def build_keras_input_amended():
filename_data, filename_w = './tmp/amended_indexed_data.p', './tmp/amended_Weight.p'
if os.path.isfile(filename_data) and os.path.isfile(filename_w):
data = load_pickle(filename_data)
W = load_pickle(filename_w)
print('Load OK.')
return (data, W)
# load data from pickle
(x_train, y_train_valence, y_train_labels,
x_test, y_test_valence, y_test_labels,
x_valid, y_valid_valence, y_valid_labels,
x_train_polarity, y_train_polarity,
x_test_polarity, y_test_polarity,
x_valid_polarity, y_valid_polarity) = load_sst(path='./resources/stanfordSentimentTreebank/')
vocab = get_vocab(x_train)
# word_vecs = load_embeddings('google_news', '/home/hs/Data/Word_Embeddings/google_news.bin')
# word_vecs = load_embeddings('glove')
# load amended word vectors
word_vecs = load_embeddings('amended_word2vec')
word_vecs = add_unknown_words(word_vecs, vocab)
W, word_idx_map = build_embedding_matrix(word_vecs, vocab)
x_train_idx_data = make_idx_data(x_train, word_idx_map)
x_test_idx_data = make_idx_data(x_test, word_idx_map)
x_valid_idx_data = make_idx_data(x_valid, word_idx_map)
x_train_polarity_idx_data = make_idx_data(x_train_polarity, word_idx_map)
x_test_polarity_idx_data = make_idx_data(x_test_polarity, word_idx_map)
x_valid_polarity_idx_data = make_idx_data(x_valid_polarity, word_idx_map)
data = (x_train_idx_data, y_train_valence, y_train_labels,
x_test_idx_data, y_test_valence, y_test_labels,
x_valid_idx_data, y_valid_valence, y_valid_labels,
x_train_polarity_idx_data, y_train_polarity,
x_test_polarity_idx_data, y_test_polarity,
x_valid_polarity_idx_data, y_valid_polarity)
dump_picle(data, filename_data)
dump_picle(W, filename_w)
return (data, W)
def build_keras_input_amended():
filename_data, filename_w = './tmp/amended_indexed_data.p', './tmp/amended_Weight.p'
if os.path.isfile(filename_data) and os.path.isfile(filename_w):
data = load_pickle(filename_data)
W = load_pickle(filename_w)
print('Load OK.')
return (data, W)
# load data from pickle
(x_train, y_train_valence, y_train_labels,
x_test, y_test_valence, y_test_labels,
x_valid, y_valid_valence, y_valid_labels,
x_train_polarity, y_train_polarity,
x_test_polarity, y_test_polarity,
x_valid_polarity, y_valid_polarity) = load_sst(path='./resources/stanfordSentimentTreebank/')
vocab = get_vocab(x_train)
# word_vecs = load_embeddings('google_news', '/home/hs/Data/Word_Embeddings/google_news.bin')
# word_vecs = load_embeddings('glove')
# load amended word vectors
word_vecs = load_embeddings('amended_word2vec')
word_vecs = add_unknown_words(word_vecs, vocab)
W, word_idx_map = build_embedding_matrix(word_vecs, vocab)
x_train_idx_data = make_idx_data(x_train, word_idx_map)
x_test_idx_data = make_idx_data(x_test, word_idx_map)
x_valid_idx_data = make_idx_data(x_valid, word_idx_map)
x_train_polarity_idx_data = make_idx_data(x_train_polarity, word_idx_map)
x_test_polarity_idx_data = make_idx_data(x_test_polarity, word_idx_map)
x_valid_polarity_idx_data = make_idx_data(x_valid_polarity, word_idx_map)
data = (x_train_idx_data, y_train_valence, y_train_labels,
x_test_idx_data, y_test_valence, y_test_labels,
x_valid_idx_data, y_valid_valence, y_valid_labels,
x_train_polarity_idx_data, y_train_polarity,
x_test_polarity_idx_data, y_test_polarity,
x_valid_polarity_idx_data, y_valid_polarity)
dump_picle(data, filename_data)
dump_picle(W, filename_w)
return (data, W)
def keras_nn_input(word_vectors_model, amending):
if word_vectors_model == 'word2vec':
if amending == True:
filename_data, filename_w = './tmp/amended_w2v_indexed_data.p', './tmp/amended_w2v_Weight.p'
elif amending == False:
filename_data, filename_w = './tmp/w2v_indexed_data.p', './tmp/w2v_Weight.p'
else:
raise Exception('Wrong!')
elif word_vectors_model == 'GloVe':
if amending == True:
filename_data, filename_w = './tmp/amended_GloVe_indexed_data.p', './tmp/amended_GloVe_Weight.p'
elif amending == False:
filename_data, filename_w = './tmp/GloVe_indexed_data.p', './tmp/GloVe_Weight.p'
else:
raise Exception('Wrong!')
else:
raise Exception('Wrong parameter!')
if os.path.isfile(filename_data) and os.path.isfile(filename_w):
data = load_pickle(filename_data)
W = load_pickle(filename_w)
print('Load OK, parameters: word_vectors_model = %s, amending = %s'%(word_vectors_model, amending))
return (data, W)
# load data from pickle
(x_train, y_train_valence, y_train_labels,
x_test, y_test_valence, y_test_labels,
x_valid, y_valid_valence, y_valid_labels,
x_train_polarity, y_train_polarity,
x_test_polarity, y_test_polarity,
x_valid_polarity, y_valid_polarity) = load_sst(path='./resources/stanfordSentimentTreebank/')
vocab = get_vocab(x_train)
if word_vectors_model == 'word2vec':
if amending == True:
word_vecs = load_embeddings('amended_word2vec')
elif amending == False:
word_vecs = load_embeddings('google_news', '/home/hs/Data/Word_Embeddings/google_news.bin')
else:
raise Exception('Wrong!')
elif word_vectors_model == 'GloVe':
if amending == True:
word_vecs = load_embeddings('amended_glove')
elif amending == False:
word_vecs = load_embeddings('glove')
else:
raise Exception('Wrong!')
else:
raise Exception('Wrong parameter!')
word_vecs = add_unknown_words(word_vecs, vocab)
W, word_idx_map = build_embedding_matrix(word_vecs, vocab)
x_train_idx_data = make_idx_data(x_train, word_idx_map)
x_test_idx_data = make_idx_data(x_test, word_idx_map)
x_valid_idx_data = make_idx_data(x_valid, word_idx_map)
x_train_polarity_idx_data = make_idx_data(x_train_polarity, word_idx_map)
x_test_polarity_idx_data = make_idx_data(x_test_polarity, word_idx_map)
x_valid_polarity_idx_data = make_idx_data(x_valid_polarity, word_idx_map)
data = (x_train_idx_data, y_train_valence, y_train_labels,
x_test_idx_data, y_test_valence, y_test_labels,
x_valid_idx_data, y_valid_valence, y_valid_labels,
x_train_polarity_idx_data, y_train_polarity,
x_test_polarity_idx_data, y_test_polarity,
x_valid_polarity_idx_data, y_valid_polarity)
dump_picle(data, filename_data)
dump_picle(W, filename_w)
print('Load OK, parameters: word_vectors_model = %s, amending = %s'%(word_vectors_model, amending))
return (data, W)
def keras_nn_input(word_vectors_model, amending):
if word_vectors_model == 'word2vec':
if amending == True:
filename_data, filename_w = './tmp/amended_w2v_indexed_data.p', './tmp/amended_w2v_Weight.p'
elif amending == False:
filename_data, filename_w = './tmp/w2v_indexed_data.p', './tmp/w2v_Weight.p'
else:
raise Exception('Wrong!')
elif word_vectors_model == 'GloVe':
if amending == True:
filename_data, filename_w = './tmp/amended_GloVe_indexed_data.p', './tmp/amended_GloVe_Weight.p'
elif amending == False:
filename_data, filename_w = './tmp/GloVe_indexed_data.p', './tmp/GloVe_Weight.p'
else:
raise Exception('Wrong!')
elif word_vectors_model == 'retrofitted_GloVe':
filename_data, filename_w = './tmp/retrofitted_GloVe_indexed_data.p', './tmp/retrofitted_GloVe_Weight.p'
elif word_vectors_model == 'retrofitted_word2vec':
filename_data, filename_w = './tmp/retrofitted_word2vec_indexed_data.p', './tmp/retrofitted_word2vec_Weight.p'
else:
raise Exception('Wrong parameter!')
if os.path.isfile(filename_data) and os.path.isfile(filename_w):
data = load_pickle(filename_data)
W = load_pickle(filename_w)
print('Load OK, parameters: word_vectors_model = %s, amending = %s'%(word_vectors_model, amending))
return (data, W)
# load data from pickle
(x_train, y_train_valence, y_train_labels,
x_test, y_test_valence, y_test_labels,
x_valid, y_valid_valence, y_valid_labels,
x_train_polarity, y_train_polarity,
x_test_polarity, y_test_polarity,
x_valid_polarity, y_valid_polarity) = load_sst(path='./resources/stanfordSentimentTreebank/')
vocab = get_vocab(x_train)
if word_vectors_model == 'word2vec':
if amending == True:
word_vecs = load_embeddings('amended_word2vec')
elif amending == False:
word_vecs = load_embeddings('google_news', '/home/hs/Data/Word_Embeddings/google_news.bin')
else:
raise Exception('Wrong!')
elif word_vectors_model == 'GloVe':
if amending == True:
word_vecs = load_embeddings('amended_glove')
elif amending == False:
word_vecs = load_embeddings('glove')
else:
raise Exception('Wrong!')
elif word_vectors_model == 'retrofitted_GloVe':
word_vecs = load_embeddings('zh_tw', 'D:\Word_Embeddings\English\glove.6B\GloVe_out_vec_file.txt')
# convert gensim model to dict type
w2v = dict()
for key in word_vecs.vocab.keys():
w2v[key] = word_vecs[key]
word_vecs = w2v
elif word_vectors_model == 'retrofitted_word2vec':
word_vecs = load_embeddings('zh_tw', 'D:\Word_Embeddings\English\word2vec_out_vec_file.txt')
# convert gensim model to dict type
w2v = dict()
for key in word_vecs.vocab.keys():
w2v[key] = word_vecs[key]
word_vecs = w2v
else:
raise Exception('Wrong parameter!')
word_vecs = add_unknown_words(word_vecs, vocab)
W, word_idx_map = build_embedding_matrix(word_vecs, vocab)
x_train_idx_data = make_idx_data(x_train, word_idx_map)
x_test_idx_data = make_idx_data(x_test, word_idx_map)
x_valid_idx_data = make_idx_data(x_valid, word_idx_map)
x_train_polarity_idx_data = make_idx_data(x_train_polarity, word_idx_map)
x_test_polarity_idx_data = make_idx_data(x_test_polarity, word_idx_map)
x_valid_polarity_idx_data = make_idx_data(x_valid_polarity, word_idx_map)
data = (x_train_idx_data, y_train_valence, y_train_labels,
x_test_idx_data, y_test_valence, y_test_labels,
x_valid_idx_data, y_valid_valence, y_valid_labels,
x_train_polarity_idx_data, y_train_polarity,
x_test_polarity_idx_data, y_test_polarity,
x_valid_polarity_idx_data, y_valid_polarity)
dump_picle(data, filename_data)
dump_picle(W, filename_w)
print('Load OK, parameters: word_vectors_model = %s, amending = %s'%(word_vectors_model, amending))
return (data, W)