Week 4 was where the branch stopped being just an algorithm branch and started becoming a package-integration branch.

The initial implementation existed, the R comparison work had begun, and now the problem became broader: the code had to survive upstream changes, static checks, and more realistic validation scenarios. This was one of those weeks where a lot of the work is not flashy, but if you skip it, the rest of the PR becomes painful.

Commits This Week

  • d2ba52d - move decomposition methods to a newer folder
  • 4c0d428 - Merge upstream main: resolve conflicts and rename gwlearn to spatialml
  • a6071dc - false-alert-due-to-local-libpysal
  • 03c8b90 - Fix all ruff and pyright typing errors
  • d72c41c - Fix remaining ty and ruff issues: Liskov LSP, y assertions, optimal_bandwidth_ typing
  • 3175c20 - Add bandwidth selection validation tests for synthetic grid and California Housing

Refactoring the Decomposition Code

One important change this week was moving the decomposition code into a clearer module layout. Early feature branches tend to accumulate logic in whatever place gets the feature working fastest. That is understandable, but once review begins, structure starts to matter much more.

Splitting the decomposition machinery into a more explicit layout helped with a few things:

  • separating generic decomposition behavior from GWPCA-specific behavior
  • making the code easier to navigate during review
  • reducing the sense that the feature was a one-file prototype

I find this kind of refactor mentally satisfying because it often signals a shift from “figuring out how to do it” to “deciding how it should live long-term.”

Dealing with Upstream Movement

This was also the week where upstream movement became very real.

The package was in the middle of rename-related churn - from gwlearn into spatialml, and later toward spml - and that meant the branch had to absorb merge conflicts while the feature itself was still evolving. This is one of the less glamorous parts of open-source work: sometimes the hardest problem on a given day is not your algorithm but the fact that the ground under the branch is moving.

Handling that well meant:

  • rebasing mental context onto the new package layout
  • resolving conflicts without losing the decomposition work
  • updating imports, paths, and tests so they still made sense after the rename

Typing and Linting Cleanup

I also spent significant time on static-check hygiene this week.

That included:

  • ruff
  • pyright
  • ty
  • signature consistency issues
  • stricter typing around estimator attributes and return values

This was not just “make CI green” work. Static checks forced me to make some decisions more explicit, especially around estimator contracts and unsupervised behavior. Those decisions later mattered a lot once decomposition had to coexist cleanly with the rest of the modeling code.

Bandwidth Validation Tests

One of the more useful additions this week was broader bandwidth-validation coverage.

I added tests using:

  • synthetic spatial grids
  • California Housing data

That helped answer a different class of question than the R comparison work. The R comparison was asking:

Is the local PCA numerically credible?

The bandwidth-validation tests were asking:

Does the estimator behave sensibly across different search settings and data regimes?

Both are necessary. A geographically weighted model can look perfectly good in one notebook and still behave badly once you put it through search and validation workflows.

Human Side of the Week

This was a slightly tiring week, mostly because it involved a lot of “invisible progress.” There is less immediate gratification in fixing typing issues, untangling merge conflicts, or moving code into better modules than there is in seeing a map render from a new method. But these weeks are often what make the later, more visible progress possible.

By the end of the week, I felt the branch was much healthier even if the visible user-facing change was smaller than in Week 2.

Main Outcome

The decomposition work now had:

  • a cleaner internal structure
  • broader validation coverage
  • better compatibility with the evolving upstream codebase
  • stronger chances of surviving detailed review

That set up the next phase well, because the PR was now ready for more serious API cleanup and output-polish work instead of still being stuck in structural instability.

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