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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 commented on SPARK-11918:
-------------------------------------
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
> 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. The failure is caused by the underneath
> 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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