"""Behaviour of the Optimiser core: a grouped-knapsack MILP over per-Option
role-1 scores (ADR-0016). Picks at most one Option per Recommendation (disjoint
groups, no cross-group constraints) to maximise total SAP gain subject to the
Scenario budget. This is the warm-start *signal* — the truthful figure comes
from the whole-package re-score + repair (a later slice); here we test the
selection with synthetic scores and no calculator.
"""

from __future__ import annotations

from domain.modelling.optimiser import ScoredOption, optimise
from domain.modelling.recommendation import Cost, MeasureOption
from domain.modelling.simulation import EpcSimulation


def _scored(measure_type: str, *, gain: float, cost: float) -> ScoredOption:
    return ScoredOption(
        option=MeasureOption(
            measure_type=measure_type,
            description=measure_type,
            overlay=EpcSimulation(),
            cost=Cost(total=cost, contingency_rate=0.0),
        ),
        sap_gain=gain,
    )


def _selected_types(selection: list[ScoredOption]) -> set[str]:
    return {scored.option.measure_type for scored in selection}


def test_grouped_knapsack_maximises_gain_within_budget() -> None:
    # Arrange — wall group has two mutually-exclusive options; roof + floor one
    # each. EWI has the best gain but is unaffordable alongside the rest.
    groups: list[list[ScoredOption]] = [
        [
            _scored("external_wall_insulation", gain=10.0, cost=8000.0),
            _scored("cavity_wall_insulation", gain=6.0, cost=1000.0),
        ],
        [_scored("loft_insulation", gain=4.0, cost=1500.0)],
        [_scored("suspended_floor_insulation", gain=3.0, cost=2000.0)],
    ]

    # Act
    selection: list[ScoredOption] = optimise(groups, budget=5000.0)

    # Assert — cavity + loft + floor (cost 4500, gain 13) beats any package
    # containing the 8000 EWI option within the 5000 budget.
    assert _selected_types(selection) == {
        "cavity_wall_insulation",
        "loft_insulation",
        "suspended_floor_insulation",
    }


def test_picks_at_most_one_option_per_group() -> None:
    # Arrange — both wall options are individually affordable.
    groups: list[list[ScoredOption]] = [
        [
            _scored("external_wall_insulation", gain=10.0, cost=2000.0),
            _scored("cavity_wall_insulation", gain=6.0, cost=1000.0),
        ],
    ]

    # Act
    selection: list[ScoredOption] = optimise(groups, budget=10000.0)

    # Assert — never both treatments of the same wall; the higher-gain one wins.
    assert len(selection) == 1
    assert _selected_types(selection) == {"external_wall_insulation"}


def test_no_budget_picks_the_best_option_in_every_group() -> None:
    # Arrange
    groups: list[list[ScoredOption]] = [
        [
            _scored("external_wall_insulation", gain=10.0, cost=8000.0),
            _scored("cavity_wall_insulation", gain=6.0, cost=1000.0),
        ],
        [_scored("loft_insulation", gain=4.0, cost=1500.0)],
    ]

    # Act — None budget = unconstrained.
    selection: list[ScoredOption] = optimise(groups, budget=None)

    # Assert
    assert _selected_types(selection) == {
        "external_wall_insulation",
        "loft_insulation",
    }


def test_budget_too_small_for_any_option_selects_nothing() -> None:
    # Arrange
    groups: list[list[ScoredOption]] = [
        [_scored("cavity_wall_insulation", gain=6.0, cost=1000.0)],
        [_scored("loft_insulation", gain=4.0, cost=1500.0)],
    ]

    # Act
    selection: list[ScoredOption] = optimise(groups, budget=500.0)

    # Assert — nothing affordable; selecting none is the optimum.
    assert selection == []


def test_no_groups_selects_nothing() -> None:
    # Act / Assert
    assert optimise([], budget=10000.0) == []


def test_within_budget_partial_selection_prefers_the_higher_gain_option() -> None:
    # Arrange — only one of the two fits the budget; pick the affordable best.
    groups: list[list[ScoredOption]] = [
        [_scored("external_wall_insulation", gain=10.0, cost=8000.0)],
        [_scored("loft_insulation", gain=4.0, cost=1500.0)],
    ]

    # Act
    selection: list[ScoredOption] = optimise(groups, budget=2000.0)

    # Assert — EWI is unaffordable; loft alone is the best within £2000.
    assert _selected_types(selection) == {"loft_insulation"}