def test_plot_intermediate_values() -> None:
# Test with no trials.
study = prepare_study_with_trials(no_trials=True)
figure = plot_intermediate_values(study)
assert len(figure.get_lines()) == 0
plt.savefig(BytesIO())
def objective(trial: Trial, report_intermediate_values: bool) -> float:
if report_intermediate_values:
trial.report(1.0, step=0)
trial.report(2.0, step=1)
return 0.0
# Test with a trial with intermediate values.
study = create_study()
study.optimize(lambda t: objective(t, True), n_trials=1)
figure = plot_intermediate_values(study)
assert len(figure.get_lines()) == 1
assert list(figure.get_lines()[0].get_xdata()) == [0, 1]
assert list(figure.get_lines()[0].get_ydata()) == [1.0, 2.0]
plt.savefig(BytesIO())
# Test a study with one trial with intermediate values and
# one trial without intermediate values.
# Expect the trial with no intermediate values to be ignored.
study.optimize(lambda t: objective(t, False), n_trials=1)
assert len(study.trials) == 2
figure = plot_intermediate_values(study)
assert len(figure.get_lines()) == 1
assert list(figure.get_lines()[0].get_xdata()) == [0, 1]
assert list(figure.get_lines()[0].get_ydata()) == [1.0, 2.0]
plt.savefig(BytesIO())
# Test a study of only one trial that has no intermediate values.
study = create_study()
study.optimize(lambda t: objective(t, False), n_trials=1)
figure = plot_intermediate_values(study)
assert len(figure.get_lines()) == 0
plt.savefig(BytesIO())
# Ignore failed trials.
def fail_objective(_: Trial) -> float:
raise ValueError
study = create_study()
study.optimize(fail_objective, n_trials=1, catch=(ValueError, ))
figure = plot_intermediate_values(study)
assert len(figure.get_lines()) == 0
plt.savefig(BytesIO())
def test_plot_intermediate_values() -> None:
# Test with no trials.
study = prepare_study_with_trials(no_trials=True)
figure = plot_intermediate_values(study)
assert not figure.has_data()
def objective(trial: Trial, report_intermediate_values: bool) -> float:
if report_intermediate_values:
trial.report(1.0, step=0)
trial.report(2.0, step=1)
return 0.0
# Test with a trial with intermediate values.
# TODO(ytknzw): Add more specific assertion with the test case.
study = create_study()
study.optimize(lambda t: objective(t, True), n_trials=1)
figure = plot_intermediate_values(study)
assert figure.has_data()
# Test a study with one trial with intermediate values and
# one trial without intermediate values.
# Expect the trial with no intermediate values to be ignored.
# TODO(ytknzw): Add more specific assertion with the test case.
study.optimize(lambda t: objective(t, False), n_trials=1)
assert len(study.trials) == 2
figure = plot_intermediate_values(study)
assert figure.has_data()
# Test a study of only one trial that has no intermediate values.
study = create_study()
study.optimize(lambda t: objective(t, False), n_trials=1)
figure = plot_intermediate_values(study)
assert not figure.has_data()
# Ignore failed trials.
def fail_objective(_: Trial) -> float:
raise ValueError
study = create_study()
study.optimize(fail_objective, n_trials=1, catch=(ValueError,))
figure = plot_intermediate_values(study)
assert not figure.has_data()
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))