optuna.visualization.matplotlib.plot_optimization_history
- optuna.visualization.matplotlib.plot_optimization_history(study, *, target=None, target_name='Objective Value', error_bar=False)[source]
Plot optimization history of all trials in a study with Matplotlib.
See also
Please refer to
optuna.visualization.plot_optimization_history()
for an example.Example
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()
Note
You need to adjust the size of the plot by yourself using
plt.tight_layout()
orplt.savefig(IMAGE_NAME, bbox_inches='tight')
.- Parameters:
study (Study | Sequence[Study]) – A
Study
object whose trials are plotted for their target values. You can pass multiple studies if you want to compare those optimization histories.target (Callable[[FrozenTrial], float] | None) –
A function to specify the value to display. If it is
None
andstudy
is being used for single-objective optimization, the objective values are plotted.Note
Specify this argument if
study
is being used for multi-objective optimization.target_name (str) – Target’s name to display on the axis label and the legend.
error_bar (bool) – A flag to show the error bar.
- Returns:
A
matplotlib.axes.Axes
object.- Return type:
Axes
Note
Added in v2.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.2.0.