Spark 多项式逻辑回归__多分类
package Spark_MLlib import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{BinaryLogisticRegressionSummary, LogisticRegression, LogisticRegressionModel}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.SparkSession object 多项式逻辑回归__多分类 {
val spark=SparkSession.builder().master("local").getOrCreate()
import spark.implicits._ //支持把一个RDD隐式转换为一个DataFrame
def main(args: Array[String]): Unit = {
val df =spark.sparkContext.textFile("file:///home/soyo/桌面/spark编程测试数据/soyo.txt")
.map(_.split(",")).map(x=>data_schema(Vectors.dense(x().toDouble,x().toDouble,x().toDouble,x().toDouble),x())).toDF()
// df.show(150)
val labelIndexer=new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(df)
val featureIndexer=new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").fit(df) //目的在特征向量中建类别索引
val Array(trainData,testData)=df.randomSplit(Array(0.7,0.3))
val lr=new LogisticRegression().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures").setMaxIter().setRegParam(0.3).setElasticNetParam(0.8).setFamily("multinomial")//设置elasticnet混合参数为0.8,setFamily("multinomial"):设置为多项逻辑回归,不设置setFamily为二项逻辑回归
val labelConverter=new IndexToString().setInputCol("prediction").setOutputCol("predictionLabel").setLabels(labelIndexer.labels) val lrPipeline=new Pipeline().setStages(Array(labelIndexer,featureIndexer,lr,labelConverter))
val lrPipeline_Model=lrPipeline.fit(trainData)
val lrPrediction=lrPipeline_Model.transform(testData)
lrPrediction.show()
// lrPrediction.take(100).foreach(println)
//模型评估
val evaluator=new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")
val lrAccuracy=evaluator.evaluate(lrPrediction)
println("准确率为： "+lrAccuracy)
val lrError=-lrAccuracy
println("错误率为: "+lrError)
val LRmodel=lrPipeline_Model.stages().asInstanceOf[LogisticRegressionModel]
println("二项逻辑回归模型系数矩阵: "+LRmodel.coefficientMatrix)
println("二项逻辑回归模型的截距向量: "+LRmodel.interceptVector)
println("类的数量(标签可以使用的值): "+LRmodel.numClasses)
println("模型所接受的特征的数量: "+LRmodel.numFeatures)
//多项式逻辑回归不包含对模型的摘要总结
println(LRmodel.hasSummary) } }
结果：
+-----------------+-----+------------+-----------------+--------------------+--------------------+----------+---------------+
| features|label|indexedLabel| indexedFeatures| rawPrediction| probability|prediction|predictionLabel|
+-----------------+-----+------------+-----------------+--------------------+--------------------+----------+---------------+
|[4.4,3.2,1.3,0.2]|soyo1| 1.0|[4.4,3.2,1.3,0.2]|[0.06313829278191...|[0.23858281707128...| 1.0| soyo1|
|[4.6,3.4,1.4,0.3]|soyo1| 1.0|[4.6,3.4,1.4,0.3]|[0.06313829278191...|[0.23750012598226...| 1.0| soyo1|
|[4.7,3.2,1.6,0.2]|soyo1| 1.0|[4.7,3.2,1.6,0.2]|[0.06313829278191...|[0.24710416166321...| 1.0| soyo1|
|[4.8,3.4,1.6,0.2]|soyo1| 1.0|[4.8,3.4,1.6,0.2]|[0.06313829278191...|[0.23716995683018...| 1.0| soyo1|
|[4.8,3.4,1.9,0.2]|soyo1| 1.0|[4.8,3.4,1.9,0.2]|[0.06313829278191...|[0.24567798276462...| 1.0| soyo1|
|[4.9,2.4,3.3,1.0]|soyo2| 0.0|[4.9,2.4,3.3,1.0]|[0.06313829278191...|[0.38071131817453...| 0.0| soyo2|
|[5.0,3.2,1.2,0.2]|soyo1| 1.0|[5.0,3.2,1.2,0.2]|[0.06313829278191...|[0.23576075216827...| 1.0| soyo1|
|[5.0,3.5,1.3,0.3]|soyo1| 1.0|[5.0,3.5,1.3,0.3]|[0.06313829278191...|[0.22978111243935...| 1.0| soyo1|
|[5.2,4.1,1.5,0.1]|soyo1| 1.0|[5.2,4.1,1.5,0.1]|[0.06313829278191...|[0.19523110424215...| 1.0| soyo1|
|[5.4,3.9,1.3,0.4]|soyo1| 1.0|[5.4,3.9,1.3,0.4]|[0.06313829278191...|[0.21630436073381...| 1.0| soyo1|
|[5.5,2.4,3.8,1.1]|soyo2| 0.0|[5.5,2.4,3.8,1.1]|[0.06313829278191...|[0.39807479409636...| 0.0| soyo2|
|[5.5,2.5,4.0,1.3]|soyo2| 0.0|[5.5,2.5,4.0,1.3]|[0.06313829278191...|[0.40810357240132...| 0.0| soyo2|
|[5.6,2.8,4.9,2.0]|soyo3| 2.0|[5.6,2.8,4.9,2.0]|[0.06313829278191...|[0.44454733071968...| 0.0| soyo2|
|[5.7,2.9,4.2,1.3]|soyo2| 0.0|[5.7,2.9,4.2,1.3]|[0.06313829278191...|[0.39634982244233...| 0.0| soyo2|
|[5.8,2.6,4.0,1.2]|soyo2| 0.0|[5.8,2.6,4.0,1.2]|[0.06313829278191...|[0.39930520027794...| 0.0| soyo2|
|[5.8,2.7,4.1,1.0]|soyo2| 0.0|[5.8,2.7,4.1,1.0]|[0.06313829278191...|[0.38762610877473...| 0.0| soyo2|
|[5.8,2.7,5.1,1.9]|soyo3| 2.0|[5.8,2.7,5.1,1.9]|[0.06313829278191...|[0.44792417666537...| 0.0| soyo2|
|[5.9,3.0,5.1,1.8]|soyo3| 2.0|[5.9,3.0,5.1,1.8]|[0.06313829278191...|[0.43418725338764...| 0.0| soyo2|
|[6.0,2.2,4.0,1.0]|soyo2| 0.0|[6.0,2.2,4.0,1.0]|[0.06313829278191...|[0.40634099537710...| 0.0| soyo2|
|[6.0,2.7,5.1,1.6]|soyo2| 0.0|[6.0,2.7,5.1,1.6]|[0.06313829278191...|[0.43688076686419...| 0.0| soyo2|
|[6.0,3.4,4.5,1.6]|soyo2| 0.0|[6.0,3.4,4.5,1.6]|[0.06313829278191...|[0.39704954911011...| 0.0| soyo2|
|[6.2,2.2,4.5,1.5]|soyo2| 0.0|[6.2,2.2,4.5,1.5]|[0.06313829278191...|[0.43847273913421...| 0.0| soyo2|
|[6.2,2.8,4.8,1.8]|soyo3| 2.0|[6.2,2.8,4.8,1.8]|[0.06313829278191...|[0.43518321759857...| 0.0| soyo2|
|[6.3,2.7,4.9,1.8]|soyo3| 2.0|[6.3,2.7,4.9,1.8]|[0.06313829278191...|[0.44055947195014...| 0.0| soyo2|
|[6.3,2.9,5.6,1.8]|soyo3| 2.0|[6.3,2.9,5.6,1.8]|[0.06313829278191...|[0.44715759200377...| 0.0| soyo2|
|[6.3,3.4,5.6,2.4]|soyo3| 2.0|[6.3,3.4,5.6,2.4]|[0.06313829278191...|[0.45196576310313...| 0.0| soyo2|
|[6.4,2.8,5.6,2.1]|soyo3| 2.0|[6.4,2.8,5.6,2.1]|[0.06313829278191...|[0.46017875340546...| 0.0| soyo2|
|[6.4,2.8,5.6,2.2]|soyo3| 2.0|[6.4,2.8,5.6,2.2]|[0.06313829278191...|[0.46321910727428...| 0.0| soyo2|
|[6.4,3.1,5.5,1.8]|soyo3| 2.0|[6.4,3.1,5.5,1.8]|[0.06313829278191...|[0.43862320280893...| 0.0| soyo2|
|[6.4,3.2,4.5,1.5]|soyo2| 0.0|[6.4,3.2,4.5,1.5]|[0.06313829278191...|[0.40056786531830...| 0.0| soyo2|
|[6.5,3.0,5.5,1.8]|soyo3| 2.0|[6.5,3.0,5.5,1.8]|[0.06313829278191...|[0.44199581778961...| 0.0| soyo2|
|[6.6,2.9,4.6,1.3]|soyo2| 0.0|[6.6,2.9,4.6,1.3]|[0.06313829278191...|[0.40579282648595...| 0.0| soyo2|
|[6.7,2.5,5.8,1.8]|soyo3| 2.0|[6.7,2.5,5.8,1.8]|[0.06313829278191...|[0.46287803722998...| 0.0| soyo2|
|[6.7,3.0,5.2,2.3]|soyo3| 2.0|[6.7,3.0,5.2,2.3]|[0.06313829278191...|[0.45387841693477...| 0.0| soyo2|
|[6.7,3.1,4.7,1.5]|soyo2| 0.0|[6.7,3.1,4.7,1.5]|[0.06313829278191...|[0.40924150360290...| 0.0| soyo2|
|[6.7,3.3,5.7,2.5]|soyo3| 2.0|[6.7,3.3,5.7,2.5]|[0.06313829278191...|[0.45972648058424...| 0.0| soyo2|
|[6.8,3.0,5.5,2.1]|soyo3| 2.0|[6.8,3.0,5.5,2.1]|[0.06313829278191...|[0.45251276088924...| 0.0| soyo2|
|[6.8,3.2,5.9,2.3]|soyo3| 2.0|[6.8,3.2,5.9,2.3]|[0.06313829278191...|[0.45975331380088...| 0.0| soyo2|
|[6.9,3.2,5.7,2.3]|soyo3| 2.0|[6.9,3.2,5.7,2.3]|[0.06313829278191...|[0.45642868507279...| 0.0| soyo2|
|[7.2,3.0,5.8,1.6]|soyo3| 2.0|[7.2,3.0,5.8,1.6]|[0.06313829278191...|[0.44031726493318...| 0.0| soyo2|
|[7.2,3.2,6.0,1.8]|soyo3| 2.0|[7.2,3.2,6.0,1.8]|[0.06313829278191...|[0.44483171938259...| 0.0| soyo2|
|[7.6,3.0,6.6,2.1]|soyo3| 2.0|[7.6,3.0,6.6,2.1]|[0.06313829278191...|[0.47047723863543...| 0.0| soyo2|
|[7.7,3.0,6.1,2.3]|soyo3| 2.0|[7.7,3.0,6.1,2.3]|[0.06313829278191...|[0.46845272424381...| 0.0| soyo2|
|[7.7,3.8,6.7,2.2]|soyo3| 2.0|[7.7,3.8,6.7,2.2]|[0.06313829278191...|[0.45233124776236...| 0.0| soyo2|
+-----------------+-----+------------+-----------------+--------------------+--------------------+----------+---------------+
准确率为： 0.36458333333333337
错误率为: 0.6354166666666666
二项逻辑回归模型系数矩阵: 3 x 4 CSCMatrix
(1,1) 0.35559564188466614
(1,2) -0.203185158868005
(1,3) -0.43876460704959996
(2,3) 0.0283914830858408
二项逻辑回归模型的截距向量: [0.06313829278191783,0.1708622138778958,-0.23400050665981365]
类的数量(标签可以使用的值): 3
模型所接受的特征的数量: 4
false
Spark 多项式逻辑回归__多分类的更多相关文章
- Spark 多项式逻辑回归__二分类
package Spark_MLlib import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.{L ...
- Spark 二项逻辑回归__二分类
package Spark_MLlib import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.{B ...
- stanford coursera 机器学习编程作业 exercise 3（逻辑回归实现多分类问题）
本作业使用逻辑回归(logistic regression)和神经网络(neural networks)识别手写的阿拉伯数字(0-9) 关于逻辑回归的一个编程练习,可参考:http://www.cnb ...
- Spark Mllib逻辑回归算法分析
原创文章,转载请注明: 转载自http://www.cnblogs.com/tovin/p/3816289.html 本文以spark 1.0.0版本MLlib算法为准进行分析 一.代码结构 逻辑回归 ...
- Spark LogisticRegression 逻辑回归之建模
导入包 import org.apache.spark.sql.SparkSession import org.apache.spark.sql.Dataset import org.apache.s ...
- [Python]数据挖掘（1）、梯度下降求解逻辑回归——考核成绩分类
ps:本博客内容根据唐宇迪的的机器学习经典算法 学习视频复制总结而来 http://www.abcplus.com.cn/course/83/tasks 逻辑回归 问题描述:我们将建立一个逻辑回归模 ...
- Spark ML逻辑回归
import org.apache.log4j.{Level, Logger} import org.apache.spark.ml.classification.LogisticRegression ...
- Spark 机器学习------逻辑回归
package Spark_MLlib import javassist.bytecode.SignatureAttribute.ArrayType import org.apache.spark.s ...
- scikit-learn机器学习(二)逻辑回归进行二分类(垃圾邮件分类),二分类性能指标，画ROC曲线，计算acc,recall,presicion,f1
数据来自UCI机器学习仓库中的垃圾信息数据集 数据可从http://archive.ics.uci.edu/ml/datasets/sms+spam+collection下载 转成csv载入数据 im ...
随机推荐
- 杭电 2124 Repair the Wall（贪心）
Description Long time ago , Kitty lived in a small village. The air was fresh and the scenery was ve ...
- [bzoj2822][AHOI2012]树屋阶梯 (卡特兰数+分解质因数+高精度)
Description 暑假期间,小龙报名了一个模拟野外生存作战训练班来锻炼体魄,训练的第一个晚上,教官就给他们出了个难题.由于地上露营湿气重,必须选择在高处的树屋露营.小龙分配的树屋建立在一颗高度为 ...
- CodeForcesGym 100517B Bubble Sort
Bubble Sort Time Limit: 2000ms Memory Limit: 262144KB This problem will be judged on CodeForcesGym. ...
- [luoguP1022] 计算器的改良（模拟）
传送门 超级大模拟.. 代码 #include <cstdio> #include <cstring> #include <iostream> #define is ...
- httpClient使用总结
前记 最近有个需求,需要根据商品id获取商品详情: 首先想到的是在浏览器里输入url按回车就可以了:或者在linux中使用curl+url来发起一个http请求; 但如果是要在java程序中发出htt ...
- 【ZJOI2017 Round1练习】D8T2 sequence（DP）
题意: 思路: #include <algorithm> #include <iostream> #include <cstring> #include <c ...
- [运维]ELK实现日志监控告警
https://blog.csdn.net/yeweiouyang/article/details/54948846
- 洛谷 P1566 加等式
P1566 加等式 题目描述 对于一个整数集合,我们定义“加等式”如下:集合中的某一个元素可以表示成集合内其他元素之和.如集合{1,2,3}中就有一个加等式:3＝1+2,而且3＝1+2 和3＝2+1是 ...
- Ubuntu 16.04安装JMeter测试工具
JMeter是Java的测试工具,由Apache开发. 同样,JMeter是跨平台的. 下载: http://jmeter.apache.org/download_jmeter.cgi 安装: 7z ...
- 我的arcgis培训照片4 来自http://www.cioiot.com/successview-549-1.html