Update BuilderConfig

NuCLS_dataset.py

@@ -23,7 +23,7 @@ Created on Tue Mar 12 16:13:56 2024
 import pandas as pd
 from PIL import Image as PilImage  # Import PIL Image with an alias
 import datasets
-from datasets import DatasetBuilder, GeneratorBasedBuilder, DownloadManager, DatasetInfo, Features, Image, ClassLabel, Value, Sequence, load_dataset, SplitGenerator
+from datasets import DatasetBuilder, GeneratorBasedBuilder, DownloadManager, DatasetInfo, Features, Image, ClassLabel, Value, Sequence, load_dataset, SplitGenerator, BuilderConfig
 import os
 import io
 from typing import Tuple, Dict, List
@@ -51,10 +51,29 @@ _LICENSE = "CC0 1.0 license"
 
 _URL = "https://www.dropbox.com/scl/fi/zsm9l3bkwx808wfryv5zm/NuCLS_dataset.zip?rlkey=x3358slgrxt00zpn7zpkpjr2h&dl=1"
 
-class NuCLSDataset(GeneratorBasedBuilder):
-    """The NuCLS dataset."""
 
-    VERSION = datasets.Version("1.1.0")
+class NuCLSDatasetConfig(BuilderConfig):
+    def __init__(self, use_fold_999=False, **kwargs):
+        super(NuCLSDatasetConfig, self).__init__(**kwargs)
+        self.use_fold_999 = use_fold_999
+
+class NuCLSDataset(GeneratorBasedBuilder):
+    # Define multiple configurations for your dataset
+    BUILDER_CONFIGS = [
+        NuCLSDatasetConfig(
+            name="default",
+            version=datasets.Version("1.1.0"),
+            description="Default configuration with the full dataset",
+        ),
+        NuCLSDatasetConfig(
+            name="debug",
+            version=datasets.Version("1.1.0"),
+            description="Debug configuration which uses fold 999 for quick tests",
+            use_fold_999=True
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
 
     def _info(self):
         """Returns the dataset info."""
@@ -118,7 +137,12 @@ class NuCLSDataset(GeneratorBasedBuilder):
         unique_filenames = [os.path.splitext(f)[0] for f in os.listdir(rgb_dir)]
 
         # 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)
+        if self.config.use_fold_999:
+            # Generate the split generators for fold 999
+            split_slide_names = self._process_train_test_split_files(split_dir, specific_fold = '999')
+        else:
+            # Generate the split generators for all folds
+            split_slide_names = self._process_train_test_split_files(split_dir)
 
         # Create the split generators for each fold
         split_generators = []
@@ -149,13 +173,17 @@ class NuCLSDataset(GeneratorBasedBuilder):
 
         return split_generators
 
-    def _process_train_test_split_files(self, split_dir):
+    def _process_train_test_split_files(self, split_dir, specific_fold=None):
         """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):
+            fold_number = split_file.split('_')[1]  # Assumes file naming format "fold_X_[train/test].csv"
+            # If specific_fold is set, skip all other folds
+            if specific_fold is not None and fold_number != specific_fold:
+                continue
             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