Update train_test_splits

NuCLS_dataset.py

@@ -20,8 +20,7 @@ Created on Tue Mar 12 16:13:56 2024
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Test 6
-
+%%writefile Test7.py
 import pandas as pd
 from PIL import Image as PilImage  # Import PIL Image with an alias
 import datasets
@@ -51,7 +50,7 @@ _HOMEPAGE = "https://sites.google.com/view/nucls/home?authuser=0"
 
 _LICENSE = "CC0 1.0 license"
 
-_URL = "https://www.dropbox.com/scl/fi/srq574rdgvp7f5gwr60xw/NuCLS_dataset.zip?rlkey=qjc9q8shgvnqpfy4bktbqybd1&dl=1"
+_URL = "https://www.dropbox.com/scl/fi/zsm9l3bkwx808wfryv5zm/NuCLS_dataset.zip?rlkey=x3358slgrxt00zpn7zpkpjr2h&dl=1"
 
 class NuCLSDataset(GeneratorBasedBuilder):
     """The NuCLS dataset."""
@@ -103,6 +102,7 @@ class NuCLSDataset(GeneratorBasedBuilder):
             citation=_CITATION,
           )
 
+
     def _split_generators(self, dl_manager: DownloadManager):
         # Download source data
         data_dir = dl_manager.download_and_extract(_URL)
@@ -113,31 +113,62 @@ class NuCLSDataset(GeneratorBasedBuilder):
         visualization_dir = os.path.join(base_dir, "visualization")
         mask_dir = os.path.join(base_dir, "mask")
         csv_dir = os.path.join(base_dir, "csv")
-        
+        split_dir = os.path.join(base_dir, "train_test_splits")
+
         # Generate a list of unique filenames (without extensions)
         unique_filenames = [os.path.splitext(f)[0] for f in os.listdir(rgb_dir)]
 
-        # Split filenames into training and testing sets
-        random.shuffle(unique_filenames)
-        split_idx = int(0.8 * len(unique_filenames))
-        train_filenames = unique_filenames[:split_idx]
-        test_filenames = unique_filenames[split_idx:]
-
-        # Map filenames to file paths for each split
-        train_filepaths = self._map_filenames_to_paths(train_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir)
-        test_filepaths = self._map_filenames_to_paths(test_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir)
-
-        # Create the split generators
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepaths": train_filepaths}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepaths": test_filepaths}
-            ),
-        ]
+        # Process train/test split files to get slide names for each split and fold
+        split_slide_names = self._process_train_test_split_files(split_dir)
+
+        # Create the split generators for each fold
+        split_generators = []
+        for fold in split_slide_names:
+            train_slide_names, test_slide_names = split_slide_names[fold]
+
+            # Filter unique filenames based on slide names
+            train_filenames = [fn for fn in unique_filenames if any(sn in fn for sn in train_slide_names)]
+            test_filenames = [fn for fn in unique_filenames if any(sn in fn for sn in test_slide_names)]
+
+            # Map filenames to file paths
+            train_filepaths = self._map_filenames_to_paths(train_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir)
+            test_filepaths = self._map_filenames_to_paths(test_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir)
+
+            # Add split generators for the fold
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=f"{datasets.Split.TRAIN}_fold_{fold}",
+                    gen_kwargs={"filepaths": train_filepaths}
+                )
+            )
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=f"{datasets.Split.TEST}_fold_{fold}",
+                    gen_kwargs={"filepaths": test_filepaths}
+                )
+            )
+
+        return split_generators
+
+    def _process_train_test_split_files(self, split_dir):
+        """Reads the train/test split CSV files and returns a dictionary with fold numbers as keys and tuple of train/test slide names as values."""
+        split_slide_names = {}
+        for split_file in os.listdir(split_dir):
+            file_path = os.path.join(split_dir, split_file)
+            fold_number = split_file.split('_')[1]  # Assumes file naming format "fold_X_[train/test].csv"
+
+            with open(file_path, 'r') as f:
+                csv_reader = csv.reader(f)
+                next(csv_reader)  # Skip header
+                for row in csv_reader:
+                    slide_name = row[1]  # Assuming slide_name is in the first column
+                    if "train" in split_file:
+                        split_slide_names.setdefault(fold_number, ([], []))[0].append(slide_name)
+                    elif "test" in split_file:
+                        split_slide_names.setdefault(fold_number, ([], []))[1].append(slide_name)
+
+        return split_slide_names
+
 
     def _map_filenames_to_paths(self, filenames, rgb_dir, visualization_dir, mask_dir, csv_dir):
         """Maps filenames to file paths for each split."""
@@ -213,5 +244,5 @@ class NuCLSDataset(GeneratorBasedBuilder):
                 coords_y = row.get('coords_y', '')
                 annotations['coords_x'].append([int(coord) if coord.isdigit() else 0 for coord in coords_x.split(',')])
                 annotations['coords_y'].append([int(coord) if coord.isdigit() else 0 for coord in coords_y.split(',')])
-        
+
             return annotations