Question

我使用ML管道和各种基于UDF的自定义变换器。我正在寻找的是一种序列化/反序列化这个管道的方法。

我使用

序列化PipelineModel

ObjectOutputStream.write()

但是每当我尝试反序列化我正在使用的管道时：

java.lang.ClassNotFoundException: org.sparkexample.DateTransformer

DateTransformer在哪里是我的自定义变换器。是否有任何方法/接口可以实现正确的序列化？

我发现有

MLWritable

我的类可能实现的接口（DateTransformer扩展Transfrormer），但找不到它的有用示例。

Answer 1

简短的回答是，你不能，至少不容易。

开发人员竭尽全力尽可能地添加新的变压器/估算器。基本上org.apache.spark.ml.util.ReadWrite中的所有内容都是私有的（MLWritable和MLReadable除外）因此无法使用任何实用方法/类/对象。还有（因为我确定你已经发现了）绝对没有关于如何做到这一点的文件，但是好的代码文件本身对吗？

在org.apache.spark.ml.util.ReadWrite和org.apache.spark.ml.feature.HashingTF中挖掘代码似乎需要覆盖MLWritable.write和MLReadable.read。似乎包含实际保存/加载实现的DefaultParamsWriter和DefaultParamsReader正在保存并加载一堆元数据：

类
时间戳
sparkVersion
UID
paramMap
（可选，额外元数据）

所以任何实现都至少需要覆盖那些，并且不需要学习任何模型的变压器可能就是这样。需要安装的模型还需要将该数据保存在save/write的实现中 - 例如，LocalLDAModel执行（https://github.com/apache/spark/blob/v1.6.3/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala#L523），因此学习的模型只是保存作为镶木地板文件（似乎）

val data = sqlContext.read.parquet(dataPath)
        .select("vocabSize", "topicsMatrix", "docConcentration", "topicConcentration",
          "gammaShape")
        .head()

作为测试，我复制了似乎需要的org.apache.spark.ml.util.ReadWrite的所有内容，并测试了以下变压器，它没有做任何有用的事情。

警告：这几乎肯定是错误的做法，并且很可能会在未来发生。 我真诚地希望我误解了某些事情并且有人会纠正我如何实际创建一个可以序列化/反序列化的变压器

这是针对spark 1.6.3的，如果您使用2.x

，可能已经被破坏了

import org.apache.spark.sql.types._
import org.apache.spark.ml.param._
import org.apache.hadoop.fs.Path
import org.apache.spark.SparkContext
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.util.{Identifiable, MLReadable, MLReader, MLWritable, MLWriter}
import org.apache.spark.sql.{SQLContext, DataFrame}
import org.apache.spark.mllib.linalg._

import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._

object CustomTransform extends DefaultParamsReadable[CustomTransform] {
  /* Companion object for deserialisation */
  override def load(path: String): CustomTransform = super.load(path)
}

class CustomTransform(override val uid: String)
  extends Transformer with DefaultParamsWritable {

  def this() = this(Identifiable.randomUID("customThing"))

  def setInputCol(value: String): this.type = set(inputCol, value)
  def setOutputCol(value: String): this.type = set(outputCol, value)
  def getOutputCol(): String = getOrDefault(outputCol)

  val inputCol = new Param[String](this, "inputCol", "input column")
  val outputCol = new Param[String](this, "outputCol", "output column")

  override def transform(dataset: DataFrame): DataFrame = {
    val sqlContext = SQLContext.getOrCreate(SparkContext.getOrCreate())
    import sqlContext.implicits._

    val outCol = extractParamMap.getOrElse(outputCol, "output")
    val inCol = extractParamMap.getOrElse(inputCol, "input")
    val transformUDF = udf({ vector: SparseVector =>
      vector.values.map( _ * 10 )
      // WHAT EVER YOUR TRANSFORMER NEEDS TO DO GOES HERE
    })

    dataset.withColumn(outCol, transformUDF(col(inCol)))
  }

  override def copy(extra: ParamMap): Transformer = defaultCopy(extra)

  override def transformSchema(schema: StructType): StructType = {
    val outputFields = schema.fields :+ StructField(extractParamMap.getOrElse(outputCol, "filtered"), new VectorUDT, nullable = false)
    StructType(outputFields)
  }
}

然后我们需要来自org.apache.spark.ml.util.ReadWrite的所有实用程序 https://github.com/apache/spark/blob/v1.6.3/mllib/src/main/scala/org/apache/spark/ml/util/ReadWrite.scala

trait DefaultParamsWritable extends MLWritable { self: Params =>
  override def write: MLWriter = new DefaultParamsWriter(this)
}

trait DefaultParamsReadable[T] extends MLReadable[T] {
  override def read: MLReader[T] = new DefaultParamsReader
}

class DefaultParamsWriter(instance: Params) extends MLWriter {
  override protected def saveImpl(path: String): Unit = {
    DefaultParamsWriter.saveMetadata(instance, path, sc)
  }
}

object DefaultParamsWriter {

  /**
    * Saves metadata + Params to: path + "/metadata"
    *  - class
    *  - timestamp
    *  - sparkVersion
    *  - uid
    *  - paramMap
    *  - (optionally, extra metadata)
    * @param extraMetadata  Extra metadata to be saved at same level as uid, paramMap, etc.
    * @param paramMap  If given, this is saved in the "paramMap" field.
    *                  Otherwise, all [[org.apache.spark.ml.param.Param]]s are encoded using
    *                  [[org.apache.spark.ml.param.Param.jsonEncode()]].
    */
  def saveMetadata(
  instance: Params,
  path: String,
  sc: SparkContext,
  extraMetadata: Option[JObject] = None,
  paramMap: Option[JValue] = None): Unit = {
    val uid = instance.uid
    val cls = instance.getClass.getName
    val params = instance.extractParamMap().toSeq.asInstanceOf[Seq[ParamPair[Any]]]
    val jsonParams = paramMap.getOrElse(render(params.map { case ParamPair(p, v) =>
      p.name -> parse(p.jsonEncode(v))
    }.toList))
    val basicMetadata = ("class" -> cls) ~
    ("timestamp" -> System.currentTimeMillis()) ~
    ("sparkVersion" -> sc.version) ~
    ("uid" -> uid) ~
    ("paramMap" -> jsonParams)
    val metadata = extraMetadata match {
      case Some(jObject) =>
        basicMetadata ~ jObject
      case None =>
        basicMetadata
    }
    val metadataPath = new Path(path, "metadata").toString
    val metadataJson = compact(render(metadata))
    sc.parallelize(Seq(metadataJson), 1).saveAsTextFile(metadataPath)
  }
}

class DefaultParamsReader[T] extends MLReader[T] {
  override def load(path: String): T = {
    val metadata = DefaultParamsReader.loadMetadata(path, sc)
    val cls = Class.forName(metadata.className, true, Option(Thread.currentThread().getContextClassLoader).getOrElse(getClass.getClassLoader))
    val instance =
    cls.getConstructor(classOf[String]).newInstance(metadata.uid).asInstanceOf[Params]
    DefaultParamsReader.getAndSetParams(instance, metadata)
    instance.asInstanceOf[T]
  }
}

object DefaultParamsReader {

  /**
    * All info from metadata file.
    *
    * @param params       paramMap, as a [[JValue]]
    * @param metadata     All metadata, including the other fields
    * @param metadataJson Full metadata file String (for debugging)
    */
  case class Metadata(
                       className: String,
                       uid: String,
                       timestamp: Long,
                       sparkVersion: String,
                       params: JValue,
                       metadata: JValue,
                       metadataJson: String)

  /**
    * Load metadata from file.
    *
    * @param expectedClassName If non empty, this is checked against the loaded metadata.
    * @throws IllegalArgumentException if expectedClassName is specified and does not match metadata
    */
  def loadMetadata(path: String, sc: SparkContext, expectedClassName: String = ""): Metadata = {
    val metadataPath = new Path(path, "metadata").toString
    val metadataStr = sc.textFile(metadataPath, 1).first()
    val metadata = parse(metadataStr)

    implicit val format = DefaultFormats
    val className = (metadata \ "class").extract[String]
    val uid = (metadata \ "uid").extract[String]
    val timestamp = (metadata \ "timestamp").extract[Long]
    val sparkVersion = (metadata \ "sparkVersion").extract[String]
    val params = metadata \ "paramMap"
    if (expectedClassName.nonEmpty) {
      require(className == expectedClassName, s"Error loading metadata: Expected class name" +
        s" $expectedClassName but found class name $className")
    }

    Metadata(className, uid, timestamp, sparkVersion, params, metadata, metadataStr)
  }

  /**
    * Extract Params from metadata, and set them in the instance.
    * This works if all Params implement [[org.apache.spark.ml.param.Param.jsonDecode()]].
    */
  def getAndSetParams(instance: Params, metadata: Metadata): Unit = {
    implicit val format = DefaultFormats
    metadata.params match {
      case JObject(pairs) =>
        pairs.foreach { case (paramName, jsonValue) =>
          val param = instance.getParam(paramName)
          val value = param.jsonDecode(compact(render(jsonValue)))
          instance.set(param, value)
        }
      case _ =>
        throw new IllegalArgumentException(
          s"Cannot recognize JSON metadata: ${metadata.metadataJson}.")
    }
  }

  /**
    * Load a [[Params]] instance from the given path, and return it.
    * This assumes the instance implements [[MLReadable]].
    */
  def loadParamsInstance[T](path: String, sc: SparkContext): T = {
    val metadata = DefaultParamsReader.loadMetadata(path, sc)
    val cls = Class.forName(metadata.className, true, Option(Thread.currentThread().getContextClassLoader).getOrElse(getClass.getClassLoader))
    cls.getMethod("read").invoke(null).asInstanceOf[MLReader[T]].load(path)
  }
}

有了这个，您可以使用CustomTransformer中的Pipeline并保存/加载管道。我在火花壳中测试得相当快，似乎工作但肯定不是很好。

Answer 2

如果您使用的是Spark 2.x +，请使用DefaultParamsWritable扩展您的变换器

例如

class ProbabilityMaxer extends Transformer with DefaultParamsWritable{

然后使用字符串参数

创建一个构造函数

 def this(_uid: String) {
    this()
  }

最后，为了成功阅读，添加一个伴侣类

object ProbabilityMaxer extends  DefaultParamsReadable[ProbabilityMaxer]

我在生产服务器上工作了。我将在以后上传时将gitlab链接添加到项目中

有没有办法在Spark ML Pipeline中序列化自定义Transformer

2 个答案: