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https://issues.apache.org/jira/browse/SYSTEMML-1238?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Niketan Pansare resolved SYSTEMML-1238.
---------------------------------------
Resolution: Fixed
Fix Version/s: SystemML 0.13
Fixed in the commit
https://github.com/apache/incubator-systemml/commit/9d0087cbbd250c9b486923555b450602f816cf19
by setting regularization to 0 (similar to that of scikit-learn).
> Python test failing for LinearRegCG
> -----------------------------------
>
> Key: SYSTEMML-1238
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1238
> Project: SystemML
> Issue Type: Bug
> Components: Algorithms, APIs
> Affects Versions: SystemML 0.13
> Reporter: Imran Younus
> Assignee: Niketan Pansare
> Fix For: SystemML 0.13
>
> Attachments: python_LinearReg_test_spark.1.6.log,
> python_LinearReg_test_spark.2.1.log
>
>
> [~deron] discovered that the one of the python test ({{test_mllearn_df.py}})
> with spark 2.1.0 was failing because the test score from linear regression
> was very low ({{~ 0.24}}). I did a some investigation and it turns out the
> the model parameters computed by the dml script are incorrect. In
> systemml.12, the values of betas from linear regression model are
> {{\[152.919, 938.237\]}}. This is what we expect from normal equation. (I
> also tested this with sklearn). But the values of betas from systemml.13
> (with spark 2.1.0) come out to be {{\[153.146, 458.489\]}}. These are not
> correct and therefore the test score is much lower than expected. The data
> going into DML script is correct. I printed out the valued of {{X}} and {{Y}}
> in dml and I didn't see any issue there.
> Attached are the log files for two different tests (systemml0.12 and 0.13)
> with explain flag.
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