数据帧成密集矢量火花

Question

我正在研究spark 2.0.2我想知道，例如，基于Spark ML进行逻辑回归。我想将数据帧的每一行都放入一个向量进行逻辑回归的向量中，你能帮忙吗？获取行导致数据帧将每行变为密集向量。谢谢。这就是我为获取数据帧所做的工作。

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.Row
import org.apache.hadoop.fs.shell.Display

object Example extends App {
val sparkSession = SparkSession.builder.master("local").appName("my-spark-app").getOrCreate()
val data=sparkSession.read.option("header", "true").csv("C://sample_lda_data.csv").toDF()
val data2=data.select("col2","col3","col4","col5","col6","col7","col8","col9")

最后我想得到这样的东西作为逻辑回归的输入在第一个位置它将是数据帧的第一列任何帮助请

val data=sparkSession.read.option("header", "true").csv("C://sample_lda_data.csv").toDF()
val data2=data.select("col2","col3","col4","col5","col6","col7","col8","col9")
val assembler = new VectorAssembler().setInputCols(Array("col2", "col3", "col4")).setOutputCol("features")
val output = assembler.transform(data2)

main" java.lang.IllegalArgumentException: Data type StringType is not supported.

我会非常感激的。谢谢你们

Answer 1

你可以使用array函数，然后映射到LabeledPoints：

import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.DoubleType

// create an array column from all but first one:
val arrayCol: Column = array(df.columns.drop(1).map(col).map(_.cast(DoubleType)): _*)

// select array column and first column, and map into LabeledPoints
val result: Dataset[LabeledPoint] = df.select(col("col1").cast(DoubleType), arrayCol)
  .map(r => LabeledPoint(
    r.getAs[Double](0),
    Vectors.dense(r.getAs[mutable.WrappedArray[Double]](1).toArray)
  ))

// You can use the Dataset or the RDD
result.show()
// +-----+---------------------+
// |label|features             |
// +-----+---------------------+
// |1.0  |[2.0,3.0,4.0,0.5]    |
// |11.0 |[12.0,13.0,14.0,15.0]|
// |21.0 |[22.0,23.0,24.0,25.0]|
// +-----+---------------------+

result.rdd.foreach(println)
// (1.0,[2.0,3.0,4.0,0.5])
// (21.0,[22.0,23.0,24.0,25.0])

Answer 2

I have wrote code to convert dataframe's numeric columns into dense vector. Please find below code. Note: here col1 and col2 are numeric type columns.

import sparksession.implicits._;
   val result: Dataset[LabeledPoint] = df.map{ x => LabeledPoint(x.getAs[Integer]("Col1").toDouble, Vectors.dense(x.getAs[Double]("col2"))) }
   result.show();
result.printSchema();

+-------+----------+
|  label|  features|
+-------+----------+
|31825.0| [75000.0]|
|58784.0| [24044.0]|
|  121.0| [41000.0]|

root
 |-- label: double (nullable = true)
 |-- features: vector (nullable = true)