https://github.com/justinalsing/pydelfi

Density Estimation Likelihood-Free Inference with neural density estimators and adaptive acquisition of simulations
https://github.com/justinalsing/pydelfi

Last synced: 3 months ago
JSON representation

Density Estimation Likelihood-Free Inference with neural density estimators and adaptive acquisition of simulations

• Host: GitHub
• URL: https://github.com/justinalsing/pydelfi
• Owner: justinalsing
• Created: 2018-07-27T18:49:46.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-09-11T08:08:21.000Z (about 1 year ago)
• Last Synced: 2024-05-22T14:31:35.347Z (6 months ago)
• Language: Python
• Homepage:
• Size: 60.2 MB
• Stars: 101
• Watchers: 7
• Forks: 31
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-neural-sbi - [Code - Free Inference (DELFI) with neural density estimators and adaptive acquisition of simulations. (Code Packages and Benchmarks)

README

        
# pydelfi

**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](https://arxiv.org/abs/1903.00007), and are based closely on [Papamakarios, Sterratt and Murray 2018](https://arxiv.org/pdf/1805.07226.pdf), [Lueckmann et al 2018](https://arxiv.org/abs/1805.09294) and [Alsing, Wandelt and Feeney, 2018](https://academic.oup.com/mnras/article-abstract/477/3/2874/4956055?redirectedFrom=fulltext). 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](https://www.tensorflow.org) (<=1.15) 
 

[getdist](http://getdist.readthedocs.io/en/latest/)


[emcee](http://dfm.io/emcee/current/) (>=3.0.2)


[tqdm](https://github.com/tqdm/tqdm)


[mpi4py](https://mpi4py.readthedocs.io/en/stable/) (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](https://arxiv.org/abs/1903.01473v1)), take a look at `jla_sne_marginalized.ipynb`.

Documentation can be found **[here](https://pydelfi.readthedocs.io/en/latest/)** (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 [email protected]) or open an issue - we welcome collaboration!