Source code for optuna.visualization.matplotlib._rank

from __future__ import annotations

from typing import Callable

from optuna._experimental import experimental_func
from optuna.logging import get_logger
from import Study
from optuna.trial import FrozenTrial
from optuna.visualization._rank import _get_rank_info
from optuna.visualization._rank import _get_tick_info
from optuna.visualization._rank import _RankPlotInfo
from optuna.visualization._rank import _RankSubplotInfo
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 PathCollection
    from optuna.visualization.matplotlib._matplotlib_imports import plt

_logger = get_logger(__name__)

[docs] @experimental_func("3.2.0") def plot_rank( study: Study, params: list[str] | None = None, *, target: Callable[[FrozenTrial], float] | None = None, target_name: str = "Objective Value", ) -> "Axes": """Plot parameter relations as scatter plots with colors indicating ranks of target value. Note that trials missing the specified parameters will not be plotted. .. seealso:: Please refer to :func:`optuna.visualization.plot_rank` for an example. Warnings: Output figures of this Matplotlib-based :func:`~optuna.visualization.matplotlib.plot_rank` function would be different from those of the Plotly-based :func:`~optuna.visualization.plot_rank`. Example: The following code snippet shows how to plot the parameter relationship as a rank plot. .. plot:: import optuna def objective(trial): x = trial.suggest_float("x", -100, 100) y = trial.suggest_categorical("y", [-1, 0, 1]) c0 = 400 - (x + y)**2 trial.set_user_attr("constraint", [c0]) return x ** 2 + y def constraints(trial): return trial.user_attrs["constraint"] sampler = optuna.samplers.TPESampler(seed=10, constraints_func=constraints) study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=30) optuna.visualization.matplotlib.plot_rank(study, params=["x", "y"]) Args: study: A :class:`` object whose trials are plotted for their target values. params: Parameter list to visualize. The default is all parameters. 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. target_name: Target's name to display on the color bar. Returns: A :class:`matplotlib.axes.Axes` object. """ _imports.check() _logger.warning( "Output figures of this Matplotlib-based `plot_rank` function would be different from " "those of the Plotly-based `plot_rank`." ) info = _get_rank_info(study, params, target, target_name) return _get_rank_plot(info)
def _get_rank_plot( info: _RankPlotInfo, ) -> "Axes": params = info.params sub_plot_infos = info.sub_plot_infos"ggplot") # Use ggplot style sheet for similar outputs to plotly. title = f"Rank ({info.target_name})" n_params = len(params) if n_params == 0: _, ax = plt.subplots() ax.set_title(title) return ax if n_params == 1 or n_params == 2: fig, axs = plt.subplots() axs.set_title(title) pc = _add_rank_subplot(axs, sub_plot_infos[0][0]) else: fig, axs = plt.subplots(n_params, n_params) fig.suptitle(title) for x_i in range(n_params): for y_i in range(n_params): ax = axs[x_i, y_i] # Set the x or y label only if the subplot is in the edge of the overall figure. pc = _add_rank_subplot( ax, sub_plot_infos[x_i][y_i], set_x_label=x_i == (n_params - 1), set_y_label=y_i == 0, ) tick_info = _get_tick_info(info.zs) pc.set_cmap(plt.get_cmap("RdYlBu_r")) cbar = fig.colorbar(pc, ax=axs, ticks=tick_info.coloridxs) cbar.outline.set_edgecolor("gray") return axs def _add_rank_subplot( ax: "Axes", info: _RankSubplotInfo, set_x_label: bool = True, set_y_label: bool = True ) -> "PathCollection": if set_x_label: ax.set_xlabel( if set_y_label: ax.set_ylabel( if not info.xaxis.is_cat: ax.set_xlim(info.xaxis.range[0], info.xaxis.range[1]) if not info.yaxis.is_cat: ax.set_ylim(info.yaxis.range[0], info.yaxis.range[1]) if info.xaxis.is_log: ax.set_xscale("log") if info.yaxis.is_log: ax.set_yscale("log") return ax.scatter(x=info.xs, y=info.ys, c=info.colors / 255, edgecolors="grey")