/**
* 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.BufferedInputStream;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.ObjectInputStream;
import java.io.PrintStream;
import org.kohsuke.args4j.Option;
import com.clearnlp.classification.model.SparseModel;
import com.clearnlp.classification.model.StringModel;
import com.clearnlp.classification.prediction.StringPrediction;
import com.clearnlp.classification.train.AbstractTrainSpace;
import com.clearnlp.classification.train.SparseTrainSpace;
import com.clearnlp.classification.train.StringInstance;
import com.clearnlp.classification.train.StringTrainSpace;
import com.clearnlp.classification.vector.SparseFeatureVector;
import com.clearnlp.util.UTInput;
import com.clearnlp.util.UTOutput;
import com.clearnlp.util.pair.Pair;
/**
* Predicts using a Liblinear model.
* @since 0.1.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class LiblinearPredict extends AbstractRun
{
@Option(name="-i", usage="the input file (input; required)", required=true, metaVar="<filename>")
private String s_testFile;
@Option(name="-o", usage="the output file (output; required)", required=true, metaVar="<filename>")
private String s_outputFile;
@Option(name="-m", usage="the model file (input; required)", required=true, metaVar="<filename>")
private String s_modelFile;
@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;
public LiblinearPredict() {}
public LiblinearPredict(String[] args)
{
initArgs(args);
try
{
predict(s_testFile, s_outputFile, s_modelFile, i_vectorType);
}
catch (Exception e) {e.printStackTrace();}
}
public void predict(String testFile, String outputFile, String modelFile, byte vectorType) throws Exception
{
BufferedReader fin = UTInput.createBufferedFileReader(testFile);
PrintStream fout = UTOutput.createPrintBufferedFileStream(outputFile);
ObjectInputStream in = new ObjectInputStream(new BufferedInputStream(new FileInputStream(modelFile)));
SparseModel pModel = null;
StringModel sModel = null;
switch (vectorType)
{
case AbstractTrainSpace.VECTOR_SPARSE:
pModel = (SparseModel)in.readObject(); break;
case AbstractTrainSpace.VECTOR_STRING:
sModel = (StringModel)in.readObject(); break;
}
in.close();
boolean hasWeight = AbstractTrainSpace.hasWeight(vectorType, testFile);
int correct = 0, total = 0;
StringPrediction r = null;
String line, label = null;
System.out.print("Predicting");
while ((line = fin.readLine()) != null)
{
if (vectorType == AbstractTrainSpace.VECTOR_SPARSE)
{
Pair<String,SparseFeatureVector> sp = SparseTrainSpace.toInstance(line, hasWeight);
r = pModel.predictBest(sp.o2);
label = sp.o1;
}
else
{
StringInstance ss = StringTrainSpace.toInstance(line, hasWeight);
r = sModel.predictBest(ss.getFeatureVector());
label = ss.getLabel();
}
fout.println(r.label+" "+r.score);
if (r.label.equals(label)) correct++;
total++;
if (total%10000 == 0) System.out.print(".");
}
fin.close();
fout.close();
System.out.println();
System.out.printf("Accuracy = %7.4f (%d/%d)\n", 100d*correct/total, correct, total);
}
static public void main(String[] args)
{
new LiblinearPredict(args);
}
}