from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from utils import load_reviews
import time
start = time.time()
filePath = '../../corpus/100k/allTrimed.csv'
_, reviews = load_reviews(filePath)
reviews = [review.split() for review in reviews]
cost = time.time() - start
print(f'Loading reviews cost: {cost:.4f} Sec')
start = time.time()
documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(reviews)]
# print(document)
model = Doc2Vec(documents, vector_size=20, window=2, min_count=5, epochs=10)
model.save_word2vec_format('./data/d2v.txt')
model.save('./data/d2v.model')
cost = time.time() - start
print(f'Training model cost: {cost:.4f} Sec')
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from utils import load_reviews, data_suffle
# stopwordPath = './data/stopword.txt'
# userDictPath = './data/user_dict.txt'
csvFilePath = '../../corpus/100k/allTrimed.csv'
modelPath = './data/bayes.model'
# 载入自定义字典
# jieba.load_userdict(userDictPath)
time_start = time.time()
labels, reviews = load_reviews(csvFilePath)
labels, reviews = data_suffle(labels, reviews)
# 1/4 分割数据集
n = len(labels) // 5
labels_train, reviews_train = labels[n:], reviews[n:]
labels_test, reviews_test = labels[:n], reviews[:n]
print(f'Load Corpus Cost {time.time() - time_start:.4f} Sec')
print(reviews[:5], type(reviews), type(reviews[0]))
time_start = time.time()
vectorizer = CountVectorizer()
vec_train = vectorizer.fit_transform(
[np.str_(review) for review in reviews_train])
# initalize Mecab tagger
tagger = Mecab()
# initalize regular expression
exp = re.compile(POS, re.IGNORECASE)
# load sentiment dictionary
bag = utils.load_dictionary()
# load model if exist
try:
with open("../Resources/models/model", "rb") as model_file:
model = pickle.load(model_file)
except IOError as err:
# load training reviews from file
train_review = utils.load_reviews("../Resources/samples/train_data")
# get feature from train data
train_data, train_label = feature_data(tagger, exp, bag, train_review)
# initalize classifer class
model = SVM()
# train model
model.train(train_data, train_label)
#save model
with open("../Resources/models/model", "wb") as model_file:
pickle.dump(model, model_file)
else:
print("use saved model..")
# load test reviews from file
test_review = utils.load_reviews("../Resources/samples/test_data")
# get feature from test data
# initalize Mecab tagger
tagger = Mecab()
# initalize regular expression
exp = re.compile(POS, re.IGNORECASE)
# load sentiment dictionary
bag = utils.load_dictionary()
# load model if exist
try:
with open("../Resources/models/model", "rb") as model_file:
model = pickle.load(model_file)
except IOError as err:
# load training reviews from file
train_review = utils.load_reviews("../Resources/samples/train_data")
# get feature from train data
train_data, train_label = feature_data(tagger, exp, bag, train_review)
# initalize classifer class
model = SVM()
# train model
model.train(train_data, train_label)
#save model
with open("../Resources/models/model", "wb") as model_file:
pickle.dump(model, model_file)
else:
print("use saved model..")
# load test reviews from file
test_review = utils.load_reviews("../Resources/samples/test_data")
# get feature from test data
# initalize Mecab tagger
tagger = Mecab()
# initalize regular expression
exp = re.compile(POS, re.IGNORECASE)
# load sentiment dictionary
bag = utils.load_dictionary()
# load model if exist
try:
with open("./models/model", "rb") as model_file:
model = pickle.load(model_file)
except IOError as err:
# load training reviews from file
train_review = utils.load_reviews("./samples/train_data")
# get feature from train data
train_data, train_label = feature_data(tagger, exp, bag, train_review)
# initalize classifer class
model = SVM()
# train model
model.train(train_data, train_label)
#save model
with open("./models/model", "wb") as model_file:
pickle.dump(model, model_file)
else:
print("use saved model..")
# load test reviews from file
#test_review = utils.load_reviews("./samples/test_data")
# get feature from test data
