[SPARK-15398][ML] Update the warning message to recommend ML usage

## What changes were proposed in this pull request?
MLlib are not recommended to use, and some methods are even deprecated.
Update the warning message to recommend ML usage.
```
  def showWarning() {
    System.err.println(
      """WARN: This is a naive implementation of Logistic Regression and is given as an example!
        |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
        |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
        |for more conventional use.
      """.stripMargin)
  }
```
To
```
  def showWarning() {
    System.err.println(
      """WARN: This is a naive implementation of Logistic Regression and is given as an example!
        |Please use org.apache.spark.ml.classification.LogisticRegression
        |for more conventional use.
      """.stripMargin)
  }
```

## How was this patch tested?
local build

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13190 from zhengruifeng/update_recd.

8 files changed, 16 insertions, 24 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
index fa10101955..97aefac025 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
@@ -24,7 +24,7 @@ import org.apache.commons.math3.linear._
  * Alternating least squares matrix factorization.
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to org.apache.spark.mllib.recommendation.ALS
+ * please refer to org.apache.spark.ml.recommendation.ALS.
  */
 object LocalALS {
 
@@ -96,7 +96,7 @@ object LocalALS {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of ALS and is given as an example!
-        |Please use the ALS method found in org.apache.spark.mllib.recommendation
+        |Please use org.apache.spark.ml.recommendation.ALS
         |for more conventional use.
       """.stripMargin)
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
index bec89f7c3d..3d02ce0561 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
@@ -26,8 +26,7 @@ import breeze.linalg.{DenseVector, Vector}
  * Logistic regression based classification.
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
- * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
+ * please refer to org.apache.spark.ml.classification.LogisticRegression.
  */
 object LocalFileLR {
   val D = 10   // Number of dimensions
@@ -43,8 +42,7 @@ object LocalFileLR {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of Logistic Regression and is given as an example!
-        |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
-        |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+        |Please use org.apache.spark.ml.classification.LogisticRegression
         |for more conventional use.
       """.stripMargin)
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
index f8961847f3..fca585c2a3 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
@@ -29,7 +29,7 @@ import breeze.linalg.{squaredDistance, DenseVector, Vector}
  * K-means clustering.
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to org.apache.spark.mllib.clustering.KMeans
+ * please refer to org.apache.spark.ml.clustering.KMeans.
  */
 object LocalKMeans {
   val N = 1000
@@ -66,7 +66,7 @@ object LocalKMeans {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of KMeans Clustering and is given as an example!
-        |Please use the KMeans method found in org.apache.spark.mllib.clustering
+        |Please use org.apache.spark.ml.clustering.KMeans
         |for more conventional use.
       """.stripMargin)
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
index 0baf6db607..13ccc2ae7c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
@@ -26,8 +26,7 @@ import breeze.linalg.{DenseVector, Vector}
  * Logistic regression based classification.
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
- * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
+ * please refer to org.apache.spark.ml.classification.LogisticRegression.
  */
 object LocalLR {
   val N = 10000  // Number of data points
@@ -50,8 +49,7 @@ object LocalLR {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of Logistic Regression and is given as an example!
-        |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
-        |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+        |Please use org.apache.spark.ml.classification.LogisticRegression
         |for more conventional use.
       """.stripMargin)
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
index 4263680c6f..b06c629802 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
@@ -26,7 +26,7 @@ import org.apache.spark._
  * Alternating least squares matrix factorization.
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to org.apache.spark.mllib.recommendation.ALS
+ * please refer to org.apache.spark.ml.recommendation.ALS.
  */
 object SparkALS {
 
@@ -81,7 +81,7 @@ object SparkALS {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of ALS and is given as an example!
-        |Please use the ALS method found in org.apache.spark.mllib.recommendation
+        |Please use org.apache.spark.ml.recommendation.ALS
         |for more conventional use.
       """.stripMargin)
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
index 7463b868ff..c514eb0fa5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -31,8 +31,7 @@ import org.apache.spark._
  * Logistic regression based classification.
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
- * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
+ * please refer to org.apache.spark.ml.classification.LogisticRegression.
  */
 object SparkHdfsLR {
   val D = 10   // Number of dimensions
@@ -54,8 +53,7 @@ object SparkHdfsLR {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of Logistic Regression and is given as an example!
-        |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
-        |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+        |Please use org.apache.spark.ml.classification.LogisticRegression
         |for more conventional use.
       """.stripMargin)
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
index d9f94a42b1..676164806e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -26,7 +26,7 @@ import org.apache.spark.{SparkConf, SparkContext}
  * K-means clustering.
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to org.apache.spark.mllib.clustering.KMeans
+ * please refer to org.apache.spark.ml.clustering.KMeans.
  */
 object SparkKMeans {
 
@@ -52,7 +52,7 @@ object SparkKMeans {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of KMeans Clustering and is given as an example!
-        |Please use the KMeans method found in org.apache.spark.mllib.clustering
+        |Please use org.apache.spark.ml.clustering.KMeans
         |for more conventional use.
       """.stripMargin)
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
index acd8656b65..718f84f645 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
@@ -31,8 +31,7 @@ import org.apache.spark._
  * Usage: SparkLR [slices]
  *
  * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
- * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
+ * please refer to org.apache.spark.ml.classification.LogisticRegression.
  */
 object SparkLR {
   val N = 10000  // Number of data points
@@ -55,8 +54,7 @@ object SparkLR {
   def showWarning() {
     System.err.println(
       """WARN: This is a naive implementation of Logistic Regression and is given as an example!
-        |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
-        |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+        |Please use org.apache.spark.ml.classification.LogisticRegression
         |for more conventional use.
       """.stripMargin)
   }