Source code for optuna.visualization._edf

from __future__ import annotations

from collections.abc import Callable
from collections.abc import Sequence
from typing import cast
from typing import NamedTuple

import numpy as np

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
from optuna.visualization._utils import _filter_nonfinite


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

_logger = get_logger(__name__)


NUM_SAMPLES_X_AXIS = 100


class _EDFLineInfo(NamedTuple):
    study_name: str
    y_values: np.ndarray


class _EDFInfo(NamedTuple):
    lines: list[_EDFLineInfo]
    x_values: np.ndarray


[docs] def plot_edf( study: Study | Sequence[Study], *, target: Callable[[FrozenTrial], float] | None = None, target_name: str = "Objective Value", ) -> "go.Figure": """Plot the objective value EDF (empirical distribution function) of a study. Note that only the complete trials are considered when plotting the EDF. .. note:: EDF is useful to analyze and improve search spaces. For instance, you can see a practical use case of EDF in the paper `Designing Network Design Spaces <https://arxiv.org/abs/2003.13678>`_. .. note:: The plotted EDF assumes that the value of the objective function is in accordance with the uniform distribution over the objective space. Example: The following code snippet shows how to plot EDF. .. plotly:: import math import optuna def ackley(x, y): a = 20 * math.exp(-0.2 * math.sqrt(0.5 * (x ** 2 + y ** 2))) b = math.exp(0.5 * (math.cos(2 * math.pi * x) + math.cos(2 * math.pi * y))) return -a - b + math.e + 20 def objective(trial, low, high): x = trial.suggest_float("x", low, high) y = trial.suggest_float("y", low, high) return ackley(x, y) sampler = optuna.samplers.RandomSampler(seed=10) # Widest search space. study0 = optuna.create_study(study_name="x=[0,5), y=[0,5)", sampler=sampler) study0.optimize(lambda t: objective(t, 0, 5), n_trials=500) # Narrower search space. study1 = optuna.create_study(study_name="x=[0,4), y=[0,4)", sampler=sampler) study1.optimize(lambda t: objective(t, 0, 4), n_trials=500) # Narrowest search space but it doesn't include the global optimum point. study2 = optuna.create_study(study_name="x=[1,3), y=[1,3)", sampler=sampler) study2.optimize(lambda t: objective(t, 1, 3), n_trials=500) fig = optuna.visualization.plot_edf([study0, study1, study2]) fig.show() Args: study: A target :class:`~optuna.study.Study` object. You can pass multiple studies if you want to compare those EDFs. 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. Returns: A :class:`plotly.graph_objs.Figure` object. """ _imports.check() layout = go.Layout( title="Empirical Distribution Function Plot", xaxis={"title": target_name}, yaxis={"title": "Cumulative Probability"}, ) info = _get_edf_info(study, target, target_name) edf_lines = info.lines if len(edf_lines) == 0: return go.Figure(data=[], layout=layout) traces = [] for study_name, y_values in edf_lines: traces.append(go.Scatter(x=info.x_values, y=y_values, name=study_name, mode="lines")) figure = go.Figure(data=traces, layout=layout) figure.update_yaxes(range=[0, 1]) return figure
def _get_edf_info( study: Study | Sequence[Study], target: Callable[[FrozenTrial], float] | None = None, target_name: str = "Objective Value", ) -> _EDFInfo: if isinstance(study, Study): studies = [study] else: studies = list(study) _check_plot_args(studies, target, target_name) if len(studies) == 0: _logger.warning("There are no studies.") return _EDFInfo(lines=[], x_values=np.array([])) if target is None: def _target(t: FrozenTrial) -> float: return cast(float, t.value) target = _target study_names = [] all_values: list[np.ndarray] = [] for study in studies: trials = _filter_nonfinite( study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target ) values = np.array([target(trial) for trial in trials]) all_values.append(values) study_names.append(study.study_name) if all(len(values) == 0 for values in all_values): _logger.warning("There are no complete trials.") return _EDFInfo(lines=[], x_values=np.array([])) min_x_value = np.min(np.concatenate(all_values)) max_x_value = np.max(np.concatenate(all_values)) x_values = np.linspace(min_x_value, max_x_value, NUM_SAMPLES_X_AXIS) edf_line_info_list = [] for study_name, values in zip(study_names, all_values): y_values = np.sum(values[:, np.newaxis] <= x_values, axis=0) / values.size edf_line_info_list.append(_EDFLineInfo(study_name=study_name, y_values=y_values)) return _EDFInfo(lines=edf_line_info_list, x_values=x_values)