Merge branch 'master' of github.com:apache/spark into dag-viz-streaming

core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala

@@ -324,9 +324,6 @@ private[worker] class Worker(
         map(e => new ExecutorDescription(e.appId, e.execId, e.cores, e.state))
       sender ! WorkerSchedulerStateResponse(workerId, execs.toList, drivers.keys.toSeq)
 
-    case Heartbeat =>
-      logInfo(s"Received heartbeat from driver ${sender.path}")
-
     case RegisterWorkerFailed(message) =>
       if (!registered) {
         logError("Worker registration failed: " + message)

core/src/main/scala/org/apache/spark/ui/WebUI.scala

@@ -94,7 +94,7 @@ private[spark] abstract class WebUI(
   }
 
   /** Detach a handler from this UI. */
-  protected def detachHandler(handler: ServletContextHandler) {
+  def detachHandler(handler: ServletContextHandler) {
     handlers -= handler
     serverInfo.foreach { info =>
       info.rootHandler.removeHandler(handler)

core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java

@@ -35,6 +35,7 @@
 import org.mockito.MockitoAnnotations;
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
+import org.xerial.snappy.buffer.CachedBufferAllocator;
 import static org.hamcrest.MatcherAssert.assertThat;
 import static org.hamcrest.Matchers.greaterThan;
 import static org.hamcrest.Matchers.lessThan;
@@ -96,6 +97,13 @@ public OutputStream apply(OutputStream stream) {
   @After
   public void tearDown() {
     Utils.deleteRecursively(tempDir);
+    // This call is a workaround for SPARK-7660, a snappy-java bug which is exposed by this test
+    // suite. Clearing the cached buffer allocator's pool of reusable buffers masks this bug,
+    // preventing a test failure in JavaAPISuite that would otherwise occur. The underlying bug
+    // needs to be fixed, but in the meantime this workaround avoids spurious Jenkins failures.
+    synchronized (CachedBufferAllocator.class) {
+      CachedBufferAllocator.queueTable.clear();
+    }
     final long leakedMemory = taskMemoryManager.cleanUpAllAllocatedMemory();
     if (leakedMemory != 0) {
       fail("Test leaked " + leakedMemory + " bytes of managed memory");

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

@@ -345,28 +345,40 @@ private[python] class PythonMLLibAPI extends Serializable {
    * Returns a list containing weights, mean and covariance of each mixture component.
    */
   def trainGaussianMixture(
-      data: JavaRDD[Vector], 
-      k: Int, 
-      convergenceTol: Double, 
+      data: JavaRDD[Vector],
+      k: Int,
+      convergenceTol: Double,
       maxIterations: Int,
-      seed: java.lang.Long): JList[Object] = {
+      seed: java.lang.Long,
+      initialModelWeights: java.util.ArrayList[Double],
+      initialModelMu: java.util.ArrayList[Vector],
+      initialModelSigma: java.util.ArrayList[Matrix]): JList[Object] = {
     val gmmAlg = new GaussianMixture()
       .setK(k)
       .setConvergenceTol(convergenceTol)
       .setMaxIterations(maxIterations)
 
+    if (initialModelWeights != null && initialModelMu != null && initialModelSigma != null) {
+      val gaussians = initialModelMu.asScala.toSeq.zip(initialModelSigma.asScala.toSeq).map {
+        case (x, y) => new MultivariateGaussian(x.asInstanceOf[Vector], y.asInstanceOf[Matrix])
+      }
+      val initialModel = new GaussianMixtureModel(
+        initialModelWeights.asScala.toArray, gaussians.toArray)
+      gmmAlg.setInitialModel(initialModel)
+    }
+
     if (seed != null) gmmAlg.setSeed(seed)
 
     try {
       val model = gmmAlg.run(data.rdd.persist(StorageLevel.MEMORY_AND_DISK))
       var wt = ArrayBuffer.empty[Double]
-      var mu = ArrayBuffer.empty[Vector]      
+      var mu = ArrayBuffer.empty[Vector]
       var sigma = ArrayBuffer.empty[Matrix]
       for (i <- 0 until model.k) {
           wt += model.weights(i)
           mu += model.gaussians(i).mu
           sigma += model.gaussians(i).sigma
-      }    
+      }
       List(Vectors.dense(wt.toArray), mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava
     } finally {
       data.rdd.unpersist(blocking = false)

mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala

@@ -38,11 +38,10 @@ import org.apache.spark.sql.{SQLContext, Row}
  * are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are 
  * the respective mean and covariance for each Gaussian distribution i=1..k. 
  * 
- * @param weight Weights for each Gaussian distribution in the mixture, where weight(i) is
- *               the weight for Gaussian i, and weight.sum == 1
- * @param mu Means for each Gaussian in the mixture, where mu(i) is the mean for Gaussian i
- * @param sigma Covariance maxtrix for each Gaussian in the mixture, where sigma(i) is the
- *              covariance matrix for Gaussian i
+ * @param weights Weights for each Gaussian distribution in the mixture, where weights(i) is
+ *                the weight for Gaussian i, and weights.sum == 1
+ * @param gaussians Array of MultivariateGaussian where gaussians(i) represents
+ *                  the Multivariate Gaussian (Normal) Distribution for Gaussian i
  */
 @Experimental
 class GaussianMixtureModel(

python/pyspark/mllib/clustering.py

@@ -142,6 +142,7 @@ class GaussianMixtureModel(object):
 
     """A clustering model derived from the Gaussian Mixture Model method.
 
+    >>> from pyspark.mllib.linalg import Vectors, DenseMatrix
     >>> clusterdata_1 =  sc.parallelize(array([-0.1,-0.05,-0.01,-0.1,
     ...                                         0.9,0.8,0.75,0.935,
     ...                                        -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2))
@@ -154,24 +155,51 @@ class GaussianMixtureModel(object):
     True
     >>> labels[4]==labels[5]
     True
-    >>> clusterdata_2 =  sc.parallelize(array([-5.1971, -2.5359, -3.8220,
-    ...                                        -5.2211, -5.0602,  4.7118,
-    ...                                         6.8989, 3.4592,  4.6322,
-    ...                                         5.7048,  4.6567, 5.5026,
-    ...                                         4.5605,  5.2043,  6.2734]).reshape(5, 3))
+    >>> data =  array([-5.1971, -2.5359, -3.8220,
+    ...                -5.2211, -5.0602,  4.7118,
+    ...                 6.8989, 3.4592,  4.6322,
+    ...                 5.7048,  4.6567, 5.5026,
+    ...                 4.5605,  5.2043,  6.2734])
+    >>> clusterdata_2 = sc.parallelize(data.reshape(5,3))
     >>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001,
     ...                               maxIterations=150, seed=10)
     >>> labels = model.predict(clusterdata_2).collect()
     >>> labels[0]==labels[1]==labels[2]
     True
     >>> labels[3]==labels[4]
     True
+    >>> clusterdata_3 = sc.parallelize(data.reshape(15, 1))
+    >>> im = GaussianMixtureModel([0.5, 0.5],
+    ...      [MultivariateGaussian(Vectors.dense([-1.0]), DenseMatrix(1, 1, [1.0])),
+    ...      MultivariateGaussian(Vectors.dense([1.0]), DenseMatrix(1, 1, [1.0]))])
+    >>> model = GaussianMixture.train(clusterdata_3, 2, initialModel=im)
     """
 
     def __init__(self, weights, gaussians):
-        self.weights = weights
-        self.gaussians = gaussians
-        self.k = len(self.weights)
+        self._weights = weights
+        self._gaussians = gaussians
+        self._k = len(self._weights)
+
+    @property
+    def weights(self):
+        """
+        Weights for each Gaussian distribution in the mixture, where weights[i] is
+        the weight for Gaussian i, and weights.sum == 1.
+        """
+        return self._weights
+
+    @property
+    def gaussians(self):
+        """
+        Array of MultivariateGaussian where gaussians[i] represents
+        the Multivariate Gaussian (Normal) Distribution for Gaussian i.
+        """
+        return self._gaussians
+
+    @property
+    def k(self):
+        """Number of gaussians in mixture."""
+        return self._k
 
     def predict(self, x):
         """
@@ -193,9 +221,9 @@ def predictSoft(self, x):
         :return:     membership_matrix. RDD of array of double values.
         """
         if isinstance(x, RDD):
-            means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
+            means, sigmas = zip(*[(g.mu, g.sigma) for g in self._gaussians])
             membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector),
-                                              _convert_to_vector(self.weights), means, sigmas)
+                                              _convert_to_vector(self._weights), means, sigmas)
             return membership_matrix.map(lambda x: pyarray.array('d', x))
 
 
@@ -208,13 +236,24 @@ class GaussianMixture(object):
     :param convergenceTol:  Threshold value to check the convergence criteria. Defaults to 1e-3
     :param maxIterations:   Number of iterations. Default to 100
     :param seed:            Random Seed
+    :param initialModel:    GaussianMixtureModel for initializing learning
     """
     @classmethod
-    def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None):
+    def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
         """Train a Gaussian Mixture clustering model."""
-        weight, mu, sigma = callMLlibFunc("trainGaussianMixture",
-                                          rdd.map(_convert_to_vector), k,
-                                          convergenceTol, maxIterations, seed)
+        initialModelWeights = None
+        initialModelMu = None
+        initialModelSigma = None
+        if initialModel is not None:
+            if initialModel.k != k:
+                raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s"
+                                % (initialModel.k, k))
+            initialModelWeights = initialModel.weights
+            initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
+            initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
+        weight, mu, sigma = callMLlibFunc("trainGaussianMixture", rdd.map(_convert_to_vector), k,
+                                          convergenceTol, maxIterations, seed, initialModelWeights,
+                                          initialModelMu, initialModelSigma)
         mvg_obj = [MultivariateGaussian(mu[i], sigma[i]) for i in range(k)]
         return GaussianMixtureModel(weight, mvg_obj)
 

python/pyspark/sql/dataframe.py

@@ -1511,13 +1511,19 @@ def inSet(self, *cols):
     isNull = _unary_op("isNull", "True if the current expression is null.")
     isNotNull = _unary_op("isNotNull", "True if the current expression is not null.")
 
-    def alias(self, alias):
-        """Return a alias for this column
+    def alias(self, *alias):
+        """Returns this column aliased with a new name or names (in the case of expressions that
+        return more than one column, such as explode).
 
         >>> df.select(df.age.alias("age2")).collect()
         [Row(age2=2), Row(age2=5)]
         """
-        return Column(getattr(self._jc, "as")(alias))
+
+        if len(alias) == 1:
+            return Column(getattr(self._jc, "as")(alias[0]))
+        else:
+            sc = SparkContext._active_spark_context
+            return Column(getattr(self._jc, "as")(_to_seq(sc, list(alias))))
 
     @ignore_unicode_prefix
     def cast(self, dataType):

python/pyspark/sql/functions.py

@@ -169,6 +169,26 @@ def approxCountDistinct(col, rsd=None):
     return Column(jc)
 
 
+def explode(col):
+    """Returns a new row for each element in the given array or map.
+
+    >>> from pyspark.sql import Row
+    >>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
+    >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
+    [Row(anInt=1), Row(anInt=2), Row(anInt=3)]
+
+    >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
+    +---+-----+
+    |key|value|
+    +---+-----+
+    |  a|    b|
+    +---+-----+
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.explode(_to_java_column(col))
+    return Column(jc)
+
+
 def coalesce(*cols):
     """Returns the first column that is not null.
 

python/pyspark/sql/tests.py

@@ -117,6 +117,21 @@ def tearDownClass(cls):
         ReusedPySparkTestCase.tearDownClass()
         shutil.rmtree(cls.tempdir.name, ignore_errors=True)
 
+    def test_explode(self):
+        from pyspark.sql.functions import explode
+        d = [Row(a=1, intlist=[1, 2, 3], mapfield={"a": "b"})]
+        rdd = self.sc.parallelize(d)
+        data = self.sqlCtx.createDataFrame(rdd)
+
+        result = data.select(explode(data.intlist).alias("a")).select("a").collect()
+        self.assertEqual(result[0][0], 1)
+        self.assertEqual(result[1][0], 2)
+        self.assertEqual(result[2][0], 3)
+
+        result = data.select(explode(data.mapfield).alias("a", "b")).select("a", "b").collect()
+        self.assertEqual(result[0][0], "a")
+        self.assertEqual(result[0][1], "b")
+
     def test_udf_with_callable(self):
         d = [Row(number=i, squared=i**2) for i in range(10)]
         rdd = self.sc.parallelize(d)