Improve performance of plot_hypervolume_history#6115
Merged
HideakiImamura merged 1 commit intooptuna:masterfrom Jun 4, 2025
Merged
Improve performance of plot_hypervolume_history#6115HideakiImamura merged 1 commit intooptuna:masterfrom
plot_hypervolume_history#6115HideakiImamura merged 1 commit intooptuna:masterfrom
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plot_hypervolume_history
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I misunderstood. I'm so sorry.
import optuna
import time
optuna.logging.set_verbosity(optuna.logging.WARNING)
def test_pr6115_with_constraints():
def objective(trial):
x = trial.suggest_float("x", 0, 1)
y = trial.suggest_float("y", 0, 1)
z = trial.suggest_float("z", 0, 1)
trial.set_user_attr("constraints", [x ** 2 + y ** 2 + z ** 2 - 1])
return x, y, z
def constraints_func(trial):
return trial.user_attrs["constraints"]
sampler = optuna.samplers.NSGAIISampler(seed=42, constraints_func=constraints_func)
study = optuna.create_study(directions=["maximize", "maximize", "maximize"], sampler=sampler)
study.optimize(objective, n_trials=10000)
reference_point=[0, 0, 0]
start = time.time()
fig = optuna.visualization.plot_hypervolume_history(study, reference_point)
end = time.time()
print(f"Time taken for hypervolume plot: {end - start} seconds")
if __name__ == "__main__":
test_pr6115_with_constraints()
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Motivation
Current implementation of
_get_hypervolume_history_infocalls_dominatesmany times. If we normalizevaluesfirst and perform checks equivalent to_dominatesonndarrays, its performance will be improvedDescription of the changes
_get_hypervolume_history_infono longer keepsbest_trials. Instead it maintainsbest_trials_values_normalizedasndarrray, for which checks like_dominatescan be efficiently performed.