[jira] [Resolved] (SPARK-1859) Linear, Ridge and Lasso Regressions with SGD yield unexpected results

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-1859?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng resolved SPARK-1859.
----------------------------------

    Resolution: Not a Problem

> Linear, Ridge and Lasso Regressions with SGD yield unexpected results
> ---------------------------------------------------------------------
>
>                 Key: SPARK-1859
>                 URL: https://issues.apache.org/jira/browse/SPARK-1859
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 0.9.1
>         Environment: OS: Ubuntu Server 12.04 x64
> PySpark
>            Reporter: Vlad Frolov
>              Labels: algorithm, machine_learning, regression
>
> Issue:
> Linear Regression with SGD don't work as expected on any data, but lpsa.dat (example one).
> Ridge Regression with SGD *sometimes* works ok.
> Lasso Regression with SGD *sometimes* works ok.
> Code example (PySpark) based on http://spark.apache.org/docs/0.9.0/mllib-guide.html#linear-regression-2 :
> {code:title=regression_example.py}
> parsedData = sc.parallelize([
>     array([2400., 1500.]),
>     array([240., 150.]),
>     array([24., 15.]),
>     array([2.4, 1.5]),
>     array([0.24, 0.15])
> ])
> # Build the model
> model = LinearRegressionWithSGD.train(parsedData)
> print model._coeffs
> {code}
> So we have a line ({{f(X) = 1.6 * X}}) here. Fortunately, {{f(X) = X}} works! :)
> The resulting model has nan coeffs: {{array([ nan])}}.
> Furthermore, if you comment records line by line you will get:
> * [-1.55897475e+296] coeff (the first record is commented), 
> * [-8.62115396e+104] coeff (the first two records are commented),
> * etc
> It looks like the implemented regression algorithms diverges somehow.
> I get almost the same results on Ridge and Lasso.
> I've also tested these inputs in scikit-learn and it works as expected there.
> However, I'm still not sure whether it's a bug or SGD 'feature'. Should I preprocess my datasets somehow?



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