我正在尝试使用 Weka 构建一个文本分类器，但distributionForInstance类的概率是1.0在一种情况下和0.0所有其他情况下，所以classifyInstance总是返回与预测相同的类。培训中的某些内容无法正常工作。
ARFF 培训
@relation test1
@attribute tweetmsg String
@attribute classValues {politica,sport,musicatvcinema,infogeneriche,fattidelgiorno,statopersonale,checkin,conversazione}
@DATA
"Renzi Berlusconi Salvini Bersani",politica
"Allegri insulta la terna arbitrale",sport
"Bravo Garcia",sport
训练方法
public void trainClassifier(final String INPUT_FILENAME) throws Exception
{
getTrainingDataset(INPUT_FILENAME);
//trainingInstances consists of feature vector of every input
for(Instance currentInstance : inputDataset)
{
Instance currentFeatureVector = extractFeature(currentInstance);
currentFeatureVector.setDataset(trainingInstances);
trainingInstances.add(currentFeatureVector);
}
classifier = new NaiveBayes();
try {
//classifier training code
classifier.buildClassifier(trainingInstances);
//storing the trained classifier to a file for future use
weka.core.SerializationHelper.write("NaiveBayes.model",classifier);
} catch (Exception ex) {
System.out.println("Exception in training the classifier."+ex);
}
}
private Instance extractFeature(Instance inputInstance) throws Exception
{
String tweet = inputInstance.stringValue(0);
StringTokenizer defaultTokenizer = new StringTokenizer(tweet);
List<String> tokens=new ArrayList<String>();
while (defaultTokenizer.hasMoreTokens())
{
String t= defaultTokenizer.nextToken();
tokens.add(t);
}
Iterator<String> a = tokens.iterator();
while(a.hasNext())
{
String token=(String) a.next();
String word = token.replaceAll("#","");
if(featureWords.contains(word))
{
double cont=featureMap.get(featureWords.indexOf(word))+1;
featureMap.put(featureWords.indexOf(word),cont);
}
else{
featureWords.add(word);
featureMap.put(featureWords.indexOf(word), 1.0);
}
}
attributeList.clear();
for(String featureWord : featureWords)
{
attributeList.add(new Attribute(featureWord));
}
attributeList.add(new Attribute("Class", classValues));
int indices[] = new int[featureMap.size()+1];
double values[] = new double[featureMap.size()+1];
int i=0;
for(Map.Entry<Integer,Double> entry : featureMap.entrySet())
{
indices[i] = entry.getKey();
values[i] = entry.getValue();
i++;
}
indices[i] = featureWords.size();
values[i] = (double)classValues.indexOf(inputInstance.stringValue(1));
trainingInstances = createInstances("TRAINING_INSTANCES");
return new SparseInstance(1.0,values,indices,1000000);
}
private void getTrainingDataset(final String INPUT_FILENAME)
{
try{
ArffLoader trainingLoader = new ArffLoader();
trainingLoader.setSource(new File(INPUT_FILENAME));
inputDataset = trainingLoader.getDataSet();
}catch(IOException ex)
{
System.out.println("Exception in getTrainingDataset Method");
}
System.out.println("dataset "+inputDataset.numAttributes());
}
private Instances createInstances(final String INSTANCES_NAME)
{
//create an Instances object with initial capacity as zero
Instances instances = new Instances(INSTANCES_NAME,attributeList,0);
//sets the class index as the last attribute
instances.setClassIndex(instances.numAttributes()-1);
return instances;
}
public static void main(String[] args) throws Exception
{
Classificatore wekaTutorial = new Classificatore();
wekaTutorial.trainClassifier("training_set_prova_tent.arff");
wekaTutorial.testClassifier("testing.arff");
}
public Classificatore()
{
attributeList = new ArrayList<Attribute>();
initialize();
}
private void initialize()
{
featureWords= new ArrayList<String>();
featureMap = new TreeMap<>();
classValues= new ArrayList<String>();
classValues.add("politica");
classValues.add("sport");
classValues.add("musicatvcinema");
classValues.add("infogeneriche");
classValues.add("fattidelgiorno");
classValues.add("statopersonale");
classValues.add("checkin");
classValues.add("conversazione");
}
测试方法
public void testClassifier(final String INPUT_FILENAME) throws Exception
{
getTrainingDataset(INPUT_FILENAME);
//trainingInstances consists of feature vector of every input
Instances testingInstances = createInstances("TESTING_INSTANCES");
for(Instance currentInstance : inputDataset)
{
//extractFeature method returns the feature vector for the current input
Instance currentFeatureVector = extractFeature(currentInstance);
//Make the currentFeatureVector to be added to the trainingInstances
currentFeatureVector.setDataset(testingInstances);
testingInstances.add(currentFeatureVector);
}
try {
//Classifier deserialization
classifier = (Classifier) weka.core.SerializationHelper.read("NaiveBayes.model");
//classifier testing code
for(Instance testInstance : testingInstances)
{
double score = classifier.classifyInstance(testInstance);
double[] vv= classifier.distributionForInstance(testInstance);
for(int k=0;k<vv.length;k++){
System.out.println("distribution "+vv[k]); //this are the probabilities of the classes and as result i get 1.0 in one and 0.0 in all the others
}
System.out.println(testingInstances.attribute("Class").value((int)score));
}
} catch (Exception ex) {
System.out.println("Exception in testing the classifier."+ex);
}
}
我想为短信创建一个文本分类器，此代码基于本教程http://preciselyconcise.com/apis_and_installations/training_a_weka_classifier_in_java.php。问题是分类器为 testing.arff 中的几乎每条消息预测错误的类，因为类的概率不正确。training_set_prova_tent.arff 每个类的消息数量相同。我正在遵循的示例使用 featureWords.dat 并将 1.0 与消息中存在的单词相关联，而不是我想创建自己的字典，其中包含 training_set_prova_tent 中存在的单词加上测试中存在的单词并与每个单词相关联出现次数。
PS我知道这正是我可以用过滤器StringToWordVector做的，但我还没有找到任何例子来解释如何使用这个过滤器和两个文件：一个用于训练集，一个用于测试集。所以改编我找到的代码似乎更容易。
非常感谢