Two tightenings, each measured by a detached-mutation run: (1) The validator now blocks a claim above the CBC nominal feasible, in ADDITION to the P90 stage. Neither dominates the other: an upward-skewed assumption band lifts P90 ABOVE nominal -- so P90 alone passed review counterexample #1 (claim 100k, nominal 90k, band [0.70, 1.40], measured P90 121057) -- while a downward-skewed band pushes P90 below it. Independent gate, same Rejection type, existing rejections keep their existing reason. (2) An assumption band must enclose its item's unit_cost (low <= unit_cost <= high, inclusive). A band that misses it states a different price rather than an uncertainty, and every Monte Carlo draw would then sample away from the item's stated cost. Checked exactly where the Monte Carlo looks bands up -- per affected item, by code; a band keyed to no affected item is never sampled and so has no unit_cost to enclose. The premise was re-verified against ground truth before building on it, not taken from STATE: 05.2 unit_cost 215 in (200,230), 03.1 310 in (290,330), ENERGI-TOTAL-EL 1.0 in [0.70,1.40] and (0.8,1.2). No fixture violates it. The LLM path already catches ValidationError as a meter-bounded retry (generate.py:138), so the new invariant cannot crash a run. Mutations, all RED: detach the nominal block; drop the model_validator decorator; make the enclosure strict. tests/test_bygg_energi_mikro.py and the commons golden are UNCHANGED and green -- the regression proof. 586 -> 589 tests. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017DDXwUqHVAQQeYE7X1TXy5
134 lines
5.4 KiB
Python
134 lines
5.4 KiB
Python
"""Step 2 tests — the promoted blocking validator + IR (B1). Crafted IR, deterministic.
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Determinism is asserted over FIXED inputs (seed 20260624), never through an LLM. The
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out-of-range path returns a ``Rejection`` that carries no percentiles, so it can never be
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consumed as validated. Pattern: tests/spikes/test_c_validator.py.
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"""
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import pytest
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from pydantic import ValidationError
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from portfolio_optimiser.ir import AffectedItem, SavingsProposal
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from portfolio_optimiser.reference_domain import load_reference_projects
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from portfolio_optimiser.validator import (
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Rejection,
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ValidatedProposal,
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_monte_carlo,
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proposal_for,
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validate_proposal,
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)
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_ASSUMPTIONS = {"05.2": (200.0, 230.0), "03.1": (290.0, 330.0)}
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@pytest.fixture(scope="module")
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def project():
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return load_reference_projects()[0] # FV42-GSV-E1
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def _valid(project) -> SavingsProposal:
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# Affected total ~1.48M NOK; 30% feasible cap ~0.44M. Claim 200k is comfortably feasible.
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return proposal_for(
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project, ["05.2", "03.1"], claimed_saving_nok=200_000, assumptions=_ASSUMPTIONS
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)
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def _out_of_range(project) -> SavingsProposal:
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# Constructs fine (800k <= 1.48M total) but far exceeds the ~0.44M feasible cap.
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return proposal_for(
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project, ["05.2", "03.1"], claimed_saving_nok=800_000, assumptions=_ASSUMPTIONS
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)
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def test_valid_proposal_yields_ordered_percentiles(project) -> None:
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result = validate_proposal(_valid(project))
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assert isinstance(result, ValidatedProposal)
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assert result.p10 <= result.p50 <= result.p90 # P10 <= P50 <= P90
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assert result.nominal_feasible > 0 # the CBC solve produced a feasible bound
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def test_out_of_range_is_structurally_blocked(project) -> None:
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result = validate_proposal(_out_of_range(project))
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assert isinstance(result, Rejection)
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assert result.reason # carries a human-readable reason
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# A Rejection carries no percentiles -> it can never be consumed as validated.
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with pytest.raises(AttributeError):
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_ = result.p50 # type: ignore[attr-defined]
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def test_monte_carlo_is_reproducible(project) -> None:
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a = validate_proposal(_valid(project))
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b = validate_proposal(_valid(project))
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assert isinstance(a, ValidatedProposal) and isinstance(b, ValidatedProposal)
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assert (a.p10, a.p50, a.p90) == (b.p10, b.p50, b.p90)
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def test_pydantic_blocks_negative_quantity() -> None:
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with pytest.raises(ValidationError):
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SavingsProposal(
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project_id="X",
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measure="bad",
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affected_items=[{"code": "A", "quantity": -1, "unit_cost": 10}],
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claimed_saving_nok=1,
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)
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def test_pydantic_blocks_claim_above_affected_total(project) -> None:
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with pytest.raises(ValidationError):
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proposal_for(project, ["05.2"], claimed_saving_nok=99_000_000)
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def _above_nominal_within_p90() -> SavingsProposal:
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"""Review counterexample #1: affected total 300000 -> nominal feasible 0.30 x 300000 =
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90000, but the band [0.70, 1.40] is skewed UPWARD around unit_cost 1.0, so the Monte
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Carlo P90 sits above it. A claim of 100000 therefore clears the P90 stage while
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exceeding the deterministic bound the CBC solve actually established."""
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return SavingsProposal(
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project_id="BYGG-KONTOR-NORD",
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measure="LED-retrofit av 200 lysrorarmaturer",
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affected_items=[AffectedItem(code="ENERGI-TOTAL-EL", quantity=300_000, unit_cost=1.0)],
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claimed_saving_nok=100_000,
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assumptions={"ENERGI-TOTAL-EL": (0.70, 1.40)},
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)
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def test_claim_above_nominal_feasible_is_blocked_though_p90_would_pass() -> None:
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"""S2.7 (1): the validator enforces its OWN stage-2 boundary. The nominal block is an
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INDEPENDENT gate — neither stage dominates the other, because an upward-skewed band
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lifts P90 above nominal while a downward-skewed one pushes it below."""
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proposal = _above_nominal_within_p90()
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# Control: without the nominal block this proposal VALIDATES — the P90 stage lets it
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# through. If this assert ever fails, the test below has stopped gating the new stage.
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_, _, p90 = _monte_carlo(proposal)
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assert proposal.claimed_saving_nok <= p90
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result = validate_proposal(proposal)
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assert isinstance(result, Rejection)
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assert "nominal" in result.reason
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def test_pydantic_blocks_assumption_band_that_excludes_unit_cost() -> None:
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"""S2.7 (2): an assumption band is uncertainty AROUND the item's own unit_cost, so it
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must enclose it. A band of [1.8, 2.2] around unit_cost 1.0 states a different price,
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not an uncertainty — every Monte Carlo sample would then exceed the item's own cost."""
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with pytest.raises(ValidationError):
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SavingsProposal(
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project_id="X",
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measure="band around the wrong centre",
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affected_items=[AffectedItem(code="A", quantity=1000, unit_cost=1.0)],
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claimed_saving_nok=100,
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assumptions={"A": (1.8, 2.2)},
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)
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def test_assumption_band_bounds_are_inclusive() -> None:
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"""The enclosure is ``low <= unit_cost <= high``: a band that touches the unit_cost at
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either end is a legitimate one-sided uncertainty, not a violation."""
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for band in ((1.0, 1.4), (0.7, 1.0)):
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SavingsProposal(
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project_id="X",
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measure="one-sided uncertainty",
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affected_items=[AffectedItem(code="A", quantity=1000, unit_cost=1.0)],
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claimed_saving_nok=100,
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assumptions={"A": band},
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)
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