log messages modified

src/syngen/ml/handlers/handlers.py

@@ -495,18 +495,18 @@ def handle(self, **kwargs):
         prepared_data = self._restore_empty_columns(prepared_data)
         # workaround for the case when all columns are dropped
         # with technical column
-        if self.dataset.tech_columns:
-            tech_columns = list(self.dataset.tech_columns)
+        tech_columns = list(self.dataset.tech_columns)
+        if tech_columns:
             prepared_data = prepared_data.drop(tech_columns, axis=1)
             logger.debug(
-                f"Technical columns "
-                f"{tech_columns} were dropped"
-                f" from the generated table"
+                "Technical columns "
+                f"{tech_columns} were removed "
+                "from the generated table."
                 )
             Report().unregister_reporters(self.table_name)
             logger.info(
-                    "Since there were no columns to train on, "
-                    "reports will be not generated "
+                    "Since there were no columns suitable for training, "
+                    "reports will not be generated "
                     f"for the table '{self.table_name}'."
                     )
 

src/syngen/ml/vae/models/dataset.py

@@ -72,6 +72,7 @@ def __init__(
         self.binary_columns: Set = set()
         self.email_columns: Set = set()
         self.long_text_columns: Set = set()
+        self.tech_columns: Set = set()
         self.primary_keys_mapping: Dict = dict()
         self.primary_keys_list: List = list()
         self.primary_key_name: Optional[str] = None
@@ -1328,11 +1329,12 @@ def pipeline(self) -> pd.DataFrame:
             tech_column = "syngen_tech_column"
             logger.info(
                 f"Since all columns in the table '{self.table_name}' "
-                f"are uuid/key/long text columns, "
-                f"there are no suitable columns to train on. "
-                f"Technical column '{tech_column}' will be added to proceed "
-                f"with the training process and will be removed afterwards."
-            )
+                "are uuid/key/long text columns, "
+                "there are no suitable columns to train on. "
+                f"A technical column '{tech_column}' will be added "
+                "to proceed with the training process "
+                "and will be removed afterwards."
+                )
             self.df[tech_column] = 1
             self._assign_float_feature(tech_column)
             self.tech_columns.add(tech_column)