feat(fase2): promote blocking validator + IR to src

src/portfolio_optimiser/ir.py

@@ -0,0 +1,44 @@
+"""Typed Pydantic IR for a candidate cost-saving measure (B1).
+
+Pure module — **no** ``agent_framework`` and no solver. The two structural invariants
+(non-negative quantities; a claimed saving may not exceed the affected items' own total)
+are enforced at construction by Pydantic, so a malformed proposal can never be built. This
+IR is the D7-portable contract both the deterministic validator and the LLM->IR generator
+speak.
+"""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class AffectedItem(BaseModel):
+    """One project cost line a proposal claims to save against."""
+
+    code: str
+    quantity: float = Field(ge=0)  # quantities must be >= 0
+    unit_cost: float = Field(gt=0)
+
+    @property
+    def total(self) -> float:
+        return self.quantity * self.unit_cost
+
+
+class SavingsProposal(BaseModel):
+    """Typed IR for a candidate cost-saving measure (B1)."""
+
+    project_id: str
+    measure: str
+    affected_items: list[AffectedItem] = Field(min_length=1)
+    claimed_saving_nok: float = Field(gt=0)
+    # code -> (low_unit_cost, high_unit_cost) for the Monte Carlo step; empty = degenerate.
+    assumptions: dict[str, tuple[float, float]] = Field(default_factory=dict)
+
+    @model_validator(mode="after")
+    def _claim_within_affected_total(self) -> SavingsProposal:
+        total = sum(item.total for item in self.affected_items)
+        if self.claimed_saving_nok > total:
+            raise ValueError(
+                f"claimed saving {self.claimed_saving_nok} exceeds affected items' total {total}"
+            )
+        return self

src/portfolio_optimiser/validator.py

@@ -0,0 +1,173 @@
+"""Deterministic, blocking hybrid validator (B1) — the obligatory, non-optional gate.
+
+Pure module: **NO** ``agent_framework`` import (D7-portable core). Three stages over the
+typed IR (``ir.py``):
+
+1. **Pydantic IR** invariants already ran at construction (``ir.SavingsProposal``).
+2. **PuLP solver-in-the-loop** — a real CBC solve bounds the maximum feasible saving (R2).
+   CBC ships in PuLP's wheel; if it is genuinely absent the step **escalates**
+   (``CbcUnavailable``) — no silent LP-relaxation fallback.
+3. **Monte Carlo** — stdlib ``random`` (seeded ``_MC_SEED``) + ``statistics.quantiles`` over
+   uncertain unit-costs give P10/P50/P90 of the feasible saving.
+
+The structural block (stage 4) returns a ``Rejection`` that is a *different type* from
+``ValidatedProposal`` and carries no percentiles, so it can never be consumed as validated.
+
+Promoted verbatim from ``spikes/c_validator.py``. The one deliberate change vs the spike:
+``self_repair`` no longer borrows the harness ``Budget`` (that pulls ``agent_framework`` via
+``spikes/_harness``) — it loops directly on ``max_attempts``; the token-budget bound is
+layered on by the Step 10 generate loop, keeping THIS module pure.
+"""
+
+from __future__ import annotations
+
+import random
+import statistics
+import warnings
+from collections.abc import Callable
+from contextlib import contextmanager
+from dataclasses import dataclass
+
+import pulp
+
+from portfolio_optimiser.ir import AffectedItem, SavingsProposal
+from portfolio_optimiser.reference_domain import Project
+
+MAX_SAVING_FRACTION = 0.30
+"""Policy cap: at most 30% of an affected item's cost is realistically recoverable as a
+saving. The LP bounds the feasible saving by this fraction."""
+
+_MC_SAMPLES = 512
+_MC_SEED = 20260624
+
+
+class CbcUnavailable(RuntimeError):
+    """PuLP's bundled CBC solver is not available — escalate (no silent fallback)."""
+
+
+@contextmanager
+def _quiet_pulp():
+    """Silence PuLP 3.x's ``PULP_CBC_CMD`` DeprecationWarning. The bundled CBC is only
+    reachable via ``PULP_CBC_CMD``; PuLP 4.0 will require ``pip install pulp[cbc]`` +
+    COIN_CMD (a migration note). The warning is cosmetic here."""
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", DeprecationWarning)
+        yield
+
+
+@dataclass(frozen=True)
+class ValidatedProposal:
+    """A proposal that passed every stage. Carries the Monte Carlo percentiles."""
+
+    proposal: SavingsProposal
+    p10: float
+    p50: float
+    p90: float
+    nominal_feasible: float
+
+
+@dataclass(frozen=True)
+class Rejection:
+    """A structurally-blocked proposal. Distinct type, no percentiles — it can never be
+    consumed as a ``ValidatedProposal``."""
+
+    proposal: SavingsProposal
+    reason: str
+
+
+def _solve_max_feasible(items: list[AffectedItem], fraction: float) -> float:
+    """Real CBC solve: maximize total saving subject to a per-item upper bound and a
+    global fraction cap. Raises ``CbcUnavailable`` if CBC is genuinely missing."""
+    with _quiet_pulp():
+        solver = pulp.PULP_CBC_CMD(msg=False)
+        if not solver.available():
+            raise CbcUnavailable("PuLP's bundled CBC solver is not available on this platform")
+        prob = pulp.LpProblem("max_feasible_saving", pulp.LpMaximize)
+        xs = [pulp.LpVariable(f"x_{i}", lowBound=0, upBound=it.total) for i, it in enumerate(items)]
+        prob += pulp.lpSum(xs)
+        prob += pulp.lpSum(xs) <= fraction * sum(it.total for it in items)
+        status = prob.solve(solver)
+    if pulp.LpStatus[status] != "Optimal":
+        raise CbcUnavailable(
+            f"CBC did not reach an optimal solution (status={pulp.LpStatus[status]})"
+        )
+    return float(pulp.value(prob.objective))
+
+
+def _monte_carlo(
+    proposal: SavingsProposal, *, fraction: float = MAX_SAVING_FRACTION
+) -> tuple[float, float, float]:
+    """Vary uncertain unit-costs (seeded) and return (P10, P50, P90) of the feasible
+    saving. Uses the LP's closed-form optimum (= fraction x sum of sampled totals), which
+    is exact here, so we do NOT spawn a CBC subprocess per sample (D6)."""
+    rng = random.Random(_MC_SEED)
+    feasibles: list[float] = []
+    for _ in range(_MC_SAMPLES):
+        total = 0.0
+        for item in proposal.affected_items:
+            rng_range = proposal.assumptions.get(item.code)
+            unit_cost = rng.uniform(*rng_range) if rng_range else item.unit_cost
+            total += item.quantity * unit_cost
+        feasibles.append(fraction * total)
+    deciles = statistics.quantiles(feasibles, n=10, method="inclusive")
+    return deciles[0], deciles[4], deciles[8]  # P10, P50, P90
+
+
+def validate_proposal(proposal: SavingsProposal) -> ValidatedProposal | Rejection:
+    """Deterministic blocking validation. Returns a ``ValidatedProposal`` only when the
+    claim is feasible; otherwise a ``Rejection`` that cannot be consumed as validated."""
+    # Stage 1 (Pydantic) already ran at construction. Stage 2: real CBC solve.
+    nominal = _solve_max_feasible(proposal.affected_items, MAX_SAVING_FRACTION)
+    # Stage 3: Monte Carlo percentiles of the feasible saving.
+    p10, p50, p90 = _monte_carlo(proposal)
+    # Stage 4: structural block — a claim above the optimistic feasible (P90) is out of range.
+    if proposal.claimed_saving_nok > p90:
+        return Rejection(
+            proposal=proposal,
+            reason=f"claimed saving {proposal.claimed_saving_nok:.0f} exceeds P90 feasible {p90:.0f}",
+        )
+    return ValidatedProposal(proposal=proposal, p10=p10, p50=p50, p90=p90, nominal_feasible=nominal)
+
+
+def self_repair(
+    generate: Callable[[int], SavingsProposal],
+    *,
+    max_attempts: int = 3,
+) -> ValidatedProposal | Rejection:
+    """Call ``generate(attempt)`` and validate; retry on rejection up to ``max_attempts``,
+    then hard-stop and return the last rejection. Attempts-bounded — never loops forever
+    (B4). The token-budget bound is layered on by the Step 10 generate loop, not here (this
+    module stays pure: no ``agent_framework`` import)."""
+    if max_attempts <= 0:
+        raise ValueError(f"max_attempts must be positive, got {max_attempts}")
+    last: Rejection | None = None
+    for attempt in range(1, max_attempts + 1):
+        result = validate_proposal(generate(attempt))
+        if isinstance(result, ValidatedProposal):
+            return result
+        last = result
+    assert last is not None
+    return last
+
+
+def proposal_for(
+    project: Project,
+    codes: list[str],
+    *,
+    claimed_saving_nok: float,
+    measure: str = "Reduce scope on selected cost codes",
+    assumptions: dict[str, tuple[float, float]] | None = None,
+) -> SavingsProposal:
+    """Build a ``SavingsProposal`` from a real reference project's cost items (helper)."""
+    items = [
+        AffectedItem(code=ci.code, quantity=ci.quantity, unit_cost=ci.unit_cost)
+        for ci in project.cost_items
+        if ci.code in codes
+    ]
+    return SavingsProposal(
+        project_id=project.id,
+        measure=measure,
+        affected_items=items,
+        claimed_saving_nok=claimed_saving_nok,
+        assumptions=assumptions or {},
+    )

tests/test_validator.py

@@ -0,0 +1,77 @@
+"""Step 2 tests — the promoted blocking validator + IR (B1). Crafted IR, deterministic.
+
+Determinism is asserted over FIXED inputs (seed 20260624), never through an LLM. The
+out-of-range path returns a ``Rejection`` that carries no percentiles, so it can never be
+consumed as validated. Pattern: tests/spikes/test_c_validator.py.
+"""
+
+import pytest
+from pydantic import ValidationError
+
+from portfolio_optimiser.ir import SavingsProposal
+from portfolio_optimiser.reference_domain import load_reference_projects
+from portfolio_optimiser.validator import (
+    Rejection,
+    ValidatedProposal,
+    proposal_for,
+    validate_proposal,
+)
+
+_ASSUMPTIONS = {"05.2": (200.0, 230.0), "03.1": (290.0, 330.0)}
+
+
+@pytest.fixture(scope="module")
+def project():
+    return load_reference_projects()[0]  # FV42-GSV-E1
+
+
+def _valid(project) -> SavingsProposal:
+    # Affected total ~1.48M NOK; 30% feasible cap ~0.44M. Claim 200k is comfortably feasible.
+    return proposal_for(
+        project, ["05.2", "03.1"], claimed_saving_nok=200_000, assumptions=_ASSUMPTIONS
+    )
+
+
+def _out_of_range(project) -> SavingsProposal:
+    # Constructs fine (800k <= 1.48M total) but far exceeds the ~0.44M feasible cap.
+    return proposal_for(
+        project, ["05.2", "03.1"], claimed_saving_nok=800_000, assumptions=_ASSUMPTIONS
+    )
+
+
+def test_valid_proposal_yields_ordered_percentiles(project) -> None:
+    result = validate_proposal(_valid(project))
+    assert isinstance(result, ValidatedProposal)
+    assert result.p10 <= result.p50 <= result.p90  # P10 <= P50 <= P90
+    assert result.nominal_feasible > 0  # the CBC solve produced a feasible bound
+
+
+def test_out_of_range_is_structurally_blocked(project) -> None:
+    result = validate_proposal(_out_of_range(project))
+    assert isinstance(result, Rejection)
+    assert result.reason  # carries a human-readable reason
+    # A Rejection carries no percentiles -> it can never be consumed as validated.
+    with pytest.raises(AttributeError):
+        _ = result.p50  # type: ignore[attr-defined]
+
+
+def test_monte_carlo_is_reproducible(project) -> None:
+    a = validate_proposal(_valid(project))
+    b = validate_proposal(_valid(project))
+    assert isinstance(a, ValidatedProposal) and isinstance(b, ValidatedProposal)
+    assert (a.p10, a.p50, a.p90) == (b.p10, b.p50, b.p90)
+
+
+def test_pydantic_blocks_negative_quantity() -> None:
+    with pytest.raises(ValidationError):
+        SavingsProposal(
+            project_id="X",
+            measure="bad",
+            affected_items=[{"code": "A", "quantity": -1, "unit_cost": 10}],
+            claimed_saving_nok=1,
+        )
+
+
+def test_pydantic_blocks_claim_above_affected_total(project) -> None:
+    with pytest.raises(ValidationError):
+        proposal_for(project, ["05.2"], claimed_saving_nok=99_000_000)