Jacek Laskowski created SPARK-14183:
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Summary: UnsupportedOperationException: empty.max when fitting
CrossValidator model
Key: SPARK-14183
URL: https://issues.apache.org/jira/browse/SPARK-14183
Project: Spark
Issue Type: Improvement
Components: ML
Affects Versions: 2.0.0
Reporter: Jacek Laskowski
Priority: Minor
The following code produces {{java.lang.UnsupportedOperationException:
empty.max}}, but it should've said what might've caused that or how to fix it.
The exception:
{code}
scala> val model = cv.fit(df)
java.lang.UnsupportedOperationException: empty.max
at scala.collection.TraversableOnce$class.max(TraversableOnce.scala:227)
at scala.collection.AbstractTraversable.max(Traversable.scala:104)
at
org.apache.spark.ml.classification.MultiClassSummarizer.numClasses(LogisticRegression.scala:739)
at
org.apache.spark.ml.classification.MultiClassSummarizer.histogram(LogisticRegression.scala:743)
at
org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:288)
at
org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:261)
at
org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:160)
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.Estimator.fit(Estimator.scala:59)
at org.apache.spark.ml.Estimator$$anonfun$fit$1.apply(Estimator.scala:78)
at org.apache.spark.ml.Estimator$$anonfun$fit$1.apply(Estimator.scala:78)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at org.apache.spark.ml.Estimator.fit(Estimator.scala:78)
at
org.apache.spark.ml.tuning.CrossValidator$$anonfun$fit$1.apply(CrossValidator.scala:110)
at
org.apache.spark.ml.tuning.CrossValidator$$anonfun$fit$1.apply(CrossValidator.scala:105)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at org.apache.spark.ml.tuning.CrossValidator.fit(CrossValidator.scala:105)
... 55 elided
{code}
The code:
{code}
import org.apache.spark.ml.tuning._
val cv = new CrossValidator
import org.apache.spark.mllib.linalg._
val features = Vectors.sparse(3, Array(1), Array(1d))
val df = Seq((0, "hello world", 0d, features)).toDF("id", "text", "label",
"features")
import org.apache.spark.ml.classification._
val lr = new LogisticRegression()
import org.apache.spark.ml.evaluation.RegressionEvaluator
val regEval = new RegressionEvaluator()
val paramGrid = new ParamGridBuilder().build()
cv.setEstimatorParamMaps(paramGrid).setEstimator(lr).setEvaluator(regEval)
val model = cv.fit(df)
{code}
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