e71531c0c9
generate/constraint_comprehension/{expr,model}.py: frozen, slots'd dataclasses, no behavior. expr = LinearExpr (sum(coeff*symbol)+constant) + LinearConstraint (lhs eq rhs, optional source_span). model = Unknown (symbol/entity/unit/finite-integer domain), AttributeFact (per-category coefficient provenance), ConstraintQuery (symbol+unit), ConstraintProblem (unknowns/facts/constraints/query).
Terms pinned as (symbol, coefficient) to match the gold serialization. Query is a minimal dedicated type, not R1's BoundUnknown (no degenerate fit). Off-serving package; no generate.derivation / reliability_gate import. 9 IR tests (shape + frozen + defaults).
91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
"""Problem model for the R2 finite-integer constraint organ.
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The structural layer above ``generate.constraint_comprehension.expr``: the unknowns, the
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raw per-category attribute coefficients (provenance), the assembled linear system, and the
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query. This is the R2 twin of the binding-graph model — a typed :class:`ConstraintProblem`
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the setup oracle grades and the solver consumes.
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Pure data — no behavior. Deterministic.
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Deviation from the design sketch: the query is a minimal dedicated :class:`ConstraintQuery`
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(symbol + unit), NOT the binding-graph ``BoundUnknown`` — R2 has no state-index /
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question-form axis, and forcing R1's unknown type onto it would be a degenerate fit.
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Multiple-choice options and the provided answer key are NOT part of the problem IR; they are
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answer-choice concerns graded separately (C4).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Literal
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from generate.constraint_comprehension.expr import LinearConstraint
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#: A finite-integer domain for an unknown. Count categories are nonnegative integers; the
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#: broader signed-integer domain is reserved for future signed-quantity problems (so the
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#: distinction is explicit, not a silent assumption that every unknown is a count).
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Domain = Literal["nonnegative_integer", "integer"]
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@dataclass(frozen=True, slots=True)
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class Unknown:
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"""One unknown category — ``large_bus``, ``chicken``, ``adult_ticket``.
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``symbol`` is the canonical identifier used in ``LinearExpr.terms``; ``entity`` is the
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surface category noun (provenance); ``unit`` is the category's own count unit (``bus``,
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``animal``); ``domain`` constrains the solution set (a count -> ``nonnegative_integer``).
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"""
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symbol: str
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entity: str
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unit: str
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domain: Domain
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@dataclass(frozen=True, slots=True)
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class AttributeFact:
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"""A per-category attribute coefficient read from the prose: ``large bus holds 50
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students`` -> ``AttributeFact("large_bus", "student", 50)``.
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This is the RAW reading (provenance); the weighted-total constraint
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``50*large_bus + 30*small_bus = 260`` is assembled FROM these. ``value`` is the integer
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coefficient — positivity and cross-category distinctness are the reader's/oracle's gate
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(C3/C6), not enforced in this pure-data layer.
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"""
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category: str
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measured_unit: str
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value: int
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@dataclass(frozen=True, slots=True)
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class ConstraintQuery:
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"""The asked unknown: which category's count is the answer, and in what unit."""
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symbol: str
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unit: str
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@dataclass(frozen=True, slots=True)
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class ConstraintProblem:
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"""A complete finite-integer constraint setup: the unknowns, the raw attribute
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coefficients, the assembled linear system, and the query.
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The setup oracle (C2) grades unknowns / units / domains / constraints / query
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canonically; the solver (C3) consumes ``unknowns`` + ``constraints``. ``facts`` is
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provenance — the coefficients the constraints were built from.
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"""
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unknowns: tuple[Unknown, ...]
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facts: tuple[AttributeFact, ...]
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constraints: tuple[LinearConstraint, ...]
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query: ConstraintQuery
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__all__ = [
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"AttributeFact",
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"ConstraintProblem",
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"ConstraintQuery",
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"Domain",
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"Unknown",
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]
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