Examples of weka.filters.supervised.attribute.AttributeSelection

Package weka.filters.supervised.attribute

Examples of weka.filters.supervised.attribute.AttributeSelection

weka.filters.supervised.attribute.AttributeSelection

A supervised attribute filter that can be used to select attributes. It is very flexible and allows various search and evaluation methods to be combined.

Valid options are:

 -S <"Name of search class [search options]"> Sets search method for subset evaluators. eg. -S "weka.attributeSelection.BestFirst -S 8"

 -E <"Name of attribute/subset evaluation class [evaluator options]"> Sets attribute/subset evaluator. eg. -E "weka.attributeSelection.CfsSubsetEval -L"

  Options specific to evaluator weka.attributeSelection.CfsSubsetEval:

 -M Treat missing values as a seperate value.

 -L Don't include locally predictive attributes.

  Options specific to search weka.attributeSelection.BestFirst:

 -P <start set> Specify a starting set of attributes. Eg. 1,3,5-7.

 -D <0 = backward | 1 = forward | 2 = bi-directional> Direction of search. (default = 1).

 -N <num> Number of non-improving nodes to consider before terminating search.

 -S <num> Size of lookup cache for evaluated subsets. Expressed as a multiple of the number of attributes in the data set. (default = 1)

@author Mark Hall (mhall@cs.waikato.ac.nz) @version $Revision: 5541 $

        ins=(Instances)Util.loadObject(f);      
      }
      else
      {
        ins=this.loadSamples(target);
        AttributeSelection filter= new AttributeSelection();
        filter.setEvaluator((ASEvaluation)Class.forName(Evaluator).newInstance());
        filter.setSearch((ASSearch)Class.forName(Search).newInstance());
        filter.setInputFormat(ins);
        ins=Filter.useFilter(ins, (Filter)filter);
        Util.writeObject(f, ins);
      }
       
    }

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        hold.setClassIndex(hold.numAttributes()-1);
       
        // att sel ranking
        Random rndd = new Random(422);


        AttributeSelection attribSel = new AttributeSelection();


        att_ev_SingleBayesROCAttributeEval roc = new att_ev_SingleBayesROCAttributeEval();
        Ranker rnkr = new Ranker();


//        roc.buildEvaluator(learn);


        attribSel.setEvaluator(roc);
        attribSel.setSearch(rnkr);


        attribSel.setInputFormat(learn);
        Instances learnPostSel = attribSel.useFilter(learn, attribSel);
        Instances holdPostSel = attribSel.useFilter(hold, attribSel);


        wrep.BGS_univariate_ROC = new double[learn.numAttributes()-1];
        for (int univ_counter = 0 ; univ_counter<learnPostSel.numAttributes()-1; univ_counter++) {
            wrep.BGS_univariate_ROC[univ_counter] = roc.evaluateAttribute(univ_counter);
        }

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    }


    public void addAttSelFilter(gui_WekaExperiment de) {
        try {


            AttributeSelection attSel = new AttributeSelection();
            CfsSubsetEval eval = new CfsSubsetEval();
            GreedyStepwise search = new GreedyStepwise();
            attSel.setEvaluator(eval);
            attSel.setSearch(search);
            //weka.filters.supervised.attribute.AttributeSelection -E "weka.attributeSelection.CfsSubsetEval " -S "weka.attributeSelection.BestFirst -D 1 -N 5"
            //String[] opt = {"-E", "\"weka.attributeSelection.CfsSubsetEval \"","-s","\"weka.attributeSelection.BestFirst -D 1 -N 5\"" };
            de.addFilter("weka.filters.supervised.attribute.AttributeSelection", attSel.getOptions());
        } catch (Exception ex) {
            System.out.println("AddAttSelFilterFail");
        }


    }

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        hold.setClassIndex(hold.numAttributes() - 1);


        // att sel ranking
        Random rndd = new Random(422);


        AttributeSelection attribSel = new AttributeSelection();


        att_ev_TTestAttributeEval pval = new att_ev_TTestAttributeEval();
        Ranker rnkr = new Ranker();
//        rnkr.setThreshold(p_val_threshold);


//        roc.buildEvaluator(learn);


        attribSel.setEvaluator(pval);
        attribSel.setSearch(rnkr);




        attribSel.setInputFormat(learn);
        Instances learnPostSel = attribSel.useFilter(learn, attribSel);
        Instances holdPostSel = attribSel.useFilter(hold, attribSel);


        wrep.TTEST_univariate_p_value_raw = new double[learnPostSel.numAttributes() - 1];
        wrep.TTEST_univariate_p_value_log = new double[learnPostSel.numAttributes() - 1];
        wrep.TTEST_univariate_fold_change_log = new double[learnPostSel.numAttributes() - 1];
        wrep.TTEST_univariate_fold_change_raw = new double[learnPostSel.numAttributes() - 1];

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        return filter;
    }
    
    protected Filter getFeatureSelectionFilter(ExampleSet examples) throws Exception {
        
        AttributeSelection filter = new AttributeSelection();  // package weka.filters.supervised.attribute!
        //CfsSubsetEval eval = new CfsSubsetEval();
        
        //CorrelationAttributeEval eval = new CorrelationAttributeEval();
        //InfoGainAttributeEval eval = new InfoGainAttributeEval();
        
        ReliefFAttributeEval eval = new ReliefFAttributeEval();
        
        //GreedyStepwise search = new GreedyStepwise();
        //search.setNumToSelect(980);
        //search.setSearchBackwards(true);
        
        Ranker search = new Ranker();
        search.setNumToSelect(980);
        
        filter.setEvaluator(eval);
        filter.setSearch(search);
        
        filter.setInputFormat(examples.getInstances());
        Instances filtered = Filter.useFilter(examples.getInstances(), filter);
        examples.setInstances(filtered);
        
        return filter;
    }

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Related Classes of weka.filters.supervised.attribute.AttributeSelection

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bnGUI.att_TTestAttSel

bnGUI.Thread_weka_run

etc.aloe.oilspill2010.FeatureGenerationImpl

gannuWSD.algorithms.Weka

weka.attributeSelection.BestFirst

weka.attributeSelection.CfsSubsetEval

weka.attributeSelection.Ranker

weka.core.Capabilities

weka.core.DenseInstance

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