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Author SHA1 Message Date
Bruno Postle
91f7626a77 bd: close homemaker-py-pek 
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014uCyZJCh5mBuA2yEFxgQpo
 2026-08-05 16:59:25 +01:00
Bruno Postle
6dd4ae6470 homemaker-py-pek: delete dead first process_storey definition 
Python silently shadowed the gnw-scope process_storey with the later
hgg-extended one; the first ~45 lines were unreachable dead code that
still read as live. Deleted; the extended definition is a strict
superset. Suite: 405 passed (pre-existing 5 CP-SAT/reassign failures
unrelated, confirmed present on main before this change).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014uCyZJCh5mBuA2yEFxgQpo
 2026-08-05 16:57:20 +01:00
2 changed files with 18 additions and 66 deletions
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@ -85,7 +85,7 @@
{"id":"homemaker-py-p6t","title":"Convergence-speed A/B for tiered comparator: evals to 0 hard fails, tiered vs flat","description":"homemaker-py-2g7.3 (DESIGN.md §37.1) validated that tiered search (-n_hard,-n_soft,fitness) reaches a strictly lower mean hard-fail count than flat (-n_fails,fitness) at a FIXED budget (20k evals) on harbor-house and maple-court. That measures fail composition at a snapshot, not time-to-solved. The natural follow-up: race the two comparators to '0 hard fails' (or a hard-fail floor) and compare evals/wall-clock to get there, ideally after 2g7.1/2g7.2 ground truth lands so there is a real target to race to instead of an arbitrary floor.","design":"Reuse experiments/tier_ab_2g7_3.py's harness; instead of a fixed budget, run until n_hard==0 or a budget cap, log evals-to-target per seed/scheme, same programmes (harbor-house, maple-court), same 3-seed protocol.","acceptance_criteria":"Report showing evals-to-0-hard-fails (or evals-to-floor) for tiered vs flat, both programmes, 3 seeds; verdict on whether tiering also wins on convergence speed, not just fixed-budget composition.","status":"open","priority":3,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-08-02T17:51:38Z","created_by":"Bruno Postle","updated_at":"2026-08-02T17:51:38Z","dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-2g7.10","title":"MAP-Elites archive over (hard-fail profile, leaf count, circulation fraction)","description":"Quality-diversity as the population-level answer to the §4.10 deceptive-valley problem: an archive keeps the elite per behavior niche, so 'transiently worse but structurally different' stepping stones survive — exactly what lex selection provably discards (§11.4's own analysis). DISTINCT from the failed §11.5/§11.8 niching: that kept diverse individuals under ONE selection pressure; MAP-Elites keeps the BEST individual per niche with no cross-niche competition. Descriptors to try: hard-fail category histogram (bucketed), total leaf count, circulation area fraction, storey balance. Emit from the existing genome.signature/score_with_grade machinery (kept default-off for exactly this reuse, §11.4 verdict). Blocked on the shape-curve DP: archive-filling needs cheap evals to be meaningful. Gate honestly per the ledger discipline: 3 seeds, control = current default stack.","acceptance_criteria":"A/B at equal budget (harbor+maple, 3 seeds): archive best hard-fails \u003c= default-stack best on mean; archive demonstrably contains the stepping stone for at least one accepted valley-crossing (traceable lineage)","status":"open","priority":3,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-08-02T09:16:00Z","created_by":"Bruno Postle","updated_at":"2026-08-02T09:16:00Z","dependencies":[{"issue_id":"homemaker-py-2g7.10","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-02T10:15:59Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-2g7.10","depends_on_id":"homemaker-py-2g7.4","type":"blocks","created_at":"2026-08-02T10:15:59Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-2g7.8","title":"LLM operator synthesis (AlphaEvolve-style): evolve mutation-operator code against the A/B harness","description":"Second LLM role, after the repair operator proves the plumbing: let the LLM propose new OPERATOR CODE (python functions with the mutate_* signature) and evaluate candidates with the exact experiment discipline DESIGN.md already enforces (control reproduces baseline, 3 seeds, 20k evals, verdict). The project's ledger of 20+ operator experiments with verdicts is unusually good few-shot material: feed it the §11-§13 history so it learns what already failed (niching, grading, annealing...) and why. Sandbox the generated code; acceptance purely empirical via the harness. This is compute-hungry — schedule after the shape-curve DP lands so each A/B is cheap.","acceptance_criteria":"one synthesized operator survives the standard 3-seed A/B gate on harbor or maple (mean fails strictly better, control reproduces baseline)","status":"open","priority":3,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-08-02T09:15:56Z","created_by":"Bruno Postle","updated_at":"2026-08-02T09:15:56Z","dependencies":[{"issue_id":"homemaker-py-2g7.8","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-02T10:15:56Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-2g7.8","depends_on_id":"homemaker-py-2g7.7","type":"blocks","created_at":"2026-08-02T10:15:56Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-pek","title":"fitness.py: delete the dead first process_storey definition (silently shadowed)","description":"Found by the homemaker-py-zrx expert review. class Fitness defines process_storey TWICE: the original gnw-scope version at fitness.py:1146 and the extended hgg version at fitness.py:1452. Python keeps only the second; the first ~45 lines are dead code that still reads as live. This is a silent-bug vector: an edit to the first definition (e.g. a fix to the covered-outside failure emission, which is duplicated verbatim in both) changes nothing at runtime and no test would notice. Delete the first definition (its docstring notes are preserved in the second). No behaviour change; run the suite to confirm 337 pass.","status":"open","priority":3,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-08-02T08:19:56Z","created_by":"Bruno Postle","updated_at":"2026-08-02T08:19:56Z","dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-pek","title":"fitness.py: delete the dead first process_storey definition (silently shadowed)","description":"Found by the homemaker-py-zrx expert review. class Fitness defines process_storey TWICE: the original gnw-scope version at fitness.py:1146 and the extended hgg version at fitness.py:1452. Python keeps only the second; the first ~45 lines are dead code that still reads as live. This is a silent-bug vector: an edit to the first definition (e.g. a fix to the covered-outside failure emission, which is duplicated verbatim in both) changes nothing at runtime and no test would notice. Delete the first definition (its docstring notes are preserved in the second). No behaviour change; run the suite to confirm 337 pass.","status":"closed","priority":3,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T08:19:56Z","created_by":"Bruno Postle","updated_at":"2026-08-05T15:57:28Z","started_at":"2026-08-05T14:43:30Z","closed_at":"2026-08-05T15:57:28Z","close_reason":"Deleted dead first process_storey definition; verified second is a strict superset; 405 tests pass (5 pre-existing unrelated failures confirmed present on main before this change)","dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-sd3","title":"driver.collapse_best bakes collapse_insearch=True into its finish evaluator, making the 94g keep-better guard vacuous","description":"Found by the homemaker-py-zrx expert review; same family as homemaker-py-7ua but in the PRODUCT (driver.py), not the experiment script. driver.collapse_best builds its evaluator as _fitness_for(str(programme_dir), leaf_sharing, superpose, multi_use=multi_use) — so collapse_insearch silently takes _fitness_for's default True. collapse_best has no collapse_insearch parameter, so evolve.py cannot thread the run's --collapse-insearch flag through even if it wanted to.\n\nConsequences, verified on 5 evolved harbor-house trees today: (1) the keep-better guard of collapse_finish is VACUOUS — base_fails is measured on a deepcopy that _evaluate_full re-collapses in-eval, so base == collapsed on 5/5 files (e.g. evolved-3M-nols-3.dom logs '12 -\u003e 12 (applied)' where the canonical evaluator shows the collapse actually did 15 -\u003e 12). The 94g safety property 'kept only if the fail count does not increase' is therefore not being checked against the true pre-collapse tree: a canonically fail-INCREASING collapse would be silently applied (collapse_global is 'monotone on harbor-house but not proven in general' per its own docstring — the guard exists precisely for that case). (2) The '[finish] collapse: N -\u003e M' log line under-reports the collapse's real effect (experiment logs quoting it understate 94g's contribution). (3) In a --no-collapse-insearch run the finish evaluator contradicts the run's objective outright — the deterministic 7ua mechanism, now in the default pipeline. (4) Minor: max_share and conn_grade are also not forwarded (matters for kpu/anneal and qi6 runs). Same pattern in search_annealed's final rescore branch: _evaluate(..., leaf_sharing=False, superpose=superpose) leaves _evaluate's collapse_insearch default True, and search_annealed has no way to pass the flag to it.\n\nOn the 5 probed files the returned tree's canonical fails happened to equal the reported number (the tree is a collapse fixpoint after iters=6 + 2-opt, so the extra in-eval collapse found nothing) — but that is not guaranteed, and the vacuous guard + misleading log line are unconditional.\n\nRecommended fix: add a collapse_insearch (and max_share/conn_grade) parameter to collapse_best, thread it from evolve.py, and make collapse_finish's keep-better measurement use a CANONICAL (collapse_insearch=False) evaluator regardless — the guard's job is to protect the canonical fail count of the written .dom, which homemaker-fitness scores with the on-disk config (no insearch override). Decide explicitly which objective the final 'best: N fails' report should quote (canonical is what the .dom.fails sidecar will say).","notes":"Fixed. Two changes: (1) fitness.collapse_finish now forces collapse_insearch=False (canonical) for its own base_fails/cand_fails measurement, saving/restoring self._collapse_insearch around the two score_with_fails calls -- regardless of how the Fitness instance itself was configured, so the guard can never again compare a pre-collapsed base against a pre-collapsed candidate. (2) driver.collapse_best now builds its evaluator with collapse_insearch=False explicitly (hardcoded, not threaded -- canonical is always the right objective for the 94g guard and the final reported fail count, matching what homemaker-fitness reports for the written .dom with no override), and threads max_share/conn_grade through to _fitness_for for config parity. Also fixed the same-family bug in search_annealed's no-polish-budget final rescore branch (was silently defaulting to collapse_insearch=True via _evaluate's default; now reads collapse_insearch/multi_use from search_kw). evolve.py forwards conn_grade to collapse_best. Added a regression test (test_collapse_finish_guard_is_canonical_even_with_insearch_collapse_on) that builds a Fitness with collapse_insearch=True in conf and asserts base_f still reflects the true pre-collapse fail count. Full suite: 405 passed (same 5 pre-existing CP-SAT/reassign failures, confirmed present on main before this change, unrelated).","status":"closed","priority":3,"issue_type":"bug","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T08:19:41Z","created_by":"Bruno Postle","updated_at":"2026-08-05T06:47:15Z","started_at":"2026-08-04T23:41:55Z","closed_at":"2026-08-05T06:47:15Z","close_reason":"94g keep-better guard is now non-vacuous; canonical scoring enforced","dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-d86","title":"Rigorously re-verify qpk/1ph historical numbers against the homemaker-py-iio fix","description":"homemaker-py-iio (fixed 2026-08-02) found a stale-leaf-share metadata leak\nin Fitness._collapse_value/_usage_quality that could corrupt one cell of\ncollapse_global's Hungarian assignment during any leaf_sharing+collapse\nrun -- i.e. essentially the entire \"full default stack\" used from\nhomemaker-py-x3b (leaf_sharing default-on) onward, including the very\nstudies that justified defaulting collapse_insearch on (94g, qpk/1ph, 8sh).\n\nA same-codebase fix-vs-no-fix re-run of the qpk protocol (harbor-house,\nbudget 2500, seeds 1-3) confirmed the bug demonstrably perturbs real\nper-seed outcomes under collapse_insearch=ON (2/3 seeds diverged by 5-8\nfails, non-directionally) -- see DESIGN.md §35 for full details. That\nre-run used TODAY's codebase, not the actual historical commit, and only 3\nharbor-house seeds, not the original seed sets -- so it establishes the bug\nwas real and non-trivial but does NOT establish whether 1ph's aggregate\nN=20 programme-house verdict (mean 7.95-\u003e7.10, paired t-test p~=0.028)\nwould have changed under the fix.\n\nThis issue is to do the rigorous version: check out the codebase near the\n1ph commit (~2026-07-24, \"post-qpk commits through 161\"), backport the iio\nfix there in an isolated worktree, and re-run the ACTUAL historical seed\nsets (programme-house N=20 seeds 1-20, harbor-house N=3 seeds 1-3) at the\n1ph protocol's exact parameters, comparing per-seed and aggregate results\nagainst the published numbers. Low priority: the qualitative direction of\nthe qpk/1ph conclusion is probably still right (noise is non-directional\nand the N=20 statistical margin is comfortably above the observed per-seed\nswing), this is about tightening confidence, not expecting a reversal.","notes":"homemaker-py-r5a (fixed 2026-08-02) also affects this: it is the COMMIT-door companion to iio (a leaf relabelled back to its own stale share_type resurrects a stale multiplicity credit). Any re-verification run here should use the codebase state after BOTH iio and r5a, not iio alone.","status":"open","priority":3,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-08-02T06:53:51Z","created_by":"Bruno Postle","updated_at":"2026-08-02T09:44:35Z","dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-7ua","title":"run_staged_search.py final rescore omits collapse_insearch override, causing false MISMATCH under leaf-sharing","description":"experiments/run_staged_search.py's _native_score() (used for the final 're-scored (native): ... -\u003e OK/MISMATCH' sanity line) calls fitness.load_config(programme_dir) with NO overrides, but driver.search_staged's internal evaluator always runs with collapse_insearch=True (baked into driver.search's default, search_staged has no param to disable it). The script's monkeypatched fitness.load_config only injects leaf_sharing/share_edge_cap/multi_use, not collapse_insearch, so the final rescore conf silently diverges from the search-time conf whenever leaf_sharing is on (the current default stack). Observed during homemaker-py-91f: a WORKERS=4 budget=2000 harbor-house run reported best fails=38 during search but re-scored fails=34 -\u003e MISMATCH (partly parallel non-determinism per homemaker-py-b8g, but the missing collapse_insearch override is a separate, deterministic contributor). Fix: add collapse_insearch=True to the monkeypatched conf alongside leaf_sharing/share_edge_cap.","status":"closed","priority":3,"issue_type":"bug","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-01T11:32:58Z","created_by":"Bruno Postle","updated_at":"2026-08-05T09:04:05Z","started_at":"2026-08-05T07:33:29Z","closed_at":"2026-08-05T09:04:05Z","close_reason":"Fixed: added collapse_insearch=True to the monkeypatched load_config conf in run_staged_search.py's leaf_share/multi_use branch. Verified with a smoke run (programme-house, LEAFSHARE=1, budget=150): pre-fix reported MISMATCH (1.51708e-08 vs 1.56663e-08), post-fix reports OK with identical values. Full pytest suite: 405 passed, 5 pre-existing failures in test_cpsat.py/test_operators.py unrelated to this change (confirmed failing on main before this fix too).","dependency_count":0,"dependent_count":0,"comment_count":0}
@ -130,27 +130,27 @@
{"id":"homemaker-py-erc.6","title":"Experiment: inner-loop slack-expansion objective term","description":"Inner-loop counterpart to plot-fill construction. If Diagnostic B shows the inner loop has room to expand leaves into slack but no objective gradient to do so (the scalar rewards hitting target area but not exceeding it where slack exists), add a term/incentive so the ratio optimiser pushes leaf boundaries out to consume neighbouring slack and satisfy size, rather than parking at target.\n\nCONDITIONAL on Diagnostic B: build this only if B localizes the gap to the inner loop (room to expand, no gradient); if B shows construction targets too-small dims, prefer the plot-fill construction sibling. Must preserve the §5.4 inner-loop cliff / §4.9 lexicographic protection — the term sits where it cannot displace the fail-count ordering. A/B vs §12.2 baseline, seeds 0/1/2, 20000 evals, staged, default-OFF. Record DESIGN.md §13.6.","notes":"DEPRIORITISED by Diagnostic B (§13.2). B shows the inner loop CANNOT repair undersize: the slack is depth-driven maldistribution baked into the frozen topology, and the equal-offset ratio DOF cannot shrink a 14x leaf to feed a starved one without trading into shape fails (0.5^n cliff). Wrong DOF and wrong direction — the blocker is slicing POSITION, not a missing expansion reward. Fix belongs upstream in construction/topology (erc.4 re-scoped, erc.3). Keep as a low-priority follow-up only if a depth-balanced construction still leaves a residual size gradient the inner loop could pick up.","status":"closed","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:24Z","created_by":"Bruno Postle","updated_at":"2026-06-28T13:22:22Z","closed_at":"2026-06-28T13:22:22Z","close_reason":"wont-fix (DESIGN §13.7): Diag B (§13.2) showed the inner loop cannot repair undersize (wrong DOF — slicing position, frozen-topology ratios). Superseded by depth-balanced construction (erc.4). Condition unmet.","dependencies":[{"issue_id":"homemaker-py-erc.6","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:23Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.6","depends_on_id":"homemaker-py-erc.2","type":"blocks","created_at":"2026-06-23T00:16:47Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-erc.5","title":"Experiment: compactness-aware cuts (minimize leaf perimeter/area)","description":"Attacks the #1 factor, crinkliness (346) — a per-leaf perimeter/area property DISTINCT from proportion (aspect ratio). Proportion-aware seeding (leu.2) sizes splits but does not bias toward balanced, square-ish subdivision. Add a KD-tree-style 'keep both children compact' cut rule (prefer the cut orientation/position that minimises summed child perimeter/area) in construction.\n\nCONDITIONAL on Diagnostic A: if A shows per-leaf shape-fail is FLAT across densities (floor intrinsic to slicing density), better cuts at the same leaf count will not pay → this should be closed wont-fix in favour of leaf-sharing. Only build if A shows shape-fail RISES with density. A/B vs §12.2 baseline, seeds 0/1/2, 20000 evals, staged, default-OFF. Record DESIGN.md §13.5.","notes":"DEPRIORITISED by erc.1 verdict (§13.1): per-leaf shape-fail flat vs slicing density and cuts already squarest (_size_divisions_from_targets picks squarest rotation) yet still ~1.8 fails/leaf =\u003e little compactness headroom at fixed leaf count. Floor is intrinsic to leaf COUNT, not cut quality. Revisit only if leaf-sharing (erc.3) underdelivers.","status":"closed","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:21Z","created_by":"Bruno Postle","updated_at":"2026-06-28T13:22:17Z","closed_at":"2026-06-28T13:22:17Z","close_reason":"wont-fix (DESIGN §13.7): Diag A (§13.1) showed the floor is intrinsic to leaf COUNT not cut quality; revisit condition was 'only if leaf-sharing underdelivers' but leaf-sharing OVER-delivered (−32…−39%, §13.3). Condition unmet.","dependencies":[{"issue_id":"homemaker-py-erc.5","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:21Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.5","depends_on_id":"homemaker-py-erc.1","type":"blocks","created_at":"2026-06-23T00:16:43Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-2g5","title":"Rebuild occlusion/daylight/sun subsystem in Python (post-Phase-5, after optimisation fully native)","description":"DESIGN.md §6 port scope — a whole subsystem, not a term. quality_daylight (Leaf.pm:281-296) needs Urb::Misc::Sun + Urb::Field::Occlusion (+CIESky); quality_uncrinkliness also takes the occlusion object. Indoor spaces return 1 for daylight; cost is outdoor spaces + crinkliness. Port Sun_horizontal (262980-minute normalisation) and the occlusion wall set from Dom-\u003eWalls.","acceptance_criteria":"Daylight and crinkliness factors match Perl (float tolerance) across the corpus, including multi-storey cases","notes":"Re-scoped 2026-06-12: occlusion disabled in the Urb oracle instead of ported (see homemaker-py-gp2). Native fitness ships with simple crinkliness (illumination factor = 1, in homemaker-py-gnw). This issue is now the eventual Python occlusion rebuild, only after optimisation works entirely in Python. Restores outdoor-daylight and shaded-wall selection pressure.\nReframed 2026-06-17: orthogonal to epic homemaker-py-c4c. This is fitness FIDELITY (restoring daylight + shaded-wall selection pressure to match Perl), not search CAPABILITY — it changes what 'good' means, not the search's ability to find good. It will NOT improve final designs in the sense currently sought. Stays P4, deferred until the topology-search-quality epic lands and optimisation is fully native.","status":"open","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-11T23:38:25Z","created_by":"Bruno Postle","updated_at":"2026-06-17T19:14:48Z","dependency_count":0,"dependent_count":0,"comment_count":0}
{"_type":"memory","key":"programme-house-optimisation-result-2026-06-14-15","value":"Programme-house optimisation result (2026-06-14/15): best achievable is 1 fail (l1 wrong level, score ~0.005). 0 fails is geometrically impossible: l1 (min 27m²) must occupy ll (~23m²) at level 0, which eliminates the t3-adj-C provider; dividing ll into lll(l1)+llr(C) gives llr proportion ~6:1 (fails). Python memetic optimizer achieves 1 fail in 50k evals vs Perl optimiser's 2-3 fails. Winning topology: TWO C nodes at level 0 — ll(C) for t3-adj-C via geometric contact, rl(C) for staircase via tree-sibling adjacency to rrr(O). Best .dom: scratch/from-warmstart-fixed.dom and scratch/from-compound3-fixed.dom."}
{"_type":"memory","key":"proportion-aware-constructive-seeding-leu-2-12-2","value":"Proportion-aware constructive seeding (leu.2/§12.2): sizing seed cuts from target AREAS only regresses (thin slivers wreck aspect); you must ALSO pick each cut's rotation for child squareness. It is a convergence ACCELERATOR via a deeper local optimum around the constructed topology: wins where that topology is roughly right and budget is scarce (harbor -13%, maple -10% at 20k evals) but DELAYS small programmes where the seed must be restructured by undivide (programme-house regresses at fixed budget, yet reaches the floor given budget - speed, not asymptote). Default-on. Also: n_storeys must honour storey_minimum, not just level: keys (programme-house storey_minimum:2, all rooms level:0 - was seeded 1 storey short; cq1)."}
{"_type":"memory","key":"run-to-run-reproducibility-in-homemaker-layout-serial","value":"Run-to-run reproducibility in homemaker-layout: serial search (workers=1) is byte-for-byte deterministic; parallel (workers\u003e1) is now deterministic too AFTER fixing driver._run_batch to admit futures in submission order (was as_completed/completion order, bug xcy). Reproducibility holds only for a FIXED worker count — serial vs parallel differ because children-per-iteration is 1 vs n_workers (different batch granularity), which is expected, not a bug. The constructive seeder was NEVER nondeterministic: _assign_adjacency_aware has unique idx tiebreaks; comparing topologies with Python builtin hash() of the signature STRING is invalid (PYTHONHASHSEED salts str hashing per process) — use a stable hash (sha1) or genome.signature equality."}
{"_type":"memory","key":"user-preference-bruno-this-is-a-fedora-system","value":"User preference (Bruno): this is a Fedora system — NEVER install Python packages via pip without asking first; always ask whether to install the rpm via dnf (e.g. python3-cma) before considering pip. Applies to any dependency additions."}
{"_type":"memory","key":"deceptive-valleys-in-topology-search-when-every-single","value":"Deceptive valleys in topology search: when every single-step mutation from a target state passes through a high-fail intermediary (e.g. level_fix displaces a room into 5+ new fails), a compound operator that atomically applies two coordinated changes can escape. Design compound operators to land on the low-fail state directly, bypassing the deceptive gradient. Programme-house example: level_compound_fix atomically moves the level-constrained room AND re-inserts the displaced room adjacent to C in one step (operators.py, 2026-06-14)."}
{"_type":"memory","key":"experiment-seeding-pitfall-run-search-scaled-py-s","value":"Experiment seeding pitfall: run_search_scaled.py's default PH_SEED (c964…dom) is a FINISHED programme-house design — passing it warm-starts and floors at ~3 fails, NOT a blank-slate topology search. For blank-slate runs comparable to §11.5/§11.6 baselines, seed from examples/programme-house/init.dom (a bare undivided plot; driver bootstrap auto-triggers only on bare plots). Bit the 6zy sweep — first pass used c964 and falsely showed 3-fail floor across the whole grid."}
{"_type":"memory","key":"ld2-13-6-interior-o-seed-diagnostic-all","value":"ld2/§13.6 interior-O seed diagnostic: ALL crinkliness fails in the constructed bal+share seed are UNDER-exposed (crink\u003c0.62, landlocked rooms with no facade + no uncovered-O neighbour) — zero over-exposed sliver fails. So the erc crinkliness residual is genuine under-daylighting, validating the interior light-well premise. Default outside_divisor=6 was too sparse (null: harbor 147-\u003e142, crinkliness even rose). odiv=3 is the seed-optimal joint setting: harbor seed fails 147-\u003e129 (-18), maple 219-\u003e206 (-14), landlocked fails drop, at cost of more leaves (harbor +4, maple +8). Because it ADDS leaves it carries the §13.4 wash-out risk; A/B to convergence pending."}
{"_type":"memory","key":"never-use-corpus-filenames-candidate-001-dom-candidate","value":"Never use corpus filenames (candidate-001.dom, candidate-002.dom, generated.dom, init.dom, etc.) as --output targets when running experiments. These are test fixtures. Always write experimental outputs to scratch/ or a timestamped path. Lesson from 2026-06-14: warm-start runs overwrote candidate-001/002.dom and broke graph tests."}
{"_type":"memory","key":"multi-storey-staircase-consistency-when-dividing-or-retyping","value":"Multi-storey staircase consistency: when dividing or retyping a circulation (C) leaf at one level, the same structural change should be propagated to the matching leaf on ALL other storeys so the stair core path is maintained. The optimizer cannot fix staircase disruptions through trial-and-error geometry alone — it requires a synchronized multi-level operator that applies the same topology change to every storey simultaneously."}
{"_type":"memory","key":"run-to-run-reproducibility-in-homemaker-layout-serial","value":"Run-to-run reproducibility in homemaker-layout: serial search (workers=1) is byte-for-byte deterministic; parallel (workers\u003e1) is now deterministic too AFTER fixing driver._run_batch to admit futures in submission order (was as_completed/completion order, bug xcy). Reproducibility holds only for a FIXED worker count — serial vs parallel differ because children-per-iteration is 1 vs n_workers (different batch granularity), which is expected, not a bug. The constructive seeder was NEVER nondeterministic: _assign_adjacency_aware has unique idx tiebreaks; comparing topologies with Python builtin hash() of the signature STRING is invalid (PYTHONHASHSEED salts str hashing per process) — use a stable hash (sha1) or genome.signature equality."}
{"_type":"memory","key":"programme-house-optimisation-result-2026-06-14-15","value":"Programme-house optimisation result (2026-06-14/15): best achievable is 1 fail (l1 wrong level, score ~0.005). 0 fails is geometrically impossible: l1 (min 27m²) must occupy ll (~23m²) at level 0, which eliminates the t3-adj-C provider; dividing ll into lll(l1)+llr(C) gives llr proportion ~6:1 (fails). Python memetic optimizer achieves 1 fail in 50k evals vs Perl optimiser's 2-3 fails. Winning topology: TWO C nodes at level 0 — ll(C) for t3-adj-C via geometric contact, rl(C) for staircase via tree-sibling adjacency to rrr(O). Best .dom: scratch/from-warmstart-fixed.dom and scratch/from-compound3-fixed.dom."}
{"_type":"memory","key":"urb-fitness-bug-found-fixed-2026-06-12","value":"Urb fitness bug found+fixed 2026-06-12 (patch in /home/bruno/src/urb, uncommitted): ProgrammeDriven.pm ratio_o/ratio_type grepped case-insensitively over the ratios hash and took the FIRST key — nondeterministic (x4.5 score swings) for designs with mixed-case type classes (both 'c' circulation and 'C' covered). Fixed to SUM the class (matches Is_Circulation//Is_Outside semantics); 35/35 corpus scores unchanged. CRITICAL for homemaker-py-3y7/gnw: the native port must implement class-SUM ratios. Building.pm has the same unpatched pattern (site-driven path, not used by our oracle). Also: the memetic search reward-hacked this bug before the fix — search results predating it are noise artifacts."}
{"_type":"memory","key":"urb-oracle-nondeterminism-urb-fitness-pl-output-varies","value":"Urb oracle nondeterminism: urb-fitness.pl output varies run-to-run from Perl hash-order randomisation — .fails line ORDER shuffles (compare sorted, use oracle.Score.fail_lines) and the score float can flip by ~1 ULP (compare with math.isclose rel_tol=1e-12, never ==). Not a batching artifact; affects single runs too. Matters for the Phase 3 native-fitness parity gate (homemaker-py-uxz)."}
{"_type":"memory","key":"collapse-global-94g-and-any-label-usage-optimisation","value":"collapse_global (94g) and any label/usage optimisation CANNOT fix geometry-intrinsic fails. The harbor-house 15-fail best layout contains long-thin cells that are useless whatever room usage is assigned — their width/proportion/crinkliness fails are shape-bound, not label slack. Two consequences: (1) do not over-claim collapse gains — only ~2-3 of that layout's fails are reclaimable relabel slack, the rest are geometry- or building-level bound; (2) the threshold objective must not be tuned to 'pass' a degenerate cell via a permissive room type — a metric-pass on a physically useless space is gaming, not a fix. Real remedies for these are geometry/topology search (cell shape) and circulation placement, filed separately, not the collapse."}
{"_type":"memory","key":"experiment-harness-gotcha-the-leaf-sharing-relaxed-objective","value":"Experiment harness gotcha: the leaf-sharing RELAXED objective (§13.3) is injected ONLY by monkeypatching fitness.load_config in the parent process (run_staged_search.py / probe scripts). This is parent-process-only and does NOT propagate into ProcessPoolExecutor workers (n_workers\u003e1), which re-import fitness fresh and score under the STRICT on-disk patterns.config -\u003e r.n_fails MISMATCH (worker strict vs parent relaxed re-score). ALL §13.x floor runs were therefore SERIAL. Any future PARALLEL leaf-sharing experiment will silently mis-score until leaf_sharing lives on disk/CLI (tracked: homemaker-py-x3b). The parallel driver itself is correct; both paths score via load_config(programme_dir)."}
{"_type":"memory","key":"homemaker-py-3l6-fix-leaf-sharing-evolve-runs","value":"homemaker-py-3l6 fix: leaf-sharing evolve runs now auto-finish before write via driver.polish_finish — unfold_shared_leaves() then a warm-started leaf_sharing=False polish search (--polish-budget, default budget//2). Makes the written .dom honest under canonical homemaker-fitness (internal==canonical when leaf_sharing off). Interrupt path forces polish_budget=0 (unfold+rescore only). This is yaa's unfold-then-polish, made automatic; Schedule B annealing is still kpu."}
{"_type":"memory","key":"cli-tool-style-prefer-python-m-homemaker-module","value":"CLI tool style: prefer python -m homemaker.module --parameters pattern, installable via pip install -e . with pyproject.toml entry_points. Not standalone bin/ scripts."}
{"_type":"memory","key":"unfold-strategy-for-shared-leaves-homemaker-py-8iv","value":"Unfold strategy for shared leaves (homemaker-py-8iv, resolved 2026-07-16): use the BALANCED GRID (operators._grow_balanced/_size_subtree_equal), NOT circulation-aware slicing. Slicing a shared leaf perpendicular to its access edge so every child touches the corridor was implemented + A/B-tested and LOST decisively (150k-eval warm-start polish from evolved-3M: slice 41 fails/3.5e-14 vs grid 25 fails/2.4e-09, grid ahead at every milestone). Reason: k rooms all touching one wall are intrinsically thin slices; that geometric debt (proportion/long/width) is unfixable without topology change, while the grid's squarer children let local search re-route access cheaply via level_retype/place_missing/level_fix. Lesson: at the sharing-\u003eno-sharing transition, prioritise squarer children and leave access to local search; do not reintroduce slicing in Schedule B (kpu)."}
{"_type":"memory","key":"warm-x0-initialization-bug-pattern-when-a-topology","value":"warm_x0 initialization bug pattern: when a topology operator explicitly sets division ratios on a newly-created node (e.g. compound_fix sets node.division=[0.25,0.25] for t3), parent.ratios has no entry for that node (it was a leaf). warm_x0 defaults it to 0.5, corrupting the inner loop's starting point and making the operator invisible to lex comparison. Fix: only propagate child ratios for nodes where the parent node was NOT already divided; stale hidden nodes revealed by structural mutations (swap flipping b.below) must NOT contribute their pre-writeback values. See driver.py lines 259-267 (fixed 2026-06-14)."}
{"_type":"memory","key":"collapse-global-s-jacobi-adjacency-relaxation-homemaker-py","value":"collapse_global's Jacobi adjacency relaxation (homemaker-py-94g) is a synchronous per-round linear-assignment re-solve, which can 2-cycle indefinitely between two labellings that each satisfy ZERO adjacency requirements even though a permutation satisfying ALL of them exists -- proven on a minimal 4-cell chain (p1-q1-p2-q2, two disjoint adjacency pairs p1\u003c-\u003ep2/q1\u003c-\u003eq2) in test_two_opt_polish_escapes_jacobi_plateau. homemaker-py-9wi added Fitness._two_opt_adjacency_polish: a same-level pairwise-swap local search run after the Jacobi fixpoint, gated behind collapse_global(local_search=True) (default off, exposed as homemaker-collapse --local-search). Monotone by construction (a swap is kept only if it strictly increases total reward). Empirically on the 11 harbor-house evolved-*.dom/3m.dom/materialised-3M.dom layouts: 10 matched Jacobi-only exactly, 0 regressed, and evolved-anneal-3M.dom improved 21-\u003e19 fails (fixed a genuine mutual da1\u003c-\u003ek1 adjacency miss the Jacobi loop couldn't reach)."}
{"_type":"memory","key":"correction-to-urb-fitness-bug-memory-bruno-2026","value":"CORRECTION to urb-fitness-bug memory (Bruno, 2026-06-12): 'C' is NOT a 'covered' type — Is_Covered is a geometric predicate (indoor space above). Urb's generic types are canonically UPPERCASE: C=circulation, O=outside, S=sahn (get_space_types qw/C O S/; corpus is 100% uppercase, never 'c'/'o' leaves). The mixed-case designs that fired the latent ratio_type first-match bug were created by homemaker's own operator type pool emitting lowercase 'c'/'o' — fixed: driver/operators now emit uppercase generics only, and class checks use t[0].lower() in 'cos'. The Urb class-sum patch stays as defensive hardening (zero impact on canonical designs). Native port (3y7/gnw): treat type classes case-insensitively, generics canonically uppercase."}
{"_type":"memory","key":"island-model-psk-14-is-a-null-priming","value":"Island model (psk, §14) is a NULL: priming a population from N converged independent elites + crossover-heavy migration does not beat best-of-N at equal total budget (maple island 124 vs control 116). The child_probe instrument shows WHY: area-matched crossover across independently-converged elites almost never synthesizes (1-3 of ~64 children beat the better parent, max drop 2-5) because the slicing encoding is non-canonical (9gp), so splices are disruptive not combinatorial. Search-machinery null #3 after graded-objective and niching/restarts; residual stays geometry/shape-bound."}
{"_type":"memory","key":"strategy-decision-2026-06-12-bruno-occlusion-daylight","value":"Strategy decision 2026-06-12 (Bruno): occlusion/daylight is ORTHOGONAL to building a scalable optimiser. Disable it in Urb (env flag, homemaker-py-gp2) rather than port it; native fitness uses simple crinkliness (illumination factor = 1); rebuild occlusion in Python only after optimisation is fully native (homemaker-py-2g5, now P4). Consequence: all scores change when the flag flips — re-baseline corpus/.score, DESIGN \\$4.5 gains, gate bars at one clean boundary AFTER homemaker-py-1p0 closes; Phase-2 urb-evolve benchmark must run with the same flag."}
{"_type":"memory","key":"deceptive-valleys-in-topology-search-when-every-single","value":"Deceptive valleys in topology search: when every single-step mutation from a target state passes through a high-fail intermediary (e.g. level_fix displaces a room into 5+ new fails), a compound operator that atomically applies two coordinated changes can escape. Design compound operators to land on the low-fail state directly, bypassing the deceptive gradient. Programme-house example: level_compound_fix atomically moves the level-constrained room AND re-inserts the displaced room adjacent to C in one step (operators.py, 2026-06-14)."}
{"_type":"memory","key":"homemaker-py-pythonpath-set-pythonpath-home-bruno-src","value":"homemaker-layout PYTHONPATH: package installed as 'homemaker-layout' via pip install -e . so 'import homemaker_layout' works from anywhere without PYTHONPATH. For running tests use 'python -m pytest' from project root /home/bruno/src/homemaker-layout (pyproject.toml adds src/ automatically). Never try pip show homemaker — that's the old homemaker-addon conflict."}
{"_type":"memory","key":"multi-storey-staircase-consistency-when-dividing-or-retyping","value":"Multi-storey staircase consistency: when dividing or retyping a circulation (C) leaf at one level, the same structural change should be propagated to the matching leaf on ALL other storeys so the stair core path is maintained. The optimizer cannot fix staircase disruptions through trial-and-error geometry alone — it requires a synchronized multi-level operator that applies the same topology change to every storey simultaneously."}
{"_type":"memory","key":"adjacency-in-binary-slicing-tree-is-structural-not","value":"Adjacency in binary slicing tree is structural, not geometric: the inner-loop NM cannot fix topological adjacency failures. Two paths exist: (1) tree-sibling adjacency — a node is adjacent to its sibling in the tree; (2) cross-zone geometric adjacency — leaves from different subtrees that happen to share a boundary. Staircase/adjacency fails require a topology mutation that changes which nodes are siblings or which zones touch. This was proved empirically on programme-house: staircase fail from rot=0 layout could not be fixed by NM but was fixed by level_retype creating a two-C topology (2026-06-14/15)."}
{"_type":"memory","key":"experiment-seeding-pitfall-run-search-scaled-py-s","value":"Experiment seeding pitfall: run_search_scaled.py's default PH_SEED (c964…dom) is a FINISHED programme-house design — passing it warm-starts and floors at ~3 fails, NOT a blank-slate topology search. For blank-slate runs comparable to §11.5/§11.6 baselines, seed from examples/programme-house/init.dom (a bare undivided plot; driver bootstrap auto-triggers only on bare plots). Bit the 6zy sweep — first pass used c964 and falsely showed 3-fail floor across the whole grid."}
{"_type":"memory","key":"never-use-corpus-filenames-candidate-001-dom-candidate","value":"Never use corpus filenames (candidate-001.dom, candidate-002.dom, generated.dom, init.dom, etc.) as --output targets when running experiments. These are test fixtures. Always write experimental outputs to scratch/ or a timestamped path. Lesson from 2026-06-14: warm-start runs overwrote candidate-001/002.dom and broke graph tests."}
{"_type":"memory","key":"9o5-multi-use-leaves-is-path-a-superposition","value":"9o5 multi-use leaves is path (a) — superposition as SEARCH RELAXATION that COLLAPSES to specific usage at the end, NOT path (b) loose-fit/no-collapse. Bruno's intent: codes with SIMILAR leaf requirements form an interchangeable equivalence class; during evolution the solver doesn't commit which leaf serves which specific usage (smoother landscape, no fighting over exact leaf usage); at the end the layout is CONDENSED to specific usages by brute-forcing the in-class assignment (3 interchangeable usages over 3 leaves = 3! = 6 combinations to check, pick best). 'Derive automatically' compatibility = requirement-similarity grouping. This reverses the issue's stated 'path b preferred' note."}
{"_type":"memory","key":"experiment-harness-gotcha-the-leaf-sharing-relaxed-objective","value":"Experiment harness gotcha: the leaf-sharing RELAXED objective (§13.3) is injected ONLY by monkeypatching fitness.load_config in the parent process (run_staged_search.py / probe scripts). This is parent-process-only and does NOT propagate into ProcessPoolExecutor workers (n_workers\u003e1), which re-import fitness fresh and score under the STRICT on-disk patterns.config -\u003e r.n_fails MISMATCH (worker strict vs parent relaxed re-score). ALL §13.x floor runs were therefore SERIAL. Any future PARALLEL leaf-sharing experiment will silently mis-score until leaf_sharing lives on disk/CLI (tracked: homemaker-py-x3b). The parallel driver itself is correct; both paths score via load_config(programme_dir)."}
{"_type":"memory","key":"collapse-global-94g-and-any-label-usage-optimisation","value":"collapse_global (94g) and any label/usage optimisation CANNOT fix geometry-intrinsic fails. The harbor-house 15-fail best layout contains long-thin cells that are useless whatever room usage is assigned — their width/proportion/crinkliness fails are shape-bound, not label slack. Two consequences: (1) do not over-claim collapse gains — only ~2-3 of that layout's fails are reclaimable relabel slack, the rest are geometry- or building-level bound; (2) the threshold objective must not be tuned to 'pass' a degenerate cell via a permissive room type — a metric-pass on a physically useless space is gaming, not a fix. Real remedies for these are geometry/topology search (cell shape) and circulation placement, filed separately, not the collapse."}
{"_type":"memory","key":"cli-tool-style-prefer-python-m-homemaker-module","value":"CLI tool style: prefer python -m homemaker.module --parameters pattern, installable via pip install -e . with pyproject.toml entry_points. Not standalone bin/ scripts."}
{"_type":"memory","key":"homemaker-py-pythonpath-set-pythonpath-home-bruno-src","value":"homemaker-layout PYTHONPATH: package installed as 'homemaker-layout' via pip install -e . so 'import homemaker_layout' works from anywhere without PYTHONPATH. For running tests use 'python -m pytest' from project root /home/bruno/src/homemaker-layout (pyproject.toml adds src/ automatically). Never try pip show homemaker — that's the old homemaker-addon conflict."}
{"_type":"memory","key":"unfold-strategy-for-shared-leaves-homemaker-py-8iv","value":"Unfold strategy for shared leaves (homemaker-py-8iv, resolved 2026-07-16): use the BALANCED GRID (operators._grow_balanced/_size_subtree_equal), NOT circulation-aware slicing. Slicing a shared leaf perpendicular to its access edge so every child touches the corridor was implemented + A/B-tested and LOST decisively (150k-eval warm-start polish from evolved-3M: slice 41 fails/3.5e-14 vs grid 25 fails/2.4e-09, grid ahead at every milestone). Reason: k rooms all touching one wall are intrinsically thin slices; that geometric debt (proportion/long/width) is unfixable without topology change, while the grid's squarer children let local search re-route access cheaply via level_retype/place_missing/level_fix. Lesson: at the sharing-\u003eno-sharing transition, prioritise squarer children and leave access to local search; do not reintroduce slicing in Schedule B (kpu)."}
{"_type":"memory","key":"urb-oracle-nondeterminism-urb-fitness-pl-output-varies","value":"Urb oracle nondeterminism: urb-fitness.pl output varies run-to-run from Perl hash-order randomisation — .fails line ORDER shuffles (compare sorted, use oracle.Score.fail_lines) and the score float can flip by ~1 ULP (compare with math.isclose rel_tol=1e-12, never ==). Not a batching artifact; affects single runs too. Matters for the Phase 3 native-fitness parity gate (homemaker-py-uxz)."}
{"_type":"memory","key":"adjacency-in-binary-slicing-tree-is-structural-not","value":"Adjacency in binary slicing tree is structural, not geometric: the inner-loop NM cannot fix topological adjacency failures. Two paths exist: (1) tree-sibling adjacency — a node is adjacent to its sibling in the tree; (2) cross-zone geometric adjacency — leaves from different subtrees that happen to share a boundary. Staircase/adjacency fails require a topology mutation that changes which nodes are siblings or which zones touch. This was proved empirically on programme-house: staircase fail from rot=0 layout could not be fixed by NM but was fixed by level_retype creating a two-C topology (2026-06-14/15)."}
{"_type":"memory","key":"island-model-psk-14-is-a-null-priming","value":"Island model (psk, §14) is a NULL: priming a population from N converged independent elites + crossover-heavy migration does not beat best-of-N at equal total budget (maple island 124 vs control 116). The child_probe instrument shows WHY: area-matched crossover across independently-converged elites almost never synthesizes (1-3 of ~64 children beat the better parent, max drop 2-5) because the slicing encoding is non-canonical (9gp), so splices are disruptive not combinatorial. Search-machinery null #3 after graded-objective and niching/restarts; residual stays geometry/shape-bound."}
{"_type":"memory","key":"proportion-aware-constructive-seeding-leu-2-12-2","value":"Proportion-aware constructive seeding (leu.2/§12.2): sizing seed cuts from target AREAS only regresses (thin slivers wreck aspect); you must ALSO pick each cut's rotation for child squareness. It is a convergence ACCELERATOR via a deeper local optimum around the constructed topology: wins where that topology is roughly right and budget is scarce (harbor -13%, maple -10% at 20k evals) but DELAYS small programmes where the seed must be restructured by undivide (programme-house regresses at fixed budget, yet reaches the floor given budget - speed, not asymptote). Default-on. Also: n_storeys must honour storey_minimum, not just level: keys (programme-house storey_minimum:2, all rooms level:0 - was seeded 1 storey short; cq1)."}
{"_type":"memory","key":"strategy-decision-2026-06-12-bruno-occlusion-daylight","value":"Strategy decision 2026-06-12 (Bruno): occlusion/daylight is ORTHOGONAL to building a scalable optimiser. Disable it in Urb (env flag, homemaker-py-gp2) rather than port it; native fitness uses simple crinkliness (illumination factor = 1); rebuild occlusion in Python only after optimisation is fully native (homemaker-py-2g5, now P4). Consequence: all scores change when the flag flips — re-baseline corpus/.score, DESIGN \\$4.5 gains, gate bars at one clean boundary AFTER homemaker-py-1p0 closes; Phase-2 urb-evolve benchmark must run with the same flag."}
{"_type":"memory","key":"user-preference-bruno-this-is-a-fedora-system","value":"User preference (Bruno): this is a Fedora system — NEVER install Python packages via pip without asking first; always ask whether to install the rpm via dnf (e.g. python3-cma) before considering pip. Applies to any dependency additions."}

48
src/homemaker_layout/fitness.py
View file

@ -1251,54 +1251,6 @@ class Fitness:
fail(f"{dom_mod.level_of(leaf)}/{leaf.id} outside edge too long")
return rate * length * _height(leaf)
# ------------------------------------------------------------------ #
# Storey processing (Storey.pm::process_storey — cost/value/leaf scope)
# ------------------------------------------------------------------ #
def process_storey(self, level_root: Node, G: nx.Graph, level_id: int, fail) -> StoreyEval:
"""Per-storey cost, value and leaf evaluations on the MERGED tree.
Covers the cost/value accumulation and per-leaf checks of
``process_storey``; circulation connectivity, roof-garden, stair fit
and tracking-driven building checks are homemaker-py-hgg.
"""
groups = geometry.boundary_groups(level_root)
cost = 0.0
value = 0.0
leaves_eval: list[LeafEval] = []
for leaf in level_root.leaves():
if dom_mod.is_outside(leaf) and dom_mod.is_covered(leaf) and level_id:
if not dom_mod.is_supported(leaf):
fail(f"{level_id}/{leaf.id} unsupported covered outside")
fail(f"{level_id}/{leaf.id} covered outside above ground")
cost += self.leaf_cost(leaf)
if not dom_mod.is_usable(leaf):
continue
quality, factors = self.evaluate_leaf(leaf, G, level_id, groups, fail)
rate = self.value_rate(leaf)
value += quality * rate * geometry.area(leaf)
leaves_eval.append(
LeafEval(
level=level_id,
id=leaf.id,
type=leaf.type or "",
area=geometry.area(leaf),
rate=rate,
quality=quality,
factors=factors,
)
)
for a, b in G.edges():
cost += self.edge_cost(G, a, b, fail)
for leaf in level_root.leaves():
cost += self.outside_edge_cost(leaf, fail)
return StoreyEval(cost=cost, value=value, leaves=leaves_eval)
def plot_cost(self, root: Node) -> float:
"""The 'initial cost' term: plot rate x lowest-root area."""
return self.cost("plot") * geometry.area(root)