# 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.

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

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.

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[Study, Sequence[Study]]) – A target `Study` object. You can pass multiple studies if you want to compare those EDFs.

• target (Optional[Callable[[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.

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.