a25dc2cb59
Answering "are we clean". Generic namespace: yes. Usage namespace: no.
FINISH §39.4. The first sweep missed sites, found by a full re-grep:
graph.py's free-area budget, operators.py host-preference / keep-type /
repair-candidate, fitness.py's ("l","c","k") public-access test, bubble.py's
generic adjacency reference, and -- the important one -- cpsat.py, which was
still matching adjacency by raw startswith. graph.code_matches_requirement is
now the single public answer to "does this leaf count as the thing the
programme asked to be next to", shared by has_adjacency, has_vertical_connection
and cpsat.
RETRACT §39.5. It concluded 2g7.5's CP-SAT seeder win did not survive the
correction. That was wrong. The cause was the missed cpsat matcher above: the
exact solver was optimising a different relation than the scorer checked, so a
failing test reporting an incomplete sweep was misread as a baseline shift.
Re-measured over 6 seeds, cpsat now wins on both programmes (harbor 102/92,
maple 156/154). xfail removed.
REAL BUG UNDERNEATH: CP-SAT was never deterministic despite
num_search_workers=1 and a comment claiming it. neighbors[slot] is a set of
dom.Node, which hashes by id() -- a memory address -- so raw iteration made the
model-build order vary and CP-SAT returned a different equally-optimal
assignment each run (measured 194/180/171/182 over four identical aggregates).
sorted() on the slot indices fixes it. Also paired the wall-clock cap with
max_deterministic_time (solves run ~124ms against a 2s cap, so nothing was
timing out -- latent hazard, not the cause). solve_room_labels is now
reproducible on every captured instance; constructive_topology on the cpsat
path still is not, filed as homemaker-py-fdp (plausible contributor to b8g).
§39.6 THE SECOND NAMESPACE. Usage prefixes b/t/l/k (bedroom/toilet/living/
kitchen) classify programme codes by first letter and stay prefix-based by
design, but they are not inert: has_circulation deletes graph edges from them.
Four corpus rooms are misclassified by spelling -- la1 "Laundry Room" and li1
"Library Corner" as living, br1 "Staff Room" as bedroom, tr1 "Treatment Room"
as toilet. Measured on a health-centre seed: tr1 loses its edge to the adjacent
O, br1 loses its edge to t10 "Staff WC" -- both feed the connectivity fails §38
found persisting. Filed homemaker-py-sel; an explicit usage: key is the fix,
but it changes fitness for correctly-spelled programmes too so it needs its own
A/B.
DOCS. README gains a "Room codes and reserved names" section; CLAUDE.md and
AGENTS.md gain the same summary for agents. audit_programme_config.py now
reports the usage class each code picks up alongside the namespace and
satisfiability checks. DESIGN §37.2's note calling the c/o/s quirk "existing
product behaviour, not a bug" is annotated as superseded.
Corpus audit: zero generic-namespace violations across all ten example
programmes. 346 passed, same 7 pre-existing fixture failures, lint unchanged.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB
97 lines
5.2 KiB
Markdown
97 lines
5.2 KiB
Markdown
# homemaker-layout
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Programme-driven building-layout search over slicing trees. A clean-room Python
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successor to the Perl [Urb](../urb) project, intended to eventually be 100% Python.
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## Why a rewrite
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Urb represents a building as a binary slicing tree where room sizes are derived
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**top-down** from division ratios. That makes room area an emergent property of
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every cut above it, which:
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- gives the genome low locality (a cut near the root rescales every descendant),
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- makes target room sizes nearly impossible to hit, so the gaussian size penalty
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dominates fitness, and
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- defeats crossover (transplanted subtrees lose their proportions).
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homemaker inverts this: leaves carry **target dimensions** from the programme and
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division ratios are **solved bottom-up** for a fixed topology. The evolutionary
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search then only explores topology + types + adjacency.
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## Phase plan
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1. ~~Solver experiment: port Urb's geometry, re-solve ratios from programme
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targets, score the result against the original via the Perl oracle.~~ ✓
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2. ~~Native Python fitness (retire the Perl oracle).~~ ✓
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3. ~~Memetic search: canonical slicing genome + high-locality operators +
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Nelder-Mead inner loop.~~ ✓
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4. ~~Penalty reshaping: lexicographic `(-n_fails, fitness)` outer-search
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comparison.~~ ✓
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5. ~~Representation upgrade: canonical slicing encoding + bottom-up shape
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feasibility, scaled to larger programmes.~~ ✓
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6. **Search-quality experiments** (current): a long running series of
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opt-in levers tried against the `harbor-house`, `health-centre`, and
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`programme-house` example corpora — leaf-sharing, finish-time cell→room
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collapse, ruin-and-recreate LNS, 2-opt polish, multi-use/co-located
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leaves, adjacency-graph and bubble-diagram fitness signals, and more.
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Most of these are negative/null results kept as opt-in flags or reference
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code rather than defaults. See `DESIGN.md` §11 onward for the full,
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numbered experiment log with methodology and results for each.
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## Layout
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- `src/homemaker_layout/dom.py` — read/write Urb `.dom` YAML into a `Node` tree.
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- `src/homemaker_layout/geometry.py` — faithful port of Urb's top-down geometry.
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- `src/homemaker_layout/programme.py` — parse `patterns.config` space requirements.
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- `src/homemaker_layout/solver.py` — bottom-up ratio solve (scipy).
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- `src/homemaker_layout/fitness.py` — native Python fitness evaluator.
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- `src/homemaker_layout/fitness_cmd.py` — `homemaker-fitness` CLI (drop-in for `urb-fitness.pl`).
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- `src/homemaker_layout/collapse_cmd.py` — `homemaker-collapse` CLI: finish-time global cell→room relabel of a `.dom`.
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- `src/homemaker_layout/graph.py` — leaf-adjacency graph for programme-driven checks.
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- `src/homemaker_layout/genome.py` — topology genome: base-floor tree + per-storey deltas.
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- `src/homemaker_layout/operators.py` — high-locality mutation and subtree crossover.
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- `src/homemaker_layout/innerloop.py` — ratio optimisation inner loop (Nelder-Mead / CMA-ES).
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- `src/homemaker_layout/driver.py` — memetic search outer loop.
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- `src/homemaker_layout/evolve.py` — `homemaker-evolve` CLI entry point.
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- `src/homemaker_layout/oracle.py` — legacy Perl shim, kept for cross-validation only.
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- `src/homemaker_layout/bubble.py` — 3D bubble-diagram adjacency fitness-signal
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prototype (DESIGN.md §27); validated null, not wired into `fitness.py` —
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reference only.
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## Room codes and reserved names
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Leaf types live in **three namespaces that share a first character**. Only the
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first is enforced; the other two are conventions the fitness function reads, so
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a room's *spelling* can change how it is scored.
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**1. Generic structural types — `C`, `O`, `S` (reserved).** The leaves the
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search itself creates: `C` circulation, `O` outside, `S` sahn (an outside court
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that also serves as circulation). Always uppercase. A programme code spelled
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exactly `C`, `O` or `S` is rejected at load.
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**2. Programme room codes — anything else, lowercase.** `k1`, `b1`, `cr1`,
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`of`, and single-character codes like `r` or `t`. These may start with **any**
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letter: since DESIGN.md §39.4 the generic tests match `C`/`O`/`S` exactly, so
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naming a room `cr1` no longer makes it circulation. (Before that fix it did —
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and silently dropped it from the required-space check entirely.)
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**3. Usage prefixes — `b` bedroom, `t` toilet, `l` living, `k` kitchen.**
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Still matched by first letter, deliberately: this is how Urb encodes room usage.
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`graph.has_circulation` deletes graph edges based on them (a "bedroom" loses its
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edges to living/kitchen/bedroom/toilet; a "toilet" loses its edges to
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outside/living/kitchen/toilet), and the access and public-access checks read
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them too.
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**So a code beginning with `b`/`t`/`l`/`k` inherits that room's connectivity
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rules whether or not you meant it** — `la1` "Laundry Room" is treated as a
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living room, `tr1` "Treatment Room" as a toilet. This is a known wart
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(`homemaker-py-sel`, DESIGN.md §39.6); an explicit `usage:` key is the planned
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fix. Until then, check any new programme with:
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```bash
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python experiments/audit_programme_config.py
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```
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which reports reserved-name collisions, the usage class each code picks up, and
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whether each room's size/width/proportion/crinkliness targets are mutually
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satisfiable at all.
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