optuna.visualization._optimization_history 源代码

from typing import Callable
from typing import Optional

import numpy as np

from optuna._study_direction import StudyDirection
from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState
from optuna.visualization._plotly_imports import _imports
from optuna.visualization._utils import _check_plot_args

if _imports.is_successful():
    from optuna.visualization._plotly_imports import go

_logger = get_logger(__name__)

[文档]def plot_optimization_history( study: Study, *, target: Optional[Callable[[FrozenTrial], float]] = None, target_name: str = "Objective Value", ) -> "go.Figure": """Plot optimization history of all trials in a study. Example: The following code snippet shows how to plot optimization history. .. plotly:: 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) fig = optuna.visualization.plot_optimization_history(study) fig.show() 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:`plotly.graph_objs.Figure` 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, ) -> "go.Figure": layout = go.Layout( title="Optimization History Plot", xaxis={"title": "#Trials"}, yaxis={"title": target_name}, ) 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 go.Figure(data=[], layout=layout) 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]) traces = [ go.Scatter( x=[t.number for t in trials], y=[t.value for t in trials], mode="markers", name=target_name, ), go.Scatter(x=[t.number for t in trials], y=best_values, name="Best Value"), ] else: traces = [ go.Scatter( x=[t.number for t in trials], y=[target(t) for t in trials], mode="markers", name=target_name, ), ] figure = go.Figure(data=traces, layout=layout) return figure