List of usage examples for weka.classifiers Evaluation toSummaryString
public String toSummaryString(String title, boolean printComplexityStatistics)
From source file:assign00.ExperimentShell.java
/** * @param args the command line arguments *//*from w w w . j a v a2 s. c o m*/ public static void main(String[] args) throws Exception { DataSource source = new DataSource(file); Instances dataSet = source.getDataSet(); //Set up data dataSet.setClassIndex(dataSet.numAttributes() - 1); dataSet.randomize(new Random(1)); //determine sizes int trainingSize = (int) Math.round(dataSet.numInstances() * .7); int testSize = dataSet.numInstances() - trainingSize; Instances training = new Instances(dataSet, 0, trainingSize); Instances test = new Instances(dataSet, trainingSize, testSize); Standardize standardizedData = new Standardize(); standardizedData.setInputFormat(training); Instances newTest = Filter.useFilter(test, standardizedData); Instances newTraining = Filter.useFilter(training, standardizedData); NeuralNetworkClassifier NWC = new NeuralNetworkClassifier(); NWC.buildClassifier(newTraining); Evaluation eval = new Evaluation(newTraining); eval.evaluateModel(NWC, newTest); System.out.println(eval.toSummaryString("\nResults\n======\n", false)); }
From source file:c4.pkg5crossv.Classifier.java
public static void trainAndTest() throws FileNotFoundException, IOException, Exception { Instances data = DataLoad.loadData("./src/data/irysy.arff"); data.setClassIndex(data.numAttributes() - 1); //Losowy podzial tablicy data.randomize(new Random()); double percent = 60.0; int trainSize = (int) Math.round(data.numInstances() * percent / 100); int testSize = data.numInstances() - trainSize; Instances trainData = new Instances(data, 0, trainSize); Instances testData = new Instances(data, trainSize, testSize); String[] options = Utils.splitOptions("-U -M 10"); J48 tree = new J48(); tree.setOptions(options);//from ww w. j av a 2 s .co m tree.buildClassifier(trainData); Evaluation eval2 = new Evaluation(trainData); eval2.crossValidateModel(tree, testData, 10, new Random(1)); // 5 - fold System.out.println(eval2.toSummaryString("Wyniki:", false)); //Wypisanie testovania cross validation }
From source file:cezeri.evaluater.FactoryEvaluation.java
public static Evaluation performCrossValidate(Classifier model, Instances datax, int folds, boolean show_text, boolean show_plot, TFigureAttribute attr) { Random rand = new Random(1); Instances randData = new Instances(datax); randData.randomize(rand);//w ww . j a v a 2s . c om if (randData.classAttribute().isNominal()) { randData.stratify(folds); } Evaluation eval = null; try { // perform cross-validation eval = new Evaluation(randData); // double[] simulated = new double[0]; // double[] observed = new double[0]; // double[] sim = new double[0]; // double[] obs = new double[0]; for (int n = 0; n < folds; n++) { Instances train = randData.trainCV(folds, n, rand); Instances validation = randData.testCV(folds, n); // build and evaluate classifier Classifier clsCopy = Classifier.makeCopy(model); clsCopy.buildClassifier(train); // sim = eval.evaluateModel(clsCopy, validation); // obs = validation.attributeToDoubleArray(validation.classIndex()); // if (show_plot) { // double[][] d = new double[2][sim.length]; // d[0] = obs; // d[1] = sim; // CMatrix f1 = CMatrix.getInstance(d); // f1.transpose().plot(attr); // } // if (show_text) { // // output evaluation // System.out.println(); // System.out.println("=== Setup for each Cross Validation fold==="); // System.out.println("Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions())); // System.out.println("Dataset: " + randData.relationName()); // System.out.println("Folds: " + folds); // System.out.println("Seed: " + 1); // System.out.println(); // System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false)); // } simulated = FactoryUtils.concatenate(simulated, eval.evaluateModel(clsCopy, validation)); observed = FactoryUtils.concatenate(observed, validation.attributeToDoubleArray(validation.classIndex())); // simulated = FactoryUtils.mean(simulated,eval.evaluateModel(clsCopy, validation)); // observed = FactoryUtils.mean(observed,validation.attributeToDoubleArray(validation.classIndex())); } if (show_plot) { double[][] d = new double[2][simulated.length]; d[0] = observed; d[1] = simulated; CMatrix f1 = CMatrix.getInstance(d); attr.figureCaption = "overall performance"; f1.transpose().plot(attr); } if (show_text) { // output evaluation System.out.println(); System.out.println("=== Setup for Overall Cross Validation==="); System.out.println( "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions())); System.out.println("Dataset: " + randData.relationName()); System.out.println("Folds: " + folds); System.out.println("Seed: " + 1); System.out.println(); System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false)); } } catch (Exception ex) { Logger.getLogger(FactoryEvaluation.class.getName()).log(Level.SEVERE, null, ex); } return eval; }
From source file:cezeri.evaluater.FactoryEvaluation.java
public static Evaluation performCrossValidateTestAlso(Classifier model, Instances datax, Instances test, boolean show_text, boolean show_plot) { TFigureAttribute attr = new TFigureAttribute(); Random rand = new Random(1); Instances randData = new Instances(datax); randData.randomize(rand);/* w ww.j ava 2 s . c om*/ Evaluation eval = null; int folds = randData.numInstances(); try { eval = new Evaluation(randData); for (int n = 0; n < folds; n++) { // randData.randomize(rand); // Instances train = randData; Instances train = randData.trainCV(folds, n); // Instances train = randData.trainCV(folds, n, rand); Classifier clsCopy = Classifier.makeCopy(model); clsCopy.buildClassifier(train); Instances validation = randData.testCV(folds, n); // Instances validation = test.testCV(test.numInstances(), n%test.numInstances()); // CMatrix.fromInstances(train).showDataGrid(); // CMatrix.fromInstances(validation).showDataGrid(); simulated = FactoryUtils.concatenate(simulated, eval.evaluateModel(clsCopy, validation)); observed = FactoryUtils.concatenate(observed, validation.attributeToDoubleArray(validation.classIndex())); } if (show_plot) { double[][] d = new double[2][simulated.length]; d[0] = observed; d[1] = simulated; CMatrix f1 = CMatrix.getInstance(d); attr.figureCaption = "overall performance"; f1.transpose().plot(attr); } if (show_text) { // output evaluation System.out.println(); System.out.println("=== Setup for Overall Cross Validation==="); System.out.println( "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions())); System.out.println("Dataset: " + randData.relationName()); System.out.println("Folds: " + folds); System.out.println("Seed: " + 1); System.out.println(); System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false)); } } catch (Exception ex) { Logger.getLogger(FactoryEvaluation.class.getName()).log(Level.SEVERE, null, ex); } return eval; }
From source file:cezeri.evaluater.FactoryEvaluation.java
private static Evaluation doTest(boolean isTrained, Classifier model, Instances train, Instances test, boolean show_text, boolean show_plot, TFigureAttribute attr) { Instances data = new Instances(train); Random rand = new Random(1); data.randomize(rand);//from w ww. j a v a2s . c o m Evaluation eval = null; try { // double[] simulated = null; eval = new Evaluation(train); if (isTrained) { simulated = eval.evaluateModel(model, test); } else { Classifier clsCopy = Classifier.makeCopy(model); clsCopy.buildClassifier(train); simulated = eval.evaluateModel(clsCopy, test); } if (show_plot) { observed = test.attributeToDoubleArray(test.classIndex()); double[][] d = new double[2][simulated.length]; d[0] = observed; d[1] = simulated; CMatrix f1 = CMatrix.getInstance(d); String[] items = { "Observed", "Simulated" }; attr.items = items; attr.figureCaption = model.getClass().getCanonicalName(); f1.transpose().plot(attr); // if (attr.axis[0].isEmpty() && attr.axis[1].isEmpty()) { // f1.transpose().plot(attr); // } else { // f1.transpose().plot(model.getClass().getCanonicalName(), attr.items, attr.axis); // } } if (show_text) { System.out.println(); System.out.println("=== Setup for Test ==="); System.out.println( "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions())); System.out.println("Dataset: " + test.relationName()); System.out.println(); System.out.println(eval.toSummaryString("=== Test Results ===", false)); } } catch (Exception ex) { Logger.getLogger(FactoryEvaluation.class.getName()).log(Level.SEVERE, null, ex); } return eval; }
From source file:cezeri.feature.selection.FeatureSelectionInfluence.java
public static Evaluation getEvaluation(Instances randData, Classifier model, int folds) { Evaluation eval = null; try {/*from w w w . j ava 2 s . c o m*/ eval = new Evaluation(randData); for (int n = 0; n < folds; n++) { Instances train = randData.trainCV(folds, n); Instances test = randData.testCV(folds, n); // build and evaluate classifier Classifier clsCopy = Classifier.makeCopy(model); clsCopy.buildClassifier(train); eval.evaluateModel(clsCopy, test); // double[] prediction = eval.evaluateModel(clsCopy, test); // double[] original = getAttributeValues(test); // double[][] d = new double[2][prediction.length]; // d[0] = prediction; // d[1] = original; // CMatrix f1 = new CMatrix(d); } // output evaluation System.out.println(); System.out.println("=== Setup ==="); System.out.println( "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions())); System.out.println("Dataset: " + randData.relationName()); System.out.println("Folds: " + folds); System.out.println(); System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false)); System.out.println(eval.toClassDetailsString("=== Detailed Accuracy By Class ===")); System.out.println(eval.toMatrixString("Confusion Matrix")); double acc = eval.correct() / eval.numInstances() * 100; System.out.println("correct:" + eval.correct() + " " + acc + "%"); } catch (Exception ex) { Logger.getLogger(FeatureSelectionInfluence.class.getName()).log(Level.SEVERE, null, ex); } return eval; }
From source file:clasificador.Perceptron.java
public void perceptron_multicapa() { try {/*from ww w . j a v a 2 s . c om*/ //INSTANCIAS PARA ENTRENAMIENTO DEL CLASIFICADOR ConverterUtils.DataSource converU = new ConverterUtils.DataSource( "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro.arff"); Instances instancias = converU.getDataSet(); instancias.setClassIndex(instancias.numAttributes() - 1); //INSTANCIAS PARA EL TEST DEL MODELO ConverterUtils.DataSource convertest = new ConverterUtils.DataSource( "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro5.arff"); Instances testInstance = convertest.getDataSet(); testInstance.setClassIndex(testInstance.numAttributes() - 1); //CONTRUCCIN DEL CLASIFICADOR MultilayerPerceptron perceptron = new MultilayerPerceptron(); perceptron.buildClassifier(instancias); //Evaluar las instancias Evaluation ev = new Evaluation(instancias); //EVALUAR MODELO DE ENTRENAMIENTO ev.evaluateModel(perceptron, instancias); //System.out.println(instancias); System.out.println("\n\nENTRENAMIENTO DEL MODELO PERCEPTRN MULTICAPA\n\n"); System.out.println(ev.toSummaryString("_____RESULTADO_____", true)); System.out.println(ev.toMatrixString("_____Matriz confusion___")); //EVALUACIN DEL MODELO ev.evaluateModel(perceptron, testInstance); //System.out.println(instancias); System.out.println("\n\nTEST DEL MODELO PERCEPTRN MULTICAPA\n\n"); System.out.println(ev.toSummaryString("_____RESULTADO_____", true)); System.out.println(ev.toMatrixString("_____Matriz confusion___")); //MOSTRAR VALORES for (int i = 0; i < ev.evaluateModel(perceptron, testInstance).length; i++) { System.out.println("Se clasifica como: " + ev.evaluateModel(perceptron, testInstance)[i]); } } catch (Exception ex) { Logger.getLogger(Perceptron.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:clasificador.Perceptron.java
public void naive_Bayes() { try {//from w w w . ja v a2 s . c om //INSTANCIAS PARA ENTRENAMIENTO DEL CLASIFICADOR ConverterUtils.DataSource converU = new ConverterUtils.DataSource( "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro.arff"); Instances instancias = converU.getDataSet(); instancias.setClassIndex(instancias.numAttributes() - 1); //INSTANCIAS PARA EL TEST DEL MODELO ConverterUtils.DataSource convertest = new ConverterUtils.DataSource( "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro5.arff"); Instances testInstance = convertest.getDataSet(); testInstance.setClassIndex(testInstance.numAttributes() - 1); //CONTRUCCIN DEL CLASIFICADOR NaiveBayes perceptron = new NaiveBayes(); perceptron.buildClassifier(instancias); //Evaluar las instancias Evaluation ev = new Evaluation(instancias); //EVALUAR MODELO DE ENTRENAMIENTO ev.evaluateModel(perceptron, instancias); //System.out.println(instancias); System.out.println("\n\nENTRENAMIENTO DEL MODELO NAIVE BAYES\n\n"); System.out.println(ev.toSummaryString("_____RESULTADO_____", true)); System.out.println(ev.toMatrixString("_____Matriz confusion___")); //EVALUACIN DEL MODELO ev.evaluateModel(perceptron, testInstance); //System.out.println(instancias); System.out.println("\n\nTEST DEL MODELO NAIVE BAYES\n\n"); System.out.println(ev.toSummaryString("_____RESULTADO_____", true)); System.out.println(ev.toMatrixString("_____Matriz confusion___")); //MOSTRAR VALORES for (int i = 0; i < ev.evaluateModel(perceptron, testInstance).length; i++) { System.out.println("Se clasifica como: " + ev.evaluateModel(perceptron, testInstance)[i]); } } catch (Exception ex) { Logger.getLogger(Perceptron.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:clasificador.Perceptron.java
public void J48() { try {//from w w w. ja v a 2 s .c o m //INSTANCIAS PARA ENTRENAMIENTO DEL CLASIFICADOR ConverterUtils.DataSource converU = new ConverterUtils.DataSource( "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro.arff"); Instances instancias = converU.getDataSet(); instancias.setClassIndex(instancias.numAttributes() - 1); //INSTANCIAS PARA TEST DEL MODELO ConverterUtils.DataSource convertest = new ConverterUtils.DataSource( "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro5.arff"); Instances testInstance = convertest.getDataSet(); testInstance.setClassIndex(testInstance.numAttributes() - 1); //INSTANCIAS PARA PREDICCIN ConverterUtils.DataSource converPredict = new ConverterUtils.DataSource( "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro1.arff"); Instances predictInstance = converPredict.getDataSet(); predictInstance.setClassIndex(predictInstance.numAttributes() - 1); //CONTRUCCIN DEL CLASIFICADOR J48 perceptron = new J48(); perceptron.buildClassifier(instancias); //Evaluar las instancias Evaluation ev = new Evaluation(instancias); //EVALUAR MODELO DE ENTRENAMIENTO ev.evaluateModel(perceptron, instancias); //System.out.println(instancias); System.out.println("\n\nENTRENAMIENTO DEL MODELO ?RBOL DE DECISIN J48\n\n"); System.out.println(ev.toSummaryString("_____RESULTADO_____", true)); System.out.println(ev.toMatrixString("_____Matriz confusion___")); //PREDECIR CON EL MODELO Evaluation evPredict = new Evaluation(instancias); evPredict.evaluateModel(perceptron, predictInstance); //System.out.println(instancias); System.out.println("\n\nPREDICCIN DEL MODELO ?RBOL DE DECISIN J48\n\n"); System.out.println(evPredict.toSummaryString("_____RESULTADO_____", false)); System.out.println(evPredict.toMatrixString("_____Matriz confusion___")); //MOSTRAR VALORES for (int i = 0; i < evPredict.evaluateModel(perceptron, predictInstance).length; i++) { resultado = evPredict.evaluateModel(perceptron, predictInstance)[i]; polaridad += polaridad(resultado) + "\n"; //System.out.println("Se clasifica como: "+resultado + "que es: " + polaridad(resultado)); } archivoResultados(polaridad); //TEST DEL MODELO CON LOS DATOS DEL CLASIFICADOR Evaluation evtesting = new Evaluation(instancias); evtesting.evaluateModel(perceptron, testInstance); //System.out.println(instancias); System.out.println("\n\nTEST DEL MODELO ?RBOL DE DECISIN J48\n\n"); System.out.println(evtesting.toSummaryString("_____RESULTADO_____", false)); System.out.println(evtesting.toMatrixString("_____Matriz confusion___")); } catch (Exception ex) { Logger.getLogger(Perceptron.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:com.github.r351574nc3.amex.assignment2.App.java
License:Open Source License
/** * Tests/evaluates the trained model. This method assumes that {@link #train()} was previously called to assign a {@link LinearRegression} * classifier. If it wasn't, an exception will be thrown. * * @throws Exception if train wasn't called prior. */// www . j a va 2 s. c o m public void test() throws Exception { if (getClassifier() == null) { throw new RuntimeException("Make sure train was run prior to this method call"); } final Evaluation eval = new Evaluation(getTrained()); eval.evaluateModel(getClassifier(), getTest()); info("%s", eval.toSummaryString("Results\n\n", false)); info("Percent of correctly classified instances: %s", eval.pctCorrect()); }