By Week 5, the branch had already gone through implementation, early validation, numerical comparison, refactoring, and a lot of upstream conflict handling. The work now shifted into a more review-sensitive phase: making the estimator feel like it naturally belongs in the package and making its outputs easier to inspect, test, and explain.

This was the week where the branch became much more readable - both for users and for reviewers.

Commits This Week

  • 8dfa9f6 - fix: enforce y: pd.Series in supervised fit() signatures
  • 97cfb8c - clean up decomposition API
  • 18b2c19 - convert decomp attrs to DataFrame
  • 3eb9049 - Enable global baseline model by default for decompositions
  • 67e1263 - Fix CI type checking for pandas Indexing
  • 5852a71 - Refactor GWPCA components to MultiIndex DataFrame and apply string labels to variance attributes
  • c5706eb - Merge upstream main, resolve spatialml->spml rename conflicts
  • 6c53999 - fix: resolve merge conflicts and update docstrings

Moving from Arrays to Labeled Outputs

One of the most important changes this week was shifting key decomposition outputs away from anonymous arrays and toward labeled pandas structures.

That sounds like a presentation change, but it is deeper than that.

For GWPCA, users need to inspect:

  • which component they are looking at
  • which feature a loading belongs to
  • which location a local statistic belongs to

Without labels, every inspection step becomes a memory exercise or a manual lookup. With labeled outputs, the estimator becomes much easier to reason about.

This led to a few concrete improvements:

  • components_ became much more explicit
  • explained variance outputs got string labels like PC0, PC1, etc.
  • more of the decomposition API started looking like something you could explore naturally in a notebook or test file

The MultiIndex components_ layout was especially useful. Instead of leaving the component-feature relationship implicit, it made that structure visible in the object itself.

Cleaner Unsupervised Estimator Behavior

Another major theme this week was cleaning up the estimator API so decomposition behaved like an unsupervised estimator should, while still fitting into the shared package machinery.

That meant being careful about things like:

  • where y should and should not be required
  • how decomposition integrates with bandwidth search
  • how much special-case logic should remain decomposition-specific
  • how to avoid making decomposition look like a supervised estimator with fake arguments

This kind of cleanup is subtle. If you get it wrong, users may not notice immediately, but maintainers definitely do. A feature can be mathematically correct and still feel awkward or inconsistent inside the package.

Global Baseline Model by Default

I also enabled a global baseline model by default for decomposition estimators.

This was a useful conceptual improvement because it makes GWPCA easier to situate:

  • the global PCA gives a reference point
  • the geographically weighted version then shows how and where local structure departs from the global summary

That comparison is valuable both in analysis and in explanation. For spatial methods, users often want to know not only what the local structure is, but how much locality is really changing the story relative to the global one.

Review Pressure and Polish

By this point, a lot of the work was being shaped not just by my own preferences, but by what would make sense to a reviewer reading the PR carefully.

Some of the reviewer-facing pressure points were becoming clearer:

  • reduce custom code where existing tools are enough
  • make outputs easier to inspect
  • avoid decomposition-specific hacks where generic behavior is possible
  • make the branch feel integrated rather than bolted on

I actually enjoyed this phase. It felt like the feature was maturing from “my branch that works” into “a branch that has a chance of being merged without people feeling nervous about it.”

Main Outcome

By the end of Week 5, the branch was much closer to package-quality:

  • the public outputs were easier to understand
  • the estimator contract was cleaner
  • the decomposition API fit more naturally into the rest of the codebase
  • the rename/conflict churn had been absorbed without losing direction

The remaining work was increasingly about final numerical validation, edge cases, reviewer comments, and tightening confidence in the implementation rather than making big conceptual changes.

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