Same-code rooms collapse into fewer, larger SHARED leaves so the ~1.8/leaf
shape tax (§13.1) is paid once per group. Multiplicity k is recovered from
area (k=clamp(round(area/target),1,max_share)) — no genome change — and used
in two default-OFF sites: graph.check_space_counts counts coverage (Σk vs
req.count) so one leaf covers several rooms without a missing fail, and
fitness.quality_size centres on k×target (σ scaled by k). Construction:
operators._share_rooms groups instances; _size_divisions_from_targets sizes
shared leaves to k×target via leaf_mult.
Floor probe (experiments/diag_leaf_sharing.py, harbor+maple, seeds 0/1/2,
+innerloop): total fails −27% harbor / −16% maple at share3, shape factors
fall ~linearly with leaf count (confirms §13.1). Cap: 17–44 missing fails
leak because depth maldistribution (§13.2) keeps shared leaves below k×target
so round() undercounts; inner loop can't close it. Net still positive.
Default-OFF reproduces baseline exactly (214 tests pass). Driver plumbing +
staged 20k A/B remain; §13.3 records the next design fork.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The constructive seeder was never nondeterministic: _assign_adjacency_aware
ends every max/min with a unique leaf-idx tiebreak and uses set unions only
for membership, so iteration order never leaks. constructive_topology(seed=0)
is byte-identical across processes for every example programme. The cited
"sig 4480 vs 16064" was a measurement artifact — Python's builtin hash() of a
str is salted per process (PYTHONHASHSEED), so an identical signature hashes to
different ints run-to-run.
The real run-to-run noise was parallel-only: driver._run_batch admitted futures
via as_completed (completion order), and admit() is order-sensitive (accrues
n_evals per result; keeps the first individual of an equal-key tie as best). A
long parallel run diverged 167 vs 161 fails (maple seed 0). Fix: admit futures
in submission order (block on each result in turn; all still run concurrently),
reproducing the serial admission sequence. Two workers=4 runs are now
byte-identical. Serial (workers=1) was already byte-for-byte reproducible.
Per-seed numbers are reproducible only at a fixed worker count; serial != parallel
is expected (children/iteration 1 vs n_workers changes batch granularity).
- driver: iterate futs in submission order, not as_completed
- test: test_search_parallel_is_reproducible (fails on pre-fix, passes on fix)
- DESIGN.md §12.4: corrected the reproducibility note
Closes homemaker-py-xcy
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Land the two evidence-supported parts of the re-scoped 9gp capstone as
operators on the existing decoded Node tree (no Polish-expression rewrite),
each default-OFF and measured against the §12.2 leu.2 baseline.
9gp.1 shape-feasibility pre-filter: operators.predicted_shape_fails lays a
topology out at its proportion-aware target geometry and counts shape fails
(size/width/proportion/crinkliness); driver._evaluate prunes clearly-infeasible
topologies before the inner loop (1 eval vs ~80), guarded so nothing that could
beat the incumbent is discarded. search/search_staged feasibility_filter,
feasibility_max_shape_fails (env FEAS/MAXSHAPE), default OFF.
9gp.2 M3 Wong-Liu reassociate: operators.mutate_reassociate adds associativity
(a|b)|c <-> a|(b|c) on same-orientation live cuts — the canonical-slicing move
missing from swap(M1)/rotate(M2), attacking the §11.4/§11.5 reachability
bottleneck. enable_reassociate (env REASSOC), default OFF (weight 0 -> baseline
byte-identical).
Unit tests (operators + driver) green, full suite 211 passed; maple-court smoke
run clean under native fitness. A/B sweep handed off per the plan; DESIGN.md
§12.3 documents the design and the pending measurement.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
_assign_adjacency_aware gains fixed_circ (seed the connected-dominating-set from
given circulation leaves) and secondary-adjacency-aware room placement: codes
with the most non-c adjacency requirements are placed first, each onto the open
slot satisfying the most of its requirements against already-typed neighbours
(clustering k1<->da1, da1<->o). lift_base_to_storeys(reqs, adjacency_aware=True)
grows the upper-floor circulation spine off the inherited vertical core and
assigns rooms around it; threaded through driver.search_staged
(seed_adjacency_aware) and run_staged_search.py (ADJ env).
End-to-end staged harbor, 20000 evals, mean total fails over 3 seeds:
ADJ=0 99.0 (reproduces the §11.4 staged lex baseline exactly), ADJ=1 85.3
(-13.7, -14%; best 78). New best harbor configuration overall: staged baseline
99.0 -> single-stage adjacency-aware (§11.6) 90.7 -> staged + adjacency-aware
lift 85.3. Staging and adjacency-aware seeding compose.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
operators._assign_adjacency_aware spends ~one extra leaf per three rooms on a
greedy connected-dominating-set of circulation leaves (read from the geometric
leaf_graph, type-independent), so every room borders a connected circulation
spine and adjacency-to-c + access are satisfied by construction. Default-on via
constructive_topology(adjacency_aware=True), threaded through
driver.search(seed_adjacency_aware) and run_search_scaled.py (ADJ env).
End-to-end single-stage, 20000 evals, mean total fails over 3 seeds:
harbor 110.0 -> 90.7 (-17.5%; ADJ=0 reproduces the §11.2 105 baseline exactly),
programme-house 12.3 -> 9.3 (-24%). Adjacency-aware single-stage harbor (mean
90.7, best 85) beats the §11.3 staged best of 95 — the first Phase-6 fail-count
reduction from seeding. Follow-ups (lift_base_to_storeys, secondary adjacencies)
filed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
genome.signature: ratio-invariant structural topology hash (per-storey tree
shape + cut orientation + leaf types), the cheap stand-in for the 9gp canonical
encoding. driver gains niche_by_signature (one individual per topology, replaces
the fitness-scalar dedup) and restart_patience (soft restart: keep elites,
refill with fresh seeds); SearchResult gains n_distinct_signatures /
diversity_history / n_restarts.
Diversity criterion MET (final-pop distinct ~5/16 -> 16/16). Gate NOT met:
blank-slate programme-house mean fails 12.3(legacy)/12.7(niche)/13.0(restart)
over 3 seeds at 20000 evals; harbor staged 95/94/108. Niching is a tie within
seed noise, restarts strictly worse — falsifies the premise that the
fitness-scalar dedup causes premature convergence. Both flags default-off,
kept for reuse. Epic c4c complete.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Implement a graded proximity comparator key (-n_fails, grade, fitness) behind
a default-off use_grade flag: fitness._leaf_grade / score_with_grade sum
f/FAIL_THRESHOLD over failing per-leaf quality factors; scalar fitness and fail
count stay untouched so the inner-loop 0.5^n cliff (§5.4) is unaffected (0/9
regression check: PASS). Read once per child in driver._evaluate off the
already-optimised tree; threaded through search_staged (Stage 2 only).
Harbor staged A/B (20000 evals, seeds 0/1/2): lex 95/96/106 (mean 99.0) vs
lex+grade 99/98/102 (mean 99.7) — grade wins 1/3, no plateau escape. Premise
falsified: within a fixed fail-tier 0.5^n is constant so fitness still spans
~6 orders of magnitude; grade above fitness displaces that working signal.
Verdict: reject; lexicographic (-n_fails, fitness) stands. Flag kept default-off
for reproducibility / possible reuse as a §11.5 diversity signal.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Search the genome in causal dependency order. Stage 1 evolves a single-storey
base over the level-0 room set (programme auto-derived to a tempdir), ranked
with a substrate-readiness bonus (reserved core × divisible capacity) so the
base is selected as a good substrate, not just a good ground floor (anti-§4.2).
Stage 2 lifts the best base into a full multi-storey design — preserving the
inherited core, instantiating each upper storey's required set by construction —
and searches the deltas with the base mutable at low probability (base_p=0.15).
New: programme.{n_storeys_required,partition_rooms_by_storey,write_stage1_programme},
graph.substrate_readiness, operators.{lift_base_to_storeys,_pick_weighted_by_storey},
base_p threading, driver.search rank_bonus_fn/seed_factory/base_p hooks +
search_staged orchestrator, experiments/run_staged_search.py, tests/test_staging.py.
Result (harbor, 20000 evals, seed 0): staged 95 fails vs single-stage 105
(-10, -9.5%), gain in crinkliness 27->18 + edge 12->8. Anti-bungalow confirmed
(Stage-2 core moves all noop — core inherited, not carved). Programme-house
regression PASS (warmstart-2f4 still reaches whole-pop 1-fail).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Make the required programme room set a constructive invariant instead of
something the topology search must stumble onto by random divide+retype.
- operators.constructive_topology: bootstrap seeder that sizes each storey to
its required rooms (partitioned by level; level-free rooms distributed),
+1 core C and +1 O per storey, then assigns types. Stochastic for population
diversity. Wired into driver bootstrap when the programme has required spaces.
- operators.mutate_place_missing: repair op that inserts a missing required
space by dividing a host leaf into [room | remainder]. Lex-safe host ranking
(generic O first, never displace a required room); honours required level.
Weight 2.0 in the mutation mix; noops cheaply once the set is complete.
A/B on harbor-house (20k evals, seed 0, identical config):
old random-bootstrap 133 fails (103 missing, 77%)
new constructive 105 fails ( 12 missing, 11%) -21% total, missing-stack
collapsed; seed head-start 163->139.
§4.10 regression PASS: warmstart-2f4 still reaches a 1-fail population at 50k.
Verdict (DESIGN.md §11.2): construction is necessary and reframes the
bottleneck to quality-fail packing of a complete dense design (crinkliness/
size/access/edge) -> unblocks §11.3 staging, motivates §11.4 graded objective.
Follow-up filed (homemaker-py-s44): adjacency-aware seeding.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Outer search now ranks individuals by (-n_fails, fitness) instead of raw
fitness scalar. This prevents high-score 3-fail designs from displacing
2-fail designs in tournament selection and population replacement — the
root cause of the §4.8 pathology where flag count dominates geometry.
Inner loop is unchanged: it still optimises against the raw 0.5^n fitness
scalar, so the cliff that prevents trading into new failures remains intact
(0/9 regressions in experiments/penalty_reshape.py).
Also removes stale _CHILD_INNER_KW = {"sigmas": (0.05,)}: this was left
over from the CMA-ES era; the NM inner loop default (homemaker-py-d6d)
does not accept a sigmas parameter.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Bakeoff with native fitness shows NM wins at all DOF sizes: +9% at
child_budget=80 for programme-house (6-7 DOF), and decisively at
harbor-house scale (35-40 DOF) where CMA-ES exhausts its convergence
detector after ~3 generations (46 evals) and adds failures on 12/15
runs. NM uses the full budget, is parameter-free, and has zero new
failures across all test cases.
- Add nm_search() to innerloop.py; change optimise() default to "nm"
- Add nm_search to parametrised test cases
- Add bakeoff_native.py and bakeoff_harbor.py experiments with results
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add n_workers parameter to driver.search(). When n_workers > 1, a
ProcessPoolExecutor evaluates the bootstrap batch and main-loop children
in parallel, giving near-linear speedup with core count. The geometry
module-level cache is cleared in each worker after fork to prevent stale
id-keyed entries. Serial behaviour (n_workers=1, default) is unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Copy programme-house corpus (36 .dom + .score + .fails + patterns.config)
into examples/ and update all 5 test files to use project-relative paths.
Native Python fitness (use_native=True) was already the default; tests now
run without /home/bruno/src/urb present.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When the seed is an undivided bare plot (init.dom), auto-generate pop_size
random topologies before the memetic loop starts, each evaluated at
child_budget. This crosses the zero-feasibility region that single-seed
chaining cannot escape — the programme-house cold start was stalling at 18
fails after 2000 evals vs urb-evolve's 6.
Auto-detection via seed_root.divided preserves the existing single-seed
path for warm starts from existing designs; all previous tests pass unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Bug fix: _entrance_bid_for_stair now returns None when the stair leaf has an
outdoor neighbour with public access — Perl's Entrances function picks the
via-outdoor priority (3.5 > 3) which maps the stair to a leaf id rather than
a boundary id, so Boundary_Id(edge) eq leaf_id never matches and no entrance
corners are added. Without this fix 7 files had an extra 'staircase volume'
failure from corners [3,1,2] giving stair_fit=0.718 instead of [3]→1.095.
New: Fitness._evaluate_full() extracts the shared pipeline so evaluate()
and score_with_fails() both use it. NativeEvaluator added to innerloop.py
as a drop-in for OracleEvaluator; optimise() defaults to use_native=True.
Gate results: 35/35 score parity (rel_tol=1e-4), 35/35 fail-set identity,
native speed ~45ms/eval vs oracle ~1000ms/eval batched = 23x speedup.
OracleEvaluator kept for validation; oracle.score_batch unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
run_urbevolve took (seed, budget, pop, cell) but cells call
fn(seed, budget, cell, **kw) — every urb-evolve cell died on TypeError,
deferred silently by pool.map. mutate_swap lacked the empty-candidates
noop guard the other operators have, crashing on init.dom-style bare
plots. Regression test: every mutation survives an undivided tree.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Two bugs fixed in boundary_id / leaf_graph:
1. 'bid not in "abcd"' used Python substring check, silently dropping the
root-division boundary (empty-string id). Fixed to frozenset membership.
2. Upper-storey nodes store their own rotation in the YAML but Urb::Quad::Rotation
delegates to Below->Rotation. boundary_id now walks the below-chain to the
ground-floor rotation, matching Perl exactly.
After fixes all 35 corpus files produce edge counts matching Perl oracle.
Added:
- src/homemaker/graph.py: build_graphs (two-phase pattern), has_adjacency,
has_vertical_connection (faithful no-overlap stub per DESIGN §8.1),
find_missing_spaces, check_adjacency, check_level_constraints,
check_vertical_connectivity
- src/homemaker/dom.py: @dataclass(eq=False) on Node for NetworkX hashability;
is_outside, is_supported, is_unsupported, merge_divided
- tests/test_graph.py: 7 tests, edge counts vs Perl oracle on all 35 files,
exact widths for 2f45907, merge_divided smoke, two-phase independence
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Bruno's correction: 'C' was never a 'covered' type — Is_Covered is a
geometric predicate. Urb generics are canonically uppercase (get_space_types
qw/C O S/; corpus 100% uppercase). The driver/operator type pool emitted
lowercase 'c'/'o', creating mixed-case designs that fragmented Dom->Ratios
class buckets and fired the latent ratio_type first-match nondeterminism
(which the search promptly reward-hacked). Operators now emit uppercase
generics only and class checks match case-insensitively (t[0].lower() in
'cos', cf. Is_Circulation/Is_Outside). The Urb-side class-sum patch remains
as defensive hardening, zero-impact on canonical designs (35/35 parity).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
driver.py (homemaker-py-b39): tournament selection, operators.mutate (storey
ops down-weighted) + area-matched crossover, every child's geometry
delegated to the warm-started inner loop (Lamarckian write-back; children
use a single local CMA phase - the exploratory ladder phase exists for cold
projections children never face). Budget stated and accounted in oracle
evaluations; near-duplicate fitness guard against population collapse
(neutral mutations are common, per 8cs).
free_with_keys/ratio_map/warm_x0 promoted from the 8cs experiment into
innerloop.py as the Lamarckian inheritance API; alignment with
solver.free_branches asserted across the corpus.
tests/test_driver.py fakes the inner loop: budget accounting, monotone
improvement history, warm-start + sigma plumbing, valid .dom output.
31 tests pass. experiments/run_search.py is the end-to-end acceptance run.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
operators.py (homemaker-py-nyb): divide/undivide/retype/swap/rotate/
level_add/level_delete + Urb-style area-matched base-storey crossover.
Operators edit the decoded Node tree; genome.encode absorbs all repair
(dangling deltas, storey misalignment) so every child is a valid genome
by construction. Geometry moves deliberately absent — the inner loop owns
continuous DOF, and 8cs made Lamarckian re-optimisation mandatory.
Fixes dom._link to CLEAR stale below-links when a path vanishes from the
storey below (undividing a base branch left upper nodes pointing at
orphaned quads; oracle scoring unaffected but in-process geometry crashed).
Acceptance (experiments/operator_locality.py, flag-on): 115/115 children
scored without error; geometry perturbation small for core ops (retype
0.07, divide/undivide 0.14, swap/crossover 0.16-0.17), fitness
perturbation large for all (0.68-0.99 rel) — the 0.5^n cliff flags most
raw moves, confirming warm-started re-optimisation + penalty reshaping
as the load-bearing design choices. 27 tests pass.
Closes homemaker-py-nyb.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The sigma-ladder default splits the budget into restart phases, so a
400-eval run reaches ~0.996 on the smooth test objective rather than
0.999+. Test now matches the component's contract. (Previous commit
landed with this failing because piping pytest to tail masked its exit
code.)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
innerloop.py: optimise(root, programme_dir, x0=None, budget, method) ->
Result, optimising equal-offset free-branch ratios (midpoint projection of
legacy unequal cuts) against full oracle fitness. OracleEvaluator scores
each population in one batched perl call. Methods: cma (default) — multi-
start sigma ladder (0.05 local, 0.15 exploratory) with IPOP-style popsize
doubling and deterministic seeding (pycma treats seed 0 as clock!) — and
compass with Hooke-Jeeves pattern moves, kept for the d0s bake-off.
Acceptance (experiments/accept_innerloop.py, §4.5 bars vs unprojected
originals, within-noise tolerance 1%): x1.65 / x1.66 / x1.58 against bars
x1.24 / x1.67 / x1.59, no new failures, 46 oracle calls vs Nelder-Mead's
200. The two near-bar results are statistically indistinguishable from the
single-NM-draw bars (measured draw spread brackets them); decision approved
by Bruno 2026-06-12.
Also: tests/ scaffold (12 oracle-free unit tests, pytest pythonpath=src),
rebaseline_no_occlusion.py for homemaker-py-gp2, cma>=3.0 dependency
(installed via dnf), dead-variable cleanup in solver.py.
Closes homemaker-py-1p0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
