List of usage examples for weka.filters.supervised.attribute AddClassification AddClassification
AddClassification
From source file:adams.gui.wizard.WekaPropertySheetPanelPage.java
License:Open Source License
/** * For testing only./*ww w .j a va2s .com*/ * * @param args ignored */ public static void main(String[] args) { Environment.setEnvironmentClass(Environment.class); final WizardPane wizard = new WizardPane(); wizard.addPage(new StartPage()); ParameterPanelPage page = new ParameterPanelPage(); page.setPageName("Parameters 1"); page.setDescription("Here is the description of the first parameter page.\n\nAnother line."); page.getParameterPanel().addPropertyType("doublevalue", PropertyType.DOUBLE); page.getParameterPanel().setLabel("doublevalue", "Double value"); page.getParameterPanel().addPropertyType("booleanvalue", PropertyType.BOOLEAN); page.getParameterPanel().setLabel("booleanvalue", "Boolean value"); page.getParameterPanel().addPropertyType("stringlist", PropertyType.LIST); page.getParameterPanel().setLabel("stringlist", "String list"); page.getParameterPanel().setList("stringlist", new String[] { "A", "B", "C" }); Properties props = new Properties(); props.setDouble("doublevalue", 1.234); props.setBoolean("booleanvalue", true); props.setProperty("stringlist", "B"); page.setProperties(props); page.setDescription("Here is the description of the first parameter page.\n\nAnother line."); wizard.addPage(page); page = new ParameterPanelPage(); page.setDescription( "<html><h3>More parameters</h3>Here is the description of the 2nd parameter page.<br><br>Another line."); page.setPageName("Parameters 2"); wizard.addPage(page); page = new ParameterPanelPage(); page.setPageName("Parameters 3"); page.setDescription("Nothing here."); wizard.addPage(page); WekaPropertySheetPanelPage shpage = new WekaPropertySheetPanelPage("Filter"); shpage.setDescription("<html><h3>Object properties</h3>Here you can change all properties of the " + AddClassification.class.getName() + " filter."); shpage.setTarget(new AddClassification()); wizard.addPage(shpage); wizard.addPage(new FinalPage()); final BaseFrame frame = new BaseFrame("Example Wizard"); wizard.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent e) { System.out.println(e.getActionCommand()); System.out.println(wizard.getProperties(true)); frame.setVisible(false); frame.dispose(); } }); frame.setDefaultCloseOperation(BaseFrame.EXIT_ON_CLOSE); frame.getContentPane().setLayout(new BorderLayout()); frame.getContentPane().add(wizard, BorderLayout.CENTER); frame.setSize(GUIHelper.getDefaultDialogDimension()); frame.setVisible(true); }
From source file:de.tudarmstadt.ukp.similarity.experiments.coling2012.util.Evaluator.java
License:Open Source License
public static void runClassifierCV(WekaClassifier wekaClassifier, Dataset dataset) throws Exception { // Set parameters int folds = 10; Classifier baseClassifier = getClassifier(wekaClassifier); // Set up the random number generator long seed = new Date().getTime(); Random random = new Random(seed); // Add IDs to the instances AddID.main(new String[] { "-i", MODELS_DIR + "/" + dataset.toString() + ".arff", "-o", MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff" }); Instances data = DataSource.read(MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff"); data.setClassIndex(data.numAttributes() - 1); // Instantiate the Remove filter Remove removeIDFilter = new Remove(); removeIDFilter.setAttributeIndices("first"); // Randomize the data data.randomize(random);/*from w w w .j a v a2 s . com*/ // Perform cross-validation Instances predictedData = null; Evaluation eval = new Evaluation(data); for (int n = 0; n < folds; n++) { Instances train = data.trainCV(folds, n, random); Instances test = data.testCV(folds, n); // Apply log filter // Filter logFilter = new LogFilter(); // logFilter.setInputFormat(train); // train = Filter.useFilter(train, logFilter); // logFilter.setInputFormat(test); // test = Filter.useFilter(test, logFilter); // 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 AddClassification filter = new AddClassification(); filter.setClassifier(filteredClassifier); filter.setOutputClassification(true); filter.setOutputDistribution(false); filter.setOutputErrorFlag(true); filter.setInputFormat(train); Filter.useFilter(train, filter); // trains the classifier Instances pred = Filter.useFilter(test, filter); // performs predictions on test set if (predictedData == null) predictedData = new Instances(pred, 0); for (int j = 0; j < pred.numInstances(); j++) predictedData.add(pred.instance(j)); } // Prepare output classification String[] scores = new String[predictedData.numInstances()]; for (Instance predInst : predictedData) { int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1; int valueIdx = predictedData.numAttributes() - 2; String value = predInst.stringValue(predInst.attribute(valueIdx)); scores[id] = value; } // Output StringBuilder sb = new StringBuilder(); for (String score : scores) sb.append(score.toString() + LF); FileUtils.writeStringToFile( new File(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString() + "/output.csv"), sb.toString()); }
From source file:dkpro.similarity.experiments.rte.util.Evaluator.java
License:Open Source License
public static void runClassifierCV(WekaClassifier wekaClassifier, Dataset dataset) throws Exception { // Set parameters int folds = 10; Classifier baseClassifier = ClassifierSimilarityMeasure.getClassifier(wekaClassifier); // Set up the random number generator long seed = new Date().getTime(); Random random = new Random(seed); // Add IDs to the instances AddID.main(new String[] { "-i", MODELS_DIR + "/" + dataset.toString() + ".arff", "-o", MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff" }); Instances data = DataSource.read(MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff"); data.setClassIndex(data.numAttributes() - 1); // Instantiate the Remove filter Remove removeIDFilter = new Remove(); removeIDFilter.setAttributeIndices("first"); // Randomize the data data.randomize(random);/*from w w w. ja v a2s .c o m*/ // Perform cross-validation Instances predictedData = null; Evaluation eval = new Evaluation(data); for (int n = 0; n < folds; n++) { Instances train = data.trainCV(folds, n, random); Instances test = data.testCV(folds, n); // Apply log filter // Filter logFilter = new LogFilter(); // logFilter.setInputFormat(train); // train = Filter.useFilter(train, logFilter); // logFilter.setInputFormat(test); // test = Filter.useFilter(test, logFilter); // 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 AddClassification filter = new AddClassification(); filter.setClassifier(classifier); filter.setOutputClassification(true); filter.setOutputDistribution(false); filter.setOutputErrorFlag(true); filter.setInputFormat(train); Filter.useFilter(train, filter); // trains the classifier Instances pred = Filter.useFilter(test, filter); // performs predictions on test set if (predictedData == null) predictedData = new Instances(pred, 0); for (int j = 0; j < pred.numInstances(); j++) predictedData.add(pred.instance(j)); } System.out.println(eval.toSummaryString()); System.out.println(eval.toMatrixString()); // Prepare output scores String[] scores = new String[predictedData.numInstances()]; for (Instance predInst : predictedData) { int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1; int valueIdx = predictedData.numAttributes() - 2; String value = predInst.stringValue(predInst.attribute(valueIdx)); scores[id] = value; } // Output classifications StringBuilder sb = new StringBuilder(); for (String score : scores) sb.append(score.toString() + LF); FileUtils.writeStringToFile(new File(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString() + "/" + dataset.toString() + ".csv"), sb.toString()); // Output prediction arff DataSink.write(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString() + "/" + dataset.toString() + ".predicted.arff", predictedData); // Output meta information sb = new StringBuilder(); sb.append(baseClassifier.toString() + LF); sb.append(eval.toSummaryString() + LF); sb.append(eval.toMatrixString() + LF); FileUtils.writeStringToFile(new File(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString() + "/" + dataset.toString() + ".meta.txt"), sb.toString()); }
From source file:dkpro.similarity.experiments.sts2013.util.Evaluator.java
License:Open Source License
public static void runLinearRegressionCV(Mode mode, Dataset... datasets) throws Exception { for (Dataset dataset : datasets) { // Set parameters int folds = 10; Classifier baseClassifier = new LinearRegression(); // Set up the random number generator long seed = new Date().getTime(); Random random = new Random(seed); // Add IDs to the instances AddID.main(new String[] { "-i", MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".arff", "-o", MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff" }); Instances data = DataSource.read( MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff"); data.setClassIndex(data.numAttributes() - 1); // Instantiate the Remove filter Remove removeIDFilter = new Remove(); removeIDFilter.setAttributeIndices("first"); // Randomize the data data.randomize(random);/*from w ww. j ava 2 s . c o m*/ // Perform cross-validation Instances predictedData = null; Evaluation eval = new Evaluation(data); for (int n = 0; n < folds; n++) { Instances train = data.trainCV(folds, n, random); Instances test = data.testCV(folds, n); // Apply log filter Filter logFilter = new LogFilter(); logFilter.setInputFormat(train); train = Filter.useFilter(train, logFilter); logFilter.setInputFormat(test); test = Filter.useFilter(test, logFilter); // 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 AddClassification filter = new AddClassification(); filter.setClassifier(classifier); filter.setOutputClassification(true); filter.setOutputDistribution(false); filter.setOutputErrorFlag(true); filter.setInputFormat(train); Filter.useFilter(train, filter); // trains the classifier Instances pred = Filter.useFilter(test, filter); // performs predictions on test set if (predictedData == null) { predictedData = new Instances(pred, 0); } for (int j = 0; j < pred.numInstances(); j++) { predictedData.add(pred.instance(j)); } } // Prepare output scores double[] scores = new double[predictedData.numInstances()]; for (Instance predInst : predictedData) { int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1; int valueIdx = predictedData.numAttributes() - 2; double value = predInst.value(predInst.attribute(valueIdx)); scores[id] = value; // Limit to interval [0;5] if (scores[id] > 5.0) { scores[id] = 5.0; } if (scores[id] < 0.0) { scores[id] = 0.0; } } // Output StringBuilder sb = new StringBuilder(); for (Double score : scores) { sb.append(score.toString() + LF); } FileUtils.writeStringToFile( new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv"), sb.toString()); } }
From source file:dkpro.similarity.experiments.sts2013baseline.util.Evaluator.java
License:Open Source License
public static void runLinearRegressionCV(Mode mode, Dataset... datasets) throws Exception { for (Dataset dataset : datasets) { // Set parameters int folds = 10; Classifier baseClassifier = new LinearRegression(); // Set up the random number generator long seed = new Date().getTime(); Random random = new Random(seed); // Add IDs to the instances AddID.main(new String[] { "-i", MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".arff", "-o", MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff" }); String location = MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff"; Instances data = DataSource.read(location); if (data == null) { throw new IOException("Could not load data from: " + location); }//from w ww . j ava 2 s . com data.setClassIndex(data.numAttributes() - 1); // Instantiate the Remove filter Remove removeIDFilter = new Remove(); removeIDFilter.setAttributeIndices("first"); // Randomize the data data.randomize(random); // Perform cross-validation Instances predictedData = null; Evaluation eval = new Evaluation(data); for (int n = 0; n < folds; n++) { Instances train = data.trainCV(folds, n, random); Instances test = data.testCV(folds, n); // Apply log filter Filter logFilter = new LogFilter(); logFilter.setInputFormat(train); train = Filter.useFilter(train, logFilter); logFilter.setInputFormat(test); test = Filter.useFilter(test, logFilter); // 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 AddClassification filter = new AddClassification(); filter.setClassifier(classifier); filter.setOutputClassification(true); filter.setOutputDistribution(false); filter.setOutputErrorFlag(true); filter.setInputFormat(train); Filter.useFilter(train, filter); // trains the classifier Instances pred = Filter.useFilter(test, filter); // performs predictions on test set if (predictedData == null) { predictedData = new Instances(pred, 0); } for (int j = 0; j < pred.numInstances(); j++) { predictedData.add(pred.instance(j)); } } // Prepare output scores double[] scores = new double[predictedData.numInstances()]; for (Instance predInst : predictedData) { int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1; int valueIdx = predictedData.numAttributes() - 2; double value = predInst.value(predInst.attribute(valueIdx)); scores[id] = value; // Limit to interval [0;5] if (scores[id] > 5.0) { scores[id] = 5.0; } if (scores[id] < 0.0) { scores[id] = 0.0; } } // Output StringBuilder sb = new StringBuilder(); for (Double score : scores) { sb.append(score.toString() + LF); } FileUtils.writeStringToFile( new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv"), sb.toString()); } }
From source file:elh.eus.absa.WekaWrapper.java
License:Open Source License
public Instances addClassification(Classifier cl, Instances data) throws Exception { //filter/*from w ww.j a v a2s .c o m*/ AddClassification add = new AddClassification(); add.setRemoveOldClass(true); //remove the old class attribute add.setOutputDistribution(true); add.setClassifier(cl); add.setInputFormat(data); // meta-classifier Instances rslt = Filter.useFilter(data, add); return rslt; }