Source code for optuna.visualization._intermediate_values

from __future__ import annotations

from typing import NamedTuple

from optuna.logging import get_logger
from optuna.samplers._base import _CONSTRAINTS_KEY
from import Study
from optuna.trial import FrozenTrial
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]]
    feasible: bool

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)

    def _satisfies_constraints(trial: FrozenTrial) -> bool:
        constraints = trial.system_attrs.get(_CONSTRAINTS_KEY)
        return constraints is None or all([x <= 0.0 for x in constraints])

    trial_infos = [
            trial.number, sorted(trial.intermediate_values.items()), _satisfies_constraints(trial)
        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:
            "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), 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) Args: study: A :class:`` object whose trials are plotted for their intermediate values. Returns: A :class:`plotly.graph_objects.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) default_marker = {"maxdisplayed": 10} 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=( default_marker if tinfo.feasible else {**default_marker, "color": "#CCCCCC"} # type: ignore[dict-item] ), name="Trial{}".format(tinfo.trial_number), ) for tinfo in trial_infos ] return go.Figure(data=traces, layout=layout)