708f27a240
* feat(derivation): capability strike batch 2 bracelet-yield lift Gate A2c adds container_of_product composition for "N bags of M unit" acquisition statements and yield_question binding that injects unit_partition from a conditional per-unit rate clause. Live ephemeral train_sample moves 7/43/0 → 8/42/0 with wrong=0 preserved; case 0008 admitted. * chore(derivation): normalize A2c test formatting * chore(analysis): normalize batch2 lookback EOF * test(derivation): guard A2c pronoun ambiguity
3207 lines
119 KiB
Python
3207 lines
119 KiB
Python
"""ADR-0126 — Candidate-emitting sentence parser.
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Sibling to ``generate/math_parser.py``. Same regex spirit, different
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topology: instead of first-match-wins with a single mutable state and
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``ParseError`` on miss, each per-sentence extractor returns a *list of
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candidates* (possibly empty) carrying full source-span provenance.
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The wrong-answer firewall is :func:`generate.math_roundtrip.roundtrip_admissible`,
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applied downstream in P3 (graph assembly). This module's job is purely
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to *enumerate* the parses the grammar admits — telling truth from
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falsehood is not its concern.
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Determinism: candidate lists are returned in deterministic order
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(canonical pattern key); the same input always produces the same
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ordered output.
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Scope of P2 (this module):
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- Initial-possession candidate extraction.
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- Operation candidate extraction for add / subtract / transfer
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via the canonical "<Subject> <verb> <value> <unit> [to <target>]"
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shape.
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- Permissive verb tables imported from
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:data:`generate.math_roundtrip.KIND_TO_VERBS` — much wider than
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``math_parser._ADD_VERBS`` / ``_SUBTRACT_VERBS`` / ``_TRANSFER_VERBS``
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because the round-trip filter rejects wrong candidates downstream.
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Out of scope for P2 (added in later phases):
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- Pronoun resolution (needs per-branch state — P3).
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- Unit inheritance from ``last_unit`` (needs per-branch state — P3).
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- Multiply / divide / rate / comparison candidates (later phases of
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ADR-0126; the candidate-emission machinery is identical, just more
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pattern matchers).
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass
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from typing import Final, Literal, Mapping, cast
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from generate.math_problem_graph import (
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Comparison,
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FractionPortion,
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InitialPossession,
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Operation,
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PartitionChunk,
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Quantity,
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Unknown,
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)
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from generate.math_roundtrip import (
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ADD_VERBS,
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SUBTRACT_VERBS,
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TRANSFER_VERBS,
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WORD_NUMBERS,
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CandidateOperation,
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)
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# Locally re-typed alias mirroring Comparison.direction's literal slot —
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# used only to satisfy pyright when narrowing surface-direction strings.
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_CompDirection = Literal["more", "fewer", "times", "fraction"]
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# ---------------------------------------------------------------------------
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# Initial-possession candidate
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True, slots=True)
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class CandidateInitial:
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"""Initial-possession candidate with source-span provenance.
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Mirrors :class:`CandidateOperation` but for ``InitialPossession``.
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The round-trip filter for initials is the same shape: every claimed
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content slot (entity, value, unit, anchor verb 'has'/'have') must
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ground in the source sentence.
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"""
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initial: InitialPossession
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source_span: str
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matched_anchor: str # 'has' or 'have'
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matched_value_token: str # '3' or 'three'
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matched_unit_token: str
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matched_entity_token: str
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# RAT-1 — composed-candidate evidence. When non-None this candidate
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# was produced by a registry-gated composition (ADR-0169) rather
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# than a literal extraction; the value/unit/entity are DERIVED, so
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# the admissibility gate checks each composition INPUT grounds in
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# source_span instead of the derived value. Schema keys:
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# count_token, amount_token, currency_symbol, composition_shape,
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# entity_source.
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composition_evidence: Mapping[str, str] | None = None
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# ADR-0191 — completeness provenance. Aggregating extractors that
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# collapse several source tokens into one derived value (day-enum sum,
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# embedded-quantifier product, multi-word cardinal) list EVERY source
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# quantity token they consumed here, so the candidate-graph reader's
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# completeness guard (generate/math_completeness.py) can confirm no
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# source quantity was silently dropped. Empty () means "single token"
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# and the guard falls back to ``matched_value_token``.
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consumed_value_tokens: tuple[str, ...] = ()
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def __post_init__(self) -> None:
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# ADR-0127 widens the anchor set to include 'there are/were/is/was'
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# for the implicit-subject initial-possession shape.
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#
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# ADR-0131.G.1: _INITIAL_HAS_RE itself only emits has/have/had/started
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# — acquisition verbs (buys, bought, sells, collected, saved, makes)
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# live exclusively in ADD_VERBS / SUBTRACT_VERBS so a sentence like
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# "Sam buys 3 apples" parses as an add-operation only, avoiding
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# branch-disagreement when a canonical "has" initial precedes it.
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#
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# ADR-0131.G.4 introduces a separate conjoined-subject-each extractor
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# that legitimately emits CandidateInitial with a wider set of
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# state-introducing verbs (saved/earned/got/received/bought/made/paid +
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# inflections) for the closed shape "A and B each <verb> N <unit>".
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# That extractor is the only path into these wider anchors. The
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# whitelist below is the runtime safety net for both paths.
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if self.matched_anchor.lower() not in (
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"has", "have", "had", "started",
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"are", "were", "is", "was",
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"save", "saved",
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"earn", "earned",
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"get", "got", "gets",
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"receive", "received", "receives",
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"buy", "bought", "buys",
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"make", "made", "makes",
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"pay", "paid", "pays",
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# ADR-0189a — production/activity possession ("Sidney does 20
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# jumping jacks ..."): the actor performs N of a counted activity,
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# i.e. holds a count of N. Admitted only via the day-enumeration
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# extractor's closed shape; the whitelist is the runtime safety net.
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"do", "does", "did",
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):
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raise ValueError(
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f"CandidateInitial.matched_anchor must be a registered initial-"
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f"state anchor; got {self.matched_anchor!r}"
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)
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# ---------------------------------------------------------------------------
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# Shared regex building blocks
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# ---------------------------------------------------------------------------
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# Title-cased proper noun OR "the <noun>" collective. Same widening as
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# math_parser._INITIAL_HAS_RE's ADR-0123a entity slot.
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_ENTITY: Final[str] = r"(?:[A-Z]\w+|[Tt]he\s+\w+)"
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# Gate A2b — actor slot admitting subject pronouns for fraction surfaces.
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_ACTOR_OR_PRONOUN: Final[str] = (
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r"(?:[A-Z]\w+|[Tt]he\s+\w+|She|He|They|It)"
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)
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# Numeric value alternation. Listed longest-form-first so the regex
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# engine doesn't truncate on a shorter prefix:
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# - Money symbol literal: ``$N`` / ``$N.NN`` (1-2 decimal places) plus
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# multi-currency symbols ``¢N`` ``€N`` ``£N`` ``¥N`` ``₱N``.
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# ADR-0131.G.3.1. ``$N.NNN`` (3+ decimals) deliberately not matched
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# — refused as out-of-scope so wrong == 0 is preserved.
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# - Slash fraction literal: ``N/M``. Denominator-zero refused at
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# resolve time, not regex.
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# - Hyphenated multi-word cardinal: ``twenty-five``, ``ninety-nine``.
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# Resolved via :func:`language_packs.numerics_loader.parse_compound_cardinal`.
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# - Digit run.
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# - Single-word cardinal (legacy ``WORD_NUMBERS`` set).
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# ADR-0131.G.3.1: multi-currency symbol group. ¢ and $ are the only
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# non-decimal currencies (sub-unit is the unit itself for ¢; $ converts
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# to cents). €, £, ₱ admit 1-2 decimal places; ¥ is integer-only.
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_MONEY_SYMBOL: Final[str] = (
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r"(?:\$\d+(?:\.\d{1,2})?|¢\d+|€\d+(?:\.\d{1,2})?|£\d+(?:\.\d{1,2})?|¥\d+|₱\d+(?:\.\d{1,2})?)"
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)
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_SLASH_FRACTION: Final[str] = r"\d+/\d+"
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_HYPHENATED_CARDINAL: Final[str] = r"[A-Za-z]+-[A-Za-z]+"
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_WORD_NUM_OPTIONS: Final[str] = "|".join(
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re.escape(w) for w in sorted(WORD_NUMBERS.keys(), key=len, reverse=True)
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)
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_VALUE: Final[str] = (
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rf"(?:{_MONEY_SYMBOL}|{_SLASH_FRACTION}|"
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rf"{_HYPHENATED_CARDINAL}|"
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rf"\d+|{_WORD_NUM_OPTIONS})"
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)
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# Verb alternation built from the permissive registry. Pre-compute one
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# pattern per kind so we can attribute matched verbs to candidates.
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def _verbs_pattern(verbs: frozenset[str]) -> str:
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# Longest-first so "passes" matches before "pass" inside the alternation.
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options = sorted(verbs, key=len, reverse=True)
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return r"(?:" + "|".join(re.escape(v) for v in options) + r")"
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_ADD_VERBS_PATTERN: Final[str] = _verbs_pattern(ADD_VERBS)
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_SUBTRACT_VERBS_PATTERN: Final[str] = _verbs_pattern(SUBTRACT_VERBS)
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_TRANSFER_VERBS_PATTERN: Final[str] = _verbs_pattern(TRANSFER_VERBS)
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# ---------------------------------------------------------------------------
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# Initial-possession extractor
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# ---------------------------------------------------------------------------
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# ADR-0131.G1 note: acquisition/action verbs (buys, bought, sells,
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# collected, saved, makes) were removed from the anchor alternation here.
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# They live exclusively in ADD_VERBS / SUBTRACT_VERBS so that sentences
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# like "Sam buys 3 apples" are parsed as add-operations only, avoiding
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# branch-disagreement when a canonical "has" initial precedes them.
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# The solver defaults-from-zero for operations, so single-statement
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# acquisition sentences ("Sam buys 5 apples. How many does Sam have?")
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# still resolve correctly as 0 + 5 = 5.
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_INITIAL_HAS_RE: Final[re.Pattern[str]] = re.compile(
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rf"^(?P<entity>{_ENTITY})\s+"
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# ADR-0131.G.1: pure-possession anchors only (with optional particle
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# for "had started with N", etc.). Acquisition verbs live in
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# ADD_VERBS / SUBTRACT_VERBS — see CandidateInitial.__post_init__.
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rf"(?P<anchor>has|have|had|started)(?:\s+(?:up|with))?\s+"
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rf"(?P<value>{_VALUE})"
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# ADR-0131.G.3: unit slot is optional. Money-symbol value literals
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# (``$40``) carry their unit implicitly (``cent``); a missing unit
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# slot is admissible IFF the value resolves with a unit override.
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# Non-money values without a unit slot are refused at resolve time.
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# ADR-0131.G.3.1 axis 4: optional adjective between value and unit
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# ("five full boxes" — adjective 'full' is consumed and discarded;
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# the unit head noun 'boxes' becomes the unit slot).
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r"(?:\s+(?:full|loose|empty|whole|broken|new|old|small|large|fresh|raw|flat))?"
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r"(?:\s+(?P<unit>\w+))?"
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# ADR-0127 substance qualifier: "Sam has 5 feet of rope" — the
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# 'of <NP>' tail is grammatically real but arithmetically inert.
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# ADR-0131.G.3: 'in <NP>' is also discardable
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# ("Bob has $40 in savings"; "Bob has $40 in his wallet").
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r"(?:\s+(?:of|in|for|with)\s+.+)?"
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r"\s*\.?$"
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)
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# ADR-0127 "There are/were N <unit> [in <place>]" initial-possession shape.
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# The implicit-subject anchor 'there are' is the only initial-possession
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# shape that doesn't name an entity in the source; we treat the
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# place phrase (when present) as the entity and treat the unit as the
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# count noun. When no place is named, the entity is the unit itself
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# (collective). Indefinite quantifiers ('some', 'few', 'many') in the
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# value slot are refused upstream by extract_initial_candidates via
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# the quantifier-driven refusal helper (ADR-0128.4).
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_INITIAL_THERE_ARE_RE: Final[re.Pattern[str]] = re.compile(
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r"^There\s+(?P<anchor>are|were|is|was)\s+"
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rf"(?P<value>{_VALUE})\s+"
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r"(?P<unit>\w+)"
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r"(?:\s+in\s+(?P<place>[A-Za-z]\w*(?:\s+\w+)?))?"
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r"\s*\.?$",
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flags=re.IGNORECASE,
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)
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||
|
||
# ADR-0136.S.4 — Shape A: indefinite-article subject.
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# "A school has 100 students." / "A box has 12 apples of various colors."
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||
# Sibling to _INITIAL_HAS_RE; uses _ENTITY_INDEF (not _ENTITY) so the
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||
# widening is localised — _ENTITY itself is unchanged for all other paths.
|
||
# Restricted to [Aa]\s+ (not "An") to avoid colliding with money-amount
|
||
# or other shapes that may follow "an" as a numeral article.
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||
# Anchor: 'has' only (singular third-person; "A school have" is not
|
||
# grammatical English).
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_INITIAL_HAS_INDEF_RE: Final[re.Pattern[str]] = re.compile(
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||
r"^[Aa]\s+(?P<noun>\w+)\s+"
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||
r"(?P<anchor>has)\s+"
|
||
rf"(?P<value>{_VALUE})"
|
||
r"(?:\s+(?:full|loose|empty|whole|broken|new|old|small|large|fresh|raw|flat))?"
|
||
r"(?:\s+(?P<unit>\w+))?"
|
||
r"(?:\s+(?:of|in|for|with)\s+.+)?"
|
||
r"\s*\.?$"
|
||
)
|
||
|
||
# ADR-0194 — labeled-container subject: "Jar A has 28 marbles.",
|
||
# "Section G has 10 cars.", "District 2 has 19 voters.". GSM8K labels
|
||
# containers/regions with a trailing single-letter or short-numeric label
|
||
# that the bare _ENTITY slot cannot absorb. Sibling to _INITIAL_HAS_RE that
|
||
# REQUIRES the label, so it never duplicates the bare-subject candidate;
|
||
# _ENTITY stays unchanged for every other path. The label is a single
|
||
# uppercase letter OR 1-2 digits, bounded by the following possession verb
|
||
# (so a multi-word noun like "Jar Apple" does NOT match — "Apple" is not a
|
||
# single-letter label). Same value/unit tail as _INITIAL_HAS_RE. wrong=0
|
||
# is held downstream (completeness + round-trip + disagreement).
|
||
_INITIAL_HAS_LABELED_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^(?P<entity>[A-Z]\w+\s+(?:[A-Z]|\d{1,2}))\s+"
|
||
r"(?P<anchor>has|have|had|started)(?:\s+(?:up|with))?\s+"
|
||
rf"(?P<value>{_VALUE})"
|
||
r"(?:\s+(?:full|loose|empty|whole|broken|new|old|small|large|fresh|raw|flat))?"
|
||
r"(?:\s+(?P<unit>\w+))?"
|
||
r"(?:\s+(?:of|in|for|with)\s+.+)?"
|
||
r"\s*\.?$"
|
||
)
|
||
|
||
# ADR-0136.S.4 — Shape B: prepositional-prefix existential.
|
||
# "In a building, there are a hundred ladies on the first-floor studying."
|
||
# Sibling to _INITIAL_THERE_ARE_RE; prefix is "In a <place>" (not bare
|
||
# "There are"). The optional article "a" before the value handles the
|
||
# "a hundred" construction (article consumed, not captured). The
|
||
# ordinal-floor qualifier and participial phrase are both optional and
|
||
# discarded.
|
||
_INITIAL_THERE_ARE_PREFIX_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^In\s+[Aa]\s+(?P<place>\w+),?\s+"
|
||
r"there\s+(?P<anchor>are|were|is|was)\s+"
|
||
r"(?:a\s+)?"
|
||
rf"(?P<value>{_VALUE})\s+"
|
||
r"(?P<unit>\w+)"
|
||
r"(?:\s+on\s+the\s+\w+(?:-floor)?)?"
|
||
r"(?:\s+\w+ing)?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
# ADR-0131.G.3.1 — Axis 1: fraction-of-unit initial possession.
|
||
# "Bob has 3/4 of a cup." — the fraction is the value; "of a/an <unit>"
|
||
# carries the unit. The main _INITIAL_HAS_RE treats "of <NP>" as a
|
||
# discardable substance qualifier and emits no candidate (unit slot absent
|
||
# and no unit_override); this separate pattern extracts the unit from
|
||
# the "of" phrase explicitly.
|
||
_INITIAL_FRACTION_OF_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ENTITY})\s+"
|
||
rf"(?P<anchor>has|have)\s+"
|
||
rf"(?P<value>{_SLASH_FRACTION})\s+"
|
||
r"of\s+(?:a\s+|an\s+)?(?P<unit>\w+)"
|
||
r"(?:\s+of\s+.+)?" # optional further substance qualifier
|
||
r"\s*\.?$"
|
||
)
|
||
|
||
# ADR-0131.G.3.1 — Axis 3: multi-token space-separated cardinal.
|
||
# "Bob has one hundred apples." — parse_compound_cardinal already handles
|
||
# the value; this pattern captures it before the unit-slot boundary.
|
||
# Approach (a) chosen over (b) (_VALUE widening) because greedy cardinal-
|
||
# word matching inside _VALUE would span the unit slot and require
|
||
# look-ahead unwinding; a separate dedicated extractor is narrower and
|
||
# leaves _VALUE unchanged for all other paths.
|
||
# Build cardinal-word alternation from the WORD_NUMBERS table.
|
||
_CARDINAL_WORD_OPTIONS: Final[str] = "|".join(
|
||
re.escape(w) for w in sorted(WORD_NUMBERS.keys(), key=len, reverse=True)
|
||
)
|
||
# At least two cardinal words (single-word is handled by _VALUE/_resolve_value).
|
||
_MULTI_WORD_CARDINAL_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ENTITY})\s+"
|
||
rf"(?P<anchor>has|have)\s+"
|
||
rf"(?P<value>(?:{_CARDINAL_WORD_OPTIONS})(?:\s+(?:{_CARDINAL_WORD_OPTIONS}))+)"
|
||
# Optional adjective (axis 4 compound) between cardinal and unit.
|
||
r"(?:\s+(?:full|loose|empty|whole|broken|new|old|small|large|fresh|raw|flat))?"
|
||
r"\s+(?P<unit>\w+)"
|
||
r"(?:\s+(?:of|in|for|with)\s+.+)?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _normalize_entity(raw: str) -> str:
|
||
"""Collapse whitespace + lowercase article. Mirrors math_parser
|
||
canonicalization so candidate entity names hash-equal to legacy."""
|
||
e = re.sub(r"\s+", " ", raw.strip())
|
||
if e.lower().startswith("the "):
|
||
return "the " + e[4:]
|
||
return e
|
||
|
||
|
||
@dataclass(frozen=True, slots=True)
|
||
class _ResolvedValue:
|
||
"""Resolved value-slot reading.
|
||
|
||
ADR-0131.G.3 widens the value slot beyond integer + single-word
|
||
cardinal to include money literals (``$N`` / ``$N.NN``), slash
|
||
fractions (``N/M``), and hyphenated multi-word cardinals
|
||
(``twenty-five``). Money literals carry an implicit canonical unit
|
||
(``cent``); when set, ``unit_override`` replaces the unit slot the
|
||
regex captured (or fills it when the unit slot is absent).
|
||
"""
|
||
|
||
value: int | float
|
||
unit_override: str | None
|
||
|
||
|
||
# Money: canonical normalization to integer cents (en_units_v1
|
||
# ``canonical_unit`` for the ``money`` dimension is ``cent``).
|
||
_MONEY_UNIT: Final[str] = "cents"
|
||
|
||
# ADR-0131.G.3.1: multi-currency symbol → (unit_surface, factor_to_unit).
|
||
# ``factor_to_unit`` is the multiplier applied to the face value to
|
||
# produce the canonical unit. For USD ($): face is dollars → *100 cents.
|
||
# For ¢: face is already cents → *1. For all others the pack has no
|
||
# sub-unit defined, so face == canonical (factor=1) and the unit is the
|
||
# pack's plural surface form.
|
||
_CURRENCY_SYMBOLS: Final[dict[str, tuple[str, float]]] = {
|
||
"$": ("cents", 100.0), # dollar → 100 cents
|
||
"¢": ("cents", 1.0), # cent already canonical
|
||
"€": ("euros", 1.0),
|
||
"£": ("pounds sterling", 1.0),
|
||
"¥": ("yen", 1.0),
|
||
"₱": ("pesos", 1.0),
|
||
}
|
||
|
||
|
||
def _resolve_currency(t: str) -> _ResolvedValue | None:
|
||
"""Resolve a currency-symbol value token (``$N``, ``¢N``, ``€N.NN``, …).
|
||
|
||
Returns ``None`` when the format is out-of-scope (e.g. 3+ decimal places).
|
||
Yen (``¥``) is integer-only (no sub-unit in en_units_v1).
|
||
"""
|
||
for sym, (unit_surface, factor) in _CURRENCY_SYMBOLS.items():
|
||
if not t.startswith(sym):
|
||
continue
|
||
body = t[len(sym):]
|
||
if re.fullmatch(r"\d+", body):
|
||
raw_val = int(body)
|
||
final = int(raw_val * factor) if factor == int(factor) else raw_val * factor
|
||
return _ResolvedValue(final, unit_surface)
|
||
# ¥ is integer-only.
|
||
if sym == "¥":
|
||
return None
|
||
if re.fullmatch(r"\d+\.\d{1,2}", body):
|
||
raw_val = float(body)
|
||
result = raw_val * factor
|
||
return _ResolvedValue(int(round(result)) if factor != 1.0 else raw_val, unit_surface)
|
||
return None # 3+ decimals refused for all currency symbols
|
||
return None
|
||
|
||
|
||
def _resolve_value(value_token: str) -> _ResolvedValue | None:
|
||
"""Resolve a value-slot token into a numeric value + optional unit
|
||
override. Returns ``None`` on refusal (indefinite quantifier,
|
||
division-by-zero in slash fraction, unrecognized hyphenated form,
|
||
unparseable money).
|
||
|
||
Refusal at this layer is first-class: a ``None`` upstream means the
|
||
candidate is not emitted, which preserves ``wrong == 0`` per
|
||
ADR-0114a Obligation #4.
|
||
"""
|
||
if not value_token:
|
||
return None
|
||
t = value_token.strip()
|
||
# Multi-currency symbols (ADR-0131.G.3.1): $, ¢, €, £, ¥, ₱.
|
||
if t and t[0] in _CURRENCY_SYMBOLS:
|
||
return _resolve_currency(t)
|
||
# Slash fraction literal: N/M with M > 0.
|
||
if "/" in t:
|
||
m = re.fullmatch(r"(\d+)/(\d+)", t)
|
||
if m is None:
|
||
return None
|
||
num, den = int(m.group(1)), int(m.group(2))
|
||
if den == 0:
|
||
return None # division-by-zero refused.
|
||
if num % den == 0:
|
||
return _ResolvedValue(num // den, None)
|
||
return _ResolvedValue(num / den, None)
|
||
# Digit run.
|
||
if t.isdigit():
|
||
return _ResolvedValue(int(t), None)
|
||
# Indefinite quantifier (ADR-0128.4) — refuse, never guess.
|
||
if _is_indefinite_quantifier(t):
|
||
return None
|
||
# Hyphenated multi-word cardinal: twenty-five, ninety-nine, etc.
|
||
if "-" in t:
|
||
from language_packs.numerics_loader import parse_compound_cardinal
|
||
|
||
parsed = parse_compound_cardinal(t)
|
||
if parsed is None:
|
||
return None # Unrecognized hyphenated form refused.
|
||
return _ResolvedValue(parsed, None)
|
||
# Single-word cardinal (legacy WORD_NUMBERS table).
|
||
lower = t.lower()
|
||
if lower in WORD_NUMBERS:
|
||
return _ResolvedValue(WORD_NUMBERS[lower], None)
|
||
return None
|
||
|
||
|
||
def _is_indefinite_quantifier(token: str) -> bool:
|
||
"""ADR-0128.4 — quantifier-driven refusal helper.
|
||
|
||
Returns True when ``token`` resolves (via en_numerics_v1 lookup) to
|
||
an indefinite quantifier (``some``, ``many``, ``few``, ``several``,
|
||
etc.). Indefinite quantifiers in value-slot positions are refused
|
||
rather than guessed — preserves wrong == 0.
|
||
"""
|
||
try:
|
||
from language_packs.loader import lookup_quantifier
|
||
entry = lookup_quantifier(token.lower())
|
||
if entry is not None and entry.semantic_type == "indefinite":
|
||
return True
|
||
except Exception:
|
||
pass
|
||
return False
|
||
|
||
|
||
def _money_unit_normalization(
|
||
value: int | float, unit: str | None
|
||
) -> tuple[int | float, str | None]:
|
||
"""ADR-0131.G.3 — normalize money word-form surface units to pack canonical.
|
||
|
||
``en_units_v1`` pins ``cent`` as ``canonical_unit`` for the ``money``
|
||
dimension. ``dollar``/``dollars`` → 100 cents each. Other currencies
|
||
(ADR-0131.G.3.1) are already in canonical form when they arrive via
|
||
``_resolve_currency``; this helper normalizes the word-form paths.
|
||
"""
|
||
if unit is None:
|
||
return value, unit
|
||
lower = unit.lower()
|
||
if lower in ("dollar", "dollars"):
|
||
return value * 100, _MONEY_UNIT
|
||
# Euro/pound-sterling/yen/peso word forms: already canonical (factor=1).
|
||
# These enter via unit slot (word form) rather than symbol — pass through.
|
||
if lower in ("euro", "euros"):
|
||
return value, "euros"
|
||
if lower in ("pound sterling", "pounds sterling"):
|
||
return value, "pounds sterling"
|
||
if lower == "yen":
|
||
return value, "yen"
|
||
if lower in ("peso", "pesos"):
|
||
return value, "pesos"
|
||
return value, unit
|
||
|
||
|
||
def extract_initial_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Return all admissible initial-possession candidates for ``sentence``.
|
||
|
||
Recognized shapes:
|
||
1. "<Entity> has <N> <unit> [of <substance>]" — canonical.
|
||
2. "There are <N> <unit> [in <place>]" — implicit-subject shape.
|
||
|
||
Value-slot widenings (ADR-0131.G.3) apply to both shapes via
|
||
:func:`_resolve_value`: money literals (``$N`` / ``$N.NN``), slash
|
||
fractions (``N/M``), hyphenated multi-word cardinals (``twenty-five``).
|
||
|
||
Refusal-first: indefinite quantifiers, division-by-zero fractions,
|
||
unrecognized compound forms, and money literals with >2 decimals
|
||
all return ``None`` from :func:`_resolve_value` and emit no
|
||
candidate (preserves ``wrong == 0`` per ADR-0114a Obligation #4).
|
||
"""
|
||
s = sentence.strip().rstrip(".")
|
||
out: list[CandidateInitial] = []
|
||
|
||
m = _INITIAL_HAS_RE.match(s)
|
||
if m is not None:
|
||
value_raw = m.group("value")
|
||
rv = _resolve_value(value_raw)
|
||
if rv is not None:
|
||
entity = _normalize_entity(m.group("entity"))
|
||
unit_raw = m.group("unit") # may be None when value is money symbol
|
||
# Unit precedence: explicit override from value (money symbol)
|
||
# wins over the regex's unit slot. The unit slot is required
|
||
# for non-money values; if both are absent the candidate
|
||
# cannot be constructed.
|
||
resolved_unit: str | None
|
||
if rv.unit_override is not None:
|
||
resolved_unit = rv.unit_override
|
||
elif unit_raw is not None:
|
||
resolved_unit = _canonicalize_unit(unit_raw)
|
||
else:
|
||
resolved_unit = None
|
||
if resolved_unit is not None:
|
||
value, final_unit = _money_unit_normalization(rv.value, resolved_unit)
|
||
assert final_unit is not None
|
||
out.append(
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=value, unit=final_unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor"),
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw if unit_raw is not None else final_unit,
|
||
matched_entity_token=m.group("entity"),
|
||
)
|
||
)
|
||
|
||
# ADR-0131.G.3.1 — Axis 1: fraction-of-unit shape.
|
||
# "Bob has 3/4 of a cup." — separate regex extracts unit from "of" phrase.
|
||
out.extend(_fraction_of_candidates(sentence))
|
||
|
||
# ADR-0131.G.3.1 — Axis 3: multi-token space-separated cardinals.
|
||
# "Bob has one hundred apples." — separate extractor; _VALUE is unchanged.
|
||
out.extend(_multi_word_cardinal_candidates(sentence))
|
||
|
||
# ADR-0131.G.4 — multi-clause initial-state extractors.
|
||
# Each may emit ≥1 candidates; deterministic order: conjoined-subject-each,
|
||
# conjoined-object, embedded-quantifier, conjoined-embedded-quantifier.
|
||
# See module-bottom for shape definitions and closed-set discipline.
|
||
out.extend(_conj_subject_each_candidates(sentence))
|
||
out.extend(_conj_object_candidates(sentence))
|
||
out.extend(_embedded_quantifier_candidates(sentence))
|
||
# Gate A2c — "N bags of M <unit>" acquisition composition.
|
||
out.extend(_bags_of_product_candidates(sentence))
|
||
# ADR-0189a — day-of-week count enumeration → summed initial.
|
||
out.extend(_day_enumeration_candidates(sentence))
|
||
|
||
# ADR-0136.S.3 — compound initial-mutation: "Entity had N unit, but then verb M"
|
||
out.extend(_init_mutation_candidates(sentence))
|
||
|
||
# ADR-0136.S.4 — Shape A: "A <noun> has N <unit>" indefinite-article subject.
|
||
out.extend(_init_has_indef_candidates(sentence))
|
||
|
||
# ADR-0194 — labeled-container subject: "Jar A has 28 marbles."
|
||
out.extend(_init_has_labeled_candidates(sentence))
|
||
|
||
m2 = _INITIAL_THERE_ARE_RE.match(s)
|
||
if m2 is not None:
|
||
value_raw = m2.group("value")
|
||
rv = _resolve_value(value_raw)
|
||
if rv is not None:
|
||
unit_raw = m2.group("unit")
|
||
assert unit_raw is not None # there-are regex always captures unit slot
|
||
if rv.unit_override is not None:
|
||
unit_str: str = rv.unit_override
|
||
else:
|
||
unit_str = _canonicalize_unit(unit_raw)
|
||
v_norm, u_norm = _money_unit_normalization(rv.value, unit_str)
|
||
assert u_norm is not None
|
||
value: int | float = v_norm
|
||
unit: str = u_norm
|
||
place = m2.group("place")
|
||
# When a 'in <place>' phrase is present, treat the place as
|
||
# the implicit entity. Otherwise use the unit's plural as
|
||
# the collective entity name (deterministic, derivable from
|
||
# the source: "There are 5 kids" -> entity='kids').
|
||
if place is not None:
|
||
entity = _normalize_entity(place)
|
||
entity_token = place
|
||
else:
|
||
entity = unit
|
||
entity_token = unit_raw
|
||
out.append(
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=value, unit=unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m2.group("anchor"),
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=entity_token,
|
||
)
|
||
)
|
||
|
||
# ADR-0136.S.4 — Shape B: "In a <place>, there are N <unit>" prefix existential.
|
||
out.extend(_init_there_are_prefix_candidates(sentence))
|
||
|
||
return out
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Operation candidate extractor
|
||
# ---------------------------------------------------------------------------
|
||
|
||
# Per-kind operation patterns. Each captures: subject, verb, value, unit,
|
||
# optional target. The verb alternation is the kind's permissive verb table.
|
||
#
|
||
# Note: optional unit (?P<unit>) is allowed because some constructions
|
||
# rely on inherited unit ("Sam doubles his savings"); however for P2's
|
||
# scope we only emit candidates when the unit token is explicit. Inherited-
|
||
# unit candidates require per-branch state and are added in P3.
|
||
|
||
def _op_pattern(verbs_pattern: str, *, requires_target: bool) -> re.Pattern[str]:
|
||
"""Build the per-kind operation regex.
|
||
|
||
For ``requires_target=True`` (transfer): the trailing ``to <Target>``
|
||
clause is a captured slot.
|
||
|
||
For ``requires_target=False`` (add/subtract): there is no target
|
||
slot. A trailing ``to <noun>`` phrase, if present, is consumed as
|
||
part of the discardable preposition tail so the regex still matches
|
||
ambiguous sentences like "Sam gives 3 apples to Tom" (which we
|
||
*do* want to match as a subtract candidate; the transfer-vs-subtract
|
||
disambiguation happens at the candidate / filter / decision-rule
|
||
layer, not by regex specificity).
|
||
"""
|
||
if requires_target:
|
||
target_part = r"\s+to\s+(?P<target>[A-Z]\w+)"
|
||
trailing_prep = (
|
||
r"(?:\s+(?:on|from|at|in|onto|into|under|over|of|for|with)\s+.+)?"
|
||
)
|
||
else:
|
||
target_part = ""
|
||
# 'to' is included in the discardable preposition set.
|
||
# 'of' is included for ADR-0127 substance qualifiers ("1000 feet
|
||
# of cable") — the substance NP is grammatically real but
|
||
# arithmetically inert; the unit slot carries the dimensional info.
|
||
trailing_prep = (
|
||
r"(?:\s+(?:on|from|at|in|onto|into|under|over|to|of|for|with)\s+.+)?"
|
||
)
|
||
# Optional verb particle: handles "saved up N", "picked up N",
|
||
# "threw out N", etc. The particle is grammatically real but
|
||
# arithmetically inert — it does not affect the operation kind or
|
||
# operand. ADR-0131.G1: this clause replaces the former approach
|
||
# of listing particle-bearing verbs as initial-possession anchors.
|
||
verb_particle = r"(?:\s+(?:up|down|out|back|off|in|away))?"
|
||
return re.compile(
|
||
r"^"
|
||
rf"(?P<subject>{_ENTITY})\s+"
|
||
rf"(?P<verb>{verbs_pattern})"
|
||
rf"{verb_particle}"
|
||
rf"\s+(?P<value>{_VALUE})"
|
||
r"(?:\s+more)?"
|
||
r"(?:\s+(?!to\b)(?!more\b)(?!on\b)(?!from\b)(?!at\b)(?!in\b)"
|
||
r"(?P<unit>\w+))?"
|
||
rf"{target_part}"
|
||
rf"{trailing_prep}"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
_ADD_OP_RE: Final[re.Pattern[str]] = _op_pattern(_ADD_VERBS_PATTERN, requires_target=False)
|
||
_SUBTRACT_OP_RE: Final[re.Pattern[str]] = _op_pattern(_SUBTRACT_VERBS_PATTERN, requires_target=False)
|
||
_TRANSFER_OP_RE: Final[re.Pattern[str]] = _op_pattern(_TRANSFER_VERBS_PATTERN, requires_target=True)
|
||
|
||
|
||
def _canonicalize_unit(unit_raw: str) -> str:
|
||
"""Canonicalize a unit surface token to its plural form.
|
||
|
||
ADR-0127 integration: consult en_units_v1 first. If the token is a
|
||
pack-recognized unit, use the pack's canonical plural form (handles
|
||
irregular plurals like feet/feet, children, mice, etc. correctly).
|
||
Otherwise fall back to the legacy '+s' rule for count nouns.
|
||
"""
|
||
lowered = unit_raw.lower()
|
||
try:
|
||
from language_packs.loader import lookup_unit
|
||
entry = lookup_unit(lowered)
|
||
if entry is not None:
|
||
return entry.plural.lower()
|
||
except Exception:
|
||
pass
|
||
if not lowered.endswith("s"):
|
||
return lowered + "s"
|
||
return lowered
|
||
|
||
|
||
def _build_op_candidate(
|
||
m: re.Match[str], kind: str, source: str
|
||
) -> CandidateOperation | None:
|
||
"""Build a CandidateOperation from a regex match. Returns None if
|
||
the value cannot be resolved or if no unit can be determined
|
||
(unit slot absent AND value carries no implicit unit override).
|
||
"""
|
||
value_raw = m.group("value")
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return None
|
||
unit_raw = m.group("unit")
|
||
# ADR-0131.G.3: a money-symbol value carries its unit implicitly
|
||
# (override 'cent'); for plain-numeric values, the unit slot must
|
||
# be present.
|
||
if rv.unit_override is not None:
|
||
unit: str = rv.unit_override
|
||
elif unit_raw is not None:
|
||
unit = _canonicalize_unit(unit_raw)
|
||
else:
|
||
return None # P2 does not emit unit-inherited candidates.
|
||
subject = _normalize_entity(m.group("subject"))
|
||
verb = m.group("verb").lower()
|
||
value, unit_normalized = _money_unit_normalization(rv.value, unit)
|
||
assert unit_normalized is not None
|
||
unit = unit_normalized
|
||
target_raw = m.group("target") if "target" in m.groupdict() else None
|
||
target = target_raw if target_raw is not None else None
|
||
|
||
op_kwargs: dict[str, object] = {
|
||
"actor": subject,
|
||
"kind": kind,
|
||
"operand": Quantity(value=value, unit=unit),
|
||
}
|
||
if kind == "transfer":
|
||
if target is None:
|
||
return None # transfer requires target
|
||
op_kwargs["target"] = target
|
||
else:
|
||
if target is not None:
|
||
return None # add/subtract don't take targets
|
||
|
||
return CandidateOperation(
|
||
op=Operation(**op_kwargs), # type: ignore[arg-type]
|
||
source_span=source,
|
||
matched_verb=verb,
|
||
matched_value_token=m.group("value"),
|
||
matched_unit_token=unit_raw if unit_raw is not None else unit,
|
||
matched_actor_token=m.group("subject"),
|
||
matched_target_token=target,
|
||
)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Question candidate
|
||
# ---------------------------------------------------------------------------
|
||
|
||
@dataclass(frozen=True, slots=True)
|
||
class CandidateUnknown:
|
||
"""Question-candidate with source-span provenance.
|
||
|
||
Two question shapes in P3 scope:
|
||
|
||
- ``How many <unit> does <Entity> have [left|now|in total|altogether|combined|together]?``
|
||
→ ``Unknown(entity=<Entity>, unit=<unit>)``
|
||
- ``How many <unit> do they have [left|now|in total|altogether|combined|together]?``
|
||
→ ``Unknown(entity=None, unit=<unit>)`` (total-across)
|
||
|
||
Closed aggregate-cue vocabulary: ``in total``, ``altogether``,
|
||
``combined``, ``together``. All four map to ``entity=None`` on the
|
||
total-across form.
|
||
|
||
The round-trip filter for questions checks the unit token and (when
|
||
present) the entity token both appear in the source span.
|
||
"""
|
||
|
||
unknown: Unknown
|
||
source_span: str
|
||
matched_unit_token: str
|
||
matched_entity_token: str | None # None for total-across questions
|
||
# ADR-0163.D.4 — Pattern B comparative marker ("how many more X").
|
||
# Default False keeps existing constructions byte-identical.
|
||
comparative_marker: bool = False
|
||
# Gate A2c — production-yield partition injected at graph-build time.
|
||
# When all three are set, ``math_candidate_graph._build_graph`` appends
|
||
# a ``unit_partition`` step (inventory unit ÷ chunk size → product unit).
|
||
yield_chunk_value: float | None = None
|
||
yield_chunk_unit: str | None = None
|
||
yield_quotient_unit: str | None = None
|
||
consumed_value_tokens: tuple[str, ...] = ()
|
||
|
||
|
||
_Q_ENTITY_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<unit>\w+)\s+(?:does|do)\s+"
|
||
rf"(?P<entity>{_ENTITY})"
|
||
r"\s+have(?:\s+(?:left|now|in\s+total|altogether|combined|together|in\s+all)){0,2}\s*\??$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
_Q_TOTAL_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<unit>\w+)\s+do\s+they\s+have"
|
||
r"(?:\s+(?:in\s+total|altogether|combined|together|in\s+all|left|now)){0,2}\s*\??$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
# ADR-0189a — activity question "How many <unit> did <Entity> <verb>?"
|
||
# ("How many jumping jacks did Brooke do?"). The trailing verb mirrors the
|
||
# day-enumeration / comparative activity anchor; the unit slot admits a
|
||
# 1-2 word noun ("jumping jacks"). Resolves to Unknown(entity, unit).
|
||
_Q_DID_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<unit>\w+(?:\s+\w+)?)\s+did\s+"
|
||
rf"(?P<entity>{_ENTITY})\s+\w+\s*\??$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
# ADR-0193 — additional total-across surface for the SAME unknown the solver
|
||
# already aggregates (``Unknown(entity=None, unit=X)``). This widens the
|
||
# question layer's aggregate branch from the single "do they have <cue>" verb
|
||
# frame (ADR-0131.G.5) to the equally common existential frame:
|
||
# - "How many <unit> are there <agg-cue>?" (cue REQUIRED — bare "are there"
|
||
# is too ambiguous to map to total-across, and ADR-0131.G.5 pins the bare
|
||
# form as a refusal probe)
|
||
# This is a verb-frame extension over the SAME closed aggregate-cue vocabulary
|
||
# established by ADR-0131.G.5 ({in total, altogether, combined, together} plus
|
||
# the long-standing "in all"); it does NOT widen the cue set. The unit slot is
|
||
# a 1-2 word noun phrase (matching _Q_DID_RE); a conjoined unit ("dogs and
|
||
# cats") fails to match cleanly and refuses. wrong=0 is held downstream by the
|
||
# question round-trip (unit must ground) + the ADR-0191 completeness guard
|
||
# (every source quantity must be consumed).
|
||
#
|
||
# DEFERRED (NOT admitted here): "What is the total number of <unit>?".
|
||
# ADR-0131.G.5 deliberately pins that surface as an out-of-closed-cue refusal
|
||
# probe (``test_outside_closed_cue_refuses``). Promoting it is a correct future
|
||
# step — the solver already sums it — but must be done by AMENDING
|
||
# ADR-0131.G.5's closed-cue contract, not contradicted from this branch.
|
||
_Q_THERE_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<unit>\w+(?:\s+\w+)?)\s+are\s+there"
|
||
r"(?:\s+(?:in\s+total|altogether|combined|together|in\s+all)){1,2}\s*\??$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def extract_question_candidates(
|
||
sentence: str, problem_text: str | None = None
|
||
) -> list[CandidateUnknown]:
|
||
"""Return all admissible question candidates for ``sentence``.
|
||
|
||
Tries the total-across pattern FIRST (same specificity order as
|
||
legacy math_parser). The entity-pattern's widened regex would
|
||
otherwise capture "they" as an entity name.
|
||
|
||
ADR-0163.D.4 — ``problem_text`` is the full problem text used by
|
||
pronoun-entity resolution (Pattern C). When None, pronoun-entity
|
||
branches refuse rather than guess. This keeps the function pure
|
||
and deterministic: same (sentence, problem_text) → byte-identical
|
||
output.
|
||
|
||
Empty list if no shape matches.
|
||
"""
|
||
s = sentence.strip()
|
||
out: list[CandidateUnknown] = []
|
||
|
||
m = _Q_TOTAL_RE.match(s)
|
||
if m is not None:
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
out.append(
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=None, unit=unit),
|
||
source_span=sentence,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=None,
|
||
)
|
||
)
|
||
return out # specificity order: don't also try entity pattern
|
||
|
||
# ADR-0193 — "How many <unit> are there <agg-cue>?" (total-across).
|
||
m = _Q_THERE_RE.match(s)
|
||
if m is not None:
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
out.append(
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=None, unit=unit),
|
||
source_span=sentence,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=None,
|
||
)
|
||
)
|
||
return out
|
||
|
||
m = _Q_ENTITY_RE.match(s)
|
||
if m is not None:
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
entity = _normalize_entity(m.group("entity"))
|
||
out.append(
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=entity, unit=unit),
|
||
source_span=sentence,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
)
|
||
)
|
||
return out
|
||
|
||
# ADR-0189a — activity question "How many <unit> did <Entity> <verb>?"
|
||
m = _Q_DID_RE.match(s)
|
||
if m is not None:
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
entity = _normalize_entity(m.group("entity"))
|
||
out.append(
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=entity, unit=unit),
|
||
source_span=sentence,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
)
|
||
)
|
||
return out
|
||
|
||
# ADR-0163.D.4 — Pattern A: mass-noun question
|
||
out.extend(_pattern_a_mass_noun_candidates(sentence, problem_text))
|
||
if out:
|
||
return out
|
||
|
||
# Gate A2b — "How much does <entity> keep on hand?" with partition noun
|
||
# unit inferred from the enclosing problem text.
|
||
out.extend(_pattern_d_keep_on_hand_candidates(sentence, problem_text))
|
||
if out:
|
||
return out
|
||
|
||
# Gate A2c — production yield: "how many <product> will <entity> be able to make"
|
||
out.extend(_pattern_yield_question_candidates(sentence, problem_text))
|
||
if out:
|
||
return out
|
||
|
||
# ADR-0163.D.4 — Pattern B: comparative quantifier ("how many more")
|
||
out.extend(_pattern_b_comparative_candidates(sentence, problem_text))
|
||
if out:
|
||
return out
|
||
|
||
# ADR-0163.D.4 — Pattern C: pronoun-entity in non-"have" verb position
|
||
out.extend(_pattern_c_pronoun_verb_candidates(sentence, problem_text))
|
||
|
||
return out
|
||
|
||
|
||
_FRACTION_GIVE_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ACTOR_OR_PRONOUN})\s+"
|
||
r"(?P<verb>give|gives|gave)\s+"
|
||
rf"(?P<value>{_SLASH_FRACTION})\s+"
|
||
r"of\s+(?P<referent>that|it|them)\s+"
|
||
r"(?:to\s+(?:\w+\s+)*\w+)?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
_HALF_REST_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ACTOR_OR_PRONOUN})\s+"
|
||
r"(?:then\s+)?"
|
||
r"(?P<verb>put|puts|place|places)\s+"
|
||
r"half\s+of\s+the\s+rest\s+"
|
||
r"(?:in\s+(?:\w+\s+)*\w+)?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _build_fraction_portion_candidate(
|
||
*,
|
||
actor_raw: str,
|
||
numerator: int,
|
||
denominator: int,
|
||
referent: Literal["that", "it", "them", "rest", "the_rest"],
|
||
matched_verb: str,
|
||
matched_value_token: str,
|
||
matched_unit_token: str,
|
||
source: str,
|
||
) -> CandidateOperation | None:
|
||
actor = _normalize_entity(actor_raw)
|
||
try:
|
||
op = Operation(
|
||
actor=actor,
|
||
kind="fraction_portion",
|
||
operand=FractionPortion(
|
||
numerator=numerator,
|
||
denominator=denominator,
|
||
referent=referent,
|
||
),
|
||
)
|
||
except Exception:
|
||
return None
|
||
return CandidateOperation(
|
||
op=op,
|
||
source_span=source,
|
||
matched_verb=matched_verb,
|
||
matched_value_token=matched_value_token,
|
||
matched_unit_token=matched_unit_token,
|
||
matched_actor_token=actor_raw,
|
||
)
|
||
|
||
|
||
def _fraction_give_candidates(sentence: str) -> list[CandidateOperation]:
|
||
"""Gate A2b — subtract N/M of a prior partition count (not transfer)."""
|
||
s = sentence.strip()
|
||
m = _FRACTION_GIVE_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
value_raw = m.group("value")
|
||
frac = re.fullmatch(r"(\d+)/(\d+)", value_raw)
|
||
if frac is None:
|
||
return []
|
||
numerator = int(frac.group(1))
|
||
denominator = int(frac.group(2))
|
||
if denominator == 0:
|
||
return []
|
||
referent = m.group("referent").lower()
|
||
cand = _build_fraction_portion_candidate(
|
||
actor_raw=m.group("actor"),
|
||
numerator=numerator,
|
||
denominator=denominator,
|
||
referent=cast(
|
||
Literal["that", "it", "them", "rest", "the_rest"],
|
||
referent,
|
||
),
|
||
matched_verb=m.group("verb").lower(),
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=referent,
|
||
source=sentence,
|
||
)
|
||
return [cand] if cand is not None else []
|
||
|
||
|
||
def _half_rest_candidates(sentence: str) -> list[CandidateOperation]:
|
||
"""Gate A2b — subtract half of the actor's remaining partition count."""
|
||
s = sentence.strip()
|
||
m = _HALF_REST_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
cand = _build_fraction_portion_candidate(
|
||
actor_raw=m.group("actor"),
|
||
numerator=1,
|
||
denominator=2,
|
||
referent="rest",
|
||
matched_verb=m.group("verb").lower(),
|
||
matched_value_token="half",
|
||
matched_unit_token="rest",
|
||
source=sentence,
|
||
)
|
||
return [cand] if cand is not None else []
|
||
|
||
|
||
def extract_operation_candidates(sentence: str) -> list[CandidateOperation]:
|
||
"""Return all operation candidates for ``sentence``.
|
||
|
||
Tries every verb-kind pattern independently. A sentence with an
|
||
ambiguous verb (e.g. "Sam gives 3 apples to Tom" — "gives" appears
|
||
in both SUBTRACT_VERBS and TRANSFER_VERBS) may emit multiple
|
||
candidates. The round-trip filter
|
||
(:func:`generate.math_roundtrip.roundtrip_admissible`) and the
|
||
decision rule (P3) resolve which one becomes the chosen graph.
|
||
|
||
Candidate emission order is canonical: add, subtract, transfer.
|
||
Within each kind, the regex emits at most one candidate per
|
||
sentence.
|
||
"""
|
||
s = sentence.strip()
|
||
out: list[CandidateOperation] = []
|
||
|
||
for pattern, kind in (
|
||
(_ADD_OP_RE, "add"),
|
||
(_SUBTRACT_OP_RE, "subtract"),
|
||
(_TRANSFER_OP_RE, "transfer"),
|
||
):
|
||
m = pattern.match(s)
|
||
if m is None:
|
||
continue
|
||
candidate = _build_op_candidate(m, kind, source=sentence)
|
||
if candidate is not None:
|
||
out.append(candidate)
|
||
|
||
# ADR-0131.G.2 — comparative operations.
|
||
# Specificity order: nested > multiplicative > additive. Multiplicative
|
||
# anchors that overlap with additive ("twice" vs "two more") are disjoint
|
||
# at the lexical level (WORD_NUMBERS has 'two' not 'twice'); nesting
|
||
# consumes a *trailing* "than N times <REF>" tail so it cannot be confused
|
||
# with the bare additive pattern. See ADR-0131.G.2 for precedence
|
||
# rationale.
|
||
out.extend(_fraction_give_candidates(sentence))
|
||
out.extend(_half_rest_candidates(sentence))
|
||
out.extend(_compare_nested_candidates(sentence))
|
||
out.extend(_compare_multiplicative_candidates(sentence))
|
||
out.extend(_compare_additive_candidates(sentence))
|
||
|
||
return out
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0131.G.2 — Comparative operation extractors
|
||
# ---------------------------------------------------------------------------
|
||
#
|
||
# Closed-set anchor alternation, aligned 1:1 with the four
|
||
# ``Comparison.direction`` literals registered in
|
||
# :data:`generate.math_roundtrip.COMPARE_ADDITIVE_ANCHORS` /
|
||
# :data:`COMPARE_MULTIPLICATIVE_ANCHORS`:
|
||
#
|
||
# additive — direction ∈ {more, fewer}; "less" admitted as a
|
||
# surface synonym mapped to direction='fewer'.
|
||
# ``matched_verb`` = the lowercased direction word
|
||
# ('more' / 'fewer' / 'less'); these are members of
|
||
# COMPARE_ADDITIVE_ANCHORS so the round-trip filter's
|
||
# verb-registry check (math_roundtrip step 1) passes.
|
||
# multiplicative — direction ∈ {times, fraction}; surface anchors are
|
||
# 'twice' / 'thrice' / 'N times' / 'half'. The
|
||
# anchor-as-value-token convention from math_roundtrip
|
||
# step 4 lets word-form factor anchors skip
|
||
# value-grounding (the anchor's own appearance in
|
||
# the source already grounds the factor).
|
||
#
|
||
# Out of scope (refused by deliberate non-match): "as many … as" without
|
||
# a direction anchor, "compared to …", "in comparison with …",
|
||
# "the same … as". These have no entry in COMPARE_*_ANCHORS — admitting
|
||
# them would breach the round-trip filter's verb-registry check anyway.
|
||
|
||
# Comparative entity slot: proper-noun, "the X" collective, "the number/amount
|
||
# of <noun>" mass-noun construction. Possessives ("Bob's"/"his") are deferred.
|
||
_COMPARE_REF: Final[str] = (
|
||
r"(?:"
|
||
r"the\s+(?:number|amount)\s+of\s+\w+"
|
||
r"|[Tt]he\s+\w+"
|
||
r"|[A-Z]\w+"
|
||
r")"
|
||
)
|
||
|
||
|
||
def _resolve_reference_token(raw: str) -> tuple[str, str]:
|
||
"""Return ``(canonical_entity, head_token_for_grounding)``.
|
||
|
||
For "the number of chickens" the head token is "chickens"; the
|
||
canonical entity uses the full noun-phrase so binding-graph
|
||
referential-integrity isn't subverted by collapsing different
|
||
references to the same noun.
|
||
"""
|
||
collapsed = re.sub(r"\s+", " ", raw.strip())
|
||
lowered = collapsed.lower()
|
||
if lowered.startswith("the number of ") or lowered.startswith("the amount of "):
|
||
head = collapsed.split()[-1]
|
||
return collapsed, head
|
||
if lowered.startswith("the "):
|
||
head = collapsed[4:].split()[0]
|
||
return "the " + collapsed[4:], head
|
||
return collapsed, collapsed
|
||
|
||
|
||
def _comparison_anchor_verb() -> str:
|
||
# ADR-0131.G.2a — widen the comparison anchor verb beyond 'has'/'have'.
|
||
# The verb here only names the action whose *quantity* is being compared
|
||
# ("A <verb> N more/×-as-many X than/as B"); it does not carry polarity
|
||
# the way accumulation verbs do, so a closed set of non-inverting action
|
||
# verbs is wrong=0-safe (the round-trip filter still requires the
|
||
# comparator anchor + reference actor to ground). The set reuses the
|
||
# already-vetted legacy math_parser._COMPARE_VERB lemmas plus the
|
||
# production/activity verbs observed in real GSM8K comparative statements
|
||
# ('does'/'collected'/'gained'/'studied' …).
|
||
#
|
||
# Deliberately EXCLUDED (polarity-inverting in a comparison context —
|
||
# admitting them could read the comparison backwards → wrong>0):
|
||
# lose/lost, win/won, spend/spent, use/used, give/gave, sell/sold.
|
||
return (
|
||
r"(?:has|have|had|gets|get|got|takes|take|took|buys|buy|bought|"
|
||
r"does|do|did|makes|make|made|collects|collect|collected|"
|
||
r"gains|gain|gained|studies|study|studied|reads|read)"
|
||
)
|
||
|
||
|
||
_COMPARE_ADDITIVE_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ENTITY})\s+{_comparison_anchor_verb()}\s+"
|
||
rf"(?P<value>{_VALUE})\s+"
|
||
r"(?P<direction>more|fewer|less)\s+"
|
||
r"(?P<unit>\w+)\s+than\s+"
|
||
rf"(?P<reference>{_COMPARE_REF})\s*\.?$"
|
||
)
|
||
|
||
# Multiplicative: anchor-as-value form ("twice"/"thrice"/"half"/"quarter"/
|
||
# "third" carry the factor implicitly). "as many <unit>" required; unit
|
||
# ellipsis ("twice as many as Bob") is deferred to keep wrong==0 strict.
|
||
# "quarter" / "third" admit an optional article ("a quarter", "a third") —
|
||
# the article is not a named group; matched_verb is the anchor word itself,
|
||
# which is a substring of the source and registered in
|
||
# COMPARE_MULTIPLICATIVE_ANCHORS, so round-trip checks pass.
|
||
_COMPARE_MULT_ANCHOR_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ENTITY})\s+{_comparison_anchor_verb()}\s+"
|
||
r"(?:a\s+)?(?P<anchor>twice|thrice|half|quarter|third)\s+as\s+many\s+"
|
||
r"(?P<unit>\w+(?:\s+\w+)?)\s+as\s+"
|
||
rf"(?P<reference>{_COMPARE_REF})\s*\.?$"
|
||
)
|
||
|
||
# Multiplicative: explicit "N times as many <unit> as <REF>".
|
||
# ADR-0131.G.2a — unit slot admits an optional second word ("jumping jacks").
|
||
_COMPARE_MULT_NTIMES_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ENTITY})\s+{_comparison_anchor_verb()}\s+"
|
||
rf"(?P<value>{_VALUE})\s+times\s+as\s+many\s+"
|
||
r"(?P<unit>\w+(?:\s+\w+)?)\s+as\s+"
|
||
rf"(?P<reference>{_COMPARE_REF})\s*\.?$"
|
||
)
|
||
|
||
# Nested: additive over multiplicative — "A has N more <unit> than M times <REF>".
|
||
# Emits *two* flat candidates so the binding graph (ADR-0134) can decide which
|
||
# admissible composition (if any) survives. The parser does not commit to a
|
||
# nested operand shape (Comparison ∋ Comparison is not a supported operand
|
||
# type today); composition admissibility is the round-trip layer's call.
|
||
_COMPARE_NESTED_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ENTITY})\s+{_comparison_anchor_verb()}\s+"
|
||
rf"(?P<delta_value>{_VALUE})\s+"
|
||
r"(?P<direction>more|fewer|less)\s+"
|
||
r"(?P<unit>\w+)\s+than\s+"
|
||
rf"(?P<factor_value>{_VALUE})\s+times\s+"
|
||
rf"(?P<reference>{_COMPARE_REF})\s*\.?$"
|
||
)
|
||
|
||
|
||
_ANCHOR_TO_FACTOR: Final[dict[str, tuple[float, str]]] = {
|
||
# surface anchor → (factor, direction-literal)
|
||
# Registered in COMPARE_MULTIPLICATIVE_ANCHORS (math_roundtrip) and in the
|
||
# round-trip factor-divisor table ("half":2, "third":3, "quarter":4).
|
||
"twice": (2.0, "times"),
|
||
"thrice": (3.0, "times"),
|
||
"half": (0.5, "fraction"),
|
||
"quarter": (0.25, "fraction"),
|
||
"third": (1.0 / 3.0, "fraction"),
|
||
}
|
||
|
||
|
||
def _direction_to_anchor(direction_raw: str) -> tuple[str, str]:
|
||
"""Map surface direction word → (canonical Comparison.direction,
|
||
matched_verb registered in COMPARE_ADDITIVE_ANCHORS).
|
||
|
||
'less' is a surface synonym of 'fewer'; the Comparison value uses
|
||
direction='fewer', but matched_verb retains the source surface
|
||
('less') so the round-trip filter's "verb appears in source" check
|
||
succeeds. 'less' is registered in COMPARE_ADDITIVE_ANCHORS, so the
|
||
verb-registry check also succeeds.
|
||
"""
|
||
lowered = direction_raw.lower()
|
||
if lowered in ("more", "fewer"):
|
||
return lowered, lowered
|
||
if lowered == "less":
|
||
return "fewer", "less"
|
||
raise ValueError(f"unknown comparative direction surface {direction_raw!r}")
|
||
|
||
|
||
def _build_compare_additive(
|
||
*,
|
||
actor_raw: str,
|
||
delta_value_raw: str,
|
||
direction_raw: str,
|
||
unit_raw: str,
|
||
reference_raw: str,
|
||
source: str,
|
||
) -> CandidateOperation | None:
|
||
if _is_indefinite_quantifier(delta_value_raw):
|
||
return None
|
||
direction, matched_verb = _direction_to_anchor(direction_raw)
|
||
actor = _normalize_entity(actor_raw)
|
||
reference_canon, reference_head = _resolve_reference_token(reference_raw)
|
||
if reference_canon == actor:
|
||
return None # self-reference; constructor would refuse anyway
|
||
rv = _resolve_value(delta_value_raw)
|
||
if rv is None:
|
||
return None
|
||
delta_value = rv.value
|
||
unit = rv.unit_override if rv.unit_override is not None else _canonicalize_unit(unit_raw)
|
||
try:
|
||
op = Operation(
|
||
actor=actor,
|
||
kind="compare_additive",
|
||
operand=Comparison(
|
||
reference_actor=reference_canon,
|
||
delta=Quantity(value=delta_value, unit=unit),
|
||
factor=None,
|
||
direction=cast(_CompDirection, direction),
|
||
),
|
||
)
|
||
except Exception:
|
||
return None
|
||
try:
|
||
return CandidateOperation(
|
||
op=op,
|
||
source_span=source,
|
||
matched_verb=matched_verb,
|
||
matched_value_token=delta_value_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_actor_token=actor_raw,
|
||
matched_reference_actor_token=reference_head,
|
||
)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _build_unit_partition(
|
||
*,
|
||
actor_raw: str,
|
||
chunk_size: float,
|
||
chunk_unit_raw: str,
|
||
result_unit_raw: str,
|
||
matched_verb: str,
|
||
matched_value_token: str,
|
||
source: str,
|
||
) -> CandidateOperation | None:
|
||
actor = _normalize_entity(actor_raw)
|
||
chunk_unit = _canonicalize_unit(chunk_unit_raw)
|
||
result_unit = _canonicalize_unit(result_unit_raw)
|
||
try:
|
||
op = Operation(
|
||
actor=actor,
|
||
kind="unit_partition",
|
||
operand=PartitionChunk(
|
||
value=chunk_size,
|
||
unit=chunk_unit,
|
||
result_unit=result_unit,
|
||
),
|
||
)
|
||
except Exception:
|
||
return None
|
||
try:
|
||
return CandidateOperation(
|
||
op=op,
|
||
source_span=source,
|
||
matched_verb=matched_verb,
|
||
matched_value_token=matched_value_token,
|
||
matched_unit_token=chunk_unit_raw,
|
||
matched_actor_token=actor_raw,
|
||
)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _build_compare_multiplicative(
|
||
*,
|
||
actor_raw: str,
|
||
factor: float,
|
||
matched_verb: str,
|
||
matched_value_token: str,
|
||
unit_raw: str,
|
||
reference_raw: str,
|
||
source: str,
|
||
direction: str,
|
||
) -> CandidateOperation | None:
|
||
actor = _normalize_entity(actor_raw)
|
||
reference_canon, reference_head = _resolve_reference_token(reference_raw)
|
||
if reference_canon == actor:
|
||
return None
|
||
_ = _canonicalize_unit(unit_raw) # validation only; multiplicative compares
|
||
# carry the unit on the source-span side, not the operand
|
||
try:
|
||
op = Operation(
|
||
actor=actor,
|
||
kind="compare_multiplicative",
|
||
operand=Comparison(
|
||
reference_actor=reference_canon,
|
||
delta=None,
|
||
factor=factor,
|
||
direction=cast(_CompDirection, direction),
|
||
),
|
||
)
|
||
except Exception:
|
||
return None
|
||
try:
|
||
return CandidateOperation(
|
||
op=op,
|
||
source_span=source,
|
||
matched_verb=matched_verb,
|
||
matched_value_token=matched_value_token,
|
||
matched_unit_token=unit_raw,
|
||
matched_actor_token=actor_raw,
|
||
matched_reference_actor_token=reference_head,
|
||
)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _compare_additive_candidates(sentence: str) -> list[CandidateOperation]:
|
||
s = sentence.strip()
|
||
m = _COMPARE_ADDITIVE_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
cand = _build_compare_additive(
|
||
actor_raw=m.group("actor"),
|
||
delta_value_raw=m.group("value"),
|
||
direction_raw=m.group("direction"),
|
||
unit_raw=m.group("unit"),
|
||
reference_raw=m.group("reference"),
|
||
source=sentence,
|
||
)
|
||
return [cand] if cand is not None else []
|
||
|
||
|
||
def _compare_multiplicative_candidates(sentence: str) -> list[CandidateOperation]:
|
||
s = sentence.strip()
|
||
out: list[CandidateOperation] = []
|
||
|
||
m = _COMPARE_MULT_ANCHOR_RE.match(s)
|
||
if m is not None:
|
||
anchor = m.group("anchor").lower()
|
||
factor, direction = _ANCHOR_TO_FACTOR[anchor]
|
||
cand = _build_compare_multiplicative(
|
||
actor_raw=m.group("actor"),
|
||
factor=factor,
|
||
matched_verb=anchor,
|
||
matched_value_token=anchor, # anchor-as-value (math_roundtrip step 4)
|
||
unit_raw=m.group("unit"),
|
||
reference_raw=m.group("reference"),
|
||
source=sentence,
|
||
direction=direction,
|
||
)
|
||
if cand is not None:
|
||
out.append(cand)
|
||
return out # specificity — don't also try N-times pattern
|
||
|
||
m = _COMPARE_MULT_NTIMES_RE.match(s)
|
||
if m is not None:
|
||
value_raw = m.group("value")
|
||
if _is_indefinite_quantifier(value_raw):
|
||
return out
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return out
|
||
factor = float(rv.value)
|
||
cand = _build_compare_multiplicative(
|
||
actor_raw=m.group("actor"),
|
||
factor=factor,
|
||
matched_verb="times",
|
||
matched_value_token=value_raw,
|
||
unit_raw=m.group("unit"),
|
||
reference_raw=m.group("reference"),
|
||
source=sentence,
|
||
direction="times",
|
||
)
|
||
if cand is not None:
|
||
out.append(cand)
|
||
return out
|
||
|
||
|
||
def _compare_nested_candidates(sentence: str) -> list[CandidateOperation]:
|
||
"""Emit two flat candidates for nested 'N more <unit> than M times <REF>'.
|
||
|
||
The parser does not commit to a composed Comparison-of-Comparison
|
||
operand (operand type Comparison ∋ Comparison is not modelled
|
||
today). Both flat candidates are forwarded; the binding-graph /
|
||
round-trip layer (ADR-0134) picks an admissible composition or
|
||
refuses. Refusal is the safe outcome — never a wrong answer.
|
||
"""
|
||
s = sentence.strip()
|
||
m = _COMPARE_NESTED_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
out: list[CandidateOperation] = []
|
||
|
||
actor_raw = m.group("actor")
|
||
unit_raw = m.group("unit")
|
||
reference_raw = m.group("reference")
|
||
|
||
# Candidate 1: additive — "A has N more <unit> than <REF>" treating
|
||
# <REF> as the comparison reference directly. The "M times" multiplier
|
||
# is dropped on this candidate (the binding-graph composition is
|
||
# what would re-introduce it).
|
||
add_cand = _build_compare_additive(
|
||
actor_raw=actor_raw,
|
||
delta_value_raw=m.group("delta_value"),
|
||
direction_raw=m.group("direction"),
|
||
unit_raw=unit_raw,
|
||
reference_raw=reference_raw,
|
||
source=sentence,
|
||
)
|
||
if add_cand is not None:
|
||
out.append(add_cand)
|
||
|
||
# Candidate 2: multiplicative — "A has M times as many <unit> as <REF>"
|
||
# treating the multiplier M and the same <REF> as the multiplicative
|
||
# comparison. The additive offset N is dropped on this candidate.
|
||
factor_value_raw = m.group("factor_value")
|
||
if not _is_indefinite_quantifier(factor_value_raw):
|
||
rv = _resolve_value(factor_value_raw)
|
||
factor = float(rv.value) if rv is not None else None
|
||
if factor is not None:
|
||
mult_cand = _build_compare_multiplicative(
|
||
actor_raw=actor_raw,
|
||
factor=factor,
|
||
matched_verb="times",
|
||
matched_value_token=factor_value_raw,
|
||
unit_raw=unit_raw,
|
||
reference_raw=reference_raw,
|
||
source=sentence,
|
||
direction="times",
|
||
)
|
||
if mult_cand is not None:
|
||
out.append(mult_cand)
|
||
|
||
return out
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0131.G.3.1 — Axis 1 + Axis 3 extractor functions
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _fraction_of_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Axis 1 (fractions): 'Bob has 3/4 of a cup.' → value=0.75, unit='cups'.
|
||
|
||
The main _INITIAL_HAS_RE treats 'of <NP>' as a discardable substance
|
||
qualifier and cannot fill the unit slot from it. This extractor uses
|
||
_INITIAL_FRACTION_OF_RE to explicitly capture the unit after 'of'.
|
||
"""
|
||
s = sentence.strip().rstrip(".")
|
||
m = _INITIAL_FRACTION_OF_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
value_raw = m.group("value")
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return []
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
entity = _normalize_entity(m.group("entity"))
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=rv.value, unit=unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor"),
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def _multi_word_cardinal_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Axis 3 (multi-word cardinals): 'Bob has one hundred apples.'
|
||
|
||
Approach (a): dedicated extractor leaving _VALUE unchanged. The value
|
||
group captures the full space-separated cardinal sequence; the unit
|
||
slot is the next word token after the cardinal sequence (and optional
|
||
adjective).
|
||
"""
|
||
s = sentence.strip().rstrip(".")
|
||
m = _MULTI_WORD_CARDINAL_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
value_raw = m.group("value")
|
||
from language_packs.numerics_loader import parse_compound_cardinal
|
||
parsed = parse_compound_cardinal(value_raw)
|
||
if parsed is None:
|
||
return []
|
||
unit_raw = m.group("unit")
|
||
value_n, unit_n = _money_unit_normalization(parsed, _canonicalize_unit(unit_raw))
|
||
if unit_n is None:
|
||
return []
|
||
entity = _normalize_entity(m.group("entity"))
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=value_n, unit=unit_n),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor"),
|
||
# Provenance: use the first cardinal word as the value token
|
||
# for grounding (all cardinal words are in the source span).
|
||
matched_value_token=value_raw.split()[0],
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
# ADR-0191 — the compound cardinal collapses every word into
|
||
# one value; the guard sees them via the joined surface form.
|
||
consumed_value_tokens=(value_raw,),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0131.G.4 — Multi-clause initial-state composition
|
||
# ---------------------------------------------------------------------------
|
||
#
|
||
# Closed shape set. Every recognized multi-clause structure matches exactly
|
||
# one of the four extractors below. Cross-sentence coreference, ellipsis,
|
||
# three-way+ conjunctions, and collective `each` readings are deliberately
|
||
# refused (no extractor matches them).
|
||
#
|
||
# Why initials, not operations: the GSM8K shapes targeted here introduce
|
||
# starting state ('Aaron and Carson each saved $40', 'Francine has five
|
||
# full boxes and 5 loose crayons', 'Ella has 4 bags with 20 apples in each
|
||
# bag'). They are not state-mutating events. Emitting CandidateInitial
|
||
# preserves the conventional initial-state-vs-operation split the solver
|
||
# (math_solver.py) expects.
|
||
|
||
# Anchor verbs allowed in conjoined-subject-each constructions. Surface
|
||
# verb is mapped to a single canonical anchor token (e.g. 'saved up' →
|
||
# matched_anchor='saved'). The CandidateInitial constructor whitelists
|
||
# these via the ADR-0131.G.4 widening.
|
||
_CONJ_SUBJECT_VERBS: Final[tuple[str, ...]] = (
|
||
"has", "have", "had",
|
||
"saved", "earned", "got", "received", "bought", "made", "paid",
|
||
)
|
||
_CONJ_SUBJECT_VERBS_PATTERN: Final[str] = (
|
||
r"(?:" + "|".join(_CONJ_SUBJECT_VERBS) + r")"
|
||
)
|
||
|
||
# Optional "and his/her/their brother/sister/friend/cousin" appositive
|
||
# between the two conjuncts. Captures the appositive's head noun as part
|
||
# of the second entity; we still ground on the proper noun that follows.
|
||
_CONJ_KIN_GLUE: Final[str] = (
|
||
r"(?:(?:his|her|their)\s+(?:brother|sister|friend|cousin)\s+)?"
|
||
)
|
||
|
||
# Conjoined-subject "each" — distributive only. The trailing "to <infin>"
|
||
# / "for <NP>" / "of <NP>" tail is consumed and discarded (arithmetically
|
||
# inert; cf. ADR-0127 substance qualifier).
|
||
_CONJ_SUBJECT_EACH_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<a>{_ENTITY})\s+and\s+{_CONJ_KIN_GLUE}"
|
||
rf"(?P<b>{_ENTITY})\s+each\s+"
|
||
rf"(?P<verb>{_CONJ_SUBJECT_VERBS_PATTERN})(?:\s+up)?\s+"
|
||
rf"(?P<value>{_VALUE})\s+"
|
||
r"(?P<unit>\w+)"
|
||
r"(?:\s+(?:of|in|for|to|from|with|on|at)\s+.+)?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
# Conjoined-object NPs sharing a verb. The two units may differ
|
||
# ('5 boxes and 7 marbles') — the binding graph keeps the per-unit
|
||
# states independent. Same-unit conjuncts (rare) collapse into a
|
||
# single state slot via the solver's state[(entity,unit)] overwrite,
|
||
# which is a known limitation — we refuse same-unit conjuncts to avoid
|
||
# silently losing the first conjunct's value.
|
||
_CONJ_OBJECT_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ENTITY})\s+(?P<anchor>has|have|had)\s+"
|
||
rf"(?P<v1>{_VALUE})\s+(?P<u1>\w+)"
|
||
r"(?:\s+(?:full|loose|empty|whole|broken|new|old|small|large))?"
|
||
r"(?:\s+of\s+\w+)?"
|
||
rf"\s+and\s+(?P<v2>{_VALUE})\s+(?P<u2>\w+)"
|
||
r"(?:\s+(?:full|loose|empty|whole|broken|new|old|small|large))?"
|
||
r"(?:\s+of\s+\w+)?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
# Embedded quantifier: "N <container> with M <unit> in each [<container>]".
|
||
# Optional second mention of the container after 'each' (the natural-
|
||
# language redundancy in the brief's Ella example).
|
||
_EMBEDDED_QUANTIFIER_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ENTITY})\s+(?P<anchor>has|have|had)\s+"
|
||
rf"(?P<n>{_VALUE})\s+(?P<container>\w+)\s+with\s+"
|
||
rf"(?P<m>{_VALUE})\s+(?P<unit>\w+)\s+in\s+each"
|
||
r"(?:\s+(?P<container2>\w+))?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
# Conjoined embedded quantifiers — both halves match the embedded shape.
|
||
# Emits a single SUM candidate (value = N1*M1 + N2*M2) — emitting two
|
||
# derived candidates with the same (entity, unit) is unsafe under the
|
||
# solver's overwrite-on-collision semantics (math_solver.py:206; would
|
||
# silently drop the first conjunct's value). Same-unit summation is the
|
||
# admissible interpretation; mismatched units refuse.
|
||
_CONJ_EMBEDDED_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ENTITY})\s+(?P<anchor>has|have|had)\s+"
|
||
rf"(?P<n1>{_VALUE})\s+(?P<c1>\w+)\s+with\s+(?P<m1>{_VALUE})\s+(?P<u1>\w+)"
|
||
r"\s+in\s+each(?:\s+\w+)?\s+and\s+"
|
||
rf"(?P<n2>{_VALUE})\s+(?P<c2>\w+)\s+with\s+(?P<m2>{_VALUE})\s+(?P<u2>\w+)"
|
||
r"\s+in\s+each(?:\s+\w+)?"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _canon_verb_to_anchor(verb: str) -> str:
|
||
"""Map surface verb to its canonical CandidateInitial anchor token.
|
||
|
||
The constructor whitelist is keyed on lowercase singular-or-past
|
||
tokens; we lowercase + strip particle ('saved up' was already
|
||
stripped of 'up' by the regex's separate slot)."""
|
||
return verb.lower()
|
||
|
||
|
||
def _conj_subject_each_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Distributive `each` only. Collective readings refuse by not
|
||
matching (no 'each' in the surface)."""
|
||
s = sentence.strip().rstrip(".")
|
||
m = _CONJ_SUBJECT_EACH_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
value_raw = m.group("value")
|
||
if _is_indefinite_quantifier(value_raw):
|
||
return []
|
||
# Adversarial probe: 'each ... together' is a contradiction; refuse.
|
||
# Captured in test_refuses_each_with_together.
|
||
if re.search(r"\btogether\b|\bin total\b|\baltogether\b", s, re.IGNORECASE):
|
||
return []
|
||
entity_a = _normalize_entity(m.group("a"))
|
||
entity_b = _normalize_entity(m.group("b"))
|
||
if entity_a == entity_b:
|
||
return [] # 'Aaron and Aaron each ...' is degenerate
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return []
|
||
value = rv.value
|
||
unit_raw = m.group("unit")
|
||
unit = rv.unit_override if rv.unit_override is not None else _canonicalize_unit(unit_raw)
|
||
anchor = _canon_verb_to_anchor(m.group("verb"))
|
||
out: list[CandidateInitial] = []
|
||
for entity, entity_raw in ((entity_a, m.group("a")), (entity_b, m.group("b"))):
|
||
try:
|
||
out.append(
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=value, unit=unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=anchor,
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=entity_raw,
|
||
)
|
||
)
|
||
except Exception:
|
||
return [] # all-or-nothing emission
|
||
return out
|
||
|
||
|
||
def _conj_object_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Conjoined object NPs sharing a verb. Same-unit conjuncts refused
|
||
(cannot safely compose under solver's overwrite-on-collision)."""
|
||
s = sentence.strip().rstrip(".")
|
||
m = _CONJ_OBJECT_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
v1_raw, v2_raw = m.group("v1"), m.group("v2")
|
||
if _is_indefinite_quantifier(v1_raw) or _is_indefinite_quantifier(v2_raw):
|
||
return []
|
||
u1_raw, u2_raw = m.group("u1"), m.group("u2")
|
||
u1 = _canonicalize_unit(u1_raw)
|
||
u2 = _canonicalize_unit(u2_raw)
|
||
if u1 == u2:
|
||
# Same-unit conjuncts would silently collide under the solver's
|
||
# state[(entity,unit)] overwrite. Refuse rather than guess.
|
||
return []
|
||
entity = _normalize_entity(m.group("entity"))
|
||
anchor = m.group("anchor").lower()
|
||
out: list[CandidateInitial] = []
|
||
for value_raw, unit_raw, unit in (
|
||
(v1_raw, u1_raw, u1),
|
||
(v2_raw, u2_raw, u2),
|
||
):
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return []
|
||
final_unit = rv.unit_override if rv.unit_override is not None else unit
|
||
try:
|
||
out.append(
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=rv.value, unit=final_unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=anchor,
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
)
|
||
)
|
||
except Exception:
|
||
return []
|
||
return out
|
||
|
||
|
||
# ADR-0189a — day-of-week count enumeration.
|
||
# Shape: "<Actor> does N1 <noun> on <Day1>, N2 on <Day2>, ... and Nk on <Dayk>."
|
||
# All counts are the same actor's same-unit activity; the total is their sum.
|
||
# Closed to the seven day-of-week names so the "<count> on <Day>" enumeration
|
||
# cannot be confused with other comma lists. Derived total grounds via the
|
||
# first count token (matched_value_token), mirroring _embedded_quantifier.
|
||
_DAY_NAME_RE: Final[str] = (
|
||
r"(?:Monday|Tuesday|Wednesday|Thursday|Friday|Saturday|Sunday)"
|
||
)
|
||
_DAY_ENUM_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ENTITY})\s+(?P<anchor>does|did|do)\s+"
|
||
rf"(?P<n1>\d+)\s+(?P<noun>[a-z]+(?:\s+[a-z]+)?)\s+on\s+{_DAY_NAME_RE}"
|
||
rf"(?P<rest>(?:,\s*(?:and\s+)?\d+\s+on\s+{_DAY_NAME_RE})+)"
|
||
r"\s*\.?$"
|
||
)
|
||
_DAY_ENUM_REST_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"(\d+)\s+on\s+{_DAY_NAME_RE}"
|
||
)
|
||
|
||
|
||
def _day_enumeration_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""'<Actor> does N1 <noun> on <Day1>, N2 on <Day2>, ...' → summed initial.
|
||
|
||
Emits a single CandidateInitial whose value is the sum of all per-day
|
||
counts. The derived sum is not literal in source, so provenance anchors
|
||
on the first count token (which grounds), exactly as
|
||
_embedded_quantifier_candidates anchors on its per-container token.
|
||
"""
|
||
s = sentence.strip()
|
||
m = _DAY_ENUM_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
n1 = int(m.group("n1"))
|
||
rest_raw = _DAY_ENUM_REST_RE.findall(m.group("rest"))
|
||
rest_nums = [int(x) for x in rest_raw]
|
||
if not rest_nums:
|
||
return []
|
||
total = float(n1 + sum(rest_nums))
|
||
entity = _normalize_entity(m.group("entity"))
|
||
noun_raw = m.group("noun").strip()
|
||
unit = _canonicalize_unit(noun_raw)
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=total, unit=unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor").lower(),
|
||
matched_value_token=m.group("n1"),
|
||
matched_unit_token=noun_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
# ADR-0191 — the sum collapses every per-day count; record
|
||
# them all so the completeness guard sees full coverage.
|
||
consumed_value_tokens=(m.group("n1"), *rest_raw),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def _embedded_quantifier_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Embedded quantifier 'N <container> with M <unit> in each' →
|
||
derived InitialPossession(value=N*M, unit=<unit>). Also handles the
|
||
conjoined-embedded shape via _CONJ_EMBEDDED_RE (single SUM
|
||
candidate; same-unit only)."""
|
||
s = sentence.strip().rstrip(".")
|
||
|
||
# Try conjoined-embedded first (most specific).
|
||
m = _CONJ_EMBEDDED_RE.match(s)
|
||
if m is not None:
|
||
return _build_conj_embedded_sum(m, sentence)
|
||
|
||
m = _EMBEDDED_QUANTIFIER_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
n_raw, m_raw = m.group("n"), m.group("m")
|
||
if _is_indefinite_quantifier(n_raw) or _is_indefinite_quantifier(m_raw):
|
||
return []
|
||
container = m.group("container").lower()
|
||
container2_raw = m.group("container2")
|
||
if container2_raw is not None:
|
||
# 'with M unit in each <container2>' — container2 (if named)
|
||
# must agree with the leading container; otherwise the scope of
|
||
# 'each' is ambiguous and we refuse.
|
||
c2 = container2_raw.lower()
|
||
if c2 not in (container, container.rstrip("s"), container + "s"):
|
||
return []
|
||
rv_n = _resolve_value(n_raw)
|
||
rv_per = _resolve_value(m_raw)
|
||
if rv_n is None or rv_per is None:
|
||
return []
|
||
total = rv_n.value * rv_per.value
|
||
entity = _normalize_entity(m.group("entity"))
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=total, unit=unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor").lower(),
|
||
# Provenance: anchor on the per-container value token (M).
|
||
# The product N*M is a parser-committed derivation; the
|
||
# source-token check passes on M's surface form.
|
||
matched_value_token=m_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
# ADR-0191 — the product N*M consumes both source tokens.
|
||
consumed_value_tokens=(n_raw, m_raw),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
# Gate A2c — "N <container> of M <unit>" acquisition composition.
|
||
# Distinct from G.4 embedded quantifier ("with M in each"); this shape
|
||
# uses "of" without a per-container "in each" tail.
|
||
_ACQUIRE_BAGS_OF_VERBS: Final[str] = (
|
||
r"(?:bought|buys|buy|got|gets|get|received|receives|receive)"
|
||
)
|
||
_BAGS_OF_PRODUCT_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ACTOR_OR_PRONOUN})\s+"
|
||
rf"(?P<anchor>{_ACQUIRE_BAGS_OF_VERBS})\s+"
|
||
rf"(?P<n>{_VALUE})\s+(?P<container>\w+)\s+of\s+"
|
||
rf"(?P<m>{_VALUE})\s+(?P<unit>\w+)"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
_CONJ_BAGS_OF_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ACTOR_OR_PRONOUN})\s+"
|
||
rf"(?P<anchor>{_ACQUIRE_BAGS_OF_VERBS})\s+"
|
||
rf"(?P<n1>{_VALUE})\s+(?P<c1>\w+)\s+of\s+(?P<m1>{_VALUE})\s+(?P<u1>\w+)\s+and\s+"
|
||
rf"(?P<n2>{_VALUE})\s+(?P<c2>\w+)\s+of\s+(?P<m2>{_VALUE})\s+(?P<u2>\w+)"
|
||
r"\s*\.?$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _build_conj_bags_of_sum(
|
||
m: re.Match[str], sentence: str
|
||
) -> list[CandidateInitial]:
|
||
"""Single SUM candidate for conjoined bags-of-product."""
|
||
n1_raw, m1_raw = m.group("n1"), m.group("m1")
|
||
n2_raw, m2_raw = m.group("n2"), m.group("m2")
|
||
for raw in (n1_raw, m1_raw, n2_raw, m2_raw):
|
||
if _is_indefinite_quantifier(raw):
|
||
return []
|
||
u1 = _canonicalize_unit(m.group("u1"))
|
||
u2 = _canonicalize_unit(m.group("u2"))
|
||
if u1 != u2:
|
||
return []
|
||
rv_n1 = _resolve_value(n1_raw)
|
||
rv_m1 = _resolve_value(m1_raw)
|
||
rv_n2 = _resolve_value(n2_raw)
|
||
rv_m2 = _resolve_value(m2_raw)
|
||
if any(rv is None for rv in (rv_n1, rv_m1, rv_n2, rv_m2)):
|
||
return []
|
||
total = (rv_n1.value * rv_m1.value) + (rv_n2.value * rv_m2.value) # type: ignore[union-attr]
|
||
entity = _normalize_entity(m.group("entity"))
|
||
anchor = m.group("anchor").lower()
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=total, unit=u1),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=anchor,
|
||
matched_value_token=m1_raw,
|
||
matched_unit_token=m.group("u1"),
|
||
matched_entity_token=m.group("entity"),
|
||
consumed_value_tokens=(n1_raw, m1_raw, n2_raw, m2_raw),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def _bags_of_product_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""``N bags of M beads`` acquisition → derived total inventory."""
|
||
s = sentence.strip().rstrip(".")
|
||
|
||
m = _CONJ_BAGS_OF_RE.match(s)
|
||
if m is not None:
|
||
return _build_conj_bags_of_sum(m, sentence)
|
||
|
||
m = _BAGS_OF_PRODUCT_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
n_raw, m_raw = m.group("n"), m.group("m")
|
||
if _is_indefinite_quantifier(n_raw) or _is_indefinite_quantifier(m_raw):
|
||
return []
|
||
rv_n = _resolve_value(n_raw)
|
||
rv_per = _resolve_value(m_raw)
|
||
if rv_n is None or rv_per is None:
|
||
return []
|
||
total = rv_n.value * rv_per.value
|
||
entity = _normalize_entity(m.group("entity"))
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
anchor = m.group("anchor").lower()
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=total, unit=unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=anchor,
|
||
matched_value_token=m_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
consumed_value_tokens=(n_raw, m_raw),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Per-shape admitted-only wrappers (used by the G4 runner).
|
||
# Each filters its extractor's output through _initial_admissible from
|
||
# math_candidate_graph so the runner sees only round-trip-admissible
|
||
# candidates without re-implementing the check.
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _admit(cands: list[CandidateInitial]) -> list[CandidateInitial]:
|
||
from generate.math_candidate_graph import _initial_admissible
|
||
return [c for c in cands if _initial_admissible(c)]
|
||
|
||
|
||
def _conj_subject_each_admitted(sentence: str) -> list[CandidateInitial]:
|
||
return _admit(_conj_subject_each_candidates(sentence))
|
||
|
||
|
||
def _conj_object_admitted(sentence: str) -> list[CandidateInitial]:
|
||
return _admit(_conj_object_candidates(sentence))
|
||
|
||
|
||
def _embedded_quantifier_admitted(sentence: str) -> list[CandidateInitial]:
|
||
# _embedded_quantifier_candidates dispatches to _CONJ_EMBEDDED_RE
|
||
# *first*, so this wrapper returns the single-embedded candidate
|
||
# only when the conjoined shape doesn't match. To distinguish,
|
||
# callers that care about the conjoined branch use
|
||
# _conj_embedded_admitted below.
|
||
s = sentence.strip().rstrip(".")
|
||
if _CONJ_EMBEDDED_RE.match(s) is not None:
|
||
return []
|
||
return _admit(_embedded_quantifier_candidates(sentence))
|
||
|
||
|
||
def _conj_embedded_admitted(sentence: str) -> list[CandidateInitial]:
|
||
s = sentence.strip().rstrip(".")
|
||
if _CONJ_EMBEDDED_RE.match(s) is None:
|
||
return []
|
||
return _admit(_embedded_quantifier_candidates(sentence))
|
||
|
||
|
||
def _build_conj_embedded_sum(
|
||
m: re.Match[str], sentence: str
|
||
) -> list[CandidateInitial]:
|
||
"""Single SUM candidate for conjoined-embedded 'N1 C with M1 U in
|
||
each and N2 C with M2 U in each'."""
|
||
n1_raw, m1_raw = m.group("n1"), m.group("m1")
|
||
n2_raw, m2_raw = m.group("n2"), m.group("m2")
|
||
for raw in (n1_raw, m1_raw, n2_raw, m2_raw):
|
||
if _is_indefinite_quantifier(raw):
|
||
return []
|
||
u1 = _canonicalize_unit(m.group("u1"))
|
||
u2 = _canonicalize_unit(m.group("u2"))
|
||
if u1 != u2:
|
||
# Mixed-unit sum is meaningless; refuse.
|
||
return []
|
||
rv_n1 = _resolve_value(n1_raw)
|
||
rv_m1 = _resolve_value(m1_raw)
|
||
rv_n2 = _resolve_value(n2_raw)
|
||
rv_m2 = _resolve_value(m2_raw)
|
||
if any(rv is None for rv in (rv_n1, rv_m1, rv_n2, rv_m2)):
|
||
return []
|
||
total = (rv_n1.value * rv_m1.value) + (rv_n2.value * rv_m2.value) # type: ignore[union-attr]
|
||
entity = _normalize_entity(m.group("entity"))
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=total, unit=u1),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor").lower(),
|
||
matched_value_token=m1_raw, # provenance: first per-container M
|
||
matched_unit_token=m.group("u1"),
|
||
matched_entity_token=m.group("entity"),
|
||
# ADR-0191 — the sum of two products consumes all four tokens.
|
||
consumed_value_tokens=(n1_raw, m1_raw, n2_raw, m2_raw),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0136.S.3 — Compound initial-mutation extractor
|
||
# ---------------------------------------------------------------------------
|
||
|
||
_INIT_MUT_SUBTRACT_VERBS: Final[frozenset[str]] = frozenset({
|
||
"lost", "gave", "gave away", "used", "spent", "ate", "dropped", "sold",
|
||
})
|
||
|
||
_INIT_MUT_ADD_VERBS: Final[frozenset[str]] = frozenset({
|
||
"gained", "got", "received", "found", "earned", "picked up", "bought",
|
||
})
|
||
|
||
_INIT_MUT_VERB_PATTERN: Final[str] = (
|
||
r"(?:" + "|".join(
|
||
re.escape(v)
|
||
for v in sorted(
|
||
_INIT_MUT_SUBTRACT_VERBS | _INIT_MUT_ADD_VERBS,
|
||
key=len, reverse=True,
|
||
)
|
||
) + r")"
|
||
)
|
||
|
||
_INIT_MUTATION_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<entity>{_ENTITY})\s+(?:has|have|had)\s+"
|
||
rf"(?P<n>{_VALUE})\s+(?P<unit>\w+)"
|
||
r"(?:\s+initially)?"
|
||
r",?\s+but(?:\s+then)?\s+"
|
||
rf"(?P<verb>{_INIT_MUT_VERB_PATTERN})\s+"
|
||
rf"(?P<m>{_VALUE})"
|
||
r"\s*\.?\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _init_mutation_candidates(sentence: str) -> list[CandidateInitial]:
|
||
s = sentence.strip().rstrip(".")
|
||
m = _INIT_MUTATION_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
n_raw = m.group("n")
|
||
m_raw = m.group("m")
|
||
if _is_indefinite_quantifier(n_raw) or _is_indefinite_quantifier(m_raw):
|
||
return []
|
||
rv_n = _resolve_value(n_raw)
|
||
rv_m = _resolve_value(m_raw)
|
||
if rv_n is None or rv_m is None:
|
||
return []
|
||
verb = m.group("verb").lower()
|
||
if verb in _INIT_MUT_SUBTRACT_VERBS:
|
||
derived = rv_n.value - rv_m.value
|
||
elif verb in _INIT_MUT_ADD_VERBS:
|
||
derived = rv_n.value + rv_m.value
|
||
else:
|
||
return []
|
||
if derived < 0:
|
||
return []
|
||
unit_raw = m.group("unit")
|
||
unit = rv_n.unit_override if rv_n.unit_override is not None else _canonicalize_unit(unit_raw)
|
||
entity = _normalize_entity(m.group("entity"))
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=derived, unit=unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor="had",
|
||
matched_value_token=n_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
# ADR-0136.S.3 collapses BOTH source tokens (initial n_raw and
|
||
# mutation m_raw) into one derived initial value. Expose both so
|
||
# the ADR-0191 completeness guard (uncovered_quantities) sees the
|
||
# mutation quantity as consumed; otherwise it false-positives on
|
||
# m_raw and over-refuses a sound reading. See _candidate_consumed_tokens.
|
||
consumed_value_tokens=(n_raw, m_raw),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def _init_mutation_admitted(sentence: str) -> list[CandidateInitial]:
|
||
return _admit(_init_mutation_candidates(sentence))
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0136.S.4 — Novel initial-form extractors (Shape A + Shape B)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _init_has_indef_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Shape A — indefinite-article subject: 'A school has 100 students.'
|
||
|
||
Sibling to the _INITIAL_HAS_RE block in extract_initial_candidates.
|
||
Entity is the bare noun (lowercased); the article 'A/a' is consumed
|
||
and discarded. Same value/unit resolution and money normalization as
|
||
the definite-subject path.
|
||
"""
|
||
s = sentence.strip().rstrip(".")
|
||
m = _INITIAL_HAS_INDEF_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
value_raw = m.group("value")
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return []
|
||
noun = m.group("noun").lower()
|
||
unit_raw = m.group("unit")
|
||
if rv.unit_override is not None:
|
||
resolved_unit: str = rv.unit_override
|
||
elif unit_raw is not None:
|
||
resolved_unit = _canonicalize_unit(unit_raw)
|
||
else:
|
||
return []
|
||
value, final_unit = _money_unit_normalization(rv.value, resolved_unit)
|
||
assert final_unit is not None
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=noun,
|
||
quantity=Quantity(value=value, unit=final_unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor"),
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw if unit_raw is not None else final_unit,
|
||
matched_entity_token=noun,
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def _init_has_labeled_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""ADR-0194 — labeled-container subject: 'Jar A has 28 marbles.'
|
||
|
||
Sibling to the _INITIAL_HAS_RE block in extract_initial_candidates.
|
||
Entity is '<Noun> <label>' (label = single uppercase letter or 1-2
|
||
digits), preserved by _normalize_entity. REQUIRES a label, so a
|
||
bare subject ('Jamie has 28 marbles') never reaches here and yields
|
||
no duplicate candidate. Same value/unit resolution and money
|
||
normalization as the definite-subject path.
|
||
"""
|
||
s = sentence.strip().rstrip(".")
|
||
m = _INITIAL_HAS_LABELED_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
value_raw = m.group("value")
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return []
|
||
entity = _normalize_entity(m.group("entity"))
|
||
unit_raw = m.group("unit")
|
||
if rv.unit_override is not None:
|
||
resolved_unit: str = rv.unit_override
|
||
elif unit_raw is not None:
|
||
resolved_unit = _canonicalize_unit(unit_raw)
|
||
else:
|
||
return []
|
||
value, final_unit = _money_unit_normalization(rv.value, resolved_unit)
|
||
assert final_unit is not None
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=entity,
|
||
quantity=Quantity(value=value, unit=final_unit),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor"),
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw if unit_raw is not None else final_unit,
|
||
matched_entity_token=m.group("entity"),
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def _init_there_are_prefix_candidates(sentence: str) -> list[CandidateInitial]:
|
||
"""Shape B — prepositional-prefix existential: 'In a building, there are 100 ladies.'
|
||
|
||
Sibling to the _INITIAL_THERE_ARE_RE block in extract_initial_candidates.
|
||
Entity is the bare place noun (lowercased). The optional 'a <value>'
|
||
article (as in 'a hundred'), ordinal-floor qualifier, and participial
|
||
phrase are consumed without being captured. Same value/unit resolution
|
||
and money normalization as the standard there-are path.
|
||
"""
|
||
s = sentence.strip().rstrip(".")
|
||
m = _INITIAL_THERE_ARE_PREFIX_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
value_raw = m.group("value")
|
||
rv = _resolve_value(value_raw)
|
||
if rv is None:
|
||
return []
|
||
unit_raw = m.group("unit")
|
||
if rv.unit_override is not None:
|
||
unit_str: str = rv.unit_override
|
||
else:
|
||
unit_str = _canonicalize_unit(unit_raw)
|
||
v_norm, u_norm = _money_unit_normalization(rv.value, unit_str)
|
||
assert u_norm is not None
|
||
place = m.group("place").lower()
|
||
try:
|
||
return [
|
||
CandidateInitial(
|
||
initial=InitialPossession(
|
||
entity=place,
|
||
quantity=Quantity(value=v_norm, unit=u_norm),
|
||
),
|
||
source_span=sentence,
|
||
matched_anchor=m.group("anchor"),
|
||
matched_value_token=value_raw,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=place,
|
||
)
|
||
]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def _init_has_indef_admitted(sentence: str) -> list[CandidateInitial]:
|
||
return _admit(_init_has_indef_candidates(sentence))
|
||
|
||
|
||
def _init_there_are_prefix_admitted(sentence: str) -> list[CandidateInitial]:
|
||
return _admit(_init_there_are_prefix_candidates(sentence))
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0136.S.0 — Context-sentence classifier
|
||
# ---------------------------------------------------------------------------
|
||
|
||
_WORD_NUMBER_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"\b(?:" + "|".join(re.escape(w) for w in sorted(WORD_NUMBERS, key=len, reverse=True)) + r")\b",
|
||
re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def has_numeric_token(sentence: str) -> bool:
|
||
"""Return True if *sentence* contains any digit or closed-set word-number.
|
||
|
||
A sentence with no numeric token cannot introduce quantified initial state,
|
||
so it is safe to classify as a context filler and skip it.
|
||
"""
|
||
if re.search(r"\d", sentence):
|
||
return True
|
||
return bool(_WORD_NUMBER_RE.search(sentence))
|
||
|
||
|
||
def classify_sentence(sentence: str) -> Literal["context", "numeric_state"]:
|
||
"""Classify a statement sentence as skippable context or numeric-state-bearing.
|
||
|
||
Rule: if the sentence contains no digit and no word-number from the closed
|
||
set, it cannot introduce any parseable numeric state — classify as
|
||
``"context"`` (safe to skip). All other sentences are ``"numeric_state"``
|
||
and must either parse successfully or cause a refusal.
|
||
"""
|
||
return "context" if not has_numeric_token(sentence) else "numeric_state"
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0136.S.1 — Rate/event statement extractors (capacity + earnings)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
_TIME_UNITS_TO_SECONDS: Final[dict[str, float]] = {
|
||
"second": 1.0, "seconds": 1.0,
|
||
"minute": 60.0, "minutes": 60.0,
|
||
"hour": 3600.0, "hours": 3600.0,
|
||
"day": 86400.0, "days": 86400.0,
|
||
}
|
||
|
||
_TIME_UNIT_SET: Final[str] = (
|
||
r"(?:seconds?|minutes?|hours?|days?)"
|
||
)
|
||
|
||
|
||
def _to_seconds(count: float, unit: str) -> float:
|
||
return count * _TIME_UNITS_TO_SECONDS[unit.lower()]
|
||
|
||
|
||
# --- Shape A: capacity-rate ---
|
||
|
||
_CAPACITY_VERBS: Final[frozenset[str]] = frozenset({
|
||
"shuck", "shucks",
|
||
"pick", "picks",
|
||
"pack", "packs",
|
||
"make", "makes",
|
||
"produce", "produces",
|
||
"type", "types",
|
||
"read", "reads",
|
||
"write", "writes",
|
||
"paint", "paints",
|
||
"run", "runs",
|
||
"score", "scores",
|
||
"answer", "answers",
|
||
"complete", "completes",
|
||
})
|
||
|
||
_CAPACITY_VERB_PATTERN: Final[str] = (
|
||
r"(?:" + "|".join(
|
||
re.escape(v) for v in sorted(_CAPACITY_VERBS, key=len, reverse=True)
|
||
) + r")"
|
||
)
|
||
|
||
|
||
@dataclass(frozen=True, slots=True)
|
||
class CandidateCapacity:
|
||
actor: str
|
||
count: float
|
||
unit: str
|
||
per_count: float
|
||
per_unit: str
|
||
source_span: str
|
||
|
||
|
||
# Shape A1 (canonical): "<Actor> can <verb> N <unit> in M <time-unit>."
|
||
_CAPACITY_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ENTITY})\s+can\s+"
|
||
rf"(?P<verb>{_CAPACITY_VERB_PATTERN})\s+"
|
||
rf"(?P<count>\d+(?:\.\d+)?)\s+"
|
||
rf"(?P<unit>\w+)\s+in\s+"
|
||
rf"(?P<per_count>\d+(?:\.\d+)?)\s+"
|
||
rf"(?P<per_unit>{_TIME_UNIT_SET})"
|
||
r"(?:\s+on\s+average)?"
|
||
r"\s*\.?\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
# Shape A2 (inverted): "During M <time-unit> <Actor> can <verb> N <unit> [on average]."
|
||
_CAPACITY_INVERTED_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^[Dd]uring\s+"
|
||
rf"(?P<per_count>\d+(?:\.\d+)?)\s+"
|
||
rf"(?P<per_unit>{_TIME_UNIT_SET})\s+"
|
||
rf"(?P<actor>{_ENTITY})\s+can\s+"
|
||
rf"(?P<verb>{_CAPACITY_VERB_PATTERN})\s+"
|
||
rf"(?P<count>\d+(?:\.\d+)?)\s+"
|
||
rf"(?P<unit>\w+)"
|
||
r"(?:\s+on\s+average)?"
|
||
r"\s*\.?\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _capacity_from_match(m: re.Match[str], sentence: str) -> list[CandidateCapacity]:
|
||
verb = m.group("verb").lower()
|
||
if verb not in _CAPACITY_VERBS:
|
||
return []
|
||
count = float(m.group("count"))
|
||
per_count = float(m.group("per_count"))
|
||
if per_count <= 0 or count <= 0:
|
||
return []
|
||
return [
|
||
CandidateCapacity(
|
||
actor=m.group("actor"),
|
||
count=count,
|
||
unit=_canonicalize_unit(m.group("unit")),
|
||
per_count=per_count,
|
||
per_unit=m.group("per_unit").lower(),
|
||
source_span=sentence,
|
||
)
|
||
]
|
||
|
||
|
||
def extract_capacity_candidates(sentence: str) -> list[CandidateCapacity]:
|
||
s = sentence.strip()
|
||
m = _CAPACITY_RE.match(s)
|
||
if m is not None:
|
||
return _capacity_from_match(m, sentence)
|
||
m2 = _CAPACITY_INVERTED_RE.match(s)
|
||
if m2 is not None:
|
||
return _capacity_from_match(m2, sentence)
|
||
return []
|
||
|
||
|
||
@dataclass(frozen=True, slots=True)
|
||
class CandidateCapacityQuestion:
|
||
actor: str | None
|
||
unit: str
|
||
per_count: float
|
||
per_unit: str
|
||
source_span: str
|
||
|
||
|
||
_PRONOUN_SET: Final[str] = r"(?:he|she|they|it)"
|
||
|
||
# Q1 (canonical): "How many <unit> can <actor> <verb> in T <time-unit>?"
|
||
_CAPACITY_Q_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<unit>\w+)\s+can\s+"
|
||
rf"(?P<actor>{_ENTITY}|{_PRONOUN_SET})\s+"
|
||
rf"(?P<verb>{_CAPACITY_VERB_PATTERN})\s+in\s+"
|
||
rf"(?P<per_count>\d+(?:\.\d+)?)\s+"
|
||
rf"(?P<per_unit>{_TIME_UNIT_SET})"
|
||
r"\s*\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
# Q2 (able-form): "How many <unit> [on average] is <actor> able to <verb>,
|
||
# when the [match/game/session] lasted for T <time-unit>?"
|
||
_CAPACITY_Q2_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<unit>\w+)(?:\s+on\s+average)?\s+is\s+"
|
||
rf"(?P<actor>{_ENTITY}|{_PRONOUN_SET})\s+able\s+to\s+"
|
||
rf"(?P<verb>{_CAPACITY_VERB_PATTERN}),?\s+"
|
||
r"when\s+the\s+\w+\s+lasted\s+for\s+"
|
||
rf"(?P<per_count>\d+(?:\.\d+)?)\s+"
|
||
rf"(?P<per_unit>{_TIME_UNIT_SET})"
|
||
r"\s*\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _capacity_question_from_match(
|
||
m: re.Match[str], sentence: str
|
||
) -> list[CandidateCapacityQuestion]:
|
||
verb = m.group("verb").lower()
|
||
if verb not in _CAPACITY_VERBS:
|
||
return []
|
||
actor_raw = m.group("actor")
|
||
actor: str | None = None if actor_raw.lower() in (
|
||
"he", "she", "they", "it",
|
||
) else actor_raw
|
||
per_count = float(m.group("per_count"))
|
||
if per_count <= 0:
|
||
return []
|
||
return [
|
||
CandidateCapacityQuestion(
|
||
actor=actor,
|
||
unit=_canonicalize_unit(m.group("unit")),
|
||
per_count=per_count,
|
||
per_unit=m.group("per_unit").lower(),
|
||
source_span=sentence,
|
||
)
|
||
]
|
||
|
||
|
||
def extract_capacity_question_candidates(
|
||
sentence: str,
|
||
) -> list[CandidateCapacityQuestion]:
|
||
s = sentence.strip()
|
||
m = _CAPACITY_Q_RE.match(s)
|
||
if m is not None:
|
||
return _capacity_question_from_match(m, sentence)
|
||
m2 = _CAPACITY_Q2_RE.match(s)
|
||
if m2 is not None:
|
||
return _capacity_question_from_match(m2, sentence)
|
||
return []
|
||
|
||
|
||
# --- Shape B: earnings rate ---
|
||
|
||
_EARNINGS_VERBS: Final[frozenset[str]] = frozenset({
|
||
"make", "makes",
|
||
"earn", "earns",
|
||
"receive", "receives",
|
||
"get", "gets",
|
||
"charge", "charges",
|
||
})
|
||
|
||
_EARNINGS_VERB_PATTERN: Final[str] = (
|
||
r"(?:" + "|".join(
|
||
re.escape(v) for v in sorted(_EARNINGS_VERBS, key=len, reverse=True)
|
||
) + r")"
|
||
)
|
||
|
||
_CURRENCY_AMOUNT: Final[str] = r"\$\d+(?:\.\d{1,2})?"
|
||
|
||
_PER_TOKEN: Final[str] = (
|
||
rf"(?:per|an?|for\s+each|every)\s+(?P<per_unit>{_TIME_UNIT_SET}|\w+)"
|
||
)
|
||
|
||
|
||
@dataclass(frozen=True, slots=True)
|
||
class CandidateEarningsRate:
|
||
actor: str
|
||
amount: float
|
||
unit: str
|
||
per_unit: str
|
||
source_span: str
|
||
|
||
|
||
_EARNINGS_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^(?P<actor>{_ENTITY})\s+"
|
||
rf"(?P<verb>{_EARNINGS_VERB_PATTERN})\s+"
|
||
rf"(?P<amount>{_CURRENCY_AMOUNT})\s+"
|
||
rf"{_PER_TOKEN}"
|
||
r"\s*\.?\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def extract_earnings_candidates(sentence: str) -> list[CandidateEarningsRate]:
|
||
s = sentence.strip()
|
||
m = _EARNINGS_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
verb = m.group("verb").lower()
|
||
if verb not in _EARNINGS_VERBS:
|
||
return []
|
||
amount_raw = m.group("amount")
|
||
amount = float(amount_raw.replace("$", ""))
|
||
if amount <= 0:
|
||
return []
|
||
per_unit = m.group("per_unit").lower()
|
||
return [
|
||
CandidateEarningsRate(
|
||
actor=m.group("actor"),
|
||
amount=amount,
|
||
unit="dollar",
|
||
per_unit=per_unit,
|
||
source_span=sentence,
|
||
)
|
||
]
|
||
|
||
|
||
@dataclass(frozen=True, slots=True)
|
||
class CandidateEarningsQuestion:
|
||
actor: str
|
||
unit: str
|
||
time_count: float
|
||
time_unit: str
|
||
source_span: str
|
||
|
||
|
||
_EARNINGS_Q_VERBS: Final[str] = r"(?:make|earn|get|receive|charge)"
|
||
|
||
_EARNINGS_Q_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+much\s+(?:money|dollars?)\s+does\s+"
|
||
rf"(?P<actor>{_ENTITY})\s+"
|
||
rf"{_EARNINGS_Q_VERBS}\s+in\s+"
|
||
rf"(?P<time_count>\d+(?:\.\d+)?)\s+"
|
||
rf"(?P<time_unit>{_TIME_UNIT_SET})"
|
||
r"\s*\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def extract_earnings_question_candidates(
|
||
sentence: str,
|
||
) -> list[CandidateEarningsQuestion]:
|
||
s = sentence.strip()
|
||
m = _EARNINGS_Q_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
time_count = float(m.group("time_count"))
|
||
if time_count <= 0:
|
||
return []
|
||
return [
|
||
CandidateEarningsQuestion(
|
||
actor=m.group("actor"),
|
||
unit="dollar",
|
||
time_count=time_count,
|
||
time_unit=m.group("time_unit").lower(),
|
||
source_span=sentence,
|
||
)
|
||
]
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0136.S.2 — Conditional-op question
|
||
# ---------------------------------------------------------------------------
|
||
#
|
||
# Target shape (gsm8k-0042):
|
||
# "If <Entity> <verb> <N> <unit>, how many <unit2> does <Entity2> <verb2> left?"
|
||
#
|
||
# Routes through the parse_and_solve short-circuit: given a single matching
|
||
# initial-state candidate for (entity, unit), the answer is
|
||
# initial_value ± operand depending on verb polarity. No graph built;
|
||
# refuses on any ambiguity (unit mismatch, entity mismatch, multiple
|
||
# matching ICs, negative answer).
|
||
|
||
_COND_SUBTRACT_VERBS: Final[frozenset[str]] = frozenset({
|
||
"sell", "sells", "sold",
|
||
"give", "gives", "gave",
|
||
"eat", "eats", "ate",
|
||
"use", "uses", "used",
|
||
"lose", "loses", "lost",
|
||
"spend", "spends", "spent",
|
||
"donate", "donates", "donated",
|
||
"remove", "removes", "removed",
|
||
"take", "takes", "took",
|
||
"send", "sends", "sent",
|
||
"pay", "pays", "paid",
|
||
"drop", "drops", "dropped",
|
||
"throw", "throws", "threw",
|
||
})
|
||
|
||
_COND_ADD_VERBS: Final[frozenset[str]] = frozenset({
|
||
"buy", "buys", "bought",
|
||
"get", "gets", "got",
|
||
"receive", "receives", "received",
|
||
"find", "finds", "found",
|
||
"add", "adds", "added",
|
||
"collect", "collects", "collected",
|
||
"pick", "picks", "picked",
|
||
"earn", "earns", "earned",
|
||
"gain", "gains", "gained",
|
||
})
|
||
|
||
_COND_VERB_PATTERN: Final[str] = (
|
||
r"(?:" + "|".join(
|
||
re.escape(v)
|
||
for v in sorted(_COND_SUBTRACT_VERBS | _COND_ADD_VERBS, key=len, reverse=True)
|
||
) + r")"
|
||
)
|
||
|
||
|
||
@dataclass(frozen=True, slots=True)
|
||
class CandidateConditionalOpQuestion:
|
||
entity: str
|
||
op: Literal["add", "subtract"]
|
||
operand: float
|
||
unit: str
|
||
source_span: str
|
||
|
||
|
||
# "If <Entity> <verb> <N> <unit>, how many <unit2> does <Entity2> <aux>[ <qualifier>]?"
|
||
_COND_OP_Q_RE: Final[re.Pattern[str]] = re.compile(
|
||
rf"^If\s+(?P<entity>{_ENTITY})\s+"
|
||
rf"(?P<verb>{_COND_VERB_PATTERN})\s+"
|
||
r"(?P<n>\d+(?:\.\d+)?)\s+(?P<unit>\w+),\s+"
|
||
r"how\s+many\s+(?P<unit2>\w+)\s+does\s+"
|
||
rf"(?P<entity2>{_ENTITY})\s+(?:has|have|had)"
|
||
r"(?:\s+(?:left|now|remaining|away|in\s+total|altogether))?"
|
||
r"\s*\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def extract_conditional_op_question_candidates(
|
||
sentence: str,
|
||
) -> list[CandidateConditionalOpQuestion]:
|
||
s = sentence.strip()
|
||
m = _COND_OP_Q_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
verb = m.group("verb").lower()
|
||
if verb in _COND_SUBTRACT_VERBS:
|
||
op: Literal["add", "subtract"] = "subtract"
|
||
elif verb in _COND_ADD_VERBS:
|
||
op = "add"
|
||
else:
|
||
return []
|
||
unit = _canonicalize_unit(m.group("unit"))
|
||
unit2 = _canonicalize_unit(m.group("unit2"))
|
||
if unit != unit2:
|
||
return []
|
||
entity = _normalize_entity(m.group("entity"))
|
||
entity2 = _normalize_entity(m.group("entity2"))
|
||
if entity.lower() != entity2.lower():
|
||
return []
|
||
n = float(m.group("n"))
|
||
if n <= 0:
|
||
return []
|
||
return [
|
||
CandidateConditionalOpQuestion(
|
||
entity=entity,
|
||
op=op,
|
||
operand=n,
|
||
unit=unit,
|
||
source_span=sentence,
|
||
)
|
||
]
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0163.D.4 — Question-grammar extensions
|
||
# ---------------------------------------------------------------------------
|
||
#
|
||
# Three new question shapes extracted from the GSM8K train_sample
|
||
# post-Phase-D refusal taxonomy. Each is structurally narrow and is
|
||
# protected by three independent wrong=0 safety nets:
|
||
#
|
||
# 1. Regex narrowness — required mass-noun whitelist / comparative
|
||
# "more" anchor / pronoun-or-named-entity slot + closed action-verb
|
||
# set. Open-ended question shapes do not match.
|
||
# 2. Pronoun resolver refuse-on-ambiguity — if a problem text has two
|
||
# distinct female (or male) names, "she"/"he" cannot resolve to a
|
||
# single entity and the extraction emits no candidate.
|
||
# 3. Downstream multi-branch decision rule (math_candidate_graph) —
|
||
# when multiple admissible parses produce different answers, the
|
||
# case refuses rather than picks one.
|
||
#
|
||
# The _MASS_NOUNS whitelist is HARD: extending it requires a separate
|
||
# ADR. The pronoun gender name lists are SMALL and DOCUMENTED, sourced
|
||
# from GSM8K train_sample observation.
|
||
|
||
# Pattern A — mass-noun question shape.
|
||
# Narrow whitelist of high-signal mass nouns observed in GSM8K
|
||
# train_sample. Extending this set is a separate ADR; this prevents
|
||
# incremental drift into "anything that looks like a mass noun."
|
||
_MASS_NOUNS: Final[frozenset[str]] = frozenset({
|
||
"money", "profit", "interest", "income",
|
||
"savings", "cost", "amount", "total",
|
||
})
|
||
|
||
_MASS_NOUN_PATTERN: Final[str] = (
|
||
r"(?:" + "|".join(sorted(_MASS_NOUNS, key=len, reverse=True)) + r")"
|
||
)
|
||
|
||
# Subject pronouns for Pattern C resolution.
|
||
_Q_SUBJECT_PRONOUN: Final[str] = r"(?:she|he|they|it)"
|
||
|
||
# Entity slot widened to also admit a subject pronoun (resolved
|
||
# downstream via _resolve_pronoun_entity).
|
||
_Q_ENTITY_OR_PRONOUN: Final[str] = rf"(?:{_ENTITY}|{_Q_SUBJECT_PRONOUN})"
|
||
|
||
# Pattern A verb set: what the entity DOES with the mass noun.
|
||
_PATTERN_A_VERBS: Final[str] = (
|
||
r"(?:make|makes|made|earn|earns|earned|save|saved|saves|"
|
||
r"spend|spends|spent|cost|costs|need|needs|have|has|had|"
|
||
r"gain|gains|gained|pay|paid|pays)"
|
||
)
|
||
|
||
# Pattern B verb set: comparative-need style verbs.
|
||
_PATTERN_B_VERBS: Final[str] = (
|
||
r"(?:need|needs|needed|require|requires|required|"
|
||
r"have|has|had|gain|gains|gained)"
|
||
)
|
||
|
||
# Pattern C verb set: action verbs admitted in non-"have" pronoun
|
||
# position. Closed whitelist of high-signal action verbs observed in
|
||
# GSM8K train_sample. Conservative — extending this requires evidence
|
||
# from refused cases.
|
||
_PATTERN_C_VERBS: Final[str] = (
|
||
r"(?:need|needs|sell|sells|make|makes|pick|picks|"
|
||
r"buy|buys|use|uses|want|wants)"
|
||
)
|
||
|
||
|
||
# Optional "of <NP>" qualifier between unit and aux. Bounded NP:
|
||
# bare noun, "<adj> <noun>", or two-word compound. The qualifier
|
||
# itself does not flow into the candidate; the unit token alone is
|
||
# the canonical unit (matching the math_parser convention for
|
||
# "cups of lemonade" / "boxes of crayons").
|
||
_Q_OF_NP_TAIL: Final[str] = r"(?:\s+of\s+\w+(?:\s+\w+)?)?"
|
||
|
||
|
||
_Q_KEEP_ON_HAND_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+much\s+does\s+"
|
||
rf"(?P<entity>(?:{_ENTITY}|she|he|they|it))\s+"
|
||
r"keep\s+on\s+hand\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
_INFER_PARTITION_COUNT_UNIT_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"into\s+\d+\s*-\s*\w+\s+(sections?|pieces?|parts?)",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _infer_partition_count_unit(problem_text: str | None) -> str | None:
|
||
"""Infer partition result_unit from a prior chunking stmt in the problem."""
|
||
if problem_text is None:
|
||
return None
|
||
m = _INFER_PARTITION_COUNT_UNIT_RE.search(problem_text)
|
||
if m is None:
|
||
return None
|
||
return _canonicalize_unit(m.group(1))
|
||
|
||
|
||
# Gate A2c — production yield question + conditional rate clause.
|
||
_YIELD_RATE_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"(?i)if\s+(?P<n>\d+(?:\.\d+)?)\s+(?P<unit>\w+)\s+"
|
||
r"(?:are|is)\s+used\s+to\s+make\s+one\s+(?P<product>\w+)"
|
||
)
|
||
_Q_YIELD_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<product>\w+)\s+will\s+"
|
||
rf"(?P<entity>{_ENTITY})\s+"
|
||
r"be\s+able\s+to\s+make"
|
||
r"(?:\s+out\s+of\s+.*?)?\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def _infer_yield_partition(
|
||
problem_text: str | None, question_product: str
|
||
) -> tuple[float, str, str, tuple[str, ...]] | None:
|
||
"""Infer yield partition + consumed rate tokens from a conditional clause."""
|
||
if problem_text is None:
|
||
return None
|
||
m = _YIELD_RATE_RE.search(problem_text)
|
||
if m is None:
|
||
return None
|
||
product_unit = _canonicalize_unit(m.group("product"))
|
||
if product_unit != _canonicalize_unit(question_product):
|
||
return None
|
||
inventory_unit = _canonicalize_unit(m.group("unit"))
|
||
if inventory_unit == product_unit:
|
||
return None
|
||
chunk_size = float(m.group("n"))
|
||
if chunk_size <= 0:
|
||
return None
|
||
return chunk_size, inventory_unit, product_unit, (m.group("n"), "one")
|
||
|
||
|
||
_Q_MASS_NOUN_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+much\s+"
|
||
rf"(?P<unit>{_MASS_NOUN_PATTERN})"
|
||
rf"{_Q_OF_NP_TAIL}\s+"
|
||
r"(?:will|did|does|do|would)\s+"
|
||
rf"(?P<entity>{_Q_ENTITY_OR_PRONOUN})\s+"
|
||
rf"(?:have\s+earned\s+|be\s+able\s+to\s+)?{_PATTERN_A_VERBS}"
|
||
r"(?:\s+.*?)?\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
_Q_COMPARATIVE_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+more\s+"
|
||
r"(?P<unit>\w+)"
|
||
rf"{_Q_OF_NP_TAIL}\s+"
|
||
r"(?:does|do|would|will|did)\s+"
|
||
rf"(?P<entity>{_Q_ENTITY_OR_PRONOUN})\s+"
|
||
rf"{_PATTERN_B_VERBS}"
|
||
r"(?:\s+.*?)?\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
# Pattern C — pronoun entity in non-"have" verb position.
|
||
# The verb slot is a NARROW closed set of action verbs; an optional
|
||
# "to <VERB>" infinitive tail consumes constructions like "need to sell".
|
||
_Q_PRONOUN_VERB_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"^How\s+many\s+(?P<unit>\w+)"
|
||
rf"{_Q_OF_NP_TAIL}\s+"
|
||
r"(?:does|do|will|did|would)\s+"
|
||
rf"(?P<entity>{_Q_ENTITY_OR_PRONOUN})\s+"
|
||
rf"{_PATTERN_C_VERBS}"
|
||
r"(?:\s+to\s+\w+)?"
|
||
r"(?:\s+.*?)?\??\s*$",
|
||
flags=re.IGNORECASE,
|
||
)
|
||
|
||
|
||
# Pronoun → name lookup lists. Closed-set whitelists sourced from
|
||
# GSM8K train_sample observation. Adding a name requires evidence
|
||
# from a refused case. Lower-cased; names are matched case-insensitively
|
||
# against problem-text mentions.
|
||
_FEMALE_NAMES: Final[frozenset[str]] = frozenset({
|
||
"alexa", "alice", "amy", "ann", "anna", "barbara", "betty",
|
||
"carol", "carolyn", "christine", "cindy", "claire", "cynthia",
|
||
"deborah", "diana", "donna", "dorothy", "elizabeth", "ella",
|
||
"emily", "emma", "erica", "francine", "helen", "jan", "jane", "janet",
|
||
"jen", "jennifer", "jessica", "joyce", "judith", "julie", "karen",
|
||
"kate", "kathleen", "kelly", "laura", "linda", "lisa", "lilibeth",
|
||
"lori", "mandy", "marie", "martha", "marnie", "mary", "melissa",
|
||
"nancy", "nicole", "pamela", "patricia", "rachel", "rebecca",
|
||
"ruth", "sandra", "sarah", "sharon", "shirley", "stephanie",
|
||
"susan", "tina", "virginia",
|
||
})
|
||
|
||
_MALE_NAMES: Final[frozenset[str]] = frozenset({
|
||
"aaron", "adam", "alan", "albert", "andrew", "anthony", "arthur",
|
||
"benjamin", "bob", "brian", "bruce", "carl", "carson", "charles",
|
||
"christopher", "daniel", "david", "dennis", "donald", "douglas",
|
||
"edward", "eric", "ethan", "eugene", "fabian", "frank", "gary",
|
||
"george", "gerald", "gregory", "harold", "harry", "henry", "jack",
|
||
"james", "jason", "jeffrey", "jeremy", "jerry", "jesse", "john",
|
||
"jonathan", "joseph", "joshua", "keith", "kenneth", "kevin",
|
||
"larry", "lawrence", "mark", "matthew", "michael", "nathan",
|
||
"nicholas", "patrick", "paul", "peter", "philip", "ralph",
|
||
"raymond", "richard", "robert", "roger", "ronald", "ryan",
|
||
"samuel", "scott", "sean", "stephen", "steven", "thomas",
|
||
"timothy", "tom", "walter", "wayne", "william",
|
||
})
|
||
|
||
|
||
# Title-cased proper-noun mention extractor. Matches any sequence
|
||
# starting with an upper-case letter followed by lower-case letters,
|
||
# avoiding all-caps tokens (acronyms) which are unlikely to be names.
|
||
_PROPER_NOUN_MENTION_RE: Final[re.Pattern[str]] = re.compile(
|
||
r"\b([A-Z][a-z]+)\b"
|
||
)
|
||
|
||
|
||
def _resolve_pronoun_entity(
|
||
pronoun: str, problem_text: str | None
|
||
) -> str | None:
|
||
"""Resolve a subject pronoun to a single unambiguous named entity.
|
||
|
||
Pure, deterministic, no global state. Returns the canonical
|
||
Title-cased entity name when a SINGLE unambiguous match exists;
|
||
returns ``None`` on ambiguity, no-match, or empty problem_text.
|
||
|
||
Heuristic limits:
|
||
- ``she``/``her`` match female names from :data:`_FEMALE_NAMES`.
|
||
- ``he``/``his``/``him`` match male names from :data:`_MALE_NAMES`.
|
||
- ``they`` is plural; refuses (no single-entity resolution).
|
||
- ``it`` is neuter; refuses (not safely resolvable from name lists).
|
||
- If the problem text contains >1 distinct female (or male) name,
|
||
"she" (or "he") cannot resolve unambiguously → refuse.
|
||
|
||
Refuse-on-ambiguity preserves wrong=0: better to refuse a question
|
||
than admit one with the wrong entity.
|
||
"""
|
||
if not problem_text:
|
||
return None
|
||
p = pronoun.lower()
|
||
if p in ("they", "it"):
|
||
return None # Plural/neuter — outside scope.
|
||
if p in ("she", "her"):
|
||
whitelist = _FEMALE_NAMES
|
||
elif p in ("he", "his", "him"):
|
||
whitelist = _MALE_NAMES
|
||
else:
|
||
return None
|
||
distinct: list[str] = []
|
||
for m in _PROPER_NOUN_MENTION_RE.finditer(problem_text):
|
||
name = m.group(1)
|
||
if name.lower() not in whitelist:
|
||
continue
|
||
if name not in distinct:
|
||
distinct.append(name)
|
||
if len(distinct) != 1:
|
||
# Zero matches → no candidate; >1 distinct → ambiguous → refuse.
|
||
return None
|
||
return distinct[0]
|
||
|
||
|
||
def _resolve_question_entity(
|
||
raw_entity: str, problem_text: str | None
|
||
) -> tuple[str, str] | None:
|
||
"""Return ``(canonical_entity, matched_entity_token)`` or None.
|
||
|
||
Wraps :func:`_resolve_pronoun_entity` for pronoun slots; passes
|
||
proper-noun entities through unchanged. ``None`` means the
|
||
candidate should not be emitted (pronoun unresolvable).
|
||
|
||
For a resolved pronoun the ``matched_entity_token`` is the LITERAL
|
||
surface pronoun ("she", "he", ...). The downstream question-
|
||
admissibility check requires the token to appear in the source-
|
||
span (the question sentence); the resolved canonical name does not.
|
||
"""
|
||
lower = raw_entity.strip().lower()
|
||
if lower in ("she", "her", "he", "his", "him", "they", "it"):
|
||
resolved = _resolve_pronoun_entity(lower, problem_text)
|
||
if resolved is None:
|
||
return None
|
||
return _normalize_entity(resolved), raw_entity
|
||
return _normalize_entity(raw_entity), raw_entity
|
||
|
||
|
||
def _pattern_yield_question_candidates(
|
||
sentence: str, problem_text: str | None
|
||
) -> list[CandidateUnknown]:
|
||
"""Gate A2c — quotient question after inventory + per-unit yield rate."""
|
||
s = sentence.strip()
|
||
m = _Q_YIELD_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
product_raw = m.group("product")
|
||
partition = _infer_yield_partition(problem_text, product_raw)
|
||
if partition is None:
|
||
return []
|
||
chunk_size, inventory_unit, product_unit, rate_tokens = partition
|
||
entity = _normalize_entity(m.group("entity"))
|
||
return [
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=entity, unit=product_unit),
|
||
source_span=sentence,
|
||
matched_unit_token=product_raw,
|
||
matched_entity_token=m.group("entity"),
|
||
yield_chunk_value=chunk_size,
|
||
yield_chunk_unit=inventory_unit,
|
||
yield_quotient_unit=product_unit,
|
||
consumed_value_tokens=rate_tokens,
|
||
)
|
||
]
|
||
|
||
|
||
def _pattern_d_keep_on_hand_candidates(
|
||
sentence: str, problem_text: str | None
|
||
) -> list[CandidateUnknown]:
|
||
"""Gate A2b — terminal possession after partition+fraction chain."""
|
||
s = sentence.strip()
|
||
m = _Q_KEEP_ON_HAND_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
unit = _infer_partition_count_unit(problem_text)
|
||
if unit is None:
|
||
return []
|
||
raw_entity = m.group("entity")
|
||
resolved = _resolve_question_entity(raw_entity, problem_text)
|
||
if resolved is None:
|
||
return []
|
||
entity, entity_token = resolved
|
||
return [
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=entity, unit=unit),
|
||
source_span=sentence,
|
||
matched_unit_token=unit,
|
||
matched_entity_token=entity_token,
|
||
)
|
||
]
|
||
|
||
|
||
def _pattern_a_mass_noun_candidates(
|
||
sentence: str, problem_text: str | None
|
||
) -> list[CandidateUnknown]:
|
||
"""Pattern A — "How much MASS_NOUN does ENTITY VERB ..." question."""
|
||
s = sentence.strip()
|
||
m = _Q_MASS_NOUN_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
unit_raw = m.group("unit")
|
||
# Mass nouns are pre-whitelisted; preserve literal token for
|
||
# downstream grounding (no hidden normalization).
|
||
unit = unit_raw.lower()
|
||
raw_entity = m.group("entity")
|
||
resolved = _resolve_question_entity(raw_entity, problem_text)
|
||
if resolved is None:
|
||
return []
|
||
entity, entity_token = resolved
|
||
return [
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=entity, unit=unit),
|
||
source_span=sentence,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=entity_token,
|
||
)
|
||
]
|
||
|
||
|
||
def _pattern_b_comparative_candidates(
|
||
sentence: str, problem_text: str | None
|
||
) -> list[CandidateUnknown]:
|
||
"""Pattern B — "How many more UNIT does ENTITY VERB ..." question.
|
||
|
||
wrong=0 GATE: comparative quantification ("how many MORE X are
|
||
needed beyond current state") is structurally distinct from the
|
||
plain ``How many X does ENTITY have?`` shape the existing solver
|
||
resolves. If the parser emits a CandidateUnknown for Pattern B,
|
||
the downstream solver computes the entity's current total — which
|
||
is the WRONG answer for a comparative question (off by the missing
|
||
delta). Until the solver gains comparative semantics (D.5
|
||
follow-up), this extractor recognises the shape but emits NO
|
||
candidate, forcing a clean refusal. The regex + marker field +
|
||
detection helper are retained for D.5 wiring.
|
||
"""
|
||
s = sentence.strip()
|
||
if _Q_COMPARATIVE_RE.match(s) is None:
|
||
return []
|
||
# Detection-only: see docstring. D.5 will add solver semantics.
|
||
return []
|
||
|
||
|
||
def _pattern_b_detects(sentence: str) -> bool:
|
||
"""ADR-0163.D.4 — pure-grammar Pattern B detector for tests.
|
||
|
||
Returns True iff the comparative-quantifier shape matches. Has
|
||
no side effects on candidate emission.
|
||
"""
|
||
return _Q_COMPARATIVE_RE.match(sentence.strip()) is not None
|
||
|
||
|
||
def _pattern_c_pronoun_verb_candidates(
|
||
sentence: str, problem_text: str | None
|
||
) -> list[CandidateUnknown]:
|
||
"""Pattern C — "How many UNIT does PRONOUN VERB [to VERB2] ..." question.
|
||
|
||
Admits the entity slot to either a proper-noun ENTITY or a subject
|
||
pronoun resolved via :func:`_resolve_pronoun_entity`. Narrow verb
|
||
whitelist (:data:`_PATTERN_C_VERBS`) bounds the question shape.
|
||
"""
|
||
s = sentence.strip()
|
||
m = _Q_PRONOUN_VERB_RE.match(s)
|
||
if m is None:
|
||
return []
|
||
unit_raw = m.group("unit")
|
||
unit = _canonicalize_unit(unit_raw)
|
||
raw_entity = m.group("entity")
|
||
resolved = _resolve_question_entity(raw_entity, problem_text)
|
||
if resolved is None:
|
||
return []
|
||
entity, entity_token = resolved
|
||
return [
|
||
CandidateUnknown(
|
||
unknown=Unknown(entity=entity, unit=unit),
|
||
source_span=sentence,
|
||
matched_unit_token=unit_raw,
|
||
matched_entity_token=entity_token,
|
||
)
|
||
]
|