When a compound operator (e.g. level_compound_fix) creates a new
internal node and explicitly sets its division ratio, that ratio was
silently overridden: warm_x0 received parent.ratios which had no entry
for the new node, so Nelder-Mead started at the default 0.5 instead
of the operator's intended 0.25 (for rrl/rrr). Result: NM evaluated
the compound topology at the wrong geometry and scored 3 fails instead
of 1 — so lex always rejected the compound child, making
level_compound_fix invisible to the outer search.
Fix: for nodes that are genuinely newly divided (not divided in the
parent tree at the same path), inherit the child's operator-set ratio
rather than defaulting to 0.5. Structural mutations (e.g. swap) can
reveal hidden level-N nodes that retain stale pre-writeback ratios —
those are correctly excluded by checking parent_node.divided.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When level_fix alone displaces a required room (e.g. t3) it triggers 5
oracle fails for the missing room — far worse than the starting 2 fails,
so lex always rejects it. level_compound_fix atomically: moves the
constrained room to its required floor AND re-inserts the displaced room
by splitting the sibling of the largest C leaf on that floor. The C
sibling is guaranteed adjacent to C (shared parent split), so the
displaced room keeps its required C-adjacency.
On programme-house this jumps the warmstart from 2 fails (l1 wrong
level + t3 size) to 1 fail (staircase volume), which lex accepts as an
improvement and provides a new base for further mutation.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Scores .dom files using fitness.Fitness.score_with_fails(), writes .score
and .fails side-cars in the same format as urb-fitness.pl, and respects the
same skip-if-up-to-date / FORCE_UPDATE caching semantics.
Closes homemaker-py-g0b.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
core_divide: divides a C leaf simultaneously on ALL storeys that share that
path, maintaining staircase consistency as an atomic invariant rather than
requiring multi-step recovery.
core_undivide: reverses core_divide consistently across all floors, merging
a C sub-core back into a single C leaf everywhere.
level_fix: atomically moves a level-constrained room to its required floor
by retyping the largest leaf there and vacating the wrong-floor leaf to C.
Requires `reqs` (SpaceReq dict); disabled (zero probability) without it.
mutate() gains `reqs=None` parameter; driver.search() passes its already-
loaded reqs so level_fix fires during the main memetic loop.
Together these let the optimiser escape the deceptive valley around the
2-fail warmstart: level_fix moves l1 to level 0 (reducing fails 2→1),
then core_divide can split the C core to accommodate the displaced t3.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Cross-storey equivalent of mutate_retype. Directly addresses
level-constraint failures ("l1 on wrong level") by moving a room type
from one floor to another without changing topology or geometry.
Registered in MUTATIONS at default weight (1.0); no drastic geometry
perturbation so it does not need the reduced level_add/delete weight.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously level_add copied the top storey exactly, duplicating all
named programme rooms and immediately triggering space-count failures
for every room on the new floor. The lex outer-search comparison
(-n_fails, score) then always rejected the multi-storey child because
its fail count was far higher than the single-storey parent.
Fix: retype all named-room leaves on the new storey to generic C or O
before admitting the child. The outer search then retypes them
incrementally via the normal retype operator. This allows level_add to
produce designs with the same fail count as the parent (storey_minimum
fail removed, no duplication fails added), making the multi-storey
transition visible to the lex selector.
Result on programme-house cold start (init.dom, 100k evals, 4 workers):
before: 6 fails, single-storey, stuck after 40k evals
after: 4 fails, two-storey, still improving at 100k
Also adds examples/harbor-house/ from urb/examples for future runs.
Co-Authored-By: Claude Sonnet 4.6 <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>
programme.load_programme_dir(directory) mirrors urb-evolve.pl: loads
../patterns.config first, then merges local patterns.config on top (shallow,
local top-level keys win). driver.search now uses load_programme_dir instead
of hardcoding the local path, so the type pool respects parent config.
fitness.load_config already had this behaviour; programme now matches.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
python -m homemaker.evolve seed.dom [--budget N] [--pop N] [--workers N] ...
Installed as homemaker-evolve entry point via pyproject.toml.
Takes a single .dom file; infers programme dir from its parent. All parameters
available as --flags or HOMEMAKER_* env vars. Output defaults to
<seed_stem>_evolved.dom; use --output - for stdout. SIGINT/SIGTERM returns
best-so-far via new driver.SearchResult.interrupted flag.
Also adds dom.dumps() for string serialisation and refactors dom.dump() to use it.
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>
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>
When a programme space has no explicit 'width' key, the fallback to
width_inside [4.0, 1.0] is geometrically impossible for small spaces
(e.g. t3 WC at 3 m²). Now compute target = sqrt(size/proportion),
sigma = max(0.1, target * size_sigma / (2 * size_target)).
Effect on 35-file corpus: 32 files score +1–307% (width quality improves
for correctly-sized small rooms); 5 files lose spurious width fail lines.
Upstream Perl fix tracked as homemaker-py-8fe.
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>
Two root causes found for ca/cb corpus parity failures:
1. _avg_path_len_from used unweighted BFS (hop count) but Perl's
Graph::average_path_length uses weighted Dijkstra with centroid-to-centroid
edge distances. This caused wrong edge removal in has_circulation, giving
wrong stack corner counts (2 instead of 3 for lr in ca9e80c5).
2. Entrance corner logic used _public_access (any street boundary) but Perl's
Entrances() picks the best entrance route — a stair only gets entrance corners
if no higher-priority non-stair C leaf has public access.
Also includes homemaker-py-hgg storey/building checks previously uncommitted:
stair fit, circ connectivity, roof-garden, public-access tracking, has_circulation,
corners_in_use, stack_corners_in_use, check_space_counts with failure stacking.
All 4 debug corpus prefixes: ratio=1.000000. 39 tests pass.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Port Urb's programme-driven fitness leaf quality factors (perpendicular,
proportion, size, width, crinkliness, daylight, access), value rates,
and cost model (per-leaf area costs, interior/exterior wall edge costs,
boundary costs) to Python. Passes 0-mismatch parity against the Urb
oracle across all 35 corpus files (407 leaves, 2849 factors), using
URB_NO_OCCLUSION=1 simple crinkliness (illumination factor pinned to 1).
Key fixes: _dist must use math.sqrt not math.hypot (1-ULP difference
flips boundary overlap predicates); leaf-scope fail regex requires ^\d+/
prefix to exclude building-level failure messages.
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>
experiments/benchmark_vs_urbevolve.py (homemaker-py-way): 3 seed designs
(init from scratch, c964435 weak, 2f45907 strong) x 2000-eval runs, the
1000-eval tier read from each run's best-so-far log; urb-evolve gets two
population sizes (default 128 = ~16 generations at this budget, and 16 =
~130) and credit for its better one. Counts via the MAX_EVALS counter
patch in urb-evolve.pl (committed in the urb repo); both systems under
URB_NO_OCCLUSION=1; all finals re-scored through urb-fitness.pl as the
common deterministic yardstick.
run_search.py generalised to (budget, rng-seed, seed.dom, out.dom);
innerloop.optimise now handles 0-DOF topologies (an undivided plot like
init.dom scores once instead of crashing CMA).
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>
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>
oracle.score_batch() writes/cleans N outputs and runs urb-fitness.pl once
with all file names; oracle.score() is now a thin wrapper. Adds
Score.fail_lines (sorted) because Perl hash-order randomisation shuffles
.fails line order between runs, and documents Urb's ~1-ULP score
nondeterminism (compare with rel tolerance, never ==).
experiments/bench_batch_oracle.py validates batch-vs-single parity on the
35-file corpus and benchmarks: 0.98 s/dom batched vs 1.27 s/dom single
(x1.30), all files identical (fitness to 1e-12 rel, exact failure sets).
Closes homemaker-py-av5.
Adds the bottom-up ratio solver, programme parser, Perl-oracle bridge,
and two experiments. Headline finding: the "isolated size solver on a
frozen topology" hypothesis is NOT validated.
- resolve_ratios.py: re-solving candidate-002 from programme targets
recovers areas accurately but scores below the original (introduces
width/perpendicular/crinkliness failures the area objective ignores).
- refine_sweep.py: warm-start refine of all 34 evolved candidates
regresses 34/34 (fails 124->297 perpendicular-tied; 124->626 area-only
with free skew). Moving cuts to fix room area breaks the coupled
adjacency/access/shape constraints those designs balanced.
Conclusion: sizing is not separable from the rest of Urb's fitness;
a geometry inner loop must optimise the full objective, not an area proxy.
Geometry port remains validated byte-identical to Urb.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Clean-room Python successor to Urb for programme-driven layout search.
This initial commit establishes the .dom bridge format and a faithful
port of Urb's top-down quad geometry, validated byte-identical against
Urb across all 35 programme-house example files (including the wall
inset and multi-storey wall-stacking inheritance).
- dom.py: .dom YAML <-> Node tree, parent/below/position linkage,
wall_outer inset on load, raw-corner stash for round-tripping
- geometry.py: Coordinate/Coordinate_a/_b/Area/Length + Coordinate_Offset
- experiments/dump_areas.{py,pl}: geometry regression harness
