From 39adba51c55b211441cc93a859beb72d008279de Mon Sep 17 00:00:00 2001 From: Sergei Lebedev Date: Mon, 28 Aug 2017 19:59:39 +0200 Subject: [PATCH] Fixed compilation on Scala 2.10 (#2629) --- .../scala/ml/dmlc/xgboost4j/scala/spark/XGBoostDFSuite.scala | 2 +- .../ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostDFSuite.scala b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostDFSuite.scala index f7bfba7c6..972971efd 100644 --- a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostDFSuite.scala +++ b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostDFSuite.scala @@ -203,7 +203,7 @@ class XGBoostDFSuite extends FunSuite with PerTest { "objective" -> "binary:logistic", "baseMarginCol" -> "margin") def trainPredict(df: Dataset[_]): Array[Float] = { - XGBoost.trainWithDataFrame(df, paramMap, round = 1, numWorkers) + XGBoost.trainWithDataFrame(df, paramMap, round = 1, nWorkers = numWorkers) .predict(testRDD) .map { case Array(p) => p } .collect() diff --git a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala index de4ab91ce..b1db09db7 100644 --- a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala +++ b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala @@ -243,7 +243,7 @@ class XGBoostGeneralSuite extends FunSuite with PerTest { val trainingRDD = sc.parallelize(Classification.train).map(_.asML).cache() val paramMap = Map("eta" -> "1", "max_depth" -> "2", "silent" -> "1", "objective" -> "binary:logistic") - val xgBoostModel = XGBoost.trainWithRDD(trainingRDD, paramMap, round = 5, numWorkers) + val xgBoostModel = XGBoost.trainWithRDD(trainingRDD, paramMap, round = 5, nWorkers = numWorkers) // Nan Zhu: deprecate it for now // xgBoostModel.eval(trainingRDD, "eval1", iter = 5, useExternalCache = false) xgBoostModel.eval(trainingRDD, "eval2", evalFunc = new EvalError, useExternalCache = false)