Density Estimation Likelihood-Free Inference with neural density estimators and adaptive acquisition of simulations. The implemented methods are described in detail in Alsing, Charnock, Feeney and Wandelt 2019, and are based closely on Papamakarios, Sterratt and Murray 2018, Lueckmann et al 2018 and Alsing, Wandelt and Feeney, 2018. Please cite these papers if you use this code!
Installation:
The code is in python3. There is a Tensorflow 1 (most stable, see below) and Tensorflow 2 version that can be installed as follows:
Tensorflow 1 (stable)
This can be found on the master branch and has the following dependencies:
tensorflow (<=1.15)
getdist
emcee (>=3.0.2)
tqdm
mpi4py (if MPI is required)
You can install the requirements and this package with,
pip install tensorflow==1.15
pip install git+https://github.com/justinalsing/pydelfi.git
(tensorflow-gpu==1.15 for GPU acceleration instead of tensorflow==1.15)
or alternatively, pip install the requirements and then clone the repo and run python setup.py install
Tensorflow 2
The Tensorflow 2 version can be found on the tf2-tom branch and can be installed as follows. We reccommend you do the install inside a virtual environment to keep version conflicts under control, ie.,
mkdir ~/envs
virtualenv ~/envs/pydelfi
source ~/envs/pydelfi/bin/activate
Followed by a pip install of pydelfi:
pip install git+https://github.com/justinalsing/pydelfi.git@tf2-tom
Note: the Mixture Density Networks (MDN) in the tf2 version are currently not performing as well as in the tf1 version (but the Masked Autoregressive Flows are fine). We are getting ot the bottom of this, and also working on expanding the suite of conditional density estimators in a coming update. Watch this space.
Documentation and tutorials:
Once everything is installed, try out either cosmic_shear.ipynb or jla_sne.ipynb as example templates for how to use the code; plugging in your own simulator and letting pydelfi do it's thing.
If you have a set of pre-run simulations you'd like to throw in rather than allowing pydelfi to run simulations on-the-fly, look at cosmic_shear_prerun_sims.ipynb as a template for how to do this.
If you are interested in using pydelfi with nuisance hardened data compression to project out nuisances (Alsing & Wandelt 2019), take a look at jla_sne_marginalized.ipynb.
Documentation can be found here (work in progress).
If you are interested in applying pydelfi to your problem but need some help getting started, or have an application that requires adaptations of the code, don't hesitate to get in touch with us (at justin.alsing@fysik.su.se) or open an issue - we welcome collaboration!