result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
if __name__ == "__main__":
WORD_VOCAB_FILE = 'data/words.vocab'
POS_VOCAB_FILE = 'data/pos.vocab'
try:
word_vocab_f = open(WORD_VOCAB_FILE, 'r')
pos_vocab_f = open(POS_VOCAB_FILE, 'r')
except FileNotFoundError:
print("Could not find vocabulary files {} and {}".format(
WORD_VOCAB_FILE, POS_VOCAB_FILE))
sys.exit(1)
extractor = FeatureExtractor(word_vocab_f, pos_vocab_f)
parser = Parser(extractor, sys.argv[1])
with open(sys.argv[2], 'r') as in_file:
for dtree in conll_reader(in_file):
words = dtree.words()
pos = dtree.pos()
deps = parser.parse_sentence(words, pos)
print(deps.print_conll())
print()
ind = [i[0] for i in sorted(enumerate(myList), key=lambda x:x[1], reverse = True)]
print(ind)
'''
model = keras.models.load_model("/Users/apple/Desktop/semester_1/5.nlp/hw/hw3/data/model.h5")
WORD_VOCAB_FILE = 'data/words.vocab'
POS_VOCAB_FILE = 'data/pos.vocab'
try:
word_vocab_f = open(WORD_VOCAB_FILE,'r')
pos_vocab_f = open(POS_VOCAB_FILE,'r')
except FileNotFoundError:
print("Could not find vocabulary files {} and {}".format(WORD_VOCAB_FILE, POS_VOCAB_FILE))
sys.exit(1)
extractor = FeatureExtractor(word_vocab_f, pos_vocab_f)
words = ["And", "they", "plan", "to", "buy", "more", "today", "."]
pos = ["CC", "PRP", "VBP", "TO", "VB", "JJR", "NN", "."]
state = State(range(1,len(words)))
state.stack.append(0)
dep_relations = ['tmod', 'vmod', 'csubjpass', 'rcmod', 'ccomp', 'poss', 'parataxis', 'appos', 'dep', 'iobj', 'pobj', 'mwe', 'quantmod', 'acomp', 'number', 'csubj', 'root', 'auxpass', 'prep', 'mark', 'expl', 'cc', 'npadvmod', 'prt', 'nsubj', 'advmod', 'conj', 'advcl', 'punct', 'aux', 'pcomp', 'discourse', 'nsubjpass', 'predet', 'cop', 'possessive', 'nn', 'xcomp', 'preconj', 'num', 'amod', 'dobj', 'neg','dt','det']
while state.buffer:
#pass
# TODO: Write the body of this loop for part 4
features = extractor.get_input_representation(words, pos, state)
soft_acts = model.predict(features.reshape(-1,6))
sort_ind = [i[0] for i in sorted(enumerate(soft_acts[0]), key=lambda x:x[1], reverse = True)]