Model/recommendations/tests/test_optimisers.py

import pytest

from recommendations.optimiser.funding_optimiser import build_heat_pump_paths
from recommendations.optimiser.funding_optimiser import run_optimizer


class DummyProp:
    """Minimal property stub exposing just what your code reads."""

    def __init__(self):
        self.data = {
            "current-energy-rating": "E",  # or "D" for the special Social+D path
            "current-energy-efficiency": 55,  # numeric SAP points used in eligibility calc
            "mainheat-energy-eff": "Very Good",
        }
        self.has_ventilation = False
        self.floor_area = 70.0
        self.main_heating_controls = {"clean_description": "time and temperature zone control"}
        self.walls = {'original_description': 'Solid brick, as built, no insulation (assumed)',
                      'thermal_transmittance': None,
                      'thermal_transmittance_unit': None, 'is_cavity_wall': False, 'is_filled_cavity': False,
                      'is_solid_brick': True,
                      'is_system_built': False, 'is_timber_frame': False, 'is_granite_or_whinstone': False,
                      'is_as_built': True,
                      'is_cob': False, 'is_assumed': True, 'is_sandstone_or_limestone': False,
                      'insulation_thickness': 'none',
                      'external_insulation': False, 'internal_insulation': False}

        self.main_heating = {
            'original_description': 'Boiler and radiators, mains gas',
            'clean_description': 'Boiler and radiators, mains gas',
            'has_radiators': True, 'has_fan_coil_units': False, 'has_pipes_in_screed_above_insulation': False,
            'has_pipes_in_insulated_timber_floor': False, 'has_pipes_in_concrete_slab': False, 'has_boiler': True,
            'has_air_source_heat_pump': False, 'has_room_heaters': False, 'has_electric_storage_heaters': False,
            'has_warm_air': False, 'has_electric_underfloor_heating': False, 'has_electric_ceiling_heating': False,
            'has_community_scheme': False, 'has_ground_source_heat_pump': False, 'has_no_system_present': False,
            'has_portable_electric_heaters': False, 'has_water_source_heat_pump': False, 'has_electric_heat_pump':
                False,
            'has_micro-cogeneration': False, 'has_solar_assisted_heat_pump': False, 'has_exhaust_source_heat_pump':
                False,
            'has_community_heat_pump': False, 'has_hot-water-only': False, 'has_electric': False, 'has_mains_gas':
                True,
            'has_wood_logs': False, 'has_coal': False, 'has_oil': False, 'has_wood_pellets': False,
            'has_anthracite': False,
            'has_dual_fuel_mineral_and_wood': False, 'has_smokeless_fuel': False, 'has_lpg': False, 'has_b30k': False,
            'has_mineral_and_wood': False, 'has_dual_fuel_appliance': False, 'has_assumed': False,
            'has_electricaire': False,
            'has_assumed_for_most_rooms': False, 'has_underfloor_heating': False
        }

        self.main_fuel = {
            'original_description': 'mains gas (not community)', 'clean_description': 'Mains gas not community',
            'fuel_type': 'mains gas', 'tariff_type': None, 'is_community': False,
            'no_individual_heating_or_community_network': False, 'complex_fuel_type': None
        }


@pytest.fixture
def p():
    return DummyProp()


def test_build_heat_pump_paths():
    eg1 = build_heat_pump_paths([], ["loft_insulation"])

    assert eg1 == [{'AND': ['loft_insulation', 'air_source_heat_pump']}]

    eg2 = build_heat_pump_paths(["internal_wall_insulation", "external_wall_insulation"], ["loft_insulation"])

    assert eg2 == [{'AND': ['internal_wall_insulation', 'loft_insulation', 'air_source_heat_pump']},
                   {'AND': ['external_wall_insulation', 'loft_insulation', 'air_source_heat_pump']}]


def test_run_optimizer_empty_input():
    solution, cost, gain = run_optimizer([])
    assert solution is None
    assert cost == 0.0
    assert gain == 0.0


def test_uses_gain_optimiser_when_budget_provided(monkeypatch):
    captured_args = {}

    class FakeGainOptimiser:
        def __init__(self, measures, max_cost, max_gain, allow_slack):
            captured_args["measures"] = measures
            captured_args["max_cost"] = max_cost
            captured_args["max_gain"] = max_gain
            captured_args["allow_slack"] = allow_slack
            self.solution = [{"cost": 100}]
            self.solution_gain = 5

        def setup(self):
            pass

        def solve(self):
            pass

    monkeypatch.setattr(
        "recommendations.optimiser.funding_optimiser.GainOptimiser",
        FakeGainOptimiser
    )

    measures = [[{"cost": 100, "gain": 5}]]

    solution, cost, gain = run_optimizer(
        measures,
        budget=500,
        sub_target_gain=10,
        allow_slack=True
    )

    assert captured_args["max_cost"] == 500
    assert captured_args["max_gain"] == 10
    assert captured_args["allow_slack"] is True
    assert cost == 100
    assert gain == 5


def test_sub_target_gain_zero_sets_max_gain_zero(monkeypatch):
    captured_args = {}

    class FakeGainOptimiser:
        def __init__(self, measures, max_cost, max_gain, allow_slack):
            captured_args["max_gain"] = max_gain
            self.solution = []
            self.solution_gain = 0

        def setup(self):
            pass

        def solve(self):
            pass

    monkeypatch.setattr(
        "recommendations.optimiser.funding_optimiser.GainOptimiser",
        FakeGainOptimiser
    )

    measures = [[{"cost": 100, "gain": 5}]]

    run_optimizer(
        measures,
        budget=500,
        sub_target_gain=0
    )

    assert captured_args["max_gain"] == 0


def test_sub_target_gain_none_sets_max_gain_infinity(monkeypatch):
    captured_args = {}

    class FakeGainOptimiser:
        def __init__(self, measures, max_cost, max_gain, allow_slack):
            captured_args["max_gain"] = max_gain
            self.solution = []
            self.solution_gain = 0

        def setup(self):
            pass

        def solve(self):
            pass

    monkeypatch.setattr(
        "recommendations.optimiser.funding_optimiser.GainOptimiser",
        FakeGainOptimiser
    )

    measures = [[{"cost": 100, "gain": 5}]]

    run_optimizer(
        measures,
        budget=500,
        sub_target_gain=None
    )

    assert captured_args["max_gain"] == float("inf")


def test_uses_cost_optimiser_when_no_budget(monkeypatch):
    captured_args = {}

    class FakeCostOptimiser:
        def __init__(self, measures, min_gain):
            captured_args["min_gain"] = min_gain
            self.solution = [{"cost": 50}]
            self.solution_gain = 10

        def setup(self):
            pass

        def solve(self):
            pass

    monkeypatch.setattr(
        "recommendations.optimiser.funding_optimiser.CostOptimiser",
        FakeCostOptimiser
    )

    measures = [[{"cost": 50, "gain": 10}]]

    solution, cost, gain = run_optimizer(
        measures,
        sub_target_gain=10
    )

    assert captured_args["min_gain"] == 10
    assert cost == 50
    assert gain == 10