def main():
if len(sys.argv) < 4:
print """
Usage:
python parse.py in.model > out.conll
Input can be provided manually via the command prompt or piped directly
to the script using cat.
"""
# END if
if sys.stdin.isatty():
rawtext = [raw_input("Please type a sentence!")]
else:
rawtext = sys.stdin.read()
# END if
out_filename = sys.argv[3]
model_filename = sys.argv[1]
try:
tp = TransitionParser.load(model_filename)
parsed = tp.parse(rawtext)
with open(out_filename, 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
# END for
# END with
except Exception:
"Error."
def main():
try:
sentences = sys.stdin.readlines()
model_file = sys.argv[1]
except:
raise ValueError('''Usage: cat <file of sentences> | python parse.py <model_file>
or, python parse.py <model_file>, type sentences and hit Ctrl+d''')
if not os.path.isfile(model_file):
raise ValueError('cant find the model file')
# scrub list / remove line breaks
sentences = [sent.rstrip() for sent in sentences]
# generate dependency graph object from sentences
depgraphs = [DependencyGraph.from_sentence(sent) for sent in sentences]
# load model and parse
tp = TransitionParser.load(model_file)
parsed = tp.parse(depgraphs)
# print to stdout.
# can cat this to a conll file for viewing with MaltEval
for p in parsed:
print(p.to_conll(10).encode('utf-8'))
return
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
def main():
file_to_parse = sys.stdin
sentences_list = [s for s in file_to_parse]
file_to_parse.close()
lang_model = sys.argv[1]
tp = TransitionParser.load(lang_model)
sentences = [DependencyGraph.from_sentence(s) for s in sentences_list]
parsed = tp.parse(sentences)
for p in parsed:
print p.to_conll(10).encode('utf-8')
def verify_lang_data(model, conll_output):
try:
lang = extract_lang_from_model_name(model)
testdata = get_data_from_lang(lang)
tp = TransitionParser.load(model)
parsed = tp.parse(testdata)
with open(conll_output, 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
ev = DependencyEvaluator(testdata, parsed)
uas, las = ev.eval()
print "\n=====Prediction of {}.model===== \nUAS: {} \nLAS: {}".format(lang, uas, las)
return las
pass
except ValueError as e:
print(e)
def parse(argv):
if len(argv) != 2:
sys.exit( "python parse.py language.model")
# data = dataset.get_english_train_corpus().parsed_sents()
# random.seed(1234)
# subdata = random.sample(data, 200)
language_model = argv[1]
try:
sentences = sys.stdin.readlines()
for i,sentence in enumerate(sentences):
dg = DependencyGraph.from_sentence(sentence)
tp = TransitionParser.load(language_model)
parsed = tp.parse([dg])
print parsed[0].to_conll(10).encode('utf-8')
# tp = TransitionParser(Transition, FeatureExtractor)
# tp.train(subdata)
# tp.save('english.model')
# testdata = dataset.get_swedish_test_corpus().parsed_sents()
# tp = TransitionParser.load('english.model')
# parsed = tp.parse(testdata)
#open new file for write on first sentence
if i == 0:
with open('test.conll', 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
#append for rest sentences
else:
with open('test.conll', 'a') 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())
except NotImplementedError:
print """
def main():
lang_train_list = []
if len(sys.argv) == 1:
lang_train_list = ['swedish', 'danish', 'english']
else:
lang_train_list = sys.argv[1:]
random.seed(1126)
for lang in lang_train_list:
whole_data = get_train_data_from_lang(lang)
subdata = random.sample(whole_data, 200)
tp = TransitionParser(Transition, FeatureExtractor)
print '\n===== Start training {} data ====='.format(lang)
tp.train(subdata)
tp.save(lang + '.model')
print '===== Sucessfully generating models ====='
def train_model(lang,training_set='train'):
# load and sample data
data = get_data(lang,dataset=training_set).parsed_sents()
if len(data) >200:
random.seed(1234)
subdata = random.sample(data, 200)
else:
subdata = data
# train model and save
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata)
tp.save('{0}.model'.format(lang))
# test performance on new data
if lang != 'english':
testdata = get_data(lang,dataset='test').parsed_sents()
# english test data not available
# so find a subset of training data
# that is disjoint from data used for training
else:
not_in_training = [sent for sent in data if sent not in subdata]
testdata = random.sample(not_in_training,200)
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
# store and print results
with open('results.txt','a') as results_file:
results_file.write('{0} model:\n'.format(lang))
results_file.write("UAS: {} \nLAS: {}\n".format(*ev.eval()))
print '{0} model:\n'.format(lang)
print "UAS: {} \nLAS: {}\n".format(*ev.eval())
return ev.eval()[1]
from transition import Transition
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, 200)
# For Swedish to get 200 projectives
#subdata = random.sample(data, 223)
# For Danish to get 200 projectives
#subdata = random.sample(data, 236)
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_dev_corpus().parsed_sents()
#testdata = dataset.get_danish_test_corpus().parsed_sents()
#tp = TransitionParser.load('badfeatures.model')
parsed = tp.parse(testdata)
with open('test.conll', 'w') as f:
for p in parsed:
__author__ = 'johnfulgoni'
import sys
from providedcode.dependencygraph import DependencyGraph
from providedcode.transitionparser import TransitionParser
# DON'T PRINT ANYTHING! OR ELSE IT MESSES THINGS UP
if __name__ == '__main__':
argc = len(sys.argv)
if argc == 2:
#print sys.argv[1] # just to see
sentence_list = []
for sent in sys.stdin: # get the sentences from the englishfile
sentence = DependencyGraph.from_sentence(sent)
sentence_list.append(sentence)
my_model = sys.argv[1] # should be 'english.model'
tp = TransitionParser.load(my_model)
parsed = tp.parse(sentence_list)
# following the example in test.py
# but we're not writing it to a file
for p in parsed:
print p.to_conll(10).encode('utf-8')
print '\n'
else:
print "Need two arguments"
exit(1)
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
data = dataset.get_swedish_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_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load('badfeatures.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())
# parsing arbitrary sentences (english):
# sentence = DependencyGraph.from_sentence('Hi, this is a test')
# tp = TransitionParser.load('english.model')
subdata = random.sample(data, 200) # use this subdata for bad features and swedish
# NEED DANISH AND ENGLISH
data_e = dataset.get_english_train_corpus().parsed_sents()
random.seed(1234)
subdata_e = random.sample(data_e, 200)
data_d = dataset.get_danish_train_corpus().parsed_sents()
random.seed(1234)
subdata_d = random.sample(data_d, 200)
try:
# BAD FEATURES MODEL (SWEDISH DATA)
print "Starting Bad Features"
testdata = dataset.get_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load('badfeatures.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 "Bad Features Results"
print "UAS: {} \nLAS: {}".format(*ev.eval())
t1 = time.time()
print "Time: "+str(t1 - t0) + '\n'
# SWEDISH FEATURE MODELS
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
from providedcode.dependencygraph import DependencyGraph
if __name__ == '__main__':
# data = dataset.get_swedish_train_corpus().parsed_sents()
# data = dataset.get_english_train_corpus().parsed_sents()
# data = dataset.get_dutch_train_corpus().parsed_sents()
data = dataset.get_danish_train_corpus().parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200)
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_danish_test_corpus().parsed_sents()
# tp = TransitionParser.load('badfeatures.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:
def handle_input(input_file, model_file):
tp = TransitionParser.load(model_file)
for line in input_file:
sentence = DependencyGraph.from_sentence(line)
parsed = tp.parse([sentence])
print parsed[0].to_conll(10).encode('utf-8')
import providedcode
from providedcode.transitionparser import TransitionParser
from providedcode.dependencygraph import DependencyGraph
from providedcode.evaluate import DependencyEvaluator
import sys
tp = TransitionParser.load('english.model')
for line in sys.stdin:
sentence = DependencyGraph.from_sentence(line)
parsed = tp.parse([sentence])
print parsed[0].to_conll(10).encode('utf-8')
#get korean training data
koreandata = dataset.get_korean_train_corpus().parsed_sents()
random.seed(1234)
koreansubdata = random.sample(koreandata, 200)
#get danish training data
danishdata = dataset.get_danish_train_corpus().parsed_sents()
random.seed(1234)
danishsubdata = random.sample(danishdata, 235)
try:
#SWEDISH TESTING
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(swedishsubdata)
tp.save('swedish.model')
#badfeatures.model...don't use for real testing
#tp = TransitionParser.load('badfeatures.model')
testdata = dataset.get_swedish_test_corpus().parsed_sents()
parsed = tp.parse(testdata)
#to write output...for badfeatures.model
'''
with open('test.conll', 'w') as f:
for p in parsed:
import nltk
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
from providedcode.dependencygraph import DependencyGraph
if __name__ == '__main__':
data = dataset.get_english_train_corpus().parsed_sents()
#data = nltk.corpus.dependency_treebank.parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200)
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata)
tp.save('english.model')
# testdata = dataset.get_english_test_corpus().parsed_sents()
# tp = TransitionParser.load('badfeatures.model')
# tp = TransitionParser.load('english.model')
# sentence = DependencyGraph.from_sentence('Hi, this is a test')
# testdata.append(sentence)
# parsed = tp.parse([sentence])
# parsed = tp.parse(testdata)
# with open('test.conll', 'w') as f:
# for p in parsed:
def handle_input(input_file, model_file):
tp = TransitionParser.load(model_file)
for line in input_file:
sentence = DependencyGraph.from_sentence(line)
parsed = tp.parse([sentence])
print parsed[0].to_conll(10).encode('utf-8')
scoreWeight = {'swedish': 25.,
'danish': 25.,
'english': 50.}
totalPoints = 0
for testName in tests.keys():
data = tests[testName]().parsed_sents()
data_1h = data[0:(len(data)/2)]
data_2h = data[(len(data)/2):-1]
random.seed(99999)
traindata = random.sample(data_1h, 200)
testdata = random.sample(data_2h, 800)
try:
print "Training {0} model...".format(testName)
tp = TransitionParser(Transition, MyFeatureExtractor)
tp.train(traindata)
tp.save(testName + ".model")
print "Testing {0} model...".format(testName)
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 "Test Results For: {0}".format(testName)
(uas, las) = ev.eval()
points = scoreWeight[testName] * (min(0.7, las)/0.7)**2
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
from providedcode.dependencygraph import DependencyGraph
import sys
tp = TransitionParser.load('english.model')
sentences = []
for sentence in sys.stdin:
sentence = sentence.strip()
sentence = DependencyGraph.from_sentence(sentence)
sentences.append(sentence)
parsed = tp.parse(sentences)
for parse in parsed:
print parse.to_conll(10).encode('utf-8')
from providedcode.dependencygraph import DependencyGraph
from providedcode.transitionparser import TransitionParser
from transition import Transition
if __name__ == '__main__':
# print 'NLP Parse Program..'
try:
model_path = sys.argv[1]
# print 'ModelPath', model_path
except IndexError as ie:
print 'Model Path Not Specified! Exiting...', ie
sys.exit(-1)
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp = TransitionParser.load(model_path) # load the trained model for parsing.
for line in sys.stdin:
# print 'Processing:', line
sentence = DependencyGraph.from_sentence(line)
parsed = tp.parse([sentence]) # parse the input line
print parsed[0].to_conll(10).encode('utf-8')
# with open('test.conll', 'w') as f:
# for p in parsed:
# f.write(p.to_conll(10).encode('utf-8'))
# f.write('\n')
# parsing arbitrary sentences (english):
# sentence = DependencyGraph.from_sentence('Hi, this is a test')
#os.chdir("/home/sidvash/NLP_coursera/Assignment1/code/")
import random
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
data = dataset.get_swedish_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_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load('swedish.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)
if __name__ == '__main__':
# 'data' is parsed sentences converted into Dependency Graph objects.
model_dict = {
'english' : ('english.model', dataset.get_english_train_corpus, dataset.get_english_test_corpus),
'danish' : ('danish.model', dataset.get_danish_train_corpus, dataset.get_danish_test_corpus),
'swedish' : ('swedish.model', dataset.get_swedish_train_corpus, dataset.get_swedish_test_corpus)
}
for model_type, model_tuple in model_dict.iteritems():
model, data, testdata = model_tuple[0], model_tuple[1]().parsed_sents(), model_tuple[2]().parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200) # 200 randomly selected DependencyGraphs(sentences) for model training.
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata) # train with 200 randomly selected dependency graphs(sentences).
tp.save(model) # save the trained model.
tp = TransitionParser.load(model) # load the trained model for parsing.
parsed = tp.parse(testdata) # parse the test data
with open('test.conll', 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
# evaluate the test parse result here...
ev = DependencyEvaluator(testdata, parsed)
print 'Model: {}'.format(model_type)
# print('Parsing dev corpus...')
# EN_testdata = dataset.get_english_dev_corpus().parsed_sents()
# EN_tp = TransitionParser.load('english.model')
# EN_parsed = EN_tp.parse(EN_testdata)
# print('Ok')
# # SE
# tp = TransitionParser(Transition, FeatureExtractor)
# tp.train(SE_subdata)
# tp.save('swedish.model')
# SE_testdata = dataset.get_swedish_test_corpus().parsed_sents()
# SE_tp = TransitionParser.load('swedish.model')
# SE_parsed = SE_tp.parse(SE_testdata)
#
# DK
tp = TransitionParser(Transition, FeatureExtractor)
print('Training...')
tp.train(DK_subdata)
print('Ok. Saving the model...')
tp.save('danish.model')
print('Ok. Parsing the test corpus...')
DK_testdata = dataset.get_danish_test_corpus().parsed_sents()
#DK_tp = TransitionParser.load('danish.model')
DK_parsed = tp.parse(DK_testdata)
print('Ok.')
# with open('english.conll', 'w') as f:
# for p in EN_parsed:
# f.write(p.to_conll(10).encode('utf-8'))
# f.write('\n')
#
# load test set in english and get 200 random sentences
english_data = dataset.get_english_train_corpus().parsed_sents()
random.seed()
english_subdata = random.sample(english_data, 200)
# load test set in danish and get 200 random sentences
danish_data = dataset.get_danish_train_corpus().parsed_sents()
random.seed()
danish_subdata = random.sample(danish_data, 200)
try:
print 'training swedish'
# swedish
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(swedish_subdata)
tp.save('swedish.model')
testdata = dataset.get_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load('swedish.model')
print 'testing swedish'
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)
import random
import nltk
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from providedcode.dependencygraph import DependencyGraph
from featureextractor import FeatureExtractor
from transition import Transition
import sys
if __name__ == '__main__':
try:
model = sys.argv[1]
tp = TransitionParser.load(model)
for line in sys.stdin:
# temp = line.strip()
# temp = str(temp)
# parsing arbitrary sentences (english):
# print "[" + temp + "]"
temp = line
# temp = "Hi, this is a test."
sentence = DependencyGraph.from_sentence(temp)
for key, dct in sentence.nodes.items():
dct['ctag'] = nltk.tag.mapping.map_tag("en-ptb", "universal", dct['ctag'])
parsed = tp.parse([sentence])
print parsed[0].to_conll(10).encode('utf-8')
data, 200) # use this subdata for bad features and swedish
# NEED DANISH AND ENGLISH
data_e = dataset.get_english_train_corpus().parsed_sents()
random.seed(1234)
subdata_e = random.sample(data_e, 200)
data_d = dataset.get_danish_train_corpus().parsed_sents()
random.seed(1234)
subdata_d = random.sample(data_d, 200)
try:
# BAD FEATURES MODEL (SWEDISH DATA)
print "Starting Bad Features"
testdata = dataset.get_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load('badfeatures.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 "Bad Features Results"
print "UAS: {} \nLAS: {}".format(*ev.eval())
t1 = time.time()
print "Time: " + str(t1 - t0) + '\n'
# SWEDISH FEATURE MODELS
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
#traindata = dataset.get_swedish_train_corpus().parsed_sents()
traindata = dataset.get_english_train_corpus().parsed_sents()
#traindata = dataset.get_danish_train_corpus().parsed_sents()
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(traindata)
#tp.save('swedish.model')
#tp.save('english.model')
### tp.save('danish.model')
#labeleddata = dataset.get_swedish_dev_corpus().parsed_sents()
labeleddata = dataset.get_english_dev_corpus().parsed_sents()
#labeleddata = dataset.get_danish_dev_corpus().parsed_sents()
#blinddata = dataset.get_swedish_dev_blind_corpus().parsed_sents()
blinddata = dataset.get_english_dev_blind_corpus().parsed_sents()
#blinddata = dataset.get_danish_dev_blind_corpus().parsed_sents()
#tp = TransitionParser.load('badfeatures.model')
parsed = tp.parse(blinddata)
F_TRAIN_ENGLISH = True
F_TRAIN_DANISH = True
F_TRAIN_KOREAN = False
#traindata = dataset.get_swedish_train_corpus().parsed_sents()
try:
if F_TEST_BADMODEL == True:
print time.ctime(), "START BADMODEL"
traindata = dataset.get_swedish_train_corpus().parsed_sents()
labeleddata = dataset.get_swedish_dev_corpus().parsed_sents()
blinddata = dataset.get_swedish_dev_blind_corpus().parsed_sents()
modelfile = 'badfeatures.model'
tp = TransitionParser.load(modelfile)
parsed = tp.parse(blinddata)
ev = DependencyEvaluator(labeleddata, parsed)
print "UAS: {} \nLAS: {}".format(*ev.eval())
conllfile = 'test.conll'
with open(conllfile, 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
print time.ctime(), "-------DONE----- BADMODEL", modelfile, conllfile
if F_TRAIN_SWEDISH == True:
print time.ctime(), "START TRAIN SWEDISH"
import random
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
data = dataset.get_swedish_train_corpus().parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200)
try:
# removed commenting from following three lines, should generate saved models
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata)
tp.save('swedish.model')
testdata = dataset.get_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load('swedish.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())
# parsing arbitrary sentences (swedish):
from transition import Transition
if __name__ == '__main__':
data = dataset.get_swedish_train_corpus().parsed_sents()
# data = dataset.get_english_test_corpus().parsed_sents()
# data = dataset.get_danish_train_corpus().parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200)
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()
#tp = TransitionParser.load('badfeatures.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')
import nltk
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
from providedcode.dependencygraph import DependencyGraph
if __name__ == '__main__':
data = dataset.get_english_train_corpus().parsed_sents()
#data = nltk.corpus.dependency_treebank.parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200)
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata)
tp.save('english.model')
# testdata = dataset.get_english_test_corpus().parsed_sents()
# tp = TransitionParser.load('badfeatures.model')
# tp = TransitionParser.load('english.model')
# sentence = DependencyGraph.from_sentence('Hi, this is a test')
# testdata.append(sentence)
# parsed = tp.parse([sentence])
# parsed = tp.parse(testdata)
# with open('test.conll', 'w') as f:
# for p in parsed:
F_TRAIN_SWEDISH = True
F_TRAIN_ENGLISH = True
F_TRAIN_DANISH = True
F_TRAIN_KOREAN = False
#traindata = dataset.get_swedish_train_corpus().parsed_sents()
try:
if F_TEST_BADMODEL == True:
print time.ctime(), "START BADMODEL"
traindata = dataset.get_swedish_train_corpus().parsed_sents()
labeleddata = dataset.get_swedish_dev_corpus().parsed_sents()
blinddata = dataset.get_swedish_dev_blind_corpus().parsed_sents()
modelfile = 'badfeatures.model'
tp = TransitionParser.load(modelfile)
parsed = tp.parse(blinddata)
ev = DependencyEvaluator(labeleddata, parsed)
print "UAS: {} \nLAS: {}".format(*ev.eval())
conllfile = 'test.conll'
with open(conllfile, 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
f.write('\n')
print time.ctime(
), "-------DONE----- BADMODEL", modelfile, conllfile
if F_TRAIN_SWEDISH == True:
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == "__main__":
data = dataset.get_swedish_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_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load("badfeatures.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())
# parsing arbitrary sentences (english):
# sentence = DependencyGraph.from_sentence('Hi, this is a test')
# tp = TransitionParser.load('english.model')
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
data = dataset.get_swedish_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_swedish_test_corpus().parsed_sents()
tp = TransitionParser.load('badfeatures.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())
# parsing arbitrary sentences (english):
# sentence = DependencyGraph.from_sentence('Hi, this is a test')
# tp = TransitionParser.load('english.model')
from providedcode.transitionparser import TransitionParser
from providedcode.dependencygraph import DependencyGraph
from nltk.tag import mapping
import sys
# check number of inputs
if len(sys.argv) != 2:
raise ValueError("Invalid arguments. Usage: python parse.py <modelfile>")
# validate & load modelfile from commandline arg
modelfile = sys.argv[1]
tp = TransitionParser.load(modelfile)
# read each line as a sentence from stdin
for line in sys.stdin:
line = line.strip()
if len(line) == 0:
continue
# convert to DependencyGraph and replace CPOS features as they are not
# originally part of from_sentence(). Piazza
sentence = DependencyGraph.from_sentence(line)
for node in sentence.nodes:
tag = sentence.nodes[node]['tag']
ctag = mapping.map_tag('wsj', 'universal', tag)
sentence.nodes[node]['ctag'] = ctag
parsed = tp.parse([sentence])
print parsed[0].to_conll(10).encode('utf-8')
#print ">>",line,"<<"
import random
from providedcode import dataset
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('english.model')
testdata = dataset.get_english_dev_corpus().parsed_sents()
#tp = TransitionParser.load('badfeatures.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())
# parsing arbitrary sentences (english):
import sys
from providedcode.transitionparser import TransitionParser
from providedcode.dependencygraph import DependencyGraph
if __name__ == '__main__':
# the raw sentences read from englishfile
lines = sys.stdin
# if no sentences read or not enough parameters retrieved, exit the program.
if not lines or not sys.argv.__len__() == 2:
exit()
# put the raw sentences in to dependency graphs and form a list of these graphs.
sentences = [DependencyGraph.from_sentence(line) for line in lines]
model_name = sys.argv[1]
# load the trained model
tp = TransitionParser.load(model_name)
# parse the sentences with the model
parsed = tp.parse(sentences)
# write the parsed sentences into the output file conll supported format.
for parsed_line in parsed:
print parsed_line.to_conll(10).encode('utf-8')
#sentence = DependencyGraph.from_sentence('Hi, this is a test')
#tp = TransitionParser.load('english.model')
#parsed = tp.parse([sentence])
#print parsed[0].to_conll(10).encode('utf-8')
# print('Parsing dev corpus...')
# EN_testdata = dataset.get_english_dev_corpus().parsed_sents()
# EN_tp = TransitionParser.load('english.model')
# EN_parsed = EN_tp.parse(EN_testdata)
# print('Ok')
# # SE
# tp = TransitionParser(Transition, FeatureExtractor)
# tp.train(SE_subdata)
# tp.save('swedish.model')
# SE_testdata = dataset.get_swedish_test_corpus().parsed_sents()
# SE_tp = TransitionParser.load('swedish.model')
# SE_parsed = SE_tp.parse(SE_testdata)
#
# DK
tp = TransitionParser(Transition, FeatureExtractor)
print('Training...')
tp.train(DK_subdata)
print('Ok. Saving the model...')
tp.save('danish.model')
print('Ok. Parsing the test corpus...')
DK_testdata = dataset.get_danish_test_corpus().parsed_sents()
#DK_tp = TransitionParser.load('danish.model')
DK_parsed = tp.parse(DK_testdata)
print('Ok.')
# with open('english.conll', 'w') as f:
# for p in EN_parsed:
# f.write(p.to_conll(10).encode('utf-8'))
# f.write('\n')
from providedcode.transitionparser import TransitionParser
from providedcode.dependencygraph import DependencyGraph
import fileinput
import sys
# parsing arbitrary sentences (english):
import nltk
from nltk.tag import map_tag
if __name__ == '__main__':
if (len(sys.argv) != 2):
print "need 1 argument for model!"
exit(1)
tp = TransitionParser.load(sys.argv[1])
line = sys.stdin.readline()
while line:
sentence = DependencyGraph.from_sentence(line)
for (index, node) in enumerate(sentence.nodes):
sentence.nodes[index]['ctag'] = map_tag(
'en-ptb', 'universal', sentence.nodes[index]['ctag'])
parsed = tp.parse([sentence])
print parsed[0].to_conll(10).encode('utf-8')
line = sys.stdin.readline()
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
# traindata = dataset.get_swedish_train_corpus().parsed_sents()
traindata = dataset.get_english_train_corpus().parsed_sents()
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(traindata)
# tp.save('swedish.model')
# labeleddata = dataset.get_swedish_dev_corpus().parsed_sents()
# blinddata = dataset.get_swedish_dev_blind_corpus().parsed_sents()
tp.save('english.model')
labeleddata = dataset.get_english_dev_corpus().parsed_sents()
blinddata = dataset.get_english_dev_blind_corpus().parsed_sents()
#tp = TransitionParser.load('badfeatures.model')
# parsed = tp.parse(labeleddata)
parsed = tp.parse(blinddata)
with open('test.conll', 'w') as f:
for p in parsed:
f.write(p.to_conll(10).encode('utf-8'))
import sys
from providedcode.transitionparser import TransitionParser
from providedcode.dependencygraph import DependencyGraph
if __name__ == '__main__':
sentences = sys.stdin.readlines()
tp = TransitionParser.load(sys.argv[1])
for sentence in sentences:
dg = DependencyGraph.from_sentence(sentence)
parsed = tp.parse([dg])
print parsed[0].to_conll(10).encode('utf-8')
#print '\n'
from providedcode.dependencygraph import DependencyGraph
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
import sys
if __name__ == "__main__":
try:
# parsing arbitrary sentences (english):
fromInput = "".join(sys.stdin.readlines())
# print fromInput
sentence = DependencyGraph.from_sentence(fromInput)
tp = TransitionParser.load("english.model")
parsed = tp.parse([sentence])
print parsed[0].to_conll(10).encode("utf-8")
except NotImplementedError:
print """
This file is currently broken! We removed the implementation of Transition
(in transition.py), which tells the transitionparser how to go from one
Configuration to another Configuration. This is an essential part of the
arc-eager dependency parsing algorithm, so you should probably fix that :)
The algorithm is described in great detail here:
http://aclweb.org/anthology//C/C12/C12-1059.pdf
We also haven't actually implemented most of the features for for the
support vector machine (in featureextractor.py), so as you might expect the
evaluator is going to give you somewhat bad results...
data = dataset.get_swedish_train_corpus().parsed_sents()
random.seed(1234)
subdata = random.sample(data, 200)
data_eng = dataset.get_english_train_corpus().parsed_sents()
random.seed(1234)
subdata_eng = random.sample(data_eng, 200)
data_dan = dataset.get_danish_train_corpus().parsed_sents()
random.seed(1234)
subdata_dan = random.sample(data_dan, 200)
try:
# BAD MODEL ###########################################################
tp = TransitionParser.load('badfeatures.model')
testdata = dataset.get_swedish_test_corpus().parsed_sents()
parsed = tp.parse(testdata)
ev = DependencyEvaluator(testdata, parsed)
print "Bad Features Model"
print "UAS: {} \nLAS: {}".format(*ev.eval())
# SWEDISH #############################################################
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata)
tp.save('swedish.model')
testdata = dataset.get_swedish_test_corpus().parsed_sents()
# tp = TransitionParser.load('badfeatures.model')
from providedcode import dataset
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == '__main__':
#traindata = dataset.get_swedish_train_corpus().parsed_sents()
traindata = dataset.get_english_train_corpus().parsed_sents()
#traindata = dataset.get_danish_train_corpus().parsed_sents()
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(traindata)
#tp.save('swedish.model')
#tp.save('english.model')
### tp.save('danish.model')
#labeleddata = dataset.get_swedish_dev_corpus().parsed_sents()
labeleddata = dataset.get_english_dev_corpus().parsed_sents()
#labeleddata = dataset.get_danish_dev_corpus().parsed_sents()
#blinddata = dataset.get_swedish_dev_blind_corpus().parsed_sents()
blinddata = dataset.get_english_dev_blind_corpus().parsed_sents()
#blinddata = dataset.get_danish_dev_blind_corpus().parsed_sents()
#tp = TransitionParser.load('badfeatures.model')
parsed = tp.parse(blinddata)
from providedcode.transitionparser import TransitionParser
from providedcode.evaluate import DependencyEvaluator
from featureextractor import FeatureExtractor
from transition import Transition
if __name__ == "__main__":
# data = dataset.get_swedish_train_corpus().parsed_sents()
data = dataset.get_english_train_corpus().parsed_sents()
# data = dataset.get_korean_train_corpus().parsed_sents()
# data = dataset.get_danish_train_corpus().parsed_sents()
#
random.seed(1234)
subdata = random.sample(data, 200)
try:
tp = TransitionParser(Transition, FeatureExtractor)
tp.train(subdata)
# tp.save('swedish.model')
tp.save("english.model")
# tp.save('korean.model')
# tp.save('danish.model')
# testdata = dataset.get_swedish_test_corpus().parsed_sents()
#
testdata = dataset.get_english_dev_corpus().parsed_sents()
# testdata = dataset.get_korean_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('korean.model')
# tp = TransitionParser.load('danish.model')