de.tudarmstadt.ukp.similarity.experiments.coling2012.Pipeline.java Source code

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/*******************************************************************************
 * Copyright 2013
 * Ubiquitous Knowledge Processing (UKP) Lab
 * Technische Universitt Darmstadt
 *
 * All rights reserved. This program and the accompanying materials
 * are made available under the terms of the GNU Public License v3.0
 * which accompanies this distribution, and is available at
 * http://www.gnu.org/licenses/gpl-3.0.txt
 ******************************************************************************/
package de.tudarmstadt.ukp.similarity.experiments.coling2012;

import static de.tudarmstadt.ukp.similarity.experiments.coling2012.Pipeline.EvaluationMetric.Accuracy;
import static de.tudarmstadt.ukp.similarity.experiments.coling2012.Pipeline.EvaluationMetric.AverageF1;

import org.apache.commons.cli.CommandLine;
import org.apache.commons.cli.CommandLineParser;
import org.apache.commons.cli.HelpFormatter;
import org.apache.commons.cli.Options;
import org.apache.commons.cli.ParseException;
import org.apache.commons.cli.PosixParser;

import de.tudarmstadt.ukp.dkpro.core.api.resources.DKProContext;
import de.tudarmstadt.ukp.similarity.experiments.coling2012.util.ColingUtils;
import de.tudarmstadt.ukp.similarity.experiments.coling2012.util.Evaluator;
import de.tudarmstadt.ukp.similarity.experiments.coling2012.util.Evaluator.WekaClassifier;
import de.tudarmstadt.ukp.similarity.experiments.coling2012.util.Features2Arff;
import edu.stanford.nlp.util.StringUtils;

/**
 * Full-featured experimental setup
 */
public class Pipeline {
    public enum Dataset {
        WikipediaRewriteCorpus, MeterCorpus, WebisCrowdParaphraseCorpus,
    }

    public enum EvaluationMetric {
        Accuracy, AverageF1
    }

    public static String DATASET_DIR;
    public static String GOLDSTANDARD_DIR;

    public static final String FEATURES_DIR = "target/features";
    public static final String MODELS_DIR = "target/models";
    public static final String UTILS_DIR = "target/utils";
    public static final String OUTPUT_DIR = "target/output";

    public static void main(String[] args) throws Exception {
        DATASET_DIR = DKProContext.getContext().getWorkspace().getAbsolutePath() + "/Datasets/###/ds";
        GOLDSTANDARD_DIR = DKProContext.getContext().getWorkspace().getAbsolutePath() + "/Datasets/###/gs";

        Options options = new Options();
        options.addOption("d", "dataset", true, "dataset to evaluate: " + StringUtils.join(Dataset.values(), ", "));
        options.addOption("c", "classifier", true,
                "classifier to use: " + StringUtils.join(WekaClassifier.values(), ", "));

        CommandLineParser parser = new PosixParser();
        try {
            CommandLine cmd = parser.parse(options, args);

            if (cmd.hasOption("d") && cmd.hasOption("c")) {
                Dataset dataset = Dataset.valueOf(cmd.getOptionValue("d"));
                WekaClassifier wekaClassifier = WekaClassifier.valueOf(cmd.getOptionValue("c"));

                runCV(dataset, wekaClassifier);
            } else {
                new HelpFormatter().printHelp(Pipeline.class.getSimpleName(), options, true);
            }
        } catch (ParseException e) {
            new HelpFormatter().printHelp(Pipeline.class.getSimpleName(), options);
        }
    }

    public static void runCV(Dataset dataset, WekaClassifier wekaClassifier) throws Exception {
        // Generate the features
        FeatureGeneration.generateFeatures(dataset);

        // Output the ordered document IDs
        ColingUtils.generateDocumentOrder(dataset);

        // Packages features in arff files
        Features2Arff.toArffFile(dataset);

        // Run the classifier
        Evaluator.runClassifierCV(wekaClassifier, dataset);

        // Evaluate
        Evaluator.runEvaluationMetric(wekaClassifier, Accuracy, dataset);
        Evaluator.runEvaluationMetric(wekaClassifier, AverageF1, dataset);
    }
}