@Override
public AnalysisCollector analyze(CollectionReader cr) throws AnalyzerFailureException {
svm_problem svmProblem = loadData(cr);
svm_problem sampled = null;
if (findBestParameters) {
if (sample < 1d) {
logger.debug("Sampling.");
sampled = do_sample(svmProblem);
}
logger.debug("Performing grid search.");
do_find_best_parameters(sampled != null ? sampled : svmProblem);
}
svm_parameter svmParam = getDefaultSvmParameters();
svmParam.probability = 1;
svmParam.C = c;
svmParam.gamma = gamma;
setWeights(svmParam);
logger.debug("Training with C=" + c + " gamma=" + gamma);
svm_model model = svm.svm_train(svmProblem, svmParam);
logger.debug("Done!");
return new SingletonAnalysisCollector(
new LibSvmTrainerAnalysis(model, scaler, labelList, c, gamma));
}
private void setWeights(svm_parameter svmParam) {
if (weights != null) {
svmParam.nr_weight = weights.size();
svmParam.weight_label = new int[weights.size()];
svmParam.weight = new double[weights.size()];
for (int i = 0; i < weights.size(); ++i) {
svmParam.weight_label[i] = i;
svmParam.weight[i] = weights.get(i);
}
logger.debug("Class weights: " + weights);
}
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
double[] outputs;
double[] outputs2;
Instance neighbor;
double dist, mean;
int actual;
int[] N = new int[nneigh];
double[] Ndist = new double[nneigh];
boolean allNull;
svm_problem SVMp = null;
svm_parameter SVMparam = new svm_parameter();
svm_model svr = null;
svm_node SVMn[];
double[] outputsCandidate = null;
boolean same = true;
Vector instancesSelected = new Vector();
Vector instancesSelected2 = new Vector();
// SVM PARAMETERS
SVMparam.C = C;
SVMparam.cache_size = 10; // 10MB of cache
SVMparam.degree = degree;
SVMparam.eps = eps;
SVMparam.gamma = gamma;
SVMparam.nr_weight = 0;
SVMparam.nu = nu;
SVMparam.p = p;
SVMparam.shrinking = shrinking;
SVMparam.probability = 0;
if (kernelType.compareTo("LINEAR") == 0) {
SVMparam.kernel_type = svm_parameter.LINEAR;
} else if (kernelType.compareTo("POLY") == 0) {
SVMparam.kernel_type = svm_parameter.POLY;
} else if (kernelType.compareTo("RBF") == 0) {
SVMparam.kernel_type = svm_parameter.RBF;
} else if (kernelType.compareTo("SIGMOID") == 0) {
SVMparam.kernel_type = svm_parameter.SIGMOID;
}
SVMparam.svm_type = svm_parameter.EPSILON_SVR;
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][2]; // matrix with transformed data
mostCommon = new String[nvariables];
SVMp = new svm_problem();
SVMp.l = ndatos;
SVMp.y = new double[SVMp.l];
SVMp.x = new svm_node[SVMp.l][nentradas + 1];
for (int l = 0; l < SVMp.l; l++) {
for (int n = 0; n < Attributes.getInputNumAttributes() + 1; n++) {
SVMp.x[l][n] = new svm_node();
}
}
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
SVMp.y[i] = inst.getAllOutputValues()[0];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMp.x[i][n].index = n;
SVMp.x[i][n].value = inst.getAllInputValues()[n];
SVMp.y[i] = inst.getAllOutputValues()[0];
}
// end of instance
SVMp.x[i][nentradas].index = -1;
}
if (svm.svm_check_parameter(SVMp, SVMparam) != null) {
System.out.println("SVM parameter error in training:");
System.out.println(svm.svm_check_parameter(SVMp, SVMparam));
System.exit(-1);
}
// train the SVM
if (ndatos > 0) {
svr = svm.svm_train(SVMp, SVMparam);
}
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));
// the values used for regression
SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMn[n] = new svm_node();
SVMn[n].index = n;
SVMn[n].value = inst.getAllInputValues()[n];
}
SVMn[nentradas] = new svm_node();
SVMn[nentradas].index = -1;
// pedict the class
X[i][1] = new String(String.valueOf((svm.svm_predict(svr, SVMn))));
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_train_name);
/** ************************************************************************************ */
try {
// Load in memory a dataset that contains a classification
// problem
IS.readSet(input_test_name, false);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][2]; // matrix with transformed data
// data
mostCommon = new String[nvariables];
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));
SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMn[n] = new svm_node();
SVMn[n].index = n;
SVMn[n].value = inst.getAllInputValues()[n];
}
SVMn[nentradas] = new svm_node();
SVMn[nentradas].index = -1;
// pedict the class
X[i][1] = new String(String.valueOf(svm.svm_predict(svr, SVMn)));
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
System.out.println("escribiendo test");
write_results(output_test_name);
}
public static svm_model svm_load_model(String model_file_name) throws IOException
{
BufferedReader fp = new BufferedReader(new FileReader(model_file_name));
// read parameters
svm_model model = new svm_model();
svm_parameter param = new svm_parameter();
model.param = param;
model.label = null;
model.nSV = null;
while(true)
{
String cmd = fp.readLine();
String arg = cmd.substring(cmd.indexOf(' ')+1);
if(cmd.startsWith("svm_type"))
{
int i;
for(i=0;i<svm_type_table.length;i++)
{
if(arg.indexOf(svm_type_table[i])!=-1)
{
param.svm_type=i;
break;
}
}
if(i == svm_type_table.length)
{
System.err.print("unknown svm type.\n");
System.exit(1);
}
}
else if(cmd.startsWith("kernel_type"))
{
int i;
for(i=0;i<kernel_type_table.length;i++)
{
if(arg.indexOf(kernel_type_table[i])!=-1)
{
param.kernel_type=i;
break;
}
}
if(i == kernel_type_table.length)
{
System.err.print("unknown kernel function.\n");
System.exit(1);
}
}
else if(cmd.startsWith("degree"))
param.degree = atof(arg);
else if(cmd.startsWith("gamma"))
param.gamma = atof(arg);
else if(cmd.startsWith("coef0"))
param.coef0 = atof(arg);
else if(cmd.startsWith("nr_class"))
model.nr_class = atoi(arg);
else if(cmd.startsWith("total_sv"))
model.l = atoi(arg);
else if(cmd.startsWith("rho"))
{
int n = model.nr_class * (model.nr_class-1)/2;
model.rho = new double[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.rho[i] = atof(st.nextToken());
}
else if(cmd.startsWith("label"))
{
int n = model.nr_class;
model.label = new int[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.label[i] = atoi(st.nextToken());
}
else if(cmd.startsWith("nr_sv"))
{
int n = model.nr_class;
model.nSV = new int[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.nSV[i] = atoi(st.nextToken());
}
else if(cmd.startsWith("SV"))
{
break;
}
else
{
System.err.print("unknown text in model file\n");
System.exit(1);
}
}
// read sv_coef and SV
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = new double[m][l];
model.SV = new svm_node[l][];
for(int i=0;i<l;i++)
{
String line = fp.readLine();
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
for(int k=0;k<m;k++)
model.sv_coef[k][i] = atof(st.nextToken());
int n = st.countTokens()/2;
model.SV[i] = new svm_node[n];
for(int j=0;j<n;j++)
{
model.SV[i][j] = new svm_node();
model.SV[i][j].index = atoi(st.nextToken());
model.SV[i][j].value = atof(st.nextToken());
}
}
fp.close();
return model;
}
public svm_parameter getDefaultSvmParameters() {
svm_parameter 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];
return param;
}
private void do_find_best_parameters(svm_problem svmProblem) {
svm_parameter svmParam = getDefaultSvmParameters();
setWeights(svmParam);
int maxIter =
((int) Math.ceil(Math.abs((log2cEnd - log2cBegin) / log2cStep)) + 1)
* ((int) Math.ceil(Math.abs((log2gEnd - log2gBegin) / log2gStep)) + 1);
// Run the grid search in separate CV threads
ExecutorService executorService = Executors.newFixedThreadPool(numberOfThreads);
List<CvParams> cvParamsList = new ArrayList<CvParams>();
for (double log2c = log2cBegin;
(log2cBegin < log2cEnd && log2c <= log2cEnd)
|| (log2cBegin >= log2cEnd && log2c >= log2cEnd);
log2c += log2cStep) {
double c1 = Math.pow(2, log2c);
for (double log2g = log2gBegin;
(log2gBegin < log2gEnd && log2g <= log2gEnd)
|| (log2gBegin >= log2gEnd && log2g >= log2gEnd);
log2g += log2gStep) {
double gamma1 = Math.pow(2, log2g);
svm_parameter svmParam1 = (svm_parameter) svmParam.clone();
svmParam1.C = c1;
svmParam1.gamma = gamma1;
executorService.execute(
new RunnableSvmCrossValidator(svmProblem, svmParam1, nrFold, cvParamsList));
}
}
// now wait for all threads to complete by calling shutdown
// note that this will NOT terminate the currently running threads, it just signals the thread
// pool to closeWriter
// once all work is completed
executorService.shutdown();
while (!executorService.isTerminated()) {
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
// don't care if we get interrupted
}
// every second, report statistics
logger.debug(
String.format("%% complete: %5.2f", cvParamsList.size() / (double) maxIter * 100));
CvParams best = getBestCvParams(cvParamsList);
CvParams worst = getWorstcvParams(cvParamsList);
if (best != null) {
logger.debug("Best accuracy: " + best.accuracy);
logger.debug("Best C: " + best.c);
logger.debug("Best Gamma: " + best.gamma);
}
if (worst != null) {
logger.debug("Worst accuracy: " + worst.accuracy);
}
}
CvParams best = getBestCvParams(cvParamsList);
CvParams worst = getWorstcvParams(cvParamsList);
if (best != null) {
logger.debug("Best accuracy: " + best.accuracy);
logger.debug("Best C: " + best.c);
logger.debug("Best Gamma: " + best.gamma);
c = best.c;
gamma = best.gamma;
} else {
logger.error("Best CV parameters is null.");
}
if (worst != null) {
logger.debug("Worst accuracy: " + worst.accuracy);
}
}
