How to test a Weka Text Classification (FilteredClassifier)

2
votes

Looked at lots of examples for this, and so far no luck. I'd like to classify free text.

  1. Configure a text classifier. (FilteredClassifier using StringToWordVector and LibSVM)
  2. Train the classifier (add in lots of documents, train on filtered text)
  3. Serialize the FilteredClassifier to disk, quit the app

Then later

  1. Load up the serialized FilteredClassifier
  2. Classify stuff!

It goes ok up to when I try to read from disk and classify things. All the documents and examples show the training list and testing list being built at the same time, and in my case, I'm trying to build a testing list after the fact.

A FilteredClassifier alone is not enough to create a testing Instance with the same "dictionary" as the original training set, so how do I save everything I need to classify at a later date?

http://weka.wikispaces.com/Use+WEKA+in+your+Java+code just says "Instances loaded from somewhere" and doesn't say anything about using a similar dictionary.

ClassifierFramework cf = new WekaSVM();
if (!cf.isTrained()) {
  train(cf); // Train, save to disk
  cf = new WekaSVM(); // reloads from file
}
cf.test("this is a test");

Ends up throwing

java.lang.ArrayIndexOutOfBoundsException: 2
at weka.core.DenseInstance.value(DenseInstance.java:332)
at weka.filters.unsupervised.attribute.StringToWordVector.convertInstancewoDocNorm(StringToWordVector.java:1587)
at weka.filters.unsupervised.attribute.StringToWordVector.input(StringToWordVector.java:688)
at weka.classifiers.meta.FilteredClassifier.filterInstance(FilteredClassifier.java:465)
at weka.classifiers.meta.FilteredClassifier.distributionForInstance(FilteredClassifier.java:495)
at weka.classifiers.AbstractClassifier.classifyInstance(AbstractClassifier.java:70)
at ratchetclassify.lab.WekaSVM.test(WekaSVM.java:125)
1
javaserializationclassificationweka

1 Answers

0
votes

Serialize your Instances which holds the definition of the trained data -similar dictionary?- while you are serializing your classifier:

Instances trainInstances = ... //

Instances trainHeader = new Instances(trainInstances, 0);
trainHeader.setClassIndex(trainInstances .classIndex());

OutputStream os = new FileOutputStream(fileName);
ObjectOutputStream objectOutputStream = new ObjectOutputStream(os);
objectOutputStream.writeObject(classifier);
if (trainHeader != null)
    objectOutputStream.writeObject(trainHeader);
objectOutputStream.flush();
objectOutputStream.close();

To desialize:

Classifier classifier = null;
Instances trainHeader = null;

InputStream is = new BufferedInputStream(new FileInputStream(fileName));
ObjectInputStream objectInputStream = new ObjectInputStream(is);
classifier = (Classifier) objectInputStream.readObject();
try { // see if we can load the header
    trainHeader = (Instances) objectInputStream.readObject();
} catch (Exception e) {
} 
objectInputStream.close();

Use trainHeader to create new Instance:

int numAttributes = trainHeader.numAttributes();
double[] vals = new double[numAttributes];

for (int i = 0; i < numAttributes - 1; i++) {
    Attribute attribute = trainHeader.attribute(i);

    //If your attribute is nominal or string:       
    double value = attribute.indexOfValue(myStrVal); //get myStrVal from your source

    //If your attribute is numeric
    double value = myNumericVal; //get myNumericVal from your source

    vals[i] = value;
}

vals[numAttributes] = Instance.missingValue();

Instance instance = new Instance(1.0, vals);
instance.setDataset(trainHeader);
return instance;