optuna.visualization.matplotlib.plot_rank
- optuna.visualization.matplotlib.plot_rank(study, params=None, *, target=None, target_name='Objective Value')[source]
Plot parameter relations as scatter plots with colors indicating ranks of target value.
Note that trials missing the specified parameters will not be plotted.
See also
Please refer to
optuna.visualization.plot_rank()
for an example.Warning
Output figures of this Matplotlib-based
plot_rank()
function would be different from those of the Plotly-basedplot_rank()
.Example
The following code snippet shows how to plot the parameter relationship as a rank plot.
import optuna 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=30) optuna.visualization.matplotlib.plot_rank(study, params=["x", "y"])
- Parameters:
study (Study) – A
Study
object whose trials are plotted for their target values.params (list[str] | None) – Parameter list to visualize. The default is all parameters.
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 color bar.
- Returns:
A
matplotlib.axes.Axes
object.- Return type:
Note
Added in v3.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.2.0.