fix: v1 to v2 dataset (#1275)

fixes: #1271

Author: shahules786

src/ragas/evaluation.py

@@ -34,7 +34,12 @@
 )
 from ragas.metrics.critique import AspectCritique
 from ragas.run_config import RunConfig
-from ragas.utils import REQUIRED_COLS_v1, get_feature_language, safe_nanmean
+from ragas.utils import (
+    convert_v1_to_v2_dataset,
+    convert_v2_to_v1_dataset,
+    get_feature_language,
+    safe_nanmean,
+)
 from ragas.validation import (
     remap_column_names,
     validate_required_columns,
@@ -164,7 +169,7 @@ def evaluate(
         # remap column names from the dataset
         v1_input = True
         dataset = remap_column_names(dataset, column_map)
-        dataset = remap_column_names(dataset, REQUIRED_COLS_v1)
+        dataset = convert_v1_to_v2_dataset(dataset)
         # validation
         dataset = EvaluationDataset.from_list(dataset.to_list())
 
@@ -310,8 +315,7 @@ def evaluate(
         # convert to v.1 dataset
         dataset = dataset.to_hf_dataset()
         if v1_input:
-            cols = {k: v for v, k in REQUIRED_COLS_v1.items()}
-            dataset = remap_column_names(dataset, cols)
+            dataset = convert_v2_to_v1_dataset(dataset)
 
         cost_cb = ragas_callbacks["cost_cb"] if "cost_cb" in ragas_callbacks else None
         result = Result(

src/ragas/utils.py

@@ -7,6 +7,7 @@
 from functools import lru_cache
 
 import numpy as np
+from datasets import Dataset
 
 if t.TYPE_CHECKING:
     from ragas.metrics.base import Metric
@@ -197,3 +198,13 @@ def get_required_columns_v1(metric: Metric):
 def convert_row_v1_to_v2(row: t.Dict[str, t.Any]) -> t.Dict[str, t.Any]:
     required_cols_v2 = {k: v for v, k in REQUIRED_COLS_v1.items()}
     return {required_cols_v2[k]: v for k, v in row.items() if k in required_cols_v2}
+
+
+def convert_v1_to_v2_dataset(dataset: Dataset) -> Dataset:
+    columns_map = {v: k for k, v in REQUIRED_COLS_v1.items() if v in dataset.features}
+    return dataset.rename_columns(columns_map)
+
+
+def convert_v2_to_v1_dataset(dataset: Dataset) -> Dataset:
+    columns_map = {k: v for k, v in REQUIRED_COLS_v1.items() if k in dataset.features}
+    return dataset.rename_columns(columns_map)