/** * Trains a classifier in the given directory. * * The directory should already contain training data as written by a {@link DataWriter} or * {@link SequenceDataWriter}. * * @param directory * The directory containing the training data. * @param trainingArguments * Additional command-line arguments that should be passed to the classifier. */ public static void main(File directory, String... trainingArguments) throws Exception { JarClassifierBuilder.trainAndPackage(directory, trainingArguments); }
@Override public void train(CollectionReader collectionReader, File directory) throws Exception { System.err.printf("%s: %s:\n", this.getClass().getSimpleName(), directory.getName()); System.err.println(this.parameterSettings); SimplePipeline.runPipeline( collectionReader, AnalysisEngineFactory.createEngineDescription(OnlyGoldModifiers.class), ModifierExtractorAnnotator.getDescription( DefaultDataWriterFactory.PARAM_DATA_WRITER_CLASS_NAME, this.parameterSettings.dataWriterClass, DirectoryDataWriterFactory.PARAM_OUTPUT_DIRECTORY, directory.getPath())); JarClassifierBuilder.trainAndPackage(directory, this.parameterSettings.trainingArguments); }
@Override public void train(CollectionReader collectionReader, File directory) throws Exception { System.err.printf("%s: %s:\n", this.getClass().getSimpleName(), directory.getName()); System.err.println(this.parameterSettings); SimplePipeline.runPipeline( collectionReader, AnalysisEngineFactory.createEngineDescription(OnlyGoldModifiers.class), ModifierExtractorAnnotator.getDescription( DefaultDataWriterFactory.PARAM_DATA_WRITER_CLASS_NAME, this.parameterSettings.dataWriterClass, DirectoryDataWriterFactory.PARAM_OUTPUT_DIRECTORY, directory.getPath())); JarClassifierBuilder.trainAndPackage(directory, this.parameterSettings.trainingArguments); }
JarClassifierBuilder.trainAndPackage(directory, parameters.toArray(new String[parameters.size()])); }else{ JarClassifierBuilder.trainAndPackage(directory, this.parameterSettings.trainingArguments);
JarClassifierBuilder.trainAndPackage(directory, parameters.toArray(new String[parameters.size()])); }else{ JarClassifierBuilder.trainAndPackage(directory, this.parameterSettings.trainingArguments);
@Override protected void train() throws Exception { AggregateBuilder builder = this.buildTrainingAggregate(); // Run preprocessing and tfidf counts analyzer SimplePipeline.runPipeline( this.getCollectionReader(items), builder.createAggregateDescription()); // For more details on the training arguments refer to SumBasicModel String[] trainingArgs = { "--max-num-sentences", Integer.toString(this.numSentences), "--seen-words-prob", Double.toString(this.seenWordsProbability), "--composition-function", this.cfType.toString() }; JarClassifierBuilder.trainAndPackage(this.modelDirectory, trainingArgs); }
JarClassifierBuilder.trainAndPackage(trainingDirectory);
JarClassifierBuilder.trainAndPackage(modelDir, modelInfo.trainingArguments);
optArray = this.kernelParams; JarClassifierBuilder.trainAndPackage(directory, optArray);
JarClassifierBuilder.trainAndPackage(directory, arguments);
JarClassifierBuilder.trainAndPackage(directory, arguments);
optArray = this.kernelParams; JarClassifierBuilder.trainAndPackage(directory, optArray);
JarClassifierBuilder.trainAndPackage( options.getModelsDirectory(), options.getTrainingArguments().toArray(new String[options.getTrainingArguments().size()]));
JarClassifierBuilder.trainAndPackage( outputDirectory, this.trainingArguments.toArray(new String[this.trainingArguments.size()]));
@Override protected void train(CollectionReader collectionReader, File directory) throws Exception { AggregateBuilder aggregateBuilder = this.getPreprocessorAggregateBuilder(); aggregateBuilder.add(PolarityCleartkAnalysisEngine.createAnnotatorDescription()); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(DocumentIDPrinter.class)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(DeterministicMarkableAnnotator.class)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(RemovePersonMarkables.class)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(SetGoldConfidence.class, SetGoldConfidence.PARAM_GOLD_VIEW, GOLD_VIEW_NAME)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(MarkableSalienceAnnotator.createDataWriterDescription( LibLinearBooleanOutcomeDataWriter.class, directory ))); SimplePipeline.runPipeline(collectionReader, aggregateBuilder.createAggregate()); // s=0 -> logistic regression with L2-norm (gives probabilistic outputs) String[] optArray = new String[]{ "-s", "0", "-c", "1", "-w1", "1"}; JarClassifierBuilder.trainAndPackage(directory, optArray); }
@Override protected void train(CollectionReader collectionReader, File directory) throws Exception { AggregateBuilder aggregateBuilder = this.getPreprocessorAggregateBuilder(); aggregateBuilder.add(PolarityCleartkAnalysisEngine.createAnnotatorDescription()); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(DocumentIDPrinter.class)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(DeterministicMarkableAnnotator.class)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(RemovePersonMarkables.class)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(SetGoldConfidence.class, SetGoldConfidence.PARAM_GOLD_VIEW, GOLD_VIEW_NAME)); aggregateBuilder.add(AnalysisEngineFactory.createEngineDescription(MarkableSalienceAnnotator.createDataWriterDescription( LibLinearBooleanOutcomeDataWriter.class, directory ))); SimplePipeline.runPipeline(collectionReader, aggregateBuilder.createAggregate()); // s=0 -> logistic regression with L2-norm (gives probabilistic outputs) String[] optArray = new String[]{ "-s", "0", "-c", "1", "-w1", "1"}; JarClassifierBuilder.trainAndPackage(directory, optArray); }