def train_doc2vec():
# def isEnglish(s):
# try:
# s.encode('ascii')
# except UnicodeEncodeError:
# return False
# else:
# return True
labeled_data, _ = load_vader('./resource/tweets.txt')
# for i,d in enumerate(labeled_data):
# print(i)
# if not isEnglish(d):
# print('*'*111)
# print(i,d)
# exit()
unlabeled_data, _ = load_sentiment140('/home/hs/Data/Corpus/training.csv')
labeled_data = preprocess(labeled_data, replace=True)
dump_picle(labeled_data, './data/acc/labeled_data.p')
unlabeled_data = preprocess(unlabeled_data, replace=True)
dump_picle(unlabeled_data, './data/acc/unlabeled_data.p')
# labeled_data = load_pickle('./data/acc/labeled_data.p')
# unlabeled_data = load_pickle('./data/acc/unlabeled_data.p')
sentence = TaggedLineSentence(labeled_data, unlabeled_data)
train_docvecs(sentence)
def train_doc2vec():
# def isEnglish(s):
# try:
# s.encode('ascii')
# except UnicodeEncodeError:
# return False
# else:
# return True
labeled_data, _ = load_vader('./resource/tweets.txt')
# for i,d in enumerate(labeled_data):
# print(i)
# if not isEnglish(d):
# print('*'*111)
# print(i,d)
# exit()
unlabeled_data, _ = load_sentiment140('/home/hs/Data/Corpus/training.csv')
labeled_data = preprocess(labeled_data, replace=True)
dump_picle(labeled_data, './data/acc/labeled_data.p')
unlabeled_data = preprocess(unlabeled_data, replace=True)
dump_picle(unlabeled_data, './data/acc/unlabeled_data.p')
# labeled_data = load_pickle('./data/acc/labeled_data.p')
# unlabeled_data = load_pickle('./data/acc/unlabeled_data.p')
sentence = TaggedLineSentence(labeled_data, unlabeled_data)
train_docvecs(sentence)
def run_build_docvecs():
model = load_embeddings('twitter')
simple_evaluate(model)
_, ratings = load_vader('./resource/tweets.txt')
# Do not account the 1240 and 3516 -th item
# r = ratings[:1240] + ratings[1241:3516] + ratings[3517:]
build_docvecs(model, ratings)
def run_build_docvecs():
model = load_embeddings('twitter')
simple_evaluate(model)
_, ratings = load_vader('./resource/tweets.txt')
# Do not account the 1240 and 3516 -th item
# r = ratings[:1240] + ratings[1241:3516] + ratings[3517:]
build_docvecs(model, ratings)
def cv(data, target, multivariant=False):
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(data, target, test_size=0.2, random_state=0)
if multivariant is False:
linear_regression(X_train, X_test, Y_train, Y_test, plot=False)
else:
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='Ridge_Regression')
if __name__ == '__main__':
from load_data import load_vader
normalize = True
corpus, ratings = load_vader(['movie_reviews'])
corpus = process(corpus)
# lexicon = load_lexicon(get_file_path('lexicon'))
from load_data import load_anew
from file_name import get_file_path
import numpy as np
words, valences, _ = load_anew(get_file_path('anew'))
mark = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
lexicon = dict()
for i, word in enumerate(words):
lexicon[word] = valences[i]
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
def get_vocab(corpus):
vocab = defaultdict(float)
for sent in corpus:
for word in clean_str(sent).split():
vocab[word] += 1
print(len(vocab))
return vocab
def process(corpus):
return [clean_str(sent) for sent in corpus]
vec_dim = 300
############################################## all ###############################################
corpus, ratings = load_vader(['tweets', 'movie_reviews', 'product_reviews', 'news_articles'])
# corpus, ratings = load_vader(['movie_reviews'])
# # name: tweets, movie_reviews, product_reviews, news_articles
lexicon_name = get_file_path('anew')
words, valences, _ = load_anew(lexicon_name)
corpus, ratings = screen_data(corpus, ratings, words)
ratings = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
print(len(corpus), len(corpus))
vocab = get_vocab(corpus)
# dump_picle(vocab, './data/corpus/vader/vocab_moview_reviews.p')
# vocab = load_pickle('./data/corpus/vader/vocab_moview_reviews.p')
print(len(vocab))
W, word_idx_map = build_embedding_matrix(load_embeddings('google_news'), vocab, k=300)
# dump_picle(word_idx_map, './data/corpus/vader/word_idx_map_movie_reviews.p')
# print('dump word_idx_map successful')
def get_vocab(corpus):
vocab = defaultdict(float)
for sent in corpus:
for word in clean_str(sent).split():
vocab[word] += 1
print(len(vocab))
return vocab
def process(corpus):
return [clean_str(sent) for sent in corpus]
vec_dim = 300
############################################## all ###############################################
corpus, ratings = load_vader(
['tweets', 'movie_reviews', 'product_reviews', 'news_articles'])
# corpus, ratings = load_vader(['movie_reviews'])
# # name: tweets, movie_reviews, product_reviews, news_articles
lexicon_name = get_file_path('anew')
words, valences, _ = load_anew(lexicon_name)
corpus, ratings = screen_data(corpus, ratings, words)
ratings = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
print(len(corpus), len(corpus))
vocab = get_vocab(corpus)
# dump_picle(vocab, './data/corpus/vader/vocab_moview_reviews.p')
# vocab = load_pickle('./data/corpus/vader/vocab_moview_reviews.p')
print(len(vocab))
W, word_idx_map = build_embedding_matrix(load_embeddings('google_news'),
vocab,
k=300)
def process(corpus):
return [clean_str(sent) for sent in corpus]
if __name__ == '__main__':
program = os.path.basename(sys.argv[0])
logger = logging.getLogger(program)
logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
logging.root.setLevel(level=logging.INFO)
logger.info(r"running %s" % ''.join(sys.argv))
from load_data import load_vader
# corpus, ratings = load_vader(['tweets', 'movie_reviews', 'product_reviews', 'news_articles'])
corpus, ratings = load_vader(['news_articles'])
lexicon_name = get_file_path('anew')
logger.info(r"loading lexicon form : " + lexicon_name)
words, valences, _ = load_anew(lexicon_name)
corpus, ratings = screen_data(corpus, ratings, words)
ratings = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
print(len(corpus))
# for i in corpus[:100]:
# print(i)
lexicon = dict()
for i, word in enumerate(words):
lexicon[word] = valences[i]
mean_ratings, tf_means, tfidf_means, geos, tf_geos, tfidf_geos = calculate_ratings(
corpus, ratings, lexicon)
dump_picle([
import numpy as np
def statistic(texts):
avg_length, vocab = 0, set()
length_list = []
for text in texts:
if type(text) is not list:
text = text.split()
length_list.append(len(text))
vocab = vocab.union(set(text))
# if len(text)>200:
# print(text)
avg_length = np.average(np.array(length_list))
return avg_length, len(vocab)
if __name__ == '__main__':
# (['tweets', 'movie_reviews', 'product_reviews', 'news_articles'])
tweets, _ = load_vader(['tweets'])
movie, _ = load_vader(['movie_reviews'])
amazon, _ = load_vader(['product_reviews'])
NYT, _ = load_vader(['news_articles'])
cvat = load_corpus(get_file_path('cn_corpus'))
print(statistic(tweets))
print(statistic(movie))
print(statistic(amazon))
print(statistic(NYT))
print(statistic(cvat))
if multivariant is False:
linear_regression(X_train, X_test, Y_train, Y_test, plot=False)
else:
linear_regression_multivariant(X_train,
X_test,
Y_train,
Y_test,
cost_fun='Ridge_Regression')
if __name__ == '__main__':
from load_data import load_vader
normalize = True
corpus, ratings = load_vader(['movie_reviews'])
corpus = process(corpus)
# lexicon = load_lexicon(get_file_path('lexicon'))
from load_data import load_anew
from file_name import get_file_path
import numpy as np
words, valences, _ = load_anew(get_file_path('anew'))
mark = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
lexicon = dict()
for i, word in enumerate(words):
lexicon[word] = valences[i]
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
for sent in corpus:
for word in clean_str(sent).split():
vocab[word] += 1
print(len(vocab))
return vocab
def process(corpus):
return [clean_str(sent) for sent in corpus]
vec_dim = 300
############################################## tweets ###############################################
# corpus, ratings = load_vader(['tweets', 'movie_reviews', 'product_reviews', 'news_articles'])
corpus, ratings = load_vader(['tweets'])
# # name: tweets, movie_reviews, product_reviews, news_articles
lexicon_name = get_file_path('anew')
words, valences, _ = load_anew(lexicon_name)
corpus, ratings = screen_data(corpus, ratings, words)
ratings = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
print(len(corpus), len(corpus))
vocab = get_vocab(corpus)
dump_picle(vocab, './data/corpus/vader/vocab_moview_tweets.p')
# vocab = load_pickle('./data/corpus/vader/vocab_moview_reviews.p')
print('词汇数量：%s' % str(len(vocab)))
W, word_idx_map = build_embedding_matrix(load_embeddings('google_news'), vocab, k=300)
dump_picle(word_idx_map, './data/corpus/vader/word_idx_map_tweets.p')
print('dump word_idx_map successful')
dump_picle(W, './data/corpus/vader/embedding_matrix_tweets.p')