Source code for optuna.visualization.matplotlib._timeline

from optuna._experimental import experimental_func
from optuna.study import Study
from optuna.trial import TrialState
from optuna.visualization._timeline import _get_timeline_info
from optuna.visualization._timeline import _TimelineInfo
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 matplotlib
    from optuna.visualization.matplotlib._matplotlib_imports import plt


[docs]@experimental_func("3.2.0") def plot_timeline(study: Study) -> "Axes": """Plot the timeline of a study. .. seealso:: Please refer to :func:`optuna.visualization.plot_timeline` for an example. Example: The following code snippet shows how to plot the timeline of a study. .. plot:: import time import optuna def objective(trial): x = trial.suggest_float("x", 0, 1) time.sleep(x * 0.1) if x > 0.8: raise ValueError() if x > 0.4: raise optuna.TrialPruned() return x ** 2 study = optuna.create_study(direction="minimize") study.optimize( objective, n_trials=50, n_jobs=2, catch=(ValueError,) ) optuna.visualization.matplotlib.plot_timeline(study) Args: study: A :class:`~optuna.study.Study` object whose trials are plotted with their lifetime. Returns: A :class:`matplotlib.axes.Axes` object. """ _imports.check() info = _get_timeline_info(study) return _get_timeline_plot(info)
def _get_timeline_plot(info: _TimelineInfo) -> "Axes": _cm = { TrialState.COMPLETE: "tab:blue", TrialState.FAIL: "tab:red", TrialState.PRUNED: "tab:orange", TrialState.RUNNING: "tab:green", TrialState.WAITING: "tab:gray", } # 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("Timeline Plot") ax.set_xlabel("Datetime") ax.set_ylabel("Trial") if len(info.bars) == 0: return ax ax.barh( y=[b.number for b in info.bars], width=[b.complete - b.start for b in info.bars], left=[b.start for b in info.bars], color=[_cm[b.state] for b in info.bars], ) # There are 5 types of TrialState in total. # However, the legend depicts only types present in the arguments. legend_handles = [] for state, color in _cm.items(): if len([b for b in info.bars if b.state == state]) > 0: legend_handles.append(matplotlib.patches.Patch(color=color, label=state.name)) ax.legend(handles=legend_handles, loc="upper left", bbox_to_anchor=(1.05, 1.0)) fig.tight_layout() assert len(info.bars) > 0 start_time = min([b.start for b in info.bars]) complete_time = max([b.complete for b in info.bars]) margin = (complete_time - start_time) * 0.05 ax.set_xlim(right=complete_time + margin, left=start_time - margin) ax.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True)) ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M:%S")) plt.gcf().autofmt_xdate() return ax