[SPARK-51060][ML][PYTHON][CONNECT][FOLLOW-UP] Fix the uid of Bucketizer fitted by QuantileDiscretizer

### What changes were proposed in this pull request?
Fix the `uid` of `Bucketizer` fitted by `QuantileDiscretizer`

### Why are the changes needed?
On connect, the `Bucketizer` fitted by `QuantileDiscretizer` has the same `uid` as `QuantileDiscretizer`.
This is not consistent with pyspark classic.

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?
added tests

### Was this patch authored or co-authored using generative AI tooling?
no

Closes apache#49770 from zhengruifeng/ml_connect_qd_uid.

Authored-by: Ruifeng Zheng <[email protected]>
Signed-off-by: Ruifeng Zheng <[email protected]>

Author: zhengruifeng

python/pyspark/ml/tests/test_feature.py

@@ -998,7 +998,9 @@ def test_quantile_discretizer_single_column(self):
         bucketizer = qds.fit(df)
         self.assertIsInstance(bucketizer, Bucketizer)
         # Bucketizer doesn't inherit uid from QuantileDiscretizer
-        # self.assertEqual(qds.uid, bucketizer.uid)
+        self.assertNotEqual(qds.uid, bucketizer.uid)
+        self.assertTrue(qds.uid.startswith("QuantileDiscretizer"))
+        self.assertTrue(bucketizer.uid.startswith("Bucketizer"))
 
         # check model coefficients
         self.assertEqual(bucketizer.getSplits(), [float("-inf"), 0.4, float("inf")])
@@ -1046,7 +1048,9 @@ def test_quantile_discretizer_multiple_columns(self):
         bucketizer = qds.fit(df)
         self.assertIsInstance(bucketizer, Bucketizer)
         # Bucketizer doesn't inherit uid from QuantileDiscretizer
-        # self.assertEqual(qds.uid, bucketizer.uid)
+        self.assertNotEqual(qds.uid, bucketizer.uid)
+        self.assertTrue(qds.uid.startswith("QuantileDiscretizer"))
+        self.assertTrue(bucketizer.uid.startswith("Bucketizer"))
 
         # check model coefficients
         self.assertEqual(

python/pyspark/ml/util.py

@@ -136,7 +136,8 @@ def wrapped(self: "JavaEstimator", dataset: "ConnectDataFrame") -> Any:
             model_info = deserialize(properties)
             client.add_ml_cache(model_info.obj_ref.id)
             model = self._create_model(model_info.obj_ref.id)
-            model._resetUid(self.uid)
+            if model.__class__.__name__ not in ["Bucketizer"]:
+                model._resetUid(self.uid)
             return self._copyValues(model)
         else:
             return f(self, dataset)