junte/Model
1
0
Fork 0
mirror of https://github.com/Hestia-Homes/Model.git synced 2026-06-08 11:17:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Trying and failing to implement the newest model

Browse source
This commit is contained in:
Khalim Conn-Kowlessar 2024-02-16 17:04:43 +00:00
parent b6eabebebf
commit 7ff3d87999
4 changed files with 66 additions and 26 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
backend/Property.py
View file

@ -230,6 +230,8 @@ class Property:
# The upgrade made here is to the u-value of the walls and the description of the
# insulation thickness
output["walls_thermal_transmittance_ending"] = recommendation["new_u_value"]
# Setting the insulation thickness here to above average should be tested further because we
# don't see a high volume of instances for this
output["walls_insulation_thickness_ending"] = "above average"
output["walls_energy_eff_ending"] = "Good"

2
backend/app/plan/router.py
View file

@ -160,6 +160,8 @@ async def trigger_plan(body: PlanTriggerRequest):
recommendations_scoring_data.extend(p.recommendations_scoring_data)
# TODO: Make sure that number_habitable_rooms has been dropped
logger.info("Preparing data for scoring in sap change api")
recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
recommendations_scoring_data = recommendations_scoring_data.drop(

42
etl/epc/generate_scenarios_data.py
View file

@ -50,9 +50,9 @@ scenario_properties = [
"postcode": "NN1 5JY",
"lmk-key": "1459796789102016070507274146560098",
"measures": [
[["internal_wall_insulation"], "11", None],
[["external_wall_insulation"], "10", None],
[["solar", "windows"], "12-15", {"photo_supply_ending": 50}],
[["internal_wall_insulation"], "11", None, [0]],
[["external_wall_insulation"], "10", None, [0]],
[["solar", "windows"], "12-15", {"photo_supply_ending": 50}, [0, 1]],
],
},
{
@ -60,7 +60,7 @@ scenario_properties = [
"postcode": "HP1 2HA",
"lmk-key": "c14029235739827d5f627dc8aa9bb567d026b267e851e0db0001db24638667b1",
"measures": [
[["cavity_wall_insulation", "loft_insulation"], "15", None],
[["cavity_wall_insulation", "loft_insulation"], "15", None, [0, 1]],
],
},
{
@ -68,7 +68,7 @@ scenario_properties = [
"postcode": "HP1 2HE",
"lmk-key": "99296a6dda21314fef3a61cda59e441e9a2aacf115eb96f4a0fa85696bf7b117",
"measures": [
[["cavity_wall_insulation", "loft_insulation"], "15", None],
[["cavity_wall_insulation", "loft_insulation"], "15", None, [0, 1]],
],
},
{
@ -76,7 +76,7 @@ scenario_properties = [
"postcode": "HP1 2AN",
"lmk-key": "d1e0534be3a44c33003323b21d0e322e3daddc65b5ee71936f89c59ddab96b50",
"measures": [
[["cavity_wall_insulation", "loft_insulation"], "15", None],
[["cavity_wall_insulation", "loft_insulation"], "15", None, [0, 1]],
],
},
{
@ -84,7 +84,7 @@ scenario_properties = [
"postcode": "HP1 2HX",
"lmk-key": "1eae354db522a95188018d9cd0502ed8c609910b6c88f8797d3a25f59b11770a",
"measures": [
[["cavity_wall_insulation", "loft_insulation"], "15", None],
[["cavity_wall_insulation", "loft_insulation"], "15", None, [0, 1]],
],
},
]
@ -195,18 +195,18 @@ for scenario_property in scenario_properties:
recommendation_record = p.base_difference_record.df.to_dict("records")[
0
].copy()
for rec in combi:
recommendation_record = p.create_recommendation_scoring_data(
property_id=rec["type"],
recommendation_record=recommendation_record,
recommendation=rec,
)
recommendation_record = p.create_recommendation_scoring_data(
property_id=i,
primary_recommendation_id=i,
recommendation_record=recommendation_record,
recommendations=combi,
)
if override is not None:
for key, value in override.items():
recommendation_record[key] = value
recommendation_record["id"] = "+".join(measure) + "+" + str(i)
recommendation_record["id"] = "&".join(measure) + "+" + str(i)
recommendation_record["impact"] = impact
scoring_list.append(recommendation_record)
@ -230,6 +230,20 @@ recommendations_scoring_data.insert(0, "impact", impact_col)
id_col = recommendations_scoring_data.pop("id")
recommendations_scoring_data.insert(0, "id", id_col)
from backend.ml_models.api import ModelApi
model_api = ModelApi(portfolio_id="generate-scenarios-data", timestamp=created_at)
all_predictions = model_api.predict_all(
df=recommendations_scoring_data,
bucket=get_settings().DATA_BUCKET,
prediction_buckets={
"sap_change_predictions": get_settings().SAP_PREDICTIONS_BUCKET,
"heat_demand_predictions": get_settings().HEAT_PREDICTIONS_BUCKET,
"carbon_change_predictions": get_settings().CARBON_PREDICTIONS_BUCKET
}
)
save_dataframe_to_s3_parquet(
recommendations_scoring_data,
"retrofit-data-dev",

46
etl/testing_data/sap_model_simulation.py
View file

@ -1935,10 +1935,10 @@ def app():
"address": "73 Long Chaulden",
"postcode": "HP1 2HX",
},
{
"address": "77 Simmons Drive",
"postcode": "B32 1SL",
},
# {
# "address": "77 Simmons Drive",
# "postcode": "B32 1SL",
# },
{
"address": "139 School Road",
"postcode": "B28 8JF",
@ -1984,6 +1984,19 @@ def app():
floor_recommendations = recommender.floor_recommender.recommendations
solar_recommendations = recommender.solar_recommender.recommendation
# We set wall recommendations to phase 0, loft to phase 1, floor to phase 2 and solar to phase 3
for rec in wall_recommendations:
rec["phase"] = 0
for rec in loft_recommendations:
rec["phase"] = 1
for rec in floor_recommendations:
rec["phase"] = 2
for rec in solar_recommendations:
rec["phase"] = 3
# TODO: TEMP!
solar_recommendations[0]["photo_supply"] = 50
@ -1994,7 +2007,8 @@ def app():
for wall_rec in wall_recommendations:
recommendation_record = p.base_difference_record.df.to_dict("records")[0].copy()
scoring_dict = p.create_recommendation_scoring_data(
property_id=p.id, recommendation_record=recommendation_record, recommendation=wall_rec,
property_id=p.id, recommendation_record=recommendation_record, recommendations=[wall_rec],
primary_recommendation_id=wall_rec["recommendation_id"]
)
wall_scoring_data.append(scoring_dict)
@ -2002,7 +2016,8 @@ def app():
for roof_rec in loft_recommendations:
recommendation_record = p.base_difference_record.df.to_dict("records")[0].copy()
scoring_dict = p.create_recommendation_scoring_data(
property_id=p.id, recommendation_record=recommendation_record, recommendation=roof_rec,
property_id=p.id, recommendation_record=recommendation_record, recommendations=[roof_rec],
primary_recommendation_id=roof_rec["recommendation_id"]
)
roof_scoring_data.append(scoring_dict)
@ -2010,7 +2025,8 @@ def app():
for floor_rec in floor_recommendations:
recommendation_record = p.base_difference_record.df.to_dict("records")[0].copy()
scoring_dict = p.create_recommendation_scoring_data(
property_id=p.id, recommendation_record=recommendation_record, recommendation=floor_rec,
property_id=p.id, recommendation_record=recommendation_record, recommendations=[floor_rec],
primary_recommendation_id=floor_rec["recommendation_id"]
)
floor_scoring_data.append(scoring_dict)
@ -2018,7 +2034,8 @@ def app():
for solar_rec in solar_recommendations:
recommendation_record = p.base_difference_record.df.to_dict("records")[0].copy()
scoring_dict = p.create_recommendation_scoring_data(
property_id=p.id, recommendation_record=recommendation_record, recommendation=solar_rec,
property_id=p.id, recommendation_record=recommendation_record, recommendations=[solar_rec],
primary_recommendation_id=solar_rec["recommendation_id"]
)
solar_scoring_data.append(scoring_dict)
@ -2029,25 +2046,30 @@ def app():
scoring_dict_with_wall = p.create_recommendation_scoring_data(
property_id=p.id,
recommendation_record=starting_record.copy(),
recommendation=wall_recommendations[0],
recommendations=[wall_recommendations[0]],
primary_recommendation_id=wall_recommendations[0]["recommendation_id"]
) if wall_recommendations else starting_record.copy()
scoring_dict_with_wall_and_roof = p.create_recommendation_scoring_data(
property_id=p.id,
recommendation_record=scoring_dict_with_wall.copy(),
recommendation=loft_recommendations[0],
recommendations=[wall_recommendations[0], loft_recommendations[0], ],
primary_recommendation_id=loft_recommendations[0]["recommendation_id"]
) if loft_recommendations else scoring_dict_with_wall.copy()
scoring_dict_with_wall_roof_floor = p.create_recommendation_scoring_data(
property_id=p.id,
recommendation_record=scoring_dict_with_wall_and_roof.copy(),
recommendation=floor_recommendations[0],
recommendations=[wall_recommendations[0], loft_recommendations[0], floor_recommendations[0]],
primary_recommendation_id=floor_recommendations[0]["recommendation_id"]
) if floor_recommendations else scoring_dict_with_wall_and_roof.copy()
scoring_dict_with_wall_roof_floor_solar = p.create_recommendation_scoring_data(
property_id=p.id,
recommendation_record=scoring_dict_with_wall_roof_floor.copy(),
recommendation=solar_recommendations[0],
recommendations=[wall_recommendations[0], loft_recommendations[0], floor_recommendations[0],
solar_recommendations[0]],
primary_recommendation_id=solar_recommendations[0]["recommendation_id"]
) if solar_recommendations else scoring_dict_with_wall_roof_floor.copy()
# We score each dataset with the model