Merge pull request #667 from Hestia-Homes/main

Added change to pass an already installed sap value from rebaselining to optimisation routine to prevent optimising for rebaselining

backend/engine/engine.py

@@ -1065,21 +1065,8 @@ async def model_engine(body: PlanTriggerRequest):
                 )
                 continue
 
-            fixed_gain = optimiser_functions.calculate_fixed_gain(
-                property_required_measures, recommendations, p, needs_ventilation
-            )
-            gain = optimiser_functions.calculate_gain(body=body, p=p, fixed_gain=fixed_gain, eco_packages=eco_packages)
-
-            # We insert the innovation uplift
-            measures_to_optimise_with_uplift = deepcopy(measures_to_optimise)
-
-            for group in measures_to_optimise_with_uplift:
-                for r in group:
-                    (r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"],
-                     r["uplift_project_score"]) = (0, 0, 0, 0)
-
             already_installed_measures = []
-            for measures in measures_to_optimise_with_uplift:
+            for measures in measures_to_optimise:
                 for m in measures:
                     # A) We're going to make the already installed measures default
                     # B) We need to SAP points for all already installed measures to avoid double counting
@@ -1096,6 +1083,22 @@ async def model_engine(body: PlanTriggerRequest):
             default_already_installed = keep_max_sap_per_measure_type(already_installed_measures)
             already_installed_sap = float(sum(d["sap_points"] for d in default_already_installed))
 
+            fixed_gain = optimiser_functions.calculate_fixed_gain(
+                property_required_measures, recommendations, p, needs_ventilation
+            )
+            gain = optimiser_functions.calculate_gain(
+                body=body, p=p, fixed_gain=fixed_gain, eco_packages=eco_packages,
+                already_installed_gain=already_installed_sap
+            )
+
+            # We insert the innovation uplift
+            measures_to_optimise_with_uplift = deepcopy(measures_to_optimise)
+
+            for group in measures_to_optimise_with_uplift:
+                for r in group:
+                    (r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"],
+                     r["uplift_project_score"]) = (0, 0, 0, 0)
+
             # Remove them from the optimisation pool
             finalised_measures_to_optimise = []
             for m in measures_to_optimise_with_uplift:

etl/customers/peabody/Nov 2025 Consulting Project/k_deck_stats.py

@@ -231,6 +231,49 @@ properties_data, plans_data, recommendations_data = get_data(
 )
 
 recommendations_df = pd.DataFrame(recommendations_data)
+properties_df = pd.DataFrame(properties_data)
 
 solar_pv_recommendations = recommendations_df[recommendations_df["measure_type"] == "solar_pv"]
 average_savings = solar_pv_recommendations.groupby("scenario_id")["energy_cost_savings"].mean().reset_index()
+
+# Check tenures
+initial_asset_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "
+    "- Data Extracts for Domna.xlsx",
+    sheet_name="Properties"
+)
+sustainability_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "
+    "- Data Extracts for Domna.xlsx",
+    sheet_name="Sustainability"
+)
+
+sustainability_sample = sustainability_data[
+    sustainability_data["UPRN"].isin(properties_df["uprn"].astype(int).astype(str).values)
+]
+
+sustainability_sample = sustainability_sample.merge(
+    initial_asset_data, left_on="Org Ref", right_on="UPRN", suffixes=("_sustainability", "_initial_asset")
+)
+
+block_sizes = initial_asset_data["BlockCode"].value_counts().reset_index().sort_values("count", ascending=False)
+block_sizes.to_excel("/Users/khalimconn-kowlessar/Downloads/peabody_block_sizes.xlsx", index=False)
+
+initial_asset_data.columns
+initial_asset_data["LeaseType"].value_counts()
+
+# sustainability_sample["Tenure Group"].value_counts()
+# Tenure Group
+# General Needs               57787
+# Home Ownership              25471
+# Care & Supported Housing     4239
+# Rental                       2677
+# Other                         188
+
+df = sustainability_sample["Ownership Type"].value_counts().to_frame().reset_index()
+df.to_excel("/Users/khalimconn-kowlessar/Downloads/sustainability_tenures.xlsx", index=False)
+
+tenure_groups = sustainability_sample["Tenure Group"].value_counts().to_frame().reset_index()
+tenure_groups.to_excel("/Users/khalimconn-kowlessar/Downloads/sustainability_tenure_groups.xlsx", index=False)
+
+initial_asset_data[~pd.isnull(initial_asset_data["BlockCode"])]["Tenure Group"].value_counts()

recommendations/optimiser/optimiser_functions.py

@@ -202,8 +202,13 @@ def calculate_fixed_gain(property_required_measures, recommendations, p, needs_v
     return fixed_gain
 
 
-def calculate_gain(body: PlanTriggerRequest, p: Property, fixed_gain: float,
+def calculate_gain(
-                   eco_packages: None | dict = None) -> float | None:
+    body: PlanTriggerRequest,
+    p: Property,
+    fixed_gain: float,
+    eco_packages: None | dict = None,
+    already_installed_gain: float = 0,
+) -> float | None:
     """
     Calculates the target gain value for optimisation based on the goal.
 
@@ -221,6 +226,7 @@ def calculate_gain(body: PlanTriggerRequest, p: Property, fixed_gain: float,
     fixed_gain : float
         Total fixed gain from required measures (returned by calculate_fixed_gain).
     eco_packages : dict, optional
+    already_installed_gain: float, optional
 
     Returns
     -------
@@ -228,13 +234,16 @@ def calculate_gain(body: PlanTriggerRequest, p: Property, fixed_gain: float,
         Required SAP gain for EPC, or None for non-EPC goals.
     """
     if body.goal == "Increasing EPC":
-        current_sap = int(p.data["current-energy-efficiency"])
+        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 target_sap == current_sap:
+            return 0
+
         gain = CostOptimiser.calculate_sap_gain_with_slack(
             target_sap - current_sap
         ) - fixed_gain

recommendations/tests/test_optimiser_functions.py

@@ -85,6 +85,22 @@ class TestCalculateGain:
         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)