Spark RDD 到 LabelPoint的轉換(包含構造臨時數據的方法)

題目: 將數據的某個特征作為label, 其他特征(或其他某幾個特征)作為Feature, 轉為LabelPoint

參考: http://www.it1352.com/220642.html

1. 首先構造數據

import scala.util.Random.{setSeed, nextDouble} setSeed(1) case class Record(foo: Double, target: Double, x1: Double, x2: Double, x3: Double) val rows = sc.parallelize( (1 to 10).map(_ => Record( nextDouble, nextDouble, nextDouble, nextDouble, nextDouble )) ) val df = sqlContext.createDataFrame(rows) df.registerTempTable("df") sqlContext.sql(""" SELECT ROUND(foo, 2) foo, ROUND(target, 2) target, ROUND(x1, 2) x1, ROUND(x2, 2) x2, ROUND(x2, 2) x3 FROM df""").show

得到的數據如下:

+----+------+----+----+----+ | foo|target| x1| x2| x3| +----+------+----+----+----+ |0.73| 0.41|0.21|0.33|0.33| |0.01| 0.96|0.94|0.95|0.95| | 0.4| 0.35|0.29|0.51|0.51| |0.77| 0.66|0.16|0.38|0.38| |0.69| 0.81|0.01|0.52|0.52| |0.14| 0.48|0.54|0.58|0.58| |0.62| 0.18|0.01|0.16|0.16| |0.54| 0.97|0.25|0.39|0.39| |0.43| 0.23|0.89|0.04|0.04| |0.66| 0.12|0.65|0.98|0.98| +----+------+----+----+----+

假設我們想排除x2和foo, 抽取 LabeledPoint(target, Array(x1, x3)):

import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint // Map feature names to indices val featInd = List("x1", "x3").map(df.columns.indexOf(_)) // Or if you want to exclude columns val ignored = List("foo", "target", "x2") val featInd = df.columns.diff(ignored).map(df.columns.indexOf(_)) // Get index of target val targetInd = df.columns.indexOf("target") df.rdd.map(r => LabeledPoint( r.getDouble(targetInd), // Get target value // Map feature indices to values Vectors.dense(featInd.map(r.getDouble(_)).toArray) ))

原文轉自 http://blog.csdn.net/zrc199021/article/details/53676116