Python rewrite of the Urb/Homemaker stack
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Claude 03c1c5edcf
CP-SAT seeding re-measured deterministically: it loses
39.5 concluded the exact CP-SAT seeder beats greedy (harbor 102 -> 92,
maple 156 -> 154). Re-checked because fdp made the arms deterministic and
3qj made the model 7.5x slower. Three findings.

A live bug in the cap, found on the way. solve_room_labels sets a
deterministic work-unit budget (4.0) and a wall-clock backstop, commented
as "a pathological-case backstop only". At 2.0s it had become THE BINDING
CONSTRAINT: 2 of 24 harbor solves returned FEASIBLE not OPTIMAL, wall time
hit exactly 2010 ms, and the deterministic budget was never reached (max
2.483/4.0). Those labellings were suboptimal AND load-dependent -- the wall
clock is exactly the cap 39.5 added the deterministic one to escape. Cause:
38.14's t -> n adjacency makes the model much harder, and the 2s value
dated from when solves took ~124 ms. Raised to 30s; 24/24 harbor and 36/36
maple now OPTIMAL, deterministic budget still in headroom (3.569/4.0).

The verdict reverses. Deterministic, 12 seeds, scored canonically:
  harbor  greedy 1323 (722h)  0.079 s/seed    cpsat 1548 (908h)  1.623
  maple   greedy 1764 (777h)  0.063 s/seed    cpsat 2256 (1213h) 1.327
cpsat loses on both, +225 and +492 fails at ~21x the seeding time,
concentrated in hard fails.

Time and quality have different causes. Removing t -> n from harbor takes
cpsat 1.623 -> 0.193 s/seed (8.4x faster) but it is still +205 vs greedy
(was +225) -- so the adjacency explains the time blow-up and ~9% of the
quality gap; the regression is otherwise pre-existing.

Squaring with 39.5: that section records cpsat returning 194/180/171/182
over four identical 10-seed aggregates before the determinism work. Its
10-fail harbor margin sits well inside a noise band that wide, and was
measured with fdp's id()-ordered room_slots live. The seeder-level claim
was never established rather than overturned. 39.5 annotated in place.

Absolute totals are ~6x 39.5's because the objective has changed, so they
are not comparable to that table; the within-measurement comparison is
like-for-like and is what the verdict rests on.

No default changes: assign_solver was already greedy for 37.7's independent
reason. What changes is that "cpsat wins the seeder A/B" should no longer
be cited as a reason to pursue it.

The cap fix takes the suite from ~4.5 to ~10 min and the tests cannot opt
out, since constructive_topology does not thread the solver limits through.
Filed as homemaker-py-2xk.

Closes homemaker-py-vjd.

Lint at parity (46); tests 384 passed, 0 failed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB
2026-08-29 14:47:36 +00:00
.beads CP-SAT seeding re-measured deterministically: it loses 2026-08-29 14:47:36 +00:00
.claude Scaffold homemaker-py with validated geometry port 2026-06-10 20:50:20 +01:00
examples Declare toilet-to-sleeping adjacency where the brief supports it 2026-08-29 10:57:44 +00:00
experiments Re-validate collapse_insearch's default under the current objective 2026-08-29 13:57:45 +00:00
src/homemaker_layout CP-SAT seeding re-measured deterministically: it loses 2026-08-29 14:47:36 +00:00
tests n_workers is an algorithm parameter, not noise 2026-08-29 12:52:33 +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 CP-SAT seeding re-measured deterministically: it loses 2026-08-29 14:47:36 +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.