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creating new aggregations for front end

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Khalim Conn-Kowlessar 2024-04-15 23:41:24 +01:00
parent 5d3440815d
commit d6fa81939d
6 changed files with 146 additions and 8 deletions
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2
.idea/Model.iml generated
View file

@ -7,7 +7,7 @@
<sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
</content>
<orderEntry type="jdk" jdkName="non_invasive_surveys-photos" jdkType="Python SDK" />
<orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="PyNamespacePackagesService">

2
.idea/misc.xml generated
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@ -3,7 +3,7 @@
<component name="Black">
<option name="sdkName" value="Python 3.10 (backend)" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="non_invasive_surveys-photos" project-jdk-type="Python SDK" />
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
<component name="PythonCompatibilityInspectionAdvertiser">
<option name="version" value="3" />
</component>

8
backend/Property.py
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@ -142,6 +142,8 @@ class Property:
self.current_adjusted_energy = None
self.expected_adjusted_energy = None
self.current_energy_bill = None
self.expected_energy_bill = None
self.recommendations_scoring_data = []
@ -892,12 +894,16 @@ class Property:
return component_data
def set_adjusted_energy(self, current_adjusted_energy, expected_adjusted_energy):
def set_adjusted_energy(
self, current_adjusted_energy, expected_adjusted_energy, current_energy_bill, expected_energy_bill
):
"""
Stores these values for usage later
"""
self.current_adjusted_energy = current_adjusted_energy
self.expected_adjusted_energy = expected_adjusted_energy
self.current_energy_bill = current_energy_bill
self.expected_energy_bill = expected_energy_bill
def set_windows_count(self):
"""

3
backend/app/db/functions/portfolio_functions.py
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@ -4,7 +4,7 @@ from backend.app.db.models.portfolio import Portfolio
def aggregate_portfolio_recommendations(
session, portfolio_id: int, total_valuation_increase: float, labour_days: float
session, portfolio_id: int, total_valuation_increase: float, labour_days: float, aggregated_data: dict
):
# Aggregate multiple fields
aggregates = (
@ -27,6 +27,7 @@ def aggregate_portfolio_recommendations(
"energy_savings": aggregates.energy_savings or 0,
"co2_equivalent_savings": aggregates.co2_equivalent_savings or 0,
"energy_cost_savings": aggregates.energy_cost_savings or 0,
**aggregated_data
}
# Get the portfolio and update the fields

128
backend/app/plan/router.py
View file

@ -1,3 +1,4 @@
import json
from datetime import datetime
from tqdm import tqdm
@ -57,6 +58,109 @@ def patch_epc(patch, epc_records):
return epc_records
def extract_portfolio_aggregation_data(
input_properties, total_valuation_increase, recommendations, new_epc_bands
):
# We aggregate a number of metrics for the portfolio:
# 1) A breakdown of the number of properties in each EPC band
# a) before retrofit
# b) after retrofit
# 2) Number of units
# 3) Co2/unit
# a) before retrofit
# b) after retrofit
# 4) Energy bulls/unit
# a) before retrofit
# b) after retrofit
# 5) Average valuation improvement/unit
# 6) Total cost
# 7) Cost per unit
# 8) £ per CO2 saved
# 9) £ per SAP point
# We need to construct the underlyind data for this
# Helper function to reformat the EPC data
def reformat_epc_data(epc_counts):
# Define all possible EPC bands in the required order
epc_bands = ["G", "F", "E", "D", "C", "B", "A"]
# Create the formatted data list by checking each band in the order
formatted_data = []
for band in epc_bands:
# Get the count from the dictionary, defaulting to 0 if not present
count = epc_counts.get(band, 0)
# Append the formatted dictionary to the list
formatted_data.append({"name": band, band: count})
return formatted_data
n_units = len(input_properties)
agg_data = []
for p in input_properties:
# Get the recommendations for the property
property_recommendations = recommendations.get(p.id, [])
if not property_recommendations:
continue
# Get just the default recommendations
default_recommendations = [r for r in property_recommendations if r["default"]]
# We can now calculate multiple outputs based on default recommendations
carbon_savings = sum([r["co2_equivalent_savings"] for r in default_recommendations])
pre_retrofit_co2 = p.data["co2-emissions-current"]
post_retrofit_co2 = pre_retrofit_co2 - carbon_savings
pre_retrofit_energy_bill = p.current_energy_bill
post_retrofit_energy_bill = p.expected_energy_bill
cost = sum([r["total"] for r in default_recommendations])
sap_point_improvement = sum([r["sap_points"] for r in default_recommendations])
agg_data.append({
"pre_retrofit_epc": p.data["current-energy-rating"],
"post_retrofit_epc": new_epc_bands[p.id],
"pre_retrofit_co2": pre_retrofit_co2,
"post_retrofit_co2": post_retrofit_co2,
"pre_retrofit_energy_bill": pre_retrofit_energy_bill,
"post_retrofit_energy_bill": post_retrofit_energy_bill,
"cost": cost,
"sap_point_improvement": sap_point_improvement
})
agg_data = pd.DataFrame(agg_data)
n_units_to_retrofit = len(agg_data)
valuation_improvment_per_unit = total_valuation_increase / n_units_to_retrofit
total_carbon_saved = agg_data["pre_retrofit_co2"].sum() - agg_data["post_retrofit_co2"].sum()
total_sap_points = agg_data["sap_point_improvement"].sum()
aggregation_data = {
"epc_breakdown_pre_retrofit": json.dumps(
reformat_epc_data(agg_data["pre_retrofit_epc"].value_counts().to_dict())
),
"epc_breakdown_post_retrofit": json.dumps(
reformat_epc_data(agg_data["post_retrofit_epc"].value_counts().to_dict())
),
"number_of_properties": n_units,
"n_units_to_retrofit": n_units_to_retrofit,
"co2_per_unit_pre_retrofit": agg_data["pre_retrofit_co2"].mean(),
"co2_per_unit_post_retrofit": agg_data["post_retrofit_co2"].mean(),
"energy_bill_per_unit_pre_retrofit": agg_data["pre_retrofit_energy_bill"].mean(),
"energy_bill_per_unit_post_retrofit": agg_data["post_retrofit_energy_bill"].mean(),
"valuation_improvement_per_unit": valuation_improvment_per_unit,
"total_cost": agg_data["cost"].sum(),
"cost_per_unit": agg_data["cost"].mean(),
"cost_per_co2_saved": agg_data["cost"].sum() / total_carbon_saved,
"cost_per_sap_point": agg_data["cost"].sum() / total_sap_points
}
return aggregation_data
router = APIRouter(
prefix="/plan",
tags=["plan"],
@ -243,7 +347,13 @@ async def trigger_plan(body: PlanTriggerRequest):
property_instance = [p for p in input_properties if p.id == property_id][0]
recommendations_with_impact, current_adjusted_energy, expected_adjusted_energy = (
(
recommendations_with_impact,
current_adjusted_energy,
expected_adjusted_energy,
current_energy_bill,
expected_energy_bill
) = (
Recommendations.calculate_recommendation_impact(
property_instance=property_instance,
all_predictions=all_predictions,
@ -254,7 +364,9 @@ async def trigger_plan(body: PlanTriggerRequest):
# Store the resulting adjusted energy in the property instance
property_instance.set_adjusted_energy(
current_adjusted_energy=current_adjusted_energy,
expected_adjusted_energy=expected_adjusted_energy
expected_adjusted_energy=expected_adjusted_energy,
current_energy_bill=current_energy_bill,
expected_energy_bill=expected_energy_bill
)
input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
@ -316,6 +428,7 @@ async def trigger_plan(body: PlanTriggerRequest):
logger.info("Uploading recommendations to the database")
property_valuation_increases = []
session.commit()
new_epc_bands = {}
for i in range(0, len(input_properties), BATCH_SIZE):
try:
# Take a slice of the input_properties list to make a batch
@ -327,6 +440,7 @@ async def trigger_plan(body: PlanTriggerRequest):
total_sap_points = sum([r["sap_points"] for r in default_recommendations])
new_sap_points = float(p.data["current-energy-efficiency"]) + total_sap_points
new_epc = sap_to_epc(new_sap_points)
new_epc_bands[p.id] = new_epc
valuations = PropertyValuation.estimate(property_instance=p, target_epc=new_epc)
@ -392,11 +506,19 @@ async def trigger_plan(body: PlanTriggerRequest):
[sum(r["labour_days"] for r in rec_group if r["default"]) for p_id, rec_group in recommendations.items()]
))
aggregated_data = extract_portfolio_aggregation_data(
input_properties=input_properties,
total_valuation_increase=total_valuation_increase,
recommendations=recommendations,
new_epc_bands=new_epc_bands
)
aggregate_portfolio_recommendations(
session,
portfolio_id=body.portfolio_id,
total_valuation_increase=total_valuation_increase,
labour_days=labour_days
labour_days=labour_days,
aggregated_data=aggregated_data
)
# Commit final changes

11
recommendations/Recommendations.py
View file

@ -281,6 +281,9 @@ class Recommendations:
current_adjusted_energy - expected_adjusted_energy
)
current_energy_bill = AnnualBillSavings.calculate_annual_bill(current_adjusted_energy)
expected_energy_bill = AnnualBillSavings.calculate_annual_bill(expected_adjusted_energy)
for recommendations_by_type in property_recommendations:
for rec in recommendations_by_type:
@ -355,4 +358,10 @@ class Recommendations:
rec["heat_demand"] is None) or (rec["energy_cost_savings"] is None):
raise ValueError("sap points, co2 or heat demand is missing")
return property_recommendations, current_adjusted_energy, expected_adjusted_energy
return (
property_recommendations,
current_adjusted_energy,
expected_adjusted_energy,
current_energy_bill,
expected_energy_bill
)