distributed in RDD

jvm-packages/pom.xml

@@ -20,6 +20,7 @@
     <modules>
         <module>xgboost4j</module>
         <module>xgboost4j-demo</module>
+        <module>xgboost4j-spark</module>
         <module>xgboost4j-flink</module>
     </modules>
     <build>
@@ -118,6 +119,19 @@
                 <artifactId>maven-surefire-plugin</artifactId>
                 <version>2.19.1</version>
             </plugin>
+            <plugin>
+                <groupId>org.scalatest</groupId>
+                <artifactId>scalatest-maven-plugin</artifactId>
+                <version>1.0</version>
+                <executions>
+                    <execution>
+                        <id>test</id>
+                        <goals>
+                            <goal>test</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
         </plugins>
     </build>
     <dependencies>
@@ -150,7 +164,7 @@
         <dependency>
             <groupId>com.typesafe</groupId>
             <artifactId>config</artifactId>
-            <version>1.3.0</version>
+            <version>1.2.1</version>
         </dependency>
     </dependencies>
 </project>

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala

@@ -28,27 +28,35 @@ import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
 
 object XGBoost {
 
-  private var _sc: Option[SparkContext] = None
-
   implicit def convertBoosterToXGBoostModel(booster: Booster): XGBoostModel = {
     new XGBoostModel(booster)
   }
 
+  private[spark] def buildDistributedBoosters(
+      trainingData: RDD[LabeledPoint],
+      xgBoostConfMap: Map[String, AnyRef],
+      numWorkers: Int, round: Int, obj: ObjectiveTrait, eval: EvalTrait): RDD[Booster] = {
+    import DataUtils._
+    val sc = trainingData.sparkContext
+    val dataUtilsBroadcast = sc.broadcast(DataUtils)
+    trainingData.repartition(numWorkers).mapPartitions {
+      trainingSamples =>
+        val dMatrix = new DMatrix(new JDMatrix(trainingSamples, null))
+        Iterator(SXGBoost.train(xgBoostConfMap, dMatrix, round,
+          watches = new HashMap[String, DMatrix], obj, eval))
+    }.cache()
+  }
+
   def train(config: Config, trainingData: RDD[LabeledPoint], obj: ObjectiveTrait = null,
       eval: EvalTrait = null): XGBoostModel = {
     import DataUtils._
-    val sc = trainingData.sparkContext
-    val dataUtilsBroadcast = sc.broadcast(DataUtils)
-    val filePath = config.getString("inputPath") // configuration entry name to be fixed
     val numWorkers = config.getInt("numWorkers")
     val round = config.getInt("round")
+    val sc = trainingData.sparkContext
     // TODO: build configuration map from config
     val xgBoostConfigMap = new HashMap[String, AnyRef]()
-    val boosters = trainingData.repartition(numWorkers).mapPartitions {
-      trainingSamples =>
-        val dMatrix = new DMatrix(new JDMatrix(trainingSamples, null))
-        Iterator(SXGBoost.train(xgBoostConfigMap, dMatrix, round, watches = null, obj, eval))
-    }.cache()
+    val boosters = buildDistributedBoosters(trainingData, xgBoostConfigMap, numWorkers, round,
+      obj, eval)
     // force the job
     sc.runJob(boosters, (boosters: Iterator[Booster]) => boosters)
     // TODO: how to choose best model

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala

@@ -0,0 +1,83 @@
+/*
+ Copyright (c) 2014 by Contributors
+
+ Licensed 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 ml.dmlc.xgboost4j.scala.spark
+
+import java.io.File
+
+import scala.collection.mutable.ListBuffer
+import scala.io.Source
+
+import org.apache.spark.mllib.linalg.DenseVector
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.{SparkConf, SparkContext}
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+class XGBoostSuite extends FunSuite with BeforeAndAfterAll {
+
+  private var sc: SparkContext = null
+  private val numWorker = 4
+
+  override def beforeAll(): Unit = {
+    // build SparkContext
+    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("XGBoostSuite")
+    sc = new SparkContext(sparkConf)
+  }
+
+  override def afterAll(): Unit = {
+    if (sc != null) {
+      sc.stop()
+    }
+  }
+
+  private def fromSVMStringToLabeledPoint(line: String): LabeledPoint = {
+    val labelAndFeatures = line.split(" ")
+    val label = labelAndFeatures(0).toInt
+    val features = labelAndFeatures.tail
+    val denseFeature = new Array[Double](129)
+    for (feature <- features) {
+      val idAndValue = feature.split(":")
+      denseFeature(idAndValue(0).toInt) = idAndValue(1).toDouble
+    }
+    LabeledPoint(label, new DenseVector(denseFeature))
+  }
+
+  private def buildRDD(filePath: String): RDD[LabeledPoint] = {
+    val file = Source.fromFile(new File(filePath))
+    val sampleList = new ListBuffer[LabeledPoint]
+    for (sample <- file.getLines()) {
+      sampleList += fromSVMStringToLabeledPoint(sample)
+    }
+    sc.parallelize(sampleList, numWorker)
+  }
+
+  private def buildTrainingAndTestRDD(): (RDD[LabeledPoint], RDD[LabeledPoint]) = {
+    val trainRDD = buildRDD(getClass.getResource("/agaricus.txt.train").getFile)
+    val testRDD = buildRDD(getClass.getResource("/agaricus.txt.test").getFile)
+    (trainRDD, testRDD)
+  }
+
+  test("build RDD containing boosters") {
+    val (trainingRDD, testRDD) = buildTrainingAndTestRDD()
+    val boosterRDD = XGBoost.buildDistributedBoosters(
+      trainingRDD,
+      Map[String, AnyRef](),
+      numWorker, 4, null, null)
+    val boosterCount = boosterRDD.count()
+    assert(boosterCount === numWorker)
+  }
+}

jvm-packages/xgboost4j/pom.xml

@@ -29,19 +29,6 @@
                     <skipAssembly>false</skipAssembly>
                 </configuration>
             </plugin>
-            <plugin>
-                <groupId>org.scalatest</groupId>
-                <artifactId>scalatest-maven-plugin</artifactId>
-                <version>1.0</version>
-                <executions>
-                    <execution>
-                        <id>test</id>
-                        <goals>
-                            <goal>test</goal>
-                        </goals>
-                    </execution>
-                </executions>
-            </plugin>
         </plugins>
     </build>
     <dependencies>