def _get_pareto_front_3d(info: _ParetoFrontInfo) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
ax.set_title("Pareto-front Plot")
cmap = plt.get_cmap(
"tab10") # Use tab10 colormap for similar outputs to plotly.
ax.set_xlabel(info.target_names[info.axis_order[0]])
ax.set_ylabel(info.target_names[info.axis_order[1]])
ax.set_zlabel(info.target_names[info.axis_order[2]])
if info.non_best_trials_with_values is not None and len(
info.non_best_trials_with_values) > 0:
ax.scatter(
xs=[
values[info.axis_order[0]]
for _, values in info.non_best_trials_with_values
],
ys=[
values[info.axis_order[1]]
for _, values in info.non_best_trials_with_values
],
zs=[
values[info.axis_order[2]]
for _, values in info.non_best_trials_with_values
],
color=cmap(0),
label="Trial",
)
if info.best_trials_with_values is not None and len(
info.best_trials_with_values):
ax.scatter(
xs=[
values[info.axis_order[0]]
for _, values in info.best_trials_with_values
],
ys=[
values[info.axis_order[1]]
for _, values in info.best_trials_with_values
],
zs=[
values[info.axis_order[2]]
for _, values in info.best_trials_with_values
],
color=cmap(3),
label="Best Trial",
)
if info.non_best_trials_with_values is not None and ax.has_data():
ax.legend()
return ax
def _get_pareto_front_3d(
study: Study,
target_names: Optional[List[str]],
include_dominated_trials: bool = False,
axis_order: Optional[List[int]] = None,
) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
ax.set_title("Pareto-front Plot")
cmap = plt.get_cmap(
"tab10") # Use tab10 colormap for similar outputs to plotly.
if target_names is None:
target_names = ["Objective 0", "Objective 1", "Objective 2"]
elif len(target_names) != 3:
raise ValueError("The length of `target_names` is supposed to be 3.")
trials = study.best_trials
if len(trials) == 0:
_logger.warning("Your study does not have any completed trials.")
if include_dominated_trials:
non_pareto_trials = _get_non_pareto_front_trials(study, trials)
trials += non_pareto_trials
if axis_order is None:
axis_order = list(range(3))
else:
if len(axis_order) != 3:
raise ValueError(
f"Size of `axis_order` {axis_order}. Expect: 3, Actual: {len(axis_order)}."
)
if len(set(axis_order)) != 3:
raise ValueError(
f"Elements of given `axis_order` {axis_order} are not unique!."
)
if max(axis_order) > 2:
raise ValueError(
f"Given `axis_order` {axis_order} contains invalid index {max(axis_order)} "
"higher than 2.")
if min(axis_order) < 0:
raise ValueError(
f"Given `axis_order` {axis_order} contains invalid index {min(axis_order)} "
"lower than 0.")
ax.set_xlabel(target_names[axis_order[0]])
ax.set_ylabel(target_names[axis_order[1]])
ax.set_zlabel(target_names[axis_order[2]])
if len(trials) - len(study.best_trials) != 0:
ax.scatter(
xs=[
t.values[axis_order[0]]
for t in trials[len(study.best_trials):]
],
ys=[
t.values[axis_order[1]]
for t in trials[len(study.best_trials):]
],
zs=[
t.values[axis_order[2]]
for t in trials[len(study.best_trials):]
],
color=cmap(0),
label="Trial",
)
if len(study.best_trials):
ax.scatter(
xs=[
t.values[axis_order[0]]
for t in trials[:len(study.best_trials)]
],
ys=[
t.values[axis_order[1]]
for t in trials[:len(study.best_trials)]
],
zs=[
t.values[axis_order[2]]
for t in trials[:len(study.best_trials)]
],
color=cmap(3),
label="Best Trial",
)
if include_dominated_trials and ax.has_data():
ax.legend()
return ax