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 |
||
|---|---|---|
| .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.
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.