Search - Sequence

from __future__ import print_function

import sys
from vowpalwabbit import pyvw


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)

        # you can test program options with sch.po_exists
        # and get their values with sch.po_get -> string and
        # sch.po_get_int -> int
        if sch.po_exists("search"):
            print("found --search")
            print(
                "--search value =",
                sch.po_get("search"),
                ", type =",
                type(sch.po_get("search")),
            )

        # 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
            ex = self.vw.example({"w": [word]})
            pred = self.sch.predict(
                examples=ex, my_tag=n + 1, oracle=pos, condition=(n, "p")
            )
            vw.finish_example(
                [ex]
            )  # must pass the example in as a list because search is a MultiEx reduction
            output.append(pred)
        return output


# 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")],
]


# initialize VW as usual, but use 'hook' as the search_task
vw = pyvw.Workspace("--search 4 --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!", file=sys.stderr)
for i in range(10):
    sequenceLabeler.learn(my_dataset)

# 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]")

found --search
--search value = 4 , type = <class 'int'>
[1, 2, 3, 1, 2]
should have printed: [1, 2, 3, 1, 2]

training!
predicting!