Source code for optuna.visualization.matplotlib._optimization_history

from typing import Callable
from typing import Optional

import numpy as np

from optuna._experimental import experimental
from optuna.logging import get_logger
from optuna.study import Study
from optuna.study import StudyDirection
from optuna.trial import FrozenTrial
from optuna.trial import TrialState
from optuna.visualization._utils import _check_plot_args
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__)


[docs]@experimental("2.2.0") def plot_optimization_history( study: Study, *, target: Optional[Callable[[FrozenTrial], float]] = None, target_name: str = "Objective Value", ) -> "Axes": """Plot optimization history of all trials in a study with Matplotlib. .. seealso:: Please refer to :func:`optuna.visualization.plot_optimization_history` for an example. Example: The following code snippet shows how to plot optimization history. .. plot:: import optuna def objective(trial): x = trial.suggest_float("x", -100, 100) y = trial.suggest_categorical("y", [-1, 0, 1]) return x ** 2 + y sampler = optuna.samplers.TPESampler(seed=10) study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=10) optuna.visualization.matplotlib.plot_optimization_history(study) Args: study: A :class:`~optuna.study.Study` object whose trials are plotted for their target values. 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 axis label and the legend. Returns: A :class:`matplotlib.axes.Axes` object. Raises: :exc:`ValueError`: If ``target`` is :obj:`None` and ``study`` is being used for multi-objective optimization. """ _imports.check() _check_plot_args(study, target, target_name) return _get_optimization_history_plot(study, target, target_name)
def _get_optimization_history_plot( study: Study, target: Optional[Callable[[FrozenTrial], float]], target_name: str, ) -> "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("Optimization History Plot") ax.set_xlabel("#Trials") ax.set_ylabel(target_name) cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly. # Prepare data for plotting. trials = [t for t in study.trials if t.state == TrialState.COMPLETE] if len(trials) == 0: _logger.warning("Study instance does not contain trials.") return ax # Draw a scatter plot and a line plot. if target is None: if study.direction == StudyDirection.MINIMIZE: best_values = np.minimum.accumulate([t.value for t in trials]) else: best_values = np.maximum.accumulate([t.value for t in trials]) ax.scatter( x=[t.number for t in trials], y=[t.value for t in trials], color=cmap(0), alpha=1, label=target_name, ) ax.plot( [t.number for t in trials], best_values, marker="o", color=cmap(3), alpha=0.5, label="Best Value", ) ax.legend() else: ax.scatter( x=[t.number for t in trials], y=[target(t) for t in trials], color=cmap(0), alpha=1, label=target_name, ) return ax