Andrew Simonson
3429bce8d0
Pass 2 previously only knew one estimator: a hardcoded physics model that matches dimensions literally named platform/actuator/energy_storage. Any domain outside that shape (e.g. archery) got all-zero estimates and failed every combo. Domains can now declare free variables and per-metric formulas as data instead; a safe AST-based evaluator (engine/formula.py, no eval()) resolves declared entity properties via dep(key, constraint_type) and generalizes the existing hand-nested mass-budget search into an N-variable recursive optimizer. Fully additive -- the legacy platform/actuator/ energy_storage path is untouched and still runs unchanged for every domain that declares no formulas. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
"""End-to-end test that pass 2 can estimate a non-transport domain entirely
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from domain-authored formulas, without touching the platform/actuator/
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energy_storage physics model in Pipeline._estimate_physics."""
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import pytest
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from physcom.engine.constraint_resolver import ConstraintResolver
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from physcom.engine.scorer import Scorer
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from physcom.engine.pipeline import Pipeline
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from physcom.models.domain import Domain, FreeVariable, MetricBound, MetricFormula
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from physcom.models.entity import Dependency, Entity
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def _build_archery_domain(repo):
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repo.add_entity(Entity(
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name="Recurve",
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dimension="bow",
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dependencies=[
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Dependency("physical", "draw_weight", "20", None, "range_min"),
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Dependency("physical", "draw_weight", "50", None, "range_max"),
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],
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))
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repo.add_entity(Entity(
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name="Carbon",
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dimension="arrow",
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dependencies=[
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Dependency("physical", "arrow_mass", "0.02", None, "provides"),
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],
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))
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return repo.add_domain(Domain(
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name="archery_test",
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metric_bounds=[
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MetricBound("drawback_force", weight=0.6, norm_min=0, norm_max=100),
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MetricBound("range", weight=0.4, norm_min=0, norm_max=300),
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],
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free_variables=[
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FreeVariable(
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name="draw_weight_chosen",
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floor_formula='dep("draw_weight", "range_min")',
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ceiling_formula='dep("draw_weight", "range_max")',
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sort_order=0,
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),
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],
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metric_formulas=[
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MetricFormula(metric_name="drawback_force", formula="draw_weight_chosen * 2"),
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MetricFormula(
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metric_name="range",
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formula='draw_weight_chosen * 5 / dep("arrow_mass")',
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),
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],
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))
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def test_formula_domain_scores_without_platform_actuator_shape(repo):
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domain = _build_archery_domain(repo)
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resolver = ConstraintResolver()
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scorer = Scorer(domain)
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pipeline = Pipeline(repo, resolver, scorer)
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result = pipeline.run(
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domain, ["bow", "arrow"], score_threshold=0.01, passes=[1, 2, 3, 5],
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)
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assert result.total_generated == 1
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assert result.pass1_failed == 0
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assert result.pass2_estimated == 1
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assert result.pass3_above_threshold == 1
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combos = repo.list_combinations()
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assert len(combos) == 1
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combo = combos[0]
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scores = {
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s["metric_name"]: s["raw_value"]
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for s in repo.get_combination_scores(combo.id, domain.id)
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}
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# Both metrics increase monotonically with draw_weight_chosen and nothing
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# trades off against it, so the optimizer should push to the declared
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# ceiling (50) -- confirms _search_free_variables is actually searching,
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# not just evaluating at the floor.
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assert scores["drawback_force"] == pytest.approx(100.0, rel=0.02)
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def test_formula_domain_zero_free_variables_direct_evaluation(repo):
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"""A domain with metric_formulas but no free_variables should evaluate
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each formula once directly -- no search loop at all."""
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repo.add_entity(Entity(
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name="Recurve",
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dimension="bow",
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dependencies=[Dependency("physical", "draw_weight", "30", None, "provides")],
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))
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repo.add_entity(Entity(name="Carbon", dimension="arrow"))
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domain = repo.add_domain(Domain(
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name="archery_direct_test",
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metric_bounds=[MetricBound("drawback_force", weight=1.0, norm_min=0, norm_max=100)],
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metric_formulas=[
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MetricFormula(metric_name="drawback_force", formula='dep("draw_weight") * 2'),
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],
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))
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resolver = ConstraintResolver()
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scorer = Scorer(domain)
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pipeline = Pipeline(repo, resolver, scorer)
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result = pipeline.run(
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domain, ["bow", "arrow"], score_threshold=0.01, passes=[1, 2, 3, 5],
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)
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assert result.pass2_estimated == 1
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combos = repo.list_combinations()
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scores = {
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s["metric_name"]: s["raw_value"]
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for s in repo.get_combination_scores(combos[0].id, domain.id)
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}
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assert scores["drawback_force"] == 60.0
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