bbd3b6678f
Close the gap between cohesion packing/search and Drive ADR-0242:
D0 — Expand ADR-0242 to five-vector + sovereignty thesis (title matches Drive).
D8 — Land docs/analysis/fibonacci_applications_in_core_substrate.md.
D1 — FibonacciSearchCertificate | OptimizationFailure (never bare float);
content-addressed cert_id; dual-run stable digest.
D2 — propose_kappa_from_search / goldtether.propose_kappa_line_search;
failure → baseline κ=1.0 (no state mutation).
D4 — ALLOCATOR_VERSION golden_angle_v1 + layout descriptor.
Fidelity §12 honest: V1/V3 green; V2 table-only; V4/V5 staged.
146 lines
4.1 KiB
Python
146 lines
4.1 KiB
Python
"""ADR-0242 V1 — evidence-gated Fibonacci section search."""
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from __future__ import annotations
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from core.physics.fibonacci_search import (
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BASELINE_KAPPA,
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BoundedUnimodalObjective,
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FibonacciSearchCertificate,
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OptimizationFailure,
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fibonacci_section_search,
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propose_kappa_from_search,
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)
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def test_fibonacci_search_returns_certificate_near_known_min():
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objective = BoundedUnimodalObjective(
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lower=0.1,
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upper=2.0,
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evaluation_budget=20,
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objective_id="test_id",
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objective_version="v1",
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)
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def func(x: float) -> float:
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return (x - 0.789) ** 2
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result = fibonacci_section_search(objective, func)
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assert isinstance(result, FibonacciSearchCertificate)
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assert abs(result.minimizer - 0.789) < 1e-3
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assert result.evaluations == 20
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assert len(result.ordered_points) == 20
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assert len(result.ordered_values) == 20
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assert result.cert_id
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assert len(result.cert_id) == 64
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def test_certificate_digest_stable_dual_run():
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objective = BoundedUnimodalObjective(
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lower=-5.0,
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upper=5.0,
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evaluation_budget=15,
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objective_id="stable",
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objective_version="v1",
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)
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def func(x: float) -> float:
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return x**2
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a = fibonacci_section_search(objective, func)
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b = fibonacci_section_search(objective, func)
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assert isinstance(a, FibonacciSearchCertificate)
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assert isinstance(b, FibonacciSearchCertificate)
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assert a.cert_id == b.cert_id
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assert a.as_dict() == b.as_dict()
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def test_fibonacci_search_eval_count_equals_budget():
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objective = BoundedUnimodalObjective(
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lower=-5.0,
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upper=5.0,
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evaluation_budget=15,
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objective_id="test_id2",
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objective_version="v1",
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)
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def func(x: float) -> float:
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return x**2
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result = fibonacci_section_search(objective, func)
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assert isinstance(result, FibonacciSearchCertificate)
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assert result.evaluations == 15
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def test_fibonacci_search_nonfinite_returns_failure():
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objective = BoundedUnimodalObjective(
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lower=-1.0,
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upper=1.0,
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evaluation_budget=10,
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objective_id="nan",
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objective_version="v1",
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)
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def func(x: float) -> float:
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return float("nan")
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result = fibonacci_section_search(objective, func)
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assert isinstance(result, OptimizationFailure)
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assert "nonfinite" in result.reason
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def test_fibonacci_search_unimodality_returns_failure():
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objective = BoundedUnimodalObjective(
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lower=-2.0,
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upper=2.0,
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evaluation_budget=10,
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objective_id="multi",
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objective_version="v1",
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)
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def func(x: float) -> float:
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return x**4 - x**2
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result = fibonacci_section_search(objective, func)
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assert isinstance(result, OptimizationFailure)
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assert "unimodality" in result.reason
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def test_never_returns_bare_float():
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objective = BoundedUnimodalObjective(
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lower=0.0,
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upper=1.0,
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evaluation_budget=8,
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objective_id="type",
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objective_version="v1",
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)
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result = fibonacci_section_search(objective, lambda x: (x - 0.3) ** 2)
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assert not isinstance(result, float)
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assert isinstance(result, (FibonacciSearchCertificate, OptimizationFailure))
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def test_propose_kappa_cert_uses_minimizer():
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objective = BoundedUnimodalObjective(
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lower=0.1,
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upper=2.0,
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evaluation_budget=16,
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objective_id="kappa",
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objective_version="v1",
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)
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result = fibonacci_section_search(objective, lambda x: (x - 0.5) ** 2)
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kappa, outcome = propose_kappa_from_search(result)
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assert isinstance(outcome, FibonacciSearchCertificate)
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assert abs(kappa - 0.5) < 1e-2
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def test_propose_kappa_failure_falls_back_to_baseline():
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objective = BoundedUnimodalObjective(
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lower=-2.0,
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upper=2.0,
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evaluation_budget=10,
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objective_id="kappa_fail",
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objective_version="v1",
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)
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result = fibonacci_section_search(objective, lambda x: x**4 - x**2)
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kappa, outcome = propose_kappa_from_search(result)
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assert isinstance(outcome, OptimizationFailure)
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assert kappa == BASELINE_KAPPA
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