remove metadata

cloudops_tsf.py

@@ -140,7 +140,6 @@ class CloudOpsTSFConfig(datasets.BuilderConfig):
     # load_dataset kwargs
     train_test: bool = field(default=True, init=False)
     pretrain: bool = field(default=False, init=False)
-    _include_metadata: tuple[str, ...] = field(default_factory=tuple, init=False)
 
     # builder kwargs
     prediction_length: int = field(default=None)
@@ -158,25 +157,6 @@ class CloudOpsTSFConfig(datasets.BuilderConfig):
     feat_static_real_dim: int = field(default=0)
     past_feat_dynamic_real_dim: int = field(default=0)
 
-    METADATA = [
-        "freq",
-        "prediction_length",
-        "stride",
-        "rolling_evaluations",
-    ]
-
-    @property
-    def include_metadata(self) -> tuple[str, ...]:
-        return self._include_metadata
-
-    @include_metadata.setter
-    def include_metadata(self, value: tuple[str, ...]):
-        assert all([v in self.METADATA for v in value]), (
-            f"Metadata: {value} is not supported, each item should be one of"
-            f" {self.METADATA}"
-        )
-        self._include_metadata = value
-
     @cached_property
     def feat_static_cat_cardinalities(self) -> Optional[list[int]]:
         if FieldName.FEAT_STATIC_CAT not in self.optional_fields:
@@ -251,14 +231,6 @@ class CloudOpsTSF(datasets.ArrowBasedBuilder):
 
             features[ts_field] = sequence_feature(dtype, univar)
 
-        for metadata in self.config.include_metadata:
-            if metadata == "freq":
-                features[metadata] = datasets.Value("string")
-            elif metadata in ("prediction_length", "stride", "rolling_evaluations"):
-                features[metadata] = datasets.Value("int32")
-            else:
-                raise ValueError(f"Invalid metadata: {metadata}")
-
         features = datasets.Features(features)
 
         return datasets.DatasetInfo(
@@ -295,23 +267,7 @@ class CloudOpsTSF(datasets.ArrowBasedBuilder):
         for batch in table.to_batches():
             columns = batch.columns
             schema = batch.schema
-            if self.config.include_metadata:
-                freq = pa.array([self.config.freq] * len(batch))
-                prediction_length = pa.array(
-                    [self.config.prediction_length] * len(batch)
-                )
-                rolling_evaluations = pa.array(
-                    [self.config.rolling_evaluations] * len(batch)
-                )
-                stride = pa.array([self.config.stride] * len(batch))
-                columns += [freq, prediction_length, rolling_evaluations, stride]
-                for pa_field in [
-                    pa.field("freq", pa.string()),
-                    pa.field("prediction_length", pa.int32()),
-                    pa.field("rolling_evaluations", pa.int32()),
-                    pa.field("stride", pa.int32()),
-                ]:
-                    schema = schema.append(pa_field)
+
             yield batch[FieldName.ITEM_ID].to_pylist(), pa.Table.from_arrays(
                 columns, schema=schema
             )