如何在Scala中将字符串读取为多边形(在Databricks上)

import com.databricks.spark.avro._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.clustering.KMeans

// 创建SparkSession
val spark = SparkSession.builder()
  .appName("Read String as Polygon")
  .getOrCreate()

// 定义多边形的Schema
val polygonSchema = StructType(Seq(
  StructField("id", IntegerType, nullable = false),
  StructField("polygon", StringType, nullable = false)
))

// 读取包含多边形字符串的数据
val polygonData = spark.read
  .format("csv")
  .option("header", "true")
  .schema(polygonSchema)
  .load("path/to/your/data.csv")

// 将字符串转换为多边形
val polygonDF = polygonData.withColumn("polygon", from_avro($"polygon"))

// 显示多边形数据
polygonDF.show()

// 进一步处理多边形数据，例如进行聚类分析
val assembler = new VectorAssembler()
  .setInputCols(Array("x", "y"))
  .setOutputCol("features")

val vectorizedDF = assembler.transform(polygonDF)

val kmeans = new KMeans()
  .setK(3)
  .setFeaturesCol("features")
  .setPredictionCol("cluster")

val model = kmeans.fit(vectorizedDF)

val clusteredDF = model.transform(vectorizedDF)

// 显示聚类结果
clusteredDF.show()