added parameter checking

SPIDER.py

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