Learning2Search_Named_Entity_Classification_sample.py

#determine the labels
import pyvw #vw python interface

DEST  = 1
PROP = 2
FAC = 3

...
#create the class for the Sequence Labeler
class SequenceLabeler(pyvw.SearchTask):
    def __init__(self, vw, sch, num_actions):
        # you must must must initialize the parent class
        # this will automatically store self.sch <- sch, self.vw <- vw
        pyvw.SearchTask.__init__(self, vw, sch, num_actions)
        
        # set whatever options you want
        sch.set_options( sch.AUTO_HAMMING_LOSS | sch.AUTO_CONDITION_FEATURES )

    def _run(self, sentence):   # it's called _run to remind you that you shouldn't call it directly!
        output = []
        for n in range(len(sentence)):
            pos,word = sentence[n]
            # use "with...as..." to guarantee that the example is finished properly
            with self.vw.example({'w': [word]}) as ex:
                pred = self.sch.predict(examples=ex, my_tag=n+1, oracle=pos, condition=[(n,'p'), (n-1, 'q')])
                output.append(pred)
        return output
...
#build the training set
...
...
...
my_dataset #training set
test_set

#train commands
vw = pyvw.vw("--search 3 --search_task hook --ring_size 1024") # 3 is the number of labels
sequenceLabeler = vw.init_search_task(SequenceLabeler)

#actual training
for i in xrange(2):
    sequenceLabeler.learn(my_dataset)
    
#predict
test_example = [ (0,w) for w in "hotel amsterdam wifi".split() ]
print test_example
#[(0, 'hotel'), (0, 'amsterdam'), (0, 'wifi')]
out = sequenceLabeler.predict(test_example)
print out
[2, 1, 3]