[SPARK-1636][MLLIB] Move main methods to examples

* `NaiveBayes` -> `SparseNaiveBayes`
* `KMeans` -> `DenseKMeans`
* `SVMWithSGD` and `LogisticRegerssionWithSGD` -> `BinaryClassification`
* `ALS` -> `MovieLensALS`
* `LinearRegressionWithSGD`, `LassoWithSGD`, and `RidgeRegressionWithSGD` -> `LinearRegression`
* `DecisionTree` -> `DecisionTreeRunner`

`scopt` is used for parsing command-line parameters. `scopt` has MIT license and it only depends on `scala-library`.

Example help message:

~~~
BinaryClassification: an example app for binary classification.
Usage: BinaryClassification [options] <input>

  --numIterations <value>
        number of iterations
  --stepSize <value>
        initial step size, default: 1.0
  --algorithm <value>
        algorithm (SVM,LR), default: LR
  --regType <value>
        regularization type (L1,L2), default: L2
  --regParam <value>
        regularization parameter, default: 0.1
  <input>
        input paths to labeled examples in LIBSVM format
~~~

Author: Xiangrui Meng <meng@databricks.com>

Closes #584 from mengxr/mllib-main and squashes the following commits:

7b58c60 [Xiangrui Meng] minor
6e35d7e [Xiangrui Meng] make imports explicit and fix code style
c6178c9 [Xiangrui Meng] update TS PCA/SVD to use new spark-submit
6acff75 [Xiangrui Meng] use scopt for DecisionTreeRunner
be86069 [Xiangrui Meng] use main instead of extending App
b3edf68 [Xiangrui Meng] move DecisionTree's main method to examples
8bfaa5a [Xiangrui Meng] change NaiveBayesParams to Params
fe23dcb [Xiangrui Meng] remove main from KMeans and add DenseKMeans as an example
67f4448 [Xiangrui Meng] remove main methods from linear regression algorithms and add LinearRegression example
b066bbc [Xiangrui Meng] remove main from ALS and add MovieLensALS example
b040f3b [Xiangrui Meng] change BinaryClassificationParams to Params
577945b [Xiangrui Meng] remove unused imports from NB
3d299bc [Xiangrui Meng] remove main from LR/SVM and add an example app for binary classification
f70878e [Xiangrui Meng] remove main from NaiveBayes and add an example NaiveBayes app
01ec2cd [Xiangrui Meng] Merge branch 'master' into mllib-main
9420692 [Xiangrui Meng] add scopt to examples dependencies

(cherry picked from commit 3f38334f441940ed0a5bbf5588ca7f22d3940359)
Signed-off-by: Reynold Xin <rxin@apache.org>

9 files changed, 7 insertions, 304 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 4f9eaacf67..780e8bae42 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.mllib.classification
 
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.{DataValidators, MLUtils}
+import org.apache.spark.mllib.util.DataValidators
 import org.apache.spark.rdd.RDD
 
 /**
@@ -183,19 +182,4 @@ object LogisticRegressionWithSGD {
       numIterations: Int): LogisticRegressionModel = {
     train(input, numIterations, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 4) {
-      println("Usage: LogisticRegression <master> <input_dir> <step_size> " +
-        "<niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "LogisticRegression")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = LogisticRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble)
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 18658850a2..f6f62ce2de 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -20,11 +20,10 @@ package org.apache.spark.mllib.classification
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
 /**
@@ -158,23 +157,4 @@ object NaiveBayes {
   def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
     new NaiveBayes(lambda).run(input)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 2 && args.length != 3) {
-      println("Usage: NaiveBayes <master> <input_dir> [<lambda>]")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "NaiveBayes")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = if (args.length == 2) {
-      NaiveBayes.train(data)
-    } else {
-      NaiveBayes.train(data, args(2).toDouble)
-    }
-
-    println("Pi\n: " + model.pi)
-    println("Theta:\n" + model.theta)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 956654b1fe..81b126717e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.mllib.classification
 
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.{DataValidators, MLUtils}
+import org.apache.spark.mllib.util.DataValidators
 import org.apache.spark.rdd.RDD
 
 /**
@@ -183,19 +182,4 @@ object SVMWithSGD {
   def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel = {
     train(input, numIterations, 1.0, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: SVM <master> <input_dir> <step_size> <regularization_parameter> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "SVM")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = SVMWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
-
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index dee9ef07e4..a64c5d44be 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -21,8 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import breeze.linalg.{DenseVector => BDV, Vector => BV, norm => breezeNorm}
 
-import org.apache.spark.annotation.Experimental
-import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLUtils
@@ -396,28 +395,6 @@ object KMeans {
       v2: BreezeVectorWithNorm): Double = {
     MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
   }
-
-  @Experimental
-  def main(args: Array[String]) {
-    if (args.length < 4) {
-      println("Usage: KMeans <master> <input_file> <k> <max_iterations> [<runs>]")
-      System.exit(1)
-    }
-    val (master, inputFile, k, iters) = (args(0), args(1), args(2).toInt, args(3).toInt)
-    val runs = if (args.length >= 5) args(4).toInt else 1
-    val sc = new SparkContext(master, "KMeans")
-    val data = sc.textFile(inputFile)
-      .map(line => Vectors.dense(line.split(' ').map(_.toDouble)))
-      .cache()
-    val model = KMeans.train(data, k, iters, runs)
-    val cost = model.computeCost(data)
-    println("Cluster centers:")
-    for (c <- model.clusterCenters) {
-      println("  " + c)
-    }
-    println("Cost: " + cost)
-    System.exit(0)
-  }
 }
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 60fb73f2b5..2a77e1a9ef 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -23,15 +23,13 @@ import scala.util.Random
 import scala.util.Sorting
 import scala.util.hashing.byteswap32
 
-import com.esotericsoftware.kryo.Kryo
 import org.jblas.{DoubleMatrix, SimpleBlas, Solve}
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.{Logging, HashPartitioner, Partitioner, SparkContext, SparkConf}
+import org.apache.spark.{Logging, HashPartitioner, Partitioner}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.rdd.RDD
-import org.apache.spark.serializer.KryoRegistrator
 import org.apache.spark.SparkContext._
 import org.apache.spark.util.Utils
 
@@ -707,45 +705,4 @@ object ALS {
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, 0.01, -1, 1.0)
   }
-
-  private class ALSRegistrator extends KryoRegistrator {
-    override def registerClasses(kryo: Kryo) {
-      kryo.register(classOf[Rating])
-    }
-  }
-
-  def main(args: Array[String]) {
-    if (args.length < 5 || args.length > 9) {
-      println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir> " +
-        "[<lambda>] [<implicitPrefs>] [<alpha>] [<blocks>]")
-      System.exit(1)
-    }
-    val (master, ratingsFile, rank, iters, outputDir) =
-      (args(0), args(1), args(2).toInt, args(3).toInt, args(4))
-    val lambda = if (args.length >= 6) args(5).toDouble else 0.01
-    val implicitPrefs = if (args.length >= 7) args(6).toBoolean else false
-    val alpha = if (args.length >= 8) args(7).toDouble else 1
-    val blocks = if (args.length == 9) args(8).toInt else -1
-    val conf = new SparkConf()
-      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-      .set("spark.kryo.registrator",  classOf[ALSRegistrator].getName)
-      .set("spark.kryo.referenceTracking", "false")
-      .set("spark.kryoserializer.buffer.mb", "8")
-      .set("spark.locality.wait", "10000")
-    val sc = new SparkContext(master, "ALS", conf)
-
-    val ratings = sc.textFile(ratingsFile).map { line =>
-      val fields = line.split(',')
-      Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
-    }
-    val model = new ALS(rank = rank, iterations = iters, lambda = lambda,
-      numBlocks = blocks, implicitPrefs = implicitPrefs, alpha = alpha).run(ratings)
-
-    model.userFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") }
-                      .saveAsTextFile(outputDir + "/userFeatures")
-    model.productFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") }
-                         .saveAsTextFile(outputDir + "/productFeatures")
-    println("Final user/product features written to " + outputDir)
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 5f0812fd2e..0e6fb1b1ca 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -17,10 +17,8 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
 /**
@@ -173,19 +171,4 @@ object LassoWithSGD {
       numIterations: Int): LassoModel = {
     train(input, numIterations, 1.0, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: Lasso <master> <input_dir> <step_size> <regularization_parameter> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "Lasso")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = LassoWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
-
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 228fa8db3e..1532ff90d8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -17,11 +17,9 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
-import org.apache.spark.mllib.util.MLUtils
 
 /**
  * Regression model trained using LinearRegression.
@@ -156,18 +154,4 @@ object LinearRegressionWithSGD {
       numIterations: Int): LinearRegressionModel = {
     train(input, numIterations, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: LinearRegression <master> <input_dir> <step_size> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "LinearRegression")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = LinearRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble)
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index e702027c7c..5f7e25a9b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -17,10 +17,8 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.linalg.Vector
 
 /**
@@ -170,21 +168,4 @@ object RidgeRegressionWithSGD {
       numIterations: Int): RidgeRegressionModel = {
     train(input, numIterations, 1.0, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: RidgeRegression <master> <input_dir> <step_size> " +
-        "<regularization_parameter> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "RidgeRegression")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = RidgeRegressionWithSGD.train(data, args(4).toInt, args(2).toDouble,
-        args(3).toDouble)
-
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index f68076f426..59ed01debf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -18,18 +18,16 @@
 package org.apache.spark.mllib.tree
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.SparkContext._
+import org.apache.spark.Logging
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
-import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance}
+import org.apache.spark.mllib.tree.impurity.Impurity
 import org.apache.spark.mllib.tree.model._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
  * :: Experimental ::
@@ -1028,129 +1026,4 @@ object DecisionTree extends Serializable with Logging {
         throw new UnsupportedOperationException("approximate histogram not supported yet.")
     }
   }
-
-  private val usage = """
-    Usage: DecisionTreeRunner <master>[slices] --algo <Classification,
-    Regression> --trainDataDir path --testDataDir path --maxDepth num [--impurity <Gini,Entropy,
-    Variance>] [--maxBins num]
-              """
-
-  def main(args: Array[String]) {
-
-    if (args.length < 2) {
-      System.err.println(usage)
-      System.exit(1)
-    }
-
-    val sc = new SparkContext(args(0), "DecisionTree")
-
-    val argList = args.toList.drop(1)
-    type OptionMap = Map[Symbol, Any]
-
-    def nextOption(map : OptionMap, list: List[String]): OptionMap = {
-      list match {
-        case Nil => map
-        case "--algo" :: string :: tail => nextOption(map ++ Map('algo -> string), tail)
-        case "--impurity" :: string :: tail => nextOption(map ++ Map('impurity -> string), tail)
-        case "--maxDepth" :: string :: tail => nextOption(map ++ Map('maxDepth -> string), tail)
-        case "--maxBins" :: string :: tail => nextOption(map ++ Map('maxBins -> string), tail)
-        case "--trainDataDir" :: string :: tail => nextOption(map ++ Map('trainDataDir -> string)
-          , tail)
-        case "--testDataDir" :: string :: tail => nextOption(map ++ Map('testDataDir -> string),
-          tail)
-        case string :: Nil =>  nextOption(map ++ Map('infile -> string), list.tail)
-        case option :: tail => logError("Unknown option " + option)
-          sys.exit(1)
-      }
-    }
-    val options = nextOption(Map(), argList)
-    logDebug(options.toString())
-
-    // Load training data.
-    val trainData = loadLabeledData(sc, options.get('trainDataDir).get.toString)
-
-    // Identify the type of algorithm.
-    val algoStr =  options.get('algo).get.toString
-    val algo = algoStr match {
-      case "Classification" => Classification
-      case "Regression" => Regression
-    }
-
-    // Identify the type of impurity.
-    val impurityStr = options.getOrElse('impurity,
-      if (algo == Classification) "Gini" else "Variance").toString
-    val impurity = impurityStr match {
-      case "Gini" => Gini
-      case "Entropy" => Entropy
-      case "Variance" => Variance
-    }
-
-    val maxDepth = options.getOrElse('maxDepth, "1").toString.toInt
-    val maxBins = options.getOrElse('maxBins, "100").toString.toInt
-
-    val strategy = new Strategy(algo, impurity, maxDepth, maxBins)
-    val model = DecisionTree.train(trainData, strategy)
-
-    // Load test data.
-    val testData = loadLabeledData(sc, options.get('testDataDir).get.toString)
-
-    // Measure algorithm accuracy
-    if (algo == Classification) {
-      val accuracy = accuracyScore(model, testData)
-      logDebug("accuracy = " + accuracy)
-    }
-
-    if (algo == Regression) {
-      val mse = meanSquaredError(model, testData)
-      logDebug("mean square error = " + mse)
-    }
-
-    sc.stop()
-  }
-
-  /**
-   * Load labeled data from a file. The data format used here is
-   * <L>, <f1> <f2> ...,
-   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
-   *
-   * @param sc SparkContext
-   * @param dir Directory to the input data files.
-   * @return An RDD of LabeledPoint. Each labeled point has two elements: the first element is
-   *         the label, and the second element represents the feature values (an array of Double).
-   */
-  private def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
-    sc.textFile(dir).map { line =>
-      val parts = line.trim().split(",")
-      val label = parts(0).toDouble
-      val features = Vectors.dense(parts.slice(1,parts.length).map(_.toDouble))
-      LabeledPoint(label, features)
-    }
-  }
-
-  // TODO: Port this method to a generic metrics package.
-  /**
-   * Calculates the classifier accuracy.
-   */
-  private def accuracyScore(model: DecisionTreeModel, data: RDD[LabeledPoint],
-      threshold: Double = 0.5): Double = {
-    def predictedValue(features: Vector) = {
-      if (model.predict(features) < threshold) 0.0 else 1.0
-    }
-    val correctCount = data.filter(y => predictedValue(y.features) == y.label).count()
-    val count = data.count()
-    logDebug("correct prediction count = " +  correctCount)
-    logDebug("data count = " + count)
-    correctCount.toDouble / count
-  }
-
-  // TODO: Port this method to a generic metrics package
-  /**
-   * Calculates the mean squared error for regression.
-   */
-  private def meanSquaredError(tree: DecisionTreeModel, data: RDD[LabeledPoint]): Double = {
-    data.map { y =>
-      val err = tree.predict(y.features) - y.label
-      err * err
-    }.mean()
-  }
 }