import itertools
from typing import List
from typing import Sequence
from typing import Union
import numpy as np
from optuna._experimental import experimental
from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import TrialState
from optuna.visualization.matplotlib._matplotlib_imports import _imports
if _imports.is_successful():
from optuna.visualization.matplotlib._matplotlib_imports import Axes
from optuna.visualization.matplotlib._matplotlib_imports import plt
_logger = get_logger(__name__)
[docs]@experimental("2.2.0")
def plot_edf(study: Union[Study, Sequence[Study]]) -> "Axes":
"""Plot the objective value EDF (empirical distribution function) of a study with Matplotlib.
.. seealso::
Please refer to :func:`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.
.. plot::
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])
Args:
study:
A target :class:`~optuna.study.Study` object.
You can pass multiple studies if you want to compare those EDFs.
Returns:
A :class:`matplotlib.axes.Axes` object.
"""
_imports.check()
if isinstance(study, Study):
studies = [study]
else:
studies = list(study)
return _get_edf_plot(studies)
def _get_edf_plot(studies: List[Study]) -> "Axes":
# Set up the graph style.
plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Empirical Distribution Function Plot")
ax.set_xlabel("Objective Value")
ax.set_ylabel("Cumulative Probability")
ax.set_ylim(0, 1)
cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots.
# Prepare data for plotting.
if len(studies) == 0:
_logger.warning("There are no studies.")
return ax
all_trials = list(
itertools.chain.from_iterable(
(
trial
for trial in study.get_trials(deepcopy=False)
if trial.state == TrialState.COMPLETE
)
for study in studies
)
)
if len(all_trials) == 0:
_logger.warning("There are no complete trials.")
return ax
min_x_value = min(trial.value for trial in all_trials)
max_x_value = max(trial.value for trial in all_trials)
x_values = np.linspace(min_x_value, max_x_value, 100)
# Draw multiple line plots.
for i, study in enumerate(studies):
values = np.asarray(
[
trial.value
for trial in study.get_trials(deepcopy=False)
if trial.state == TrialState.COMPLETE
]
)
y_values = np.sum(values[:, np.newaxis] <= x_values, axis=0) / values.size
ax.plot(x_values, y_values, color=cmap(i), alpha=0.7, label=study.study_name)
return ax