Source code for optuna.visualization._edf

import itertools
from typing import List
from typing import Sequence
from typing import Union

import numpy as np

from optuna.logging import get_logger
from 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__)

[docs]def plot_edf(study: Union[Study, Sequence[Study]]) -> "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 <>`_. .. 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. .. testcode:: 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() # 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) optuna.visualization.plot_edf([study0, study1, study2]) .. raw:: html <iframe src="../../_static/plot_edf.html" width="100%" height="500px" frameborder="0"> </iframe> Args: study: A target :class:`` object. You can pass multiple studies if you want to compare those EDFs. Returns: A :class:`plotly.graph_objs.Figure` object. """ _imports.check() if isinstance(study, Study): studies = [study] else: studies = list(study) return _get_edf_plot(studies)
def _get_edf_plot(studies: List[Study]) -> "go.Figure": layout = go.Layout( title="Empirical Distribution Function Plot", xaxis={"title": "Objective Value"}, yaxis={"title": "Cumulative Probability"}, ) if len(studies) == 0: _logger.warning("There are no studies.") return go.Figure(data=[], layout=layout) all_trials = list( itertools.chain.from_iterable( ( trial for trial in study.get_trials(deepcopy=False) if trial.state == TrialState.COMPLETE ) for study in studies ) ) if len(all_trials) == 0: _logger.warning("There are no complete trials.") return go.Figure(data=[], layout=layout) min_x_value = min(trial.value for trial in all_trials) max_x_value = max(trial.value for trial in all_trials) x_values = np.linspace(min_x_value, max_x_value, 100) traces = [] for study in studies: values = np.asarray( [ trial.value for trial in study.get_trials(deepcopy=False) if trial.state == TrialState.COMPLETE ] ) y_values = np.sum(values[:, np.newaxis] <= x_values, axis=0) / values.size traces.append(go.Scatter(x=x_values, y=y_values, name=study.study_name, mode="lines")) figure = go.Figure(data=traces, layout=layout) figure.update_yaxes(range=[0, 1]) return figure