Bruno Postle
de60200bbc
Make the required programme room set a constructive invariant instead of something the topology search must stumble onto by random divide+retype. - operators.constructive_topology: bootstrap seeder that sizes each storey to its required rooms (partitioned by level; level-free rooms distributed), +1 core C and +1 O per storey, then assigns types. Stochastic for population diversity. Wired into driver bootstrap when the programme has required spaces. - operators.mutate_place_missing: repair op that inserts a missing required space by dividing a host leaf into [room | remainder]. Lex-safe host ranking (generic O first, never displace a required room); honours required level. Weight 2.0 in the mutation mix; noops cheaply once the set is complete. A/B on harbor-house (20k evals, seed 0, identical config): old random-bootstrap 133 fails (103 missing, 77%) new constructive 105 fails ( 12 missing, 11%) -21% total, missing-stack collapsed; seed head-start 163->139. §4.10 regression PASS: warmstart-2f4 still reaches a 1-fail population at 50k. Verdict (DESIGN.md §11.2): construction is necessary and reframes the bottleneck to quality-fail packing of a complete dense design (crinkliness/ size/access/edge) -> unblocks §11.3 staging, motivates §11.4 graded objective. Follow-up filed (homemaker-py-s44): adjacency-aware seeding. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
840 lines
46 KiB
Markdown
840 lines
46 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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### 4.9 Penalty reshaping decision: lexicographic outer search (measured 2026-06-14)
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`experiments/penalty_reshape.py`, `URB_NO_OCCLUSION=1`, programme-house.
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**Inner-loop protection** (nm_search, budget 80, 3 files × 3 seeds = 9 runs):
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All runs show `n_fails ≤ x0_n_fails`. **0/9 regressions.** The `0.5^n` cliff
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in the native fitness scalar is unchanged and continues to protect the inner
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loop.
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**Outer-search comparison** (budget 3000, 3 seeds, seed = 2f45907):
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| scheme | seed | best | fails | note |
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|--------|------|------|-------|------|
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| lex | 0 | 0.01781 | 2 | |
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| lex | 1 | 0.01793 | 2 | |
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| lex | 2 | 0.01785 | 2 | |
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| scalar | 0 | 0.01781 | 2 | (same outcome) |
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| scalar | 1 | **0.01890** | **3** | trapped by high-score 3-fail design |
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| scalar | 2 | 0.02632 | 2 | (different topology path) |
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`lex` mean: 0.01786 / 2.00 fails. `scalar` mean: 0.02101 / 2.33 fails.
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Key result (seed 1): scalar promoted a 3-fail design whose raw score (×0.125
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penalty) beat the pool's 2-fail candidates — exactly the §4.8 pathology.
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Lexicographic comparison (`-n_fails` first, then `fitness`) is immune: any
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2-fail design beats any 3-fail design regardless of raw score. Within a
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homogeneous fail tier both schemes are identical (seeds 0 and 2 agree in
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serendipitous runs where scalar also stays in the 2-fail tier).
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**Decision: lexicographic. `0.5^n` stays in the fitness scalar (inner loop
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unchanged). Outer search uses `(-n_fails, fitness)` as comparison key.**
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### 4.10 Deceptive level-fix valley and compound operators (measured 2026-06-14/15)
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**Context:** programme-house, Phase 3 native fitness + Phase 4 lex search, seed
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`warmstart-2f4.dom` (best Phase-3 result, 2 fails at score 0.032). Goal: reach
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≤ 1 fail, beating the Perl optimiser (2–3 fails).
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**The deceptive valley.** The 2-fail state has l1 (living room, min 27 m²,
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required level 0) on level 1. The obvious repair is `level_fix`: swap l1 with a
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leaf on level 0. But every single-step `level_fix` move creates 5+ new fails
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because the displaced room (t3, the WC) is dropped into an arbitrary slot that
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violates adjacency, size, and access constraints simultaneously. The lex
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comparator (`-n_fails, fitness`) correctly rejects these — but the result is that
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the 2-fail state appears completely surrounded by ≥ 5-fail states, and the search
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stalls. This is a textbook deceptive valley: the fitness gradient points away from
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the global optimum.
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**Compound operator.** `mutate_level_compound_fix` (added `operators.py`) escapes
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the valley by doing two things atomically:
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1. Move l1 to level 0 by swapping it with the *largest* leaf there (the
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circulation C node, because C is generic and can absorb the swap without
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producing a new structural failure).
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2. Re-insert the displaced t3 by dividing the sibling of that C node (so t3
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lands adjacent to C, satisfying the adjacency requirement).
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The new split gets `division=[0.25,0.25]` (giving t3 ≈ 3.4 m², barely in range)
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and `rotation=0` (t3 on the left, adjacent to the C sibling).
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**The `warm_x0` initialization bug.** The compound operator sets specific ratios
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on a newly-created split node. But `driver.py` was initialising the NM inner loop
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from `parent.ratios`, which has no entry for the new node (it was a leaf).
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`warm_x0` defaulted the new node to 0.5, giving t3 ≈ 6.8 m² — a size fail —
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so NM started at 3 fails instead of 1. Lex then always rejected the compound
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child; `level_compound_fix` was completely invisible to the outer search for
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~12 000 evals (until `warm_x0` was fixed).
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The correct fix distinguishes genuinely-new split nodes from stale hidden nodes
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that become visible after structural mutations (e.g. `swap` can flip a `b.below`
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pointer, revealing pre-writeback division values from a different topology). Only
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use the child's explicit ratio for node `(li, path)` if the matching node in the
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parent was *not already divided*; everything else falls through to `parent.ratios`
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or defaults to 0.5. Fix in `driver.py` lines 259–267.
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**Results (50 000 evals each, pop 8, child_budget 80, 4 workers):**
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| seed | event | eval | fails | score |
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|------|-------|------|-------|-------|
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| warmstart-2f4 | seed | 200 | 2 | 0.032 |
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| warmstart-2f4 | `level_compound_fix` fires | 12 280 | 1 | 0.000122 |
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| warmstart-2f4 | `level_retype 0/ll<->1/l` | 17 880 | 1 | 0.00497 |
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| warmstart-2f4 | final | 50 040 | **1** | **0.00518** |
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| compound3-raw | seed (1-fail hand-built) | 200 | 1 | 0.000118 |
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| compound3-raw | `level_retype 0/ll<->1/l` | 18 360 | 1 | 0.00383 |
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| compound3-raw | final | 50 040 | **1** | **0.00523** |
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Perl optimiser reference: **2–3 fails**.
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**The two-C topology breakthrough.** After `level_compound_fix` fires, the
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topology is: level 0 = `ll(l1), lr(t2), rl(C), rrl(t3), rrr(O)` — but now l1
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is at level 0 (correct) and t3 is adjacent to rl(C) (staircase). However l1
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is occupying ll, and rl(C) is the staircase core — so t3-adj-C is satisfied
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via rl, but there is no second C to satisfy staircase independently. Score
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≈ 0.000157 (1 fail).
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At eval ≈ 18 000, `level_retype 0/ll<->1/l` (swap the type of ll on level 0
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with l on level 1) creates a TWO-C configuration at level 0:
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`ll(C), lr(t2), rl(C), rrl(t3), rrr(O)`, with l1 moving to level 1. The score
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jumps 25× to ≈ 0.005. Why two C nodes work:
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- `ll(C)` (bottom-left, 23 m²) satisfies t3-adj-C via geometric contact at the
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l/r zone boundary with `rrl(t3)`.
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- `rl(C)` (top-right, 8.5 m²) satisfies staircase adjacency via tree adjacency
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to `rrr(O)` (its right sibling when `r.rotation=3`).
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Both constraints are simultaneously met because binary-tree sibling adjacency and
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cross-zone geometric adjacency provide *independent* paths.
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**Why 0 fails is geometrically impossible on this programme + plot.** l1 needs
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min 27 m² at level 0. The only space large enough is `ll` (≈ 23 m², the entire
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left half of level 0). Putting l1 at `ll` removes the t3-adj-C provider.
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The alternative — dividing `ll` into `lll(l1)+llr(C)` — gives `llr` a proportion
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of ≈ 6:1 (width ≈ 0.73 m), failing both the proportion and width constraints.
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0 fails is not achievable on this programme+plot with a binary slicing tree
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representation; 1 fail is the geometric optimum.
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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
|
||
regulariser, via `Below`-inheritance).
|
||
3. **Prefer population/batch-evaluating optimisers** so the batched oracle is
|
||
efficient (§4.6). A **native Python fitness** (faithful to Urb, validated
|
||
against the oracle on the 35-file corpus) **gates topology search at scale**
|
||
(§4.6 arithmetic); the oracle suffices for the inner loop and a small-scale
|
||
topology-search proof only.
|
||
4. **Reshape the failure penalty** (§4.8) — additive/soft or multi-objective —
|
||
so the search has a gradient and isn't dominated by flag-count. **Caution:**
|
||
the `0.5^n` cliff is what *protects* the inner loop from trading into new
|
||
failures (§4.5); reshaping must not lose that property. Candidate
|
||
resolutions: keep the cliff inside the inner loop only, lexicographic
|
||
ordering (failure count first, score second), or genuine multi-objective
|
||
Pareto. Decide in Phase 4 with measurements.
|
||
5. **Representation upgrade (later):** canonical slicing encoding (normalized
|
||
Polish expression / skewed slicing tree, Wong–Liu) for redundancy-free,
|
||
high-locality topology moves; bottom-up shape feasibility checks. Defer until
|
||
the inner loop + native fitness are in place.
|
||
6. **Lamarckian geometry inheritance.** A child topology's inner loop
|
||
warm-starts from the parent's optimised ratios (cuts that survive the
|
||
topology move keep their values; new cuts get heuristic defaults). This is
|
||
the main cost lever for the memetic loop (§4.6) and a standard memetic
|
||
design choice (Lamarckian vs Baldwinian — we write the optimised geometry
|
||
back into the genome). Validate the warm-vs-cold speedup in Phase 1.
|
||
|
||
What we are **not** doing: the bottom-up area-proxy solver; independent-offset
|
||
cuts; non-slicing representations (sequence-pair/B*-tree — excluded by §2).
|
||
|
||
---
|
||
|
||
## 6. Component plan
|
||
|
||
| component | status | notes |
|
||
|---|---|---|
|
||
| `dom.py` (I/O + linkage) | ✅ done | round-trips byte-perfect; keep |
|
||
| `geometry.py` (port + cache) | ✅ done, validated | the trusted geometry kernel |
|
||
| `programme.py` | ✅ done | extend as fitness needs grow |
|
||
| `oracle.py` (Perl bridge) | ✅ done | throwaway; the validation reference |
|
||
| `solver.py` (area proxy) | ⚠️ keep as artifact | falsified; do not build on it |
|
||
| **geometry inner loop** | ❌ to build | full-objective ratio optimiser (DOF = free branches); batch/population so the oracle batches; warm-start support (§5.6) |
|
||
| **topology genome + operators** | ❌ to build | base tree + per-floor deltas; high-locality moves |
|
||
| **search driver** | ❌ to build | memetic EA / SA over topology; small-scale on oracle, full-scale needs native fitness |
|
||
| **native fitness** | ❌ to build | **gates topology search at scale** (§4.6); port + validate vs oracle; scope is larger than the term list — see below |
|
||
| **penalty reshaping** | ❌ to design | additive/soft or multi-objective; must preserve inner-loop cliff protection (§5.4) |
|
||
| canonical encoding (Polish expr.) | ❌ later | representation upgrade once core lands |
|
||
|
||
Urb fitness terms the native port must reproduce (all couple to geometry):
|
||
**size, width, proportion, adjacency, access/inaccessible, crinkliness,
|
||
perpendicular, level, staircase volume/count, public access, circulation &
|
||
outside ratios, min internal area.** Source of truth:
|
||
`/home/bruno/src/urb/lib/Urb/Dom/Fitness/ProgrammeDriven.pm` and the `Storey`/
|
||
`Building`/`Leaf`/`Base` submodules.
|
||
|
||
**Port scope beyond the term list** (found by source review — budget for these):
|
||
|
||
- **Daylight + occlusion subsystem — DESCOPED (decision 2026-06-12).**
|
||
Occlusion is orthogonal to building a scalable optimiser. Instead of porting
|
||
`Urb::Misc::Sun`/`Urb::Field::Occlusion`/CIESky, disable it in Urb behind an
|
||
env flag (`quality_daylight` → 1 everywhere; `Crinkliness`/`Area_Outside`
|
||
pins the `CIEsky_vertical` illumination factor to 1 — *simple crinkliness* =
|
||
unweighted external wall area / floor area). The boundary-overlap geometry
|
||
(`Dom->Walls`) stays in scope; the sky model does not. The native fitness
|
||
ports simple crinkliness only; a Python occlusion subsystem is rebuilt
|
||
post-Phase-5 once optimisation is fully native. **Flipping the flag changes
|
||
every score** — re-baseline the corpus, the §4.5 table, and gate bars at one
|
||
clean boundary, and run the Phase-2 urb-evolve benchmark under the same flag.
|
||
- **The cost denominator.** Fitness is value/**cost**: per-leaf area costs,
|
||
interior/exterior wall edge costs, boundary costs
|
||
(Leaf.pm:194-251, Storey.pm:122-147). Cost couples to geometry too.
|
||
- **Structural failures** not in the term list: "edge too long" (>8 m, two
|
||
variants), "unsupported covered outside", "covered outside above ground",
|
||
"level N not connected".
|
||
- **Missing-space failure stacking** (ProgrammeDriven.pm:192-212): a missing
|
||
space generates 2 base failures plus one per size/width/proportion/adjacency/
|
||
level requirement — up to ~7 failures. Penalty reshaping (Phase 4) must
|
||
preserve this hierarchy or the search will happily drop rooms.
|
||
- **Two-phase graph build**: adjacency/level/vertical checks run on the
|
||
*unmerged* tree; graphs are rebuilt after `Merge_Divided` for storey
|
||
processing (ProgrammeDriven.pm:83-103). Easy to get subtly wrong; the
|
||
35-file validation gate will catch it, but anticipate it.
|
||
- **Known stub to decide on** (fidelity-vs-fix, §8.1):
|
||
`has_vertical_connection` (ProgrammeDriven.pm:399-423) matches any leaf of
|
||
the target type anywhere on the level below — no spatial-overlap check. A
|
||
faithful port reproduces the bug; decide explicitly.
|
||
|
||
---
|
||
|
||
## 7. Phased roadmap
|
||
|
||
- **Phase 0 — diagnostics** *(done)*: geometry port validated; proxy solver
|
||
falsified; full-fitness geometry headroom validated; oracle throughput
|
||
measured (~1 s/dom batched).
|
||
- **Phase 1 — geometry inner loop (on batched oracle)**: full-objective ratio
|
||
optimiser; use a population/batch optimiser so a generation scores in one
|
||
oracle call. Reproduce/exceed the §4.5 gains. Integrate as
|
||
`optimise(topology, x0=None) -> (geometry, fitness)`. Two cheap experiments
|
||
belong here: (a) **warm-vs-cold start** — quantify the §5.6 speedup;
|
||
(b) **optimiser bake-off** — DOF is only ≈ rooms−1, so batched multi-start
|
||
pattern search may beat CMA-ES on simplicity; measure, don't commit blind.
|
||
*Gate:* match §4.5 gains at materially lower oracle-call budget.
|
||
- **Phase 2 — topology search, small-scale proof (on batched oracle)**:
|
||
base-tree + per-floor-delta genome, high-locality operators, memetic driver
|
||
wrapping the Phase-1 inner loop. **Explicitly small** (§4.6 arithmetic):
|
||
tens of topologies, budgets counted in **oracle evaluations**, not
|
||
generations. Compare against `urb-evolve` from the same seeds/programmes *at
|
||
equal oracle-call budget* (urb-evolve has diversity injection/culling baked
|
||
in, so generations are not comparable). *Gate:* memetic loop beats
|
||
equal-budget urb-evolve. Scaling up waits for Phase 3.
|
||
|
||
**Gate result (homemaker-py-way, 2026-06-13, `URB_NO_OCCLUSION=1`, budget 2000):**
|
||
`experiments/benchmark_vs_urbevolve.py`; urb-evolve scores unchanged,
|
||
memetic scores corrected (patterns.config missing from re-score cwd in first
|
||
run, fixed in same session).
|
||
|
||
| seed | system | best@1000 | final@2000 | fails |
|
||
|------|--------|-----------|------------|-------|
|
||
| init.dom | memetic | 8.84e-10 | 3.37e-09 | 18 |
|
||
| init.dom | urb-evolve p16 | 9.10e-06 | 9.36e-05 | 6 |
|
||
| init.dom | urb-evolve p128 | 4.83e-09 | 3.27e-05 | 6 |
|
||
| c964435 | memetic | 7.65e-03 | **7.65e-03** | 2 |
|
||
| 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** *(done, homemaker-py-yg5, 2026-06-14)*:
|
||
**Decision: lexicographic outer-search comparison** (see §4.9).
|
||
Inner loop unchanged — still uses raw `0.5^n` fitness scalar (cliff protection
|
||
preserved, §5.4). Outer search compares individuals by `(-n_fails, fitness)`:
|
||
fewer fails always beats more fails; within a tier, compare by score.
|
||
Implemented in `driver.search(use_lex=True)`. `_CHILD_INNER_KW` stale
|
||
`sigmas` entry also removed (NM default has no `sigmas` parameter).
|
||
- **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 — RESOLVED (homemaker-py-yg5,
|
||
2026-06-14).** Lexicographic outer-search comparison (§4.9). Inner loop
|
||
unchanged.
|
||
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.
|
||
|
||
---
|
||
|
||
## 11. Phase 6 — topology-search quality for full / multi-storey programmes
|
||
|
||
**Epic:** `homemaker-py-c4c`. **Status:** scoped 2026-06-17, pre-implementation.
|
||
This section is the experiment ledger for the epic; each subsection is stubbed
|
||
now and **filled in by the session that runs the experiment** (record the
|
||
command, the numbers, and a one-line verdict, in the style of §4).
|
||
|
||
### 11.0 Diagnosis (why this phase exists)
|
||
|
||
The delivered speedups landed in the two layers that were **never the
|
||
bottleneck**. The native fitness (~140× over the oracle, §7 Phase 3) and the
|
||
geometry inner loop (~1.6×, §4.5/§4.7) both operate *within a fixed topology*:
|
||
the inner loop polishes geometry **inside a failure tier** and, by design, the
|
||
`0.5^n` cliff stops it ever changing the failure count (§4.5: 0-fail-change
|
||
across the headroom table). But final design quality is dominated by **failure
|
||
count**, which is almost entirely a **topology** property. So faster fitness and
|
||
better geometry do not move the number an architect would notice.
|
||
|
||
Topology search on full programmes is the weakness:
|
||
|
||
- **blank-slate programme-house** (`init.dom`): memetic stalls at **18 fails**;
|
||
urb-evolve reaches **6** (§7 Phase 2 verdict).
|
||
- **harbor-house** (16 rooms): `out1.dom` = **74 fails**, `generated.dom` =
|
||
**130 fails**, both at ~machine-epsilon score; failures dominated by
|
||
**`missing`-room stacking** (each missing room stacks critical + size + width
|
||
+ adjacency + level, §6).
|
||
|
||
**Smoking gun:** `operators.mutate_divide` (operators.py:71) types each new leaf
|
||
**at random** from `programme-codes + C + O`. Nothing makes the required
|
||
programme spaces a constructive invariant, so on a large programme required
|
||
rooms simply go missing → catastrophic `0.5^n` stacking, and the search is a
|
||
random walk over type assignments with a flat-and-catastrophic gradient in the
|
||
high-fail regime.
|
||
|
||
**Causal frame for the fixes.** The base-floor tree is the *master* genome;
|
||
upper storeys are divide/undivide deltas (`Below`-inheritance); the programme
|
||
partitions rooms by required level (harbor: **10 on L0, 4 on L1, 2 free**). So
|
||
construction and search should follow the genome's dependency order — credible
|
||
base floor first, upper floors as deltas, with each floor's required-room set
|
||
known from the programme. **Do not hard-freeze the base** when adding floors:
|
||
that recreates the §4.2 partial-objective trap at the topology level (a base
|
||
optimised purely as a ground floor can be a bad *substrate* — the vertical core
|
||
must stay aligned and load-bearing walls must stack).
|
||
|
||
### 11.1 Premise experiment: single-storey harbor (`homemaker-py-c4c.1`) — DONE
|
||
|
||
Built `examples/harbor-house-l0/` from harbor by retaining only the 10 space
|
||
codes explicitly marked `level: 0` (cr1, ef1, da1, k1, ws1, m×3, la1, st1, me1,
|
||
of×2 → 13 room instances), pruning adjacencies to the retained codes, and
|
||
setting single-storey constraints (`storey_minimum: 1`, `storey_limit: 1`). The
|
||
straddling anonymous spaces `n`/`t` (no explicit level key) were dropped so the
|
||
set is an unambiguous single floor. Seeded from the bare plot (`init.dom`).
|
||
|
||
- *Expectation / decision rule:* near-zero fails ⇒ bottleneck is multi-storey
|
||
*coupling* (staging is the lever); still stalls (esp. `missing`) ⇒ per-floor
|
||
*construction* itself is the bottleneck (§11.2 required first).
|
||
- *Command (reproduce):*
|
||
```bash
|
||
URB_NO_OCCLUSION=1 python3 experiments/run_search_scaled.py \
|
||
examples/harbor-house-l0 20000 0 \
|
||
examples/harbor-house-l0/init.dom examples/harbor-house-l0/generated.dom
|
||
```
|
||
- *Result:* 20000 native evals across 250 topologies (234 s, 85 evals/s).
|
||
Best **33 fails**, fitness 2.25e-12 — deep in the 0.5ⁿ high-fail penalty
|
||
regime, with the whole 16-member population stuck at 33–35 fails. The smaller
|
||
budget-300 smoke run sat at 40 fails; full budget only crept 40 → 33. **Not
|
||
near zero.** Fail histogram of the best `generated.dom`:
|
||
|
||
| count | category |
|
||
|------:|----------|
|
||
| 13 | **missing** (all 3 `m` meeting rooms never constructed: required/critical + per-instance size/width/adjacency sub-checks) |
|
||
| 6 | adjacency (ws1→c, k1→da1, da1→c, da1→k1, me1→c, la1→c) |
|
||
| 4 | access |
|
||
| 4 | size |
|
||
| 2 | edge too long |
|
||
| 2 | crinkliness |
|
||
| 1 | proportion |
|
||
| 1 | too few stairs — single-storey artifact (`staircase_min` floored to 1 by the fitness `or 1` default; constant across runs) |
|
||
| **33** | total |
|
||
|
||
- *Verdict: per-floor CONSTRUCTION is the bottleneck, not multi-storey coupling.*
|
||
Even on a single floor with only 13 rooms and zero delta/core-alignment
|
||
complexity, the search cannot assemble the required room set: the dominant
|
||
category (13/33 = 39 %) is `missing` — the counted anonymous space `m×3` is
|
||
entirely absent — and the remaining fails are downstream adjacency/access/size
|
||
consequences of a room set the mutation operators never managed to construct.
|
||
This matches the §11.0 prediction's "still stalls (esp. `missing`)" branch:
|
||
**§11.2 programme-aware construction + missing-room repair is the prerequisite,
|
||
and staging alone (§11.3) will not rescue it.** §11.3 stays blocked on §11.2.
|
||
|
||
### 11.2 Programme-aware construction + missing-room repair (`homemaker-py-c4c.2`) — DONE
|
||
|
||
Two changes (`operators.py`, wired in `driver.py`):
|
||
|
||
1. **`constructive_topology`** — bootstrap seeder that makes the required room
|
||
set a *constructive invariant*. It sizes each storey to its required rooms
|
||
(partitioning by `level`; level-free rooms distributed round-robin over a
|
||
shuffled order), plus one circulation `C` and one outside `O` per storey,
|
||
grows the slicing tree to that leaf count, and assigns the types. Stochastic
|
||
(random splits/rotations, shuffled type→leaf assignment) so a bootstrap batch
|
||
is still a diverse population. Replaces the random `random_topology` bootstrap
|
||
whenever the programme has required spaces.
|
||
2. **`mutate_place_missing`** — repair operator. Detects a required-but-absent
|
||
space (`graph.check_space_counts`) and inserts one by dividing a host leaf
|
||
into `[room | remainder]`. Lex-safe host ranking (cf. §4.10): generic `O`
|
||
leaves first (unbounded, nothing displaced), then other non-required leaves,
|
||
circulation/stairs only as last resort; a required room is never displaced.
|
||
Forced onto the room's required storey when the programme constrains its
|
||
level. Weight 2.0 in the mutation mix (noops cheaply once complete).
|
||
|
||
- *Gate:* `missing`-type failures collapse to ~0; net-fail improvement vs the
|
||
blank-slate baseline; no regression on the seeded programme-house 1-fail
|
||
optimum (§4.10).
|
||
- *Commands (reproduce):*
|
||
```bash
|
||
# A/B at identical budget+seed (old = git HEAD before this change):
|
||
URB_NO_OCCLUSION=1 python3 experiments/run_search_scaled.py \
|
||
examples/harbor-house 20000 0 examples/harbor-house/init.dom out.dom
|
||
# §4.10 regression: warmstart-2f4 seed, 50000 evals, pop 8, 4 workers
|
||
```
|
||
- *Result (harbor-house, 20000 native evals, seed 0, identical config):*
|
||
|
||
| metric | OLD (random bootstrap) | NEW (constructive) |
|
||
|--------|-----------------------:|-------------------:|
|
||
| seed best fails | 163 | 139 |
|
||
| final total fails | 133 | **105** |
|
||
| `missing` fails | **103** (77 %) | **12** (11 %) |
|
||
| missing-records | 22 | 2 |
|
||
| dominant remaining | `missing` | crinkliness 27, size 23, access 13, edge 12 |
|
||
|
||
Constructive seeding alone gives a **24-fail head start at the seed**
|
||
(163 → 139) and the run ends at **105 vs 133 (−21 %)**, with the
|
||
`missing` stack collapsed **103 → 12**. **§4.10 regression: PASS** — the
|
||
warmstart-2f4 seed still reaches a **1-fail** population (whole pop 1f at
|
||
50 040 evals; `place_missing` noops harmlessly when the set is complete).
|
||
|
||
- *Verdict: construction works and is necessary, but reframes the bottleneck.*
|
||
Making the required set a constructive invariant removes the catastrophic
|
||
`missing`-room stacking that dominated the blank-slate baseline (77 % → 11 %
|
||
of fails). But a *complete* 36-room harbor design then carries a large
|
||
**quality-fail load** — crinkliness/size/access/edge-too-long packing of two
|
||
fully-populated floors — that the current geometry inner loop + topology
|
||
operators reduce only partway in 20k evals. So total fails improve but stay
|
||
high. The dominant categories are now exactly what **§11.4 (graded objective,
|
||
to navigate the dense quality-fail regime)** and **§11.3 (staging — build one
|
||
credible floor at a time instead of cramming both)** target; §11.3 is
|
||
unblocked by this result. A concrete next seeder refinement (filed): the
|
||
type→leaf assignment is currently random, ignoring adjacency — clustering each
|
||
room near its required `c`/neighbour at construction time should cut the
|
||
adjacency (8) and downstream access (13) fails directly.
|
||
|
||
*Note on the baseline:* DESIGN cited a "74-fail `out1.dom`", but the on-disk
|
||
`out1.dom` is untracked and was overwritten by a prior experiment (it now
|
||
re-scores to 37 fails; the committed `out1.dom.fails` of 74 lines belongs to
|
||
the superseded `.dom`). The honest, reproducible comparison is therefore the
|
||
identical-config A/B against the pre-change code (133 fails), not the stale
|
||
`out1.dom` number.
|
||
|
||
### 11.3 Staged per-floor search (`homemaker-py-c4c.3`)
|
||
|
||
*Stub.* Stage 1: base floor over the level-0 room set (one tree, no deltas) +
|
||
reserved core + substrate-readiness term. Stage 2: upper floors as deltas seeded
|
||
with their required room sets, base kept mutable at low probability. Gated by
|
||
§11.1 premise.
|
||
|
||
- *Gate:* staged beats single-stage on harbor at equal native-fitness budget;
|
||
reserved-core + readiness shown to prevent the bungalow trap (stage 2 does not
|
||
carve a core from scratch); no programme-house regression.
|
||
- *Result:* TODO.
|
||
|
||
### 11.4 Graded high-fail objective (`homemaker-py-c4c.4`)
|
||
|
||
*Stub.* Extends Phase 4 (§4.9). Lexicographic-by-total-count gives ~zero signal
|
||
when every candidate sits at ~49–74 fails. Add partial credit (proximity per
|
||
unsatisfied constraint and/or count of *distinct* unsatisfied requirements) as a
|
||
secondary key beneath fail-count, preserving the inner-loop cliff (§5.4) and the
|
||
missing-space hierarchy (§6).
|
||
|
||
- *Gate:* measured escape from a high-fail plateau the current lex comparator
|
||
cannot escape at equal budget; inner-loop 0/9-regression check (§4.9) still
|
||
clean.
|
||
- *Result:* TODO.
|
||
|
||
### 11.5 Topology diversity: structural niching + restarts (`homemaker-py-c4c.5`)
|
||
|
||
*Stub.* Replace the fitness-scalar dedup (driver.py:174) with a topology
|
||
signature so niching is by *structure*, not score; add crowding/restarts/islands
|
||
to match urb-evolve's upfront diversity on blank slate.
|
||
|
||
- *Gate:* blank-slate programme-house reaches ≤ 6 fails at equal budget; distinct
|
||
topology-signature count over time quantified before/after.
|
||
- *Result:* TODO. (Capstone `homemaker-py-9gp` canonical Polish encoding is the
|
||
principled long-term signature — `(a|b)|c == a|(b|c)` collapse — and lands
|
||
after §11.2.)
|