8000 [DL Edition] T037: E(3)-invariant graph neural networks by joschka-gross · Pull Request #326 · volkamerlab/teachopencadd · GitHub
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[DL Edition] T037: E(3)-invariant graph neural networks #326

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Merged
merged 17 commits into from
Apr 11, 2023
Merged

[DL Edition] T037: E(3)-invariant graph neural networks #326

merged 17 commits into from
Apr 11, 2023

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joschka-gross
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@joschka-gross joschka-gross commented Mar 15, 2023 •
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Details

  • Talktorial ID: 037
  • Title: [DL Edition] T037: E(3)-invariant graph neural networks
  • Original authors: Joschka Groß
  • Reviewer(s): XXX
  • Date of review: DD-MM-YYYY

Content

  • One line summary: Introduction to E(3) In-/Equivariant Graph Neural Networks
  • Potential labels or categories (e.g. machine learning, small molecules, online APIs): Machine learning, small molecules, graph neural networks, point clouds, 3D information
  • Time it took to execute (approx.):
  • I have used the talktorial template and followed the content and formatting suggestions there
  • Packages must be open-sourced and should be installable from conda-forge. If you are adding new packages to the TeachOpenCADD environment, please check if already installed packages can perform the same functionality and if not leave a sentence explaining why the new addition is needed. If the new package is not on conda-forge, please list them and their intended usage here.
    • numpy, matplotlib: Already in TeachOpenCADD
    • pytorch 1.12.1, pytorch-cluster 1.6.0, pytorch-scatter 2.1.0, pytorch-sparse 0.6.15, pyg 2.2.0 (conda-forge): pyg (torch_geometric) includes graph neural network and the QM9 dataset we use for the practical part, everzthing else is a dependency of torch_geometric, however we also use pytorch-scatter explicitly in our EGNN implementation
  • Data must be publicly available, preferably accessible via a webserver or downloadable via a URL. Please list the data resources that you use and how to access them:

Content style

  • Talktorial includes cross-references to other talktorials if applicable
  • The table of contents reflects the talktorial story-line; order of #, ##, ### headers is correct
  • URLs are linked with meaningful words, instead of pasting the URL directly or linking words like here.
  • I have spell-checked the notebook
  • Images have enough resolution to be rendered with quality, without being too heavy.
  • All figures have a description
  • Markdown cell content is still in-line with code cell output (whenever results are discussed)
  • I have checked that cell outputs are not incredibly long (this applies also to DataFrames) - is 100 lines okay?
  • Formatting looks correctly on the Sphinx render (bold, italics, figure placing)

Code style

  • Variable and function names follow snake case rules (e.g. a_variable_name vs aVariableName)
  • Spacing follows PEP8 (run Black on the code cells if needed)
  • Code line are under 99 characters each (run black-nb -l 99)
  • Comments are useful and well placed
  • There are no unpythonic idioms like for i in range(len(list)) (see slides)
  • All 3rd party dependencies are listed at the top of the notebook
  • I have marked all code cell with output referenced in markdown cells with the label # NBVAL_CHECK_OUTPUT
  • I have identified potential candidates for a code refactor / useful functions
  • All import ... lines are at the top (practice part) cell, ordered by standard library / 3rd party packages / our own (teachopencadd.*)
  • I have used absolute paths instead of relative paths 
    HERE = Path(_dh[-1])
DATA = HERE / "data"

Website

We present our talktorials on our TeachOpenCADD website (https://projects.volkamerlab.org/teachopencadd/), so we have to check as well if the Jupyter notebook renders nicely there.

  • If this PR adds a new talktorial, please follow these steps:
    • Add your talktorial to the complete list of talktorials here (at the end).
    • Add your talktorial to one or multiple of the collections here. Or propose a new collection section in your PR.
    • Add your talktorial's nblink file by running python generate_nblinks.py from within the directory teachopencadd/docs/talktorials.
    • Please complile the website following the instructions here.
  • Check the rendering of the talktorial of this PR.
  • Is your talktorial listed in the talktorial list?
  • Is your talktorial listed in the talktorial collections?
    • Add a picture for your talktorial in the collection view by following these instructions.

Status

  • Ready to go

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@AndreaVolkamer AndreaVolkamer mentioned this pull request Mar 16, 2023
Merged
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@gerritgr gerritgr merged commit 980c52d into DL_edition Apr 11, 2023
@mbackenkoehler mbackenkoehler deleted the jg-037-e3-equivariant-gnn branch January 29, 2024 10:02
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@joschka-gross @gerritgr @mbackenkoehler
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