be.uza.keratoconus.analysis.impl.PreTrainedModel.java Source code

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Here is the source code for be.uza.keratoconus.analysis.impl.PreTrainedModel.java

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/*
This file is part of Keratoconus Assistant.
    
Keratoconus Assistant is free software: you can redistribute it 
and/or modify it under the terms of the GNU General Public License 
as published by the Free Software Foundation, either version 3 of 
the License, or (at your option) any later version.
    
Keratoconus Assistant is distributed in the hope that it will be 
useful, but WITHOUT ANY WARRANTY; without even the implied warranty 
of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.
    
You should have received a copy of the GNU General Public License
along with Keratoconus Assistant.  If not, see 
<http://www.gnu.org/licenses/>.
 */

package be.uza.keratoconus.analysis.impl;

import java.io.ByteArrayInputStream;
import java.nio.charset.Charset;
import java.util.HashMap;
import java.util.Map;

import org.osgi.service.component.ComponentContext;
import org.osgi.service.log.LogService;

import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.CSVLoader;
import aQute.bnd.annotation.component.Activate;
import aQute.bnd.annotation.component.Component;
import aQute.bnd.annotation.component.Reference;
import be.uza.keratoconus.analysis.api.Analyser;
import be.uza.keratoconus.datafiles.api.PatientExam;
import be.uza.keratoconus.model.api.Face;
import be.uza.keratoconus.model.api.ModelService;

@Component(enabled = true)
public class PreTrainedModel implements Analyser {

    private static final String SEMICOLON = ";";

    private weka.classifiers.functions.SMO classifier;
    private ModelService classificationModelService;
    private LogService logService;

    private Map<String, String> examData;
    private Instance instance;
    private String[] classAttributeNames;

    private ComponentContext ownComponentContext;

    @Reference
    protected void setClassificationModelService(ModelService cms) {
        classificationModelService = cms;
    }

    @Reference
    protected void setLogService(LogService ls) {
        logService = ls;
    }

    @Activate
    public void activate(ComponentContext cc) throws Exception {
        this.ownComponentContext = cc;
        classifier = classificationModelService.getSMO();
        classAttributeNames = classifier.classAttributeNames();
    }

    @Override
    public void processPatientExam(PatientExam exam) {
        examData = exam.getExamData();
        String headerLine = "";
        String dataLine = "";
        int nColumns = 0;
        for (String fieldName : classificationModelService.getUsedFields()) {
            if (examData.containsKey(fieldName)) {
                headerLine += fieldName + SEMICOLON;
                final String fieldValue = examData.get(fieldName);
                // TODO fatal error if fieldValue is null?
                dataLine += fieldValue + SEMICOLON;
                ++nColumns;
            } else if (examData.containsKey(fieldName + " " + Face.FRONT)) {
                headerLine += fieldName + " " + Face.FRONT + SEMICOLON;
                final String frontFieldValue = examData.get(fieldName + " " + Face.FRONT);
                // TODO fatal error if fieldValue is null?
                dataLine += frontFieldValue + SEMICOLON;
                ++nColumns;
                headerLine += fieldName + " " + Face.BACK + SEMICOLON;
                final String backFieldValue = examData.get(fieldName + " " + Face.BACK);
                // TODO fatal error if fieldValue is null?
                dataLine += backFieldValue + SEMICOLON;
                ++nColumns;
            }
        }

        String csv = headerLine + "Class\n" + dataLine + "?\n";
        CSVLoader csvLoader = new CSVLoader();
        csvLoader.setFieldSeparator(SEMICOLON);
        try {
            csvLoader.setSource(new ByteArrayInputStream(csv.getBytes(Charset.forName("windows-1252"))));
            final Instances dataSet = csvLoader.getDataSet();
            dataSet.setClassIndex(nColumns);
            instance = dataSet.get(0);
        } catch (Exception e) {
            logService.log(ownComponentContext.getServiceReference(), LogService.LOG_WARNING,
                    "Exception thrown when reading CSV record", e);
        }
    }

    @Override
    public Map<String, Double> getDistribution() throws Exception {
        if (instance == null) {
            throw new IllegalStateException("no exam data is available");
        }
        double[] distribution = classifier.distributionForInstance(instance);
        Map<String, Double> result = new HashMap<String, Double>();
        for (int i = 0; i < distribution.length; ++i) {
            result.put(classAttributeNames[i], distribution[i]);
        }
        return result;
    }

}