Xgboost安装、使用和算法原理理解

一、Xgboost相关重要文档

1、官方文档

官方文档中可查询到各语言版本的安装方法、官方用例等

XGBoost Documentation — xgboost 1.6.0-dev documentationhttps://xgboost.readthedocs.io/en/latest/index.html2、github

github源码可查看代码实现、下载数据样例等GitHub - dmlc/xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlowhttps://github.com/dmlc/xgboost3、maven仓库

使用IDEA配置xgboost4j的时候，根据scala版本配置依赖

https://mvnrepository.com/artifact/ml.dmlc/xgboost4jhttps://mvnrepository.com/artifact/ml.dmlc/xgboost4j

POM文件配置：

 2.12
 2.12.6
 3.0.2
 1.2.0





 ml.dmlc
 xgboost4j-spark_${spark.version.scala}
 ${xgboost.version}

二、xgboost使用方法

参考官方github给出的spark分布式训练的代码例子

https://github.com/dmlc/xgboost/blob/master/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scalahttps://github.com/dmlc/xgboost/blob/master/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala

或者官网教程讲解的例子。这与上面的是同一个例子，只不过这里是教程，上面的是代码。

XGBoost4J-Spark Tutorial (version 0.9+) — xgboost 1.6.0-dev documentation
​​​​​​​​​​​​​​

其他博客中的代码例子 ：

[机器学习] XGBoost on Spark 分布式使用完全手册_小墨鱼的专栏-CSDN博客_spark xgboost

XGBoost实践经验：

 经验1：二分类中不要设置参数，"num_class" -> 2, 会训练失败。多元分类时设置。
 经验2：为了防止block，将训练数据分区数和申请的工作节点数numWorkers保持一致。即对训练数据做repartition处理。numWorkers默认值是32.
 经验3:numWorker参数应该与executor数量设置一致，executor-cores设置小一些
 经验4:在train的过程中，每个partition占用的内存最好限制在executor-memory的1/3以内，因为除了本来训练样本需要驻留的内存外，xgboost为了速度的提升，为每个线程申请了额外的内存，并且这些内存是JVM所管理不到的
 经验5：检查训练数据，如果有缺失值，XGB4j会training failed
 经验6：XGB4j 1.3.0以上版本，解决了 spark.speculation机制 和xgboost4j的worker的逻辑冲突问题，但1.2.0版本比1.3.1 、1.5.0运行更稳定
 经验7：kill_spark_context_on_worker_failure 设置为false，默认值是true，如果worker训练失败会杀掉spark进程

注意区分：xgboost本地单机模式 和xgboost spark分布式模式 ！！！
上手机器学习系列-第5篇（中）-XGBoost+Scala/Spark_a_step_further的博客-CSDN博客

代码示例1: 多元分类 + pipeline

import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature._
import org.apache.spark.ml.tuning._
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types._
import ml.dmlc.xgboost4j.scala.spark.{XGBoostClassifier, XGBoostClassificationModel}

object xgboostExample {

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

    val spark = SparkSession.builder().appName("xgboost").getOrCreate()

    val cpuGpu = args(0)

    //0-用GPU还是CPU
    val (treeMethod, numWorkers) = if (cpuGpu == "gpu") {
      ("gpu_hist", 4)
    } else ("auto", 8)

    // 1-加载数据集
    val masterPath = "hdfs://gaoToby/ml_data/xgboost_data/"
    val irisPath = masterPath + "iris.data"

    val schema = new StructType(Array(
      StructField("sepal length", DoubleType, true),
      StructField("sepal width", DoubleType, true),
      StructField("petal length", DoubleType, true),
      StructField("petal width", DoubleType, true),
      StructField("class", StringType, true)))

    val rawInput = spark.read.schema(schema).csv(irisPath)

    // Split training and test dataset
    val Array(training, test) = rawInput.randomSplit(Array(0.8, 0.2), 123)
    training.cache()
    training.show(5,false)

    //2- 创建Pipeline
    // Build ML pipeline, it includes 4 stages:
    // 1, Assemble all features into a single vector column.
    // 2, From string label to indexed double label.
    // 3, Use XGBoostClassifier to train classification model.
    // 4, Convert indexed double label back to original string label.
    val assembler = new VectorAssembler()
      .setInputCols(Array("sepal length", "sepal width", "petal length", "petal width"))
      .setOutputCol("features")
    val labelIndexer = new StringIndexer()
      .setInputCol("class")
      .setOutputCol("classIndex")
      .fit(training)
    val booster = new XGBoostClassifier(
      Map("eta" -> 0.1f,
        "max_depth" -> 2,
        "objective" -> "multi:softprob",
        "num_class" -> 3,
        "num_round" -> 100,
        "num_workers" -> numWorkers,
        "tree_method" -> treeMethod
      )
    )
    booster.setFeaturesCol("features")
    booster.setLabelCol("classIndex")
    val labelConverter = new IndexToString()
      .setInputCol("prediction")
      .setOutputCol("realLabel")
      .setLabels(labelIndexer.labels)

    val pipeline = new Pipeline()
      .setStages(Array(assembler, labelIndexer, booster, labelConverter))

    //3-模型训练
    val model = pipeline.fit(training)

    //4-模型预测
    val prediction = model.transform(test)
    prediction.show(false)

    //5-模型评价
    val evaluator = new MulticlassClassificationEvaluator()
    evaluator.setLabelCol("classIndex")
    evaluator.setPredictionCol("prediction")
    val accuracy = evaluator.evaluate(prediction)
    println("The model accuracy is : " + accuracy)

    //6-模型调参 Tune model using cross validation
    val paramGrid = new ParamGridBuilder()
      .addGrid(booster.maxDepth, Array(3, 8))
      .addGrid(booster.eta, Array(0.2, 0.6))
      .build()
    val cv = new CrossValidator()
      .setEstimator(pipeline)
      .setEvaluator(evaluator)
      .setEstimatorParamMaps(paramGrid)
      .setNumFolds(3)

    val cvModel = cv.fit(training)

    val bestModel = cvModel.bestModel.asInstanceOf[PipelineModel].stages(2)
      .asInstanceOf[XGBoostClassificationModel]
    println("The params of best XGBoostClassification model : " +
      bestModel.extractParamMap())
    println("The training summary of best XGBoostClassificationModel : " +
      bestModel.summary)

    //7- 模型保存
    val pipelineModelPath = "hdfs://gaoToby/model/xgboost/"
    model.write.overwrite().save(pipelineModelPath)

    //8-模型加载
    val model2 = PipelineModel.load(pipelineModelPath)
    model2.transform(test).show(false)

    //9-保存为单机版本的模型，可以本地加载模型
    // Export the XGBoostClassificationModel as local XGBoost model,
    // then you can load it back in local Python environment.
    val nativeModelPath = "/home/gaoToby/xgboost/nativeModel"
    bestModel.nativeBooster.saveModel(nativeModelPath)
    
  }
}

结果展示：

(1)原始训练数据

（2）模型预测结果

代码示例2: 二元分类 + 无pipeline直接训练

import ml.dmlc.xgboost4j.scala.spark.{XGBoostClassificationModel, XGBoostClassifier}
import org.apache.spark.sql.SparkSession

/**
 * XGBoost例子： 二元分类、CPU计算、不用Pipeline
 */

object xgboost {

  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().appName("xgboost").enableHiveSupport().getOrCreate()

    val masterPath = "hdfs://gaoToby/ml_data/xgboost_data/"
    val trainPath = masterPath + "agaricus.txt.train"
    val testPath = masterPath + "agaricus.txt.test"
    //1-加载训练数据
    val trainData = spark.read.format("libsvm").load(trainPath)
      .dropDuplicates()
    trainData.cache()
    trainData.show(5,false)

    val testData = spark.read.format("libsvm").load(testPath)
    testData.cache()
    testData.show(5,false)

    //2- 创建模型实例
    val xgbClassifier = new XGBoostClassifier(
      Map("eta" -> 0.1f,
        "missing" -> -999.0,
        "max_depth" -> 4,
        "objective" -> "binary:logistic", //二元分类
//        "num_class" -> 2,  //二分类中不要设置这个参数，会训练失败。多元分类时设置。
        "num_round" -> 100,
        "num_workers" -> 4,
        "tree_method" -> "hist", //GPU不能用，用CPU的方法
        "train_test_ratio" -> 0.7
      )
    ).setFeaturesCol("features")
      .setLabelCol("label")
      .setAllowNonZeroForMissing(true) //允许非零的缺失值存在

    //3-模型训练
    val modelFitted =xgbClassifier.fit(trainData)

    //4-模型预测
    val predicts = modelFitted.transform(testData) //对DataFrame用transform进行转换
    predicts.show(5,false)

//    import org.apache.spark.ml.linalg.Vector
//    val features = trainData.head().getAs[Vector]("features")
//    val result: Double = modelFitted.predict(features) //对一条向量的特征数据进行预测用predict
//    println(result)

    //5-模型评价 --二元分类模型评价器
    import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
    val evaluator = new BinaryClassificationEvaluator()
      .setMetricName("areaUnderROC")
      .setRawPredictionCol("rawPrediction")
      .setLabelCol("label")

    val auc = evaluator.evaluate(predicts)
    println(s"xgboost Model evaluate trainData areaUnderROC i.e AUC: $auc")

    //6-模型保存
    val modelPath = "hdfs://gaoToby/model/xgboost/"
    modelFitted.write.overwrite().save(modelPath)

    //7-模型加载
    val xgboostModel1 = XGBoostClassificationModel.load(modelPath)
    val testPrediction = xgboostModel1.transform(testData)
    testPrediction.cache()
    testPrediction.show(5,false)

 }
}

结果展示：

1、训练数据

2、模型预测数据

三、xgboost原理理解

xgboost参数解读：XGBoost Parameters — xgboost 1.6.0-dev documentation

xgboost参数解释_lc574260570的博客-CSDN博客

论文：XGBoost: A Scalable Tree Boosting System​​​​​​​

上手机器学习系列-第5篇（下）-XGBoost原理_a_step_further的博客-CSDN博客

原理介绍文档：中文版：https://xgboost.apachecn.org/#/docs/3

英文版： ​​​​​​​Introduction to Boosted Trees — xgboost 1.6.0-dev documentation