import pytest import numpy as np from types import SimpleNamespace from recommendations.tests.test_data.measures_to_optimise import measures_to_optimise from recommendations.optimiser import optimiser_functions from recommendations.optimiser.GainOptimiser import GainOptimiser from recommendations.optimiser.CostOptimiser import CostOptimiser class TestPrepareInputMeasures: def test_returns_expected_structure_without_ventilation(self): recs = [ [ # loft insulation measure {"recommendation_id": "loft1", "type": "loft_insulation", "total": 100, "kwh_savings": 200, "energy_cost_savings": 10, "has_battery": False, "measure_type": "loft_insulation", "partial_project_funding": 0, "partial_project_score": 0, "uplift_project_score": 0, }, ], ] measures = optimiser_functions.prepare_input_measures(recs, goal="Energy Savings", needs_ventilation=False) assert isinstance(measures, list) assert measures[0][0]["id"] == "loft1" assert measures[0][0]["cost"] == 100 assert measures[0][0]["gain"] == 200 def test_bundles_ventilation_when_needed(self, monkeypatch): # patch measures_needing_ventilation so that "wall_insulation" needs ventilation monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation", ["internal_wall_insulation"]) recs = [ [{"recommendation_id": "wall1", "type": "internal_wall_insulation", "total": 500, "kwh_savings": 300, "energy_cost_savings": 5, "has_battery": False, "measure_type": "internal_wall_insulation", "partial_project_funding": 0, "partial_project_score": 0, "uplift_project_score": 0, }], [{"recommendation_id": "vent1", "type": "mechanical_ventilation", "total": 50, "kwh_savings": 30, "energy_cost_savings": 5, "has_battery": False, "measure_type": "mechanical_ventilation", "partial_project_funding": 0, "partial_project_score": 0, "uplift_project_score": 0, }], ] measures = optimiser_functions.prepare_input_measures(recs, goal="Energy Savings", needs_ventilation=True) wall_option = measures[0][0] assert wall_option["cost"] == 550 assert wall_option["gain"] == 330 assert "+mechanical_ventilation" in wall_option["type"] def test_filters_out_negative_cost_savings(self): recs = [ [{"recommendation_id": "bad1", "type": "loft_insulation", "total": 200, "kwh_savings": 100, "energy_cost_savings": -5, "has_battery": False, "partial_project_funding": 0, "partial_project_score": 0, "uplift_project_score": 0, }], ] measures = optimiser_functions.prepare_input_measures(recs, goal="Energy Savings", needs_ventilation=False) assert measures == [] # should skip negative cost saving recs class TestCalculateFixedGain: def test_no_required_measures_returns_zero(self): fixed_gain = optimiser_functions.calculate_fixed_gain( [], {}, SimpleNamespace(id="P1"), needs_ventilation=False ) assert fixed_gain == 0 def test_sums_max_sap_points_per_type(self, monkeypatch): monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation", ["internal_wall_insulation"]) required_measures = [ [{"type": "internal_wall_insulation", "sap_points": 5}, {"type": "internal_wall_insulation", "sap_points": 10}], [{"type": "loft_insulation", "sap_points": 3}] ] recommendations = {"P1": [[{"type": "mechanical_ventilation", "sap_points": 2}]]} prop = SimpleNamespace(id="P1") gain = optimiser_functions.calculate_fixed_gain( required_measures, recommendations, prop, needs_ventilation=True ) # Should take max of wall (10) + loft (3) + ventilation (2) assert gain == 15 class TestCalculateGain: def test_returns_none_for_energy_savings_goal(self): body = SimpleNamespace(goal="Energy Savings") prop = SimpleNamespace(data={"current-energy-efficiency": "50"}) gain = optimiser_functions.calculate_gain(body, prop, fixed_gain=0) assert gain is None def test_returns_zero_for_already_installed_getting_to_target(self): body = SimpleNamespace(goal="Increasing EPC", goal_value="C") p = SimpleNamespace(data={"current-energy-efficiency": "67"}, id=1) fixed_gain = 0 eco_packages = {1: (None, None, None, [])} already_installed_sap = 2 gain = optimiser_functions.calculate_gain( body=body, p=p, fixed_gain=fixed_gain, eco_packages=eco_packages, already_installed_gain=already_installed_sap ) assert gain == 0 def test_calculates_gain_for_epc(self, monkeypatch): # patch cost optimiser calculation monkeypatch.setattr(optimiser_functions, "epc_to_sap_lower_bound", lambda goal_value: 69) body = SimpleNamespace(goal="Increasing EPC", goal_value="C", simulate_sap_10=False) prop = SimpleNamespace(data={"current-energy-efficiency": "50"}) gain = optimiser_functions.calculate_gain(body, prop, fixed_gain=2) assert gain == 17.5 class TestAddRequiredMeasures: def test_adds_cheapest_required_measure(self): property_id = "P1" required_measures = [ [{"recommendation_id": "a", "total": 100, "sap_points": 5, "type": "loft_insulation"}, {"recommendation_id": "b", "total": 80, "sap_points": 6, "type": "loft_insulation"}] ] recommendations = { "P1": [[{"recommendation_id": "a", "total": 100, "sap_points": 5, "type": "loft_insulation"}, {"recommendation_id": "b", "total": 80, "sap_points": 6, "type": "loft_insulation"}]] } selected = set() result = optimiser_functions.add_required_measures(property_id, required_measures, recommendations, selected) # cheapest should be b assert "b" in selected assert any(rec["id"] == "b" for rec in result) class TestAddBestPracticeMeasures: def test_adds_ventilation_and_trickle_vents(self, monkeypatch): monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation", ["internal_wall_insulation"]) property_id = "P1" solution = [{"type": "internal_wall_insulation", "id": "w1", "gain": 10, "cost": 100}] recommendations = { "P1": [ [{"type": "mechanical_ventilation", "recommendation_id": "vent1"}], [{"type": "trickle_vents", "recommendation_id": "trickle1"}] ] } selected = set() updated = optimiser_functions.add_best_practice_measures(property_id, solution, recommendations, selected) assert "vent1" in updated assert "trickle1" in updated class TestFlattenRecommendationsWithDefaults: def test_marks_selected_and_flattens(self): property_id = "P1" recommendations = { "P1": [ [{"recommendation_id": "a", "foo": 1}, {"recommendation_id": "b", "foo": 2}], [{"recommendation_id": "c", "foo": 3}] ] } selected = {"b", "c"} result = optimiser_functions.flatten_recommendations_with_defaults(property_id, recommendations, selected) # All recs should now have a default key assert all("default" in rec for rec in result) assert next(r for r in result if r["recommendation_id"] == "b")["default"] is True assert next(r for r in result if r["recommendation_id"] == "a")["default"] is False class TestIncreasingEpcE2e: """ Test out the classic increasing EPC optimisation flow end-to-end. We have a goal (Increasing EPC), no budget, and we expect the optimiser to choose the best set of measures and include best-practice ventilation. """ @pytest.fixture def setup_case(self): # Dummy property object p = SimpleNamespace( id="P1", has_ventilation=False, data={"current-energy-efficiency": "52"}, ) # Dummy request body body = SimpleNamespace( goal="Increasing EPC", goal_value="C", optimise=True, budget=None, simulate_sap_10=False, required_measures=[] ) recommendations = {"P1": measures_to_optimise} return p, body, recommendations def test_end_to_end_increasing_epc(self, setup_case): p, body, recommendations = setup_case # --------------------- # RUN THE OPTIMISATION LOOP # --------------------- property_measure_types = {rec["type"] for recs in recommendations[p.id] for rec in recs} property_required_measures = [m for m in recommendations[p.id] if m[0]["type"] in body.required_measures] measures_to_optimise = [m for m in recommendations[p.id] if m[0]["type"] not in body.required_measures] # ventilation flag needs_ventilation = any( x in property_measure_types for x in optimiser_functions.assumptions.measures_needing_ventilation ) and not p.has_ventilation assert needs_ventilation # Input the various things we need - set all to 0 for group in measures_to_optimise: for r in group: ( r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"], r["uplift_project_score"], ) = ( 0, 0, 0, 0 ) input_measures = optimiser_functions.prepare_input_measures(measures_to_optimise, body.goal, needs_ventilation) assert input_measures, "Expected measures to optimise" assert len(input_measures) == 7 fixed_gain = optimiser_functions.calculate_fixed_gain( property_required_measures, recommendations, p, needs_ventilation ) assert fixed_gain == 0, "No required measures should mean fixed gain is 0" gain = optimiser_functions.calculate_gain(body=body, p=p, fixed_gain=fixed_gain) assert gain == 17.5, "Expected gain to be calculated correctly based on fixed gain and SAP target" optimiser = ( GainOptimiser( input_measures, max_cost=body.budget, max_gain=gain, allow_slack=body.goal == "Increasing EPC" ) if body.budget else CostOptimiser(input_measures, min_gain=gain) ) optimiser.setup() optimiser.solve() solution = optimiser.solution assert solution, "Optimiser should return a non-empty solution" assert all("id" in m for m in solution) assert any("solar_pv" in m["type"] for m in solution), "Expected solar PV to be included" # Collect selected measure IDs selected = {r["id"] for r in solution} assert selected == {'7_phase=6', '5_phase=4', '10_phase=7'} assert float(optimiser.solution_gain) == 17.6 # Add required measures (none here) solution = optimiser_functions.add_required_measures( property_id=p.id, property_required_measures=property_required_measures, recommendations=recommendations, selected=selected, ) assert solution == [ {'id': '5_phase=4', 'cost': 58.8, 'gain': 2, 'type': 'low_energy_lighting'}, {'id': '7_phase=6', 'cost': 30.0, 'gain': np.float64(3.6), 'type': 'secondary_heating'}, {'id': '10_phase=7', 'cost': 5826.491999999999, 'gain': np.float64(12.0), 'type': 'solar_pv'} ] total_optimised_gain = sum(m["gain"] for m in solution) assert total_optimised_gain == 17.6, "Total gain of optimised measures should meet or exceed target gain" selected = optimiser_functions.add_best_practice_measures(p.id, solution, recommendations, selected) # Flatten recommendations for output flattened = optimiser_functions.flatten_recommendations_with_defaults(p.id, recommendations, selected) # --------------------- # FINAL ASSERTIONS # --------------------- assert isinstance(flattened, list) assert all("default" in rec for rec in flattened) assert any(rec["default"] for rec in flattened), "Some measures should be marked as default" # We don't add ventilation as major insulation work isn't done ventilation_added = any(rec["recommendation_id"] == "3_phase=2" and rec["default"] for rec in flattened) assert not ventilation_added, "Ventilation should not be added without major insulation work"