[SPARK-15141][EXAMPLE][DOC] Update OneVsRest Examples

## What changes were proposed in this pull request?
1, Add python example for OneVsRest
2, remove args-parsing

## How was this patch tested?
manual tests
`./bin/spark-submit examples/src/main/python/ml/one_vs_rest_example.py`

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12920 from zhengruifeng/ovr_pe.

1 files changed, 24 insertions, 132 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
index fc73ae07ff..0b333cf629 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
@@ -18,171 +18,63 @@
 // scalastyle:off println
 package org.apache.spark.examples.ml
 
-import java.util.concurrent.TimeUnit.{NANOSECONDS => NANO}
-
-import scopt.OptionParser
-
 // $example on$
-import org.apache.spark.examples.mllib.AbstractParams
 import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
-import org.apache.spark.ml.util.MetadataUtils
-import org.apache.spark.mllib.evaluation.MulticlassMetrics
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 import org.apache.spark.sql.DataFrame
 // $example off$
 import org.apache.spark.sql.SparkSession
 
 /**
- * An example runner for Multiclass to Binary Reduction with One Vs Rest.
- * The example uses Logistic Regression as the base classifier. All parameters that
- * can be specified on the base classifier can be passed in to the runner options.
+ * An example of Multiclass to Binary Reduction with One Vs Rest,
+ * using Logistic Regression as the base classifier.
  * Run with
  * {{{
- * ./bin/run-example ml.OneVsRestExample [options]
- * }}}
- * For local mode, run
- * {{{
- * ./bin/spark-submit --class org.apache.spark.examples.ml.OneVsRestExample --driver-memory 1g
- *   [examples JAR path] [options]
+ * ./bin/run-example ml.OneVsRestExample
  * }}}
- * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */
-object OneVsRestExample {
-
-  case class Params private[ml] (
-      input: String = null,
-      testInput: Option[String] = None,
-      maxIter: Int = 100,
-      tol: Double = 1E-6,
-      fitIntercept: Boolean = true,
-      regParam: Option[Double] = None,
-      elasticNetParam: Option[Double] = None,
-      fracTest: Double = 0.2) extends AbstractParams[Params]
 
+object OneVsRestExample {
   def main(args: Array[String]) {
-    val defaultParams = Params()
-
-    val parser = new OptionParser[Params]("OneVsRest Example") {
-      head("OneVsRest Example: multiclass to binary reduction using OneVsRest")
-      opt[String]("input")
-        .text("input path to labeled examples. This path must be specified")
-        .required()
-        .action((x, c) => c.copy(input = x))
-      opt[Double]("fracTest")
-        .text(s"fraction of data to hold out for testing.  If given option testInput, " +
-        s"this option is ignored. default: ${defaultParams.fracTest}")
-        .action((x, c) => c.copy(fracTest = x))
-      opt[String]("testInput")
-        .text("input path to test dataset.  If given, option fracTest is ignored")
-        .action((x, c) => c.copy(testInput = Some(x)))
-      opt[Int]("maxIter")
-        .text(s"maximum number of iterations for Logistic Regression." +
-          s" default: ${defaultParams.maxIter}")
-        .action((x, c) => c.copy(maxIter = x))
-      opt[Double]("tol")
-        .text(s"the convergence tolerance of iterations for Logistic Regression." +
-          s" default: ${defaultParams.tol}")
-        .action((x, c) => c.copy(tol = x))
-      opt[Boolean]("fitIntercept")
-        .text(s"fit intercept for Logistic Regression." +
-        s" default: ${defaultParams.fitIntercept}")
-        .action((x, c) => c.copy(fitIntercept = x))
-      opt[Double]("regParam")
-        .text(s"the regularization parameter for Logistic Regression.")
-        .action((x, c) => c.copy(regParam = Some(x)))
-      opt[Double]("elasticNetParam")
-        .text(s"the ElasticNet mixing parameter for Logistic Regression.")
-        .action((x, c) => c.copy(elasticNetParam = Some(x)))
-      checkConfig { params =>
-        if (params.fracTest < 0 || params.fracTest >= 1) {
-          failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1).")
-        } else {
-          success
-        }
-      }
-    }
-    parser.parse(args, defaultParams).map { params =>
-      run(params)
-    }.getOrElse {
-      sys.exit(1)
-    }
-  }
-
-  private def run(params: Params) {
     val spark = SparkSession
       .builder
-      .appName(s"OneVsRestExample with $params")
+      .appName(s"OneVsRestExample")
       .getOrCreate()
 
     // $example on$
-    val inputData = spark.read.format("libsvm").load(params.input)
-    // compute the train/test split: if testInput is not provided use part of input.
-    val data = params.testInput match {
-      case Some(t) =>
-        // compute the number of features in the training set.
-        val numFeatures = inputData.first().getAs[Vector](1).size
-        val testData = spark.read.option("numFeatures", numFeatures.toString)
-          .format("libsvm").load(t)
-        Array[DataFrame](inputData, testData)
-      case None =>
-        val f = params.fracTest
-        inputData.randomSplit(Array(1 - f, f), seed = 12345)
-    }
-    val Array(train, test) = data.map(_.cache())
+    // load data file.
+    val inputData: DataFrame = spark.read.format("libsvm")
+      .load("data/mllib/sample_multiclass_classification_data.txt")
+
+    // generate the train/test split.
+    val Array(train, test) = inputData.randomSplit(Array(0.8, 0.2))
 
     // instantiate the base classifier
     val classifier = new LogisticRegression()
-      .setMaxIter(params.maxIter)
-      .setTol(params.tol)
-      .setFitIntercept(params.fitIntercept)
-
-    // Set regParam, elasticNetParam if specified in params
-    params.regParam.foreach(classifier.setRegParam)
-    params.elasticNetParam.foreach(classifier.setElasticNetParam)
+      .setMaxIter(10)
+      .setTol(1E-6)
+      .setFitIntercept(true)
 
     // instantiate the One Vs Rest Classifier.
-
-    val ovr = new OneVsRest()
-    ovr.setClassifier(classifier)
+    val ovr = new OneVsRest().setClassifier(classifier)
 
     // train the multiclass model.
-    val (trainingDuration, ovrModel) = time(ovr.fit(train))
+    val ovrModel = ovr.fit(train)
 
     // score the model on test data.
-    val (predictionDuration, predictions) = time(ovrModel.transform(test))
-
-    // evaluate the model
-    val predictionsAndLabels = predictions.select("prediction", "label")
-      .rdd.map(row => (row.getDouble(0), row.getDouble(1)))
-
-    val metrics = new MulticlassMetrics(predictionsAndLabels)
-
-    val confusionMatrix = metrics.confusionMatrix
+    val predictions = ovrModel.transform(test)
 
-    // compute the false positive rate per label
-    val predictionColSchema = predictions.schema("prediction")
-    val numClasses = MetadataUtils.getNumClasses(predictionColSchema).get
-    val fprs = Range(0, numClasses).map(p => (p, metrics.falsePositiveRate(p.toDouble)))
+    // obtain evaluator.
+    val evaluator = new MulticlassClassificationEvaluator()
+      .setMetricName("precision")
 
-    println(s" Training Time ${trainingDuration} sec\n")
-
-    println(s" Prediction Time ${predictionDuration} sec\n")
-
-    println(s" Confusion Matrix\n ${confusionMatrix.toString}\n")
-
-    println("label\tfpr")
-
-    println(fprs.map {case (label, fpr) => label + "\t" + fpr}.mkString("\n"))
+    // compute the classification error on test data.
+    val precision = evaluator.evaluate(predictions)
+    println(s"Test Error : ${1 - precision}")
     // $example off$
 
     spark.stop()
   }
 
-  private def time[R](block: => R): (Long, R) = {
-    val t0 = System.nanoTime()
-    val result = block    // call-by-name
-    val t1 = System.nanoTime()
-    (NANO.toSeconds(t1 - t0), result)
-  }
 }
 // scalastyle:on println