Python rewrite of the Urb/Homemaker stack
Bruno Postle
995342d0a4
Size each constructive-seed cut from leaf TARGET areas (division=[f,f] gives left area-fraction f) and pick each cut's rotation for child squareness — both derived from target dims, topology/type assignment untouched. Area-only regressed (slivers); rotation choice is what makes it pay. End-to-end (20000 evals, 3 seeds, staged): harbor 85.3->74.0 (-13%, best 69), maple-court 151.7->136.0 (-10%, best 126). PROP=0 reproduces the §11.7/§12.1 baselines exactly. programme-house regresses at fixed budget (deeper local optimum walls off the undivide restructuring path) but a budget sweep shows it's convergence speed, not a worse asymptote (PROP=1 reaches 1 fail at 150k). Default-on (seed_proportion_aware=True, env PROP=1). cq1: n_storeys now honours storey_minimum, not just level: keys — programme-house (storey_minimum:2, all rooms level:0) was seeded one storey short and fell through to plain search. New programme.storey_minimum()/n_storeys_for(); driver.search passes min_storeys to the seeder; search_staged routes on the max. No-op for harbor/maple; programme-house single-stage 8.0->5.0. New maple-court best (126) saved as generated.dom. 204 tests pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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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.