Source code for optuna.visualization.matplotlib._hypervolume_history

from __future__ import annotations

from typing import Sequence

import numpy as np

from optuna._experimental import experimental_func
from optuna.study import Study
from optuna.visualization._hypervolume_history import _get_hypervolume_history_info
from optuna.visualization._hypervolume_history import _HypervolumeHistoryInfo
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


[docs]@experimental_func("3.3.0") def plot_hypervolume_history( study: Study, reference_point: Sequence[float], ) -> "Axes": """Plot hypervolume history of all trials in a study with Matplotlib. Example: The following code snippet shows how to plot optimization history. .. plot:: import optuna import matplotlib.pyplot as plt def objective(trial): x = trial.suggest_float("x", 0, 5) y = trial.suggest_float("y", 0, 3) v0 = 4 * x ** 2 + 4 * y ** 2 v1 = (x - 5) ** 2 + (y - 5) ** 2 return v0, v1 study = optuna.create_study(directions=["minimize", "minimize"]) study.optimize(objective, n_trials=50) reference_point=[100, 50] optuna.visualization.matplotlib.plot_hypervolume_history(study, reference_point) plt.tight_layout() .. note:: You need to adjust the size of the plot by yourself using ``plt.tight_layout()`` or ``plt.savefig(IMAGE_NAME, bbox_inches='tight')``. Args: study: A :class:`~optuna.study.Study` object whose trials are plotted for their hypervolumes. The number of objectives must be 2 or more. reference_point: A reference point to use for hypervolume computation. The dimension of the reference point must be the same as the number of objectives. Returns: A :class:`matplotlib.axes.Axes` object. """ _imports.check() if not study._is_multi_objective(): raise ValueError( "Study must be multi-objective. For single-objective optimization, " "please use plot_optimization_history instead." ) if len(reference_point) != len(study.directions): raise ValueError( "The dimension of the reference point must be the same as the number of objectives." ) info = _get_hypervolume_history_info(study, np.asarray(reference_point, dtype=np.float64)) return _get_hypervolume_history_plot(info)
def _get_hypervolume_history_plot( info: _HypervolumeHistoryInfo, ) -> "Axes": # Set up the graph style. plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. _, ax = plt.subplots() ax.set_title("Hypervolume History Plot") ax.set_xlabel("Trial") ax.set_ylabel("Hypervolume") cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly. ax.plot( info.trial_numbers, info.values, marker="o", color=cmap(0), alpha=0.5, ) return ax