List of usage examples for weka.classifiers Evaluation Evaluation
public Evaluation(Instances data) throws Exception
From source file:core.classification.Classifiers.java
License:Open Source License
public void trainSC() throws Exception { String sql;/*from w w w . ja v a 2 s .c o m*/ // --- // Connect to the database // --- InstanceQuery query = new InstanceQuery(); query.setDatabaseURL(dbase); query.setUsername(""); query.setPassword(""); // --- // --- // SCA // --- // --- sql = "SELECT "; sql += "CR.ratio, CR.class "; sql += "FROM Class_ratio AS CR;"; query.setQuery(sql); Instances data = query.retrieveInstances(); // --- // Setting options // --- String[] options = Utils.splitOptions( "-D -Q weka.classifiers.bayes.net.search.local.K2 -- -P 1 -S BAYES -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5"); SCA.setOptions(options); data.setClassIndex(data.numAttributes() - 1); // --- // Train the classifier // --- System.out.println("Building SCA ..."); SCA.buildClassifier(data); System.out.println("Done."); // --- // Classifier evaluation // --- System.out.println("Cross-validation for SCA..."); Evaluation eval = new Evaluation(data); eval.crossValidateModel(SCA, data, 10, new Random(1)); System.out.println("Done."); System.out.println(eval.toSummaryString("\n Results for SCA: \n\n", false)); // --- // --- // SCB // --- // --- sql = "SELECT "; sql += "Data.H2, Data.D2, Data.DX, "; sql += "Data.PARENT_CHAR AS PCLASS, "; sql += "Data.CLASS "; sql += "FROM Data "; sql += "WHERE (((Data.SEGERR)=0) AND (Data.PARENT_CHAR<>'0') );"; query.setQuery(sql); data = query.retrieveInstances(); // --- // Setting options // --- options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"); SCB.setOptions(options); data.setClassIndex(data.numAttributes() - 1); // --- // Train the classifier // --- System.out.println("Building SCB ..."); SCB.buildClassifier(data); System.out.println("Done."); // --- // Classifier evaluation // --- System.out.println("Cross-validation for SCB..."); eval = new Evaluation(data); eval.crossValidateModel(SCB, data, 10, new Random(1)); System.out.println("Done."); System.out.println(eval.toSummaryString("\n Results for SCB: \n\n", false)); // --- // --- // SCC // --- // --- // ---- // SCC1 // ---- sql = "SELECT "; sql += "Data.LH, Data.LD, Data.LDX, Data.LCLASS, "; sql += "Data.CLASS "; sql += "FROM Data "; sql += "WHERE ( (Data.SEGERR)=0 AND ( (Data.LCLASS)<>'0' ) );"; query.setQuery(sql); data = query.retrieveInstances(); // --- // Setting options // --- options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"); SCC1.setOptions(options); data.setClassIndex(data.numAttributes() - 1); // --- // Train the classifier // --- System.out.println("Building SCC1 ..."); SCC1.buildClassifier(data); System.out.println("Done."); // --- // Classifier evaluation // --- System.out.println("Cross-validation for SCC1..."); eval = new Evaluation(data); eval.crossValidateModel(SCC1, data, 10, new Random(1)); System.out.println("Done."); System.out.println(eval.toSummaryString("\n Results for SCC1: \n\n", false)); // ---- // SCC2 // ---- sql = "SELECT "; sql += "Data.EH, Data.ED, Data.EDX, Data.ECLASS, "; sql += "Data.CLASS "; sql += "FROM Data "; sql += "WHERE ( (Data.SEGERR)=0 AND ( (Data.ECLASS)<>'0' ) );"; query.setQuery(sql); data = query.retrieveInstances(); // --- // Setting options // --- // options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"); SCC2.setOptions(options); data.setClassIndex(data.numAttributes() - 1); // --- // Train the classifier // --- System.out.println("Building SCC2 ..."); SCC2.buildClassifier(data); System.out.println("Done."); // --- // Classifier evaluation // --- System.out.println("Cross-validation for SCC2..."); eval = new Evaluation(data); eval.crossValidateModel(SCC2, data, 10, new Random(1)); System.out.println("Done."); System.out.println(eval.toSummaryString("\n Results for SCC2: \n\n", false)); // ---- // SCC3 // ---- sql = "SELECT "; sql += "Data.SH, Data.SD, Data.SDX, Data.SCLASS, "; sql += "Data.CLASS "; sql += "FROM Data "; sql += "WHERE ( (Data.SEGERR)=0 AND ( (Data.SCLASS)<>'0' ) );"; query.setQuery(sql); data = query.retrieveInstances(); // --- // Setting options // --- // options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"); SCC3.setOptions(options); data.setClassIndex(data.numAttributes() - 1); // --- // Train the classifier // --- System.out.println("Building SCC3 ..."); SCC3.buildClassifier(data); System.out.println("Done."); // --- // Classifier evaluation // --- System.out.println("Cross-validation for SCC3..."); eval = new Evaluation(data); eval.crossValidateModel(SCC3, data, 10, new Random(1)); System.out.println("Done."); System.out.println(eval.toSummaryString("\n Results for SCC3: \n\n", false)); }
From source file:core.classification.Classifiers.java
License:Open Source License
public void trainRC() throws Exception { // ---/*from www. j ava 2 s.co m*/ // Retrieve the instances in the database // --- InstanceQuery query = new InstanceQuery(); query.setDatabaseURL(dbase); query.setUsername(""); query.setPassword(""); String sql = "SELECT "; sql += "Data.H2, Data.D2, Data.DX, "; sql += "Data.CLASS, Data.PARENT_CHAR AS PCLASS, "; sql += "Data.RELID "; sql += "FROM Data "; sql += "WHERE (((Data.SEGERR)=0) AND (Data.PARENT_CHAR<>'0') );"; query.setQuery(sql); Instances data = query.retrieveInstances(); // --- // Setting options // --- // String[] options = Utils.splitOptions("-L 0.2 -M 0.2 -N 50 -V 0 -S 0 -E 20 -H 5 "); String[] options = Utils.splitOptions( "-cost-matrix \"[0.0 1.0 1.0 0.1 0.1; 1.0 0.0 1.0 0.1 0.1; 1.0 1.0 0.0 0.1 0.1; 10.0 10.0 10.0 0.0 1.0; 10.0 10.0 10.0 1.0 0.0]\" -S 1 -W weka.classifiers.functions.MultilayerPerceptron -- -L 0.2 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"); RC.setOptions(options); data.setClassIndex(data.numAttributes() - 1); // --- // Train // --- System.out.println("Building RC..."); RC.buildClassifier(data); System.out.println("Done."); // --- // Evaluation // --- System.out.println("Cross-validation for RC..."); Evaluation eval = new Evaluation(data); eval.crossValidateModel(RC, data, 10, new Random(1)); System.out.println("Done."); System.out.println(eval.toSummaryString("\n Results for RC: \n\n", false)); }
From source file:core.classification.Classifiers.java
License:Open Source License
public void trainYNC() throws Exception { // ---/*from www .j a va 2 s . c om*/ // Retrieve the instances in the database // --- InstanceQuery query = new InstanceQuery(); query.setDatabaseURL(dbase); query.setUsername(""); query.setPassword(""); String sql = "SELECT "; sql += "YNCdata.PCLASS, YNCdata.CCLASS, YNCdata.RAREA, YNCdata.H, YNCdata.D, YNCdata.V, "; sql += "YNCdata.YN "; sql += "FROM YNCdata "; query.setQuery(sql); Instances data = query.retrieveInstances(); // --- // Setting options // --- String[] options = Utils.splitOptions("-R -N 3 -Q 1 -M 30"); YNC.setOptions(options); data.setClassIndex(data.numAttributes() - 1); // --- // Train // --- System.out.println("Building YC..."); YNC.buildClassifier(data); System.out.println("Done."); // --- // Evaluation // --- System.out.println("Cross-validation for YNC..."); Evaluation eval = new Evaluation(data); eval.crossValidateModel(YNC, data, 10, new Random(1)); System.out.println("Done."); System.out.println(eval.toSummaryString("\n Results for YNC: \n\n", false)); }
From source file:cs.man.ac.uk.classifiers.GetAUC.java
License:Open Source License
/** * Computes the AUC for the supplied learner. * @return the AUC as a double value.//from ww w.ja v a 2 s . co m */ @SuppressWarnings("unused") private static double validate5x2CV() { try { // other options int runs = 5; int folds = 2; double AUC_SUM = 0; // perform cross-validation for (int i = 0; i < runs; i++) { // randomize data int seed = i + 1; Random rand = new Random(seed); Instances randData = new Instances(data); randData.randomize(rand); if (randData.classAttribute().isNominal()) { System.out.println("Stratifying..."); randData.stratify(folds); } Evaluation eval = new Evaluation(randData); for (int n = 0; n < folds; n++) { Instances train = randData.trainCV(folds, n); Instances test = randData.testCV(folds, n); // the above code is used by the StratifiedRemoveFolds filter, the // code below by the Explorer/Experimenter: // Instances train = randData.trainCV(folds, n, rand); // build and evaluate classifier String[] options = { "-U", "-A" }; J48 classifier = new J48(); //HTree classifier = new HTree(); classifier.setOptions(options); classifier.buildClassifier(train); eval.evaluateModel(classifier, test); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve vmc = new ThresholdVisualizePanel(); AUC_SUM += ThresholdCurve.getROCArea(result); System.out.println("AUC: " + ThresholdCurve.getROCArea(result) + " \tAUC SUM: " + AUC_SUM); } } return AUC_SUM / ((double) runs * (double) folds); } catch (Exception e) { System.out.println("Exception validating data!"); return 0; } }
From source file:cs.man.ac.uk.classifiers.GetAUC.java
License:Open Source License
/** * Computes the AUC for the supplied learner. * @param learner the learning algorithm to use. * @return the AUC as a double value./*from w w w . ja va 2s . com*/ */ @SuppressWarnings("unused") private static double validate(Classifier learner) { try { Evaluation eval = new Evaluation(data); eval.crossValidateModel(learner, data, 2, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve vmc = new ThresholdVisualizePanel(); double AUC = ThresholdCurve.getROCArea(result); vmc.setROCString( "(Area under ROC = " + Utils.doubleToString(ThresholdCurve.getROCArea(result), 9) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) cp[n] = true; tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); return AUC; } catch (Exception e) { System.out.println("Exception validating data!"); return 0; } }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile1(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial1.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES/*from w ww .j a va 2 s . co m*/ Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[1] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(2); for (int j = 0; j < 2; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 1) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(1); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_autosentiment.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(1); test.setClassIndex(1); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile2(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial2.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES//from w w w . j a va2 s .c o m Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[3] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(4); for (int j = 0; j < 4; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 3) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(3); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_asAndpolarwords.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(3); test.setClassIndex(3); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile3(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial3.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES/*from ww w . jav a2 s .co m*/ Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); attr[3] = new Attribute("FW"); attr[4] = new Attribute("JJ"); attr[5] = new Attribute("RB"); attr[6] = new Attribute("RB_JJ"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[7] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); attrs.addElement(attr[4]); attrs.addElement(attr[5]); attrs.addElement(attr[6]); attrs.addElement(attr[7]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(8); for (int j = 0; j < 8; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 7) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(7); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_asAndpolarwordsAndpos.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(7); test.setClassIndex(7); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile4(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial4.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES/*from w w w . j a va 2s . com*/ Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); attr[3] = new Attribute("FW"); attr[4] = new Attribute("JJ"); attr[5] = new Attribute("RB"); attr[6] = new Attribute("RB_JJ"); attr[7] = new Attribute("amod"); attr[8] = new Attribute("acomp"); attr[9] = new Attribute("advmod"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[10] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); attrs.addElement(attr[4]); attrs.addElement(attr[5]); attrs.addElement(attr[6]); attrs.addElement(attr[7]); attrs.addElement(attr[8]); attrs.addElement(attr[9]); attrs.addElement(attr[10]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(11); for (int j = 0; j < 11; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 10) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(10); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_asAndpolarwordsAndposAnddep.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(10); test.setClassIndex(10); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile5(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial5.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES//from www . jav a 2 s. c om Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); attr[3] = new Attribute("FW"); attr[4] = new Attribute("JJ"); attr[5] = new Attribute("RB"); attr[6] = new Attribute("RB_JJ"); attr[7] = new Attribute("amod"); attr[8] = new Attribute("acomp"); attr[9] = new Attribute("advmod"); attr[10] = new Attribute("BLPos"); attr[11] = new Attribute("BLNeg"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[12] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); attrs.addElement(attr[4]); attrs.addElement(attr[5]); attrs.addElement(attr[6]); attrs.addElement(attr[7]); attrs.addElement(attr[8]); attrs.addElement(attr[9]); attrs.addElement(attr[10]); attrs.addElement(attr[11]); attrs.addElement(attr[12]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(13); for (int j = 0; j < 13; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 12) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(12); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_asAndpolarwordsAndposAnddepAndbl.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(12); test.setClassIndex(12); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }