add random splits to the filtered data

ppb_affinity.py

@@ -1,8 +1,9 @@
 import datasets
 import csv
+import random
 
 class ppb_affinity(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.1")
+    VERSION = datasets.Version("1.0.2")
     
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name="raw", description="Raw parsed PDBs dataset with critical filtrations only."),
@@ -11,38 +12,67 @@ class ppb_affinity(datasets.GeneratorBasedBuilder):
     
     def _info(self):
         return datasets.DatasetInfo()
-    
+
     def _split_generators(self, dl_manager):
-        if self.config.name == "raw":
-            filepath = dl_manager.download_and_extract("raw.csv")
-            return [datasets.SplitGenerator(
+        """Downloads and defines dataset splits"""
+        filepath = dl_manager.download_and_extract("filtered.csv")
+        
+        return [
+            datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": filepath}
-            )]
-        else:
-            filepath = dl_manager.download_and_extract("filtered.csv")
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={"filepath": filepath, "split": "train"},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={"filepath": filepath, "split": "val"},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"filepath": filepath, "split": "test"},
-                ),
-            ]
-    
-    def _generate_examples(self, filepath, split=None):
+                gen_kwargs={"filepath": filepath, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": filepath, "split": "val"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": filepath, "split": "test"},
+            ),
+
+            datasets.SplitGenerator(
+                name="train_rand",
+                gen_kwargs={"filepath": filepath, "split": "train_rand", "random_split": True},
+            ),
+            datasets.SplitGenerator(
+                name="val_rand",
+                gen_kwargs={"filepath": filepath, "split": "val_rand", "random_split": True},
+            ),
+            datasets.SplitGenerator(
+                name="test_rand",
+                gen_kwargs={"filepath": filepath, "split": "test_rand", "random_split": True},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split=None, random_split=False):
+        """Generates examples, either using predefined splits or random splits"""
         with open(filepath, encoding="utf-8") as f:
             reader = csv.DictReader(f)
-            for idx, row in enumerate(reader):
-                if self.config.name == "raw":
-                    yield idx, row
-                else:
-                    if row["split"] == split:
-                        del row["split"]  # Remove split column from examples
-                        yield idx, row
+            data = list(reader)
+
+        if random_split:
+            return self._generate_examples_rand(data, split)
+        
+        for idx, row in enumerate(data):
+            if row["split"] == split:
+                del row["split"]  # Remove split column from examples
+                yield idx, row
+
+    def _generate_examples_rand(self, data, split):
+        """Randomly splits the dataset into 80% train, 10% val, 10% test with a fixed seed"""
+        random.seed(42)
+        random.shuffle(data)
+
+        total = len(data)
+        train_end = int(0.8 * total)
+        val_end = train_end + int(0.1 * total)
+
+        split_map = {
+            "train_rand": data[:train_end],
+            "val_rand": data[train_end:val_end],
+            "test_rand": data[val_end:]
+        }
+
+        for idx, row in enumerate(split_map[split]):
+            yield idx, row