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K-fold-m-step forward cross-validation is a new approach of evaluating extrapolation performance in materials property prediction.

Python 100.00%
machine-learning cross-validation materials-discoveries

kmfcv's Issues

pip or conda installs? Use of DataFrames?

Might be worth considering hosting via PyPI and/or Anaconda. For PyPI, something as simple as:
Create __init__.py:

"""<one-line description>"""
__version__ = "1.0.0"

Create ~/.pypirc

[distutils]
index-servers =
   pypi

[pypi]
repository = https://upload.pypi.org/legacy/
username = __token__
password = <my-pypi-token>

Then:

pip install flit
flit init
# double check `pyproject.toml` file
flit publish

See flit (I'm a user, not affiliated).

The code will also be a lot more portable if you enable the use of passing Pandas DataFrames (or similar) rather than depending on absolute or relative filepaths.

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