81 lines
3.4 KiB
Java
81 lines
3.4 KiB
Java
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package de.unidue.ltl.escrito.examples.local.models;
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import java.io.File;
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import java.util.ArrayList;
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import java.util.List;
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import org.apache.uima.analysis_engine.AnalysisEngine;
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import org.apache.uima.fit.factory.AnalysisEngineFactory;
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import org.apache.uima.fit.factory.JCasFactory;
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import org.apache.uima.fit.util.JCasUtil;
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import org.apache.uima.jcas.JCas;
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import org.dkpro.tc.api.type.TextClassificationOutcome;
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import org.dkpro.tc.ml.model.PreTrainedModelProviderUnitMode;
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import de.unidue.ltl.escrito.core.types.LearnerAnswer;
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import de.unidue.ltl.escrito.examples.basics.Experiments_ImplBase;
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public class StoredModelPredictor extends Experiments_ImplBase{
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public static final String LANG_CODE = "de";
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public static void main(String[] args) {
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//String experimentName = "Me_n-SMO-C-1.0-NormalizedPolyKernel-E-3.0";
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File modelOutputFolder = new File(TrainAndSaveModel.OUTPUT_DIR, args[0]);
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//String exampleAnswer = "Die Chromosomen, welche aus zwei Chromatiden bestehen, bewegen sich zum zentralen Äquator."; // GT = 1
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//for debugging:
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System.out.println("Total number of arguments passed: " + args.length);
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for (int i = 0; i < args.length; i++) {
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System.out.println("Argument " + i + ": " + args[i]);
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}
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try {
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documentLoadModelSingleLabel(LANG_CODE, modelOutputFolder, args[1]);
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} catch (Exception e) {
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System.out.println("Exception while processing answer. Please verify the following:");
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System.out.println("--> a) The correct name of an existing directory in " + TrainAndSaveModel.OUTPUT_DIR
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+ " is passed as first argument.");
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System.out.println("--> b) A string representing the learner's answer to be classified is passed as second argument.");
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System.out.println("--> c) Both passed arguments are wrapped in double quotes: \"...\".");
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e.printStackTrace();
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}
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}
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// from de.unidue.ltl.escrito.examples.io.StoredModelApplicationExample
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private static void documentLoadModelSingleLabel(String languageCode, File modelOutputFile, String exampleAnswer)
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throws Exception
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{
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//System.out.println("Path to model: " + modelOutputFile.getAbsolutePath());
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AnalysisEngine preprocessing = AnalysisEngineFactory.createEngine(Experiments_ImplBase.getPreprocessing(languageCode));
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AnalysisEngine tcAnno = AnalysisEngineFactory.createEngine(PreTrainedModelProviderUnitMode.class,
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PreTrainedModelProviderUnitMode.PARAM_NAME_TARGET_ANNOTATION, LearnerAnswer.class,
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// Achtung: It seems like you MAY NOT use the class TextClassificationTarget (as we do in the reader)
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// to indicate the unit to be considered
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// as far as I can see, a TextClassifcationTarget is produced by the classifier and we only want to have one in the end!
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PreTrainedModelProviderUnitMode.PARAM_TC_MODEL_LOCATION, modelOutputFile.getAbsolutePath());
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JCas jcas = JCasFactory.createJCas();
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jcas.setDocumentText(exampleAnswer);
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jcas.setDocumentLanguage(languageCode);
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LearnerAnswer unit = new LearnerAnswer(jcas, 0, jcas.getDocumentText().length());
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unit.addToIndexes();
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// redo the preprocessing
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preprocessing.process(jcas);
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tcAnno.process(jcas);
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// redo the processing done by the classifier
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List<TextClassificationOutcome> outcomes = new ArrayList<>(
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JCasUtil.select(jcas, TextClassificationOutcome.class));
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//System.out.println(jcas.getDocumentText()+"\nOutcome: "+outcomes.get(0).getOutcome());
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System.out.println(outcomes.get(0).getOutcome()); // only print (binary) outcome
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}
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}
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