基于ML库机器学习的步骤：
1）样本数据导入；
2）样本数据特征抽取和特征值处理（结合模型需要归一化或离散化）；这里本文没有做处理，特征选择和特征值处理本身就很大；
3）样本集划分训练集和验证集；
4）根据训练集训练模型；
5）用验证集评价模型；
6）导入测试集，并用模型预测输出预测结果；
package com.vip;import java.io.File;import be.abeel.util.Pair;import net.sf.javaml.classification.Classifier;import net.sf.javaml.classification.KNearestNeighbors;import net.sf.javaml.core.Dataset;import net.sf.javaml.core.DefaultDataset;import net.sf.javaml.core.DenseInstance;import net.sf.javaml.core.Instance;import net.sf.javaml.featureselection.scoring.GainRatio;import net.sf.javaml.sampling.Sampling;import net.sf.javaml.tools.data.FileHandler;public class VIPClassifer {public static void main(String[] args)throws Exception {if (args.length != 2) {System.err.println("Usage: 输入训练集和测试集路径");System.exit(2);}/* Load a data set 前面13列是训练特征，最后1列标记*/Dataset ori_data = FileHandler.loadDataset(new File(args[0]), 13, "\\s+");//特征评分，可独立//GainRatio ga = new GainRatio(); //ga.build(ori_data); /* Apply the algorithm to the data set */ //for (int i = 0; i < ga.noAttributes(); i++) // System.out.println(ga.score(i)); //抽样训练集和验证集Sampling s = Sampling.SubSampling;Pair<Dataset, Dataset> sam_data = s.sample(ori_data, (int) (ori_data.size() * 0.8));/*Dataset train_data = new DefaultDataset();//80%训练Dataset test_data = new DefaultDataset();//20%验证int sample=0;for(Instance inst:ori_data){double[] values = new double[] { inst.value(5),inst.value(6),inst.value(7),inst.value(8), inst.value(9),inst.value(16),inst.value(17)};Instance train_inst = new DenseInstance(values, inst.classValue()); if(sample<4){sample++;train_data.add(train_inst);}else {sample=0;test_data.add(train_inst); } }*///Contruct a KNN classifier that uses 5 neighbors to make a decision.Classifier knn = new KNearestNeighbors(5);knn.buildClassifier(sam_data.x());//验证集int correct = 0, wrong = 0;/* Classify all instances and check with the correct class values */for (Instance inst : sam_data.y()) {Object predictedClassValue = knn.classify(inst);Object realClassValue = inst.classValue();if (predictedClassValue.equals(realClassValue))correct++;elsewrong++;}System.out.println("Correct predictions " + correct);System.out.println("Wrong predictions " + wrong);//模型预测/* Load a data set 前面13列是训练特征，最后2列是uid和spuid联合标识*/Dataset pre_data = FileHandler.loadDataset(new File(args[1]),"\\s+");System.out.println(pre_data.instance(0));Dataset out_data = new DefaultDataset();for(Instance inst:pre_data){double[] values = new double[13]; for(int i=0;i<13;i++) values[i]=inst.value(i);Instance pre_inst = new DenseInstance(values); //无标记，13列特征参与训练Object pre_classvalue = knn.classify(pre_inst);//预测结果//pre_inst.setClassValue(pre_classvalue);//标注预测结果double[] u_spu_id=new double[]{inst.value(13),inst.value(14)};Instance out_inst = new DenseInstance(u_spu_id,pre_classvalue); //带标记out_data.add(out_inst);}//输出u_Id+spu_id+action_typeFileHandler.exportDataset(out_data, new File("/data1/DataFountain/output.txt"));}}//java -XX:-UseGCOverheadLimit -Xmx10240m -jar vip.jar train_features_new.txt test_features_new.txt
在上面这个代码框架内，可以用不同模型，如SVM、RF(随机森林）等，也可以对特征值进行处理后选择特征来训练。
