コード例 #1
0
ファイル: MultiScheme.java プロジェクト: bigbigbug/wekax
  /**
   * Parses a given list of options. Valid options are:
   *
   * <p>-D <br>
   * Turn on debugging output.
   *
   * <p>-S seed <br>
   * Random number seed (default 1).
   *
   * <p>-B classifierstring <br>
   * Classifierstring should contain the full class name of a scheme included for selection followed
   * by options to the classifier (required, option should be used once for each classifier).
   *
   * <p>-X num_folds <br>
   * Use cross validation error as the basis for classifier selection. (default 0, is to use error
   * on the training data instead)
   *
   * <p>
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {

    setDebug(Utils.getFlag('D', options));

    String numFoldsString = Utils.getOption('X', options);
    if (numFoldsString.length() != 0) {
      setNumFolds(Integer.parseInt(numFoldsString));
    } else {
      setNumFolds(0);
    }

    String randomString = Utils.getOption('S', options);
    if (randomString.length() != 0) {
      setSeed(Integer.parseInt(randomString));
    } else {
      setSeed(1);
    }

    // Iterate through the schemes
    FastVector classifiers = new FastVector();
    while (true) {
      String classifierString = Utils.getOption('B', options);
      if (classifierString.length() == 0) {
        break;
      }
      String[] classifierSpec = Utils.splitOptions(classifierString);
      if (classifierSpec.length == 0) {
        throw new Exception("Invalid classifier specification string");
      }
      String classifierName = classifierSpec[0];
      classifierSpec[0] = "";
      classifiers.addElement(Classifier.forName(classifierName, classifierSpec));
    }
    if (classifiers.size() <= 1) {
      throw new Exception("At least two classifiers must be specified" + " with the -B option.");
    } else {
      Classifier[] classifiersArray = new Classifier[classifiers.size()];
      for (int i = 0; i < classifiersArray.length; i++) {
        classifiersArray[i] = (Classifier) classifiers.elementAt(i);
      }
      setClassifiers(classifiersArray);
    }
  }
コード例 #2
0
  /** * Create a FastVector containing the set of values found in the given col... */
  public FastVector getColValues(Table t, int colIdx) {
    HashSet<String> valSet = new HashSet<String>();
    for (int r = 0; r < t.rows(); r++) {
      String val = (String) t.matrix.getQuick(r, colIdx);

      if (val == null) {
        System.err.println("\nDEBUG r=" + r + "\tc=" + colIdx + "\tval=" + val);
        System.err.println("\nDEBUG r=" + t.rowNames[r] + "\tc=" + t.colNames[colIdx]);
      }

      // Don't want to include "missing value" as one of the nominal values...
      if (val != null) {
        if (!val.equals("?")) {
          valSet.add(val);
        }
      }
    }
    FastVector attVals = new FastVector();
    for (Object v : valSet) {
      attVals.addElement(v);
    }
    return (attVals);
  }
コード例 #3
0
  /**
   * * Create a FastVector containing the set of values found in the given row...
   *
   * <p>NOTE: As it happens, this function determines the order of the attribute values, an order
   * that will percolate throughout wekaMine and influence all subsequent displays.
   *
   * <p>Originally the valSet was a HashSet and the iteration order of that will depend on the hash
   * code for the key, and may seem random. While we would not like to rely on the order of
   * attributes, it seems desirable to make the attribute order somehow comprehensible, either sort
   * order or insertion order. Insertion order will seem random also because it is determined by the
   * arbitrary order of the instances. So it has been changed to a TreeSet which will be ordered by
   * the natural ordering of it's elements.
   */
  public FastVector getRowValues(Table t, int rowIdx) {
    TreeSet<String> valSet = new TreeSet<String>();
    for (int c = 0; c < t.cols(); c++) {
      String val = (String) t.matrix.getQuick(rowIdx, c);

      if (val == null) {
        System.err.println("null value in row:" + rowIdx + " col:" + c);
        for (int i = 0; i < t.cols(); i++) {
          System.err.println("\t" + i + "\t" + t.matrix.getQuick(rowIdx, c));
        }
      }

      // Don't want to include "missing value" as one of the nominal values...
      if (!val.equals("?")) {
        valSet.add(val);
      }
    }

    FastVector attVals = new FastVector();
    for (Object v : valSet) {
      attVals.addElement(v);
    }
    return (attVals);
  }
コード例 #4
0
  /**
   * ************************************************** Convert a table to a set of instances, with
   * <b>columns</b> representing individual </b>instances</b> and <b>rows</b> representing
   * <b>attributes</b> (e.g. as is common with microarray data)
   */
  public Instances tableColsToInstances(Table t, String relationName) {

    System.err.print("Converting table cols to instances...");

    // Set up attributes, which for colInstances will be the rowNames...
    FastVector atts = new FastVector();
    ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
    ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...

    System.err.print("creating attributes...");

    for (int r = 0; r < t.numRows; r++) {
      if (rowIsNumeric(t, r)) {
        isNominal.add(false);
        atts.addElement(new Attribute(t.rowNames[r]));
        allAttVals.add(null); // No enumeration of attribute values.
      } else {
        // It's nominal... determine the range of values and create a nominal attribute...
        isNominal.add(true);
        FastVector attVals = getRowValues(t, r);
        atts.addElement(new Attribute(t.rowNames[r], attVals));
        // Save it for later
        allAttVals.add(attVals);
      }
    }

    System.err.print("creating instances...");

    // Create Instances object..
    Instances data = new Instances(relationName, atts, 0);
    data.setRelationName(relationName);

    /** ***** CREATE INSTANCES ************* */
    // Fill the instances with data...
    // For each instance...
    for (int c = 0; c < t.numCols; c++) {
      double[] vals =
          new double[data.numAttributes()]; // Even nominal values are stored as double pointers.

      // For each attribute fill in the numeric or attributeValue index...
      for (int r = 0; r < t.numRows; r++) {
        String val = (String) t.matrix.getQuick(r, c);
        if (val == "?") vals[r] = Instance.missingValue();
        else if (isNominal.get(r)) {
          vals[r] = allAttVals.get(r).indexOf(val);
        } else {
          vals[r] = Double.parseDouble((String) val);
        }
      }
      // Add the a newly minted instance with those attribute values...
      data.add(new Instance(1.0, vals));
    }

    System.err.print("add feature names...");

    /** ***** ADD FEATURE NAMES ************* */
    // takes basically zero time... all time is in previous 2 chunks.
    if (addInstanceNamesAsFeatures) {
      Instances newData = new Instances(data);
      newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
      int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0

      // We save the instanceNames in a list because it's handy later on...
      instanceNames = new ArrayList<String>();

      for (int c = 0; c < t.colNames.length; c++) {
        instanceNames.add(t.colNames[c]);
        newData.instance(c).setValue(attrIdx, t.colNames[c]);
      }
      data = newData;
    }

    System.err.println("done.");

    return (data);
  }
コード例 #5
0
  /**
   * ************************************************** Convert a table to a set of instances, with
   * <b>rows</b> representing individual </b>instances</b> and <b>columns</b> representing
   * <b>attributes</b>
   */
  public Instances tableRowsToNominalInstances(Table t, String relationName) {

    System.err.print("Converting table rows to instances...");

    // Set up attributes, which for rowInstances will be the colNames...
    FastVector atts = new FastVector();
    ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
    ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...			

    System.err.print("creating attributes...");

    for (int c = 0; c < t.numCols; c++) {
      // It's nominal... determine the range of values
      isNominal.add(true);
      FastVector attVals = getColValues(t, c);
      atts.addElement(new Attribute(t.colNames[c], attVals));
      // Save it for later
      allAttVals.add(attVals);
    }

    System.err.print("creating instances...");

    // Create Instances object..
    Instances data = new Instances(relationName, atts, 0);
    data.setRelationName(relationName);

    // Fill the instances with data...
    // For each instance...
    for (int r = 0; r < t.numRows; r++) {
      double[] vals = new double[data.numAttributes()];

      // for each attribute
      for (int c = 0; c < t.numCols; c++) {
        String val = (String) t.matrix.getQuick(r, c);
        if (val == "?") vals[c] = Instance.missingValue();
        else if (isNominal.get(c)) {
          vals[c] = allAttVals.get(c).indexOf(val);
        } else {
          vals[c] = Double.parseDouble((String) val);
        }
      }
      // Add the a newly minted instance with those attribute values...
      data.add(new Instance(1.0, vals));
    }

    System.err.print("add feature names...");

    if (addInstanceNamesAsFeatures) {
      Instances newData = new Instances(data);
      newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
      int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0

      // We save the instanceNames in a list because it's handy later on...
      instanceNames = new ArrayList<String>();

      for (int r = 0; r < t.rowNames.length; r++) {
        instanceNames.add(t.rowNames[r]);
        newData.instance(r).setValue(attrIdx, t.rowNames[r]);
      }
      data = newData;
    }

    System.err.println("done.");

    return (data);
  }
コード例 #6
0
  /**
   * If we know in advance that the table is numeric, can optimize a lot... For example, on 9803 x
   * 294 table, TableFileLoader.readNumeric takes 6s compared to 12s for WekaMine readFromTable.
   */
  public static Instances readNumeric(String fileName, String relationName, String delimiter)
      throws Exception {

    int numAttributes = FileUtils.fastCountLines(fileName) - 1; // -1 exclude heading.
    String[] attrNames = new String[numAttributes];

    // Read the col headings and figure out the number of columns in the table..
    BufferedReader reader = new BufferedReader(new FileReader(fileName), 4194304);
    String line = reader.readLine();
    String[] instanceNames = parseColNames(line, delimiter);
    int numInstances = instanceNames.length;

    System.err.print("reading " + numAttributes + " x " + numInstances + " table..");

    // Create an array to hold the data as we read it in...
    double dataArray[][] = new double[numAttributes][numInstances];

    // Populate the matrix with values...
    String valToken = "";
    try {
      int rowIdx = 0;
      while ((line = reader.readLine()) != null) {

        String[] tokens = line.split(delimiter, -1);
        attrNames[rowIdx] = tokens[0].trim();
        for (int colIdx = 0; colIdx < (tokens.length - 1); colIdx++) {
          valToken = tokens[colIdx + 1];
          double value;

          if (valToken.equals("null")) {
            value = Instance.missingValue();
          } else if (valToken.equals("?")) {
            value = Instance.missingValue();
          } else if (valToken.equals("NA")) {
            value = Instance.missingValue();
          } else if (valToken.equals("")) {
            value = Instance.missingValue();
            // }else value = DoubleParser.lightningParse(valToken); // faster double parser with
            // MANY assumptions
          } else value = Double.parseDouble(valToken);
          dataArray[rowIdx][colIdx] = value;
        }
        rowIdx++;
      }
    } catch (NumberFormatException e) {
      System.err.println(e.toString());
      System.err.println("Parsing line: " + line);
      System.err.println("Parsing token: " + valToken);
    }

    // Set up attributes, which for colInstances will be the rowNames...
    FastVector atts = new FastVector();
    for (int a = 0; a < numAttributes; a++) {
      atts.addElement(new Attribute(attrNames[a]));
    }

    // Create Instances object..
    Instances data = new Instances(relationName, atts, 0);
    data.setRelationName(relationName);

    System.err.print("creating instances..");

    // System.err.println("DEBUG: numAttributes "+numAttributes);

    /** ***** CREATE INSTANCES ************* */
    // Fill the instances with data...
    // For each instance...
    for (int c = 0; c < numInstances; c++) {
      double[] vals =
          new double[data.numAttributes()]; // Even nominal values are stored as double pointers.

      for (int r = 0; r < numAttributes; r++) {
        double val = dataArray[r][c];
        vals[r] = val;
      }
      // Add the a newly minted instance with those attribute values...
      data.add(new Instance(1.0, vals));
    }

    // System.err.println("DEBUG: data.numInstances: "+data.numInstances());
    // System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
    // System.err.println("DEBUG: data.relationNAme"+data.relationName());
    System.err.print("add feature names..");

    /** ***** ADD FEATURE NAMES ************* */
    // takes basically zero time... all time is in previous 2 chunks.
    Instances newData = new Instances(data);
    newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
    int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0

    for (int c = 0; c < numInstances; c++) {
      newData.instance(c).setValue(attrIdx, instanceNames[c]);
    }
    data = newData;

    // System.err.println("DEBUG: data.numInstances: "+data.numInstances());
    // System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());

    return (data);
  }