plot_parallel_coordinate

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

Plot the high-dimensional parameter relationships in a study.

If a trial does not contain a parameter, the line is connected to a special None tick. If at least one completed trial contains constraint values, feasible trials are drawn with solid lines and infeasible trials with dotted lines. In this case, trials without constraint values are treated as infeasible.

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. For a multi-objective study, all objective values are plotted and the lines are colored by Pareto rank. Pareto ranks are computed from the objective values without considering constraints.

• target_name (str) – Target’s name to display on the axis label and the legend.

Returns:

A plotly.graph_objects.Figure object.

Return type:

go.Figure

Note

The colormap is reversed when the target argument isn’t None or direction of Study is minimize. The Pareto-rank colormap is always reversed for multi-objective studies.

The following code snippet shows how to plot the high-dimensional parameter relationships.

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])
    return x**2 + y


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

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