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https://github.com/Hestia-Homes/Model.git
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Adding new materials to Materials enum
This commit is contained in:
Khalim Conn-Kowlessar
parent
46f08c4aab
commit
d703447c6a
5 changed files with 12 additions and 188 deletions
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.idea/Model.iml
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.idea/Model.iml
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@ -7,7 +7,7 @@
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<sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
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<sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
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</content>
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<orderEntry type="jdk" jdkName="Python 3.10 (model_data)" jdkType="Python SDK" />
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<orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PyNamespacePackagesService">
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2
.idea/misc.xml
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.idea/misc.xml
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@ -3,7 +3,7 @@
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<component name="Black">
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<option name="sdkName" value="Python 3.10 (backend)" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (model_data)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
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<component name="PythonCompatibilityInspectionAdvertiser">
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<option name="version" value="3" />
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</component>
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@ -15,6 +15,9 @@ class MaterialType(enum.Enum):
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cavity_wall_insulation = "cavity_wall_insulation"
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mechanical_ventilation = "mechanical_ventilation"
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loft_insulation = "loft_insulation"
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exposed_floor_insulation = "exposed_floor_insulation"
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flat_roof_insulation = "flat_roof_insulation"
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room_roof_insulation = "room_roof_insulation"
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class DepthUnit(enum.Enum):
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@ -1,176 +0,0 @@
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from datetime import datetime
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import pandas as pd
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from epc_api.client import EpcClient
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from fastapi import APIRouter, Depends
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from sqlalchemy.exc import IntegrityError, OperationalError
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from sqlalchemy.orm import sessionmaker
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from starlette.responses import Response
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from backend.app.config import get_settings
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from backend.app.db.connection import db_engine
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from backend.app.db.functions.materials_functions import get_materials
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from backend.app.db.functions.portfolio_functions import aggregate_portfolio_recommendations
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from backend.app.db.functions.property_functions import (
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create_property, create_property_details_epc, create_property_targets, update_property_data
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)
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from backend.app.db.functions.recommendations_functions import (
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create_plan, create_plan_recommendations, upload_recommendations
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)
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from backend.app.db.models.portfolio import rating_lookup
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from backend.app.dependencies import validate_token
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from backend.app.plan.schemas import PlanTriggerRequest
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from backend.app.plan.utils import (
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create_recommendation_scoring_data, filter_materials, get_cleaned, insert_temp_recommendation_id
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)
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from backend.app.utils import epc_to_sap_lower_bound, read_csv_from_s3, read_parquet_from_s3
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from backend.ml_models.sap_change_model.api import SAPChangeModelAPI
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from backend.Property import Property
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from etl.epc.DataProcessor import DataProcessor
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from etl.epc.settings import COLUMNS_TO_MERGE_ON
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from recommendations.FloorRecommendations import FloorRecommendations
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from recommendations.optimiser.CostOptimiser import CostOptimiser
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from recommendations.optimiser.GainOptimiser import GainOptimiser
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from recommendations.optimiser.optimiser_functions import prepare_input_measures
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from recommendations.WallRecommendations import WallRecommendations
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from utils.logger import setup_logger
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from utils.s3 import read_dataframe_from_s3_parquet
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logger = setup_logger()
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import pickle
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with open('local_data.pickle', 'rb') as f:
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local_data = pickle.load(f)
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with open("property_dimensions.pickle", "rb") as f:
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property_dimensions = pickle.load(f)
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with open("sap_change_dataset.pickle", "rb") as f:
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sap_change_dataset = pickle.load(f)
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created_at = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
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plan_input = local_data["plan_input"]
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uprn_filenames = local_data["uprn_filenames"]
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local_property_data = local_data["local_property_data"]
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materials = local_data["materials"]
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materials_by_type = filter_materials(materials)
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cleaned = local_data["cleaned"]
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cleaning_data = local_data["cleaning_data"]
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# Need to find some proper materials
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materials_by_type["walls"] += [
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{'id': 4, 'type': 'cavity_wall_insulation', 'description': 'Example Material 1',
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'depths': None,
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'depth_unit': None, 'cost': 20,
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'cost_unit': 'gbp_sq_meter', 'r_value_per_mm': 0.0278, 'r_value_unit': 'square_meter_kelvin_per_watt',
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'thermal_conductivity': 0.036, 'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': None, 'created_at': None, 'is_active': True},
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{'id': 10, 'type': "cavity_wall_insulation", 'description': 'Example Material 2',
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'depths': None, 'depth_unit': None, 'cost': 25, 'cost_unit': 'gbp_sq_meter',
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'r_value_per_mm': 0.02631579, 'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.038,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': None,
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'created_at': None, 'is_active': True}
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]
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epc_client = EpcClient(auth_token="NO-TOKEN")
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input_properties = []
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for i, config in enumerate(plan_input):
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property_id = local_property_data[i]["id"]
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input_properties.append(
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Property(
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postcode=config['postcode'],
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address1=config['address'],
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epc_client=epc_client,
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id=property_id
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)
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)
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logger.info("Getting EPC, and spatial data")
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for i, p in enumerate(input_properties):
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p.data = local_property_data[i]["data"]
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p.uprn = local_property_data[i]["uprn"]
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p.id = local_property_data[i]["id"]
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p.full_sap_epc = local_property_data[i]["full_sap_epc"]
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p.old_data = local_property_data[i]["old_data"]
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p.is_listed = False
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p.in_conservation_area = False
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p.is_heritage = False
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p.set_year_built()
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# TODO: TESTING
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p.data['number-habitable-rooms'] = 3
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recommendations = {}
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recommendations_scoring_data = []
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for p in input_properties:
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property_recommendations = []
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# Property recommendations
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p.get_components(cleaned)
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# Floor recommendations
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floor_recommender = FloorRecommendations(
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property_instance=p,
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materials=materials_by_type["floor"],
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)
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floor_recommender.recommend()
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if floor_recommender.recommendations:
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property_recommendations.append(floor_recommender.recommendations)
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# Wall recommendations
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wall_recomender = WallRecommendations(
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property_instance=p,
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materials=materials_by_type["walls"]
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)
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wall_recomender.recommend()
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if wall_recomender.recommendations:
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property_recommendations.append(wall_recomender.recommendations)
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# We insert temporary ids into the recommendations which is important for the optimiser later
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property_recommendations = insert_temp_recommendation_id(property_recommendations)
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if not property_recommendations:
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continue
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recommendations[p.id] = property_recommendations
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# Finally, we'll prepare data for predicting the impact on SAP
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# TODO: We should use the cleaned data from get_components in the data rather than the raw
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# values. We should create a method in Property which takes the EPC data and inserts the cleaned
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# data
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data_processor = DataProcessor(None, newdata=True)
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data_processor.insert_data(pd.DataFrame([p.data.copy()]))
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data_processor.pre_process()
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starting_epc_data = data_processor.get_component_features(suffix="_STARTING")
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ending_epc_data = data_processor.get_component_features(suffix="_ENDING")
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fixed_data = data_processor.get_fixed_features()
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# We update the ending record with the recommended updates and we set lodgement date to today
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ending_epc_data["LODGEMENT_DATE_ENDING"] = created_at
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for recommendations_by_type in property_recommendations:
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for rec in recommendations_by_type:
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scoring_dict = create_recommendation_scoring_data(
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property=p,
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recommendation=rec,
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starting_epc_data=starting_epc_data,
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ending_epc_data=ending_epc_data,
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fixed_data=fixed_data,
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)
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recommendations_scoring_data.append(scoring_dict)
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# cleanup
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del data_processor
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@ -47,7 +47,8 @@ class RoofRecommendations:
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# Building regulations part L recommend installing at least 270mm of insulation, however generally we
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# experience diminishing returns in terms of SAP once we go beyond around 150mm of insulation
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if insulation_thickness >= self.MINIMUM_LOFT_ISULATION_MM:
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# This only holds true for pitched roofs
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if (insulation_thickness >= self.MINIMUM_LOFT_ISULATION_MM) and self.property.roof["is_pitched"]:
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return
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# If we have a u-value already, need to implement this
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@ -61,7 +62,7 @@ class RoofRecommendations:
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return
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if self.property.roof["is_roof_room"]:
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self.recommend_room_roof_insulation(u_value, insulation_thickness)
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self.recommend_room_roof_insulation(u_value)
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return
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raise NotImplementedError("Implement me")
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@ -125,8 +126,9 @@ class RoofRecommendations:
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for depth, cost_per_unit in zip(material["depths"], material["cost"]):
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# We make sure we hit a depth of 270mm. We should factor in any existing insulation if the
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# loft is already partially insulated
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if (depth + insulation_thickness) < self.MINIMUM_LOFT_ISULATION_MM:
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# loft is already partially insulated.
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# Note: This requirement is only for loft insulation
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if ((depth + insulation_thickness) < self.MINIMUM_LOFT_ISULATION_MM) and roof["is_pitched"]:
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continue
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part_u_value = r_value_per_mm_to_u_value(depth, material["r_value_per_mm"])
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@ -178,7 +180,7 @@ class RoofRecommendations:
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self.recommendations = recommendations
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def recommend_room_roof_insulation(self, u_value, insulation_thickness):
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def recommend_room_roof_insulation(self, u_value):
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"""
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This method recommends room in roof insulation for properties that have been identified
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to possess a room in roof.
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@ -217,7 +219,6 @@ class RoofRecommendations:
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- Flat ceilings can be insulated like a standard loft.
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:param u_value: Current u-value of the roof
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:param insulation_thickness: Current insulation thickness of the roof
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:return:
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"""
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@ -232,10 +233,6 @@ class RoofRecommendations:
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recommendations = []
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for material in roof_roof_insulation_materials:
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for depth, cost_per_unit in zip(material["depths"], material["cost"]):
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# We make sure we hit a depth of 270mm. We should factor in any existing insulation if the
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# loft is already partially insulated
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if (depth + insulation_thickness) < self.MINIMUM_LOFT_ISULATION_MM:
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continue
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part_u_value = r_value_per_mm_to_u_value(depth, material["r_value_per_mm"])
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