matbench-genmetrics 0.2.2

Generative materials benchmarking metrics, inspired by CDVAE.

NOTE: This is a WIP repository (as of 2022-06-21) being developed in parallel with xtal2png. Feedback and contributions welcome!

matbench-genmetrics

Generative materials benchmarking metrics, inspired by CDVAE.

This repository provides standardized benchmarks for benchmarking generative models for crystal structure. Each benchmark has a fixed dataset, a predefined split, and a notion of best (i.e. metric) associated with it.

Getting Started

Installation

Create a conda environment with the matbench-genmetrics package installed from the conda-forge channel. Then activate the environment.

conda create --name matbench-genmetrics --channel conda-forge python==3.9.* matbench-genmetrics
conda activate matbench-genmetrics

NOTE: It doesn't have to be Python 3.9; you can remove python==3.9.* altogether or change this to e.g. python==3.8.*. See Advanced Installation

Basic Usage

from mp_time_split.utils.gen import DummyGenerator
from matbench_genmetrics.core import MPTSMetrics

mptm = MPTSMetrics(dummy=False)
for fold in mptm.folds:
    train_val_inputs = mptm.get_train_and_val_data(fold)

    dg = DummyGenerator()
    dg.fit(train_val_inputs)
    gen_structures = dg.gen(n=10000)

    mptm.record(fold, gen_structures)

print(mptm.recorded_metrics)

Advanced Installation

In order to set up the necessary environment:

1. review and uncomment what you need in environment.yml and create an environment matbench-genmetrics with the help of conda:

   conda env create -f environment.yml
   

2. activate the new environment with:

   conda activate matbench-genmetrics
   

NOTE: The conda environment will have matbench-genmetrics 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:

   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:

   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.

Dependency Management & Reproducibility

1. Always keep your abstract (unpinned) dependencies updated in environment.yml and eventually in setup.cfg if you want to ship and install your package via pip later on.
2. Create concrete dependencies as environment.lock.yml for the exact reproduction of your environment with:

   conda env export -n matbench-genmetrics -f environment.lock.yml
   

   For multi-OS development, consider using --no-builds during the export.
3. Update your current environment with respect to a new environment.lock.yml using:

   conda env update -f environment.lock.yml --prune
   

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
│   └── matbench_genmetrics <- 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.post1.dev2+ge50b5e1 and the dsproject extension 0.7.2.post1.dev2+geb5d6b6.