Remove model_missing_values from ClusterBasedNormalizer call (#310)

* Warning fix

* Feedback

* Up rdt version

Author: fealho

ctgan/data_transformer.py

@@ -46,7 +46,8 @@ def _fit_continuous(self, data):
                 A ``ColumnTransformInfo`` object.
         """
         column_name = data.columns[0]
-        gm = ClusterBasedNormalizer(model_missing_values=True, max_clusters=min(len(data), 10))
+        gm = ClusterBasedNormalizer(
+            missing_value_generation='from_column', max_clusters=min(len(data), 10))
         gm.fit(data, column_name)
         num_components = sum(gm.valid_component_indicator)
 

setup.py

@@ -21,7 +21,7 @@
     "torch>=1.8.0;python_version<'3.10'",
     "torch>=1.11.0;python_version>='3.10' and python_version<'3.11'",
     "torch>=2.0.0;python_version>='3.11'",
-    'rdt>=1.3.0,<2.0',
+    'rdt>=1.6.1,<2.0',
 ]
 
 setup_requires = [

tests/unit/test_data_transformer.py

@@ -75,7 +75,8 @@ def test__fit_continuous_max_clusters(self, MockCBN):
         transformer._fit_continuous(data)
 
         # Assert
-        MockCBN.assert_called_once_with(model_missing_values=True, max_clusters=len(data))
+        MockCBN.assert_called_once_with(
+            missing_value_generation='from_column', max_clusters=len(data))
 
     @patch('ctgan.data_transformer.OneHotEncoder')
     def test___fit_discrete(self, MockOHE):