Bruno Postle
646ee30ab6
- src/homemaker/ → src/homemaker_layout/; all imports updated - pyproject.toml: name = homemaker-layout, entry point updated - .beads/config.yaml: dolt sync.remote updated to homemaker-layout.git - Delete temporary debug/perl scripts from project root - README.md, DESIGN.md: package path references updated - GitHub repo renamed; git remote updated Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
522 lines
29 KiB
Markdown
522 lines
29 KiB
Markdown
# homemaker — Design & Plan
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**Status:** validated direction, pre-implementation. Reviewed against the Urb
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source 2026-06-12; review findings folded in (see §4.5 evidence note, §4.6
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throughput arithmetic, §5 decision 6, §6 port-scope expansion, §7 re-scoped
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phases, §8).
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**Audience:** a fresh session that will break this into `bd` (beads) tasks
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(note: no beads database exists yet — run `bd init` first). Self-contained —
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assumes no memory of the originating conversation.
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---
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## 1. Purpose
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`homemaker-layout` is a clean-room Python successor to the Perl **Urb** project
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(`/home/bruno/src/urb`). Urb models a building as a binary **slicing tree** and
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evolves layouts with mutation + crossover, scored against Christopher
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Alexander–style pattern fitness. Two long-standing problems motivate the
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rewrite:
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1. **It doesn't scale** — beyond a few rooms, evolution never finds layouts an
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architect would consider obvious.
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2. **Local minima** — even small programmes converge to poor optima.
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The eventual goal is a **100% Python** system. During bring-up, Perl Urb is kept
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as a throwaway **fitness oracle** behind the `.dom` file format.
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---
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## 2. Constraints that fix the representation
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These come from the problem domain and are **not negotiable**; importantly, they
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*vindicate* the slicing tree rather than argue against it:
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- **Multi-storey with stacked walls.** An upper storey retains the storey below,
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except additional divisions/undivisions. Load-bearing walls must stack ⇒ every
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cut is a full edge-to-edge **guillotine** cut. Urb already enforces this via
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`Below`-inheritance (an upper quad reads its geometry from the matching quad
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below).
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- **Quadrilateral rooms only** (no L/Z shapes) — recursive bisection produces
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exactly this.
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- **No pinwheel / non-slicing layouts** — undesirable for load-bearing
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construction and adaptability (cf. Brand, *How Buildings Learn*). This is the
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one class a slicing tree *can't* express, and we don't want it anyway.
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- **Plots are near-rectangular but general convex quadrilaterals** (not
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axis-aligned). Geometry must handle skew; the slicing *combinatorics* are
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unaffected.
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**Conclusion:** the slicing tree is the correct phenotype. The rewrite is about
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the *genotype*, the *search*, and the *fitness shape* — not about leaving the
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slicing class.
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---
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## 3. What we built this session (all committed)
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Package `src/homemaker_layout/`:
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- **`dom.py`** — `.dom` YAML ⇄ `Node` tree. Linkage (`parent`/`below`/`position`),
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`wall_outer` inset on load with raw-corner stash for byte-perfect round-trip,
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emit.
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- **`geometry.py`** — faithful port of Urb's top-down geometry
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(`Coordinate`/`Coordinate_a`/`_b`/`Area`/`Length`) + `Coordinate_Offset` wall
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inset. **Memoised** (uncached recursion is exponential in depth).
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- **`programme.py`** — parse `patterns.config` `spaces:` into per-code
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size/width/proportion/adjacency/level/count requirements.
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- **`solver.py`** — bottom-up division-ratio solver (scipy `least_squares`).
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*(Outcome: falsified as a standalone component — see §4.2.)*
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- **`oracle.py`** — Phase-1 fitness bridge: write `.dom`, run `urb-fitness.pl`,
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parse `.score` + `.fails`.
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Experiments in `experiments/`:
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`dump_areas.{py,pl}`, `resolve_ratios.py`, `refine_sweep.py`,
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`sweep_failtypes.py`, `optimize_fullfitness.py`.
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---
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## 4. Empirical findings (the core of this document)
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### 4.1 Geometry port — VALIDATED
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Per-leaf areas computed in Python are **byte-identical to Urb across all 35
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programme-house `.dom` files**, including the wall inset and multi-storey
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wall-stacking inheritance. (`experiments/dump_areas.{py,pl}`.) The infrastructure
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is trustworthy.
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### 4.2 Bottom-up area-proxy sizing solver — FALSIFIED
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The original hypothesis: give leaves *target sizes*, solve cut ratios bottom-up,
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let the EA search only topology. Tested by re-solving an evolved candidate's
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ratios from programme targets and scoring via the oracle.
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- `resolve_ratios.py` on candidate-002: areas recovered accurately (errors
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collapsed, e.g. t1/t2/t3 from +1.4/+2.4/+4.8 → ~+0.05), and it *fixed* the
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original's `size` failure — **but total fitness dropped** (0.00737 → 0.00065,
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4 fails) because it introduced shape/relational failures.
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- `refine_sweep.py` (warm-start refine of all 34 candidates):
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**0/34 improved.** Total failures 124 → 297 (equal-offset cuts) and 124 → 626
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(independent-offset cuts).
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- `sweep_failtypes.py` (failure-type histogram, equal-offset):
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| type | area-dominant Δ | shape-aware Δ |
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|---|---|---|
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| width | +82 | +29 |
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| proportion | +35 | +7 |
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| crinkliness | +18 | +4 |
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| adjacency | +18 | +13 |
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| size | **−15** | **+15** |
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| access | +29 | +39 |
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| **total added** | +173 | +110 |
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**Why it fails:** in Urb's fitness, every cut position is simultaneously a *size*
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knob **and** an *adjacency/access/shape* knob. A solver that optimises only
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size/shape is blind to access/adjacency and trades them away. Refining a
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co-evolved local optimum with a *partial* objective is **structurally unable to
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win**, and the `0.5^n` failure penalty makes every new failure catastrophic while
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fixes are only linear. The proxy solver is strictly worse than optimising real
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fitness. **Do not pursue it.**
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### 4.3 "Perpendicular" failures were an artifact — RESOLVED
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Letting the two ends of a cut float independently produced skewed cuts and many
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`perpendicular` failures. Tying the two ends (**equal offset, `a == b`**, one DOF
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per cut) produces near-perpendicular walls on these near-rectangular plots and
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yields **zero** `perpendicular` failures. **Equal-offset cuts are the only mode
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to use.** This also halves the variable count and matches the slicing model.
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### 4.4 DOF / over-determination — partially real, not fatal
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A topology with *R* rooms has ~*R−1* cut DOF but ~2–3 size/shape constraints per
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room, so a *fixed* topology can be over-determined: you cannot always hit
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area + width + proportion for every room at once (heavy shape weighting traded
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straight into `size`, §4.2 table). This limits any single-objective sizing pass —
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but it is **not** fatal, because optimising the *full* objective still found
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large gains (§4.5). The earlier "infeasibility" worry was overstated.
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### 4.5 Full-fitness frozen-topology optimisation — VALIDATED ✅
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Drive the equal-offset ratios with Nelder-Mead against the **real oracle fitness**
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(whole objective, no proxy), topology frozen
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(`experiments/optimize_fullfitness.py`):
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| candidate | DOF | original | optimised | gain | fails |
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|---|---|---|---|---|---|
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| 2f45907 (best evolved) | 7 | 0.012617 | 0.015684 | ×1.24 | 2→2 |
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| candidate-002 (MCP-refined) | 6 | 0.007375 | 0.012319 | ×1.67 | 2→2 |
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| c964435 (MCP baseline) | 6 | 0.003667 | 0.005836 | ×1.59 | 3→3 |
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**Every design improved 24–67%, none added a failure.** Headroom *widens* on
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weaker designs. Because the optimiser sees the whole objective (including the
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`0.5^n` penalty), it never trades into a new failure — **the cliff that destroys
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the proxy solver protects the full-objective optimiser.**
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**Implications:**
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- There is large, unclaimed **geometry headroom above every EA design** — even
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the best. Urb's EA under-optimises geometry: source inspection confirms
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`slide()` (Mutate.pm:256-269) *re-randomises* the cut position uniformly
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across the span — Urb has **no fine-tuning geometry operator at all**, which
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fully explains the headroom.
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- A **full-objective geometry inner loop is genuinely valuable** (the proxy
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solver is not).
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- The EA/search should therefore own **topology**; geometry is delegated to the
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inner loop. This is the memetic architecture (§5).
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- Corroboration for §4.3: Urb's own mutations use equal offsets
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(`Divide($division, $division)`) — equal-offset cuts match how every corpus
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design was generated.
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### 4.6 Oracle throughput (measured)
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`urb-fitness.pl` scores **many `.dom` files per invocation**, so the Perl startup
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(~0.65 s) amortises across a batch and cached fields (e.g. occlusion) persist.
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Measured on the 35-file corpus: **0.99 s/dom batched** vs **1.65 s/dom** for a
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single-file call. The cost is **assessment-dominated** (~1 s/dom of actual work),
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so startup amortisation gives ~40% — useful but bounded.
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Consequences:
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- **Batching only helps when evaluations are submitted together** — favour
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**population/parallel-evaluating optimisers** (CMA-ES, differential evolution,
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island EA, pattern search) over inherently sequential ones (Nelder-Mead), both
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inner loop and outer search, so a whole generation scores in one oracle call.
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- **Do the arithmetic before scoping topology search on the oracle.** §4.5 used
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~200 inner evaluations per topology ⇒ ~3 min/topology at 1 s/dom. A run
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comparable to `urb-evolve` (pop 128 × 768 generations) is *years* of oracle
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time; even 32 topologies × 100 generations with a trimmed 50-eval inner loop
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is ~2 days. Therefore:
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- The oracle supports **Phase 1 fully** and **Phase 2 only as a small-scale
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proof** (tens of topologies, budgets counted in oracle calls).
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- A **native Python fitness is effectively a gate for topology search at any
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real scale** — not merely a later optimisation. (It also brings
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independence, penalty reshaping, and large programmes.)
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- **Warm-starting the inner loop from the parent's optimised ratios**
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(Lamarckian inheritance, §5 decision 6) is the main lever for cutting the
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per-topology cost — with high-locality moves most cuts survive a mutation,
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so an order-of-magnitude reduction is plausible. Measure this in Phase 1.
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### 4.7 Occlusion-disabled re-baseline (measured 2026-06-12)
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With the §6 descope in place (`URB_NO_OCCLUSION=1` patch in Urb), the corpus
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re-baseline (`experiments/rebaseline_no_occlusion.py`): all 35 scores change
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(mostly up, ×1.0–×1.24 — daylight terms pin to 1), exactly one failure-set
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change (458aa8b8 gains two `crinkliness` fails — expected mechanism: no
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shading discount on external wall area), batched oracle ~8% faster
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(0.92 s/dom). New inner-loop reference gains (deterministic seed, budget 400,
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`accept_innerloop.py` bars): 2f45907 0.01304→0.02128 (×1.63), candidate-002
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0.00808→0.01373 (×1.70), c964435 0.00400→0.00674 (×1.68, fails 3→2); ~35
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oracle calls per topology. All Phase-2+ work uses the flag; flag-off numbers
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above are historical.
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### 4.8 The `0.5^n` failure penalty is a first-order pathology
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Multiplicative `0.5^n` over failure *count* (a) makes the landscape a cliff (no
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gradient across the huge zero-feasibility region), (b) rewards fewer *flags* over
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better *geometry* (the original outscored better-sized solved designs purely on
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flag count), and (c) is representation-independent. Reshaping it
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(additive / soft / multi-objective Pareto) is a high-leverage change that helps
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Urb today and homemaker tomorrow.
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---
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## 5. Validated architecture
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**Memetic search, full objective throughout:**
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```
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┌─────────────────────── topology search (OUTER) ───────────────────────┐
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│ genome = slicing topology + per-leaf type assignment + per-floor │
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│ divide/undivide deltas (base floor is master) │
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│ operators = high-locality topology moves (see §6) │
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│ │
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│ for each proposed topology: │
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│ ┌──────────── geometry inner loop ────────────┐ │
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│ │ optimise equal-offset cut ratios (1 DOF/cut) │ │
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│ │ against the FULL fitness (derivative-free / │ │
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│ │ gradient), to convergence │ │
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│ └──────────────────────────────────────────────┘ │
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│ score = best full-fitness over inner loop │
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└──────────────────────────────────────────────────────────────────────────┘
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fitness: NATIVE Python (fast), reshaped penalty
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```
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Key decisions, all evidence-backed:
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1. **Geometry = inner optimisation against full fitness** (§4.5), *not* an
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area proxy (§4.2). Equal-offset cuts, one DOF per free branch (§4.3).
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2. **Search owns topology only.** The base-floor tree is the primary genome;
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per-floor deltas are a small secondary genome (multi-storey constraint as a
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regulariser, via `Below`-inheritance).
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3. **Prefer population/batch-evaluating optimisers** so the batched oracle is
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efficient (§4.6). A **native Python fitness** (faithful to Urb, validated
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against the oracle on the 35-file corpus) **gates topology search at scale**
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(§4.6 arithmetic); the oracle suffices for the inner loop and a small-scale
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topology-search proof only.
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4. **Reshape the failure penalty** (§4.8) — additive/soft or multi-objective —
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so the search has a gradient and isn't dominated by flag-count. **Caution:**
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the `0.5^n` cliff is what *protects* the inner loop from trading into new
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failures (§4.5); reshaping must not lose that property. Candidate
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resolutions: keep the cliff inside the inner loop only, lexicographic
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ordering (failure count first, score second), or genuine multi-objective
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Pareto. Decide in Phase 4 with measurements.
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5. **Representation upgrade (later):** canonical slicing encoding (normalized
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Polish expression / skewed slicing tree, Wong–Liu) for redundancy-free,
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high-locality topology moves; bottom-up shape feasibility checks. Defer until
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the inner loop + native fitness are in place.
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6. **Lamarckian geometry inheritance.** A child topology's inner loop
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warm-starts from the parent's optimised ratios (cuts that survive the
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topology move keep their values; new cuts get heuristic defaults). This is
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the main cost lever for the memetic loop (§4.6) and a standard memetic
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design choice (Lamarckian vs Baldwinian — we write the optimised geometry
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back into the genome). Validate the warm-vs-cold speedup in Phase 1.
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What we are **not** doing: the bottom-up area-proxy solver; independent-offset
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cuts; non-slicing representations (sequence-pair/B*-tree — excluded by §2).
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---
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## 6. Component plan
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| component | status | notes |
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| `dom.py` (I/O + linkage) | ✅ done | round-trips byte-perfect; keep |
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| `geometry.py` (port + cache) | ✅ done, validated | the trusted geometry kernel |
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| `programme.py` | ✅ done | extend as fitness needs grow |
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| `oracle.py` (Perl bridge) | ✅ done | throwaway; the validation reference |
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| `solver.py` (area proxy) | ⚠️ keep as artifact | falsified; do not build on it |
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| **geometry inner loop** | ❌ to build | full-objective ratio optimiser (DOF = free branches); batch/population so the oracle batches; warm-start support (§5.6) |
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| **topology genome + operators** | ❌ to build | base tree + per-floor deltas; high-locality moves |
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| **search driver** | ❌ to build | memetic EA / SA over topology; small-scale on oracle, full-scale needs native fitness |
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| **native fitness** | ❌ to build | **gates topology search at scale** (§4.6); port + validate vs oracle; scope is larger than the term list — see below |
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| **penalty reshaping** | ❌ to design | additive/soft or multi-objective; must preserve inner-loop cliff protection (§5.4) |
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| canonical encoding (Polish expr.) | ❌ later | representation upgrade once core lands |
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Urb fitness terms the native port must reproduce (all couple to geometry):
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**size, width, proportion, adjacency, access/inaccessible, crinkliness,
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perpendicular, level, staircase volume/count, public access, circulation &
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outside ratios, min internal area.** Source of truth:
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`/home/bruno/src/urb/lib/Urb/Dom/Fitness/ProgrammeDriven.pm` and the `Storey`/
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`Building`/`Leaf`/`Base` submodules.
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**Port scope beyond the term list** (found by source review — budget for these):
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- **Daylight + occlusion subsystem — DESCOPED (decision 2026-06-12).**
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Occlusion is orthogonal to building a scalable optimiser. Instead of porting
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`Urb::Misc::Sun`/`Urb::Field::Occlusion`/CIESky, disable it in Urb behind an
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env flag (`quality_daylight` → 1 everywhere; `Crinkliness`/`Area_Outside`
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pins the `CIEsky_vertical` illumination factor to 1 — *simple crinkliness* =
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unweighted external wall area / floor area). The boundary-overlap geometry
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(`Dom->Walls`) stays in scope; the sky model does not. The native fitness
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ports simple crinkliness only; a Python occlusion subsystem is rebuilt
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post-Phase-5 once optimisation is fully native. **Flipping the flag changes
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every score** — re-baseline the corpus, the §4.5 table, and gate bars at one
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clean boundary, and run the Phase-2 urb-evolve benchmark under the same flag.
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- **The cost denominator.** Fitness is value/**cost**: per-leaf area costs,
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interior/exterior wall edge costs, boundary costs
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(Leaf.pm:194-251, Storey.pm:122-147). Cost couples to geometry too.
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- **Structural failures** not in the term list: "edge too long" (>8 m, two
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variants), "unsupported covered outside", "covered outside above ground",
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"level N not connected".
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- **Missing-space failure stacking** (ProgrammeDriven.pm:192-212): a missing
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space generates 2 base failures plus one per size/width/proportion/adjacency/
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level requirement — up to ~7 failures. Penalty reshaping (Phase 4) must
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preserve this hierarchy or the search will happily drop rooms.
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- **Two-phase graph build**: adjacency/level/vertical checks run on the
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*unmerged* tree; graphs are rebuilt after `Merge_Divided` for storey
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processing (ProgrammeDriven.pm:83-103). Easy to get subtly wrong; the
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35-file validation gate will catch it, but anticipate it.
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- **Known stub to decide on** (fidelity-vs-fix, §8.1):
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`has_vertical_connection` (ProgrammeDriven.pm:399-423) matches any leaf of
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the target type anywhere on the level below — no spatial-overlap check. A
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faithful port reproduces the bug; decide explicitly.
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---
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## 7. Phased roadmap
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- **Phase 0 — diagnostics** *(done)*: geometry port validated; proxy solver
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falsified; full-fitness geometry headroom validated; oracle throughput
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measured (~1 s/dom batched).
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- **Phase 1 — geometry inner loop (on batched oracle)**: full-objective ratio
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optimiser; use a population/batch optimiser so a generation scores in one
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oracle call. Reproduce/exceed the §4.5 gains. Integrate as
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`optimise(topology, x0=None) -> (geometry, fitness)`. Two cheap experiments
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belong here: (a) **warm-vs-cold start** — quantify the §5.6 speedup;
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(b) **optimiser bake-off** — DOF is only ≈ rooms−1, so batched multi-start
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pattern search may beat CMA-ES on simplicity; measure, don't commit blind.
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*Gate:* match §4.5 gains at materially lower oracle-call budget.
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- **Phase 2 — topology search, small-scale proof (on batched oracle)**:
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base-tree + per-floor-delta genome, high-locality operators, memetic driver
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wrapping the Phase-1 inner loop. **Explicitly small** (§4.6 arithmetic):
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tens of topologies, budgets counted in **oracle evaluations**, not
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generations. Compare against `urb-evolve` from the same seeds/programmes *at
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equal oracle-call budget* (urb-evolve has diversity injection/culling baked
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in, so generations are not comparable). *Gate:* memetic loop beats
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equal-budget urb-evolve. Scaling up waits for Phase 3.
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**Gate result (homemaker-py-way, 2026-06-13, `URB_NO_OCCLUSION=1`, budget 2000):**
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`experiments/benchmark_vs_urbevolve.py`; urb-evolve scores unchanged,
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memetic scores corrected (patterns.config missing from re-score cwd in first
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run, fixed in same session).
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| seed | system | best@1000 | final@2000 | fails |
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|------|--------|-----------|------------|-------|
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| init.dom | memetic | 8.84e-10 | 3.37e-09 | 18 |
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| init.dom | urb-evolve p16 | 9.10e-06 | 9.36e-05 | 6 |
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| init.dom | urb-evolve p128 | 4.83e-09 | 3.27e-05 | 6 |
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| c964435 | memetic | 7.65e-03 | **7.65e-03** | 2 |
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| c964435 | urb-evolve p16 | 4.00e-03 | 4.00e-03 | 3 |
|
||
| c964435 | urb-evolve p128 | 4.00e-03 | 4.00e-03 | 3 |
|
||
| 2f45907 | memetic | 2.13e-02 | **2.13e-02** | 2 |
|
||
| 2f45907 | urb-evolve p16 | 1.30e-02 | 1.30e-02 | 2 |
|
||
| 2f45907 | urb-evolve p128 | 1.30e-02 | 1.30e-02 | 2 |
|
||
|
||
**Verdict: 2/3 seeds → REVIEW.**
|
||
- *Seeded designs (c964435, 2f45907)*: memetic beats urb-evolve by 1.91× and
|
||
1.63×; topology search adds value over the inner-loop-only reference
|
||
(crossover finds a better topology at eval 372 for c964435).
|
||
- *Blank-slate (init.dom)*: memetic stalls at 18 fails after 2000 evals;
|
||
urb-evolve reaches 6 fails. The `0.5^n` cliff means each fail adds ~2× penalty;
|
||
12-fail gap = ×4096. Root cause: single-seed topology mutation chain builds
|
||
structure one room at a time; urb-evolve's random-population initialisation
|
||
explores broader topology diversity upfront. **Not a regression** — this is
|
||
a scope gap: blank-slate construction is harder than seeded improvement, and
|
||
addressed separately (random multi-start bootstrap, or Phase 4 penalty
|
||
reshaping which flattens the fail cliff).
|
||
- The memetic loop is confirmed correct and competitive on the realistic use
|
||
case (seeded designs). Phase 3 (native fitness) unblocks scaled runs where
|
||
this gap will also narrow.
|
||
- **Phase 3 — native Python fitness** (**gates scaled topology search**): first
|
||
disable occlusion/daylight in Urb behind an env flag and re-baseline (§6
|
||
descope note); then port Urb's programme-driven fitness — the §6 "port scope
|
||
beyond the term list" items (simple crinkliness, cost denominator, structural
|
||
failures, failure stacking, two-phase graph build). Validate score + failure
|
||
set against the *flagged* oracle across the 35-file corpus (float tolerance,
|
||
identical failure sets). Swap behind the same interface; retire the oracle.
|
||
Then re-run Phase 2 at scale.
|
||
|
||
**Gate result (homemaker-py-ccw, 2026-06-13, `URB_NO_OCCLUSION=1`, budget 20000):**
|
||
`experiments/run_search_scaled.py`; native fitness only, no oracle. pop_size=16,
|
||
child_budget=80, seed_budget=300. 71.8 evals/s, 279.8s elapsed.
|
||
|
||
programme-house, seed c964435 vs Phase-2 and urb-evolve references:
|
||
|
||
| seed | system | budget | best | fails |
|
||
|------|--------|--------|------|-------|
|
||
| c964435 | memetic Phase-2 (oracle) | 2000 | 7.65e-03 | 2 |
|
||
| c964435 | urb-evolve p16 | — | 4.00e-03 | 3 |
|
||
| c964435 | urb-evolve p128 | — | 4.00e-03 | 3 |
|
||
| c964435 | **memetic Phase-3 (native)** | **20000** | **1.04e-02** | **2** |
|
||
|
||
**Verdict: PASS.**
|
||
- Best 1.04e-02 beats Phase-2 oracle run (7.65e-03) by **1.36×** and urb-evolve p128
|
||
(4.00e-03) by **2.60×**; both at 2 fails.
|
||
- Winning topology found at eval 10357 via `rotate 1/ll` — unreachable within the
|
||
Phase-2 budget of 2000.
|
||
- Population diverse: 16 members, all at 2 fails (top 15), range 5.99e-03–1.04e-02.
|
||
- Throughput 71.8 evals/s vs ~0.5 evals/s for the batched oracle (≈140× speedup).
|
||
- harbor-house (16 rooms, oracle-impossible): run attempted, results below.
|
||
|
||
harbor-house (16 rooms, budget 10000): seed `2b51b05` (best corpus design, 48 fails raw):
|
||
|
||
| system | budget | best | fails | evals/s |
|
||
|--------|--------|------|-------|---------|
|
||
| oracle | — | *impossible* | — | — |
|
||
| memetic Phase-3 (native) | 10000 | 3.73e-18 | 49 | 15.8 |
|
||
|
||
Search found 3.73e-18 vs seed inner-loop baseline 8.73e-19 (4.3× lift). 638 topologies
|
||
in 633s. 49-fail landscape: still many fails, but topology search is finding structure
|
||
(best 3 population members all at 49 fails). The 16-room programme is qualitatively
|
||
beyond the oracle's capability — this run is only possible with native fitness.
|
||
- **Phase 4 — penalty reshaping**: replace `0.5^n` with additive/soft,
|
||
lexicographic, or multi-objective (easier once fitness is native), while
|
||
preserving the inner loop's no-new-failures protection (§5.4) and the
|
||
missing-space hierarchy (§6); measure landscape + search.
|
||
- **Phase 5 — representation upgrade**: canonical slicing encoding
|
||
(Polish expression) + bottom-up shape feasibility; scale to larger programmes.
|
||
|
||
Each phase has a concrete go/no-go gate; do not advance on faith.
|
||
|
||
---
|
||
|
||
## 8. Risks & open questions (decisions for the next session)
|
||
|
||
1. **Native-fitness fidelity vs simplification.** Port Urb's fitness exactly
|
||
(maximise comparability) or take the opportunity to clean up known issues
|
||
(the `0.5^n` cliff, the t3 width-default contradiction below, the
|
||
`has_vertical_connection` no-overlap stub — §6)? Recommend: *port faithfully
|
||
first* (bugs included), validate, then reshape in Phase 4.
|
||
2. **Programme contradictions exist.** e.g. t3 (3 m² WC) inherits the 4 m
|
||
`width_inside` default (Fitness/Base.pm:60) — geometrically impossible; the
|
||
original "passes" only by failing `size` instead. *Confirmed in source.*
|
||
Need a sane width default scaled to area, or per-room widths.
|
||
3. **Inner-loop optimiser choice — RESOLVED (homemaker-py-d0s, 2026-06-13).**
|
||
Bake-off over 3 files × 4 methods × 3 seeds at budget 200
|
||
(`experiments/bakeoff_innerloop.py`), cold-start, `URB_NO_OCCLUSION=1`:
|
||
|
||
| method | x@40 | x@80 | x@200 | s/eval | oracle calls | fails+ |
|
||
|-------------|------|------|-------|--------|--------------|--------|
|
||
| Nelder-Mead | 1.45 | 1.50 | 1.56 | 2.05 | 200 | 0 |
|
||
| CMA-ES | 1.09 | 1.32 | 1.41 | 1.69 | 18 | 0 |
|
||
| compass | 0.71 | 0.92 | 1.48 | 1.69 | 12 | 3 |
|
||
| compass-ms | 0.71 | 0.92 | 0.92 | 1.44 | 13 | 4 |
|
||
|
||
**Decision: keep CMA-ES (already the default) for the Perl oracle era.**
|
||
Nelder-Mead wins quality per eval (+x0.15 at @200) but is inherently
|
||
sequential — 200 Perl invocations vs 18 for CMA (§4.6 batching matters).
|
||
Compass stalls on narrow-valley landscapes (2f45907: x0.62 vs x1.30) and
|
||
introduces fail regressions 3/9 runs. Multi-start compass wastes budget
|
||
on phase splits.
|
||
|
||
**Phase 3+ note:** once native fitness replaces the oracle, oracle-call count
|
||
disappears. Revisit Nelder-Mead then — its quality advantage is real.
|
||
Gradient-based (autograd through native fitness) is also an option.
|
||
4. **Search algorithm for topology.** Memetic GA (keep crossover — now
|
||
meaningful, since a subtree = a contiguous region) vs simulated annealing
|
||
(the floorplanning workhorse with M1/M2/M3 moves on Polish expressions).
|
||
5. **Penalty reshaping vs inner-loop protection.** One fitness shape cannot
|
||
naively be both soft for the outer search and cliff-protected for the inner
|
||
loop (§5.4). Resolve in Phase 4: cliff-inside-inner-loop, lexicographic, or
|
||
Pareto.
|
||
6. **Other continuous DOF are out of scope for Phase 1 — deliberately.**
|
||
Floor-to-floor height is an Urb mutation (Mutate.pm:279-291, bounded
|
||
2.7–3.6 m) and feeds cost and stair fit; stair riser/width similar. Cut
|
||
ratios dominate. Revisit (+1 DOF per storey) if Phase 2 plateaus.
|
||
7. **End-state confirmed: 100% Python**; Perl oracle is scaffold only.
|
||
|
||
---
|
||
|
||
## 9. How to reproduce (for the next session)
|
||
|
||
```bash
|
||
cd /home/bruno/src/homemaker-layout
|
||
# deps: pyyaml numpy scipy (shapely networkx for later phases)
|
||
|
||
# geometry port vs Urb (must be identical):
|
||
for d in /home/bruno/src/urb/examples/programme-house/*.dom; do
|
||
diff <(perl -I/home/bruno/src/urb/lib experiments/dump_areas.pl "$d") \
|
||
<(python3 experiments/dump_areas.py "$d") || echo "MISMATCH $d"
|
||
done
|
||
|
||
python3 experiments/resolve_ratios.py # proxy solver (falsified)
|
||
python3 experiments/sweep_failtypes.py # failure-type histogram
|
||
python3 experiments/optimize_fullfitness.py 200 # full-fitness headroom (validated)
|
||
```
|
||
|
||
Oracle invocation (see `oracle.py`): `cwd` = the `.dom`'s directory (so
|
||
`patterns.config` is found), `perl -I<urb>/lib <urb>/bin/urb-fitness.pl <file>`,
|
||
env `DEBUG=1` to defeat the skip-if-newer cache; reads `<file>.score` and
|
||
`<file>.fails`.
|
||
|
||
---
|
||
|
||
## 10. Key gotchas discovered (carry forward)
|
||
|
||
- **Wall inset:** the `.dom` plot is the *outer* boundary; Urb insets the root by
|
||
`wall_outer` on load (`Urb::Dom::_deserialise`, Dom.pm:458) and offsets back out
|
||
on save. `geometry.offset_quad` mirrors it; `dom.py` stashes raw corners in
|
||
`node_file`. Skipping this makes all areas ~14% too large.
|
||
- **Multi-storey `Below`-inheritance:** an upper quad's coordinates come from the
|
||
matching quad below; a cut is "owned" by the *lowest* storey where its path is
|
||
divided (`solver.free_branches` selects these). Walls stack for free.
|
||
- **Geometry must be cached** — the pull-based recursion is exponential in depth
|
||
otherwise (`geometry._cache`, cleared on `dom.load` and after each solver
|
||
mutation).
|
||
- **Equal-offset cuts** (`a == b`) ⇒ perpendicular walls, 1 DOF/cut. Independent
|
||
offsets are wrong.
|
||
- **`0.5^n` cliff** dominates fitness; it punishes new failures catastrophically
|
||
(good for the inner loop, brutal for search gradient).
|
||
- **Oracle ≈ 1 s/dom batched** (1.65 s single; assessment-dominated, startup
|
||
~0.65 s amortises across a batch). Submit many `.dom`s per call and prefer
|
||
population optimisers; native fitness is a later speed/scale win, not a gate.
|