optuna.visualization._intermediate_values 源代码

from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import TrialState
from optuna.visualization._plotly_imports import _imports

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

_logger = get_logger(__name__)

[文档]def plot_intermediate_values(study: Study) -> "go.Figure": """Plot intermediate values of all trials in a study. Example: The following code snippet shows how to plot intermediate values. .. plotly:: import optuna def f(x): return (x - 2) ** 2 def df(x): return 2 * x - 4 def objective(trial): lr = trial.suggest_float("lr", 1e-5, 1e-1, log=True) x = 3 for step in range(128): y = f(x) trial.report(y, step=step) if trial.should_prune(): raise optuna.TrialPruned() gy = df(x) x -= gy * lr return y sampler = optuna.samplers.TPESampler(seed=10) study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=16) fig = optuna.visualization.plot_intermediate_values(study) fig.show() Args: study: A :class:`~optuna.study.Study` object whose trials are plotted for their intermediate values. Returns: A :class:`plotly.graph_objs.Figure` object. """ _imports.check() return _get_intermediate_plot(study)
def _get_intermediate_plot(study: Study) -> "go.Figure": layout = go.Layout( title="Intermediate Values Plot", xaxis={"title": "Step"}, yaxis={"title": "Intermediate Value"}, showlegend=False, ) target_state = [TrialState.PRUNED, TrialState.COMPLETE, TrialState.RUNNING] trials = [trial for trial in study.trials if trial.state in target_state] if len(trials) == 0: _logger.warning("Study instance does not contain trials.") return go.Figure(data=[], layout=layout) traces = [] for trial in trials: if trial.intermediate_values: sorted_intermediate_values = sorted(trial.intermediate_values.items()) trace = go.Scatter( x=tuple((x for x, _ in sorted_intermediate_values)), y=tuple((y for _, y in sorted_intermediate_values)), mode="lines+markers", marker={"maxdisplayed": 10}, name="Trial{}".format(trial.number), ) traces.append(trace) if not traces: _logger.warning( "You need to set up the pruning feature to utilize `plot_intermediate_values()`" ) return go.Figure(data=[], layout=layout) figure = go.Figure(data=traces, layout=layout) return figure