def _get_importances_plot(info: _ImportancesInfo,
hover_template: List[str]) -> "go.Figure":
layout = go.Layout(
title="Hyperparameter Importances",
xaxis={"title": f"Importance for {info.target_name}"},
yaxis={"title": "Hyperparameter"},
showlegend=False,
)
param_names = info.param_names
importance_values = info.importance_values
if len(importance_values) == 0:
return go.Figure(data=[], layout=layout)
fig = go.Figure(
data=[
go.Bar(
x=importance_values,
y=param_names,
text=info.importance_labels,
textposition="outside",
cliponaxis=False, # Ensure text is not clipped.
hovertemplate=hover_template,
marker_color=plotly.colors.sequential.Blues[-4],
orientation="h",
)
],
layout=layout,
)
return fig
def plot_param_importances(
study: Study,
evaluator: Optional[BaseImportanceEvaluator] = None,
params: Optional[List[str]] = None,
*,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "go.Figure":
"""Plot hyperparameter importances.
Example:
The following code snippet shows how to plot hyperparameter importances.
.. plotly::
import optuna
def objective(trial):
x = trial.suggest_int("x", 0, 2)
y = trial.suggest_float("y", -1.0, 1.0)
z = trial.suggest_float("z", 0.0, 1.5)
return x ** 2 + y ** 3 - z ** 4
sampler = optuna.samplers.RandomSampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=100)
optuna.visualization.plot_param_importances(study)
.. seealso::
This function visualizes the results of :func:`optuna.importance.get_param_importances`.
Args:
study:
An optimized study.
evaluator:
An importance evaluator object that specifies which algorithm to base the importance
assessment on.
Defaults to
:class:`~optuna.importance.FanovaImportanceEvaluator`.
params:
A list of names of parameters to assess.
If :obj:`None`, all parameters that are present in all of the completed trials are
assessed.
target:
A function to specify the value to evaluate importances. If it is :obj:`None`, the
objective values are used.
target_name:
Target's name to display on the axis label.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_imports.check()
layout = go.Layout(
title="Hyperparameter Importances",
xaxis={"title": f"Importance for {target_name}"},
yaxis={"title": "Hyperparameter"},
showlegend=False,
)
# Importances cannot be evaluated without completed trials.
# Return an empty figure for consistency with other visualization functions.
trials = [trial for trial in study.trials if trial.state == TrialState.COMPLETE]
if len(trials) == 0:
logger.warning("Study instance does not contain completed trials.")
return go.Figure(data=[], layout=layout)
importances = optuna.importance.get_param_importances(
study, evaluator=evaluator, params=params, target=target
)
importances = OrderedDict(reversed(list(importances.items())))
importance_values = list(importances.values())
param_names = list(importances.keys())
fig = go.Figure(
data=[
go.Bar(
x=importance_values,
y=param_names,
text=importance_values,
texttemplate="%{text:.2f}",
textposition="outside",
cliponaxis=False, # Ensure text is not clipped.
hovertemplate=[
_make_hovertext(param_name, importance, study)
for param_name, importance in importances.items()
],
marker_color=[_get_color(param_name, study) for param_name in param_names],
orientation="h",
)
],
layout=layout,
)
return fig
def plot_param_importances(study: Study,
evaluator: BaseImportanceEvaluator = None,
params: Optional[List[str]] = None) -> "go.Figure":
"""Plot hyperparameter importances.
Example:
The following code snippet shows how to plot hyperparameter importances.
.. testcode::
import optuna
def objective(trial):
x = trial.suggest_int("x", 0, 2)
y = trial.suggest_float("y", -1.0, 1.0)
z = trial.suggest_float("z", 0.0, 1.5)
return x ** 2 + y ** 3 - z ** 4
study = optuna.create_study(sampler=optuna.samplers.RandomSampler())
study.optimize(objective, n_trials=100)
optuna.visualization.plot_param_importances(study)
.. raw:: html
<iframe src="../../_static/plot_param_importances.html"
width="100%" height="500px" frameborder="0">
</iframe>
.. seealso::
This function visualizes the results of :func:`optuna.importance.get_param_importances`.
Args:
study:
An optimized study.
evaluator:
An importance evaluator object that specifies which algorithm to base the importance
assessment on.
Defaults to
:class:`~optuna.importance._mean_decrease_impurity.MeanDecreaseImpurityImportanceEvaluator`.
params:
A list of names of parameters to assess.
If :obj:`None`, all parameters that are present in all of the completed trials are
assessed.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_imports.check()
layout = go.Layout(
title="Hyperparameter Importances",
xaxis={"title": "Importance"},
yaxis={"title": "Hyperparameter"},
showlegend=False,
)
# Importances cannot be evaluated without completed trials.
# Return an empty figure for consistency with other visualization functions.
trials = [
trial for trial in study.trials if trial.state == TrialState.COMPLETE
]
if len(trials) == 0:
logger.warning("Study instance does not contain completed trials.")
return go.Figure(data=[], layout=layout)
importances = optuna.importance.get_param_importances(study,
evaluator=evaluator,
params=params)
importances = OrderedDict(reversed(list(importances.items())))
importance_values = list(importances.values())
param_names = list(importances.keys())
fig = go.Figure(
data=[
go.Bar(
x=importance_values,
y=param_names,
text=importance_values,
texttemplate="%{text:.2f}",
textposition="outside",
cliponaxis=False, # Ensure text is not clipped.
hovertemplate=[
_make_hovertext(param_name, importance, study)
for param_name, importance in importances.items()
],
marker_color=[
_get_color(param_name, study) for param_name in param_names
],
orientation="h",
)
],
layout=layout,
)
return fig