def get_data_from_lang(lang):
if lang == 'swedish':
return dataset.get_swedish_test_corpus().parsed_sents()
elif lang == 'danish':
return dataset.get_danish_test_corpus().parsed_sents()
elif lang == 'english':
return dataset.get_english_test_corpus().parsed_sents()
else:
raise ValueError("Please don't use {}, only use english, swedish or danish".format(lang))
def evaluate_parse(partIdx):
if partIdx == 3:
print 'Evaluating your swedish model ... '
testdata = dataset.get_swedish_test_corpus().parsed_sents()
if not os.path.exists('./swedish.model'):
print 'No model. Please save your model as swedish.model at current directory before submission.'
sys.exit(0)
tp = TransitionParser.load('swedish.model')
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
uas, las = ev.eval()
print 'UAS:', uas
print 'LAS:', las
swed_score = (min(las, 0.7) / 0.7)**2
return swed_score
if partIdx == 1:
print 'Evaluating your english model ... '
testdata = dataset.get_english_test_corpus().parsed_sents()
if not os.path.exists('./english.model'):
print 'No model. Please save your model as english.model at current directory before submission.'
sys.exit(0)
tp = TransitionParser.load('english.model')
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
uas, las = ev.eval()
print 'UAS:', uas
print 'LAS:', las
eng_score = (min(las, 0.7) / 0.7)**2
return eng_score
if partIdx == 2:
print 'Evaluating your danish model ... '
testdata = dataset.get_danish_test_corpus().parsed_sents()
if not os.path.exists('./danish.model'):
print 'No model. Please save your model danish.model at current directory before submission.'
sys.exit(0)
tp = TransitionParser.load('danish.model')
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
uas, las = ev.eval()
print 'UAS:', uas
print 'LAS:', las
dan_score = (min(las, 0.7) / 0.7)**2
return dan_score
def evaluate_parse(partIdx):
if partIdx == 3:
print 'Evaluating your swedish model ... '
testdata = dataset.get_swedish_test_corpus().parsed_sents()
if not os.path.exists('./swedish.model'):
print 'No model. Please save your model as swedish.model at current directory before submission.'
sys.exit(0)
tp = TransitionParser.load('swedish.model')
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
uas, las = ev.eval()
print 'UAS:',uas
print 'LAS:',las
swed_score = (min(las, 0.7) / 0.7) ** 2
return swed_score
if partIdx == 1:
print 'Evaluating your english model ... '
testdata = dataset.get_english_test_corpus().parsed_sents()
if not os.path.exists('./english.model'):
print 'No model. Please save your model as english.model at current directory before submission.'
sys.exit(0)
tp = TransitionParser.load('english.model')
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
uas, las = ev.eval()
print 'UAS:',uas
print 'LAS:',las
eng_score = (min(las, 0.7) / 0.7) ** 2
return eng_score
if partIdx == 2:
print 'Evaluating your danish model ... '
testdata = dataset.get_danish_test_corpus().parsed_sents()
if not os.path.exists('./danish.model'):
print 'No model. Please save your model danish.model at current directory before submission.'
sys.exit(0)
tp = TransitionParser.load('danish.model')
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
uas, las = ev.eval()
print 'UAS:',uas
print 'LAS:',las
dan_score = (min(las, 0.7) / 0.7) ** 2
return dan_score
if __name__ == '__main__':
# data = dataset.get_swedish_train_corpus().parsed_sents()
data = dataset.get_english_train_corpus().parsed_sents()
# data = dataset.get_danish_train_corpus().parsed_sents()
random.seed(1234)
subdata = random.sample(data, 400)
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata)
# tp.save('swedish.model')
tp.save('english.model')
# tp.save('danish.model')
# testdata = dataset.get_swedish_test_corpus().parsed_sents()
testdata = dataset.get_english_test_corpus().parsed_sents()
# testdata = dataset.get_danish_test_corpus().parsed_sents()
# tp = TransitionParser.load('swedish.model')
tp = TransitionParser.load('english.model')
# tp = TransitionParser.load('danish.model')
parsed = tp.parse(testdata)
with open('test.conll', 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
ev = DependencyEvaluator(testdata, parsed)
print "LAS: {} \nUAS: {}".format(*ev.eval())
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
data = dataset.get_english_train_corpus().parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200)
try:
#tp = TransitionParser(Transition, FeatureExtractor)
#tp.train(subdata)
#tp.save('swedish.model')
testdata = dataset.get_english_test_corpus().parsed_sents()
tp = TransitionParser.load('english.model')
parsed = tp.parse(testdata)
with open('test.conll', 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
ev = DependencyEvaluator(testdata, parsed)
print "UAS: {} \nLAS: {}".format(*ev.eval())
f.close()
# parsing arbitrary sentences (english):
# sentence = DependencyGraph.from_sentence('Hi, this is a test')
