def load_data(file_dir):
file_names = os.listdir(file_dir)
for file_name in file_names:
text = ' '.join(
codecs.open(os.path.join(file_dir, file_name), 'r',
'utf-8').readlines())
for word in clean_str(text).split():
vocab[word] += 1
def load_data(file_dir):
file_names = os.listdir(file_dir)
data = []
length = len(file_names)
for file_name in file_names:
text = ' '.join(codecs.open(os.path.join(file_dir, file_name), 'r', 'utf-8').readlines())
data.append(clean_str(text))
idx_data = make_idx_data(data, word_idx_map, max_len=200, kernel_size=5)
return idx_data, length
def cnn(text=None):
request_text = text
# Test
[idx_data, ratings] = load_pickle('./data/corpus/vader/vader_processed_data_tweets.p')
# print(idx_data[2])
# print(ratings[2])
W = load_pickle('./data/corpus/vader/embedding_matrix_tweets.p')
# print(len(W[1]))
if request_text is None:
request_text = 'why you are not happy'
request_text = clean_str(request_text)
# print(request_text)
word_idx_map = load_pickle('./data/corpus/vader/word_idx_map_tweets.p')
idx_request_text = get_idx_from_sent(request_text, word_idx_map)
# print(idx_request_text) # type: list
max_len = len(idx_request_text)
idx_request_text = np.array(idx_request_text).reshape((1,max_len))
# print(idx_request_text.shape)
def cnn_model():
N_fm = 100 # number of filters
kernel_size = 5
conv_input_height, conv_input_width = max_len, len(W[1])
model = Sequential()
model.add(Embedding(input_dim=W.shape[0], output_dim=W.shape[1], weights=[W], W_constraint=unitnorm()))
model.add(Reshape(dims=(1, conv_input_height, conv_input_width)))
model.add(Convolution2D(nb_filter=N_fm,
nb_row=kernel_size,
nb_col=conv_input_width,
border_mode='valid',
W_regularizer=l2(0.0001)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(conv_input_height - kernel_size + 1, 1), ignore_border=True))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('linear'))
sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mse', optimizer='adagrad')
return model
model = cnn_model()
model.load_weights('./data/corpus/vader/cnn_model_weights.hdf5')
predict_value = model.predict(idx_request_text)
return [predict_value[0], 5.0]
def load_data(file_dir):
file_names = os.listdir(file_dir)
data = []
length = len(file_names)
for file_name in file_names:
text = ' '.join(
codecs.open(os.path.join(file_dir, file_name), 'r',
'utf-8').readlines())
data.append(clean_str(text))
idx_data = make_idx_data(data,
word_idx_map,
max_len=200,
kernel_size=5)
return idx_data, length
def screen_data(corpus, ratings, words):
processed_texts, processed_ratings = [], []
size = len(corpus)
# sums = 0
for i, sentence in enumerate(corpus):
sentence = clean_str(sentence)
same_words = list(set(sentence.split()).intersection(set(words)))
nb_same_words = len(same_words)
if nb_same_words >= 1:
# max_freq = max([sentence.split().count(w) for w in same_words])
# if max_freq >= 6:
# sums = sums + 1
# print('*'*100)
# print(sentence, str(same_words),max_freq, sums)
processed_texts.append(sentence)
processed_ratings.append(ratings[i])
if i % 500 == 0:
print(sentence)
print('the %i/%i is processing: %s' % (i, size, str(same_words)))
print('size of corpus is %i' % len(processed_texts))
# print(sums)
return processed_texts, processed_ratings
def process(corpus):
return [clean_str(sent) for sent in corpus]
def process(corpus):
return [clean_str(sent) for sent in corpus]
def load_data(file_dir):
file_names = os.listdir(file_dir)
for file_name in file_names:
text = ' '.join(codecs.open(os.path.join(file_dir, file_name), 'r', 'utf-8').readlines())
for word in clean_str(text).split():
vocab[word] += 1
