def main():
from ngram import Ngram
from model import Model
from forest import Forest
flags.DEFINE_integer("beam", 100, "beam size", short_name="b")
flags.DEFINE_integer("debuglevel", 0, "debug level")
flags.DEFINE_boolean("mert", True, "output mert-friendly info (<hyp><cost>)")
flags.DEFINE_boolean("cube", True, "using cube pruning to speedup")
flags.DEFINE_integer("kbest", 1, "kbest output", short_name="k")
flags.DEFINE_integer("ratio", 3, "the maximum items (pop from PQ): ratio*b", short_name="r")
argv = FLAGS(sys.argv)
weights = Model.cmdline_model()
lm = Ngram.cmdline_ngram()
false_decoder = CYKDecoder(weights, lm)
def non_local_scorer(cedge, ders):
(lmsc, alltrans, sig) = false_decoder.deltLMScore(cedge.lhsstr, ders)
fv = Vector()
fv["lm"] = lmsc
return ((weights.dot(fv), fv), alltrans, sig)
cube_prune = CubePruning(FeatureScorer(weights), non_local_scorer, FLAGS.k, FLAGS.ratio)
for i, forest in enumerate(Forest.load("-", is_tforest=True, lm=lm), 1):
a = false_decoder.beam_search(forest, b = FLAGS.beam)
b = cube_prune.run(forest.root)
assert a[0], b[0].score[0]
assert a[1], b[0].score[1]
print a
print b[0]
def oracle_extracter(forest, weights, false_decoder, k, ratio, extract=1):
"reimplementation of forest.compute_oracle using cube pruning to get oracle forest"
flen = len(forest)
fbleu = forest.bleu
def non_local_scorer(cedge, ders):
bleu = fbleu.copy()
node = cedge.head
ratio = node.span_width() / float(flen)
bleu.special_reflen = fbleu.single_reflen() * ratio # proportional reflen
wlen = ratio * flen
hyp = cedge.assemble(ders)
#print wlen, ratio, flen, bleu.rescore(hyp), hyp,ders
bleu_score = bleu.rescore(hyp) #- (float(len(hyp.split()))* 1e-5)
fv = Vector()
if false_decoder:
(lmsc, alltrans, sig) = false_decoder.deltLMScore(cedge.lhsstr, ders)
fv["lm"] = lmsc
return ((bleu_score * wlen, -weights.dot(fv), fv), hyp, hyp)
decoder = CubePruning(BleuScorer(weights, 1.0, 0.0), non_local_scorer, k, ratio, find_min=False)
start = time.time()
best = decoder.run(forest.root)
end = time.time()
print >> logs, "Cube Bleu %s"%((end - start))
start = time.time()
dec_forest = decoder.extract_kbest_forest(forest, extract)
end = time.time()
print >> logs, "Extracting Forest %s"%((end - start))
return dec_forest, best
