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https://issues.apache.org/jira/browse/SPARK-11918?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15021729#comment-15021729
]
Yanbo Liang edited comment on SPARK-11918 at 11/23/15 8:44 AM:
---------------------------------------------------------------
Further more, I use the breeze library to train the model by local normal
equation method.
{code}
import sqlCtx.implicits._
import org.apache.spark.mllib.linalg.Vector
import breeze.linalg.DenseMatrix
import breeze.linalg._
val df = MLUtils.loadLibSVMFile(sqlCtx.sparkContext,
"/Users/yanboliang/data/trunk/spark/data/mllib/sample_libsvm_data.txt").toDF()
val features = df.select(col("features")).map { r =>
r.getAs[Vector](0)
}.collect().flatMap { v => v.toArray }
val labelArray = df.select(col("label")).map { r =>
r.getDouble(0)
}.collect()
val Xt = new DenseMatrix[Double](692, 100, features)
val X = Xt.t
val y = new DenseMatrix[Double](100, 1, labelArray)
val XtXi = inv(Xt * X)
val XtY = Xt * y
val coefs = XtXi * XtY
println(coefs.toString)
{code}
It also throw exception like:
{code}
breeze.linalg.MatrixSingularException:
at breeze.linalg.inv$$anon$1.apply(inv.scala:36)
at breeze.linalg.inv$$anon$1.apply(inv.scala:19)
at breeze.generic.UFunc$class.apply(UFunc.scala:48)
at breeze.linalg.inv$.apply(inv.scala:17)
{code}
breeze.linalg.inv is also call netlib lapack library which is the same as
Spark. Tracking the breeze code, we can get this exception is thrown at here
(https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/linalg/functions/inv.scala#L33)
also caused by the underneath lapack error.
was (Author: yanboliang):
Further more, I use the breeze library to train the model by local normal
equation method.
{code}
import sqlCtx.implicits._
import org.apache.spark.mllib.linalg.Vector
import breeze.linalg.DenseMatrix
import breeze.linalg._
val df = MLUtils.loadLibSVMFile(sqlCtx.sparkContext,
"/Users/yanboliang/data/trunk/spark/data/mllib/sample_libsvm_data.txt").toDF()
val features = df.select(col("features")).map { r =>
r.getAs[Vector](0)
}.collect().flatMap { v => v.toArray }
val labelArray = df.select(col("label")).map { r =>
r.getDouble(0)
}.collect()
val Xt = new DenseMatrix[Double](692, 100, features)
val X = Xt.t
val y = new DenseMatrix[Double](100, 1, labelArray)
val XtXi = inv(Xt * X)
val XtY = Xt * y
val coefs = XtXi * XtY
println(coefs.toString)
{code}
It also throw exception like:
{code}
breeze.linalg.MatrixSingularException:
at breeze.linalg.inv$$anon$1.apply(inv.scala:36)
at breeze.linalg.inv$$anon$1.apply(inv.scala:19)
at breeze.generic.UFunc$class.apply(UFunc.scala:48)
at breeze.linalg.inv$.apply(inv.scala:17)
{code}
The breeze.linalg.inv is also call netlib LAPACK package which is the same
library as Spark. Tracking the breeze code, we can get this exception is thrown
at here
(https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/linalg/functions/inv.scala#L33)
which is also caused by the underneath lapack error.
> WLS can not resolve some kinds of equation
> ------------------------------------------
>
> Key: SPARK-11918
> URL: https://issues.apache.org/jira/browse/SPARK-11918
> Project: Spark
> Issue Type: Bug
> Components: ML
> Reporter: Yanbo Liang
> Attachments: R_GLM_output
>
>
> Weighted Least Squares (WLS) is one of the optimization method for solve
> Linear Regression (when #feature < 4096). But if the dataset is very ill
> condition (such as 0-1 based label used for classification and the equation
> is underdetermined), the WLS failed (But "l-bfgs" can train and get the
> model). The failure is caused by the underneath lapack library return error
> value when Cholesky decomposition.
> This issue is easy to reproduce, you can train a LinearRegressionModel by
> "normal" solver with the example
> dataset(https://github.com/apache/spark/blob/master/data/mllib/sample_libsvm_data.txt).
> The following is the exception:
> {code}
> assertion failed: lapack.dpotrs returned 1.
> java.lang.AssertionError: assertion failed: lapack.dpotrs returned 1.
> at scala.Predef$.assert(Predef.scala:179)
> at
> org.apache.spark.mllib.linalg.CholeskyDecomposition$.solve(CholeskyDecomposition.scala:42)
> at
> org.apache.spark.ml.optim.WeightedLeastSquares.fit(WeightedLeastSquares.scala:117)
> at
> org.apache.spark.ml.regression.LinearRegression.train(LinearRegression.scala:180)
> at
> org.apache.spark.ml.regression.LinearRegression.train(LinearRegression.scala:67)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
> {code}
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