[jira] [Commented] (SPARK-15153) SparkR spark.naiveBayes throws error when label is numeric type
[
https://issues.apache.org/jira/browse/SPARK-15153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15566251#comment-15566251
]
Joseph K. Bradley commented on SPARK-15153:
---
Note I'm setting the target version for 2.1, not 2.0.x, since the fix requires
a public API change in the preceding PR.
> SparkR spark.naiveBayes throws error when label is numeric type
> ---
>
> Key: SPARK-15153
> URL: https://issues.apache.org/jira/browse/SPARK-15153
> Project: Spark
> Issue Type: Bug
> Components: ML, SparkR
>Reporter: Yanbo Liang
>Assignee: Yanbo Liang
>
> When the label of dataset is numeric type, SparkR spark.naiveBayes will throw
> error. This bug is easy to reproduce:
> {code}
> t <- as.data.frame(Titanic)
> t1 <- t[t$Freq > 0, -5]
> t1$NumericSurvived <- ifelse(t1$Survived == "No", 0, 1)
> t2 <- t1[-4]
> df <- suppressWarnings(createDataFrame(sqlContext, t2))
> m <- spark.naiveBayes(df, NumericSurvived ~ .)
> 16/05/05 03:26:17 ERROR RBackendHandler: fit on
> org.apache.spark.ml.r.NaiveBayesWrapper failed
> Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
> java.lang.ClassCastException:
> org.apache.spark.ml.attribute.UnresolvedAttribute$ cannot be cast to
> org.apache.spark.ml.attribute.NominalAttribute
> at
> org.apache.spark.ml.r.NaiveBayesWrapper$.fit(NaiveBayesWrapper.scala:66)
> at org.apache.spark.ml.r.NaiveBayesWrapper.fit(NaiveBayesWrapper.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:141)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:86)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:38)
> at
> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
> at io.netty.channel.AbstractChannelHandlerContext.invo
> {code}
> In RFormula, the response variable type could be string or numeric. If it's
> string, RFormula will transform it to label of DoubleType by StringIndexer
> and set corresponding column metadata; otherwise, RFormula will directly use
> it as label when training model (and assumes that it was numbered from 0,
> ..., maxLabelIndex).
> When we extract labels at ml.r.NaiveBayesWrapper, we should handle it
> according the type of the response variable (string or numeric).
> cc [~mengxr] [~josephkb]
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[jira] [Commented] (SPARK-15153) SparkR spark.naiveBayes throws error when label is numeric type
[
https://issues.apache.org/jira/browse/SPARK-15153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15565004#comment-15565004
]
Apache Spark commented on SPARK-15153:
--
User 'yanboliang' has created a pull request for this issue:
https://github.com/apache/spark/pull/15431
> SparkR spark.naiveBayes throws error when label is numeric type
> ---
>
> Key: SPARK-15153
> URL: https://issues.apache.org/jira/browse/SPARK-15153
> Project: Spark
> Issue Type: Bug
> Components: ML, SparkR
>Reporter: Yanbo Liang
>Assignee: Yanbo Liang
>
> When the label of dataset is numeric type, SparkR spark.naiveBayes will throw
> error. This bug is easy to reproduce:
> {code}
> t <- as.data.frame(Titanic)
> t1 <- t[t$Freq > 0, -5]
> t1$NumericSurvived <- ifelse(t1$Survived == "No", 0, 1)
> t2 <- t1[-4]
> df <- suppressWarnings(createDataFrame(sqlContext, t2))
> m <- spark.naiveBayes(df, NumericSurvived ~ .)
> 16/05/05 03:26:17 ERROR RBackendHandler: fit on
> org.apache.spark.ml.r.NaiveBayesWrapper failed
> Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
> java.lang.ClassCastException:
> org.apache.spark.ml.attribute.UnresolvedAttribute$ cannot be cast to
> org.apache.spark.ml.attribute.NominalAttribute
> at
> org.apache.spark.ml.r.NaiveBayesWrapper$.fit(NaiveBayesWrapper.scala:66)
> at org.apache.spark.ml.r.NaiveBayesWrapper.fit(NaiveBayesWrapper.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:141)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:86)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:38)
> at
> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
> at io.netty.channel.AbstractChannelHandlerContext.invo
> {code}
> In RFormula, the response variable type could be string or numeric. If it's
> string, RFormula will transform it to label of DoubleType by StringIndexer
> and set corresponding column metadata; otherwise, RFormula will directly use
> it as label when training model (and assumes that it was numbered from 0,
> ..., maxLabelIndex).
> When we extract labels at ml.r.NaiveBayesWrapper, we should handle it
> according the type of the response variable (string or numeric).
> cc [~mengxr] [~josephkb]
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