def test_get_class_counts_binary(test_output_dirs: OutputFolderForTests) -> None:
"""
Test the get_class_counts method for binary scalar datasets.
"""
dataset_folder = Path(test_output_dirs.make_sub_dir("dataset"))
dataset_contents = """subject,channel,path,label,numerical1,numerical2,CAT1
S1,week0,scan1.npy,,1,10,A
S1,week1,scan2.npy,True,2,20,A
S2,week0,scan3.npy,,3,30,A
S2,week1,scan4.npy,False,4,40,A
S3,week0,scan1.npy,,5,50,A
S3,week1,scan3.npy,True,6,60,A
"""
config = ScalarModelBase(
local_dataset=dataset_folder,
label_channels=["week1"],
label_value_column="label",
non_image_feature_channels=["week0", "week1"],
numerical_columns=["numerical1", "numerical2"],
should_validate=False
)
config.set_output_to(test_output_dirs.root_dir)
train_dataset = ScalarDataset(config, pd.read_csv(StringIO(dataset_contents), dtype=str))
class_counts = train_dataset.get_class_counts()
assert class_counts == {0: 2}
def test_get_class_counts_multilabel(test_output_dirs: OutputFolderForTests) -> None:
"""
Test the get_class_counts method for multilabel scalar datasets.
"""
dataset_folder = Path(test_output_dirs.make_sub_dir("dataset"))
dataset_contents = """subject,channel,path,label,CAT1
S1,week0,scan1.npy,,A
S1,week1,scan2.npy,0|1|2,A
S2,week0,scan3.npy,,A
S2,week1,scan4.npy,1|2,A
S3,week0,scan1.npy,,A
S3,week1,scan3.npy,1,A
"""
config = ScalarModelBase(
local_dataset=dataset_folder,
class_names=["class0", "class1", "class2", "class3"],
label_channels=["week1"],
label_value_column="label",
non_image_feature_channels=["week0", "week1"],
should_validate=False
)
config.set_output_to(test_output_dirs.root_dir)
train_dataset = ScalarDataset(config, pd.read_csv(StringIO(dataset_contents), dtype=str))
class_counts = train_dataset.get_class_counts()
assert class_counts == {0: 1, 1: 3, 2: 2, 3: 0}
def test_get_class_weights_dataset(test_output_dirs: OutputFolderForTests) -> None:
"""
Test training and testing of sequence models that predicts at multiple time points,
when it is started via run_ml.
"""
dataset_folder = Path(test_output_dirs.make_sub_dir("dataset"))
dataset_contents = """subject,channel,path,label,numerical1,numerical2,CAT1
S1,week0,scan1.npy,,1,10,A
S1,week1,scan2.npy,True,2,20,A
S2,week0,scan3.npy,,3,30,A
S2,week1,scan4.npy,False,4,40,A
S3,week0,scan1.npy,,5,50,A
S3,week1,scan3.npy,True,6,60,A
"""
config = ScalarModelBase(
local_dataset=dataset_folder,
label_channels=["week1"],
label_value_column="label",
non_image_feature_channels=["week0", "week1"],
numerical_columns=["numerical1", "numerical2"],
should_validate=False
)
config.set_output_to(test_output_dirs.root_dir)
train_dataset = ScalarDataset(config, pd.read_csv(StringIO(dataset_contents), dtype=str))
class_counts = train_dataset.get_class_counts()
assert class_counts == {0.0: 1, 1.0: 2}