Example usage for weka.core Instances numAttributes

Introduction

In this page you can find the example usage for weka.core Instances numAttributes.

Prototype

publicint numAttributes() 

Source Link

Document

Returns the number of attributes.

Usage

From source file:fantail.core.Tools.java

License:Open Source License

public static double[] getTargetVector(Instance inst) {
    Instances targetBag = inst.relationalValue(inst.classIndex());
    double[] values = new double[targetBag.numAttributes()];
    for (int i = 0; i < values.length; i++) {
        values[i] = targetBag.instance(0).value(i);
    }/*from   w  w w  .  j ava  2s  . co m*/
    return values;
}

---

From source file:fantail.core.Tools.java

License:Open Source License

public static String[] getTargetNames(Instance inst) {
    Instances targetBag = inst.relationalValue(inst.classIndex());
    String[] names = new String[targetBag.numAttributes()];
    for (int i = 0; i < names.length; i++) {
        names[i] = targetBag.attribute(i).name();
    }//from  w w  w.ja va 2 s  .  c o  m
    return names;
}

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From source file:fantail.core.Tools.java

License:Open Source License

public static ReasonerComponent[] getTargetObjects(Instance inst) {

    Instances targetBag = inst.relationalValue(inst.classIndex());
    String[] names = new String[targetBag.numAttributes()];
    ReasonerComponent res[] = new ReasonerComponent[names.length];

    for (int i = 0; i < names.length; i++) {
        ReasonerComponent cl = new ReasonerComponent(i, targetBag.instance(0).value(i));
        res[i] = cl;//from   www  . j av a 2s .  co m
    }
    return res;
}

---

From source file:fantail.core.Tools.java

License:Open Source License

public static Instances loadFantailARFFInstances(String arffPath) throws Exception {
    ArffLoader loader = new ArffLoader();
    loader.setSource(new File(arffPath));
    Instances data = loader.getDataSet();
    data.setClassIndex(data.numAttributes() - 1);
    if (data.classAttribute().isRelationValued() != true) {
        throw new Exception("The last attribute needs to be 'RelationValued'");
    }/*from  www  .  j ava  2s . co  m*/
    return data;
}

---

From source file:fantail.core.WekaLRHelper.java

License:Open Source License

public static Instances covertArff2Xarff(Instances data) {

    Instances xarffData = null;// w  w w.  j a  v  a 2  s .  c  o  m

    try {
        String userDIR = System.getProperty("user.dir");
        String randFileName = Long.toString(System.nanoTime()).substring(10)
                + ".fantail.algorithms.LRT.temp.xarff";
        String path_separator = System.getProperty("file.separator");
        String xarffPath = userDIR + path_separator + randFileName;
        //System.out.println(m_xarffPath);
        int numObjects = Tools.getNumberTargets(data);

        StringBuilder sb = new StringBuilder();
        sb.append("@relation arff2xarff").append(System.getProperty("line.separator"));
        for (int i = 0; i < data.numAttributes() - 1; i++) {
            sb.append("@attribute ");
            sb.append(data.attribute(i).name());
            sb.append(" numeric").append(System.getProperty("line.separator"));
        }
        sb.append("@attribute L RANKING {");
        for (int i = 0; i < numObjects; i++) {
            String spr = ",";
            if (i == numObjects - 1) {
                spr = "";
            }
            String targetName = "T" + (i);
            sb.append(targetName).append(spr);
        }
        sb.append("}").append(System.getProperty("line.separator"));
        sb.append("@data ").append(System.getProperty("line.separator"));

        for (int i = 0; i < data.numInstances(); i++) {
            Instance inst = data.instance(i);
            for (int j = 0; j < data.numAttributes() - 1; j++) {
                sb.append(inst.value(j)).append(",");
            }
            for (int x = 1; x <= numObjects; x++) {
                int rank = x;

                String[] names = Tools.getTargetNames(inst);
                String algo = getName(rank, Tools.getTargetVector(inst), names);

                System.out.println("\t algo: " + algo + ". rank: " + rank + ", Tools.getTargetVector(inst):"
                        + Arrays.toString(Tools.getTargetVector(inst)) + ", " + "names:"
                        + Arrays.toString(names));

                String sprr = ">";
                if (x == names.length) {
                    sprr = "";
                }
                sb.append(algo).append(sprr);
            }
            sb.append(System.getProperty("line.separator"));
        }

        File file = new File(xarffPath);
        Writer output = new BufferedWriter(new FileWriter(file));
        output.write(sb.toString());
        output.close();

        System.out.println(file.getAbsoluteFile());

        weka.core.converters.XArffLoader xarffLoader = new weka.core.converters.XArffLoader();
        xarffLoader.setSource(new File(xarffPath));
        xarffData = xarffLoader.getDataSet();
        //
        File tmpxarffFile = new File(xarffPath);
        if (tmpxarffFile.exists()) {
            tmpxarffFile.delete();
        }
    } catch (Exception e) {
        e.printStackTrace();
        System.exit(-1);
    }

    return xarffData;
}

---

From source file:fantail.core.WekaLRHelper.java

License:Open Source License

public static Instances covertArff2Xarff2(DATA_TYPE data_type, Instances data) {

    Instances xarffData = null;//from w  w w  .j  a va  2  s.  co  m

    try {
        String userDIR = System.getProperty("user.dir");
        //String randFileName = Long.toString(System.nanoTime()).substring(10) + ".LRT.temp.xarff";
        String randFileName = UUID.randomUUID().toString() + ".LRT.temp.xarff";
        String path_separator = System.getProperty("file.separator");
        String xarffPath = userDIR + path_separator + randFileName;
        //System.out.println(m_xarffPath);
        int numObjects = Tools.getNumberTargets(data);

        StringBuilder sb = new StringBuilder();
        sb.append("@relation arff2xarff").append(System.getProperty("line.separator"));
        for (int i = 0; i < data.numAttributes() - 1; i++) {
            sb.append("@attribute ");
            sb.append(data.attribute(i).name());
            sb.append(" numeric").append(System.getProperty("line.separator"));
        }
        sb.append("@attribute L RANKING {");
        for (int i = 0; i < numObjects; i++) {
            String spr = ",";
            if (i == numObjects - 1) {
                spr = "";
            }
            String targetName = "T" + (i);
            sb.append(targetName).append(spr);
        }
        sb.append("}").append(System.getProperty("line.separator"));
        sb.append("@data ").append(System.getProperty("line.separator"));

        for (int i = 0; i < data.numInstances(); i++) {
            Instance inst = data.instance(i);

            // determine a ranking of the class labels
            String ranking_result = determineRanking(Tools.getTargetObjects(inst));
            if (ranking_result == null)
                continue;
            //System.out.println("\t ranking_result:" + ranking_result);

            // looking at a>b>c, if the 'a' part consists of more than two partial relations, we need to split them.
            List<ArrayList<String>> label_collection = new ArrayList<ArrayList<String>>();

            // generate feature string
            String attr_set_str = "";
            for (int j = 0; j < data.numAttributes() - 1; j++) {
                attr_set_str += (inst.value(j) + ",");
            }

            // split label string via ">"
            String items[] = ranking_result.split(">");
            for (int j = 0; j < items.length; j++) {

                String labels[] = items[j].split("\\|");

                // if the first label has more than or equal to 2 partial relations, we split it.
                ArrayList<String> label_list = new ArrayList<String>();
                if (j == 0) {
                    if (labels.length >= 2) {
                        for (int k = 0; k < labels.length; k++) {
                            label_list.add(labels[k]);
                        }
                    } else {
                        label_list.add(items[j]);
                    }
                    label_collection.add(label_list);
                } else {
                    if (labels.length >= 3) {
                        for (int k = 0; k < labels.length; k++) {
                            label_list.add(labels[k]);
                        }

                    } else {
                        label_list.add(items[j]);
                    }
                    label_collection.add(label_list);
                }
            }

            List<String> prev_items_in_label_collection = new ArrayList<String>();
            for (int j = 0; j < label_collection.size(); j++) {
                List<String> items_in_label_collection = new ArrayList<String>();
                if (j == 0) {
                    for (int k = 0; k < label_collection.get(j).size(); k++) {
                        items_in_label_collection.add(label_collection.get(j).get(k));
                    }
                } else {
                    for (int k = 0; k < label_collection.get(j).size(); k++) {
                        for (int l = 0; l < prev_items_in_label_collection.size(); l++) {
                            items_in_label_collection.add(prev_items_in_label_collection.get(l) + ">"
                                    + label_collection.get(j).get(k));
                        }
                    }
                }
                prev_items_in_label_collection = items_in_label_collection;
            }

            for (int j = 0; j < prev_items_in_label_collection.size(); j++) {
                //System.out.println("\t\t line:" + prev_items_in_label_collection.get(j));
                sb.append(attr_set_str + prev_items_in_label_collection.get(j) + "\n");
            }

            InputStream is = new ByteArrayInputStream(sb.toString().getBytes());
            weka.core.converters.XArffLoader xarffLoader = new weka.core.converters.XArffLoader();
            xarffLoader.setSource(is);
            xarffData = xarffLoader.getDataSet();
        }
    } catch (Exception e) {
        e.printStackTrace();
        System.exit(-1);
    }

    return xarffData;
}

---

From source file:fantail.examples.LabelRankingAlgorithmsMultiRunEvalExample01.java

License:Open Source License

public static void main(String[] args) throws Exception {

    String arffPath = "/Users/Quan/Dropbox/Ranking_Datasets/LabelRankingSemiSyntheticData/iris_dense.csv.arff";
    Instances data = Tools.loadFantailARFFInstances(arffPath);

    int numRuns = 30;
    int randSeed = 1;
    double trainsetRatio = 0.90;

    System.out.println(arffPath);
    System.out.println("Num of labels: " + Tools.getNumberTargets(data));
    System.out.println("Num of instances: " + data.numInstances());
    System.out.println("Num of attributes (incl. target att): " + data.numAttributes());
    System.out.println();//from w  w  w. j  av  a  2 s .  co m
    System.out.println("Num of runs: " + numRuns);
    System.out.println("trainsetRatio: " + trainsetRatio);
    System.out.println();

    AbstractRanker ranker;
    MultiRunEvaluation eval;

    //
    String strFormat = "%-30s %-30s %-30s";
    System.out.println(String.format(strFormat, "<Algorithms>", "<Kendall's tau>", "<Spearman Correlation>"));
    //
    ranker = new AverageRanking();
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);

    ranker = new RankingWithBinaryPCT();
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);

    ranker = new RankingByPairwiseComparison();
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);

    ranker = new RankingWithkNN();
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);

    ranker = new BinaryART();
    ((BinaryART) ranker).setK(9999);
    ((BinaryART) ranker).setMiniLeaf(1);
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);

    ranker = new ARTForests();
    ((ARTForests) ranker).setNumIterations(50);
    ((ARTForests) ranker).setK(0);
    ((ARTForests) ranker).setNumMinInstances(1);
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);
    //
    ranker = new RankingViaRegression();
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);
}

---

From source file:fantail.examples.LabelRankingSingleAlgoExample01.java

License:Open Source License

public static void main(String[] args) throws Exception {

    String arffPath = "/Users/Quan/Dropbox/Ranking_Datasets/LabelRankingSemiSyntheticData/glass_dense.csv.arff";
    Instances data = Tools.loadFantailARFFInstances(arffPath);

    int numRuns = 30;
    int randSeed = 1;
    double trainsetRatio = 0.50;

    System.out.println(arffPath);
    System.out.println("Num of labels: " + Tools.getNumberTargets(data));
    System.out.println("Num of instances: " + data.numInstances());
    System.out.println("Num of attributes (incl. target att): " + data.numAttributes());
    System.out.println();//from  w w w.  j a v  a 2s.  c o m

    AbstractRanker ranker;
    MultiRunEvaluation eval;

    String strFormat = "%-30s %-30s %-30s";
    System.out.println(String.format(strFormat, "<Algorithms>", "<Kendall>", "<SpearmanCC>"));

    ranker = new ARTForests();
    eval = new MultiRunEvaluation(data);
    eval.multiRunEvaluate(ranker, numRuns, trainsetRatio, randSeed);
    printResult(strFormat, ranker, eval);

}

---

From source file:farm_ads.MyClassifier.java

public Instances readIntances(String URL) throws Exception {
    FarmAds fa = new FarmAds(URL);
    FarmAdsVector fav = new FarmAdsVector();
    fav.writeFile("data\\dataVecto.dat", fa);
    DataSource source = new DataSource("data\\dataVecto.dat");
    Instances instances = source.getDataSet();
    if (instances.classIndex() == -1) {
        instances.setClassIndex(instances.numAttributes() - 1);
    }//www  . j  a v  a  2  s .  c o  m
    return instances;
}

---

From source file:farm_ads.MyClassifier.java

public Instances readIntances(String URL, Hashtable att, Hashtable numAtt, String iv) throws Exception {
    FarmAds fa = new FarmAds(att, numAtt, iv, URL);
    FarmAdsVector fav = new FarmAdsVector();
    fav.writeFile("data\\dataVecto.dat", fa);
    DataSource source = new DataSource("data\\dataVecto.dat");
    Instances instances = source.getDataSet();
    if (instances.classIndex() == -1) {
        instances.setClassIndex(instances.numAttributes() - 1);
    }/*from www  .  j a v  a 2 s .c  o  m*/
    return instances;
}

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