Examples of weka.classifiers.meta.FilteredClassifier

weka.classifiers.meta.FilteredClassifier

Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.

Valid options are:

 -F <filter specification> Full class name of filter to use, followed by filter options. eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

 -D If set, classifier is run in debug mode and may output additional info to the console

 -W Full name of base classifier. (default: weka.classifiers.trees.J48)

  Options specific to classifier weka.classifiers.trees.J48:

 -U Use unpruned tree.

 -C <pruning confidence> Set confidence threshold for pruning. (default 0.25)

 -M <minimum number of instances> Set minimum number of instances per leaf. (default 2)

 -R Use reduced error pruning.

 -N <number of folds> Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)

 -B Use binary splits only.

 -S Don't perform subtree raising.

 -L Do not clean up after the tree has been built.

 -A Laplace smoothing for predicted probabilities.

 -Q <seed> Seed for random data shuffling (default 1).

@author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 1.28 $

          
          // Copy the classifier
          Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);
                                 
          // Instantiate the FilteredClassifier
          FilteredClassifier filteredClassifier = new FilteredClassifier();
          filteredClassifier.setFilter(removeIDFilter);
          filteredClassifier.setClassifier(classifier);
             
          // Build the classifier
          filteredClassifier.buildClassifier(train);
           
          // Evaluate
          eval.evaluateModel(filteredClassifier, test);
          
          // Add predictions

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            // Copy the classifier
            Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);
                                   
            // Instantiate the FilteredClassifier
            FilteredClassifier filteredClassifier = new FilteredClassifier();
            filteredClassifier.setFilter(removeIDFilter);
            filteredClassifier.setClassifier(classifier);
               
            // Build the classifier
            filteredClassifier.buildClassifier(train);
             
            // Evaluate
            eval.evaluateModel(classifier, test);
            
            // Add predictions

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        // Copy the classifier
        Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);
          
        // Instantiate the FilteredClassifier
        FilteredClassifier filteredClassifier = new FilteredClassifier();
        filteredClassifier.setFilter(removeIDFilter);
        filteredClassifier.setClassifier(classifier);
           
        // Build the classifier
        filteredClassifier.buildClassifier(train);
    
        // Prepare the output buffer 
        AbstractOutput output = new PlainText();
        output.setBuffer(new StringBuffer());
        output.setHeader(test);

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          // Copy the classifier
          Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);
                                 
          // Instantiate the FilteredClassifier
          FilteredClassifier filteredClassifier = new FilteredClassifier();
          filteredClassifier.setFilter(removeIDFilter);
          filteredClassifier.setClassifier(classifier);
             
          // Build the classifier
          filteredClassifier.buildClassifier(train);
          
          // Evaluate
          eval.evaluateModel(filteredClassifier, test);
          
          // Add predictions

View Full Code Here

    MakeIndicator filter = new MakeIndicator();
    filter.setAttributeIndex("" + (data.classIndex() + 1));
    filter.setValueIndices(m_Range.getRanges());
    filter.setNumeric(false);
    filter.setInputFormat(data);
    m_FilteredClassifier = new FilteredClassifier();
    if (data.numInstances() > 0) {
      m_FilteredClassifier.setClassifier(Classifier.makeCopies(classifier, 1)[0]);
    } else {
      m_FilteredClassifier.setClassifier(new weka.classifiers.rules.ZeroR());
    }

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    MakeIndicator filter = new MakeIndicator();
    filter.setAttributeIndex("" + (data.classIndex() + 1));
    filter.setValueIndices(m_Range.getRanges());
    filter.setNumeric(false);
    filter.setInputFormat(data);
    m_FilteredClassifier = new FilteredClassifier();
    if (data.numInstances() > 0) {
      m_FilteredClassifier.setClassifier(Classifier.makeCopies(classifier, 1)[0]);
    } else {
      m_FilteredClassifier.setClassifier(new weka.classifiers.rules.ZeroR());
    }

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      MakeIndicator filter = new MakeIndicator();
      filter.setAttributeIndex("" + (data.classIndex() + 1));
      filter.setValueIndices(node.m_right.getString());
      filter.setNumeric(false);
      filter.setInputFormat(data);
      FilteredClassifier classifier = new FilteredClassifier();
      if (data.numInstances() > 0) {
  classifier.setClassifier(Classifier.makeCopies(m_Classifier, 1)[0]);
      } else {
  classifier.setClassifier(new ZeroR());
      }
      classifier.setFilter(filter);
      
      if (!m_classifiers.containsKey(node.m_left.getString() + "|" + node.m_right.getString())) {
  classifier.buildClassifier(data);
  m_classifiers.put(node.m_left.getString() + "|" + node.m_right.getString(), classifier);
      } else {
  classifier=(FilteredClassifier)m_classifiers.get(node.m_left.getString() + "|" + 
               node.m_right.getString());
      }

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   * determined heuristically, derived tests might need to adjust it.
   * 
   * @return the configured FilteredClassifier
   */
  protected FilteredClassifier getFilteredClassifier() {
    FilteredClassifier  result;
    Filter    filter;
    Classifier    cls;
    
    result = new FilteredClassifier();
    
    // set filter
    filter = getFilter();
    result.setFilter(filter);
    
    // set classifier
    if (filter.getCapabilities().handles(Capability.NOMINAL_CLASS))
      cls = new weka.classifiers.trees.J48();
    else if (filter.getCapabilities().handles(Capability.BINARY_CLASS))
      cls = new weka.classifiers.trees.J48();
    else if (filter.getCapabilities().handles(Capability.UNARY_CLASS))
      cls = new weka.classifiers.trees.J48();
    else if (filter.getCapabilities().handles(Capability.NUMERIC_CLASS))
      cls = new weka.classifiers.trees.M5P();
    else if (filter.getCapabilities().handles(Capability.DATE_CLASS))
      cls = new weka.classifiers.trees.M5P();
    else
      throw new IllegalStateException("Cannot determine base classifier for FilteredClassifier!");
    result.setClassifier(cls);
    
    return result;
  }

View Full Code Here

    }


    // Build classifier


    
    FilteredClassifier fclass = new FilteredClassifier();    
    fclass.setClassifier(new weka.classifiers.bayes.NaiveBayesSimple());
    fclass.setFilter(new Discretize());
    m_Classifier = fclass;
    
    
    // Uncomment if you want to use a different classifier
    // Caution: Other places in the code will have to be adjusted!!

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