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https://issues.apache.org/jira/browse/SPARK-11918?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yanbo Liang updated SPARK-11918:
--------------------------------
Description:
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}
was:
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 the "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}
> 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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