[SPARK-16750][FOLLOW-UP][ML] Add transformSchema for StringIndexer/VectorAssembler and fix failed tests.

## What changes were proposed in this pull request?
This is follow-up for #14378. When we add ```transformSchema``` for all estimators and transformers, I found there are tests failed for ```StringIndexer``` and ```VectorAssembler```. So I moved these parts of work separately in this PR, to make it more clear to review.
The corresponding tests should throw ```IllegalArgumentException``` at schema validation period after we add ```transformSchema```. It's efficient that to throw exception at the start of ```fit``` or ```transform``` rather than during the process.

## How was this patch tested?
Modified unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14455 from yanboliang/transformSchema.

2 files changed, 4 insertions, 1 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index fe79e2ec80..80fe46796f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -85,6 +85,7 @@ class StringIndexer @Since("1.4.0") (
 
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): StringIndexerModel = {
+    transformSchema(dataset.schema, logging = true)
     val counts = dataset.select(col($(inputCol)).cast(StringType))
       .rdd
       .map(_.getString(0))
@@ -160,7 +161,7 @@ class StringIndexerModel (
         "Skip StringIndexerModel.")
       return dataset.toDF
     }
-    validateAndTransformSchema(dataset.schema)
+    transformSchema(dataset.schema, logging = true)
 
     val indexer = udf { label: String =>
       if (labelToIndex.contains(label)) {
@@ -305,6 +306,7 @@ class IndexToString private[ml] (@Since("1.5.0") override val uid: String)
 
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
     val inputColSchema = dataset.schema($(inputCol))
     // If the labels array is empty use column metadata
     val values = if (!isDefined(labels) || $(labels).isEmpty) {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 142a2ae44c..ca900536bc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -51,6 +51,7 @@ class VectorAssembler @Since("1.4.0") (@Since("1.4.0") override val uid: String)
 
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
     // Schema transformation.
     val schema = dataset.schema
     lazy val first = dataset.toDF.first()