add E2ET method

[pakamienny]

Competition Checklist:

• [x] title of this PR is meaningful, i.e. "method X for comp"
• [x] A folder has been added to submission/ with a meaningful name corresponding to your method name.

• The added folder includes these elements:

  • [x] metadata.yml (required): A file describing your submission, following the descriptions in example/metadata.yml.

  • [x] regressor.py (required): a Python file that defines your method, named appropriately. See submission/feat-example/regressor.py for complete documentation. It contains:

    • [x] est: a sklearn-compatible Regressor object.
    • [x] model(est, X=None): a function that returns a sympy-compatible string specifying the final model. It can optionally take the training data as an input argument. See guidance below.
    • [x] eval_kwargs (optional): a dictionary that can specify method-specific arguments to evaluate_model.py.

  • [ ] LICENSE (optional) A license file

  • [ ] environment.yml (optional): a conda environment file that specifies dependencies for your submission.

  • [ ] install.sh (optional): a bash script that installs your method.

  • [ ] additional files (optional): you may include a folder containing the code for your method in the submission.

I have verified that:

• [x] install scripts do not require sudo permissions.
• [x] if pulled remotely, the source code is a fixed version (i.e., rerunning install.sh shouldn't pulll a different version of the code when run multiple times.)

Refer to the competition guide if you are unsure about any steps. If you don't find an answer, ping us!

[pakamienny]

Hi ! this is my first try to submit for the SRBench's competition. I tried following William's answer here https://github.com/cavalab/srbench/discussions/81#discussioncomment-2546257 on how to get the submission running. Running directly test_evaluate on our method (E2ET) goes perfect, however there is an issue when importing stuff using pytest (which seems to be a common issue on stackoverflow, though I could not really get around it). Any idea?

What about running with CI (which I am not familiar at all with). what is the associated command?

Please let me know how I can improve my PR :) thanks!

[lacava]

hey @pakamienny , great, thanks for your submission! The install and tests are running and can be checked above, e.g.: https://github.com/cavalab/srbench/runs/5996899965?check_suite_focus=true

[lacava]

Looks like there's an issue with pip finding the right torch version:

Pip subprocess error:
ERROR: Could not find a version that satisfies the requirement torch==1.11.0+cu113 (from versions: 1.0.0, 1.0.1, 1.0.1.post2, 1.1.0, 1.2.0, 1.3.0, 1.3.1, 1.4.0, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.8.1, 1.9.0, 1.9.1, 1.10.0, 1.10.1, 1.10.2, 1.11.0)
ERROR: No matching distribution found for torch==1.11.0+cu113

[pakamienny]

Hi @lacava, it looks like we finally passed the test, but the CI somehow fails when adding us as a competitor :) do you know whether this comes from us? Thanks!

[lacava]

hi @pakamienny , great. Yes, it looks like the error is on our end.. just a bad commit ref I think. bear with us, we'll get it fixed!

[pakamienny]

perfect, thanks, please let me know when this is solved if I have to do something for the submission.

[lacava]

this is the issue: https://github.com/github/docs/issues/15319

I think I fixed it; @pakamienny could you merge with the upstream commits on Competition2022?

EDIT: i'm going to try a merge commit and see what happens :)

[pakamienny]

this is the issue: github/docs#15319

I think I fixed it; @pakamienny could you merge with the upstream commits on Competition2022?

EDIT: i'm going to try a merge commit and see what happens :)

Looks like it worked @lacava. This mean we are official competitors right? Just to be sure, we can still update the model until end of competition, via PR?

[lacava]

@pakamienny yes! thanks for your submission, you are official competitors. I will be in touch if we run into any issues.

And yes, you are free to update your submission until the submission deadline. Just submit a PR that changes your submission folder. The tests are triggered by changes in that path.

Note: you can totally remove eval_kwargs and the pretrain function if you aren't using them.