public void train() throws Exception {
/*
* Read the training dataset into an object which implements DataIter
* interface(trainData). Each of the training instance is encapsulated
* in the object which provides DataSequence interface. The DataIter
* interface returns object of DataSequence (training instance) in
* next() routine.
*/
DataIterImpl trainData = new DataIterImpl();
/*
* Once you have loaded the training dataset, you need to allocate
* objects for the model to be learned. allocmodel() method does that
* allocation.
*/
allocModel();
/*
* You may need to train some of the feature types class. This training
* is needed for features which need to learn from the training data for
* instance dictionary features build generated from the training set.
*/
featureGen.train(trainData);
/*
* Call train routine of the CRF model to train the model using the
* train data. This routine returns the learned weight for the features.
*/
double featureWts[] = crfModel.train(trainData);
/*
* You can store the learned model for later use into disk. For this you
* will have to store features as well as their corresponding weights.
*/
crfModel.write(baseDir + "/learntModels/" + outDir + "/crf");
featureGen.write(baseDir + "/learntModels/" + outDir + "/features");
}
public void test() throws Exception {
/*
* Read the test dataset. Each of the test instance is encapsulated in the
* object which provides DataSequence interface.
*/
/*
* Once you have loaded the test dataset, you need to allocate objects
* for the model to be learned. allocmodel() method does that allocation.
* Also, you need to read learned parameters from the disk stored after
* training. If the model is already available in the memory, then you do
* not need to reallocate the model i.e. you can skip the next step in that
* case.
*/
allocModel();
featureGen.read(baseDir+"/learntModels/"+outDir+"/features");
crfModel.read(baseDir+"/learntModels/"+outDir+"/crf");
/*
* Iterate over test data set and apply the crf model to each test instance.
*/
while(...) {
/*
* Now apply CRF model to each test instance.
*/
crfModel.apply(testRecord);
/*
* The labeled instance have value of the states as labels.
* These state values are not labels as supplied during training.
* To map this state to one of the labels you need to call following
* method on the labled testRecord.
*/
featureGen.mapStatesToLabels(testRecord);
}
}
void allocModel() throws Exception {
/*
* A CRF model consists of features and corresponding weights. The
* features are stored in FeatureGenImpl and weights and other CRF
* parameters are encapsulated in CRF object.
*
* Here, you will call appropriate constructor for a feature generator
* and a CRF model. You can use feature generator available in the
* package or use your own implemented feature generator.
*
* There are two CRF model classes: CRF and NestedCRF. The CRF class is
* flat CRF model while NestedCRF is a segment(semi-)CRF model.
*/
featureGen = new FeatureGenImpl(options.getProperty("modelGraph"),
Integer.parseInt(options.getProperty("numLabels")));
crfModel = new CRF(featureGen.numFeatures(), featureGen, options);
}
public WordFeatures(FeatureGenImpl m, WordsInTrain d) {
super(m);
dict = d;
frequency_cutOff = RARE_THRESHOLD;
numStates = m.numStates();
}