阿帕奇星火 - java.lang.NoSuchMethodError：breeze.linalg.Vector $ .scalarOf（）Lbreeze/linalg /支持/ ScalarOf

以下是错误：

Exception in thread "main" java.lang.NoSuchMethodError: breeze.linalg.Vector$.scalarOf()Lbreeze/linalg/support/ScalarOf; 
at org.apache.spark.ml.knn.Leaf$$anonfun$4.apply(MetricTree.scala:95) 
at org.apache.spark.ml.knn.Leaf$$anonfun$4.apply(MetricTree.scala:93) 
at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) 
at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) 
at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:48) 
at org.apache.spark.ml.knn.Leaf$.apply(MetricTree.scala:93) 
at org.apache.spark.ml.knn.MetricTree$.build(MetricTree.scala:169) 
at org.apache.spark.ml.knn.KNN.fit(KNN.scala:388) 
at org.apache.spark.ml.classification.KNNClassifier.train(KNNClassifier.scala:109) 
at org.apache.spark.ml.classification.KNNClassifier.fit(KNNClassifier.scala:117) 
at SparkKNN$.main(SparkKNN.scala:23) 
at SparkKNN.main(SparkKNN.scala)

这里是触发错误的程序：

object SparkKNN { 
    def main(args: Array[String]) { 
     val spark = SparkSession.builder().master("local").config("spark.sql.warehouse.dir", "file:///c:/tmp/spark-warehouse").getOrCreate() 
     val sc = spark.sparkContext 
     import spark.implicits._ 
     //read in raw label and features 
     val training = spark.read.format("com.databricks.spark.csv").option("header", true).load("E:/Machine Learning/knn_input.csv") 
     var df = training.selectExpr("cast(label as double) label", "cast(feature1 as int) feature1","cast(feature2 as int) feature2","cast(feature3 as int) feature3") 
     val assembler = new VectorAssembler().setInputCols(Array("feature1","feature2","feature3")).setOutputCol("features") 
     df = assembler.transform(df) 
     //MLUtils.loadLibSVMFile(sc, "C:/Program Files (x86)/spark-2.0.0-bin-hadoop2.7/data/mllib/sample_libsvm_data.txt").toDF() 

     val knn = new KNNClassifier() 
      .setTopTreeSize(df.count().toInt/2) 
      .setK(10) 
     val splits = df.randomSplit(Array(0.7, 0.3)) 
     val (trainingData, testData) = (splits(0), splits(1)) 
     val knnModel = knn.fit(trainingData) 

     val predicted = knnModel.transform(testData) 
     predicted.show() 
    } 
}

我使用Apache Spark 2.0和Scala版本2.11.8。它看起来像一个版本差异问题。有任何想法吗？

来源

2017-04-02 Swetha Boga

星火MLLib 2.0带来了这个版本的微风：

"org.scalanlp" % "breeze_2.11" % "0.11.2"

您必须在您的类路径也有微风，但不同版本的依赖另一个库，这是一个被加载。因此，MLLib在运行时使用不同版本的Breeze，而不是在编译时。

您有多个选项。你可以在Breeze上发现不希望的传递依赖并排除它。您可以添加一个直接依赖于具有与MLLib相同的Breeze依赖项的库的版本。或者您可以添加对Breeze MLLib需求的直接依赖。

来源

2017-04-02 16:01:23 Vidya

RE：火花2.1 MLIB取决于微风0.12 https://github.com/apache/spark/blob/v2.1.0/pom.xml#L655-L671 –

的OP是使用2.0。你是否说升级Spark会解决问题？ – Vidya

阿帕奇星火 - java.lang.NoSuchMethodError：breeze.linalg.Vector $ .scalarOf（）Lbreeze/linalg /支持/ ScalarOf

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