Spark特征(提取，转换，选择)extracting, transforming and selecting features
VectorAssembler字段转换成特征向量
import org.apache.spark.ml.feature.VectorAssembler
val colArray = Array("age", "yearsmarried", "religiousness", "education", "occupation", "rating")
// 字段转换成特征向量
val assembler = new VectorAssembler().setInputCols(colArray).setOutputCol("features")
val vecDF: DataFrame = assembler.transform(data)
vecDF: org.apache.spark.sql.DataFrame = [affairs: double, gender: string ... 8 more fields]
vecDF.select("features", colArray: _*).show(10, truncate = false)
+----------------------------+----+------------+-------------+---------+----------+------+
|features |age |yearsmarried|religiousness|education|occupation|rating|
+----------------------------+----+------------+-------------+---------+----------+------+
|[37.0,10.0,3.0,18.0,7.0,4.0]|37.0|10.0 |3.0 |18.0 |7.0 |4.0 |
|[27.0,4.0,4.0,14.0,6.0,4.0] |27.0|4.0 |4.0 |14.0 |6.0 |4.0 |
|[32.0,15.0,1.0,12.0,1.0,4.0]|32.0|15.0 |1.0 |12.0 |1.0 |4.0 |
|[57.0,15.0,5.0,18.0,6.0,5.0]|57.0|15.0 |5.0 |18.0 |6.0 |5.0 |
|[22.0,0.75,2.0,17.0,6.0,3.0]|22.0|0.75 |2.0 |17.0 |6.0 |3.0 |
|[32.0,1.5,2.0,17.0,5.0,5.0] |32.0|1.5 |2.0 |17.0 |5.0 |5.0 |
|[22.0,0.75,2.0,12.0,1.0,3.0]|22.0|0.75 |2.0 |12.0 |1.0 |3.0 |
|[57.0,15.0,2.0,14.0,4.0,4.0]|57.0|15.0 |2.0 |14.0 |4.0 |4.0 |
|[32.0,15.0,4.0,16.0,1.0,2.0]|32.0|15.0 |4.0 |16.0 |1.0 |2.0 |
|[22.0,1.5,4.0,14.0,4.0,5.0] |22.0|1.5 |4.0 |14.0 |4.0 |5.0 |
+----------------------------+----+------------+-------------+---------+----------+------+
only showing top 10 rows
VectorIndexer自动识别分类的特征，并对它们进行索引
import org.apache.spark.ml.feature.VectorIndexer
val colArray = Array("age", "yearsmarried", "religiousness", "education", "occupation", "rating")
// 自动识别分类的特征，并对它们进行索引
// 具有大于7个不同的值的特征被视为连续。
val featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.setMaxCategories(7)
.fit(vecDF)
val categoricalFeatures: Set[Int] = featureIndexer.categoryMaps.keys.toSet
categoricalFeatures: Set[Int] = Set(2, 3, 4, 5)
println(s"Chose ${categoricalFeatures.size} categorical features: " +
categoricalFeatures.mkString(", "))
Chose 4 categorical features: 2, 3, 4, 5
// 由此看出，当MaxCategories=7，从6个字段中识别出了4个“类别特征字段”，
// 他们的下标索引为(2, 3, 4, 5),分别对应colArray中的(2, 3, 4, 5)元素，即"religiousness", "education", "occupation", "rating"
// 为什么识别出了4个“类别特征字段”呢，请看本人博客http://www.cnblogs.com/wwxbi/p/6125363.html“统计字段中元素的个数”
// 从“统计字段中元素的个数”看出，("religiousness", "education", "occupation", "rating")这4个字段的元素个数<=7
// Create new column "indexedFeatures" with categorical values transformed to indices
val indexedData = featureIndexer.transform(vecDF)
indexedData: org.apache.spark.sql.DataFrame = [affairs: double, gender: string ... 9 more fields]
val resColArray = Array("indexedFeatures", "features", "age", "yearsmarried", "religiousness", "education", "occupation", "rating")
resColArray: Array[String] = Array(indexedFeatures, features, age, yearsmarried, religiousness, education, occupation, rating)
indexedData.selectExpr(resColArray: _*).show(10, truncate = false)
+---------------------------+----------------------------+----+------------+-------------+---------+----------+------+
|indexedFeatures |features |age |yearsmarried|religiousness|education|occupation|rating|
+---------------------------+----------------------------+----+------------+-------------+---------+----------+------+
|[37.0,10.0,2.0,5.0,6.0,3.0]|[37.0,10.0,3.0,18.0,7.0,4.0]|37.0|10.0 |3.0 |18.0 |7.0 |4.0 |
|[27.0,4.0,3.0,2.0,5.0,3.0] |[27.0,4.0,4.0,14.0,6.0,4.0] |27.0|4.0 |4.0 |14.0 |6.0 |4.0 |
|[32.0,15.0,0.0,1.0,0.0,3.0]|[32.0,15.0,1.0,12.0,1.0,4.0]|32.0|15.0 |1.0 |12.0 |1.0 |4.0 |
|[57.0,15.0,4.0,5.0,5.0,4.0]|[57.0,15.0,5.0,18.0,6.0,5.0]|57.0|15.0 |5.0 |18.0 |6.0 |5.0 |
|[22.0,0.75,1.0,4.0,5.0,2.0]|[22.0,0.75,2.0,17.0,6.0,3.0]|22.0|0.75 |2.0 |17.0 |6.0 |3.0 |
|[32.0,1.5,1.0,4.0,4.0,4.0] |[32.0,1.5,2.0,17.0,5.0,5.0] |32.0|1.5 |2.0 |17.0 |5.0 |5.0 |
|[22.0,0.75,1.0,1.0,0.0,2.0]|[22.0,0.75,2.0,12.0,1.0,3.0]|22.0|0.75 |2.0 |12.0 |1.0 |3.0 |
|[57.0,15.0,1.0,2.0,3.0,3.0]|[57.0,15.0,2.0,14.0,4.0,4.0]|57.0|15.0 |2.0 |14.0 |4.0 |4.0 |
|[32.0,15.0,3.0,3.0,0.0,1.0]|[32.0,15.0,4.0,16.0,1.0,2.0]|32.0|15.0 |4.0 |16.0 |1.0 |2.0 |
|[22.0,1.5,3.0,2.0,3.0,4.0] |[22.0,1.5,4.0,14.0,4.0,5.0] |22.0|1.5 |4.0 |14.0 |4.0 |5.0 |
+---------------------------+----------------------------+----+------------+-------------+---------+----------+------+
only showing top 10 rows
import org.apache.spark.ml.feature.VectorSlicer
val slicer = new VectorSlicer().setInputCol("indexedFeatures").setOutputCol("slicerFeatures")
slicer.setIndices(Array(3)) // 此处的3对应“索引化”之前的字段“education”
val output = slicer.transform(indexedData)
output.select("indexedFeatures",
"slicerFeatures",
"education").limit(10).orderBy($"education").show(10, truncate = false)
+---------------------------+--------------+---------+
|indexedFeatures |slicerFeatures|education|
+---------------------------+--------------+---------+
|[32.0,15.0,0.0,1.0,0.0,3.0]|[1.0] |12.0 |
|[22.0,0.75,1.0,1.0,0.0,2.0]|[1.0] |12.0 |
|[27.0,4.0,3.0,2.0,5.0,3.0] |[2.0] |14.0 |
|[57.0,15.0,1.0,2.0,3.0,3.0]|[2.0] |14.0 |
|[22.0,1.5,3.0,2.0,3.0,4.0] |[2.0] |14.0 |
|[32.0,15.0,3.0,3.0,0.0,1.0]|[3.0] |16.0 |
|[32.0,1.5,1.0,4.0,4.0,4.0] |[4.0] |17.0 |
|[22.0,0.75,1.0,4.0,5.0,2.0]|[4.0] |17.0 |
|[37.0,10.0,2.0,5.0,6.0,3.0]|[5.0] |18.0 |
|[57.0,15.0,4.0,5.0,5.0,4.0]|[5.0] |18.0 |
+---------------------------+--------------+---------+
// 由此看出，“类别特征字段”被索引化后，索引的编号是跟“原字段值的大小顺序”对照的，索引从0开始
// 索引编号(0,1,2,3,4,5,6)对应[9.0, 12.0, 14.0, 16.0, 17.0, 18.0, 20.0]
VectorSlicer向量切割
import org.apache.spark.ml.feature.VectorSlicer
val colArray = Array("age", "yearsmarried", "religiousness", "education", "occupation", "rating")
// 字段转换成特征向量
val assembler = new VectorAssembler().setInputCols(colArray).setOutputCol("features")
val vecDF = assembler.transform(data)
val slicer = new VectorSlicer().setInputCol("features").setOutputCol("slicerFeatures")
// 指定“向量字段features”中的下标索引
// (2, 3, 4)分别对应字段("religiousness", "education", "occupation")
slicer.setIndices(Array(2, 3, 4))
val output = slicer.transform(vecDF)
output.select("features", "slicerFeatures","religiousness", "education", "occupation").show(10, truncate = false)
+----------------------------+--------------+-------------+---------+----------+
|features |slicerFeatures|religiousness|education|occupation|
+----------------------------+--------------+-------------+---------+----------+
|[37.0,10.0,3.0,18.0,7.0,4.0]|[3.0,18.0,7.0]|3.0 |18.0 |7.0 |
|[27.0,4.0,4.0,14.0,6.0,4.0] |[4.0,14.0,6.0]|4.0 |14.0 |6.0 |
|[32.0,15.0,1.0,12.0,1.0,4.0]|[1.0,12.0,1.0]|1.0 |12.0 |1.0 |
|[57.0,15.0,5.0,18.0,6.0,5.0]|[5.0,18.0,6.0]|5.0 |18.0 |6.0 |
|[22.0,0.75,2.0,17.0,6.0,3.0]|[2.0,17.0,6.0]|2.0 |17.0 |6.0 |
|[32.0,1.5,2.0,17.0,5.0,5.0] |[2.0,17.0,5.0]|2.0 |17.0 |5.0 |
|[22.0,0.75,2.0,12.0,1.0,3.0]|[2.0,12.0,1.0]|2.0 |12.0 |1.0 |
|[57.0,15.0,2.0,14.0,4.0,4.0]|[2.0,14.0,4.0]|2.0 |14.0 |4.0 |
|[32.0,15.0,4.0,16.0,1.0,2.0]|[4.0,16.0,1.0]|4.0 |16.0 |1.0 |
|[22.0,1.5,4.0,14.0,4.0,5.0] |[4.0,14.0,4.0]|4.0 |14.0 |4.0 |
+----------------------------+--------------+-------------+---------+----------+
only showing top 10 rows
output.printSchema()
root
|-- affairs: double (nullable = false)
|-- gender: string (nullable = true)
|-- age: double (nullable = false)
|-- yearsmarried: double (nullable = false)
|-- children: string (nullable = true)
|-- religiousness: double (nullable = false)
|-- education: double (nullable = false)
|-- occupation: double (nullable = false)
|-- rating: double (nullable = false)
|-- features: vector (nullable = true)
|-- slicerFeatures: vector (nullable = true)
Bucketizer将连续数据离散化到指定的范围区间
import org.apache.spark.ml.feature.Bucketizer // Double.NegativeInfinity：负无穷；Double.PositiveInfinity：正无穷
// 分为6个组：[负无穷,-100),[-100,-10),[-10,0),[0,10),[10,90),[90,正无穷)
val splits = Array(Double.NegativeInfinity, -100, -10, 0.0, 10, 90, Double.PositiveInfinity) val data: Array[Double] = Array(-180,-160,-100,-50,-70,-20,-8,-5,-3, 0.0, 1,3,7,10,30,60,90,100,120,150) val dataFrame = spark.createDataFrame(data.map(Tuple1.apply)).toDF("features")
dataFrame: org.apache.spark.sql.DataFrame = [features: double] val bucketizer = new Bucketizer()
.setInputCol("features")
.setOutputCol("bucketedFeatures")
.setSplits(splits) // 将原始数据转换为桶索引
val bucketedData = bucketizer.transform(dataFrame)
bucketedData: org.apache.spark.sql.DataFrame = [features: double, bucketedFeatures: double] bucketedData.show(50,truncate=false)
+--------+----------------+
|features|bucketedFeatures|
+--------+----------------+
|-180.0 |0.0 |
|-160.0 |0.0 |
|-100.0 |1.0 |
|-50.0 |1.0 |
|-70.0 |1.0 |
|-20.0 |1.0 |
|-8.0 |2.0 |
|-5.0 |2.0 |
|-3.0 |2.0 |
|0.0 |3.0 |
|1.0 |3.0 |
|3.0 |3.0 |
|7.0 |3.0 |
|10.0 |4.0 |
|30.0 |4.0 |
|60.0 |4.0 |
|90.0 |5.0 |
|100.0 |5.0 |
|120.0 |5.0 |
|150.0 |5.0 |
+--------+----------------+
Spark特征(提取，转换，选择)extracting, transforming and selecting features的更多相关文章
- Spark Extracting,transforming,selecting features
Spark(3) - Extracting, transforming, selecting features 官方文档链接:https://spark.apache.org/docs/2.2.0/m ...
- Extracting and composing robust features with denosing autoencoders 论文
这是一篇发表于2008年初的论文. 文章主要讲了利用 denosing autoencoder来学习 robust的中间特征..进上步,说明,利用这个方法,可以初始化神经网络的权值..这就相当于一种非 ...
- 【Spark篇】---Spark中Transformations转换算子
一.前述 Spark中默认有两大类算子,Transformation(转换算子),懒执行.action算子,立即执行,有一个action算子 ,就有一个job. 通俗些来说由RDD变成RDD就是Tra ...
- 【转】Spark实现行列转换pivot和unpivot
背景 做过数据清洗ETL工作的都知道,行列转换是一个常见的数据整理需求.在不同的编程语言中有不同的实现方法,比如SQL中使用case+group,或者Power BI的M语言中用拖放组件实现.今天正好 ...
- Spark中RDD转换成DataFrame的两种方式（分别用Java和Scala实现）
一:准备数据源 在项目下新建一个student.txt文件,里面的内容为: ,zhangsan, ,lisi, ,wanger, ,fangliu, 二:实现 Java版: 1.首先新建一个s ...
- Spark之 RDD转换成DataFrame的Scala实现
依赖 <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2. ...
- Android oat文件提取转换
说明: 1.手机厂商可以修改Android源码并进行编译后再生成oat格式文件在手机上存储,比如boot-okhttp.oat,boot-framework.oat. 2.自带的apk可以调用这些模块 ...
- 【spark】常用转换操作：join
join就表示内连接. 对于内链接,对于给定的两个输入数据集(k,v1)和(k,v2) 根据相同的k进行连接,最终得到(k,(v1,v2))的数据集. 示例 val arr1 = Array((&qu ...
- 【spark】常用转换操作：keys 、values和mapValues
1.keys 功能: 返回所有键值对的key 示例 val list = List("hadoop","spark","hive",&quo ...
随机推荐
- QT 运行崩溃：The inferior stopped because it received a signal from the Operating System
最近在研究QT自带的boxes例子,自己派生一个图形项,但是在运行生成该图形项时程序直接退出了~ Qt Creater调试代码,问题定位如下代码行: 执行1270行时弹出错误消息框: 于是上网查找资料 ...
- Rob Whitfield：银行已准备好避免下一场金融危机的爆发
2014夏季达沃斯论坛于9月10日至12日在天津举行.来自全球90多个国家和地区的1600多位精英将就“推动创新 创造价值”这一主题展开激烈讨论.中国电子银行网全程直击,第一时间为您带来达沃斯最前方的 ...
- SQL Compare数据库比较工具 完全破解+使用教程
来源http://www.cnblogs.com/duci/articles/4482665.html 一.使用教程 SQL Compare是编程人员常用的比较两个数据库之间差异的工具.可以用来比较数 ...
- Android零碎知识点 1
Android零碎知识点 1 Android在2.3版本上开始支持KeyEvent.KEYCODE_PAGE_DOWN以及KeyEvent.KEYCODE_PAGE_UP的操作. Androi ...
- Images.xcassets 的简单使用
总结起来Images.xcassets 就这么几句话 (1)以下開始简单整理 进入images.xcassets 右键--->New Image Set 或者点XCode的images.xcas ...
- my-small.ini、my-medium.ini、my-large.ini、my-huge.ini文件的作用
安装完mysql之后或者是下载的免安装版解压之后,默认是没有my.ini文件的.但是,有几个类似的文件,如my-small.ini.my-medium.ini.my-large.ini.my-huge ...
- iOS 图片剪切和压缩的几个方法
// 图片剪切 - (UIImage*)clipImageWithImage:(UIImage*)image inRect:(CGRect)rect { CGImageRef imageRef ...
- pgpool-II 的使用
1.pgpool-II的概念 pgpool-II 是一个位于 PostgreSQL 服务器和 PostgreSQL 数据库客户端之间的中间件,它提供以下功能: 连接池 pgpool-II 保持已经连接 ...
- 【代码审计】CmsEasy_v5.7 代码执行漏洞分析
0x00 环境准备 CmsEasy官网:http://www.cmseasy.cn/ 网站源码版本:CmsEasy_v5.7_UTF8-0208 程序源码下载: http://ftp.cmseas ...
- It is possible that this issue is resolved by uninstalling an existing version of the apk if it is present, and then re-installing ___Error Installing APK
一 : 根据以下路径,找到Instant Run中的选项 File —— Settings——Build,Execution,Deployment——Instant Run ...