optuna.visualization.matplotlib.plot_parallel_coordinate
- optuna.visualization.matplotlib.plot_parallel_coordinate(study, params=None, *, target=None, target_name='Objective Value')[source]
Plot the high-dimensional parameter relationships in a study with Matplotlib.
Note that, if a parameter contains missing values, a trial with missing values is not plotted.
See also
Please refer to
optuna.visualization.plot_parallel_coordinate()
for an example.Example
The following code snippet shows how to plot the high-dimensional parameter relationships.
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=10) optuna.visualization.matplotlib.plot_parallel_coordinate(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 axis label and the legend.
- Returns:
A
matplotlib.axes.Axes
object.- Return type:
Note
The colormap is reversed when the
target
argument isn’tNone
ordirection
ofStudy
isminimize
.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.