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]")
