Python rewrite of the Urb/Homemaker stack
Bruno Postle
6ee5d4b4ae
Land the two evidence-supported parts of the re-scoped 9gp capstone as operators on the existing decoded Node tree (no Polish-expression rewrite), each default-OFF and measured against the §12.2 leu.2 baseline. 9gp.1 shape-feasibility pre-filter: operators.predicted_shape_fails lays a topology out at its proportion-aware target geometry and counts shape fails (size/width/proportion/crinkliness); driver._evaluate prunes clearly-infeasible topologies before the inner loop (1 eval vs ~80), guarded so nothing that could beat the incumbent is discarded. search/search_staged feasibility_filter, feasibility_max_shape_fails (env FEAS/MAXSHAPE), default OFF. 9gp.2 M3 Wong-Liu reassociate: operators.mutate_reassociate adds associativity (a|b)|c <-> a|(b|c) on same-orientation live cuts — the canonical-slicing move missing from swap(M1)/rotate(M2), attacking the §11.4/§11.5 reachability bottleneck. enable_reassociate (env REASSOC), default OFF (weight 0 -> baseline byte-identical). Unit tests (operators + driver) green, full suite 211 passed; maple-court smoke run clean under native fitness. A/B sweep handed off per the plan; DESIGN.md §12.3 documents the design and the pending measurement. 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.