[jvm-packages] fix early stopping doesn't work even without custom_ev…

…al setting (#6738) * [jvm-packages] fix early stopping doesn't work even without custom_eval setting * remove debug info * resolve comment
dmlc · Mar 7, 2021 · 49c22c2 · 49c22c2
1 parent 5ae7f99
commit 49c22c2
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diff --git a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
@@ -149,7 +149,7 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
     overridedParams += "num_early_stopping_rounds" -> numEarlyStoppingRounds
     if (numEarlyStoppingRounds > 0 &&
       !overridedParams.contains("maximize_evaluation_metrics")) {
-      if (overridedParams.contains("custom_eval")) {
+      if (overridedParams.getOrElse("custom_eval", null) != null) {
         throw new IllegalArgumentException("custom_eval does not support early stopping")
       }
       val eval_metric = overridedParams("eval_metric").toString

diff --git a/...ackages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/ParameterSuite.scala b/...ackages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/ParameterSuite.scala
@@ -78,4 +78,26 @@ class ParameterSuite extends FunSuite with PerTest with BeforeAndAfterAll {
       waitForSparkContextShutdown()
     }
   }
+
+  test("custom_eval does not support early stopping") {
+    val paramMap = Map("eta" -> "0.1", "custom_eval" -> new EvalError, "silent" -> "1",
+      "objective" -> "multi:softmax", "num_class" -> "6", "num_round" -> 5,
+      "num_workers" -> numWorkers, "num_early_stopping_rounds" -> 2)
+    val trainingDF = buildDataFrame(MultiClassification.train)
+
+    val thrown = intercept[IllegalArgumentException] {
+      new XGBoostClassifier(paramMap).fit(trainingDF)
+    }
+
+    assert(thrown.getMessage.contains("custom_eval does not support early stopping"))
+  }
+
+  test("early stopping should work without custom_eval setting") {
+    val paramMap = Map("eta" -> "0.1", "silent" -> "1",
+      "objective" -> "multi:softmax", "num_class" -> "6", "num_round" -> 5,
+      "num_workers" -> numWorkers, "num_early_stopping_rounds" -> 2)
+    val trainingDF = buildDataFrame(MultiClassification.train)
+
+    new XGBoostClassifier(paramMap).fit(trainingDF)
+  }
 }