2a3f422783
A1 of the refined sequencing — the binary-relation reader was inert w.r.t. the yardstick (contributing 0). This adds a comprehension_relational_predicate domain: binary-relation prose scored against hand-authored independent gold (predicate, subject, object) triples — INV-25 independent / INV-27 reader-disjoint (the reader never produced the gold). Index breadth 8->9, capability_score 0.937258->0.944030, wrong_total still 0; baseline.json re-frozen to digest 1ea91c1e. Rigor split: the index lane is POSITIVE-ONLY (clean coverage, consistent with the other 8 lanes — mixing adversarial refuse-cases into the coverage denominator would make 'added capability' read as a score drop). The #596 fabrication-catch lives in a dedicated falsification test (evals/relational/v1/refusals.jsonl): the trailing- qualifier / dangling-copula / negation / verb-form cases MUST refuse — bites if the reader ever fabricates. Honest coverage gap recorded: overlaps_event has no copular surface form (verb-form 'A overlaps B' refuses), so 17 positives cover 15/16 predicates.
122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
"""Per-lane adapters — normalize each independent-gold lane to a DomainResult.
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These are thin COUNT extractors, not capability logic: each calls a lane's own
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self-loading runner and reads its correct/wrong/refused counts. A lane that fails
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to run is recorded as ``not_covered`` (no silent drop), never faked.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from evals.capability_index.index import DomainResult
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def _counts(report: dict) -> tuple[int, int, int]:
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c = report.get("counts", report)
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return int(c["correct"]), int(c["wrong"]), int(c["refused"])
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def deductive_logic_result() -> DomainResult:
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from evals.deductive_logic.runner import build_combined_report
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agg = build_combined_report()["aggregate"] # {n, correct, wrong, refused}
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return DomainResult(
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"deductive_logic", int(agg["correct"]), int(agg["wrong"]), int(agg["refused"])
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)
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def relational_metric_result() -> DomainResult:
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from evals.relational_metric.runner import run
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r = run()
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return DomainResult(
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"relational_metric", int(r["correct"]), int(r["wrong"]), int(r["refused"])
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)
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def dimensional_result() -> DomainResult:
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from evals.dimensional.runner import _ROOT, _load, build_report
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correct, wrong, refused = _counts(build_report(_load(_ROOT / "v1" / "cases.jsonl")))
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return DomainResult("dimensional", correct, wrong, refused)
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def comprehension_set_membership_result() -> DomainResult:
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from evals.comprehension.set_membership_runner import run
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c, w, r = _counts(run())
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return DomainResult("comprehension_set_membership", c, w, r)
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def comprehension_syllogism_result() -> DomainResult:
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from evals.comprehension.syllogism_runner import run
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c, w, r = _counts(run())
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return DomainResult("comprehension_syllogism", c, w, r)
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def comprehension_total_ordering_result() -> DomainResult:
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from evals.comprehension.total_ordering_runner import run
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c, w, r = _counts(run())
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return DomainResult("comprehension_total_ordering", c, w, r)
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def comprehension_propositional_result() -> DomainResult:
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from evals.comprehension.propositional_runner import run
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c, w, r = _counts(run())
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return DomainResult("comprehension_propositional", c, w, r)
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def comprehension_relational_metric_result() -> DomainResult:
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from evals.comprehension.relational_metric_runner import run
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c, w, r = _counts(run())
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return DomainResult("comprehension_relational_metric", c, w, r)
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def comprehension_relational_predicate_result() -> DomainResult:
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from evals.comprehension.relational_predicate_runner import run
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c, w, r = _counts(run())
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return DomainResult("comprehension_relational_predicate", c, w, r)
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#: The reasoning domains currently composed into the index (self-loading lanes).
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#: The six ``comprehension_*`` lanes score the GENERAL comprehension reader; the
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#: relational_metric one reads arithmetic prose into the binding-graph quantity
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#: substrate (admissibility-checked) and projects to the arithmetic oracle, and the
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#: relational_predicate one (#596) reads binary-relation prose into pack-named
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#: predicates — so the index now measures comprehension breadth across categorical,
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#: ordering, propositional, quantitative, AND relational reasoning.
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ADAPTERS = (
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deductive_logic_result,
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relational_metric_result,
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dimensional_result,
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comprehension_set_membership_result,
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comprehension_syllogism_result,
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comprehension_total_ordering_result,
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comprehension_propositional_result,
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comprehension_relational_metric_result,
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comprehension_relational_predicate_result,
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)
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@dataclass(frozen=True, slots=True)
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class Collection:
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results: tuple[DomainResult, ...]
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not_covered: tuple[tuple[str, str], ...] # (adapter_name, error) — no silent drop
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def collect_domain_results() -> Collection:
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"""Run every adapter; surface any that fail rather than dropping them."""
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results: list[DomainResult] = []
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not_covered: list[tuple[str, str]] = []
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for adapter in ADAPTERS:
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try:
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results.append(adapter())
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except Exception as exc: # noqa: BLE001 — surfacing is the contract
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not_covered.append((adapter.__name__, repr(exc)))
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return Collection(results=tuple(results), not_covered=tuple(not_covered))
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