GBDT分为ML Classification API和ML Regression API两大类模型接口。

模型接口类别	函数接口
ML Classification API	def fit(dataset: Dataset[_]): GBTClassificationModel
	def fit(dataset: Dataset[_], paramMap: ParamMap): GBTClassificationModel
	def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[GBTClassificationModel]
	def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GBTClassificationModel
ML Regression API	def fit(dataset: Dataset[_]): GBTRegressionModel
	def fit(dataset: Dataset[_], paramMap: ParamMap): GBTRegressionModel
	def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[GBTRegressionModel]
	def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GBTRegressionModel

ML Classification API

功能描述
 传入Dataset格式的样本数据，调用训练接口，输出GBDT分类模型。

输入输出

包名：package org.apache.spark.ml.classification
类名：GBTClassifier
方法名：fit

输入：Dataset[_]，训练样本数据，必须字段如下。

Param name	Type(s)	Default	Description
labelCol	Double	"label"	预测标签
featuresCol	Vector	"features"	特征标签

输入：paramMap、paramMaps、firstParamPair、otherParamPairs，fit接口的模型参数，说明如下。

Param name	Type(s)	Example	Description
paramMap	ParamMap	ParamMap(A.c -> b)	将b的值赋给模型A的参数c
paramMaps	Array[ParamMa p]	Array[ParamMa p](n)	形成n个 ParamMap模型参数列表
firstParamPair	ParamPair	ParamPair(A.c, b)	将b的值赋给模型A的参数c
otherParamPair s	ParamPair	ParamPair(A.e, f)	将f的值赋给模型 A的参数e

算法参数

算法参数
def setCheckpointInterval(value: Int): GBTClassifier.this.type def setFeatureSubsetStrategy(value: String): GBTClassifier.this.type def setFeaturesCol(value: String): GBTClassifier def setImpurity(value: String): GBTClassifier.this.type def setLabelCol(value: String): GBTClassifier def setLossType(value: String): GBTClassifier.this.type def setMaxBins(value: Int): GBTClassifier.this.type def setMaxDepth(value: Int): GBTClassifier.this.type def setMaxIter(value: Int): GBTClassifier.this.type def setMinInfoGain(value: Double): GBTClassifier.this.type def setMinInstancesPerNode(value: Int): GBTClassifier.this.type def setPredictionCol(value: String): GBTClassifier def setProbabilityCol(value: String): GBTClassifierdoUseAcc def setRawPredictionCol(value: String): GBTClassifier def setSeed(value: Long): GBTClassifier.this.type def setStepSize(value: Double): GBTClassifier.this.type def setSubsamplingRate(value: Double): GBTClassifier.this.type def setThresholds(value: Array[Double]): GBTClassifier

def setCheckpointInterval(value: Int): GBTClassifier.this.type def setFeatureSubsetStrategy(value: String): GBTClassifier.this.type def setFeaturesCol(value: String): GBTClassifier def setImpurity(value: String): GBTClassifier.this.type def setLabelCol(value: String): GBTClassifier def setLossType(value: String): GBTClassifier.this.type def setMaxBins(value: Int): GBTClassifier.this.type def setMaxDepth(value: Int): GBTClassifier.this.type def setMaxIter(value: Int): GBTClassifier.this.type def setMinInfoGain(value: Double): GBTClassifier.this.type def setMinInstancesPerNode(value: Int): GBTClassifier.this.type def setPredictionCol(value: String): GBTClassifier def setProbabilityCol(value: String): GBTClassifierdoUseAcc def setRawPredictionCol(value: String): GBTClassifier def setSeed(value: Long): GBTClassifier.this.type def setStepSize(value: Double): GBTClassifier.this.type def setSubsamplingRate(value: Double): GBTClassifier.this.type def setThresholds(value: Array[Double]): GBTClassifier

新增算法参数。

参数名称	参数含义	取值类型
doUseAcc	特征并行训练模式开关	True/False[Boolean]

参数及fit代码接口示例：

import org.apache.spark.ml.param.{ParamMap, ParamPair}
val gbdt = new GBTClassifier()
//定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数
val paramMap = ParamMap(gbdt.maxDepth -> maxDepth) .put(gbdt.maxIter, maxIter)

// 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数
val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
for (i <- 0 to  2) {
paramMaps(i) = ParamMap(gbdt.maxDepth -> maxDepth)
.put(gbdt.maxIter, maxIter)
}//对paramMaps进行赋值

// 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数
val maxDepthParamPair = ParamPair(gbdt.maxDepth, maxDepth)
val maxIterParamPair = ParamPair(gbdt.maxIter, maxIter)
val maxBinsParamPair = ParamPair(gbdt.maxBins, maxBins)

// 调用各个fit接口
model = gbdt.fit(trainingData)
model = gbdt.fit(trainingData, paramMap)
models = gbdt.fit(trainingData, paramMaps)
model = gbdt.fit(trainingData, maxDepthParamPair, maxIterParamPair, maxBinsParamPair)

输出：GBTClassificationModel，GBDT分类模型，模型预测时的输出字段。

Param name	Type(s)	Default	Description
predictionCol	Double	"prediction"	Predicted label

使用样例

fit(dataset: Dataset[_]): GBTClassificationModel样例：

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{GBTClassificationModel, GBTClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}

// Load and parse the data file, converting it to a DataFrame.
val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

// Index labels, adding metadata to the label column.
// Fit on whole dataset to include all labels in index.
val labelIndexer = new StringIndexer()
.setInputCol("label")
.setOutputCol("indexedLabel")
.fit(data)
// Automatically identify categorical features, and index them.
// Set maxCategories so features with > 4 distinct values are treated as continuous.
val featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.setMaxCategories(4)
.fit(data)

// Split the data into training and test sets (30% held out for testing).
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a GBT model.
val gbt = new GBTClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
.setMaxIter(10)

// Convert indexed labels back to original labels.
val labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels)

// Chain indexers and GBT in a Pipeline.
val pipeline = new Pipeline()
.setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter))

// Train model. This also runs the indexers.
val model = pipeline.fit(trainingData)

// Make predictions.
val predictions = model.transform(testData)

// Select (prediction, true label) and compute test error.
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("indexedLabel")
.setPredictionCol("prediction") .setMetricName("accuracy")
val accuracy = evaluator.evaluate(predictions)
println("Test Error = " + (1.0 - accuracy))
val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel]
println("Learned classification GBT model:\n" + gbtModel.toDebugString)

结果样例

Test Error = 0.0714285714285714
Learned classification GBT model:
GBTClassificationModel (uid=gbtc_72086dba9af5) with 10 trees
Tree 0 (weight 1.0):
If (feature 406 <= 9.5)
Predict: 1.0
Else (feature 406 > 9.5)
Predict: -1.0
Tree 1 (weight 0.1):
If (feature 406 <= 9.5)
If (feature 209 <= 241.5)
If (feature 154 <= 55.0)
Predict: 0.4768116880884702
Else (feature 154 > 55.0)
Predict: 0.4768116880884703
Else (feature 209 > 241.5)
Predict: 0.47681168808847035
Else (feature 406 > 9.5)
If (feature 461 <= 143.5)
Predict: -0.47681168808847024
Else (feature 461 > 143.5) Predict: -0.47681168808847035
Tree 2 (weight 0.1):
If (feature 406 <= 9.5)
If (feature 657 <= 116.5)
If (feature 154 <= 9.5)
Predict: 0.4381935810427206
Else (feature 154 > 9.5)
Predict: 0.43819358104272066
Else (feature 657 > 116.5)
Predict: 0.43819358104272066
Else (feature 406 > 9.5)
If (feature 322 <= 16.0)
Predict: -0.4381935810427206
Else (feature 322 > 16.0) Predict: -0.4381935810427206 Tree 3 (weight 0.1):
If (feature 406 <= 9.5)
If (feature 598 <= 166.5)
If (feature 180 <= 3.0)
Predict: 0.4051496802845983
Else (feature 180 > 3.0)
Predict: 0.4051496802845984
Else (feature 598 > 166.5)
Predict: 0.4051496802845983
Else (feature 406 > 9.5) Predict: -0.4051496802845983 Tree 4 (weight 0.1):
If (feature 406 <= 9.5)
If (feature 537 <= 47.5)
If (feature 606 <= 7.0)
Predict: 0.3765841318352991
Else (feature 606 > 7.0)
Predict: 0.37658413183529926
Else (feature 537 > 47.5)
Predict: 0.3765841318352994
Else (feature 406 > 9.5) If (feature 124 <= 35.5)
If (feature 376 <= 1.0)
If (feature 516 <= 26.5) If (feature 266 <= 50.5)
Predict: -0.3765841318352991
Else (feature 266 > 50.5)
Predict: -0.37658413183529915
Else (feature 516 > 26.5)
Predict: -0.3765841318352992
Else (feature 376 > 1.0)
Predict: -0.3765841318352994
Else (feature 124 > 35.5) Predict: -0.3765841318352994 Tree 5 (weight 0.1):
If (feature 406 <= 9.5) If (feature 570 <= 3.5)
Predict: 0.35166478958101005
Else (feature 570 > 3.5)
Predict: 0.35166478958101
Else (feature 406 > 9.5) If (feature 266 <= 14.0) If (feature 267 <= 12.5)
Predict: -0.35166478958101005
Else (feature 267 > 12.5)
If (feature 267 <= 36.0)
Predict: -0.35166478958101005
Else (feature 267 > 36.0)
Predict: -0.3516647895810101
Else (feature 266 > 14.0) Predict: -0.35166478958101005 Tree 6 (weight 0.1):
If (feature 406 <= 9.5) If (feature 207 <= 7.5)
Predict: 0.32974984655529926
Else (feature 207 > 7.5)
Predict: 0.3297498465552993
Else (feature 406 > 9.5)
If (feature 490 <= 185.0)
Predict: -0.32974984655529926
Else (feature 490 > 185.0) Predict: -0.3297498465552993 Tree 7 (weight 0.1):
If (feature 406 <= 9.5)
If (feature 568 <= 22.0)
Predict: 0.3103372455197956
Else (feature 568 > 22.0)
Predict: 0.31033724551979563
Else (feature 406 > 9.5)
If (feature 379 <= 133.5) If (feature 237 <= 250.5)
Predict: -0.3103372455197956
Else (feature 237 > 250.5)
Predict: -0.3103372455197957
Else (feature 379 > 133.5)
If (feature 433 <= 183.5)
If (feature 516 <= 9.0)
Predict: -0.3103372455197956
Else (feature 516 > 9.0)
Predict: -0.3103372455197957
Else (feature 433 > 183.5) Predict: -0.3103372455197957 Tree 8 (weight 0.1):
If (feature 406 <= 9.5)
If (feature 184 <= 19.0)
Predict: 0.2930291649125433
Else (feature 184 > 19.0) If (feature 155 <= 147.0)
If (feature 180 <= 3.0)
Predict: 0.2930291649125433
Else (feature 180 > 3.0)
Predict: 0.2930291649125433
Else (feature 155 > 147.0)
Predict: 0.2930291649125434
Else (feature 406 > 9.5)
If (feature 379 <= 133.5)
Predict: -0.2930291649125433
Else (feature 379 > 133.5)
If (feature 433 <= 52.5)
Predict: -0.2930291649125433
Else (feature 433 > 52.5) If (feature 462 <= 143.5)
Predict: -0.2930291649125433
Else (feature 462 > 143.5) Predict: -0.2930291649125434 Tree 9 (weight 0.1):
If (feature 406 <= 9.5) If (feature 183 <= 3.0)
Predict: 0.27750666438358246
Else (feature 183 > 3.0) If (feature 183 <= 19.5)
Predict: 0.27750666438358246
Else (feature 183 > 19.5)
Predict: 0.2775066643835825
Else (feature 406 > 9.5) If (feature 239 <= 50.5)
If (feature 435 <= 102.0)
Predict: -0.27750666438358246
Else (feature 435 > 102.0)
Predict: -0.2775066643835825
Else (feature 239 > 50.5)
Predict: -0.27750666438358257

Regression API

功能描述
 传入Dataset格式的样本数据，调用训练接口，输出GBDT回归模型。

输入输出

包名：package org.apache.spark.ml.classification
类名：GBTRegressor
方法名：fit

输入：Dataset[_]，训练样本数据，必须字段如下。

Param name	Type(s)	Default	Description
labelCol	Double	"label"	预测标签
featuresCol	Vector	"features"	特征标签

输入：paramMap、paramMaps、firstParamPair、otherParamPairs，fit接口的模型参数，说明如下。

Param name	Type(s)	Example	Description
paramMap	ParamMap	ParamMap(A.c -> b)	将b的值赋给模型A的参数c
paramMaps	Array[ParamMa p]	Array[ParamMa p](n)	形成n个ParamMap模型参数列表
firstParamPair	ParamPair	ParamPair(A.c, b)	将b的值赋给模型A的参数c
otherParamPair s	ParamPair	ParamPair(A.e, f)	将f的值赋给模型A的参数e

算法参数

算法参数
def setCheckpointInterval(value: Int): GBTRegressor.this.type def setFeatureSubsetStrategy(value: String): GBTRegressor.this.type def setFeaturesCol(value: String): GBTRegressor def setImpurity(value: String): GBTRegressor.this.type def setLabelCol(value: String): GBTRegressor def setLossType(value: String): GBTRegressor.this.type def setMaxBins(value: Int): GBTRegressor.this.type def setMaxDepth(value: Int): GBTRegressor.this.type def setMaxIter(value: Int): GBTRegressor.this.type def setMinInfoGain(value: Double): GBTRegressor.this.type def setMinInstancesPerNode(value: Int): GBTRegressor.this.type def setPredictionCol(value: String): GBTRegressor def setSeed(value: Long): GBTRegressor.this.type def setStepSize(value: Double): GBTRegressor.this.type def setSubsamplingRate(value: Double): GBTRegressor.this.type

def setCheckpointInterval(value: Int): GBTRegressor.this.type

def setFeatureSubsetStrategy(value: String): GBTRegressor.this.type

def setFeaturesCol(value: String): GBTRegressor

def setImpurity(value: String): GBTRegressor.this.type

def setLabelCol(value: String): GBTRegressor

def setLossType(value: String): GBTRegressor.this.type

def setMaxBins(value: Int): GBTRegressor.this.type

def setMaxDepth(value: Int): GBTRegressor.this.type

def setMaxIter(value: Int): GBTRegressor.this.type

def setMinInfoGain(value: Double): GBTRegressor.this.type

def setMinInstancesPerNode(value: Int): GBTRegressor.this.type

def setPredictionCol(value: String): GBTRegressor

def setSeed(value: Long): GBTRegressor.this.type

def setStepSize(value: Double): GBTRegressor.this.type

def setSubsamplingRate(value: Double): GBTRegressor.this.type

参数及fit代码接口示例：

import org.apache.spark.ml.param.{ParamMap, ParamPair}

val gbdt = new GBTRegressor() //定义回归模型

//定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数
val paramMap = ParamMap(gbdt.maxDepth -> maxDepth) .put(gbdt.maxIter, maxIter)

// 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数
val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
for (i <- 0 to  2) {
paramMaps(i) = ParamMap(gbdt.maxDepth -> maxDepth)
.put(gbdt.maxIter, maxIter)
} //对paramMaps进行赋值

// 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数
val maxDepthParamPair = ParamPair(gbdt.maxDepth, maxDepth)
val maxIterParamPair = ParamPair(gbdt.maxIter, maxIter)
val maxBinsParamPair = ParamPair(gbdt.maxBins, maxBins)

// 调用各个fit接口
model = gbdt.fit(trainingData)  //返回GBTRegressionModel
model = gbdt.fit(trainingData, paramMap)  //返回GBTRegressionModel
models = gbdt.fit(trainingData, paramMaps)  //返回Seq[GBTRegressionModel]
model = gbdt.fit(trainingData, maxDepthParamPair, maxIterParamPair, maxBinsParamPair) //返回GBTRegressionModel

输出：GBTRegressionModel或Seq[GBTRegressionModel]，GBDT回归模型，模型预测时的输出字段如下。

Param name	Type(s)	Default	Description
predictionCol	Double	"prediction"	Predicted label

使用样例

fit(dataset: Dataset[_]): GBTRegressionModel样例：

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.feature.VectorIndexer
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}

// Load and parse the data file, converting it to a DataFrame.
val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

// Automatically identify categorical features, and index them.
// Set maxCategories so features with > 4 distinct values are treated as continuous.
val featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.setMaxCategories(4) .fit(data)

// Split the data into training and test sets (30% held out for testing).
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a GBT model.
val gbt = new GBTRegressor()
.setLabelCol("label")
.setFeaturesCol("indexedFeatures")
.setMaxIter(10)

// Chain indexer and GBT in a Pipeline.
val pipeline = new Pipeline()
.setStages(Array(featureIndexer, gbt))

// Train model. This also runs the indexer.
val model = pipeline.fit(trainingData)

// Make predictions.
val predictions = model.transform(testData)

// Select example rows to display.
predictions.select("prediction", "label", "features").show(5)

// Select (prediction, true label) and compute test error.
val evaluator = new RegressionE
val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel] println("Learned regression GBT model:\n" + gbtModel.toDebugString)

结果样例

Root Mean Squared Error (RMSE) on test data = 0.0
Learned regression GBT model:
GBTRegressionModel (uid=gbtr_842c8acff963) with 10 trees
Tree 0 (weight 1.0):
If (feature 434 <= 70.5)
If (feature 99 in {0.0,3.0})
Predict: 0.0
Else (feature 99 not in {0.0,3.0})
Predict: 1.0
Else (feature 434 > 70.5)
Predict: 1.0
Tree 1 (weight 0.1):
Predict: 0.0
Tree 2 (weight 0.1):
Predict: 0.0
Tree 3 (weight 0.1):
Predict: 0.0
Tree 4 (weight 0.1):
Predict: 0.0
Tree 5 (weight 0.1):
Predict: 0.0
Tree 6 (weight 0.1):
Predict: 0.0
Tree 7 (weight 0.1):
Predict: 0.0
Tree 8 (weight 0.1):
Predict: 0.0
Tree 9 (weight 0.1):
Predict: 0.0

接口适用性说明：

本算法接口适用于基于鲲鹏服务器的HDP大数据平台，其中Java开发环境要求1.8及以上版本，Spark开发环境要求2.3.2版本，低的HDP版本是3.1.0。
本算法运行在HDP大数据平台需部署的组件需要包括：HDFS、Spark2、Yarn、 ZooKeeper、Hive、MapReduce2。