Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6115#discussion_r30284085
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala ---
@@ -0,0 +1,169 @@
+/*
+ * 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.ml
+
+import java.util.concurrent.TimeUnit.{NANOSECONDS => NANO}
+
+import scopt.OptionParser
+
+import org.apache.spark.{SparkContext, SparkConf}
+import org.apache.spark.examples.mllib.AbstractParams
+import org.apache.spark.ml.classification.{OneVsRest, LogisticRegression}
+import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SQLContext
+
+/**
+ * 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.
+ * 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]
+ * }}}
+ * 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]
+
+ def main(args: Array[String]) {
+ val defaultParams = Params()
+
+ val parser = new OptionParser[Params]("OneVsRest Example") {
+ head("OneVsRest Example: multiclass to binary reduction using
OneVsRest")
+ arg[String]("<input>")
+ .text("input path to labeled examples")
+ .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. default:
${defaultParams.maxIter}")
+ .action((x, c) => c.copy(maxIter = x))
+ opt[Double]("tol")
+ .text(s"the convergence tolerance of iterations. default:
${defaultParams.tol}")
+ .action((x, c) => c.copy(tol = x))
+ opt[Double]("regParam")
+ .text(s"the regularization parameter")
+ .action((x,c) => c.copy(regParam = Some(x)))
+ opt[Double]("elasticNetParam")
+ .text(s"the ElasticNet mixing parameter")
+ .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 conf = new SparkConf().setAppName(s"OneVsRestExample with $params")
+ val sc = new SparkContext(conf)
+ val inputData = MLUtils.loadLibSVMFile(sc, params.input)
+ val sqlContext = new SQLContext(sc)
+ import sqlContext.implicits._
+
+ // compute the train/test split: if testInput is not provided use part
of input.
+ val data = params.testInput match {
+ case Some(t) => Array[RDD[LabeledPoint]](inputData,
MLUtils.loadLibSVMFile(sc, t))
+ case None => {
+ val f = params.fracTest
+ inputData.randomSplit(Array(1 - f, f), seed = 12345)
+ }
+ }
+ val Array(train, test) = data.map(_.toDF().cache())
+ // instantiate the base classifier
+ val classifier = new LogisticRegression()
+ classifier.setMaxIter(params.maxIter)
+ classifier.setTol(params.tol)
+ params.regParam.foreach(classifier.setRegParam)
--- End diff --
The use of foreach here may confuse people not used to Scala. Add comment:
```
// Set regParam, elasticNetParam if specified in params
```
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