def _get_param_importance_plot(
study: Study,
evaluator: Optional[BaseImportanceEvaluator] = None,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
# Set up the graph style.
plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly.
fig, ax = plt.subplots()
ax.set_title("Hyperparameter Importances")
ax.set_xlabel(f"Importance for {target_name}")
ax.set_ylabel("Hyperparameter")
# Prepare data for plotting.
# 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 ax
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())
pos = np.arange(len(param_names))
# Draw horizontal bars.
ax.barh(
pos,
importance_values,
align="center",
color=cm.get_cmap("tab20c")(0),
tick_label=param_names,
)
renderer = fig.canvas.get_renderer()
for idx, val in enumerate(importance_values):
label = f" {val:.2f}" if val >= 0.01 else " <0.01"
text = ax.text(val, idx, label, va="center")
# Sometimes horizontal axis needs to be re-scaled
# to avoid text going over plot area.
bbox = text.get_window_extent(renderer)
bbox = bbox.transformed(ax.transData.inverted())
_, plot_xmax = ax.get_xlim()
bbox_xmax = bbox.xmax
if bbox_xmax > plot_xmax:
ax.set_xlim(xmax=AXES_PADDING_RATIO * bbox_xmax)
return ax
def _get_param_importance_plot(
study: Study,
evaluator: Optional[BaseImportanceEvaluator] = None,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
# Set up the graph style.
_, ax = plt.subplots()
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
ax.set_title("Hyperparameter Importances")
ax.set_xlabel(f"Importance for {target_name}")
ax.set_ylabel("Hyperparameter")
# Prepare data for plotting.
# 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 ax
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())
pos = np.arange(len(param_names))
# Draw horizontal bars.
ax.barh(
pos,
importance_values,
align="center",
color=cm.get_cmap("tab20c")(0),
tick_label=param_names,
)
return ax
def _get_importances_plot(info: _ImportancesInfo) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
fig, ax = plt.subplots()
ax.set_title("Hyperparameter Importances")
ax.set_xlabel(f"Importance for {info.target_name}")
ax.set_ylabel("Hyperparameter")
param_names = info.param_names
pos = np.arange(len(param_names))
importance_values = info.importance_values
if len(importance_values) == 0:
return ax
# Draw horizontal bars.
ax.barh(
pos,
importance_values,
align="center",
color=cm.get_cmap("tab20c")(0),
tick_label=param_names,
)
renderer = fig.canvas.get_renderer()
for idx, (val, label) in enumerate(
zip(importance_values, info.importance_labels)):
text = ax.text(val, idx, label, va="center")
# Sometimes horizontal axis needs to be re-scaled
# to avoid text going over plot area.
bbox = text.get_window_extent(renderer)
bbox = bbox.transformed(ax.transData.inverted())
_, plot_xmax = ax.get_xlim()
bbox_xmax = bbox.xmax
if bbox_xmax > plot_xmax:
ax.set_xlim(xmax=AXES_PADDING_RATIO * bbox_xmax)
return ax
from optuna.distributions import UniformDistribution
from optuna.importance._base import BaseImportanceEvaluator
from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import FrozenTrial
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 cm
from optuna.visualization.matplotlib._matplotlib_imports import plt
from optuna.visualization.matplotlib._matplotlib_imports import Rectangle
# Color definitions.
cmap = cm.get_cmap("tab20c")
_distribution_colors = {
UniformDistribution: cmap(0),
LogUniformDistribution: cmap(0),
DiscreteUniformDistribution: cmap(0),
IntUniformDistribution: cmap(1),
IntLogUniformDistribution: cmap(1),
CategoricalDistribution: cmap(3),
}
_legend_elements = [
Rectangle([0, 0],
0,
0,
facecolor=cmap(0),
label="Uniform Distribution"),
Rectangle([0, 0],