2017) using the Python package zuko
. It has been tested with version 1.1.0 and 1.2.0 of that package. Note that objects created by its version 1.2.0 cannot be read with its version 1.1.0 (i.e., when saved in and read from pickle
files).
The simplest portable way to get mafR working may be to install it in a conda environment. Below is a complete installation recipe. More information about alternative installation procedure may be found on the Git repository for mafR, https://github.com/f-rousset/mafR.
mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 rm ~/miniconda3/miniconda.sh ~/miniconda3/bin/conda init bash conda create --name maf-conda python==3.10 conda activate maf-conda pip install zuko conda install R conda install conda-forge::r-gmp conda install conda-forge::gsl
and, in an R session within the maf-conda
environment:
install.packages("reticulate") library(reticulate) use_condaenv(condaenv="maf-conda", conda="~/miniconda3/bin/conda") install.packages("mafR") # 'mafR' was first designed for use with 'Infusion': install.packages("Infusion") install.packages("Rmixmod") # only a Suggested dependency of Infusion, but needed.
References
Papamakarios, G., D. Sterratt, and I. Murray. 2019. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:837-848, 2019. https://doi.org/10.48550/arXiv.1705.07057 ; https://proceedings.mlr.press/v89/papamakarios19a.html
Rozet, F., Divo, F., Schnake, S (2023) Zuko: Normalizing flows in PyTorch. https://doi.org/10.5281/zenodo.7625672