a7ef9e2fa0
Owner: "I think a corridor could be worth a sixth of a room, this is ok. maybe we should dump the ratio_circulation altogether if there is already a pressure in circulation caused by the cost benefit ratio per msq. this is the kind of thing we want to root out of the scoring model: anything that is double counting, or using a gaussian where a linear ramp is appropriate, etc." value_circulation = 50 stands; hxi's rate question is closed. The duplication argument is stronger than it first looks. score = value/cost is already a ratio, so the per-m2 economics (50 against a build cost of 200) is not merely an absolute pressure -- adding corridor moves value/cost by an amount that depends on how much of the building is already corridor. It is ALREADY proportional. ratio_circulation said the same thing again as a whole-building multiplier, on a curve where twice the corridor is far more than twice as bad. Correction to my own first measurement: I overrode ratio_circulation and got scores going DOWN when a <=1 multiplier was removed, which is impossible. All four corpus programmes DECLARE ratio_circulation, so the CONF_DEFAULTS value I had changed was never in play and the two arms differed only in sigma. Same trap as value_supported in 39.19. Against the keep-it case, recorded because it is the one real argument: three of four declare a POSITIVE target (harbor/maple 0.08, health-centre 0.10), making the term formally two-sided rather than "less is better". It does not survive the numbers -- the lower side is worth at most 13.3% on the large programmes against 99% on the upper side, and "a building needs some circulation" is enforced structurally by access and connectivity, which no amount of value can buy off. Disabled in CONF_DEFAULTS and the four corpus configs, each with the reason inline and a note that a [target, sigma] pair re-enables it. Fail sets unchanged; it was always a value multiplier. Scores +42% to +7712%. 39.24 also sweeps every remaining term against the owner's two tests. Verdicts: perpendicular, proportion, width, crinkliness, access, size's lower side, ratio_outside, staircase volume and the count/limit fails are all sound. Filed as homemaker-py-dpt: size's UPPER side (cost already charges area; 82% of size fails are over-target), the minimum-internal-area factor (a third statement of "build the rooms"), the 0.5**n_fails curve (a ruling, not a measurement), and two dead paths -- ratio_public/private_outside, which no config declares, and the daylight factor pinned to 1.0 since the descope. Also updated a test I added last turn which asserted ratio_circulation was the second charge; it now pins that the linear ramp is the ONLY one. 419 passed. Closes homemaker-py-hxi. 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.
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.