mirror of
https://github.com/Hestia-Homes/Model.git
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Merge branch 'main' into feature/automate-categorisation-of-works
This commit is contained in:
Daniel Roth
commit
8e5016978e
6 changed files with 303 additions and 62 deletions
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@ -865,7 +865,7 @@ async def model_engine(body: PlanTriggerRequest):
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check_duplicate_property_ids(input_properties)
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logger.info("Inserting property data")
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# We now bulk upload all of the EPC data
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# We now bulk upload all the EPC data
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with db_session() as session:
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db_funcs.epc_functions.EpcStoreService.bulk_upsert_epc_data(session, epc_upserts)
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@ -768,6 +768,24 @@ class Recommendations:
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# Update the current phase values
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current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"]
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# This is very much an edge case but we also the end result taking the property
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# below a SAP rating of 1, which is the minimum SAP rating
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if previous_phase_values["sap"] + property_phase_impact["sap"] < 1:
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sap_adjustment = 1 - (previous_phase_values["sap"] + property_phase_impact["sap"])
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adjustments.append(
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{
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"recommendation_id": rec["recommendation_id"],
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"phase": rec["phase"],
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"sap_adjustment": sap_adjustment,
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}
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)
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# The new impact should be the current impact plus the adjustment
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property_phase_impact["sap"] = property_phase_impact["sap"] + sap_adjustment
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# Update the current phase values
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current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"]
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elif rec["type"] == "loft_insulation":
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# When we have a loft insulation recommendation, where there is an extension and the existing
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# amount of loft insulation is already good, we limit the SAP points
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@ -10,6 +10,7 @@ In the future, we will adapt this into a class-based structure to allow for more
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from copy import deepcopy
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import pandas as pd
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import numpy as np
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from typing import Mapping, Union
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from itertools import product
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from backend.app.plan.schemas import (
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@ -823,21 +824,23 @@ def optimise_with_scenarios(
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# No special path; just exclude ASHP from options and allow us to optimise.
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measures_no_heat_pump = exclude_measure_types(optimisation_measures, ["air_source_heat_pump"])
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picked, total_cost, total_gain = run_optimizer(
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measures_no_heat_pump,
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budget=budget,
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sub_target_gain=target_gain,
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)
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if target_gain > 0:
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# If we don't have any gain, we don't actually need to do this
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picked, total_cost, total_gain = run_optimizer(
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measures_no_heat_pump,
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budget=budget,
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sub_target_gain=target_gain,
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)
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if picked is not None:
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solutions.append({
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"scenario": "no_heat_pump",
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"items": picked,
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"fixed_items": [],
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"total_cost": total_cost,
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"total_gain": total_gain,
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"already_installed_gain": sum([x["gain"] for x in picked if x["already_installed"]])
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})
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if picked is not None:
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solutions.append({
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"scenario": "no_heat_pump",
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"items": picked,
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"fixed_items": [],
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"total_cost": total_cost,
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"total_gain": total_gain,
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"already_installed_gain": sum([x["gain"] for x in picked if x["already_installed"]])
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})
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solutions_df = append_solution_metrics(solutions, target_gain, p, already_installed_sap)
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@ -1101,7 +1104,12 @@ def contributes_min_insulation(opt_types):
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})
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def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack=False):
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def run_optimizer(
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input_measures: list[list[Mapping[str, int | float | str]]],
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budget: Union[float, None] = None,
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sub_target_gain: Union[float, None] = None,
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allow_slack: bool = False
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):
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"""
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Thin wrapper over your optimisers.
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Returns: list[dict] selected_options
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@ -1112,7 +1120,7 @@ def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack
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if budget is not None:
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opt = GainOptimiser(
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input_measures, max_cost=budget, max_gain=(sub_target_gain or float("inf")),
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input_measures, max_cost=budget, max_gain=0 if sub_target_gain == 0 else (sub_target_gain or float("inf")),
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allow_slack=allow_slack
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)
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else:
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@ -1123,6 +1131,7 @@ def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack
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opt.setup()
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opt.solve()
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cost = sum([x["cost"] for x in opt.solution])
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return opt.solution, cost, opt.solution_gain
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@ -1,6 +1,7 @@
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import pytest
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from recommendations.optimiser.funding_optimiser import build_heat_pump_paths
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from recommendations.optimiser.funding_optimiser import run_optimizer
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class DummyProp:
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@ -68,3 +69,143 @@ def test_build_heat_pump_paths():
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assert eg2 == [{'AND': ['internal_wall_insulation', 'loft_insulation', 'air_source_heat_pump']},
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{'AND': ['external_wall_insulation', 'loft_insulation', 'air_source_heat_pump']}]
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def test_run_optimizer_empty_input():
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solution, cost, gain = run_optimizer([])
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assert solution is None
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assert cost == 0.0
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assert gain == 0.0
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def test_uses_gain_optimiser_when_budget_provided(monkeypatch):
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captured_args = {}
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class FakeGainOptimiser:
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def __init__(self, measures, max_cost, max_gain, allow_slack):
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captured_args["measures"] = measures
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captured_args["max_cost"] = max_cost
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captured_args["max_gain"] = max_gain
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captured_args["allow_slack"] = allow_slack
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self.solution = [{"cost": 100}]
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self.solution_gain = 5
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def setup(self):
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pass
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def solve(self):
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pass
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monkeypatch.setattr(
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"recommendations.optimiser.funding_optimiser.GainOptimiser",
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FakeGainOptimiser
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)
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measures = [[{"cost": 100, "gain": 5}]]
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solution, cost, gain = run_optimizer(
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measures,
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budget=500,
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sub_target_gain=10,
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allow_slack=True
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)
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assert captured_args["max_cost"] == 500
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assert captured_args["max_gain"] == 10
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assert captured_args["allow_slack"] is True
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assert cost == 100
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assert gain == 5
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def test_sub_target_gain_zero_sets_max_gain_zero(monkeypatch):
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captured_args = {}
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class FakeGainOptimiser:
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def __init__(self, measures, max_cost, max_gain, allow_slack):
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captured_args["max_gain"] = max_gain
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self.solution = []
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self.solution_gain = 0
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def setup(self):
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pass
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def solve(self):
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pass
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monkeypatch.setattr(
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"recommendations.optimiser.funding_optimiser.GainOptimiser",
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FakeGainOptimiser
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)
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measures = [[{"cost": 100, "gain": 5}]]
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run_optimizer(
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measures,
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budget=500,
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sub_target_gain=0
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)
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assert captured_args["max_gain"] == 0
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def test_sub_target_gain_none_sets_max_gain_infinity(monkeypatch):
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captured_args = {}
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class FakeGainOptimiser:
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def __init__(self, measures, max_cost, max_gain, allow_slack):
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captured_args["max_gain"] = max_gain
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self.solution = []
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self.solution_gain = 0
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def setup(self):
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pass
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def solve(self):
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pass
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monkeypatch.setattr(
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"recommendations.optimiser.funding_optimiser.GainOptimiser",
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FakeGainOptimiser
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)
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measures = [[{"cost": 100, "gain": 5}]]
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run_optimizer(
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measures,
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budget=500,
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sub_target_gain=None
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)
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assert captured_args["max_gain"] == float("inf")
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def test_uses_cost_optimiser_when_no_budget(monkeypatch):
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captured_args = {}
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class FakeCostOptimiser:
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def __init__(self, measures, min_gain):
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captured_args["min_gain"] = min_gain
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self.solution = [{"cost": 50}]
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self.solution_gain = 10
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def setup(self):
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pass
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def solve(self):
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pass
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monkeypatch.setattr(
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"recommendations.optimiser.funding_optimiser.CostOptimiser",
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FakeCostOptimiser
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)
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measures = [[{"cost": 50, "gain": 10}]]
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solution, cost, gain = run_optimizer(
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measures,
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sub_target_gain=10
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)
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assert captured_args["min_gain"] == 10
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assert cost == 50
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assert gain == 10
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@ -347,21 +347,21 @@ def property_instance():
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"input_data, expected",
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[
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(
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[
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{"recommendation_id": "a", "phase": 0, "sap_adjustment": 1.7},
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{"recommendation_id": "b", "phase": 0, "sap_adjustment": 1.7},
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],
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[{"recommendation_id": "a", "phase": 0, "sap_adjustment": 1.7}],
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[
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{"recommendation_id": "a", "phase": 0, "sap_adjustment": 1.7},
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{"recommendation_id": "b", "phase": 0, "sap_adjustment": 1.7},
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],
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[{"recommendation_id": "a", "phase": 0, "sap_adjustment": 1.7}],
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),
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(
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[
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{"recommendation_id": "a", "phase": 1, "sap_adjustment": 2},
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{"recommendation_id": "b", "phase": 2, "sap_adjustment": 3},
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],
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[
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{"recommendation_id": "a", "phase": 1, "sap_adjustment": 2},
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{"recommendation_id": "b", "phase": 2, "sap_adjustment": 3},
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],
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[
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{"recommendation_id": "a", "phase": 1, "sap_adjustment": 2},
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{"recommendation_id": "b", "phase": 2, "sap_adjustment": 3},
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],
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[
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{"recommendation_id": "a", "phase": 1, "sap_adjustment": 2},
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{"recommendation_id": "b", "phase": 2, "sap_adjustment": 3},
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],
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),
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],
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)
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@ -1478,3 +1478,103 @@ def test_lighting_and_loft_adjustment_combined(property_instance, heat_demand_pr
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{'recommendation_id': '0_phase=0', 'phase': 0, 'sap_adjustment': np.float64(1.7)},
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{'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)}
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]
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def test_mechanical_ventilation_sap_floor(property_instance):
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rec = {
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"type": "mechanical_ventilation",
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"recommendation_id": "mv_test",
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"phase": 1,
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}
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previous_phase_values = {"sap": 2.0}
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current_phase_values = {"sap": 0.5} # model prediction already below 1
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property_phase_impact = {"sap": -1.5, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec=rec,
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property_phase_impact=property_phase_impact,
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previous_phase_values=previous_phase_values,
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current_phase_values=current_phase_values,
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adjustments=adjustments,
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property_instance=property_instance
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)
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)
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# SAP should be clamped to minimum 1
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assert updated_current["sap"] == 1.0
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# Original final SAP would have been 0.5 → so adjustment = 1 - 0.5 = 0.5
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assert updated_adjustments == [
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{
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"recommendation_id": "mv_test",
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"phase": 1,
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"sap_adjustment": 0.5,
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}
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]
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# Impact should now reflect new clamped SAP
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assert updated_impact["sap"] == -1.0 # 2.0 → 1.0
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def test_mechanical_ventilation_no_floor_adjustment(property_instance):
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rec = {
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"type": "mechanical_ventilation",
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"recommendation_id": "mv_test",
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"phase": 1,
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}
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previous_phase_values = {"sap": 5.0}
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current_phase_values = {"sap": 3.0}
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property_phase_impact = {"sap": -2.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec=rec,
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property_phase_impact=property_phase_impact,
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previous_phase_values=previous_phase_values,
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current_phase_values=current_phase_values,
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adjustments=adjustments,
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property_instance=property_instance
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)
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)
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# No adjustment expected
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assert updated_adjustments == []
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# SAP unchanged
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assert updated_current["sap"] == 3.0
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assert updated_impact["sap"] == -2.0
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def test_mechanical_ventilation_exactly_one_no_adjustment(property_instance):
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# Test when SAP = 1
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rec = {
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"type": "mechanical_ventilation",
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"recommendation_id": "mv_test",
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"phase": 1,
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}
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previous_phase_values = {"sap": 2.0}
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current_phase_values = {"sap": 1.0}
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property_phase_impact = {"sap": -1.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
|
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|
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updated_impact, updated_current, updated_adjustments = (
|
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Recommendations._apply_measure_specific_rules(
|
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rec=rec,
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property_phase_impact=property_phase_impact,
|
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previous_phase_values=previous_phase_values,
|
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current_phase_values=current_phase_values,
|
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adjustments=adjustments,
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property_instance=property_instance
|
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)
|
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)
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# Exactly 1 → no adjustment
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assert updated_adjustments == []
|
||||
assert updated_current["sap"] == 1.0
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assert updated_impact["sap"] == -1.0
|
||||
|
|
|
|||
|
|
@ -28,12 +28,12 @@ from sqlalchemy import func
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|||
|
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# PORTFOLIO_ID = 206
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# SCENARIOS = [389]
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PORTFOLIO_ID = 524
|
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PORTFOLIO_ID = 568
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SCENARIOS = [
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1009,
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1059,
|
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]
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scenario_names = {
|
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1009: "EPC C; Most Economic",
|
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1059: "EPC C - 10k budget",
|
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}
|
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|
||||
|
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|
|
@ -232,7 +232,7 @@ for scenario_id in SCENARIOS:
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|||
# Get recs for this scenario
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||||
recommended_measures_df = recommendations_df[
|
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recommendations_df["scenario_id"] == scenario_id
|
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][["property_id", "measure_type", "estimated_cost", "default"]]
|
||||
][["property_id", "measure_type", "estimated_cost", "default"]]
|
||||
recommended_measures_df = recommended_measures_df[
|
||||
recommended_measures_df["default"]
|
||||
]
|
||||
|
|
@ -240,7 +240,7 @@ for scenario_id in SCENARIOS:
|
|||
|
||||
post_install_sap = recommendations_df[
|
||||
recommendations_df["scenario_id"] == scenario_id
|
||||
][["property_id", "default", "sap_points"]]
|
||||
][["property_id", "default", "sap_points"]]
|
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post_install_sap = post_install_sap[post_install_sap["default"]]
|
||||
# Sum up the sap points by property id
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||||
post_install_sap = (
|
||||
|
|
@ -303,33 +303,6 @@ for scenario_id in SCENARIOS:
|
|||
)
|
||||
df["uprn"] = df["uprn"].astype(str)
|
||||
|
||||
relevant_plans = plans_df[plans_df["scenario_id"] == scenario_id]
|
||||
df2 = df.merge(
|
||||
relevant_plans[["property_id", "post_sap_points", "post_epc_rating"]],
|
||||
how="left",
|
||||
on="property_id",
|
||||
suffixes=("", "_plan"),
|
||||
)
|
||||
print(df2["predicted_post_works_epc"].value_counts())
|
||||
print(df2["post_epc_rating"].value_counts())
|
||||
|
||||
z = df2[
|
||||
(df2["predicted_post_works_epc"] != "D")
|
||||
& (df2["post_epc_rating"].astype(str) == "Epc.D")
|
||||
]
|
||||
|
||||
df2["predicted_post_works_epc"].value_counts()
|
||||
df2["post_epc_rating"].astype(str).value_counts()
|
||||
|
||||
df2[df2["total_retrofit_cost"] > 0].shape
|
||||
|
||||
getting_works = df[df["total_retrofit_cost"] > 0]
|
||||
getting_works["predicted_post_works_epc"].value_counts()
|
||||
|
||||
32565 / getting_works.shape[0]
|
||||
|
||||
df[df["predicted_post_works_sap"] == ""]
|
||||
|
||||
# Create excel to store to
|
||||
filename = f"{scenario_names[scenario_id]} - 20250113 final.xlsx"
|
||||
with pd.ExcelWriter(filename) as writer:
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue