Source code for optuna.visualization._hypervolume_history

from __future__ import annotations

from typing import NamedTuple
from typing import Sequence

import numpy as np

from optuna._experimental import experimental_func
from optuna._hypervolume import WFG
from optuna.logging import get_logger
from optuna.samplers._base import _CONSTRAINTS_KEY
from optuna.study import Study
from optuna.study._multi_objective import _dominates
from optuna.study._study_direction import StudyDirection
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 _HypervolumeHistoryInfo(NamedTuple):
    trial_numbers: list[int]
    values: list[float]


[docs]@experimental_func("3.3.0") def plot_hypervolume_history( study: Study, reference_point: Sequence[float], ) -> "go.Figure": """Plot hypervolume 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", 0, 5) y = trial.suggest_float("y", 0, 3) v0 = 4 * x ** 2 + 4 * y ** 2 v1 = (x - 5) ** 2 + (y - 5) ** 2 return v0, v1 study = optuna.create_study(directions=["minimize", "minimize"]) study.optimize(objective, n_trials=50) reference_point=[100., 50.] fig = optuna.visualization.plot_hypervolume_history(study, reference_point) fig.show() Args: study: A :class:`~optuna.study.Study` object whose trials are plotted for their hypervolumes. The number of objectives must be 2 or more. reference_point: A reference point to use for hypervolume computation. The dimension of the reference point must be the same as the number of objectives. Returns: A :class:`plotly.graph_objs.Figure` object. """ _imports.check() if not study._is_multi_objective(): raise ValueError( "Study must be multi-objective. For single-objective optimization, " "please use plot_optimization_history instead." ) if len(reference_point) != len(study.directions): raise ValueError( "The dimension of the reference point must be the same as the number of objectives." ) info = _get_hypervolume_history_info(study, np.asarray(reference_point, dtype=np.float64)) return _get_hypervolume_history_plot(info)
def _get_hypervolume_history_plot( info: _HypervolumeHistoryInfo, ) -> "go.Figure": layout = go.Layout( title="Hypervolume History Plot", xaxis={"title": "Trial"}, yaxis={"title": "Hypervolume"}, ) data = go.Scatter( x=info.trial_numbers, y=info.values, mode="lines+markers", ) return go.Figure(data=data, layout=layout) def _get_hypervolume_history_info( study: Study, reference_point: np.ndarray, ) -> _HypervolumeHistoryInfo: completed_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)) if len(completed_trials) == 0: _logger.warning("Your study does not have any completed trials.") # Our hypervolume computation module assumes that all objectives are minimized. # Here we transform the objective values and the reference point. signs = np.asarray([1 if d == StudyDirection.MINIMIZE else -1 for d in study.directions]) minimization_reference_point = signs * reference_point # Only feasible trials are considered in hypervolume computation. trial_numbers = [] values = [] best_trials: list[FrozenTrial] = [] hypervolume = 0.0 for trial in completed_trials: trial_numbers.append(trial.number) has_constraints = _CONSTRAINTS_KEY in trial.system_attrs if has_constraints: constraints_values = trial.system_attrs[_CONSTRAINTS_KEY] if any(map(lambda x: x > 0.0, constraints_values)): # The trial is infeasible. values.append(hypervolume) continue if any(map(lambda t: _dominates(t, trial, study.directions), best_trials)): # The trial is not on the Pareto front. values.append(hypervolume) continue best_trials = list( filter(lambda t: not _dominates(trial, t, study.directions), best_trials) ) + [trial] solution_set = np.asarray( list( filter( lambda v: (v <= minimization_reference_point).all(), [signs * trial.values for trial in best_trials], ) ) ) if solution_set.size > 0: hypervolume = WFG().compute(solution_set, minimization_reference_point) values.append(hypervolume) if len(best_trials) == 0: _logger.warning("Your study does not have any feasible trials.") return _HypervolumeHistoryInfo(trial_numbers, values)