Question

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到一个向量中进行逻辑回归的输入，你能帮助得到数据帧得到的行吗？每一行都是一个密集的向量。谢谢。这就是我做的数据帧。

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")

最后我希望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框的第一列任何帮助，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。谢谢你们。

Answer 1

您可以使用array功能，然后映射到LabeledPoint s：

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)

数据帧成密集矢量火花

2 个答案: