Python rewrite of the Urb/Homemaker stack
Bruno Postle
479d4a57d5
Leaf-sharing evolve runs optimised a sharing-credited objective (a shared leaf of code X with share=k counts as k programme rooms, size re-centred on k*target) but wrote the un-materialised genome, which the canonical homemaker-fitness rates catastrophically worse (harbor-house: internal 1.03e-05 vs canonical 6.73e-29, 15 critical missing-room fails). Fix: before write, driver.polish_finish() unfolds every live shared leaf into k distinct rooms (operators.unfold_shared_leaves — pays down the count deficit) then warm-starts a leaf_sharing=False polish search from the unfolded genome so the materialised rooms get proportion/width/size cleanup. Returned best.fitness is the canonical score (leaf_sharing off => internal == canonical). This is yaa's proven unfold-then-polish path, made automatic. evolve.py: new --polish-budget (env HOMEMAKER_POLISH_BUDGET; -1=auto= budget//2, 0=unfold+rescore only). Interrupt forces polish_budget=0 for a fast honest output. Default stays --leaf-sharing on (its topology-search speed retained; output made honest by the finish). Schedule B in-run annealing remains homemaker-py-kpu. Verified (harbor-house 3000+1500): reported polish fitness 4.79788e-27 matches canonical homemaker-fitness exactly, 0 critical fails. Tests: 254 pass (+3 polish_finish). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm |
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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.