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parser.py
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parser.py
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from create_features import features
from sklearn.externals import joblib
from pymystem3 import Mystem
from collections import OrderedDict
from string import whitespace, punctuation
clf = joblib.load('frame_parser.pkl')
vec = joblib.load('feature_transformer.pkl')
m = Mystem()
def frames(sentence):
header = ('word', 'lex', 'pos', 'gram', 'prev_gr', 'prev_lex', 'rel', 'pred_lemma')
anas = m.analyze(sentence)
fr = OrderedDict()
data = OrderedDict()
for w in anas:
word = w['text']
#if word in whitespace or word in punctuation:
# continue
try:
gr = w['analysis'][0]['gr']
lex = w['analysis'][0]['lex']
except:
gr, lex = None, None
data[word] = [lex, gr, None, None, None, None]
vector = features(data)
vectors = OrderedDict((k, v) for k,v in vector.items() if k not in whitespace and k not in punctuation)
for w in vectors:
feats = [w] + [str(x) for x in vectors[w]][:-1]
feats = dict(zip(header, feats))
v = vec.transform(feats)
role = clf.predict(v)[0]
fr[w] = role
#print(fr)
return fr
#vector = vec.transform(features)
#return clf.predict(vector)[0][0]
if __name__ == "__main__":
# clf = joblib.load('model.pkl')
# vec = joblib.load('feature_transformer.pkl')
# demo part
phrase = input('Введите фразу: ')
fr = frames(phrase)
#print(fr)
for f in fr:
print(f, fr[f])
#words = phrase.split()
#for word in words:
# print(word, pos(word))