Community-contributed extensions to the Chemprop machine learning package
chemprop-contrib can either be pip installed directly
pip install chemprop-contrib
or installed as an optional dependency when installing Chemprop
pip install chemprop[contrib]
Then, the contributed packages can be imported in Python as
from chemprop_contrib import <package_name>Each package includes tests, which can be used to demonstrate the use of the package.
moe_regressor: Implements the "Adaptive Mixture of Local Experts" model for regression.
All community-contributed packages should fork the repository and in a branch on the fork, make a new directory under the chemprop_contrib directory with their name. Then add the following:
- License for the code (preferably MIT)
- All associated code files
- Data files, if needed, and their provenance
- Documentation (perhaps a Notebook (*.ipynb) or README.md) with instructions on how to use code contributed
- Tests demonstrating that the code works as intended
If your package has additional dependencies, add them to the list of optional dependencies in the pyproject.toml with the same name as your directory.
Once the package is ready, make a pull request to the main chemprop-contrib repository for a brief review. We provide a template for these pull requests.
For packages which do not require any new dependencies, see moe_regressor as an example.
For packages which do require additional dependencies, see mcp as an example.
Note that one must edit the pyproject.toml and add these dependencies, as mentioned in Contributing.