package org.apache.spark.examples.examplesforml

import org.apache.spark.ml.classification.LogisticRegression

import org.apache.spark.ml.feature.VectorAssembler

import org.apache.spark.ml.regression.{IsotonicRegression, LinearRegression}

import org.apache.spark.sql.SparkSession

import org.apache.spark.{SparkConf, SparkContext}

import scala.util.Random

/*

日期：2018.10.15

描述：

7-14

保序回归算法

实现房价预测

数据集：

house.csv

 */

object IstonicRegression {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf()

      .setAppName("linear")

      .setMaster("local")

    val sc = new SparkContext(conf)

    val spark = SparkSession

      .builder()

      .config(conf)

      .getOrCreate()

    val file = spark.read

      .format("csv")

      .option("sep",";")

      .option("header","true")

      .load("D:\\7-6线性回归-预测房价\\house.csv")

    import spark.implicits._

    //打乱顺序

    val rand = new Random()

    val data = file.select("square","price")

      .map(

      row => (row.getAs[String](0).toDouble,row.getString(1).toDouble,rand.nextDouble()))

      .toDF("square","price","rand").sort("rand") //强制类型转换过程

    val ass = new VectorAssembler()

      .setInputCols(Array("square"))

      .setOutputCol("features")

    val dataset = ass.transform(data)//特征包装

    val Array(train,test) = dataset.randomSplit(Array(0.8,0.2))//拆分成训练数据集和测试数据集

    val isotonic = new IsotonicRegression()

      .setFeaturesCol("features")

      .setLabelCol("price")

    val model = isotonic.fit(train)

    model.transform(test).show()

  }

}