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https://github.com/Hestia-Homes/Model.git
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Added KwhData client to router
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Khalim Conn-Kowlessar
parent
fffb179219
commit
c9720cd78c
3 changed files with 70 additions and 67 deletions
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@ -247,8 +247,8 @@ def create_epc_records(epc_searcher: SearchEpc, energy_assessment: dict):
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# We insert county into the epc, since right now this isn't something that we pull out from the energy
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# assessment
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epc["county"] = epc_searcher.newest_epc["county"]
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epc["constituency"] = epc_searcher.newest_epc["constituency"]
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for col in ["county", "constituency", "constituency-label", "local-authority", "local-authority-label"]:
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epc[col] = epc_searcher.newest_epc[col]
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# We check if the energy assessment is newer than the newest EPC
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if pd.to_datetime(energy_assessment_date) > pd.to_datetime(epc_searcher.newest_epc["inspection-date"]):
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@ -433,10 +433,7 @@ async def trigger_plan(body: PlanTriggerRequest):
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model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
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epcs_for_scoring = KwhData.transform(
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data=pd.DataFrame([p.epc_record.original_epc for p in input_properties]),
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cleaned=cleaned,
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)
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epcs_for_scoring = KwhData().transform(data=KwhData().prepare_epc(input_properties), cleaned=cleaned)
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kwh_predictions = model_api.predict_all(
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df=epcs_for_scoring,
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@ -507,66 +507,6 @@ class EnergyConsumptionModel:
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return prediction
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@staticmethod
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def _prepare_new_data(p: Property):
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"""
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Given an instance of the property class, this method will ensure that the EPC is ready for scoring with the
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kwh models. In the backend, we perform some cleaning and transformation on an EPC so we just ensure that the
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data is in the format required by the model
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:return:
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"""
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epc = p.data.copy()
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numeric_cols = [
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'current-energy-efficiency',
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'potential-energy-efficiency', 'environment-impact-current',
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'environment-impact-potential', 'energy-consumption-current',
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'energy-consumption-potential', 'co2-emissions-current',
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'co2-emiss-curr-per-floor-area', 'co2-emissions-potential',
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'lighting-cost-current', 'lighting-cost-potential',
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'heating-cost-current', 'heating-cost-potential',
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'hot-water-cost-current', 'hot-water-cost-potential',
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'total-floor-area', 'multi-glaze-proportion',
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'extension-count', 'number-habitable-rooms', 'number-heated-rooms',
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'low-energy-lighting', 'number-open-fireplaces',
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'wind-turbine-count', 'unheated-corridor-length',
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'floor-height', 'photo-supply', 'fixed-lighting-outlets-count',
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'low-energy-fixed-light-count',
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]
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for v in numeric_cols:
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if epc[v] is not None:
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epc[v] = float(epc[v])
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bools_to_remap = ['mains-gas-flag', 'flat-top-storey']
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bool_map = {
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True: "Y",
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False: "N",
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None: "N",
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"Y": "Y",
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"N": "N"
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}
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for v in bools_to_remap:
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epc[v] = bool_map[epc[v]]
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no_data = {
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"floor-level": "NODATA!",
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"floor-energy-eff": "NO DATA!"
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}
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for v, fill_val in no_data.items():
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if pd.isnull(epc[v]):
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epc[v] = fill_val
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return epc
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def prepare_new_data(self, input_properties: list[Property]):
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scoring_data = pd.DataFrame([self._prepare_new_data(p) for p in input_properties])
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scoring_data["lodgement-year"] = pd.to_datetime(scoring_data["lodgement-date"]).dt.year
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scoring_data["lodgement-month"] = pd.to_datetime(scoring_data["lodgement-date"]).dt.month
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scoring_data["id"] = scoring_data["uprn"].copy()
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return scoring_data
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@staticmethod
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def calculate_percentage_decrease(start_efficiency, end_efficiency, consumption_averages):
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@ -5,6 +5,7 @@ from datetime import datetime
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from tqdm import tqdm
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from utils.logger import setup_logger
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from utils.s3 import list_files_in_s3_folder, read_pickle_from_s3, save_dataframe_to_s3_parquet
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from backend.Property import Property
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logger = setup_logger()
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@ -29,7 +30,7 @@ class KwhData:
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'heating-cost-potential', 'hot-water-cost-current', 'current-energy-efficiency'
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]
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def __init__(self, bucket):
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def __init__(self, bucket=None):
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self.run_date = datetime.now().strftime("%Y-%m-%d")
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self.bucket = bucket
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self.data = None
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@ -144,6 +145,8 @@ class KwhData:
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This method can be used to transform the training data, or new epcs within the backend engine
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:return:
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"""
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if save and self.bucket is None:
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raise Exception("bucket not set, cannot save data")
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# TODO: New is a temporary parameter, which will transform the epc descriptions to their transformed features
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# in anticipation of the new model
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@ -216,3 +219,66 @@ class KwhData:
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file_key=self.model_training_data_filepath,
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df=data
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)
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return
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return data
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@staticmethod
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def _prepare_epc(p: Property):
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"""
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Given an instance of the property class, this method will ensure that the EPC is ready for scoring with the
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kwh models. In the backend, we perform some cleaning and transformation on an EPC so we just ensure that the
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data is in the format required by the model
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:return:
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"""
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epc = p.data.copy()
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numeric_cols = [
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'current-energy-efficiency',
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'potential-energy-efficiency', 'environment-impact-current',
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'environment-impact-potential', 'energy-consumption-current',
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'energy-consumption-potential', 'co2-emissions-current',
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'co2-emiss-curr-per-floor-area', 'co2-emissions-potential',
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'lighting-cost-current', 'lighting-cost-potential',
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'heating-cost-current', 'heating-cost-potential',
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'hot-water-cost-current', 'hot-water-cost-potential',
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'total-floor-area', 'multi-glaze-proportion',
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'extension-count', 'number-habitable-rooms', 'number-heated-rooms',
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'low-energy-lighting', 'number-open-fireplaces',
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'wind-turbine-count', 'unheated-corridor-length',
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'floor-height', 'photo-supply', 'fixed-lighting-outlets-count',
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'low-energy-fixed-light-count',
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]
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for v in numeric_cols:
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if epc[v] is not None:
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epc[v] = float(epc[v])
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bools_to_remap = ['mains-gas-flag', 'flat-top-storey']
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bool_map = {
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True: "Y",
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False: "N",
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None: "N",
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"Y": "Y",
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"N": "N"
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}
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for v in bools_to_remap:
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epc[v] = bool_map[epc[v]]
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no_data = {
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"floor-level": "NODATA!",
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"floor-energy-eff": "NO DATA!"
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}
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for v, fill_val in no_data.items():
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if pd.isnull(epc[v]):
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epc[v] = fill_val
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return epc
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def prepare_epc(self, input_properties: list[Property]):
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scoring_data = pd.DataFrame([self._prepare_epc(p) for p in input_properties])
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scoring_data["lodgement-year"] = pd.to_datetime(scoring_data["lodgement-date"]).dt.year
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scoring_data["lodgement-month"] = pd.to_datetime(scoring_data["lodgement-date"]).dt.month
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scoring_data["id"] = scoring_data["uprn"].copy()
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return scoring_data
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