phuysmans · May 15, 2019 13:26
diff --git a/Learning2Search_Named_Entity_Classification_sample.py b/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]
	#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]