Bruno Postle
0dcdf1f29f
innerloop.py: optimise(root, programme_dir, x0=None, budget, method) -> Result, optimising equal-offset free-branch ratios (midpoint projection of legacy unequal cuts) against full oracle fitness. OracleEvaluator scores each population in one batched perl call. Methods: cma (default) — multi- start sigma ladder (0.05 local, 0.15 exploratory) with IPOP-style popsize doubling and deterministic seeding (pycma treats seed 0 as clock!) — and compass with Hooke-Jeeves pattern moves, kept for the d0s bake-off. Acceptance (experiments/accept_innerloop.py, §4.5 bars vs unprojected originals, within-noise tolerance 1%): x1.65 / x1.66 / x1.58 against bars x1.24 / x1.67 / x1.59, no new failures, 46 oracle calls vs Nelder-Mead's 200. The two near-bar results are statistically indistinguishable from the single-NM-draw bars (measured draw spread brackets them); decision approved by Bruno 2026-06-12. Also: tests/ scaffold (12 oracle-free unit tests, pytest pythonpath=src), rebaseline_no_occlusion.py for homemaker-py-gp2, cma>=3.0 dependency (installed via dnf), dead-variable cleanup in solver.py. Closes homemaker-py-1p0. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
77 lines
3 KiB
Python
77 lines
3 KiB
Python
#!/usr/bin/env python3
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"""Acceptance run for the geometry inner loop (homemaker-py-1p0).
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Gate (DESIGN.md §4.5, issue acceptance criteria): reproduce or exceed the
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Nelder-Mead diagnostic gains — x1.24 / x1.67 / x1.59, no new failures — on
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2f45907, candidate-002 and c964435, using the batched compass search.
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Usage: accept_innerloop.py [budget] [method] (default: 200 oracle evals, cma)
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"""
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from __future__ import annotations
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import shutil
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import sys
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import tempfile
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import time
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
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from homemaker import dom, innerloop, oracle # noqa: E402
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URB = Path("/home/bruno/src/urb")
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EX = URB / "examples" / "programme-house"
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# name -> the x-gain the §4.5 Nelder-Mead diagnostic achieved (the bar)
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GATE = {
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"2f45907abd9accac2a124d311732f749.dom": 1.24,
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"candidate-002.dom": 1.67,
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"c964435454c459f86c3ed9a5a7621132.dom": 1.59,
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}
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# The §4.5 bars are single Nelder-Mead draws and the inner loop's per-run
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# variance brackets them (candidate-002 drew 0.0117-0.0160 against a 0.0123
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# bar). Reproduction within 1% counts as met — decision approved 2026-06-12
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# (homemaker-py-1p0); chasing the last fraction with seed rolls would be
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# cherry-picking.
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NOISE_TOL = 0.99
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def main() -> int:
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budget = int(sys.argv[1]) if len(sys.argv) > 1 else 200
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method = sys.argv[2] if len(sys.argv) > 2 else "cma"
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print(f"method={method} budget={budget}")
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all_ok = True
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with tempfile.TemporaryDirectory(prefix="accept_innerloop_") as tmp:
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scratch = Path(tmp)
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shutil.copy(EX / "patterns.config", scratch)
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for name, bar in GATE.items():
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# Baseline = the UNMODIFIED original file, exactly as §4.5 measured
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# it. The inner loop's own x0 score is the equal-offset *projection*
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# of the original (a==b forced, clipped), which is lower for legacy
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# designs with unequal cuts — gains must not be measured from there.
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s_orig = oracle.score(shutil.copy(EX / name, scratch), URB)
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root = dom.load(str(EX / name))
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t0 = time.perf_counter()
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r = innerloop.optimise(root, EX, budget=budget, method=method, urb_root=URB)
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dt = time.perf_counter() - t0
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gain = r.fitness / s_orig.fitness if s_orig.fitness else float("inf")
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ok = gain >= bar * NOISE_TOL and r.n_fails <= s_orig.n_fails
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all_ok &= ok
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print(
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f"{name:42s} dof={len(r.x):2d} "
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f"orig={s_orig.fitness:.6g}(fails {s_orig.n_fails}) "
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f"projected x0={r.x0_fitness:.6g}(fails {r.x0_n_fails}) "
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f"opt={r.fitness:.6g}(fails {r.n_fails}) "
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f"x{gain:.2f} (bar x{bar:.2f}) "
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f"{r.n_evals} evals / {r.n_oracle_calls} oracle calls / {dt:.0f}s "
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f"{'PASS' if ok else 'FAIL'}",
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flush=True,
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)
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print("\nGATE " + ("PASSED" if all_ok else "FAILED"))
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return 0 if all_ok else 1
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if __name__ == "__main__":
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sys.exit(main())
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