Scala DoubleType类代码示例

//设置package包名称以及导入依赖的类
package com.drakeconsulting.big_data_maker

import org.scalatest.FunSuite
import com.holdenkarau.spark.testing.SharedSparkContext
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types.{StructField, StringType, LongType, DoubleType}

class ColumnsTest extends FunSuite with SharedSparkContext {
  val numLoops = 100

  test("test StringConstant") {
    val s1 = new StringConstant("f1", "abc")
    assert("abc" === s1.getValue(1))
    assert(StructField("f1", StringType, false) == s1.getStructField)
  }

  test("test RandomLong") {
    val s1 = new RandomLong("f1", 666666L)
    for (x <- 1 to numLoops) {
      assert(s1.getValue(1) >= 0)
      assert(s1.getValue(1) <= 666666L)
    }
    assert(StructField("f1", LongType, false) == s1.getStructField)
  }

  test("test RandomDouble") {
    val s1 = new RandomDouble("f1", 666666.00)
    for (x <- 1 to numLoops) {
      assert(s1.getValue(1) >= 0)
      assert(s1.getValue(1) <= 666666.00)
    }
    assert(StructField("f1", DoubleType, false) == s1.getStructField)
  }

  test("test Categorical") {
    val list = List("a", "b", "c", "d")
    val s1 = new Categorical("f1", list)
    for (x <- 1 to numLoops) {
      val v = s1.getValue(1)
      assert(list.exists(key => v.contains(key)))
    }
    assert(StructField("f1", StringType, false) == s1.getStructField)
  }
} 

//设置package包名称以及导入依赖的类
package fr.ippon.spark.ml

import org.apache.spark.sql.types.DoubleType
import org.apache.spark.sql.{functions, Column, DataFrame, SQLContext}


object Titanic {

  // Fonction de récupération des données d'un fichier de Titanic dans un DataFrame
  def dataframeFromTitanicFile(sqlc: SQLContext, file: String): DataFrame = sqlc.read
    .format("com.databricks.spark.csv")
    .option("header", "true")
    .option("inferSchema", "true")
    .load(file)

  // Fonction de calcul de l'age moyen
  def calcMeanAge(df: DataFrame, inputCol: String): Double = df
    .agg(functions.avg(df(inputCol)))
    .head
    .getDouble(0)

  // Fonction nous donnant l'age ou la moyenne des ages
  def fillMissingAge(df: DataFrame, inputCol: String, outputCol: String, replacementValue: Double): DataFrame = {
    val ageValue: (Any) => Double = age => age match {
      case age: Double => age
      case _ => replacementValue
    }
    df.withColumn(outputCol, functions.callUDF(ageValue, DoubleType, df(inputCol)))
  }
} 

//设置package包名称以及导入依赖的类
package org.wikimedia.research.recommendation.job.translation

import java.io.File

import org.apache.log4j.{LogManager, Logger}
import org.apache.spark.ml.regression.RandomForestRegressionModel
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Row, SaveMode, SparkSession}

import scala.collection.parallel.mutable.ParArray

object ScorePredictor {
  val log: Logger = LogManager.getLogger(ScorePredictor.getClass)

  def predictScores(spark: SparkSession,
                    modelsInputDir: File,
                    predictionsOutputDir: Option[File],
                    sites: ParArray[String],
                    featureData: DataFrame): Unit = {
    log.info("Scoring items")

    val predictions: Array[DataFrame] = sites.map(target => {
      try {
        log.info("Scoring for " + target)
        log.info("Getting work data for " + target)
        val workData: DataFrame = Utils.getWorkData(spark, featureData, target, exists = false)
        log.info("Loading model for " + target)
        val model = RandomForestRegressionModel.load(
          new File(modelsInputDir, target).getAbsolutePath)
        log.info("Scoring data for " + target)
        val predictions = model
          .setPredictionCol(target)
          .transform(workData)
          .select("id", target)

        predictions
      } catch {
        case unknown: Throwable =>
          log.error("Score for " + target + " failed", unknown)
          val schema = StructType(Seq(
            StructField("id", StringType, nullable = false),
            StructField(target, DoubleType, nullable = true)))
          spark.createDataFrame(spark.sparkContext.emptyRDD[Row], schema)
      }
    }).toArray

    val predictedScores = predictions.reduce((left, right) => left.join(right, Seq("id"), "outer"))

    log.info("Saving predictions")
    predictionsOutputDir.foreach(f = o =>
      predictedScores.coalesce(1)
        .write
        .mode(SaveMode.ErrorIfExists)
        .option("header", value = true)
        .option("compression", "bzip2")
        .csv(new File(o, "allPredictions").getAbsolutePath))
  }
} 

//设置package包名称以及导入依赖的类
package ru.ispras.atr.rank

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.functions.desc
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
import ru.ispras.atr.datamodel.{DSDataset, TermCandidate}
import ru.ispras.atr.features.FeatureConfig


abstract class SparkTermCandidatesWeighter(docsToShow:Int) extends TermCandidatesWeighter {
  val termDFName = "Term"

  def allFeatures: Seq[FeatureConfig]

  def convert2FeatureSpace(candidates: Seq[TermCandidate], dataset: DSDataset):Seq[Seq[Double]] = {
    val resByFeatures: Seq[Seq[Double]] = allFeatures.map(f => {
      //iterate by features first, because it lets to estimate time per feature and (maybe) it is faster due to caching
      log.info(s"Initializing feature ${f.id}...")
      val featureComputer = f.build(candidates, dataset)
      log.info(s"Computing feature ${f.id}...")
      featureComputer.compute(candidates)
    })
    log.info(s"${allFeatures.size} features have been computed")
    resByFeatures.transpose
    }

  def convertToDF(termNames: Seq[String], featureNames: Seq[String], resByTerms: Seq[Seq[Double]]): DataFrame = {
    val header = StructField(termDFName, StringType) +: featureNames.map(f => StructField(f, DoubleType))
    val schema = StructType(header)
    val rows = termNames.zip(resByTerms).map(a => Row.fromSeq(a._1 +: a._2))
    val rowsRDD: RDD[Row] = SparkConfigs.sc.parallelize(rows)
    val df = SparkConfigs.sqlc.createDataFrame(rowsRDD, schema)
    df
  }

  def weightAndSort(candidates: Seq[TermCandidate], dataset: DSDataset): Iterable[(String, Double)] = {
    val featureValues = convert2FeatureSpace(candidates, dataset)
    val initDF = convertToDF(candidates.map(_.verboseRepr(docsToShow)), allFeatures.map(_.id), featureValues)
    val weightedDF = weight(initDF)
    val termNamesDF = weightedDF.select(termDFName,id).sort(desc(id))
    val weightColId: String = id //for serialization
    val termColId: String = termDFName
    val terms = termNamesDF.rdd.map(r => (r.getAs[String](termColId), r.getAs[Double](weightColId))).collect()
    terms
  }

  def weight(df: DataFrame) : DataFrame
}

object SparkConfigs {
  val sparkConf = new SparkConf()
    .setAppName("ATR Evaluation System")
    .setMaster("local[16]")
    .set("spark.driver.memory", "1g")
  val sc = new SparkContext(sparkConf)
  val sqlc = new HiveContext(sc)
} 

//设置package包名称以及导入依赖的类
package org.tensorframes

import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{DoubleType, StructType}
import org.scalatest.FunSuite
import org.tensorframes.impl.{DebugRowOpsImpl, ScalarDoubleType}
import org.tensorframes.dsl._

class DebugRowOpsSuite
  extends FunSuite with TensorFramesTestSparkContext with GraphScoping with Logging {
  lazy val sql = sqlContext
  import ColumnInformation.structField
  import Shape.Unknown

  testGraph("Simple identity") {
    val rows = Array(Row(1.0))
    val input = StructType(Array(structField("x", ScalarDoubleType, Shape(Unknown))))
    val p2 = placeholder[Double](1) named "x"
    val out = identity(p2) named "y"
    val outputSchema = StructType(Array(structField("y", ScalarDoubleType, Shape(Unknown))))
    val (g, _) = TestUtilities.analyzeGraph(out)
    logDebug(g.toString)
    val res = DebugRowOpsImpl.performMap(rows, input, Array(0), g, outputSchema)
    assert(res === Array(Row(1.0, 1.0)))
  }

  testGraph("Simple add") {
    val rows = Array(Row(1.0))
    val input = StructType(Array(structField("x", ScalarDoubleType, Shape(Unknown))))
    val p2 = placeholder[Double](1) named "x"
    val out = p2 + p2 named "y"
    val outputSchema = StructType(Array(structField("y", ScalarDoubleType, Shape(Unknown))))
    val (g, _) = TestUtilities.analyzeGraph(out)
    logDebug(g.toString)
    val res = DebugRowOpsImpl.performMap(rows, input, Array(0), g, outputSchema)
    assert(res === Array(Row(2.0, 1.0)))
  }

}