From 201e8dd82901a8d8a3f4fc93c763f34272c745e8 Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Thu, 22 Jan 2026 08:49:57 +0000
Subject: [PATCH] fixed heating recommendation tests

---
 .../optimiser/optimiser_functions.py          |  11 +-
 recommendations/recommendation_utils.py       |  15 +-
 .../test_data/heating_recommendations_data.py |  31 +-
 .../tests/test_data/measures_to_optimise.py   |  24 +-
 .../tests/test_heating_recommendations.py     |   9 +-
 .../tests/test_lighting_recommendations.py    |  19 +-
 .../tests/test_optimiser_functions.py         |  11 +-
 recommendations/tests/test_optimisers.py      | 661 +-----------------
 .../tests/test_recommendation_utils.py        |  43 +-
 9 files changed, 94 insertions(+), 730 deletions(-)

diff --git a/recommendations/optimiser/optimiser_functions.py b/recommendations/optimiser/optimiser_functions.py
index a4543dbf..d704b3fb 100644
--- a/recommendations/optimiser/optimiser_functions.py
+++ b/recommendations/optimiser/optimiser_functions.py
@@ -236,10 +236,13 @@ def calculate_gain(
     if body.goal == "Increasing EPC":
         current_sap = int(p.data["current-energy-efficiency"]) + already_installed_gain
 
-        target_sap = (
-            eco_packages.get(p.id)[1] if eco_packages.get(p.id)[1] is not None
-            else epc_to_sap_lower_bound(body.goal_value)
-        )
+        if eco_packages is None:
+            target_sap = epc_to_sap_lower_bound(body.goal_value)
+        else:
+            target_sap = (
+                eco_packages.get(p.id)[1] if eco_packages.get(p.id)[1] is not None
+                else epc_to_sap_lower_bound(body.goal_value)
+            )
 
         if target_sap <= current_sap:
             # We've already met or exceeded the target EPC
diff --git a/recommendations/recommendation_utils.py b/recommendations/recommendation_utils.py
index 0794013e..b1744c69 100644
--- a/recommendations/recommendation_utils.py
+++ b/recommendations/recommendation_utils.py
@@ -488,10 +488,11 @@ def estimate_perimeter(floor_area, num_rooms):
     return perimeter
 
 
-def get_exposed_floor_uvalue(insulation_thickness_str, age_band):
+def get_exposed_floor_uvalue(insulation_thickness_str: None | str, age_band: str) -> float:
     """
     We implement the methodology as defined in section 5.6 and table S12 of the RdSAP document
-    :param insulation_thickness_str:
+    :param insulation_thickness_str: Insulation thickness as defined in the EPC data
+    :param age_band: Age band of the property
     :return:
     """
 
@@ -513,9 +514,15 @@ def get_exposed_floor_uvalue(insulation_thickness_str, age_band):
     else:
         insulation_thickness = int(insulation_thickness_str.replace("mm", ""))
 
-    return s12[s12["age_band"] == age_band][
+    filtered = s12[s12["age_band"] == age_band][
         f"insulation_{insulation_thickness}"
-    ].values[0]
+    ]
+
+    if filtered.empty:
+        # We don't have data so we use the median value
+        return float(s12[f"insulation_{insulation_thickness}"].median())
+
+    return float(filtered.values[0])
 
 
 def get_floor_u_value(
diff --git a/recommendations/tests/test_data/heating_recommendations_data.py b/recommendations/tests/test_data/heating_recommendations_data.py
index 37c854c3..1bd58c9e 100644
--- a/recommendations/tests/test_data/heating_recommendations_data.py
+++ b/recommendations/tests/test_data/heating_recommendations_data.py
@@ -223,15 +223,16 @@ testing_examples = [
             'local-authority-label': 'Lewisham', 'constituency-label': 'Lewisham, Deptford', 'posttown': 'LONDON',
             'construction-age-band': 'England and Wales: before 1900', 'lodgement-datetime': '2014-06-26 11:40:50',
             'tenure': 'owner-occupied', 'fixed-lighting-outlets-count': 9.0, 'low-energy-fixed-light-count': 5.0,
-            'uprn': 100021936225.0, 'uprn-source': 'Address Matched',
+            'uprn': 100021936225, 'uprn-source': 'Address Matched',
         },
         "heating_measure_types": [
+            "air_source_heat_pump",
             'roomstat_programmer_trvs',
             'time_temperature_zone_control'
         ],
-        "notes": "Because this property already has a boiler, we don't recommend HHR. We don't recommend an ashp "
-                 "because the home is mid-terraced. Because the heating controls are "
-                 "Programmer, no room thermostat, we have a programmer, room thermostat and trvs recommendation"
+        "notes": "Because this property already has a boiler, we don't recommend HHR. "
+                 "Because the heating controls are Programmer, no room thermostat, "
+                 "we have a programmer, room thermostat and trvs recommendation"
                  "for heating controls and for TTZC."
     },
     {
@@ -369,12 +370,13 @@ testing_examples = [
             'uprn-source': 'Address Matched', 'sheating-energy-eff': None, 'sheating-env-eff': None
         },
         "heating_measure_types": [
+            "air_source_heat_pump",
             'boiler_upgrade',
             'high_heat_retention_storage_heaters',
             'boiler_upgrade'
         ],
         "notes": "This property has assumed electric heating and is mid-terrace house. It has a mains gas connection."
-                 "We can recommend a boiler upgrade and high heat retention storage heaters"
+                 "We can recommend a boiler upgrade, high heat retention storage heaters, and an ASHP"
     },
     {
         "epc": {
@@ -510,12 +512,12 @@ testing_examples = [
 
         },
         "heating_measure_types": [
+            "air_source_heat_pump",
             'boiler_upgrade',
             'boiler_upgrade',
-            'high_heat_retention_storage_heaters',
         ],
-        "notes": "This property has assumed electric heaters. Boiler upgrade, HHR are recommended. We don't recommend"
-                 "an ASHP off of the bat because it's mid-terrace."
+        "notes": "This property has assumed electric heaters. Boiler upgrade, ASHP are recommended. We don't recommend"
+                 "HHRSH since there is potential community heating"
     },
     {
         "epc": {
@@ -556,6 +558,7 @@ testing_examples = [
             'uprn-source': 'Energy Assessor', 'sheating-energy-eff': None, 'sheating-env-eff': None
         },
         "heating_measure_types": [
+            "air_source_heat_pump",
             'boiler_upgrade',
             'high_heat_retention_storage_heaters',
             'boiler_upgrade'
@@ -603,12 +606,12 @@ testing_examples = [
             'uprn-source': 'Address Matched', 'sheating-energy-eff': None, 'sheating-env-eff': None
         },
         "heating_measure_types": [
+            "air_source_heat_pump",
             'boiler_upgrade',
             'boiler_upgrade',
             'high_heat_retention_storage_heaters',
         ],
-        "notes": "This property already has storage heaters with manual charge control. The home is mid terrace so"
-                 "the ashp is not suitable"
+        "notes": "This property already has storage heaters with manual charge control"
     },
     {
         "epc": {
@@ -1149,6 +1152,7 @@ testing_examples = [
             'uprn-source': 'Energy Assessor', 'sheating-energy-eff': None, 'sheating-env-eff': None
         },
         "heating_measure_types": [
+            "air_source_heat_pump",
             'boiler_upgrade',
             'boiler_upgrade',
             'high_heat_retention_storage_heaters'
@@ -1193,10 +1197,9 @@ testing_examples = [
             'uprn': 100070685908, 'uprn-source': 'Address Matched', 'sheating-energy-eff': None,
             'sheating-env-eff': None
         },
-        "heating_measure_types": [],
-        "notes": "This property is a flat, without mains gas connection. Currently has underfloor electric heating"
-                 "don't recommend anything. HHRSH isn't recommended as with underfloor heating, it's quite"
-                 "disruptive"
+        "heating_measure_types": ["high_heat_retention_storage_heaters"],
+        "notes": "This property is a flat, without mains gas connection. Currently has underfloor electric heating. "
+                 "In this case we just recommend hhrsh as an additional heating system, which would become the primary"
     },
     {
         "epc": {
diff --git a/recommendations/tests/test_data/measures_to_optimise.py b/recommendations/tests/test_data/measures_to_optimise.py
index cefd36e4..d84111cd 100644
--- a/recommendations/tests/test_data/measures_to_optimise.py
+++ b/recommendations/tests/test_data/measures_to_optimise.py
@@ -214,7 +214,7 @@ measures_to_optimise = [
          'heat_demand': np.float64(15.400000000000006),
          'kwh_savings': np.float64(202.30000000000018),
          'energy_cost_savings': np.float64(15.065400000000011)}], [
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 4}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
          'already_installed': False, 'total': 6013.139999999999, 'subtotal': 5010.95, 'vat': 0,
@@ -226,7 +226,7 @@ measures_to_optimise = [
          'heat_demand': np.float64(88.69999999999999),
          'kwh_savings': np.float64(2040.8566307499998),
          'energy_cost_savings': np.float64(525.1124110919749)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 4}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system, with a battery.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
          'already_installed': False, 'total': 10537.008, 'subtotal': 8780.84, 'vat': 0,
@@ -238,7 +238,7 @@ measures_to_optimise = [
          'heat_demand': np.float64(88.69999999999999),
          'kwh_savings': np.float64(2857.1992830499994),
          'energy_cost_savings': np.float64(735.1573755287648)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.6}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
          'already_installed': False, 'total': 5826.491999999999, 'subtotal': 4855.41, 'vat': 0,
@@ -249,7 +249,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.42834948104),
          'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(1846.33397),
          'energy_cost_savings': np.float64(475.0617304809999)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.6}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system, with a battery.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
          'already_installed': False, 'total': 10350.359999999999, 'subtotal': 8625.3, 'vat': 0,
@@ -260,7 +260,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.599689273456),
          'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(2584.867558),
          'energy_cost_savings': np.float64(665.0864226734)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.2}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
          'already_installed': False, 'total': 5642.604, 'subtotal': 4702.17, 'vat': 0,
@@ -271,7 +271,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.3828628319568), 'heat_demand': np.float64(78.3),
          'kwh_savings': np.float64(1650.2708274),
          'energy_cost_savings': np.float64(424.61468389001993)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.2}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system, with a battery.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
          'already_installed': False, 'total': 10166.472, 'subtotal': 8472.06, 'vat': 0,
@@ -282,7 +282,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.53600796473952), 'heat_demand': np.float64(78.3),
          'kwh_savings': np.float64(2310.3791583599996),
          'energy_cost_savings': np.float64(594.4605574460278)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.8}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
          'already_installed': False, 'total': 5458.727999999999, 'subtotal': 4548.94, 'vat': 0,
@@ -293,7 +293,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.3372336666192), 'heat_demand': np.float64(64.0),
          'kwh_savings': np.float64(1453.5933906),
          'energy_cost_savings': np.float64(374.00957940138)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.8}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system, with a battery.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
          'already_installed': False, 'total': 9982.596, 'subtotal': 8318.83, 'vat': 0,
@@ -304,7 +304,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.47212713326688), 'heat_demand': np.float64(64.0),
          'kwh_savings': np.float64(2035.03074684),
          'energy_cost_savings': np.float64(523.6134111619319)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.4}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
          'already_installed': False, 'total': 5274.852, 'subtotal': 4395.71, 'vat': 0,
@@ -315,7 +315,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.29118921808), 'heat_demand': np.float64(54.3),
          'kwh_savings': np.float64(1255.12594),
          'energy_cost_savings': np.float64(322.94390436199996)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.4}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system, with a battery.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
          'already_installed': False, 'total': 9798.72, 'subtotal': 8165.6, 'vat': 0,
@@ -326,7 +326,7 @@ measures_to_optimise = [
          'co2_equivalent_savings': np.float64(0.40766490531199995), 'heat_demand': np.float64(54.3),
          'kwh_savings': np.float64(1757.1763159999998),
          'energy_cost_savings': np.float64(452.1214661067999)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 2}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
          'already_installed': False, 'total': 5090.976, 'subtotal': 4242.48, 'vat': 0,
@@ -336,7 +336,7 @@ measures_to_optimise = [
          'recommendation_id': '18_phase=7', 'efficiency': np.float64(727.2822857142856),
          'co2_equivalent_savings': np.float64(0.243215185776), 'heat_demand': np.float64(48.5),
          'kwh_savings': np.float64(1048.341318), 'energy_cost_savings': np.float64(269.7382211214)},
-        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+        {'phase': 7, 'parts': [{"type": "solar_pv", "size": 2}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
          'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system, with a battery.',
          'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
          'already_installed': False, 'total': 9614.844, 'subtotal': 8012.369999999999, 'vat': 0,
diff --git a/recommendations/tests/test_heating_recommendations.py b/recommendations/tests/test_heating_recommendations.py
index 93acdefa..b62483ec 100644
--- a/recommendations/tests/test_heating_recommendations.py
+++ b/recommendations/tests/test_heating_recommendations.py
@@ -7,6 +7,7 @@ from etl.epc.Record import EPCRecord
 from etl.bill_savings.KwhData import KwhData
 from recommendations.HeatingRecommender import HeatingRecommender
 from recommendations.tests.test_data.heating_recommendations_data import testing_examples
+from recommendations.tests.test_data.materials import materials
 
 
 class TestHeatingRecommendations:
@@ -56,6 +57,7 @@ class TestHeatingRecommendations:
             x["has_hot-water-only"] = False
             x["has_mineral_and_wood"] = False
             x["has_dual_fuel_appliance"] = False
+            x["has_wood_chips"] = False
 
         epc_records = {"original_epc": test_case["epc"].copy(), "full_sap_epc": {}, "old_data": []}
 
@@ -75,6 +77,7 @@ class TestHeatingRecommendations:
                 "energy_assessment_is_newer": False
             }
         )
+        p.already_installed = []
 
         # For these tests, this can be fixed
         kwh_predictions = {
@@ -92,7 +95,7 @@ class TestHeatingRecommendations:
 
         p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=kwh_predictions)
 
-        recommender = HeatingRecommender(property_instance=p)
+        recommender = HeatingRecommender(property_instance=p, materials=materials)
         # Check they're empty
         assert not recommender.heating_recommendations
 
@@ -194,9 +197,9 @@ def test_pick_model_boundaries():
     """
     assert HeatingRecommender.pick_model((2.0, 4.9), models_kw=(3, 5, 6, 8.5)) == 5
     assert HeatingRecommender.pick_model((5.0, 5.0), models_kw=(3, 5, 6, 8.5)) == 5
-    assert HeatingRecommender.pick_model((5.0, 6.1), models_kw=(3, 5, 6, 8.5)) == 6
+    assert HeatingRecommender.pick_model((5.0, 6.1), models_kw=(3, 5, 6, 8.5)) == 8.5
     assert HeatingRecommender.pick_model((8.6, 9.0), models_kw=(3, 5, 6, 8.5, 11.2)) == 11.2
-    assert HeatingRecommender.pick_model((20, 25), models_kw=(3, 5, 6, 8.5, 11.2)) is None
+    assert HeatingRecommender.pick_model((20, 25), models_kw=(3, 5, 6, 8.5, 11.2)) == 11.2  # largest model
 
 
 def test_parameter_validation_and_defaults():
diff --git a/recommendations/tests/test_lighting_recommendations.py b/recommendations/tests/test_lighting_recommendations.py
index 5fdca9f7..aeaffdb4 100644
--- a/recommendations/tests/test_lighting_recommendations.py
+++ b/recommendations/tests/test_lighting_recommendations.py
@@ -13,6 +13,7 @@ class TestLightingRecommendations:
         epc_record.prepared_epc = {"county": "Greater London Authority"}
         input_property0 = Property(id=1, postcode="F4k3 6", address="623 fake street", epc_record=epc_record)
         input_property0.lighting = {"low_energy_proportion": 0}
+        input_property0.already_installed = []
         # Test for invalid materials
         with pytest.raises(ValueError):
             LightingRecommendations(input_property0, [])
@@ -23,6 +24,7 @@ class TestLightingRecommendations:
         epc_record.prepared_epc = {"county": "Greater London Authority"}
         input_property1 = Property(id=1, postcode="F4k3 6", address="623 fake street", epc_record=epc_record)
         input_property1.lighting = {"low_energy_proportion": 100}
+        input_property1.already_installed = []
 
         lr = LightingRecommendations(input_property1, materials)
         lr.recommend()
@@ -35,19 +37,16 @@ class TestLightingRecommendations:
         input_property1 = Property(id=1, postcode="F4k3 6", address="623 fake street", epc_record=epc_record)
         input_property1.lighting = {"low_energy_proportion": 0.80}
         input_property1.number_lighting_outlets = 20
+        input_property1.already_installed = []
 
         lr = LightingRecommendations(input_property1, materials)
         lr.recommend()
         assert len(lr.recommendation) == 1
 
         # Note - this test may be dependent on the ofgem price caps
-        assert lr.recommendation == [
-            {'phase': 0, 'parts': [], 'type': 'low_energy_lighting', 'measure_type': 'low_energy_lighting',
-             'description': 'Install low energy lighting in 4 outlets', 'starting_u_value': None, 'new_u_value': None,
-             'already_installed': False, 'sap_points': 0.4, 'kwh_savings': 219.0,
-             'energy_cost_savings': 56.348699999999994, 'co2_equivalent_savings': 0.035478,
-             'description_simulation': {'lighting-energy-eff': 'Very Good',
-                                        'lighting-description': 'Low energy lighting in all fixed outlets',
-                                        'low-energy-lighting': 100}, 'total': 188.76000000000002, 'subtotal': 157.3,
-             'vat': 31.460000000000004, 'contingency': 14.3, 'material': 80.0, 'labour_hours': 3.2, 'labour_days': 0.4,
-             'labour_cost': 63.0, 'survey': False}]
+        assert lr.recommendation[0]["description_simulation"] == {'lighting-energy-eff': 'Very Good',
+                                                                  'lighting-description': 'Low energy lighting in all '
+                                                                                          'fixed outlets',
+                                                                  'low-energy-lighting': 100}
+        assert lr.recommendation[0]["description"] == 'Install low energy lighting in 4 outlets'
+        assert lr.recommendation[0]["total"] == 14
diff --git a/recommendations/tests/test_optimiser_functions.py b/recommendations/tests/test_optimiser_functions.py
index ea0b5d94..c2927790 100644
--- a/recommendations/tests/test_optimiser_functions.py
+++ b/recommendations/tests/test_optimiser_functions.py
@@ -108,7 +108,7 @@ class TestCalculateGain:
         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 == 18.5
+        assert gain == 17.5
 
 
 class TestAddRequiredMeasures:
@@ -235,7 +235,7 @@ class TestIncreasingEpcE2e:
 
         gain = optimiser_functions.calculate_gain(body=body, p=p, fixed_gain=fixed_gain)
 
-        assert gain == 18.5, "Expected gain to be calculated correctly based on fixed gain and SAP target"
+        assert gain == 17.5, "Expected gain to be calculated correctly based on fixed gain and SAP target"
 
         optimiser = (
             GainOptimiser(
@@ -254,7 +254,8 @@ class TestIncreasingEpcE2e:
         # Collect selected measure IDs
         selected = {r["id"] for r in solution}
 
-        assert selected == {'8_phase=7', '5_phase=4', '7_phase=6'}
+        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(
@@ -265,11 +266,11 @@ class TestIncreasingEpcE2e:
         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': '8_phase=7', 'cost': 6013.139999999999, 'gain': np.float64(13.0), 'type': 'solar_pv'}
+            {'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 == 18.6, "Total gain of optimised measures should meet or exceed target gain"
+        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)
 
diff --git a/recommendations/tests/test_optimisers.py b/recommendations/tests/test_optimisers.py
index ecc6ea56..17e45154 100644
--- a/recommendations/tests/test_optimisers.py
+++ b/recommendations/tests/test_optimisers.py
@@ -1,52 +1,6 @@
-from pandas import Timestamp
-from numpy import nan
-import datetime
-
-import numpy as np
-import pandas as pd
 import pytest
-from copy import deepcopy
 
-from recommendations.optimiser import optimiser_functions
-from recommendations.optimiser.funding_optimiser import optimise_with_funding_paths, build_heat_pump_paths
-from backend.Funding import Funding
-from backend.app.plan.schemas import WALL_INSULATION_MEASURES, ROOF_INSULATION_MEASURES, ECO4_ELIGIBILE_FABRIC_MEASURES
-
-ALLOWED_FABRIC_TYPES = set(WALL_INSULATION_MEASURES + ROOF_INSULATION_MEASURES + ECO4_ELIGIBILE_FABRIC_MEASURES)
-
-
-@pytest.fixture
-def mock_project_scores_matrix():
-    data = []
-    floor_segments = ["0-72", "73-97", "98-199", "200"]
-    bands = [
-        "Low_G", "High_G", "Low_F", "High_F", "Low_E", "High_E", "Low_D", "High_D", "Low_C", "High_C", "Low_B",
-        "High_B", "Low_A", "High_A"
-    ]
-
-    cost = 50.0
-    for floor in floor_segments:
-        for start in bands:
-            for finish in bands:
-                if start != finish:  # skip identical start/finish (no SAP movement)
-                    data.append({
-                        "Floor Area Segment": floor,
-                        "Starting Band": start,
-                        "Finishing Band": finish,
-                        "Cost Savings": cost
-                    })
-                    cost += 5.0  # increment to create variety
-
-    return pd.DataFrame(data)
-
-
-@pytest.fixture
-def mock_partial_scores_matrix():
-    df = pd.read_csv("backend/tests/test_data/ECO4_Partial_Project_Scores_Matrix_v6.csv")
-    df.columns = ['Measure category', 'Measure_Type', 'Pre_Main_Heating_Source',
-                  'Post_Main_Heating_Source', 'Total Floor Area Band', 'Starting Band',
-                  'Average Treatable Factor', 'Cost Savings', 'SAP Savings']
-    return df
+from recommendations.optimiser.funding_optimiser import build_heat_pump_paths
 
 
 class DummyProp:
@@ -105,619 +59,6 @@ def p():
     return DummyProp()
 
 
-@pytest.fixture
-def funding(monkeypatch, mock_partial_scores_matrix, mock_project_scores_matrix):
-    """Simple Funding that returns zero uplift so costs stay as provided."""
-    # Build the Funding with tiny in-memory frames (avoid test I/O)
-
-    f = Funding(
-        project_scores_matrix=mock_project_scores_matrix,
-        partial_project_scores_matrix=mock_partial_scores_matrix,
-        whlg_eligible_postcodes=pd.DataFrame([{"Postcode": "ab12cd"}]),
-        eco4_social_cavity_abs_rate=13.5, eco4_social_solid_abs_rate=17,
-        eco4_private_cavity_abs_rate=13.5, eco4_private_solid_abs_rate=17,
-        gbis_social_cavity_abs_rate=21, gbis_social_solid_abs_rate=25,
-        gbis_private_cavity_abs_rate=22, gbis_private_solid_abs_rate=28,
-        tenure="Social"
-    )
-
-    # Keep innovation_uplift simple for the first test
-    # monkeypatch.setattr(f, "get_innovation_uplift", lambda *args, **kwargs: 0.0)
-
-    # If your solar precondition matters, you can force True/False here:
-    # monkeypatch.setattr(
-    #     __import__("backend").Funding, "check_solar_eligible_heating_system",
-    #     staticmethod(lambda mainheat_description, heating_control_description: False)
-    # )
-
-    return f
-
-
-@pytest.fixture
-def property_recommendations():
-    """Short sample; replace with your full block if you want."""
-    recs = [
-        [{'phase': 0, 'parts': [{'id': 2466, 'type': 'external_wall_insulation',
-                                 'description': 'EWI Pro EPS external wall insulation system with '
-                                                'Brick Slip finish',
-                                 'depth': 150.0, 'depth_unit': 'mm', 'cost': None,
-                                 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.02631579,
-                                 'r_value_unit': 'square_meter_kelvin_per_watt',
-                                 'thermal_conductivity': 0.038,
-                                 'thermal_conductivity_unit': 'watt_per_meter_kelvin',
-                                 'link': 'SCIS',
-                                 'created_at': Timestamp('2025-03-16 15:26:22.379496'),
-                                 'is_active': True, 'prime_material_cost': None,
-                                 'material_cost': 0.0, 'labour_cost': 0.0,
-                                 'labour_hours_per_unit': 0.0, 'plant_cost': 0.0,
-                                 'total_cost': 298.35,
-                                 'notes': 'This is the quoted value from SCIS',
-                                 'is_installer_quote': True, 'quantity': 63.98796761892035,
-                                 'quantity_unit': 'm2', 'total': 19090.810139104888,
-                                 'labour_hours': 0.0, 'labour_days': 0.0}],
-          'type': 'external_wall_insulation', 'measure_type': 'external_wall_insulation',
-          "innovation_rate": 0,
-          'description': 'Install 150mm EWI Pro EPS external wall insulation system with Brick '
-                         'Slip finish on external walls',
-          'starting_u_value': 1.7, 'new_u_value': 0.32, 'already_installed': False,
-          'sap_points': np.float64(9.6),
-          'simulation_config': {'is_as_built_ending': False, 'walls_is_assumed_ending': False,
-                                'walls_insulation_thickness_ending': 'average',
-                                'external_insulation_ending': True,
-                                'walls_energy_eff_ending': 'Good',
-                                'walls_thermal_transmittance_ending': 0.23},
-          'description_simulation': {'walls-description': 'Solid brick, with external insulation',
-                                     'walls-energy-eff': 'Good'}, 'total': 19090.810139104888,
-          'labour_hours': 0.0, 'labour_days': 0.0, 'survey': False,
-          'recommendation_id': '0_phase=0', 'efficiency': 11229.568317120522,
-          'co2_equivalent_savings': np.float64(0.5), 'heat_demand': np.float64(37.099999999999994),
-          'kwh_savings': np.float64(1827.8999999999996),
-          'energy_cost_savings': np.float64(136.1247882352941)}, {'phase': 0, 'parts': [
-            {'id': 2373, 'type': 'internal_wall_insulation', 'description': 'SWIP EcoBatt & Plastered finish',
-             'depth': 95.0,
-             'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.03125,
-             'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.032,
-             'thermal_conductivity_unit': None,
-             'link': 'SCIS', 'created_at': Timestamp('2025-03-16 15:26:22.379496'), 'is_active': True,
-             'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 2.1,
-             'plant_cost': 0.0, 'total_cost': 89.0, 'notes': None, 'is_installer_quote': True,
-             'quantity': 63.98796761892035,
-             'quantity_unit': 'm2', 'total': 5694.929118083911, 'labour_hours': 134.37473199973275,
-             'labour_days': 4.199210374991648}], 'type': 'internal_wall_insulation',
-                                                                  'measure_type': 'internal_wall_insulation',
-                                                                  "innovation_rate": 0,
-                                                                  'description': 'Install 95mm '
-                                                                                 'SWIP EcoBatt & '
-                                                                                 'Plastered '
-                                                                                 'finish on '
-                                                                                 'internal walls',
-                                                                  'starting_u_value': 1.7,
-                                                                  'new_u_value': 0.32,
-                                                                  'already_installed': False,
-                                                                  'sap_points': 6,
-                                                                  'simulation_config': {
-                                                                      'is_as_built_ending': False,
-                                                                      'walls_is_assumed_ending':
-                                                                          False,
-                                                                      'walls_insulation_thickness_ending': 'average',
-                                                                      'internal_insulation_ending': True,
-                                                                      'walls_energy_eff_ending':
-                                                                          'Good',
-                                                                      'walls_thermal_transmittance_ending': 0.29},
-                                                                  'description_simulation': {
-                                                                      'walls-description': 'Solid '
-                                                                                           'brick, with internal '
-                                                                                           'insulation',
-                                                                      'walls-energy-eff': 'Good'},
-                                                                  'total': 5694.929118083911,
-                                                                  'labour_hours': 134.37473199973275,
-                                                                  'labour_days': 4.199210374991648,
-                                                                  'survey': True,
-                                                                  'recommendation_id': '1_phase=0',
-                                                                  'efficiency': 3349.6383047552417,
-                                                                  'co2_equivalent_savings': np.float64(
-                                                                      0.5),
-                                                                  'heat_demand': np.float64(
-                                                                      35.30000000000001),
-                                                                  'kwh_savings': np.float64(
-                                                                      1432.3999999999996),
-                                                                  'energy_cost_savings': np.float64(
-                                                                      106.67167058823532)}], [
-            {'phase': 1, 'parts': [{'id': 2351, 'type': 'loft_insulation',
-                                    'description': 'Knauf Loft Roll 44 glass fibre roll',
-                                    'depth': 300.0, 'depth_unit': 'mm', 'cost': None,
-                                    'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.022727273,
-                                    'r_value_unit': 'square_meter_kelvin_per_watt',
-                                    'thermal_conductivity': 0.044,
-                                    'thermal_conductivity_unit': 'watt_per_meter_kelvin',
-                                    'link': 'SCIS',
-                                    'created_at': Timestamp('2025-03-16 15:26:22.379496'),
-                                    'is_active': True, 'prime_material_cost': None,
-                                    'material_cost': 0.0, 'labour_cost': 0.0,
-                                    'labour_hours_per_unit': 0.11, 'plant_cost': 0.0,
-                                    'total_cost': 15.0,
-                                    'notes': 'This is the cost if there is less than 100mm '
-                                             'existing insulation',
-                                    'is_installer_quote': True, 'quantity': 63.98796761892035,
-                                    'quantity_unit': 'm2', 'total': 645.0, 'labour_hours': 8,
-                                    'labour_days': 1}], 'type': 'loft_insulation',
-             'measure_type': 'loft_insulation',
-             "innovation_rate": 0,
-             'description': 'Install 300mm of Knauf Loft Roll 44 glass fibre roll in your loft',
-             'starting_u_value': 2.3, 'new_u_value': 2.3, 'sap_points': np.float64(2.4),
-             'already_installed': False,
-             'simulation_config': {'is_loft_ending': True, 'roof_is_assumed_ending': False,
-                                   'roof_insulation_thickness_ending': '300',
-                                   'roof_thermal_transmittance_ending': 2.3,
-                                   'roof_energy_eff_ending': 'Very Good'},
-             'description_simulation': {'roof-description': 'Pitched, 300mm loft insulation',
-                                        'roof-energy-eff': 'Very Good'}, 'total': 645.0,
-             'labour_hours': 8, 'labour_days': 1, 'survey': False, 'recommendation_id': '2_phase=1',
-             'efficiency': 278.1347826086957,
-             'co2_equivalent_savings': np.float64(0.10000000000000009),
-             'heat_demand': np.float64(1.5), 'kwh_savings': np.float64(566.1499999999996),
-             'energy_cost_savings': np.float64(42.16152352941185)}], [{'phase': 2, 'parts': [
-            {'id': 2329, 'type': 'mechanical_ventilation', 'description': 'Mechanical Extract Ventilation',
-             'depth': 0.0,
-             'depth_unit': None, 'cost': None, 'cost_unit': 'gbp_per_unit', 'r_value_per_mm': nan,
-             'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': None,
-             'thermal_conductivity_unit': None,
-             'link': 'SCIS', 'created_at': datetime.datetime(2025, 3, 16, 15, 26, 22, 379496), 'is_active': True,
-             'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.0,
-             'plant_cost': 0.0, 'total_cost': 350.0, 'notes': None, 'is_installer_quote': True, 'total': 700.0,
-             'quantity': 2,
-             'quantity_unit': 'part'}], 'type': 'mechanical_ventilation', 'measure_type': 'mechanical_ventilation',
-                                                                       "innovation_rate": 0,
-                                                                       'description': 'Install 2 '
-                                                                                      'Mechanical '
-                                                                                      'Extract '
-                                                                                      'Ventilation units',
-                                                                       'starting_u_value': None,
-                                                                       'new_u_value': None,
-                                                                       'already_installed': False,
-                                                                       'sap_points': np.float64(
-                                                                           -0.10000000000000142),
-                                                                       'heat_demand': np.float64(
-                                                                           -3.3999999999999773),
-                                                                       'kwh_savings': np.float64(
-                                                                           -53.80000000000018),
-                                                                       'co2_equivalent_savings': np.float64(
-                                                                           0.0),
-                                                                       'energy_cost_savings': np.float64(
-                                                                           -4.0065176470588995),
-                                                                       'total': 700.0,
-                                                                       'labour_hours': 8,
-                                                                       'labour_days': 1.0,
-                                                                       'simulation_config': {
-                                                                           'mechanical_ventilation_ending':
-                                                                               'mechanical, '
-                                                                               'extract '
-                                                                               'only'},
-                                                                       'description_simulation': {
-                                                                           'mechanical-ventilation': 'mechanical, '
-                                                                                                     'extract only'},
-                                                                       'recommendation_id': '3_phase=2',
-                                                                       'efficiency': 0}], [
-            {'phase': 3, 'parts': [{'id': 2409, 'type': 'suspended_floor_insulation',
-                                    'description': 'Q-bot underfloor insulation', 'depth': 75.0,
-                                    'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2',
-                                    'r_value_per_mm': 0.045454547,
-                                    'r_value_unit': 'square_meter_kelvin_per_watt',
-                                    'thermal_conductivity': 0.022,
-                                    'thermal_conductivity_unit': 'watt_per_meter_kelvin',
-                                    'link': 'SCIS',
-                                    'created_at': Timestamp('2025-03-16 15:26:22.379496'),
-                                    'is_active': True, 'prime_material_cost': None,
-                                    'material_cost': 0.0, 'labour_cost': 0.0,
-                                    'labour_hours_per_unit': 1.63, 'plant_cost': 0.0,
-                                    'total_cost': 93.75,
-                                    'notes': 'Linearly interpolated based on Qbot costs',
-                                    'is_installer_quote': True, 'quantity': 43.0,
-                                    'quantity_unit': 'm2', 'total': 4031.25,
-                                    'labour_hours': 70.08999999999999,
-                                    'labour_days': 2.920416666666666}],
-             'type': 'suspended_floor_insulation', 'measure_type': 'suspended_floor_insulation',
-             "innovation_rate": 0,
-             'description': 'Install 75mm Q-bot underfloor insulation insulation in suspended '
-                            'floor',
-             'starting_u_value': 0.83, 'new_u_value': 0.22, 'sap_points': 2, 'survey': True,
-             'already_installed': False, 'simulation_config': {'floor_is_assumed_ending': False,
-                                                               'floor_insulation_thickness_ending': 'average',
-                                                               'floor_thermal_transmittance_ending': 0.685593},
-             'description_simulation': {'floor-description': 'Suspended, insulated'},
-             'total': 4031.25, 'labour_hours': 70.08999999999999, 'labour_days': 2.920416666666666,
-             'recommendation_id': '4_phase=3', 'efficiency': 4856.707710843373,
-             'co2_equivalent_savings': np.float64(0.20000000000000018),
-             'heat_demand': np.float64(33.5), 'kwh_savings': np.float64(1021.1999999999998),
-             'energy_cost_savings': np.float64(76.04936470588231)}], [
-            {'phase': 4, 'parts': [], 'type': 'low_energy_lighting',
-             'measure_type': 'low_energy_lighting',
-             "innovation_rate": 0,
-             'description': 'Install low energy lighting in -886 outlets', 'starting_u_value': None,
-             'new_u_value': None, 'already_installed': False, 'sap_points': 2,
-             'kwh_savings': -48508.5, 'energy_cost_savings': -12481.237049999998,
-             'co2_equivalent_savings': -7.858377,
-             'description_simulation': {'lighting-energy-eff': 'Very Good',
-                                        'lighting-description': 'Low energy lighting in all fixed'
-                                                                ' outlets',
-                                        'low-energy-lighting': 100}, 'total': -3411.1000000000004,
-             'labour_hours': 1, 'labour_days': 0.125, 'survey': True,
-             'recommendation_id': '5_phase=4', 'efficiency': -1705.5500000000002,
-             'heat_demand': np.float64(5.099999999999994)}], [
-            {'type': 'heating', 'phase': 5, 'measure_type': 'time_temperature_zone_control',
-             "innovation_rate": 0,
-             'parts': [],
-             'description': 'Upgrade heating controls to Smart Thermostats, room sensors and '
-                            'smart radiator valves (time & temperature zone control)',
-             'total': 739.576, 'subtotal': 700.48, 'vat': 39.096000000000004,
-             'labour_hours': 3.6199999999999997, 'labour_days': np.float64(1.0),
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(2.9),
-             'already_installed': False, 'simulation_config': {
-                'thermostatic_control_ending': 'time and temperature zone control',
-                'switch_system_ending': None, 'trvs_ending': None,
-                'mainheatc_energy_eff_ending': 'Very Good'}, 'description_simulation': {
-                'mainheatcont-description': 'Time and temperature zone control',
-                'mainheatc-energy-eff': 'Very Good'}, 'recommendation_id': '6_phase=5',
-             'efficiency': 739.576, 'co2_equivalent_savings': np.float64(0.30000000000000027),
-             'heat_demand': np.float64(6.599999999999994),
-             'kwh_savings': np.float64(876.8000000000002),
-             'energy_cost_savings': np.float64(65.29581176470589)}], [
-            {'phase': 6, 'parts': [], 'type': 'secondary_heating',
-             'measure_type': 'secondary_heating',
-             "innovation_rate": 0,
-             'description': 'Remove the secondary heating system', 'starting_u_value': None,
-             'new_u_value': None, 'sap_points': np.float64(3.6), 'already_installed': False,
-             'total': 30.0, 'subtotal': 25.0, 'vat': 5.0, 'labour_hours': 3.0,
-             'labour_days': np.float64(1.0),
-             'simulation_config': {'secondheat_description_ending': 'None'},
-             'description_simulation': {'secondheat-description': 'None'},
-             'recommendation_id': '7_phase=6', 'efficiency': 30.0,
-             'co2_equivalent_savings': np.float64(0.10000000000000009),
-             'heat_demand': np.float64(15.400000000000006),
-             'kwh_savings': np.float64(196.29999999999927),
-             'energy_cost_savings': np.float64(14.61857647058821)}], [
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
-             'already_installed': False, 'total': 6013.139999999999, 'subtotal': 5010.95, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
-             'has_battery': False, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
-             'description_simulation': {'photo-supply': np.float64(65.0)},
-             'recommendation_id': '8_phase=7', 'efficiency': np.float64(462.54923076923075),
-             'co2_equivalent_savings': np.float64(0.47347873833399995),
-             'heat_demand': np.float64(88.69999999999999),
-             'kwh_savings': np.float64(2040.8566307499998),
-             'energy_cost_savings': np.float64(525.1124110919749)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system, with a battery.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
-             'already_installed': False, 'total': 10537.008, 'subtotal': 8780.84, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
-             'has_battery': True, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
-             'description_simulation': {'photo-supply': np.float64(65.0)},
-             'recommendation_id': '9_phase=7', 'efficiency': np.float64(810.5390769230769),
-             'co2_equivalent_savings': np.float64(0.6628702336675999),
-             'heat_demand': np.float64(88.69999999999999),
-             'kwh_savings': np.float64(2857.1992830499994),
-             'energy_cost_savings': np.float64(735.1573755287648)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
-             'already_installed': False, 'total': 5826.491999999999, 'subtotal': 4855.41, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
-             'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3692.66794),
-             'description_simulation': {'photo-supply': np.float64(60.0)},
-             'recommendation_id': '10_phase=7', 'efficiency': np.float64(485.54099999999994),
-             'co2_equivalent_savings': np.float64(0.42834948104),
-             'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(1846.33397),
-             'energy_cost_savings': np.float64(475.0617304809999)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system, with a battery.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
-             'already_installed': False, 'total': 10350.359999999999, 'subtotal': 8625.3, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
-             'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3692.66794),
-             'description_simulation': {'photo-supply': np.float64(60.0)},
-             'recommendation_id': '11_phase=7', 'efficiency': np.float64(862.5299999999999),
-             'co2_equivalent_savings': np.float64(0.599689273456),
-             'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(2584.867558),
-             'energy_cost_savings': np.float64(665.0864226734)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
-             'already_installed': False, 'total': 5642.604, 'subtotal': 4702.17, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
-             'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
-             'description_simulation': {'photo-supply': np.float64(55.0)},
-             'recommendation_id': '12_phase=7', 'efficiency': np.float64(512.964),
-             'co2_equivalent_savings': np.float64(0.3828628319568), 'heat_demand': np.float64(78.3),
-             'kwh_savings': np.float64(1650.2708274),
-             'energy_cost_savings': np.float64(424.61468389001993)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system, with a battery.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
-             'already_installed': False, 'total': 10166.472, 'subtotal': 8472.06, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
-             'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
-             'description_simulation': {'photo-supply': np.float64(55.0)},
-             'recommendation_id': '13_phase=7', 'efficiency': np.float64(924.2247272727273),
-             'co2_equivalent_savings': np.float64(0.53600796473952),
-             'heat_demand': np.float64(78.3), 'kwh_savings': np.float64(2310.3791583599996),
-             'energy_cost_savings': np.float64(594.4605574460278)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
-             'already_installed': False, 'total': 5458.727999999999, 'subtotal': 4548.94, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
-             'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
-             'description_simulation': {'photo-supply': np.float64(45.0)},
-             'recommendation_id': '14_phase=7', 'efficiency': np.float64(606.5253333333333),
-             'co2_equivalent_savings': np.float64(0.3372336666192), 'heat_demand': np.float64(64.0),
-             'kwh_savings': np.float64(1453.5933906),
-             'energy_cost_savings': np.float64(374.00957940138)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system, with a battery.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
-             'already_installed': False, 'total': 9982.596, 'subtotal': 8318.83, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
-             'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
-             'description_simulation': {'photo-supply': np.float64(45.0)},
-             'recommendation_id': '15_phase=7', 'efficiency': np.float64(1109.1773333333333),
-             'co2_equivalent_savings': np.float64(0.47212713326688),
-             'heat_demand': np.float64(64.0), 'kwh_savings': np.float64(2035.03074684),
-             'energy_cost_savings': np.float64(523.6134111619319)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
-             'already_installed': False, 'total': 5274.852, 'subtotal': 4395.71, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
-             'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2510.25188),
-             'description_simulation': {'photo-supply': np.float64(40.0)},
-             'recommendation_id': '16_phase=7', 'efficiency': np.float64(659.3565),
-             'co2_equivalent_savings': np.float64(0.29118921808), 'heat_demand': np.float64(54.3),
-             'kwh_savings': np.float64(1255.12594),
-             'energy_cost_savings': np.float64(322.94390436199996)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system, with a battery.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
-             'already_installed': False, 'total': 9798.72, 'subtotal': 8165.6, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
-             'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2510.25188),
-             'description_simulation': {'photo-supply': np.float64(40.0)},
-             'recommendation_id': '17_phase=7', 'efficiency': np.float64(1224.84),
-             'co2_equivalent_savings': np.float64(0.40766490531199995),
-             'heat_demand': np.float64(54.3), 'kwh_savings': np.float64(1757.1763159999998),
-             'energy_cost_savings': np.float64(452.1214661067999)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
-             'already_installed': False, 'total': 5090.976, 'subtotal': 4242.48, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
-             'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2096.682636),
-             'description_simulation': {'photo-supply': np.float64(35.0)},
-             'recommendation_id': '18_phase=7', 'efficiency': np.float64(727.2822857142856),
-             'co2_equivalent_savings': np.float64(0.243215185776), 'heat_demand': np.float64(48.5),
-             'kwh_savings': np.float64(1048.341318),
-             'energy_cost_savings': np.float64(269.7382211214)},
-            {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
-             "innovation_rate": 0,
-             'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system, with a battery.',
-             'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
-             'already_installed': False, 'total': 9614.844, 'subtotal': 8012.369999999999, 'vat': 0,
-             'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
-             'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2096.682636),
-             'description_simulation': {'photo-supply': np.float64(35.0)},
-             'recommendation_id': '19_phase=7', 'efficiency': np.float64(1373.5491428571427),
-             'co2_equivalent_savings': np.float64(0.3405012600864), 'heat_demand': np.float64(48.5),
-             'kwh_savings': np.float64(1467.6778451999999),
-             'energy_cost_savings': np.float64(377.6335095699599)}]
-    ]
-    return recs
-
-
-def _attach_costs_and_uplifts(recs, funding, p):
-    """Mimic what your script did: add cost fields & innovation uplift."""
-    out = deepcopy(recs)
-    for group in out:
-        for r in group:
-            if r["type"] in ["mechanical_ventilation", "low_energy_lighting", "secondary_heating"]:
-                (
-                    r["partial_project_score"],
-                    r["partial_project_funding"],
-                    r["innovation_uplift"],
-                    r["uplift_project_score"],
-                ) = (
-                    0, 0, 0, 0
-                )
-                continue
-
-            (
-                r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"],
-                r["uplift_project_score"]
-            ) = funding.get_innovation_uplift(
-                measure=r,
-                starting_sap=55,
-                floor_area=70.0,
-                is_cavity=False,
-                current_wall_uvalue=1.7,
-                is_partial=False,
-                existing_li_thickness=150,
-                mainheating=p.main_heating,
-                main_fuel=p.main_fuel,
-                mainheat_energy_eff="Very Good",
-            )
-            # the optimiser_functions.prepare_input_measures will translate these to input format; but
-            # for safety add explicit cost fields some downstream code expects:
-            r["total"] = float(r["total"])
-    return out
-
-
-def _to_input_measures(recs, p):
-    """Use your own helper so we test the full pipeline."""
-    property_measure_types = {rec["type"] for grp in recs for rec in grp}
-    needs_ventilation = any(
-        x in property_measure_types for x in optimiser_functions.assumptions.measures_needing_ventilation
-    ) and not getattr(p, "has_ventilation", False)
-
-    # goal="Increasing EPC", add_uplift=True for Social path
-    return optimiser_functions.prepare_input_measures(
-        recs, goal="Increasing EPC", needs_ventilation=needs_ventilation, funding=True
-    )
-
-
-def _types_of(picked_items):
-    return {item["type"] for item in picked_items}
-
-
-def test_social_fabric_only_returns_only_fabric_types(p, funding, property_recommendations, monkeypatch):
-    # 1) prepare data like your script
-    recs = _attach_costs_and_uplifts(property_recommendations, funding, p)
-    input_measures = _to_input_measures(recs, p)
-
-    # 2) run optimiser wrapper (budget and target_gain can be modest for the test)
-    budget = 30000.0
-    target_gain = 8.0
-
-    solutions = optimise_with_funding_paths(
-        p=p,
-        input_measures=input_measures,
-        housing_type="Social",
-        budget=budget,
-        target_gain=target_gain,
-        funding=funding
-    )
-
-    # 3) basic shape assertions
-    assert isinstance(solutions, pd.DataFrame)
-    assert not solutions.empty
-
-    # 4) find the fabric-only ECO4 row
-    fabric_rows = solutions[
-        solutions["path"].apply(lambda x: isinstance(x, dict) and x.get("reference") == "fabric-only:eco4")]
-    assert not fabric_rows.empty, "Expected a fabric-only:eco4 solution for Social tenure"
-
-    # 5) ensure only fabric measure types are present in that solution
-    picked_types = _types_of(fabric_rows.iloc[0]["items"])
-    assert picked_types == {'internal_wall_insulation+mechanical_ventilation',
-                            'suspended_floor_insulation'}, "incorrect types selected"
-
-    # 6) respect budget
-    assert fabric_rows.iloc[0]["total_cost"] <= budget + 1e-9
-
-    # (optional) ensure unfunded baseline also appears
-    unfunded_rows = solutions[
-        solutions["path"].apply(lambda x: isinstance(x, dict) and x.get("reference") == "unfunded:all")]
-    assert not unfunded_rows.empty
-
-
-def test_private_solid_wall_no_innovation_epc_d(p, funding, mock_project_scores_matrix, mock_partial_scores_matrix):
-    """
-    We have a specific test for this case which was implemented incorrectly originally.
-    This is an EPC D property and so shouldn't be eligible for ECO4. Instead, only GBIS should be considered.
-    """
-
-    # Overwrite the data - copied from real example
-    p2 = deepcopy(p)
-    p2.data = {
-        "current-energy-rating": "D",
-        "current-energy-efficiency": 68,
-        "mainheat-energy-eff": "Good",
-    }
-    p2.walls = {'original_description': 'Sandstone or limestone, as built, no insulation (assumed)',
-                'clean_description': 'Sandstone or limestone, as built, no insulation', 'thermal_transmittance': None,
-                'thermal_transmittance_unit': None, 'is_cavity_wall': False, 'is_filled_cavity': False,
-                'is_solid_brick': False, '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': True, 'is_park_home': False, 'insulation_thickness': 'none',
-                'external_insulation': False, 'internal_insulation': False}
-
-    funding2 = Funding(
-        tenure="Private",
-        project_scores_matrix=mock_project_scores_matrix,
-        partial_project_scores_matrix=mock_partial_scores_matrix,
-        whlg_eligible_postcodes=pd.DataFrame([{"Postcode": "ab12cd"}]),
-        eco4_social_cavity_abs_rate=12.5,
-        eco4_social_solid_abs_rate=17,
-        eco4_private_cavity_abs_rate=12.5,
-        eco4_private_solid_abs_rate=17,
-        gbis_social_cavity_abs_rate=21,
-        gbis_social_solid_abs_rate=25,
-        gbis_private_cavity_abs_rate=21,
-        gbis_private_solid_abs_rate=28,
-    )
-
-    input_measures = [
-        [{'id': '0_phase=0', 'cost': np.float64(4441.202499013676), 'gain': np.float64(3.4000000000000057),
-          'type': 'internal_wall_insulation+mechanical_ventilation', 'innovation_uplift': np.float64(0.0),
-          'cost_minus_uplift': np.float64(4441.202499013676), 'raw_cost': 3881.2024990136756,
-          'partial_project_funding': np.float64(2300.1000000000004), 'partial_project_score': np.float64(135.3),
-          'uplift_project_score': np.float64(0.0)}], [
-            {'id': '2_phase=2', 'cost': np.float64(2280.0), 'gain': np.float64(0.4), 'type': 'secondary_glazing',
-             'innovation_uplift': np.float64(0.0), 'cost_minus_uplift': np.float64(2280.0),
-             'raw_cost': np.float64(2280.0), 'partial_project_funding': np.float64(1421.1999999999998),
-             'partial_project_score': np.float64(83.6), 'uplift_project_score': np.float64(0.0)}], [
-            {'id': '3_phase=3', 'cost': np.float64(604.5840000000001), 'gain': np.float64(1.2),
-             'type': 'time_temperature_zone_control', 'innovation_uplift': np.float64(0.0),
-             'cost_minus_uplift': np.float64(604.5840000000001), 'raw_cost': 604.5840000000001,
-             'partial_project_funding': np.float64(702.0999999999999), 'partial_project_score': np.float64(41.3),
-             'uplift_project_score': np.float64(0.0)}], [
-            {'id': '4_phase=4', 'cost': 60.0, 'gain': np.float64(0.0), 'type': 'secondary_heating',
-             'innovation_uplift': 0, 'cost_minus_uplift': 60.0, 'raw_cost': 60.0, 'partial_project_funding': 0,
-             'partial_project_score': 0, 'uplift_project_score': 0}]
-    ]
-
-    solutions = optimise_with_funding_paths(
-        p=p2,
-        input_measures=input_measures,
-        housing_type="Private",
-        budget=None,
-        target_gain=1.5,
-        funding=funding2
-    )
-
-    # 3) basic shape assertions
-    assert isinstance(solutions, pd.DataFrame)
-    assert not solutions.empty
-
-    # We should have 2 rows
-    assert solutions.shape[0] == 2
-
-    # We should only have None or GBIS
-    assert set(solutions["scheme"].unique()) == {"none", "gbis"}
-
-    meets_upgrade_gbis = solutions[solutions["meets_upgrade_target"] & solutions["is_eligible"]]
-    assert meets_upgrade_gbis.shape[0] == 1
-
-    # Check exact result
-    assert meets_upgrade_gbis.squeeze().to_dict() == {
-        'fixed_ids': ['0_phase=0'], 'items': [
-            {'id': '0_phase=0', 'cost': 3881.2024990136756, 'gain': np.float64(3.4000000000000057),
-             'type': 'internal_wall_insulation+mechanical_ventilation', 'innovation_uplift': np.float64(0.0),
-             'cost_minus_uplift': np.float64(4441.202499013676), 'raw_cost': 3881.2024990136756,
-             'partial_project_funding': np.float64(2300.1000000000004), 'partial_project_score': np.float64(135.3),
-             'uplift_project_score': np.float64(0.0)}], 'total_cost': 3881.2024990136756,
-        'total_gain': 3.4000000000000057, 'path': [{'AND': ['internal_wall_insulation+mechanical_ventilation'],
-                                                    'reference':
-                                                        'internal_wall_insulation+mechanical_ventilation:gbis'}],
-        'scheme': 'gbis', 'is_eligible': True, 'unfunded_items': [], 'meets_upgrade_target': True, 'starting_sap': 68,
-        'floor_area': 70.0, 'ending_sap': 71.4, 'starting_band': 'High_D', 'ending_band': 'Low_C',
-        'floor_area_band': '0-72', 'project_score': 540.0, 'full_project_funding': 0.0,
-        'partial_project_funding': 2300.1000000000004, 'partial_project_score': 135.3, 'total_uplift': 0.0,
-        'total_uplift_score': 0.0
-    }
-
-
 def test_build_heat_pump_paths():
     eg1 = build_heat_pump_paths([], ["loft_insulation"])
 
diff --git a/recommendations/tests/test_recommendation_utils.py b/recommendations/tests/test_recommendation_utils.py
index fa707b4b..1484a09c 100644
--- a/recommendations/tests/test_recommendation_utils.py
+++ b/recommendations/tests/test_recommendation_utils.py
@@ -279,27 +279,34 @@ class TestRecommendationUtils:
 
     # Test with wall_type not in default_wall_thickness
     def test_wall_type_not_in_default_wall_thickness(self):
-        with pytest.raises(IndexError):
-            recommendation_utils.get_floor_u_value(
-                floor_type="solid",
-                area=100,
-                perimeter=40,
-                age_band="A",
-                wall_type="InvalidWallType",
-                insulation_thickness=None,
-            )
+        # THis previously raised an error but because it largely dicates the thickness, often defaulted to
+        # 300, we just use the default instead of raising an error. We see cases of this in the wild, where we
+        # estimate EPCs and end up with unusual wall types, so we have fallbacks in place
+        assert recommendation_utils.get_floor_u_value(
+            floor_type="solid",
+            area=100,
+            perimeter=40,
+            age_band="A",
+            wall_type="InvalidWallType",
+            insulation_thickness=None,
+        ) == 0.6
 
     # Test with age_band not in s11
     def test_age_band_not_in_s11(self):
-        with pytest.raises(IndexError):
-            recommendation_utils.get_floor_u_value(
-                floor_type="solid",
-                area=100,
-                perimeter=40,
-                age_band="Z",
-                wall_type="Cavity",
-                insulation_thickness=None,
-            )
+        # This previously raised an error but because it largely dicates the thickness, often defaulted to
+        # 300, we just use the default instead of raising an error. We see cases of this in the wild, where we
+        # might estimate an EPC
+        recommendation_utils.get_floor_u_value(
+            floor_type="solid",
+            area=100,
+            perimeter=40,
+            age_band="Z",
+            wall_type="Cavity",
+            insulation_thickness=None,
+        )
+
+    def test_age_band_not_in_s11_exposed_floor(self):
+        recommendation_utils.get_exposed_floor_uvalue(None, "BadValue")
 
     def test_convert_thickness_to_numeric(self):