本文整理汇总了Java中weka.classifiers.Evaluation类的典型用法代码示例。如果您正苦于以下问题:Java Evaluation类的具体用法?Java Evaluation怎么用?Java Evaluation使用的例子?那么, 这里精选的类代码示例或许可以为您提供帮助。
Evaluation类属于weka.classifiers包,在下文中一共展示了Evaluation类的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: useClassifier
import weka.classifiers.Evaluation; //导入依赖的package包/类
/**
* 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();
GeneticSearch search = new GeneticSearch();
// search.setSearchBackwards(false);
RandomForest base = new RandomForest();
classifier.setClassifier(base);
System.out.println("Set the classifier : " + base.toString());
classifier.setEvaluator(eval);
System.out.println("Set the evaluator : " + eval.toString());
// classifier.setSearch( search );
System.out.println("Set the search : " + search.toString());
Evaluation evaluation = new Evaluation(data);
evaluation.crossValidateModel(classifier, data, 10, new Random(1));
System.out.println(evaluation.toSummaryString());
}
示例2: performTestSetEvaluation
import weka.classifiers.Evaluation; //导入依赖的package包/类
/**
* Splits the dataset between training set and test set according with the percentage given.
* < br/>Then, build the classifier based on the training set and apply to predict the test set.
* @param dataset
* Dataset to be divided
* @param percentageSplit
* Rate of split
* @return
* An Evaluation Object with the results
* @throws Exception
*/
public Evaluation[] performTestSetEvaluation(Instances dataset, int percentageSplit) throws Exception{
int trainSetSize = Math.round((dataset.numInstances() * percentageSplit)/100);
int testSetSize = dataset.numInstances() - trainSetSize;
dataset = randomizeSet(dataset);
trainingSet = new Instances(dataset, 0, trainSetSize);
testingSet = new Instances(dataset, trainSetSize, testSetSize);
for(int i = 0;i < cls.length;i++){
cls[i].buildClassifier(trainingSet);
eval[i] = new Evaluation(trainingSet);
eval[i].evaluateModel(cls[i], testingSet);
}
return eval;
}
示例3: crossValidate
import weka.classifiers.Evaluation; //导入依赖的package包/类
/**
* 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();
}
示例4: trainRandomForest
import weka.classifiers.Evaluation; //导入依赖的package包/类
public static void trainRandomForest(final Instances trainingSet) throws Exception {
// Create a classifier
final RandomForest tree = new RandomForest();
tree.buildClassifier(trainingSet);
// Test the model
final Evaluation eval = new Evaluation(trainingSet);
// eval.crossValidateModel(tree, trainingSet, 10, new Random(1));
eval.evaluateModel(tree, trainingSet);
// Print the result à la Weka explorer:
logger.info(eval.toSummaryString());
logger.info(eval.toMatrixString());
logger.info(tree.toString());
}
示例5: modelErrors
import weka.classifiers.Evaluation; //导入依赖的package包/类
/**
*Updates the numIncorrectModel field for all nodes. This is needed for calculating the alpha-values.
*/
public void modelErrors() throws Exception{
Evaluation eval = new Evaluation(m_train);
if (!m_isLeaf) {
m_isLeaf = true;
eval.evaluateModel(this, m_train);
m_isLeaf = false;
m_numIncorrectModel = eval.incorrect();
for (int i = 0; i < m_sons.length; i++) m_sons[i].modelErrors();
} else {
eval.evaluateModel(this, m_train);
m_numIncorrectModel = eval.incorrect();
}
}
示例6: crossValidate
import weka.classifiers.Evaluation; //导入依赖的package包/类
/**
* 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 = Classifier.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();
}
示例7: Classification
import weka.classifiers.Evaluation; //导入依赖的package包/类
public Classification(ArrayList<ClassifierType> cType) {
cls = new Classifier[cType.size()];
eval = new Evaluation[cType.size()];
for(int i = 0; i < cType.size();i++){
switch(cType.get(i)){
// TODO Will we use J48 or ID3 implementation of decision trees?
case J48:
cls[i] = new J48();
break;
case NAIVE_BAYES:
// If bType == Incremental then cls = new UpdateableNaiveBayes(); else
cls[i] = new NaiveBayes();
break;
case IBK:
cls[i] = new IBk();
break;
case COSINE:
cls[i] = useCosine();
// TODO Add other cases: Decision Rule, KNN and so on.
}
}
}
示例8: Main
import weka.classifiers.Evaluation; //导入依赖的package包/类
public Main() {
try {
BufferedReader datafile;
datafile = readDataFile("camping.txt");
Instances data = new Instances(datafile);
data.setClassIndex(data.numAttributes() - 1);
Instances trainingData = new Instances(data, 0, 14);
Instances testingData = new Instances(data, 14, 5);
Evaluation evaluation = new Evaluation(trainingData);
SMO smo = new SMO();
smo.buildClassifier(data);
evaluation.evaluateModel(smo, testingData);
System.out.println(evaluation.toSummaryString());
// Test instance
Instance instance = new DenseInstance(3);
instance.setValue(data.attribute("age"), 78);
instance.setValue(data.attribute("income"), 125700);
instance.setValue(data.attribute("camps"), 1);
instance.setDataset(data);
System.out.println("The instance: " + instance);
System.out.println(smo.classifyInstance(instance));
} catch (Exception ex) {
ex.printStackTrace();
}
}
开发者ID:PacktPublishing,项目名称:Machine-Learning-End-to-Endguide-for-Java-developers,代码行数:30,代码来源:Main-SVG.java
示例9: writeCrossValidationResults
import weka.classifiers.Evaluation; //导入依赖的package包/类
@TimeThis(task="write-results", category=TimerCategory.EXPORT)
protected void writeCrossValidationResults(ProcessingContext<Corpus> ctx, TargetStream evaluationFile, Evaluation evaluation, String[] classes) throws Exception {
Logger logger = getLogger(ctx);
logger.info("writing test results into " + evaluationFile.getName());
try (PrintStream out = evaluationFile.getPrintStream()) {
for (int i = 0; i < classes.length; ++i) {
out.printf("Results for class %d (%s):\n", i, classes[i]);
out.printf(" True positives : %8.0f\n", evaluation.numTruePositives(i));
out.printf(" False positives: %8.0f\n", evaluation.numFalsePositives(i));
out.printf(" True negatives : %8.0f\n", evaluation.numTrueNegatives(i));
out.printf(" False negatives: %8.0f\n", evaluation.numFalseNegatives(i));
out.printf(" Recall: %6.4f\n", evaluation.recall(i));
out.printf(" Precision: %6.4f\n", evaluation.precision(i));
out.printf(" F-Measure: %6.4f\n", evaluation.fMeasure(i));
out.println();
}
out.println(evaluation.toMatrixString("Confusion matrix:"));
}
}
示例10: evaluateResults
import weka.classifiers.Evaluation; //导入依赖的package包/类
public static void evaluateResults(Evaluation evaluation) {
for (Prediction p : evaluation.predictions()) {
System.out.println(p.actual() + " " + p.predicted());
}
System.out.println(evaluation.toSummaryString("\nResults\n======\n", true));
// System.out.println(evaluation.toSummaryString(evaluation.correlationCoefficient() + " " + evaluation.errorRate() + " " + evaluation.meanAbsoluteError() + " ");
}
示例11: evaluate
import weka.classifiers.Evaluation; //导入依赖的package包/类
public static void evaluate(Classifier clf, Instances data, double minPerfomance)
throws Exception {
Instances[] split = TestUtil.splitTrainTest(data);
Instances train = split[0];
Instances test = split[1];
clf.buildClassifier(train);
Evaluation trainEval = new Evaluation(train);
trainEval.evaluateModel(clf, train);
Evaluation testEval = new Evaluation(train);
testEval.evaluateModel(clf, test);
final double testPctCorrect = testEval.pctCorrect();
final double trainPctCorrect = trainEval.pctCorrect();
log.info("Train: {}, Test: {}", trainPctCorrect, testPctCorrect);
boolean success =
testPctCorrect > minPerfomance && trainPctCorrect > minPerfomance;
Assert.assertTrue(success);
}
示例12: holdout
import weka.classifiers.Evaluation; //导入依赖的package包/类
/**
* Perform simple holdout with a given percentage
*
* @param clf Classifier
* @param data Full dataset
* @param p Split percentage
* @throws Exception
*/
public static void holdout(Classifier clf, Instances data, double p) throws Exception {
Instances[] split = splitTrainTest(data, p);
Instances train = split[0];
Instances test = split[1];
clf.buildClassifier(train);
Evaluation trainEval = new Evaluation(train);
trainEval.evaluateModel(clf, train);
logger.info("Weka Train Evaluation:");
logger.info(trainEval.toSummaryString());
if (!data.classAttribute().isNumeric()) {
logger.info(trainEval.toMatrixString());
}
Evaluation testEval = new Evaluation(train);
logger.info("Weka Test Evaluation:");
testEval.evaluateModel(clf, test);
logger.info(testEval.toSummaryString());
if (!data.classAttribute().isNumeric()) {
logger.info(testEval.toMatrixString());
}
}
示例13: getErrorPercent
import weka.classifiers.Evaluation; //导入依赖的package包/类
@Override
public double getErrorPercent() {
this.splitInstances();
try {
this.getClassifier().buildClassifier(getTrainInstances());
Evaluation eval = new Evaluation(getTestInstances());
eval.evaluateModel(getClassifier(), getTestInstances());
return eval.pctIncorrect();
} catch (Exception e) {
e.printStackTrace();
return -1;
}
}
示例14: trainRandomCommittee
import weka.classifiers.Evaluation; //导入依赖的package包/类
public static void trainRandomCommittee(final Instances trainingSet) throws Exception {
logger.info("Create a classifier");
final RandomTree classifier = new RandomTree();
classifier.setKValue(0);
classifier.setMaxDepth(0);
classifier.setMinNum(0.001);
classifier.setAllowUnclassifiedInstances(false);
classifier.setNumFolds(0);
final RandomCommittee tree = new RandomCommittee();
tree.setClassifier(classifier);
tree.setNumIterations(10);
tree.buildClassifier(trainingSet);
logger.info("Test the model");
final Evaluation eval = new Evaluation(trainingSet);
// eval.crossValidateModel(tree, trainingSet, 10, new Random(1));
eval.evaluateModel(tree, trainingSet);
// Print the result à la Weka explorer:
logger.info(eval.toSummaryString());
logger.info(tree.toString());
logger.info(eval.toMatrixString());
logger.info(eval.toClassDetailsString());
logger.info(eval.toCumulativeMarginDistributionString());
// logger.info("coefficients");
// for(int i = 0; i < tree.coefficients().length; ++i){
// logger.info("{} | {}", trainingSet.attribute(i).name(), tree.coefficients()[i]);
// }
// try {
// final File file = new File(GitHubPublisher.localPath + RetailSalePrediction.predict_retail_sales + File.separator + "prediction_set_script.js");
// FileUtils.writeStringToFile(file, ClassifierToJs.compress(ClassifierToJs.toSource(tree, "predictCommonBySet")), "UTF-8");
// } catch (final Exception e) {
// logger.error(e.getLocalizedMessage(), e);
// }
}
示例15: trainDecisionTable
import weka.classifiers.Evaluation; //导入依赖的package包/类
public static void trainDecisionTable(final Instances trainingSet) throws Exception {
// Create a classifier
final DecisionTable tree = new DecisionTable();
tree.buildClassifier(trainingSet);
// Test the model
final Evaluation eval = new Evaluation(trainingSet);
// eval.crossValidateModel(tree, trainingSet, 10, new Random(1));
eval.evaluateModel(tree, trainingSet);
// Print the result à la Weka explorer:
logger.info(eval.toSummaryString());
logger.info(tree.toString());
// try {
// final File file = new File(GitHubPublisher.localPath + RetailSalePrediction.predict_retail_sales + File.separator + "prediction_set_script.js");
// FileUtils.writeStringToFile(file, ClassifierToJs.compress(ClassifierToJs.toSource(tree, "predictCommonBySet")), "UTF-8");
// } catch (final Exception e) {
// logger.error(e.getLocalizedMessage(), e);
// }
}