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Python rewrite of the Urb/Homemaker stack
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Bruno Postle 3d141a619d homemaker-py-1s3: multi-use leaves N=15 confirmation -- does not replicate 
The N=3 A/B (previous commits) found the precision-weighted shape
combination improved both example programmes (harbor-house -1.4%,
health-centre -13.9%), but N=3 is a thin sample by this project's own
standard (xyu/9yx use N=15). Two confirmations:

- N=15, plain search, budget=3000 (mirrors xyu/9yx's own protocol exactly):
  both programmes trend NEGATIVE (harbor +6.1%, health-centre +6.6%,
  Wilcoxon p=0.044)
- N=15, staged search, budget=20000 (true same-conditions replication --
  identical to the original A/B except seed count): both programmes AGAIN
  trend negative (harbor +6.6% p=0.15, health-centre +4.7% p=0.48)

The same-conditions replication disagrees with the original result's
direction on both programmes. Conclusion: the N=3 positive signal was
sampling noise, not a real effect -- health-centre's -13.9% was driven
substantially by one seed (71->43 fails) that didn't hold up.

multi_use stays default OFF and is not recommended even as a promising
lever -- this is a clean NULL, closing out both halves of §26's original
multi-use-leaves question (path a was NULL/NEGATIVE, path b is NULL after
replication). Mechanism itself is unchanged, complete, and fully tested.
DESIGN.md §33 rewritten with all three measurements and the honest verdict.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
2026-08-01 08:52:24 +01:00
.beads homemaker-py-1s3: multi-use leaves N=15 confirmation -- does not replicate 2026-08-01 08:52:24 +01:00
.claude Scaffold homemaker-py with validated geometry port 2026-06-10 20:50:20 +01:00
examples homemaker-py-1s3: multi-use leaves as permanent design goal (§26 path b) 2026-07-31 00:16:12 +01:00
experiments homemaker-py-1s3: multi-use leaves N=15 confirmation -- does not replicate 2026-08-01 08:52:24 +01:00
src/homemaker_layout homemaker-py-1s3: land precision-weighted shape combination for multi-use leaves 2026-07-31 10:20:49 +01:00
tests homemaker-py-1s3: land precision-weighted shape combination for multi-use leaves 2026-07-31 10:20:49 +01:00
.gitignore bd init: initialize beads issue tracking 2026-06-11 23:27:11 +01:00
AGENTS.md bd init: initialize beads issue tracking 2026-06-11 23:27:11 +01:00
CLAUDE.md docs: DESIGN.md §26/§27 — backfill 9o5/xi7/b3v (type superposition) and mi7 (bubble-diagram signal) 2026-07-26 23:16:21 +01:00
DESIGN.md homemaker-py-1s3: multi-use leaves N=15 confirmation -- does not replicate 2026-08-01 08:52:24 +01:00
pyproject.toml 94g: public-access pin + keep-better wrapper + CLI/finish-hook wiring 2026-07-18 10:29:44 +01:00
README.md update 2026-07-31 09:57:45 +01:00

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

  1. Solver experiment: port Urb's geometry, re-solve ratios from programme targets, score the result against the original via the Perl oracle. ✓
  2. Native Python fitness (retire the Perl oracle). ✓
  3. Memetic search: canonical slicing genome + high-locality operators + Nelder-Mead inner loop. ✓
  4. Penalty reshaping: lexicographic (-n_fails, fitness) outer-search comparison. ✓
  5. Representation upgrade: canonical slicing encoding + bottom-up shape feasibility, scaled to larger programmes. ✓
  6. Search-quality experiments (current): a long running series of opt-in levers tried against the harbor-house, health-centre, and programme-house example 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. See DESIGN.md §11 onward for the full, numbered experiment log with methodology and results for each.

Layout

  • src/homemaker_layout/dom.py — read/write Urb .dom YAML into a Node tree.
  • src/homemaker_layout/geometry.py — faithful port of Urb's top-down geometry.
  • src/homemaker_layout/programme.py — parse patterns.config space 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-fitness CLI (drop-in for urb-fitness.pl).
  • src/homemaker_layout/collapse_cmd.py — homemaker-collapse CLI: 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-evolve CLI 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 into fitness.py — reference only.