fabric measures for ECO

backend/app/plan/schemas.py

@@ -11,13 +11,23 @@ TYPICAL_MEASURE_TYPES = [
 WALL_INSULATION_MEASURES = ["internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"]
 ROOF_INSULATION_MEASURES = ["loft_insulation", "flat_roof_insulation", "room_roof_insulation"]
 
-SPECIFIC_MEASURES = WALL_INSULATION_MEASURES + ROOF_INSULATION_MEASURES + [
-    "suspended_floor_insulation", "solid_floor_insulation",
-    "boiler_upgrade", "high_heat_retention_storage_heater", "air_source_heat_pump",
-    "secondary_heating", "solar_pv", "double_glazing", "secondary_glazing",
-    "ventilation", "low_energy_lighting", "fireplace", "hot_water_tank_insulation",
-    "cylinder_thermostat"
+# Both all and roof insulaiton measures are eligible for ECO4. These are the remaining fabric and heating measures
+# This is based on th measures we have recommendations for
+ECO4_ELIGIBILE_FABRIC_MEASURES = [
+    "suspended_floor_insulation", "solid_floor_insulation", "double_glazing", "secondary_glazing"
 ]
+ECO4_ELIGIBLE_HEATING_MEASURES = [
+    "boiler_upgrade", "high_heat_retention_storage_heater", "air_source_heat_pump", "solar_pv"
+]
+
+SPECIFIC_MEASURES = (
+    WALL_INSULATION_MEASURES + ROOF_INSULATION_MEASURES + ECO4_ELIGIBILE_FABRIC_MEASURES +
+    ECO4_ELIGIBLE_HEATING_MEASURES + [
+        "secondary_heating" "ventilation", "low_energy_lighting", "fireplace",
+        "hot_water_tank_insulation",
+        "cylinder_thermostat"
+    ]
+)
 
 INSULATION_MEASURES = [
     "internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation",

recommendations/tests/test_optimisers.py

@@ -5,6 +5,9 @@ from numpy import nan
 import datetime
 from copy import deepcopy
 
+from app.plan.schemas import (
+    WALL_INSULATION_MEASURES, ROOF_INSULATION_MEASURES, ECO4_ELIGIBILE_FABRIC_MEASURES, ECO4_ELIGIBLE_HEATING_MEASURES
+)
 from recommendations.optimiser.CostOptimiser import CostOptimiser
 from recommendations.optimiser.GainOptimiser import GainOptimiser
 from backend.Funding import Funding
@@ -472,7 +475,7 @@ def _find_measure(input_measures, measure_type):
     return False
 
 
-def _make_generic_eco4_funding_paths(p, input_measures, funding_paths, remaining_insulation_type):
+def _make_solar_heating_funding_paths(p, input_measures, funding_paths, remaining_insulation_type):
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     # Solar PV with existing eligible heating system
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -624,7 +627,7 @@ def make_funding_paths(p, input_measures, tenure):
                 ):
                     input_gbis_measures_innovation.append([measure])
 
-        funding_paths = _make_generic_eco4_funding_paths(
+        funding_paths = _make_solar_heating_funding_paths(
             p, input_measures_innovation, funding_paths, remaining_insulation_type
         )
 
@@ -653,7 +656,7 @@ def make_funding_paths(p, input_measures, tenure):
             ewi_or_iwi[0]["reference"] = "+".join(reference_measures) + ":eco4"
             funding_paths.append(ewi_or_iwi)
 
-    funding_paths = _make_generic_eco4_funding_paths(
+    funding_paths = _make_solar_heating_funding_paths(
         p, input_measures, funding_paths, remaining_insulation_type
     )
 
@@ -758,6 +761,24 @@ def violates_min_insulation(fixed):
     return is_heating and not has_insul
 
 
+# Treat "type" like "external_wall_insulation+mechanical_ventilation" → "external_wall_insulation"
+def _base_type(s: str) -> str:
+    return s.split("+", 1)[0]
+
+
+def _filter_measures_by_types(input_measures, allowed_types):
+    """
+    Keep only groups that have ≥1 allowed option; inside each group keep only allowed options.
+    """
+    allowed_set = set(allowed_types)
+    filtered = []
+    for group in input_measures:
+        kept_opts = [opt for opt in group if _base_type(opt["type"]) in allowed_set]
+        if kept_opts:
+            filtered.append(kept_opts)
+    return filtered
+
+
 def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, social=False):
     """
     run_optimizer(sub_measures, budget, target_gain) -> (picked_options, sub_cost, sub_gain)
@@ -765,8 +786,40 @@ def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, s
 
     funding_paths = make_funding_paths(p, input_measures, body.housing_type)
 
+    # We now produce a fabric only path for ECO4
+    # We add in generic insulation funding paths (where there is no fixed measure)
+    # Heating controls are only eligible if installed as part of a heating upgrade and so we do not include them
+    # here
+    allowed_types = WALL_INSULATION_MEASURES + ROOF_INSULATION_MEASURES + ECO4_ELIGIBILE_FABRIC_MEASURES
+    funding_paths = [{'AND': [], 'reference': 'fabric-only:eco4'}] + funding_paths
+
     solutions = []
     for path_spec in funding_paths:
+
+        if path_spec["reference"] == "fabric-only:eco4":
+            sub_measures = _filter_measures_by_types(input_measures, allowed_types)
+            if not sub_measures:
+                continue
+
+            picked, sub_cost, sub_gain = run_optimizer(
+                sub_measures,
+                budget=budget,  # no fixed items; budget unchanged
+                sub_target_gain=target_gain
+            )
+
+            if picked is None:
+                continue
+
+            solutions.append(
+                {
+                    "fixed_ids": [],
+                    "items": picked,
+                    "total_cost": sub_cost,
+                    "total_gain": sub_gain,
+                    "path": path_spec,
+                }
+            )
+
         # 1) expand fixed selections for this path
         fixed_selections = expand_funding_path(input_measures, path_spec) if path_spec else [[]]
         if not fixed_selections:
@@ -1031,8 +1084,9 @@ def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack
     else:
         if sub_target_gain is None:
             raise ValueError("Either budget or target_gain must be provided.")
-        opt = CostOptimiser(input_measures, min_gain=sub_target_gain)
+        opt = CostOptimiser(sub_measures, min_gain=sub_target_gain)
 
     opt.setup()
     opt.solve()
-    return opt.solution, opt.solution_cost, opt.solution_gain
+    cost = sum([x["cost"] for x in opt.solution])
+    return opt.solution, cost, opt.solution_gain