Source code for optuna.visualization.matplotlib._edf

from __future__ import annotations

from typing import Callable
from typing import Sequence

from optuna._experimental import experimental_func
from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.visualization._edf import _get_edf_info
from optuna.visualization.matplotlib._matplotlib_imports import _imports


if _imports.is_successful():
    from optuna.visualization.matplotlib._matplotlib_imports import Axes
    from optuna.visualization.matplotlib._matplotlib_imports import plt

_logger = get_logger(__name__)


[docs]@experimental_func("2.2.0") def plot_edf( study: Study | Sequence[Study], *, target: Callable[[FrozenTrial], float] | None = None, target_name: str = "Objective Value", ) -> "Axes": """Plot the objective value EDF (empirical distribution function) of a study with Matplotlib. Note that only the complete trials are considered when plotting the EDF. .. seealso:: Please refer to :func:`optuna.visualization.plot_edf` for an example, where this function can be replaced with it. .. note:: Please refer to `matplotlib.pyplot.legend <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html>`_ to adjust the style of the generated legend. Example: The following code snippet shows how to plot EDF. .. plot:: 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) optuna.visualization.matplotlib.plot_edf([study0, study1, study2]) 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:`matplotlib.axes.Axes` object. """ _imports.check() # Set up the graph style. plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. _, ax = plt.subplots() ax.set_title("Empirical Distribution Function Plot") ax.set_xlabel(target_name) ax.set_ylabel("Cumulative Probability") ax.set_ylim(0, 1) cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots. info = _get_edf_info(study, target, target_name) edf_lines = info.lines if len(edf_lines) == 0: return ax for i, (study_name, y_values) in enumerate(edf_lines): ax.plot(info.x_values, y_values, color=cmap(i), alpha=0.7, label=study_name) if len(edf_lines) >= 2: ax.legend() return ax