/**
* 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.tag;
import org.apache.commons.cli.BasicParser;
import org.apache.commons.cli.CommandLine;
import org.apache.commons.cli.HelpFormatter;
import org.apache.commons.cli.Options;
import org.fnlp.ml.types.alphabet.AlphabetFactory;
import org.fnlp.ml.types.alphabet.IFeatureAlphabet;
import org.fnlp.ml.types.alphabet.LabelAlphabet;
import org.fnlp.nlp.pipe.Pipe;
import org.fnlp.nlp.pipe.SeriesPipes;
import org.fnlp.nlp.pipe.Target2Label;
import org.fnlp.nlp.pipe.seq.Sequence2FeatureSequence;
import org.fnlp.nlp.pipe.seq.templet.TempletGroup;
/**
* 序列标注器训练和测试程序
*
* @author xpqiu
*
*/
public class Tagger extends AbstractTagger{
/**
* 序列标注训练和测试主程序
* 训练： java -classpath fnlp-core.jar org.fnlp.nlp.tag.Tagger -train template train model
* 测试： java -classpath fnlp-core.jar org.fnlp.nlp.tag.Tagger [-haslabel] model test [result]
*
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
Options opt = new Options();
opt.addOption("h", false, "Print help for this application");
opt.addOption("iter", true, "iterative num, default 50");
opt.addOption("c", true, "parameters C in PA algorithm, default 0.8");
opt.addOption("train", false,
"switch to training mode(Default: test model");
opt.addOption("retrain", false,
"switch to retraining mode(Default: test model");
opt.addOption("margin", false, "use hamming loss as margin threshold");
opt.addOption("interim", false, "save interim model file");
opt.addOption("haslabel", false, "test file has includes label or not");
BasicParser parser = new BasicParser();
CommandLine cl;
try {
cl = parser.parse(opt, args);
} catch (Exception e) {
System.err.println("Parameters format error");
return;
}
if (args.length == 0 || cl.hasOption('h')) {
HelpFormatter f = new HelpFormatter();
f.printHelp(
"Tagger:\n"
+ "tagger [option] -train templet_file train_file model_file [test_file];\n"
+ "tagger [option] -retrain train_file model_file newmodel_file [test_file];\n"
+ "tagger [option] -label model_file test_file output_file\n",
opt);
return;
}
Tagger tagger = new Tagger();
tagger.iterNum = Integer.parseInt(cl.getOptionValue("iter", "50"));
tagger.c = Float.parseFloat(cl.getOptionValue("c", "0.8"));
tagger.useLoss = cl.hasOption("margin");
tagger.interim = cl.hasOption("interim");
tagger.hasLabel = cl.hasOption("haslabel");
String[] arg = cl.getArgs();
if (cl.hasOption("train") && arg.length == 3) {
tagger.templateFile = arg[0];
tagger.train = arg[1];
tagger.model = arg[2];
System.out.println("Training model ...");
tagger.train();
} else if (cl.hasOption("train") && arg.length == 4) {
tagger.templateFile = arg[0];
tagger.train = arg[1];
tagger.model = arg[2];
tagger.testfile = arg[3];
System.out.println("Training model ...");
tagger.train();
} else if (cl.hasOption("train") && arg.length == 5) {
tagger.templateFile = arg[0];
tagger.train = arg[1];
tagger.model = arg[2];
tagger.testfile = arg[3];
System.out.println("Training model ...");
tagger.train();
System.gc();
tagger.output = arg[4];
tagger.test();
} else if (cl.hasOption("retrain") && arg.length == 3) {
tagger.train = arg[0];
tagger.model = arg[1];
tagger.newmodel = arg[2];
System.out.println("Re-Training model ...");
tagger.loadFrom(tagger.model);
tagger.train();
} else if (cl.hasOption("retrain") && arg.length == 4) {
tagger.train = arg[0];
tagger.model = arg[1];
tagger.newmodel = arg[2];
tagger.testfile = arg[3];
System.out.println("Re-Training model ...");
tagger.loadFrom(tagger.model);
tagger.train();
} else if (cl.hasOption("retrain") && arg.length == 5) {
tagger.train = arg[0];
tagger.model = arg[1];
tagger.newmodel = arg[2];
tagger.testfile = arg[3];
System.out.println("Re-Training model ...");
tagger.loadFrom(tagger.model);
tagger.train();
System.gc();
tagger.output = arg[4];
tagger.test();
} else if (arg.length == 3) {
tagger.model = arg[0];
tagger.testfile = arg[1];
tagger.output = arg[2];
tagger.test();
} else if (arg.length == 2) {
tagger.model = arg[0];
tagger.testfile = arg[1];
tagger.test();
} else {
System.err.println("paramenters format error!");
System.err.println("Print option \"-h\" for help.");
return;
}
System.gc();
}
@Override
public Pipe createProcessor() throws Exception {
if (cl != null){
factory = cl.getAlphabetFactory();
}
else{
factory = AlphabetFactory.buildFactory();
templets = new TempletGroup();
templets.load(templateFile);
}
//类别集合
LabelAlphabet labels = factory.DefaultLabelAlphabet();
// 特征集合
IFeatureAlphabet features = factory.DefaultFeatureAlphabet();
featurePipe = new Sequence2FeatureSequence(templets, features, labels);
Pipe pipe = new SeriesPipes(new Pipe[] { new Target2Label(labels), featurePipe });
return pipe;
}
}