Merge pull request #706 from Hestia-Homes/bug/ignored-cost-caps

bug fixed for 0 target gain where infinity was being selected

backend/engine/engine.py

@@ -865,7 +865,7 @@ async def model_engine(body: PlanTriggerRequest):
         check_duplicate_property_ids(input_properties)
 
         logger.info("Inserting property data")
-        # We now bulk upload all of the EPC data
+        # We now bulk upload all the EPC data
         with db_session() as session:
             db_funcs.epc_functions.EpcStoreService.bulk_upsert_epc_data(session, epc_upserts)
 

recommendations/optimiser/funding_optimiser.py

@@ -10,6 +10,7 @@ In the future, we will adapt this into a class-based structure to allow for more
 from copy import deepcopy
 import pandas as pd
 import numpy as np
+from typing import Mapping, Union
 from itertools import product
 
 from backend.app.plan.schemas import (
@@ -823,21 +824,23 @@ def optimise_with_scenarios(
     # No special path; just exclude ASHP from options and allow us to optimise.
     measures_no_heat_pump = exclude_measure_types(optimisation_measures, ["air_source_heat_pump"])
 
-    picked, total_cost, total_gain = run_optimizer(
-        measures_no_heat_pump,
-        budget=budget,
-        sub_target_gain=target_gain,
-    )
+    if target_gain > 0:
+        # If we don't have any gain, we don't actually need to do this
+        picked, total_cost, total_gain = run_optimizer(
+            measures_no_heat_pump,
+            budget=budget,
+            sub_target_gain=target_gain,
+        )
 
-    if picked is not None:
-        solutions.append({
-            "scenario": "no_heat_pump",
-            "items": picked,
-            "fixed_items": [],
-            "total_cost": total_cost,
-            "total_gain": total_gain,
-            "already_installed_gain": sum([x["gain"] for x in picked if x["already_installed"]])
-        })
+        if picked is not None:
+            solutions.append({
+                "scenario": "no_heat_pump",
+                "items": picked,
+                "fixed_items": [],
+                "total_cost": total_cost,
+                "total_gain": total_gain,
+                "already_installed_gain": sum([x["gain"] for x in picked if x["already_installed"]])
+            })
 
     solutions_df = append_solution_metrics(solutions, target_gain, p, already_installed_sap)
 
@@ -1101,7 +1104,12 @@ def contributes_min_insulation(opt_types):
     })
 
 
-def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack=False):
+def run_optimizer(
+    input_measures: list[list[Mapping[str, int | float | str]]],
+    budget: Union[float, None] = None,
+    sub_target_gain: Union[float, None] = None,
+    allow_slack: bool = False
+):
     """
     Thin wrapper over your optimisers.
     Returns: list[dict] selected_options
@@ -1112,7 +1120,7 @@ def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack
 
     if budget is not None:
         opt = GainOptimiser(
-            input_measures, max_cost=budget, max_gain=(sub_target_gain or float("inf")),
+            input_measures, max_cost=budget, max_gain=0 if sub_target_gain == 0 else (sub_target_gain or float("inf")),
             allow_slack=allow_slack
         )
     else:
@@ -1123,6 +1131,7 @@ def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack
     opt.setup()
     opt.solve()
     cost = sum([x["cost"] for x in opt.solution])
+
     return opt.solution, cost, opt.solution_gain
 
 

recommendations/tests/test_optimisers.py

@@ -1,6 +1,7 @@
 import pytest
 
 from recommendations.optimiser.funding_optimiser import build_heat_pump_paths
+from recommendations.optimiser.funding_optimiser import run_optimizer
 
 
 class DummyProp:
@@ -68,3 +69,143 @@ def test_build_heat_pump_paths():
 
     assert eg2 == [{'AND': ['internal_wall_insulation', 'loft_insulation', 'air_source_heat_pump']},
                    {'AND': ['external_wall_insulation', 'loft_insulation', 'air_source_heat_pump']}]
+
+
+def test_run_optimizer_empty_input():
+    solution, cost, gain = run_optimizer([])
+    assert solution is None
+    assert cost == 0.0
+    assert gain == 0.0
+
+
+def test_uses_gain_optimiser_when_budget_provided(monkeypatch):
+    captured_args = {}
+
+    class FakeGainOptimiser:
+        def __init__(self, measures, max_cost, max_gain, allow_slack):
+            captured_args["measures"] = measures
+            captured_args["max_cost"] = max_cost
+            captured_args["max_gain"] = max_gain
+            captured_args["allow_slack"] = allow_slack
+            self.solution = [{"cost": 100}]
+            self.solution_gain = 5
+
+        def setup(self):
+            pass
+
+        def solve(self):
+            pass
+
+    monkeypatch.setattr(
+        "recommendations.optimiser.funding_optimiser.GainOptimiser",
+        FakeGainOptimiser
+    )
+
+    measures = [[{"cost": 100, "gain": 5}]]
+
+    solution, cost, gain = run_optimizer(
+        measures,
+        budget=500,
+        sub_target_gain=10,
+        allow_slack=True
+    )
+
+    assert captured_args["max_cost"] == 500
+    assert captured_args["max_gain"] == 10
+    assert captured_args["allow_slack"] is True
+    assert cost == 100
+    assert gain == 5
+
+
+def test_sub_target_gain_zero_sets_max_gain_zero(monkeypatch):
+    captured_args = {}
+
+    class FakeGainOptimiser:
+        def __init__(self, measures, max_cost, max_gain, allow_slack):
+            captured_args["max_gain"] = max_gain
+            self.solution = []
+            self.solution_gain = 0
+
+        def setup(self):
+            pass
+
+        def solve(self):
+            pass
+
+    monkeypatch.setattr(
+        "recommendations.optimiser.funding_optimiser.GainOptimiser",
+        FakeGainOptimiser
+    )
+
+    measures = [[{"cost": 100, "gain": 5}]]
+
+    run_optimizer(
+        measures,
+        budget=500,
+        sub_target_gain=0
+    )
+
+    assert captured_args["max_gain"] == 0
+
+
+def test_sub_target_gain_none_sets_max_gain_infinity(monkeypatch):
+    captured_args = {}
+
+    class FakeGainOptimiser:
+        def __init__(self, measures, max_cost, max_gain, allow_slack):
+            captured_args["max_gain"] = max_gain
+            self.solution = []
+            self.solution_gain = 0
+
+        def setup(self):
+            pass
+
+        def solve(self):
+            pass
+
+    monkeypatch.setattr(
+        "recommendations.optimiser.funding_optimiser.GainOptimiser",
+        FakeGainOptimiser
+    )
+
+    measures = [[{"cost": 100, "gain": 5}]]
+
+    run_optimizer(
+        measures,
+        budget=500,
+        sub_target_gain=None
+    )
+
+    assert captured_args["max_gain"] == float("inf")
+
+
+def test_uses_cost_optimiser_when_no_budget(monkeypatch):
+    captured_args = {}
+
+    class FakeCostOptimiser:
+        def __init__(self, measures, min_gain):
+            captured_args["min_gain"] = min_gain
+            self.solution = [{"cost": 50}]
+            self.solution_gain = 10
+
+        def setup(self):
+            pass
+
+        def solve(self):
+            pass
+
+    monkeypatch.setattr(
+        "recommendations.optimiser.funding_optimiser.CostOptimiser",
+        FakeCostOptimiser
+    )
+
+    measures = [[{"cost": 50, "gain": 10}]]
+
+    solution, cost, gain = run_optimizer(
+        measures,
+        sub_target_gain=10
+    )
+
+    assert captured_args["min_gain"] == 10
+    assert cost == 50
+    assert gain == 10

sfr/principal_pitch/2_export_data.py

@@ -28,12 +28,12 @@ from sqlalchemy import func
 
 # PORTFOLIO_ID = 206
 # SCENARIOS = [389]
-PORTFOLIO_ID = 524
+PORTFOLIO_ID = 568
 SCENARIOS = [
-    1009,
+    1059,
 ]
 scenario_names = {
-    1009: "EPC C; Most Economic",
+    1059: "EPC C - 10k budget",
 }
 
 
@@ -230,7 +230,7 @@ for scenario_id in SCENARIOS:
     # Get recs for this scenario
     recommended_measures_df = recommendations_df[
         recommendations_df["scenario_id"] == scenario_id
-    ][["property_id", "measure_type", "estimated_cost", "default"]]
+        ][["property_id", "measure_type", "estimated_cost", "default"]]
     recommended_measures_df = recommended_measures_df[
         recommended_measures_df["default"]
     ]
@@ -238,7 +238,7 @@ 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"]]
     post_install_sap = post_install_sap[post_install_sap["default"]]
     # Sum up the sap points by property id
     post_install_sap = (
@@ -301,33 +301,6 @@ 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: