core/tests/test_labeled_container_subject.py
Shay 84db129629
feat(adr-0194): labeled-container subject entity shape — 'Jar A has N' parses, wrong=0-proven (substrate) (#499)
GSM8K labels containers/regions with a trailing single-letter or short-numeric
label ('Jar A has 28 marbles', 'Section G has 10 cars', 'District 2 has 19
voters'); the initial-possession entity slot captured only 'Jar' and the label
broke the match. Adds a separate sibling pattern _INITIAL_HAS_LABELED_RE
(mirroring ADR-0136.S.4 localisation) that REQUIRES the label, so the global
_ENTITY is unchanged and bare subjects yield no duplicate candidate.

- Composes with ADR-0193 aggregate question: 'Jar A has 28 marbles. Jar B has
  12 marbles. How many marbles are there in total?' -> 40.0.
- 0 real-corpus metric flip (honest substrate): the one real multi-container
  aggregate additionally needs comparative + multiplicative + lowercase-ref.
- wrong=0 HOLDS full corpus (7,473 q); train_sample byte-identical 4/46/0;
  synthetic-registry capability-axis gate + G5 lane green; smoke 67 passed.
- Label bounded by the possession verb: multi-word nouns ('Jar Apple') do NOT
  match. wrong=0 held downstream by completeness + round-trip + disagreement.

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
2026-05-30 16:56:09 -07:00

81 lines
3.6 KiB
Python

"""ADR-0194 — labeled-container subject entity shape.
GSM8K routinely labels containers/regions with a trailing single-letter or
short-numeric label: "Jar A has 28 marbles.", "Section G has 15 rows.",
"District 2 has 19 voters.". The initial-possession parser's entity slot
(``_ENTITY = (?:[A-Z]\\w+|[Tt]he\\s+\\w+)``) captures only "Jar" and then
expects the possession verb, so the label breaks the match and the statement
produces no candidate.
This adds a SEPARATE sibling pattern ``_INITIAL_HAS_LABELED_RE`` (mirroring
ADR-0136.S.4's ``_INITIAL_HAS_INDEF_RE`` localisation) that REQUIRES a label,
so it never duplicates the bare-subject main pattern. The global ``_ENTITY``
is unchanged. wrong=0 is held downstream by the completeness guard
(ADR-0191) + round-trip + branch disagreement — the label widening only makes
a statement *parse*; a mis-parse leaves quantities uncovered and refuses.
SUBSTRATE: 0 real-corpus metric flip (the one real multi-container aggregate,
"Jar A has 28 marbles. Jar B has 12 more than jar A. Jar C has twice as many
as jar B. ...altogether?", additionally needs comparative + multiplicative
reading). Its value is the entity-shape generalisation + that it composes
with the ADR-0193 aggregate question (proven below).
"""
from __future__ import annotations
import pytest
from generate.math_candidate_parser import extract_initial_candidates
from generate.math_candidate_graph import parse_and_solve
# --- Labeled-container subjects now parse as initial possessions -----------
@pytest.mark.parametrize("sentence,entity,value,unit", [
("Jar A has 28 marbles.", "Jar A", 28.0, "marbles"),
("Box B has 15 marbles.", "Box B", 15.0, "marbles"),
("Section G has 10 cars.", "Section G", 10.0, "cars"),
("District 2 has 19 voters.", "District 2", 19.0, "voters"),
("Tank 1 has 40 liters.", "Tank 1", 40.0, "liters"),
])
def test_labeled_container_parses(sentence, entity, value, unit) -> None:
cands = extract_initial_candidates(sentence)
assert len(cands) == 1, f"expected one candidate for {sentence!r}"
c = cands[0]
assert c.initial.entity == entity
assert c.initial.quantity.value == value
assert c.initial.quantity.unit == unit
# --- No duplicate candidates for bare (unlabeled) subjects -----------------
def test_bare_subject_single_candidate_unchanged() -> None:
"""The labeled pattern requires a label, so 'Jamie has 28 marbles'
still yields exactly one candidate (from the main pattern only)."""
cands = extract_initial_candidates("Jamie has 28 marbles.")
assert len(cands) == 1
assert cands[0].initial.entity == "Jamie"
# --- The label must not swallow a following content word -------------------
@pytest.mark.parametrize("sentence", [
"Jar Apple has 5 marbles.", # 'Apple' is not a single-letter label
"Box Set has 12 items.", # 'Set' is not a label
])
def test_multiword_noun_not_a_label(sentence: str) -> None:
cands = extract_initial_candidates(sentence)
assert cands == [], f"{sentence!r} must not parse as a labeled container"
# --- Composes with the ADR-0193 aggregate question -------------------------
def test_composes_with_aggregate_question() -> None:
res = parse_and_solve(
"Jar A has 28 marbles. Jar B has 12 marbles. "
"How many marbles are there in total?"
)
assert res.answer == 40.0, res.refusal_reason
def test_composes_three_containers() -> None:
res = parse_and_solve(
"Jar A has 5 marbles. Jar B has 3 marbles. Jar C has 2 marbles. "
"How many marbles are there altogether?"
)
assert res.answer == 10.0, res.refusal_reason