// 计算h1,h2分类器共同的分类错误率；
public double measureBothError(Classifier h1, Classifier h2, Instances test) {
int m = test.numInstances();
double value1, value2, value;
int error = 0, total = 0;
try {
for (int i = 0; i < m; i++) {
value = test.instance(i).classValue();
value1 = h1.classifyInstance(test.instance(i));
value2 = h2.classifyInstance(test.instance(i));
// 两分类器做出相同决策
if (value1 == value2) {
// 两分类器做出相同决策的样本数量
total++;
// 两分类器做出相同错误决策
if (value != value1) {
// 两分类器做出相同错误决策的样本数量
error++;
}
}
}
} catch (Exception e) {
System.out.println(e);
}
// System.out.println("m:=" + m);
// System.out.println("error:=" + error +"; total:=" + total);
// 两个分类器的分类错误率= 两分类器做出相同错误决策的样本数量/两分类器做出相同决策的样本数量
return (error * 1.0) / total;
}
public static void main(String[] args) throws Exception {
/*
* First we load the test data from our ARFF file
*/
ArffLoader testLoader = new ArffLoader();
testLoader.setSource(new File("data/titanic/test.arff"));
testLoader.setRetrieval(Loader.BATCH);
Instances testDataSet = testLoader.getDataSet();
/*
* Now we tell the data set which attribute we want to classify, in our
* case, we want to classify the first column: survived
*/
Attribute testAttribute = testDataSet.attribute(0);
testDataSet.setClass(testAttribute);
testDataSet.deleteStringAttributes();
/*
* Now we read in the serialized model from disk
*/
Classifier classifier = (Classifier) SerializationHelper.read("data/titanic/titanic.model");
/*
* This part may be a little confusing. We load up the test data again
* so we have a prediction data set to populate. As we iterate over the
* first data set we also iterate over the second data set. After an
* instance is classified, we set the value of the prediction data set
* to be the value of the classification
*/
ArffLoader test1Loader = new ArffLoader();
test1Loader.setSource(new File("data/titanic/test.arff"));
Instances test1DataSet = test1Loader.getDataSet();
Attribute test1Attribute = test1DataSet.attribute(0);
test1DataSet.setClass(test1Attribute);
/*
* Now we iterate over the test data and classify each entry and set the
* value of the 'survived' column to the result of the classification
*/
Enumeration testInstances = testDataSet.enumerateInstances();
Enumeration test1Instances = test1DataSet.enumerateInstances();
while (testInstances.hasMoreElements()) {
Instance instance = (Instance) testInstances.nextElement();
Instance instance1 = (Instance) test1Instances.nextElement();
double classification = classifier.classifyInstance(instance);
instance1.setClassValue(classification);
}
/*
* Now we want to write out our predictions. The resulting file is in a
* format suitable to submit to Kaggle.
*/
CSVSaver predictedCsvSaver = new CSVSaver();
predictedCsvSaver.setFile(new File("data/titanic/predict.csv"));
predictedCsvSaver.setInstances(test1DataSet);
predictedCsvSaver.writeBatch();
System.out.println("Prediciton saved to predict.csv");
}
private static int calculatenpeople(Classifier ibkregression, Instance instregression) {
Double predictValue = null;
try {
predictValue = ibkregression.classifyInstance(instregression);
} catch (Exception e) {
e.printStackTrace();
}
return predictValue.intValue();
}
// 通过h1,h2分类器学习样本集，将h1,h2分类决策相同的样本放入L中，得到标记集合；
public void updateL(Classifier h1, Classifier h2, Instances L, Instances test) {
int length = unlabeledIns.numInstances();
double value1 = 0.0, value2 = 0.0;
try {
for (int i = 0; i < length; i++) {
value1 = h1.classifyInstance(test.instance(i));
value2 = h2.classifyInstance(test.instance(i));
if (value1 == value2) {
// 当两个分类器做出相同决策时重新标记样本的类别；
test.instance(i).setClassValue(value1);
L.add(test.instance(i));
}
}
} catch (Exception e) {
System.out.println(e);
}
// return false;
}
/** 分类过程 */
public double classifyMessage(String message) throws Exception {
filter.input(makeInstance(message, instances.stringFreeStructure()));
Instance filteredInstance = filter.output(); // 必须使用原来的filter
double predicted = classifier.classifyInstance(filteredInstance); // (int)predicted是类标索引
// System.out.println("Message classified as : "
// + instances.classAttribute().value((int) predicted));
return predicted;
}
public double getAccuracy(Classifier classifier, Instances test) throws Exception {
for (Instance instance : test) {
int predClass = (int) classifier.classifyInstance(instance);
int realClass = (int) instance.classValue();
if (predClass == realClass) {
corrCnt++;
}
}
return (double) corrCnt / totCnt;
}
public int SelectRow_KLDivergenceMisclassified(
Instances pool, Classifier myEstimator, int desiredAttr) {
// for each instance with unbought desiredAttr and label = desiredLabel
// measure KL-divergence (relative entropy between two prob distributions):
// KL(P||Q) = sum_i p_i log (p_i/q_i)
// withr respect to Q = Uniform, we have
// KL(P||U) = sum_i p_i log(p_i)
// choose (row) that is minimum (i.e. closest to uniform)
int numInstances = pool.numInstances();
double[] KLDivs = new double[numInstances];
boolean[] isValidInstance = new boolean[numInstances];
boolean misclassified = false;
double[] probs = null;
Instance inst;
for (int i = 0; i < numInstances; i++) {
inst = pool.instance(i);
try {
if (inst.classValue() != myEstimator.classifyInstance(inst)) misclassified = true;
else misclassified = false;
} catch (Exception e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
if (inst.isMissing(desiredAttr) && misclassified) {
try {
probs = myEstimator.distributionForInstance(inst);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
for (int j = 0; j < probs.length; j++) KLDivs[i] += MyXLogX(probs[j]);
isValidInstance[i] = true;
} else {
KLDivs[i] = Double.MAX_VALUE;
isValidInstance[i] = false;
}
}
double leastDivergence = KLDivs[Utils.minIndex(KLDivs)];
int numLeastDivs = 0;
for (int i = 0; i < numInstances; i++)
if (isValidInstance[i] && KLDivs[i] == leastDivergence) numLeastDivs++;
int randomInstance = r.nextInt(numLeastDivs);
int index = 0;
for (int i = 0; i < numInstances; i++) {
if (isValidInstance[i] && KLDivs[i] == leastDivergence) {
if (index == randomInstance) return i;
else index++;
}
}
return -1;
}
private static boolean calcultateifhotspot(
Classifier ibkclassification, Instance instclassification) {
Double predictValue = null;
try {
predictValue = ibkclassification.classifyInstance(instclassification);
} catch (Exception e) {
e.printStackTrace();
}
if (predictValue == 0.0) return true;
else return false;
}
public void classify(String filename) throws Exception {
Instances unLabeledData = DataSource.read(filename);
unLabeledData.setClassIndex(unLabeledData.numAttributes() - 1);
Instances LabeledData = new Instances(unLabeledData);
for (int i = 0; i < unLabeledData.numInstances(); ++i) {
double clsLabel = classifier.classifyInstance(unLabeledData.instance(i));
LabeledData.instance(i).setClassValue(clsLabel);
}
System.out.println(LabeledData.toString());
}
@Override
public double classifyInstance(Instance instance) throws Exception {
double sum = 0.0;
for (Classifier classifier : classifiers) {
double classification = classifier.classifyInstance(instance);
sum += classification;
}
if (sum >= classifiers.size() / 2) {
return 1.0;
}
return 0.0;
}
public String classifyInstance(Instance wekaInstance) {
String label = null;
try {
double labelIndex = bayesNet.classifyInstance(wekaInstance);
wekaInstance.setClassValue(labelIndex);
label = wekaInstance.toString(wekaInstance.classIndex());
} catch (Exception e) {
System.err.println(e.getMessage());
e.printStackTrace();
System.exit(-1);
}
return label;
}
/**
* classifies a tweet
*
* @param stringa the tweet to classify
* @return the tweet polarity
*/
public double classifyDouble(String stringa)
throws FileNotFoundException, IOException, Exception {
String string_new;
Preprocesser pre = new Preprocesser();
string_new = pre.preprocessDocument(stringa);
String tmp = "";
StringTokenizer st = new StringTokenizer(string_new, " ");
// Instances unlabeled = new Instances(new BufferedReader(new
// FileReader("D:/prova.arff")));
Instances unlabeled = new Instances(_train, 1);
Instance inst = new Instance(unlabeled.numAttributes());
// load unlabeled data
inst.setDataset(unlabeled);
int j = 0;
while (j < unlabeled.numAttributes()) {
inst.setValue(j, "0");
j++;
}
while (st.hasMoreTokens()) {
tmp = st.nextToken();
if (unlabeled.attribute(tmp) != null) inst.setValue(unlabeled.attribute(tmp), "1");
}
unlabeled.add(inst);
// set class attribute
unlabeled.setClassIndex(unlabeled.numAttributes() - 1);
// create copy
Instances labeled = new Instances(unlabeled);
// label instances
for (int i = 0; i < unlabeled.numInstances(); i++) {
double clsLabel = _cl.classifyInstance(unlabeled.instance(i));
labeled.instance(i).setClassValue(clsLabel);
}
// return labeled.instance(0).stringValue(unlabeled.numAttributes()-1);
// System.out.println("weight: " + labeled.instance(0).weight());
return labeled.instance(0).classValue();
}
/**
* Classifies a given instance using the selected classifier.
*
* @param instance the instance to be classified
* @exception Exception if instance could not be classified successfully
*/
public double classifyInstance(Instance instance) throws Exception {
return m_Classifier.classifyInstance(instance);
}
public static void main(String[] args) throws Exception {
// NaiveBayesSimple nb = new NaiveBayesSimple();
// BufferedReader br_train = new BufferedReader(new FileReader("src/train.arff.txt"));
// String s = null;
// long st_time = System.currentTimeMillis();
// Instances inst_train = new Instances(br_train);
// System.out.println(inst_train.numAttributes());
// inst_train.setClassIndex(inst_train.numAttributes()-1);
// System.out.println("train time"+(System.currentTimeMillis()-st_time));
// NaiveBayes nb1 = new NaiveBayes();
// nb1.buildClassifier(inst_train);
// br_train.close();
long st_time = System.currentTimeMillis();
st_time = System.currentTimeMillis();
Classifier classifier = (Classifier) SerializationHelper.read("NaiveBayes.model");
// BufferedReader br_test = new BufferedReader(new FileReader("src/test.arff.txt"));
// Instances inst_test = new Instances(br_test);
// inst_test.setClassIndex(inst_test.numAttributes()-1);
// System.out.println("test time"+(System.currentTimeMillis()-st_time));
//
ArffLoader testLoader = new ArffLoader();
testLoader.setSource(new File("src/test.arff"));
testLoader.setRetrieval(Loader.BATCH);
Instances testDataSet = testLoader.getDataSet();
Attribute testAttribute = testDataSet.attribute("class");
testDataSet.setClass(testAttribute);
int correct = 0;
int incorrect = 0;
FastVector attInfo = new FastVector();
attInfo.addElement(new Attribute("Id"));
attInfo.addElement(new Attribute("Category"));
Instances outputInstances = new Instances("predict", attInfo, testDataSet.numInstances());
Enumeration testInstances = testDataSet.enumerateInstances();
int index = 1;
while (testInstances.hasMoreElements()) {
Instance instance = (Instance) testInstances.nextElement();
double classification = classifier.classifyInstance(instance);
Instance predictInstance = new Instance(outputInstances.numAttributes());
predictInstance.setValue(0, index++);
predictInstance.setValue(1, (int) classification + 1);
outputInstances.add(predictInstance);
}
System.out.println("Correct Instance: " + correct);
System.out.println("IncCorrect Instance: " + incorrect);
double accuracy = (double) (correct) / (double) (correct + incorrect);
System.out.println("Accuracy: " + accuracy);
CSVSaver predictedCsvSaver = new CSVSaver();
predictedCsvSaver.setFile(new File("predict.csv"));
predictedCsvSaver.setInstances(outputInstances);
predictedCsvSaver.writeBatch();
System.out.println("Prediciton saved to predict.csv");
}
// 输入问题，输出问题所属类型。
public double classifyByBayes(String question) throws Exception {
double label = -1;
List<Question> questionID = questionDAO.getQuestionIDLabeled();
// 定义数据格式
Attribute att1 = new Attribute("法律政策");
Attribute att2 = new Attribute("位置交通");
Attribute att3 = new Attribute("风水");
Attribute att4 = new Attribute("房价");
Attribute att5 = new Attribute("楼层");
Attribute att6 = new Attribute("户型");
Attribute att7 = new Attribute("小区配套");
Attribute att8 = new Attribute("贷款");
Attribute att9 = new Attribute("买房时机");
Attribute att10 = new Attribute("开发商");
FastVector labels = new FastVector();
labels.addElement("1");
labels.addElement("2");
labels.addElement("3");
labels.addElement("4");
labels.addElement("5");
labels.addElement("6");
labels.addElement("7");
labels.addElement("8");
labels.addElement("9");
labels.addElement("10");
Attribute att11 = new Attribute("类别", labels);
FastVector attributes = new FastVector();
attributes.addElement(att1);
attributes.addElement(att2);
attributes.addElement(att3);
attributes.addElement(att4);
attributes.addElement(att5);
attributes.addElement(att6);
attributes.addElement(att7);
attributes.addElement(att8);
attributes.addElement(att9);
attributes.addElement(att10);
attributes.addElement(att11);
Instances dataset = new Instances("Test-dataset", attributes, 0);
dataset.setClassIndex(10);
Classifier classifier = null;
if (!new File("naivebayes.model").exists()) {
// 添加数据
double[] values = new double[11];
for (int i = 0; i < questionID.size(); i++) {
for (int m = 0; m < 11; m++) {
values[m] = 0;
}
int whitewordcount = 0;
whitewordcount = questionDAO.getHitWhiteWordNum(questionID.get(i).getId());
if (whitewordcount != 0) {
List<QuestionWhiteWord> questionwhiteword =
questionDAO.getHitQuestionWhiteWord(questionID.get(i).getId());
for (int j = 0; j < questionwhiteword.size(); j++) {
values[getAttIndex(questionwhiteword.get(j).getWordId()) - 1]++;
}
for (int m = 0; m < 11; m++) {
values[m] = values[m] / whitewordcount;
}
}
values[10] = questionID.get(i).getType() - 1;
Instance inst = new Instance(1.0, values);
dataset.add(inst);
}
// 构造分类器
classifier = new NaiveBayes();
classifier.buildClassifier(dataset);
SerializationHelper.write("naivebayes.model", classifier);
} else {
classifier = (Classifier) SerializationHelper.read("naivebayes.model");
}
System.out.println("*************begin evaluation*******************");
Evaluation evaluation = new Evaluation(dataset);
evaluation.evaluateModel(classifier, dataset); // 按道理说，这里应该使用另一份数据，而不是训练集data。
System.out.println(evaluation.toSummaryString());
// 分类
System.out.println("*************begin classification*******************");
Instance subject = new Instance(1.0, getQuestionVector(question));
subject.setDataset(dataset);
label = classifier.classifyInstance(subject);
System.out.println("label: " + label);
// double dis[]=classifier.distributionForInstance(inst);
// for(double i:dis){
// System.out.print(i+" ");
// }
System.out.println(questionID.size());
return label + 1;
}
/**
* Classifies a given instance.
*
* @param instance the instance to be classified
* @return index of the predicted class
* @throws Exception if an error occurred during the prediction
*/
public double classifyInstance(Instance instance) throws Exception {
if (m_GroovyObject != null) return m_GroovyObject.classifyInstance(instance);
else return Utils.missingValue();
}
/**
* Cleanses the data based on misclassifications when used training data.
*
* @param data the data to train with and cleanse
* @return the cleansed data
* @throws Exception if something goes wrong
*/
private Instances cleanseTrain(Instances data) throws Exception {
Instance inst;
Instances buildSet = new Instances(data);
Instances temp = new Instances(data, data.numInstances());
Instances inverseSet = new Instances(data, data.numInstances());
int count = 0;
double ans;
int iterations = 0;
int classIndex = m_classIndex;
if (classIndex < 0) {
classIndex = data.classIndex();
}
if (classIndex < 0) {
classIndex = data.numAttributes() - 1;
}
// loop until perfect
while (count != buildSet.numInstances()) {
// check if hit maximum number of iterations
iterations++;
if (m_numOfCleansingIterations > 0 && iterations > m_numOfCleansingIterations) {
break;
}
// build classifier
count = buildSet.numInstances();
buildSet.setClassIndex(classIndex);
m_cleansingClassifier.buildClassifier(buildSet);
temp = new Instances(buildSet, buildSet.numInstances());
// test on training data
for (int i = 0; i < buildSet.numInstances(); i++) {
inst = buildSet.instance(i);
ans = m_cleansingClassifier.classifyInstance(inst);
if (buildSet.classAttribute().isNumeric()) {
if (ans >= inst.classValue() - m_numericClassifyThreshold
&& ans <= inst.classValue() + m_numericClassifyThreshold) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
} else { // class is nominal
if (ans == inst.classValue()) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
}
}
buildSet = temp;
}
if (m_invertMatching) {
inverseSet.setClassIndex(data.classIndex());
return inverseSet;
} else {
buildSet.setClassIndex(data.classIndex());
return buildSet;
}
}
/**
* @param args the command line arguments
* @throws Exception
*/
public static void main(String[] args) throws Exception {
PreProcessor p = new PreProcessor("census-income.data", "census-income-preprocessed.arff");
p.smote();
PreProcessor p_test =
new PreProcessor("census-income.test", "census-income-test-preprocessed.arff");
p_test.run();
BufferedReader traindata =
new BufferedReader(new FileReader("census-income-preprocessed.arff"));
BufferedReader testdata =
new BufferedReader(new FileReader("census-income-test-preprocessed.arff"));
Instances traininstance = new Instances(traindata);
Instances testinstance = new Instances(testdata);
traindata.close();
testdata.close();
traininstance.setClassIndex(traininstance.numAttributes() - 1);
testinstance.setClassIndex(testinstance.numAttributes() - 1);
int numOfAttributes = testinstance.numAttributes();
int numOfInstances = testinstance.numInstances();
NaiveBayesClassifier nb = new NaiveBayesClassifier("census-income-preprocessed.arff");
Classifier cnaive = nb.NBClassify();
DecisionTree dt = new DecisionTree("census-income-preprocessed.arff");
Classifier cls = dt.DTClassify();
AdaBoost ab = new AdaBoost("census-income-preprocessed.arff");
AdaBoostM1 m1 = ab.AdaBoostDTClassify();
BaggingMethod b = new BaggingMethod("census-income-preprocessed.arff");
Bagging bag = b.BaggingDTClassify();
SVM s = new SVM("census-income-preprocessed.arff");
SMO svm = s.SMOClassifier();
knn knnclass = new knn("census-income-preprocessed.arff");
IBk knnc = knnclass.knnclassifier();
Logistic log = new Logistic();
log.buildClassifier(traininstance);
int match = 0;
int error = 0;
int greater = 0;
int less = 0;
for (int i = 0; i < numOfInstances; i++) {
String predicted = "";
greater = 0;
less = 0;
double predictions[] = new double[8];
double pred = cls.classifyInstance(testinstance.instance(i));
predictions[0] = pred;
double abpred = m1.classifyInstance(testinstance.instance(i));
predictions[1] = abpred;
double naivepred = cnaive.classifyInstance(testinstance.instance(i));
predictions[2] = naivepred;
double bagpred = bag.classifyInstance(testinstance.instance(i));
predictions[3] = bagpred;
double smopred = svm.classifyInstance(testinstance.instance(i));
predictions[4] = smopred;
double knnpred = knnc.classifyInstance(testinstance.instance(i));
predictions[5] = knnpred;
for (int j = 0; j < 6; j++) {
if ((testinstance.instance(i).classAttribute().value((int) predictions[j]))
.compareTo(">50K")
== 0) greater++;
else less++;
}
if (greater > less) predicted = ">50K";
else predicted = "<=50K";
if ((testinstance.instance(i).stringValue(numOfAttributes - 1)).compareTo(predicted) == 0)
match++;
else error++;
}
System.out.println("Correctly classified Instances: " + match);
System.out.println("Misclassified Instances: " + error);
double accuracy = (double) match / (double) numOfInstances * 100;
double error_percent = 100 - accuracy;
System.out.println("Accuracy: " + accuracy + "%");
System.out.println("Error: " + error_percent + "%");
}
/**
* Cleanses the data based on misclassifications when performing cross-validation.
*
* @param data the data to train with and cleanse
* @return the cleansed data
* @throws Exception if something goes wrong
*/
private Instances cleanseCross(Instances data) throws Exception {
Instance inst;
Instances crossSet = new Instances(data);
Instances temp = new Instances(data, data.numInstances());
Instances inverseSet = new Instances(data, data.numInstances());
int count = 0;
double ans;
int iterations = 0;
int classIndex = m_classIndex;
if (classIndex < 0) {
classIndex = data.classIndex();
}
if (classIndex < 0) {
classIndex = data.numAttributes() - 1;
}
// loop until perfect
while (count != crossSet.numInstances()
&& crossSet.numInstances() >= m_numOfCrossValidationFolds) {
count = crossSet.numInstances();
// check if hit maximum number of iterations
iterations++;
if (m_numOfCleansingIterations > 0 && iterations > m_numOfCleansingIterations) {
break;
}
crossSet.setClassIndex(classIndex);
if (crossSet.classAttribute().isNominal()) {
crossSet.stratify(m_numOfCrossValidationFolds);
}
// do the folds
temp = new Instances(crossSet, crossSet.numInstances());
for (int fold = 0; fold < m_numOfCrossValidationFolds; fold++) {
Instances train = crossSet.trainCV(m_numOfCrossValidationFolds, fold);
m_cleansingClassifier.buildClassifier(train);
Instances test = crossSet.testCV(m_numOfCrossValidationFolds, fold);
// now test
for (int i = 0; i < test.numInstances(); i++) {
inst = test.instance(i);
ans = m_cleansingClassifier.classifyInstance(inst);
if (crossSet.classAttribute().isNumeric()) {
if (ans >= inst.classValue() - m_numericClassifyThreshold
&& ans <= inst.classValue() + m_numericClassifyThreshold) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
} else { // class is nominal
if (ans == inst.classValue()) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
}
}
}
crossSet = temp;
}
if (m_invertMatching) {
inverseSet.setClassIndex(data.classIndex());
return inverseSet;
} else {
crossSet.setClassIndex(data.classIndex());
return crossSet;
}
}
public HashMap<String, String> process(
Sentence sent,
String dep,
HashSet<String> terms,
List<NamedEntity> entities,
String author,
String aidx) {
try {
// System.out.println("ML start!");
// System.out.println("List : " + terms);
HashMap<String, String> ht = new HashMap<String, String>();
List<NamedEntity> newEntities = new ArrayList<NamedEntity>();
for (NamedEntity entity : entities) {
// System.out.println("original: " + entity.entity);
boolean check = false;
for (NamedEntity temp : entities) {
if (entity == temp) continue;
if (entity.entity.contains(temp.entity)) {
check = true;
}
}
if (!check) newEntities.add(entity);
}
List<DependencyTriple> dtl = getDependencyTripleList(dep);
List<NamedEntity> targetCands = new ArrayList<NamedEntity>();
HashMap<NamedEntity, String> tOpinTerm = new HashMap<NamedEntity, String>();
List<NamedEntity> holderCands = new ArrayList<NamedEntity>();
HashMap<NamedEntity, String> hOpinTerm = new HashMap<NamedEntity, String>();
BufferedWriter writer = new BufferedWriter(new FileWriter("weka_target.csv"));
writer.write("A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12,A13,Class\n");
boolean check = false;
List<NamedEntity> targetTmp = new ArrayList<NamedEntity>();
for (NamedEntity entity : newEntities) {
// System.out.println("extracted: " + entity.entity);
String temp = getTargetFeatures(entity, author, terms, dtl);
// System.out.println(temp);
if (temp.length() > 1) {
check = true;
writer.write(temp);
String[] toks = temp.split("\n");
for (int i = 0; i < toks.length; i++) {
targetTmp.add(entity);
tOpinTerm.put(entity, toks[i].substring(0, toks[i].indexOf(",")));
}
}
}
writer.close();
if (check) {
DataSource source = new DataSource("weka_target.csv");
Instances testdata = source.getDataSet();
testdata.setClassIndex(testdata.numAttributes() - 1);
Classifier models = (Classifier) weka.core.SerializationHelper.read("target_smoreg.model");
if (testdata.numInstances() != targetTmp.size())
System.out.println("wrong number of instances");
for (int i = 0; i < testdata.numInstances(); i++) {
double pred = models.classifyInstance(testdata.instance(i));
if (pred >= 1.0) {
// System.out.println(pred + " , " + targetTmp.get(i).entity);
targetCands.add(targetTmp.get(i));
}
}
}
writer = new BufferedWriter(new FileWriter("weka_holder.csv"));
writer.write("A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12,A13,A14,A15,Class\n");
check = false;
List<NamedEntity> holderTmp = new ArrayList<NamedEntity>();
for (NamedEntity entity : newEntities) {
// System.out.println("extracted: " + entity.entity);
String temp = getHolderFeatures(entity, author, terms, dtl);
// System.out.println(temp);
if (temp.length() > 1) {
check = true;
writer.write(temp);
String[] toks = temp.split("\n");
for (int i = 0; i < toks.length; i++) {
holderTmp.add(entity);
hOpinTerm.put(entity, toks[i].substring(0, toks[i].indexOf(",")));
}
}
}
writer.close();
if (check) {
DataSource source = new DataSource("weka_holder.csv");
Instances testdata = source.getDataSet();
testdata.setClassIndex(testdata.numAttributes() - 1);
Classifier models = (Classifier) weka.core.SerializationHelper.read("holder_smoreg.model");
if (testdata.numInstances() != holderTmp.size())
System.out.println("wrong number of instances");
for (int i = 0; i < testdata.numInstances(); i++) {
double pred = models.classifyInstance(testdata.instance(i));
if (pred >= 1.0) {
// System.out.println(pred + " , " + holderTmp.get(i).entity);
holderCands.add(holderTmp.get(i));
}
}
}
if ((targetCands.size() == 0) || (holderCands.size() == 0)) return ht;
List<NamedEntity> holderCandTmp = new ArrayList<NamedEntity>();
for (NamedEntity holderCand : holderCands) {
boolean hasLonger = false;
for (NamedEntity temp : holderCands) {
if (temp.entity.compareTo(holderCand.entity) == 0) continue;
if (temp.entity.contains(holderCand.entity)) {
hasLonger = true;
break;
}
}
if (!hasLonger) holderCandTmp.add(holderCand);
}
List<NamedEntity> targetCandTmp = new ArrayList<NamedEntity>();
for (NamedEntity targetCand : targetCands) {
boolean hasLonger = false;
for (NamedEntity temp : targetCands) {
if (temp.entity.compareTo(targetCand.entity) == 0) continue;
if (temp.entity.contains(targetCand.entity)) {
hasLonger = true;
break;
}
}
if (!hasLonger) targetCandTmp.add(targetCand);
}
for (NamedEntity targetCand : targetCandTmp) {
if (targetCand.entity.compareTo(author) == 0) continue;
for (NamedEntity holderCand : holderCandTmp) {
if (targetCand.entity.compareTo(holderCand.entity) == 0) continue;
String targetOpin = tOpinTerm.get(targetCand);
String holderOpin = hOpinTerm.get(holderCand);
// System.out.println(targetOpin + ", " + holderOpin);
if (targetOpin.compareTo(holderOpin) != 0) continue;
String opin =
targetOpin
.concat("\t")
.concat(
Integer.toString(sent.sent.indexOf(targetOpin) + sent.beg)
.concat("-")
.concat(
Integer.toString(
sent.sent.indexOf(targetOpin) + sent.beg + targetOpin.length())));
String holder =
holderCand
.entity
.concat("\t")
.concat(
Integer.toString(holderCand.beg)
.concat("-")
.concat(Integer.toString(holderCand.end)));
String target =
targetCand
.entity
.concat("\t")
.concat(
Integer.toString(targetCand.beg)
.concat("-")
.concat(Integer.toString(targetCand.end)));
ht.put(targetOpin, opin.concat("\t").concat(holder).concat("\t").concat(target));
}
}
return ht;
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
return null;
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
return null;
}
}
