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Expand optimizer seeding and cache fitness

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This commit is contained in:
Andreas
2026-07-16 10:35:52 +02:00
parent b0b437f1d7
commit 5b8f7de113
7 changed files with 1139 additions and 837 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test_genetic_seeding.py
+102 -1
View File
@@ -52,6 +52,63 @@ def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEO
assert individual[opt.total_slots :] == [0] * opt.total_slots
def test_fitness_cache_restores_canonical_ev_genome(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)
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.full(opt.total_slots, 100.0),
}
first = creator.Individual([0] * opt.total_slots + [1] * opt.total_slots)
duplicate = creator.Individual(first)
opt._fitness_cache_enabled = True
with patch.object(opt, "evaluate_inner", return_value=result) as evaluate:
first_fitness = opt.evaluate(first, parameters, 0, False) # type: ignore[arg-type]
duplicate_fitness = opt.evaluate(duplicate, parameters, 0, False) # type: ignore[arg-type]
# The miss evaluates and then re-evaluates the repaired EV plan. The duplicate
# is served directly from the original-key alias and receives the canonical genome.
assert evaluate.call_count == 2
assert first_fitness == duplicate_fitness
assert duplicate == first
assert duplicate[opt.total_slots :] == [0] * opt.total_slots
assert duplicate.extra_data == first.extra_data
assert opt._fitness_cache_hits == 1
assert opt._fitness_cache_misses == 1
def test_fitness_cache_never_stores_failed_evaluations(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)
parameters = SimpleNamespace(
ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
eauto=None,
)
first = creator.Individual([0] * opt.total_slots)
duplicate = creator.Individual(first)
opt._fitness_cache_enabled = True
with patch.object(opt, "evaluate_inner", side_effect=RuntimeError("transient")) as evaluate:
assert opt.evaluate(first, parameters, 0, False) == (100000.0,) # type: ignore[arg-type]
assert opt.evaluate(duplicate, parameters, 0, False) == (100000.0,) # type: ignore[arg-type]
assert evaluate.call_count == 2
assert opt._fitness_cache_hits == 0
assert opt._fitness_cache_misses == 2
assert opt._fitness_cache == {}
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)
@@ -67,6 +124,50 @@ def test_mutated_warm_start_neighbors_keep_elapsed_slots(config_eos: ConfigEOS):
assert all(neighbor != start_solution for neighbor in neighbors)
def test_initial_population_uses_fixed_seed_budget_and_150_survivors(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
config_eos.optimization.genetic.individuals = 300
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
start_solution = [5] * opt.total_slots
warm_neighbors = [[6] * opt.total_slots for _ in range(50)]
educated = [[7] * opt.total_slots for _ in range(100)]
captured: dict[str, object] = {}
def fake_ea(population, toolbox, **kwargs):
captured["population"] = list(population)
captured["mu"] = kwargs["mu"]
captured["lambda"] = kwargs["lambda_"]
for individual in population:
individual.fitness.values = (float(sum(individual)),)
individual.extra_data = (0.0, 0.0, 0.0)
kwargs["halloffame"].update(population)
return population, SimpleNamespace(select=lambda _name: [])
with (
patch.object(opt, "_mutated_warm_start_neighbors", return_value=warm_neighbors),
patch.object(opt, "_educated_guess_individuals", return_value=educated),
patch.object(
opt.toolbox,
"population",
side_effect=lambda n: [creator.Individual([9] * opt.total_slots) for _ in range(n)],
),
patch("akkudoktoreos.optimization.genetic.genetic.algorithms.eaMuPlusLambda", fake_ea),
):
opt.optimize(start_solution=start_solution, ngen=1)
population = captured["population"]
first_genes = [individual[0] for individual in population] # type: ignore[union-attr]
assert len(population) == 300 # type: ignore[arg-type]
assert first_genes.count(5) == 10
assert first_genes.count(6) == 50
assert first_genes.count(7) == 100
assert first_genes.count(9) == 140
assert captured["mu"] == 150
assert captured["lambda"] == 150
def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
opt = GeneticOptimization(fixed_seed=42)
@@ -86,7 +187,7 @@ def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigE
dc_allowed_state = 4
export_state = 5
assert len(guesses) >= 4
assert len(guesses) == opt.EDUCATED_GUESS_TARGET
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)
tests/testdata/optimize_result_1.json
Vendored
+144 -144
View File
@@ -14,13 +14,6 @@
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@@ -157,7 +157,7 @@
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"Gesamtbilanz_Euro": 1.251565700139288,
"Gesamteinnahmen_Euro": 1.1316277804018695,
"Gesamtkosten_Euro": 2.3831934805411574,
"Gesamt_Verluste": 3421.6165248449383,
"Gesamtbilanz_Euro": 0.5951993628823129,
"Gesamteinnahmen_Euro": 1.1298399163065491,
"Gesamtkosten_Euro": 1.725039279188862,
"Home_appliance_wh_per_hour": [
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@@ -318,17 +318,17 @@
],
"home_appliance_energy_wh": {},
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tests/testdata/optimize_result_1_be.json
Vendored
+72 -72
View File
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