plot_rank

optuna.visualization.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.

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 plotly.graph_objects.Figure object.

Return type:

Figure

Note

This function requires plotly >= 5.0.0.

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

/home/docs/checkouts/readthedocs.org/user_builds/optuna/envs/latest/lib/python3.12/site-packages/optuna/_experimental.py:31: ExperimentalWarning:

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

import optuna
from plotly.io import show


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)

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