How to use

edu.illinois.cs.cogcomp.indsup.learning.L2Loss.L2LossParallelJLISLearner

constructor

public static RerankerModel trainRerankerModel(double C, int n_thread,
    StructuredProblem train) throws Exception {
  RerankerModel model = new RerankerModel();
  
  model.para = new JLISParameters();
  // para.total_number_features = train.label_mapping.size() *
  // train.n_base_feature_in_train;
  model.para.c_struct = C;
  model.para.TRAINMINI = true;
  // play with the following two parameters if you want to solve SSVM more
  // tightly
  model.para.DUAL_GAP = 0.01;
  model.para.WORKINGSETSVM_STOP = 0.01;
  System.out.println("Initializing Solvers...");
  System.out.flush();
  AbstractLossSensitiveStructureFinder[] s_finder_list = new AbstractLossSensitiveStructureFinder[n_thread];
  for (int i = 0; i < s_finder_list.length; i++) {
    s_finder_list[i] = new RerankerBestItemFinder();
  }
  System.out.println("Done!");
  System.out.flush();
  
  L2LossParallelJLISLearner learner = new L2LossParallelJLISLearner();
  // train model
  model.wv = learner.parallelTrainStructuredSVM(s_finder_list, train,
      model.para);
  return model;
}

public static MulticlassModel trainMultiClassModel(double C, int n_thread,
    LabeledMulticlassData train) throws Exception {
  MulticlassModel model = new MulticlassModel();
  model.lab_mapping = train.label_mapping; // for the bias term
  model.n_base_feature_in_train = train.n_base_feature_in_train;
  model.para = new JLISParameters();
  // para.total_number_features = train.label_mapping.size() *
  // train.n_base_feature_in_train;
  model.para.c_struct = C;
  model.para.TRAINMINI = true;
  // play with the following two parameters if you want to solve SSVM more
  // tightly
  model.para.DUAL_GAP = 0.01;
  model.para.WORKINGSETSVM_STOP = 0.01;
  System.out.println("Initializing Solvers...");
  System.out.flush();
  AbstractLossSensitiveStructureFinder[] s_finder_list = new AbstractLossSensitiveStructureFinder[n_thread];
  for (int i = 0; i < s_finder_list.length; i++) {
    s_finder_list[i] = new MultiClassStructureFinder();
  }
  System.out.println("Done!");
  System.out.flush();
  model.s_finder = s_finder_list[0];
  L2LossParallelJLISLearner learner = new L2LossParallelJLISLearner();
  // train model
  model.wv = learner.parallelTrainStructuredSVM(s_finder_list, train.sp,
      model.para);
  return model;
}

L2LossParallelJLISLearner learner = new L2LossParallelJLISLearner();