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sentiment_classification.py
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sentiment_classification.py
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from load_data import load_sst
import numpy as np
from collections import defaultdict
from load_data import load_embeddings
from save_data import dump_picle
import os
from load_data import load_pickle
def clean_str(sent):
sent = sent.strip().replace('.', '').replace(',', '')
sent = sent.replace(';', '').replace('<br />', ' ')
sent = sent.replace(':', '').replace('"', '')
sent = sent.replace('(', '').replace(')', '')
sent = sent.replace('!', '').replace('*', '')
sent = sent.replace(' - ', ' ').replace(' -- ', '')
sent = sent.replace('?', '')
sent = sent.lower()
return ' '.join(sent.split())
def get_vocab(corpus):
vocab = defaultdict(int)
for sent in corpus:
for word in clean_str(sent).split():
vocab[word] += 1
print('The total number of vocabulary is: %s. ' % len(vocab))
return vocab
def add_unknown_words(word_vecs, vocab, min_df=3, k=300):
"""
For words that occur in at least min_df documents, create a separate word vector.
0.25 is chosen so the unknown vectors have (approximately) same variance as pre-trained ones
"""
for word in vocab:
if word not in word_vecs and vocab[word] >= min_df:
word_vecs[word] = np.random.uniform(-0.25,0.25,k)
return word_vecs
# word_vecs is the model of word2vec
def build_embedding_matrix(word_vecs, vocab, k=300):
"""
Get word matrix. W[i] is the vector for word indexed by i
"""
union = (set(word_vecs.keys()) & set(vocab.keys()))
vocab_size = len(union)
print('The number of words occuring in corpus and word2vec simutaneously: %s.' % vocab_size)
word_idx_map = dict()
W = np.zeros(shape=(vocab_size + 1, k))
W[0] = np.zeros(k, dtype=np.float32)
for i, word in enumerate(union, start=1):
print(word, i)
W[i] = word_vecs[word]
word_idx_map[word] = i # dict
return W, word_idx_map
def sent2ind(sent, word_idx_map):
"""
Transforms sentence into a list of indices.
"""
x = []
words = sent.split()
for word in words:
if word in word_idx_map:
x.append(word_idx_map[word])
return x
def make_idx_data(sentences, word_idx_map):
"""
Transforms sentences (corpus, a list of sentence) into a 2-d matrix.
"""
idx_data = []
for sent in sentences:
idx_sent = sent2ind(clean_str(sent), word_idx_map)
idx_data.append(idx_sent)
# idx_data = np.array(idx_data, dtype=np.int)
return idx_data
def build_keras_input():
filename_data, filename_w = './tmp/indexed_data.p', './tmp/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')
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 nn_input(word_vectors_model = 'word2vec', amending = False):
###################################### Hyper-parameters #######################################
word_vectors_model = 'GloVe' # values: 'word2vec', 'GloVe'
amending = True # values: True, False
###############################################################################################
data = keras_nn_input(word_vectors_model, amending)
return data
if __name__ == '__main__':
# ((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), W) = build_keras_input()
# build_keras_input_amended() # using amended word2vec
nn_input()