svm_train.java

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import libsvm.*;
import java.io.*;
import java.util.*;

class svm_train {
        private svm_parameter param;            // set by parse_command_line
        private svm_problem prob;               // set by read_problem
        private svm_model model;
        private String input_file_name;         // set by parse_command_line
        private String model_file_name;         // set by parse_command_line
        private String error_msg;
        private int cross_validation;
        private int nr_fold;

        private static svm_print_interface svm_print_null = new svm_print_interface()
        {
                public void print(String s) {}
        };

        private static void exit_with_help()
        {
                System.out.print(
                 "Usage: svm_train [options] training_set_file [model_file]\n"
                +"options:\n"
                +"-s svm_type : set type of SVM (default 0)\n"
                +"      0 -- C-SVC              (multi-class classification)\n"
                +"      1 -- nu-SVC             (multi-class classification)\n"
                +"      2 -- one-class SVM\n"
                +"      3 -- epsilon-SVR        (regression)\n"
                +"      4 -- nu-SVR             (regression)\n"
                +"-t kernel_type : set type of kernel function (default 2)\n"
                +"      0 -- linear: u'*v\n"
                +"      1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
                +"      2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
                +"      3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
                +"      4 -- precomputed kernel (kernel values in training_set_file)\n"
                +"-d degree : set degree in kernel function (default 3)\n"
                +"-g gamma : set gamma in kernel function (default 1/num_features)\n"
                +"-r coef0 : set coef0 in kernel function (default 0)\n"
                +"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
                +"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
                +"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
                +"-m cachesize : set cache memory size in MB (default 100)\n"
                +"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
                +"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
                +"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
                +"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
                +"-v n : n-fold cross validation mode\n"
                +"-q : quiet mode (no outputs)\n"
                );
                System.exit(1);
        }

        private void do_cross_validation()
        {
                int i;
                int total_correct = 0;
                double total_error = 0;
                double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
                double[] target = new double[prob.l];

                svm.svm_cross_validation(prob,param,nr_fold,target);
                if(param.svm_type == svm_parameter.EPSILON_SVR ||
                   param.svm_type == svm_parameter.NU_SVR)
                {
                        for(i=0;i<prob.l;i++)
                        {
                                double y = prob.y[i];
                                double v = target[i];
                                total_error += (v-y)*(v-y);
                                sumv += v;
                                sumy += y;
                                sumvv += v*v;
                                sumyy += y*y;
                                sumvy += v*y;
                        }
                        System.out.print("Cross Validation Mean squared error = "+total_error/prob.l+"\n");
                        System.out.print("Cross Validation Squared correlation coefficient = "+
                                ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
                                ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))+"\n"
                                );
                }
                else
                {
                        for(i=0;i<prob.l;i++)
                                if(target[i] == prob.y[i])
                                        ++total_correct;
                        System.out.print("Cross Validation Accuracy = "+100.0*total_correct/prob.l+"%\n");
                }
        }
        
        private void run(String argv[]) throws IOException
        {
                parse_command_line(argv);
                read_problem();
                error_msg = svm.svm_check_parameter(prob,param);

                if(error_msg != null)
                {
                        System.err.print("ERROR: "+error_msg+"\n");
                        System.exit(1);
                }

                if(cross_validation != 0)
                {
                        do_cross_validation();
                }
                else
                {
                        model = svm.svm_train(prob,param);
                        svm.svm_save_model(model_file_name,model);
                }
        }

        public static void main(String argv[]) throws IOException
        {
                svm_train t = new svm_train();
                t.run(argv);
        }

        private static double atof(String s)
        {
                double d = Double.valueOf(s).doubleValue();
                if (Double.isNaN(d) || Double.isInfinite(d))
                {
                        System.err.print("NaN or Infinity in input\n");
                        System.exit(1);
                }
                return(d);
        }

        private static int atoi(String s)
        {
                return Integer.parseInt(s);
        }

        private void parse_command_line(String argv[])
        {
                int i;
                svm_print_interface print_func = null;  // default printing to stdout

                param = new svm_parameter();
                // default values
                param.svm_type = svm_parameter.C_SVC;
                param.kernel_type = svm_parameter.RBF;
                param.degree = 3;
                param.gamma = 0;        // 1/num_features
                param.coef0 = 0;
                param.nu = 0.5;
                param.cache_size = 100;
                param.C = 1;
                param.eps = 1e-3;
                param.p = 0.1;
                param.shrinking = 1;
                param.probability = 0;
                param.nr_weight = 0;
                param.weight_label = new int[0];
                param.weight = new double[0];
                cross_validation = 0;

                // parse options
                for(i=0;i<argv.length;i++)
                {
                        if(argv[i].charAt(0) != '-') break;
                        if(++i>=argv.length)
                                exit_with_help();
                        switch(argv[i-1].charAt(1))
                        {
                                case 's':
                                        param.svm_type = atoi(argv[i]);
                                        break;
                                case 't':
                                        param.kernel_type = atoi(argv[i]);
                                        break;
                                case 'd':
                                        param.degree = atoi(argv[i]);
                                        break;
                                case 'g':
                                        param.gamma = atof(argv[i]);
                                        break;
                                case 'r':
                                        param.coef0 = atof(argv[i]);
                                        break;
                                case 'n':
                                        param.nu = atof(argv[i]);
                                        break;
                                case 'm':
                                        param.cache_size = atof(argv[i]);
                                        break;
                                case 'c':
                                        param.C = atof(argv[i]);
                                        break;
                                case 'e':
                                        param.eps = atof(argv[i]);
                                        break;
                                case 'p':
                                        param.p = atof(argv[i]);
                                        break;
                                case 'h':
                                        param.shrinking = atoi(argv[i]);
                                        break;
                                case 'b':
                                        param.probability = atoi(argv[i]);
                                        break;
                                case 'q':
                                        print_func = svm_print_null;
                                        i--;
                                        break;
                                case 'v':
                                        cross_validation = 1;
                                        nr_fold = atoi(argv[i]);
                                        if(nr_fold < 2)
                                        {
                                                System.err.print("n-fold cross validation: n must >= 2\n");
                                                exit_with_help();
                                        }
                                        break;
                                case 'w':
                                        ++param.nr_weight;
                                        {
                                                int[] old = param.weight_label;
                                                param.weight_label = new int[param.nr_weight];
                                                System.arraycopy(old,0,param.weight_label,0,param.nr_weight-1);
                                        }

                                        {
                                                double[] old = param.weight;
                                                param.weight = new double[param.nr_weight];
                                                System.arraycopy(old,0,param.weight,0,param.nr_weight-1);
                                        }

                                        param.weight_label[param.nr_weight-1] = atoi(argv[i-1].substring(2));
                                        param.weight[param.nr_weight-1] = atof(argv[i]);
                                        break;
                                default:
                                        System.err.print("Unknown option: " + argv[i-1] + "\n");
                                        exit_with_help();
                        }
                }

                svm.svm_set_print_string_function(print_func);

                // determine filenames

                if(i>=argv.length)
                        exit_with_help();

                input_file_name = argv[i];

                if(i<argv.length-1)
                        model_file_name = argv[i+1];
                else
                {
                        int p = argv[i].lastIndexOf('/');
                        ++p;    // whew...
                        model_file_name = argv[i].substring(p)+".model";
                }
        }

        // read in a problem (in svmlight format)

        private void read_problem() throws IOException
        {
                BufferedReader fp = new BufferedReader(new FileReader(input_file_name));
                Vector<Double> vy = new Vector<Double>();
                Vector<svm_node[]> vx = new Vector<svm_node[]>();
                int max_index = 0;

                while(true)
                {
                        String line = fp.readLine();
                        if(line == null) break;

                        StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");

                        vy.addElement(atof(st.nextToken()));
                        int m = st.countTokens()/2;
                        svm_node[] x = new svm_node[m];
                        for(int j=0;j<m;j++)
                        {
                                x[j] = new svm_node();
                                x[j].index = atoi(st.nextToken());
                                x[j].value = atof(st.nextToken());
                        }
                        if(m>0) max_index = Math.max(max_index, x[m-1].index);
                        vx.addElement(x);
                }

                prob = new svm_problem();
                prob.l = vy.size();
                prob.x = new svm_node[prob.l][];
                for(int i=0;i<prob.l;i++)
                        prob.x[i] = vx.elementAt(i);
                prob.y = new double[prob.l];
                for(int i=0;i<prob.l;i++)
                        prob.y[i] = vy.elementAt(i);

                if(param.gamma == 0 && max_index > 0)
                        param.gamma = 1.0/max_index;

                if(param.kernel_type == svm_parameter.PRECOMPUTED)
                        for(int i=0;i<prob.l;i++)
                        {
                                if (prob.x[i][0].index != 0)
                                {
                                        System.err.print("Wrong kernel matrix: first column must be 0:sample_serial_number\n");
                                        System.exit(1);
                                }
                                if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
                                {
                                        System.err.print("Wrong input format: sample_serial_number out of range\n");
                                        System.exit(1);
                                }
                        }

                fp.close();
        }
}