Spark ML包中的几种归一化方法总结

还不错！！！

org.apache.spark.ml.feature包中包含了4种不同的归一化方法：

• Normalizer
• StandardScaler
• MinMaxScaler
• MaxAbsScaler

有时感觉会容易混淆，借助官方文档和实际数据的变换，在这里做一次总结。

原文地址：http://www.neilron.xyz/spark-ml-feature-scaler/

0 数据准备

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import org.apache.spark.ml.linalg.Vectors val dataFrame = spark.createDataFrame(Seq( (0, Vectors.dense(1.0, 0.5, -1.0)), (1, Vectors.dense(2.0, 1.0, 1.0)), (2, Vectors.dense(4.0, 10.0, 2.0)) )).toDF("id", "features") dataFrame.show // 原始数据 +---+--------------+ | id| features| +---+--------------+ | 0|[1.0,0.5,-1.0]| | 1| [2.0,1.0,1.0]| | 2|[4.0,10.0,2.0]| +---+--------------+

1 Normalizer

Normalizer的作用范围是每一行，使每一个行向量的范数变换为一个单位范数，下面的示例代码都来自spark官方文档加上少量改写和注释。

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import org.apache.spark.ml.feature.Normalizer // 正则化每个向量到1阶范数 val normalizer = new Normalizer() .setInputCol("features") .setOutputCol("normFeatures") .setP(1.0) val l1NormData = normalizer.transform(dataFrame) println("Normalized using L^1 norm") l1NormData.show() // 将每一行的规整为1阶范数为1的向量，1阶范数即所有值绝对值之和。 +---+--------------+------------------+ | id| features| normFeatures| +---+--------------+------------------+ | 0|[1.0,0.5,-1.0]| [0.4,0.2,-0.4]| | 1| [2.0,1.0,1.0]| [0.5,0.25,0.25]| | 2|[4.0,10.0,2.0]|[0.25,0.625,0.125]| +---+--------------+------------------+ // 正则化每个向量到无穷阶范数 val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.PositiveInfinity) println("Normalized using L^inf norm") lInfNormData.show() // 向量的无穷阶范数即向量中所有值中的最大值 +---+--------------+--------------+ | id| features| normFeatures| +---+--------------+--------------+ | 0|[1.0,0.5,-1.0]|[1.0,0.5,-1.0]| | 1| [2.0,1.0,1.0]| [1.0,0.5,0.5]| | 2|[4.0,10.0,2.0]| [0.4,1.0,0.2]| +---+--------------+--------------+

 

2 StandardScaler

StandardScaler处理的对象是每一列，也就是每一维特征，将特征标准化为单位标准差或是0均值，或是0均值单位标准差。
主要有两个参数可以设置：

• withStd: 默认为真。将数据标准化到单位标准差。
• withMean: 默认为假。是否变换为0均值。

StandardScaler需要fit数据，获取每一维的均值和标准差，来缩放每一维特征。

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import org.apache.spark.ml.feature.StandardScaler val scaler = new StandardScaler() .setInputCol("features") .setOutputCol("scaledFeatures") .setWithStd(true) .setWithMean(false) // Compute summary statistics by fitting the StandardScaler. val scalerModel = scaler.fit(dataFrame) // Normalize each feature to have unit standard deviation. val scaledData = scalerModel.transform(dataFrame) scaledData.show // 将每一列的标准差缩放到1。 +---+--------------+------------------------------------------------------------+ |id |features |scaledFeatures | +---+--------------+------------------------------------------------------------+ |0 |[1.0,0.5,-1.0]|[0.6546536707079772,0.09352195295828244,-0.6546536707079771]| |1 |[2.0,1.0,1.0] |[1.3093073414159544,0.1870439059165649,0.6546536707079771] | |2 |[4.0,10.0,2.0]|[2.618614682831909,1.870439059165649,1.3093073414159542] | +---+--------------+------------------------------------------------------------+

 

3 MinMaxScaler

MinMaxScaler作用同样是每一列，即每一维特征。将每一维特征线性地映射到指定的区间，通常是[0, 1]。
它也有两个参数可以设置：

• min: 默认为0。指定区间的下限。
• max: 默认为1。指定区间的上限。

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import org.apache.spark.ml.feature.MinMaxScaler val scaler = new MinMaxScaler() .setInputCol("features") .setOutputCol("scaledFeatures") // Compute summary statistics and generate MinMaxScalerModel val scalerModel = scaler.fit(dataFrame) // rescale each feature to range [min, max]. val scaledData = scalerModel.transform(dataFrame) println(s"Features scaled to range: [${scaler.getMin}, ${scaler.getMax}]") scaledData.select("features", "scaledFeatures").show // 每维特征线性地映射，最小值映射到0，最大值映射到1。 +--------------+-----------------------------------------------------------+ |features |scaledFeatures | +--------------+-----------------------------------------------------------+ |[1.0,0.5,-1.0]|[0.0,0.0,0.0] | |[2.0,1.0,1.0] |[0.3333333333333333,0.05263157894736842,0.6666666666666666]| |[4.0,10.0,2.0]|[1.0,1.0,1.0] | +--------------+-----------------------------------------------------------+ 筛选出label和新的featuresval featuresdatatran = scaledData.map{row=>(row.getAs[String]("id"),row.getAs[Vector]("scaledFeatures"))}

4 MaxAbsScaler

MaxAbsScaler将每一维的特征变换到[-1, 1]闭区间上，通过除以每一维特征上的最大的绝对值，它不会平移整个分布，也不会破坏原来每一个特征向量的稀疏性。

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import org.apache.spark.ml.feature.MaxAbsScaler val scaler = new MaxAbsScaler() .setInputCol("features") .setOutputCol("scaledFeatures") // Compute summary statistics and generate MaxAbsScalerModel val scalerModel = scaler.fit(dataFrame) // rescale each feature to range [-1, 1] val scaledData = scalerModel.transform(dataFrame) scaledData.select("features", "scaledFeatures").show() // 每一维的绝对值的最大值为[4, 10, 2] +--------------+----------------+ | features| scaledFeatures| +--------------+----------------+ |[1.0,0.5,-1.0]|[0.25,0.05,-0.5]| | [2.0,1.0,1.0]| [0.5,0.1,0.5]| |[4.0,10.0,2.0]| [1.0,1.0,1.0]| +--------------+----------------+

 

总结

所有4种归一化方法都是线性的变换，当某一维特征上具有非线性的分布时，还需要配合其它的特征预处理方法。