public static void wekaAlgorithms(Instances data) throws Exception {
classifier = new FilteredClassifier(); // new instance of tree
classifier.setClassifier(new NaiveBayes());
// classifier.setClassifier(new J48());
// classifier.setClassifier(new RandomForest());
// classifier.setClassifier(new ZeroR());
// classifier.setClassifier(new NaiveBayes());
// classifier.setClassifier(new IBk());
data.setClassIndex(data.numAttributes() - 1);
Evaluation eval = new Evaluation(data);
int folds = 10;
eval.crossValidateModel(classifier, data, folds, new Random(1));
System.out.println("===== Evaluating on filtered (training) dataset =====");
System.out.println(eval.toSummaryString());
System.out.println(eval.toClassDetailsString());
double[][] mat = eval.confusionMatrix();
System.out.println("========= Confusion Matrix =========");
for (int i = 0; i < mat.length; i++) {
for (int j = 0; j < mat.length; j++) {
System.out.print(mat[i][j] + " ");
}
System.out.println(" ");
}
}
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
Instances isTrainingSet = createSet(4);
Instance instance1 = createInstance(new double[] {1, 0.7, 0.1, 0.7}, "S1", isTrainingSet);
Instance instance2 = createInstance(new double[] {0.1, 0.2, 1, 0.3}, "S2", isTrainingSet);
Instance instance22 = createInstance(new double[] {0, 0, 0, 0}, "S3", isTrainingSet);
isTrainingSet.add(instance1);
isTrainingSet.add(instance2);
isTrainingSet.add(instance22);
Instances isTestingSet = createSet(4);
Instance instance3 = createInstance(new double[] {1, 0.7, 0.1, 0.7}, "S1", isTrainingSet);
Instance instance4 = createInstance(new double[] {0.1, 0.2, 1, 0.3}, "S2", isTrainingSet);
isTestingSet.add(instance3);
isTestingSet.add(instance4);
// Create a naïve bayes classifier
Classifier cModel = (Classifier) new BayesNet(); // M5P
cModel.buildClassifier(isTrainingSet);
// Test the model
Evaluation eTest = new Evaluation(isTrainingSet);
eTest.evaluateModel(cModel, isTestingSet);
// Print the result à la Weka explorer:
String strSummary = eTest.toSummaryString();
System.out.println(strSummary);
// Get the likelihood of each classes
// fDistribution[0] is the probability of being “positive”
// fDistribution[1] is the probability of being “negative”
double[] fDistribution = cModel.distributionForInstance(instance4);
for (int i = 0; i < fDistribution.length; i++) {
System.out.println(fDistribution[i]);
}
}
/**
* Creates an evaluation overview of the built classifier.
*
* @return the panel to be displayed as result evaluation view for the current decision point
*/
protected JPanel createEvaluationVisualization(Instances data) {
// build text field to display evaluation statistics
JTextPane statistic = new JTextPane();
try {
// build evaluation statistics
Evaluation evaluation = new Evaluation(data);
evaluation.evaluateModel(myClassifier, data);
statistic.setText(
evaluation.toSummaryString()
+ "\n\n"
+ evaluation.toClassDetailsString()
+ "\n\n"
+ evaluation.toMatrixString());
} catch (Exception ex) {
ex.printStackTrace();
return createMessagePanel("Error while creating the decision tree evaluation view");
}
statistic.setFont(new Font("Courier", Font.PLAIN, 14));
statistic.setEditable(false);
statistic.setCaretPosition(0);
JPanel resultViewPanel = new JPanel();
resultViewPanel.setLayout(new BoxLayout(resultViewPanel, BoxLayout.PAGE_AXIS));
resultViewPanel.add(new JScrollPane(statistic));
return resultViewPanel;
}
public static void run(String[] args) throws Exception {
/**
* *************************************************
*
* @param args[0]: train arff path
* @param args[1]: test arff path
*/
DataSource source = new DataSource(args[0]);
Instances data = source.getDataSet();
data.setClassIndex(data.numAttributes() - 1);
NaiveBayes model = new NaiveBayes();
model.buildClassifier(data);
// Evaluation:
Evaluation eval = new Evaluation(data);
Instances testData = new DataSource(args[1]).getDataSet();
testData.setClassIndex(testData.numAttributes() - 1);
eval.evaluateModel(model, testData);
System.out.println(model.toString());
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
System.out.println("======\nConfusion Matrix:");
double[][] confusionM = eval.confusionMatrix();
for (int i = 0; i < confusionM.length; ++i) {
for (int j = 0; j < confusionM[i].length; ++j) {
System.out.format("%10s ", confusionM[i][j]);
}
System.out.print("\n");
}
}
/**
* Finds the best parameter combination. (recursive for each parameter being optimised).
*
* @param depth the index of the parameter to be optimised at this level
* @param trainData the data the search is based on
* @param random a random number generator
* @throws Exception if an error occurs
*/
protected void findParamsByCrossValidation(int depth, Instances trainData, Random random)
throws Exception {
if (depth < m_CVParams.size()) {
CVParameter cvParam = (CVParameter) m_CVParams.elementAt(depth);
double upper;
switch ((int) (cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
case 1:
upper = m_NumAttributes;
break;
case 2:
upper = m_TrainFoldSize;
break;
default:
upper = cvParam.m_Upper;
break;
}
double increment = (upper - cvParam.m_Lower) / (cvParam.m_Steps - 1);
for (cvParam.m_ParamValue = cvParam.m_Lower;
cvParam.m_ParamValue <= upper;
cvParam.m_ParamValue += increment) {
findParamsByCrossValidation(depth + 1, trainData, random);
}
} else {
Evaluation evaluation = new Evaluation(trainData);
// Set the classifier options
String[] options = createOptions();
if (m_Debug) {
System.err.print("Setting options for " + m_Classifier.getClass().getName() + ":");
for (int i = 0; i < options.length; i++) {
System.err.print(" " + options[i]);
}
System.err.println("");
}
((OptionHandler) m_Classifier).setOptions(options);
for (int j = 0; j < m_NumFolds; j++) {
// We want to randomize the data the same way for every
// learning scheme.
Instances train = trainData.trainCV(m_NumFolds, j, new Random(1));
Instances test = trainData.testCV(m_NumFolds, j);
m_Classifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(m_Classifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println("Cross-validated error rate: " + Utils.doubleToString(error, 6, 4));
}
if ((m_BestPerformance == -99) || (error < m_BestPerformance)) {
m_BestPerformance = error;
m_BestClassifierOptions = createOptions();
}
}
}
/** evaluates the classifier */
@Override
public void evaluate() throws Exception {
// evaluate classifier and print some statistics
if (_test.classIndex() == -1) _test.setClassIndex(_test.numAttributes() - 1);
Evaluation eval = new Evaluation(_train);
eval.evaluateModel(_cl, _test);
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
System.out.println(eval.toMatrixString());
}
/**
* Buildclassifier selects a classifier from the set of classifiers by minimising error on the
* training data.
*
* @param data the training data to be used for generating the boosted classifier.
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifiers.length == 0) {
throw new Exception("No base classifiers have been set!");
}
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
newData.randomize(new Random(m_Seed));
if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1))
newData.stratify(m_NumXValFolds);
Instances train = newData; // train on all data by default
Instances test = newData; // test on training data by default
Classifier bestClassifier = null;
int bestIndex = -1;
double bestPerformance = Double.NaN;
int numClassifiers = m_Classifiers.length;
for (int i = 0; i < numClassifiers; i++) {
Classifier currentClassifier = getClassifier(i);
Evaluation evaluation;
if (m_NumXValFolds > 1) {
evaluation = new Evaluation(newData);
for (int j = 0; j < m_NumXValFolds; j++) {
train = newData.trainCV(m_NumXValFolds, j);
test = newData.testCV(m_NumXValFolds, j);
currentClassifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(currentClassifier, test);
}
} else {
currentClassifier.buildClassifier(train);
evaluation = new Evaluation(train);
evaluation.evaluateModel(currentClassifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println(
"Error rate: "
+ Utils.doubleToString(error, 6, 4)
+ " for classifier "
+ currentClassifier.getClass().getName());
}
if ((i == 0) || (error < bestPerformance)) {
bestClassifier = currentClassifier;
bestPerformance = error;
bestIndex = i;
}
}
m_ClassifierIndex = bestIndex;
m_Classifier = bestClassifier;
if (m_NumXValFolds > 1) {
m_Classifier.buildClassifier(newData);
}
}
public Evaluation evaluateClassifier(Instances trainInstances, Instances testInstances) {
try {
Evaluation eval = new Evaluation(trainInstances);
eval.evaluateModel(bayesNet, testInstances);
return eval;
} catch (Exception e) {
System.err.println(e.getMessage());
e.printStackTrace();
return null;
}
}
/** uses the meta-classifier */
protected static void useClassifier(Instances data) throws Exception {
System.out.println("\n1. Meta-classfier");
AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
J48 base = new J48();
classifier.setClassifier(base);
classifier.setEvaluator(eval);
classifier.setSearch(search);
Evaluation evaluation = new Evaluation(data);
evaluation.crossValidateModel(classifier, data, 10, new Random(1));
System.out.println(evaluation.toSummaryString());
}
public static Double runClassify(String trainFile, String testFile) {
double predictOrder = 0.0;
double trueOrder = 0.0;
try {
String trainWekaFileName = trainFile;
String testWekaFileName = testFile;
Instances train = DataSource.read(trainWekaFileName);
Instances test = DataSource.read(testWekaFileName);
train.setClassIndex(0);
test.setClassIndex(0);
train.deleteAttributeAt(8);
test.deleteAttributeAt(8);
train.deleteAttributeAt(6);
test.deleteAttributeAt(6);
train.deleteAttributeAt(5);
test.deleteAttributeAt(5);
train.deleteAttributeAt(4);
test.deleteAttributeAt(4);
// AdditiveRegression classifier = new AdditiveRegression();
// NaiveBayes classifier = new NaiveBayes();
RandomForest classifier = new RandomForest();
// LibSVM classifier = new LibSVM();
classifier.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(classifier, test);
System.out.println(eval.toSummaryString("\nResults\n\n", true));
// System.out.println(eval.toClassDetailsString());
// System.out.println(eval.toMatrixString());
int k = 892;
for (int i = 0; i < test.numInstances(); i++) {
predictOrder = classifier.classifyInstance(test.instance(i));
trueOrder = test.instance(i).classValue();
System.out.println((k++) + "," + (int) predictOrder);
}
} catch (Exception e) {
e.printStackTrace();
}
return predictOrder;
}
public static Instances getKnowledgeBase() {
if (knowledgeBase == null) {
try {
// load knowledgebase from file
CreateAppInsertIntoVm.knowledgeBase =
Action.loadKnowledge(Configuration.getInstance().getKBCreateAppInsertIntoVm());
// prediction is also performed therefore the classifier and the evaluator must be
// instantiated
if (!isOnlyLearning()) {
System.out.println("Classify data CreateAppInsertInto");
if (knowledgeBase.numInstances() > 0) {
classifier = new MultilayerPerceptron();
classifier.buildClassifier(knowledgeBase);
evaluation = new Evaluation(knowledgeBase);
evaluation.crossValidateModel(
classifier,
knowledgeBase,
10,
knowledgeBase.getRandomNumberGenerator(randomData.nextLong(1, 1000)));
System.out.println("Classified data CreateAppInsertInto");
} else {
System.out.println("No Instancedata for classifier CreateAppInsertIntoVm");
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
return knowledgeBase;
}
/**
* Process a classifier's prediction for an instance and update a set of plotting instances and
* additional plotting info. m_PlotShape for nominal class datasets holds shape types (actual data
* points have automatic shape type assignment; classifier error data points have box shape type).
* For numeric class datasets, the actual data points are stored in m_PlotInstances and m_PlotSize
* stores the error (which is later converted to shape size values).
*
* @param toPredict the actual data point
* @param classifier the classifier
* @param eval the evaluation object to use for evaluating the classifier on the instance to
* predict
* @see #m_PlotShapes
* @see #m_PlotSizes
* @see #m_PlotInstances
*/
public void process(Instance toPredict, Classifier classifier, Evaluation eval) {
double pred;
double[] values;
int i;
try {
pred = eval.evaluateModelOnceAndRecordPrediction(classifier, toPredict);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
toPredict =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.constructMappedInstance(toPredict);
}
if (!m_SaveForVisualization) return;
if (m_PlotInstances != null) {
values = new double[m_PlotInstances.numAttributes()];
for (i = 0; i < m_PlotInstances.numAttributes(); i++) {
if (i < toPredict.classIndex()) {
values[i] = toPredict.value(i);
} else if (i == toPredict.classIndex()) {
values[i] = pred;
values[i + 1] = toPredict.value(i);
i++;
} else {
values[i] = toPredict.value(i - 1);
}
}
m_PlotInstances.add(new DenseInstance(1.0, values));
if (toPredict.classAttribute().isNominal()) {
if (toPredict.isMissing(toPredict.classIndex()) || Utils.isMissingValue(pred)) {
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
} else if (pred != toPredict.classValue()) {
// set to default error point shape
m_PlotShapes.addElement(new Integer(Plot2D.ERROR_SHAPE));
} else {
// otherwise set to constant (automatically assigned) point shape
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
}
m_PlotSizes.addElement(new Integer(Plot2D.DEFAULT_SHAPE_SIZE));
} else {
// store the error (to be converted to a point size later)
Double errd = null;
if (!toPredict.isMissing(toPredict.classIndex()) && !Utils.isMissingValue(pred)) {
errd = new Double(pred - toPredict.classValue());
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
} else {
// missing shape if actual class not present or prediction is missing
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
}
m_PlotSizes.addElement(errd);
}
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
// calculate if an App fits to a pm
// TODO: gst: use WEKA to calc fit factor!!
private int calculateFit(App app2, VirtualMachine vm) {
int output = 0;
if (Action.isOnlyLearning() == false && CreateAppInsertIntoVm.evaluation != null) {
// is free space available in the VM
if (app2.getCpu() + vm.getCurrentCpuUsage() < vm.getCurrentCpuAllocation()
&& app2.getMemory() + vm.getCurrentMemoryUsage() < vm.getCurrentMemoryAllocation()
&& app2.getStorage() + vm.getCurrentStorageUsage() < vm.getCurrentCpuAllocation()) {
Instance instance = createInstance(Instance.missingValue(), vm);
instance.setDataset(CreateAppInsertIntoVm.getKnowledgeBase());
try {
output = (int) (evaluation.evaluateModelOnce(classifier, instance) * 100);
} catch (Exception e) {
e.printStackTrace();
}
}
} else {
if (app2.getCpu() + vm.getCurrentCpuUsage() < vm.getCurrentCpuAllocation()
&& app2.getMemory() + vm.getCurrentMemoryUsage() < vm.getCurrentMemoryAllocation()
&& app2.getStorage() + vm.getCurrentStorageUsage() < vm.getCurrentCpuAllocation()) {
output = randomData.nextInt(1, 100);
}
}
return output;
}
/**
* Adds the prediction intervals as additional attributes at the end. Since classifiers can
* returns varying number of intervals per instance, the dataset is filled with missing values for
* non-existing intervals.
*/
protected void addPredictionIntervals() {
int maxNum;
int num;
int i;
int n;
FastVector preds;
FastVector atts;
Instances data;
Instance inst;
Instance newInst;
double[] values;
double[][] predInt;
// determine the maximum number of intervals
maxNum = 0;
preds = m_Evaluation.predictions();
for (i = 0; i < preds.size(); i++) {
num = ((NumericPrediction) preds.elementAt(i)).predictionIntervals().length;
if (num > maxNum) maxNum = num;
}
// create new header
atts = new FastVector();
for (i = 0; i < m_PlotInstances.numAttributes(); i++)
atts.addElement(m_PlotInstances.attribute(i));
for (i = 0; i < maxNum; i++) {
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-lowerBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-upperBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-width"));
}
data = new Instances(m_PlotInstances.relationName(), atts, m_PlotInstances.numInstances());
data.setClassIndex(m_PlotInstances.classIndex());
// update data
for (i = 0; i < m_PlotInstances.numInstances(); i++) {
inst = m_PlotInstances.instance(i);
// copy old values
values = new double[data.numAttributes()];
System.arraycopy(inst.toDoubleArray(), 0, values, 0, inst.numAttributes());
// add interval data
predInt = ((NumericPrediction) preds.elementAt(i)).predictionIntervals();
for (n = 0; n < maxNum; n++) {
if (n < predInt.length) {
values[m_PlotInstances.numAttributes() + n * 3 + 0] = predInt[n][0];
values[m_PlotInstances.numAttributes() + n * 3 + 1] = predInt[n][1];
values[m_PlotInstances.numAttributes() + n * 3 + 2] = predInt[n][1] - predInt[n][0];
} else {
values[m_PlotInstances.numAttributes() + n * 3 + 0] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n * 3 + 1] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n * 3 + 2] = Utils.missingValue();
}
}
// create new Instance
newInst = new DenseInstance(inst.weight(), values);
data.add(newInst);
}
m_PlotInstances = data;
}
public static void trainModel(Instances dataTrain, Instances dataTest) {
try {
LibLINEAR classifier = new LibLINEAR();
classifier.setBias(10);
classifier.buildClassifier(dataTrain);
Evaluation eval = new Evaluation(dataTrain);
eval.evaluateModel(classifier, dataTest);
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String[] argv) {
try {
System.out.println(Evaluation.evaluateModel(new Decorate(), argv));
} catch (Exception e) {
System.err.println(e.getMessage());
}
}
@Override
protected pikater.ontology.messages.Evaluation evaluateCA() {
Evaluation eval = test();
pikater.ontology.messages.Evaluation result = new pikater.ontology.messages.Evaluation();
result.setError_rate((float) eval.errorRate());
try {
result.setKappa_statistic((float) eval.kappa());
} catch (Exception e) {
result.setKappa_statistic(-1);
}
result.setMean_absolute_error((float) eval.meanAbsoluteError());
try {
result.setRelative_absolute_error((float) eval.relativeAbsoluteError());
} catch (Exception e) {
result.setRelative_absolute_error(-1);
}
result.setRoot_mean_squared_error((float) eval.rootMeanSquaredError());
result.setRoot_relative_squared_error((float) eval.rootRelativeSquaredError());
return result;
}
/**
* Main method for testing this class
*
* @param argv options
*/
public static void main(String[] argv) {
try {
System.out.println(Evaluation.evaluateModel(new UnivariateLinearRegression(), argv));
} catch (Exception e) {
System.out.println(e.getMessage());
e.printStackTrace();
}
}
/**
* Utility method for fast 5-fold cross validation of a naive bayes model
*
* @param fullModel a <code>NaiveBayesUpdateable</code> value
* @param trainingSet an <code>Instances</code> value
* @param r a <code>Random</code> value
* @return a <code>double</code> value
* @exception Exception if an error occurs
*/
public static double crossValidate(
NaiveBayesUpdateable fullModel, Instances trainingSet, Random r) throws Exception {
// make some copies for fast evaluation of 5-fold xval
Classifier[] copies = AbstractClassifier.makeCopies(fullModel, 5);
Evaluation eval = new Evaluation(trainingSet);
// make some splits
for (int j = 0; j < 5; j++) {
Instances test = trainingSet.testCV(5, j);
// unlearn these test instances
for (int k = 0; k < test.numInstances(); k++) {
test.instance(k).setWeight(-test.instance(k).weight());
((NaiveBayesUpdateable) copies[j]).updateClassifier(test.instance(k));
// reset the weight back to its original value
test.instance(k).setWeight(-test.instance(k).weight());
}
eval.evaluateModel(copies[j], test);
}
return eval.incorrect();
}
@Override
protected Evaluation test() {
working = true;
System.out.println("Agent " + getLocalName() + ": Testing...");
// evaluate classifier and print some statistics
Evaluation eval = null;
try {
eval = new Evaluation(train);
eval.evaluateModel(cls, test);
System.out.println(
eval.toSummaryString(getLocalName() + " agent: " + "\nResults\n=======\n", false));
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
working = false;
return eval;
} // end test
@Override
public void crossValidation(String traindata) throws Exception {
DataSource ds = new DataSource(traindata);
Instances instances = ds.getDataSet();
StringToWordVector stv = new StringToWordVector();
stv.setOptions(
weka.core.Utils.splitOptions(
"-R first-last -W 1000 "
+ "-prune-rate -1.0 -N 0 "
+ "-stemmer weka.core.stemmers.NullStemmer -M 1 "
+ "-tokenizer \"weka.core.tokenizers.WordTokenizer -delimiters \\\" \\r\\n\\t.,;:\\\'\\\"()?!\""));
stv.setInputFormat(instances);
instances = Filter.useFilter(instances, stv);
instances.setClassIndex(0);
Evaluation eval = new Evaluation(instances);
eval.crossValidateModel(this.classifier, instances, 10, new Random(1));
System.out.println(eval.toSummaryString());
System.out.println(eval.toMatrixString());
}
private static void evaluateClassifier(Classifier c, Instances trainData, Instances testData)
throws Exception {
System.err.println(
"INFO: Starting split validation to predict '"
+ trainData.classAttribute().name()
+ "' using '"
+ c.getClass().getCanonicalName()
+ ":"
+ Arrays.toString(c.getOptions())
+ "' (#train="
+ trainData.numInstances()
+ ",#test="
+ testData.numInstances()
+ ") ...");
if (trainData.classIndex() < 0) throw new IllegalStateException("class attribute not set");
c.buildClassifier(trainData);
Evaluation eval = new Evaluation(testData);
eval.useNoPriors();
double[] predictions = eval.evaluateModel(c, testData);
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
// write predictions to file
{
System.err.println("INFO: Writing predictions to file ...");
Writer out = new FileWriter("prediction.trec");
writePredictionsTrecEval(predictions, testData, 0, trainData.classIndex(), out);
out.close();
}
// write predicted distributions to CSV
{
System.err.println("INFO: Writing predicted distributions to CSV ...");
Writer out = new FileWriter("predicted_distribution.csv");
writePredictedDistributions(c, testData, 0, out);
out.close();
}
}
protected static Evaluation adjustForInputMappedClassifier(
Evaluation eval,
weka.classifiers.Classifier classifier,
Instances inst,
ClassifierErrorsPlotInstances plotInstances)
throws Exception {
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
Instances mappedClassifierHeader =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.getModelHeader(new Instances(inst, 0));
eval = new Evaluation(new Instances(mappedClassifierHeader, 0));
if (!eval.getHeader().equalHeaders(inst)) {
// When the InputMappedClassifier is loading a model,
// we need to make a new dataset that maps the test instances to
// the structure expected by the mapped classifier - this is only
// to ensure that the ClassifierPlotInstances object is configured
// in accordance with what the embeded classifier was trained with
Instances mappedClassifierDataset =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.getModelHeader(new Instances(mappedClassifierHeader, 0));
for (int zz = 0; zz < inst.numInstances(); zz++) {
Instance mapped =
((weka.classifiers.misc.InputMappedClassifier) classifier)
.constructMappedInstance(inst.instance(zz));
mappedClassifierDataset.add(mapped);
}
eval.setPriors(mappedClassifierDataset);
plotInstances.setInstances(mappedClassifierDataset);
plotInstances.setClassifier(classifier);
plotInstances.setClassIndex(mappedClassifierDataset.classIndex());
plotInstances.setEvaluation(eval);
}
}
return eval;
}
/**
* 用分类器测试
*
* @param trainFileName
* @param testFileName
*/
public static void classify(String trainFileName, String testFileName) {
try {
File inputFile = new File(fileName + trainFileName); // 训练语料文件
ArffLoader atf = new ArffLoader();
atf.setFile(inputFile);
Instances instancesTrain = atf.getDataSet(); // 读入训练文件
// 设置类标签类
inputFile = new File(fileName + testFileName); // 测试语料文件
atf.setFile(inputFile);
Instances instancesTest = atf.getDataSet(); // 读入测试文件
instancesTest.setClassIndex(instancesTest.numAttributes() - 1);
instancesTrain.setClassIndex(instancesTrain.numAttributes() - 1);
classifier = (Classifier) Class.forName(CLASSIFIERNAME).newInstance();
classifier.buildClassifier(instancesTrain);
Evaluation eval = new Evaluation(instancesTrain);
// 第一个为一个训练过的分类器，第二个参数是在某个数据集上评价的数据集
eval.evaluateModel(classifier, instancesTest);
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString());
System.out.println(eval.toMatrixString());
System.out.println("precision is :" + (1 - eval.errorRate()));
} catch (Exception e) {
e.printStackTrace();
}
}
/** outputs some data about the classifier */
public String toString() {
StringBuffer result;
result = new StringBuffer();
result.append("Weka - Demo\n===========\n\n");
result.append(
"Classifier...: "
+ m_Classifier.getClass().getName()
+ " "
+ Utils.joinOptions(m_Classifier.getOptions())
+ "\n");
if (m_Filter instanceof OptionHandler)
result.append(
"Filter.......: "
+ m_Filter.getClass().getName()
+ " "
+ Utils.joinOptions(((OptionHandler) m_Filter).getOptions())
+ "\n");
else result.append("Filter.......: " + m_Filter.getClass().getName() + "\n");
result.append("Training file: " + m_TrainingFile + "\n");
result.append("\n");
result.append(m_Classifier.toString() + "\n");
result.append(m_Evaluation.toSummaryString() + "\n");
try {
result.append(m_Evaluation.toMatrixString() + "\n");
} catch (Exception e) {
e.printStackTrace();
}
try {
result.append(m_Evaluation.toClassDetailsString() + "\n");
} catch (Exception e) {
e.printStackTrace();
}
return result.toString();
}
/** runs 10fold CV over the training file */
public void execute() throws Exception {
// run filter
m_Filter.setInputFormat(m_Training);
Instances filtered = Filter.useFilter(m_Training, m_Filter);
// train classifier on complete file for tree
m_Classifier.buildClassifier(filtered);
// 10fold CV with seed=1
m_Evaluation = new Evaluation(filtered);
m_Evaluation.crossValidateModel(
m_Classifier, filtered, 10, m_Training.getRandomNumberGenerator(1));
}
private static void run() throws Exception {
DataSource source = new DataSource("src/files/powerpuffgirls.arff");
int folds = 10;
int runs = 30;
HashMap<String, Classifier> hash = new HashMap<>();
hash.put("J48", new J48());
hash.put("NaiveBayes", new NaiveBayes());
hash.put("IBk=1", new IBk(1));
hash.put("IBk=3", new IBk(3));
hash.put("MultilayerPerceptron", new MultilayerPerceptron());
// LibSVM svm = new LibSVM();
// svm.setOptions(new String[]{"-S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5 -M 0.40 -C 1.0 -E
// 0.001 -P 0.1"});
// hash.put("LibSVM", svm);
Instances data = source.getDataSet();
data.setClassIndex(4);
System.out.println("#seed \t correctly instances \t percentage of corrects\n");
for (Entry<String, Classifier> entry : hash.entrySet()) {
System.out.println("\n Algorithm: " + entry.getKey() + "\n");
for (int i = 1; i <= runs; i++) {
Evaluation eval = new Evaluation(data);
eval.crossValidateModel(entry.getValue(), data, folds, new Random(i));
System.out.println(summary(eval));
}
}
}
private double[] makePredictions(
Classifier classifier, Instances validationSet, Evaluation evaluation) {
double[] predictions = null;
try {
predictions = evaluation.evaluateModel(classifier, validationSet);
} catch (ArrayIndexOutOfBoundsException e) {
throw new ClassifierPredictionException(
"Error applying the trained classifier to the train instances. The number of features of the instance exceeds the number of features the classifier was trained on.",
e);
} catch (Exception e) {
throw new ClassifierPredictionException(
"Error applying the trained classifier to the test instances.", e);
}
return predictions;
}
@Override
protected DataInstances getPredictions(Instances test, DataInstances onto_test) {
Evaluation eval = test();
double pre[] = new double[test.numInstances()];
for (int i = 0; i < test.numInstances(); i++) {
try {
pre[i] = eval.evaluateModelOnce((Classifier) getModelObject(), test.instance(i));
} catch (Exception e) {
pre[i] = Integer.MAX_VALUE;
}
}
// copy results to the DataInstancs
int i = 0;
Iterator itr = onto_test.getInstances().iterator();
while (itr.hasNext()) {
Instance next_instance = (Instance) itr.next();
next_instance.setPrediction(pre[i]);
i++;
}
return onto_test;
}
static void evaluateClassifier(Classifier c, Instances data, int folds) throws Exception {
System.err.println(
"INFO: Starting crossvalidation to predict '"
+ data.classAttribute().name()
+ "' using '"
+ c.getClass().getCanonicalName()
+ ":"
+ Arrays.toString(c.getOptions())
+ "' ...");
StringBuffer sb = new StringBuffer();
Evaluation eval = new Evaluation(data);
eval.crossValidateModel(c, data, folds, new Random(1), sb, new Range("first"), Boolean.FALSE);
// write predictions to file
{
Writer out = new FileWriter("cv.log");
out.write(sb.toString());
out.close();
}
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}
