:lipstick: Better splitting

california_burned_areas.py

@@ -60,19 +60,9 @@ class CaBuArConfig(datasets.BuilderConfig):
         keyword arguments forwarded to super.
     """
 
-    def __init__(
-        self,
-        load_prefire: bool,
-        train_folds: List[int],
-        validation_folds: List[int],
-        test_folds: List[int],
-        **kwargs
-    ):
+    def __init__(self, load_prefire: bool, **kwargs):
         super(CaBuArConfig, self).__init__(**kwargs)
         self.load_prefire = load_prefire
-        self.train_folds = train_folds
-        self.validation_folds = validation_folds
-        self.test_folds = test_folds
 
 
 class CaBuAr(datasets.GeneratorBasedBuilder):
@@ -86,18 +76,12 @@ class CaBuAr(datasets.GeneratorBasedBuilder):
             version=VERSION,
             description="Post-fire only version of the dataset",
             load_prefire=False,
-            train_folds=None,
-            validation_folds=None,
-            test_folds=None,
         ),
         CaBuArConfig(
             name="pre-post-fire",
             version=VERSION,
             description="Pre-fire and post-fire version of the dataset",
             load_prefire=True,
-            train_folds=None,
-            validation_folds=None,
-            test_folds=None,
         ),
     ]
 
@@ -136,49 +120,25 @@ class CaBuAr(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         h5_file = dl_manager.download(_URLS)
-        # Raise ValueError if train_folds, validation_folds or test_folds are not set
-        if (
-            self.config.train_folds is None
-            or self.config.validation_folds is None
-            or self.config.test_folds is None
-        ):
-            raise ValueError("train_folds, validation_folds and test_folds must be set")
 
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
+                name=fold,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "folds": self.config.train_folds,
+                    "fold": fold,
                     "load_prefire": self.config.load_prefire,
                     "filepath": h5_file,
                 },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "folds": self.config.validation_folds,
-                    "load_prefire": self.config.load_prefire,
-                    "filepath": h5_file,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "folds": self.config.test_folds,
-                    "load_prefire": self.config.load_prefire,
-                    "filepath": h5_file,
-                },
-            ),
+            )
+            for fold in range(0, 5)
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, folds: List[int], load_prefire: bool, filepath):
+    def _generate_examples(self, fold: int, load_prefire: bool, filepath):
         with h5py.File(filepath, "r") as f:
             for uuid, values in f.items():
-                if values.attrs["fold"] not in folds:
+                if values.attrs["fold"] != fold:
                     continue
                 if load_prefire and "pre_fire" not in values:
                     continue