Improve genetic optimizer seeding

tests/test_genetic_seeding.py

@@ -0,0 +1,113 @@
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+from deap import creator
+
+from akkudoktoreos.config.config import ConfigEOS
+from akkudoktoreos.core.coreabc import get_ems
+from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
+from akkudoktoreos.utils.datetimeutil import to_datetime
+
+
+def _configure_hourly_grid(config_eos: ConfigEOS, *, start_hour: int = 0) -> None:
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 3600},
+        }
+    )
+    get_ems(init=True).set_start_datetime(to_datetime().set(hour=start_hour, minute=0))
+
+
+def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEOS):
+    _configure_hourly_grid(config_eos)
+    opt = GeneticOptimization(fixed_seed=42)
+    opt.optimize_ev = True
+    opt.ev_possible_charge_values = [0.0, 1.0]
+    opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
+    individual = creator.Individual([0] * opt.total_slots + [1] * opt.total_slots)
+
+    first_result = {
+        "Gesamtbilanz_Euro": 10.0,
+        "Gesamt_Verluste": 0.0,
+        "EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
+    }
+    repaired_result = {
+        "Gesamtbilanz_Euro": 1.0,
+        "Gesamt_Verluste": 0.0,
+        "EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
+    }
+    parameters = SimpleNamespace(
+        ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
+        eauto=None,
+    )
+
+    with patch.object(opt, "evaluate_inner", side_effect=[first_result, repaired_result]) as evaluate:
+        fitness = opt.evaluate(individual, parameters, start_hour=0, worst_case=False)  # type: ignore[arg-type]
+
+    assert evaluate.call_count == 2
+    assert fitness == pytest.approx((1.0,))
+    assert individual[opt.total_slots :] == [0] * opt.total_slots
+
+
+def test_mutated_warm_start_neighbors_keep_elapsed_slots(config_eos: ConfigEOS):
+    _configure_hourly_grid(config_eos, start_hour=10)
+    opt = GeneticOptimization(fixed_seed=42)
+    opt.optimize_ev = False
+    opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
+    start_solution = [0] * opt.total_slots
+
+    neighbors = opt._mutated_warm_start_neighbors(start_solution, count=5)
+
+    assert len(neighbors) == 5
+    assert len({tuple(neighbor) for neighbor in neighbors}) == 5
+    assert all(neighbor[:10] == start_solution[:10] for neighbor in neighbors)
+    assert all(neighbor != start_solution for neighbor in neighbors)
+
+
+def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigEOS):
+    _configure_hourly_grid(config_eos)
+    opt = GeneticOptimization(fixed_seed=42)
+    opt.optimize_ev = False
+    opt.optimize_dc_charge = True
+    opt.optimize_battery_grid_export = True
+    opt.bat_possible_charge_values = [1.0]
+    opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
+
+    slots = opt.total_slots
+    opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
+    opt.simulation.elect_revenue_per_hour_arr = np.linspace(0.00001, 0.0003, slots)
+    opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
+    opt.simulation.load_energy_array = np.full(slots, 500.0)
+
+    guesses = opt._educated_guess_individuals()
+
+    dc_allowed_state = 4
+    export_state = 5
+    assert len(guesses) >= 4
+    assert all(len(guess) == slots for guess in guesses)
+    assert any(guess[0] == dc_allowed_state for guess in guesses)
+    assert any(guess[-1] == export_state for guess in guesses)
+
+
+def test_flat_feed_in_tariff_does_not_seed_direct_marketing(config_eos: ConfigEOS):
+    _configure_hourly_grid(config_eos)
+    opt = GeneticOptimization(fixed_seed=42)
+    opt.optimize_ev = False
+    opt.optimize_dc_charge = True
+    opt.optimize_battery_grid_export = True
+    opt.bat_possible_charge_values = [1.0]
+    opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
+
+    slots = opt.total_slots
+    opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
+    opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.00005)
+    opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
+    opt.simulation.load_energy_array = np.full(slots, 500.0)
+
+    guesses = opt._educated_guess_individuals()
+
+    export_state = 5
+    assert all(export_state not in guess for guess in guesses)