plot_contour

optuna.visualization.matplotlib.plot_contour(study, params=None, *, target=None, target_name='Objective Value')

Plot the parameter relationship as contour plot 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_contour() for an example.

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 and study 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:

Axes

Note

The colormap is reversed when the target argument isn’t None or direction of Study is minimize.

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.

The following code snippet shows how to plot the parameter relationship as contour plot.

/home/docs/checkouts/readthedocs.org/user_builds/optuna/checkouts/stable/docs/visualization_matplotlib_examples/optuna.visualization.matplotlib.contour.py:25: ExperimentalWarning: optuna.visualization.matplotlib._contour.plot_contour is experimental (supported from v2.2.0). The interface can change in the future.
  optuna.visualization.matplotlib.plot_contour(study, params=["x", "y"])

<Axes: title={'center': 'Contour Plot'}, xlabel='x', ylabel='y'>

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_contour(study, params=["x", "y"])