updated the tests

Author: Omswastik-11

openml/runs/run.py

@@ -9,6 +9,7 @@
 from typing import (
     TYPE_CHECKING,
     Any,
+    cast,
 )
 
 import arff
@@ -154,11 +155,14 @@ def __init__(  # noqa: PLR0913
     def predictions(self) -> pd.DataFrame:
         """Return a DataFrame with predictions for this run"""
         if self._predictions is None:
+            arff_dict: dict[str, Any]
             if self.data_content:
                 arff_dict = self._generate_arff_dict()
             elif self.predictions_url:
                 arff_text = openml._api_calls._download_text_file(self.predictions_url)
-                arff_dict = arff.loads(arff_text)
+                if arff_text is None:
+                    raise RuntimeError("Could not download predictions ARFF content.")
+                arff_dict = self._load_predictions_arff(arff_text)
             else:
                 raise RuntimeError("Run has no predictions.")
             self._predictions = pd.DataFrame(
@@ -167,6 +171,21 @@ def predictions(self) -> pd.DataFrame:
             )
         return self._predictions
 
+    @staticmethod
+    def _load_predictions_arff(arff_text: str) -> dict[str, Any]:
+        try:
+            return cast("dict[str, Any]", arff.loads(arff_text))
+        except arff.ArffException:
+            normalized = arff_text.lstrip("\ufeff \t\r\n")
+            relation_indexes = [
+                idx
+                for idx in [normalized.find("@relation"), normalized.find("@RELATION")]
+                if idx >= 0
+            ]
+            if relation_indexes:
+                return cast("dict[str, Any]", arff.loads(normalized[min(relation_indexes) :]))
+            raise
+
     @property
     def id(self) -> int | None:
         """The ID of the run, None if not uploaded to the server yet."""
@@ -525,6 +544,7 @@ def get_metric_fn(self, sklearn_fn: Callable, kwargs: dict | None = None) -> np.
             metric results
         """
         kwargs = kwargs if kwargs else {}
+        predictions_arff: dict[str, Any]
         if self.data_content is not None and self.task_id is not None:
             predictions_arff = self._generate_arff_dict()
         elif (self.output_files is not None) and ("predictions" in self.output_files):
@@ -533,7 +553,9 @@ def get_metric_fn(self, sklearn_fn: Callable, kwargs: dict | None = None) -> np.
                 "predictions.arff",
             )
             response = openml._api_calls._download_text_file(predictions_file_url)
-            predictions_arff = arff.loads(response)
+            if response is None:
+                raise ValueError("Could not download predictions ARFF content.")
+            predictions_arff = self._load_predictions_arff(response)
             # TODO: make this a stream reader
         else:
             raise ValueError(