ha7

etl/eligibility/ha_15_32/ha7_app.py

@@ -137,6 +137,131 @@ def get_ha7_data(data, cleaned, cleaning_data, created_at):
             }
         )
 
+    scoring_df = pd.DataFrame(scoring_data)
+    # Implement the same process that is being used in the recommendation engine to cleaning scoring_df
+
+    # Perform the same cleaning as in the model - first clean number of room variables though
+    scoring_df = DataProcessor.apply_averages_cleaning(
+        data_to_clean=scoring_df,
+        cleaning_data=cleaning_data,
+        cols_to_merge_on=['PROPERTY_TYPE', 'BUILT_FORM', 'CONSTRUCTION_AGE_BAND', 'LOCAL_AUTHORITY'],
+        colnames=["NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"],
+    )
+
+    scoring_df = DataProcessor.apply_averages_cleaning(
+        data_to_clean=scoring_df,
+        cleaning_data=cleaning_data,
+        cols_to_merge_on=COLUMNS_TO_MERGE_ON + ["LOCAL_AUTHORITY"],
+    ).drop(columns=["LOCAL_AUTHORITY"])
+
+    scoring_df = DataProcessor.clean_missings_after_description_process(
+        scoring_df,
+        ignore_cols=[c for c in scoring_df.columns if ("thermal_transmittance" in c) or (
+            "insulation_thickness" in c) or ("ENERGY_EFF" in c)]
+    )
+
+    scoring_df = DataProcessor.clean_efficiency_variables(scoring_df)
+
+    model_api = ModelApi(portfolio_id="ha33-eligibility", timestamp=created_at)
+    all_predictions = model_api.predict_all(
+        df=scoring_df,
+        bucket="retrofit-data-dev",
+        prediction_buckets={
+            "sap_change_predictions": "retrofit-sap-predictions-dev",
+            "heat_demand_predictions": "retrofit-heat-predictions-dev",
+            "carbon_change_predictions": "retrofit-carbon-predictions-dev"
+        }
+    )
+
+    predictions = all_predictions["sap_change_predictions"].copy()
+
+    results_df = pd.DataFrame(results)
+
+    predictions = predictions.rename(columns={"property_id": "row_id"}).merge(
+        results_df[["row_id", "sap"]], how="left", on="row_id"
+    )
+    predictions["sap_uplift"] = predictions["predictions"] - predictions["sap"]
+    predictions = predictions.groupby("row_id")["sap_uplift"].sum().reset_index()
+
+    results_df = results_df.merge(
+        predictions[["sap_uplift", "row_id"]],
+        how="left",
+        on="row_id"
+    )
+
+    results_df["post_install_sap"] = results_df["sap"] + results_df["sap_uplift"]
+
+    eligibility_assessment = []
+    for _, row in results_df[results_df["eco4_eligible"] == True].iterrows():
+        # The upgrade requirements are dependent on the current SAP
+
+        # If the property is an F or G, it only needs to upgrade to an %
+        if row["sap"] <= 38:
+            if row["post_install_sap"] >= 57:
+                eligibility_classification = "highest confidence"
+            elif row["post_install_sap"] >= 55:
+                eligibility_classification = "high confidence"
+            elif row["post_install_sap"] >= 53:
+                eligibility_classification = "medium confidence"
+            else:
+                eligibility_classification = "unlikely"
+        else:
+
+            if row["post_install_sap"] >= 71:
+                eligibility_classification = "highest confidence"
+            elif row["post_install_sap"] >= 69:
+                eligibility_classification = "high confidence"
+            elif row["post_install_sap"] >= 67:
+                eligibility_classification = "medium confidence"
+            else:
+                eligibility_classification = "unlikely"
+
+        eligibility_assessment.append(
+            {
+                "row_id": row["row_id"],
+                "eligibility_classification": eligibility_classification
+            }
+        )
+
+    eligibility_assessment = pd.DataFrame(eligibility_assessment)
+
+    results_df = results_df.merge(
+        eligibility_assessment, how="left", on="row_id"
+    )
+
+    return results_df, scoring_data, nodata
+
+
+def analyse_ha_7(results_df, data):
+    df = results_df.merge(
+        data[["row_id", "row_code", "Property Type"]], how="left", on="row_id"
+    )
+    warmfront_identification = df["row_code"].value_counts()
+    warmfront_identified = df[df["row_code"] == "potential ECO4"]
+
+    property_types = df["Property Type"].value_counts()
+
+    n_identified = (results_df["gbis_eligible"] | results_df["eco4_eligible"]).sum()
+
+    eco_identified = results_df[results_df["eco4_eligible"]]
+    n_eco4 = eco_identified["eco4_eligible"].sum()
+    gbis_identified = results_df[~results_df["eco4_eligible"] & results_df["gbis_eligible"]]
+    n_gbis = results_df[~results_df["eco4_eligible"]]["gbis_eligible"].sum()
+
+    eco_eligibile = results_df[results_df["eco4_eligible"]]
+    eco_eligibile["eligibility_classification"].value_counts()
+
+    future_possibilities_eco = results_df[
+        (results_df["eco4_eligible_future"] == True) & (~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
+        ].copy()
+
+    future_possibilities_gbis = results_df[
+        (results_df["gbis_eligible_future"] == True) & (results_df["eco4_eligible_future"] == False) & (
+            ~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
+        ].copy()
+
+    total_future_possibilities = future_possibilities_eco.shape[0] + future_possibilities_gbis.shape[0]
+
 
 def app():
     data = load_data()
@@ -153,3 +278,10 @@ def app():
     )
 
     created_at = datetime.now().isoformat()
+
+    results_df, scoring_data, nodata = get_ha7_data(data, cleaned, cleaning_data, created_at)
+
+    # Pickle results
+    # import pickle
+    # with open("ha7_results.pkl", "wb") as f:
+    #     pickle.dump({"results_df": results_df, "scoring_data": scoring_data, "nodata": nodata}, f)