import json
from typing import List
from typing import Optional
import optuna
from optuna import multi_objective
from optuna._deprecated import deprecated
from optuna.multi_objective.study import MultiObjectiveStudy
from optuna.multi_objective.trial import FrozenMultiObjectiveTrial
from optuna.trial import TrialState
from optuna.visualization._plotly_imports import _imports
if _imports.is_successful():
from optuna.visualization._plotly_imports import go
_logger = optuna.logging.get_logger(__name__)
[文档]@deprecated("2.4.0", "4.0.0")
def plot_pareto_front(
study: MultiObjectiveStudy,
names: Optional[List[str]] = None,
include_dominated_trials: bool = False,
axis_order: Optional[List[int]] = None,
) -> "go.Figure":
"""Plot the pareto front of a study.
Example:
The following code snippet shows how to plot the pareto front of a study.
.. plotly::
import optuna
def objective(trial):
x = trial.suggest_float("x", 0, 5)
y = trial.suggest_float("y", 0, 3)
v0 = 4 * x ** 2 + 4 * y ** 2
v1 = (x - 5) ** 2 + (y - 5) ** 2
return v0, v1
study = optuna.multi_objective.create_study(["minimize", "minimize"])
study.optimize(objective, n_trials=50)
fig = optuna.multi_objective.visualization.plot_pareto_front(study)
fig.show()
Args:
study:
A :class:`~optuna.multi_objective.study.MultiObjectiveStudy` object whose trials
are plotted for their objective values.
names:
Objective name list used as the axis titles. If :obj:`None` is specified,
"Objective {objective_index}" is used instead.
include_dominated_trials:
A flag to include all dominated trial's objective values.
axis_order:
A list of indices indicating the axis order. If :obj:`None` is specified,
default order is used.
Returns:
A :class:`plotly.graph_objs.Figure` object.
Raises:
:exc:`ValueError`:
If the number of objectives of ``study`` isn't 2 or 3.
"""
_imports.check()
if study.n_objectives == 2:
return _get_pareto_front_2d(study, names, include_dominated_trials, axis_order)
elif study.n_objectives == 3:
return _get_pareto_front_3d(study, names, include_dominated_trials, axis_order)
else:
raise ValueError("`plot_pareto_front` function only supports 2 or 3 objective studies.")
def _get_non_pareto_front_trials(
study: MultiObjectiveStudy,
pareto_trials: List["multi_objective.trial.FrozenMultiObjectiveTrial"],
) -> List["multi_objective.trial.FrozenMultiObjectiveTrial"]:
non_pareto_trials = []
for trial in study.get_trials():
if trial.state == TrialState.COMPLETE and trial not in pareto_trials:
non_pareto_trials.append(trial)
return non_pareto_trials
def _get_pareto_front_2d(
study: MultiObjectiveStudy,
names: Optional[List[str]],
include_dominated_trials: bool = False,
axis_order: Optional[List[int]] = None,
) -> "go.Figure":
if names is None:
names = ["Objective 0", "Objective 1"]
elif len(names) != 2:
raise ValueError("The length of `names` is supposed to be 2.")
trials = study.get_pareto_front_trials()
if len(trials) == 0:
_logger.warning("Your study does not have any completed trials.")
point_colors = ["blue"] * len(trials)
if include_dominated_trials:
non_pareto_trials = _get_non_pareto_front_trials(study, trials)
point_colors += ["red"] * len(non_pareto_trials)
trials += non_pareto_trials
if axis_order is None:
axis_order = list(range(2))
else:
if len(axis_order) != 2:
raise ValueError(
f"Size of `axis_order` {axis_order}. Expect: 2, Actual: {len(axis_order)}."
)
if len(set(axis_order)) != 2:
raise ValueError(f"Elements of given `axis_order` {axis_order} are not unique!")
if max(axis_order) > 1:
raise ValueError(
f"Given `axis_order` {axis_order} contains invalid index {max(axis_order)} "
"higher than 1."
)
if min(axis_order) < 0:
raise ValueError(
f"Given `axis_order` {axis_order} contains invalid index {min(axis_order)} "
"lower than 0."
)
data = go.Scatter(
x=[t.values[axis_order[0]] for t in trials],
y=[t.values[axis_order[1]] for t in trials],
text=[_make_hovertext(t) for t in trials],
mode="markers",
hovertemplate="%{text}<extra></extra>",
marker={"color": point_colors},
)
layout = go.Layout(
title="Pareto-front Plot",
xaxis_title=names[axis_order[0]],
yaxis_title=names[axis_order[1]],
)
return go.Figure(data=data, layout=layout)
def _get_pareto_front_3d(
study: MultiObjectiveStudy,
names: Optional[List[str]],
include_dominated_trials: bool = False,
axis_order: Optional[List[int]] = None,
) -> "go.Figure":
if names is None:
names = ["Objective 0", "Objective 1", "Objective 2"]
elif len(names) != 3:
raise ValueError("The length of `names` is supposed to be 3.")
trials = study.get_pareto_front_trials()
if len(trials) == 0:
_logger.warning("Your study does not have any completed trials.")
point_colors = ["blue"] * len(trials)
if include_dominated_trials:
non_pareto_trials = _get_non_pareto_front_trials(study, trials)
point_colors += ["red"] * len(non_pareto_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."
)
data = go.Scatter3d(
x=[t.values[axis_order[0]] for t in trials],
y=[t.values[axis_order[1]] for t in trials],
z=[t.values[axis_order[2]] for t in trials],
text=[_make_hovertext(t) for t in trials],
mode="markers",
hovertemplate="%{text}<extra></extra>",
marker={"color": point_colors},
)
layout = go.Layout(
title="Pareto-front Plot",
scene={
"xaxis_title": names[axis_order[0]],
"yaxis_title": names[axis_order[1]],
"zaxis_title": names[axis_order[2]],
},
)
return go.Figure(data=data, layout=layout)
def _make_hovertext(trial: FrozenMultiObjectiveTrial) -> str:
text = json.dumps(
{"number": trial.number, "values": trial.values, "params": trial.params}, indent=2
)
return text.replace("\n", "<br>")