https://github.com/statbiophys/MINIMALIST

Companion Github of the MINIMALIST preprint.
https://github.com/statbiophys/MINIMALIST

Last synced: 3 months ago
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Companion Github of the MINIMALIST preprint.

• Host: GitHub
• URL: https://github.com/statbiophys/MINIMALIST
• Owner: statbiophys
• License: gpl-3.0
• Created: 2021-05-24T09:44:23.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-06-03T10:34:21.000Z (over 3 years ago)
• Last Synced: 2024-07-03T23:44:31.934Z (4 months ago)
• Language: Python
• Size: 104 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## MINIMALIST

Written by Giulio Isacchini, MPIDS Göttingen - ENS Paris and Natanael Spisak, ENS Paris

The code is written in Python3. Last updated on 24-05-2021

Reference: MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories, Giulio Isacchini, Natanael Spisak, Armita Nourmohammad, Thierry Mora and Aleksandra M. Walczak

### To reproduce the figures

In order to reproduce the plots you need to run the following commands.

1) Install the `mimsbi` package

Enter the mimsbi folder and run the command `python setup.py install`

2) Run analysis

To run the analysis for the the 4 task run the script `run_analysis.py` with the options: `ou`,`bd`,`sir` and/or `lorenz`. 

3) Plot the results.

For Figure 2, run `fig2.py`

For Figure 3 run `fig3.py`

For Figure 4, run `fig4.py`

### Extended usage

This directory includes a stable version of the `mimsbi` package.  

The package allows to infer the likelihood-to-evidence ratio model using one of three objective functions: MINE, FDIV or BCE. The package has implemented simulators for the processes studied in the MINIMALIST paper: Ornstein-Uhlenbeck, birth-death, SIR and Lorenz processes. To add another functionality one needs to add a new `Simulator` class to `mimsbi/models`. Then, inference can be performed using the `DensityRatioEstimator` class. For example of usage go to the `scripts` directory where separate files can be used to 

1) simulate the data  `scripts/simulate.py`

2) tune network hyperparameters  `scripts/infer_hyperpars.py`

3) likelihood-to-evidence ratio inference  `scripts/infer_estimators.py`

4) posterior evaluation  `scripts/compare_estimators.py`

To use the above scripts with a new model, its specifications need to be added in `scripts/utils.py` `return_pars` function. A simple data generation to posterior evaluation protocol is also available in the `mimsbi/tutorial.ipynb` Jupyter notebook.

### Requisites

- `tensorflow>2.1`

- `numpy`

- `pandas`

- `scipy`

- `matplotlib`

- `tqdm`