Python rewrite of the Urb/Homemaker stack
Bruno Postle
3b3eef68a7
Ramp the leaf-share grain down within one continuous run (e.g. 4->3->2->off), carrying the whole population across each step — graduated non-convexity over the single hard sharing->off transition of the §15 finish. - operators.unfold_shared_leaves(above=cap): unfold only leaves whose share exceeds the new grain cap, leaving smaller-share leaves collapsed for the next step. above=1 (default) keeps the full-unfold §15 behaviour. - driver: max_share override threaded through _overrides_for/_fitness_for/ _evaluate so a phase can rebuild the evaluator at a lower leaf_share_max cap; search(seed_pop=) evaluates an explicit initial population so a phase hands its whole population to the next instead of restarting from a single best. - driver.search_annealed: one phase per descending grain then a de-share polish; unfold-above-cap between steps; cumulative accounting + grain-tagged history; honest canonical best (byte-for-byte verified vs homemaker-fitness). - evolve: --anneal-grain LADDER CLI (self-finishing; §15 finish not applied). 8iv settled the primitive (grid unfold beat the circulation-aware slice), so the ramp reuses the plain balanced-grid unfold at every step. Tests: unfold above-cap selectivity, seed_pop seeding, search_annealed phase stitching / honest finish / degenerate-ladder fallback. 258 pass. DESIGN §16. Head-to-head A/B on harbor-house still to run; verdict pending (issue open). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm |
||
|---|---|---|
| .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.