Qmlearn #82
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Qmlearn #82
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zaspel
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Jul 29, 2019
* Made base representations * started CM and data class * Working on generate routine * Working basic example * Mostly hacked the searchcv routines to work * Implementing atomic gaussian kernel * working atomic krr * Restructure and started global slatm * Slatm * Started acsf * stash before merging acsf bugfix * acsf bugfix cherrypick * sigma='auto' option added to kernels * Started fchl * Working fchl * Started preprocessing * Mostly working atom scaler * Made several attributes private * Restructured how the data object is passed, to avoid possible memory issues * Started alchemy in kernels * Minor change to kernel alchemy * Working feature trick in kernels * Cleaned up code * daily * Finished examples
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Scikit-learn high level interface. I think that this should be the default interface for qml and that we should hide more low level stuff a bit away.
The implementation is about 3/4 done and is ready for beta testing. 'examples/qmlearn.py' shows how to use it.
I had to restructure and modify quite a lot of files, but only major changes was made in the qmlearn submodule.
It is hardcoded to predict energies at the moment, but energies can be replaced with any molecular property. Atomic properties is not supported yet.
Tests are on the todo list.