Python rewrite of the Urb/Homemaker stack
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Claude 4c95505907
Shape-curve DP models leaf-sharing, so it can fire on real runs
shapecurve.leaf_constraints derived each leaf's feasible area from its own
type's base (target, sigma). quality_size does not: a leaf holding k
same-code rooms is centred on k*target with sigma*k, and a co-typed leaf
adds both codes' targets. The DP modelled neither, so eligible() excluded
leaf_sharing/max_share/multi_use -- and leaf_sharing defaults True in
driver.search, so the guard excluded essentially every real run. The DP was
correct and unreachable.

Why the guard could not just be dropped, measured before touching it: on 6
harbor constructed seeds, 24 of 24 shared leaves (100%) have a real area
outside the unscaled single-room bounds. Relaxing eligible without
modelling k would have made the DP call every one of those topologies
infeasible -- false negatives that prune feasible topologies and misdirect
the NM warm-start. The guard was load-bearing.

Fix: mirror quality_size by asking the SAME Fitness object -- k =
graph.leaf_share(leaf, fit._max_share) when fit._leaf_sharing, then
target*k / sigma*k, else fit._leaf_co_type for the additive case. Same
object, same flags, same branch order, deliberately not re-derived: 39.5's
cpsat._matches bug was a solver optimising a relation the scorer had moved,
and this is the same hazard class.

Verified as an exact inversion: for every shared leaf in a real seed,
quality_size evaluated at the DP's amin and amax returns FAIL_THRESHOLD to
1e-9 (k=3 n-leaf: bounds [128.50, 231.50], both 0.100000).

superpose stays excluded for a different reason than the others: it does
not rescale a target, it changes which type the leaf is scored as, and the
collapse happens after the DP has read leaf.type.

shapecurve_warmstart/shapecurve_prune remain default off, so no current run
changes -- including the cold-start baseline in progress. They are now
applicable, which unblocks homemaker-py-v4s.

Closes homemaker-py-tym.

Lint at parity (46); tests 387 passed (3 new, 1 legacy rewritten to the new
contract rather than deleted), 0 failed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB
2026-08-29 21:36:45 +00:00
.beads Shape-curve DP models leaf-sharing, so it can fire on real runs 2026-08-29 21:36:45 +00:00
.claude Scaffold homemaker-py with validated geometry port 2026-06-10 20:50:20 +01:00
examples coldstart programme-house seed 0 @ 500000: 1 fails (0h/1s) 2026-08-29 18:27:40 +01:00
experiments A/Bs now report what their sample could resolve 2026-08-29 19:51:47 +00:00
src/homemaker_layout Shape-curve DP models leaf-sharing, so it can fire on real runs 2026-08-29 21:36:45 +00:00
tests Shape-curve DP models leaf-sharing, so it can fire on real runs 2026-08-29 21:36:45 +00:00
.gitignore bd init: initialize beads issue tracking 2026-06-11 23:27:11 +01:00
AGENTS.md §39.7: access requirements become a declared usage: attribute (homemaker-py-sel) 2026-08-26 13:39:41 +00:00
CLAUDE.md §39.7: access requirements become a declared usage: attribute (homemaker-py-sel) 2026-08-26 13:39:41 +00:00
DESIGN.md Shape-curve DP models leaf-sharing, so it can fire on real runs 2026-08-29 21:36:45 +00:00
pyproject.toml homemaker-py-2g7.5: CP-SAT exact room-code assignment (seeder + reassign op) 2026-08-04 09:19:36 +01:00
README.md §39.7: access requirements become a declared usage: attribute (homemaker-py-sel) 2026-08-26 13:39:41 +00:00

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.pyhomemaker-fitness CLI (drop-in for urb-fitness.pl).
  • src/homemaker_layout/collapse_cmd.pyhomemaker-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.pyhomemaker-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.

Room codes and reserved names

Leaf types live in three namespaces that share a first character. Only the first is enforced; the other two are conventions the fitness function reads, so a room's spelling can change how it is scored.

1. Generic structural types — C, O, S (reserved). The leaves the search itself creates: C circulation, O outside, S sahn (an outside court that also serves as circulation). Always uppercase. A programme code spelled exactly C, O or S is rejected at load.

2. Programme room codes — anything else, lowercase. k1, b1, cr1, of, and single-character codes like r or t. These may start with any letter: since DESIGN.md §39.4 the generic tests match C/O/S exactly, so naming a room cr1 no longer makes it circulation. (Before that fix it did — and silently dropped it from the required-space check entirely.)

3. Access requirements — the usage: attribute. Every space declares one of living, kitchen, bedroom, toilet, utility, none. Mandatory, no fallback, and a missing or unknown value is a load error. It replaced a first-character convention (b/t/l/k) under which a room silently inherited another room's connectivity rules from its spelling — la1 "Laundry Room" was trimmed as a living room (DESIGN.md §39.7).

spaces:
  la1:
    usage: utility          # controlled, drives engine behaviour
    name: Laundry Room      # free text, building-specific

A usage value exists only where the engine treats it differently, so the vocabulary is closed: a new access class means new code, not new config. Check a programme with:

python experiments/audit_programme_config.py

which reports reserved-name collisions, the usage class each code picks up, and whether each room's size/width/proportion/crinkliness targets are mutually satisfiable at all.