Examples of weka.filters.unsupervised.attribute.Remove

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.Remove

weka.filters.unsupervised.attribute.Remove
A filter that removes a range of attributes from the dataset. Will re-order the remaining attributes if invert matching sense is turned on and the attribute column indices are not specified in ascending order.
Valid options are:
```
 -R <index1,index2-index4,...> Specify list of columns to delete. First and last are valid indexes. (default none)
```
```
 -V Invert matching sense (i.e. only keep specified columns)
```
@author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 7107 $

    return retI;
  }


  private Instances removeIgnoreCols(Instances inst, int[] toIgnore) {


    Remove af = new Remove();
    Instances retI = null;


    try {
      af.setAttributeIndicesArray(toIgnore);
      af.setInvertSelection(false);
      af.setInputFormat(inst);
      retI = Filter.useFilter(inst, af);
    } catch (Exception e) {
      e.printStackTrace();
    }

View Full Code Here

    m_RemoveButton.setEnabled(false);
    m_RemoveButton.setToolTipText(Messages.getInstance().getString("PreprocessPanel_RemoveButton_SetToolTipText_Text"));
    m_RemoveButton.addActionListener(new ActionListener() {
  public void actionPerformed(ActionEvent e) {
    try {
      Remove r = new Remove();
      int [] selected = m_AttPanel.getSelectedAttributes();
      if (selected.length == 0) {
        return;
      }
      if (selected.length == m_Instances.numAttributes()) {
        // Pop up an error optionpane
        JOptionPane.showMessageDialog(PreprocessPanel.this,
            Messages.getInstance().getString("PreprocessPanel_JOptionPaneShowMessageDialog_Text_First"),
            Messages.getInstance().getString("PreprocessPanel_JOptionPaneShowMessageDialog_Text_Second"),
              JOptionPane.ERROR_MESSAGE);
        m_Log.logMessage(Messages.getInstance().getString("PreprocessPanel_Log_LogMessage_Text_First"));
        m_Log.statusMessage(Messages.getInstance().getString("PreprocessPanel_Log_StatusMessage_Text_First"));
        return;
      }
      r.setAttributeIndicesArray(selected);
      applyFilter(r);
    } catch (Exception ex) {
      if (m_Log instanceof TaskLogger) {
        ((TaskLogger)m_Log).taskFinished();
      }

View Full Code Here

    m_decisionFeatures = new int [selected.length+1];
    System.arraycopy(selected, 0, m_decisionFeatures, 0, selected.length);
    m_decisionFeatures[m_decisionFeatures.length-1] = m_theInstances.classIndex();


    // reduce instances to selected features
    m_delTransform = new Remove();
    m_delTransform.setInvertSelection(true);


    // set features to keep
    m_delTransform.setAttributeIndicesArray(m_decisionFeatures); 
    m_delTransform.setInputFormat(m_theInstances);

View Full Code Here

    double errorRate = 0;
    double[] repError = new double[5];
    int numAttributes = 0;
    int i, j;
    Random Rnd = new Random(m_seed);
    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the instances
    Instances trainCopy = new Instances(m_trainInstances);


    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }


    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];


    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }


    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);


    // max of 5 repititions ofcross validation
    for (i = 0; i < 5; i++) {
      m_Evaluation = new Evaluation(trainCopy);

View Full Code Here

      }
    }
    
    // remove columns from the data if necessary
    if (attributesToRemove.size() > 0) {
      m_attributeFilter = new Remove();
      int [] todelete = new int[attributesToRemove.size()];
      for (int i = 0; i < attributesToRemove.size(); i++) {
        todelete[i] = ((Integer)(attributesToRemove.elementAt(i))).intValue();
      }
      m_attributeFilter.setAttributeIndicesArray(todelete);

View Full Code Here

    double errorRate = 0;
    int numAttributes = 0;
    Instances trainCopy=null;
    Instances testCopy=null;


    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the training instances
    trainCopy = new Instances(m_trainingInstances);
    
    if (!m_useTraining) {
      if (m_holdOutInstances == null) {
  throw new Exception("Must specify a set of hold out/test instances "
          +"with -H");
      } 
      // copy the test instances
      testCopy = new Instances(m_holdOutInstances);
    }
    
    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }
    
    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];
    
    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }
    
    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);
    if (!m_useTraining) {
      testCopy = Filter.useFilter(testCopy, delTransform);
    }

View Full Code Here


    if (m_trainingInstances.equalHeaders(holdOut) == false) {
      throw new Exception("evaluateSubset : Incompatable instance types.");
    }


    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the training instances
    trainCopy = new Instances(m_trainingInstances);
    
    testCopy = new Instances(holdOut);


    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }
    
    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];
    
    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }
    
    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);
    testCopy = Filter.useFilter(testCopy, delTransform);


    // build the classifier
    m_Classifier.buildClassifier(trainCopy);

View Full Code Here


    if (m_trainingInstances.equalHeaders(holdOut.dataset()) == false) {
      throw new Exception("evaluateSubset : Incompatable instance types.");
    }


    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the training instances
    trainCopy = new Instances(m_trainingInstances);
    
    testCopy = (Instance)holdOut.copy();


    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }
    
    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];
    
    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }
    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);


    if (retrain) {
      trainCopy = Filter.useFilter(trainCopy, delTransform);
      // build the classifier
      m_Classifier.buildClassifier(trainCopy);
    }


    delTransform.input(testCopy);
    testCopy = delTransform.output();


    double pred;
    double [] distrib;
    distrib = m_Classifier.distributionForInstance(testCopy);
    if (m_trainingInstances.classAttribute().isNominal()) {

View Full Code Here

    attributeList2[count] = noa;
    count++;
  }
      }
      
      m_filter = new Remove();
      ((Remove)m_filter).setInvertSelection(true);
      ((Remove)m_filter).setAttributeIndicesArray(attributeList2);
      m_filter.setInputFormat(m_training);
      
      Instances temp2 = Filter.useFilter(m_training, m_filter);

View Full Code Here

      }
    }
    
    // remove columns from the data if necessary
    if (attributesToRemove.size() > 0) {
      m_attributeFilter = new Remove();
      int [] todelete = new int[attributesToRemove.size()];
      for (int i = 0; i < attributesToRemove.size(); i++) {
        todelete[i] = ((Integer)(attributesToRemove.elementAt(i))).intValue();
      }
      m_attributeFilter.setAttributeIndicesArray(todelete);

View Full Code Here

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