de.tudarmstadt.ukp.dkpro.tc.ml.liblinear.LiblinearDataWriter.java Source code

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/*******************************************************************************
 * Copyright 2015
 * Ubiquitous Knowledge Processing (UKP) Lab
 * Technische Universitt Darmstadt
 * 
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 * 
 *   http://www.apache.org/licenses/LICENSE-2.0
 * 
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 ******************************************************************************/
package de.tudarmstadt.ukp.dkpro.tc.ml.liblinear;

import java.io.File;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;

import org.apache.commons.io.FileUtils;
import org.apache.commons.lang.StringUtils;

import de.bwaldvogel.liblinear.FeatureNode;
import de.tudarmstadt.ukp.dkpro.tc.api.features.FeatureStore;
import de.tudarmstadt.ukp.dkpro.tc.api.features.Instance;
import de.tudarmstadt.ukp.dkpro.tc.core.io.DataWriter;
import de.tudarmstadt.ukp.dkpro.tc.core.ml.TCMachineLearningAdapter.AdapterNameEntries;

/**
 * Format is
 * outcome TAB index:value TAB index:value TAB ...
 * 
 * Zeros are omitted.
 * Indexes need to be sorted.
 * 
 * For example:
 *  1 1:1  3:1  4:1   6:1
 *  2 2:1  3:1  5:1   7:1
 *  1 3:1  5:1
 */
public class LiblinearDataWriter implements DataWriter {

    @Override
    public void write(File outputDirectory, FeatureStore featureStore, boolean useDenseInstances,
            String learningMode, boolean applyWeighting) throws Exception {
        FeatureNodeArrayEncoder encoder = new FeatureNodeArrayEncoder();
        FeatureNode[][] nodes = encoder.featueStore2FeatureNode(featureStore);

        // liblinear only supports integer outcomes, thus we need to create a mapping
        Map<String, Integer> outcomeMapping = getOutcomeMapping(featureStore.getUniqueOutcomes());

        StringBuilder sb = new StringBuilder();
        for (int i = 0; i < nodes.length; i++) {
            Instance instance = featureStore.getInstance(i);
            List<String> elements = new ArrayList<String>();
            for (int j = 0; j < nodes[i].length; j++) {
                FeatureNode node = nodes[i][j];
                int index = node.getIndex();
                double value = node.getValue();

                // write sparse values, i.e. skip zero values
                if (Math.abs(value) > 0.00000000001) {
                    elements.add(index + ":" + value);
                }
            }
            sb.append(outcomeMapping.get(instance.getOutcome()));
            sb.append("\t");
            sb.append(StringUtils.join(elements, "\t"));
            sb.append("\n");
        }

        File outputFile = new File(outputDirectory,
                LiblinearAdapter.getInstance().getFrameworkFilename(AdapterNameEntries.featureVectorsFile));
        FileUtils.writeStringToFile(outputFile, sb.toString());

        File mappingFile = new File(outputDirectory, LiblinearAdapter.getOutcomeMappingFilename());
        FileUtils.writeStringToFile(mappingFile, LiblinearUtils.outcomeMap2String(outcomeMapping));
    }

    private Map<String, Integer> getOutcomeMapping(Set<String> outcomes) {
        Map<String, Integer> outcomeMapping = new HashMap<String, Integer>();
        int i = 1;
        for (String outcome : outcomes) {
            outcomeMapping.put(outcome, i);
            i++;
        }
        return outcomeMapping;
    }

}