SparkMLlib分类算法之支持向量机
(一),概念
支持向量机(support vector machine)是一种分类算法,通过寻求结构化风险最小来提高学习机泛化能力,实现经验风险和置信范围的最小化,从而达到在统计样本量较少的情况下,亦能获得良好统计规律的目的。通俗来讲,它是一种二类分类模型,其基本模型定义为特征空间上的间隔最大的线性分类器,即支持向量机的学习策略便是间隔最大化,最终可转化为一个凸二次规划问题的求解。参考网址:http://www.cnblogs.com/end/p/3848740.html
(二),SparkMLlib中SVM回归应用
1,数据集:参考这篇SparkMLlib学习分类算法之逻辑回归算法
2,处理数据及获取训练集和测试集
val orig_file=sc.textFile("train_nohead.tsv") //println(orig_file.first()) val data_file=orig_file.map(_.split(" ")).map{ r => val trimmed =r.map(_.replace(""","")) val lable=trimmed(r.length-1).toDouble val feature=trimmed.slice(4,r.length-1).map(d => if(d=="?")0.0 else d.toDouble) LabeledPoint(lable,Vectors.dense(feature)) } /*特征标准化优化*/ val vectors=data_file.map(x =>x.features) val rows=new RowMatrix(vectors) println(rows.computeColumnSummaryStatistics().variance)//每列的方差 val scaler=new StandardScaler(withMean=true,withStd=true).fit(vectors)//标准化 val scaled_data=data_file.map(point => LabeledPoint(point.label,scaler.transform(point.features))) .randomSplit(Array(0.7,0.3),11L) val data_train=scaled_data(0) val data_test=scaled_data(1)
2,建立支持向量机模型及模型评估
/*训练 SVM 模型**/ val model_Svm=SVMWithSGD.train(data_train,numIteration) val correct_svm=data_test.map{ point => if(model_Svm.predict(point.features)==point.label) 1 else 0 }.sum()/data_test.count()//精确度:0.6060885608856088 val metrics=Seq(model_Svm).map{ model => val socreAndLabels=data_test.map { point => (model.predict(point.features), point.label) } val metrics=new BinaryClassificationMetrics(socreAndLabels) (model.getClass.getSimpleName,metrics.areaUnderPR(),metrics.areaUnderROC()) } val allMetrics = metrics allMetrics.foreach{ case (m, pr, roc) => println(f"$m, Area under PR: ${pr * 100.0}%2.4f%%, Area under ROC: ${roc * 100.0}%2.4f%%") } /* SVMModel, Area under PR: 72.5527%, Area under ROC: 60.4180%*/
3,模型参数调优
逻辑回归(SGD)和 SVM 模型有相同的参数,原因是它们都使用随机梯度下降( SGD )作为基础优化技术。不同点在于二者采用的损失函数不同
3.1 定义调参函数及模型评估函数
/*调参函数*/ def trainWithParams(input: RDD[LabeledPoint], regParam: Double, numIterations: Int, updater: Updater, stepSize: Double) = { val svm = new SVMWithSGD svm.optimizer.setNumIterations(numIterations). setUpdater(updater).setRegParam(regParam).setStepSize(stepSize) svm.run(input) } /*评估函数*/ def createMetrics(label: String, data: RDD[LabeledPoint], model: ClassificationModel) = { val scoreAndLabels = data.map { point => (model.predict(point.features), point.label) } val metrics = new BinaryClassificationMetrics(scoreAndLabels) (label, metrics.areaUnderROC) }
3.2 改变迭代次数(发现一旦完成特定次数的迭代,再增大迭代次数对结果的影响较小)
val iterResults = Seq(1, 5, 10, 50).map { param => val model = trainWithParams(data_train, 0.0, param, new SimpleUpdater, 1.0) createMetrics(s"$param iterations", data_test, model) } iterResults.foreach { case (param, auc) => println(f"$param, AUC = ${auc * 100}%2.2f%%") } /* 1 iterations, AUC = 59.02% 5 iterations, AUC = 60.04% 10 iterations, AUC = 60.42% 50 iterations, AUC = 60.42% */
3.3 ,改变步长(以看出步长增长过大对性能有负面影响)
在 SGD 中,在训练每个样本并更新模型的权重向量时,步长用来控制算法在最陡的梯度方向上应该前进多远。较大的步长收敛较快,但是步长太大可能导致收敛到局部最优解。
val stepResults = Seq(0.001, 0.01, 0.1, 1.0, 10.0).map { param => val model = trainWithParams(data_train, 0.0, numIteration, new SimpleUpdater, param) createMetrics(s"$param step size", data_test, model) } stepResults.foreach { case (param, auc) => println(f"$param, AUC = ${auc * 100}%2.2f%%") } /* 0.001 step size, AUC = 59.02% 0.01 step size, AUC = 59.02% 0.1 step size, AUC = 59.01% 1.0 step size, AUC = 60.42% 10.0 step size, AUC = 56.09% */
3.4 正则化
val regResults = Seq(0.001, 0.01, 0.1, 1.0, 10.0).map { param => val model = trainWithParams(data_train, param, numIteration, new SquaredL2Updater, 1.0) createMetrics(s"$param L2 regularization parameter", data_test, model) } regResults.foreach { case (param, auc) => println(f"$param, AUC = ${auc * 100}%2.2f%%") } /* 0.001 L2 regularization parameter, AUC = 60.42% 0.01 L2 regularization parameter, AUC = 60.42% 0.1 L2 regularization parameter, AUC = 60.37% 1.0 L2 regularization parameter, AUC = 60.56% 10.0 L2 regularization parameter, AUC = 41.54% */
可以看出,低等级的正则化对模型的性能影响不大。然而,增大正则化可以看到欠拟合会导致较低模型性能。
(三),总结
1,提高精确度感觉蛮难的,前提还是要先分析数据,对不同特征加以处理吧。。。。。
2,以后多学习。。。。