[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

examples/pom.xml

@@ -166,6 +166,11 @@
         </exclusion>
       </exclusions>
     </dependency>
+    <dependency>
+      <groupId>com.github.scopt</groupId>
+      <artifactId>scopt_${scala.binary.version}</artifactId>
+      <version>3.2.0</version>
+    </dependency>
   </dependencies>
 
   <build>

examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala

@@ -0,0 +1,145 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.log4j.{Level, Logger}
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.classification.{LogisticRegressionWithSGD, SVMWithSGD}
+import org.apache.spark.mllib.evaluation.binary.BinaryClassificationMetrics
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}
+
+/**
+ * An example app for binary classification. Run with
+ * {{{
+ * ./bin/run-example org.apache.spark.examples.mllib.BinaryClassification
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object BinaryClassification {
+
+  object Algorithm extends Enumeration {
+    type Algorithm = Value
+    val SVM, LR = Value
+  }
+
+  object RegType extends Enumeration {
+    type RegType = Value
+    val L1, L2 = Value
+  }
+
+  import Algorithm._
+  import RegType._
+
+  case class Params(
+      input: String = null,
+      numIterations: Int = 100,
+      stepSize: Double = 1.0,
+      algorithm: Algorithm = LR,
+      regType: RegType = L2,
+      regParam: Double = 0.1)
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("BinaryClassification") {
+      head("BinaryClassification: an example app for binary classification.")
+      opt[Int]("numIterations")
+        .text("number of iterations")
+        .action((x, c) => c.copy(numIterations = x))
+      opt[Double]("stepSize")
+        .text(s"initial step size, default: ${defaultParams.stepSize}")
+        .action((x, c) => c.copy(stepSize = x))
+      opt[String]("algorithm")
+        .text(s"algorithm (${Algorithm.values.mkString(",")}), " +
+        s"default: ${defaultParams.algorithm}")
+        .action((x, c) => c.copy(algorithm = Algorithm.withName(x)))
+      opt[String]("regType")
+        .text(s"regularization type (${RegType.values.mkString(",")}), " +
+        s"default: ${defaultParams.regType}")
+        .action((x, c) => c.copy(regType = RegType.withName(x)))
+      opt[Double]("regParam")
+        .text(s"regularization parameter, default: ${defaultParams.regParam}")
+      arg[String]("<input>")
+        .required()
+        .text("input paths to labeled examples in LIBSVM format")
+        .action((x, c) => c.copy(input = x))
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    } getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"BinaryClassification with $params")
+    val sc = new SparkContext(conf)
+
+    Logger.getRootLogger.setLevel(Level.WARN)
+
+    val examples = MLUtils.loadLibSVMData(sc, params.input).cache()
+
+    val splits = examples.randomSplit(Array(0.8, 0.2))
+    val training = splits(0).cache()
+    val test = splits(1).cache()
+
+    val numTraining = training.count()
+    val numTest = test.count()
+    println(s"Training: $numTraining, test: $numTest.")
+
+    examples.unpersist(blocking = false)
+
+    val updater = params.regType match {
+      case L1 => new L1Updater()
+      case L2 => new SquaredL2Updater()
+    }
+
+    val model = params.algorithm match {
+      case LR =>
+        val algorithm = new LogisticRegressionWithSGD()
+        algorithm.optimizer
+          .setNumIterations(params.numIterations)
+          .setStepSize(params.stepSize)
+          .setUpdater(updater)
+          .setRegParam(params.regParam)
+        algorithm.run(training).clearThreshold()
+      case SVM =>
+        val algorithm = new SVMWithSGD()
+        algorithm.optimizer
+          .setNumIterations(params.numIterations)
+          .setStepSize(params.stepSize)
+          .setUpdater(updater)
+          .setRegParam(params.regParam)
+        algorithm.run(training).clearThreshold()
+    }
+
+    val prediction = model.predict(test.map(_.features))
+    val predictionAndLabel = prediction.zip(test.map(_.label))
+
+    val metrics = new BinaryClassificationMetrics(predictionAndLabel)
+
+    println(s"Test areaUnderPR = ${metrics.areaUnderPR()}.")
+    println(s"Test areaUnderROC = ${metrics.areaUnderROC()}.")
+
+    sc.stop()
+  }
+}

examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala

@@ -0,0 +1,161 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.{DecisionTree, impurity}
+import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.model.DecisionTreeModel
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+
+/**
+ * An example runner for decision tree. Run with
+ * {{{
+ * ./bin/spark-example org.apache.spark.examples.mllib.DecisionTreeRunner [options]
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object DecisionTreeRunner {
+
+  object ImpurityType extends Enumeration {
+    type ImpurityType = Value
+    val Gini, Entropy, Variance = Value
+  }
+
+  import ImpurityType._
+
+  case class Params(
+      input: String = null,
+      algo: Algo = Classification,
+      maxDepth: Int = 5,
+      impurity: ImpurityType = Gini,
+      maxBins: Int = 20)
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("DecisionTreeRunner") {
+      head("DecisionTreeRunner: an example decision tree app.")
+      opt[String]("algo")
+        .text(s"algorithm (${Algo.values.mkString(",")}), default: ${defaultParams.algo}")
+        .action((x, c) => c.copy(algo = Algo.withName(x)))
+      opt[String]("impurity")
+        .text(s"impurity type (${ImpurityType.values.mkString(",")}), " +
+          s"default: ${defaultParams.impurity}")
+        .action((x, c) => c.copy(impurity = ImpurityType.withName(x)))
+      opt[Int]("maxDepth")
+        .text(s"max depth of the tree, default: ${defaultParams.maxDepth}")
+        .action((x, c) => c.copy(maxDepth = x))
+      opt[Int]("maxBins")
+        .text(s"max number of bins, default: ${defaultParams.maxBins}")
+        .action((x, c) => c.copy(maxBins = x))
+      arg[String]("<input>")
+        .text("input paths to labeled examples in dense format (label,f0 f1 f2 ...)")
+        .required()
+        .action((x, c) => c.copy(input = x))
+      checkConfig { params =>
+        if (params.algo == Classification &&
+            (params.impurity == Gini || params.impurity == Entropy)) {
+          success
+        } else if (params.algo == Regression && params.impurity == Variance) {
+          success
+        } else {
+          failure(s"Algo ${params.algo} is not compatible with impurity ${params.impurity}.")
+        }
+      }
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    }.getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName("DecisionTreeRunner")
+    val sc = new SparkContext(conf)
+
+    // Load training data and cache it.
+    val examples = MLUtils.loadLabeledData(sc, params.input).cache()
+
+    val splits = examples.randomSplit(Array(0.8, 0.2))
+    val training = splits(0).cache()
+    val test = splits(1).cache()
+
+    val numTraining = training.count()
+    val numTest = test.count()
+
+    println(s"numTraining = $numTraining, numTest = $numTest.")
+
+    examples.unpersist(blocking = false)
+
+    val impurityCalculator = params.impurity match {
+      case Gini => impurity.Gini
+      case Entropy => impurity.Entropy
+      case Variance => impurity.Variance
+    }
+
+    val strategy = new Strategy(params.algo, impurityCalculator, params.maxDepth, params.maxBins)
+    val model = DecisionTree.train(training, strategy)
+
+    if (params.algo == Classification) {
+      val accuracy = accuracyScore(model, test)
+      println(s"Test accuracy = $accuracy.")
+    }
+
+    if (params.algo == Regression) {
+      val mse = meanSquaredError(model, test)
+      println(s"Test mean squared error = $mse.")
+    }
+
+    sc.stop()
+  }
+
+  /**
+   * Calculates the classifier accuracy.
+   */
+  private def accuracyScore(
+      model: DecisionTreeModel,
+      data: RDD[LabeledPoint],
+      threshold: Double = 0.5): Double = {
+    def predictedValue(features: Vector): Double = {
+      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()
+    correctCount.toDouble / count
+  }
+
+  /**
+   * 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()
+  }
+}