ammended RIR conditions

.idea/Model.iml

@@ -7,7 +7,7 @@
       <sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
       <sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="Route-March" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Fastapi-backend" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyNamespacePackagesService">

.idea/misc.xml

@@ -3,7 +3,7 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.10 (backend)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Route-March" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Fastapi-backend" project-jdk-type="Python SDK" />
   <component name="PyCharmProfessionalAdvertiser">
     <option name="shown" value="true" />
   </component>

backend/app/plan/router.py

@@ -759,7 +759,11 @@ async def trigger_plan(body: PlanTriggerRequest):
                     new_epc = sap_to_epc(new_sap_points)
                     new_epc_bands[p.id] = new_epc
 
-                    valuations = PropertyValuation.estimate(property_instance=p, target_epc=new_epc)
+                    total_cost = sum([r["total"] for r in default_recommendations])
+
+                    valuations = PropertyValuation.estimate(
+                        property_instance=p, target_epc=new_epc, total_cost=total_cost
+                    )
                     property_value_increase_ranges[p.id] = valuations
 
                     if p.is_new:

backend/ml_models/Valuation.py

@@ -203,7 +203,14 @@ class PropertyValuation:
         return msm_increase, lloyds_increase
 
     @classmethod
-    def estimate(cls, property_instance, target_epc):
+    def estimate(cls, property_instance, target_epc, total_cost=None):
+        """
+        This function estimates the value of a property based on the current EPC rating and the target EPC rating
+        :param property_instance: An instance of the Property class
+        :param target_epc: The target EPC rating
+        :param total_cost: The total cost of the retrofit
+        :return:
+        """
         current_value = (
             property_instance.valuation if property_instance.valuation else
             cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
@@ -242,6 +249,19 @@ class PropertyValuation:
 
         avg_increase = np.mean(all_increases)
 
+        if total_cost is not None:
+            # We CAP the retrofit ROI at 2
+            avg_increase_value = current_value * avg_increase
+            if avg_increase_value / total_cost > 2:
+                # We re-scale the % so that the average value increase is no more than 2 times the total cost
+                double_cost = 2 * total_cost
+                new_avg_increase = double_cost / current_value
+                scalar = new_avg_increase / avg_increase
+                # We scale the min and max increases by the same scalar
+                min_increase *= scalar
+                max_increase *= scalar
+                avg_increase = new_avg_increase
+
         return {
             "current_value": current_value,
             "lower_bound_increased_value": float(current_value * (1 + min_increase)),

etl/customers/warwick/remote_assessments.py

@@ -0,0 +1,123 @@
+import pandas as pd
+from utils.s3 import save_csv_to_s3
+
+PORTFOLIO_ID = 115
+USER_ID = 8
+
+
+def app():
+    """
+    Used to set up the remote assessments for Warwick
+    """
+
+    asset_list = [
+        {
+            "uprn": 10033604792,
+            "address": "Flat 2, 3 Green Street",
+            "postcode": "W1K 6RN"
+        },
+        {
+            "uprn": 10033604794,
+            "address": "Flat 4, 3 Green Street",
+            "postcode": "W1K 6RN"
+        },
+        {
+            "uprn": 10033615515,
+            "address": "Apartment 4, 52 Green Street",
+            "postcode": "W1K 6RS"
+        }
+    ]
+    asset_list = pd.DataFrame(asset_list)
+
+    # Store the asset list in s3
+    filename = f"{USER_ID}/{PORTFOLIO_ID}/asset_list.csv"
+    save_csv_to_s3(
+        dataframe=asset_list,
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=filename
+    )
+
+    non_invasive_recommendations = [
+        {
+            "uprn": 10033604792,
+            "recommendations": [
+                {
+                    "type": "internal_wall_insulation",
+                    "sap_points": 16,
+                    "survey": True
+                }
+            ]
+        },
+        {
+            "uprn": 10033604794,
+            "recommendations": [
+                {
+                    "type": "internal_wall_insulation",
+                    "sap_points": 14,
+                    "survey": True
+                }
+            ]
+        },
+        {
+            "uprn": 10033615515,
+            "recommendations": [
+                {
+                    "type": "room_roof_insulation",
+                    "sap_points": 12,
+                    "survey": True
+                },
+                {
+                    "type": "internal_wall_insulation",
+                    "sap_points": 2,
+                    "survey": True
+                }
+            ]
+        }
+    ]
+
+    # Store non-invasive recommendations in S3
+    non_invasive_recommendations_filename = f"{USER_ID}/{PORTFOLIO_ID}/non_invasive_recommendations.csv"
+    save_csv_to_s3(
+        dataframe=pd.DataFrame(non_invasive_recommendations),
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=non_invasive_recommendations_filename
+    )
+
+    valuation_data = [
+        {
+            "uprn": 10033604792,
+            "value": 3_692_000
+        },
+        {
+            "uprn": 10033604794,
+            "value": 3_789_000
+        },
+        {
+            "uprn": 10033615515,
+            "value": 3_499_000
+        }
+    ]
+
+    # Store valuation data to s3
+    valuation_filename = f"{USER_ID}/{PORTFOLIO_ID}/valuation.csv"
+    save_csv_to_s3(
+        dataframe=pd.DataFrame(valuation_data),
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=valuation_filename
+    )
+
+    body = {
+        "portfolio_id": str(PORTFOLIO_ID),
+        "housing_type": "Private",
+        "goal": "Increasing EPC",
+        "goal_value": "C",
+        "trigger_file_path": filename,
+        "already_installed_file_path": "",
+        "patches_file_path": "",
+        "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
+        "valuation_file_path": valuation_filename,
+        "scenario_name": "Full package remote assessment",
+        "multi_plan": True,
+        "budget": None,
+    }
+    print(body)

recommendations/Recommendations.py

@@ -519,6 +519,7 @@ class Recommendations:
                     # heating_cost_starting and heating_cost_ending are just the values in the EPC. However, with
                     # heating_cost_ending, we expect that the EPC will predict a heating cost based on what would happen
                     # if we implemented the recommendation today, so our starting value is the EPC
+
                     previous_phase_values = {
                         "sap": float(property_instance.data["current-energy-efficiency"]),
                         "carbon": float(property_instance.data["co2-emissions-current"]),
@@ -541,8 +542,13 @@ class Recommendations:
                         previous_phase_values = previous_phase_values_multiple[0]
 
                 # We extract the values for the current phase
+                if rec.get("survey", False):
+                    current_phase_sap = rec["sap_points"] + previous_phase_values["sap"]
+                else:
+                    current_phase_sap = phase_energy_efficiency_metrics["sap_change"]
+
                 current_phase_values = {
-                    "sap": phase_energy_efficiency_metrics["sap_change"],
+                    "sap": current_phase_sap,
                     "carbon": phase_energy_efficiency_metrics["carbon_change"],
                     "heat_demand": phase_energy_efficiency_metrics["heat_demand"],
                 }

recommendations/RoofRecommendations.py

@@ -123,7 +123,11 @@ class RoofRecommendations:
             self.property.roof["insulation_thickness"] in ["average", "above_average"]
         )
 
-        return full_insulated_room_roof or room_roof_insulated_at_rafters
+        has_non_invasive_recommendation = any(
+            x["type"] == "room_roof_insulation" for x in self.property.non_invasive_recommendations
+        )
+
+        return (full_insulated_room_roof or room_roof_insulated_at_rafters) and not has_non_invasive_recommendation
 
     def recommend(self, phase, measures=None, default_u_values=False):
 
@@ -181,7 +185,8 @@ class RoofRecommendations:
 
         # We firstly handle non-intrusive recommendations, which may override the normal roof insulation recommendations
         if ("loft_insulation" in [x["type"] for x in non_invasive_recommendations]) or (
-            self.property.roof["is_pitched"] and "loft_insulation" in measures
+            self.property.roof["is_pitched"] and "loft_insulation" in measures and
+            not self.property.roof["is_at_rafters"]
         ):
             self.recommend_roof_insulation(
                 u_value=u_value,
@@ -512,8 +517,6 @@ class RoofRecommendations:
                     rir_non_invasive_recommendation.get("cost")
                 )
 
-                sap_points = rir_non_invasive_recommendation.get("sap_points", None)
-
                 # Could also be Roof room(s), ceiling insulated
                 new_descriptin = "Roof room(s), insulated"
                 roof_ending_config = RoofAttributes(new_descriptin).process()
@@ -562,7 +565,7 @@ class RoofRecommendations:
                         "description": "Insulate room in roof at rafters and re-decorate",
                         "starting_u_value": u_value,
                         "new_u_value": new_u_value,
-                        "sap_points": sap_points,
+                        "sap_points": rir_non_invasive_recommendation.get("sap_points", None),
                         "simulation_config": simulation_config,
                         "description_simulation": {
                             "roof-description": new_descriptin,