本文整理汇总了Java中weka.classifiers.evaluation.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？那么恭喜您, 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.evaluation包，在下文中一共展示了Evaluation类的20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，您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: getEvalResultbySMOTE
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbySMOTE(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:40，代码来源:ImbalanceProcessingAve.java
示例2: evalToBytes
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/**
* Serializes and compresses an Evaluation object to an array of bytes
*
* @param eval the Evaluation object to serialize
* @return an array of bytes
* @throws IOException if a problem occurs
*/
protected static byte[] evalToBytes(Evaluation eval) throws IOException {
ObjectOutputStream p = null;
byte[] bytes = null;
try {
ByteArrayOutputStream ostream = new ByteArrayOutputStream();
OutputStream os = ostream;
p = new ObjectOutputStream(new BufferedOutputStream(new GZIPOutputStream(
os)));
p.writeObject(eval);
p.flush();
p.close();
bytes = ostream.toByteArray();
p = null;
} finally {
if (p != null) {
p.close();
}
}
return bytes;
}
开发者ID:mydzigear，项目名称:repo.kmeanspp.silhouette_score，代码行数:32，代码来源:WekaFoldBasedClassifierEvaluationHadoopMapper.java
示例3: deserialize
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/**
* Helper function to deserialize a Evaluation object from an array of bytes
*
* @param bytes the array containing the compressed serialized Evaluation
* object
* @return the deserialized Evaluation object
* @throws Exception if a problem occurs
*/
protected Evaluation deserialize(byte[] bytes) throws Exception {
ByteArrayInputStream istream = new ByteArrayInputStream(bytes);
ObjectInputStream p = null;
Object toReturn = null;
try {
p = new ObjectInputStream(new BufferedInputStream(new GZIPInputStream(
istream)));
toReturn = p.readObject();
if (!(toReturn instanceof Evaluation)) {
throw new Exception("Object deserialized was not an Evaluation object!");
}
} finally {
if (p != null) {
p.close();
}
}
return (Evaluation) toReturn;
}
开发者ID:mydzigear，项目名称:repo.kmeanspp.silhouette_score，代码行数:30，代码来源:WekaClassifierEvaluationHadoopReducer.java
示例4: aggregate
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/**
* Aggregate a list of Evaluation objects.
*
* @param evals the list of Evaluation objects to aggregate
* @return a single eval Evaluation
* @throws Exception if a problem occurs
*/
public Evaluation aggregate(List<Evaluation> evals) throws Exception {
if (evals.size() == 0) {
throw new Exception("Nothing to aggregate!");
}
AggregateableEvaluation aggEval = new AggregateableEvaluation(evals.get(0));
for (Evaluation e : evals) {
aggEval.aggregate(e);
}
aggEval.finalizeAggregation();
return aggEval;
}
开发者ID:mydzigear，项目名称:repo.kmeanspp.silhouette_score，代码行数:24，代码来源:WekaClassifierEvaluationReduceTask.java
示例5: main
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
public static void main(String[] args) throws Exception{
String databasePath = "data/features.arff";
// Load the data in arff format
Instances data = new Instances(new BufferedReader(new FileReader(databasePath)));
// Set class the last attribute as class
data.setClassIndex(data.numAttributes() - 1);
// Build a basic decision tree model
String[] options = new String[]{};
J48 model = new J48();
model.setOptions(options);
model.buildClassifier(data);
// Output decision tree
System.out.println("Decision tree model:\n"+model);
// Output source code implementing the decision tree
System.out.println("Source code:\n"+model.toSource("ActivityRecognitionEngine"));
// Check accuracy of model using 10-fold cross-validation
Evaluation eval = new Evaluation(data);
eval.crossValidateModel(model, data, 10, new Random(1), new String[] {});
System.out.println("Model performance:\n"+eval.toSummaryString());
String[] activities = new String[]{"Walk", "Walk", "Walk", "Run", "Walk", "Run", "Run", "Sit", "Sit", "Sit"};
DiscreteLowPass dlpFilter = new DiscreteLowPass(3);
for(String str : activities){
System.out.println(str +" -> "+ dlpFilter.filter(str));
}
}
开发者ID:PacktPublishing，项目名称:Machine-Learning-End-to-Endguide-for-Java-developers，代码行数:35，代码来源:ActivityRecognition.java
示例6: getEvalResultbyNo
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Only use C4.5 to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyNo(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(j48, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:33，代码来源:ImbalanceProcessingAve.java
示例7: getEvalResultbyResampling
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>Resampling</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyResampling(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
Resample resample = new Resample();
resample.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(resample);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:40，代码来源:ImbalanceProcessingAve.java
示例8: getEvalResultbyCost
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>Cost-sensitive learning</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyCost(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/**Classifier setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
CostSensitiveClassifier csc = new CostSensitiveClassifier();
csc.setClassifier(j48);
csc.setCostMatrix(new CostMatrix(new BufferedReader(new FileReader("files/costm"))));
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(csc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:38，代码来源:ImbalanceProcessingAve.java
示例9: getEvalResultbyDefault
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyDefault(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:40，代码来源:FeatureSelectionAve.java
示例10: testCOMT2
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
public static void testCOMT2() throws Exception{
BestConf bestconf = new BestConf();
Instances trainingSet = DataIOFile.loadDataFromArffFile("data/trainingBestConf0.arff");
trainingSet.setClassIndex(trainingSet.numAttributes()-1);
Instances samplePoints = LHSInitializer.getMultiDimContinuous(bestconf.getAttributes(), InitialSampleSetSize, false);
samplePoints.insertAttributeAt(trainingSet.classAttribute(), samplePoints.numAttributes());
samplePoints.setClassIndex(samplePoints.numAttributes()-1);
COMT2 comt = new COMT2(samplePoints, COMT2Iteration);
comt.buildClassifier(trainingSet);
Evaluation eval = new Evaluation(trainingSet);
eval.evaluateModel(comt, trainingSet);
System.err.println(eval.toSummaryString());
Instance best = comt.getInstanceWithPossibleMaxY(samplePoints.firstInstance());
Instances bestInstances = new Instances(trainingSet,2);
bestInstances.add(best);
DataIOFile.saveDataToXrffFile("data/trainingBestConf_COMT2.arff", bestInstances);
//now we output the training set with the class value updated as the predicted value
Instances output = new Instances(trainingSet, trainingSet.numInstances());
Enumeration<Instance> enu = trainingSet.enumerateInstances();
while(enu.hasMoreElements()){
Instance ins = enu.nextElement();
double[] values = ins.toDoubleArray();
values[values.length-1] = comt.classifyInstance(ins);
output.add(ins.copy(values));
}
DataIOFile.saveDataToXrffFile("data/trainingBestConf0_predict.xrff", output);
}
开发者ID:zhuyuqing，项目名称:bestconf，代码行数:34，代码来源:BestConf.java
示例11: cleanup
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
@Override
public void cleanup(Context context) throws IOException {
try {
// aggregate the stats over all folds in this chunk
AggregateableEvaluation agg = null;
for (int i = 0; i < m_totalFolds; i++) {
if (!m_classifierIsUpdateable || m_forceBatch) {
String modelToLoad = "" + (i + 1) + "_" + m_originalModelFileName;
Classifier foldModel = WekaClassifierHadoopMapper
.loadClassifier(modelToLoad);
m_tasks[i].setClassifier(foldModel);
}
m_tasks[i].finalizeTask();
Evaluation eval = m_tasks[i].getEvaluation();
// save memory
m_tasks[i] = null;
if (agg == null) {
agg = new AggregateableEvaluation(eval);
}
agg.aggregate(eval);
}
if (agg != null) {
byte[] bytes = evalToBytes(agg);
String constantKey = "evaluation";
Text key = new Text();
key.set(constantKey);
BytesWritable value = new BytesWritable();
value.set(bytes, 0, bytes.length);
context.write(key, value);
}
} catch (Exception ex) {
throw new IOException(ex);
}
}
开发者ID:mydzigear，项目名称:repo.kmeanspp.silhouette_score，代码行数:41，代码来源:WekaFoldBasedClassifierEvaluationHadoopMapper.java
示例12: getEvalResultbyChiSquare
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b>, combined with <b>Chi-Square</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyChiSquare(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/**chi-squared filter to process the whole dataset first*/
ChiSquaredAttributeEval evall = new ChiSquaredAttributeEval();
Ranker ranker = new Ranker();
AttributeSelection selector = new AttributeSelection();
selector.setEvaluator(evall);
selector.setSearch(ranker);
selector.setInputFormat(ins);
ins = Filter.useFilter(ins, selector);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:50，代码来源:FeatureSelectionAve.java
示例13: getEvalResultbyInfoGain
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b>, combined with <b>Information Gain</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyInfoGain(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/**information gain filter to process the whole dataset first*/
InfoGainAttributeEval evall = new InfoGainAttributeEval();
Ranker ranker = new Ranker();
AttributeSelection selector = new AttributeSelection();
selector.setEvaluator(evall);
selector.setSearch(ranker);
selector.setInputFormat(ins);
ins = Filter.useFilter(ins, selector);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:50，代码来源:FeatureSelectionAve.java
示例14: getEvalResultbyGainRatio
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b>, combined with <b>Information Gain Ratio</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyGainRatio(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/**information gain ratio filter to process the whole dataset first*/
GainRatioAttributeEval evall = new GainRatioAttributeEval();
Ranker ranker = new Ranker();
AttributeSelection selector = new AttributeSelection();
selector.setEvaluator(evall);
selector.setSearch(ranker);
selector.setInputFormat(ins);
ins = Filter.useFilter(ins, selector);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:50，代码来源:FeatureSelectionAve.java
示例15: getEvalResultbyCorrelation
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b>, combined with <b>Correlation</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyCorrelation(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/** correlation filter to process the whole dataset first*/
CorrelationAttributeEval evall = new CorrelationAttributeEval();
Ranker ranker = new Ranker();
AttributeSelection selector = new AttributeSelection();
selector.setEvaluator(evall);
selector.setSearch(ranker);
selector.setInputFormat(ins);
ins = Filter.useFilter(ins, selector);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:50，代码来源:FeatureSelectionAve.java
示例16: getEvalResultbyReliefF
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b>, combined with <b>ReliefF</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyReliefF(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/** correlation filter to process the whole dataset first*/
ReliefFAttributeEval evall = new ReliefFAttributeEval();
Ranker ranker = new Ranker();
AttributeSelection selector = new AttributeSelection();
selector.setEvaluator(evall);
selector.setSearch(ranker);
selector.setInputFormat(ins);
ins = Filter.useFilter(ins, selector);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:50，代码来源:FeatureSelectionAve.java
示例17: showFolds
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/***
* <p>Using Feature Selection method to get 10 folds results in the given project</p>
* @param path project path
* @param sel label means we use the Feature Selection method
* @throws Exception
*/
public static void showFolds(String path, int k, int flag) throws Exception{
Instances data1 = DataSource.read(path);
data1.setClassIndex(data1.numAttributes()-1);
if(!data1.classAttribute().isNominal()) // in case of noisy data, return
return;
/** Feature Selection: Correlation */
CorrelationAttributeEval evall = new CorrelationAttributeEval();
Ranker ranker = new Ranker();
AttributeSelection selector = new AttributeSelection();
selector.setEvaluator(evall);
selector.setSearch(ranker);
selector.setInputFormat(data1);
data1 = Filter.useFilter(data1, selector);
/** Randomize and stratify the dataset*/
data1.randomize(new Random(1));
data1.stratify(10); // 10 folds
double[][] matrix = new double[10][7];
for(int i=0; i<10; i++){ // To calculate the results in each fold
Instances test = data1.testCV(10, i);
Instances train = data1.trainCV(10, i);
/** SMOTE */
SMOTE smote = new SMOTE();
smote.setInputFormat(train);
train = Filter.useFilter(train, smote);
/** C4.5 */
J48 rf = new J48();
// RandomForest rf = new RandomForest();
// rf.setNumIterations(300);
rf.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(rf, test);
matrix[i][6] = 1-eval.errorRate();
matrix[i][0] = eval.precision(0);
matrix[i][1] = eval.recall(0);
matrix[i][2] = eval.fMeasure(0);
matrix[i][3] = eval.precision(1);
matrix[i][4] = eval.recall(1);
matrix[i][5] = eval.fMeasure(1);
}
for(int i=0;i<10;i++){
for(int j=0;j<7;j++){
System.out.printf("%15.8f", matrix[i][j]);
}
System.out.println("");
}
}
开发者ID:Gu-Youngfeng，项目名称:CraTer，代码行数:71，代码来源:FoldResultsAve.java
示例18: learnParameters
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
/**
*
* Learns the rule from parsed features in a cross validation and the set
* parameters. Additionally feature subset selection is conducted, if the
* parameters this.forwardSelection or this.backwardSelection are set
* accordingly.
*
* @param features
* Contains features to learn a classifier
*/
@Override
public Performance learnParameters(FeatureVectorDataSet features) {
// create training
Instances trainingData = transformToWeka(features, this.trainingSet);
try {
Evaluation eval = new Evaluation(trainingData);
// apply feature subset selection
if (this.forwardSelection || this.backwardSelection) {
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(this.backwardSelection);
this.fs = new AttributeSelection();
WrapperSubsetEval wrapper = new WrapperSubsetEval();
// Do feature subset selection, but using a 10-fold cross
// validation
wrapper.buildEvaluator(trainingData);
wrapper.setClassifier(this.classifier);
wrapper.setFolds(10);
wrapper.setThreshold(0.01);
this.fs.setEvaluator(wrapper);
this.fs.setSearch(search);
this.fs.SelectAttributes(trainingData);
trainingData = fs.reduceDimensionality(trainingData);
}
// perform 10-fold Cross Validation to evaluate classifier
eval.crossValidateModel(this.classifier, trainingData, 10, new Random(1));
System.out.println(eval.toSummaryString("\nResults\n\n", false));
this.classifier.buildClassifier(trainingData);
int truePositive = (int) eval.numTruePositives(trainingData.classIndex());
int falsePositive = (int) eval.numFalsePositives(trainingData.classIndex());
int falseNegative = (int) eval.numFalseNegatives(trainingData.classIndex());
Performance performance = new Performance(truePositive, truePositive + falsePositive,
truePositive + falseNegative);
return performance;
} catch (Exception e) {
e.printStackTrace();
return null;
}
}
开发者ID:olehmberg，项目名称:winter，代码行数:62，代码来源:WekaMatchingRule.java
示例19: main
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
public static void main(String[] args) {
try {
Instances inst =
new Instances(new java.io.BufferedReader(
new java.io.FileReader(args[0])));
inst.setClassIndex(inst.numAttributes() - 1);
weka.classifiers.evaluation.AggregateableEvaluation agg = null;
WekaClassifierEvaluationMapTask task =
new WekaClassifierEvaluationMapTask();
weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
WekaClassifierMapTask trainer = new WekaClassifierMapTask();
trainer.setClassifier(classifier);
trainer.setTotalNumFolds(10);
for (int i = 0; i < 10; i++) {
System.err.println("Processing fold " + (i + 1));
trainer.setFoldNumber((i + 1));
trainer.setup(new Instances(inst, 0));
trainer.addToTrainingHeader(inst);
trainer.finalizeTask();
Classifier c = trainer.getClassifier();
task.setClassifier(c);
task.setTotalNumFolds(10);
task.setFoldNumber((i + 1));
// TODO set the priors properly here.
task.setup(new Instances(inst, 0), null, -1, 1L, 0);
for (int j = 0; j < inst.numInstances(); j++) {
task.processInstance(inst.instance(j));
}
task.finalizeTask();
Evaluation eval = task.getEvaluation();
if (agg == null) {
agg = new weka.classifiers.evaluation.AggregateableEvaluation(eval);
}
agg.aggregate(eval);
}
System.err.println(agg.toSummaryString());
System.err.println("\n" + agg.toClassDetailsString());
} catch (Exception ex) {
ex.printStackTrace();
}
}
开发者ID:mydzigear，项目名称:repo.kmeanspp.silhouette_score，代码行数:50，代码来源:WekaClassifierEvaluationMapTask.java
示例20: testCrossValidateBatchMapOnly
import weka.classifiers.evaluation.Evaluation; //导入依赖的package包/类
@Test
public void testCrossValidateBatchMapOnly() throws Exception {
Instances train = new Instances(new BufferedReader(new StringReader(
CorrelationMatrixMapTaskTest.IRIS)));
train.setClassIndex(train.numAttributes() - 1);
WekaClassifierEvaluationMapTask evaluator = new WekaClassifierEvaluationMapTask();
WekaClassifierMapTask trainer = new WekaClassifierMapTask();
trainer.setClassifier(new weka.classifiers.trees.J48());
trainer.setTotalNumFolds(10);
for (int i = 0; i < 10; i++) {
trainer.setFoldNumber((i + 1));
trainer.setup(new Instances(train, 0));
trainer.addToTrainingHeader(train);
trainer.finalizeTask();
Classifier c = trainer.getClassifier();
evaluator.setClassifier(c);
evaluator.setTotalNumFolds(10);
evaluator.setFoldNumber(i + 1);
// priors for iris (just using priors + count from all the data for
// simplicity)
double[] priors = { 50.0, 50.0, 50.0 };
evaluator.setup(new Instances(train, 0), priors, 150, 1L, 0);
for (int j = 0; j < train.numInstances(); j++) {
evaluator.processInstance(train.instance(j));
}
evaluator.finalizeTask();
Evaluation eval = evaluator.getEvaluation();
assertTrue(eval != null);
// there should be predictions for exactly 15 instances per test fold
assertEquals(15, (int) eval.numInstances());
// there shouldn't be any AUC/AUPRC stats computed
assertTrue(Utils.isMissingValue(eval.areaUnderROC(0)));
}
}
开发者ID:mydzigear，项目名称:repo.kmeanspp.silhouette_score，代码行数:45，代码来源:WekaClassifierEvaluationTest.java
注：本文中的weka.classifiers.evaluation.Evaluation类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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