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from typing import Sequence
import numpy as np
import pytest
from optuna.samplers import NSGAIISampler
from optuna.study import create_study
from optuna.trial import FrozenTrial
from optuna.trial import Trial
from optuna.visualization._hypervolume_history import _get_hypervolume_history_info
from optuna.visualization._hypervolume_history import _HypervolumeHistoryInfo
@pytest.mark.parametrize(
"directions",
[
["minimize", "minimize"],
["minimize", "maximize"],
["maximize", "minimize"],
["maximize", "maximize"],
],
)
def test_get_optimization_history_info(directions: str) -> None:
signs = [1 if d == "minimize" else -1 for d in directions]
def objective(trial: Trial) -> Sequence[float]:
def impl(trial: Trial) -> Sequence[float]:
if trial.number == 0:
return 1.5, 1.5 # dominated by the reference_point
elif trial.number == 1:
return 0.75, 0.75
elif trial.number == 2:
return 0.5, 0.5 # dominates Trial #1
elif trial.number == 3:
return 0.5, 0.5 # dominates Trial #1
elif trial.number == 4:
return 0.75, 0.25 # incomparable
return 0.0, 0.0 # dominates all
values = impl(trial)
return signs[0] * values[0], signs[1] * values[1]
def constraints(trial: FrozenTrial) -> Sequence[float]:
if trial.number == 2:
return (1,) # infeasible
return (0,) # feasible
sampler = NSGAIISampler(constraints_func=constraints)
study = create_study(directions=directions, sampler=sampler)
study.optimize(objective, n_trials=6)
reference_point = np.asarray(signs)
info = _get_hypervolume_history_info(study, reference_point)
assert info == _HypervolumeHistoryInfo(
trial_numbers=[0, 1, 2, 3, 4, 5], values=[0.0, 0.0625, 0.0625, 0.25, 0.3125, 1.0]
)
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