梯度迭代树（GBDT）算法原理及Spark MLlib调用实例（Scala/Java/python）

梯度迭代树

算法简介：

        梯度提升树是一种决策树的集成算法。它通过反复迭代训练决策树来最小化损失函数。决策树类似，梯度提升树具有可处理类别特征、易扩展到多分类问题、不需特征缩放等性质。Spark.ml通过使用现有decision tree工具来实现。

       梯度提升树依次迭代训练一系列的决策树。在一次迭代中，算法使用现有的集成来对每个训练实例的类别进行预测，然后将预测结果与真实的标签值进行比较。通过重新标记，来赋予预测结果不好的实例更高的权重。所以，在下次迭代中，决策树会对先前的错误进行修正。

       对实例标签进行重新标记的机制由损失函数来指定。每次迭代过程中，梯度迭代树在训练数据上进一步减少损失函数的值。spark.ml为分类问题提供一种损失函数（Log Loss），为回归问题提供两种损失函数（平方误差与绝对误差）。

       Spark.ml支持二分类以及回归的随机森林算法，适用于连续特征以及类别特征。

＊注意梯度提升树目前不支持多分类问题。

参数：

checkpointInterval:

类型：整数型。

含义：设置检查点间隔（>=1），或不设置检查点（-1）。

featuresCol:

类型：字符串型。

含义：特征列名。

impurity:

类型：字符串型。

含义：计算信息增益的准则（不区分大小写）。

labelCol:

类型：字符串型。

含义：标签列名。

lossType:

类型：字符串型。

含义：损失函数类型。

maxBins:

类型：整数型。

含义：连续特征离散化的最大数量，以及选择每个节点分裂特征的方式。

maxDepth:

类型：整数型。

含义：树的最大深度（>=0）。

maxIter:

类型：整数型。

含义：迭代次数（>=0）。

minInfoGain:

类型：双精度型。

含义：分裂节点时所需最小信息增益。

minInstancesPerNode:

类型：整数型。

含义：分裂后自节点最少包含的实例数量。

predictionCol:

类型：字符串型。

含义：预测结果列名。

rawPredictionCol:

类型：字符串型。

含义：原始预测。

seed:

类型：长整型。

含义：随机种子。

subsamplingRate:

类型：双精度型。

含义：学习一棵决策树使用的训练数据比例，范围[0,1]。

stepSize:

类型：双精度型。

含义：每次迭代优化步长。

示例：

       下面的例子导入LibSVM格式数据，并将之划分为训练数据和测试数据。使用第一部分数据进行训练，剩下数据来测试。训练之前我们使用了两种数据预处理方法来对特征进行转换，并且添加了元数据到DataFrame。

Scala:

import org.apache.spark.ml.Pipelineimport org.apache.spark.ml.classification.{GBTClassificationModel, GBTClassifier}import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluatorimport 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 example rows to display.predictions.select("predictedLabel", "label", "features").show(5)// 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)

Java:

import org.apache.spark.ml.Pipeline;import org.apache.spark.ml.PipelineModel;import org.apache.spark.ml.PipelineStage;import org.apache.spark.ml.classification.GBTClassificationModel;import org.apache.spark.ml.classification.GBTClassifier;import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;import org.apache.spark.ml.feature.*;import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Row;import org.apache.spark.sql.SparkSession;// Load and parse the data file, converting it to a DataFrame.Dataset<Row> 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.StringIndexerModel 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.VectorIndexerModel featureIndexer = new VectorIndexer()  .setInputCol("features")  .setOutputCol("indexedFeatures")  .setMaxCategories(4)  .fit(data);// Split the data into training and test sets (30% held out for testing)Dataset<Row>[] splits = data.randomSplit(new double[] {0.7, 0.3});Dataset<Row> trainingData = splits[0];Dataset<Row> testData = splits[1];// Train a GBT model.GBTClassifier gbt = new GBTClassifier()  .setLabelCol("indexedLabel")  .setFeaturesCol("indexedFeatures")  .setMaxIter(10);// Convert indexed labels back to original labels.IndexToString labelConverter = new IndexToString()  .setInputCol("prediction")  .setOutputCol("predictedLabel")  .setLabels(labelIndexer.labels());// Chain indexers and GBT in a Pipeline.Pipeline pipeline = new Pipeline()  .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter});// Train model. This also runs the indexers.PipelineModel model = pipeline.fit(trainingData);// Make predictions.Dataset<Row> predictions = model.transform(testData);// Select example rows to display.predictions.select("predictedLabel", "label", "features").show(5);// Select (prediction, true label) and compute test error.MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()  .setLabelCol("indexedLabel")  .setPredictionCol("prediction")  .setMetricName("accuracy");double accuracy = evaluator.evaluate(predictions);System.out.println("Test Error = " + (1.0 - accuracy));GBTClassificationModel gbtModel = (GBTClassificationModel)(model.stages()[2]);System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString());

Python：

from pyspark.ml import Pipelinefrom pyspark.ml.classification import GBTClassifierfrom pyspark.ml.feature import StringIndexer, VectorIndexerfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator# Load and parse the data file, converting it to a DataFrame.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.labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)# Automatically identify categorical features, and index them.# Set maxCategories so features with > 4 distinct values are treated as continuous.featureIndexer =\    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)# Split the data into training and test sets (30% held out for testing)(trainingData, testData) = data.randomSplit([0.7, 0.3])# Train a GBT model.gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10)# Chain indexers and GBT in a Pipelinepipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt])# Train model.  This also runs the indexers.model = pipeline.fit(trainingData)# Make predictions.predictions = model.transform(testData)# Select example rows to display.predictions.select("prediction", "indexedLabel", "features").show(5)# Select (prediction, true label) and compute test errorevaluator = MulticlassClassificationEvaluator(    labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")accuracy = evaluator.evaluate(predictions)print("Test Error = %g" % (1.0 - accuracy))gbtModel = model.stages[2]print(gbtModel)  # summary only