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Add adaptive genetic evolution

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This commit is contained in:
Andreas
2026-07-16 15:14:13 +02:00
parent 4dfd4b275b
commit f7e2ac3619
8 changed files with 1294 additions and 844 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test_genetic_seeding.py
+65 -5
View File
@@ -3,7 +3,7 @@ from unittest.mock import MagicMock, patch
import numpy as np
import pytest
from deap import creator
from deap import creator, tools
from akkudoktoreos.config.config import ConfigEOS
from akkudoktoreos.core.coreabc import get_ems
@@ -142,6 +142,34 @@ def test_fitness_cache_never_stores_failed_evaluations(config_eos: ConfigEOS):
assert opt._fitness_cache == {}
def test_fitness_cache_ignores_elapsed_control_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)
parameters = SimpleNamespace(
ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
eauto=None,
)
result = {
"Gesamtbilanz_Euro": 1.0,
"Gesamt_Verluste": 0.0,
"EAuto_SoC_pro_Stunde": np.zeros(opt.total_slots),
}
first = creator.Individual([0] * opt.total_slots)
elapsed_variant = creator.Individual(first)
elapsed_variant[0] = 1
opt._fitness_cache_enabled = True
with patch.object(opt, "evaluate_inner", return_value=result) as evaluate:
first_fitness = opt.evaluate(first, parameters, 10, False) # type: ignore[arg-type]
variant_fitness = opt.evaluate(elapsed_variant, parameters, 10, False) # type: ignore[arg-type]
assert evaluate.call_count == 1
assert first_fitness == variant_fitness
assert opt._fitness_cache_hits == 1
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)
@@ -170,7 +198,7 @@ def test_initial_population_uses_fixed_seed_budget_and_configured_population(
educated = [[7] * opt.total_slots for _ in range(100)]
captured: dict[str, object] = {}
def fake_ea(population, toolbox, **kwargs):
def fake_evolution(population, **kwargs):
captured["population"] = list(population)
captured["mu"] = kwargs["mu"]
captured["lambda"] = kwargs["lambda_"]
@@ -188,7 +216,7 @@ def test_initial_population_uses_fixed_seed_budget_and_configured_population(
"population",
side_effect=lambda n: [creator.Individual([9] * opt.total_slots) for _ in range(n)],
),
patch("akkudoktoreos.optimization.genetic.genetic.algorithms.eaMuPlusLambda", fake_ea),
patch.object(opt, "_evolve_population_adaptive", side_effect=fake_evolution),
):
opt.optimize(start_solution=start_solution, ngen=1)
@@ -220,7 +248,7 @@ def test_small_population_scales_warm_and_educated_seed_families(config_eos: Con
captured["educated_count"] = count
return [[7] * opt.total_slots for _ in range(count)]
def fake_ea(population, toolbox, **kwargs):
def fake_evolution(population, **kwargs):
captured["population"] = list(population)
captured["mu"] = kwargs["mu"]
captured["lambda"] = kwargs["lambda_"]
@@ -238,7 +266,7 @@ def test_small_population_scales_warm_and_educated_seed_families(config_eos: Con
"population",
side_effect=lambda n: [creator.Individual([9] * opt.total_slots) for _ in range(n)],
),
patch("akkudoktoreos.optimization.genetic.genetic.algorithms.eaMuPlusLambda", fake_ea),
patch.object(opt, "_evolve_population_adaptive", side_effect=fake_evolution),
):
opt.optimize(start_solution=start_solution, ngen=1)
@@ -255,6 +283,38 @@ def test_small_population_scales_warm_and_educated_seed_families(config_eos: Con
assert captured["lambda"] == 100
def test_adaptive_evolution_soft_restarts_collapsed_population(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
opt.toolbox.register("evaluate", lambda individual: (float(sum(individual)),))
population = [creator.Individual([0] * opt.total_slots) for _ in range(20)]
stats = tools.Statistics(lambda individual: individual.fitness.values)
stats.register("min", np.min)
stats.register("avg", np.mean)
stats.register("max", np.max)
halloffame = tools.HallOfFame(1)
fresh = [creator.Individual([value] + [0] * (opt.total_slots - 1)) for value in range(1, 20)]
with patch.object(opt, "_fresh_population", return_value=fresh) as create_fresh:
evolved, log = opt._evolve_population_adaptive(
population,
mu=20,
lambda_=20,
ngen=1,
stats=stats,
halloffame=halloffame,
)
create_fresh.assert_called_once_with(19, educated_fraction=0.40)
assert log.select("restart") == [0, 1]
assert log.select("immigrants") == [0, 19]
assert opt._adaptive_evolution_metrics["soft_restarts"] == 1
assert opt._population_diversity(evolved) == pytest.approx(1.0)
assert halloffame[0].fitness.values == (0.0,)
def test_local_search_moves_weak_export_to_later_expensive_import(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
opt = GeneticOptimization(fixed_seed=42)
tests/test_optimization_interval.py
+25 -6
View File
@@ -227,8 +227,8 @@ def test_hourly_start_solution_is_expanded_to_slots(config_eos: ConfigEOS):
assert migrated[:8] == [0, 0, 0, 0, 1, 1, 1, 1]
def test_quarter_hour_mutation_probability_preserves_hourly_rate(config_eos: ConfigEOS):
"""A finer genome does not mutate four times as many controls per hour."""
def test_quarter_hour_mutation_targets_three_future_controls(config_eos: ConfigEOS):
"""Point mutation scales to roughly three effective future controls."""
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
@@ -239,7 +239,28 @@ def test_quarter_hour_mutation_probability_preserves_hourly_rate(config_eos: Con
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
assert opt.toolbox.mutate_charge_discharge.keywords["indpb"] == pytest.approx(0.05)
active_slots = opt.total_slots - opt._start_day_slot()
expected = min(0.10, opt.POINT_MUTATION_EXPECTED_GENES / active_slots)
assert opt.toolbox.mutate_charge_discharge.keywords["indpb"] == pytest.approx(expected)
def test_point_mutation_keeps_elapsed_slots_unchanged(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
}
)
get_ems(init=True).set_start_datetime(to_datetime().set(hour=10, minute=0))
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
individual = [0] * opt.total_slots
changed = opt._mutate_point_controls(individual)
assert changed
assert individual[: opt._start_day_slot()] == [0] * opt._start_day_slot()
def test_sub_hourly_home_appliance_is_scheduled(config_eos: ConfigEOS):
@@ -253,9 +274,7 @@ def test_sub_hourly_home_appliance_is_scheduled(config_eos: ConfigEOS):
parameters = load_hourly_parameters().model_copy(
update={
"home_appliances": [
HomeApplianceParameters(
device_id="dishwasher1", consumption_wh=1200, duration_h=2
)
HomeApplianceParameters(device_id="dishwasher1", consumption_wh=1200, duration_h=2)
]
},
deep=True,
tests/testdata/optimize_result_1.json
Vendored
+99 -99
View File
@@ -116,24 +116,24 @@
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@@ -239,9 +239,9 @@
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@@ -272,10 +272,10 @@
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],
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"Gesamtbilanz_Euro": 0.5951993628823129,
"Gesamteinnahmen_Euro": 1.1298399163065491,
"Gesamtkosten_Euro": 1.725039279188862,
"Gesamt_Verluste": 3807.1176630027076,
"Gesamtbilanz_Euro": 0.22702041221600888,
"Gesamteinnahmen_Euro": 1.0402628835513343,
"Gesamtkosten_Euro": 1.2672832957673432,
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@@ -324,14 +324,6 @@
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@@ -352,10 +352,10 @@
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@@ -364,14 +364,6 @@
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"Electricity_price": [
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@@ -709,15 +709,12 @@
"initial_soc_percentage": 54
},
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tests/testdata/optimize_result_1_be.json
Vendored
+56 -56
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@@ -318,10 +318,10 @@
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tests/testdata/optimize_result_2.json
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tests/testdata/optimize_result_2_be.json
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