com.googlecode.clearnlp.experiment.DEPDevelop Maven / Gradle / Ivy

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/**
* Copyright (c) 2009-2012, 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:
*
* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* 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.
* Neither the name of the University of Colorado at Boulder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
*
* 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
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* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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package com.googlecode.clearnlp.experiment;

import java.io.FileInputStream;
import java.util.Arrays;
import java.util.Set;
import java.util.concurrent.TimeUnit;

import org.kohsuke.args4j.Option;
import org.w3c.dom.Element;

import com.googlecode.clearnlp.classification.model.StringModel;
import com.googlecode.clearnlp.dependency.DEPLib;
import com.googlecode.clearnlp.dependency.DEPParser;
import com.googlecode.clearnlp.dependency.DEPTree;
import com.googlecode.clearnlp.feature.xml.DEPFtrXml;
import com.googlecode.clearnlp.reader.DEPReader;
import com.googlecode.clearnlp.run.DEPTrain;
import com.googlecode.clearnlp.util.UTArray;
import com.googlecode.clearnlp.util.UTFile;
import com.googlecode.clearnlp.util.UTInput;
import com.googlecode.clearnlp.util.UTXml;
import com.googlecode.clearnlp.util.pair.Pair;
import com.googlecode.clearnlp.util.pair.StringIntPair;


/**
 * Trains a liblinear model.
 * @since v0.1
 * @author Jinho D. Choi ({@code [email protected]})
 */
public class DEPDevelop extends DEPTrain
{
	@Option(name="-d", usage="the directory containing development file (input; required)", required=true, metaVar="")
	private String s_devDir;
	
	public DEPDevelop() {}
	
	public DEPDevelop(String[] args)
	{
		initArgs(args);
		
		try
		{
			run(s_configXml, s_featureXml, s_trainDir, s_devDir);	
		}
		catch (Exception e) {e.printStackTrace();}
	}
	
	private void run(String configXml, String featureXml, String trainDir, String devDir) throws Exception
	{
		Element   eConfig = UTXml.getDocumentElement(new FileInputStream(configXml));
		DEPReader reader = (DEPReader)getReader(eConfig).o1;
		DEPFtrXml xml = new DEPFtrXml(new FileInputStream(featureXml));
		String[]  trainFiles = UTFile.getSortedFileListBySize(trainDir, ".*", true);
		String[]  devFiles = UTFile.getSortedFileList(devDir); 
		Set sPunc = getLexica(eConfig, xml, trainFiles, -1);
		
		Pair model = new Pair(null, 0d);
		double prevScore;	int i = 0;
		
		develop(eConfig, reader, xml, sPunc, trainFiles, devFiles, model, i++);
		
		do
		{
			prevScore = model.o2;
			develop(eConfig, reader, xml, sPunc, trainFiles, devFiles, model, i++);
		}
		while (model.o2 > prevScore);
	}
	
	/** @param devId if {@code -1}, train the models using all training files. */
	protected void develop(Element eConfig, DEPReader reader, DEPFtrXml xml, Set sPunc, String[] trainFiles, String[] devFiles, Pair model, int boot) throws Exception
	{
		long st = System.currentTimeMillis();
		int[] lCounts = {0,0,0,0}, gCounts = {0,0,0,0}, counts;
		StringIntPair[] gHeads;
		DEPParser parser;
		DEPTree tree;
		int i;
		
		parser = getTrainedParser(eConfig, xml, sPunc, trainFiles, model.o1, -1, boot);
		model.o1 = parser.getModel();
		
		for (String devFile : devFiles)
		{
		//	PrintStream fout = UTOutput.createPrintBufferedFileStream(devFile+".out");
			reader.open(UTInput.createBufferedFileReader(devFile));
			Arrays.fill(lCounts, 0);
			
			System.out.println("Predicting: "+devFile);
			for (i=0; (tree = reader.next()) != null; i++)
			{
				gHeads = tree.getHeads();
				parser.parse(tree);
		//		fout.println(tree.toStringDEP()+"\n");
				counts = DEPLib.getScores(tree, gHeads);
				UTArray.add(lCounts, counts);
				if (i%1000 == 0)	System.out.print(".");
			}
			System.out.println();
			reader.close();
			
			System.out.printf("LAS: %5.2f (%d/%d)\n", 100d*lCounts[1]/lCounts[0], lCounts[1], lCounts[0]);
			System.out.printf("UAS: %5.2f (%d/%d)\n", 100d*lCounts[2]/lCounts[0], lCounts[2], lCounts[0]);
			System.out.printf("LS : %5.2f (%d/%d)\n", 100d*lCounts[3]/lCounts[0], lCounts[3], lCounts[0]);
			
			for (i=0; i