def run_interaction(self, train, compose):
data = [{
"image": np.ones((1, 2, 2, 2)).astype(np.float32),
"label": np.ones((1, 2, 2, 2))
} for _ in range(5)]
network = torch.nn.Linear(2, 2)
lr = 1e-3
opt = torch.optim.SGD(network.parameters(), lr)
loss = torch.nn.L1Loss()
train_transforms = Compose([
FindAllValidSlicesd(label="label", sids="sids"),
AddInitialSeedPointd(label="label",
guidance="guidance",
sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
])
dataset = Dataset(data, transform=train_transforms)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
iteration_transforms = [
Activationsd(keys="pred", sigmoid=True),
ToNumpyd(keys=["image", "label", "pred"]),
FindDiscrepancyRegionsd(label="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanced(guidance="guidance",
discrepancy="discrepancy",
probability="probability"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
]
iteration_transforms = Compose(
iteration_transforms) if compose else iteration_transforms
i = Interaction(transforms=iteration_transforms,
train=train,
max_interactions=5)
self.assertEqual(len(i.transforms.transforms), 6,
"Mismatch in expected transforms")
# set up engine
engine = SupervisedTrainer(
device=torch.device("cpu"),
max_epochs=1,
train_data_loader=data_loader,
network=network,
optimizer=opt,
loss_function=loss,
iteration_update=i,
)
engine.add_event_handler(IterationEvents.INNER_ITERATION_STARTED,
add_one)
engine.add_event_handler(IterationEvents.INNER_ITERATION_COMPLETED,
add_one)
engine.run()
self.assertIsNotNone(engine.state.batch[0].get("guidance"),
"guidance is missing")
self.assertEqual(engine.state.best_metric, 9)
def train_pre_transforms(self, context: Context):
return [
LoadImaged(keys=("image", "label"), dtype=np.uint8),
FilterImaged(keys="image", min_size=5),
AsChannelFirstd(keys="image"),
AddChanneld(keys="label"),
ToTensord(keys="image"),
TorchVisiond(keys="image",
name="ColorJitter",
brightness=64.0 / 255.0,
contrast=0.75,
saturation=0.25,
hue=0.04),
ToNumpyd(keys="image"),
RandRotate90d(keys=("image", "label"),
prob=0.5,
spatial_axes=(0, 1)),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
AddInitialSeedPointExd(label="label", guidance="guidance"),
AddGuidanceSignald(image="image",
guidance="guidance",
number_intensity_ch=3),
EnsureTyped(keys=("image", "label")),
]
def run_interaction(self, train, compose):
data = []
for i in range(5):
data.append({"image": torch.tensor([float(i)]), "label": torch.tensor([float(i)])})
network = torch.nn.Linear(1, 1)
lr = 1e-3
opt = torch.optim.SGD(network.parameters(), lr)
loss = torch.nn.L1Loss()
dataset = Dataset(data, transform=None)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
iteration_transforms = [Activationsd(keys="pred", sigmoid=True), ToNumpyd(keys="pred")]
iteration_transforms = Compose(iteration_transforms) if compose else iteration_transforms
i = Interaction(transforms=iteration_transforms, train=train, max_interactions=5)
self.assertEqual(len(i.transforms.transforms), 2, "Mismatch in expected transforms")
# set up engine
engine = SupervisedTrainer(
device=torch.device("cpu"),
max_epochs=1,
train_data_loader=data_loader,
network=network,
optimizer=opt,
loss_function=loss,
iteration_update=i,
)
engine.add_event_handler(IterationEvents.INNER_ITERATION_STARTED, add_one)
engine.add_event_handler(IterationEvents.INNER_ITERATION_COMPLETED, add_one)
engine.run()
self.assertIsNotNone(engine.state.batch.get("probability"), "Probability is missing")
self.assertEqual(engine.state.best_metric, 9)
def test_numpy_input(self):
test_data = np.array([[1, 2], [3, 4]])
test_data = np.rot90(test_data)
self.assertFalse(test_data.flags["C_CONTIGUOUS"])
result = ToNumpyd(keys="img")({"img": test_data})["img"]
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.flags["C_CONTIGUOUS"])
np.testing.assert_allclose(result, test_data)
def test_tensor_input(self):
test_data = torch.tensor([[1, 2], [3, 4]])
test_data = test_data.rot90()
self.assertFalse(test_data.is_contiguous())
result = ToNumpyd(keys="img")({"img": test_data})["img"]
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.flags["C_CONTIGUOUS"])
np.testing.assert_allclose(result, test_data.numpy())
def post_transforms(self, data=None):
return [
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True, logit_thresh=0.5),
ToNumpyd(keys="pred"),
RestoreLabeld(keys="pred", ref_image="image", mode="nearest"),
AsChannelLastd(keys="pred"),
]
def post_transforms(self, data=None) -> Sequence[Callable]:
return [
EnsureTyped(keys="pred", device=data.get("device") if data else None),
Activationsd(keys="pred", softmax=True),
AsDiscreted(keys="pred", argmax=True),
SqueezeDimd(keys="pred", dim=0),
ToNumpyd(keys="pred"),
Restored(keys="pred", ref_image="image"),
]
def post_transforms(self, data=None) -> Sequence[Callable]:
return [
EnsureTyped(keys="pred",
device=data.get("device") if data else None),
Activationsd(keys="pred", softmax=True),
AsDiscreted(keys="pred", argmax=True),
ToNumpyd(keys="pred"),
Restored(keys="pred", ref_image="image"),
BoundingBoxd(keys="pred", result="result", bbox="bbox"),
]
def post_transforms(self, data=None) -> Sequence[Callable]:
return [
EnsureTyped(keys="pred",
device=data.get("device") if data else None),
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True, logit_thresh=0.5),
ToNumpyd(keys="pred"),
RestoreLabeld(keys="pred", ref_image="image", mode="nearest"),
AsChannelLastd(keys="pred"),
]
def post_transforms(self, data=None) -> Sequence[Callable]:
return [
EnsureTyped(keys="pred", device=data.get("device") if data else None),
Activationsd(keys="pred", softmax=len(self.labels) > 1, sigmoid=len(self.labels) == 1),
AsDiscreted(keys="pred", argmax=len(self.labels) > 1, threshold=0.5 if len(self.labels) == 1 else None),
SqueezeDimd(keys="pred", dim=0),
ToNumpyd(keys=("image", "pred")),
PostFilterLabeld(keys="pred", image="image"),
FindContoursd(keys="pred", labels=self.labels),
]
def _default_transforms(image_key, label_key, pixdim):
keys = [image_key] if label_key is None else [image_key, label_key]
mode = [GridSampleMode.BILINEAR, GridSampleMode.NEAREST
] if len(keys) == 2 else [GridSampleMode.BILINEAR]
return Compose([
LoadImaged(keys=keys),
AsChannelFirstd(keys=keys),
Orientationd(keys=keys, axcodes="RAS"),
Spacingd(keys=keys, pixdim=pixdim, mode=mode),
FromMetaTensord(keys=keys),
ToNumpyd(keys=keys + [PostFix.meta(k) for k in keys]),
])
def get_click_transforms(self, context: Context):
return [
Activationsd(keys="pred", sigmoid=True),
ToNumpyd(keys=("image", "label", "pred")),
FindDiscrepancyRegionsd(label="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanced(guidance="guidance",
discrepancy="discrepancy",
probability="probability"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
]
def get_click_transforms():
return Compose([
Activationsd(keys="pred", sigmoid=True),
ToNumpyd(keys=("image", "label", "pred")),
FindDiscrepancyRegionsd(label="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanced(
guidance="guidance",
discrepancy="discrepancy",
probability="probability",
),
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys=("image", "label")),
])
def get_click_transforms():
return Compose([
Activationsd(keys='pred', sigmoid=True),
ToNumpyd(keys=('image', 'label', 'pred', 'probability', 'guidance')),
FindDiscrepancyRegionsd(label='label',
pred='pred',
discrepancy='discrepancy',
batched=True),
AddRandomGuidanced(guidance='guidance',
discrepancy='discrepancy',
probability='probability',
batched=True),
AddGuidanceSignald(image='image', guidance='guidance', batched=True),
ToTensord(keys=('image', 'label'))
])
def get_click_transforms(self, context: Context):
return [
Activationsd(keys="pred", softmax=True),
AsDiscreted(keys="pred", argmax=True),
ToNumpyd(keys=("image", "label", "pred")),
# Transforms for click simulation
FindDiscrepancyRegionsCustomd(keys="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanceCustomd(
keys="NA",
guidance="guidance",
discrepancy="discrepancy",
probability="probability",
),
AddGuidanceSignalCustomd(
keys="image",
guidance="guidance",
number_intensity_ch=self.number_intensity_ch),
#
ToTensord(keys=("image", "label")),
]
def post_transforms(self, data=None) -> Sequence[Callable]:
largest_cc = False if not data else data.get("largest_cc", False)
applied_labels = list(self.labels.values()) if isinstance(
self.labels, dict) else self.labels
t = [
EnsureTyped(keys="pred",
device=data.get("device") if data else None),
Activationsd(keys="pred",
softmax=len(self.labels) > 1,
sigmoid=len(self.labels) == 1),
AsDiscreted(keys="pred",
argmax=len(self.labels) > 1,
threshold=0.5 if len(self.labels) == 1 else None),
]
if largest_cc:
t.append(
KeepLargestConnectedComponentd(keys="pred",
applied_labels=applied_labels))
t.extend([
ToNumpyd(keys="pred"),
Restored(keys="pred", ref_image="image"),
])
return t
def test_values(self):
with tempfile.TemporaryDirectory() as tempdir:
test_data1 = [
["subject_id", "label", "image", "ehr_0", "ehr_1", "ehr_2"],
[
"s000000", 5, "./imgs/s000000.png", 2.007843256,
2.29019618, 2.054902077
],
[
"s000001", 0, "./imgs/s000001.png", 6.839215755,
6.474509716, 5.862744808
],
[
"s000002", 4, "./imgs/s000002.png", 3.772548914,
4.211764812, 4.635294437
],
[
"s000003", 1, "./imgs/s000003.png", 3.333333254,
3.235294342, 3.400000095
],
[
"s000004", 9, "./imgs/s000004.png", 6.427451134,
6.254901886, 5.976470947
],
]
test_data2 = [
[
"subject_id", "ehr_3", "ehr_4", "ehr_5", "ehr_6", "ehr_7",
"ehr_8"
],
[
"s000000", 3.019608021, 3.807843208, 3.584313869,
3.141176462, 3.1960783, 4.211764812
],
[
"s000001", 5.192157269, 5.274509907, 5.250980377,
4.647058964, 4.886274338, 4.392156601
],
[
"s000002", 5.298039436, 9.545097351, 12.57254887,
6.799999714, 2.1960783, 1.882352948
],
[
"s000003", 3.164705753, 3.086274624, 3.725490093,
3.698039293, 3.698039055, 3.701960802
],
[
"s000004", 6.26274538, 7.717647076, 9.584313393,
6.082352638, 2.662744999, 2.34117651
],
]
test_data3 = [
[
"subject_id", "ehr_9", "ehr_10", "meta_0", "meta_1",
"meta_2"
],
["s000000", 6.301961422, 6.470588684, "TRUE", "TRUE", "TRUE"],
[
"s000001", 5.219608307, 7.827450752, "FALSE", "TRUE",
"FALSE"
],
["s000002", 1.882352948, 2.031372547, "TRUE", "FALSE", "TRUE"],
[
"s000003", 3.309803963, 3.729412079, "FALSE", "FALSE",
"TRUE"
],
["s000004", 2.062745094, 2.34117651, "FALSE", "TRUE", "TRUE"],
]
def prepare_csv_file(data, filepath):
with open(filepath, "a") as f:
for d in data:
f.write((",".join([str(i) for i in d])) + "\n")
filepath1 = os.path.join(tempdir, "test_data1.csv")
filepath2 = os.path.join(tempdir, "test_data2.csv")
filepath3 = os.path.join(tempdir, "test_data3.csv")
prepare_csv_file(test_data1, filepath1)
prepare_csv_file(test_data2, filepath2)
prepare_csv_file(test_data3, filepath3)
# test single CSV file
dataset = CSVIterableDataset(filepath1)
for i, item in enumerate(dataset):
if i == 2:
self.assertDictEqual(
{
k: round(v, 4) if not isinstance(v, str) else v
for k, v in item.items()
},
{
"subject_id": "s000002",
"label": 4,
"image": "./imgs/s000002.png",
"ehr_0": 3.7725,
"ehr_1": 4.2118,
"ehr_2": 4.6353,
},
)
break
# test reset iterables
dataset.reset(filename=filepath3)
for i, item in enumerate(dataset):
if i == 3:
self.assertEqual(item["meta_0"], False)
# test multiple CSV files, join tables with kwargs
dataset = CSVIterableDataset([filepath1, filepath2, filepath3],
on="subject_id")
for i, item in enumerate(dataset):
if i == 3:
self.assertDictEqual(
{
k: round(v, 4)
if not isinstance(v, (str, np.bool_)) else v
for k, v in item.items()
},
{
"subject_id": "s000003",
"label": 1,
"image": "./imgs/s000003.png",
"ehr_0": 3.3333,
"ehr_1": 3.2353,
"ehr_2": 3.4000,
"ehr_3": 3.1647,
"ehr_4": 3.0863,
"ehr_5": 3.7255,
"ehr_6": 3.6980,
"ehr_7": 3.6980,
"ehr_8": 3.7020,
"ehr_9": 3.3098,
"ehr_10": 3.7294,
"meta_0": False,
"meta_1": False,
"meta_2": True,
},
)
# test selected columns and chunk size
dataset = CSVIterableDataset(
filename=[filepath1, filepath2, filepath3],
chunksize=2,
col_names=["subject_id", "image", "ehr_1", "ehr_7", "meta_1"],
)
for i, item in enumerate(dataset):
if i == 3:
self.assertDictEqual(
{
k: round(v, 4)
if not isinstance(v, (str, np.bool_)) else v
for k, v in item.items()
},
{
"subject_id": "s000003",
"image": "./imgs/s000003.png",
"ehr_1": 3.2353,
"ehr_7": 3.6980,
"meta_1": False,
},
)
# test group columns
dataset = CSVIterableDataset(
filename=[filepath1, filepath2, filepath3],
col_names=[
"subject_id", "image", *[f"ehr_{i}" for i in range(11)],
"meta_0", "meta_1", "meta_2"
],
col_groups={
"ehr": [f"ehr_{i}" for i in range(11)],
"meta12": ["meta_1", "meta_2"]
},
)
for i, item in enumerate(dataset):
if i == 3:
np.testing.assert_allclose(
[round(i, 4) for i in item["ehr"]],
[
3.3333, 3.2353, 3.4000, 3.1647, 3.0863, 3.7255,
3.6980, 3.6980, 3.7020, 3.3098, 3.7294
],
)
np.testing.assert_allclose(item["meta12"], [False, True])
# test transform
dataset = CSVIterableDataset(
chunksize=2,
buffer_size=4,
filename=[filepath1, filepath2, filepath3],
col_groups={"ehr": [f"ehr_{i}" for i in range(5)]},
transform=ToNumpyd(keys="ehr"),
shuffle=True,
)
dataset.set_random_state(123)
expected = [
[3.7725, 4.2118, 4.6353, 5.298, 9.5451],
[2.0078, 2.2902, 2.0549, 3.0196, 3.8078],
[6.4275, 6.2549, 5.9765, 6.2627, 7.7176],
[6.8392, 6.4745, 5.8627, 5.1922, 5.2745],
]
for item, exp in zip(dataset, expected):
self.assertTrue(isinstance(item["ehr"], np.ndarray))
np.testing.assert_allclose(np.around(item["ehr"], 4), exp)
# test multiple processes loading
dataset = CSVIterableDataset(filepath1,
transform=ToNumpyd(keys="label"))
# set num workers = 0 for mac / win
num_workers = 2 if sys.platform == "linux" else 0
dataloader = DataLoader(dataset=dataset,
num_workers=num_workers,
batch_size=2)
for item in dataloader:
# test the last item which only has 1 data
if len(item) == 1:
self.assertListEqual(item["subject_id"], ["s000002"])
np.testing.assert_allclose(item["label"], [4])
self.assertListEqual(item["image"], ["./imgs/s000002.png"])
def test_values(self):
with tempfile.TemporaryDirectory() as tempdir:
test_data1 = [
["subject_id", "label", "image", "ehr_0", "ehr_1", "ehr_2"],
[
"s000000", 5, "./imgs/s000000.png", 2.007843256,
2.29019618, 2.054902077
],
[
"s000001", 0, "./imgs/s000001.png", 6.839215755,
6.474509716, 5.862744808
],
[
"s000002", 4, "./imgs/s000002.png", 3.772548914,
4.211764812, 4.635294437
],
[
"s000003", 1, "./imgs/s000003.png", 3.333333254,
3.235294342, 3.400000095
],
[
"s000004", 9, "./imgs/s000004.png", 6.427451134,
6.254901886, 5.976470947
],
]
test_data2 = [
[
"subject_id", "ehr_3", "ehr_4", "ehr_5", "ehr_6", "ehr_7",
"ehr_8"
],
[
"s000000", 3.019608021, 3.807843208, 3.584313869,
3.141176462, 3.1960783, 4.211764812
],
[
"s000001", 5.192157269, 5.274509907, 5.250980377,
4.647058964, 4.886274338, 4.392156601
],
[
"s000002", 5.298039436, 9.545097351, 12.57254887,
6.799999714, 2.1960783, 1.882352948
],
[
"s000003", 3.164705753, 3.086274624, 3.725490093,
3.698039293, 3.698039055, 3.701960802
],
[
"s000004", 6.26274538, 7.717647076, 9.584313393,
6.082352638, 2.662744999, 2.34117651
],
]
test_data3 = [
[
"subject_id", "ehr_9", "ehr_10", "meta_0", "meta_1",
"meta_2"
],
["s000000", 6.301961422, 6.470588684, "TRUE", "TRUE", "TRUE"],
[
"s000001", 5.219608307, 7.827450752, "FALSE", "TRUE",
"FALSE"
],
["s000002", 1.882352948, 2.031372547, "TRUE", "FALSE", "TRUE"],
[
"s000003", 3.309803963, 3.729412079, "FALSE", "FALSE",
"TRUE"
],
["s000004", 2.062745094, 2.34117651, "FALSE", "TRUE", "TRUE"],
# generate NaN values in the row
["s000005", 3.353655643, 1.675674543, "TRUE", "TRUE", "FALSE"],
]
def prepare_csv_file(data, filepath):
with open(filepath, "a") as f:
for d in data:
f.write((",".join([str(i) for i in d])) + "\n")
filepath1 = os.path.join(tempdir, "test_data1.csv")
filepath2 = os.path.join(tempdir, "test_data2.csv")
filepath3 = os.path.join(tempdir, "test_data3.csv")
filepaths = [filepath1, filepath2, filepath3]
prepare_csv_file(test_data1, filepath1)
prepare_csv_file(test_data2, filepath2)
prepare_csv_file(test_data3, filepath3)
# test single CSV file
dataset = CSVDataset(filepath1)
self.assertDictEqual(
{
k: round(v, 4) if not isinstance(v, str) else v
for k, v in dataset[2].items()
},
{
"subject_id": "s000002",
"label": 4,
"image": "./imgs/s000002.png",
"ehr_0": 3.7725,
"ehr_1": 4.2118,
"ehr_2": 4.6353,
},
)
# test multiple CSV files, join tables with kwargs
dataset = CSVDataset(filepaths, on="subject_id")
self.assertDictEqual(
{
k: round(v, 4) if not isinstance(v, (str, np.bool_)) else v
for k, v in dataset[3].items()
},
{
"subject_id": "s000003",
"label": 1,
"image": "./imgs/s000003.png",
"ehr_0": 3.3333,
"ehr_1": 3.2353,
"ehr_2": 3.4000,
"ehr_3": 3.1647,
"ehr_4": 3.0863,
"ehr_5": 3.7255,
"ehr_6": 3.6980,
"ehr_7": 3.6980,
"ehr_8": 3.7020,
"ehr_9": 3.3098,
"ehr_10": 3.7294,
"meta_0": False,
"meta_1": False,
"meta_2": True,
},
)
# test selected rows and columns
dataset = CSVDataset(
src=filepaths,
row_indices=[[0, 2], 3], # load row: 0, 1, 3
col_names=["subject_id", "image", "ehr_1", "ehr_7", "meta_1"],
)
self.assertEqual(len(dataset), 3)
self.assertDictEqual(
{
k: round(v, 4) if not isinstance(v, (str, np.bool_)) else v
for k, v in dataset[-1].items()
},
{
"subject_id": "s000003",
"image": "./imgs/s000003.png",
"ehr_1": 3.2353,
"ehr_7": 3.6980,
"meta_1": False,
},
)
# test group columns
dataset = CSVDataset(
src=filepaths,
row_indices=[1, 3], # load row: 1, 3
col_names=[
"subject_id", "image", *[f"ehr_{i}" for i in range(11)],
"meta_0", "meta_1", "meta_2"
],
col_groups={
"ehr": [f"ehr_{i}" for i in range(11)],
"meta12": ["meta_1", "meta_2"]
},
)
np.testing.assert_allclose(
[round(i, 4) for i in dataset[-1]["ehr"]],
[
3.3333, 3.2353, 3.4000, 3.1647, 3.0863, 3.7255, 3.6980,
3.6980, 3.7020, 3.3098, 3.7294
],
)
np.testing.assert_allclose(dataset[-1]["meta12"], [False, True])
# test transform
dataset = CSVDataset(
src=filepaths,
col_groups={"ehr": [f"ehr_{i}" for i in range(5)]},
transform=ToNumpyd(keys="ehr"))
self.assertEqual(len(dataset), 5)
expected = [
[2.0078, 2.2902, 2.0549, 3.0196, 3.8078],
[6.8392, 6.4745, 5.8627, 5.1922, 5.2745],
[3.7725, 4.2118, 4.6353, 5.2980, 9.5451],
[3.3333, 3.2353, 3.4000, 3.1647, 3.0863],
[6.4275, 6.2549, 5.9765, 6.2627, 7.7176],
]
for item, exp in zip(dataset, expected):
self.assertTrue(isinstance(item["ehr"], np.ndarray))
np.testing.assert_allclose(np.around(item["ehr"], 4), exp)
# test default values and dtype
dataset = CSVDataset(
src=filepaths,
col_names=["subject_id", "image", "ehr_1", "ehr_9", "meta_1"],
col_types={
"image": {
"type": str,
"default": "No image"
},
"ehr_1": {
"type": int,
"default": 0
}
},
how="outer", # generate NaN values in this merge mode
)
self.assertEqual(len(dataset), 6)
self.assertEqual(dataset[-1]["image"], "No image")
self.assertEqual(type(dataset[-1]["ehr_1"]), int)
np.testing.assert_allclose(dataset[-1]["ehr_9"], 3.3537, rtol=1e-2)
# test pre-loaded DataFrame
df = pd.read_csv(filepath1)
dataset = CSVDataset(src=df)
self.assertDictEqual(
{
k: round(v, 4) if not isinstance(v, str) else v
for k, v in dataset[2].items()
},
{
"subject_id": "s000002",
"label": 4,
"image": "./imgs/s000002.png",
"ehr_0": 3.7725,
"ehr_1": 4.2118,
"ehr_2": 4.6353,
},
)
# test pre-loaded multiple DataFrames, join tables with kwargs
dfs = [pd.read_csv(i) for i in filepaths]
dataset = CSVDataset(src=dfs, on="subject_id")
self.assertEqual(dataset[3]["subject_id"], "s000003")
self.assertEqual(dataset[3]["label"], 1)
self.assertEqual(round(dataset[3]["ehr_0"], 4), 3.3333)
self.assertEqual(dataset[3]["meta_0"], False)