def test_get_contour_info_nonfinite_removed(value: float) -> None:
study = prepare_study_with_trials(value_for_first_trial=value)
info = _get_contour_info(study, params=["param_b", "param_d"])
assert info == _ContourInfo(
sorted_params=["param_b", "param_d"],
sub_plot_infos=[[
_SubContourInfo(
xaxis=_AxisInfo(
name="param_b",
range=(-0.05, 1.05),
is_log=False,
is_cat=False,
indices=[-0.05, 0.0, 1.0, 1.05],
values=[0.0, 1.0],
),
yaxis=_AxisInfo(
name="param_d",
range=(1.9, 4.1),
is_log=False,
is_cat=False,
indices=[1.9, 2.0, 4.0, 4.1],
values=[4.0, 2.0],
),
z_values={
(1, 2): 2.0,
(2, 1): 1.0
},
)
]],
reverse_scale=True,
target_name="Objective Value",
)
def test_get_contour_info_mixture_category_types() -> None:
study = _create_study_mixture_category_types()
info = _get_contour_info(study)
assert info == _ContourInfo(
sorted_params=["param_a", "param_b"],
sub_plot_infos=[[
_SubContourInfo(
xaxis=_AxisInfo(
name="param_a",
range=(-0.05, 1.05),
is_log=False,
is_cat=True,
indices=["100", "None"],
values=["None", "100"],
),
yaxis=_AxisInfo(
name="param_b",
range=(100.95, 102.05),
is_log=False,
is_cat=False,
indices=[100.95, 101, 102, 102.05],
values=[101.0, 102.0],
),
z_values={
(0, 2): 0.5,
(1, 1): 0.0
},
)
]],
reverse_scale=True,
target_name="Objective Value",
)
def test_get_contour_info_2_params() -> None:
params = ["param_a", "param_b"]
study = prepare_study_with_trials()
info = _get_contour_info(study, params=params)
assert info == _ContourInfo(
sorted_params=params,
sub_plot_infos=[[
_SubContourInfo(
xaxis=_AxisInfo(
name="param_a",
range=(0.925, 2.575),
is_log=False,
is_cat=False,
indices=[0.925, 1.0, 2.5, 2.575],
values=[1.0, None, 2.5],
),
yaxis=_AxisInfo(
name="param_b",
range=(-0.1, 2.1),
is_log=False,
is_cat=False,
indices=[-0.1, 0.0, 1.0, 2.0, 2.1],
values=[2.0, 0.0, 1.0],
),
z_values={
(1, 3): 0.0,
(2, 2): 1.0
},
)
]],
reverse_scale=True,
target_name="Objective Value",
)
def test_generate_contour_plot_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_contour_info(study, params=params)
assert info == _ContourInfo(
sorted_params=sorted(params),
sub_plot_infos=[[
_SubContourInfo(
xaxis=_AxisInfo(
name=sorted(params)[0],
range=(1.0, 1.0),
is_log=False,
is_cat=False,
indices=[1.0],
values=[1.0, None],
),
yaxis=_AxisInfo(
name=sorted(params)[1],
range=(-0.1, 2.1),
is_log=False,
is_cat=False,
indices=[-0.1, 0.0, 2.0, 2.1],
values=[2.0, 0.0],
),
z_values={},
)
]],
reverse_scale=True,
target_name="Objective Value",
)
def test_get_contour_info_log_scale_and_str_category_2_params() -> None:
# If the search space has two parameters, plot_contour generates a single plot.
study = _create_study_with_log_scale_and_str_category_2d()
info = _get_contour_info(study)
assert info == _ContourInfo(
sorted_params=["param_a", "param_b"],
sub_plot_infos=[[
_SubContourInfo(
xaxis=_AxisInfo(
name="param_a",
range=(math.pow(10, -6.05), math.pow(10, -4.95)),
is_log=True,
is_cat=False,
indices=[
math.pow(10, -6.05), 1e-6, 1e-5,
math.pow(10, -4.95)
],
values=[1e-6, 1e-5],
),
yaxis=_AxisInfo(
name="param_b",
range=(-0.05, 1.05),
is_log=False,
is_cat=True,
indices=["100", "101"],
values=["101", "100"],
),
z_values={
(1, 1): 0.0,
(2, 0): 1.0
},
)
]],
reverse_scale=True,
target_name="Objective Value",
)