def main():
usage = "%prog project documents.json"
parser = OptionParser(usage=usage)
parser.add_option('-a', dest='alpha', default=0.00001,
help='Regularization strength: default=%default')
parser.add_option('-d', dest='hidden_dim', default=50,
help='Hidden node dimension: default=%default')
parser.add_option('-e', dest='epochs', default=10,
help='Number of epochs: default=%default')
parser.add_option('-i', dest='iter_display', default=5000,
help='Number of iterations between output: default=%default')
parser.add_option('-o', dest='optimization', default='sgd',
help='Optimization method [sgd|sgdm|adagrad]: default=%default')
parser.add_option('-l', dest='learning_rate', default=0.1,
help='Initial learning rate: default=%default')
parser.add_option('--decay', dest='decay', default=1.00,
help='Learning rate decay: default=%default')
parser.add_option('--momentum', dest='momentum', default=0.5,
help='Momentum parameter (sgdm only): default=%default')
parser.add_option('--word2vec_file', dest='word2vec_file', default='',
help='Location of word2vec file: default=do not load')
parser.add_option('--glove_file', dest='glove_file', default='',
help='Location of glove file: default=do not load')
parser.add_option('--save_vectors', action="store_true", dest="save_vectors", default=False,
help='Save loaded vectors for faster loading next time: default=%default')
parser.add_option('-s', dest='seed', default=42,
help='Random seed: default=%default')
parser.add_option('--no_eval', action="store_true", dest="no_eval", default=False,
help='Skip the evaluation between epochs: default=%default')
parser.add_option('--test_fold', dest='test_fold', default=0,
help='Test fold: default=%default')
parser.add_option('--dev_fold', dest='dev_fold', default=0,
help='Dev fold: default=%default')
parser.add_option('--n_labels', dest='n_labels', default=14,
help='Number of labels to use (max 15): default=%default')
parser.add_option('--w_word', dest='w_word', default=1.0,
help='Weight on word prediction: default=%default')
parser.add_option('--w_sentence', dest='w_sentence', default=1.0,
help='Weight on word prediction: default=%default')
parser.add_option('--w_article', dest='w_article', default=1.0,
help='Weight on word prediction: default=%default')
(options, args) = parser.parse_args()
project_name = args[0]
input_filename = args[1]
dirs.make_base_dir(project_name)
sents_dir = dirs.data_raw_sentences_dir
seed = int(options.seed)
n_epochs = int(options.epochs)
alpha = float(options.alpha)
lr = float(options.learning_rate)
iter_display = int(options.iter_display)
opti_method = options.optimization
lr_decay = float(options.decay)
momentum = float(options.momentum)
no_eval = options.no_eval
word2vec_file = options.word2vec_file
glove_file = options.glove_file
save_vectors = options.save_vectors
test_fold = int(options.test_fold)
dev_fold = int(options.dev_fold)
n_labels = int(options.n_labels)
w_word = float(options.w_word)
w_sentence = float(options.w_sentence)
w_article = float(options.w_article)
if seed > 0:
np.random.seed(seed)
random.seed(seed)
dh = int(options.hidden_dim)
dx = 300
np.__config__.show()
article_sent_words, article_word_labels, vocab, n_labels, n_unique_articles, annotation_counts = load_data(input_filename, n_labels)
train_keys, dev_keys, test_keys = ds.get_all_splits(test_fold=test_fold, dev_subfold=dev_fold)
vocab = vocab.keys()
vocab.sort()
vocab_size = len(vocab)
vocab_index = dict(zip(vocab, range(vocab_size)))
print "Vocab size =", vocab_size
n_articles = len(article_sent_words)
keys = article_sent_words.keys()
keys.sort()
print keys[:10]
print "Loaded %d annotations for %d articles using %d labels" % (n_articles, n_unique_articles, n_labels)
print list(train_keys)[:10]
train_keys = [k for k in keys if k.split('__')[0] in train_keys]
dev_keys = [k for k in keys if k.split('__')[0] in dev_keys]
test_keys = [k for k in keys if k.split('__')[0] in test_keys]
#dev_indices = np.random.choice(n_articles, n_dev, replace=False).tolist()
#train_indices = list(set(range(n_articles)) - set(dev_indices))
#train_keys = [keys[i] for i in train_indices]
#dev_keys = [keys[i] for i in dev_indices]
if glove_file != '':
initial_embeddings = vector_utils.load_glove_vectors(glove_file, vocab, dx)
elif word2vec_file != '':
initial_embeddings = vector_utils.load_word2vec_vectors(word2vec_file, vocab, dx)
else:
initial_embeddings, vocab, vocab_index = vector_utils.load_from_file(input_filename)
vocab_size = len(vocab)
if save_vectors:
vector_utils.save_vectors(input_filename, initial_embeddings, vocab)
# index words into vocabulary and make mask and label arrays
idxs_dict = {}
mask_dict = {}
label_dict = {}
for key, sent_words in article_sent_words.items():
n_sents = len(sent_words)
max_len = max([len(s) for s in sent_words])
word_idxs = np.zeros([max_len, n_sents], dtype=np.int32)
mask = np.zeros([max_len, n_sents], dtype=np.int32)
labels = np.zeros([max_len, n_sents, n_labels], dtype=np.int32)
for s_i, s in enumerate(sent_words):
n_words = len(s)
word_idxs[:n_words, s_i] = [vocab_index[w] for w in s]
mask[:n_words, s_i] = 1
labels[:n_words, s_i, :] = article_word_labels[key][s_i][:, :]
idxs_dict[key] = word_idxs
mask_dict[key] = mask
label_dict[key] = labels
article_lengths = [(idxs_dict[k].size, k) for k in train_keys]
article_lengths.sort()
# create the LSTM
theano_seed = np.random.randint(2 ** 30)
print "Number of distributions =", 2
print "Building RNN"
optimizer, opti_params = get_optimizer(opti_method, momentum)
bilstm = BiLSTM(vocab_size, dh, dx, n_labels, optimizer, opti_params, initial_embeddings=initial_embeddings,
alpha=alpha, update=opti_method, seed=theano_seed, momentum=momentum,
word_weight=w_word, sent_weight=w_sentence, article_weight=w_article) # create RNN
best_dev_f1 = np.zeros(n_labels)
corr_test_f1 = np.zeros(n_labels)
print "Training"
for epoch in range(n_epochs):
sum_log_loss = 0
sum_loss = 0
mistakes = 0
# sort by keys on the first pass, then shuffle
if epoch == 0:
keys = [key for length, key in article_lengths]
else:
keys = train_keys
random.shuffle(keys)
print "epoch\titems\tloss\tl+reg\terrs"
# consider each sentence in turn
for k_i, k in enumerate(keys):
idxs = idxs_dict[k]
mask = mask_dict[k]
word_labels = label_dict[k]
p_word_labels, p_sent_labels, p_article_labels, log_loss, loss = bilstm.train(idxs, mask, word_labels, lr, 1)
sum_log_loss += log_loss
sum_loss += loss
y_pred_words = np.array(p_word_labels > 0.5, dtype=int) # (n_words, n_sents, n_labels)
y_pred_sents = np.array(p_sent_labels > 0.5, dtype=int)
y_pred_article = np.array(p_article_labels > 0.5, dtype=int)
sent_labels = np.max(word_labels, axis=0)
article_labels = np.max(sent_labels, axis=0)
mistakes += np.sum(np.abs(article_labels - y_pred_article))/float(n_labels)
to_print = False
if k_i == 0 and to_print:
print "\tTraining example:", k
print article_labels
print np.array(y_pred_article, dtype=int)
max_len, n_sents = mask.shape
for s_i in range(n_sents):
if np.max(y_pred_words[:, s_i, :]) == 1:
n_words = np.argmin(mask[:, s_i]) - 1
sentence = [vocab[c] for c in idxs[:n_words, s_i]]
print "Full:", k_i, ' '.join(sentence)
for code in range(n_labels):
if y_pred_sents[s_i, code] == 1:
highlight = [w if word_labels[w_i, s_i, code] else ' ' * len(w) for w_i, w in enumerate(sentence)]
print '-------------------------------------'
print "True:", k_i, code, ' '.join(highlight)
highlight = [w if y_pred_words[w_i, s_i, code] else ' ' * len(w) for w_i, w in enumerate(sentence)]
#highlight = [vocab[c][1:2] if (p_y_given_x[c_i, code] > 0.5 or vocab[c][1:2] == '\n') else ' ' for c_i, c in enumerate(idxs)]
print '-------------------------------------'
print "Pred:", k_i, code, ' '.join(highlight)
print ""
if k_i % iter_display == 0 and k_i > 0:
d = float(k_i+1)
print '%d\t%d\t%.4f\t%.4f\t%.4f' % \
(epoch, k_i, sum_log_loss/d, sum_loss/d, mistakes/d)
if not no_eval:
print "\nDev evaluation"
valid_z_o_loss, valid_log_loss, valid_f1, valid_per_class_f1 = evaluate(idxs_dict, mask_dict, label_dict, dev_keys, bilstm, vocab, annotation_counts)
print "\nTest evaluation"
test_z_o_loss, test_log_loss, test_f1, test_per_class_f1 = evaluate(idxs_dict, mask_dict, label_dict, test_keys, bilstm, vocab, annotation_counts)
print ('epoch=%d\tdev_log_loss=%.3f\tdev_0/1=%.3f\tdev_f1=%.3f\ttest_log_loss=%.3f\ttest_0/1=%.3f\ttest_f1=%.3f\t') % (epoch, valid_log_loss, valid_z_o_loss, valid_f1, test_log_loss, test_z_o_loss, test_f1)
for k in range(n_labels):
if valid_per_class_f1[k] > best_dev_f1[k]:
best_dev_f1[k] = valid_per_class_f1[k]
corr_test_f1[k] = test_per_class_f1[k]
print "Best valid f1s:", best_dev_f1
print "Corr. test f1s:", corr_test_f1
# decay learning rate
lr *= lr_decay
def main():
usage = "%prog project documents.json"
parser = OptionParser(usage=usage)
parser.add_option('-a', dest='alpha', default=0.00001,
help='Regularization strength: default=%default')
parser.add_option('-g', dest='gamma', default=0.5,
help='Gamma (proportional weight on words): default=%default')
parser.add_option('-d', dest='hidden_dim', default=50,
help='Hidden node dimension: default=%default')
parser.add_option('-e', dest='epochs', default=20,
help='Number of epochs: default=%default')
parser.add_option('-i', dest='iter_display', default=5000,
help='Number of iterations between output: default=%default')
parser.add_option('-o', dest='optimization', default='adagrad',
help='Optimization method [sgd|sgdm|adagrad]: default=%default')
parser.add_option('-l', dest='learning_rate', default=0.05,
help='Initial learning rate: default=%default')
parser.add_option('--decay', dest='decay', default=1.00,
help='Learning rate decay: default=%default')
parser.add_option('--momentum', dest='momentum', default=0.5,
help='Momentum parameter (sgdm only): default=%default')
parser.add_option('--word2vec_file', dest='word2vec_file', default='',
help='Location of word2vec file: default=do not load')
parser.add_option('--glove_file', dest='glove_file', default='',
help='Location of glove file: default=do not load')
parser.add_option('--save_vectors', action="store_true", dest="save_vectors", default=False,
help='Save loaded vectors for faster loading next time: default=%default')
parser.add_option('-s', dest='seed', default=42,
help='Random seed: default=%default')
parser.add_option('--no_eval', action="store_true", dest="no_eval", default=False,
help='Skip the evaluation between epochs: default=%default')
parser.add_option('--n_dev', dest='n_dev', default=500,
help='Number of random dev sentences: default=%default')
(options, args) = parser.parse_args()
project_name = args[0]
input_filename = args[1]
dirs.make_base_dir(project_name)
sents_dir = dirs.data_raw_sentences_dir
seed = int(options.seed)
n_epochs = int(options.epochs)
alpha = float(options.alpha)
gamma = float(options.gamma)
lr = float(options.learning_rate)
iter_display = int(options.iter_display)
opti_method = options.optimization
lr_decay = float(options.decay)
momentum = float(options.momentum)
no_eval = options.no_eval
n_dev = int(options.n_dev)
word2vec_file = options.word2vec_file
glove_file = options.glove_file
save_vectors = options.save_vectors
if seed > 0:
np.random.seed(seed)
random.seed(seed)
dh = int(options.hidden_dim)
dx = 300
nd = 2
np.__config__.show()
sents, word_labels, vocab, n_labels = load_data(input_filename)
vocab = vocab.keys()
vocab.sort()
vocab_size = len(vocab)
vocab_index = dict(zip(vocab, range(vocab_size)))
print "Vocab size =", vocab_size
n_articles = len(sents)
print "Loaded", n_articles, "annotated sentences"
dev_indices = np.random.choice(n_articles, n_dev, replace=False).tolist()
train_indices = list(set(range(n_articles)) - set(dev_indices))
n_train = len(train_indices)
n_dev = len(dev_indices)
if glove_file != '':
initial_embeddings = vector_utils.load_glove_vectors(glove_file, vocab, dx)
elif word2vec_file != '':
initial_embeddings = vector_utils.load_word2vec_vectors(word2vec_file, vocab, dx)
else:
initial_embeddings, vocab, vocab_index = vector_utils.load_from_file(input_filename)
vocab_size = len(vocab)
if save_vectors:
vector_utils.save_vectors(input_filename, initial_embeddings, vocab)
idxs_dict = {}
for k in range(n_articles):
words = sents[k]
idxs_dict[k] = np.array([vocab_index[w] for w_i, w in enumerate(words)], dtype=np.int32)
sent_lengths = [(len(idxs_dict[k]), k) for k in train_indices]
sent_lengths.sort()
# create the LSTM
theano_seed = np.random.randint(2 ** 30)
print "Number of distributions =", 2
print "Building RNN"
bilstm = BiLSTM(vocab_size, dh, dx, n_labels, initial_embeddings=initial_embeddings, alpha=alpha,
update=opti_method, seed=theano_seed, momentum=momentum, gamma=gamma) # create RNN
print "Training"
for epoch in range(n_epochs):
sum_log_loss = 0
sum_loss = 0
mistakes = 0
# sort by keys on the first pass, then shuffle
if epoch == 0:
keys = [key for length, key in sent_lengths]
else:
keys = train_indices
random.shuffle(keys)
print "epoch\titems\tloss\tl+reg\terrs"
# consider each sentence in turn
for k_i, k in enumerate(keys):
text = sents[k]
idxs = idxs_dict[k]
codes = word_labels[k]
y_pred, y_pred_max, log_loss, loss = bilstm.train(np.array(idxs, dtype=np.int32), codes, lr, 1)
sum_log_loss += log_loss
sum_loss += loss
codes_max = np.max(codes, axis=0)
mistakes += np.sum(np.abs(codes_max - y_pred_max))/float(len(codes_max))
if k_i == -1:
print "Training example:"
sentence = [vocab[c] for c in idxs]
print k_i, ' '.join(sentence)
print codes_max
print y_pred_max
for code in range(len(y_pred_max)):
if y_pred_max[code] == 1:
highlight = [w if codes[w_i, code] else ' ' * len(w) for w_i, w in enumerate(sentence)]
print '-------------------------------------'
print "True:", k_i, code, ' '.join(highlight)
highlight = [w if y_pred[w_i, code] else ' ' * len(w) for w_i, w in enumerate(sentence)]
#highlight = [vocab[c][1:2] if (p_y_given_x[c_i, code] > 0.5 or vocab[c][1:2] == '\n') else ' ' for c_i, c in enumerate(idxs)]
print '-------------------------------------'
print "Pred:", k_i, code, ' '.join(highlight)
print ""
print np.abs(codes_max - y_pred_max)/float(len(codes_max))
if k_i % iter_display == 0 and k_i > 0:
d = float(k_i+1)
print '%d\t%d\t%.4f\t%.4f\t%.4f' % \
(epoch, k_i, sum_log_loss/d, sum_loss/d, mistakes/d)
if not no_eval:
valid_z_o_loss, valid_log_loss, valid_f1 = evaluate(idxs_dict, word_labels, dev_indices, bilstm, vocab)
test_z_o_loss = 0
print ('epoch=%d\tdev_log_loss=%.3f\tdev_0/1=%.3f\tvalid_f1=%.3f') % (epoch, valid_log_loss, valid_z_o_loss, valid_f1)
# decay learning rate
lr *= lr_decay
