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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2018/01/24 18:16:00 UTC
[jira] [Resolved] (SPARK-23152) Invalid guard condition in
org.apache.spark.ml.classification.Classifier
[ https://issues.apache.org/jira/browse/SPARK-23152?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-23152.
-------------------------------
Resolution: Fixed
Fix Version/s: 2.4.0
Issue resolved by pull request 20321
[https://github.com/apache/spark/pull/20321]
> Invalid guard condition in org.apache.spark.ml.classification.Classifier
> ------------------------------------------------------------------------
>
> Key: SPARK-23152
> URL: https://issues.apache.org/jira/browse/SPARK-23152
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
> Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.3.0, 2.3.1
> Reporter: Matthew Tovbin
> Assignee: Matthew Tovbin
> Priority: Minor
> Labels: easyfix
> Fix For: 2.4.0
>
>
> When fitting a classifier that extends "org.apache.spark.ml.classification.Classifier" (NaiveBayes, DecisionTreeClassifier, RandomForestClassifier) a misleading NullPointerException is thrown.
> Steps to reproduce:
> {code:java}
> val data = spark.createDataset(Seq.empty[(Double, org.apache.spark.ml.linalg.Vector)])
> new DecisionTreeClassifier().setLabelCol("_1").setFeaturesCol("_2").fit(data)
> {code}
> The error:
> {code:java}
> java.lang.NullPointerException: Value at index 0 is null
> at org.apache.spark.sql.Row$class.getAnyValAs(Row.scala:472)
> at org.apache.spark.sql.Row$class.getDouble(Row.scala:248)
> at org.apache.spark.sql.catalyst.expressions.GenericRow.getDouble(rows.scala:165)
> at org.apache.spark.ml.classification.Classifier.getNumClasses(Classifier.scala:115)
> at org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:102)
> at org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:45)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:118){code}
>
> The problem happens due to an incorrect guard condition in function getNumClasses at org.apache.spark.ml.classification.Classifier:106
> {code:java}
> val maxLabelRow: Array[Row] = dataset.select(max($(labelCol))).take(1)
> if (maxLabelRow.isEmpty) {
> throw new SparkException("ML algorithm was given empty dataset.")
> }
> {code}
> When the input data is empty the result "maxLabelRow" array is not. Instead it contains a single Row(null) element.
>
> Proposed solution: the condition can be modified to verify that.
> {code:java}
> if (maxLabelRow.isEmpty || maxLabelRow(0).get(0) == null) {
> throw new SparkException("ML algorithm was given empty dataset.")
> }
> {code}
>
>
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