fix groupbys

skpro/distributions/mixture.py

@@ -134,7 +134,7 @@ def _average_df(self, df_list, weights=None):
         n_df = len(df_list)
         df_weighted = [df * w for df, w in zip(df_list, weights)]
         df_concat = pd.concat(df_weighted, axis=1, keys=range(n_df))
-        df_res = df_concat.T.groupby(level=-1).sum()
+        df_res = df_concat.T.groupby(level=-1).T.sum()
         return df_res
 
     def pdf(self, x):

skpro/metrics/base.py

@@ -116,9 +116,9 @@ def evaluate(self, y_true, y_pred, **kwargs):
         if self.score_average and multioutput == "uniform_average":
             out = out.mean(axis=1).iloc[0]  # average over all
         if self.score_average and multioutput == "raw_values":
-            out = out.T.groupby(level=0).mean()  # average over scores
+            out = out.T.groupby(level=0).T.mean()  # average over scores
         if not self.score_average and multioutput == "uniform_average":
-            out = out.T.groupby(level=1).mean()  # average over variables
+            out = out.T.groupby(level=1).T.mean()  # average over variables
         if not self.score_average and multioutput == "raw_values":
             out = out  # don't average
 
@@ -202,9 +202,9 @@ def evaluate_by_index(self, y_true, y_pred, **kwargs):
         if self.score_average and multioutput == "uniform_average":
             out = out.mean(axis=1)  # average over all
         if self.score_average and multioutput == "raw_values":
-            out = out.T.groupby(level=0).mean()  # average over scores
+            out = out.T.groupby(level=0).T.mean()  # average over scores
         if not self.score_average and multioutput == "uniform_average":
-            out = out.T.groupby(level=1).mean()  # average over variables
+            out = out.T.groupby(level=1).T.mean()  # average over variables
         if not self.score_average and multioutput == "raw_values":
             out = out  # don't average