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.
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.
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 (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
andstudy
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:
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.