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"""
plot_optimization_history
=========================
.. autofunction:: optuna.visualization.matplotlib.plot_optimization_history
The following code snippet shows how to plot optimization history.
"""
import optuna
import matplotlib.pyplot as plt
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y = trial.suggest_categorical("y", [-1, 0, 1])
return x**2 + y
sampler = optuna.samplers.TPESampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=10)
optuna.visualization.matplotlib.plot_optimization_history(study)
plt.tight_layout()
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