[GitHub] flink pull request: [FLINK-3919][flink-ml] Distributed Linear Algebra: row-b...

[chiwanpark <gi...@git.apache.org>]

Github user chiwanpark commented on a diff in the pull request:

    https://github.com/apache/flink/pull/1996#discussion_r65309134
  
    --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/math/distributed/DistributedRowMatrix.scala ---
    @@ -0,0 +1,182 @@
    +/*
    + * 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.flink.ml.math.distributed
    +
    +import java.lang
    +
    +import breeze.linalg.{CSCMatrix => BreezeSparseMatrix, Matrix => BreezeMatrix, Vector => BreezeVector}
    +import org.apache.flink.api.common.functions.RichGroupReduceFunction
    +import org.apache.flink.api.common.typeinfo.TypeInformation
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml.math.{Matrix => FlinkMatrix, _}
    +import org.apache.flink.util.Collector
    +import org.apache.flink.ml.math.Breeze._
    +import scala.collection.JavaConversions._
    +
    +/**
    +  * Distributed row-major matrix representation.
    +  * @param numRowsOpt If None, will be calculated from the DataSet.
    +  * @param numColsOpt If None, will be calculated from the DataSet.
    +  */
    +class DistributedRowMatrix(data: DataSet[IndexedRow],
    +                           numRowsOpt: Option[Int] = None,
    +                           numColsOpt: Option[Int] = None)
    +    extends DistributedMatrix {
    +
    +  lazy val getNumRows: Int = numRowsOpt match {
    +    case Some(rows) => rows
    +    case None => data.count().toInt
    +  }
    +
    +  lazy val getNumCols: Int = numColsOpt match {
    +    case Some(cols) => cols
    +    case None => calcCols
    +  }
    +
    +  val getRowData = data
    +
    +  private def calcCols: Int =
    +    data.first(1).collect().headOption match {
    +      case Some(vector) => vector.values.size
    +      case None => 0
    +    }
    +
    +  /**
    +    * Collects the data in the form of a sequence of coordinates associated with their values.
    +    * @return
    +    */
    +  def toCOO: Seq[(Int, Int, Double)] = {
    +
    +    val localRows = data.collect()
    +
    +    for (IndexedRow(rowIndex, vector) <- localRows;
    +         (columnIndex, value) <- vector) yield (rowIndex, columnIndex, value)
    +  }
    +
    +  /**
    +    * Collects the data in the form of a SparseMatrix
    +    * @return
    +    */
    +  def toLocalSparseMatrix: SparseMatrix = {
    +    val localMatrix =
    +      SparseMatrix.fromCOO(this.getNumRows, this.getNumCols, this.toCOO)
    +    require(localMatrix.numRows == this.getNumRows)
    +    require(localMatrix.numCols == this.getNumCols)
    +    localMatrix
    +  }
    +
    +  //TODO: convert to dense representation on the distributed matrix and collect it afterward
    +  def toLocalDenseMatrix: DenseMatrix = this.toLocalSparseMatrix.toDenseMatrix
    +
    +  /**
    +    * Apply a high-order function to couple of rows
    +    * @param fun
    +    * @param other
    +    * @return
    +    */
    +  def byRowOperation(fun: (Vector, Vector) => Vector,
    +                     other: DistributedRowMatrix): DistributedRowMatrix = {
    +    val otherData = other.getRowData
    +    require(this.getNumCols == other.getNumCols)
    +    require(this.getNumRows == other.getNumRows)
    +
    +
    +    val result = this.data
    +      .fullOuterJoin(otherData)
    +      .where("rowIndex")
    +      .equalTo("rowIndex")(
    +          (left: IndexedRow, right: IndexedRow) => {
    +            val row1 = Option(left).getOrElse(IndexedRow(
    +                    right.rowIndex,
    +                    SparseVector.fromCOO(right.values.size, List((0, 0.0)))))
    +            val row2 = Option(right).getOrElse(IndexedRow(
    +                    left.rowIndex,
    +                    SparseVector.fromCOO(left.values.size, List((0, 0.0)))))
    +
    +            IndexedRow(row1.rowIndex, fun(row1.values, row2.values))
    +          }
    +      )
    +    new DistributedRowMatrix(result, numRowsOpt, numColsOpt)
    +  }
    +
    +  /**
    +    * Add the matrix to another matrix.
    +    * @param other
    +    * @return
    +    */
    +  def sum(other: DistributedRowMatrix): DistributedRowMatrix = {
    +    val sumFunction: (Vector, Vector) => Vector = (x: Vector, y: Vector) =>
    +      (x.asBreeze + y.asBreeze).fromBreeze
    +    this.byRowOperation(sumFunction, other)
    +  }
    +
    +  /**
    +    * Subtracts another matrix.
    +    * @param other
    +    * @return
    +    */
    +  def subtract(other: DistributedRowMatrix): DistributedRowMatrix = {
    +    val subFunction: (Vector, Vector) => Vector = (x: Vector, y: Vector) =>
    +      (x.asBreeze - y.asBreeze).fromBreeze
    +    this.byRowOperation(subFunction, other)
    +  }
    +}
    +
    +object DistributedRowMatrix {
    +
    +  type MatrixRowIndex = Int
    +
    +  /**
    +    * Builds a DistributedRowMatrix from a dataset in COO
    +    * @param isSorted If false, sorts the row to properly build the matrix representation.
    +    *                 If already sorted, set this parameter to true to skip sorting.
    +    * @return
    +    */
    +  def fromCOO(data: DataSet[(Int, Int, Double)],
    +              numRows: Int,
    +              numCols: Int,
    +              isSorted: Boolean = false): DistributedRowMatrix = {
    +    val vectorData: DataSet[(Int, SparseVector)] = data
    +      .groupBy(0)
    +      .reduceGroup(sparseRow => {
    +        require(sparseRow.nonEmpty)
    +        val sortedRow =
    +          if (isSorted) {
    +            sparseRow.toList
    +          } else {
    +            sparseRow.toList.sortBy(row => row._2)
    +          }
    +        val (indices, values) = sortedRow.map(x => (x._2, x._3)).unzip
    +        (sortedRow.head._1,
    +         SparseVector(numCols, indices.toArray, values.toArray))
    +      })
    +
    +    val zippedData = vectorData.map(x => IndexedRow(x._1.toInt, x._2))
    +
    +    new DistributedRowMatrix(zippedData, Some(numRows), Some(numCols))
    +  }
    +}
    +
    +case class IndexedRow(rowIndex: Int, values: Vector)
    +    extends Ordered[IndexedRow] {
    --- End diff --
    
    Ah, okay. Let's leave it.


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