[SPARK-16750][ML] Fix GaussianMixture training failed due to feature column type mistake

## What changes were proposed in this pull request?
ML ```GaussianMixture``` training failed due to feature column type mistake. The feature column type should be ```ml.linalg.VectorUDT``` but got ```mllib.linalg.VectorUDT``` by mistake.
See [SPARK-16750](https://issues.apache.org/jira/browse/SPARK-16750) for how to reproduce this bug.
Why the unit tests did not complain this errors? Because some estimators/transformers missed calling ```transformSchema(dataset.schema)``` firstly during ```fit``` or ```transform```. I will also add this function to all estimators/transformers who missed in this PR.

## How was this patch tested?
No new tests, should pass existing ones.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14378 from yanboliang/spark-16750.

1 files changed, 2 insertions, 2 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
index 2b9912657f..d4ae59deff 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
@@ -196,7 +196,7 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
 
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): AFTSurvivalRegressionModel = {
-    validateAndTransformSchema(dataset.schema, fitting = true)
+    transformSchema(dataset.schema, logging = true)
     val instances = extractAFTPoints(dataset)
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
@@ -326,7 +326,7 @@ class AFTSurvivalRegressionModel private[ml] (
 
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
-    transformSchema(dataset.schema)
+    transformSchema(dataset.schema, logging = true)
     val predictUDF = udf { features: Vector => predict(features) }
     val predictQuantilesUDF = udf { features: Vector => predictQuantiles(features)}
     if (hasQuantilesCol) {