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https://github.com/statphysandml/MCMCEvaluationLib

Python library for the evaluation of simulation data. The library provides functionalities to load simulation results into Python, to perform standard evaluation algorithms for Markov Chain Monte Carlo algorithms. It further can be used to generate a pytorch dataset from the simulation data.
https://github.com/statphysandml/MCMCEvaluationLib

markov-chain-monte-carlo numerics pytorch-dataset statistics

Last synced: 4 months ago
JSON representation

Host: GitHub
URL: https://github.com/statphysandml/MCMCEvaluationLib
Owner: statphysandml
License: mit
Created: 2020-12-17T17:21:10.000Z (over 3 years ago)
Default Branch: master
Last Pushed: 2022-11-13T19:02:53.000Z (over 1 year ago)
Last Synced: 2024-01-16T10:46:01.042Z (5 months ago)
Topics: markov-chain-monte-carlo, numerics, pytorch-dataset, statistics
Language: Python
Homepage:
Size: 44.9 KB
Stars: 4
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-stars - statphysandml/MCMCEvaluationLib - Python library for the evaluation of simulation data. The library provides functionalities to load simulation results into Python, to perform standard evaluation algorithms for Markov Chain Monte Carl (Python)

README

MCMCEvaluationLib
=================

The MCMCEvaluationLib is a Python library that implements important algorithms for an evaluation of results of a Markov Chain Monte Carlo algorithms. This includes the computation of expectation values, error estimation and the computation of the autocorrelation time. The library is used by the MCMCSimulationLib (https://github.com/statphysandml/MCMCSimulationLib) which provides generic code for Markov Chain Monte Carlo algorithms in C++. Further, the library enables an easy loading of the simulation data and provides a convenient way to convert the data into a PyTorch dataset.

The library currently consists of the following main modules:

- **loading** - Class for loading the simulation data. The simulation data is assumed to be stored columns wise in a .txt file. The ConfigurationLoader supports a simultaneous loading from multiple files and a piecewise loading (chunk by chunk).
- **modes** - Code that is used by the C++ MCMCSimulationLib for the computation of expectation values and further important results of a Markov Chain Monte Carlo Simulation.
- **pytorch** - Classes for a generation of a dataset consisting of samples/configurations of a Markov Chain Monte Carlo simulation. A possible batch-wise loading of the data boosts the computationl performance of the loading process. Samples of simulations with different hyperparameters can be mixed and loaded simultaneously.

Examples
--------

Examples to the different Python modules can be found here: https://github.com/statphysandml/MCMCSimulationLib/tree/master/examples/python_scripts/examples. Simulation results of the Ising model are discussed as a more detailed example here: https://github.com/statphysandml/MCMCSimulationLib/blob/master/examples/jupyter_notebooks/ising_model_cheat_sheet.ipynb. The example covers almost all functionalities of the library and shows additionally possible ways to make use of the pystatplottools library (https://github.com/statphysandml/pystatplottools) to analyse the data in more detail.

Integration
-----------

So far, the library needs to be build locally. This can be done by

```bash
cd path_to_mcmcevaluationlib/

python setup.py sdist
pip install -e .
```

For virtual enviroments, the library needs to be activate beforehand.

After this step, the different modules of the library can be used, for example, by

```python
import mcmctools

from mcmctools.pytorch.data_generation.configdatagenerator import ConfigDataGenerator
```

Dependencies
------------

- matplotlib
- numpy
- pandas (version >= 1.1)
- scipy
- pytorch
- (jupyter lab)

- pystatplottools (https://github.com/statphysandml/pystatplottools)

Projects using the MCMCEvaluationLib
----------------------------------

- MCMCSimulationLib (https://github.com/statphysandml/MCMCSimulationLib)
- LatticeModelSimulationLib (https://github.com/statphysandml/LatticeModelSimulationLib)
- LatticeModelImplementations (https://github.com/statphysandml/LatticeModelImplementations)

Support and Development
----------------------

For bug reports/suggestions/complaints please file an issue on GitHub.

Or start a discussion on our mailing list: [email protected]