use all available datasets for computing the encoding of the categori…

…cal data in the metrics evaluater (#300)

src/synthcity/metrics/eval.py

@@ -203,12 +203,16 @@ def evaluate(
 
         """
         We need to encode the categorical data in the real and synthetic data.
-        To ensure each category in the two datasets are mapped to the same one hot vector, we merge X_syn into X_gt for computing the encoder.
-        TODO: Check whether the optional datasets also need to be taking into account when getting the encoder.
+        To ensure each category in the two datasets are mapped to the same one hot vector, we merge all avalable datasets for computing the encoder.
         """
-        X_gt_df = X_gt.dataframe()
-        X_syn_df = X_syn.dataframe()
-        X_enc = create_from_info(pd.concat([X_gt_df, X_syn_df]), X_gt.info())
+        all_df = pd.concat([X_gt.dataframe(), X_syn.dataframe()])
+        if X_train:
+            all_df = pd.concat([all_df, X_train.dataframe()])
+        if X_ref_syn:
+            all_df = pd.concat([all_df, X_ref_syn.dataframe()])
+        if X_augmented:
+            all_df = pd.concat([all_df, X_augmented.dataframe()])
+        X_enc = create_from_info(all_df, X_gt.info())
         _, encoders = X_enc.encode()
 
         # now we encode the data