[FIX] Fix spis (#190)

Author: elephaint

tests/test_losses.py

@@ -178,7 +178,7 @@ def test_spis_numerical(self):
         Validates scaled sum of absolute errors per series (Pandas and Polars).
         """
         # In-sample data for scaling (mean per series)
-        df_train = pd.DataFrame({
+        train_df = pd.DataFrame({
             "unique_id": ["A", "A", "B", "B"],
             "y": [1, 3, 2, 6]
         })
@@ -200,7 +200,7 @@ def test_spis_numerical(self):
         # Pandas test
         out_pd = spis(
             df=df,
-            df_train=df_train,
+            train_df=train_df,
             models=["y_hat"],
             id_col="unique_id",
             target_col="y",
@@ -210,7 +210,7 @@ def test_spis_numerical(self):
         # Polars test
         out_pl = spis(
             df=pl.DataFrame(df),
-            df_train=pl.DataFrame(df_train),
+            train_df=pl.DataFrame(train_df),
             models=["y_hat"],
             id_col="unique_id",
             target_col="y",

utilsforecast/losses.py

@@ -292,7 +292,7 @@ def pis(
 @_base_docstring
 def spis(
     df: DFType,
-    df_train: DFType,
+    train_df: DFType,
     models: List[str],
     id_col: str = "unique_id",
     target_col: str = "y",
@@ -304,7 +304,7 @@ def spis(
     yielding a scale-independent bias measure that can be aggregated across series.
     """
     if isinstance(df, pd.DataFrame):
-        ins_means = df_train.groupby(id_col)[target_col].mean().rename("insample_mean")
+        ins_means = train_df.groupby(id_col)[target_col].mean().rename("insample_mean")
         abs_err_sum = (
             (df[models].sub(df[target_col], axis=0))
             .abs()
@@ -315,7 +315,7 @@ def spis(
         res.index.name = id_col
         return res.reset_index()
     else:
-        ins_means = df_train.group_by(id_col).agg(
+        ins_means = train_df.group_by(id_col).agg(
             pl.col(target_col).mean().alias("insample_mean")
         )
         abs_err = _pl_agg_expr(