From 1b4ae239a3a052d49835d902aaefb68758b97e9c Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Thu, 4 Jan 2024 14:56:33 +0000
Subject: [PATCH] testing different weight methods - to run full test on

---
 backend/SearchEpc.py               | 24 ++++++++++++++++--------
 recommendations/Recommendations.py |  2 ++
 2 files changed, 18 insertions(+), 8 deletions(-)

diff --git a/backend/SearchEpc.py b/backend/SearchEpc.py
index 6e17d200..b3f58b04 100644
--- a/backend/SearchEpc.py
+++ b/backend/SearchEpc.py
@@ -472,13 +472,21 @@ class SearchEpc:
                         epc_data["house_number_distance"] = abs(
                             epc_data["numeric_house_number"] - self.numeric_house_number
                         )
-                        # We add 1, just in case we have a 0 weight (e.g. comparing house number 7a to 7b, or 9A to 9)
-                        epc_data["weight"] = 1 / (epc_data["house_number_distance"] + 1)
-                        # If we have a home without a house number, fill that weight with average
-                        epc_data["weight"] = epc_data["weight"].fillna(epc_data["weight"].mean())
-                        # Finally, we might not have any house numbers whatsoever so everything could be
-                        # missing, so we fill with 1
-                        epc_data["weight"] = epc_data["weight"].fillna(1)
+                        # # We add 1, just in case we have a 0 weight (e.g. comparing house number 7a to 7b, or 9A to 9)
+                        # epc_data["weight"] = 1 / (epc_data["house_number_distance"] + 1)
+                        # # If we have a home without a house number, fill that weight with average
+                        # epc_data["weight"] = epc_data["weight"].fillna(epc_data["weight"].mean())
+                        # # Finally, we might not have any house numbers whatsoever so everything could be
+                        # # missing, so we fill with 1
+                        # epc_data["weight"] = epc_data["weight"].fillna(1)
+                        # TODO: Testing
+                        # If the postcode is different from the initial postcode, it doesn't make sense to have
+                        # any weightings
+                        if all(pd.isnull(epc_data["house_number_distance"])) or (postcode != initial_postcode):
+                            epc_data["weight"] = 1
+                        else:
+                            epc_data["weight"] = 1 / np.sqrt(epc_data["house_number_distance"] + 1)
+                            epc_data["weight"] = epc_data["weight"].fillna(epc_data["weight"].mean())
 
                     estimation_property_type = self._estimate_str(
                         key="property-type", estimation_data=epc_data
@@ -621,7 +629,7 @@ class SearchEpc:
 
     @staticmethod
     def _estimate_float(estimation_data, key):
-        return np.average(a=estimation_data[key], weights=estimation_data["weight"])
+        return round(np.average(a=estimation_data[key], weights=estimation_data["weight"]), 2)
 
     @staticmethod
     def _estimate_str(estimation_data, key):
diff --git a/recommendations/Recommendations.py b/recommendations/Recommendations.py
index 4b54cb52..a5d1f35c 100644
--- a/recommendations/Recommendations.py
+++ b/recommendations/Recommendations.py
@@ -155,6 +155,8 @@ class Recommendations:
                     # For the moment, we cap the number of SAP points that can be achieved by ventilation at 2
                     rec["sap_points"] = min(rec["sap_points"], VentilationRecommendations.SAP_LIMIT)
 
+                # Round to 2 decimal places
+                rec["sap_points"] = round(rec["sap_points"], 2)
                 rec["co2_equivalent_savings"] = float(property_instance.data["co2-emissions-current"]) - new_carbon
 
                 # Energy consumption current is per meter squared, so we need to multiply by the floor area to get