From 076df2efb1ba1286269b9769c6002598033bea71 Mon Sep 17 00:00:00 2001
From: Weichen Xu <weichen.xu@databricks.com>
Date: Mon, 15 Jul 2024 12:28:20 +0800
Subject: [PATCH] rename

Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
---
 .../org/apache/spark/ml/feature/StringIndexer.scala  | 12 ++++++------
 .../apache/spark/ml/feature/StringIndexerSuite.scala |  6 +++---
 2 files changed, 9 insertions(+), 9 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 9db3d44fb9abc..281f50a4773e0 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
@@ -43,7 +43,7 @@ import org.apache.spark.util.collection.OpenHashMap
 private[feature] trait StringIndexerBase extends Params with HasHandleInvalid with HasInputCol
   with HasOutputCol with HasInputCols with HasOutputCols {
 
-  @transient private[ml] var _transformDataset: Dataset[_] = _
+  @transient private[ml] var transformDataset: Dataset[_] = _
 
   /**
    * Param for how to handle invalid data (unseen labels or NULL values).
@@ -127,7 +127,7 @@ private[feature] trait StringIndexerBase extends Params with HasHandleInvalid wi
     val outputFields = inputColNames.zip(outputColNames).flatMap {
       case (inputColName, outputColName) =>
         try {
-          val dtype = _transformDataset.col(inputColName).expr.dataType
+          val dtype = transformDataset.col(inputColName).expr.dataType
           Some(
             validateAndTransformField(schema, inputColName, dtype, outputColName)
           )
@@ -246,9 +246,9 @@ class StringIndexer @Since("1.4.0") (
 
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): StringIndexerModel = {
-    _transformDataset = dataset
+    transformDataset = dataset
     transformSchema(dataset.schema, logging = true)
-    _transformDataset = null
+    transformDataset = null
 
     // In case of equal frequency when frequencyDesc/Asc, the strings are further sorted
     // alphabetically.
@@ -425,9 +425,9 @@ class StringIndexerModel (
 
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
-    _transformDataset = dataset
+    transformDataset = dataset
     transformSchema(dataset.schema, logging = true)
-    _transformDataset = null
+    transformDataset = null
 
     val (inputColNames, outputColNames) = getInOutCols()
     val outputColumns = new Array[Column](outputColNames.length)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 71ffe37e58305..47ff7e8a917a3 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -102,7 +102,7 @@ class StringIndexerSuite extends MLTest with DefaultReadWriteTest {
   test("StringIndexer.transformSchema)") {
     val idxToStr = new StringIndexer().setInputCol("input").setOutputCol("output")
     val inSchema = StructType(Seq(StructField("input", StringType)))
-    idxToStr._transformDataset = spark.createDataFrame(List(Row("a")).asJava, schema = inSchema)
+    idxToStr.transformDataset = spark.createDataFrame(List(Row("a")).asJava, schema = inSchema)
     val outSchema = idxToStr.transformSchema(inSchema)
     assert(outSchema("output").dataType === DoubleType)
   }
@@ -112,7 +112,7 @@ class StringIndexerSuite extends MLTest with DefaultReadWriteTest {
       setOutputCols(Array("output", "output2"))
     val inSchema = StructType(Seq(StructField("input", StringType),
       StructField("input2", StringType)))
-    idxToStr._transformDataset = spark.createDataFrame(
+    idxToStr.transformDataset = spark.createDataFrame(
       List(Row("a", "b")).asJava,
       schema = inSchema
     )
@@ -131,7 +131,7 @@ class StringIndexerSuite extends MLTest with DefaultReadWriteTest {
       "input1 struct<a struct<f1 string, f2 string>>, " +
       "input2 struct<b1 string, b2 string>, input3 string"
     )
-    idxToStr._transformDataset = spark.createDataFrame(
+    idxToStr.transformDataset = spark.createDataFrame(
       List(Row(Row(Row("a", "b")), Row("c", "d"), "e")).asJava,
       schema = inSchema
     )