[SPARK-15449][MLLIB][EXAMPLE] Wrong Data Format - Documentation Issue

## What changes were proposed in this pull request?

(Please fill in changes proposed in this fix)
In the MLLib naivebayes example, scala and python example doesn't use libsvm data, but Java does.

I make changes in scala and python example to use the libsvm data as the same as Java example.

## How was this patch tested?

Manual tests

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #13301 from wangmiao1981/example.

4 files changed, 10 insertions, 33 deletions

diff --git a/data/mllib/sample_naive_bayes_data.txt b/data/mllib/sample_naive_bayes_data.txt
deleted file mode 100644
index bd22bea3a5..0000000000
--- a/data/mllib/sample_naive_bayes_data.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-0,1 0 0
-0,2 0 0
-0,3 0 0
-0,4 0 0
-1,0 1 0
-1,0 2 0
-1,0 3 0
-1,0 4 0
-2,0 0 1
-2,0 0 2
-2,0 0 3
-2,0 0 4
\ No newline at end of file
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
index 2b17dbb963..f4ec04b0c6 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
@@ -36,9 +36,9 @@ public class JavaNaiveBayesExample {
     SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample");
     JavaSparkContext jsc = new JavaSparkContext(sparkConf);
     // $example on$
-    String path = "data/mllib/sample_naive_bayes_data.txt";
+    String path = "data/mllib/sample_libsvm_data.txt";
     JavaRDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();
-    JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4}, 12345);
+    JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4});
     JavaRDD<LabeledPoint> training = tmp[0]; // training set
     JavaRDD<LabeledPoint> test = tmp[1]; // test set
     final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
diff --git a/examples/src/main/python/mllib/naive_bayes_example.py b/examples/src/main/python/mllib/naive_bayes_example.py
index 35724f7d6a..749353b20e 100644
--- a/examples/src/main/python/mllib/naive_bayes_example.py
+++ b/examples/src/main/python/mllib/naive_bayes_example.py
@@ -29,15 +29,9 @@ import shutil
 from pyspark import SparkContext
 # $example on$
 from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
-from pyspark.mllib.linalg import Vectors
-from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.util import MLUtils
 
 
-def parseLine(line):
-    parts = line.split(',')
-    label = float(parts[0])
-    features = Vectors.dense([float(x) for x in parts[1].split(' ')])
-    return LabeledPoint(label, features)
 # $example off$
 
 if __name__ == "__main__":
@@ -45,10 +39,11 @@ if __name__ == "__main__":
     sc = SparkContext(appName="PythonNaiveBayesExample")
 
     # $example on$
-    data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
+    # Load and parse the data file.
+    data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 
     # Split data approximately into training (60%) and test (40%)
-    training, test = data.randomSplit([0.6, 0.4], seed=0)
+    training, test = data.randomSplit([0.6, 0.4])
 
     # Train a naive Bayes model.
     model = NaiveBayes.train(training, 1.0)
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
index 0187ad603a..b321d8e127 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
@@ -21,8 +21,7 @@ package org.apache.spark.examples.mllib
 import org.apache.spark.{SparkConf, SparkContext}
 // $example on$
 import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel}
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLUtils
 // $example off$
 
 object NaiveBayesExample {
@@ -31,16 +30,11 @@ object NaiveBayesExample {
     val conf = new SparkConf().setAppName("NaiveBayesExample")
     val sc = new SparkContext(conf)
     // $example on$
-    val data = sc.textFile("data/mllib/sample_naive_bayes_data.txt")
-    val parsedData = data.map { line =>
-      val parts = line.split(',')
-      LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
-    }
+    // Load and parse the data file.
+    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 
     // Split data into training (60%) and test (40%).
-    val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
-    val training = splits(0)
-    val test = splits(1)
+    val Array(training, test) = data.randomSplit(Array(0.6, 0.4))
 
     val model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial")