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From jkbradley <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-3974][MLlib] Distributed Block Matrix A...
Date Fri, 16 Jan 2015 20:23:13 GMT
Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3200#discussion_r23106799
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
    @@ -0,0 +1,217 @@
    +/*
    + * 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.distributed
    +
    +import breeze.linalg.{DenseMatrix => BDM}
    +
    +import org.apache.spark._
    +import org.apache.spark.mllib.linalg._
    +import org.apache.spark.mllib.rdd.RDDFunctions._
    +import org.apache.spark.rdd.RDD
    +import org.apache.spark.storage.StorageLevel
    +import org.apache.spark.util.Utils
    +
    +/**
    + * A grid partitioner, which stores every block in a separate partition.
    + *
    + * @param numRowBlocks Number of blocks that form the rows of the matrix.
    + * @param numColBlocks Number of blocks that form the columns of the matrix.
    + * @param rowPerBlock Number of rows that make up each block.
    + * @param colPerBlock Number of columns that make up each block.
    + */
    +private[mllib] class GridPartitioner(
    +    val numRowBlocks: Int,
    +    val numColBlocks: Int,
    +    val rowPerBlock: Int,
    +    val colPerBlock: Int,
    +    override val numPartitions: Int) extends Partitioner {
    +
    +  /**
    +   * Returns the index of the partition the SubMatrix belongs to.
    +   *
    +   * @param key The key for the SubMatrix. Can be its position in the grid (its column
major index)
    +   *            or a tuple of three integers that are the final row index after the multiplication,
    +   *            the index of the block to multiply with, and the final column index after
the
    +   *            multiplication.
    +   * @return The index of the partition, which the SubMatrix belongs to.
    +   */
    +  override def getPartition(key: Any): Int = {
    +    key match {
    +      case ind: (Int, Int) =>
    +        Utils.nonNegativeMod(ind._1 + ind._2 * numRowBlocks, numPartitions)
    +      case indices: (Int, Int, Int) =>
    +        Utils.nonNegativeMod(indices._1 + indices._3 * numRowBlocks, numPartitions)
    +      case _ =>
    +        throw new IllegalArgumentException("Unrecognized key")
    +    }
    +  }
    +
    +  /** Checks whether the partitioners have the same characteristics */
    +  override def equals(obj: Any): Boolean = {
    +    obj match {
    +      case r: GridPartitioner =>
    +        (this.numPartitions == r.numPartitions) && (this.rowPerBlock == r.rowPerBlock)
&&
    +          (this.colPerBlock == r.colPerBlock)
    +      case _ =>
    +        false
    +    }
    +  }
    +}
    +
    +/**
    + * Represents a distributed matrix in blocks of local matrices.
    + *
    + * @param numRowBlocks Number of blocks that form the rows of this matrix
    + * @param numColBlocks Number of blocks that form the columns of this matrix
    + * @param rdd The RDD of SubMatrices (local matrices) that form this matrix
    + */
    +class BlockMatrix(
    +    val numRowBlocks: Int,
    +    val numColBlocks: Int,
    +    val rdd: RDD[((Int, Int), Matrix)]) extends DistributedMatrix with Logging {
    +
    +  type SubMatrix = ((Int, Int), Matrix) // ((blockRowIndex, blockColIndex), matrix)
    +
    +  /**
    +   * Alternate constructor for BlockMatrix without the input of a partitioner. Will use
a Grid
    +   * Partitioner by default.
    +   *
    +   * @param numRowBlocks Number of blocks that form the rows of this matrix
    +   * @param numColBlocks Number of blocks that form the columns of this matrix
    +   * @param rdd The RDD of SubMatrices (local matrices) that form this matrix
    +   * @param rowPerBlock Number of rows that make up each block.
    +   * @param colPerBlock Number of columns that make up each block.
    +   */
    +  def this(
    +      numRowBlocks: Int,
    +      numColBlocks: Int,
    +      rdd: RDD[((Int, Int), Matrix)],
    +      rowPerBlock: Int,
    +      colPerBlock: Int) = {
    +    this(numRowBlocks, numColBlocks, rdd)
    +    val part = new GridPartitioner(numRowBlocks, numColBlocks, rowPerBlock,
    +      colPerBlock, rdd.partitions.length)
    +    setPartitioner(part)
    +  }
    +
    +  private[mllib] var partitioner: GridPartitioner = {
    +    val firstSubMatrix = rdd.first()._2
    +    new GridPartitioner(numRowBlocks, numColBlocks,
    +      firstSubMatrix.numRows, firstSubMatrix.numCols, rdd.partitions.length)
    +  }
    +
    +  /**
    +   * Set the partitioner for the matrix. For internal use only. Users should use `repartition`.
    +   * @param part A partitioner that specifies how SubMatrices are stored in the cluster
    +   */
    +  private def setPartitioner(part: GridPartitioner): Unit = {
    +    partitioner = part
    +  }
    +
    +  private lazy val dims: (Long, Long) = getDim
    +
    +  override def numRows(): Long = dims._1
    +  override def numCols(): Long = dims._2
    +
    +  /** Returns the dimensions of the matrix. */
    +  def getDim: (Long, Long) = {
    +    // picks the sizes of the matrix with the maximum indices
    +    def pickSizeByGreaterIndex(
    +        example: (Int, Int, Int, Int),
    +        base: (Int, Int, Int, Int)): (Int, Int, Int, Int) = {
    +      if (example._1 > base._1 && example._2 > base._2) {
    +        (example._1, example._2, example._3, example._4)
    +      } else if (example._1 > base._1) {
    +        (example._1, base._2, example._3, base._4)
    +      } else if (example._2 > base._2) {
    +        (base._1, example._2, base._3, example._4)
    +      } else {
    +        (base._1, base._2, base._3, base._4)
    +      }
    +    }
    +
    +    val lastRowCol = rdd.treeAggregate((0, 0, 0, 0))(
    +      seqOp = (c, v) => (c, v) match { case (base, ((blockXInd, blockYInd), mat))
=>
    +        pickSizeByGreaterIndex((blockXInd, blockYInd, mat.numRows, mat.numCols), base)
    +      },
    +      combOp = (c1, c2) => (c1, c2) match {
    +        case (res1, res2) =>
    +          pickSizeByGreaterIndex(res1, res2)
    +      })
    +
    +    (lastRowCol._1.toLong * partitioner.rowPerBlock + lastRowCol._3,
    +      lastRowCol._2.toLong * partitioner.colPerBlock + lastRowCol._4)
    +  }
    +
    +  /** Returns the Frobenius Norm of the matrix */
    +  def normFro(): Double = {
    +    math.sqrt(rdd.map { mat => mat._2 match {
    +      case sparse: SparseMatrix =>
    +        sparse.values.map(x => math.pow(x, 2)).sum
    +      case dense: DenseMatrix =>
    +        dense.values.map(x => math.pow(x, 2)).sum
    +    }
    +    }.reduce(_ + _))
    +  }
    +
    +  /** Cache the underlying RDD. */
    +  def cache(): DistributedMatrix = {
    +    rdd.cache()
    +    this
    +  }
    +
    +  /** Set the storage level for the underlying RDD. */
    +  def persist(storageLevel: StorageLevel): DistributedMatrix = {
    +    rdd.persist(storageLevel)
    +    this
    +  }
    +
    +  /** Collect the distributed matrix on the driver as a local matrix. */
    +  def toLocalMatrix(): Matrix = {
    +    val parts = rdd.collect().sortBy(x => (x._1._2, x._1._1))
    +    val nRows = numRows().toInt
    +    val nCols = numCols().toInt
    +    val values = new Array[Double](nRows * nCols)
    +
    +    parts.foreach { part =>
    +      val rowOffset = part._1._1 * partitioner.rowPerBlock
    +      val colOffset = part._1._2 * partitioner.colPerBlock
    +      val block = part._2
    +      var j = 0
    +      while (j < block.numCols) {
    +        var i = 0
    +        val indStart = (j + colOffset) * nRows + rowOffset
    +        val indEnd = block.numRows
    +        val matStart = j * block.numRows
    +        val mat = block.toArray
    --- End diff --
    
    Move outside loop


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