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

Question

以下是錯誤：

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) 

這裏是觸發錯誤的程序：

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。它看起來像一個版本差異問題。有任何想法嗎？

Answer 1

星火MLLib 2.0帶來了這個版本的微風：

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

您必須在您的類路徑也有微風，但不同版本的依賴另一個庫，這是一個被加載。因此，MLLib在運行時使用不同版本的Breeze，而不是在編譯時。

您有多個選項。你可以在Breeze上發現不希望的傳遞依賴並排除它。您可以添加一個直接依賴於具有與MLLib相同的Breeze依賴項的庫的版本。或者您可以添加對Breeze MLLib需求的直接依賴。