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Python rewrite of the Urb/Homemaker stack
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Bruno Postle 33d79be3fe Cold-start bootstrap: diverse initial population for blank-slate search 
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>
2026-06-13 23:29:12 +01:00
.beads Cold-start bootstrap: diverse initial population for blank-slate search 2026-06-13 23:29:12 +01:00
.claude Scaffold homemaker-py with validated geometry port 2026-06-10 20:50:20 +01:00
experiments Phase 3 gate (homemaker-py-ccw): scaled search on native fitness 2026-06-13 22:10:38 +01:00
src/homemaker Cold-start bootstrap: diverse initial population for blank-slate search 2026-06-13 23:29:12 +01:00
tests Cold-start bootstrap: diverse initial population for blank-slate search 2026-06-13 23:29:12 +01:00
.gitignore bd init: initialize beads issue tracking 2026-06-11 23:27:11 +01:00
AGENTS.md bd init: initialize beads issue tracking 2026-06-11 23:27:11 +01:00
CLAUDE.md Fix stair-fit parity: entrance corners + weighted has_circulation (homemaker-py-w1e/q70) 2026-06-13 20:55:25 +01:00
DESIGN.md Phase 3 gate (homemaker-py-ccw): scaled search on native fitness 2026-06-13 22:10:38 +01:00
pyproject.toml Geometry inner loop: batched full-objective ratio optimiser (CMA-ES) 2026-06-12 09:42:24 +01:00
README.md Scaffold homemaker-py with validated geometry port 2026-06-10 20:50:20 +01:00

README.md

homemaker-py

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

  1. Solver experiment (current): port Urb's geometry, re-solve ratios from programme targets, score the result against the original via the Perl oracle.
  2. Native Python fitness (retire the Perl oracle).
  3. Canonical slicing encoding (normalized Polish expression) + memetic search.

Layout

  • src/homemaker/dom.py — read/write Urb .dom YAML into a Node tree.
  • src/homemaker/geometry.py — faithful port of Urb's top-down geometry.
  • src/homemaker/programme.py — parse patterns.config space requirements.
  • src/homemaker/solver.py — bottom-up ratio solve (scipy).
  • src/homemaker/oracle.py — Phase-1 scaffold: score a .dom via Urb's urb-fitness.pl.

The Perl oracle is the only throwaway component; everything else is permanent.