org.openml.webapplication.evaluate.EvaluateSurvivalAnalysisPredictions.java Source code

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Here is the source code for org.openml.webapplication.evaluate.EvaluateSurvivalAnalysisPredictions.java

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/*
 *  Webapplication - Java library that runs on OpenML servers
 *  Copyright (C) 2014 
 *  @author Jan N. van Rijn (j.n.van.rijn@liacs.leidenuniv.nl)
 *  
 *  This program 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.
 *  
 *  This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 *  
 */
package org.openml.webapplication.evaluate;

import java.io.BufferedReader;
import java.net.URL;
import java.util.ArrayList;
import java.util.List;

import org.openml.apiconnector.algorithms.Input;
import org.openml.apiconnector.xml.EvaluationScore;
import org.openml.apiconnector.xml.Task;
import org.openml.webapplication.algorithm.InstancesHelper;
import org.openml.webapplication.predictionCounter.FoldsPredictionCounter;
import org.openml.webapplication.predictionCounter.PredictionCounter;

import weka.core.Instance;
import weka.core.Instances;

public class EvaluateSurvivalAnalysisPredictions implements PredictionEvaluator {

    private final int ATT_PREDICTION_ROWID;
    private final int ATT_PREDICTION_FOLD;
    private final int ATT_PREDICTION_REPEAT;

    private final Instances dataset;
    private final Instances splits;
    private final Instances predictions;

    private final PredictionCounter predictionCounter;

    private EvaluationScore[] evaluationScores;

    public EvaluateSurvivalAnalysisPredictions(Task task, URL datasetPath, URL splitsPath, URL predictionsPath)
            throws Exception {
        // set all arff files needed for this operation. 
        dataset = new Instances(new BufferedReader(Input.getURL(datasetPath)));
        predictions = new Instances(new BufferedReader(Input.getURL(predictionsPath)));
        splits = new Instances(new BufferedReader(Input.getURL(splitsPath)));

        // initiate a class that will help us with checking the prediction count. 
        predictionCounter = new FoldsPredictionCounter(splits);

        // register row indexes. 
        ATT_PREDICTION_ROWID = InstancesHelper.getRowIndex("row_id", predictions);
        ATT_PREDICTION_REPEAT = InstancesHelper.getRowIndex(new String[] { "repeat", "repeat_nr" }, predictions);
        ATT_PREDICTION_FOLD = InstancesHelper.getRowIndex(new String[] { "fold", "fold_nr" }, predictions);

        // and do the actual evaluation. 
        doEvaluation();
    }

    private void doEvaluation() throws Exception {
        for (int i = 0; i < predictions.numInstances(); i++) {
            Instance prediction = predictions.instance(i);
            int repeat = ATT_PREDICTION_REPEAT < 0 ? 0 : (int) prediction.value(ATT_PREDICTION_REPEAT);
            int fold = ATT_PREDICTION_FOLD < 0 ? 0 : (int) prediction.value(ATT_PREDICTION_FOLD);
            int rowid = (int) prediction.value(ATT_PREDICTION_ROWID);

            predictionCounter.addPrediction(repeat, fold, 0, rowid);
            if (dataset.numInstances() <= rowid) {
                throw new RuntimeException("Making a prediction for row_id" + rowid
                        + " (0-based) while dataset has only " + dataset.numInstances() + " instances. ");
            }
        }

        if (predictionCounter.check() == false) {
            throw new RuntimeException("Prediction count does not match: " + predictionCounter.getErrorMessage());
        }

        List<EvaluationScore> evaluationMeasuresList = new ArrayList<EvaluationScore>();

        evaluationScores = evaluationMeasuresList.toArray(new EvaluationScore[evaluationMeasuresList.size()]);
    }

    public EvaluationScore[] getEvaluationScores() {
        return evaluationScores;
    }

    @Override
    public PredictionCounter getPredictionCounter() {
        return predictionCounter;
    }

}