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.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 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 Pipeline
from pyspark.ml.classification import GBTClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer
from 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 Pipeline
pipeline = 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 error
evaluator = 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