/** * Sets up a dummy subset evaluator that basically just delegates evaluation * to the estimatePerformance method in DecisionTable */ @Override protected void setUpEvaluator() throws Exception { m_evaluator = new EvalWithDelete(); m_evaluator.buildEvaluator(m_theInstances); }
/** Constructor that uses an evaluator on a multi-label dataset using a transformation * @param x * @param dt * @param mlData */ public MultiClassAttributeEvaluator(ASEvaluation x, MultiClassTransformation dt, MultiLabelInstances mlData) { baseAttributeEvaluator = x; Instances data; try { data = dt.transformInstances(mlData); ((ASEvaluation) baseAttributeEvaluator).buildEvaluator(data); } catch (Exception ex) { Logger.getLogger(MultiClassAttributeEvaluator.class.getName()).log(Level.SEVERE, null, ex); } }
/** Constructor that uses an evaluator on a multi-label dataset * @param x * @param mlData */ public LabelPowersetAttributeEvaluator(ASEvaluation x, MultiLabelInstances mlData) { baseAttributeEvaluator = x; LabelPowersetTransformation lpt = new LabelPowersetTransformation(); Instances data; try { data = lpt.transformInstances(mlData); ((ASEvaluation) baseAttributeEvaluator).buildEvaluator(data); } catch (Exception ex) { Logger.getLogger(LabelPowersetAttributeEvaluator.class.getName()).log(Level.SEVERE, null, ex); } }
/** * Select attributes for a split of the data. Calling this function updates * the statistics on attribute selection. CVResultsString() returns a string * summarizing the results of repeated calls to this function. Assumes that * splits are from the same dataset--- ie. have the same number and types of * attributes as previous splits. * * @param split the instances to select attributes from * @exception Exception if an error occurs */ public void selectAttributesCVSplit(Instances split) throws Exception { m_ASEvaluator.buildEvaluator(split); // Do the search int[] attributeSet = m_searchMethod.search(m_ASEvaluator, split); // Do any postprocessing that a attribute selection method might // require attributeSet = m_ASEvaluator.postProcess(attributeSet); updateStatsForModelCVSplit(split, m_ASEvaluator, m_searchMethod, attributeSet, m_doRank); }
/** * Select attributes for a split of the data. Calling this function updates * the statistics on attribute selection. CVResultsString() returns a string * summarizing the results of repeated calls to this function. Assumes that * splits are from the same dataset--- ie. have the same number and types of * attributes as previous splits. * * @param split the instances to select attributes from * @exception Exception if an error occurs */ public void selectAttributesCVSplit(Instances split) throws Exception { m_ASEvaluator.buildEvaluator(split); // Do the search int[] attributeSet = m_searchMethod.search(m_ASEvaluator, split); // Do any postprocessing that a attribute selection method might // require attributeSet = m_ASEvaluator.postProcess(attributeSet); updateStatsForModelCVSplit(split, m_ASEvaluator, m_searchMethod, attributeSet, m_doRank); }
m_ASEval.buildEvaluator(m_Instances); if (m_ASEval instanceof AttributeTransformer) { m_SubsetEval.buildEvaluator(m_Instances); double[][] rankres; fs.setGenerateRanking(true); m_ASEval.buildEvaluator(m_Instances); fs.search(m_ASEval, m_Instances); rankres = fs.rankedAttributes();
ase.buildEvaluator(labelInstances);
ASEval.buildEvaluator(data); ranking = LSF.rankAttributes(data, (SubsetEvaluator) ASEval, m_verbose); } else { m_setSizeEval.buildEvaluator(trainData[f]); searchResults[f] = new LFSMethods(); searchResults[f].forwardSearch(m_cacheSize, new BitSet(m_numAttribs), m_setSizeEval.buildEvaluator(trainData[f]); searchResults[f].forwardSearch(m_cacheSize, searchResults[f].getBestGroup(), ranking, m_numUsedAttributes, for (int s = 1; s <= maxSubsetSize; s++) { if (HoldOutSubsetEvaluator.class.isInstance(m_setSizeEval)) { m_setSizeEval.buildEvaluator(trainData[f]); testMerit[f][s] = ((HoldOutSubsetEvaluator) m_setSizeEval).evaluateSubset(searchResults[f].getBestGroupOfSize( s), testData[f]); } else { m_setSizeEval.buildEvaluator(testData[f]); testMerit[f][s] = ((SubsetEvaluator)m_setSizeEval).evaluateSubset(searchResults[f].getBestGroupOfSize( s)); ASEval.buildEvaluator(data); LSF.forwardSearch(m_cacheSize, new BitSet(m_numAttribs), ranking, m_numUsedAttributes, m_linearSelectionType == TYPE_FIXED_WIDTH, 1,
m_ASEvaluator.buildEvaluator(m_trainInstances); if (m_ASEvaluator instanceof AttributeTransformer) { m_trainInstances =
m_ASEvaluator.buildEvaluator(m_trainInstances); if (m_ASEvaluator instanceof AttributeTransformer) { m_trainInstances =
((ASEvaluation) ASEval).buildEvaluator(data);
getStepManager().statusMessage(message); getStepManager().logBasic(message); evalCopy.buildEvaluator(train); if (evalCopy instanceof AttributeTransformer) { m_transformerStore.put(setNum != null ? setNum : -1,
getStepManager().statusMessage(message); getStepManager().logBasic(message); evalCopy.buildEvaluator(train); if (evalCopy instanceof AttributeTransformer) { m_transformerStore.put(setNum != null ? setNum : -1,