Spark 决策树--分类模型
package Spark_MLlib import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, DecisionTreeClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.sql.SparkSession /**
* Created by soyo on 17-11-5.
*/
case class data_schemas(features:Vector,label:String)
object 决策树 {
val spark=SparkSession.builder().master("local").appName("决策树").getOrCreate()
import spark.implicits._
def main(args: Array[String]): Unit = { val source_DF=spark.sparkContext.textFile("file:///home/soyo/桌面/spark编程测试数据/soyo2.txt")
.map(_.split(",")).map(x=>data_schemas(Vectors.dense(x().toDouble,x().toDouble,x().toDouble,x().toDouble),x())).toDF()
source_DF.createOrReplaceTempView("decisonTree")
val DF=spark.sql("select * from decisonTree")
DF.show()
//分别获取标签列和特征列,进行索引和重命名(索引的目的是将字符串label数值化方便机器学习算法学习)
val lableIndexer=new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(DF)
val featureIndexer= new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories().fit(DF)
val labelConverter= new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(lableIndexer.labels)
// 训练数据和测试数据
val Array(trainData,testData)=DF.randomSplit(Array(0.7,0.3))
val decisionTreeClassifier=new DecisionTreeClassifier().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures")
//构建机器学习工作流
val dt_pipeline=new Pipeline().setStages(Array(lableIndexer,featureIndexer,decisionTreeClassifier,labelConverter))
val dt_model=dt_pipeline.fit(trainData)
//进行预测
val dtprediction=dt_model.transform(testData)
dtprediction.show()
//评估决策树模型
val evaluatorClassifier=new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("accuracy")
val accuracy=evaluatorClassifier.evaluate(dtprediction)
println("准确率为: "+accuracy)
val error=-accuracy
println("错误率为: "+error)
val treeModelClassifier=dt_model.stages().asInstanceOf[DecisionTreeClassificationModel]
val schema_DecisionTree=treeModelClassifier.toDebugString
println("决策树的模型结构为: "+schema_DecisionTree) }
}
结果为：
+-----------------+------+
| features| label|
+-----------------+------+
|[5.1,3.5,1.4,0.2]|hadoop|
|[4.9,3.0,1.4,0.2]|hadoop|
|[4.7,3.2,1.3,0.2]|hadoop|
|[4.6,3.1,1.5,0.2]|hadoop|
|[5.0,3.6,1.4,0.2]|hadoop|
|[5.4,3.9,1.7,0.4]|hadoop|
|[4.6,3.4,1.4,0.3]|hadoop|
|[5.0,3.4,1.5,0.2]|hadoop|
|[4.4,2.9,1.4,0.2]|hadoop|
|[4.9,3.1,1.5,0.1]|hadoop|
|[5.4,3.7,1.5,0.2]|hadoop|
|[4.8,3.4,1.6,0.2]|hadoop|
|[4.8,3.0,1.4,0.1]|hadoop|
|[4.3,3.0,1.1,0.1]|hadoop|
|[5.8,4.0,1.2,0.2]|hadoop|
|[5.7,4.4,1.5,0.4]|hadoop|
|[5.4,3.9,1.3,0.4]|hadoop|
|[5.1,3.5,1.4,0.3]|hadoop|
|[5.7,3.8,1.7,0.3]|hadoop|
|[5.1,3.8,1.5,0.3]|hadoop|
+-----------------+------+
only showing top 20 rows
+-----------------+------+------------+-----------------+--------------+-------------+----------+--------------+
| features| label|indexedLabel| indexedFeatures| rawPrediction| probability|prediction|predictedLabel|
+-----------------+------+------------+-----------------+--------------+-------------+----------+--------------+
|[4.4,3.0,1.3,0.2]|hadoop| 1.0|[4.4,3.0,1.3,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[4.6,3.4,1.4,0.3]|hadoop| 1.0|[4.6,3.4,1.4,0.3]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[4.6,3.6,1.0,0.2]|hadoop| 1.0|[4.6,3.6,1.0,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[4.9,2.4,3.3,1.0]| spark| 0.0|[4.9,2.4,3.3,1.0]| [0.0,0.0,1.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[5.0,2.0,3.5,1.0]| spark| 0.0|[5.0,2.0,3.5,1.0]| [1.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.0,2.3,3.3,1.0]| spark| 0.0|[5.0,2.3,3.3,1.0]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.0,3.2,1.2,0.2]|hadoop| 1.0|[5.0,3.2,1.2,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.0,3.3,1.4,0.2]|hadoop| 1.0|[5.0,3.3,1.4,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.0,3.4,1.6,0.4]|hadoop| 1.0|[5.0,3.4,1.6,0.4]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.0,3.6,1.4,0.2]|hadoop| 1.0|[5.0,3.6,1.4,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.1,3.5,1.4,0.2]|hadoop| 1.0|[5.1,3.5,1.4,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.1,3.7,1.5,0.4]|hadoop| 1.0|[5.1,3.7,1.5,0.4]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.2,3.4,1.4,0.2]|hadoop| 1.0|[5.2,3.4,1.4,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.2,4.1,1.5,0.1]|hadoop| 1.0|[5.2,4.1,1.5,0.1]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.4,3.0,4.5,1.5]| spark| 0.0|[5.4,3.0,4.5,1.5]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.4,3.9,1.7,0.4]|hadoop| 1.0|[5.4,3.9,1.7,0.4]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.5,2.4,3.7,1.0]| spark| 0.0|[5.5,2.4,3.7,1.0]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.5,2.4,3.8,1.1]| spark| 0.0|[5.5,2.4,3.8,1.1]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.5,2.5,4.0,1.3]| spark| 0.0|[5.5,2.5,4.0,1.3]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.5,2.6,4.4,1.2]| spark| 0.0|[5.5,2.6,4.4,1.2]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.5,4.2,1.4,0.2]|hadoop| 1.0|[5.5,4.2,1.4,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[5.6,2.5,3.9,1.1]| spark| 0.0|[5.6,2.5,3.9,1.1]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.6,2.7,4.2,1.3]| spark| 0.0|[5.6,2.7,4.2,1.3]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.6,3.0,4.1,1.3]| spark| 0.0|[5.6,3.0,4.1,1.3]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.7,2.6,3.5,1.0]| spark| 0.0|[5.7,2.6,3.5,1.0]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.8,2.6,4.0,1.2]| spark| 0.0|[5.8,2.6,4.0,1.2]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[5.8,4.0,1.2,0.2]|hadoop| 1.0|[5.8,4.0,1.2,0.2]|[0.0,36.0,0.0]|[0.0,1.0,0.0]| 1.0| hadoop|
|[6.1,2.6,5.6,1.4]| Scala| 2.0|[6.1,2.6,5.6,1.4]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[6.2,2.2,4.5,1.5]| spark| 0.0|[6.2,2.2,4.5,1.5]| [0.0,0.0,1.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[6.2,3.4,5.4,2.3]| Scala| 2.0|[6.2,3.4,5.4,2.3]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[6.3,2.5,5.0,1.9]| Scala| 2.0|[6.3,2.5,5.0,1.9]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[6.3,2.8,5.1,1.5]| Scala| 2.0|[6.3,2.8,5.1,1.5]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[6.4,2.8,5.6,2.1]| Scala| 2.0|[6.4,2.8,5.6,2.1]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[6.4,2.8,5.6,2.2]| Scala| 2.0|[6.4,2.8,5.6,2.2]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[6.4,3.2,4.5,1.5]| spark| 0.0|[6.4,3.2,4.5,1.5]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[6.4,3.2,5.3,2.3]| Scala| 2.0|[6.4,3.2,5.3,2.3]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[6.5,2.8,4.6,1.5]| spark| 0.0|[6.5,2.8,4.6,1.5]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[6.6,2.9,4.6,1.3]| spark| 0.0|[6.6,2.9,4.6,1.3]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[6.6,3.0,4.4,1.4]| spark| 0.0|[6.6,3.0,4.4,1.4]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[6.8,3.2,5.9,2.3]| Scala| 2.0|[6.8,3.2,5.9,2.3]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[6.9,3.1,4.9,1.5]| spark| 0.0|[6.9,3.1,4.9,1.5]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[6.9,3.2,5.7,2.3]| Scala| 2.0|[6.9,3.2,5.7,2.3]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[7.2,3.0,5.8,1.6]| Scala| 2.0|[7.2,3.0,5.8,1.6]|[29.0,0.0,0.0]|[1.0,0.0,0.0]| 0.0| spark|
|[7.2,3.2,6.0,1.8]| Scala| 2.0|[7.2,3.2,6.0,1.8]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[7.6,3.0,6.6,2.1]| Scala| 2.0|[7.6,3.0,6.6,2.1]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[7.7,3.0,6.1,2.3]| Scala| 2.0|[7.7,3.0,6.1,2.3]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[7.7,3.8,6.7,2.2]| Scala| 2.0|[7.7,3.8,6.7,2.2]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
|[7.9,3.8,6.4,2.0]| Scala| 2.0|[7.9,3.8,6.4,2.0]|[0.0,0.0,31.0]|[0.0,0.0,1.0]| 2.0| Scala|
+-----------------+------+------------+-----------------+--------------+-------------+----------+--------------+
准确率为: 0.8958333333333334
错误率为: 0.10416666666666663
决策树的结构为: DecisionTreeClassificationModel (uid=dtc_218264842cd2) of depth 5 with 15 nodes
If (feature 2 <= 1.9)
Predict: 1.0
Else (feature 2 > 1.9)
If (feature 3 <= 1.7)
If (feature 0 <= 4.9)
Predict: 2.0
Else (feature 0 > 4.9)
If (feature 1 <= 2.2)
If (feature 2 <= 4.0)
Predict: 0.0
Else (feature 2 > 4.0)
Predict: 2.0
Else (feature 1 > 2.2)
Predict: 0.0
Else (feature 3 > 1.7)
If (feature 2 <= 4.8)
If (feature 0 <= 5.9)
Predict: 0.0
Else (feature 0 > 5.9)
Predict: 2.0
Else (feature 2 > 4.8)
Predict: 2.0
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