/**
* Copyright (c) 2009/09-2012/08, Regents of the University of Colorado
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* Copyright 2012/09-2013/04, 2013/11-Present, University of Massachusetts Amherst
* Copyright 2013/05-2013/10, IPSoft Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.clearnlp.run;
import java.io.BufferedOutputStream;
import java.io.FileOutputStream;
import java.io.ObjectOutputStream;
import org.kohsuke.args4j.Option;
import com.clearnlp.classification.algorithm.old.AbstractAlgorithm;
import com.clearnlp.classification.algorithm.old.AbstractLiblinear;
import com.clearnlp.classification.algorithm.old.LiblinearL2LR;
import com.clearnlp.classification.algorithm.old.LiblinearL2SVC;
import com.clearnlp.classification.model.AbstractModel;
import com.clearnlp.classification.train.AbstractTrainSpace;
import com.clearnlp.classification.train.OneVsAllTrainer;
import com.clearnlp.classification.train.SparseTrainSpace;
import com.clearnlp.classification.train.StringTrainSpace;
import com.clearnlp.util.UTInput;
/**
* Trains a Liblinear model.
* @since 0.1.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class LiblinearTrain extends AbstractRun
{
@Option(name="-i", usage="the training file (input; required)", required=true, metaVar="<filename>")
private String s_trainFile;
@Option(name="-m", usage="the model file (output; required)", required=true, metaVar="<filename>")
private String s_modelFile;
@Option(name="-nl", usage="label frequency cutoff (default: 0)\n"+"exclusive, string vector space only", required=false, metaVar="<integer>")
private int i_labelCutoff = 0;
@Option(name="-nf", usage="feature frequency cutoff (default: 0)\n"+"exclusive, string vector space only", required=false, metaVar="<integer>")
private int i_featureCutoff = 0;
@Option(name="-nt", usage="the number of threads to be used (default: 1)", required=false, metaVar="<integer>")
private int i_numThreads = 1;
@Option(name="-v", usage="the type of vector space (default: "+AbstractTrainSpace.VECTOR_STRING+")\n"+
AbstractTrainSpace.VECTOR_SPARSE+": sparse vector space\n"+
AbstractTrainSpace.VECTOR_STRING+": string vector space\n",
required=false, metaVar="<byte>")
private byte i_vectorType = AbstractTrainSpace.VECTOR_STRING;
@Option(name="-s", usage="the type of solver (default: "+AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_L1_SVC+")\n"+
AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_L1_SVC+": L2-regularized L1-loss support vector classification (dual)\n"+
AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_L2_SVC+": L2-regularized L2-loss support vector classification (dual)\n"+
AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_LR +": L2-regularized logistic regression (dual)",
required=false, metaVar="<byte>")
private byte i_solver = AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_L1_SVC;
@Option(name="-c", usage="the cost (default: 0.1)", required=false, metaVar="<double>")
private double d_cost = 0.1;
@Option(name="-e", usage="the tolerance of termination criterion (default: 0.1)", required=false, metaVar="<double>")
private double d_eps = 0.1;
@Option(name="-b", usage="the bias (default: 0)", required=false, metaVar="<double>")
private double d_bias = 0.0;
public LiblinearTrain() {}
public LiblinearTrain(String[] args)
{
initArgs(args);
try
{
train(s_trainFile, s_modelFile, i_vectorType, i_labelCutoff, i_featureCutoff, i_numThreads, i_solver, d_cost, d_eps, d_bias);
}
catch (Exception e) {e.printStackTrace();}
}
public void train(String trainFile, String modelFile, byte vectorType, int labelCutoff, int featureCutoff, int numThreads, byte solver, double cost, double eps, double bias) throws Exception
{
AbstractTrainSpace space = null;
boolean hasWeight = AbstractTrainSpace.hasWeight(vectorType, trainFile);
switch (vectorType)
{
case AbstractTrainSpace.VECTOR_SPARSE:
space = new SparseTrainSpace(hasWeight); break;
case AbstractTrainSpace.VECTOR_STRING:
space = new StringTrainSpace(hasWeight, labelCutoff, featureCutoff); break;
}
space.readInstances(UTInput.createBufferedFileReader(trainFile));
space.build();
AbstractModel model = getModel(space, numThreads, solver, cost, eps, bias);
ObjectOutputStream out = new ObjectOutputStream(new BufferedOutputStream(new FileOutputStream(modelFile)));
out.writeObject(model);
out.close();
}
static public AbstractModel getModel(AbstractTrainSpace space, int numThreads, byte solver, double cost, double eps, double bias)
{
AbstractLiblinear algorithm = null;
switch (solver)
{
case AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_L1_SVC:
algorithm = new LiblinearL2SVC((byte)1, cost, eps, bias); break;
case AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_L2_SVC:
algorithm = new LiblinearL2SVC((byte)2, cost, eps, bias); break;
case AbstractAlgorithm.SOLVER_LIBLINEAR_LR2_LR:
algorithm = new LiblinearL2LR(cost, eps, bias); break;
}
new OneVsAllTrainer(space, algorithm, numThreads);
return space.getModel();
}
static public void main(String[] args)
{
new LiblinearTrain(args);
}
}