Java tutorial
/* * To change this license header, choose License Headers in Project Properties. * To change this template file, choose Tools | Templates * and open the template in the editor. */ package recsys; import java.io.File; import java.io.FileInputStream; import java.io.FileOutputStream; import java.io.ObjectInputStream; import java.io.ObjectOutputStream; import java.util.ArrayList; import java.util.HashMap; import java.util.Scanner; import java.util.StringTokenizer; import weka.classifiers.Classifier; import weka.classifiers.bayes.BayesNet; import weka.classifiers.bayes.BayesianLogisticRegression; import weka.classifiers.bayes.NaiveBayes; import weka.classifiers.functions.Logistic; import weka.classifiers.meta.AdaBoostM1; import weka.classifiers.meta.FilteredClassifier; import weka.classifiers.trees.RandomForest; import weka.core.Attribute; import weka.core.Instance; import weka.core.Instances; import weka.core.converters.ConverterUtils.DataSource; import weka.filters.Filter; import weka.filters.unsupervised.attribute.Remove; /** * * @author Sarwar */ public class ResultProcessing { public static void main(String args[]) throws Exception { int own_training = StaticVariables.own_training; //opening the testing file DataSource sourceTest; if (own_training == 1) { sourceTest = new DataSource("D://own_training//item//feature data//test_feature.arff"); } else { sourceTest = new DataSource("E://recsys//item//feature data//test_feature.arff"); } //DataSource sourceTest = new DataSource("D://own_training//test_featureFile.arff"); //System.out.println("working"); Instances test = sourceTest.getDataSet(); PrintFile solutionFile; if (own_training == 1) { solutionFile = new PrintFile(null, new File("D://own_training//item//solution//solution.dat")); } else { solutionFile = new PrintFile(null, new File("E://solution.dat")); } //PrintFile solutionFile = new PrintFile(null, new File("D://own_training//solution.dat")); if (test.classIndex() == -1) { test.setClassIndex(test.numAttributes() - 1); } //System.out.println("hello"); ObjectInputStream ois; if (own_training == 1) { ois = new ObjectInputStream(new FileInputStream("D://own_training//item//model//train.model")); } else { ois = new ObjectInputStream(new FileInputStream("E:\\recsys\\item\\model\\train.model")); //sois = new ObjectInputStream(new FileInputStream("E:\\recsys\\my best performances\\39127.6\\train.model")); } //AdaBoostM1 cls = (AdaBoostM1)ois.readObject(); //BayesNet cls = (BayesNet)ois .readObject(); RandomForest cls = (RandomForest) ois.readObject(); //Logistic cls = (Logistic) ois.readObject(); //System.out.println(cls.globalInfo()); //System.out.println(cls.getNumFeatures()); //System.out.println(cls.toString()); //BayesianLogisticRegression cls = (BayesianLogisticRegression)ois.readObject(); //NaiveBayes cls = (NaiveBayes) ois.readObject(); //FilteredClassifier fc = (FilteredClassifier) ois.readObject(); System.out.println(cls.toString()); ois.close(); String[] options = new String[2]; options[0] = "-R"; // "range" options[1] = "1,2,4"; // first attribute //options[2] = "2"; //options[3] = "4"; Remove remove = new Remove(); // new instance of filter remove.setOptions(options); // set options remove.setInputFormat(test); // inform filter about dataset **AFTER** setting options Instances newData = Filter.useFilter(test, remove); // apply filter System.out.println(newData.firstInstance()); int totalSessionCount = 0; int buySessionCount = 0; int b = 0; Scanner sc; if (own_training == 0) sc = new Scanner(new File("E:\\recsys\\session\\solution\\solution.dat")); //sc = new Scanner(new File("E:\\recsys\\my best performances\\best performance\\solution_session.dat")); else sc = new Scanner(new File("D:\\own_training\\session\\solution\\solution.dat")); //sc = new Scanner(new File("D:\\own_training\\session\\data\\original_solution.csv")); HashMap<Integer, Integer> a = new HashMap<Integer, Integer>(); while (sc.hasNext()) { String temp = sc.next(); StringTokenizer st = new StringTokenizer(temp, ",;"); a.put(Integer.parseInt(st.nextToken()), 1); } System.out.println("size " + a.size()); Integer tempSessionId = (int) test.instance(0).value(0); ArrayList<Integer> buy = new ArrayList<>(); String result = String.valueOf(tempSessionId) + ";"; //int lengthVector[] = new int[300]; int testSessionCount = 0, currentSessionLength = 0; //int sessionLengthCount=0; for (int i = 0; i < test.numInstances(); i++) { if ((int) test.instance(i).value(0) != tempSessionId) { if (a.containsKey(tempSessionId)) { //if(test.instance(i-1).value(3)< StaticVariables.length) { //System.out.println(test.instance(i-1).value(3)); totalSessionCount++; if (buy.size() > 0) { for (int j = 0; j < buy.size(); j++) { result += buy.get(j) + ","; } solutionFile.writeFile(result.substring(0, result.length() - 1)); } //lengthVector[sessionLengthCount]++; } /*}else{ if(buy.size()>= 3){ for (int j = 0; j < buy.size(); j++) { result += buy.get(j) + ","; } solutionFile.writeFile(result.substring(0, result.length() - 1)); } }*/ //testSessionCount=0; tempSessionId = (int) test.instance(i).value(0); result = String.valueOf(tempSessionId) + ";"; //sessionLengthCount=0; buy.clear(); } //currentSessionLength = test.instance(i).value(3); //testSessionCount++; //System.out.println("working"); //sessionLengthCount++; double pred = cls.classifyInstance(newData.instance(i)); if (test.classAttribute().value((int) pred).equals("buy")) { b++; Integer item = (int) test.instance(i).value(1); buy.add(item); } //System.out.print(", actual: " + test.classAttribute().value((int) test.instance(i).classValue())); //System.out.println(", predicted: " + test.classAttribute().value((int) pred)); } System.out.println(totalSessionCount); //System.out.println(totalSessionCount); //System.out.println(b); if (buy.size() > 0) { solutionFile.writeFile(result.substring(0, result.length() - 1)); } /*for(int p:lengthVector) System.out.println(p);*/ solutionFile.closeFile(); } }