/**
* This file is part of FNLP (formerly FudanNLP).
*
* FNLP is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* FNLP is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with FudanNLP. If not, see <http://www.gnu.org/licenses/>.
*
* Copyright 2009-2014 www.fnlp.org. All rights reserved.
*/
package org.fnlp.nlp.cn.anaphora.train;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import org.fnlp.data.reader.ListReader;
import org.fnlp.data.reader.SimpleFileReader;
import org.fnlp.data.reader.SimpleFileReader.Type;
import org.fnlp.ml.classifier.linear.Linear;
import org.fnlp.ml.classifier.linear.OnlineTrainer;
import org.fnlp.ml.classifier.linear.inf.Inferencer;
import org.fnlp.ml.classifier.linear.inf.LinearMax;
import org.fnlp.ml.classifier.linear.update.LinearMaxPAUpdate;
import org.fnlp.ml.classifier.linear.update.Update;
import org.fnlp.ml.feature.Generator;
import org.fnlp.ml.feature.SFGenerator;
import org.fnlp.ml.loss.ZeroOneLoss;
import org.fnlp.ml.types.Instance;
import org.fnlp.ml.types.InstanceSet;
import org.fnlp.ml.types.alphabet.AlphabetFactory;
import org.fnlp.ml.types.alphabet.LabelAlphabet;
import org.fnlp.nlp.pipe.Pipe;
import org.fnlp.nlp.pipe.SeriesPipes;
import org.fnlp.nlp.pipe.StringArray2IndexArray;
import org.fnlp.nlp.pipe.Target2Label;
/**
* 训练分类器
* @author jszhao
* @version 1.0
* @since FudanNLP 1.5
*/
public class ARClassifier {
static InstanceSet train;
static InstanceSet test;
static AlphabetFactory factory = AlphabetFactory.buildFactory();
static LabelAlphabet al = factory.DefaultLabelAlphabet();
static String path = null;
static Pipe pipe;
/**
* 训练文件
*/
private String trainFile = "../tmp/ar-train.txt";
/**
* 模型文件
*/
private static String modelFile = "../models/ar.m";
public static void main(String[] args) throws Exception {
ARClassifier tc = new ARClassifier();
tc.train();
Linear cl =Linear.loadFrom(modelFile);
int i = 0;int j = 0;double ij = 0.0;int kk = 0;int jj = 0;int nn = 0;int n = 0;
InstanceSet test = new InstanceSet(cl.getPipe(),cl.getAlphabetFactory());
SimpleFileReader sfr = new SimpleFileReader("../tmp/ar-train.txt",true);
ArrayList<Instance> list1 = new ArrayList<Instance>();
while (sfr.hasNext())
{
list1.add(sfr.next());
}
List<String>[] str1 = new List[list1.size()];
String[] str2 = new String[list1.size()];
Iterator it = list1.iterator();
while(it.hasNext()){
Instance in = (Instance) it.next();
str1[i] = (List<String>) in.getData();
str2[i] = (String) in.getTarget();
i++;
}
for(int k = 0;k<str2.length;k++)
{
if(str2[k].equals("1"))
kk++;
}
String ss =null;
test.loadThruPipes(new ListReader(str1));
for(int ii=0;ii<str1.length;ii++){
ss = cl.getStringLabel(test.getInstance(ii));
if(ss.equals("1"))
j++;
if(ss.equals("1")&&ss.equals(str2[ii]))
jj++;
if(ss.equals("0")&&ss.equals(str2[ii]))
n++;
if(ss.equals(str2[ii]))
nn++;
}
ij = (nn+0.0)/str2.length;
System.out.print("整体正确率："+ij);System.out.print('\n');
ij = (jj+0.0)/kk;
System.out.print("判断为指代关系的正确率："+ij);System.out.print('\n');
ij = (n+0.0)/(str2.length-kk);
System.out.print("判断为非指代关系的正确率："+ij);System.out.print('\n');
System.gc();
}
/**
* 训练
* @throws Exception
*/
public void train() throws Exception {
//建立字典管理器
Pipe lpipe = new Target2Label(al);
Pipe fpipe = new StringArray2IndexArray(factory, true);
//构造转换器组
SeriesPipes pipe = new SeriesPipes(new Pipe[]{lpipe,fpipe});
InstanceSet instset = new InstanceSet(pipe,factory);
instset.loadThruStagePipes(new SimpleFileReader(trainFile," ",true,Type.LabelData));
Generator gen = new SFGenerator();
ZeroOneLoss l = new ZeroOneLoss();
Inferencer ms = new LinearMax(gen, factory.getLabelSize());
Update update = new LinearMaxPAUpdate(l);
OnlineTrainer trainer = new OnlineTrainer(ms, update,l, factory.getFeatureSize(), 50,0.005f);
Linear pclassifier = trainer.train(instset,instset);
pipe.removeTargetPipe();
pclassifier.setPipe(pipe);
factory.setStopIncrement(true);
pclassifier.saveTo(modelFile);
}
}