Chris Oswald
commited on
Commit
•
d004e7b
1
Parent(s):
5fa2de2
added parameter checking
Browse files
SPIDER.py
CHANGED
@@ -170,13 +170,15 @@ class SPIDER(datasets.GeneratorBasedBuilder):
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def _generate_examples(
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self,
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paths_dict: Dict[str, str],
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-
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validate_share: float = 0.3,
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test_share: float = 0.2,
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raw_image: bool = True,
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numeric_array: bool = True,
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metadata: bool = True,
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rad_gradings: bool = True,
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) -> Tuple[str, Dict]:
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"""
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This method handles input defined in _split_generators to yield
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@@ -185,6 +187,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
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Args
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paths_dict
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split:
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validate_share
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test_share
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@@ -194,24 +197,47 @@ class SPIDER(datasets.GeneratorBasedBuilder):
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rad_gradings
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Yields
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"""
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#
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# Generate train/validate/test partitions of patient IDs
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partition = np.random.choice(
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['train', 'dev', 'test'],
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p=[
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size=N_PATIENTS,
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)
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patient_ids = (np.arange(N_PATIENTS) + 1)
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@@ -270,12 +296,6 @@ class SPIDER(datasets.GeneratorBasedBuilder):
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subset_ids = validate_ids
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elif split == 'test':
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subset_ids = test_ids
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else:
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subset_ids = None
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raise ValueError( #TODO: move all parameter checking to beginning
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f'Split argument "{split}" is not recognized. \
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Please enter one of ["train", "validate", "test"]'
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)
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image_files = [
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file for file in image_files
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@@ -313,11 +333,14 @@ class SPIDER(datasets.GeneratorBasedBuilder):
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# Extract overview data corresponding to image
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image_overview = overview_dict[scan_id]
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# Extract patient radiological gradings corresponding to
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patient_grades_dict = {}
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for item in grades_dict[patient_id]:
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key = f'IVD{item["IVD label"]}'
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value = {
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patient_grades_dict[key] = value
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# Prepare example return dict
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def _generate_examples(
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self,
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paths_dict: Dict[str, str],
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scan_types: List[str] = ['t1', 't2', 't2_SPACE'],
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split: str = 'train',
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validate_share: float = 0.3,
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test_share: float = 0.2,
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raw_image: bool = True,
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numeric_array: bool = True,
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metadata: bool = True,
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rad_gradings: bool = True,
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random_seed: int = 9999,
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) -> Tuple[str, Dict]:
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"""
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This method handles input defined in _split_generators to yield
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Args
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paths_dict
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scan_types:
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split:
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validate_share
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test_share
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rad_gradings
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Yields
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Tuple (unique patient-scan ID, dict of
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"""
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# Set constants
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N_PATIENTS = 257
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train_share = (1.0 - validate_share - test_share)
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np.random.seed(int(random_seed))
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# Validate params
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for item in scan_types:
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if item not in ['t1', 't2', 't2_SPACE']:
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raise ValueError(
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'Scan type "{item}" not recognized as valid scan type.\
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Verify scan type argument.'
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)
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if split not in ['train', 'validate', 'test']:
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raise ValueError(
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f'Split argument "{split}" is not recognized. \
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Please enter one of ["train", "validate", "test"]'
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)
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if train_share <= 0.0:
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raise ValueError(
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f'Training share is calculated as (1 - validate_share - test_share) \
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and must be greater than 0. Current calculated value is \
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{round(train_share, 3)}. Adjust validate_share and/or \
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test_share parameters.'
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)
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if validate_share > 1.0 or validate_share < 0.0:
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raise ValueError(
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f'Validation share must be between (0, 1). Current value is \
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{validate_share}.'
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)
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if test_share > 1.0 or test_share < 0.0:
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raise ValueError(
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f'Testing share must be between (0, 1). Current value is \
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{test_share}.'
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)
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# Generate train/validate/test partitions of patient IDs
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partition = np.random.choice(
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['train', 'dev', 'test'],
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p=[train_share, validate_share, test_share],
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size=N_PATIENTS,
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)
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patient_ids = (np.arange(N_PATIENTS) + 1)
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subset_ids = validate_ids
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elif split == 'test':
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subset_ids = test_ids
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image_files = [
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file for file in image_files
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# Extract overview data corresponding to image
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image_overview = overview_dict[scan_id]
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# Extract patient radiological gradings corresponding to patient
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patient_grades_dict = {}
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for item in grades_dict[patient_id]:
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key = f'IVD{item["IVD label"]}'
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value = {
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k:v for k,v in item.items()
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if k not in ['Patient', 'IVD label']
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}
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patient_grades_dict[key] = value
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# Prepare example return dict
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