[SPARK-1390] Refactoring of matrices backed by RDDs

examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala

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+/*
+ * 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.mllib
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.mllib.linalg.Vectors
+
+/**
+ * Compute the principal components of a tall-and-skinny matrix, whose rows are observations.
+ *
+ * The input matrix must be stored in row-oriented dense format, one line per row with its entries
+ * separated by space. For example,
+ * {{{
+ * 0.5 1.0
+ * 2.0 3.0
+ * 4.0 5.0
+ * }}}
+ * represents a 3-by-2 matrix, whose first row is (0.5, 1.0).
+ */
+object TallSkinnyPCA {
+  def main(args: Array[String]) {
+    if (args.length != 2) {
+      System.err.println("Usage: TallSkinnyPCA <master> <file>")
+      System.exit(1)
+    }
+
+    val conf = new SparkConf()
+      .setMaster(args(0))
+      .setAppName("TallSkinnyPCA")
+      .setSparkHome(System.getenv("SPARK_HOME"))
+      .setJars(SparkContext.jarOfClass(this.getClass))
+    val sc = new SparkContext(conf)
+
+    // Load and parse the data file.
+    val rows = sc.textFile(args(1)).map { line =>
+      val values = line.split(' ').map(_.toDouble)
+      Vectors.dense(values)
+    }
+    val mat = new RowMatrix(rows)
+
+    // Compute principal components.
+    val pc = mat.computePrincipalComponents(mat.numCols().toInt)
+
+    println("Principal components are:\n" + pc)
+
+    sc.stop()
+  }
+}

examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala

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+/*
+ * 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.mllib
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.mllib.linalg.Vectors
+
+/**
+ * Compute the singular value decomposition (SVD) of a tall-and-skinny matrix.
+ *
+ * The input matrix must be stored in row-oriented dense format, one line per row with its entries
+ * separated by space. For example,
+ * {{{
+ * 0.5 1.0
+ * 2.0 3.0
+ * 4.0 5.0
+ * }}}
+ * represents a 3-by-2 matrix, whose first row is (0.5, 1.0).
+ */
+object TallSkinnySVD {
+  def main(args: Array[String]) {
+    if (args.length != 2) {
+      System.err.println("Usage: TallSkinnySVD <master> <file>")
+      System.exit(1)
+    }
+
+    val conf = new SparkConf()
+      .setMaster(args(0))
+      .setAppName("TallSkinnySVD")
+      .setSparkHome(System.getenv("SPARK_HOME"))
+      .setJars(SparkContext.jarOfClass(this.getClass))
+    val sc = new SparkContext(conf)
+
+    // Load and parse the data file.
+    val rows = sc.textFile(args(1)).map { line =>
+      val values = line.split(' ').map(_.toDouble)
+      Vectors.dense(values)
+    }
+    val mat = new RowMatrix(rows)
+
+    // Compute SVD.
+    val svd = mat.computeSVD(mat.numCols().toInt)
+
+    println("Singular values are " + svd.s)
+
+    sc.stop()
+  }
+}

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

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+/*
+ * 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.mllib.linalg
+
+import breeze.linalg.{Matrix => BM, DenseMatrix => BDM}
+
+/**
+ * Trait for a local matrix.
+ */
+trait Matrix extends Serializable {
+
+  /** Number of rows. */
+  def numRows: Int
+
+  /** Number of columns. */
+  def numCols: Int
+
+  /** Converts to a dense array in column major. */
+  def toArray: Array[Double]
+
+  /** Converts to a breeze matrix. */
+  private[mllib] def toBreeze: BM[Double]
+
+  /** Gets the (i, j)-th element. */
+  private[mllib] def apply(i: Int, j: Int): Double = toBreeze(i, j)
+
+  override def toString: String = toBreeze.toString()
+}
+
+/**
+ * Column-majored dense matrix.
+ * The entry values are stored in a single array of doubles with columns listed in sequence.
+ * For example, the following matrix
+ * {{{
+ *   1.0 2.0
+ *   3.0 4.0
+ *   5.0 6.0
+ * }}}
+ * is stored as `[1.0, 3.0, 5.0, 2.0, 4.0, 6.0]`.
+ *
+ * @param numRows number of rows
+ * @param numCols number of columns
+ * @param values matrix entries in column major
+ */
+class DenseMatrix(val numRows: Int, val numCols: Int, val values: Array[Double]) extends Matrix {
+
+  require(values.length == numRows * numCols)
+
+  override def toArray: Array[Double] = values
+
+  private[mllib] override def toBreeze: BM[Double] = new BDM[Double](numRows, numCols, values)
+}
+
+/**
+ * Factory methods for [[org.apache.spark.mllib.linalg.Matrix]].
+ */
+object Matrices {
+
+  /**
+   * Creates a column-majored dense matrix.
+   *
+   * @param numRows number of rows
+   * @param numCols number of columns
+   * @param values matrix entries in column major
+   */
+  def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = {
+    new DenseMatrix(numRows, numCols, values)
+  }
+
+  /**
+   * Creates a Matrix instance from a breeze matrix.
+   * @param breeze a breeze matrix
+   * @return a Matrix instance
+   */
+  private[mllib] def fromBreeze(breeze: BM[Double]): Matrix = {
+    breeze match {
+      case dm: BDM[Double] =>
+        require(dm.majorStride == dm.rows,
+          "Do not support stride size different from the number of rows.")
+        new DenseMatrix(dm.rows, dm.cols, dm.data)
+      case _ =>
+        throw new UnsupportedOperationException(
+          s"Do not support conversion from type ${breeze.getClass.getName}.")
+    }
+  }
+}