Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/458#discussion_r12023406
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/ADMMLasso.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.optimization
+
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{Vector => BV, DenseVector => BDV, DenseMatrix =>
BDM, cholesky, norm}
+
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.{Partitioner, HashPartitioner, Logging}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.SparkContext._
+import org.apache.spark.annotation.DeveloperApi
+
+
+/**
+ * :: DeveloperApi ::
+ * Class used to solve the optimization problem in ADMMLasso
+ */
+@DeveloperApi
+class ADMMLasso
+ extends Optimizer with Logging
+{
+ private var numPartitions: Int = 10
+ private var numIterations: Int = 100
+ private var l1RegParam: Double = 1.0
+ private var l2RegParam: Double = .0
+ private var penalty: Double = 10.0
+
+
+ /**
+ * Set the number of partitions for ADMM. Default 10
+ */
+ def setNumPartitions(parts: Int): this.type = {
+ this.numPartitions = parts
+ this
+ }
+
+ /**
+ * Set the number of iterations for ADMM. Default 100.
+ */
+ def setNumIterations(iters: Int): this.type = {
+ this.numIterations = iters
+ this
+ }
+
+ /**
+ * Set the l1-regularization parameter. Default 1.0.
+ */
+ def setL1RegParam(regParam: Double): this.type = {
+ this.l1RegParam = regParam
+ this
+ }
+
+ /**
+ * Set the l2-regularization parameter. Default .0
+ */
+ def setL2RegParam(regParam: Double): this.type = {
+ this.l2RegParam = regParam
+ this
+ }
+
+ /**
+ * Set the penalty parameter. Default 10.0
+ */
+ def setPenalty(penalty: Double): this.type = {
+ this.penalty = penalty
+ this
+ }
+
+ def optimize(data: RDD[(Double, Vector)], initialWeights: Vector):
Vector = {
+ val (weights, _) = ADMMLasso.runADMM(data, numPartitions,
numIterations, l1RegParam,
+ l2RegParam, penalty, initialWeights)
+ weights
+ }
+
+}
+
+/**
+ * :: DeveloperApi ::
+ * Top-level method to run ADMMLasso.
+ */
+@DeveloperApi
+object ADMMLasso extends Logging {
+
+ /**
+ * @param data Input data for ADMMLasso. RDD of the set of data
examples, each of
+ * the form (label, [feature values]).
+ * @param numPartitions number of data blocks to partition the RDD into
+ * @param numIterations number of iterations that ADMM should be run.
+ * @param l1RegParam l1-regularization parameter
+ * @param l2RegParam l2-regularization parameter
+ * @param penalty The penalty parameter in ADMM
+ * @param initialWeights Initial set of weights to be used. Array
should be equal in size to
+ * the number of features in the data.
+ * @return A tuple containing two elements. The first element is a
column matrix containing
+ * weights for every feature, and the second element is an array
containing the loss
+ * computed for every iteration.
+ */
+ def runADMM(
+ data: RDD[(Double, Vector)],
+ numPartitions: Int,
+ numIterations: Int,
+ l1RegParam: Double,
+ l2RegParam: Double,
+ penalty: Double,
+ initialWeights: Vector): (Vector, Array[Double]) = {
+
+ val lossHistory = new ArrayBuffer[Double](numIterations)
+
+ // Initialize weights as a column vector
+ val p = initialWeights.size
+
+ // Consensus variable
+ var z = BDV.zeros[Double](p)
+
+ // Transform the input data into ADMM format
+ def collectBlock(it: Iterator[(Vector, Double)]):
+ Iterator[((BDV[Double], BDM[Double], BDM[Double]), (BDV[Double],
BDV[Double]))] = {
+ val lab = new ArrayBuffer[Double]()
+ val features = new ArrayBuffer[Double]()
+ var row = 0
+ it.foreach {case (f, l) =>
+ lab += l
+ features ++= f.toArray
+ row += 1
+ }
+ val col = features.length/row
+
+ val designMatrix = new BDM(col, features.toArray).t
+
+ // Precompute the cholesky decomposition for solving linear system
inside each partition
+ val chol = if (row >= col) {
+ cholesky((designMatrix.t * designMatrix) + (BDM.eye[Double](col)
:* penalty))
+ }
+ else cholesky(((designMatrix * designMatrix.t) :/ penalty) +
BDM.eye[Double](row))
+
+ Iterator(((BDV(lab.toArray), designMatrix, chol),
+ (BDV(initialWeights.toArray), BDV.zeros[Double](col))))
+ }
+
+ val partitionedData = data.map{case (x, y) => (y, x)}
+ .partitionBy(new HashPartitioner(numPartitions)).cache()
+
+ // ((lab, design, chol), (x, u))
+ var dividedData = partitionedData.mapPartitions(collectBlock, true)
+
+ var iter = 1
+ var minorChange: Boolean = false
+ while (iter <= numIterations && !minorChange) {
+ val zBroadcast = z
--- End diff --
Do you mean real broadcast variable in Spark? If not, the `zBroadcast`
could be removed, since `=` here is a shallow copy.
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