Merge branch 'master' of github.com:apache/spark into rest

core/src/main/resources/org/apache/spark/log4j-defaults.properties

@@ -10,4 +10,3 @@ log4j.logger.org.eclipse.jetty=WARN
 log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.org.apache.hadoop.yarn.util.RackResolver=WARN

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -1986,7 +1986,7 @@ object SparkContext extends Logging {
       case "yarn-client" =>
         val scheduler = try {
           val clazz =
-            Class.forName("org.apache.spark.scheduler.cluster.YarnClientClusterScheduler")
+            Class.forName("org.apache.spark.scheduler.cluster.YarnScheduler")
           val cons = clazz.getConstructor(classOf[SparkContext])
           cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl]
 

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -67,17 +67,16 @@ private[spark] class PythonRDD(
       envVars += ("SPARK_REUSE_WORKER" -> "1")
     }
     val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
+    // Whether is the worker released into idle pool
+    @volatile var released = false
 
     // Start a thread to feed the process input from our parent's iterator
     val writerThread = new WriterThread(env, worker, split, context)
 
-    var complete_cleanly = false
     context.addTaskCompletionListener { context =>
       writerThread.shutdownOnTaskCompletion()
       writerThread.join()
-      if (reuse_worker && complete_cleanly) {
-        env.releasePythonWorker(pythonExec, envVars.toMap, worker)
-      } else {
+      if (!reuse_worker || !released) {
         try {
           worker.close()
         } catch {
@@ -145,8 +144,12 @@ private[spark] class PythonRDD(
                 stream.readFully(update)
                 accumulator += Collections.singletonList(update)
               }
+              // Check whether the worker is ready to be re-used.
               if (stream.readInt() == SpecialLengths.END_OF_STREAM) {
-                complete_cleanly = true
+                if (reuse_worker) {
+                  env.releasePythonWorker(pythonExec, envVars.toMap, worker)
+                  released = true
+                }
               }
               null
           }

core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala

@@ -149,7 +149,7 @@ class SparkContextSchedulerCreationSuite
   }
 
   test("yarn-client") {
-    testYarn("yarn-client", "org.apache.spark.scheduler.cluster.YarnClientClusterScheduler")
+    testYarn("yarn-client", "org.apache.spark.scheduler.cluster.YarnScheduler")
   }
 
   def testMesos(master: String, expectedClass: Class[_], coarse: Boolean) {

examples/src/main/python/mllib/gradient_boosted_trees.py

@@ -0,0 +1,76 @@
+#
+# 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.
+#
+
+"""
+Gradient boosted Trees classification and regression using MLlib.
+"""
+
+import sys
+
+from pyspark.context import SparkContext
+from pyspark.mllib.tree import GradientBoostedTrees
+from pyspark.mllib.util import MLUtils
+
+
+def testClassification(trainingData, testData):
+    # Train a GradientBoostedTrees model.
+    #  Empty categoricalFeaturesInfo indicates all features are continuous.
+    model = GradientBoostedTrees.trainClassifier(trainingData, categoricalFeaturesInfo={},
+                                                 numIterations=30, maxDepth=4)
+    # Evaluate model on test instances and compute test error
+    predictions = model.predict(testData.map(lambda x: x.features))
+    labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
+    testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() \
+        / float(testData.count())
+    print('Test Error = ' + str(testErr))
+    print('Learned classification ensemble model:')
+    print(model.toDebugString())
+
+
+def testRegression(trainingData, testData):
+    # Train a GradientBoostedTrees model.
+    #  Empty categoricalFeaturesInfo indicates all features are continuous.
+    model = GradientBoostedTrees.trainRegressor(trainingData, categoricalFeaturesInfo={},
+                                                numIterations=30, maxDepth=4)
+    # Evaluate model on test instances and compute test error
+    predictions = model.predict(testData.map(lambda x: x.features))
+    labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
+    testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() \
+        / float(testData.count())
+    print('Test Mean Squared Error = ' + str(testMSE))
+    print('Learned regression ensemble model:')
+    print(model.toDebugString())
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        print >> sys.stderr, "Usage: gradient_boosted_trees"
+        exit(1)
+    sc = SparkContext(appName="PythonGradientBoostedTrees")
+
+    # Load and parse the data file into an RDD of LabeledPoint.
+    data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
+    # Split the data into training and test sets (30% held out for testing)
+    (trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+    print('\nRunning example of classification using GradientBoostedTrees\n')
+    testClassification(trainingData, testData)
+
+    print('\nRunning example of regression using GradientBoostedTrees\n')
+    testRegression(trainingData, testData)
+
+    sc.stop()

mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala

@@ -41,10 +41,11 @@ import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
 import org.apache.spark.mllib.stat.correlation.CorrelationNames
 import org.apache.spark.mllib.stat.test.ChiSqTestResult
-import org.apache.spark.mllib.tree.{RandomForest, DecisionTree}
-import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
+import org.apache.spark.mllib.tree.{GradientBoostedTrees, RandomForest, DecisionTree}
+import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo, Strategy}
 import org.apache.spark.mllib.tree.impurity._
-import org.apache.spark.mllib.tree.model.{RandomForestModel, DecisionTreeModel}
+import org.apache.spark.mllib.tree.loss.Losses
+import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel, RandomForestModel, DecisionTreeModel}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -532,6 +533,35 @@ class PythonMLLibAPI extends Serializable {
     }
   }
 
+  /**
+   * Java stub for Python mllib GradientBoostedTrees.train().
+   * This stub returns a handle to the Java object instead of the content of the Java object.
+   * Extra care needs to be taken in the Python code to ensure it gets freed on exit;
+   * see the Py4J documentation.
+   */
+  def trainGradientBoostedTreesModel(
+      data: JavaRDD[LabeledPoint],
+      algoStr: String,
+      categoricalFeaturesInfo: JMap[Int, Int],
+      lossStr: String,
+      numIterations: Int,
+      learningRate: Double,
+      maxDepth: Int): GradientBoostedTreesModel = {
+    val boostingStrategy = BoostingStrategy.defaultParams(algoStr)
+    boostingStrategy.setLoss(Losses.fromString(lossStr))
+    boostingStrategy.setNumIterations(numIterations)
+    boostingStrategy.setLearningRate(learningRate)
+    boostingStrategy.treeStrategy.setMaxDepth(maxDepth)
+    boostingStrategy.treeStrategy.categoricalFeaturesInfo = categoricalFeaturesInfo.asScala.toMap
+
+    val cached = data.rdd.persist(StorageLevel.MEMORY_AND_DISK)
+    try {
+      GradientBoostedTrees.train(cached, boostingStrategy)
+    } finally {
+      cached.unpersist(blocking = false)
+    }
+  }
+
   /**
    * Java stub for mllib Statistics.colStats(X: RDD[Vector]).
    * TODO figure out return type.

mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala

@@ -290,6 +290,13 @@ class Word2Vec extends Serializable with Logging {
 
     val newSentences = sentences.repartition(numPartitions).cache()
     val initRandom = new XORShiftRandom(seed)
+
+    if (vocabSize.toLong * vectorSize * 8 >= Int.MaxValue) {
+      throw new RuntimeException("Please increase minCount or decrease vectorSize in Word2Vec" +
+        " to avoid an OOM. You are highly recommended to make your vocabSize*vectorSize, " +
+        "which is " + vocabSize + "*" + vectorSize + " for now, less than `Int.MaxValue/8`.")
+    }
+
     val syn0Global =
       Array.fill[Float](vocabSize * vectorSize)((initRandom.nextFloat() - 0.5f) / vectorSize)
     val syn1Global = new Array[Float](vocabSize * vectorSize)

mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala

@@ -78,7 +78,7 @@ sealed trait Vector extends Serializable {
         result = 31 * result + (bits ^ (bits >>> 32)).toInt
       }
     }
-    return result
+    result
   }
 
   /**

mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala

@@ -232,6 +232,15 @@ class BlockMatrix(
     new DenseMatrix(m, n, values)
   }
 
+  /** Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
+    * same underlying data. Is a lazy operation. */
+  def transpose: BlockMatrix = {
+    val transposedBlocks = blocks.map { case ((blockRowIndex, blockColIndex), mat) =>
+      ((blockColIndex, blockRowIndex), mat.transpose)
+    }
+    new BlockMatrix(transposedBlocks, colsPerBlock, rowsPerBlock, nCols, nRows)
+  }
+
   /** Collects data and assembles a local dense breeze matrix (for test only). */
   private[mllib] def toBreeze(): BDM[Double] = {
     val localMat = toLocalMatrix()

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala

@@ -29,8 +29,8 @@ object Algo extends Enumeration {
   val Classification, Regression = Value
 
   private[mllib] def fromString(name: String): Algo = name match {
-    case "classification" => Classification
-    case "regression" => Regression
+    case "classification" | "Classification" => Classification
+    case "regression" | "Regression" => Regression
     case _ => throw new IllegalArgumentException(s"Did not recognize Algo name: $name")
   }
 }

mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala

@@ -146,4 +146,33 @@ class BlockMatrixSuite extends FunSuite with MLlibTestSparkContext {
     assert(gridBasedMat.toLocalMatrix() === dense)
     assert(gridBasedMat.toBreeze() === expected)
   }
+
+  test("transpose") {
+    val expected = BDM(
+      (1.0, 0.0, 3.0, 0.0, 0.0),
+      (0.0, 2.0, 1.0, 1.0, 0.0),
+      (0.0, 1.0, 1.0, 2.0, 1.0),
+      (0.0, 0.0, 0.0, 1.0, 5.0))
+
+    val AT = gridBasedMat.transpose
+    assert(AT.numRows() === gridBasedMat.numCols())
+    assert(AT.numCols() === gridBasedMat.numRows())
+    assert(AT.toBreeze() === expected)
+
+    // partitioner must update as well
+    val originalPartitioner = gridBasedMat.partitioner
+    val ATpartitioner = AT.partitioner
+    assert(originalPartitioner.colsPerPart === ATpartitioner.rowsPerPart)
+    assert(originalPartitioner.rowsPerPart === ATpartitioner.colsPerPart)
+    assert(originalPartitioner.cols === ATpartitioner.rows)
+    assert(originalPartitioner.rows === ATpartitioner.cols)
+
+    // make sure it works when matrices are cached as well
+    gridBasedMat.cache()
+    val AT2 = gridBasedMat.transpose
+    AT2.cache()
+    assert(AT2.toBreeze() === AT.toBreeze())
+    val A = AT2.transpose
+    assert(A.toBreeze() === gridBasedMat.toBreeze())
+  }
 }

mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala

@@ -128,6 +128,11 @@ class GradientBoostedTreesSuite extends FunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("SPARK-5496: BoostingStrategy.defaultParams should recognize Classification") {
+    for (algo <- Seq("classification", "Classification", "regression", "Regression")) {
+      BoostingStrategy.defaultParams(algo)
+    }
+  }
 }
 
 object GradientBoostedTreesSuite {

python/pyspark/mllib/tests.py

@@ -169,7 +169,7 @@ def test_kmeans_deterministic(self):
 
     def test_classification(self):
         from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
-        from pyspark.mllib.tree import DecisionTree
+        from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees
         data = [
             LabeledPoint(0.0, [1, 0, 0]),
             LabeledPoint(1.0, [0, 1, 1]),
@@ -198,18 +198,31 @@ def test_classification(self):
         self.assertTrue(nb_model.predict(features[3]) > 0)
 
         categoricalFeaturesInfo = {0: 3}  # feature 0 has 3 categories
-        dt_model = \
-            DecisionTree.trainClassifier(rdd, numClasses=2,
-                                         categoricalFeaturesInfo=categoricalFeaturesInfo)
+        dt_model = DecisionTree.trainClassifier(
+            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo)
         self.assertTrue(dt_model.predict(features[0]) <= 0)
         self.assertTrue(dt_model.predict(features[1]) > 0)
         self.assertTrue(dt_model.predict(features[2]) <= 0)
         self.assertTrue(dt_model.predict(features[3]) > 0)
 
+        rf_model = RandomForest.trainClassifier(
+            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100)
+        self.assertTrue(rf_model.predict(features[0]) <= 0)
+        self.assertTrue(rf_model.predict(features[1]) > 0)
+        self.assertTrue(rf_model.predict(features[2]) <= 0)
+        self.assertTrue(rf_model.predict(features[3]) > 0)
+
+        gbt_model = GradientBoostedTrees.trainClassifier(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+        self.assertTrue(gbt_model.predict(features[0]) <= 0)
+        self.assertTrue(gbt_model.predict(features[1]) > 0)
+        self.assertTrue(gbt_model.predict(features[2]) <= 0)
+        self.assertTrue(gbt_model.predict(features[3]) > 0)
+
     def test_regression(self):
         from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
             RidgeRegressionWithSGD
-        from pyspark.mllib.tree import DecisionTree
+        from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees
         data = [
             LabeledPoint(-1.0, [0, -1]),
             LabeledPoint(1.0, [0, 1]),
@@ -238,13 +251,27 @@ def test_regression(self):
         self.assertTrue(rr_model.predict(features[3]) > 0)
 
         categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
-        dt_model = \
-            DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+        dt_model = DecisionTree.trainRegressor(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
         self.assertTrue(dt_model.predict(features[0]) <= 0)
         self.assertTrue(dt_model.predict(features[1]) > 0)
         self.assertTrue(dt_model.predict(features[2]) <= 0)
         self.assertTrue(dt_model.predict(features[3]) > 0)
 
+        rf_model = RandomForest.trainRegressor(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100)
+        self.assertTrue(rf_model.predict(features[0]) <= 0)
+        self.assertTrue(rf_model.predict(features[1]) > 0)
+        self.assertTrue(rf_model.predict(features[2]) <= 0)
+        self.assertTrue(rf_model.predict(features[3]) > 0)
+
+        gbt_model = GradientBoostedTrees.trainRegressor(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+        self.assertTrue(gbt_model.predict(features[0]) <= 0)
+        self.assertTrue(gbt_model.predict(features[1]) > 0)
+        self.assertTrue(gbt_model.predict(features[2]) <= 0)
+        self.assertTrue(gbt_model.predict(features[3]) > 0)
+
 
 class StatTests(PySparkTestCase):
     # SPARK-4023