generated examples

SPIDER.py

@@ -14,17 +14,28 @@
 # TODO: Address all TODOs and remove all explanatory comments
 """TODO: Add a description here."""
 
-
+# Import packages
 import csv
 import json
 import os
-from typing import Dict, List, Optional, Set
+from typing import Dict, List, Optional, Set, Tuple
 
 import numpy as np
 
 import datasets
 import SimpleITK as sitk
 
+# Define functions
+def import_csv_data(filepath: str) -> List[Dict[str, str]]:
+    """Import all rows of CSV file."""
+    results = []
+    with open(filepath, encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for line in reader:
+            results.append(line)
+    return results
+    
+
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
@@ -156,11 +167,36 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, paths_dict, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+    def _generate_examples(
+        self,
+        paths_dict: Dict[str, str],
+        split: str = 'train', # ['train', 'validate', 'test']
+        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,
+    ) -> Tuple[str, Dict]:
+        """
+        This method handles input defined in _split_generators to yield 
+        (key, example) tuples from the dataset. The `key` is for legacy reasons 
+        (tfds) and is not important in itself, but must be unique for each example.
+        
+        Args
+            paths_dict
+            split:
+            validate_share
+            test_share
+            raw_image
+            numeric_array
+            metadata
+            rad_gradings
         
-        # Config params
+        Yields
+            
+        """
+        # Configure params
         #TODO: make hardcoded values dynamic
         np.random.seed(9999)
         N_PATIENTS = 257 
@@ -188,6 +224,21 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         overview_data = import_csv_data(paths_dict['overview'])
         grades_data = import_csv_data(paths_dict['gradings'])
         
+        # Convert overview data list of dicts to dict of dicts
+        overview_dict = {}
+        for item in overview_data:
+            key = item['new_file_name']
+            overview_dict[key] = item
+        
+        # Merge patient records for radiological gradings data
+        grades_dict = {}
+        for patient_id in patient_ids:
+            patient_grades = [
+                x for x in grades_data if x['Patient'] == str(patient_id)
+            ]
+            if patient_grades:
+                grades_dict[str(patient_id)] = patient_grades
+    
         # Import image and mask data
         image_files = [
             file for file in os.listdir(os.path.join(paths_dict['images'], 'images'))
@@ -221,8 +272,10 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             subset_ids = test_ids
         else:
             subset_ids = None
-            raise ValueError(f'Split argument "{split}" is not recognized. \
-                             Please enter one of ["train", "validate", "test"]')
+            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
@@ -258,40 +311,25 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             image_array = sitk.GetArrayFromImage(image)
             
             # Extract overview data corresponding to image
-            
-            # Extract patient radiological gradings corresponding to image
-            
-            
-
-        
-
-
+            image_overview = overview_dict[scan_id]
 
-
-    def import_csv_data(filepath: str) -> List[Dict[str, str]]:
-        """Import all rows of CSV file."""
-        results = []
-        with open(filepath, encoding='utf-8') as f:
-            reader = csv.DictReader(f)
-            for line in reader:
-                results.append(line)
-        return results
-                
-        
+            # Extract patient radiological gradings corresponding to image
+            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']}
+                patient_grades_dict[key] = value
         
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
-                    }
+            # Prepare example return dict
+            return_dict = {'patient_id':patient_id, 'scan_type':scan_type}
+            if raw_image:
+                return_dict['raw_image'] = image
+            if numeric_array:
+                return_dict['numeric_array'] = image_array
+            if metadata:
+                return_dict['metadata'] = image_overview
+            if rad_gradings:
+                return_dict['rad_gradings'] = patient_grades_dict
+            
+            # Yield example
+            yield (scan_id, return_dict)