Java tutorial
/* * To change this license header, choose License Headers in Project Properties. * To change this template file, choose Tools | Templates * and open the template in the editor. */ package qa.experiment; import Util.ClearParserUtil; import Util.Constant; import Util.GlobalV; import Util.ProcessFrameUtil; import Util.StdUtil; import Util.StringUtil; import clear.engine.SRLPredict; import clear.engine.SRLTrain; import qa.util.FileUtil; import java.io.BufferedReader; import java.io.File; import java.io.FileNotFoundException; import java.io.IOException; import java.io.InputStreamReader; import java.io.PrintWriter; import java.lang.reflect.InvocationTargetException; import java.util.ArrayList; import java.util.Arrays; import java.util.Collections; import java.util.HashMap; import java.util.stream.Collectors; import java.util.stream.IntStream; import org.apache.commons.io.FileUtils; import org.kohsuke.args4j.CmdLineException; import org.kohsuke.args4j.CmdLineParser; import org.kohsuke.args4j.Option; import qa.ProcessFrame; import qa.ProcessFrameProcessor; import qa.srl.SRLEvaluate; import qa.srl.SRLWrapper; /** * * @author samuellouvan */ public class SRLCombinedModelCrossValidation { ProcessFrameProcessor proc; private ArrayList<String> blackList; @Option(name = "-f", usage = "process file", required = true, metaVar = "REQUIRED") private String processTsvFileName; @Option(name = "-o", usage = "output directory name", required = true, metaVar = "REQUIRED") private String outDirName; @Option(name = "-k", usage = "number of fold", required = true, metaVar = "REQUIRED") private int fold; @Option(name = "-n", usage = "number of processes to test", required = false, metaVar = "OPTIONAL") private int nbProcess = 0; @Option(name = "-srl", usage = "SRL type", required = true, metaVar = "OPTIONAL") private int srlType; @Option(name = "-p", usage = "Process to test", required = false, metaVar = "OPTIONAL") private String processToTest = ""; @Option(name = "-pi", usage = "predicate/trigger identification", required = false, metaVar = "OPTIONAL") private boolean pi = false; boolean limitedProcess = false; private ArrayList<ProcessFrame> frameArr; ArrayList<ProcessFrame> inverseData; private HashMap<String, Integer> processFold; private ArrayList<String> processNames; ArrayList<String> testFilePath; ArrayList<String> trainingModelFilePath; /*String[] blackListProcess = {"Salivating", "composted", "decant_decanting", "dripping", "magneticseparation", "loosening", "momentum", "seafloorspreadingtheory", "sedimentation", "spear_spearing", "retract"}*/ String[] blackListProcess = { "Salivating", "composted", "decant_decanting", "dripping", "magneticseparation", "loosening", "momentum", "seafloorspreadingtheory", "sedimentation", "spear_spearing", "retract", "drop_dropping", "Feelsleepy", "harden", "positivetropism", "Resting", "separated", "revising", "sight" }; public SRLCombinedModelCrossValidation() throws FileNotFoundException { trainingModelFilePath = new ArrayList<String>(); testFilePath = new ArrayList<String>(); processFold = new HashMap<String, Integer>(); processNames = new ArrayList<String>(); blackList = new ArrayList<String>(); frameArr = new ArrayList<ProcessFrame>(); } public void init() throws FileNotFoundException, IOException, ClassNotFoundException { proc = new ProcessFrameProcessor(processTsvFileName); proc.loadProcessData(); blackList = new ArrayList(Arrays.asList(blackListProcess)); if (nbProcess > 0) { limitedProcess = true; for (int i = 0; i < proc.getProcArr().size() && processNames.size() < nbProcess; i++) { String normProcessName = ProcessFrameUtil .normalizeProcessName(proc.getProcArr().get(i).getProcessName()); if (!processNames.contains(normProcessName)) { processNames.add(normProcessName); } } } if (processToTest.isEmpty()) { frameArr = proc.getProcArr(); for (int i = 0; i < frameArr.size(); i++) { String normName = ProcessFrameUtil.normalizeProcessName(frameArr.get(i).getProcessName()); if (!processNames.contains(normName)) { processNames.add(normName); } processFold.put(normName, 0); } } else { for (int i = 0; i < proc.getProcArr().size(); i++) { String normName = ProcessFrameUtil.normalizeProcessName(proc.getProcArr().get(i).getProcessName()); String[] normNames = normName.split("_"); if (StringUtil.contains(processToTest, normNames)) { frameArr.add(proc.getProcArr().get(i)); if (!processNames.contains(normName)) { processNames.add(normName); } processFold.put(normName, 0); } } if (processFold.size() == 0) { System.out.println("Cannot find the process to test!"); System.exit(0); } } File outDirHandler = new File(outDirName); if (outDirHandler.exists()) { FileUtils.cleanDirectory(outDirHandler); } else { boolean success = outDirHandler.mkdir(); if (!success) { System.out.println("FAILED to create output directory"); System.exit(0); } } } public void doTrain(String trainingFileName, String modelFileName) throws IOException, FileNotFoundException, NoSuchMethodException, IllegalAccessException, IllegalArgumentException, InvocationTargetException { new SRLWrapper().doTrain(trainingFileName, modelFileName, srlType, false); } public void doPredict() throws NoSuchMethodException, IllegalAccessException, IllegalArgumentException, InvocationTargetException { for (int i = 0; i < testFilePath.size(); i++) { new SRLWrapper().doPredict(testFilePath.get(i), testFilePath.get(i).replace("test.", "combined.predict."), trainingModelFilePath.get(i), srlType, pi, false); } } public void trainAndPredict() throws FileNotFoundException, IOException, InterruptedException, NoSuchMethodException, IllegalAccessException, IllegalArgumentException, InvocationTargetException { /*testFilePath.clear(); trainingModelFilePath.clear(); int startIdx = 0; int testSize = frameArr.size() / fold; int endIdx = startIdx + testSize; Collections.shuffle(frameArr); for (int currentFold = 0; currentFold < fold; currentFold++) { ArrayList<ProcessFrame> testingFrames = new ArrayList<ProcessFrame>(frameArr.subList(startIdx, endIdx)); ArrayList<ProcessFrame> trainingFrames = new ArrayList<ProcessFrame>(frameArr.subList(0, startIdx)); trainingFrames.addAll(new ArrayList<ProcessFrame>(frameArr.subList(endIdx, frameArr.size()))); String trainingFileName = outDirName + "/train.combined.cv." + currentFold; String testingFileName = outDirName + "/test.cv." + currentFold; String modelName = outDirName + "/combinedmodel.cv." + currentFold; testFilePath.add(testingFileName); trainingModelFilePath.add(modelName); ProcessFrameUtil.toParserFormat(trainingFrames, trainingFileName, srlType); ProcessFrameUtil.toParserFormat(testingFrames, testingFileName, srlType); doTrain(trainingFileName, modelName); startIdx = endIdx; if (currentFold == fold - 2) { endIdx = frameArr.size(); } else { endIdx = startIdx + testSize; } } doPredict();*/ testFilePath.clear(); trainingModelFilePath.clear(); for (String processName : processNames) { if (!blackList.contains(processName)) { ArrayList<ProcessFrame> processData = proc.getProcessFrameByNormalizedName(processName); Collections.shuffle(processData); inverseData = proc.getInverseProcessFrameByNormalizedName(processName); if (processData.size() < 5) // Special case { System.out.println("Less than 5 " + processName + " size " + processData.size()); doCrossValidation(processName, processData, processData.size()); } else { doCrossValidation(processName, processData, fold); } } } doPredict(); } public void doCrossValidation(String processName, ArrayList<ProcessFrame> selectedProcessFrame, int foldSize) throws IOException, InterruptedException, FileNotFoundException, NoSuchMethodException, IllegalAccessException, IllegalArgumentException, InvocationTargetException { int startIdx = 0; int testSize = selectedProcessFrame.size() / foldSize; int endIdx = testSize; for (int currentFold = 0; currentFold < foldSize; currentFold++) { ArrayList<ProcessFrame> testingFrames = new ArrayList<ProcessFrame>( selectedProcessFrame.subList(startIdx, endIdx)); ArrayList<ProcessFrame> trainingFrames = new ArrayList<ProcessFrame>( selectedProcessFrame.subList(0, startIdx)); trainingFrames.addAll( new ArrayList<ProcessFrame>(selectedProcessFrame.subList(endIdx, selectedProcessFrame.size()))); trainingFrames.addAll(inverseData); String trainingFileName = outDirName + "/" + processName + ".train.combined.cv." + currentFold; String testingFileName = outDirName + "/" + processName + ".test.cv." + currentFold; String modelName = outDirName + "/" + processName + ".combinedmodel.cv." + currentFold; testFilePath.add(testingFileName); trainingModelFilePath.add(modelName); new ProcessFrameUtil().toParserFormat(trainingFrames, trainingFileName, srlType); new ProcessFrameUtil().toParserFormat(testingFrames, testingFileName, srlType); doTrain(trainingFileName, modelName); startIdx = endIdx; if (currentFold == foldSize - 2) { endIdx = selectedProcessFrame.size(); } else { endIdx = startIdx + testSize; } } } /** * Compute the precision, recall, F1 of the predictions by executing * combine.py and evaluate.py */ public void evaluate() throws FileNotFoundException, IOException { new SRLEvaluate().evaluateOverall(testFilePath, "test.", "combined.predict.", srlType); } public static void main(String[] args) throws FileNotFoundException { CmdLineParser cmd = null; try { SRLCombinedModelCrossValidation srlExp = new SRLCombinedModelCrossValidation(); cmd = new CmdLineParser(srlExp); cmd.parseArgument(args); srlExp.init(); srlExp.trainAndPredict(); Thread.sleep(5000); srlExp.evaluate(); System.out.println("FINISH"); } catch (CmdLineException e) { System.err.println(e.getMessage()); cmd.printUsage(System.err); } catch (Exception e) { e.printStackTrace(); } } }