Python rewrite of the Urb/Homemaker stack
Bruno Postle
bb9b355f14
Make the §13.3 lever a first-class feature, not experiment-only.
- programme.py: SpaceReq.share (default 1) + has_share, parsed from
patterns.config 'share: N'.
- operators._share_grain: resolve per-code grain from leaf_share_factor
selector — 0 = per-code opt-in (share iff share:N>=2), >=2 = global with
per-code override (share:1 opts OUT, share:N sets grain). _share_rooms
groups per resolved grain.
- End-to-end conf injection without monkeypatch: load_config(overrides=)
merges run-level keys last; driver.search / innerloop.optimise /
NativeEvaluator / _fitness_for thread conf_overrides={leaf_sharing:True}
through both inner-loop and off-tree scorers when sharing is on.
- homemaker-evolve: --leaf-sharing/--no-leaf-sharing + --leaf-share-factor
(env HOMEMAKER_LEAF_SHARING / HOMEMAKER_LEAF_SHARE_FACTOR).
- Example programmes untouched (§13.3/§13.9 stay reproducible). Experiment
load_config monkeypatches updated to accept overrides=.
Tests: grain modes, opt-out, default-OFF parity, load_config overrides,
programme parse, CLI parse. 233 pass. Smoke: harbor 37 vs 95 fails on/off.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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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 (current): canonical slicing genome + high-locality operators + Nelder-Mead inner loop.
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/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.