feat(derivation): Workstream A inc 3 — support 'one' connector in rate_with_currency injector (post docs ratification)
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7 changed files with 830 additions and 284 deletions
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@ -57,91 +57,214 @@ from generate.math_problem_graph import Comparison, Operation, Quantity, Rate
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# Surface verbs that grammatically place the actor as the *gainer* of the
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# operand quantity. Past tense and present tense both registered.
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ADD_VERBS: Final[frozenset[str]] = frozenset({
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# acquisition
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"buy", "buys", "bought",
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"get", "gets", "got",
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"find", "finds", "found",
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"receive", "receives", "received",
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"earn", "earns", "earned",
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"add", "adds", "added",
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"pick", "picks", "picked", # "picks up N"
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"collect", "collects", "collected",
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"gather", "gathers", "gathered",
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"catch", "catches", "caught",
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"save", "saves", "saved",
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# production (actor creates instances of the unit)
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"bake", "bakes", "baked",
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"make", "makes", "made",
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"cook", "cooks", "cooked",
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"slice", "slices", "sliced",
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"pack", "packs", "packed",
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"build", "builds", "built",
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"grow", "grows", "grew",
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})
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ADD_VERBS: Final[frozenset[str]] = frozenset(
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{
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# acquisition
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"buy",
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"buys",
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"bought",
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"get",
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"gets",
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"got",
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"find",
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"finds",
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"found",
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"receive",
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"receives",
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"received",
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"earn",
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"earns",
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"earned",
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"add",
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"adds",
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"added",
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"pick",
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"picks",
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"picked", # "picks up N"
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"collect",
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"collects",
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"collected",
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"gather",
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"gathers",
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"gathered",
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"catch",
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"catches",
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"caught",
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"save",
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"saves",
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"saved",
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# production (actor creates instances of the unit)
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"bake",
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"bakes",
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"baked",
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"make",
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"makes",
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"made",
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"cook",
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"cooks",
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"cooked",
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"slice",
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"slices",
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"sliced",
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"pack",
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"packs",
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"packed",
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"build",
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"builds",
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"built",
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"grow",
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"grows",
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"grew",
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}
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)
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# Surface verbs that grammatically place the actor as the *loser* of the
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# operand quantity.
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SUBTRACT_VERBS: Final[frozenset[str]] = frozenset({
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"eat", "eats", "ate",
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"lose", "loses", "lost",
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"sell", "sells", "sold",
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"donate", "donates", "donated",
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"use", "uses", "used",
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"spend", "spends", "spent",
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"drop", "drops", "dropped",
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"remove", "removes", "removed",
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"break", "breaks", "broke",
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"destroy", "destroys", "destroyed",
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"throw", "throws", "threw", # "throws out N"
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"discard", "discards", "discarded",
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"return", "returns", "returned", # ambiguous — see TRANSFER_VERBS
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"consume", "consumes", "consumed",
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"give", "gives", "gave", # ambiguous — see TRANSFER_VERBS
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"send", "sends", "sent", # ambiguous — see TRANSFER_VERBS
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})
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SUBTRACT_VERBS: Final[frozenset[str]] = frozenset(
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{
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"eat",
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"eats",
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"ate",
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"lose",
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"loses",
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"lost",
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"sell",
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"sells",
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"sold",
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"donate",
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"donates",
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"donated",
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"use",
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"uses",
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"used",
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"spend",
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"spends",
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"spent",
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"drop",
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"drops",
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"dropped",
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"remove",
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"removes",
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"removed",
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"break",
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"breaks",
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"broke",
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"destroy",
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"destroys",
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"destroyed",
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"throw",
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"throws",
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"threw", # "throws out N"
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"discard",
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"discards",
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"discarded",
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"return",
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"returns",
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"returned", # ambiguous — see TRANSFER_VERBS
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"consume",
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"consumes",
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"consumed",
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"give",
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"gives",
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"gave", # ambiguous — see TRANSFER_VERBS
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"send",
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"sends",
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"sent", # ambiguous — see TRANSFER_VERBS
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}
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)
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# Surface verbs that grammatically place the actor as the *sender* and a
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# named target as the *receiver*. These verbs ALSO appear in SUBTRACT_VERBS
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# because the same surface token can take a transfer reading (with target)
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# or a subtract reading (without target) — both candidates fire and the
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# decision rule picks based on whether a target slot was grounded.
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TRANSFER_VERBS: Final[frozenset[str]] = frozenset({
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"give", "gives", "gave",
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"send", "sends", "sent",
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"hand", "hands", "handed",
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"pass", "passes", "passed",
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"mail", "mails", "mailed",
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"deliver", "delivers", "delivered",
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"return", "returns", "returned",
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})
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TRANSFER_VERBS: Final[frozenset[str]] = frozenset(
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{
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"give",
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"gives",
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"gave",
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"send",
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"sends",
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"sent",
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"hand",
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"hands",
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"handed",
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"pass",
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"passes",
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"passed",
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"mail",
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"mails",
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"mailed",
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"deliver",
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"delivers",
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"delivered",
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"return",
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"returns",
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"returned",
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}
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)
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MULTIPLY_VERBS: Final[frozenset[str]] = frozenset({
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"double", "doubles", "doubled",
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"triple", "triples", "tripled",
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"quadruple", "quadruples", "quadrupled",
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"multiply", "multiplies", "multiplied",
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})
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MULTIPLY_VERBS: Final[frozenset[str]] = frozenset(
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{
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"double",
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"doubles",
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"doubled",
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"triple",
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"triples",
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"tripled",
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"quadruple",
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"quadruples",
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"quadrupled",
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"multiply",
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"multiplies",
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"multiplied",
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}
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)
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DIVIDE_VERBS: Final[frozenset[str]] = frozenset({
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"halve", "halves", "halved",
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"split", "splits", "split",
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"divide", "divides", "divided",
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"share", "shares", "shared",
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})
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DIVIDE_VERBS: Final[frozenset[str]] = frozenset(
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{
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"halve",
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"halves",
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"halved",
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"split",
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"splits",
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"split",
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"divide",
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"divides",
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"divided",
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"share",
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"shares",
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"shared",
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}
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)
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# Comparison "verbs" — the surface anchor for compare_additive /
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# compare_multiplicative is usually 'has'/'have' + comparator phrase
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# ('N more than', 'twice as many as', etc.). The matched_verb slot for
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# comparison candidates carries the comparator phrase head ('more',
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# 'fewer', 'twice', 'times', 'half').
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COMPARE_ADDITIVE_ANCHORS: Final[frozenset[str]] = frozenset({
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"more", "fewer", "less", "additional", "extra",
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})
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COMPARE_MULTIPLICATIVE_ANCHORS: Final[frozenset[str]] = frozenset({
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"twice", "thrice", "times", "half", "double", "triple",
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"quadruple", "third", "quarter",
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})
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COMPARE_ADDITIVE_ANCHORS: Final[frozenset[str]] = frozenset(
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{
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"more",
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"fewer",
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"less",
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"additional",
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"extra",
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}
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)
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COMPARE_MULTIPLICATIVE_ANCHORS: Final[frozenset[str]] = frozenset(
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{
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"twice",
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"thrice",
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"times",
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"half",
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"double",
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"triple",
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"quadruple",
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"third",
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"quarter",
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}
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)
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# Rate anchors (ADR-0122): "per", "each", "every", "a"/"an" (when followed
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# by a unit in a rate surface such as "$18 an hour" or "$2 a cup").
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@ -149,9 +272,16 @@ COMPARE_MULTIPLICATIVE_ANCHORS: Final[frozenset[str]] = frozenset({
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# so that roundtrip_admissible / CandidateOperation post-init grounding
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# succeeds. "a"/"an" were documented in the comment but missing from the
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# set; added here (Inc 2) with corresponding injector tests.
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RATE_ANCHORS: Final[frozenset[str]] = frozenset({
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"per", "each", "every", "a", "an",
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})
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RATE_ANCHORS: Final[frozenset[str]] = frozenset(
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{
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"per",
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"each",
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"every",
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"a",
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"an",
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"one",
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}
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)
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KIND_TO_VERBS: Final[Mapping[str, frozenset[str]]] = {
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@ -172,15 +302,40 @@ KIND_TO_VERBS: Final[Mapping[str, frozenset[str]]] = {
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# ---------------------------------------------------------------------------
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WORD_NUMBERS: Final[Mapping[str, int]] = {
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"zero": 0, "one": 1, "two": 2, "three": 3, "four": 4,
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"five": 5, "six": 6, "seven": 7, "eight": 8, "nine": 9,
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"ten": 10, "eleven": 11, "twelve": 12, "thirteen": 13,
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"fourteen": 14, "fifteen": 15, "sixteen": 16, "seventeen": 17,
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"eighteen": 18, "nineteen": 19, "twenty": 20, "thirty": 30,
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"forty": 40, "fifty": 50, "sixty": 60, "seventy": 70,
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"eighty": 80, "ninety": 90, "hundred": 100, "thousand": 1000,
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"zero": 0,
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"one": 1,
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"two": 2,
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"three": 3,
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"four": 4,
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"five": 5,
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"six": 6,
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"seven": 7,
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"eight": 8,
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"nine": 9,
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"ten": 10,
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"eleven": 11,
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"twelve": 12,
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"thirteen": 13,
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"fourteen": 14,
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"fifteen": 15,
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"sixteen": 16,
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"seventeen": 17,
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"eighteen": 18,
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"nineteen": 19,
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"twenty": 20,
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"thirty": 30,
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"forty": 40,
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"fifty": 50,
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"sixty": 60,
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"seventy": 70,
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"eighty": 80,
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"ninety": 90,
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"hundred": 100,
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"thousand": 1000,
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# ordinals as factor-bearing forms ("a third", "a quarter")
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"half": 2, "third": 3, "quarter": 4,
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"half": 2,
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"third": 3,
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"quarter": 4,
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}
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@ -188,6 +343,7 @@ WORD_NUMBERS: Final[Mapping[str, int]] = {
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# Public dataclass — what the candidate-graph parser will emit per match.
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True, slots=True)
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class CandidateOperation:
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"""An Operation candidate plus the source-span provenance proving it.
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@ -233,29 +389,26 @@ class CandidateOperation:
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raise ValueError("CandidateOperation.source_span must be non-empty")
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if not isinstance(self.matched_verb, str) or not self.matched_verb:
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raise ValueError("CandidateOperation.matched_verb must be non-empty")
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if not isinstance(self.matched_actor_token, str) or not self.matched_actor_token:
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raise ValueError(
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"CandidateOperation.matched_actor_token must be non-empty"
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)
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if (
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not isinstance(self.matched_actor_token, str)
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or not self.matched_actor_token
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):
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raise ValueError("CandidateOperation.matched_actor_token must be non-empty")
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if self.op.kind == "transfer":
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if not self.matched_target_token:
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raise ValueError(
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"matched_target_token required when op.kind='transfer'"
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)
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elif self.matched_target_token is not None:
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raise ValueError(
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"matched_target_token only valid when op.kind='transfer'"
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)
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raise ValueError("matched_target_token only valid when op.kind='transfer'")
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if isinstance(self.op.operand, Comparison):
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if not self.matched_reference_actor_token:
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raise ValueError(
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"matched_reference_actor_token required when operand is "
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"Comparison"
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"matched_reference_actor_token required when operand is Comparison"
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)
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elif self.matched_reference_actor_token is not None:
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raise ValueError(
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"matched_reference_actor_token only valid when operand is "
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"Comparison"
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"matched_reference_actor_token only valid when operand is Comparison"
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)
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@ -378,6 +531,7 @@ def _value_grounds(value_token: str, haystack_tokens: frozenset[str]) -> bool:
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if "-" in value_token and not value_token[0].isdigit():
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try:
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from language_packs.numerics_loader import parse_compound_cardinal
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parsed = parse_compound_cardinal(value_token)
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if parsed is not None:
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components = [c for c in value_token.lower().split("-") if c]
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@ -397,6 +551,7 @@ def _value_grounds(value_token: str, haystack_tokens: frozenset[str]) -> bool:
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# through to the hard-coded table.
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try:
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from language_packs.loader import lookup_cardinal
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entry = lookup_cardinal(lowered)
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if entry is not None:
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digit = str(entry.numeric_value)
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@ -421,6 +576,7 @@ def _value_grounds(value_token: str, haystack_tokens: frozenset[str]) -> bool:
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# Pack-backed reverse lookup: digit -> cardinal surface in haystack
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try:
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from language_packs.loader import lookup_cardinal
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for tok in haystack_tokens:
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entry = lookup_cardinal(tok)
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if entry is not None and entry.numeric_value == n:
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@ -434,6 +590,7 @@ def _value_grounds(value_token: str, haystack_tokens: frozenset[str]) -> bool:
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# The load-bearing primitive.
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# ---------------------------------------------------------------------------
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def roundtrip_admissible(c: CandidateOperation) -> bool:
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"""True iff every content slot in ``c`` grounds in ``c.source_span``
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AND the matched verb is registered for the operation kind.
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@ -462,7 +619,10 @@ def roundtrip_admissible(c: CandidateOperation) -> bool:
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# Skipped only for multiplicative comparison anchors that carry
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# the factor implicitly ("twice", "half", "thrice") — those use
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# the anchor itself as the value token and pass via step (2).
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if c.op.kind == "compare_multiplicative" and c.matched_value_token == c.matched_verb:
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if (
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c.op.kind == "compare_multiplicative"
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and c.matched_value_token == c.matched_verb
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):
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pass # anchor already grounded by verb check
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elif not _value_grounds(c.matched_value_token, haystack):
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return False
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@ -248,9 +248,7 @@ def inject_discrete_count_statement(
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anchor, sentence
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)
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elif anchor_kind == "acquisition":
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cand = _build_operation_from_discrete_count_acquisition(
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anchor, sentence
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)
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cand = _build_operation_from_discrete_count_acquisition(anchor, sentence)
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else:
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# Unknown anchor_kind — under-admit. Future widenings (e.g.
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# "depletion" verbs as CandidateOperation(subtract)) extend
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@ -296,10 +294,13 @@ def _build_initial_from_discrete_count(
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counted_noun = anchor.get("counted_noun")
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if (
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not isinstance(subject_role, str) or not subject_role
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or not isinstance(count_token, str) or not count_token
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not isinstance(subject_role, str)
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or not subject_role
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or not isinstance(count_token, str)
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or not count_token
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or not isinstance(count_kind, str)
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or not isinstance(counted_noun, str) or not counted_noun
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or not isinstance(counted_noun, str)
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or not counted_noun
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):
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return None
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@ -390,11 +391,15 @@ def _build_operation_from_discrete_count_acquisition(
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verb_token = anchor.get("verb_token")
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||||
if (
|
||||
not isinstance(subject_role, str) or not subject_role
|
||||
or not isinstance(count_token, str) or not count_token
|
||||
not isinstance(subject_role, str)
|
||||
or not subject_role
|
||||
or not isinstance(count_token, str)
|
||||
or not count_token
|
||||
or not isinstance(count_kind, str)
|
||||
or not isinstance(counted_noun, str) or not counted_noun
|
||||
or not isinstance(verb_token, str) or not verb_token
|
||||
or not isinstance(counted_noun, str)
|
||||
or not counted_noun
|
||||
or not isinstance(verb_token, str)
|
||||
or not verb_token
|
||||
):
|
||||
return None
|
||||
|
||||
|
|
@ -461,10 +466,7 @@ def _count_token_followed_by_times(sentence: str, count_token: str) -> bool:
|
|||
admitting path.
|
||||
"""
|
||||
target = count_token.lower()
|
||||
tokens = [
|
||||
raw.strip(".,;:!?\"'()[]{}").lower()
|
||||
for raw in sentence.split()
|
||||
]
|
||||
tokens = [raw.strip(".,;:!?\"'()[]{}").lower() for raw in sentence.split()]
|
||||
for i, tok in enumerate(tokens[:-1]):
|
||||
if tok == target and tokens[i + 1] == "times":
|
||||
return True
|
||||
|
|
@ -520,9 +522,11 @@ def _locate_possession_verb(sentence: str) -> str | None:
|
|||
# registers its injector. No global state, no side effects.
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_WAVE_A_INJECTABLE_ANCHOR_KINDS: frozenset[str] = frozenset({
|
||||
"multiplicative_aggregate_each_weighing",
|
||||
})
|
||||
_WAVE_A_INJECTABLE_ANCHOR_KINDS: frozenset[str] = frozenset(
|
||||
{
|
||||
"multiplicative_aggregate_each_weighing",
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def inject_multiplicative_aggregation(
|
||||
|
|
@ -591,7 +595,7 @@ def _locate_rate_verb(sentence: str) -> str | None:
|
|||
apply_rate. The literal form is required so CandidateOperation
|
||||
post-init + roundtrip_admissible grounding checks pass.
|
||||
"""
|
||||
rate_verbs = ("per", "each", "every", "a", "an")
|
||||
rate_verbs = ("per", "each", "every", "a", "an", "one")
|
||||
for raw in sentence.split():
|
||||
tok = raw.strip(".,;:!?\"'()[]{}").lower()
|
||||
if tok in rate_verbs:
|
||||
|
|
@ -670,11 +674,17 @@ def inject_rate_with_currency(
|
|||
# No whole-sentence fallback is allowed, because _locate_rate_verb
|
||||
# can still pick an unrelated earlier "a".
|
||||
rate_anchor_token = anchor.get("rate_anchor_token")
|
||||
if not rate_anchor_token or rate_anchor_token not in ("per", "each", "every", "a", "an"):
|
||||
# Missing or invalid connector for this rate surface (e.g. "one"
|
||||
# from "for one cup", or absent token). Refuse — do not emit
|
||||
# a CandidateOperation with a verb that does not belong to the
|
||||
# matched rate expression.
|
||||
if not rate_anchor_token or rate_anchor_token not in (
|
||||
"per",
|
||||
"each",
|
||||
"every",
|
||||
"a",
|
||||
"an",
|
||||
"one",
|
||||
):
|
||||
# Missing or invalid connector for this rate surface (e.g. absent
|
||||
# token). "one" (from "for one cup") is now supported (Inc 3).
|
||||
# Refuse on anything else.
|
||||
return ()
|
||||
verb_token = rate_anchor_token
|
||||
|
||||
|
|
|
|||
|
|
@ -39,18 +39,51 @@ from generate.recognizer_registry import RatifiedRecognizer
|
|||
# multipliers ("dozen"). Mirrors the Phase A categorizer's
|
||||
# _NUMBER_WORDS so the matcher's "has any quantity marker" predicate
|
||||
# is the same shape as Phase A's "has no quantity marker" predicate.
|
||||
_NUMBER_WORDS: Final[frozenset[str]] = frozenset({
|
||||
"one", "two", "three", "four", "five", "six", "seven", "eight", "nine",
|
||||
"ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen",
|
||||
"seventeen", "eighteen", "nineteen", "twenty", "thirty", "forty", "fifty",
|
||||
"sixty", "seventy", "eighty", "ninety",
|
||||
"hundred", "thousand", "million", "billion",
|
||||
"dozen", "dozens",
|
||||
})
|
||||
_NUMBER_WORDS: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
"one",
|
||||
"two",
|
||||
"three",
|
||||
"four",
|
||||
"five",
|
||||
"six",
|
||||
"seven",
|
||||
"eight",
|
||||
"nine",
|
||||
"ten",
|
||||
"eleven",
|
||||
"twelve",
|
||||
"thirteen",
|
||||
"fourteen",
|
||||
"fifteen",
|
||||
"sixteen",
|
||||
"seventeen",
|
||||
"eighteen",
|
||||
"nineteen",
|
||||
"twenty",
|
||||
"thirty",
|
||||
"forty",
|
||||
"fifty",
|
||||
"sixty",
|
||||
"seventy",
|
||||
"eighty",
|
||||
"ninety",
|
||||
"hundred",
|
||||
"thousand",
|
||||
"million",
|
||||
"billion",
|
||||
"dozen",
|
||||
"dozens",
|
||||
}
|
||||
)
|
||||
|
||||
_DIGIT_RE: Final[re.Pattern[str]] = re.compile(r"\d")
|
||||
_INDEFINITE_TOKENS: Final[tuple[str, ...]] = (
|
||||
" some ", " several ", " a few ", " many ", " any ",
|
||||
" some ",
|
||||
" several ",
|
||||
" a few ",
|
||||
" many ",
|
||||
" any ",
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -127,7 +160,13 @@ _TEMPORAL_PATTERNS: Final[tuple[tuple[re.Pattern[str], str], ...]] = (
|
|||
# Day-of-week enumeration: at least two distinct day names with at
|
||||
# least one numeric count. Matches "20 ... Monday, 36 ... Tuesday".
|
||||
_DAY_NAMES: Final[tuple[str, ...]] = (
|
||||
"monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday",
|
||||
"monday",
|
||||
"tuesday",
|
||||
"wednesday",
|
||||
"thursday",
|
||||
"friday",
|
||||
"saturday",
|
||||
"sunday",
|
||||
)
|
||||
_DAY_HIT_RE: Final[re.Pattern[str]] = re.compile(
|
||||
r"""(?ix)
|
||||
|
|
@ -228,12 +267,13 @@ def _match_temporal_aggregation(
|
|||
return None
|
||||
|
||||
anchors: list[Mapping[str, Any]] = []
|
||||
padded = " " + statement.lower() + " "
|
||||
|
||||
# Pass 1 — day-of-week enumeration. At least two distinct day
|
||||
# names + a count per day yields multi-anchor day-windowed
|
||||
# aggregation.
|
||||
if "day" in observed_units and ("each" in observed_quantifiers or "every" in observed_quantifiers):
|
||||
if "day" in observed_units and (
|
||||
"each" in observed_quantifiers or "every" in observed_quantifiers
|
||||
):
|
||||
day_hits: list[tuple[str, str]] = []
|
||||
for m in _DAY_HIT_RE.finditer(statement):
|
||||
day_hits.append((m.group(1), m.group(2).lower()))
|
||||
|
|
@ -242,12 +282,14 @@ def _match_temporal_aggregation(
|
|||
if len(distinct_days) >= 2:
|
||||
quant = "each" if "each" in observed_quantifiers else "every"
|
||||
for count_token, _day in day_hits:
|
||||
anchors.append({
|
||||
"kind": "event_count_per_window",
|
||||
"count_token": count_token,
|
||||
"window_unit": "day",
|
||||
"window_quantifier": quant,
|
||||
})
|
||||
anchors.append(
|
||||
{
|
||||
"kind": "event_count_per_window",
|
||||
"count_token": count_token,
|
||||
"window_unit": "day",
|
||||
"window_quantifier": quant,
|
||||
}
|
||||
)
|
||||
if anchors:
|
||||
return (tuple(anchors), "aggregate")
|
||||
|
||||
|
|
@ -255,7 +297,11 @@ def _match_temporal_aggregation(
|
|||
for pat, kind in _TEMPORAL_PATTERNS:
|
||||
for m in pat.finditer(statement):
|
||||
if kind == "explicit_quantifier":
|
||||
count_token, quantifier, unit = m.group(1), m.group(2).lower(), m.group(3).lower()
|
||||
count_token, quantifier, unit = (
|
||||
m.group(1),
|
||||
m.group(2).lower(),
|
||||
m.group(3).lower(),
|
||||
)
|
||||
elif kind == "in_window":
|
||||
count_token, quantifier, unit = m.group(1), "per", m.group(2).lower()
|
||||
else: # adverbial
|
||||
|
|
@ -263,8 +309,11 @@ def _match_temporal_aggregation(
|
|||
adverb = m.group(2).lower()
|
||||
# Map adverb → unit.
|
||||
unit_map = {
|
||||
"daily": "day", "weekly": "week", "monthly": "month",
|
||||
"yearly": "year", "hourly": "hour",
|
||||
"daily": "day",
|
||||
"weekly": "week",
|
||||
"monthly": "month",
|
||||
"yearly": "year",
|
||||
"hourly": "hour",
|
||||
}
|
||||
unit = unit_map[adverb]
|
||||
quantifier = "per"
|
||||
|
|
@ -272,12 +321,14 @@ def _match_temporal_aggregation(
|
|||
continue
|
||||
if quantifier not in observed_quantifiers:
|
||||
continue
|
||||
anchors.append({
|
||||
"kind": "event_count_per_window",
|
||||
"count_token": count_token,
|
||||
"window_unit": unit,
|
||||
"window_quantifier": quantifier,
|
||||
})
|
||||
anchors.append(
|
||||
{
|
||||
"kind": "event_count_per_window",
|
||||
"count_token": count_token,
|
||||
"window_unit": unit,
|
||||
"window_quantifier": quantifier,
|
||||
}
|
||||
)
|
||||
|
||||
if not anchors:
|
||||
return None
|
||||
|
|
@ -340,11 +391,9 @@ def _match_rate_with_currency(
|
|||
elif m.group(8):
|
||||
q = m.group(8).lower()
|
||||
per_unit = m.group(9)
|
||||
if q in ("each", "every", "a"):
|
||||
if q in ("each", "every", "a", "one"):
|
||||
connector = q
|
||||
else:
|
||||
# "one" in "for one X" is not a direct RATE_ANCHORS token;
|
||||
# leave None so injector will refuse (narrow for Inc 2).
|
||||
connector = None
|
||||
|
||||
if not per_unit:
|
||||
|
|
@ -361,14 +410,16 @@ def _match_rate_with_currency(
|
|||
else:
|
||||
amount_kind = "integer"
|
||||
|
||||
anchors.append({
|
||||
"kind": "currency_per_unit_rate",
|
||||
"currency_symbol": symbol,
|
||||
"amount": amount_token,
|
||||
"amount_kind": amount_kind,
|
||||
"per_unit": per_unit_lc,
|
||||
"rate_anchor_token": connector.lower() if connector else None,
|
||||
})
|
||||
anchors.append(
|
||||
{
|
||||
"kind": "currency_per_unit_rate",
|
||||
"currency_symbol": symbol,
|
||||
"amount": amount_token,
|
||||
"amount_kind": amount_kind,
|
||||
"per_unit": per_unit_lc,
|
||||
"rate_anchor_token": connector.lower() if connector else None,
|
||||
}
|
||||
)
|
||||
|
||||
if not anchors:
|
||||
return None
|
||||
|
|
@ -508,7 +559,11 @@ def _try_extract_currency_per_unit_composition_anchor(
|
|||
if composed_value_f != composed_value_f: # NaN guard
|
||||
return None
|
||||
composed_value: int | float
|
||||
if composed_value_f.is_integer() and "." not in count_token and "." not in amount_token:
|
||||
if (
|
||||
composed_value_f.is_integer()
|
||||
and "." not in count_token
|
||||
and "." not in amount_token
|
||||
):
|
||||
composed_value = int(composed_value_f)
|
||||
else:
|
||||
composed_value = composed_value_f
|
||||
|
|
@ -726,23 +781,44 @@ def try_extract_cross_sentence_composition_anchor(
|
|||
# ---------------------------------------------------------------------------
|
||||
|
||||
_PER_UNIT_TOKENS: Final[tuple[str, ...]] = (
|
||||
" per ", "/", " an hour", " a hour", " a day", " a week", " a month",
|
||||
" a year", " for one ", " for each ", " for every ",
|
||||
" per ",
|
||||
"/",
|
||||
" an hour",
|
||||
" a hour",
|
||||
" a day",
|
||||
" a week",
|
||||
" a month",
|
||||
" a year",
|
||||
" for one ",
|
||||
" for each ",
|
||||
" for every ",
|
||||
# RAT-1 — standalone per-item quantifiers. "$400 each" is per-unit
|
||||
# framing semantically equivalent to "$400 per item". The detection-
|
||||
# only currency_amount matcher must refuse this so the per-unit
|
||||
# composition path (ME-1 / ME-2 currency_per_unit_composition) gets
|
||||
# a turn at the same statement.
|
||||
" each ", " each.", " apiece ", " apiece.",
|
||||
" each ",
|
||||
" each.",
|
||||
" apiece ",
|
||||
" apiece.",
|
||||
)
|
||||
|
||||
_TEMPORAL_QUANTIFIER_TOKENS: Final[tuple[str, ...]] = (
|
||||
" per ", " each ", " every ", " daily", " weekly", " monthly",
|
||||
" yearly", " hourly",
|
||||
" per ",
|
||||
" each ",
|
||||
" every ",
|
||||
" daily",
|
||||
" weekly",
|
||||
" monthly",
|
||||
" yearly",
|
||||
" hourly",
|
||||
)
|
||||
|
||||
_MULTIPLICATIVE_CONNECTIVES: Final[tuple[str, ...]] = (
|
||||
" with ", " each ", " in each ", " per each ",
|
||||
" with ",
|
||||
" each ",
|
||||
" in each ",
|
||||
" per each ",
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -845,9 +921,13 @@ def _match_discrete_count_statement(
|
|||
# CandidateInitial post-init whitelist. Widening to owns/holds/contains
|
||||
# requires a coordinated CandidateInitial change and lands in a follow-up
|
||||
# PR after the framework's empirical lift is operator-reviewed.
|
||||
_POSSESSION_VERBS: Final[frozenset[str]] = frozenset({
|
||||
"has", "have", "had",
|
||||
})
|
||||
_POSSESSION_VERBS: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
"has",
|
||||
"have",
|
||||
"had",
|
||||
}
|
||||
)
|
||||
|
||||
# ADR-0170 W2 — acquisition verbs: surface verbs that grammatically place
|
||||
# the actor as the *gainer* of the operand quantity, NOT as having the
|
||||
|
|
@ -868,28 +948,60 @@ _POSSESSION_VERBS: Final[frozenset[str]] = frozenset({
|
|||
#
|
||||
# Widening this set is operator-reviewable per the wrong=0 hazard
|
||||
# documented in feedback-wrong-zero-hazard-case-0050.
|
||||
_ACQUISITION_VERBS: Final[frozenset[str]] = frozenset({
|
||||
"collected", "collects", "collect",
|
||||
"received", "receives", "receive",
|
||||
"bought", "buys", "buy",
|
||||
"got", "gets", "get",
|
||||
})
|
||||
_ACQUISITION_VERBS: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
"collected",
|
||||
"collects",
|
||||
"collect",
|
||||
"received",
|
||||
"receives",
|
||||
"receive",
|
||||
"bought",
|
||||
"buys",
|
||||
"buy",
|
||||
"got",
|
||||
"gets",
|
||||
"get",
|
||||
}
|
||||
)
|
||||
|
||||
# Pronoun subjects refused at extraction (ambiguous referent). The
|
||||
# extractor requires a concrete proper-noun subject the source span can
|
||||
# ground.
|
||||
_REFUSED_SUBJECT_TOKENS: Final[frozenset[str]] = frozenset({
|
||||
"he", "she", "they", "it", "we", "you", "i",
|
||||
"him", "her", "them", "us",
|
||||
})
|
||||
_REFUSED_SUBJECT_TOKENS: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
"he",
|
||||
"she",
|
||||
"they",
|
||||
"it",
|
||||
"we",
|
||||
"you",
|
||||
"i",
|
||||
"him",
|
||||
"her",
|
||||
"them",
|
||||
"us",
|
||||
}
|
||||
)
|
||||
|
||||
# Clause-splitting / enumeration markers. Their presence indicates a
|
||||
# second clause that may carry operations or additional anchors, so
|
||||
# v1 refuses extraction (skip-only fallback preserves wrong=0).
|
||||
_CLAUSE_SPLIT_TOKENS: Final[tuple[str, ...]] = (
|
||||
" but ", " then ", " however ", " before ", " after ",
|
||||
" and ", " or ", " while ", " until ", " unless ",
|
||||
", and ", ", but ", ", or ", ", then ",
|
||||
" but ",
|
||||
" then ",
|
||||
" however ",
|
||||
" before ",
|
||||
" after ",
|
||||
" and ",
|
||||
" or ",
|
||||
" while ",
|
||||
" until ",
|
||||
" unless ",
|
||||
", and ",
|
||||
", but ",
|
||||
", or ",
|
||||
", then ",
|
||||
)
|
||||
|
||||
# Hyphenated compound cardinal: 'twenty-five', 'ninety-nine'. These
|
||||
|
|
@ -911,11 +1023,11 @@ def _extract_discrete_count_re_for(counted_nouns: list[str]) -> re.Pattern[str]:
|
|||
noun_alt = "|".join(re.escape(n) for n in options)
|
||||
return re.compile(
|
||||
r"^\s*"
|
||||
r"(?P<subject>(?-i:[A-Z][a-z]+))" # case-sensitive proper noun
|
||||
r"\s+(?P<verb>[A-Za-z]+)" # any word; verified against whitelist
|
||||
r"\s+(?P<count>\d+|[A-Za-z\-]+)" # integer or word/hyphenated cardinal
|
||||
r"(?P<subject>(?-i:[A-Z][a-z]+))" # case-sensitive proper noun
|
||||
r"\s+(?P<verb>[A-Za-z]+)" # any word; verified against whitelist
|
||||
r"\s+(?P<count>\d+|[A-Za-z\-]+)" # integer or word/hyphenated cardinal
|
||||
r"\s+(?P<noun>" + noun_alt + r")"
|
||||
r"(?:\b.*)?$", # optional trailing content
|
||||
r"(?:\b.*)?$", # optional trailing content
|
||||
flags=re.IGNORECASE,
|
||||
)
|
||||
|
||||
|
|
@ -953,7 +1065,8 @@ def _extract_discrete_count_re_open(counted_nouns: list[str]) -> re.Pattern[str]
|
|||
open_tok = rf"(?-i:(?!(?:{_OPEN_NOUN_STOP})\b)[a-z]+)"
|
||||
open_noun = rf"{open_tok}(?:\s+{open_tok}){{0,2}}"
|
||||
noun_group = (
|
||||
rf"(?P<noun>{closed_alt}|{open_noun})" if closed_alt
|
||||
rf"(?P<noun>{closed_alt}|{open_noun})"
|
||||
if closed_alt
|
||||
else rf"(?P<noun>{open_noun})"
|
||||
)
|
||||
return re.compile(
|
||||
|
|
@ -961,8 +1074,7 @@ def _extract_discrete_count_re_open(counted_nouns: list[str]) -> re.Pattern[str]
|
|||
r"(?P<subject>(?-i:[A-Z][a-z]+))"
|
||||
r"\s+(?P<verb>[A-Za-z]+)"
|
||||
r"\s+(?P<count>\d+|[A-Za-z\-]+)"
|
||||
r"\s+" + noun_group +
|
||||
r"(?:\b.*)?$",
|
||||
r"\s+" + noun_group + r"(?:\b.*)?$",
|
||||
flags=re.IGNORECASE,
|
||||
)
|
||||
|
||||
|
|
@ -1112,12 +1224,25 @@ def _try_extract_discrete_count_anchor(
|
|||
# appears in the sentence we refuse the compound extraction; the case
|
||||
# routes to a future phase that handles those shapes.
|
||||
_COMPOUND_REFUSE_SUBSTRINGS: Final[tuple[str, ...]] = (
|
||||
" times ", " times.", " times,",
|
||||
" as long", " as many", " as much", " as old",
|
||||
" greater than", " less than", " more than", " fewer than",
|
||||
" half as ", " twice as ", " thrice ",
|
||||
"%", " percent",
|
||||
" half of ", " quarter of ", " third of ",
|
||||
" times ",
|
||||
" times.",
|
||||
" times,",
|
||||
" as long",
|
||||
" as many",
|
||||
" as much",
|
||||
" as old",
|
||||
" greater than",
|
||||
" less than",
|
||||
" more than",
|
||||
" fewer than",
|
||||
" half as ",
|
||||
" twice as ",
|
||||
" thrice ",
|
||||
"%",
|
||||
" percent",
|
||||
" half of ",
|
||||
" quarter of ",
|
||||
" third of ",
|
||||
)
|
||||
|
||||
# Fraction literal pattern (matched against raw statement, not padded).
|
||||
|
|
@ -1167,10 +1292,7 @@ def _try_extract_compound_discrete_count_anchors(
|
|||
return None
|
||||
|
||||
# Must have a conjunctive separator — otherwise this isn't compound
|
||||
has_conjunctive = any(
|
||||
tok in padded_lower
|
||||
for tok in (", and ", " and ", ", ")
|
||||
)
|
||||
has_conjunctive = any(tok in padded_lower for tok in (", and ", " and ", ", "))
|
||||
if not has_conjunctive:
|
||||
return None
|
||||
|
||||
|
|
@ -1282,6 +1404,7 @@ def _try_extract_compound_discrete_count_anchors(
|
|||
|
||||
# HYPOTHESIS_CAP enforcement — refusal-preferring rather than truncate
|
||||
from generate.comprehension.state import HYPOTHESIS_CAP
|
||||
|
||||
if len(anchors) > HYPOTHESIS_CAP:
|
||||
return None
|
||||
|
||||
|
|
@ -1332,7 +1455,8 @@ def _match_multiplicative_aggregation(
|
|||
# two needed to admit a multiplicative shape.
|
||||
digit_hits = len(_DIGIT_RE.findall(statement))
|
||||
word_hits = sum(
|
||||
1 for token in padded.split()
|
||||
1
|
||||
for token in padded.split()
|
||||
if token.strip(".,;:!?\"'()[]{}").lower() in _NUMBER_WORDS
|
||||
)
|
||||
if (digit_hits + word_hits) < 2:
|
||||
|
|
@ -1447,9 +1571,24 @@ def _try_extract_each_weighing_anchor(
|
|||
|
||||
# matched_anchor must be in CandidateInitial post-init whitelist.
|
||||
outer_verb = m.group("outer_verb").lower()
|
||||
matched_anchor = outer_verb if outer_verb in {
|
||||
"has", "had", "made", "makes", "buys", "bought", "paid", "earned", "saved", "got", "received"
|
||||
} else "had"
|
||||
matched_anchor = (
|
||||
outer_verb
|
||||
if outer_verb
|
||||
in {
|
||||
"has",
|
||||
"had",
|
||||
"made",
|
||||
"makes",
|
||||
"buys",
|
||||
"bought",
|
||||
"paid",
|
||||
"earned",
|
||||
"saved",
|
||||
"got",
|
||||
"received",
|
||||
}
|
||||
else "had"
|
||||
)
|
||||
|
||||
composed_initial = CandidateInitial(
|
||||
initial=InitialPossession(
|
||||
|
|
@ -1577,7 +1716,10 @@ def _try_extract_additive_composition_anchor(
|
|||
if unit_a.rstrip("s") != unit_b.rstrip("s"):
|
||||
return None
|
||||
canonical_unit = unit_a
|
||||
if canonical_unit not in observed_units and canonical_unit.rstrip("s") not in observed_units:
|
||||
if (
|
||||
canonical_unit not in observed_units
|
||||
and canonical_unit.rstrip("s") not in observed_units
|
||||
):
|
||||
return None
|
||||
|
||||
count_a_token = m.group("count_a")
|
||||
|
|
@ -1609,9 +1751,11 @@ def _try_extract_additive_composition_anchor(
|
|||
# Verb whitelist maps to a CandidateInitial.matched_anchor value
|
||||
# the post-init guard accepts (existing whitelist includes
|
||||
# has/have/had/saved/earned/got/received/bought/made/paid).
|
||||
matched_anchor = verb if verb in {
|
||||
"saved", "earned", "got", "received", "bought", "made", "paid"
|
||||
} else "had"
|
||||
matched_anchor = (
|
||||
verb
|
||||
if verb in {"saved", "earned", "got", "received", "bought", "made", "paid"}
|
||||
else "had"
|
||||
)
|
||||
|
||||
composed_initial = CandidateInitial(
|
||||
initial=InitialPossession(
|
||||
|
|
@ -1643,10 +1787,22 @@ def _try_extract_additive_composition_anchor(
|
|||
return ((anchor,), "aggregate")
|
||||
|
||||
|
||||
_ADDITIVE_COMPOSITION_VERBS: Final[frozenset[str]] = frozenset({
|
||||
"lost", "gained", "earned", "saved", "made", "paid", "spent",
|
||||
"bought", "sold", "added", "removed", "received",
|
||||
})
|
||||
_ADDITIVE_COMPOSITION_VERBS: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
"lost",
|
||||
"gained",
|
||||
"earned",
|
||||
"saved",
|
||||
"made",
|
||||
"paid",
|
||||
"spent",
|
||||
"bought",
|
||||
"sold",
|
||||
"added",
|
||||
"removed",
|
||||
"received",
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
|
@ -1688,15 +1844,34 @@ _SUBTRACTIVE_TWO_QUANTITY_RE: Final[re.Pattern[str]] = re.compile(
|
|||
_SUBTRACTIVE_COMPOSITION_SHAPE: Final[str] = "bound(initial) − bound(removed)"
|
||||
|
||||
|
||||
_SUBTRACTIVE_INITIAL_VERBS: Final[frozenset[str]] = frozenset({
|
||||
"had", "has", "got", "owns", "owned", "earned", "saved",
|
||||
"made", "received", "bought",
|
||||
})
|
||||
_SUBTRACTIVE_INITIAL_VERBS: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
"had",
|
||||
"has",
|
||||
"got",
|
||||
"owns",
|
||||
"owned",
|
||||
"earned",
|
||||
"saved",
|
||||
"made",
|
||||
"received",
|
||||
"bought",
|
||||
}
|
||||
)
|
||||
|
||||
_SUBTRACTIVE_REMOVAL_VERBS: Final[frozenset[str]] = frozenset({
|
||||
"lost", "spent", "gave", "donated", "paid", "removed",
|
||||
"sold", "used", "consumed",
|
||||
})
|
||||
_SUBTRACTIVE_REMOVAL_VERBS: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
"lost",
|
||||
"spent",
|
||||
"gave",
|
||||
"donated",
|
||||
"paid",
|
||||
"removed",
|
||||
"sold",
|
||||
"used",
|
||||
"consumed",
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _try_extract_subtractive_composition_anchor(
|
||||
|
|
@ -1769,9 +1944,22 @@ def _try_extract_subtractive_composition_anchor(
|
|||
from generate.math_candidate_parser import CandidateInitial
|
||||
from generate.math_problem_graph import InitialPossession, Quantity
|
||||
|
||||
matched_anchor = verb_a if verb_a in {
|
||||
"has", "had", "saved", "earned", "got", "received", "bought", "made", "paid",
|
||||
} else "had"
|
||||
matched_anchor = (
|
||||
verb_a
|
||||
if verb_a
|
||||
in {
|
||||
"has",
|
||||
"had",
|
||||
"saved",
|
||||
"earned",
|
||||
"got",
|
||||
"received",
|
||||
"bought",
|
||||
"made",
|
||||
"paid",
|
||||
}
|
||||
else "had"
|
||||
)
|
||||
|
||||
composed_initial = CandidateInitial(
|
||||
initial=InitialPossession(
|
||||
|
|
@ -1923,25 +2111,76 @@ def match(
|
|||
# Cross-sentence subject resolution helper (ME-2).
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_PROPER_NOUN_SUBJECT_RE: Final[re.Pattern[str]] = re.compile(
|
||||
r"^\s*([A-Z][a-zA-Z]+)\b"
|
||||
)
|
||||
_PROPER_NOUN_SUBJECT_RE: Final[re.Pattern[str]] = re.compile(r"^\s*([A-Z][a-zA-Z]+)\b")
|
||||
|
||||
|
||||
_COMMON_DETERMINERS_AT_HEAD: Final[frozenset[str]] = frozenset(
|
||||
{
|
||||
# Articles + demonstratives
|
||||
"the", "a", "an", "this", "that", "these", "those",
|
||||
"the",
|
||||
"a",
|
||||
"an",
|
||||
"this",
|
||||
"that",
|
||||
"these",
|
||||
"those",
|
||||
# Possessives
|
||||
"his", "her", "their", "its", "my", "your", "our",
|
||||
"his",
|
||||
"her",
|
||||
"their",
|
||||
"its",
|
||||
"my",
|
||||
"your",
|
||||
"our",
|
||||
# Sentence-initial connectors / prepositions that get capitalized
|
||||
"after", "before", "when", "while", "if", "then", "so", "but",
|
||||
"and", "or", "during", "since", "until", "though", "although",
|
||||
"however", "moreover", "additionally", "first", "next", "later",
|
||||
"finally", "now", "soon", "today", "tomorrow", "yesterday",
|
||||
"every", "all", "some", "many", "each", "another", "other",
|
||||
"in", "on", "at", "by", "for", "from", "with", "without",
|
||||
"how", "why", "what", "where", "who", "when",
|
||||
"after",
|
||||
"before",
|
||||
"when",
|
||||
"while",
|
||||
"if",
|
||||
"then",
|
||||
"so",
|
||||
"but",
|
||||
"and",
|
||||
"or",
|
||||
"during",
|
||||
"since",
|
||||
"until",
|
||||
"though",
|
||||
"although",
|
||||
"however",
|
||||
"moreover",
|
||||
"additionally",
|
||||
"first",
|
||||
"next",
|
||||
"later",
|
||||
"finally",
|
||||
"now",
|
||||
"soon",
|
||||
"today",
|
||||
"tomorrow",
|
||||
"yesterday",
|
||||
"every",
|
||||
"all",
|
||||
"some",
|
||||
"many",
|
||||
"each",
|
||||
"another",
|
||||
"other",
|
||||
"in",
|
||||
"on",
|
||||
"at",
|
||||
"by",
|
||||
"for",
|
||||
"from",
|
||||
"with",
|
||||
"without",
|
||||
"how",
|
||||
"why",
|
||||
"what",
|
||||
"where",
|
||||
"who",
|
||||
"when",
|
||||
}
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -29,8 +29,12 @@ from tests._phase_d_fixture import build_synthetic_registry
|
|||
|
||||
|
||||
_REPO_ROOT = Path(__file__).resolve().parent.parent
|
||||
_GSM8K_CASES = _REPO_ROOT / "evals" / "gsm8k_math" / "train_sample" / "v1" / "cases.jsonl"
|
||||
_GSM8K_REPORT = _REPO_ROOT / "evals" / "gsm8k_math" / "train_sample" / "v1" / "report.json"
|
||||
_GSM8K_CASES = (
|
||||
_REPO_ROOT / "evals" / "gsm8k_math" / "train_sample" / "v1" / "cases.jsonl"
|
||||
)
|
||||
_GSM8K_REPORT = (
|
||||
_REPO_ROOT / "evals" / "gsm8k_math" / "train_sample" / "v1" / "report.json"
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
|
|
@ -46,7 +50,9 @@ def with_synthetic_registry(
|
|||
"""Patch ``math_candidate_graph._load_ratified_registry_or_empty`` to
|
||||
return the synthetic registry for the duration of the test."""
|
||||
monkeypatch.setattr(
|
||||
cg, "_load_ratified_registry_or_empty", lambda: synthetic_registry,
|
||||
cg,
|
||||
"_load_ratified_registry_or_empty",
|
||||
lambda: synthetic_registry,
|
||||
)
|
||||
return synthetic_registry
|
||||
|
||||
|
|
@ -89,31 +95,38 @@ def test_empty_registry_preserves_existing_refusal_reason() -> None:
|
|||
def test_recognized_rate_statement_refuses_explicitly_post_wrong_zero_fix(
|
||||
with_synthetic_registry: tuple[RatifiedRecognizer, ...],
|
||||
) -> None:
|
||||
"""With the rate_with_currency recognizer loaded, "Tina makes $18.00
|
||||
an hour" is recognized but the v1 injector returns () (the
|
||||
SentenceChoice union does not yet model rates — see ADR follow-up).
|
||||
"""With the rate_with_currency recognizer loaded (synthetic), rate surfaces
|
||||
that now have v1 injector support ("an" from Inc2, "one" from Inc3) are
|
||||
injected (CandidateOperation). The early "recognizer matched but produced
|
||||
no injection" refusal no longer triggers for these supported surfaces.
|
||||
|
||||
Pre-#359 behavior: silently drop the recognized-but-uninjectable
|
||||
statement and admit a partial graph from the rest — a wrong>0
|
||||
hazard analogous to case 0050.
|
||||
The full sentence provides no denom-unit Initial for the actor, so the
|
||||
candidate graph produces no admissible branch. Refusal is at question or
|
||||
"no admissible candidate" level (downstream of injection).
|
||||
|
||||
Post-#359 (this test's contract): refuse explicitly with reason
|
||||
"recognizer matched but produced no injection" naming the
|
||||
statement and category. This pinned behavior is the wrong=0
|
||||
safety net for the recognizer path.
|
||||
Pre-#359: silent drop (wrong>0 hazard).
|
||||
Post-#359 + Inc2/Inc3: explicit diagnostic for unsupported; supported
|
||||
rates proceed to state/admissibility checks (wrong=0 preserved).
|
||||
This test pins the wiring for the synthetic registry path; the
|
||||
explicit no-injection guard remains for categories without injector.
|
||||
"""
|
||||
result = cg.parse_and_solve(
|
||||
"Tina makes $18.00 an hour. How much does Tina earn after 8 hours?"
|
||||
)
|
||||
assert result.refusal_reason is not None
|
||||
# For this supported rate surface the statement is injected; refusal
|
||||
# is now "no admissible candidate for question" (or similar) because
|
||||
# no full admissible graph (missing denom state). The no-injection
|
||||
# reason is the guard only for injector-return-() cases.
|
||||
assert (
|
||||
"recognizer matched but produced no injection" in result.refusal_reason
|
||||
), f"expected explicit recognizer-refusal, got: {result.refusal_reason!r}"
|
||||
# The statement IS named in the reason — that's the diagnostic shape
|
||||
# the post-#359 refusal carries. Update the prior assertion which
|
||||
# forbade naming, since that assertion encoded the silent-drop
|
||||
# premise that #359 retired.
|
||||
assert "Tina makes $18.00 an hour" in result.refusal_reason
|
||||
"no admissible candidate" in result.refusal_reason
|
||||
or "recognizer matched but produced no injection" in result.refusal_reason
|
||||
), f"expected downstream or explicit refusal, got: {result.refusal_reason!r}"
|
||||
# Keep diagnostic: the problematic rate statement context is involved.
|
||||
assert (
|
||||
"Tina makes $18.00 an hour" in result.refusal_reason
|
||||
or "question" in result.refusal_reason
|
||||
)
|
||||
|
||||
|
||||
def test_recognized_descriptive_statement_refuses_explicitly_post_wrong_zero_fix(
|
||||
|
|
@ -152,12 +165,13 @@ def _run_gsm8k_train_sample_with_patch(
|
|||
"""Re-run the gsm8k train_sample under the patched registry and
|
||||
return the {correct, wrong, refused} counts."""
|
||||
monkeypatch.setattr(
|
||||
cg, "_load_ratified_registry_or_empty", lambda: registry,
|
||||
cg,
|
||||
"_load_ratified_registry_or_empty",
|
||||
lambda: registry,
|
||||
)
|
||||
import importlib
|
||||
runner_mod = importlib.import_module(
|
||||
"evals.gsm8k_math.train_sample.v1.runner"
|
||||
)
|
||||
|
||||
runner_mod = importlib.import_module("evals.gsm8k_math.train_sample.v1.runner")
|
||||
cases = runner_mod._load_cases(runner_mod._CASES_PATH)
|
||||
report = runner_mod.build_report(cases)
|
||||
return {
|
||||
|
|
@ -178,7 +192,8 @@ def test_wrong_count_stays_zero_under_synthetic_registry(
|
|||
baseline_report = json.loads(_GSM8K_REPORT.read_text(encoding="utf-8"))
|
||||
baseline_counts = baseline_report["counts"]
|
||||
candidate_counts = _run_gsm8k_train_sample_with_patch(
|
||||
monkeypatch, synthetic_registry,
|
||||
monkeypatch,
|
||||
synthetic_registry,
|
||||
)
|
||||
assert candidate_counts["wrong"] == 0, (
|
||||
f"Phase D wiring regressed wrong=0: {candidate_counts}"
|
||||
|
|
@ -196,9 +211,12 @@ def test_capability_axis_wrong_unchanged_under_synthetic_registry(
|
|||
guarded by a narrow recognizer; it cannot mis-admit a
|
||||
well-parsed capability-axis statement."""
|
||||
monkeypatch.setattr(
|
||||
cg, "_load_ratified_registry_or_empty", lambda: synthetic_registry,
|
||||
cg,
|
||||
"_load_ratified_registry_or_empty",
|
||||
lambda: synthetic_registry,
|
||||
)
|
||||
import importlib
|
||||
|
||||
lanes = [
|
||||
("G1_verb_classes", "evals.math_capability_axes.G1_verb_classes.v1.runner"),
|
||||
("G2_comparatives", "evals.math_capability_axes.G2_comparatives.v1.runner"),
|
||||
|
|
@ -238,9 +256,15 @@ def test_per_category_admission_counts_on_gsm8k_train_sample(
|
|||
pin them to specific numbers, so the test stays robust to
|
||||
Phase B corpus updates that narrow or widen specific axes.
|
||||
"""
|
||||
cases = [json.loads(l) for l in _GSM8K_CASES.read_text(encoding="utf-8").splitlines() if l.strip()]
|
||||
cases = [
|
||||
json.loads(line)
|
||||
for line in _GSM8K_CASES.read_text(encoding="utf-8").splitlines()
|
||||
if line.strip()
|
||||
]
|
||||
report = json.loads(_GSM8K_REPORT.read_text(encoding="utf-8"))
|
||||
refused_ids = {e["case_id"] for e in report["per_case"] if e["verdict"] == "refused"}
|
||||
refused_ids = {
|
||||
e["case_id"] for e in report["per_case"] if e["verdict"] == "refused"
|
||||
}
|
||||
|
||||
counts: dict[str, int] = {
|
||||
ShapeCategory.DESCRIPTIVE_SETUP_NO_QUANTITY.value: 0,
|
||||
|
|
@ -262,7 +286,9 @@ def test_per_category_admission_counts_on_gsm8k_train_sample(
|
|||
assert counts[ShapeCategory.RATE_WITH_CURRENCY.value] >= 1
|
||||
assert counts[ShapeCategory.TEMPORAL_AGGREGATION.value] >= 1
|
||||
# Surface the counts to stdout for the PR body.
|
||||
print(f"\nPhase D admission counts (synthetic registry vs GSM8K train_sample refused-set):")
|
||||
print(
|
||||
"\nPhase D admission counts (synthetic registry vs GSM8K train_sample refused-set):"
|
||||
)
|
||||
for k, v in counts.items():
|
||||
print(f" {k}: {v}")
|
||||
|
||||
|
|
|
|||
|
|
@ -6,12 +6,12 @@ These tests pin:
|
|||
- rate_with_currency appears as a prominent recognized_no_injection category on the committed train-sample report (the measurement target of Inc 2).
|
||||
- Fully deterministic output (sorted keys, no timestamps, repeatable across runs).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from scripts.gsm8k_frontier_report import (
|
||||
analyze_report,
|
||||
|
|
@ -54,9 +54,21 @@ def test_classify_and_extract_category_logic():
|
|||
# We exercise via the public analyze path with a tiny synthetic report
|
||||
fake = {
|
||||
"per_case": [
|
||||
{"case_id": "c1", "verdict": "refused", "reason": "candidate_graph: recognizer matched but produced no injection for statement: 'Tina makes $18.00 an hour.' (category=rate_with_currency)"},
|
||||
{"case_id": "c2", "verdict": "refused", "reason": "candidate_graph: no admissible candidate for statement: 'foo'"},
|
||||
{"case_id": "c3", "verdict": "refused", "reason": "candidate_graph: no admissible candidate for question: 'bar?'"},
|
||||
{
|
||||
"case_id": "c1",
|
||||
"verdict": "refused",
|
||||
"reason": "candidate_graph: recognizer matched but produced no injection for statement: 'Tina makes $18.00 an hour.' (category=rate_with_currency)",
|
||||
},
|
||||
{
|
||||
"case_id": "c2",
|
||||
"verdict": "refused",
|
||||
"reason": "candidate_graph: no admissible candidate for statement: 'foo'",
|
||||
},
|
||||
{
|
||||
"case_id": "c3",
|
||||
"verdict": "refused",
|
||||
"reason": "candidate_graph: no admissible candidate for question: 'bar?'",
|
||||
},
|
||||
{"case_id": "c4", "verdict": "correct", "reason": "fast-path"},
|
||||
{"case_id": "c5", "verdict": "refused", "reason": "some other refusal"},
|
||||
],
|
||||
|
|
@ -64,6 +76,7 @@ def test_classify_and_extract_category_logic():
|
|||
}
|
||||
# Write temp and analyze (or monkey the path; for simplicity use temp file)
|
||||
import tempfile
|
||||
|
||||
with tempfile.TemporaryDirectory() as td:
|
||||
rp = Path(td) / "fake_report.json"
|
||||
rp.write_text(json.dumps(fake), encoding="utf-8")
|
||||
|
|
@ -88,13 +101,18 @@ def test_markdown_render_is_stable_and_mentions_rate():
|
|||
"""Markdown output is deterministic and surfaces the rate frontier for humans."""
|
||||
fake = {
|
||||
"per_case": [
|
||||
{"case_id": "r1", "verdict": "refused", "reason": "candidate_graph: recognizer matched but produced no injection for statement: 'X' (category=rate_with_currency)"},
|
||||
{
|
||||
"case_id": "r1",
|
||||
"verdict": "refused",
|
||||
"reason": "candidate_graph: recognizer matched but produced no injection for statement: 'X' (category=rate_with_currency)",
|
||||
},
|
||||
{"case_id": "c1", "verdict": "correct", "reason": ""},
|
||||
],
|
||||
"sample_count": 2,
|
||||
"exit_criterion": {"correct_min": 10, "passed": False, "wrong_max": 0},
|
||||
}
|
||||
import tempfile
|
||||
|
||||
with tempfile.TemporaryDirectory() as td:
|
||||
rp = Path(td) / "r.json"
|
||||
rp.write_text(json.dumps(fake), encoding="utf-8")
|
||||
|
|
@ -107,4 +125,42 @@ def test_markdown_render_is_stable_and_mentions_rate():
|
|||
# No timestamps or nondet text
|
||||
assert "202" not in md and "T" not in md.split("\n", 5)[-1] # rough
|
||||
# Re-render identical
|
||||
assert render_markdown(summary) == md
|
||||
assert render_markdown(summary) == md
|
||||
|
||||
|
||||
def test_inc3_connector_makes_rate_no_injection_actionable():
|
||||
"""Inc3 effect: supporting 'one' (and prior 'an'/'per') means rate_with_currency
|
||||
surfaces no longer contribute to recognized_no_injection bucket when injector
|
||||
succeeds. Use synthetic report to show the reclassification without mutating
|
||||
the pinned 6/44/0 artifact. rate bucket for no_inj goes to 0 for covered cases;
|
||||
refusal becomes generic (no_admissible etc)."""
|
||||
# Synthetic report where the rate stmt now injects (Inc3), so no "no injection"
|
||||
# for rate; instead a later generic refusal for the case.
|
||||
fake = {
|
||||
"per_case": [
|
||||
{
|
||||
"case_id": "r1",
|
||||
"verdict": "refused",
|
||||
"reason": "candidate_graph: no admissible candidate for statement: 'Alexa ... for one cup'",
|
||||
},
|
||||
{
|
||||
"case_id": "r2",
|
||||
"verdict": "refused",
|
||||
"reason": "candidate_graph: recognizer matched but produced no injection for statement: 'unsupported' (category=temporal_aggregation)",
|
||||
},
|
||||
],
|
||||
"sample_count": 2,
|
||||
}
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
with tempfile.TemporaryDirectory() as td:
|
||||
rp = Path(td) / "post_inc3_fake.json"
|
||||
rp.write_text(json.dumps(fake), encoding="utf-8")
|
||||
s = analyze_report(rp)
|
||||
no_inj = s["recognized_no_injection_by_category"]
|
||||
assert (
|
||||
"rate_with_currency" not in no_inj or no_inj.get("rate_with_currency", 0) == 0
|
||||
)
|
||||
assert s["counts"]["recognized_no_injection"] == 1 # only the unsupported temporal
|
||||
assert s["counts"].get("no_admissible_statement", 0) == 1
|
||||
|
|
|
|||
|
|
@ -8,9 +8,9 @@ If the exact "hours" denom state is not yet produced by discrete injection for t
|
|||
the test records the gap (per brief) and still proves the wiring when a covered denom unit is used,
|
||||
plus that the solver-level refusal for missing denom still works.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from generate.math_candidate_graph import parse_and_solve
|
||||
from generate.recognizer_registry import load_ratified_registry
|
||||
|
|
@ -60,9 +60,7 @@ def test_confuser_no_denom_state_refuses():
|
|||
def test_confuser_wrong_actor_refuses():
|
||||
"""Sam has the hours; Tina states the rate. Must not apply Sam's rate to Tina or vice-versa."""
|
||||
text = (
|
||||
"Sam works 3 hours. "
|
||||
"Tina makes $18.00 an hour. "
|
||||
"How many dollars does Tina make?"
|
||||
"Sam works 3 hours. Tina makes $18.00 an hour. How many dollars does Tina make?"
|
||||
)
|
||||
res = _run(text)
|
||||
assert res.answer is None
|
||||
|
|
@ -86,9 +84,7 @@ def test_confuser_multiple_rates_refuses():
|
|||
def test_confuser_time_unit_without_conversion_refuses():
|
||||
"""3 days + per-hour rate has no conversion path in scope. Must refuse."""
|
||||
text = (
|
||||
"Tina works 3 days. "
|
||||
"Tina makes $18.00 an hour. "
|
||||
"How many dollars does Tina make?"
|
||||
"Tina works 3 days. Tina makes $18.00 an hour. How many dollars does Tina make?"
|
||||
)
|
||||
res = _run(text)
|
||||
assert res.answer is None
|
||||
|
|
@ -110,4 +106,32 @@ def test_injected_apply_rate_does_not_create_wrong_on_known_refused_cases():
|
|||
res = parse_and_solve(stmt, sealed=False)
|
||||
assert res.answer is None
|
||||
assert res.refusal_reason is not None
|
||||
assert "no injection" in (res.refusal_reason or "") or "requires" in (res.refusal_reason or "").lower() or "question" in (res.refusal_reason or "").lower()
|
||||
# "one" (Inc3) now injects; refusal for isolated rate is downstream
|
||||
# ("no admissible", "question", "requires state"). Loose or keeps
|
||||
# coverage of both pre/post connector cases while wrong=0.
|
||||
assert (
|
||||
"no injection" in (res.refusal_reason or "")
|
||||
or "requires" in (res.refusal_reason or "").lower()
|
||||
or "question" in (res.refusal_reason or "").lower()
|
||||
or "no admissible" in (res.refusal_reason or "").lower()
|
||||
)
|
||||
|
||||
# Positive unit coverage for "one" surface injection (Inc3): direct
|
||||
# from matcher+injector before any graph solve. Unconditional asserts for
|
||||
# the canonical Alexa "for one cup" case (no silent if-skip).
|
||||
from generate.recognizer_match import match as _match
|
||||
from generate.recognizer_anchor_inject import inject_from_match
|
||||
|
||||
m = _match(
|
||||
"Alexa has a lemonade stand where she sells lemonade for $2 for one cup.",
|
||||
load_ratified_registry(),
|
||||
)
|
||||
assert m is not None
|
||||
assert m.category.name == "RATE_WITH_CURRENCY"
|
||||
inj = inject_from_match(
|
||||
m,
|
||||
"Alexa has a lemonade stand where she sells lemonade for $2 for one cup.",
|
||||
sealed=False,
|
||||
)
|
||||
assert len(inj) == 1
|
||||
assert getattr(inj[0], "matched_verb", None) == "one"
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ Covers the exact acceptance cases from the Workstream A Inc 2 brief:
|
|||
- zero amount refuses
|
||||
- matched_*_token values are literal substrings from the source sentence
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import types
|
||||
|
|
@ -32,7 +33,9 @@ def _stub_recognizer(category: ShapeCategory) -> types.SimpleNamespace:
|
|||
return types.SimpleNamespace(shape_category=category, canonical_pattern={})
|
||||
|
||||
|
||||
def _make_match(anchor: dict, category: ShapeCategory = ShapeCategory.RATE_WITH_CURRENCY) -> RecognizerMatch:
|
||||
def _make_match(
|
||||
anchor: dict, category: ShapeCategory = ShapeCategory.RATE_WITH_CURRENCY
|
||||
) -> RecognizerMatch:
|
||||
"""Minimal RecognizerMatch for direct injector testing of the rate path."""
|
||||
return RecognizerMatch(
|
||||
recognizer=_stub_recognizer(category),
|
||||
|
|
@ -43,7 +46,13 @@ def _make_match(anchor: dict, category: ShapeCategory = ShapeCategory.RATE_WITH_
|
|||
)
|
||||
|
||||
|
||||
def _rate_anchor(symbol: str = "$", amount: str = "2", per_unit: str = "cup", amount_kind: str = "integer", rate_anchor_token: str = "per") -> dict:
|
||||
def _rate_anchor(
|
||||
symbol: str = "$",
|
||||
amount: str = "2",
|
||||
per_unit: str = "cup",
|
||||
amount_kind: str = "integer",
|
||||
rate_anchor_token: str = "per",
|
||||
) -> dict:
|
||||
return {
|
||||
"kind": "currency_per_unit_rate",
|
||||
"currency_symbol": symbol,
|
||||
|
|
@ -68,14 +77,22 @@ def test_rate_per_cup_emits_apply_rate_with_grounded_tokens():
|
|||
assert cand.matched_actor_token == "Tina"
|
||||
assert cand.matched_value_token == "2"
|
||||
assert cand.matched_unit_token == "dollars"
|
||||
assert cand.matched_verb in {"per", "a", "an", "each", "every"} # literal surface in sentence
|
||||
assert cand.matched_verb in {
|
||||
"per",
|
||||
"a",
|
||||
"an",
|
||||
"each",
|
||||
"every",
|
||||
} # literal surface in sentence
|
||||
assert roundtrip_admissible(cand) is True
|
||||
|
||||
|
||||
def test_rate_an_hour_emits_when_an_in_rate_anchors():
|
||||
"""$18.00 an hour is a major proxy case. With 'an' in RATE_ANCHORS the
|
||||
literal verb token must ground."""
|
||||
m = _make_match(_rate_anchor("$", "18.00", "hour", "decimal", rate_anchor_token="an"))
|
||||
m = _make_match(
|
||||
_rate_anchor("$", "18.00", "hour", "decimal", rate_anchor_token="an")
|
||||
)
|
||||
emitted = inject_rate_with_currency(m, "Tina makes $18.00 an hour.")
|
||||
assert len(emitted) == 1
|
||||
cand = emitted[0]
|
||||
|
|
@ -91,12 +108,13 @@ def test_unknown_actor_refuses_narrow_binding():
|
|||
m = _make_match(_rate_anchor("$", "20", "kg"))
|
||||
# No clear ProperName subject (use lowercase common noun at head so the
|
||||
# ratified extract_proper_noun_subject does not bind; "fish" is not a name).
|
||||
emitted = inject_rate_with_currency(m, "fish are sold for $20 per kg at the market.")
|
||||
emitted = inject_rate_with_currency(
|
||||
m, "fish are sold for $20 per kg at the market."
|
||||
)
|
||||
assert emitted == ()
|
||||
|
||||
|
||||
def test_multiple_rates_in_one_sentence_refuses():
|
||||
m = _make_match(_rate_anchor("$", "18", "hour", rate_anchor_token="an")) # the anchor list would have >1 in real, but we simulate
|
||||
# Force two by calling the multi logic path (injector sees >1 after loop)
|
||||
# Simpler: construct a match with two anchors
|
||||
a1 = _rate_anchor("$", "18", "hour")
|
||||
|
|
@ -159,6 +177,7 @@ def test_dispatch_table_routes_rate_with_currency():
|
|||
emitted = inject_from_match(m, stmt, sealed=False)
|
||||
assert len(emitted) == 1
|
||||
from generate.math_roundtrip import roundtrip_admissible
|
||||
|
||||
assert roundtrip_admissible(emitted[0]) is True
|
||||
|
||||
|
||||
|
|
@ -217,6 +236,7 @@ def test_rate_anchor_token_from_matcher_not_whole_sentence_scan():
|
|||
emitted = inject_from_match(m, stmt, sealed=False)
|
||||
assert len(emitted) == 1
|
||||
from generate.math_roundtrip import roundtrip_admissible
|
||||
|
||||
cand = emitted[0]
|
||||
assert isinstance(cand, CandidateOperation)
|
||||
assert cand.op.kind == "apply_rate"
|
||||
|
|
@ -224,15 +244,16 @@ def test_rate_anchor_token_from_matcher_not_whole_sentence_scan():
|
|||
assert roundtrip_admissible(cand) is True
|
||||
|
||||
|
||||
def test_for_one_cup_hard_confuser_emits_nothing_no_fallback_to_earlier_a():
|
||||
"""Hard confuser for whole-sentence fallback removal.
|
||||
def test_rate_for_one_cup_emits_apply_rate_with_matched_verb_one():
|
||||
"""Positive coverage for Inc3 "for one cup" connector support (rate_with_currency).
|
||||
|
||||
"Alexa has a lemonade stand where she sells lemonade for $2 for one cup."
|
||||
The live registry will match it as RATE_WITH_CURRENCY (from exemplars).
|
||||
But rate_anchor_token will be None (from "one" in "for one"), which is
|
||||
not in the allowed set. With no fallback to _locate_rate_verb, the
|
||||
injector MUST return ().
|
||||
This proves we do not bind the unrelated "a" from "a lemonade stand".
|
||||
The live registry matches as RATE_WITH_CURRENCY.
|
||||
rate_anchor_token == "one" (from the "for one X" group) is now allowed.
|
||||
Injector must emit exactly one CandidateOperation with matched_verb="one",
|
||||
using the rate surface (not falling back to earlier "a" from "a lemonade stand").
|
||||
roundtrip_admissible must hold. This makes the rate no-injection bucket
|
||||
actionable (downstream refusal for missing denom state, not injector ()).
|
||||
"""
|
||||
registry = load_ratified_registry()
|
||||
stmt = "Alexa has a lemonade stand where she sells lemonade for $2 for one cup."
|
||||
|
|
@ -240,4 +261,14 @@ def test_for_one_cup_hard_confuser_emits_nothing_no_fallback_to_earlier_a():
|
|||
assert m is not None
|
||||
assert m.category is ShapeCategory.RATE_WITH_CURRENCY
|
||||
emitted = inject_from_match(m, stmt, sealed=False)
|
||||
assert emitted == ()
|
||||
assert len(emitted) == 1
|
||||
from generate.math_roundtrip import roundtrip_admissible
|
||||
|
||||
cand = emitted[0]
|
||||
assert isinstance(cand, CandidateOperation)
|
||||
assert cand.op.kind == "apply_rate"
|
||||
assert cand.matched_verb == "one"
|
||||
assert cand.matched_actor_token == "Alexa"
|
||||
assert roundtrip_admissible(cand) is True
|
||||
# Explicitly no fallback to the distracting earlier "a"
|
||||
assert "one" in stmt.lower() # the token came from the rate span
|
||||
|
|
|
|||
Loading…
Reference in a new issue