def train_and_trial(train_file, test_file, train_parse='', test_parse='', pickled=True, use_dep=False):
global use_dep_parse
if use_dep:
use_dep_parse = True
if pickled:
f = open(train_file, 'rb')
traind = cPickle.load(f)
f.close()
f = open(test_file, 'rb')
testd = cPickle.load(f)
f.close()
else:
traind = XMLParser.create_exs(train_file)
testd = XMLParser.create_exs(test_file)
posi_words = semeval_util.get_liu_lexicon('positive-words.txt')
negi_words = semeval_util.get_liu_lexicon('negative-words.txt')
print "should really use better dictionary for sentence senti labels"
senti_dictionary = semeval_util.get_mpqa_lexicon()
train_sentiment = [senti_classify(sent, posi_words, negi_words) for sent in traind['orig']]
dep_parses = []
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'], train_parse, dictionary=True, iobs=True)
chunker = ConsecutiveChunkTagger(zip(traind['iob'],traind['polarity']), senti_dictionary,
train_sentiment, dep_parses)
print "done training"
test_sentiment = [senti_classify(sent, posi_words, negi_words) for sent in testd['orig']]
dep_parses = [[]] * len(test_sentiment)
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(testd['iob'], test_parse, dictionary=True, iobs=True)
results = []
for i in range(len(test_sentiment)):
results.append(chunker.parse((testd['iob'][i], test_sentiment[i], dep_parses[i])))
return results
def train_and_trial(trn_file,
test_file,
parse_file_train,
parse_file_test,
use_dep=False,
posit_lex_file='positive-words.txt',
nega_lex_file='negative-words.txt',
pickled=False):
""" Train on the training file and test on the testing file
"""
global use_dep_parse
if use_dep:
use_dep_parse = True
if pickled:
f = open(trn_file, 'rb')
traind = cPickle.load(f)
f.close()
f = open(test_file, 'rb')
testd = cPickle.load(f)
f.close()
else:
traind = XMLParser.create_exs(trn_file)
testd = XMLParser.create_exs(test_file)
#posi_words = semeval_util.get_liu_lexicon(posit_lex_file)
#negi_words = semeval_util.get_liu_lexicon(nega_lex_file)
dep_parses = []
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'],
parse_file_train,
dictionary=True,
iobs=True)
senti_dictionary = semeval_util.get_mpqa_lexicon()
chunker = ConsecutiveChunker(traind['iob'], testd['iob'], senti_dictionary,
dep_parses)
print "done training on %d examples" % len(traind['iob'])
'''
f = open('learned.pkl','wb')
cPickle.dump(chunker,f)
f.close()
'''
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'],
parse_file_test,
dictionary=True,
iobs=True)
guessed_iobs = chunker.evaluate([testd['iob'], dep_parses])
###semeval_util.compute_pr(testd['iob'], guessed_iobs)
return guessed_iobs
def train_and_test(filename, parse_file, use_deps=False,
posit_lex_file='positive-words.txt', nega_lex_file='negative-words.txt'):
"""Creates an 80/20 split of the examples in filename,
trains the chunker on 80%, and evaluates the learned chunker on 20%.
"""
global use_dep_parse
if use_deps:
use_dep_parse = True
traind = XMLParser.create_exs(filename)
dep_parses = []
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'], parse_file, dictionary=True, iobs=True)
n = len(traind['iob'])
split_size = int(n * 0.8)
train = traind['iob'][:split_size]
test = traind['iob'][split_size:]
test_deps = []
if use_dep_parse:
test_deps = dep_parses[split_size:]
#Liu not in use for now
#posi_words = semeval_util.get_liu_lexicon(posit_lex_file)
#negi_words = semeval_util.get_liu_lexicon(nega_lex_file)
senti_dictionary = semeval_util.get_mpqa_lexicon()
chunker = ConsecutiveChunker(train, test, senti_dictionary, dep_parses)
guessed_iobs = chunker.evaluate([test,test_deps])
semeval_util.compute_pr(test, guessed_iobs)
def train_and_test(filename,
parse_file,
use_deps=False,
posit_lex_file='positive-words.txt',
nega_lex_file='negative-words.txt'):
"""Creates an 80/20 split of the examples in filename,
trains the chunker on 80%, and evaluates the learned chunker on 20%.
"""
global use_dep_parse
if use_deps:
use_dep_parse = True
traind = XMLParser.create_exs(filename)
dep_parses = []
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'],
parse_file,
dictionary=True,
iobs=True)
n = len(traind['iob'])
split_size = int(n * 0.8)
train = traind['iob'][:split_size]
test = traind['iob'][split_size:]
test_deps = []
if use_dep_parse:
test_deps = dep_parses[split_size:]
#Liu not in use for now
#posi_words = semeval_util.get_liu_lexicon(posit_lex_file)
#negi_words = semeval_util.get_liu_lexicon(nega_lex_file)
senti_dictionary = semeval_util.get_mpqa_lexicon()
chunker = ConsecutiveChunker(train, test, senti_dictionary, dep_parses)
guessed_iobs = chunker.evaluate([test, test_deps])
semeval_util.compute_pr(test, guessed_iobs)
def K_fold_train_and_test(filename,
parse_file,
use_dep=False,
posit_lex_file='positive-words.txt',
nega_lex_file='negative-words.txt',
k=5,
pickled=False):
"""Does K-fold cross-validation on the given filename
"""
global use_dep_parse
if use_dep:
print "using dependency parses"
use_dep_parse = True
if pickled:
f = open(filename, 'rb')
traind = cPickle.load(f)
f.close()
else:
traind = XMLParser.create_exs(filename)
n = len(traind['iob'])
dep_parses = traind['iob']
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'],
parse_file,
dictionary=True,
iobs=True)
#posi_words = semeval_util.get_liu_lexicon(posit_lex_file)
#negi_words = semeval_util.get_liu_lexicon(nega_lex_file)
senti_dictionary = semeval_util.get_mpqa_lexicon()
kf = cross_validation.KFold(n, n_folds=k, indices=True)
tot_p, tot_r, tot_f1 = 0, 0, 0
for train, test in kf:
print "next fold, split size: %d/%d" % (len(train), len(test))
#print train
train_set = []
test_set = []
train_parse = []
test_parse = []
for i in train:
train_set.append(traind['iob'][i])
train_parse.append(dep_parses[i])
for i in test:
test_set.append(traind['iob'][i])
test_parse.append(dep_parses[i])
chunker = ConsecutiveChunker(train_set, test_set, senti_dictionary,
train_parse)
guesses = chunker.evaluate([test_set, test_parse])
#print test_set
#print guesses
r, p, f = semeval_util.compute_pr(test_set, guesses)
tot_p += p
tot_r += r
tot_f1 += f
print "ave Prec: %.2f, Rec: %.2f, F1: %.2f" % (tot_p / float(k), tot_r /
float(k), tot_f1 / float(k))
def train_and_trial(trn_file, test_file, parse_file_train, parse_file_test, use_dep=False,
posit_lex_file='positive-words.txt', nega_lex_file='negative-words.txt', pickled=False):
""" Train on the training file and test on the testing file
"""
global use_dep_parse
if use_dep:
use_dep_parse = True
if pickled:
f = open(trn_file, 'rb')
traind = cPickle.load(f)
f.close()
f = open(test_file, 'rb')
testd = cPickle.load(f)
f.close()
else:
traind = XMLParser.create_exs(trn_file)
testd = XMLParser.create_exs(test_file)
#posi_words = semeval_util.get_liu_lexicon(posit_lex_file)
#negi_words = semeval_util.get_liu_lexicon(nega_lex_file)
dep_parses = []
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'], parse_file_train, dictionary=True, iobs=True)
senti_dictionary = semeval_util.get_mpqa_lexicon()
chunker = ConsecutiveChunker(traind['iob'], testd['iob'], senti_dictionary, dep_parses)
print "done training on %d examples" % len(traind['iob'])
'''
f = open('learned.pkl','wb')
cPickle.dump(chunker,f)
f.close()
'''
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'], parse_file_test, dictionary=True, iobs=True)
guessed_iobs = chunker.evaluate([testd['iob'], dep_parses])
###semeval_util.compute_pr(testd['iob'], guessed_iobs)
return guessed_iobs
def k_fold(filename, parse_filename, k=5, pickled=True, use_dep=False):
global use_dep_parse
if use_dep:
use_dep_parse = True
if pickled:
f = open(filename, 'rb')
traind = cPickle.load(f)
f.close()
else:
traind = XMLParser.create_exs(filename)
n = len(traind['iob'])
posi_words = semeval_util.get_liu_lexicon('positive-words.txt')
negi_words = semeval_util.get_liu_lexicon('negative-words.txt')
senti_dictionary = semeval_util.get_mpqa_lexicon()
full_senti_label = [senti_classify(sentence, posi_words, negi_words) for sentence in traind['orig']]
dep_parses = [[]] * n
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'], parse_filename, dictionary=True, iobs=True)
kf = cross_validation.KFold(n, n_folds=k, indices=True)
tot_acc = 0.
for train, test in kf:
print "next fold, split size: %d/%d" %(len(train), len(test))
#print train
train_set = []
train_sentis = []
train_parse = []
test_set = []
test_sentis = []
test_parse = []
for i in train:
train_set.append((traind['iob'][i], traind['polarity'][i]))
train_sentis.append(full_senti_label[i])
train_parse.append(dep_parses[i])
for i in test:
test_set.append((traind['iob'][i], traind['polarity'][i]))
test_sentis.append((full_senti_label[i]))
test_parse.append(dep_parses[i])
chunker = ConsecutiveChunkTagger(train_set, senti_dictionary, train_sentis, train_parse)
acc = chunker.evaluate(zip(test_set, test_sentis, test_parse))
print "acc:", acc
tot_acc += acc
print "average acc:", tot_acc/k
def K_fold_train_and_test(filename, parse_file, use_dep=False,
posit_lex_file='positive-words.txt', nega_lex_file='negative-words.txt', k=5, pickled=False):
"""Does K-fold cross-validation on the given filename
"""
global use_dep_parse
if use_dep:
print "using dependency parses"
use_dep_parse = True
if pickled:
f = open(filename, 'rb')
traind = cPickle.load(f)
f.close()
else:
traind = XMLParser.create_exs(filename)
n = len(traind['iob'])
dep_parses = traind['iob']
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'], parse_file, dictionary=True, iobs=True)
#posi_words = semeval_util.get_liu_lexicon(posit_lex_file)
#negi_words = semeval_util.get_liu_lexicon(nega_lex_file)
senti_dictionary = semeval_util.get_mpqa_lexicon()
kf = cross_validation.KFold(n, n_folds=k, indices=True)
tot_p, tot_r, tot_f1 = 0, 0, 0
for train, test in kf:
print "next fold, split size: %d/%d" %(len(train), len(test))
#print train
train_set = []
test_set = []
train_parse = []
test_parse = []
for i in train:
train_set.append(traind['iob'][i])
train_parse.append(dep_parses[i])
for i in test:
test_set.append(traind['iob'][i])
test_parse.append(dep_parses[i])
chunker = ConsecutiveChunker(train_set, test_set, senti_dictionary, train_parse)
guesses = chunker.evaluate([test_set, test_parse])
#print test_set
#print guesses
r, p, f = semeval_util.compute_pr(test_set, guesses)
tot_p += p
tot_r += r
tot_f1 += f
print "ave Prec: %.2f, Rec: %.2f, F1: %.2f" %(tot_p/float(k), tot_r/float(k), tot_f1/float(k))
def train_and_test(filename, parse_file,
posit_lex_file='positive-words.txt', nega_lex_file='negative-words.txt',
pickled=False, use_dep=False):
"""Creates an 80/20 split of the examples in filename,
trains the sentiment classifier on 80%, and evaluates the learned classifier on 20%.
"""
global use_dep_parse
if use_dep:
use_dep_parse = True
if pickled:
f = open(filename, 'rb')
traind = cPickle.load(f)
f.close()
else:
traind = XMLParser.create_exs(filename)
n = len(traind['iob'])
split_size = int(n * 0.8)
train = zip(traind['iob'][:split_size], traind['polarity'][:split_size])
test = zip(traind['iob'][split_size:], traind['polarity'][split_size:])
posi_words = semeval_util.get_liu_lexicon(posit_lex_file)
negi_words = semeval_util.get_liu_lexicon(nega_lex_file)
senti_dictionary = semeval_util.get_mpqa_lexicon()
full_senti_label = [senti_classify(sentence, posi_words, negi_words) for sentence in traind['orig']]
dep_parses = []
if use_dep_parse:
dep_parses = semeval_util.add_dep_parse_features(traind['iob'], parse_file, dictionary=True, iobs=True)
print "first dep_parse:", dep_parses[0]
print "first train ex:", train[0]
print "size parses all:", len(dep_parses), "vs train:", len(dep_parses[:split_size])
chunker = ConsecutiveChunkTagger(train, senti_dictionary, full_senti_label, dep_parses[:split_size])
print "done training"
if use_dep_parse:
dep_parses = dep_parses[split_size:]
print "first test dep parse:", dep_parses[0]
print "first test ex:", test[0]
else:
#artifact of using zip, even if not using parses, need to have same # of elements in all lists
dep_parses = [[]] * split_size
print chunker.evaluate(zip(test, full_senti_label[split_size:], dep_parses))