from typing import Callable
from typing import List
from typing import Optional
import numpy as np
from optuna._experimental import experimental_func
from optuna.importance._base import BaseImportanceEvaluator
from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.visualization._param_importances import _get_importances_info
from optuna.visualization._param_importances import _ImportancesInfo
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
_logger = get_logger(__name__)
AXES_PADDING_RATIO = 1.05
[docs]@experimental_func("2.2.0")
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",
) -> "Axes":
"""Plot hyperparameter importances with Matplotlib.
.. seealso::
Please refer to :func:`optuna.visualization.plot_param_importances` for an example.
Example:
The following code snippet shows how to plot hyperparameter importances.
.. plot::
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.matplotlib.plot_param_importances(study)
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 display. If it is :obj:`None` and ``study`` is being
used for single-objective optimization, the objective values are plotted.
.. note::
Specify this argument if ``study`` is being used for multi-objective
optimization. For example, to get the hyperparameter importance of the first
objective, use ``target=lambda t: t.values[0]`` for the target parameter.
target_name:
Target's name to display on the axis label.
Returns:
A :class:`matplotlib.axes.Axes` object.
"""
_imports.check()
importances_info = _get_importances_info(study, evaluator, params, target, target_name)
return _get_importances_plot(importances_info)
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