List of usage examples for weka.core Instance setClassMissing
public void setClassMissing();
From source file:hsa_jni.hsa_jni.EvaluatePeriodicHeldOutTestBatch.java
License:Open Source License
@Override protected Object doMainTask(TaskMonitor monitor, ObjectRepository repository) { Classifier learner = (Classifier) getPreparedClassOption(this.learnerOption); InstanceStream stream = (InstanceStream) getPreparedClassOption(this.streamOption); ClassificationPerformanceEvaluator evaluator = (ClassificationPerformanceEvaluator) getPreparedClassOption( this.evaluatorOption); learner.setModelContext(stream.getHeader()); long instancesProcessed = 0; LearningCurve learningCurve = new LearningCurve("evaluation instances"); File dumpFile = this.dumpFileOption.getFile(); PrintStream immediateResultStream = null; if (dumpFile != null) { try {//w w w . j a va 2 s.c om if (dumpFile.exists()) { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile, true), true); } else { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open immediate result file: " + dumpFile, ex); } } boolean firstDump = true; InstanceStream testStream = null; int testSize = this.testSizeOption.getValue(); if (this.cacheTestOption.isSet()) { monitor.setCurrentActivity("Caching test examples...", -1.0); Instances testInstances = new Instances(stream.getHeader(), this.testSizeOption.getValue()); while (testInstances.numInstances() < testSize) { testInstances.add(stream.nextInstance()); if (testInstances.numInstances() % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) testInstances.numInstances() / (double) (this.testSizeOption.getValue())); } } testStream = new CachedInstancesStream(testInstances); } else { //testStream = (InstanceStream) stream.copy(); testStream = stream; /*monitor.setCurrentActivity("Skipping test examples...", -1.0); for (int i = 0; i < testSize; i++) { stream.nextInstance(); }*/ } instancesProcessed = 0; TimingUtils.enablePreciseTiming(); double totalTrainTime = 0.0; while ((this.trainSizeOption.getValue() < 1 || instancesProcessed < this.trainSizeOption.getValue()) && stream.hasMoreInstances() == true) { monitor.setCurrentActivityDescription("Training..."); long instancesTarget = instancesProcessed + this.sampleFrequencyOption.getValue(); ArrayList<Instance> instanceCache = new ArrayList<Instance>(); long trainStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); double lastTrainTime = 0; while (instancesProcessed < instancesTarget && stream.hasMoreInstances() == true) { instanceCache.add(stream.nextInstance()); instancesProcessed++; if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) (instancesProcessed) / (double) (this.trainSizeOption.getValue())); } if (instanceCache.size() % 1000 == 0) { trainStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); for (Instance inst : instanceCache) { learner.trainOnInstance(inst); } lastTrainTime += TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - trainStartTime); instanceCache.clear(); } } trainStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); for (Instance inst : instanceCache) { learner.trainOnInstance(inst); } if (learner instanceof BatchClassifier) ((BatchClassifier) learner).commit(); lastTrainTime += TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - trainStartTime); totalTrainTime += lastTrainTime; if (totalTrainTime > this.trainTimeOption.getValue()) { break; } if (this.cacheTestOption.isSet()) { testStream.restart(); } evaluator.reset(); long testInstancesProcessed = 0; monitor.setCurrentActivityDescription("Testing (after " + StringUtils.doubleToString( ((double) (instancesProcessed) / (double) (this.trainSizeOption.getValue()) * 100.0), 2) + "% training)..."); long testStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); int instCount = 0; for (instCount = 0; instCount < testSize; instCount++) { if (stream.hasMoreInstances() == false) { break; } Instance testInst = (Instance) testStream.nextInstance().copy(); double trueClass = testInst.classValue(); testInst.setClassMissing(); double[] prediction = learner.getVotesForInstance(testInst); testInst.setClassValue(trueClass); evaluator.addResult(testInst, prediction); testInstancesProcessed++; if (testInstancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) testInstancesProcessed / (double) (testSize)); } } if (instCount != testSize) { break; } double testTime = TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - testStartTime); List<Measurement> measurements = new ArrayList<Measurement>(); measurements.add(new Measurement("evaluation instances", instancesProcessed)); measurements.add(new Measurement("total train time", totalTrainTime)); measurements.add(new Measurement("total train speed", instancesProcessed / totalTrainTime)); measurements.add(new Measurement("last train time", lastTrainTime)); measurements.add( new Measurement("last train speed", this.sampleFrequencyOption.getValue() / lastTrainTime)); measurements.add(new Measurement("test time", testTime)); measurements.add(new Measurement("test speed", this.testSizeOption.getValue() / testTime)); Measurement[] performanceMeasurements = evaluator.getPerformanceMeasurements(); for (Measurement measurement : performanceMeasurements) { measurements.add(measurement); } Measurement[] modelMeasurements = learner.getModelMeasurements(); for (Measurement measurement : modelMeasurements) { measurements.add(measurement); } learningCurve.insertEntry( new LearningEvaluation(measurements.toArray(new Measurement[measurements.size()]))); if (immediateResultStream != null) { if (firstDump) { immediateResultStream.println(learningCurve.headerToString()); firstDump = false; } immediateResultStream.println(learningCurve.entryToString(learningCurve.numEntries() - 1)); immediateResultStream.flush(); } if (monitor.resultPreviewRequested()) { monitor.setLatestResultPreview(learningCurve.copy()); } // if (learner instanceof HoeffdingTree // || learner instanceof HoeffdingOptionTree) { // int numActiveNodes = (int) Measurement.getMeasurementNamed( // "active learning leaves", // modelMeasurements).getValue(); // // exit if tree frozen // if (numActiveNodes < 1) { // break; // } // int numNodes = (int) Measurement.getMeasurementNamed( // "tree size (nodes)", modelMeasurements) // .getValue(); // if (numNodes == lastNumNodes) { // noGrowthCount++; // } else { // noGrowthCount = 0; // } // lastNumNodes = numNodes; // } else if (learner instanceof OzaBoost || learner instanceof // OzaBag) { // double numActiveNodes = Measurement.getMeasurementNamed( // "[avg] active learning leaves", // modelMeasurements).getValue(); // // exit if all trees frozen // if (numActiveNodes == 0.0) { // break; // } // int numNodes = (int) (Measurement.getMeasurementNamed( // "[avg] tree size (nodes)", // learner.getModelMeasurements()).getValue() * Measurement // .getMeasurementNamed("ensemble size", // modelMeasurements).getValue()); // if (numNodes == lastNumNodes) { // noGrowthCount++; // } else { // noGrowthCount = 0; // } // lastNumNodes = numNodes; // } } if (immediateResultStream != null) { immediateResultStream.close(); } return learningCurve; }
From source file:meddle.PredictByDomainOS.java
License:Open Source License
private static boolean predictOneFlow(String line, String domainOS) { if (!domainOSModel.containsKey(domainOS)) return false; else {/* w w w .j ava2 s.co m*/ try { Classifier classifier = domainOSModel.get(domainOS); Map<String, Integer> fi = domainOSFeature.get(domainOS); Instances structure = domainOSStruct.get(domainOS); Instance current = getInstance(line, fi, fi.size()); Instances is = new Instances(structure); is.setClassIndex(is.numAttributes() - 1); is.add(current); current = is.get(is.size() - 1); current.setClassMissing(); double predicted = classifier.classifyInstance(current); if (predicted > 0) { return true; } else return false; } catch (Exception e) { e.printStackTrace(); } } return false; }
From source file:meka.classifiers.multilabel.cc.CNode.java
License:Open Source License
/** * Transform - turn [y1,y2,y3,x1,x2] into [y1,y2,x1,x2]. * @return transformed Instance/*w w w. j a va2s . c om*/ */ public Instance transform(Instance x, double ypred[]) throws Exception { x = (Instance) x.copy(); int L = x.classIndex(); int L_c = (paY.length + 1); x.setDataset(null); for (int j = 0; j < (L - L_c); j++) { x.deleteAttributeAt(0); } for (int pa : paY) { //System.out.println("x_["+map[pa]+"] <- "+ypred[pa]); x.setValue(map[pa], ypred[pa]); } x.setDataset(T); x.setClassMissing(); return x; }
From source file:meka.core.PSUtils.java
License:Open Source License
/** * Transform instances into a multi-class representation. * @param D original dataset//from ww w. j a v a 2s . c om * @param L number of labels in the original dataset * @param cname class name for the new dataset (may want to encode the list of indices here for RAkEL-like methods) * @param p pruning value * @param n restoration value * @return transformed dataset */ public static Instances PSTransformation(Instances D, int L, String cname, int p, int n) { D = new Instances(D); // Gather combinations HashMap<LabelSet, Integer> distinctCombinations = PSUtils.countCombinationsSparse(D, L); // Prune combinations if (p > 0) MLUtils.pruneCountHashMap(distinctCombinations, p); // Check there are > 2 if (distinctCombinations.size() <= 1 && p > 0) { // ... or try again if not ... System.err.println("[Warning] You did too much pruning, setting P = P-1"); return PSTransformation(D, L, cname, p - 1, n); } // Create class attribute ArrayList<String> ClassValues = new ArrayList<String>(); for (LabelSet y : distinctCombinations.keySet()) ClassValues.add(y.toString()); Attribute C = new Attribute(cname, ClassValues); // Insert new special attribute (which has all possible combinations of labels) D.insertAttributeAt(C, L); D.setClassIndex(L); //Add class values int N = D.numInstances(); for (int i = 0; i < N; i++) { Instance x = D.instance(i); LabelSet y = new LabelSet(MLUtils.toSparseIntArray(x, L)); String y_string = y.toString(); // add it if (ClassValues.contains(y_string)) //if its class value exists x.setClassValue(y_string); // decomp else if (n > 0) { //String d_subsets[] = getTopNSubsets(comb,distinctCombinations,n); LabelSet d_subsets[] = PSUtils.getTopNSubsets(y, distinctCombinations, n); //LabelSet d_subsets[] = PSUtils.cover(y,distinctCombinations); if (d_subsets.length > 0) { // fast x.setClassValue(d_subsets[0].toString()); // additional if (d_subsets.length > 1) { for (int s_i = 1; s_i < d_subsets.length; s_i++) { Instance x_ = (Instance) (x).copy(); x_.setClassValue(d_subsets[s_i].toString()); D.add(x_); } } } else { x.setClassMissing(); } } } // remove with missing class D.deleteWithMissingClass(); try { D = F.removeLabels(D, L); } catch (Exception e) { // should never happen } D.setClassIndex(0); return D; }
From source file:meka.core.PSUtils.java
License:Open Source License
/** * Transform instances into a multi-class representation. * @param D original dataset/*from ww w . j a va 2 s. c o m*/ * @param L number of labels in that dataset * @param cname class name for the new dataset (may want to encode the list of indices here for RAkEL-like methods) * @param p pruning value * @param n restoration value * @return transformed dataset */ public static Instances SLTransformation(Instances D, int L, String cname, int p, int n) { D = new Instances(D); // Gather combinations HashMap<LabelSet, Integer> distinctCombinations = PSUtils.countCombinationsSparse(D, L); // Prune combinations if (p > 0) MLUtils.pruneCountHashMap(distinctCombinations, p); // Check there are > 2 if (distinctCombinations.size() <= 1 && p > 0) { // ... or try again if not ... System.err.println("[Warning] You did too much pruning, setting P = P-1"); return PSTransformation(D, L, cname, p - 1, n); } // Create class attribute ArrayList<String> ClassValues = new ArrayList<String>(); for (LabelSet y : distinctCombinations.keySet()) ClassValues.add(y.toString()); Attribute C = new Attribute(cname, ClassValues); // Insert new special attribute (which has all possible combinations of labels) D.insertAttributeAt(C, L); D.setClassIndex(L); //Add class values int N = D.numInstances(); for (int i = 0; i < N; i++) { Instance x = D.instance(i); LabelSet y = new LabelSet(MLUtils.toSparseIntArray(x, L)); String y_string = y.toString(); // add it if (ClassValues.contains(y_string)) //if its class value exists x.setClassValue(y_string); // decomp else if (n > 0) { //String d_subsets[] = getTopNSubsets(comb,distinctCombinations,n); LabelSet d_subsets[] = PSUtils.getTopNSubsets(y, distinctCombinations, n); //LabelSet d_subsets[] = PSUtils.cover(y,distinctCombinations); if (d_subsets.length > 0) { // fast x.setClassValue(d_subsets[0].toString()); // additional if (d_subsets.length > 1) { for (int s_i = 1; s_i < d_subsets.length; s_i++) { Instance x_ = (Instance) (x).copy(); x_.setClassValue(d_subsets[s_i].toString()); D.add(x_); } } } else { x.setClassMissing(); } } } // remove with missing class D.deleteWithMissingClass(); try { D = F.removeLabels(D, L); } catch (Exception e) { // should never happen } D.setClassIndex(0); return D; }
From source file:moa.tasks.EvaluatePeriodicHeldOutTestLA.java
License:Open Source License
@Override protected Object doMainTask(TaskMonitor monitor, ObjectRepository repository) { int numberOfCheckCalls = 0; double modelTime = 0; Classifier learner = (Classifier) getPreparedClassOption(this.learnerOption); InstanceStream stream = (InstanceStream) getPreparedClassOption(this.streamOption); ClassificationPerformanceEvaluator evaluator = (ClassificationPerformanceEvaluator) getPreparedClassOption( this.evaluatorOption); learner.setModelContext(stream.getHeader()); long instancesProcessed = 0; LearningCurve learningCurve = new LearningCurve("evaluation instances") { @Override/* w ww. ja va2 s . co m*/ public String entryToString(int entryIndex) { // TODO Auto-generated method stub return super.entryToString(entryIndex).replace(',', '\t'); } }; File dumpFile = this.dumpFileOption.getFile(); PrintStream immediateResultStream = null; if (dumpFile != null) { try { if (dumpFile.exists()) { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile, true), true); } else { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open immediate result file: " + dumpFile, ex); } } boolean firstDump = true; InstanceStream testStream = null; int testSize = this.testSizeOption.getValue(); if (this.cacheTestOption.isSet()) { monitor.setCurrentActivity("Caching test examples...", -1.0); Instances testInstances = new Instances(stream.getHeader(), this.testSizeOption.getValue()); while (testInstances.numInstances() < testSize) { testInstances.add(stream.nextInstance()); if (testInstances.numInstances() % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) testInstances.numInstances() / (double) (this.testSizeOption.getValue())); } } testStream = new CachedInstancesStream(testInstances); } else { //testStream = (InstanceStream) stream.copy(); testStream = stream; /*monitor.setCurrentActivity("Skipping test examples...", -1.0); for (int i = 0; i < testSize; i++) { stream.nextInstance(); }*/ } instancesProcessed = 0; TimingUtils.enablePreciseTiming(); double totalTrainTime = 0.0; while ((this.trainSizeOption.getValue() < 1 || instancesProcessed < this.trainSizeOption.getValue()) && stream.hasMoreInstances() == true) { monitor.setCurrentActivityDescription("Training..."); long instancesTarget = instancesProcessed + this.sampleFrequencyOption.getValue(); long fixup = 0; long trainStartTime = System.nanoTime(); while (instancesProcessed < instancesTarget && stream.hasMoreInstances() == true) { long start = System.nanoTime(); Instance i = stream.nextInstance(); fixup += System.nanoTime() - start; learner.trainOnInstance(i); instancesProcessed++; if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) (instancesProcessed) / (double) (this.trainSizeOption.getValue())); } } double lastTrainTime = TimingUtils.nanoTimeToSeconds(System.nanoTime() - trainStartTime); totalTrainTime += lastTrainTime; if (totalTrainTime > this.trainTimeOption.getValue()) { break; } if (this.cacheTestOption.isSet()) { testStream.restart(); } evaluator.reset(); long testInstancesProcessed = 0; monitor.setCurrentActivityDescription("Testing (after " + StringUtils.doubleToString( ((double) (instancesProcessed) / (double) (this.trainSizeOption.getValue()) * 100.0), 2) + "% training)..."); long testStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); int instCount = 0; long count = 0; long period = 0; for (instCount = 0; instCount < testSize; instCount++) { if (stream.hasMoreInstances() == false) { break; } Instance testInst = (Instance) testStream.nextInstance().copy(); if (instCount == 0) { long modelBuildStart = System.nanoTime(); learner.getVotesForInstance(testInst); long modelBuildEnd = System.nanoTime(); modelTime = (modelBuildEnd - modelBuildStart) / 1000000.0; } double trueClass = testInst.classValue(); testInst.setClassMissing(); double[] prediction = learner.getVotesForInstance(testInst); do // tune to get more or less stable data { long start = System.nanoTime(); for (int i = 0; i < numberOfCheckCalls; ++i) prediction = learner.getVotesForInstance(testInst); long end = System.nanoTime(); if ((end - start) / 1000000.0 < 30) { numberOfCheckCalls += 10; } else { count += numberOfCheckCalls; period += (end - start); break; } } while (true); testInst.setClassValue(trueClass); evaluator.addResult(testInst, prediction); testInstancesProcessed++; if (testInstancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) testInstancesProcessed / (double) (testSize)); } } if (instCount != testSize) { break; } double final_period = ((double) period / count) / 1000000.0; // msec double testTime = TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - testStartTime); List<Measurement> measurements = new ArrayList<Measurement>(); measurements.add(new Measurement("number of attributes", testStream.getHeader().numAttributes() - 1)); measurements.add(new Measurement("evaluation instances", instancesProcessed)); measurements.add(new Measurement("test latency", final_period)); measurements.add(new Measurement("model build time", modelTime)); measurements.add(new Measurement("trainTime", (totalTrainTime / instancesProcessed))); Measurement[] performanceMeasurements = evaluator.getPerformanceMeasurements(); for (Measurement measurement : performanceMeasurements) { measurements.add(measurement); } Measurement[] modelMeasurements = learner.getModelMeasurements(); for (Measurement measurement : modelMeasurements) { measurements.add(measurement); } learningCurve.insertEntry( new LearningEvaluation(measurements.toArray(new Measurement[measurements.size()]))); if (immediateResultStream != null) { if (firstDump) { immediateResultStream.println(learningCurve.headerToString()); firstDump = false; } immediateResultStream.println(learningCurve.entryToString(learningCurve.numEntries() - 1)); immediateResultStream.flush(); } //if (monitor.resultPreviewRequested()) { // monitor.setLatestResultPreview(learningCurve.copy()); //} } if (immediateResultStream != null) { immediateResultStream.close(); } return learningCurve; }
From source file:moa.tasks.EvaluatePeriodicHeldOutTestLatency.java
License:Open Source License
@Override protected Object doMainTask(TaskMonitor monitor, ObjectRepository repository) { Classifier learner = (Classifier) getPreparedClassOption(this.learnerOption); InstanceStream stream = (InstanceStream) getPreparedClassOption(this.streamOption); ClassificationPerformanceEvaluator evaluator = (ClassificationPerformanceEvaluator) getPreparedClassOption( this.evaluatorOption); learner.setModelContext(stream.getHeader()); long instancesProcessed = 0; LearningCurve learningCurve = new LearningCurve("evaluation instances"); File dumpFile = this.dumpFileOption.getFile(); PrintStream immediateResultStream = null; if (dumpFile != null) { try {//from w ww. j a v a 2 s .c o m if (dumpFile.exists()) { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile, true), true); } else { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open immediate result file: " + dumpFile, ex); } } boolean firstDump = true; InstanceStream testStream = null; int testSize = this.testSizeOption.getValue(); if (this.cacheTestOption.isSet()) { monitor.setCurrentActivity("Caching test examples...", -1.0); Instances testInstances = new Instances(stream.getHeader(), this.testSizeOption.getValue()); while (testInstances.numInstances() < testSize) { testInstances.add(stream.nextInstance()); if (testInstances.numInstances() % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) testInstances.numInstances() / (double) (this.testSizeOption.getValue())); } } testStream = new CachedInstancesStream(testInstances); } else { //testStream = (InstanceStream) stream.copy(); testStream = stream; /*monitor.setCurrentActivity("Skipping test examples...", -1.0); for (int i = 0; i < testSize; i++) { stream.nextInstance(); }*/ } instancesProcessed = 0; TimingUtils.enablePreciseTiming(); double totalTrainTime = 0.0; while ((this.trainSizeOption.getValue() < 1 || instancesProcessed < this.trainSizeOption.getValue()) && stream.hasMoreInstances() == true) { monitor.setCurrentActivityDescription("Training..."); long instancesTarget = instancesProcessed + this.sampleFrequencyOption.getValue(); long trainStartTime = System.nanoTime(); while (instancesProcessed < instancesTarget && stream.hasMoreInstances() == true) { learner.trainOnInstance(stream.nextInstance()); instancesProcessed++; if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) (instancesProcessed) / (double) (this.trainSizeOption.getValue())); } } double lastTrainTime = TimingUtils.nanoTimeToSeconds(System.nanoTime() - trainStartTime); totalTrainTime += lastTrainTime; if (totalTrainTime > this.trainTimeOption.getValue()) { break; } if (this.cacheTestOption.isSet()) { testStream.restart(); } evaluator.reset(); long testInstancesProcessed = 0; monitor.setCurrentActivityDescription("Testing (after " + StringUtils.doubleToString( ((double) (instancesProcessed) / (double) (this.trainSizeOption.getValue()) * 100.0), 2) + "% training)..."); long testStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); int instCount = 0; long count = 0; long period = 0; for (instCount = 0; instCount < testSize; instCount++) { if (stream.hasMoreInstances() == false) { break; } Instance testInst = (Instance) testStream.nextInstance().copy(); if (instCount == 0) learner.getVotesForInstance(testInst); double trueClass = testInst.classValue(); testInst.setClassMissing(); double[] prediction = learner.getVotesForInstance(testInst); long start = System.nanoTime(); long end = System.nanoTime(); count += 10; period += (end - start); testInst.setClassValue(trueClass); evaluator.addResult(testInst, prediction); testInstancesProcessed++; if (testInstancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } monitor.setCurrentActivityFractionComplete( (double) testInstancesProcessed / (double) (testSize)); } } if (instCount != testSize) { break; } double final_period = ((double) period / count) / 1000000.0; // msec double testTime = TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - testStartTime); List<Measurement> measurements = new ArrayList<Measurement>(); measurements.add(new Measurement("evaluation instances", instancesProcessed)); measurements.add(new Measurement("total train speed", instancesProcessed / totalTrainTime)); measurements.add( new Measurement("last train speed", this.sampleFrequencyOption.getValue() / lastTrainTime)); Measurement[] performanceMeasurements = evaluator.getPerformanceMeasurements(); for (Measurement measurement : performanceMeasurements) { measurements.add(measurement); } Measurement[] modelMeasurements = learner.getModelMeasurements(); for (Measurement measurement : modelMeasurements) { measurements.add(measurement); } learningCurve.insertEntry( new LearningEvaluation(measurements.toArray(new Measurement[measurements.size()]))); if (immediateResultStream != null) { if (firstDump) { immediateResultStream.println(learningCurve.headerToString()); firstDump = false; } immediateResultStream.println(learningCurve.entryToString(learningCurve.numEntries() - 1)); immediateResultStream.flush(); } if (monitor.resultPreviewRequested()) { monitor.setLatestResultPreview(learningCurve.copy()); } } if (immediateResultStream != null) { immediateResultStream.close(); } return learningCurve; }
From source file:semana07.IrisKnn.java
public static void main(String[] args) throws FileNotFoundException, IOException, Exception { // DEFININDO CONJUNTO DE TREINAMENTO // - Definindo o leitor do arquivo arff FileReader baseIris = new FileReader("iris.arff"); // - Definindo o grupo de instancias a partir do arquivo "simpsons.arff" Instances iris = new Instances(baseIris); // - Definindo o indice do atributo classe iris.setClassIndex(4);//from w ww . ja va 2 s .c o m iris = iris.resample(new Debug.Random()); Instances irisTreino = iris.trainCV(3, 0); Instances irisTeste = iris.testCV(3, 0); // DEFININDO EXEMPLO DESCONHECIDO //5.9,3.0,5.1,1.8,Iris-virginica Instance irisInst = new DenseInstance(iris.numAttributes()); irisInst.setDataset(iris); irisInst.setValue(0, 5.9); irisInst.setValue(1, 3.0); irisInst.setValue(2, 5.1); irisInst.setValue(3, 1.8); // DEFININDO ALGORITMO DE CLASSIFICAO //NN IBk vizinhoIris = new IBk(); //kNN IBk knnIris = new IBk(3); // MONTANDO CLASSIFICADOR //NN vizinhoIris.buildClassifier(irisTreino); //kNN knnIris.buildClassifier(irisTreino); // Definindo arquivo a ser escrito FileWriter writer = new FileWriter("iris.csv"); // Escrevendo o cabealho do arquivo writer.append("Classe Real;Resultado NN;Resultado kNN"); writer.append(System.lineSeparator()); // Sada CLI / Console System.out.println("Classe Real;Resultado NN;Resultado kNN"); //Cabealho for (int i = 0; i <= irisTeste.numInstances() - 1; i++) { Instance testeIris = irisTeste.instance(i); // Sada CLI / Console do valor original System.out.print(testeIris.stringValue(4) + ";"); // Escrevendo o valor original no arquivo writer.append(testeIris.stringValue(4) + ";"); // Definindo o atributo classe como indefinido testeIris.setClassMissing(); // CLASSIFICANDO A INSTANCIA // NN double respostaVizinho = vizinhoIris.classifyInstance(testeIris); testeIris.setValue(4, respostaVizinho); String stringVizinho = testeIris.stringValue(4); //kNN double respostaKnn = knnIris.classifyInstance(testeIris); // Atribuindo respota ao valor do atributo do index 4(classe) testeIris.setValue(4, respostaKnn); String stringKnn = testeIris.stringValue(4); // Adicionando resultado ao grupo de instancia iris iris.add(irisInst); //Escrevendo os resultados no arquivo iris.csv writer.append(stringVizinho + ";"); writer.append(stringKnn + ";"); writer.append(System.lineSeparator()); // Exibindo via CLI / Console o resultado System.out.print(respostaVizinho + ";"); System.out.print(respostaKnn + ";"); System.out.println(testeIris.stringValue(4)); } writer.flush(); writer.close(); }
From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java
License:Open Source License
protected int selfTest(InstanceStream testStream) { int returnStatus = 1; Instance testInst = null; int maxInstances = this.maxInstancesOption.getValue(); long instancesProcessed = 0; //InstanceStream testStream = (InstanceStream) getPreparedClassOption(this.testStreamOption); ClassificationPerformanceEvaluator evaluator = new BasicClassificationPerformanceEvaluator(); evaluator.reset();//from w ww .j a v a 2 s .co m while (testStream.hasMoreInstances() && ((maxInstances < 0) || (instancesProcessed < maxInstances))) { testInst = (Instance) testStream.nextInstance().copy(); int trueClass = (int) testInst.classValue(); testInst.setClassMissing(); double[] prediction = model.getVotesForInstance(testInst); evaluator.addClassificationAttempt(trueClass, prediction, testInst.weight()); instancesProcessed++; if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { long estimatedRemainingInstances = testStream.estimatedRemainingInstances(); if (maxInstances > 0) { long maxRemaining = maxInstances - instancesProcessed; if ((estimatedRemainingInstances < 0) || (maxRemaining < estimatedRemainingInstances)) { estimatedRemainingInstances = maxRemaining; } } System.out.println(estimatedRemainingInstances < 0 ? -1.0 : (double) instancesProcessed / (double) (instancesProcessed + estimatedRemainingInstances)); } } return returnStatus; }
From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java
License:Open Source License
public LearningEvaluation evalModel(InstanceStream trainStream, InstanceStream testStream, AbstractClassifier model) {/* w ww . j a v a 2 s . c om*/ model = new SelfOzaBoostID(); InstanceStream stream = (InstanceStream) trainStream.copy(); ClassificationPerformanceEvaluator evaluator = new BasicClassificationPerformanceEvaluator(); Instance testInst = null; int maxInstances = this.maxInstancesOption.getValue(); long instancesProcessed = 0; System.out.println("Evaluating model..."); while (stream.hasMoreInstances() && ((maxInstances < 0) || (instancesProcessed < maxInstances))) { testInst = (Instance) stream.nextInstance().copy(); int trueClass = (int) testInst.classValue(); testInst.setClassMissing(); double[] prediction = model.getVotesForInstance(testInst); evaluator.addClassificationAttempt(trueClass, prediction, testInst.weight()); instancesProcessed++; if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { long estimatedRemainingInstances = stream.estimatedRemainingInstances(); if (maxInstances > 0) { long maxRemaining = maxInstances - instancesProcessed; if ((estimatedRemainingInstances < 0) || (maxRemaining < estimatedRemainingInstances)) { estimatedRemainingInstances = maxRemaining; } } System.out.println(estimatedRemainingInstances < 0 ? -1.0 : (double) instancesProcessed / (double) (instancesProcessed + estimatedRemainingInstances)); } } System.out.println("Accuracy result before self-train: " + evaluator.getPerformanceMeasurements()[1]); selfTrain(testInst); int returnStatus = selfTest(testStream); EvalActiveBoostingID.model.resetLearningImpl(); //Learning is completed so we can reset return new LearningEvaluation(evaluator.getPerformanceMeasurements()); }