Python rewrite of the Urb/Homemaker stack
c0c47a6e2d
Diagnostic investigation of why search stalls in local minima. Adds experiments/diag_exposure_frontage.py (frontage/exposure/value reports, no search run required) and records the findings as §38. Core mechanism: quality_uncrinkliness returns a hard 0.0 for any leaf with no daylit wall, and since leaf quality is a product feeding value += quality * rate * area, every buried room contributes exactly zero value while still costing. 45-56% of interior leaves are in this state under the default construction stack. Consequences measured, not inferred: - Deleting a buried O leaf improves the score x85, a buried C leaf x62. Nothing pins circulation or outside space, so the search is rewarded by two orders of magnitude for deleting the circulation spine. This retro-explains §18, §21/§22 and the level-not-connected fails surviving >1M evals. - Closed-form frontage bound: every interior leaf needs exposed wall L >= A/(1.6202*h). harbor-house supplies 54m against 148m needed (2.7x short), maple-court 56 vs 162; health-centre and programme-house are feasible. The corpus plateau is predicted by frontage deficit alone. - Crinkliness is tiered SOFT but 60-100% of its fails are zero-exposure, which is topological, so §37.1's tiered comparator is mis-informed about the largest fail category. - The missing-space cascade emits one extra fail per declared size/width/ proportion key, so under 0.5^n a missing room is weighted 4x differently depending on patterns.config verbosity. Filed as homemaker-py-ssz, hxi, tdp, gvb, 1i8 (plus bdf for the pre-existing fresh-clone test failures found en route). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB |
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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: canonical slicing genome + high-locality operators + Nelder-Mead inner loop.✓Penalty reshaping: lexicographic✓(-n_fails, fitness)outer-search comparison.Representation upgrade: canonical slicing encoding + bottom-up shape feasibility, scaled to larger programmes.✓- Search-quality experiments (current): a long running series of
opt-in levers tried against the
harbor-house,health-centre, andprogramme-houseexample corpora — leaf-sharing, finish-time cell→room collapse, ruin-and-recreate LNS, 2-opt polish, multi-use/co-located leaves, adjacency-graph and bubble-diagram fitness signals, and more. Most of these are negative/null results kept as opt-in flags or reference code rather than defaults. SeeDESIGN.md§11 onward for the full, numbered experiment log with methodology and results for each.
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/collapse_cmd.py—homemaker-collapseCLI: finish-time global cell→room relabel of a.dom.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.src/homemaker_layout/bubble.py— 3D bubble-diagram adjacency fitness-signal prototype (DESIGN.md §27); validated null, not wired intofitness.py— reference only.