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This was referenced Mar 5, 2026
ballPointPenguin
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NOTE: pca.py’s module docstring still describes the deleted handwritten PCA implementation, even though this PR replaces it with sklearn.
NOTE: the new benchmark helper’s usage examples assume ../.venv/bin/python, which doesn’t match the repo’s documented/canonical delphi-env workflow in delphi/Makefile
-> we should pick either delphi-env/bin/python OR .venv/bin/python and use that everywhere
otherwise: looks good, tests pass. Nice cleanup 🧽
Note: we will update the golden records in a later commit.
I took care to keep the "sparsity-aware" scaling, computed in a vectorized way.
It's not needed anymore: the PCA is done by Sklearn, and the edge cases are already handled by wrapped_pca caller, i.e. pca_project_dataframe. Less code, less complexity, less maintenance.
Re-recorded golden snapshots using the new sklearn SVD-based PCA. These snapshots now serve as the baseline for regression tests.
Tests that only verify sorting/moderation now use recompute=False to skip unnecessary PCA computation. Manager tests that need PCA but use minimal data have targeted filterwarnings with explanations. Changes: - Add recompute=False to sorting tests (no PCA needed for ID ordering) - Add recompute=False to test_moderation (only checks filtering) - Add @pytest.mark.filterwarnings to TestConversationManager tests that use minimal data through the manager interface 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Fix division by zero when participant has no votes (pca.py) Mirrors Clojure's (max n-votes 1) approach - Fix return type annotation for _log_projection_metrics (comparer.py) - Add explanatory comment for intentional except pass (comparer.py) - Remove unused variable in test (test_regression.py) - Add unit test for no-votes edge case (test_pca_unit.py) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Use pca.mean_ instead of manual centering (sklearn handles centering internally) - Replace np.random.seed(42) with random_state=42 on PCA constructor - Update docstring to reflect sklearn PCA + column-mean imputation - Fix _pca_sign_flips type annotation to Dict[str, Dict[int, int]] - Scope outlier_fraction to PCA-related paths only (non-PCA data stays strict) - Add try/except around symlink creation for cross-platform robustness - Remove unused normalize_vector/proj_vec test helpers Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- pca.py: describe sklearn wrapper instead of deleted power iteration impl - bench_pca.py: use tool-agnostic `python` instead of hardcoded venv path Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Thanks @ballPointPenguin !
As to what should be a "canonical" venv place, in a way that would be "least surprising" to most python developers likely to work on delphi, or even might be tool-agnostic, this deserves a discussion of its own, and eventually its own PR. |
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Summary
The main deliverable of the #2311 split. Replaces ~900 lines of hand-rolled power iteration PCA with ~50 lines using sklearn's SVD-based PCA.
Net deletion: ~1000+ lines of hand-rolled linear algebra replaced by battle-tested sklearn.
Reviewer note
The golden record JSON diffs (~15k lines) can be skipped — they are the expected output changes from switching to sklearn PCA. The projection metrics from #2415 validate equivalence. Focus review on `pca.py`.
Test plan
🤖 Generated with Claude Code