SPARK-2434: Warning messages redirecting to original implementaions a…

…dded.
tdas · Jul 17, 2014 · 2cb5301 · 2cb5301
1 parent 9c24974
commit 2cb5301
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Showing 8 changed files with 101 additions and 7 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
@@ -117,11 +117,20 @@ object LocalALS {
       }
       case _ => {
         System.err.println("Usage: LocalALS <M> <U> <F> <iters>")
+        System.err.println(
+          """WARNING: 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 FOR
+            |MORE CONVENTIONAL USE
+          """.stripMargin)
         System.exit(1)
       }
     }
     printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
-
+    System.err.println(
+      """WARNING: 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 FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     val R = generateR()
 
     // Initialize m and u randomly
@@ -136,5 +145,10 @@ object LocalALS {
       println("RMSE = " + rmse(R, ms, us))
       println()
     }
+    System.err.println(
+      """WARNING: 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 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
@@ -36,7 +36,11 @@ object LocalFileLR {
     val lines = scala.io.Source.fromFile(args(0)).getLines().toArray
     val points = lines.map(parsePoint _)
     val ITERATIONS = args(1).toInt
-
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
@@ -52,5 +56,10 @@ object LocalFileLR {
     }
 
     println("Final w: " + w)
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification 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
@@ -71,6 +71,11 @@ object LocalKMeans {
       points.add(data(rand.nextInt(N)))
     }
 
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF K-MEANS CLUSTERING AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE KMeans METHOD FOUND IN org.apache.spark.mllib.clustering FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     val iter = points.iterator
     for (i <- 1 to points.size) {
       kPoints.put(i, iter.next())
@@ -103,5 +108,10 @@ object LocalKMeans {
     }
 
     println("Final centers: " + kPoints)
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF K-MEANS CLUSTERING AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE KMeans METHOD FOUND IN org.apache.spark.mllib.clustering 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
@@ -44,7 +44,11 @@ object LocalLR {
 
   def main(args: Array[String]) {
     val data = generateData
-
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
@@ -60,5 +64,10 @@ object LocalLR {
     }
 
     println("Final w: " + w)
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+      |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification 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
@@ -101,9 +101,19 @@ object SparkALS {
         slices = slices_.getOrElse("2").toInt
       case _ =>
         System.err.println("Usage: SparkALS [M] [U] [F] [iters] [slices]")
+        System.err.println(
+          """WARNING: 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 FOR
+            |MORE CONVENTIONAL USE
+          """.stripMargin)
         System.exit(1)
     }
     printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
+    System.err.println(
+      """WARNING: 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 FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     val sparkConf = new SparkConf().setAppName("SparkALS")
     val sc = new SparkContext(sparkConf)
 
@@ -130,7 +140,11 @@ object SparkALS {
       println("RMSE = " + rmse(R, ms, us))
       println()
     }
-
+    System.err.println(
+      """WARNING: 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 FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     sc.stop()
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -51,9 +51,18 @@ object SparkHdfsLR {
   def main(args: Array[String]) {
     if (args.length < 2) {
       System.err.println("Usage: SparkHdfsLR <file> <iters>")
+      System.err.println(
+        """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+          |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification FOR
+          |MORE CONVENTIONAL USE
+        """.stripMargin)
       System.exit(1)
     }
-
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     val sparkConf = new SparkConf().setAppName("SparkHdfsLR")
     val inputPath = args(0)
     val conf = SparkHadoopUtil.get.newConfiguration()
@@ -78,6 +87,11 @@ object SparkHdfsLR {
     }
 
     println("Final w: " + w)
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     sc.stop()
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -48,9 +48,19 @@ object SparkKMeans {
 
   def main(args: Array[String]) {
     if (args.length < 3) {
-      System.err.println("Usage: SparkKMeans <file> <k> <convergeDist>")
+      System.err.println("Usage: SparkKMeans <file> <k> <convergeDist>"
+        System.err.println(
+          """WARNING: THIS IS A NAIVE IMPLEMENTATION OF K-MEANS CLUSTERING AND IS GIVEN AS AN EXAMPLE!
+            |PLEASE USE THE KMeans METHOD FOUND IN org.apache.spark.mllib.clustering FOR
+            |MORE CONVENTIONAL USE
+          """.stripMargin)
       System.exit(1)
     }
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF K-MEANS CLUSTERING AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE KMeans METHOD FOUND IN org.apache.spark.mllib.clustering FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     val sparkConf = new SparkConf().setAppName("SparkKMeans")
     val sc = new SparkContext(sparkConf)
     val lines = sc.textFile(args(0))
@@ -82,6 +92,11 @@ object SparkKMeans {
 
     println("Final centers:")
     kPoints.foreach(println)
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF K-MEANS CLUSTERING AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE KMeans METHOD FOUND IN org.apache.spark.mllib.clustering FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     sc.stop()
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
@@ -52,7 +52,11 @@ object SparkLR {
     val sc = new SparkContext(sparkConf)
     val numSlices = if (args.length > 0) args(0).toInt else 2
     val points = sc.parallelize(generateData, numSlices).cache()
-
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
@@ -66,6 +70,11 @@ object SparkLR {
     }
 
     println("Final w: " + w)
+    System.err.println(
+      """WARNING: THIS IS A NAIVE IMPLEMENTATION OF LOGISTIC REGRESSION AND IS GIVEN AS AN EXAMPLE!
+        |PLEASE USE THE LogisticRegression METHOD FOUND IN org.apache.spark.mllib.classification FOR
+        |MORE CONVENTIONAL USE
+      """.stripMargin)
     sc.stop()
   }
 }