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 $

  public double evaluateAttribute (int attribute)
    throws Exception {
    int[] featArray = new int[2]; // feat + class
    double errorRate;
    Evaluation o_Evaluation;
    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the instances
    Instances trainCopy = new Instances(m_trainInstances);
    featArray[0] = attribute;
    featArray[1] = trainCopy.classIndex();
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);
    o_Evaluation = new Evaluation(trainCopy);
    String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
    Classifier oneR = Classifier.forName("weka.classifiers.rules.OneR", oneROpts);
    if (m_evalUsingTrainingData) {

View Full Code Here

   */
  private void buildLinearModel(int [] indices) throws Exception {
    // copy the training instances and remove all but the tested
    // attributes
    Instances reducedInst = new Instances(m_instances);
    Remove attributeFilter = new Remove();
    
    attributeFilter.setInvertSelection(true);
    attributeFilter.setAttributeIndicesArray(indices);
    attributeFilter.setInputFormat(reducedInst);


    reducedInst = Filter.useFilter(reducedInst, attributeFilter);
    
    // build a linear regression for the training data using the
    // tested attributes

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      m_RunThread.start();
    }
  }


  private Instances removeClass(Instances inst) {
    Remove af = new Remove();
    Instances retI = null;
    
    try {
      if (inst.classIndex() < 0) {
  retI = inst;
      } else {
  af.setAttributeIndices(""+(inst.classIndex()+1));
  af.setInvertSelection(false);
  af.setInputFormat(inst);
  retI = Filter.useFilter(inst, af);
      }
    } catch (Exception e) {
      e.printStackTrace();
    }

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      if (m_ignoreKeyList.isSelectedIndex(classIndex)) {
  m_ignoreKeyList.removeSelectionInterval(classIndex, classIndex);
      }
    }
    int [] selected = m_ignoreKeyList.getSelectedIndices();
    Remove af = new Remove();
    Instances retI = null;


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

View Full Code Here

    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();
    }

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    m_RemoveButton.setEnabled(false);
    m_RemoveButton.setToolTipText("Remove selected attributes.");
    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,
              "Can't remove all attributes from data!\n",
              "Remove Attributes",
              JOptionPane.ERROR_MESSAGE);
        m_Log.logMessage("Can't remove all attributes from data!");
        m_Log.statusMessage("Problem removing attributes");
        return;
      }
      r.setAttributeIndicesArray(selected);
      applyFilter(r);
    } catch (Exception ex) {
      if (m_Log instanceof TaskLogger) {
        ((TaskLogger)m_Log).taskFinished();
      }

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         +m_upperBoundMinSupport);
      }
    }


    if (deleteString.toString().length() > 0) {
      Remove af = new Remove();
      af.setAttributeIndices(deleteString.toString());
      af.setInvertSelection(false);
      af.setInputFormat(instances);
      Instances newInst = Filter.useFilter(instances, af);


      return newInst;
    }
    return instances;

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        // a classifier for decision trees:
        J48 j48 = new J48();
        
        // filter for removing samples:
        Remove rm = new Remove();
        rm.setAttributeIndices("1");  // remove 1st attribute


        // filtered classifier
        FilteredClassifier fc = new FilteredClassifier();
        fc.setFilter(rm);
        fc.setClassifier(j48);

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            if (indexes.contains(i) == false) {
                to_remove.add(i+1);
            }
        } // FOR
        
        Remove filter = new Remove();
        filter.setInputFormat(data);
        String options[] = { "-R", StringUtil.join(",", to_remove) };
        filter.setOptions(options);
        return (filter);
    }

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          }
          
          data.setClassIndex(data.numAttributes() - 1);
            
          // Instantiate the Remove filter
          Remove removeIDFilter = new Remove();
          removeIDFilter.setAttributeIndices("first");
      
      // Randomize the data
      data.randomize(random);
    
      // Perform cross-validation

View Full Code Here

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