plot_edf

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

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

Note that only the complete trials are considered when plotting the EDF.

See also

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

Note

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

Parameters:

• study (Study | Sequence[Study]) – A target Study object. You can pass multiple studies if you want to compare those EDFs.

• 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 axis label.

Returns:

A matplotlib.axes.Axes object.

Return type:

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.

The following code snippet shows how to plot EDF.

/home/docs/checkouts/readthedocs.org/user_builds/optuna/checkouts/stable/docs/visualization_matplotlib_examples/optuna.visualization.matplotlib.edf.py:43: ExperimentalWarning:

plot_edf is experimental (supported from v2.2.0). The interface can change in the future.


<Axes: title={'center': 'Empirical Distribution Function Plot'}, xlabel='Objective Value', ylabel='Cumulative Probability'>

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])