Merge branch 'master' of git://github.com/mesos/spark into sgd-cleanup

Conflicts:
	mllib/src/main/scala/spark/mllib/util/MLUtils.scala

6 files changed, 366 insertions, 6 deletions

diff --git a/mllib/pom.xml b/mllib/pom.xml
new file mode 100644
index 0000000000..f3928cc73d
--- /dev/null
+++ b/mllib/pom.xml
@@ -0,0 +1,165 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ 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.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.spark-project</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.8.0-SNAPSHOT</version>
+    <relativePath>../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.spark-project</groupId>
+  <artifactId>spark-mllib</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project ML Library</name>
+  <url>http://spark-project.org/</url>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.eclipse.jetty</groupId>
+      <artifactId>jetty-server</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.jblas</groupId>
+      <artifactId>jblas</artifactId>
+      <version>1.2.3</version>
+    </dependency>
+
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
+
+  <profiles>
+    <profile>
+      <id>hadoop1</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.spark-project</groupId>
+          <artifactId>spark-core</artifactId>
+          <version>${project.version}</version>
+          <classifier>hadoop1</classifier>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-core</artifactId>
+          <scope>provided</scope>
+        </dependency>
+      </dependencies>
+      <build>
+        <plugins>
+          <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-jar-plugin</artifactId>
+            <configuration>
+              <classifier>hadoop1</classifier>
+            </configuration>
+          </plugin>
+        </plugins>
+      </build>
+    </profile>
+    <profile>
+      <id>hadoop2</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.spark-project</groupId>
+          <artifactId>spark-core</artifactId>
+          <version>${project.version}</version>
+          <classifier>hadoop2</classifier>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-core</artifactId>
+          <scope>provided</scope>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-client</artifactId>
+          <scope>provided</scope>
+        </dependency>
+      </dependencies>
+      <build>
+        <plugins>
+          <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-jar-plugin</artifactId>
+            <configuration>
+              <classifier>hadoop2</classifier>
+            </configuration>
+          </plugin>
+        </plugins>
+      </build>
+    </profile>
+    <profile>
+      <id>hadoop2-yarn</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.spark-project</groupId>
+          <artifactId>spark-core</artifactId>
+          <version>${project.version}</version>
+          <classifier>hadoop2-yarn</classifier>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-client</artifactId>
+          <scope>provided</scope>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-api</artifactId>
+          <scope>provided</scope>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-common</artifactId>
+          <scope>provided</scope>
+        </dependency>
+      </dependencies>
+      <build>
+        <plugins>
+          <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-jar-plugin</artifactId>
+            <configuration>
+              <classifier>hadoop2-yarn</classifier>
+            </configuration>
+          </plugin>
+        </plugins>
+      </build>
+    </profile>
+  </profiles>
+</project>
diff --git a/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
index d875d6de50..b402c71ed2 100644
--- a/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
@@ -315,14 +315,15 @@ object KMeans {
   }
 
   def main(args: Array[String]) {
-    if (args.length != 4) {
-      println("Usage: KMeans <master> <input_file> <k> <max_iterations>")
+    if (args.length < 4) {
+      println("Usage: KMeans <master> <input_file> <k> <max_iterations> [<runs>]")
       System.exit(1)
     }
     val (master, inputFile, k, iters) = (args(0), args(1), args(2).toInt, args(3).toInt)
+    val runs = if (args.length >= 5) args(4).toInt else 1
     val sc = new SparkContext(master, "KMeans")
-    val data = sc.textFile(inputFile).map(line => line.split(' ').map(_.toDouble))
-    val model = KMeans.train(data, k, iters)
+    val data = sc.textFile(inputFile).map(line => line.split(' ').map(_.toDouble)).cache()
+    val model = KMeans.train(data, k, iters, runs)
     val cost = model.computeCost(data)
     println("Cluster centers:")
     for (c <- model.clusterCenters) {
diff --git a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
index 7281b2fcb9..6ecf0151a1 100644
--- a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
@@ -418,6 +418,7 @@ object ALS {
     System.setProperty("spark.serializer", "spark.KryoSerializer")
     System.setProperty("spark.kryo.registrator", classOf[ALSRegistrator].getName)
     System.setProperty("spark.kryo.referenceTracking", "false")
+    System.setProperty("spark.kryoserializer.buffer.mb", "8")
     System.setProperty("spark.locality.wait", "10000")
     val sc = new SparkContext(master, "ALS")
     val ratings = sc.textFile(ratingsFile).map { line =>
diff --git a/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala
new file mode 100644
index 0000000000..8f95cf7479
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala
@@ -0,0 +1,80 @@
+/*
+ * 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 spark.mllib.util
+
+import scala.util.Random
+
+import spark.{RDD, SparkContext}
+
+object KMeansDataGenerator {
+
+  /**
+   * Generate an RDD containing test data for KMeans. This function chooses k cluster centers
+   * from a d-dimensional Gaussian distribution scaled by factor r, then creates a Gaussian
+   * cluster with scale 1 around each center.
+   *
+   * @param sc SparkContext to use for creating the RDD
+   * @param numPoints Number of points that will be contained in the RDD
+   * @param k Number of clusters
+   * @param d Number of dimensions
+   * @parak r Scaling factor for the distribution of the initial centers
+   * @param numPartitions Number of partitions of the generated RDD; default 2
+   */
+  def generateKMeansRDD(
+      sc: SparkContext,
+      numPoints: Int,
+      k: Int,
+      d: Int,
+      r: Double,
+      numPartitions: Int = 2)
+    : RDD[Array[Double]] =
+  {
+    // First, generate some centers
+    val rand = new Random(42)
+    val centers = Array.fill(k)(Array.fill(d)(rand.nextGaussian() * r))
+    // Then generate points around each center
+    sc.parallelize(0 until numPoints, numPartitions).map { idx =>
+      val center = centers(idx % k)
+      val rand2 = new Random(42 + idx)
+      Array.tabulate(d)(i => center(i) + rand2.nextGaussian())
+    }
+  }
+
+  def main(args: Array[String]) {
+    if (args.length < 6) {
+      println("Usage: KMeansGenerator " +
+        "<master> <output_dir> <num_points> <k> <d> <r> [<num_partitions>]")
+      System.exit(1)
+    }
+
+    val sparkMaster = args(0)
+    val outputPath = args(1)
+    val numPoints = args(2).toInt
+    val k = args(3).toInt
+    val d = args(4).toInt
+    val r = args(5).toDouble
+    val parts = if (args.length >= 7) args(6).toInt else 2
+
+    val sc = new SparkContext(sparkMaster, "KMeansDataGenerator")
+    val data = generateKMeansRDD(sc, numPoints, k, d, r, parts)
+    data.map(_.mkString(" ")).saveAsTextFile(outputPath)
+
+    System.exit(0)
+  }
+}
+
diff --git a/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala
new file mode 100644
index 0000000000..88992cde0c
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala
@@ -0,0 +1,113 @@
+/*
+ * 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 spark.mllib.recommendation
+
+import scala.util.Random
+
+import org.jblas.DoubleMatrix
+
+import spark.{RDD, SparkContext}
+import spark.mllib.util.MLUtils
+
+/**
+* Generate RDD(s) containing data for Matrix Factorization.
+*
+* This method samples training entries according to the oversampling factor
+* 'trainSampFact', which is a multiplicative factor of the number of
+* degrees of freedom of the matrix: rank*(m+n-rank).
+* 
+* It optionally samples entries for a testing matrix using 
+* 'testSampFact', the percentage of the number of training entries 
+* to use for testing.
+*
+* This method takes the following inputs:
+*   sparkMaster    (String) The master URL.
+*   outputPath     (String) Directory to save output.
+*   m              (Int) Number of rows in data matrix.
+*   n              (Int) Number of columns in data matrix.
+*   rank           (Int) Underlying rank of data matrix.
+*   trainSampFact  (Double) Oversampling factor.
+*   noise          (Boolean) Whether to add gaussian noise to training data.
+*   sigma          (Double) Standard deviation of added gaussian noise.
+*   test           (Boolean) Whether to create testing RDD.
+*   testSampFact   (Double) Percentage of training data to use as test data.
+*/
+
+object MFDataGenerator{
+
+  def main(args: Array[String]) {
+    if (args.length < 2) {
+      println("Usage: MFDataGenerator " +
+        "<master> <outputDir> [m] [n] [rank] [trainSampFact] [noise] [sigma] [test] [testSampFact]")
+      System.exit(1)
+    }
+
+    val sparkMaster: String = args(0)
+    val outputPath: String = args(1)
+    val m: Int = if (args.length > 2) args(2).toInt else 100
+    val n: Int = if (args.length > 3) args(3).toInt else 100
+    val rank: Int = if (args.length > 4) args(4).toInt else 10
+    val trainSampFact: Double = if (args.length > 5) args(5).toDouble else 1.0
+    val noise: Boolean = if (args.length > 6) args(6).toBoolean else false
+    val sigma: Double = if (args.length > 7) args(7).toDouble else 0.1
+    val test: Boolean = if (args.length > 8) args(8).toBoolean else false
+    val testSampFact: Double = if (args.length > 9) args(9).toDouble else 0.1
+
+    val sc = new SparkContext(sparkMaster, "MFDataGenerator")
+
+    val A = DoubleMatrix.randn(m, rank)
+    val B = DoubleMatrix.randn(rank, n)
+    val z = 1 / (scala.math.sqrt(scala.math.sqrt(rank)))
+    A.mmuli(z)
+    B.mmuli(z)
+    val fullData = A.mmul(B)
+
+    val df = rank * (m + n - rank)
+    val sampSize = scala.math.min(scala.math.round(trainSampFact * df),
+      scala.math.round(.99 * m * n)).toInt
+    val rand = new Random()
+    val mn = m * n
+    val shuffled = rand.shuffle(1 to mn toIterable)
+
+    val omega = shuffled.slice(0, sampSize)
+    val ordered = omega.sortWith(_ < _).toArray
+    val trainData: RDD[(Int, Int, Double)] = sc.parallelize(ordered)
+      .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1)))
+
+    // optionally add gaussian noise
+    if (noise) { 
+      trainData.map(x => (x._1, x._2, x._3 + rand.nextGaussian * sigma))
+    }
+
+    trainData.map(x => x._1 + "," + x._2 + "," + x._3).saveAsTextFile(outputPath)
+
+    // optionally generate testing data
+    if (test) {
+      val testSampSize = scala.math
+        .min(scala.math.round(sampSize * testSampFact),scala.math.round(mn - sampSize)).toInt
+      val testOmega = shuffled.slice(sampSize, sampSize + testSampSize)
+      val testOrdered = testOmega.sortWith(_ < _).toArray
+      val testData: RDD[(Int, Int, Double)] = sc.parallelize(testOrdered)
+        .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1)))
+      testData.map(x => x._1 + "," + x._2 + "," + x._3).saveAsTextFile(outputPath)
+    }
+        
+    sc.stop()
+  
+  }
+}
\ No newline at end of file
diff --git a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
index e45eda2c99..9174e8cea7 100644
--- a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
@@ -39,9 +39,9 @@ object MLUtils {
    */
   def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
     sc.textFile(dir).map { line =>
-      val parts = line.split(",")
+      val parts = line.split(',')
       val label = parts(0).toDouble
-      val features = parts(1).trim().split(" ").map(_.toDouble)
+      val features = parts(1).trim().split(' ').map(_.toDouble)
       LabeledPoint(label, features)
     }
   }