rsmine
A Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.
Science Score: 77.0%
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Keywords
Repository
A Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.
Basic Info
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- Stars: 32
- Watchers: 2
- Forks: 3
- Open Issues: 3
- Releases: 2
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Metadata Files
README.md
RSMI-NE
rsmine is a Python package, implemented using Tensorflow, for optimising coarse-graining rules for real-space renormalisation group by maximising real-space mutual information. This work results from the Horizon 2020 COMPLEX ML project.
Overview
rsmine employs state-of-the-art results for estimating mutual information (MI) by maximising its lower-bounds parametrised by deep neural networks [Poole et al. (2019), arXiv:1905.06922v1]. This allows it to overcome the severe limitations of the initial proposals for constructing real-space RG transformations by MI-maximization in [M. Koch-Janusz and Z. Ringel, Nature Phys. 14, 578-582 (2018), P.M. Lenggenhager et al., Phys.Rev. X 10, 011037 (2020)], and to reconstruct the relevant operators of the theory, as detailed in the manuscripts accompanying this code [D.E. Gökmen, Z. Ringel, S.D. Huber and M. Koch-Janusz, Phys. Rev. Lett. 127, 240603 (2021) and Phys. Rev. E 104, 064106 (2021)].
System requirements
Hardware requirements
rsmine can be run on a standard personal computer. It has been tested on the following setup (without GPU):
- CPU: 2.3 GHz Quad-Core Intel Core i5, Memory: 8 GB 2133 MHz LPDDR3
Software requirements
This package has been tested on the following systems with Python 3.8.5:
- macOS:
- Catalina (10.15)
- Big Sur (11.1)
- Monterey (12.5.1)
rsmine mainly depends on the following Python packages:
- matplotlib
- numpy
- pandas
- scipy
- scikit-learn
- tensorflow 2.0
- tensorflow-probability
Installation
From pip:
bash
pip install rsmine
Or directly from the source: clone RSMI-NE from GitHub
bash
git clone https://github.com/RSMI-NE/RSMI-NE
cd RSMI-NE
and install the rsmine package via pip in editable mode
bash
pip install -e .
or create a virtual environment and install there:
bash
./install.sh
Getting started
The package can be used by importing the rsmine module and its submodules:
```python
import rsmine
import rsmine.coarsegrainer.builddataset as ds import rsmine.coarsegrainer.cgoptimisers as cg_opt ```
Jupyter notebooks demonstrating the basic usage in simple examples are provided in https://github.com/RSMI-NE/RSMI-NE/tree/main/examples.
Citation
If you use RSMI-NE in your work, please cite our publications Phys. Rev. Lett. 127, 240603 (2021) and Phys. Rev. E 104, 064106 (2021):
```latex @article{PhysRevLett.127.240603, title = {Statistical Physics through the Lens of Real-Space Mutual Information}, author = {G\"okmen, Doruk Efe and Ringel, Zohar and Huber, Sebastian D. and Koch-Janusz, Maciej}, journal = {Phys. Rev. Lett.}, volume = {127}, issue = {24}, pages = {240603}, numpages = {7}, year = {2021}, month = {Dec}, publisher = {American Physical Society}, doi = {10.1103/PhysRevLett.127.240603}, url = {https://link.aps.org/doi/10.1103/PhysRevLett.127.240603} }
@article{PhysRevE.104.064106, title = {Symmetries and phase diagrams with real-space mutual information neural estimation}, author = {G\"okmen, Doruk Efe and Ringel, Zohar and Huber, Sebastian D. and Koch-Janusz, Maciej}, journal = {Phys. Rev. E}, volume = {104}, issue = {6}, pages = {064106}, numpages = {17}, year = {2021}, month = {Dec}, publisher = {American Physical Society}, doi = {10.1103/PhysRevE.104.064106}, url = {https://link.aps.org/doi/10.1103/PhysRevE.104.064106} } ```
Owner
- Name: RSMI
- Login: RSMI-NE
- Kind: organization
- Repositories: 2
- Profile: https://github.com/RSMI-NE
Citation (CITATION.cff)
cff-version: 0.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Gökmen
given-names: Doruk Efe
orcid: https://orcid.org/0000-0002-5536-1941
- family-names: Ringel
given-names: Zohar
- family-names: Huber
given-names: Sebastian D.
- family-names: Koch-Janusz
given-names: Maciej
title: Statistical Physics through the Lens of Real-Space Mutual Information
journal: Phys. Rev. Lett.
volume: 127
issue: 24
pages: 240603
numpages: 7
publisher: American Physical Socoety
doi: 10.1103/PhysRevLett.127.240603
url: https://link.aps.org/doi/10.1103/PhysRevLett.127.240603
date-released: 2021-12-06
GitHub Events
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- Release event: 1
- Watch event: 5
- Delete event: 1
- Member event: 1
- Push event: 3
- Create event: 2
Last Year
- Release event: 1
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Last synced: about 2 years ago
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Last synced: 6 months ago
All Time
- Total issues: 9
- Total pull requests: 3
- Average time to close issues: 8 days
- Average time to close pull requests: less than a minute
- Total issue authors: 1
- Total pull request authors: 2
- Average comments per issue: 0.33
- Average comments per pull request: 0.0
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
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- Issue authors: 0
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- Average comments per issue: 0
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- Merged pull requests: 0
- Bot issues: 0
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Top Authors
Issue Authors
- dgoekmen (9)
Pull Request Authors
- maciekkj (2)
- dgoekmen (1)
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Packages
- Total packages: 1
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Total downloads:
- pypi 8 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
pypi.org: rsmine
Optimal coarse graining transformations with RSMI neural estimation
- Homepage: https://github.com/RSMI-NE/RSMI-NE
- Documentation: https://rsmine.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 0.3.0a1
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
- matplotlib *
- networkx *
- numpy *
- pandas *
- requests *
- scikit-learn *
- scipy *
- seaborn *
- tensorflow-probability *
- tf_package_name ,
- tqdm *
- wandb *