implement op that reads multiple rows from dataframe

fuse/data/ops/ops_read.py

@@ -117,6 +117,71 @@ def get_all_keys(self) -> List[Hashable]:
         return list(self.data.keys())
 
 
+class OpReadMultiFromDataframe(OpReadDataframe):
+    """
+    Read multiple entries from dataframe at once.
+    In that case the key expected a string that build from multiple dataframe indices separated by "@SEP@"
+    For example
+    df = pd.DataFrame({
+        "sample_id": [0, 1, 2, 3, 4]
+        "my_data": [10, 11, 12, 13, 14]
+    })
+    sample_dict = {
+        "data.sample_id": "3@SEP@4"
+    }
+    will read row 3 from dataframe into sample_dict[f"my_data.0"]=13
+    And  row 4 into sample_dict[f"my_data.1"]=14
+    """
+
+    def __init__(
+        self,
+        data: Optional[pd.DataFrame] = None,
+        data_filename: Optional[str] = None,
+        columns_to_extract: Optional[List[str]] = None,
+        rename_columns: Optional[Dict[str, str]] = None,
+        key_name: str = "data.sample_id",
+        key_column: str = "sample_id",
+        multi_key_sep: str = "@SEP@",
+    ):
+        super().__init__(
+            data,
+            data_filename,
+            columns_to_extract,
+            rename_columns,
+            key_name,
+            key_column,
+        )
+
+        self._multi_key_sep = multi_key_sep
+
+        # convert ids to strings to support simple split and concat
+        if not isinstance(next(iter(self._data.keys())), str):
+            self._data = {str(k): v for k, v in self._data.items()}
+
+    def __call__(self, sample_dict: NDict, prefix: Optional[str] = None) -> NDict:
+        multi_key = sample_dict[self._key_name]
+
+        assert isinstance(multi_key, str), "Error: only str sample ids are supported"
+
+        if self._multi_key_sep in multi_key:
+            keys = multi_key.split(self._multi_key_sep)
+        else:
+            keys = [multi_key]
+
+        for key_index, key in enumerate(keys):
+            # locate the required item
+            sample_data = self._data[key].copy()
+
+            # add values tp sample_dict
+            for name, value in sample_data.items():
+                if prefix is None:
+                    sample_dict[f"{name}.{key_index}"] = value
+                else:
+                    sample_dict[f"{prefix}.{name}.{key_index}"] = value
+
+        return sample_dict
+
+
 class OpReadHDF5(OpBase):
     """
     Op reading data from hd5f based dataset

fuse/data/tokenizers/modular_tokenizer/modular_tokenizer.py

@@ -1137,7 +1137,7 @@ def encode_list(
             #   of input mapped to unk.
             if on_unknown == "raise":
                 raise RuntimeError(
-                    f"Encountered {unk_count} unknown tokens out of {len(merged_encoding.ids)} in input starting with {typed_input_list[0].input_string}"
+                    f"Encountered {unk_count} unknown tokens out of {len(merged_encoding.ids)} in input starting with {[typed_input.input_string for typed_input in typed_input_list]}"
                 )
             elif on_unknown == "warn":
                 if verbose == 0: