# homemaker — Design & Plan **Status:** validated direction, pre-implementation. **Audience:** a fresh session that will break this into `bd` (beads) tasks. Self-contained — assumes no memory of the originating conversation. --- ## 1. Purpose `homemaker-py` is a clean-room Python successor to the Perl **Urb** project (`/home/bruno/src/urb`). Urb models a building as a binary **slicing tree** and evolves layouts with mutation + crossover, scored against Christopher Alexander–style pattern fitness. Two long-standing problems motivate the rewrite: 1. **It doesn't scale** — beyond a few rooms, evolution never finds layouts an architect would consider obvious. 2. **Local minima** — even small programmes converge to poor optima. The eventual goal is a **100% Python** system. During bring-up, Perl Urb is kept as a throwaway **fitness oracle** behind the `.dom` file format. --- ## 2. Constraints that fix the representation These come from the problem domain and are **not negotiable**; importantly, they *vindicate* the slicing tree rather than argue against it: - **Multi-storey with stacked walls.** An upper storey retains the storey below, except additional divisions/undivisions. Load-bearing walls must stack ⇒ every cut is a full edge-to-edge **guillotine** cut. Urb already enforces this via `Below`-inheritance (an upper quad reads its geometry from the matching quad below). - **Quadrilateral rooms only** (no L/Z shapes) — recursive bisection produces exactly this. - **No pinwheel / non-slicing layouts** — undesirable for load-bearing construction and adaptability (cf. Brand, *How Buildings Learn*). This is the one class a slicing tree *can't* express, and we don't want it anyway. - **Plots are near-rectangular but general convex quadrilaterals** (not axis-aligned). Geometry must handle skew; the slicing *combinatorics* are unaffected. **Conclusion:** the slicing tree is the correct phenotype. The rewrite is about the *genotype*, the *search*, and the *fitness shape* — not about leaving the slicing class. --- ## 3. What we built this session (all committed) Package `src/homemaker/`: - **`dom.py`** — `.dom` YAML ⇄ `Node` tree. Linkage (`parent`/`below`/`position`), `wall_outer` inset on load with raw-corner stash for byte-perfect round-trip, emit. - **`geometry.py`** — faithful port of Urb's top-down geometry (`Coordinate`/`Coordinate_a`/`_b`/`Area`/`Length`) + `Coordinate_Offset` wall inset. **Memoised** (uncached recursion is exponential in depth). - **`programme.py`** — parse `patterns.config` `spaces:` into per-code size/width/proportion/adjacency/level/count requirements. - **`solver.py`** — bottom-up division-ratio solver (scipy `least_squares`). *(Outcome: falsified as a standalone component — see §4.2.)* - **`oracle.py`** — Phase-1 fitness bridge: write `.dom`, run `urb-fitness.pl`, parse `.score` + `.fails`. Experiments in `experiments/`: `dump_areas.{py,pl}`, `resolve_ratios.py`, `refine_sweep.py`, `sweep_failtypes.py`, `optimize_fullfitness.py`. --- ## 4. Empirical findings (the core of this document) ### 4.1 Geometry port — VALIDATED Per-leaf areas computed in Python are **byte-identical to Urb across all 35 programme-house `.dom` files**, including the wall inset and multi-storey wall-stacking inheritance. (`experiments/dump_areas.{py,pl}`.) The infrastructure is trustworthy. ### 4.2 Bottom-up area-proxy sizing solver — FALSIFIED The original hypothesis: give leaves *target sizes*, solve cut ratios bottom-up, let the EA search only topology. Tested by re-solving an evolved candidate's ratios from programme targets and scoring via the oracle. - `resolve_ratios.py` on candidate-002: areas recovered accurately (errors collapsed, e.g. t1/t2/t3 from +1.4/+2.4/+4.8 → ~+0.05), and it *fixed* the original's `size` failure — **but total fitness dropped** (0.00737 → 0.00065, 4 fails) because it introduced shape/relational failures. - `refine_sweep.py` (warm-start refine of all 34 candidates): **0/34 improved.** Total failures 124 → 297 (equal-offset cuts) and 124 → 626 (independent-offset cuts). - `sweep_failtypes.py` (failure-type histogram, equal-offset): | type | area-dominant Δ | shape-aware Δ | |---|---|---| | width | +82 | +29 | | proportion | +35 | +7 | | crinkliness | +18 | +4 | | adjacency | +18 | +13 | | size | **−15** | **+15** | | access | +29 | +39 | | **total added** | +173 | +110 | **Why it fails:** in Urb's fitness, every cut position is simultaneously a *size* knob **and** an *adjacency/access/shape* knob. A solver that optimises only size/shape is blind to access/adjacency and trades them away. Refining a co-evolved local optimum with a *partial* objective is **structurally unable to win**, and the `0.5^n` failure penalty makes every new failure catastrophic while fixes are only linear. The proxy solver is strictly worse than optimising real fitness. **Do not pursue it.** ### 4.3 "Perpendicular" failures were an artifact — RESOLVED Letting the two ends of a cut float independently produced skewed cuts and many `perpendicular` failures. Tying the two ends (**equal offset, `a == b`**, one DOF per cut) produces near-perpendicular walls on these near-rectangular plots and yields **zero** `perpendicular` failures. **Equal-offset cuts are the only mode to use.** This also halves the variable count and matches the slicing model. ### 4.4 DOF / over-determination — partially real, not fatal A topology with *R* rooms has ~*R−1* cut DOF but ~2–3 size/shape constraints per room, so a *fixed* topology can be over-determined: you cannot always hit area + width + proportion for every room at once (heavy shape weighting traded straight into `size`, §4.2 table). This limits any single-objective sizing pass — but it is **not** fatal, because optimising the *full* objective still found large gains (§4.5). The earlier "infeasibility" worry was overstated. ### 4.5 Full-fitness frozen-topology optimisation — VALIDATED ✅ Drive the equal-offset ratios with Nelder-Mead against the **real oracle fitness** (whole objective, no proxy), topology frozen (`experiments/optimize_fullfitness.py`): | candidate | DOF | original | optimised | gain | fails | |---|---|---|---|---|---| | 2f45907 (best evolved) | 7 | 0.012617 | 0.015684 | ×1.24 | 2→2 | | candidate-002 (MCP-refined) | 6 | 0.007375 | 0.012319 | ×1.67 | 2→2 | | c964435 (MCP baseline) | 6 | 0.003667 | 0.005836 | ×1.59 | 3→3 | **Every design improved 24–67%, none added a failure.** Headroom *widens* on weaker designs. Because the optimiser sees the whole objective (including the `0.5^n` penalty), it never trades into a new failure — **the cliff that destroys the proxy solver protects the full-objective optimiser.** **Implications:** - There is large, unclaimed **geometry headroom above every EA design** — even the best. Urb's EA under-optimises geometry (its slide mutations are weak/ under-applied). - A **full-objective geometry inner loop is genuinely valuable** (the proxy solver is not). - The EA/search should therefore own **topology**; geometry is delegated to the inner loop. This is the memetic architecture (§5). ### 4.6 Oracle throughput (measured) `urb-fitness.pl` scores **many `.dom` files per invocation**, so the Perl startup (~0.65 s) amortises across a batch and cached fields (e.g. occlusion) persist. Measured on the 35-file corpus: **0.99 s/dom batched** vs **1.65 s/dom** for a single-file call. The cost is **assessment-dominated** (~1 s/dom of actual work), so startup amortisation gives ~40% — useful but bounded. Consequences: - **Batching only helps when evaluations are submitted together** — favour **population/parallel-evaluating optimisers** (CMA-ES, differential evolution, island EA, pattern search) over inherently sequential ones (Nelder-Mead), both inner loop and outer search, so a whole generation scores in one oracle call. - The memetic search can be **prototyped on the batched oracle** before any fitness port. A **native Python fitness is strongly desirable** (faster assessment, independence, enables penalty reshaping and large programmes) but is **a later speed/scale optimisation, not a hard gate**. ### 4.7 The `0.5^n` failure penalty is a first-order pathology Multiplicative `0.5^n` over failure *count* (a) makes the landscape a cliff (no gradient across the huge zero-feasibility region), (b) rewards fewer *flags* over better *geometry* (the original outscored better-sized solved designs purely on flag count), and (c) is representation-independent. Reshaping it (additive / soft / multi-objective Pareto) is a high-leverage change that helps Urb today and homemaker tomorrow. --- ## 5. Validated architecture **Memetic search, full objective throughout:** ``` ┌─────────────────────── topology search (OUTER) ───────────────────────┐ │ genome = slicing topology + per-leaf type assignment + per-floor │ │ divide/undivide deltas (base floor is master) │ │ operators = high-locality topology moves (see §6) │ │ │ │ for each proposed topology: │ │ ┌──────────── geometry inner loop ────────────┐ │ │ │ optimise equal-offset cut ratios (1 DOF/cut) │ │ │ │ against the FULL fitness (derivative-free / │ │ │ │ gradient), to convergence │ │ │ └──────────────────────────────────────────────┘ │ │ score = best full-fitness over inner loop │ └──────────────────────────────────────────────────────────────────────────┘ fitness: NATIVE Python (fast), reshaped penalty ``` Key decisions, all evidence-backed: 1. **Geometry = inner optimisation against full fitness** (§4.5), *not* an area proxy (§4.2). Equal-offset cuts, one DOF per free branch (§4.3). 2. **Search owns topology only.** The base-floor tree is the primary genome; 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) is a later speed/scale optimisation — desirable, not a gate. 4. **Reshape the failure penalty** (§4.7) — additive/soft or multi-objective — so the search has a gradient and isn't dominated by flag-count. 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. 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 | | **topology genome + operators** | ❌ to build | base tree + per-floor deltas; high-locality moves | | **search driver** | ❌ to build | memetic EA / SA over topology | | **native fitness** | ❌ later | speed/scale optimisation (not gating); port + validate vs oracle | | **penalty reshaping** | ❌ to design | additive/soft or multi-objective | | 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. --- ## 7. Phased roadmap - **Phase 0 — diagnostics** *(done this session)*: 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) -> (geometry, fitness)`. - **Phase 2 — topology search (on batched oracle)**: base-tree + per-floor-delta genome, high-locality operators, memetic driver wrapping the Phase-1 inner loop. Compare against `urb-evolve` from the same seeds/programmes. - **Phase 3 — native Python fitness**: port Urb's programme-driven fitness; validate score + failure set against the oracle across the 35-file corpus (float tolerance, identical failure sets). Swap behind the same interface for speed/scale; retire the oracle. - **Phase 4 — penalty reshaping**: replace `0.5^n` with additive/soft or multi-objective (easier once fitness is native); 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)? Recommend: *port faithfully first*, validate, then reshape in Phase 3. 2. **Programme contradictions exist.** e.g. t3 (3 m² WC) inherits a 4 m default width — geometrically impossible; the original "passes" only by failing `size` instead. Need a sane width default scaled to area, or per-room widths. 3. **Inner-loop optimiser choice.** Nelder-Mead worked for diagnostics; for production consider CMA-ES, Powell, or gradient-based once native fitness is differentiable-ish. DOF is small (≈ rooms−1). 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. **End-state confirmed: 100% Python**; Perl oracle is scaffold only. --- ## 9. How to reproduce (for the next session) ```bash cd /home/bruno/src/homemaker-py # 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/lib /bin/urb-fitness.pl `, env `DEBUG=1` to defeat the skip-if-newer cache; reads `.score` and `.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.