def test_get_intermediate_plot():
# type: () -> None
# Test with no trials.
study = _prepare_study_with_trials(no_trials=True)
figure = _get_intermediate_plot(study)
assert not figure.data
def objective(trial, report_intermediate_values):
# type: (Trial, 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 = _get_intermediate_plot(study)
assert len(figure.data) == 1
assert figure.data[0].x == (0, 1)
assert figure.data[0].y == (1.0, 2.0)
# 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 = _get_intermediate_plot(study)
assert len(figure.data) == 1
assert figure.data[0].x == (0, 1)
assert figure.data[0].y == (1.0, 2.0)
# 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 = _get_intermediate_plot(study)
assert not figure.data
# Ignore failed trials.
def fail_objective(_):
# type: (Trial) -> float
raise ValueError
study = create_study()
study.optimize(fail_objective, n_trials=1, catch=(ValueError, ))
figure = _get_intermediate_plot(study)
assert not figure.data
def test_get_intermediate_plot():
# type: () -> None
# Test with no trial
study = create_study()
figure = _get_intermediate_plot(study)
assert len(figure.data) == 0
def objective(trial, report_intermediate_values):
# type: (Trial, 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 = _get_intermediate_plot(study)
assert len(figure.data) == 1
assert tuple(figure.data[0].x) == (0, 1)
assert tuple(figure.data[0].y) == (1.0, 2.0)
# Test with trials, one of which contains no intermediate value.
study = create_study()
study.optimize(lambda t: objective(t, False), n_trials=1)
figure = _get_intermediate_plot(study)
assert len(figure.data) == 1
assert len(figure.data[0].x) == 0
assert len(figure.data[0].y) == 0
# Ignore failed trials.
def fail_objective(_):
# type: (Trial) -> float
raise ValueError
study = create_study()
study.optimize(fail_objective, n_trials=1)
figure = _get_intermediate_plot(study)
assert len(figure.data) == 0