Python rewrite of the Urb/Homemaker stack
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Follow-up measurement corrects the first draft of §38 in two ways. 1. Harbor-house's floor is 15 fails (evolved-3M-nols-3, 1.7M evals), not the 30-40 I quoted from §13.11's 20k-budget runs. Frontage deficit predicts the COST of solving, not impossibility: ~150x budget gap between a frontage-short and a frontage-surplus programme. Table corrected. 2. Zero-exposure is only half the mechanism, and not the dominant half. Splitting the deletion test by lit vs buried shows a WELL-DAYLIT corridor (q_crink=0.736) is still worth x4.06 to delete. Cause: value_circulation=50 vs value_inside=300, so merging corridor into room is a flat x6 gain, while 'level N not connected' costs only x0.5. Break-even needs 0.5^k < 50/300, i.e. k > 2.58 -- severing must cost at least 3 fails and costs 1. Net x3.0 predicted, x4.06 measured. The objective is net-positive on severing the spine even when the circulation is perfectly lit, which explains why both 'level N not connected' fails survive in the best layout after 1.7M evals. Adds fitness.quality_uncrinkliness crinkliness_mode (EXPERIMENTAL, default "urb" = stock hard 0.0, byte-identical: 336 passed vs 331 before, same 7 pre-existing fixture failures). A/B harness ab_crinkliness_mode_ssz.py shows none of the three modes removes the incentive, and the lit column is 3/8 under every mode including stock -- clean isolation of the two mechanisms. Filed homemaker-py-2v1 (P0) for the pricing fix; ssz/hxi now depend on it. Acceptance test recorded up front: harbor must reach 15 fails in materially fewer than 1.7M evals AND without either not-connected fail. 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.