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[jvm-packages] fix "key not found: train" issue (#6842)

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* [jvm-packages] fix "key not found: train" issue

* fix bug
This commit is contained in:
Bobby Wang 2021-04-19 14:28:39 +08:00 committed by GitHub
parent 556a83022d
commit 2c684ffd32
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2 changed files with 16 additions and 2 deletions
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2
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
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@ -377,7 +377,7 @@ object XGBoost extends Serializable {
// to workaround the empty partitions in training dataset, // to workaround the empty partitions in training dataset,
// this might not be the best efficient implementation, see // this might not be the best efficient implementation, see
// (https://github.com/dmlc/xgboost/issues/1277) // (https://github.com/dmlc/xgboost/issues/1277)
if (watches.toMap("train").rowNum == 0) { if (!watches.toMap.contains("train")) {
throw new XGBoostError( throw new XGBoostError(
s"detected an empty partition in the training data, partition ID:" + s"detected an empty partition in the training data, partition ID:" +
s" ${TaskContext.getPartitionId()}") s" ${TaskContext.getPartitionId()}")

16
jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala
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@ -16,10 +16,12 @@
package ml.dmlc.xgboost4j.scala.spark package ml.dmlc.xgboost4j.scala.spark
import ml.dmlc.xgboost4j.java.XGBoostError
import scala.util.Random import scala.util.Random
import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint} import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
import ml.dmlc.xgboost4j.scala.DMatrix import ml.dmlc.xgboost4j.scala.DMatrix
import org.apache.spark.{TaskContext} import org.apache.spark.TaskContext
import org.scalatest.FunSuite import org.scalatest.FunSuite
import org.apache.spark.ml.feature.VectorAssembler import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.sql.functions.lit import org.apache.spark.sql.functions.lit
@ -367,4 +369,16 @@ class XGBoostGeneralSuite extends FunSuite with TmpFolderPerSuite with PerTest {
df2.collect() df2.collect()
} }
test("throw exception for empty partition in trainingset") {
val paramMap = Map("eta" -> "0.1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "multi:softmax", "num_class" -> "2", "num_round" -> 5,
"num_workers" -> numWorkers, "tree_method" -> "auto")
// The Dmatrix will be empty
val trainingDF = buildDataFrame(Seq(XGBLabeledPoint(1.0f, 1, Array(), Array())))
val xgb = new XGBoostClassifier(paramMap)
intercept[XGBoostError] {
val model = xgb.fit(trainingDF)
}
}
} }