import sys
from vowpalwabbit import pyvw
# wow! your data can be ANY type you want... does NOT have to be VW examples
DET = 1
NOUN = 2
VERB = 3
ADJ = 4
my_dataset = [ [(DET , 'the'),
(NOUN, 'monster'),
(VERB, 'ate'),
(DET , 'a'),
(ADJ , 'big'),
(NOUN, 'sandwich')],
[(DET , 'the'),
(NOUN, 'sandwich'),
(VERB, 'was'),
(ADJ , 'tasty')],
[(NOUN, 'it'),
(VERB, 'ate'),
(NOUN, 'it'),
(ADJ , 'all')] ]
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 | sch.IS_LDF )
def makeExample(self, word, p):
ex = self.example({'w': [word + '_' + str(p)]}, labelType=self.vw.lCostSensitive)
ex.set_label_string(str(p) + ':0')
return ex
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
ex = [ self.makeExample(word,p) for p in [DET,NOUN,VERB,ADJ] ]
pred = self.sch.predict(examples=ex, my_tag=n+1, oracle=pos, condition=(n,'p'))
vw.finish_example(ex)
output.append(pred)
return output
# initialize VW as usual, but use 'hook' as the search_task
vw = pyvw.Workspace("--search 0 --csoaa_ldf m --search_task hook", quiet=True)
# tell VW to construct your search task object
sequenceLabeler = vw.init_search_task(SequenceLabeler)
# train it on the above dataset ten times; the my_dataset.__iter__ feeds into _run above
print('training!')
i = 0
while i < 10:
sequenceLabeler.learn(my_dataset)
i += 1
# now see the predictions on a test sentence
print('predicting!', file=sys.stderr)
print(sequenceLabeler.predict( [(1,w) for w in "the sandwich ate a monster".split()] ))
print('should have printed: [1, 2, 3, 1, 2]')
