def test_plot_optimization_history(direction: str) -> None:
# Test with no studies.
figure = plot_edf([])
assert len(figure.get_lines()) == 0
# Test with no trials.
figure = plot_edf(create_study(direction=direction))
assert len(figure.get_lines()) == 0
figure = plot_edf(
[create_study(direction=direction),
create_study(direction=direction)])
assert len(figure.get_lines()) == 0
# Test with a study.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0)
lines = figure.get_lines()
_validate_edf_values(lines[0].get_ydata())
assert len(lines) == 1
assert figure.xaxis.label.get_text() == "Objective Value"
# Test with two studies.
study1 = create_study(direction=direction)
study1.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf([study0, study1])
lines = figure.get_lines()
for line in lines:
_validate_edf_values(line.get_ydata())
assert len(lines) == 2
figure = plot_edf((study0, study1))
lines = figure.get_lines()
for line in lines:
_validate_edf_values(line.get_ydata())
assert len(lines) == 2
# Test with a customized target value.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
with pytest.warns(UserWarning):
figure = plot_edf(study0, target=lambda t: t.params["x"])
lines = figure.get_lines()
_validate_edf_values(lines[0].get_ydata())
assert len(lines) == 1
# Test with a customized target name.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0, target_name="Target Name")
lines = figure.get_lines()
_validate_edf_values(lines[0].get_ydata())
assert len(figure.get_lines()) == 1
assert figure.xaxis.label.get_text() == "Target Name"
def _log_plots(run,
study: optuna.Study,
visualization_backend='plotly',
log_plot_contour=True,
log_plot_edf=True,
log_plot_parallel_coordinate=True,
log_plot_param_importances=True,
log_plot_pareto_front=True,
log_plot_slice=True,
log_plot_intermediate_values=True,
log_plot_optimization_history=True,
):
if visualization_backend == 'matplotlib':
import optuna.visualization.matplotlib as vis
elif visualization_backend == 'plotly':
import optuna.visualization as vis
else:
raise NotImplementedError(f'{visualization_backend} visualisation backend is not implemented')
if vis.is_available:
params = list(p_name for t in study.trials for p_name in t.params.keys())
if log_plot_contour and any(params):
run['visualizations/plot_contour'] = neptune.types.File.as_html(vis.plot_contour(study))
if log_plot_edf:
run['visualizations/plot_edf'] = neptune.types.File.as_html(vis.plot_edf(study))
if log_plot_parallel_coordinate:
run['visualizations/plot_parallel_coordinate'] = \
neptune.types.File.as_html(vis.plot_parallel_coordinate(study))
if log_plot_param_importances and len(study.get_trials(states=(optuna.trial.TrialState.COMPLETE, optuna.trial.TrialState.PRUNED,))) > 1:
try:
run['visualizations/plot_param_importances'] = neptune.types.File.as_html(vis.plot_param_importances(study))
except (RuntimeError, ValueError, ZeroDivisionError):
# Unable to compute importances
pass
if log_plot_pareto_front and study._is_multi_objective() and visualization_backend == 'plotly':
run['visualizations/plot_pareto_front'] = neptune.types.File.as_html(vis.plot_pareto_front(study))
if log_plot_slice and any(params):
run['visualizations/plot_slice'] = neptune.types.File.as_html(vis.plot_slice(study))
if log_plot_intermediate_values and any(trial.intermediate_values for trial in study.trials):
# Intermediate values plot if available only if the above condition is met
run['visualizations/plot_intermediate_values'] = \
neptune.types.File.as_html(vis.plot_intermediate_values(study))
if log_plot_optimization_history:
run['visualizations/plot_optimization_history'] = \
neptune.types.File.as_html(vis.plot_optimization_history(study))
def test_plot_optimization_history(direction: str) -> None:
# Test with no studies.
figure = plot_edf([])
assert not figure.has_data()
# Test with no trials.
figure = plot_edf(create_study(direction=direction))
assert not figure.has_data()
figure = plot_edf(
[create_study(direction=direction),
create_study(direction=direction)])
assert not figure.has_data()
# Test with a study.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0)
assert figure.has_data()
# Test with two studies.
# TODO(ytknzw): Add more specific assertion with the numbers of the studies.
study1 = create_study(direction=direction)
study1.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf([study0, study1])
assert figure.has_data()
figure = plot_edf((study0, study1))
assert figure.has_data()
def test_inconsistent_number_of_trial_values() -> None:
studies: List[Study] = []
n_studies = 5
for i in range(n_studies):
study = prepare_study_with_trials()
if i % 2 == 0:
study.add_trial(create_trial(value=1.0))
studies.append(study)
figure = plot_edf(studies)
assert len(figure.get_lines()) == n_studies
plt.savefig(BytesIO())
def test_plot_optimization_history(direction: str) -> None:
# Test with no studies.
figure = plot_edf([])
assert not figure.has_data()
# Test with no trials.
figure = plot_edf(create_study(direction=direction))
assert not figure.has_data()
figure = plot_edf(
[create_study(direction=direction),
create_study(direction=direction)])
assert not figure.has_data()
# Test with a study.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0)
assert figure.has_data()
# Test with two studies.
# TODO(ytknzw): Add more specific assertion with the numbers of the studies.
study1 = create_study(direction=direction)
study1.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf([study0, study1])
assert figure.has_data()
figure = plot_edf((study0, study1))
assert figure.has_data()
# Test with a customized target value.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
with pytest.warns(UserWarning):
figure = plot_edf(study0, target=lambda t: t.params["x"])
assert figure.has_data()
# Test with a customized target name.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0, target_name="Target Name")
assert figure.has_data()
def test_target_is_none_and_study_is_multi_obj() -> None:
study = create_study(directions=["minimize", "minimize"])
with pytest.raises(ValueError):
plot_edf(study)
def test_nonfinite_removed(value: int) -> None:
study = prepare_study_with_trials(value_for_first_trial=value)
figure = plot_edf(study)
assert all(np.isfinite(figure.get_lines()[0].get_xdata()))
plt.savefig(BytesIO())
def test_nonfinite_multiobjective(objective: int, value: int) -> None:
study = prepare_study_with_trials(n_objectives=2, value_for_first_trial=value)
figure = plot_edf(study, target=lambda t: t.values[objective])
assert all(np.isfinite(figure.get_lines()[0].get_xdata()))
plt.savefig(BytesIO())