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https://github.com/sparks-baird/mp-time-split
Use time-splits for Materials Project entries for generative modeling benchmarking.
https://github.com/sparks-baird/mp-time-split
generative materials-informatics materials-project materials-science time-series-forecasting
Last synced: 28 days ago
JSON representation
Use time-splits for Materials Project entries for generative modeling benchmarking.
- Host: GitHub
- URL: https://github.com/sparks-baird/mp-time-split
- Owner: sparks-baird
- License: mit
- Archived: true
- Created: 2022-06-02T01:13:26.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-17T21:30:44.000Z (over 1 year ago)
- Last Synced: 2024-10-03T12:10:54.822Z (2 months ago)
- Topics: generative, materials-informatics, materials-project, materials-science, time-series-forecasting
- Language: Jupyter Notebook
- Homepage: https://mp-time-split.readthedocs.io/
- Size: 1.79 MB
- Stars: 11
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-materials-informatics - mp-time-split - Use time-based cross-validation splits from Materials Project for generative modeling benchmarking (**Python**). [](https://github.com/sparks-baird/mp-time-split) (Software and products)
README
[](https://pyscaffold.org/)
[](https://mp-time-split.readthedocs.io/en/stable/)
[](https://pypi.org/project/mp-time-split/)
[](https://anaconda.org/conda-forge/mp_time_split)
[](https://coveralls.io/github/sparks-baird/mp-time-split?branch=main)
> **⚠️`mp-time-split` has been moved to [`matbench-genmetrics.mp_time_split`](https://github.com/sparks-baird/matbench-genmetrics) as a namespace package⚠️**
# mp-time-split
> Use Materials Project time-splits for generative modeling benchmarking.
While methods for cross-validating accuracy of materials informatics models is well
estabilished (see for example [Matbench](https://matbench.materialsproject.org/)),
evaluating the performance of generative models such as
[FTCP](https://github.com/PV-Lab/FTCP) or
[imatgen](https://github.com/kaist-amsg/imatgen), and [many
others](https://github.com/stars/sgbaird/lists/materials-generative-models) is less
straightforward. Recently, [Xie et al.](http://arxiv.org/abs/2110.06197) introduced new
benchmark datasets and metrics in [CDVAE](https://github.com/txie-93/cdvae) for several
state-of-the-art algorithms. This repository acts as a supplement to CDVAE benchmarks,
delivering [a new benchmark dataset](https://figshare.com/articles/dataset/Materials_Project_Time_Split_Data/19991516) (`Materials_Project_Time_Split_52` or **MPTS-52**) with time-based (5 $\times$ train/val)
+train/test splits suitable for cross-validated hyperparameter optimization and
subsequent benchmarking via the test split.
**MPTS-52** is most comparable to **MP-20**
from [Xie et al.](http://arxiv.org/abs/2110.06197), with the difference that up to 52
atoms are allowed and possibly a difference in the unique elements, as no elemental
filtering was applied (e.g. removal of radioactive elements).
## Quick Start
### Installation
```bash
conda env create -n mp-time-split -c conda-forge mp-time-split
conda activate mp-time-split
```
### Example
```python
from mp_time_split.core import MPTimeSplit
mpt = MPTimeSplit(target="energy_above_hull")
mpt.load(dummy=False)
for fold in mpt.folds:
train_inputs, val_inputs, train_outputs, val_outputs = mpt.get_train_and_val_data(
fold
)
final_train_inputs, test_inputs, final_train_outputs, test_outputs = mpt.get_test_data()
```
### Output
```python
print(train_inputs.iloc[0], train_outputs)
```
`train_inputs.iloc[0]`
`train_outputs`
```python
Structure Summary
Lattice
abc : 2.591619125942699 2.591619125942699 2.591619125942699
angles : 109.47122063449069 109.47122063449069 109.47122063449069
volume : 13.399593956465264
A : -1.496272 1.496272 1.496272
B : 1.496272 -1.496272 1.496272
C : 1.496272 1.496272 -1.496272
PeriodicSite: V (0.0000, 0.0000, 0.0000) [0.0000, 0.0000, 0.0000]
```
```python
146 0.000000
925 0.190105
1282 0.087952
1335 0.022710
12778 0.003738
2540 0.000000
316 0.000000
```
```{note}
Data (e.g. `train_inputs`, `train_outputs`) is sorted by earliest publication year.
The Materials Project ID number is used as the pandas `Series` index. The "mp-" and
"mvc" suffixes are dropped, except that "-" is retained in case of "mvc-" to make it
negative in the index and distinguish between "mp-" and "mvc-" type entries. This is
to make it easy to sort the entries based on the index. Ensure that you apply `abs` to
the index prior to sorting, otherwise the `mvc-` entries will be in the wrong order.
See [how-do-i-do-a-time-split-of-materials-project-entries-e-g-pre-2018-vs-post-2018](https://matsci.org/t/how-do-i-do-a-time-split-of-materials-project-entries-e-g-pre-2018-vs-post-2018/42584/2?u=sgbaird)
for more information. More detailed information (e.g. BibTeX references) can be accessed
via `mpt.data`.
```
For additional examples, see the [notebooks](notebooks) directory.
## Installation
### Anaconda
Create an environment named (`-n`) `mp-time-split` with the Anaconda package `mp-time-split` intalled from the `conda-forge` channel (`-c`).
```bash
conda env create -n mp-time-split -c conda-forge mp-time-split
```
### PyPI
Optionally create and activate a conda environment (recommended to use an isolated environment of some kind):
```bash
conda env create -n mp-time-split python==3.9.* # 3.7.* or 3.8.* also OK
conda activate mp-time-split
```
Install the `mp-time-split` package from PyPI.
```bash
pip install mp-time-split
```
### `environment.yml`
In order to set up the necessary environment:
1. review and uncomment what you need in `environment.yml` and create an environment `mp-time-split` with the help of [conda]:
```
conda env create -f environment.yml
```
2. activate the new environment with:
```
conda activate mp-time-split
```
### Local Installation
> **_NOTE:_** The conda environment will have mp-time-split installed in editable mode.
> Some changes, e.g. in `setup.cfg`, might require you to run `pip install -e .` again.
Optional and needed only once after `git clone`:
3. install several [pre-commit] git hooks with:
```bash
pre-commit install
# You might also want to run `pre-commit autoupdate`
```
and checkout the configuration under `.pre-commit-config.yaml`.
The `-n, --no-verify` flag of `git commit` can be used to deactivate pre-commit hooks temporarily.
4. install [nbstripout] git hooks to remove the output cells of committed notebooks with:
```bash
nbstripout --install --attributes notebooks/.gitattributes
```
This is useful to avoid large diffs due to plots in your notebooks.
A simple `nbstripout --uninstall` will revert these changes.
Then take a look into the `scripts` and `notebooks` folders.
## Project Organization
```
├── AUTHORS.md <- List of developers and maintainers.
├── CHANGELOG.md <- Changelog to keep track of new features and fixes.
├── CONTRIBUTING.md <- Guidelines for contributing to this project.
├── Dockerfile <- Build a docker container with `docker build .`.
├── LICENSE.txt <- License as chosen on the command-line.
├── README.md <- The top-level README for developers.
├── configs <- Directory for configurations of model & application.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
├── docs <- Directory for Sphinx documentation in rst or md.
├── environment.yml <- The conda environment file for reproducibility.
├── models <- Trained and serialized models, model predictions,
│ or model summaries.
├── notebooks <- Jupyter notebooks. Naming convention is a number (for
│ ordering), the creator's initials and a description,
│ e.g. `1.0-fw-initial-data-exploration`.
├── pyproject.toml <- Build configuration. Don't change! Use `pip install -e .`
│ to install for development or to build `tox -e build`.
├── references <- Data dictionaries, manuals, and all other materials.
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated plots and figures for reports.
├── scripts <- Analysis and production scripts which import the
│ actual PYTHON_PKG, e.g. train_model.
├── setup.cfg <- Declarative configuration of your project.
├── setup.py <- [DEPRECATED] Use `python setup.py develop` to install for
│ development or `python setup.py bdist_wheel` to build.
├── src
│ └── mp_time_split <- Actual Python package where the main functionality goes.
├── tests <- Unit tests which can be run with `pytest`.
├── .coveragerc <- Configuration for coverage reports of unit tests.
├── .isort.cfg <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.
```
## Note
This project has been set up using [PyScaffold] 4.2.2 and the [dsproject extension] 0.7.post1.dev8+g43a905e.
[conda]: https://docs.conda.io/
[pre-commit]: https://pre-commit.com/
[Jupyter]: https://jupyter.org/
[nbstripout]: https://github.com/kynan/nbstripout
[Google style]: http://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings
[PyScaffold]: https://pyscaffold.org/
[dsproject extension]: https://github.com/pyscaffold/pyscaffoldext-dsproject
To create the same starting point for this repository, as of 2022-06-01 on Windows you will need the development versions of PyScaffold and extensions, however this will not be necessary once certain bugfixes have been introduced in the next stable releases:
```bash
pip install git+https://github.com/pyscaffold/pyscaffold.git git+https://github.com/pyscaffold/pyscaffoldext-dsproject.git git+https://github.com/pyscaffold/pyscaffoldext-markdown.git
```
The following `pyscaffold` command creates a starting point for this repository:
```bash
putup xtal2png --github-actions --markdown --dsproj
```
Alternatively, you can edit a file interactively and update and uncomment relevant lines, which saves some of the additional setup:
```bash
putup --interactive xtal2png
```