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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/10/06 08:55:20 UTC
[jira] [Reopened] (SPARK-17797) getNumClasses support non-double
datatypes
[ https://issues.apache.org/jira/browse/SPARK-17797?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen reopened SPARK-17797:
-------------------------------
> getNumClasses support non-double datatypes
> ------------------------------------------
>
> Key: SPARK-17797
> URL: https://issues.apache.org/jira/browse/SPARK-17797
> Project: Spark
> Issue Type: Bug
> Components: ML
> Reporter: zhengruifeng
>
> Without precomputed meta {{numValues}}, method {{Classifier.getNumClasses()}} do not support numeric types other than Double.
> {code}
> scala> val path = "/Users/zrf/.dev/spark-2.0.1-bin-hadoop2.7/data/mllib/sample_libsvm_data.txt"
> path: String = /Users/zrf/.dev/spark-2.0.1-bin-hadoop2.7/data/mllib/sample_libsvm_data.txt
> scala> val data = spark.read.format("libsvm").load(path).persist()
> data: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [label: double, features: vector]
> scala>
> scala> val data2 = data.select(col("label").cast(LongType), col("features"))
> data2: org.apache.spark.sql.DataFrame = [label: bigint, features: vector]
> scala> val model = new NaiveBayes().fit(data)
> model: org.apache.spark.ml.classification.NaiveBayesModel = NaiveBayesModel (uid=nb_1e27d7acf0b3) with 2 classes
> scala> val model = new NaiveBayes().fit(data2)
> java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Double
> at scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:114)
> at org.apache.spark.sql.Row$class.getDouble(Row.scala:242)
> at org.apache.spark.sql.catalyst.expressions.GenericRow.getDouble(rows.scala:192)
> at org.apache.spark.ml.classification.Classifier.getNumClasses(Classifier.scala:115)
> at org.apache.spark.ml.classification.NaiveBayes.train(NaiveBayes.scala:104)
> at org.apache.spark.ml.classification.NaiveBayes.train(NaiveBayes.scala:76)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
> ... 54 elided
> {code}
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