使用Spark MLlib中决策树分类器API，训练出一个决策树模型，使用Python开发。
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"""
Decision Tree Classification Example.
"""from __future__ import print_functionfrom pyspark import SparkContextfrom pyspark.mllib.tree import DecisionTree, DecisionTreeModelfrom pyspark.mllib.util import MLUtilsif __name__ == "__main__":
sc = SparkContext(appName="PythonDecisionTreeClassificationExample") # 加载和解析数据文件为RDD
dataPath = "/home/zhb/Desktop/work/DecisionTreeShareProject/app/sample_libsvm_data.txt"
print(dataPath)
data = MLUtils.loadLibSVMFile(sc,dataPath) # 将数据集分割为训练数据集和测试数据集
(trainingData,testData) = data.randomSplit([0.7,0.3])
print("train data count: " + str(trainingData.count()))
print("test data count : " + str(testData.count())) # 训练决策树分类器
# categoricalFeaturesInfo 为空，表示所有的特征均为连续值
model = DecisionTree.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},
impurity='gini', maxDepth=5, maxBins=32) # 测试数据集上预测
predictions = model.predict(testData.map(lambda x: x.features)) # 打包真实值与预测值
labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) # 统计预测错误的样本的频率
testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count())
print('Decision Tree Test Error = %5.3f%%'%(testErr*100))
print("Decision Tree Learned classifiction tree model : ")
print(model.toDebugString()) # 保存和加载训练好的模型
modelPath = "/home/zhb/Desktop/work/DecisionTreeShareProject/app/myDecisionTreeClassificationModel"
model.save(sc, modelPath)
sameModel = DecisionTreeModel.load(sc, modelPath)决策树分类器API为DecisionTree.trainClassifier，进入源码分析。
源码文件所在路径为，spark-1.6/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala。
@Since("1.1.0")
def trainClassifier(
input: RDD[LabeledPoint],
numClasses: Int,
categoricalFeaturesInfo: Map[Int, Int],
impurity: String,
maxDepth: Int,
maxBins: Int): DecisionTreeModel = {
val impurityType = Impurities.fromString(impurity)
train(input, Classification, impurityType, maxDepth, numClasses, maxBins, Sort,
categoricalFeaturesInfo)
}训练出一个分类器，然后调用了train方法。
@Since("1.0.0")
def train(
input: RDD[LabeledPoint],
algo: Algo,
impurity: Impurity,
maxDepth: Int,
numClasses: Int,
maxBins: Int,
quantileCalculationStrategy: QuantileStrategy,
categoricalFeaturesInfo: Map[Int, Int]): DecisionTreeModel = {
val strategy = new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
quantileCalculationStrategy, categoricalFeaturesInfo)
new DecisionTree(strategy).run(input)
}train方法首先将模型类型（分类或者回归）、信息增益指标、决策树深度、分类数目、最大切分箱子数等参数封装为Strategy，然后新建一个DecisionTree对象，并调用run方法。
@Since("1.0.0")class DecisionTree private[spark] (private val strategy: Strategy, private val seed: Int)
extends Serializable with Logging { /**
* @param strategy The configuration parameters for the tree algorithm which specify the type
* of decision tree (classification or regression), feature type (continuous,
* categorical), depth of the tree, quantile calculation strategy, etc.
*/
@Since("1.0.0") def this(strategy: Strategy) = this(strategy, seed = 0)
strategy.assertValid() /**
* Method to train a decision tree model over an RDD
*
* @param input Training data: RDD of `org`.`apache`.`spark`.`mllib`.`regression`.`LabeledPoint`.
* @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.2.0") def run(input: RDD[LabeledPoint]): DecisionTreeModel = { val rf = new RandomForest(strategy, numTrees = 1, featureSubsetStrategy = "all", seed = seed) val rfModel = rf.run(input)
rfModel.trees(0)
}
}run方法中首先新建一个RandomForest对象，将strategy、决策树数目设置为1，子集选择策略为"all"传递给RandomForest对象，然后调用RandomForest中的run方法，最后返回随机森林模型中的第一棵决策树。
也就是，决策树模型使用了随机森林模型进行训练，将决策树数目设置为1，然后将随机森林模型中的第一棵决策树作为结果，返回作为决策树训练模型。
随机森林的源码文件所在路径为，spark-1.6/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala。
private class RandomForest ( private val strategy: Strategy, private val numTrees: Int,
featureSubsetStrategy: String, private val seed: Int)
extends Serializable with Logging {
strategy.assertValid() require(numTrees > 0, s"RandomForest requires numTrees > 0, but was given numTrees = $numTrees.") require(RandomForest.supportedFeatureSubsetStrategies.contains(featureSubsetStrategy)
|| Try(featureSubsetStrategy.toInt).filter(_ > 0).isSuccess
|| Try(featureSubsetStrategy.toDouble).filter(_ > 0).filter(_ <= 1.0).isSuccess,
s"RandomForest given invalid featureSubsetStrategy: $featureSubsetStrategy." +
s" Supported values: ${NewRFParams.supportedFeatureSubsetStrategies.mkString(", ")}," +
s" (0.0-1.0], [1-n].") /**
* Method to train a decision tree model over an RDD
*
* @param input Training data: RDD of `org`.`apache`.`spark`.`mllib`.`regression`.`LabeledPoint`.
* @return RandomForestModel that can be used for prediction.
*/
def run(input: RDD[LabeledPoint]): RandomForestModel = {
val trees: Array[NewDTModel] = NewRandomForest.run(input.map(_.asML), strategy, numTrees,
featureSubsetStrategy, seed.toLong, None) new RandomForestModel(strategy.algo, trees.map(_.toOld))
}
}在该文件开头，通过"import org.apache.spark.ml.tree.impl.{RandomForest => NewRandomForest}"将ml中的RandomForest引入，重新命名为NewRandomForest。
在RandomForest.run方法中，首先新建NewRandomForest模型，并调用该类的run方法，然后将生成的trees作为新建RandomForestModel的入参。
NewRandomForest，源码文件所在路径为，spark-1.6/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala。
由于涉及代码量较大，因此无法将代码展开，run方法主要有如下调用。
run方法
--->1. val metadata = DecisionTreeMetadata.buildMetadata(retaggedInput, strategy, numTrees,featureSubsetStrategy) # 对输入数据建立元数据--->2. val splits = findSplits(retaggedInput, metadata, seed) # 对元数据中的特征进行切分
--->2.1 计算采样率，对输入样本进行采样
--->2.2 findSplitsBySorting(sampledInput, metadata, continuousFeatures) # 对采样后的样本中的特征进行切分
--->2.2.1 val thresholds = findSplitsForContinuousFeature(samples, metadata, idx) # 针对连续型特征
--->2.2.2 val categories = extractMultiClassCategories(splitIndex + 1, featureArity) # 针对分类型特征，且特征无序
--->2.2.3 Array.empty[Split] # 针对分类型特征，且特征有序，训练时直接构造即可--->3. val treeInput = TreePoint.convertToTreeRDD(retaggedInput, splits, metadata) # 将输入数据转换为树形数据
--->3.1 input.map { x => TreePoint.labeledPointToTreePoint(x, thresholds, featureArity) # 将LabeledPoint数据转换为TreePoint数据
--->3.2 arr(featureIndex) = findBin(featureIndex, labeledPoint, featureArity(featureIndex), thresholds(featureIndex)) # 在(labeledPoint,feature)中找出一个离散值--->4. val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, strategy.subsamplingRate, numTrees,withReplacement, seed) # 对输入数据进行采样
--->4.1 convertToBaggedRDDSamplingWithReplacement(input, subsamplingRate, numSubsamples, seed) #有放回采样
--->4.2 convertToBaggedRDDWithoutSampling(input) # 样本数为1，采样率为100%
--->4.3 convertToBaggedRDDSamplingWithoutReplacement(input, subsamplingRate, numSubsamples, seed) # 无放回采样--->5. val (nodesForGroup, treeToNodeToIndexInfo) = RandomForest.selectNodesToSplit(nodeQueue, maxMemoryUsage,metadata, rng) # 取得每棵树所有需要切分的结点
--->5.1 val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) { Some(SamplingUtils.reservoirSampleAndCount(Range(0, metadata.numFeatures).iterator, metadata.numFeaturesPerNode, rng.nextLong())._1)} # 如果需要子采样，选择特征子集
--->5.2 val nodeMemUsage = RandomForest.aggregateSizeForNode(metadata, featureSubset) * 8L # 计算添加这个结点之后，是否有足够的内存--->6. RandomForest.findBestSplits(baggedInput, metadata, topNodes, nodesForGroup, treeToNodeToIndexInfo, splits, nodeQueue, timer, nodeIdCache) # 找出最优切分点
--->6.1 val (split: Split, stats: ImpurityStats) = binsToBestSplit(aggStats, splits, featuresForNode, nodes(nodeIndex)) #找出每个结点最好的切分--->7. new DecisionTreeClassificationModel(uid, rootNode.toNode, numFeatures, strategy.getNumClasses) # 返回决策树分类模型
