Python rewrite of the Urb/Homemaker stack
Bruno Postle
393183b356
§13.7 flagged edge-too-long as harbor's top fail class. Dissection showed the bulk are a leaf-sharing REPRESENTATION ARTIFACT: a share=k leaf aggregates k same-code rooms, so its walls run ~k× the flat 8 m cap purely for being big — the same §13.3 leak (size/missing relaxed for shared leaves) on the wall measure, since edge_cost/outside_edge_cost ignored leaf.share. Fix: Fitness._edge_cap(*leaves) scales the 8 m cap by the largest type-guarded leaf_share among adjoining leaves, mirroring quality_size's k×target; non-shared leaves keep the flat cap so genuine narrow/oversize pathologies stay flagged. Gated behind a share_edge_cap config knob (SHAREEDGE env), default OFF so the §13.x controls reproduce. A/B (full Phase-8 stack, staged, 20k evals, seeds 0/1/2): control reproduces §13.7 (maple 80.3 exact, harbor 34.7≈34.0); share-aware arm maple 80.3→74.0 (−7.9%), harbor 34.7→31.0 (−10.6%), zero regressions across 6 seeds. Positive and monotone-harmless (only ever removes a false-positive fail). Verdict: recommend default-ON; follow-up issue flips the default + rebaselines the floor. Tests: 6 new unit tests for _edge_cap (221 pass). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JygRv4n2dcyDQqMiDRe7TN |
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| .beads | ||
| .claude | ||
| examples | ||
| experiments | ||
| src/homemaker_layout | ||
| tests | ||
| .gitignore | ||
| AGENTS.md | ||
| CLAUDE.md | ||
| DESIGN.md | ||
| pyproject.toml | ||
| README.md | ||
homemaker-layout
Programme-driven building-layout search over slicing trees. A clean-room Python successor to the Perl Urb project, intended to eventually be 100% Python.
Why a rewrite
Urb represents a building as a binary slicing tree where room sizes are derived top-down from division ratios. That makes room area an emergent property of every cut above it, which:
- gives the genome low locality (a cut near the root rescales every descendant),
- makes target room sizes nearly impossible to hit, so the gaussian size penalty dominates fitness, and
- defeats crossover (transplanted subtrees lose their proportions).
homemaker inverts this: leaves carry target dimensions from the programme and division ratios are solved bottom-up for a fixed topology. The evolutionary search then only explores topology + types + adjacency.
Phase plan
Solver experiment: port Urb's geometry, re-solve ratios from programme targets, score the result against the original via the Perl oracle.✓Native Python fitness (retire the Perl oracle).✓- Memetic search (current): canonical slicing genome + high-locality operators + Nelder-Mead inner loop.
Layout
src/homemaker_layout/dom.py— read/write Urb.domYAML into aNodetree.src/homemaker_layout/geometry.py— faithful port of Urb's top-down geometry.src/homemaker_layout/programme.py— parsepatterns.configspace requirements.src/homemaker_layout/solver.py— bottom-up ratio solve (scipy).src/homemaker_layout/fitness.py— native Python fitness evaluator.src/homemaker_layout/fitness_cmd.py—homemaker-fitnessCLI (drop-in forurb-fitness.pl).src/homemaker_layout/graph.py— leaf-adjacency graph for programme-driven checks.src/homemaker_layout/genome.py— topology genome: base-floor tree + per-storey deltas.src/homemaker_layout/operators.py— high-locality mutation and subtree crossover.src/homemaker_layout/innerloop.py— ratio optimisation inner loop (Nelder-Mead / CMA-ES).src/homemaker_layout/driver.py— memetic search outer loop.src/homemaker_layout/evolve.py—homemaker-evolveCLI entry point.src/homemaker_layout/oracle.py— legacy Perl shim, kept for cross-validation only.