Python rewrite of the Urb/Homemaker stack
Bruno Postle
94d4223a55
The dominant post-collapse fail is the binary "level N not connected", which is flat across fragmentation (a 7-component storey scores the same as a 2-component one), so the outer search has no gradient toward connected circulation. A finish-time convert-to-circulation repair was prototyped and measured NEGATIVE (195->560 fails: bridging needed rooms costs more missing-room fails than the one binary fail it clears). Instead add graph.circulation_connectivity(G) = largest-circ-component fraction, summed over storeys onto the score_with_grade proximity channel (conf flag conn_grade; replaces the §11.4 leaf-grade there). It is a secondary comparator key only — scalar fitness and fail count stay byte-identical — restoring the gradient the binary fail lacks. Threaded through driver (_overrides_for/_fitness_for/_evaluate/search; enabling it implies the grade key) and evolve --conn-grade (default off). A/B on full-budget runs pending; short smoke run confirms plumbing. Tests: tests/test_conn_grade.py x9 (fraction contract, non-circ ignored, monotone under (dis)connection, score/fail invariance); 276 pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm |
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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/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.