plot_rank

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

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-based plot_rank().

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

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.

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

/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/stable/lib/python3.11/site-packages/optuna/_experimental.py:31: ExperimentalWarning:

Argument ``constraints_func`` is an experimental feature. The interface can change in the future.

/home/docs/checkouts/readthedocs.org/user_builds/optuna/checkouts/stable/docs/visualization_matplotlib_examples/optuna.visualization.matplotlib.rank.py:33: ExperimentalWarning:

plot_rank is experimental (supported from v3.2.0). The interface can change in the future.


<Axes: title={'center': 'Rank (Objective Value)'}, xlabel='x', ylabel='y'>

import optuna


def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])

    c0 = 400 - (x + y) ** 2
    trial.set_user_attr("constraint", [c0])

    return x**2 + y


def constraints(trial):
    return trial.user_attrs["constraint"]


sampler = optuna.samplers.TPESampler(seed=10, constraints_func=constraints)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=30)

optuna.visualization.matplotlib.plot_rank(study, params=["x", "y"])