Fixing example(for now)

sdgx/models/components/transform.py

@@ -50,21 +50,7 @@ def _fit_continuous(self, data):
                 A ``ColumnTransformInfo`` object.
         """
         column_name = data.columns[0]
-
-        # 这里进行了修改，用于防止未来的兼容性问题，同时避免出现以下警告
-        # ======================================================
-        # FutureWarning: Future versions of RDT
-        # will not support the 'model_missing_values' parameter.
-        # Please switch to using the 'missing_value_generation'
-        # parameter to select your strategy.
-        # ======================================================
-
-        # 修改前
-        # gm = ClusterBasedNormalizer(model_missing_values=True, max_clusters=min(len(data), 10))
-        # 修改后
-        gm = ClusterBasedNormalizer(
-            missing_value_generation="from_column", max_clusters=min(len(data), 10)
-        )
+        gm = ClusterBasedNormalizer(model_missing_values=True, max_clusters=min(len(data), 10))
         gm.fit(data, column_name)
         num_components = sum(gm.valid_component_indicator)
 

sdgx/models/ml/single_table/ctgan.py

@@ -24,6 +24,7 @@
 from sdgx.models.components.transform import DataTransformer
 from sdgx.models.components.utils import random_state
 from sdgx.models.extension import hookimpl
+from sdgx.models.ml.single_table.base import TorchSynthesizerModel
 
 
 class Discriminator(Module):
@@ -110,7 +111,7 @@ def forward(self, input_):
 
 # 目前针对了较大的数据进行内存占用上的优化
 # 总体思路是采用分批 load 进内存的方法，优化其占用空间的大小
-class CTGAN(SynthesizerModel):
+class CTGAN(TorchSynthesizerModel):
     """Conditional Table GAN Synthesizer.
 
     This is the core class of the CTGAN project, where the different components