edu.illinois.cs.cogcomp.core.io.IOUtils.mkdir()方法的使用及代码示例

x33g5p2x  于2022-01-21 转载在 其他  
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本文整理了Java中edu.illinois.cs.cogcomp.core.io.IOUtils.mkdir()方法的一些代码示例，展示了IOUtils.mkdir()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。IOUtils.mkdir()方法的具体详情如下：
包路径：edu.illinois.cs.cogcomp.core.io.IOUtils
类名称：IOUtils
方法名：mkdir

IOUtils.mkdir介绍

[英]Create a directory, if it does not exist.
[中]创建一个目录（如果它不存在）。

代码示例

代码示例来源：origin: CogComp/cogcomp-nlp

public void writeModelsToDisk(String dir, String modelName){
  IOUtils.mkdir(dir);
  chunker.write(dir + File.separator + modelName + ".lc", dir + File.separator + modelName + ".lex");
  logger.info("Done training, models are in " + dir+File.separator+modelName+".lc (.lex)");
}
public static void main(String[] args) {

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-chunker

public void writeModelsToDisk(String dir, String modelName){
  IOUtils.mkdir(dir);
  chunker.write(dir + File.separator + modelName + ".lc", dir + File.separator + modelName + ".lex");
  logger.info("Done training, models are in " + dir+File.separator+modelName+".lc (.lex)");
}
public static void main(String[] args) {

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-nlp-readers

/**
 * print output into a file in directory specified, with name based on annotationFile.
 * Should not create an empty file (i.e., if columnOutput is empty).
 *
 * @param nerOutputDir  directory to write output file
 * @param annotationFile    used as prefix for the name of the new file
 * @param columnOutput  a list of strings to be printed to the output file
 * @throws IOException
 */
private static void printOut(String nerOutputDir, String annotationFile, List<String> columnOutput) throws IOException {
  String outFile = nerOutputDir + "/" + annotationFile + ".ner.column.txt" ;
  if ( !columnOutput.isEmpty() ) {
    if ( !IOUtils.exists( nerOutputDir ) )
      IOUtils.mkdir( nerOutputDir );
    LineIO.write(outFile, columnOutput);
  }
}

代码示例来源：origin: CogComp/cogcomp-nlp

/**
 * Saves the ".lc" and ".lex" models to disk in the modelPath specified by the constructor The
 * modelName ("Chunker", as specified in ChunkerConfigurator) is fixed
 */
public void writeModelsToDisk() {
  IOUtils.mkdir(rm.getString("modelDirPath"));
  chunker.save();
  logger.info("Done training, models are in " + rm.getString("modelDirPath"));
}
public void writeModelsToDisk(String dir, String modelName){

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-chunker

/**
 * Saves the ".lc" and ".lex" models to disk in the modelPath specified by the constructor The
 * modelName ("Chunker", as specified in ChunkerConfigurator) is fixed
 */
public void writeModelsToDisk() {
  IOUtils.mkdir(rm.getString("modelDirPath"));
  chunker.save();
  logger.info("Done training, models are in " + rm.getString("modelDirPath"));
}
public void writeModelsToDisk(String dir, String modelName){

代码示例来源：origin: CogComp/cogcomp-nlp

IOUtils.mkdir(outDir);

代码示例来源：origin: CogComp/cogcomp-nlp

String tmpFile = tmpDir + "/google.ngrams.get1t" + (new Random()).nextInt();
IOUtils.mkdir(tmpDir);

代码示例来源：origin: CogComp/talen

/**
 * This saves an individual TextAnnotation to the desired output folder.
 * @param foldertype
 * @param path
 * @param ta
 * @throws IOException
 */
public static void save(String foldertype, String path, TextAnnotation ta) throws IOException {
  if(!IOUtils.exists(path)) {
    IOUtils.mkdir(path);
  }
  if(foldertype.equals(Common.FOLDERTA)) {
    SerializationHelper.serializeTextAnnotationToFile(ta, path + "/" + ta.getId(), true);
  }else if(foldertype.equals(Common.FOLDERTAJSON)) {
    SerializationHelper.serializeTextAnnotationToFile(ta, path + "/" + ta.getId(), true, true);
  }else if(foldertype.equals(Common.FOLDERCONLL)) {
    CoNLLNerReader.TaToConll(Collections.singletonList(ta), path);
  }else if(foldertype.equals(Common.FOLDERCOLUMN)) {
    ColumnReader.TaToColumn(Collections.singletonList(ta), path);
  }
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-core-utilities

String tmpFile = tmpDir + "/google.ngrams.get1t" + (new Random()).nextInt();
IOUtils.mkdir(tmpDir);

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-corpusreaders

IOUtils.mkdir(outDir);

代码示例来源：origin: CogComp/cogcomp-nlp

IOUtils.mkdir(outDir);

代码示例来源：origin: CogComp/cogcomp-nlp

String cacheDBDir = "data-cached";
if (!IOUtils.exists(cacheDBDir))
  IOUtils.mkdir(cacheDBDir);
String cacheDB = cacheDBDir + File.separator + viewName + "-cache.db";
dbHandler = new TextAnnotationMapDBHandler(cacheDB);

代码示例来源：origin: edu.illinois.cs.cogcomp/LBJava-NLP-tools

String cacheDBDir = "data-cached";
if (!IOUtils.exists(cacheDBDir))
  IOUtils.mkdir(cacheDBDir);
String cacheDB = cacheDBDir + File.separator + viewName + "-cache.db";
dbHandler = new TextAnnotationMapDBHandler(cacheDB);

代码示例来源：origin: CogComp/cogcomp-nlp

String outDir = args[1];
IOUtils.mkdir(outDir);

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-corpusreaders

System.exit(-1);
} else
  IOUtils.mkdir(conllDir);

代码示例来源：origin: CogComp/cogcomp-nlp

System.exit(-1);
} else
  IOUtils.mkdir(conllDir);

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-prep-srl

public void train() {
  if (!IOUtils.exists(modelsDir))
    IOUtils.mkdir(modelsDir);
  Learner classifier = new PrepSRLClassifier(modelName + ".lc", modelName + ".lex");
  Parser trainDataReader = new PrepSRLDataReader(dataDir, "train");
  BatchTrainer trainer = new BatchTrainer(classifier, trainDataReader, 1000);
  trainer.train(20);
  classifier.save();
  trainDataReader.close();
}

代码示例来源：origin: CogComp/cogcomp-nlp

public void train() {
  if (!IOUtils.exists(modelsDir))
    IOUtils.mkdir(modelsDir);
  Learner classifier = new PrepSRLClassifier(modelName + ".lc", modelName + ".lex");
  Parser trainDataReader = new PrepSRLDataReader(dataDir, "train");
  BatchTrainer trainer = new BatchTrainer(classifier, trainDataReader, 1000);
  trainer.train(20);
  classifier.save();
  trainDataReader.close();
}

代码示例来源：origin: CogComp/cogcomp-nlp

@CommandDescription(
    description = "Pre-extracts the features for the verb-sense model. Run this before training.",
    usage = "preExtract")
public static void preExtract() throws Exception {
  SenseManager manager = getManager(true);
  ResourceManager conf = new VerbSenseConfigurator().getDefaultConfig();
  // If models directory doesn't exist create it
  if (!IOUtils.isDirectory(conf.getString(conf
      .getString(VerbSenseConfigurator.MODELS_DIRECTORY))))
    IOUtils.mkdir(conf.getString(conf.getString(VerbSenseConfigurator.MODELS_DIRECTORY)));
  int numConsumers = Runtime.getRuntime().availableProcessors();
  Dataset dataset = Dataset.PTBTrainDev;
  log.info("Pre-extracting features");
  ModelInfo modelInfo = manager.getModelInfo();
  String featureSet = "" + modelInfo.featureManifest.getIncludedFeatures().hashCode();
  String allDataCacheFile =
      VerbSenseConfigurator.getFeatureCacheFile(featureSet, dataset, rm);
  FeatureVectorCacheFile featureCache =
      preExtract(numConsumers, manager, dataset, allDataCacheFile);
  pruneFeatures(numConsumers, manager, featureCache,
      VerbSenseConfigurator.getPrunedFeatureCacheFile(featureSet, rm));
  Lexicon lexicon = modelInfo.getLexicon().getPrunedLexicon(manager.getPruneSize());
  log.info("Saving lexicon  with {} features to {}", lexicon.size(),
      manager.getLexiconFileName());
  log.info(lexicon.size() + " features in the lexicon");
  lexicon.save(manager.getLexiconFileName());
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-verbsense

@CommandDescription(
    description = "Pre-extracts the features for the verb-sense model. Run this before training.",
    usage = "preExtract")
public static void preExtract() throws Exception {
  SenseManager manager = getManager(true);
  ResourceManager conf = new VerbSenseConfigurator().getDefaultConfig();
  // If models directory doesn't exist create it
  if (!IOUtils.isDirectory(conf.getString(conf
      .getString(VerbSenseConfigurator.MODELS_DIRECTORY))))
    IOUtils.mkdir(conf.getString(conf.getString(VerbSenseConfigurator.MODELS_DIRECTORY)));
  int numConsumers = Runtime.getRuntime().availableProcessors();
  Dataset dataset = Dataset.PTBTrainDev;
  log.info("Pre-extracting features");
  ModelInfo modelInfo = manager.getModelInfo();
  String featureSet = "" + modelInfo.featureManifest.getIncludedFeatures().hashCode();
  String allDataCacheFile =
      VerbSenseConfigurator.getFeatureCacheFile(featureSet, dataset, rm);
  FeatureVectorCacheFile featureCache =
      preExtract(numConsumers, manager, dataset, allDataCacheFile);
  pruneFeatures(numConsumers, manager, featureCache,
      VerbSenseConfigurator.getPrunedFeatureCacheFile(featureSet, rm));
  Lexicon lexicon = modelInfo.getLexicon().getPrunedLexicon(manager.getPruneSize());
  log.info("Saving lexicon  with {} features to {}", lexicon.size(),
      manager.getLexiconFileName());
  log.info(lexicon.size() + " features in the lexicon");
  lexicon.save(manager.getLexiconFileName());
}

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