spark上运行xgboost-scala接口
概述
xgboost可以在spark上运行，我用的xgboost的版本是0.7的版本，目前只支持spark2.0以上版本上运行,
编译好jar包，加载到maven仓库里面去：
mvn install:install-file -Dfile=xgboost4j-spark-0.7-jar-with-dependencies.jar -DgroupId=ml.dmlc -DartifactId=xgboost4j-spark -Dversion=0.7 -Dpackaging=jar
添加依赖:
RDD接口：
package com.meituan.spark_xgboost
import org.apache.log4j.{ Level, Logger }
import org.apache.spark.{ SparkConf, SparkContext }
import ml.dmlc.xgboost4j.scala.spark.XGBoost
import org.apache.spark.sql.{ SparkSession, Row }
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.linalg.Vectors
object XgboostR {
def main(args: Array[String]): Unit = {
Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
val spark = SparkSession.builder.master("local").appName("example").
config("spark.sql.warehouse.dir", s"file:///Users/shuubiasahi/Documents/spark-warehouse").
config("spark.sql.shuffle.partitions", "20").getOrCreate()
spark.conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
val path = "/Users/shuubiasahi/Documents/workspace/xgboost/demo/data/"
val trainString = "agaricus.txt.train"
val testString = "agaricus.txt.test"
val train = MLUtils.loadLibSVMFile(spark.sparkContext, path + trainString)
val test = MLUtils.loadLibSVMFile(spark.sparkContext, path + testString)
val traindata = train.map { x =>
val f = x.features.toArray
val v = x.label
LabeledPoint(v, Vectors.dense(f))
}
val testdata = test.map { x =>
val f = x.features.toArray
val v = x.label
Vectors.dense(f)
}
val numRound = 15
//"objective" -> "reg:linear", //定义学习任务及相应的学习目标
//"eval_metric" -> "rmse", //校验数据所需要的评价指标 用于做回归
val paramMap = List(
"eta" -> 1f,
"max_depth" ->5, //数的最大深度。缺省值为6 ,取值范围为：[1,∞]
"silent" -> 1, //取0时表示打印出运行时信息，取1时表示以缄默方式运行，不打印运行时信息。缺省值为0
"objective" -> "binary:logistic", //定义学习任务及相应的学习目标
"lambda"->2.5,
"nthread" -> 1 //XGBoost运行时的线程数。缺省值是当前系统可以获得的最大线程数
).toMap
println(paramMap)
val model = XGBoost.trainWithRDD(traindata, paramMap, numRound, 55, null, null, useExternalMemory = false, Float.NaN)
print("sucess")
val result=model.predict(testdata)
result.take(10).foreach(println)
spark.stop();
}
}
DataFrame接口：
package com.meituan.spark_xgboost
import org.apache.log4j.{ Level, Logger }
import org.apache.spark.{ SparkConf, SparkContext }
import ml.dmlc.xgboost4j.scala.spark.XGBoost
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.sql.{ SparkSession, Row }
object XgboostD {
def main(args: Array[String]): Unit = {
Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
val spark = SparkSession.builder.master("local").appName("example").
config("spark.sql.warehouse.dir", s"file:///Users/shuubiasahi/Documents/spark-warehouse").
config("spark.sql.shuffle.partitions", "20").getOrCreate()
spark.conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
val path = "/Users/shuubiasahi/Documents/workspace/xgboost/demo/data/"
val trainString = "agaricus.txt.train"
val testString = "agaricus.txt.test"
val train = spark.read.format("libsvm").load(path + trainString).toDF("label", "feature")
val test = spark.read.format("libsvm").load(path + testString).toDF("label", "feature")
val numRound = 15
//"objective" -> "reg:linear", //定义学习任务及相应的学习目标
//"eval_metric" -> "rmse", //校验数据所需要的评价指标 用于做回归
val paramMap = List(
"eta" -> 1f,
"max_depth" -> 5, //数的最大深度。缺省值为6 ,取值范围为：[1,∞]
"silent" -> 1, //取0时表示打印出运行时信息，取1时表示以缄默方式运行，不打印运行时信息。缺省值为0
"objective" -> "binary:logistic", //定义学习任务及相应的学习目标
"lambda" -> 2.5,
"nthread" -> 1 //XGBoost运行时的线程数。缺省值是当前系统可以获得的最大线程数
).toMap
val model = XGBoost.trainWithDataFrame(train, paramMap, numRound, 45, obj = null, eval = null, useExternalMemory = false, Float.NaN, "feature", "label")
val predict = model.transform(test)
val scoreAndLabels = predict.select(model.getPredictionCol, model.getLabelCol)
.rdd
.map { case Row(score: Double, label: Double) => (score, label) }
//get the auc
val metric = new BinaryClassificationMetrics(scoreAndLabels)
val auc = metric.areaUnderROC()
println("auc:" + auc)
}
}
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作者：旭旭_哥
来源：CSDN
原文：https://blog.csdn.net/luoyexuge/article/details/71422270
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