def compare(dataset, model_name, pre_model_name):
# build model
if(os.path.isfile(model_name)):
model = Word2Vec.load(model_name)
logger.debug("model %s already exist, stop training wordvector", model_name)
else:
logger.info("start trainning word vector")
start_time = timeit.default_timer()
model = wordvector.build_word_vector(dataset, save=True, save_file=model_name)
logger.info("model %s trained in %.4lfs", model_name, timeit.default_timer() - start_time)
# find most similar words:
for word in keywords:
print word
print model.most_similar(word, topn=10);
# load pre-trained google news model
logger.info("start loading pre-trained dataset")
start_time = timeit.default_timer()
pre_model = Word2Vec.load_word2vec_format(pre_model_name, binary=True)
logger.info("pre-trained dataset loaded in %.4lfs", timeit.default_timer() - start_time)
# find most similar words:
for word in keywords:
print word
print pre_model.most_similar(word, topn=10);
def main(train_dir, test_dir):
# these may be function parameters
w2v_option = Word2VecOption(num_features=300, min_word_count=40, \
num_workers=4, context=10, downsampling=1e-3)
csv_option = CsvOption(deli=",", title=["review", "sentiment"], \
chunksize=100, review_name="review", sentiment_name="sentiment")
process_option = ProcessOption(rm_html=True, rm_punc=True, rm_num=True, \
lower_case=True, rm_stop_words=False)
model_name = "model.bin"
save_model = True
save_fv = True
train_fv_name = "train_fv.bin"
test_fv_name = "test_fv.bin"
build_option = 2
save_classifier = True
classifier_name = "classifier/classifier.bin"
to_normalize = True
to_scale = False
# logger info
build_method = "average word vector"
if build_option == 2:
build_method = "average word vector with tf-idf"
elif build_option == 3:
build_method = "cluster word vector"
logger.debug("text process option: %s", str(process_option))
logger.debug("use %s to build doc vector", build_method)
train_sentences = Sentences(train_dir, csv_option, process_option)
logger.info("number of docs: %d", train_sentences.doc_num)
# train word2vec
if (os.path.isfile(model_name)):
model = Word2Vec.load(model_name)
logger.debug("model %s already exist, stop training wordvector",
model_name)
else:
logger.info("start trainning word vector")
start_time = timeit.default_timer()
model = wordvector.build_word_vector(train_sentences,
w2v_option,
save=True,
save_file=model_name)
logger.info("model %s trained in %.4lfs", model_name,
timeit.default_timer() - start_time)
# get doc vector
logger.info("start building training set doc vector")
start_time = timeit.default_timer()
train_fv = wordvector.build_doc_vector(
train_dir,
model,
build_option,
process_option,
save_fv,
train_fv_name,
)
print train_fv
logger.info("training set doc vector built in %.4lfs",
timeit.default_timer() - start_time)
logger.info("training set doc vector saved to %s", train_fv_name)
logger.debug("training size: %s", str(train_fv.shape))
# train classifier
logger.info("start training classifier")
start_time = timeit.default_timer()
forest = grid_search.GridSearchCV(RandomForestClassifier(), {
'n_estimators': [100],
'n_jobs': [100]
},
cv=5,
scoring='f1_weighted',
n_jobs=100)
best_model = forest.fit(train_fv,
list(train_sentences.sentiment_iterator()))
logger.info("finished training classifier in %.4lfs",
timeit.default_timer() - start_time)
if save_classifier:
joblib.dump(best_model, classifier_name)
# evaluate on test set
logger.info("start building test set doc vector")
start_time = timeit.default_timer()
test_sentences = Sentences(test_dir, csv_option, process_option)
test_fv = wordvector.build_doc_vector(test_dir, model, build_option,
process_option, save_fv,
test_fv_name)
print test_fv
logger.info("test set doc vector built in %.4lfs",
timeit.default_timer() - start_time)
logger.info("test set doc vector saved to %s", test_fv_name)
logger.debug("test size: %s", str(test_fv.shape))
logger.info("start predicting test set sentiment")
start_time = timeit.default_timer()
predicted_sentiment = best_model.predict(test_fv)
logger.info("finished prediction in %.4lfs",
timeit.default_timer() - start_time)
accuracy = np.mean(
predicted_sentiment == list(test_sentences.sentiment_iterator()))
print "Test Set Accuracy = ", accuracy
print metrics.classification_report(list(test_sentences.sentiment_iterator()), \
predicted_sentiment, target_names=['0', '1', '2', '3'])
chunksize=100, review_name="review", sentiment_name="sentiment")
process_option = ProcessOption(rm_html=True, rm_punc=True, rm_num=True, \
lower_case=True, rm_stop_words=False)
train_sentences = Sentences(dataset, csv_option, process_option)
# build model
if (os.path.isfile(model_name)):
model = Word2Vec.load(model_name)
logger.debug("model %s already exist, stop training wordvector",
model_name)
else:
logger.info("start trainning word vector")
start_time = timeit.default_timer()
model = wordvector.build_word_vector(train_sentences,
w2v_option,
save=True,
save_file=model_name)
logger.info("model %s trained in %.4lfs", model_name,
timeit.default_timer() - start_time)
# find most similar words:
for word in keywords:
print word
print model.most_similar(word, topn=10)
# load pre-trained google news model
logger.info("start loading pre-trained dataset")
start_time = timeit.default_timer()
pre_model = Word2Vec.load_word2vec_format(pre_model_name, binary=True)
logger.info("pre-trained dataset loaded in %.4lfs",
timeit.default_timer() - start_time)
def main(train_dir, test_dir):
# these may be function parameters
w2v_option = Word2VecOption(num_features=300, min_word_count=40, \
num_workers=4, context=10, downsampling=1e-3)
csv_option = CsvOption(deli=",", title=["review", "sentiment"], \
chunksize=100, review_name="review", sentiment_name="sentiment")
process_option = ProcessOption(rm_html=True, rm_punc=True, rm_num=True, \
lower_case=True, rm_stop_words=False)
model_name = "model.bin"
save_model = True
save_fv = True
train_fv_name = "train_fv.bin"
test_fv_name = "test_fv.bin"
build_option = 2
save_classifier = True
classifier_name = "classifier/classifier.bin"
to_normalize = True
to_scale = False
# logger info
build_method = "average word vector"
if build_option == 2:
build_method = "average word vector with tf-idf"
elif build_option == 3:
build_method = "cluster word vector"
logger.debug("text process option: %s", str(process_option))
logger.debug("use %s to build doc vector", build_method)
train_sentences = Sentences(train_dir, csv_option, process_option)
logger.info("number of docs: %d", train_sentences.doc_num)
# train word2vec
if(os.path.isfile(model_name)):
model = Word2Vec.load(model_name)
logger.debug("model %s already exist, stop training wordvector", model_name)
else:
logger.info("start trainning word vector")
start_time = timeit.default_timer()
model = wordvector.build_word_vector(train_sentences, w2v_option, save=True, save_file=model_name)
logger.info("model %s trained in %.4lfs", model_name, timeit.default_timer() - start_time)
# get doc vector
logger.info("start building training set doc vector")
start_time = timeit.default_timer()
train_fv = wordvector.build_doc_vector(train_dir, model, build_option, process_option, save_fv, train_fv_name, )
print train_fv
logger.info("training set doc vector built in %.4lfs", timeit.default_timer() - start_time)
logger.info("training set doc vector saved to %s", train_fv_name)
logger.debug("training size: %s", str(train_fv.shape))
# train classifier
logger.info("start training classifier")
start_time = timeit.default_timer()
forest = grid_search.GridSearchCV(RandomForestClassifier(), {'n_estimators':[100], 'n_jobs':[100]}, cv=5, scoring = 'f1_weighted', n_jobs=100)
best_model = forest.fit(train_fv, list(train_sentences.sentiment_iterator()))
logger.info("finished training classifier in %.4lfs", timeit.default_timer() - start_time)
if save_classifier:
joblib.dump(best_model, classifier_name)
# evaluate on test set
logger.info("start building test set doc vector")
start_time = timeit.default_timer()
test_sentences = Sentences(test_dir, csv_option, process_option)
test_fv = wordvector.build_doc_vector(test_dir, model, build_option, process_option, save_fv, test_fv_name)
print test_fv
logger.info("test set doc vector built in %.4lfs", timeit.default_timer() - start_time)
logger.info("test set doc vector saved to %s", test_fv_name)
logger.debug("test size: %s", str(test_fv.shape))
logger.info("start predicting test set sentiment")
start_time = timeit.default_timer()
predicted_sentiment = best_model.predict(test_fv)
logger.info("finished prediction in %.4lfs", timeit.default_timer() - start_time)
accuracy = np.mean(predicted_sentiment == list(test_sentences.sentiment_iterator()))
print "Test Set Accuracy = ", accuracy
print metrics.classification_report(list(test_sentences.sentiment_iterator()), \
predicted_sentiment, target_names=['0', '1', '2', '3'])
num_workers=4, context=10, downsampling=1e-7)
csv_option = CsvOption(deli=",", title=["review", "sentiment"], \
chunksize=100, review_name="review", sentiment_name="sentiment")
process_option = ProcessOption(rm_html=True, rm_punc=True, rm_num=True, \
lower_case=True, rm_stop_words=False)
train_sentences = Sentences(dataset, csv_option, process_option)
# build model
if(os.path.isfile(model_name)):
model = Word2Vec.load(model_name)
logger.debug("model %s already exist, stop training wordvector", model_name)
else:
logger.info("start trainning word vector")
start_time = timeit.default_timer()
model = wordvector.build_word_vector(train_sentences, w2v_option, save=True, save_file=model_name)
logger.info("model %s trained in %.4lfs", model_name, timeit.default_timer() - start_time)
# find most similar words:
for word in keywords:
print word
print model.most_similar(word, topn=10);
# load pre-trained google news model
logger.info("start loading pre-trained dataset")
start_time = timeit.default_timer()
pre_model = Word2Vec.load_word2vec_format(pre_model_name, binary=True)
logger.info("pre-trained dataset loaded in %.4lfs", timeit.default_timer() - start_time)
# find most similar words:
for word in keywords:
