[
https://issues.apache.org/jira/browse/SPARK-23152?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Matthew Tovbin updated SPARK-23152:
-----------------------------------
Description:
When fitting a classifier that extends
"org.apache.spark.ml.classification.Classifier" (NaiveBayes,
DecisionTreeClassifier, RandomForestClassifier) a 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
org.apache.spark.ml.classification.Classifier:getNumClasses
{code:java}
val maxLabelRow: Array[Row] = dataset.select(max($(labelCol))).take(1)
if (maxLabelRow.isEmpty) {
throw new SparkException("ML algorithm was given empty dataset.")
}
{code}
When the input data is empty the result "maxLabelRow" array is not. Instead it
contains a single Row(null) element.
Proposed solution: the condition can be modified to verify that.
{code:java}
if (maxLabelRow.isEmpty || maxLabelRow(0).get(0) == null) {
throw new SparkException("ML algorithm was given empty dataset.")
}
{code}
was:
When fitting a classifier that extends
"org.apache.spark.ml.classification.Classifier" (NaiveBayes,
DecisionTreeClassifier, RandomForestClassifier) a NullPointerException is
thrown.
Steps to reproduce:
{code:java}
val data = spark.createDataset(Seq.empty[(Double,
org.apache.spark.ml.linalg.Vector)])
new DecisionTreeClassifier().setLabelCol("_1").setFeaturesCol("_2").fit(data)
{code}
The error:
{code:java}
java.lang.NullPointerException: Value at index 0 is null
at org.apache.spark.sql.Row$class.getAnyValAs(Row.scala:472)
at org.apache.spark.sql.Row$class.getDouble(Row.scala:248)
at
org.apache.spark.sql.catalyst.expressions.GenericRow.getDouble(rows.scala:165)
at
org.apache.spark.ml.classification.Classifier.getNumClasses(Classifier.scala:115)
at
org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:102)
at
org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:45)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:118){code}
The problem happens due to an incorrect guard condition in
org.apache.spark.ml.classification.Classifier:getNumClasses
{code:java}
val maxLabelRow: Array[Row] = dataset.select(max($(labelCol))).take(1)
if (maxLabelRow.isEmpty) {
throw new SparkException("ML algorithm was given empty dataset.")
}
{code}
When the input data is empty the "maxLabelRow" array is not. Instead it
contains a single null element WrappedArray(null).
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}
> 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
> Priority: Minor
> Labels: easyfix
>
> When fitting a classifier that extends
> "org.apache.spark.ml.classification.Classifier" (NaiveBayes,
> DecisionTreeClassifier, RandomForestClassifier) a 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
> org.apache.spark.ml.classification.Classifier:getNumClasses
> {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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