List of usage examples for weka.classifiers Evaluation toMatrixString
public String toMatrixString(String title) throws Exception
From source file:Controller.CtlDataMining.java
public String arbolJ48(Instances data) { try {//from w ww . j a v a 2 s. co m // Creamos un clasidicador J48 J48 j48 = new J48(); //creamos el clasificador del J48 con los datos j48.buildClassifier(data); //Creamos un objeto para la validacion del modelo con redBayesiana Evaluation evalJ48 = new Evaluation(data); /*Aplicamos el clasificador J48 hacemos validacion cruzada, de redBayesiana, con 10 campos, y el aleatorio arrancando desde 1 para la semilla*/ evalJ48.crossValidateModel(j48, data, 10, new Random(1)); //Obtenemos resultados String resJ48 = "<br><b><center>Resultados Arbol de decision J48" + "</center><br>========<br>Modelo generado indica los " + "siguientes resultados:<br>========<br></b>"; resJ48 = resJ48 + ("<b>1. Numero de instancias clasificadas:</b> " + (int) evalJ48.numInstances() + "<br>"); resJ48 = resJ48 + ("<b>2. Porcentaje de instancias correctamente " + "clasificadas:</b> " + formato.format(evalJ48.pctCorrect()) + "<br>"); resJ48 = resJ48 + ("<b>3. Numero de instancias correctamente " + "clasificadas:</b>" + (int) evalJ48.correct() + "<br>"); resJ48 = resJ48 + ("<b>4. Porcentaje de instancias incorrectamente " + "clasificadas:</b> " + formato.format(evalJ48.pctIncorrect()) + "<br>"); resJ48 = resJ48 + ("<b>5. Numero de instancias incorrectamente " + "clasificadas:</b> " + (int) evalJ48.incorrect() + "<br>"); resJ48 = resJ48 + ("<b>6. Media del error absoluto:</b> " + formato.format(evalJ48.meanAbsoluteError()) + "<br>"); resJ48 = resJ48 + ("<b>7. " + evalJ48.toMatrixString("Matriz de" + " confusion</b>").replace("\n", "<br>")); // SE GRAFICA EL ARBOL GENERADO //Se crea un Jframe Temporal final javax.swing.JFrame jf = new javax.swing.JFrame("Arbol de decision: J48"); /*Se asigna un tamao*/ jf.setSize(500, 400); /*Se define un borde*/ jf.getContentPane().setLayout(new BorderLayout()); /*Se instancia la grafica del arbol, estableciendo el tipo J48 Parametros (Listener, Tipo de arbol, Tipo de nodos) El placeNode2 colocar los nodos para que caigan en forma uniforme por debajo de su padre*/ TreeVisualizer tv = new TreeVisualizer(null, j48.graph(), new PlaceNode2()); /*Aade el arbol centrandolo*/ jf.getContentPane().add(tv, BorderLayout.CENTER); /*Aadimos un listener para la X del close*/ jf.addWindowListener(new java.awt.event.WindowAdapter() { @Override public void windowClosing(java.awt.event.WindowEvent e) { jf.dispose(); } }); /*Lo visualizamos*/ jf.setVisible(true); /*Ajustamos el arbol al ancho del JFRM*/ tv.fitToScreen(); return resJ48; } catch (Exception e) { return "El error es" + e.getMessage(); } }
From source file:farm_ads.MyClassifier.java
public String printEvaluation(Evaluation e) throws Exception { String s = new String(); s += e.toSummaryString("\nResults\n======\n", false); s += "\n" + e.toMatrixString("Matrix String"); s += "\n" + e.toClassDetailsString(); return s;/* www. j a v a 2 s . c o m*/ }
From source file:main.mFFNN.java
public static void main(String[] args) throws Exception { mFFNN m = new mFFNN(); BufferedReader breader = null; breader = new BufferedReader(new FileReader("src\\main\\iris.arff")); Instances fileTrain = new Instances(breader); fileTrain.setClassIndex(fileTrain.numAttributes() - 1); System.out.println(fileTrain); breader.close();/*from w ww. ja v a 2 s .c o m*/ System.out.println("mFFNN!!!\n\n"); FeedForwardNeuralNetwork FFNN = new FeedForwardNeuralNetwork(); Evaluation eval = new Evaluation(fileTrain); FFNN.buildClassifier(fileTrain); eval.evaluateModel(FFNN, fileTrain); //OUTPUT Scanner scan = new Scanner(System.in); System.out.println(eval.toSummaryString("=== Stratified cross-validation ===\n" + "=== Summary ===", true)); System.out.println(eval.toClassDetailsString("=== Detailed Accuracy By Class ===")); System.out.println(eval.toMatrixString("===Confusion matrix===")); System.out.println(eval.fMeasure(1) + " " + eval.recall(1)); System.out.println("\nDo you want to save this model(1/0)? "); FFNN.distributionForInstance(fileTrain.get(0)); /* int c = scan.nextInt(); if (c == 1 ){ System.out.print("Please enter your file name (*.model) : "); String infile = scan.next(); m.saveModel(FFNN,infile); } else { System.out.print("Model not saved."); } */ }
From source file:mao.datamining.ModelProcess.java
private void testWithExtraDS(Classifier classifier, Instances finalTrainDataSet, Instances finalTestDataSet, FileOutputStream testCaseSummaryOut, TestResult result) { //Use final training dataset and final test dataset double confusionMatrix[][] = null; long start, end, trainTime = 0, testTime = 0; if (finalTestDataSet != null) { try {//from www . ja v a2 s . c o m //counting training time start = System.currentTimeMillis(); classifier.buildClassifier(finalTrainDataSet); end = System.currentTimeMillis(); trainTime += end - start; //counting test time start = System.currentTimeMillis(); Evaluation testEvalOnly = new Evaluation(finalTrainDataSet); testEvalOnly.evaluateModel(classifier, finalTestDataSet); end = System.currentTimeMillis(); testTime += end - start; testCaseSummaryOut.write("=====================================================\n".getBytes()); testCaseSummaryOut.write((testEvalOnly.toSummaryString("=== Test Summary ===", true)).getBytes()); testCaseSummaryOut.write("\n".getBytes()); testCaseSummaryOut .write((testEvalOnly.toClassDetailsString("=== Test Class Detail ===\n")).getBytes()); testCaseSummaryOut.write("\n".getBytes()); testCaseSummaryOut .write((testEvalOnly.toMatrixString("=== Confusion matrix for Test ===\n")).getBytes()); testCaseSummaryOut.flush(); confusionMatrix = testEvalOnly.confusionMatrix(); result.setConfusionMatrix4Test(confusionMatrix); result.setAUT(testEvalOnly.areaUnderROC(1)); result.setPrecision(testEvalOnly.precision(1)); result.setRecall(testEvalOnly.recall(1)); } catch (Exception e) { ModelProcess.logging(null, e); } result.setTrainingTime(trainTime); result.setTestTime(testTime); } //using test data set , end }
From source file:mao.datamining.ModelProcess.java
private void testCV(Classifier classifier, Instances finalTrainDataSet, FileOutputStream testCaseSummaryOut, TestResult result) {/* ww w . j a va 2s . co m*/ long start, end, trainTime = 0, testTime = 0; Evaluation evalAll = null; double confusionMatrix[][] = null; // randomize data, and then stratify it into 10 groups Random rand = new Random(1); Instances randData = new Instances(finalTrainDataSet); randData.randomize(rand); if (randData.classAttribute().isNominal()) { //always run with 10 cross validation randData.stratify(folds); } try { evalAll = new Evaluation(randData); for (int i = 0; i < folds; i++) { Evaluation eval = new Evaluation(randData); Instances train = randData.trainCV(folds, i); Instances test = randData.testCV(folds, i); //counting traininig time start = System.currentTimeMillis(); Classifier j48ClassifierCopy = Classifier.makeCopy(classifier); j48ClassifierCopy.buildClassifier(train); end = System.currentTimeMillis(); trainTime += end - start; //counting test time start = System.currentTimeMillis(); eval.evaluateModel(j48ClassifierCopy, test); evalAll.evaluateModel(j48ClassifierCopy, test); end = System.currentTimeMillis(); testTime += end - start; } } catch (Exception e) { ModelProcess.logging(null, e); } //end test by cross validation // output evaluation try { ModelProcess.logging(""); //write into summary file testCaseSummaryOut .write((evalAll.toSummaryString("=== Cross Validation Summary ===", true)).getBytes()); testCaseSummaryOut.write("\n".getBytes()); testCaseSummaryOut.write( (evalAll.toClassDetailsString("=== " + folds + "-fold Cross-validation Class Detail ===\n")) .getBytes()); testCaseSummaryOut.write("\n".getBytes()); testCaseSummaryOut .write((evalAll.toMatrixString("=== Confusion matrix for all folds ===\n")).getBytes()); testCaseSummaryOut.flush(); confusionMatrix = evalAll.confusionMatrix(); result.setConfusionMatrix10Folds(confusionMatrix); } catch (Exception e) { ModelProcess.logging(null, e); } }
From source file:myclassifier.wekaCode.java
public static void foldValidation(Instances dataSet, Classifier classifiers) throws Exception { Evaluation evaluation = new Evaluation(dataSet); evaluation.crossValidateModel(classifiers, dataSet, 10, new Random(1)); //Evaluates the classifier on a given set of instances. System.out.println(evaluation.toSummaryString("\n 10-fold cross validation", false)); System.out.println(evaluation.toMatrixString("\n Confusion Matrix")); }
From source file:textmining.TextMining.java
/** * Decision Table/*from w ww . j a v a 2 s . c o m*/ * * @param instances * @return string * @throws Exception */ private static String C_DecisionTable(Instances instances) throws Exception { Classifier decisionTable = (Classifier) new DecisionTable(); String[] options = weka.core.Utils.splitOptions("-X 1 -S \"weka.attributeSelection.BestFirst -D 1 -N 5\""); decisionTable.setOptions(options); decisionTable.buildClassifier(instances); Evaluation eval = new Evaluation(instances); // eval.evaluateModel(decisionTable, instances); eval.crossValidateModel(decisionTable, instances, 5, new Random(1)); String resume = eval.toSummaryString(); return eval.toMatrixString(resume); }
From source file:textmining.TextMining.java
private static String setOptions(Classifier classifier, Instances instances, String[] options) throws Exception { classifier.setOptions(options);//w ww .j a v a2s. c o m classifier.buildClassifier(instances); Evaluation eval = new Evaluation(instances); eval.crossValidateModel(classifier, instances, 5, new Random(1)); eval.evaluateModel(classifier, instances); String resume = eval.toSummaryString(); return eval.toMatrixString(resume); }
From source file:tucil.dua.ai.TucilDuaAi.java
public static void fullTrainingSet() throws Exception { Classifier j48 = new J48(); j48.buildClassifier(datas);//from w w w .j a va 2 s .co m Evaluation eval = new Evaluation(datas); eval.evaluateModel(j48, datas); System.out.println("=====Run Information======"); System.out.println("======Classifier Model======"); System.out.println(j48.toString()); System.out.println(eval.toSummaryString("====Stats======\n", false)); System.out.println(eval.toClassDetailsString("====Detailed Result=====\n")); System.out.println(eval.toMatrixString("======Confusion Matrix======\n")); }
From source file:tucil.dua.ai.TucilDuaAi.java
public static void crossValidation() throws Exception { Evaluation evaluation = new Evaluation(datas); Classifier attr_tree = new J48(); attr_tree.buildClassifier(datas);// ww w . j av a 2 s . c om evaluation.crossValidateModel(attr_tree, datas, 10, new Random(1)); System.out.println("=====Run Information======"); System.out.println("======Classifier Model======"); System.out.println(attr_tree.toString()); System.out.println(evaluation.toSummaryString("====Stats======\n", false)); System.out.println(evaluation.toClassDetailsString("====Detailed Result=====\n")); System.out.println(evaluation.toMatrixString("======Confusion Matrix======\n")); }