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 $

                  "-o", MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff" });
    Instances data = DataSource.read(MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff");
    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

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                     "-o", MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff" });
      Instances data = DataSource.read(MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff");
      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

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                  "-o", MODELS_DIR + "/" + testDataset.toString() + "-plusIDs.arff" });
    Instances test = DataSource.read(MODELS_DIR + "/" + testDataset.toString() + "-plusIDs.arff");
    test.setClassIndex(test.numAttributes() - 1);    
    
    // Instantiate the Remove filter
        Remove removeIDFilter = new Remove();
      removeIDFilter.setAttributeIndices("first");
        
    // Randomize the data
    test.randomize(random);
    
    // Apply log filter

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                  "-o", MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff" });
    Instances data = DataSource.read(MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff");
    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

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    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);

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      m_selectionResults.append(CrossValidateAttributes()); 
    }


    // set up the attribute filter with the selected attributes
    if (m_selectedAttributeSet != null && !m_doXval) {
      m_attributeFilter = new Remove();
      m_attributeFilter.setAttributeIndicesArray(m_selectedAttributeSet);
      m_attributeFilter.setInvertSelection(true);
      m_attributeFilter.setInputFormat(m_trainInstances);  
    }

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    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);

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  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 = AbstractClassifier.forName("weka.classifiers.rules.OneR", oneROpts);
    if (m_evalUsingTrainingData) {

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  ((Randomizable) m_Classifiers[j]).setSeed(random.nextInt());
      }
      FilteredClassifier fc = new FilteredClassifier();
      fc.setClassifier(m_Classifiers[j]);
      m_Classifiers[j] = fc;
      Remove rm = new Remove();
      rm.setOptions(new String[]{"-V", "-R", randomSubSpace(indices,subSpaceSize,classIndex+1,random)});
      fc.setFilter(rm);


      // build the classifier
      //m_Classifiers[j].buildClassifier(m_data);
    }

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    double evalMetric = 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 repetitions of cross validation
    for (i = 0; i < 5; i++) {
      m_Evaluation = new Evaluation(trainCopy);

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