Spark ML 正则化标准化归一化 ---- spark 中的正则化

拼搏现实的明天。 2022-09-09 04:38 188阅读 0赞

### 文章大纲 ###

*  spark 中的正则化
 *   *  Normalizer
     *  源代码
 *  参考文献

--------------------

# spark 中的正则化 #

## Normalizer ##

标准化文档：

*  http://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/feature/Normalizer.html

标准化源代码：

*  https://github.com/apache/spark/blob/v3.1.2/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala

文档中就这么一句话:

Normalize a vector to have unit norm using the given p-norm.  
使用给定的p-范数规范化向量，使其具有单位范数。

## 源代码 ##

package org.apache.spark.ml.feature
    
    import org.apache.spark.annotation.Since
    import org.apache.spark.ml.UnaryTransformer
    import org.apache.spark.ml.attribute.AttributeGroup
    import org.apache.spark.ml.linalg.{
        Vector, VectorUDT}
    import org.apache.spark.ml.param.{
        DoubleParam, ParamValidators}
    import org.apache.spark.ml.util._
    import org.apache.spark.mllib.feature
    import org.apache.spark.mllib.linalg.{
        Vectors => OldVectors}
    import org.apache.spark.sql.types._
    
    /**
     * Normalize a vector to have unit norm using the given p-norm.
     */
    @Since("1.4.0")
    class Normalizer @Since("1.4.0") (@Since("1.4.0") override val uid: String)
      extends UnaryTransformer[Vector, Vector, Normalizer] with DefaultParamsWritable {
        
    
      @Since("1.4.0")
      def this() = this(Identifiable.randomUID("normalizer"))
    
      /**
       * Normalization in L^p^ space. Must be greater than equal to 1.
       * (default: p = 2)
       * @group param
       */
      @Since("1.4.0")
      val p = new DoubleParam(this, "p", "the p norm value", ParamValidators.gtEq(1))
    
      setDefault(p -> 2.0)
    
      /** @group getParam */
      @Since("1.4.0")
      def getP: Double = $(p)
    
      /** @group setParam */
      @Since("1.4.0")
      def setP(value: Double): this.type = set(p, value)
    
      override protected def createTransformFunc: Vector => Vector = {
        
        val normalizer = new feature.Normalizer($(p))
        vector => normalizer.transform(OldVectors.fromML(vector)).asML
      }
    
      override protected def validateInputType(inputType: DataType): Unit = {
        
        require(inputType.isInstanceOf[VectorUDT],
          s"Input type must be ${(new VectorUDT).catalogString} but got ${inputType.catalogString}.")
      }
    
      override protected def outputDataType: DataType = new VectorUDT()
    
      @Since("1.4.0")
      override def transformSchema(schema: StructType): StructType = {
        
        var outputSchema = super.transformSchema(schema)
        if ($(inputCol).nonEmpty && $(outputCol).nonEmpty) {
        
          val size = AttributeGroup.fromStructField(schema($(inputCol))).size
          if (size >= 0) {
        
            outputSchema = SchemaUtils.updateAttributeGroupSize(outputSchema,
              $(outputCol), size)
          }
        }
        outputSchema
      }
    
      @Since("3.0.0")
      override def toString: String = {
        
        s"Normalizer: uid=$uid, p=${$(p)}"
      }
    }
    
    @Since("1.6.0")
    object Normalizer extends DefaultParamsReadable[Normalizer] {
        
    
      @Since("1.6.0")
      override def load(path: String): Normalizer = super.load(path)
    }

--------------------

# 参考文献 #

系列文章：

*  [正则化、标准化、归一化基本概念简介][Link 1]
 *  [ spark 中的正则化][spark]
 *  [spark 中的标准化][spark 1]
 *  [spark 中的归一化][spark 2]
 *  [扩展spark 的归一化函数][spark 3]

spark 中的 特征相关内容处理的文档

*  http://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/feature/index.html

概念简介

*  https://blog.csdn.net/u014381464/article/details/81101551

参考：

*  https://segmentfault.com/a/1190000014042959
 *  https://www.cnblogs.com/nucdy/p/7994542.html
 *  https://blog.csdn.net/weixin\_34117522/article/details/88875270
 *  https://blog.csdn.net/xuejianbest/article/details/85779029

[Link 1]: https://blog.csdn.net/wangyaninglm/article/details/119728871
[spark]: https://season.blog.csdn.net/article/details/119975289
[spark 1]: https://season.blog.csdn.net/article/details/119975304
[spark 2]: https://blog.csdn.net/wangyaninglm/article/details/119975312
[spark 3]: https://blog.csdn.net/wangyaninglm/article/details/119975330