[
https://issues.apache.org/jira/browse/SPARK-16064?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15362328#comment-15362328
]
Yanbo Liang edited comment on SPARK-16064 at 7/5/16 10:22 AM:
--------------------------------------------------------------
[~April_ZMQ] After in-depth study of your problem, I think the major cause of
this error is bad modeling method rather than GLM disadvantage. The error
seemingly is cause by NA produced by reweight function, but I found we can not
handle your case even we add more constraint to the scope of {{(offset,
weight)}} which is produced by reweight function. Since your data is very ill
condition and the error of your response variable is not following poisson
distribution at all. You can refer the following std/variance of features and
label for your fitted model:
{code}
features std:
[0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555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label variance = 1.4239918875179388E7
{code}
So to resolve this problem, you should think more about better modeling method
such as other families/links or different features ensemble.
I will solve this JIRA as {{Not A Problem}}. If you have more questions, please
feel free to comment here or email to me. Thanks!
was (Author: yanboliang):
[~April_ZMQ] After in-depth study of your problem, I think the major cause of
this error is bad modeling method rather than GLM disadvantage. The error
seemingly is cause by NA produced by reweight function, but I found we can not
handle your case even we add more constraint to the scope of {{(offset,
weight)}} which is produced by reweight function. Since your data is very ill
condition and the error of your response variable is not following poisson
distribution at all. You can refer the following std/variance of features and
label for your fitted model:
{code}
features std:
[0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555,0.05555555555555555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label variance = 1.4239918875179388E7
{code}
So to resolve this problem, you should think more about better modeling method
such as other families/links or different features ensemble.
I will solve this JIRA as won't {{Not A Problem}}. If you have more questions,
please feel free to comment here or email to me. Thanks!
> Fix the GLM error caused by NA produced by reweight function
> ------------------------------------------------------------
>
> Key: SPARK-16064
> URL: https://issues.apache.org/jira/browse/SPARK-16064
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.0.0
> Reporter: Zhang Mengqi
> Assignee: Yanbo Liang
> Priority: Minor
>
> This case happens when users run GLM in with SparkR, the same dataset runs
> GLM well in native R.
> When users run the GLM model using glm with family of poisson, it generates a
> assertion errors by NA produced by reweight function.
> 16/06/20 16:40:22 ERROR RBackendHandler: fit on
> org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper failed
> Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
> java.lang.AssertionError: assertion failed: Sum of weights cannot be zero.
> at scala.Predef$.assert(Predef.scala:170)
> at
> org.apache.spark.ml.optim.WeightedLeastSquares$Aggregator.validate(WeightedLeastSquares.scala:248)
> at
> org.apache.spark.ml.optim.WeightedLeastSquares.fit(WeightedLeastSquares.scala:82)
> at
> org.apache.spark.ml.optim.IterativelyReweightedLeastSquares.fit(IterativelyReweightedLeastSquares.scala:85)
> at
> org.apache.spark.ml.regression.GeneralizedLinearRegression.train(GeneralizedLinearRegression.scala:276)
> at
> org.apache.spark.ml.regression.GeneralizedLinearRegression.train(GeneralizedLinearRegression.scala:134)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:71)
> at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:148)
> at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:144)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> at scala.collection.Abstra
> P.S The dataset is about a city ride flow between several planning area in
> Singapore.
> ride_flow_exp <- glm(flow~Origin+Destination+distance,ride_flow,family =
> poisson(link = "log"))
> SparkDataFrame[Origin:string, Destination:string, flow:double, Oi:int,
> Dj:int, distance:double]
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