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https://issues.apache.org/jira/browse/SYSTEMML-1238?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15858442#comment-15858442
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Niketan Pansare commented on SYSTEMML-1238:
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Looks like both script have same plan. This looks like an algorithm-related or
repeatability issue as the statistics after training are as follows:
python_LinearReg_test_spark.1.6.log:
||r|| initial value = 64725.64237405237, target value = 0.06472564237405237
Iteration 1: ||r|| / ||r init|| = 0.013822097249150999
Iteration 2: ||r|| / ||r init|| = 7.063617429825055E-14
The CG algorithm is done.
Computing the statistics...
938.237
152.919
AVG_TOT_Y,153.36255924170615
STDEV_TOT_Y,77.21853383600028
AVG_RES_Y,-1.081722178918495E-11
STDEV_RES_Y,63.03850633761024
DISPERSION,3973.8532812769263
PLAIN_R2,0.3351312506863876
ADJUSTED_R2,0.33354822985468857
PLAIN_R2_NOBIAS,0.3351312506863876
ADJUSTED_R2_NOBIAS,0.33354822985468857
python_LinearReg_test_spark.2.1.log:
||r|| initial value = 64725.64237405237, target value = 0.06472564237405237
Iteration 1: ||r|| / ||r init|| = 0.01378813951373333
Iteration 2: ||r|| / ||r init|| = 4.3730800595678527E-14
The CG algorithm is done.
Computing the statistics...
458.489
153.146
AVG_TOT_Y,153.36255924170615
STDEV_TOT_Y,77.21853383600028
AVG_RES_Y,-6.688193969161777E-12
STDEV_RES_Y,67.06389890324985
DISPERSION,4497.566536105316
PLAIN_R2,0.24750834362605834
ADJUSTED_R2,0.24571669682516795
PLAIN_R2_NOBIAS,0.24750834362605834
ADJUSTED_R2_NOBIAS,0.24571669682516795
> 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
> 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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