Source code for optuna.visualization.matplotlib._param_importances

from __future__ import annotations

from typing import Callable

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 plt


_logger = get_logger(__name__)


AXES_PADDING_RATIO = 1.05


[docs]@experimental_func("2.2.0") def plot_param_importances( study: Study, evaluator: BaseImportanceEvaluator | None = None, params: list[str] | None = None, *, target: Callable[[FrozenTrial], float] | None = 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=plt.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