def plot_optimization_history(study):
# type: (Study) -> None
"""Plot optimization history of all trials in a study.
Example:
The following code snippet shows how to plot optimization history.
.. code::
import optuna
def objective(trial):
...
study = optuna.create_study()
study.optimize(objective, n_trials=100)
optuna.visualization.plot_optimization_history(study)
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their objective
values.
"""
_check_plotly_availability()
figure = _get_optimization_history_plot(study)
figure.show()
def plot_contour(study, params=None):
# type: (Study, Optional[List[str]]) -> None
"""Plot the parameter relationship as contour plot in a study.
Note that, If a parameter contains missing values, a trial with missing values is not
plotted.
Example:
The following code snippet shows how to plot the parameter relationship as contour plot.
.. code::
import optuna
def objective(trial):
...
study = optuna.create_study()
study.optimize(objective, n_trials=100)
optuna.visualization.plot_contour(study, params=['param_a', 'param_b'])
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their objective
values.
params:
Parameter list to visualize. The default is all parameters.
"""
_check_plotly_availability()
figure = _get_contour_plot(study, params)
figure.show()
def plot_parallel_coordinate(study, params=None):
# type: (Study, Optional[List[str]]) -> go.Figure
"""Plot the high-dimentional parameter relationships in a study.
Note that, If a parameter contains missing values, a trial with missing values is not plotted.
Example:
The following code snippet shows how to plot the high-dimentional parameter relationships.
.. code::
import optuna
def objective(trial):
...
study = optuna.create_study()
study.optimize(objective, n_trials=100)
optuna.visualization.plot_parallel_coordinate(study, params=['param_a', 'param_b'])
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their objective
values.
params:
Parameter list to visualize. The default is all parameters.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_check_plotly_availability()
return _get_parallel_coordinate_plot(study, params)
def plot_intermediate_values(study):
# type: (Study) -> go.Figure
"""Plot intermediate values of all trials in a study.
Example:
The following code snippet shows how to plot intermediate values.
.. code::
import optuna
def objective(trial):
# Intermediate values are supposed to be reported inside the objective function.
...
study = optuna.create_study()
study.optimize(objective, n_trials=100)
optuna.visualization.plot_intermediate_values(study)
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their intermediate
values.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_check_plotly_availability()
return _get_intermediate_plot(study)
def plot_optimization_history(study):
# type: (Study) -> go.Figure
"""Plot optimization history of all trials in a study.
Example:
The following code snippet shows how to plot optimization history.
.. testcode::
import optuna
def objective(trial):
x = trial.suggest_uniform('x', -100, 100)
y = trial.suggest_categorical('y', [-1, 0, 1])
return x ** 2 + y
study = optuna.create_study()
study.optimize(objective, n_trials=10)
optuna.visualization.plot_optimization_history(study)
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their objective
values.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_check_plotly_availability()
return _get_optimization_history_plot(study)
def plot_intermediate_values(study):
# type: (Study) -> go.Figure
"""Plot intermediate values of all trials in a study.
Example:
The following code snippet shows how to plot intermediate values.
.. testcode::
import optuna
def f(x):
return (x - 2) ** 2
def df(x):
return 2 * x - 4
def objective(trial):
lr = trial.suggest_loguniform("lr", 1e-5, 1e-1)
x = 3
for step in range(128):
y = f(x)
trial.report(y, step=step)
if trial.should_prune():
raise optuna.exceptions.TrialPruned()
gy = df(x)
x -= gy * lr
return y
study = optuna.create_study()
study.optimize(objective, n_trials=16)
optuna.visualization.plot_intermediate_values(study)
.. raw:: html
<iframe src="../_static/plot_intermediate_values.html"
width="100%" height="500px" frameborder="0">
</iframe>
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their intermediate
values.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_check_plotly_availability()
return _get_intermediate_plot(study)
def plot_parallel_coordinate(study: Study,
params: Optional[List[str]] = None
) -> "go.Figure":
"""Plot the high-dimentional parameter relationships in a study.
Note that, If a parameter contains missing values, a trial with missing values is not plotted.
Example:
The following code snippet shows how to plot the high-dimentional parameter relationships.
.. testcode::
import optuna
def objective(trial):
x = trial.suggest_uniform('x', -100, 100)
y = trial.suggest_categorical('y', [-1, 0, 1])
return x ** 2 + y
study = optuna.create_study()
study.optimize(objective, n_trials=10)
optuna.visualization.plot_parallel_coordinate(study, params=['x', 'y'])
.. raw:: html
<iframe src="../_static/plot_parallel_coordinate.html"
width="100%" height="500px" frameborder="0">
</iframe>
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their objective
values.
params:
Parameter list to visualize. The default is all parameters.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_check_plotly_availability()
return _get_parallel_coordinate_plot(study, params)
def plot_intermediate_values(study):
# type: (Study) -> go.Figure
"""Plot intermediate values of all trials in a study.
Example:
The following code snippet shows how to plot intermediate values.
.. testcode::
import optuna
def objective(trial):
x = trial.suggest_uniform('x', -100, 100)
y = trial.suggest_categorical('y', [-1, 0, 1])
return x ** 2 + y
study = optuna.create_study()
study.optimize(objective, n_trials=10)
optuna.visualization.plot_intermediate_values(study)
.. raw:: html
<iframe src="../_static/plot_intermediate_values.html"
width="100%" height="500px" frameborder="0">
</iframe>
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their intermediate
values.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_check_plotly_availability()
return _get_intermediate_plot(study)
def plot_contour(study, params=None):
# type: (Study, Optional[List[str]]) -> go.Figure
"""Plot the parameter relationship as contour plot in a study.
Note that, If a parameter contains missing values, a trial with missing values is not plotted.
Example:
The following code snippet shows how to plot the parameter relationship as contour plot.
.. testcode::
import optuna
def objective(trial):
x = trial.suggest_uniform('x', -100, 100)
y = trial.suggest_categorical('y', [-1, 0, 1])
return x ** 2 + y
study = optuna.create_study()
study.optimize(objective, n_trials=10)
optuna.visualization.plot_contour(study, params=['x', 'y'])
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their objective
values.
params:
Parameter list to visualize. The default is all parameters.
Returns:
A :class:`plotly.graph_objs.Figure` object.
"""
_check_plotly_availability()
return _get_contour_plot(study, params)
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.
"""
_check_plotly_availability()
layout = go.Layout(
title="Hyperparameter Importances",
xaxis={"title": "Feature"},
yaxis={"title": "Importance"},
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)
fig = go.Figure(data=[
go.Bar(x=list(importances.keys()), y=list(importances.values()))
],
layout=layout)
return fig
