add post sap 10 feature

etl/epc/DataProcessor.py

@@ -4,6 +4,7 @@ import pandas as pd
 from etl.epc.settings import (
     DATA_PROCESSOR_SETTINGS,
     EARLIEST_EPC_DATE,
+    POST_SAP10_DATE,
     # IGNORED_TRANSACTION_TYPES,
     IGNORED_FLOOR_LEVELS,
     IGNORED_PROPERTY_TYPES,
@@ -159,6 +160,9 @@ class EPCDataProcessor:
         #     colnames=["NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"],
         # )
 
+        # Create post sap10 flag
+        self.create_post_sap10_flag()
+
         # When running in newdata mode, cleaning_averages has lower cases so we co-erce back to upper
         cleaning_averages = self.cleaning_averages.copy()
         if self.run_mode == "newdata":
@@ -175,6 +179,13 @@ class EPCDataProcessor:
         self.cast_cleaning_averages_columns_to_lower(ignore_step=ignore_step)
         self.cast_data_columns_to_lower()
 
+    def create_post_sap10_flag(self):
+        """
+        Create a flag to indicate if the epc is post sap10
+        """
+
+        self.data["is_post_sap10"] = self.data["LODGEMENT_DATE"] >= POST_SAP10_DATE
+
     def cast_data_columns_to_lower(self):
         """
         Convert all columns names to lower

etl/epc/Pipeline.py

@@ -23,6 +23,7 @@ from etl.epc.settings import (
     POTENTIAL_COLUMNS,
     ROOM_FEATURES,
     COST_FEATURES,
+    POST_SAP10_FEATURE,
 )
 
 # TODO: change in setting file
@@ -325,7 +326,9 @@ class EPCPipeline:
 
         # We include the lodgement date here as we probably need to factor time into the
         # model, since EPC standards and rigour have changed over time
-        variable_data = property_data[VARIABLE_DATA_FEATURES + COST_FEATURES]
+        variable_data = property_data[
+            VARIABLE_DATA_FEATURES + COST_FEATURES + POST_SAP10_FEATURE
+        ]
 
         uprn = str(uprn)
         epc_records = [

etl/epc/Record.py

@@ -20,6 +20,7 @@ from etl.epc.settings import (
     COMPONENT_FEATURES,
     EFFICIENCY_FEATURES,
     ROOM_FEATURES,
+    POST_SAP10_FEATURE,
 )
 from recommendations.recommendation_utils import estimate_number_of_floors
 from utils.s3 import read_dataframe_from_s3_parquet
@@ -89,6 +90,7 @@ class EPCRecord:
     co2_emissions_current: float = None
     number_habitable_rooms: float = None
     number_heated_rooms: float = None
+    is_post_sap10: bool = None
 
     # u_values_walls = None
     # u_values_roof = None
@@ -277,6 +279,7 @@ class EPCRecord:
         self.number_heated_rooms: float = float(
             self.prepared_epc["number_heated_rooms"]
         )
+        self.is_post_sap10: bool = bool(self.prepared_epc["is_post_sap10"])
 
     def _identify_delta_between_prepared_and_original_records(self):
         """
@@ -385,11 +388,11 @@ class EPCRecord:
         return df
 
     def _clean_floor_height(self):
-        """ Remaps anomalies in floor height to the average floor height for the property type """
+        """Remaps anomalies in floor height to the average floor height for the property type"""
         floor_height_data = self.cleaning_data[
-            (self.cleaning_data["property_type"] == self.prepared_epc["property-type"]) &
-            (self.cleaning_data["built_form"] == self.prepared_epc["built-form"])
-            ]
+            (self.cleaning_data["property_type"] == self.prepared_epc["property-type"])
+            & (self.cleaning_data["built_form"] == self.prepared_epc["built-form"])
+        ]
         average = floor_height_data["floor_height"].mean()
         sd = floor_height_data["floor_height"].std()
         # If we're in the top 0.5 percentile of floor heights, we'll set it to the average
@@ -399,14 +402,16 @@ class EPCRecord:
             self.prepared_epc["floor-height"] = average
 
     def _clean_new_build_descriptions(self):
-        for col in ['roof-description', 'walls-description', 'floor-description']:
+        for col in ["roof-description", "walls-description", "floor-description"]:
             self.prepared_epc[col] = self.prepared_epc[col].replace("W/m²K", "W/m-¦K")
 
     def _clean_constituency(self):
         """
         We handle the single case of finding a missing constituency by using the local authority
         """
-        if pd.isnull(self.prepared_epc["constituency"]) or (self.prepared_epc["constituency"] == ""):
+        if pd.isnull(self.prepared_epc["constituency"]) or (
+            self.prepared_epc["constituency"] == ""
+        ):
             if self.prepared_epc["local-authority"] != "E06000044":
                 raise NotImplementedError(
                     "This function is only implemented for Portsmouth, in the single edgecase seen"
@@ -595,12 +600,12 @@ class EPCRecord:
 
         # We handle the edge case of floor area being 0. We set it to zero and it is cleaned by
         # _clean_with_data_processor
-        if self.prepared_epc['total-floor-area'] == 0:
+        if self.prepared_epc["total-floor-area"] == 0:
             print(
                 "Edge case of floor area being zero - will set to none and will be cleaned in "
                 "_clean_with_data_processor"
             )
-            self.prepared_epc['total-floor-area'] = None
+            self.prepared_epc["total-floor-area"] = None
 
     def _clean_mains_gas(self):
         """
@@ -609,12 +614,7 @@ class EPCRecord:
         if not self.prepared_epc:
             raise ValueError("EPC Recrod doesn not contain epc data")
 
-        mains_gas_map = {
-            "Y": True,
-            "N": False,
-            True: True,
-            False: False
-        }
+        mains_gas_map = {"Y": True, "N": False, True: True, False: False}
 
         self.prepared_epc["mains-gas-flag"] = (
             None
@@ -1064,7 +1064,12 @@ class EPCDifferenceRecord:
             CARBON_RESPONSE
         )
 
-        component_variables = COMPONENT_FEATURES + EFFICIENCY_FEATURES + ROOM_FEATURES
+        component_variables = (
+            COMPONENT_FEATURES
+            + EFFICIENCY_FEATURES
+            + ROOM_FEATURES
+            + POST_SAP10_FEATURE
+        )
         ending_record = self.record2.get(
             component_variables + ["lodgement_date"],
             return_asdict=True,

etl/epc/settings.py

@@ -52,6 +52,9 @@ DATA_ANOMALY_MATCHES = {
     "Unknown",
 }
 
+# Add the post_sap10 date to indicate if the epc is post sap10
+POST_SAP10_DATE = "2025-06-22"
+
 DATA_ANOMALY_SUBSTRINGS = {
     # Where values in a ‘pick’ list that have been superseded by another value. For example, where a value for
     # ‘pitched roof’ has been replaced by three sub-categories of pitched roof. The original value is retained
@@ -184,6 +187,8 @@ EFFICIENCY_FEATURES = [
 
 ROOM_FEATURES = ["number_habitable_rooms", "number_heated_rooms"]
 
+POST_SAP10_FEATURE = ["is_post_sap10"]
+
 COMPONENT_FEATURES = CORE_COMPONENT_FEATURES + [
     "TRANSACTION_TYPE",
     "ENERGY_TARIFF",  # Not sure if this is relevant