# optuna.visualization.matplotlib.plot_edf¶

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.

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

Example

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]) Parameters
• study (Union[optuna.study.Study, Sequence[optuna.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.

Note

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

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

Returns

A matplotlib.axes.Axes object.

Raises

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

Return type

matplotlib.axes._axes.Axes

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.