[SPARK-14907][MLLIB] Use repartition in GLMRegressionModel.save

## What changes were proposed in this pull request?

This PR changes `GLMRegressionModel.save` function like the following code that is similar to other algorithms' parquet write.
```
- val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
- // TODO: repartition with 1 partition after SPARK-5532 gets fixed
- dataRDD.write.parquet(Loader.dataPath(path))
+ sqlContext.createDataFrame(Seq(data)).repartition(1).write.parquet(Loader.dataPath(path))
```

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12676 from dongjoon-hyun/SPARK-14907.

1 files changed, 1 insertions, 4 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala
index a6e1767fe2..7696fdf2dc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala
@@ -48,7 +48,6 @@ private[regression] object GLMRegressionModel {
         weights: Vector,
         intercept: Double): Unit = {
       val sqlContext = SQLContext.getOrCreate(sc)
-      import sqlContext.implicits._
 
       // Create JSON metadata.
       val metadata = compact(render(
@@ -58,9 +57,7 @@ private[regression] object GLMRegressionModel {
 
       // Create Parquet data.
       val data = Data(weights, intercept)
-      val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
-      // TODO: repartition with 1 partition after SPARK-5532 gets fixed
-      dataRDD.write.parquet(Loader.dataPath(path))
+      sqlContext.createDataFrame(Seq(data)).repartition(1).write.parquet(Loader.dataPath(path))
     }
 
     /**