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我尝试复制此Spark / Scala 示例，但是当我尝试从已处理的 .csv 文件中提取一些指标时出现错误。

我的代码片段：

val splitSeed = 5043
val Array(trainingData, testData) = df3.randomSplit(Array(0.7, 0.3), splitSeed)

val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8)

trainingData.show(20);

// Fit the model
val model = lr.fit(trainingData)

// Print the coefficients and intercept for logistic regression
println(s"Coefficients: ${model.coefficients} Intercept: ${model.intercept}")

// run the  model on test features to get predictions**
val predictions = model.transform(testData)
//As you can see, the previous model transform produced a new columns: rawPrediction, probablity and prediction.**
testData.show()

// run the  model on test features to get predictions**
val predictions = model.transform(testData)
//As you can see, the previous model transform produced a new columns: rawPrediction, probablity and prediction.**
predictions.show()

// use MLlib to evaluate, convert DF to RDD**
val myRdd = predictions.select("rawPrediction", "label").rdd

val predictionAndLabels = myRdd.map(x => (x(0).asInstanceOf[DenseVector](1), x(1).asInstanceOf[Double]))
// Instantiate metrics object
val metrics = new BinaryClassificationMetrics(predictionAndLabels)
println("area under the precision-recall curve: " + metrics.areaUnderPR)
println("area under the receiver operating characteristic (ROC) curve : " + metrics.areaUnderROC)
// A Precision-Recall curve plots (precision, recall) points for different threshold values, while a
// receiver operating characteristic, or ROC, curve plots (recall, false positive rate) points.
// The closer  the area Under ROC is to 1, the better the model is making predictions.**

当我尝试了解该属性时areaUnderPR，出现此错误：

20/01/10 10:41:02 WARN TaskSetManager：在阶段 56.0 中丢失任务 0.0（TID 246、10.10.252.172、执行程序 1）：java.lang.ClassNotFoundException：在 java.net.URLClassLoader 的预测.TestCancerOriginal$$anonfun$1 .findClass(URLClassLoader.java:382) 在 java.lang.ClassLoader.loadClass(ClassLoader.java:424) 在 java.lang.ClassLoader.loadClass(ClassLoader.java:357) 在 java.lang.Class.forName0(Native Method ) 在 java.lang.Class.forName(Class.java:348) 在 org.apache.spark.serializer.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:67) 在 java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream. java:1868) 在 java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1751) 在 java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2042) 在 java.io.ObjectInputStream。readObject0(ObjectInputStream.java:1573) 在 java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2287) 在 java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2211) 在 java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java :2069) 在 java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1573) 在 java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2287) 在 java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2211) 在 java .io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2069) 在 java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1573) 在 java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2287) 在 java.io.ObjectInputStream。在 java.io 上读取串行数据（ObjectInputStream.java:2211）。ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2069) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1573) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:431) at org.apache.spark.serializer.JavaDeserializationStream .readObject(JavaSerializer.scala:75) 在 org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:114) 在 org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:88) 在 org. apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun $10.apply(Executor.scala:408) 在 org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) 在 org.apache.spark.executor.Executor$TaskRunner。在 java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 在 java.lang.Thread 的 java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 运行(Executor.scala:414) .run(Thread.java:748)

我的 predictions.show 结果：

+------+---------+----+-----+----+------+----+------+----+---+----+------------+--------------------+-----+--------------------+--------------------+----------+
|    id|thickness|size|shape|madh|epsize|bnuc|bchrom|nNuc|mit|clas|clasLogistic|            features|label|       rawPrediction|         probability|prediction|
+------+---------+----+-----+----+------+----+------+----+---+----+------------+--------------------+-----+--------------------+--------------------+----------+
| 63375|      9.0| 1.0|  2.0| 6.0|   4.0|10.0|   7.0| 7.0|2.0|   4|           1|[9.0,1.0,2.0,6.0,...|  1.0|[0.36391634252951...|[0.58998813846052...|       0.0|
|128059|      1.0| 1.0|  1.0| 1.0|   2.0| 5.0|   5.0| 1.0|1.0|   2|           0|[1.0,1.0,1.0,1.0,...|  0.0|[0.81179252636135...|[0.69249134920886...|       0.0|
|145447|      8.0| 4.0|  4.0| 1.0|   2.0| 9.0|   3.0| 3.0|1.0|   4|           1|[8.0,4.0,4.0,1.0,...|  1.0|[0.06964047482828...|[0.51740308582457...|       0.0|
|183913|      1.0| 2.0|  2.0| 1.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[1.0,2.0,2.0,1.0,...|  0.0|[0.96139876234944...|[0.72340177322811...|       0.0|
|342245|      1.0| 1.0|  3.0| 1.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[1.0,1.0,3.0,1.0,...|  0.0|[0.95750903648839...|[0.72262279564412...|       0.0|
|434518|      3.0| 1.0|  1.0| 1.0|   2.0| 1.0|   2.0| 1.0|1.0|   2|           0|[3.0,1.0,1.0,1.0,...|  0.0|[1.10995557408198...|[0.75212082898242...|       0.0|
|493452|      1.0| 1.0|  3.0| 1.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[1.0,1.0,3.0,1.0,...|  0.0|[0.95750903648839...|[0.72262279564412...|       0.0|
|508234|      7.0| 4.0|  5.0|10.0|   2.0|10.0|   3.0| 8.0|2.0|   4|           1|[7.0,4.0,5.0,10.0...|  1.0|[-0.0809133769755...|[0.47978268474014...|       1.0|
|521441|      5.0| 1.0|  1.0| 2.0|   2.0| 1.0|   2.0| 1.0|1.0|   2|           0|[5.0,1.0,1.0,2.0,...|  0.0|[1.10995557408198...|[0.75212082898242...|       0.0|
|527337|      4.0| 1.0|  1.0| 1.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[4.0,1.0,1.0,1.0,...|  0.0|[1.11079628977456...|[0.75227753466134...|       0.0|
|534555|      1.0| 1.0|  1.0| 1.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[1.0,1.0,1.0,1.0,...|  0.0|[1.11079628977456...|[0.75227753466134...|       0.0|
|535331|      3.0| 1.0|  1.0| 1.0|   3.0| 1.0|   2.0| 1.0|1.0|   2|           0|[3.0,1.0,1.0,1.0,...|  0.0|[1.10995557408198...|[0.75212082898242...|       0.0|
|558538|      4.0| 1.0|  3.0| 3.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[4.0,1.0,3.0,3.0,...|  0.0|[0.95750903648839...|[0.72262279564412...|       0.0|
|560680|      1.0| 1.0|  1.0| 1.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[1.0,1.0,1.0,1.0,...|  0.0|[1.11079628977456...|[0.75227753466134...|       0.0|
|601265|     10.0| 4.0|  4.0| 6.0|   2.0|10.0|   2.0| 3.0|1.0|   4|           1|[10.0,4.0,4.0,6.0...|  1.0|[-0.0034290346398...|[0.49914274218002...|       1.0|
|603148|      4.0| 1.0|  1.0| 1.0|   2.0| 1.0|   1.0| 1.0|1.0|   2|           0|[4.0,1.0,1.0,1.0,...|  0.0|[1.11079628977456...|[0.75227753466134...|       0.0|
|606722|      5.0| 5.0|  7.0| 8.0|   6.0|10.0|   7.0| 4.0|1.0|   4|           1|[5.0,5.0,7.0,8.0,...|  1.0|[-0.3103173938140...|[0.42303726852941...|       1.0|
|616240|      5.0| 3.0|  4.0| 3.0|   4.0| 5.0|   4.0| 7.0|1.0|   2|           0|[5.0,3.0,4.0,3.0,...|  0.0|[0.43719456056061...|[0.60759034803682...|       0.0|
|640712|      1.0| 1.0|  1.0| 1.0|   2.0| 1.0|   2.0| 1.0|1.0|   2|           0|[1.0,1.0,1.0,1.0,...|  0.0|[1.10995557408198...|[0.75212082898242...|       0.0|
|654546|      1.0| 1.0|  1.0| 1.0|   2.0| 1.0|   1.0| 1.0|8.0|   2|           0|[1.0,1.0,1.0,1.0,...|  0.0|[1.11079628977456...|[0.75227753466134...|       0.0|
+------+---------+----+-----+----+------+----+------+----+---+----+------------+--------------------+-----+--------------------+--------------------+----------+
only showing top 20 rows
4

1 回答 1

1

我在这里看到的一个错误是您将rawPrediction列传递给BinaryClassificationMetrics对象，而不是prediction列。rawPrediction包含一个数组，每个类都有某种“概率”，同时BinaryClassificationMetrics期望一个双精度值，如其签名所指定：

new BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)])

您可以在此处查看详细信息。

我已经对该修改进行了快速测试，它似乎有效，这是代码段：

import org.apache.spark.sql.{Encoders, SparkSession}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.sql.functions._
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics



case class Obs(id: Int, thickness: Double, size: Double, shape: Double, madh: Double,
               epsize: Double, bnuc: Double, bchrom: Double, nNuc: Double, mit: Double, clas: Double)
val obsSchema = Encoders.product[Obs].schema

val spark = SparkSession.builder
  .appName("StackoverflowQuestions")
  .master("local[*]")
  .getOrCreate()
// Implicits necessary to transform DataFrame to Dataset using .as[] method
import spark.implicits._


val df = spark.read
              .schema(obsSchema)
              .csv("breast-cancer-wisconsin.data")
              .drop("id")
              .withColumn("clas", when(col("clas").equalTo(4.0), 1.0).otherwise(0.0))
              .na.drop() // Make sure to drop nulls, or the feature assemble will fail

//define the feature columns to put in the feature vector**
val featureCols = Array("thickness", "size", "shape", "madh", "epsize", "bnuc", "bchrom", "nNuc", "mit")
//set the input and output column names**
val assembler = new VectorAssembler().setInputCols(featureCols).setOutputCol("features")
//return a dataframe with all of the  feature columns in  a vector column**
val df2 = assembler.transform(df)
//  Create a label column with the StringIndexer**
val labelIndexer = new StringIndexer().setInputCol("clas").setOutputCol("label")
val df3 = labelIndexer.fit(df2).transform(df2)

val splitSeed = 5043
val Array(trainingData, testData) = df3.randomSplit(Array(0.7, 0.3), splitSeed)

val lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.3)
  .setElasticNetParam(0.8)

trainingData.show(20);

// Fit the model
val model = lr.fit(trainingData)

// Print the coefficients and intercept for logistic regression
println(s"Coefficients: ${model.coefficients} Intercept: ${model.intercept}")

// run the  model on test features to get predictions**
val predictions = model.transform(testData)
//As you can see, the previous model transform produced a new columns: rawPrediction, probablity and prediction.**
predictions.show(truncate=false)

// use MLlib to evaluate, convert DF to RDD**
val predictionAndLabels = predictions.select("prediction", "label").as[(Double, Double)].rdd

// Instantiate metrics object
val metrics = new BinaryClassificationMetrics(predictionAndLabels)
println("area under the precision-recall curve: " + metrics.areaUnderPR)
println("area under the receiver operating characteristic (ROC) curve : " + metrics.areaUnderROC)
于 2020-01-12T10:58:02.370 回答