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Implementing sap model api to backend wip

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This commit is contained in:
Khalim Conn-Kowlessar 2023-09-05 15:47:26 +01:00
parent 66b20c2223
commit 637ada9cb3
4 changed files with 115 additions and 2 deletions
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110
backend/app/plan/router.py
View file

@ -15,6 +15,8 @@ from backend.app.db.utils import row2dict
from starlette.responses import Response
from sqlalchemy.orm import sessionmaker
from sqlalchemy.exc import IntegrityError, OperationalError
from datetime import datetime
import pandas as pd
# database interaction functions
from backend.app.db.functions.property_functions import (
@ -212,6 +214,8 @@ async def trigger_plan(body: PlanTriggerRequest):
# TODO: Move this to a class. We probably was a Recommender class which takes the injects the optimisers
# in as a dependency and then the optimisers can take the input measures in as part of the setup() method
recommendations = {}
recommendations_scoring_data = []
for p in input_properties:
property_recommendations = []
@ -323,7 +327,111 @@ async def trigger_plan(body: PlanTriggerRequest):
recommendations[p.id] = property_recommendations
# Once we're done, we'll store:
# Finally, we'll prepare data for predicting the impact on SAP
from model_data.simulation_system.core.Settings import FIXED_FEATURES, COMPONENT_FEATURES
epc_data = p.data.copy()
epc_data = pd.DataFrame([epc_data])
epc_data.columns = [col.upper().replace("-", "_") for col in epc_data.columns]
starting_epc_data = epc_data[COMPONENT_FEATURES + ["LODGEMENT_DATE"]].copy().add_suffix("_STARTING")
ending_epc_data = epc_data[COMPONENT_FEATURES + ["LODGEMENT_DATE"]].copy().add_suffix("_ENDING")
fixed_data = epc_data[FIXED_FEATURES]
# We update the ending record with the recommended updates and we set lodgement date to today
ending_epc_data["LODGEMENT_DATE_ENDING"] = datetime.now().strftime("%Y-%m-%d")
scoring_map = {
'Solid brick, as built, no insulation (assumed)': 'Solid brick, as built, insulated (assumed)',
'Suspended, no insulation (assumed)': 'Suspended, insulated (assumed)',
'Solid, no insulation (assumed)': 'Solid, insulated (assumed)',
}
for rec in property_recommendations:
scoring_dict = {
"UPRN": p.data["uprn"],
"id": "+".join([str(p.id), str(rec["recommendation_id"])]),
**starting_epc_data.to_dict("records")[0],
**ending_epc_data.to_dict("records")[0],
**fixed_data.to_dict("records")[0]
}
# We update the description to indicate it's insulated
if rec["type"] == "wall_insulation":
scoring_dict["WALLS_DESCRIPTION_ENDING"] = scoring_map[p.walls["clean_description"]]
elif rec["type"] == "floor_insulation":
scoring_dict["FLOOR_DESCRIPTION_ENDING"] = scoring_map[p.floor["clean_description"]]
else:
raise NotImplementedError("Implement me")
recommendations_scoring_data.append(scoring_dict)
recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
# TODO: We need to clean the data
column_types = data.dtypes.to_dict()
columntypes = {}
for k, v in column_types.items():
if k not in recommendations_scoring_data.columns:
continue
columntypes[k] = v.name
columntypes["id"] = "object"
for col in recommendations_scoring_data.columns:
recommendations_scoring_data[col] = recommendations_scoring_data[col].astype(columntypes[col])
recommendations_scoring_data = recommendations_scoring_data.astype(columntypes)
column_types = {'UPRN': dtype('O'), 'RDSAP_CHANGE': dtype('int64'), 'HEAT_DEMAND_CHANGE': dtype('int64'),
'TOTAL_FLOOR_AREA': dtype('float64'), 'FLOOR_HEIGHT': dtype('float64'), 'PROPERTY_TYPE': dtype('O'),
'BUILT_FORM': dtype('O'), 'CONSTITUENCY': dtype('O'), 'NUMBER_HABITABLE_ROOMS': dtype('float64'),
'NUMBER_HEATED_ROOMS': dtype('float64'), 'FIXED_LIGHTING_OUTLETS_COUNT': dtype('float64'),
'FLOOR_LEVEL': dtype('float64'), 'CONSTRUCTION_AGE_BAND': dtype('O'), 'TRANSACTION_TYPE_STARTING': dtype('O'),
'WALLS_DESCRIPTION_STARTING': dtype('O'), 'FLOOR_DESCRIPTION_STARTING': dtype('O'),
'LIGHTING_DESCRIPTION_STARTING': dtype('O'), 'ROOF_DESCRIPTION_STARTING': dtype('O'),
'MAINHEAT_DESCRIPTION_STARTING': dtype('O'), 'HOTWATER_DESCRIPTION_STARTING': dtype('O'),
'MAIN_FUEL_STARTING': dtype('O'), 'MECHANICAL_VENTILATION_STARTING': dtype('O'),
'SECONDHEAT_DESCRIPTION_STARTING': dtype('O'), 'ENERGY_TARIFF_STARTING': dtype('O'),
'SOLAR_WATER_HEATING_FLAG_STARTING': dtype('O'), 'PHOTO_SUPPLY_STARTING': dtype('float64'),
'WINDOWS_DESCRIPTION_STARTING': dtype('O'), 'GLAZED_TYPE_STARTING': dtype('O'),
'MULTI_GLAZE_PROPORTION_STARTING': dtype('float64'), 'LOW_ENERGY_LIGHTING_STARTING': dtype('float64'),
'NUMBER_OPEN_FIREPLACES_STARTING': dtype('float64'), 'MAINHEATCONT_DESCRIPTION_STARTING': dtype('O'),
'EXTENSION_COUNT_STARTING': dtype('float64'), 'LODGEMENT_DATE_STARTING': dtype('O'),
'TRANSACTION_TYPE_ENDING': dtype('O'), 'WALLS_DESCRIPTION_ENDING': dtype('O'),
'FLOOR_DESCRIPTION_ENDING': dtype('O'), 'LIGHTING_DESCRIPTION_ENDING': dtype('O'),
'ROOF_DESCRIPTION_ENDING': dtype('O'), 'MAINHEAT_DESCRIPTION_ENDING': dtype('O'),
'HOTWATER_DESCRIPTION_ENDING': dtype('O'), 'MAIN_FUEL_ENDING': dtype('O'),
'MECHANICAL_VENTILATION_ENDING': dtype('O'), 'SECONDHEAT_DESCRIPTION_ENDING': dtype('O'),
'ENERGY_TARIFF_ENDING': dtype('O'), 'SOLAR_WATER_HEATING_FLAG_ENDING': dtype('O'),
'PHOTO_SUPPLY_ENDING': dtype('float64'), 'WINDOWS_DESCRIPTION_ENDING': dtype('O'),
'GLAZED_TYPE_ENDING': dtype('O'), 'MULTI_GLAZE_PROPORTION_ENDING': dtype('float64'),
'LOW_ENERGY_LIGHTING_ENDING': dtype('float64'), 'NUMBER_OPEN_FIREPLACES_ENDING': dtype('float64'),
'MAINHEATCONT_DESCRIPTION_ENDING': dtype('O'), 'EXTENSION_COUNT_ENDING': dtype('float64'),
'LODGEMENT_DATE_ENDING': dtype('O'), 'id': dtype('int64')}
# Example data file
from io import BytesIO
import boto3
def read_parquet_from_s3(bucket_name, file_key):
client = boto3.client('s3')
# Get the object
s3_object = client.get_object(Bucket=bucket_name, Key=file_key)
# Read the CSV body into a DataFrame
csv_body = s3_object["Body"].read()
df = pd.read_parquet(BytesIO(csv_body))
return df
data = read_parquet_from_s3(
bucket_name="retrofit-data-dev", file_key="model_build_data/change_data/rdsap_full/test_data_with_id.parquet"
)
data = data.head(5)
# We query the sap difference model api to get the estimated impact on sap
for property_id, recommendations in recommendations.items():
# 1) the property data
# 2) the property details (epc)
# 3) the recommendations

2
backend/requirements/base.txt
View file

@ -32,3 +32,5 @@ psycopg2-binary
pytz==2023.3
mip==1.15.0
boto3==1.28.3
pandas==1.5.3
pyarrow==12.0.1

1
model_data/simulation_system/core/Settings.py
View file

@ -99,7 +99,6 @@ COMPONENT_FEATURES = [
"WINDOWS_DESCRIPTION",
"GLAZED_TYPE",
"MULTI_GLAZE_PROPORTION",
"LIGHTING_DESCRIPTION",
"LOW_ENERGY_LIGHTING",
"NUMBER_OPEN_FIREPLACES",
"MAINHEATCONT_DESCRIPTION",

4
recommendations/WallRecommendations.py
View file

@ -256,6 +256,10 @@ class WallRecommendations(Definitions):
is_solid_brick = self.property.walls["is_solid_brick"]
insulation_thickness = self.property.walls["insulation_thickness"]
# We check if the wall is already insulated and if so, we exit
if insulation_thickness in ["average", "above average"]:
return
if u_value:
if self.property.walls["thermal_transmittance_unit"] != self.U_VALUE_UNIT:
raise NotImplementedError("Haven't handled the case of other u value units yet")