streamline splits

cloudops_tsf.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 from dataclasses import dataclass, field
 from typing import Iterator, Optional
-from functools import cached_property
 
 import datasets
 import pandas as pd
@@ -157,19 +156,12 @@ class CloudOpsTSFConfig(datasets.BuilderConfig):
     feat_static_real_dim: int = field(default=0)
     past_feat_dynamic_real_dim: int = field(default=0)
 
-    @cached_property
-    def feat_static_cat_cardinalities(self) -> Optional[list[int]]:
+    def feat_static_cat_cardinalities(
+        self, split: str = "train_test"
+    ) -> Optional[list[int]]:
         if FieldName.FEAT_STATIC_CAT not in self.optional_fields:
             return None
 
-        if self.pretrain:
-            split = "pretrain"
-        elif self.train_test:
-            split = "train_test"
-        else:
-            raise ValueError(
-                "At least one of `train_test` and `pretrain` should be True"
-            )
         return [c[1] for c in self._feat_static_cat_cardinalities[split]]
 
 
@@ -239,26 +231,24 @@ class CloudOpsTSF(datasets.ArrowBasedBuilder):
         )
 
     def _split_generators(self, dl_manager) -> list[datasets.SplitGenerator]:
-        generators = []
-        if self.config.train_test:
-            downloaded_files = dl_manager.download_and_extract(
-                f"{self.config.name}/train_test.zip"
-            )
-            generators.append(
-                datasets.SplitGenerator(
-                    name=TRAIN_TEST if self.config.train_test else PRETRAIN,
-                    gen_kwargs={"filepath": downloaded_files},
-                )
-            )
-        if self.config.pretrain:
-            downloaded_files = dl_manager.download_and_extract(
-                f"{self.config.name}/pretrain.zip"
-            )
-            generators.append(
-                datasets.SplitGenerator(
-                    name=PRETRAIN, gen_kwargs={"filepath": downloaded_files}
-                )
-            )
+        downloaded_files = dl_manager.download_and_extract(
+            [
+                f"{self.config.name}/train_test.zip",
+                f"{self.config.name}/pretrain.zip",
+            ]
+        )
+
+        generators = [
+            datasets.SplitGenerator(
+                name=TRAIN_TEST,
+                gen_kwargs={"filepath": downloaded_files[0]},
+            ),
+            datasets.SplitGenerator(
+                name=PRETRAIN,
+                gen_kwargs={"filepath": downloaded_files[1]},
+            ),
+        ]
+
         return generators
 
     def _generate_tables(self, filepath: str) -> Iterator[pa.Table]: