Source code for optuna.visualization._intermediate_values
from typing import List
from typing import NamedTuple
from typing import Tuple
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__)
class _TrialInfo(NamedTuple):
trial_number: int
sorted_intermediate_values: List[Tuple[int, float]]
class _IntermediatePlotInfo(NamedTuple):
trial_infos: List[_TrialInfo]
def _get_intermediate_plot_info(study: Study) -> _IntermediatePlotInfo:
trials = study.get_trials(
deepcopy=False, states=(TrialState.PRUNED, TrialState.COMPLETE, TrialState.RUNNING)
)
trial_infos = [
_TrialInfo(trial.number, sorted(trial.intermediate_values.items()))
for trial in trials
if len(trial.intermediate_values) > 0
]
if len(trials) == 0:
_logger.warning("Study instance does not contain trials.")
elif len(trial_infos) == 0:
_logger.warning(
"You need to set up the pruning feature to utilize `plot_intermediate_values()`"
)
return _IntermediatePlotInfo(trial_infos)
[docs]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(_get_intermediate_plot_info(study))
def _get_intermediate_plot(info: _IntermediatePlotInfo) -> "go.Figure":
layout = go.Layout(
title="Intermediate Values Plot",
xaxis={"title": "Step"},
yaxis={"title": "Intermediate Value"},
showlegend=False,
)
trial_infos = info.trial_infos
if len(trial_infos) == 0:
return go.Figure(data=[], layout=layout)
traces = [
go.Scatter(
x=tuple((x for x, _ in tinfo.sorted_intermediate_values)),
y=tuple((y for _, y in tinfo.sorted_intermediate_values)),
mode="lines+markers",
marker={"maxdisplayed": 10},
name="Trial{}".format(tinfo.trial_number),
)
for tinfo in trial_infos
]
return go.Figure(data=traces, layout=layout)