optuna.visualization.matplotlib.plot_intermediate_values¶
-
optuna.visualization.matplotlib.
plot_intermediate_values
(study: optuna.study.Study) → matplotlib.axes._axes.Axes[source]¶ Plot intermediate values of all trials in a study with Matplotlib.
Example
The following code snippet shows how to plot intermediate values.
import optuna def f(x): return (x - 2) ** 2 def df(x): return 2 * x - 4 def objective(trial): lr = trial.suggest_loguniform("lr", 1e-5, 1e-1) x = 3 for step in range(128): y = f(x) trial.report(y, step=step) if trial.should_prune(): raise optuna.TrialPruned() gy = df(x) x -= gy * lr return y sampler = optuna.samplers.TPESampler(seed=10) study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=16) optuna.visualization.matplotlib.plot_intermediate_values(study)
See also
Please refer to
optuna.visualization.plot_intermediate_values()
for an example.- Parameters
study – A
Study
object whose trials are plotted for their intermediate values.- Returns
A
matplotlib.axes.Axes
object.
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.