基于spark的波士顿房价预测中，各个属性与房价的相关系数图代码

以下是基于Spark的波士顿房价预测中，各个属性与房价的相关系数图的代码：

import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.stat.Correlation
import org.apache.spark.sql.functions._

// 读取数据
val data = spark.read.format("csv")
  .option("header", "true")
  .option("inferSchema", "true")
  .load("boston_housing.csv")

// 合并特征向量
val assembler = new VectorAssembler()
  .setInputCols(Array("crim", "zn", "indus", "chas", "nox", "rm", "age", "dis", "rad", "tax", "ptratio", "b", "lstat"))
  .setOutputCol("features")
val dataWithFeatures = assembler.transform(data)

// 计算相关系数
val Row(coeff1: Matrix) = Correlation.corr(dataWithFeatures, "features").head
val corrArray = coeff1.toArray

// 获取属性名
val cols = Array("crim", "zn", "indus", "chas", "nox", "rm", "age", "dis", "rad", "tax", "ptratio", "b", "lstat")

// 打印相关系数矩阵
println("Correlation Matrix:")
for (i <- 0 until corrArray.length) {
  if (i % cols.length == 0) {
    print("\n")
  }
  print(f"${corrArray(i)}%1.2f\t")
}

// 画相关系数图
val corrMatrix = coeff1.toArray
val corrMatrixRows = corrMatrix.grouped(cols.length).toArray
val corrMatrixCols = corrMatrixRows.transpose
val corrMatrixColsRows = corrMatrixCols.flatten.grouped(cols.length).toArray

val corrMatrixColsRowsWithHeader = Array(cols) ++ corrMatrixColsRows

val corrMatrixString = corrMatrixColsRowsWithHeader.map(_.mkString(",")).mkString("\n")
println("\n\nCorrelation Matrix as CSV:")
println(corrMatrixString)

这段代码会输出两个内容，一个是相关系数矩阵，另一个是相关系数图的CSV格式数据。你可以使用该CSV数据绘制相关系数图。注意，该代码假设数据文件名为"boston_housing.csv"，属性名称为"crim", "zn", "indus", "chas", "nox", "rm", "age", "dis", "rad", "tax", "ptratio", "b", "lstat"。如果你的数据文件名或属性名称不同，需要对代码进行相应修改。