Vectorize participant info computation (3-15x speedup)#2516
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ballPointPenguin
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Pull request overview
Vectorizes _compute_participant_info_optimized in Conversation by replacing the per-participant Python loop with bulk NumPy operations: matrix-wide np.sum for vote counts and a per-group P @ g matmul for Pearson correlation, yielding 3–15x speedup. Removes the now-unused legacy participant_stats() function from repness.py and updates all callers/imports. Adds a comprehensive unit test file and an A/B benchmark script.
Changes:
- Replace per-participant loop with vectorized vote counting and matmul-based Pearson correlation in
_compute_participant_info_optimized - Delete dead
participant_stats()fromrepness.py; update imports inconversation.py,pca_kmeans_rep/__init__.py,notebooks/run_analysis.py, and 4 test files to call the optimized method instead - Add
tests/test_participant_info.py(31 tests covering vote counts, group correlations, edge cases, real-data equivalence, golden snapshot regression) andscripts/benchmark_participant_info.py
Reviewed changes
Copilot reviewed 12 out of 12 changed files in this pull request and generated no comments.
Show a summary per file
| File | Description |
|---|---|
| delphi/polismath/conversation/conversation.py | Vectorize participant info using np.sum and P @ g Pearson formula |
| delphi/polismath/pca_kmeans_rep/repness.py | Delete dead participant_stats(); clarify PSEUDO_COUNT (MAP) comment |
| delphi/polismath/pca_kmeans_rep/init.py | Drop participant_stats from public exports |
| delphi/notebooks/run_analysis.py | Remove unused participant_stats import |
| delphi/tests/test_participant_info.py | New comprehensive unit + golden + real-data equivalence tests |
| delphi/tests/test_repness_unit.py | Switch test_participant_stats to call optimized method; centralize PSEUDO_COUNT import |
| delphi/tests/test_old_format_repness.py | Same switch and import centralization |
| delphi/tests/test_repness_smoke.py | Replace participant_stats call with _compute_participant_info_optimized |
| delphi/tests/test_legacy_repness_comparison.py | Drop unused participant_stats import |
| delphi/tests/simplified_repness_test.py | Import PSEUDO_COUNT from repness instead of hardcoding |
| delphi/scripts/benchmark_participant_info.py | New A/B benchmark of old loop vs vectorized impl |
| delphi/docs/PLAN_DISCREPANCY_FIXES.md | Add stack cross-reference table and GitHub PR numbers |
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## Summary - Replaces the O(N×G×C) per-participant Python loop in `_compute_participant_info_optimized` with bulk NumPy operations: matrix-wide vote counting (`np.sum` over axis) and per-group Pearson correlation via `P @ g` matrix multiply - Adds 31 unit tests covering vote counts, group correlations, edge cases (small groups, zero-std, NaN handling, missing members), and golden snapshot regression - Correlations now return Python `float` instead of `numpy.float64` - Includes a benchmark script (`scripts/benchmark_participant_info.py`) that runs old vs new on the same data ## Benchmark results Measured on real datasets (5 runs, median), old per-participant loop vs new vectorized: | Dataset | Size | Old | New | Speedup | |---------|------|-----|-----|---------| | vw | 69p × 125c × 4g | 0.011s | 0.001s | **14.6x** | | biodiversity | 536p × 314c × 2g | 0.047s | 0.006s | **8.1x** | | _(larger private datasets)_ | | | | **3–6x** | Speedup is higher on smaller datasets (loop overhead dominates) and lower on very large ones (matrix materialization dominates). Overall **3–15x** depending on size. ## Test plan - [x] 31 unit tests pass (pre-vectorization baseline established first, then re-run post) - [x] Golden snapshot regression passes for biodiversity + vw - [x] Full regression test suite passes (40/40) - [x] Benchmark run on all datasets including private (results above) - [x] Max correlation diff across all datasets: < 2e-15 🤖 Generated with [Claude Code](https://claude.com/claude-code) ## Squashed commits - Address Copilot review: clarify MAP estimate, centralize PSEUDO_COUNT imports - Update plan: add stack cross-reference and GitHub PR numbers - Vectorize _compute_participant_info_optimized for ~100x speedup - Add benchmark script for participant info vectorization - Address Copilot review: drop unused cache, validate --runs, tighten type assert - Remove old participant_stats() in favor of vectorized replacement commit-id:ea747196
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Summary
_compute_participant_info_optimizedwith bulk NumPy operations: matrix-wide vote counting (np.sumover axis) and per-group Pearson correlation viaP @ gmatrix multiplyfloatinstead ofnumpy.float64scripts/benchmark_participant_info.py) that runs old vs new on the same dataBenchmark results
Measured on real datasets (5 runs, median), old per-participant loop vs new vectorized:
Speedup is higher on smaller datasets (loop overhead dominates) and lower on very large ones (matrix materialization dominates). Overall 3–15x depending on size.
Test plan
🤖 Generated with Claude Code
Squashed commits
commit-id:ea747196
Stack: