Repository: spark Updated Branches: refs/heads/master dd95abada -> 52beb20f7
[SPARK-2477][MLlib] Using appendBias for adding intercept in GeneralizedLinearAlgorithm Instead of using prependOne currently in GeneralizedLinearAlgorithm, we would like to use appendBias for 1) keeping the indices of original training set unchanged by adding the intercept into the last element of vector and 2) using the same public API for consistently adding intercept. Author: DB Tsai <dbt...@alpinenow.com> Closes #1410 from dbtsai/SPARK-2477_intercept_with_appendBias and squashes the following commits: 011432c [DB Tsai] From Alpine Data Labs Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/52beb20f Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/52beb20f Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/52beb20f Branch: refs/heads/master Commit: 52beb20f7904e0333198b9b14619366ddf53ab85 Parents: dd95aba Author: DB Tsai <dbt...@alpinenow.com> Authored: Tue Jul 15 02:14:58 2014 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Tue Jul 15 02:14:58 2014 -0700 ---------------------------------------------------------------------- .../regression/GeneralizedLinearAlgorithm.scala | 21 +++++--------------- 1 file changed, 5 insertions(+), 16 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/52beb20f/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 8cca926..fe41863 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -17,13 +17,12 @@ package org.apache.spark.mllib.regression -import breeze.linalg.{DenseVector => BDV, SparseVector => BSV} - import org.apache.spark.annotation.DeveloperApi import org.apache.spark.{Logging, SparkException} import org.apache.spark.rdd.RDD import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.linalg.{Vectors, Vector} +import org.apache.spark.mllib.util.MLUtils._ /** * :: DeveloperApi :: @@ -124,16 +123,6 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] run(input, initialWeights) } - /** Prepends one to the input vector. */ - private def prependOne(vector: Vector): Vector = { - val vector1 = vector.toBreeze match { - case dv: BDV[Double] => BDV.vertcat(BDV.ones[Double](1), dv) - case sv: BSV[Double] => BSV.vertcat(new BSV[Double](Array(0), Array(1.0), 1), sv) - case v: Any => throw new IllegalArgumentException("Do not support vector type " + v.getClass) - } - Vectors.fromBreeze(vector1) - } - /** * Run the algorithm with the configured parameters on an input RDD * of LabeledPoint entries starting from the initial weights provided. @@ -147,23 +136,23 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] // Prepend an extra variable consisting of all 1.0's for the intercept. val data = if (addIntercept) { - input.map(labeledPoint => (labeledPoint.label, prependOne(labeledPoint.features))) + input.map(labeledPoint => (labeledPoint.label, appendBias(labeledPoint.features))) } else { input.map(labeledPoint => (labeledPoint.label, labeledPoint.features)) } val initialWeightsWithIntercept = if (addIntercept) { - prependOne(initialWeights) + appendBias(initialWeights) } else { initialWeights } val weightsWithIntercept = optimizer.optimize(data, initialWeightsWithIntercept) - val intercept = if (addIntercept) weightsWithIntercept(0) else 0.0 + val intercept = if (addIntercept) weightsWithIntercept(weightsWithIntercept.size - 1) else 0.0 val weights = if (addIntercept) { - Vectors.dense(weightsWithIntercept.toArray.slice(1, weightsWithIntercept.size)) + Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)) } else { weightsWithIntercept }
