def test_get_slice_plot_info_nonfinite_multiobjective(objective: int,
value: float) -> None:
study = prepare_study_with_trials(n_objectives=2,
value_for_first_trial=value)
info = _get_slice_plot_info(
study,
params=["param_b", "param_d"],
target=lambda t: t.values[objective],
target_name="Objective Value",
)
assert info == _SlicePlotInfo(
target_name="Objective Value",
subplots=[
_SliceSubplotInfo(
param_name="param_b",
x=[0.0, 1.0],
y=[2.0, 1.0],
trial_numbers=[1, 2],
is_log=False,
is_numerical=True,
),
_SliceSubplotInfo(
param_name="param_d",
x=[4.0, 2.0],
y=[2.0, 1.0],
trial_numbers=[1, 2],
is_log=False,
is_numerical=True,
),
],
)
def test_get_slice_plot_info_mixture_category_types() -> None:
study = _create_study_mixture_category_types()
info = _get_slice_plot_info(study, None, None, "Objective Value")
assert info == _SlicePlotInfo(
target_name="Objective Value",
subplots=[
_SliceSubplotInfo(
param_name="param_a",
x=[None, "100"],
y=[0.0, 0.5],
trial_numbers=[0, 1],
is_log=False,
is_numerical=False,
),
_SliceSubplotInfo(
param_name="param_b",
x=[101, 102.0],
y=[0.0, 0.5],
trial_numbers=[0, 1],
is_log=False,
is_numerical=True,
),
],
)
def test_get_slice_plot_info_log_scale_and_str_category_2_params() -> None:
study = _create_study_with_log_scale_and_str_category_2d()
info = _get_slice_plot_info(study, None, None, "Objective Value")
assert info == _SlicePlotInfo(
target_name="Objective Value",
subplots=[
_SliceSubplotInfo(
param_name="param_a",
x=[1e-6, 1e-5],
y=[0.0, 1.0],
trial_numbers=[0, 1],
is_log=True,
is_numerical=True,
),
_SliceSubplotInfo(
param_name="param_b",
x=["101", "100"],
y=[0.0, 1.0],
trial_numbers=[0, 1],
is_log=False,
is_numerical=False,
),
],
)
def test_get_slice_plot_info_params(params: Optional[List[str]]) -> None:
study = prepare_study_with_trials()
params = ["param_a", "param_b", "param_c", "param_d"
] if params is None else params
expected_subplot_infos = {
"param_a":
_SliceSubplotInfo(
param_name="param_a",
x=[1.0, 2.5],
y=[0.0, 1.0],
trial_numbers=[0, 2],
is_log=False,
is_numerical=True,
),
"param_b":
_SliceSubplotInfo(
param_name="param_b",
x=[2.0, 0.0, 1.0],
y=[0.0, 2.0, 1.0],
trial_numbers=[0, 1, 2],
is_log=False,
is_numerical=True,
),
"param_c":
_SliceSubplotInfo(
param_name="param_c",
x=[3.0, 4.5],
y=[0.0, 1.0],
trial_numbers=[0, 2],
is_log=False,
is_numerical=True,
),
"param_d":
_SliceSubplotInfo(
param_name="param_d",
x=[4.0, 4.0, 2.0],
y=[0.0, 2.0, 1.0],
trial_numbers=[0, 1, 2],
is_log=False,
is_numerical=True,
),
}
info = _get_slice_plot_info(study,
params=params,
target=None,
target_name="Objective Value")
assert info == _SlicePlotInfo(
target_name="Objective Value",
subplots=[expected_subplot_infos[p] for p in params],
)
def test_get_slice_plot_info_for_few_observations(params: List[str]) -> None:
study = create_study(direction="minimize")
study.add_trial(
create_trial(
values=[0.0],
params={
"param_a": 1.0,
"param_b": 2.0
},
distributions={
"param_a": FloatDistribution(0.0, 3.0),
"param_b": FloatDistribution(0.0, 3.0),
},
))
study.add_trial(
create_trial(
values=[2.0],
params={"param_b": 0.0},
distributions={"param_b": FloatDistribution(0.0, 3.0)},
))
info = _get_slice_plot_info(study, params, None, "Objective Value")
assert info == _SlicePlotInfo(
target_name="Objective Value",
subplots=[
_SliceSubplotInfo(
param_name="param_a",
x=[1.0],
y=[0.0],
trial_numbers=[0],
is_log=False,
is_numerical=True,
),
_SliceSubplotInfo(
param_name="param_b",
x=[2.0, 0.0],
y=[0.0, 2.0],
trial_numbers=[0, 1],
is_log=False,
is_numerical=True,
),
],
)
def test_get_slice_plot_info_customized_target() -> None:
params = ["param_a"]
study = prepare_study_with_trials()
info = _get_slice_plot_info(study,
params=params,
target=lambda t: t.params["param_d"],
target_name="Objective Value")
assert info == _SlicePlotInfo(
target_name="Objective Value",
subplots=[
_SliceSubplotInfo(
param_name="param_a",
x=[1.0, 2.5],
y=[4.0, 2.0],
trial_numbers=[0, 2],
is_log=False,
is_numerical=True,
),
],
)