Scala crossvalidator的sparkml设置并行性

因此，我试图使用SparkML设置交叉验证，但我得到了一个运行时错误

"value setParallelism is not a member of org.apache.spark.ml.tuning.CrossValidator" 

我目前正在学习spark页面教程。我是新手，所以非常感谢您的帮助。下面是我的代码片段：

import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.Row
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}

// Tokenizer
val tokenizer = new Tokenizer().setInputCol("tweet").setOutputCol("words")

// HashingTF
val hash_tf = new HashingTF().setInputCol(tokenizer.getOutputCol).setOutputCol("features")

// ML models
val l_regression = new LogisticRegression().setMaxIter(100).setRegParam(0.15)

// Pipeline
val pipe = new Pipeline().setStages(Array(tokenizer, hash_tf, l_regression))

val paramGrid = new ParamGridBuilder()
.addGrid(hash_tf.numFeatures, Array(10,100,1000))
.addGrid(l_regression.regParam, Array(0.1,0.01,0.001))
.build()

val c_validator = new CrossValidator()
.setEstimator(pipe)
.setEvaluator(new BinaryClassificationEvaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(3)
.setParallelism(2)

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setParallelism

。您必须使用早期版本：

（仅限专家）参数设置器

（……）

def setParallelism（值：Int）：CrossValidator.this.type

设置最大并行级别以并行评估模型。串行评估的默认值为1

注释@自（“2.3.0”）

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