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https://github.com/Hestia-Homes/Model.git
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removed temp debugging code
This commit is contained in:
Khalim Conn-Kowlessar
parent
33bc38fdc8
commit
97dfa3e572
3 changed files with 14 additions and 173 deletions
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@ -639,15 +639,16 @@ class Property:
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else:
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default_roof_area = self.insulation_floor_area
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# Keep a record
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self.roof_area_comparison = {
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"api": roof_area,
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"estimated": default_roof_area
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}
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# We also set the roof area
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if roof_area is None:
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self.roof_area = default_roof_area
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else:
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# Perform a comparison between the default_roof_area and roof_area
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difference = abs(default_roof_area - roof_area)
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if difference / default_roof_area > 0.1:
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raise Exception("Investigate difference in roof area")
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self.roof_area = roof_area
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def set_current_energy_bill(self, kwh_client, kwh_predictions):
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@ -511,12 +511,12 @@ async def trigger_plan(body: PlanTriggerRequest):
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input_properties = OpenUprnClient.set_spatial_data(input_properties, bucket_name=get_settings().DATA_BUCKET)
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[p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=kwh_preds) for p in input_properties]
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logger.info("Performing solar analysis")
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# TODO: If a property is semi-detached, we might get roof surfaces for the main building + the neighbour
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# TODO: If we can't get high image quality, should we use the solar API? Maybe just for semi-detached units with
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# extensions, since it doesn't seem to do a great job
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logger.info("Performing solar analysis")
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building_solar_config, unit_solar_config = GoogleSolarApi.prepare_input_data(
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input_properties=input_properties,
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energy_consumption_client=energy_consumption_client,
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@ -595,171 +595,6 @@ async def trigger_plan(body: PlanTriggerRequest):
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scoring_epcs.extend(property_instance.updated_simulation_epcs)
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recommendations[property_id] = recommendations_with_impact
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# For Debugging
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# recommendation_impact_df = []
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# for property_id in recommendations.keys():
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# for recs_by_type in recommendations[property_id]:
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# for rec in recs_by_type:
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# recommendation_impact_df.append(
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# {
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# "property_id": property_id,
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# "uprn": [p.uprn for p in input_properties if p.id == property_id][0],
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# "address": [p.address for p in input_properties if p.id == property_id][0],
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# "recommendation_id": rec["recommendation_id"],
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# "type": rec["type"],
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# "description": rec["description"],
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# "sap_points": rec["sap_points"],
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# "co2_equivalent_savings": rec["co2_equivalent_savings"],
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# "heat_demand": rec["heat_demand"]
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# }
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# )
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# recommendation_impact_df = pd.DataFrame(recommendation_impact_df)
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#
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# surveyed_uprns = [
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# 10024087855, 121016117, 121016124,
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# 10024087902, 121016121, 121016128
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# ]
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# recommendation_impact_df = recommendation_impact_df[recommendation_impact_df["uprn"].isin(surveyed_uprns)]
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# # recommendation_impact_df = recommendation_impact_df[recommendation_impact_df["type"].isin(
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# # ["windows_glazing", "internal_wall_insulation"])
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# # ]
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#
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# actual_impacts_df = pd.DataFrame(
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# [
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# # 10024087855
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# {"uprn": 10024087855, "type": "internal_wall_insulation", "actual_sap_points": 5},
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# {"uprn": 10024087855, "type": "draught_proofing", "actual_sap_points": 2},
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# {"uprn": 10024087855, "type": "low_energy_lighting", "actual_sap_points": 0},
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# {"uprn": 10024087855, "type": "windows_glazing", "actual_sap_points": 4},
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# # 121016117
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# {"uprn": 121016117, "type": "internal_wall_insulation", "actual_sap_points": 6},
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# {"uprn": 121016117, "type": "draught_proofing", "actual_sap_points": 1},
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# {"uprn": 121016117, "type": "low_energy_lighting", "actual_sap_points": 1},
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# {"uprn": 121016117, "type": "windows_glazing", "actual_sap_points": 4},
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# # 121016124
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# {"uprn": 121016124, "type": "internal_wall_insulation", "actual_sap_points": 8},
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# {"uprn": 121016124, "type": "low_energy_lighting", "actual_sap_points": 2},
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# {"uprn": 121016124, "type": "windows_glazing", "actual_sap_points": 5},
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# # 10024087902
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# {"uprn": 10024087902, "type": "room_roof_insulation", "actual_sap_points": 16},
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# {"uprn": 10024087902, "type": "internal_wall_insulation", "actual_sap_points": 2},
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# {"uprn": 10024087902, "type": "low_energy_lighting", "actual_sap_points": 0},
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# # 121016121
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# {"uprn": 121016121, "type": "internal_wall_insulation", "actual_sap_points": 5},
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# {"uprn": 121016121, "type": "suspended_floor_insulation", "actual_sap_points": 2},
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# {"uprn": 121016121, "type": "draught_proofing", "actual_sap_points": 1},
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# {"uprn": 121016121, "type": "windows_glazing", "actual_sap_points": 3},
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# # 121016128
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# {"uprn": 121016128, "type": "internal_wall_insulation", "actual_sap_points": 6},
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# {"uprn": 121016128, "type": "suspended_floor_insulation", "actual_sap_points": 1},
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# {"uprn": 121016128, "type": "draught_proofing", "actual_sap_points": 1},
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# {"uprn": 121016128, "type": "low_energy_lighting", "actual_sap_points": 1},
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# {"uprn": 121016128, "type": "windows_glazing", "actual_sap_points": 3},
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# ]
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# )
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#
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# comparison = recommendation_impact_df.merge(
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# actual_impacts_df, how="inner", on=["uprn", "type"]
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# )
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#
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# print(recommendation_impact_df.groupby(["uprn"])["sap_points"].sum())
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# property_recs = recommendation_impact_df[recommendation_impact_df["uprn"] == 121016128]
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# property = [p for p in input_properties if p.uprn == 121016128][0]
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# print(property.data["current-energy-efficiency"])
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# print(property_recs["sap_points"].sum())
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# print(property_recs["type"])
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# print(float(property.data["current-energy-efficiency"]) + property_recs["sap_points"].sum())
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# recommendations[property.id][2][0]["simulation_config"]
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# from utils.s3 import read_dataframe_from_s3_parquet
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# training_data = read_dataframe_from_s3_parquet(
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# bucket_name="retrofit-data-dev",
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# file_key="sap_change_model/2024-08-06-11-19-49/dataset_rooms.parquet"
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# )
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# import pickle
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# with open("delete_me.pkl", "wb") as f:
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# pickle.dump(training_data, f)
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# Read in the pickle
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import pickle
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with open("delete_me.pkl", "rb") as f:
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training_data = pickle.load(f)
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# How do we simulate windows:
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ending_cols = [col for col in training_data.columns if col.endswith("_ending")]
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starting = {}
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for c in ending_cols:
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starting_colname = c.replace("_ending", "_starting")
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if starting_colname in training_data.columns:
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starting[c] = starting_colname
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else:
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starting[c] = c.replace("_ending", "")
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allowed_to_change = [
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# Windows
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"windows_energy_eff_ending",
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"glazed_type_ending",
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"glazing_type_ending",
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"multi_glaze_proportion_ending",
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# Other
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"sap_ending",
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"heat_demand_ending",
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"carbon_ending",
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"estimated_perimeter_ending",
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"lodgement_year_ending",
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"lodgement_month_ending",
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"days_to_ending",
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"number_habitable_rooms_ending",
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"number_heated_rooms_ending",
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]
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fixed = [c for c in ending_cols if c not in allowed_to_change + ["uprn"]]
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training_fixed = training_data.copy()
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for col in fixed:
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starting_col = starting[col]
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training_fixed = training_fixed[training_fixed[col] == training_fixed[starting_col]]
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training_fixed = training_fixed.reset_index(drop=True)
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# Get the recommendation config for this uprn
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uprn = 121016121
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property_instance = [p for p in input_properties if p.uprn == uprn][0]
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property_recs = recommendations[property_instance.id]
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window_recs = [r for r in property_recs if r[0]["type"] == "windows_glazing"][0]
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window_recs[0].keys()
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window_recs[0]["description_simulation"]["multi-glaze-proportion"]
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# TODO: - In description_simulation for windows, we update glazed-type but in the model training data there
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# is a column called "glazing-type".
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# - We don't update glazed-area (should be "Much More Than Typical" most likely? Or Normal??)
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# TODO: I think we update eveything that we actually need to, when simulating the recommendation impact for the
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# ML models
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# TODO: Secondary glazing appears to go to "Good", not "Average". Investigate why
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# TODO: For the two properties, force recommendations for double glazing and check impact
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z = training_data[training_data["glazed_type_ending"] == "secondary glazing"]
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z = z[z["multi_glaze_proportion_ending"] == 100]
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z["windows_energy_eff_ending"].value_counts()
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# Find the things that change
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example = training_fixed.iloc[3]
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for _, example in training_fixed.iterrows():
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things_that_change = []
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for c in ending_cols:
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if example[c] != example[starting[c]]:
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things_that_change.append(c)
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if len(things_that_change) > 4:
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print(things_that_change)
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print(example["uprn"])
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# blah
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# 100051011370 (doesn't change in actual glazing)
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# example["glazed_type_ending"]
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# double glazing installed before 2002
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# example["glazed_type_starting"]
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# double glazing, unknown install date
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# 100040925015
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# We call the API with the scoring epcs
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scoring_epcs = pd.DataFrame(scoring_epcs)
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scoring_epcs = kwh_client.transform(data=scoring_epcs, cleaned=cleaned)
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@ -84,12 +84,17 @@ class RoofRecommendations:
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return (self.insulation_thickness > self.MINIMUM_LOFT_ISULATION_MM) and self.property.roof["is_pitched"]
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def is_room_roof_insulated(self):
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def is_room_roof_insulated_or_unsuitable(self, measures):
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"""
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Check if the room roof is already insulated
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"""
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# If the roof is a room roof room roof is not included in the measures, we deem the recommendation unsuitable
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unsuitable = "room_roof_insulation" not in measures and self.property.roof["is_roof_room"]
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if unsuitable:
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return True
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full_insulated_room_roof = (
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self.property.roof["is_roof_room"] and
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self.property.roof["insulation_thickness"] in ["average", "above_average"]
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@ -123,7 +128,7 @@ class RoofRecommendations:
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if (self.insulation_thickness >= self.MINIMUM_FLAT_ROOF_ISULATION_MM) and self.property.roof["is_flat"]:
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return
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if self.is_room_roof_insulated():
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if self.is_room_roof_insulated_or_unsuitable(measures):
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return
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# If we have a u-value already, need to implement this
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