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from __future__ import annotations
from typing import Callable
from matplotlib.axes._axes import Axes
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import pytest
import optuna
from optuna.study.study import ObjectiveFuncType
from optuna.visualization import plot_contour
from optuna.visualization import plot_edf
from optuna.visualization import plot_optimization_history
from optuna.visualization import plot_parallel_coordinate
from optuna.visualization import plot_param_importances
from optuna.visualization import plot_rank
from optuna.visualization import plot_slice
from optuna.visualization import plot_timeline
from optuna.visualization.matplotlib import (
plot_optimization_history as matplotlib_plot_optimization_history,
)
from optuna.visualization.matplotlib import (
plot_parallel_coordinate as matplotlib_plot_parallel_coordinate,
)
from optuna.visualization.matplotlib import (
plot_param_importances as matplotlib_plot_param_importances,
)
from optuna.visualization.matplotlib import plot_contour as matplotlib_plot_contour
from optuna.visualization.matplotlib import plot_edf as matplotlib_plot_edf
from optuna.visualization.matplotlib import plot_rank as matplotlib_plot_rank
from optuna.visualization.matplotlib import plot_slice as matplotlib_plot_slice
from optuna.visualization.matplotlib import plot_timeline as matplotlib_plot_timeline
parametrize_visualization_functions_for_single_objective = pytest.mark.parametrize(
"plot_func",
[
plot_optimization_history,
plot_edf,
plot_contour,
plot_parallel_coordinate,
plot_rank,
plot_slice,
plot_timeline,
plot_param_importances,
matplotlib_plot_optimization_history,
matplotlib_plot_edf,
matplotlib_plot_contour,
matplotlib_plot_parallel_coordinate,
matplotlib_plot_rank,
matplotlib_plot_slice,
matplotlib_plot_timeline,
matplotlib_plot_param_importances,
],
)
def objective_single_dynamic_with_categorical(trial: optuna.Trial) -> float:
category = trial.suggest_categorical("category", ["foo", "bar"])
if category == "foo":
return (trial.suggest_float("x1", 0, 10) - 2) ** 2
else:
return -((trial.suggest_float("x2", -10, 0) + 5) ** 2)
def objective_single_none_categorical(trial: optuna.Trial) -> float:
x = trial.suggest_float("x", -100, 100)
trial.suggest_categorical("y", ["foo", None])
return x**2
parametrize_single_objective_functions = pytest.mark.parametrize(
"objective_func",
[
objective_single_dynamic_with_categorical,
objective_single_none_categorical,
],
)
@parametrize_visualization_functions_for_single_objective
@parametrize_single_objective_functions
def test_visualizations_with_single_objectives(
plot_func: Callable[[optuna.study.Study], go.Figure | Axes], objective_func: ObjectiveFuncType
) -> None:
study = optuna.create_study(sampler=optuna.samplers.RandomSampler())
study.optimize(objective_func, n_trials=20)
fig = plot_func(study) # Must not raise an exception here.
if isinstance(fig, Axes):
plt.close()
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