optuna.visualization.matplotlib.plot_edf(study, *, target=None, target_name='Objective Value')[source]

Plot the objective value EDF (empirical distribution function) of a study with Matplotlib.

See also

Please refer to optuna.visualization.plot_edf() for an example, where this function can be replaced with it.


Please refer to matplotlib.pyplot.legend to adjust the style of the generated legend.


The following code snippet shows how to plot EDF.

import math

import optuna

def ackley(x, y):
    a = 20 * math.exp(-0.2 * math.sqrt(0.5 * (x ** 2 + y ** 2)))
    b = math.exp(0.5 * (math.cos(2 * math.pi * x) + math.cos(2 * math.pi * y)))
    return -a - b + math.e + 20

def objective(trial, low, high):
    x = trial.suggest_float("x", low, high)
    y = trial.suggest_float("y", low, high)
    return ackley(x, y)

sampler = optuna.samplers.RandomSampler(seed=10)

# Widest search space.
study0 = optuna.create_study(study_name="x=[0,5), y=[0,5)", sampler=sampler)
study0.optimize(lambda t: objective(t, 0, 5), n_trials=500)

# Narrower search space.
study1 = optuna.create_study(study_name="x=[0,4), y=[0,4)", sampler=sampler)
study1.optimize(lambda t: objective(t, 0, 4), n_trials=500)

# Narrowest search space but it doesn't include the global optimum point.
study2 = optuna.create_study(study_name="x=[1,3), y=[1,3)", sampler=sampler)
study2.optimize(lambda t: objective(t, 1, 3), n_trials=500)

optuna.visualization.matplotlib.plot_edf([study0, study1, study2])
  • study (Union[optuna.study.study.Study, Sequence[optuna.study.study.Study]]) – A target Study object. You can pass multiple studies if you want to compare those EDFs.

  • target (Optional[Callable[[optuna.trial._frozen.FrozenTrial], float]]) –

    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.


    Specify this argument if study is being used for multi-objective optimization.

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


A matplotlib.axes.Axes object.


ValueError – If target is None and study is being used for multi-objective optimization.

Return type



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.