Move contraint reverese_transform order

sdv/data_processing/data_processor.py

@@ -710,9 +710,6 @@ def reverse_transform(self, data, reset_keys=False):
         except rdt.errors.NotFittedError:
             LOGGER.info(f'HyperTransformer has not been fitted for table {self.table_name}')
 
-        for constraint in reversed(self._constraints_to_reverse):
-            reversed_data = constraint.reverse_transform(reversed_data)
-
         num_rows = len(reversed_data)
         sampled_columns = list(reversed_data.columns)
         missing_columns = [
@@ -731,6 +728,13 @@ def reverse_transform(self, data, reset_keys=False):
             generated_keys = self.generate_keys(num_rows, reset_keys)
             sampled_columns.extend(self._keys)
 
+        for constraint in reversed(self._constraints_to_reverse):
+            reversed_data = constraint.reverse_transform(reversed_data)
+
+        # Add new columns generated by the constraint
+        new_columns = list(set(reversed_data.columns) - set(sampled_columns))
+        sampled_columns.extend(new_columns)
+
         # Sort the sampled columns in the order of the metadata
         # In multitable there may be missing columns in the sample such as foreign keys
         # And alternate keys. Thats the reason of ensuring that the metadata column is within

sdv/data_processing/test.py

@@ -0,0 +1,17 @@
+from sdv.data_processing.data_processor import DataProcessor
+from sdv.metadata.single_table import SingleTableMetadata
+from sdv.single_table.copulas import GaussianCopulaSynthesizer
+import pandas as pd
+
+data = pd.DataFrame({
+    'low': [1, 2, 3],
+})
+metadata = SingleTableMetadata()
+metadata.add_column('low', sdtype='numerical')
+metadata.update_column('low', sdtype='job', pii=True)
+
+dp = DataProcessor(metadata)
+dp.fit(data)
+transformed = dp.transform(data)
+reverse_transformed = dp.reverse_transform(transformed)
+print(reverse_transformed)