Codecov Report

Attention: Patch coverage is 88.12500% with 19 lines in your changes missing coverage. Please review.

Project coverage is 81.27%. Comparing base (05a9456) to head (3af8c66).
Report is 4 commits behind head on main.

@@            Coverage Diff             @@
##             main     #236      +/-   ##
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+ Coverage   80.87%   81.27%   +0.39%     
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  Files          50       51       +1     
  Lines        4135     4314     +179     
  Branches      845      981     +136     
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+ Hits         3344     3506     +162     
- Misses        668      670       +2     
- Partials      123      138      +15     

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hi @adamrupe - can you add a checklist into the PR description with the tasks to complete for this PR before it needs review?

@cthoyt What's your recommendation for handling merge conflicts with jupyter notebooks? I need to do this before I can pull your updates. I'm also not familiar with the CI/CD system, so if you could talk me through it that would be great.

@adamrupe before merging, copy your local notebook to your desktop. While merging, throw away everything from your repository's copy and overwrite it with remote. Then, you can think about manually inspecting your notebook on your desktop, and the new version from the remote repo side-by-side.

The best way to avoid this kind of thing is never to leave changes unpushed when you finish working, and to always pull before you start working again

The short explanation of how to use the CI/CD system is: you can always scroll to the bottom of this pull request (#236) and look at the feedback given by GitHub running our unit tests, linting, and code quality checks.

This is what it looks like to me right now:

You can click on any of the rows with the red x's, and then it will bring you to the page that ran the tests for you. Right now, you will be able to see all of the output from running pytest. You have to scroll up a bit since unfortunately, pytest reports timings and warnings after test failures, but you can see https://github.com/y0-causal-inference/y0/actions/runs/13134504627/job/36646756591?pr=236#step:6:69 for the currently failing test.

Similarly, while you're still getting used to having code quality checks, you will probably see that the linting or type checking scripts also give errors, which you can view in the same way..

It's sort of the expectation in a team setting for coding that you make pushes often, and each time check out what kind of feedback CI gives. This will help you iteratively make your code better, with fully objective feedback that you don't have to wait on someone else to give you. Alternatively to CI/CD in GitHub, you can run tox which also creates a reproducible execution of all of the testing suite.

There's documentation in the README on how to use all of the nice development tools built into this repo at https://github.com/y0-causal-inference/y0?tab=readme-ov-file#%EF%B8%8F-for-developers

If you get caught up on any parts of this that aren't self-explanatory, I'm happy to plan a video chat tomorrow, or sometime next before 6PM germany time

Awesome, thanks @cthoyt! That makes sense, and Jeremy and Richard have already shown me how to use tox a bit. I've pulled your changes and I'm going through them now. I'll add a test_to_hscm.

@cthoyt I've refactored HSCMs and filled in the HCM.to_hscm() tests, so all tests are now passing. However, there is a depreciation warning I'm getting from another part of the codebase:

src/y0/examples/__init__.py:1173: FutureWarning: 
Downcasting behavior in `replace` is deprecated and will be removed in a future version. 
To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. 
To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
    asia_df = pd.read_csv(ASIA_PATH).replace({"yes": 1, "no": -1})

There is also a mypy error from graph.py

mypy: commands[0]> mypy --ignore-missing-imports --strict src/ tests/test_hierarchical.py
src/y0/graph.py:480: error: Function is missing a type annotation for one or more arguments  [no-untyped-def]

Since I didn't write this code, I didn't want to make edits to fix these, but they seem straightforward fixes.

I'll add a test for augment_collapsed_model, and Jeremy and I have discussed a high-level notebook that I'll create as well. This should then complete your requested changes. I'll ping again when they are all complete.

@adamrupe you should be unblocked on the CI/CD pipeline now. looking forward to seeing a nice case study notebook, then we can finish this PR!