removing weird ashp recommendation

backend/app/plan/router.py

@@ -642,7 +642,6 @@ async def trigger_plan(body: PlanTriggerRequest):
             recommendations_scoring_data.extend(p.recommendations_scoring_data)
 
         # TODO: Make sure that number_habitable_rooms has been dropped
-
         logger.info("Preparing data for scoring in sap change api")
         recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
 
@@ -651,18 +650,11 @@ async def trigger_plan(body: PlanTriggerRequest):
                      "carbon_ending"]
         )
 
-        all_predictions = model_api.predictions_template()
-        to_loop_over = range(0, recommendations_scoring_data.shape[0], SCORING_BATCH_SIZE)
-        for chunk in tqdm(to_loop_over, total=len(to_loop_over)):
-            predictions_dict = model_api.predict_all(
-                df=recommendations_scoring_data.iloc[chunk:chunk + SCORING_BATCH_SIZE],
-                bucket=get_settings().DATA_BUCKET,
-                prediction_buckets=get_prediction_buckets(),
-            )
-
-            # Append the predictions to the predictions dictionary
-            for key, scored in predictions_dict.items():
-                all_predictions[key] = pd.concat([all_predictions[key], scored])
+        all_predictions = model_api.paginated_predictions(
+            data=recommendations_scoring_data,
+            bucket=get_settings().DATA_BUCKET,
+            batch_size=SCORING_BATCH_SIZE
+        )
 
         # Insert the predictions into the recommendations, and get the impact summary
         scoring_epcs = []  # For scoring the kwh models
@@ -687,14 +679,29 @@ async def trigger_plan(body: PlanTriggerRequest):
         scoring_epcs = pd.DataFrame(scoring_epcs)
         scoring_epcs = kwh_client.transform(data=scoring_epcs, cleaned=cleaned)
 
-        kwh_simulation_predictions = model_api.predict_all(
-            df=scoring_epcs,
+        # There should be no difference between index 9 and index 8, apart from photo-supply (other that sap, etc)
+        a = scoring_epcs[scoring_epcs.index == 6]
+        b = scoring_epcs[scoring_epcs.index == 11]
+        difference = []
+        for col in a.columns:
+            if a[col].values[0] != b[col].values[0]:
+                difference.append(
+                    {
+                        "col": col,
+                        "without_solar": a[col].values[0],
+                        "with_solar": b[col].values[0]
+                    }
+                )
+        difference = pd.DataFrame(difference)
+
+        kwh_simulation_predictions = model_api.paginated_predictions(
+            data=scoring_epcs,
             bucket=get_settings().DATA_BUCKET,
             model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
+            batch_size=SCORING_BATCH_SIZE
         )
 
-        # TODO: Costing model, which should include today's costs!
-        # We now insert into the recommendations
+        # We now insert kwh estimates and costs into the recommendations
         for property_id in recommendations.keys():
             property_recommendations = recommendations[property_id]
             property_instance = [p for p in input_properties if p.id == property_id][0]
@@ -1128,7 +1135,9 @@ async def build_mds(body: MdsRequest):
                      "carbon_ending"]
         )
 
-        model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
+        model_api = ModelApi(
+            portfolio_id=body.portfolio_id, timestamp=created_at, prediction_buckets=get_prediction_buckets()
+        )
 
         all_predictions = {
             "sap_change_predictions": pd.DataFrame(),

backend/ml_models/api.py

@@ -1,4 +1,5 @@
 import pandas as pd
+from tqdm import tqdm
 import requests
 from requests.exceptions import RequestException
 from utils.logger import setup_logger
@@ -55,9 +56,8 @@ class ModelApi:
             "sap_change_predictions": pd.DataFrame(),
             "heat_demand_predictions": pd.DataFrame(),
             "carbon_change_predictions": pd.DataFrame(),
-            "lighting_cost_predictions": pd.DataFrame(),
-            "heating_cost_predictions": pd.DataFrame(),
-            "hot_water_cost_predictions": pd.DataFrame(),
+            "hotwater_kwh_predictions": pd.DataFrame(),
+            "heating_kwh_predictions": pd.DataFrame(),
         }
 
     def upload_scoring_data(self, df: pd.DataFrame, bucket: str, model_prefix: str) -> str:
@@ -179,3 +179,20 @@ class ModelApi:
             predictions[model_prefix] = predictions_df
 
         return predictions
+
+    def paginated_predictions(self, data, bucket, batch_size, model_prefixes=None, extract_ids=True):
+        all_predictions = self.predictions_template()
+        to_loop_over = range(0, data.shape[0], batch_size)
+        for chunk in tqdm(to_loop_over, total=len(to_loop_over)):
+            predictions_dict = self.predict_all(
+                df=data.iloc[chunk:chunk + batch_size],
+                bucket=bucket,
+                model_prefixes=model_prefixes,
+                extract_ids=extract_ids
+            )
+
+            # Append the predictions to the predictions dictionary
+            for key, scored in predictions_dict.items():
+                all_predictions[key] = pd.concat([all_predictions[key], scored])
+
+        return all_predictions

recommendations/HeatingControlRecommender.py

@@ -43,7 +43,7 @@ class HeatingControlRecommender:
             # For an ASHP, we can recommend time and temperature zone controls, as well as programmer, trvs and a bypass
             # which are common configurations for ASHPs
             self.recommend_time_temperature_zone_controls()
-            self.recommend_programmer_trvs_bypass()
+            # self.recommend_programmer_trvs_bypass()
 
     def recommend_room_heaters_electric_controls(self):
         """

recommendations/Recommendations.py

@@ -419,10 +419,7 @@ class Recommendations:
                     previous_phase_values_multiple = [x for x in impact_summary if x["phase"] == (rec["phase"] - 1)]
                     if len(previous_phase_values_multiple) != 1:
                         # Take an average of each of the previous phases
-                        keys_to_median = [
-                            "sap", "carbon", "heat_demand", "epc_heating_cost", "epc_hot_water_cost",
-                            "epc_lighting_cost"
-                        ]
+                        keys_to_median = ["sap", "carbon", "heat_demand"]
 
                         previous_phase_values = {}
                         for key in keys_to_median: