leojklarner / gauche Goto Github PK
View Code? Open in Web Editor NEWA Library for Gaussian Processes in Chemistry
Home Page: https://leojklarner.github.io/gauche/
License: MIT License
A Library for Gaussian Processes in Chemistry
Home Page: https://leojklarner.github.io/gauche/
License: MIT License
The Google colab links for the notebook tutorials have no instructions for installing dependencies.
All dependencies mentioned in the readme are available as conda packages.
Always nicer to avoid pip when using anaconda imho. Also, much easier to prepare a yaml file?
you already have a requirements file. then you wouldn't need a setup.py file?
you know, a simple
Just a thought, not an issue per se.
Apologies for the ad-like post. Figured this was the easiest way to bring it on your radar though. It's a 2-day event happening next week, and details are at https://ac-bo-hackathon.github.io/. It would be great to have a Gauche contribution! Feel free to close the issue at any point.
It may be worth investigating whether convolutional kernel networks [1] can be integrated as a GP graph kernel.
[1] Chen, D., Jacob, L. and Mairal, J., Convolutional kernel networks for graph-structured data. ICML, 2020.
The buckminsterfullerene gif has stopped working in the README :)
Currently the python versioning of this project is unclear: the README has no info, the supplied conda env says python 3.7, while the internal setup.py
says python>=3.8
. Maybe this should be standardized? It looks like any python version should do as long as the dependencies are supported, no?
I think moving the contents of the benchmarks
directory into the codebase (.e.g gprotorch.benchmarks
) will make organisation and docs clearer. It also helps (modestly) to enforce a consistent API across the library.
Also, we should rename from gprotorch
to gauche
.
Hi, thanks for your work! I'm wondering how exactly do I install gauche as a library. It seems that the instructions in the README are only for installing dependencies. Meanwhile, when I do pip install git+https://github.com/leojklarner/gauche.git
, I get an error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [1 lines of output]
error in gauche setup command: 'extras_require' must be a dictionary whose values are strings or lists of strings containing valid project/version requirement specifiers.
[end of output]
Thanks!
Parent:
Molecular property prediction
Protein ligand binding affinity
First off, great work, this is a really cool package!
I've been playing with the graph representation inputs using graphein
to a model building off of SIGP
(some examples in your codebase call it GraphGP
) and have been getting some really great performance out of it. However, I'm struggling to understand how to correctly save and then load the model back into memory for inference after training. If I save the state dict then re-init using that state dict, the model performs as if it had been randomly initialized. I also tried pickling the model (not the ideal solution) I get the following exception:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[88], [line 4](vscode-notebook-cell:?execution_count=88&line=4)
[1](vscode-notebook-cell:?execution_count=88&line=1) import pickle
[3](vscode-notebook-cell:?execution_count=88&line=3) with open('model.pkl', 'wb') as file:
----> [4](vscode-notebook-cell:?execution_count=88&line=4) pickle.dump(model, file)
RuntimeError: Pickling of "rdkit.Chem.rdchem.Atom" instances is not enabled (http://www.boost.org/libs/python/doc/v2/pickle.html)
I tried setting train_inputs
to None
before saving. This took care of the exception, however I'm back to the original issue where the model seems to be randomly initialized.
I was wondering if you had any guidance here, or if there was something in the docs that I missed. Thanks!
@a-r-j will address this in the next release of graphein I believe. Just leaving a TODO note here.
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