Java Util.deserialize方法代码示例

import edu.uw.easysrl.util.Util; //导入方法依赖的package包/类
private void evaluate(final double testingSupertaggerBeam, final Optional<Double> supertaggerWeight)
		throws IOException {
	final int maxSentenceLength = 70;
	final POSTagger posTagger = POSTagger
			.getStanfordTagger(new File(dataParameters.getExistingModel(), "posTagger"));

	final SRLParser parser = new JointSRLParser(EasySRL.makeParser(trainingParameters.getModelFolder()
			.getAbsolutePath(), testingSupertaggerBeam, ParsingAlgorithm.ASTAR, 20000, true, supertaggerWeight, 1,
			70), posTagger);

	final SRLParser backoff = new BackoffSRLParser(parser, new PipelineSRLParser(EasySRL.makeParser(dataParameters
			.getExistingModel().getAbsolutePath(), 0.0001, ParsingAlgorithm.ASTAR, 100000, false, Optional.empty(),
			1, 70), Util.deserialize(new File(dataParameters.getExistingModel(), "labelClassifier")), posTagger));

	final Results results = SRLEvaluation
			.evaluate(backoff, ParallelCorpusReader.getPropBank00(), maxSentenceLength);

	System.out.println("Final result: F1=" + results.getF1());

}
 

import edu.uw.easysrl.util.Util; //导入方法依赖的package包/类
public AbstractParser build() {
	try {
		if (modelFactory == null) {
			if (jointModel) {
				final Map<FeatureKey, Integer> keyToIndex = Util.deserialize(new File(modelFolder, "featureToIndex"));
				final double[] weights = Util.deserialize(new File(modelFolder, "weights"));
				if (supertaggerWeight != null) {
					weights[0] = supertaggerWeight;
				}

				modelFactory = new SRLFactoredModelFactory(weights,
						Util.<FeatureSet>deserialize(new File(modelFolder, "features"))
								.setSupertaggingFeature(new File(modelFolder, "/pipeline"), supertaggerBeam),
						lexicalCategories, cutoffs, keyToIndex);

			} else {
				final Tagger tagger = !useSupertaggedInput ?
						Tagger.make(modelFolder, supertaggerBeam, 50, cutoffs) :
						null;

				modelFactory = new SupertagFactoredModelFactory(tagger, lexicalCategories, nbest > 1);

			}
		}
		return build2();
	} catch (final IOException e) {
		throw new UncheckedIOException(e);
	}
}
 

import edu.uw.easysrl.util.Util; //导入方法依赖的package包/类
public static void main(final String[] args) throws IOException {

		final String folder = Util.getHomeFolder() + "/Downloads/lstm_models/model";
		final String pipelineFolder = folder + "/pipeline";
		final POSTagger posTagger = POSTagger.getStanfordTagger(new File(pipelineFolder, "posTagger"));
		final PipelineSRLParser pipeline = new PipelineSRLParser(new ParserAStar.Builder(new File(pipelineFolder))
				.supertaggerBeam(0.00001).build(), Util.deserialize(new File(pipelineFolder, "labelClassifier")),
				posTagger);

		for (final double beta : Arrays.asList(0.01, 0.005, 0.001)) {
			// for (final Double supertaggerWeight : Arrays.asList(null)) {
			final Double supertaggerWeight = null;

			final SRLParser jointAstar = new BackoffSRLParser(

			new JointSRLParser(new ParserAStar.Builder(new File(folder)).maxChartSize(20000).supertaggerBeam(beta)
							.supertaggerWeight(supertaggerWeight).build(), posTagger), pipeline);

			evaluate(jointAstar,
			// pipeline,
			// // BrownPropbankReader.readCorpus()//
					ParallelCorpusReader.getPropBank00()
					// ParallelCorpusReader.getPropBank23()
					, 70);
			// }
		}
	}
 

import edu.uw.easysrl.util.Util; //导入方法依赖的package包/类
private static PipelineSRLParser makePipelineParser(final File folder,
		final CommandLineArguments commandLineOptions, final double supertaggerBeam,
		final boolean outputDependencies) throws IOException {
	final POSTagger posTagger = POSTagger.getStanfordTagger(new File(folder, "posTagger"));
	final File labelClassifier = new File(folder, "labelClassifier");
	final LabelClassifier classifier = labelClassifier.exists() && outputDependencies ? Util
			.deserialize(labelClassifier) : CCGBankEvaluation.dummyLabelClassifier;

			return new PipelineSRLParser(new ParserAStar.Builder(folder).maxChartSize(100000)
			.supertaggerBeam(supertaggerBeam).nBest(commandLineOptions.getNbest())
			.maximumSentenceLength(commandLineOptions.getMaxLength()).build(), classifier, posTagger);
}
 

import edu.uw.easysrl.util.Util; //导入方法依赖的package包/类
@Deprecated
public static Parser makeParser(final CommandLineArguments commandLineOptions, final int maxChartSize,
		final boolean joint, final Optional<Double> supertaggerWeight, final boolean loadSupertagger)
		throws IOException {
	final File modelFolder = Util.getFile(commandLineOptions.getModel());
	Coindexation.parseMarkedUpFile(new File(modelFolder, "markedup"));
	final File cutoffsFile = new File(modelFolder, "cutoffs");
	final CutoffsDictionaryInterface cutoffs = cutoffsFile.exists() ? Util.deserialize(cutoffsFile) : null;

	ModelFactory modelFactory;

	final Collection<Category> lexicalCategories = TaggerEmbeddings.loadCategories(new File(modelFolder,
			"categories"));

	if (joint) {
		final Map<FeatureKey, Integer> keyToIndex = Util.deserialize(new File(modelFolder, "featureToIndex"));
		final double[] weights = Util.deserialize(new File(modelFolder, "weights"));
		if (supertaggerWeight.isPresent()) {
			weights[0] = supertaggerWeight.get();
		}

		modelFactory = new SRLFactoredModelFactory(weights, Util.<FeatureSet> deserialize(
				new File(modelFolder, "features")).setSupertaggingFeature(new File(modelFolder, "/pipeline"),
				commandLineOptions.getSupertaggerbeam()), lexicalCategories, cutoffs, keyToIndex);

	} else {
		final Tagger tagger = loadSupertagger ? Tagger.make(modelFolder, commandLineOptions.getSupertaggerbeam(),
				50, cutoffs) : null;

		modelFactory = new SupertagFactoredModelFactory(tagger, lexicalCategories,
				commandLineOptions.getNbest() > 1);

	}

	return makeParser(commandLineOptions, maxChartSize, modelFactory);
}
 

import edu.uw.easysrl.util.Util; //导入方法依赖的package包/类
private static SRLParser makeParser(final String folder) throws IOException {
	final int nbest = 10;
	final String pipelineFolder = folder + "/pipeline";
	final POSTagger posTagger = POSTagger.getStanfordTagger(new File(pipelineFolder, "posTagger"));
	final PipelineSRLParser pipeline = new PipelineSRLParser(new ParserAStar.Builder(new File(pipelineFolder))
	.supertaggerBeam(0.0001).nBest(nbest).maximumSentenceLength(100).build(), Util.deserialize(new File(
			pipelineFolder, "labelClassifier")), posTagger);

	final SRLParser jointAstar = new SemanticParser(new BackoffSRLParser(new JointSRLParser(
			new ParserAStar.Builder(new File(folder)).supertaggerBeam(0.005).nBest(nbest)
				.maximumSentenceLength(100).build(), posTagger), pipeline),
			CompositeLexicon.makeDefault(new File(folder, "lexicon")));

	return jointAstar;
}
 

import edu.uw.easysrl.util.Util; //导入方法依赖的package包/类
@Override
public void loadData(final int firstSentence, final int lastSentence,
		final TrainingDataParameters dataParameters,
		final File modelFolder, final Logger trainingLogger)
		throws IOException, RemoteException {

	System.out.println("Loading sentences from: " + firstSentence + "\t"
			+ lastSentence);
	final Collection<Sentence> sentences = new ArrayList<>();
	final Iterator<Sentence> sentenceIt = ParallelCorpusReader.READER
			.readCorpus(false);
	int i = 0;
	while (sentenceIt.hasNext()) {
		final Sentence sentence = sentenceIt.next();
		if (i < firstSentence) {

		} else if (i == lastSentence) {
			break;
		} else {
			System.out.println("Sentence "
					+ sentence.getCCGBankDependencyParse().getFile()
					+ "."
					+ sentence.getCCGBankDependencyParse()
					.getSentenceNumber());
			sentences.add(sentence);

		}

		i++;
	}

	final Training.TrainingParameters trainingParameters = Util
			.deserialize(new File(modelFolder, "parameters"));
	final CutoffsDictionaryInterface cutoffs = Util.deserialize(new File(
			modelFolder, "cutoffs"));
	final Map<FeatureKey, Integer> featureToIndex = Util
			.deserialize(trainingParameters.getFeatureToIndexFile());

	final List<TrainingExample> data = new TrainingDataLoader(cutoffs,
			dataParameters, true).makeTrainingData(sentences.iterator(),
			false);
	lossFunction = Optimization.getUnregularizedLossFunction(data,
			featureToIndex, trainingParameters, trainingLogger);
}