1、概述
决策树及树集(算法)是用于机器学习任务的分类和回归的流行方法。决策树被广泛使用,因为它们易于解释,处理分类特征,扩展到多类分类设置,不需要特征缩放,并且能够捕获非线性和特征交互。树集分类算法(例如随机森林和boosting)在分类和回归任务中表现最佳。
spark.ml实现使用连续和分类特征,支持用于二元分类和多类分类以及用于回归的决策树。该实现按行对数据进行分区,从而允许对数百万甚至数十亿个实例进行分布式训练。
2、输入和输出
所有输出列都是可选的;要排除输出列,请将其对应的Param设置为空字符串。
Input Columns
Param name | Type(s) | Default | Description |
labelCol | Double | "label" | Label to predict |
featuresCol | Vector | "features" | Feature vector |
Output Columns
Param name | Type(s) | Default | Description | Notes |
predictionCol | Double | "prediction" | Predicted label | |
rawPredictionCol | Vector | "rawPrediction" | Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction | Classification only |
probabilityCol | Vector | "probability" | Vector of length # classes equal to rawPrediction normalized to a multinomial distribution | Classification only |
varianceCol | Double | | The biased sample variance of prediction | Regression only |
3、code
package com.home.spark.ml
import org.apache.spark.SparkConf
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, DecisionTreeClassifier}
import org.apache.spark.ml.evaluation.{MulticlassClassificationEvaluator, RegressionEvaluator}
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.regression.DecisionTreeRegressor
import org.apache.spark.sql.{Dataset, Row, SparkSession}
object Ex_DecisionTree {
def main(args: Array[String]): Unit = {
val conf: SparkConf = new SparkConf(true).setMaster("local[2]").setAppName("spark ml")
val spark = SparkSession.builder().config(conf).getOrCreate()
//rdd转换成df或者ds需要SparkSession实例的隐式转换
//导入隐式转换,注意这里的spark不是包名,而是SparkSession的对象名
import spark.implicits._
val data = spark.sparkContext.textFile("input/iris.data.txt")
.map(_.split(","))
.map(a => Iris(
Vectors.dense(a(0).toDouble, a(1).toDouble, a(2).toDouble, a(3).toDouble),
a(4))
).toDF()
data.createOrReplaceTempView("iris")
val df = spark.sql("select * from iris")
df.map(r => r(1) + " : " + r(0)).collect().take(10).foreach(println)
////对特征列和标签列进行索引转换
val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(df)
val featureIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures")
.setMaxCategories(4).fit(df)
//决策树分类器
val dtClassifier = new DecisionTreeClassifier().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures")
//将预测的类别重新转成字符型
val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictionLabel").setLabels(labelIndexer.labels)
//将原数据集拆分成两个部分,一部分用于训练,一部分用于测试
val Array(trainingData, testData): Array[Dataset[Row]] = df.randomSplit(Array(0.7,0.3))
//建立工作流
val pipeline = new Pipeline().setStages(Array(labelIndexer,featureIndexer,dtClassifier,labelConverter))
//生成训练模型
val modelDecisionTreeClassifier = pipeline.fit(trainingData)
//预测
val result = modelDecisionTreeClassifier.transform(testData)
result.show(150,false)
/**
* 样本分为:正类样本和负类样本。
* TP:被分类器正确分类的正类样本数。
* TN: 被分类器正确分类的负类样本数。
* FP: 被分类器错误分类的正类样本数。(本来是负,被预测为正) ---------->正
* FN: 被分类器错误分类的负类样本数。 (本来是正, 被预测为负) ---------->负
*
* 准确率(Accuracy ACC)
* 总样本数=TP+TN+FP+FN
* ACC=(TP+TN)/(总样本数)
* 该评价指标主要针对分类均匀的数据集。
*/
val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy: Double = evaluator.evaluate(result)
println("Accuracy = " + accuracy)
/**
* 精确率(Precision 查准率)
* Precision = TP / (TP+ FP) 准确率,表示模型预测为正样本的样本中真正为正的比例
*/
val evaluator2 = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")
.setMetricName("weightedPrecision")
val weightedPrecision: Double = evaluator2.evaluate(result)
println("weightedPrecision = " + weightedPrecision)
/**
* 召回率(查全率)
* Recall = TP /(TP + FN) 召回率,表示模型准确预测为正样本的数量占所有正样本数量的比例
*/
val evaluator3 = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")
.setMetricName("weightedRecall")
val weightedRecall: Double = evaluator3.evaluate(result)
println("weightedRecall = " + weightedRecall)
val treeModel = modelDecisionTreeClassifier.stages(2).asInstanceOf[DecisionTreeClassificationModel]
println("Learned classification tree model:\n" + treeModel.toDebugString)
//决策树回归器
val dtRegressor = new DecisionTreeRegressor().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures")
val pipelineRegressor = new Pipeline()
.setStages(Array(labelIndexer,featureIndexer,dtRegressor,labelConverter))
val modelRegressor = pipelineRegressor.fit(trainingData)
val result2 = modelRegressor.transform(testData)
result2.show(150,false)
//评估
val regressionEvaluator = new RegressionEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")
.setMetricName("rmse")
val rmse = regressionEvaluator.evaluate(result2)
println("rmse = " + rmse)
spark.stop()
}
}
case class Iris(features: Vector, label: String)