portfolio-optimiser/tests/test_verdicts.py

"""Step 3 tests — non-tautological top-K retrieval + REAL-SessionContext two-arg injection.

The true match shares the *structured* similarity fields with the query but uses different
description text; the decoys share surface text but differ structurally. The injection test
uses a REAL ``agent_framework.SessionContext`` (not a single-arg fake), exercising the
genuine two-arg ``extend_instructions(source_id, instructions)`` GA signature — retiring the
Critical Fase 1 risk. Pattern: tests/spikes/test_d_verdictstore.py + real SessionContext.
"""

from pathlib import Path

import pytest
from agent_framework import SessionContext

from portfolio_optimiser.verdicts import (
    ExpeLContextProvider,
    ProposalFeatures,
    Verdict,
    VerdictStore,
    bundle_candidate_features,
    capture_verdict,
    seed_store_from_bundle,
)

_BUNDLE_DIR = Path(__file__).resolve().parents[1] / "shared" / "examples" / "bygg-energi-mikro"

_QUERY = ProposalFeatures(
    affected_codes=frozenset({"05.2", "03.1"}),
    measure_type="scope_reduction",
    claimed_saving_nok=220_000,  # bucket [100k, 500k)
    description="asphalt base course reduction near school",
)


def _store_with_true_match_and_decoys() -> tuple[VerdictStore, str]:
    true_match = Verdict(
        id="TRUE",
        proposal_features=ProposalFeatures(
            affected_codes=frozenset({"05.2", "03.1"}),  # same codes
            measure_type="scope_reduction",  # same measure type
            claimed_saving_nok=200_000,  # same magnitude bucket
            description="zzz totally unrelated wording alpha beta",  # DIFFERENT text
        ),
        decision="approved",
        rationale="prior scope reduction on the same codes was approved",
    )
    decoy_low = Verdict(
        id="DECOY-LOW",
        proposal_features=ProposalFeatures(
            affected_codes=frozenset({"09.1"}),
            measure_type="rate_renegotiation",
            claimed_saving_nok=50_000,
            description="asphalt base course reduction near school",  # same words as query
        ),
        decision="rejected",
        rationale="surface-text decoy",
    )
    decoy_high = Verdict(
        id="DECOY-HIGH",
        proposal_features=ProposalFeatures(
            affected_codes=frozenset({"21.2"}),
            measure_type="material_substitution",
            claimed_saving_nok=700_000,
            description="asphalt base course reduction extra words",
        ),
        decision="rejected",
        rationale="surface-text decoy",
    )
    return VerdictStore(verdicts=[decoy_low, true_match, decoy_high]), "TRUE"


def test_retrieve_finds_structural_match_over_text_decoys() -> None:
    store, true_id = _store_with_true_match_and_decoys()
    hits = store.retrieve(_QUERY, k=3)
    assert hits[0].id == true_id  # structural match ranks #1 despite different wording


def test_retrieve_is_deterministic() -> None:
    store, _ = _store_with_true_match_and_decoys()
    assert [h.id for h in store.retrieve(_QUERY, k=3)] == [
        h.id for h in store.retrieve(_QUERY, k=3)
    ]


def test_retrieve_rejects_non_positive_k() -> None:
    store, _ = _store_with_true_match_and_decoys()
    with pytest.raises(ValueError):
        store.retrieve(_QUERY, k=0)


def test_capture_verdict_mints_stable_id() -> None:
    a = capture_verdict(_QUERY, "approved", "ok")
    b = capture_verdict(
        ProposalFeatures(
            affected_codes=frozenset({"03.1", "05.2"}),  # same set, different order
            measure_type="scope_reduction",
            claimed_saving_nok=220_000,
            description="DIFFERENT surface wording entirely",  # text excluded from the id
        ),
        "approved",
        "ok",
    )
    assert a.id == b.id  # structurally identical -> stable id
    assert len(a.id) == 16


async def test_before_run_populates_real_sessioncontext_two_arg() -> None:
    store, true_id = _store_with_true_match_and_decoys()
    provider = ExpeLContextProvider(store, _QUERY, k=2)
    # A REAL SessionContext (not a single-arg fake) — exercises the genuine GA two-arg
    # extend_instructions(source_id, instructions) signature.
    ctx = SessionContext(input_messages=[], instructions=[])
    await provider.before_run(agent=None, session=None, context=ctx, state={})
    assert any(true_id in instr for instr in ctx.instructions)


# --- OKF-bundle seeding (Fase 2a): the pre-hypothesis ExpeL query key + the seed store ---


def test_bundle_candidate_features_keys_on_the_ir_projection() -> None:
    """The pre-hypothesis ExpeL query is the candidate measure's cost-IR features (from the
    bundle's ``validator-input.json``) — available BEFORE any proposal is generated."""
    features = bundle_candidate_features(str(_BUNDLE_DIR))
    assert features.affected_codes == frozenset({"ENERGI-TOTAL-EL"})
    assert "LED-retrofit" in features.measure_type
    assert features.claimed_saving_nok == 30000


def test_seed_store_from_bundle_carries_the_realization_signal() -> None:
    """Each ``type: verdict`` file becomes a structurally-keyed ``Verdict`` whose rationale carries
    the learning signal the validator cannot compute (the realization rate 0.82)."""
    store = seed_store_from_bundle(str(_BUNDLE_DIR))
    assert len(store.verdicts) == 1
    seed = store.verdicts[0]
    assert seed.proposal_features.affected_codes == frozenset({"ENERGI-TOTAL-EL"})
    assert "approved" in seed.decision
    assert "0.82" in seed.rationale


def test_seed_store_retrieval_matches_the_candidate() -> None:
    """A3: seed and query derive from the SAME IR -> similarity 1.0 -> the lone seed is retrieved
    for the candidate (the structural match the Step-1 wiring relies on)."""
    store = seed_store_from_bundle(str(_BUNDLE_DIR))
    query = bundle_candidate_features(str(_BUNDLE_DIR))
    hits = store.retrieve(query, k=3)
    assert len(hits) == 1
    assert "0.82" in hits[0].rationale