log messages are added

tdspora · Oct 29, 2024 · 08c0086 · 08c0086
1 parent 8130e07
commit 08c0086
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Showing 2 changed files with 18 additions and 7 deletions.
diff --git a/src/syngen/ml/handlers/handlers.py b/src/syngen/ml/handlers/handlers.py
@@ -498,10 +498,17 @@ def handle(self, **kwargs):
         if self.dataset.tech_columns:
             tech_columns = list(self.dataset.tech_columns)
             prepared_data = prepared_data.drop(tech_columns, axis=1)
+            logger.debug(
+                f"Technical columns "
+                f"{self.dataset.tech_columns} were dropped"
+                f" from the generated table"
+                )
             Report().unregister_reporters(self.dataset.table_name)
-            logger.debug(f"Technical columns "
-                         f"{self.dataset.tech_columns} are dropped"
-                         f" from the generated table")
+            logger.info(
+                "Since there were no columns to train on, "
+                "reports will not be generated "
+                f"for the table '{self.dataset.table_name}'."
+                )
 
         is_pk = self._is_pk()
 

diff --git a/src/syngen/ml/vae/models/dataset.py b/src/syngen/ml/vae/models/dataset.py
@@ -1324,12 +1324,16 @@ def pipeline(self) -> pd.DataFrame:
 
         # workaround for the case when all columns are dropped
         # add a technical column to proceed with the training process
+        print(f'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
+        print(f"self.features: {self.features}")
         if not self.features:
             tech_column = "syngen_tech_column"
-            logger.warning(
-                f"There are no columns left to train on for '{self.table_name}'. "
-                f"Adding a technical column '{tech_column}' to proceed "
-                f"with the training process."
+            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."
             )
             self.df[tech_column] = 1
             self._assign_float_feature(tech_column)