/**
* 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.app.tc;
import org.fnlp.data.reader.Reader;
import org.fnlp.ml.classifier.LabelParser.Type;
import org.fnlp.ml.classifier.Predict;
import org.fnlp.ml.classifier.TPredict;
import org.fnlp.ml.classifier.linear.Linear;
import org.fnlp.ml.classifier.linear.OnlineTrainer;
import org.fnlp.ml.types.Instance;
import org.fnlp.ml.types.InstanceSet;
import org.fnlp.ml.types.alphabet.AlphabetFactory;
import org.fnlp.nlp.pipe.NGram;
import org.fnlp.nlp.pipe.Pipe;
import org.fnlp.nlp.pipe.SeriesPipes;
import org.fnlp.nlp.pipe.StringArray2IndexArray;
import org.fnlp.nlp.pipe.Target2Label;
import org.fnlp.util.exception.LoadModelException;
/**
* 文本分类简单封装
* @author xpqiu
*
*/
public class TextClassifier {
private Linear pclassifier;
private Pipe prePipe = null;
public TextClassifier(String modelFile) throws LoadModelException {
load(modelFile);
}
public TextClassifier() {
// TODO Auto-generated constructor stub
}
/**
* 训练
* @param reader
* @param modelFile
* @throws Exception
*/
public void train(Reader reader, String modelFile) throws Exception {
/**
* 分类模型
*/
//建立字典管理器
AlphabetFactory af = AlphabetFactory.buildFactory();
//使用n元特征
Pipe ngrampp = new NGram(new int[] {1,2});
//将字符特征转换成字典索引
Pipe indexpp = new StringArray2IndexArray(af);
//将目标值对应的索引号作为类别
Pipe targetpp = new Target2Label(af.DefaultLabelAlphabet());
//建立pipe组合
SeriesPipes pp = new SeriesPipes(new Pipe[]{ngrampp,targetpp,indexpp});
SeriesPipes pp2 = new SeriesPipes(new Pipe[]{prePipe, ngrampp,targetpp,indexpp});
InstanceSet instset = new InstanceSet(pp2,af);
//读入数据，并进行数据处理
instset.loadThruStagePipes(reader);
/**
* 建立分类器
*/
OnlineTrainer trainer = new OnlineTrainer(af,100);
trainer.c = 0.01f;
pclassifier = trainer.train(instset);
pp.removeTargetPipe();
pclassifier.setPipe(pp);
af.setStopIncrement(true);
//将分类器保存到模型文件
pclassifier.saveTo(modelFile);
}
/**
* 从模型文件读入分类器
* @param modelFile
* @throws LoadModelException
*/
public void load(String modelFile) throws LoadModelException{
pclassifier =Linear.loadFrom(modelFile);
}
public TPredict<String> classify(String str){
Pipe p = pclassifier.getPipe();
Instance inst = new Instance(str);
try {
//特征转换
if(prePipe!=null)
prePipe.addThruPipe(inst);
p.addThruPipe(inst);
} catch (Exception e) {
e.printStackTrace();
}
TPredict<String> res = pclassifier.classify(inst,Type.STRING);
return res;
}
public Pipe getPrePipe() {
return prePipe;
}
public void setPrePipe(Pipe p) {
this.prePipe = p;
}
}