Metrics: ensure ordinal encoder of classes is the same in real and sy…

…nthetic datasets [type:bug] (#257)

* reuse encoders

* ensure categorical encoder is trained on real and synthetic

* better transformer

* remove unnecessary imports

* better error message

* compatbility with DDIM

src/synthcity/metrics/eval.py

@@ -200,15 +200,26 @@ def evaluate(
         if metrics is None:
             metrics = Metrics.list()
 
-        X_gt, _ = X_gt.encode()
-        X_syn, _ = X_syn.encode()
+        """
+        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.
+        """
+        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())
+        _, encoders = X_enc.encode()
+
+        # now we encode the data
+        X_gt, _ = X_gt.encode(encoders)
+        X_syn, _ = X_syn.encode(encoders)
 
         if X_train:
-            X_train, _ = X_train.encode()
+            X_train, _ = X_train.encode(encoders)
         if X_ref_syn:
-            X_ref_syn, _ = X_ref_syn.encode()
+            X_ref_syn, _ = X_ref_syn.encode(encoders)
         if X_augmented:
-            X_augmented, _ = X_augmented.encode()
+            X_augmented, _ = X_augmented.encode(encoders)
 
         scores = ScoreEvaluator()