def train(self, x, y, window_size, learning_rate):
cwords = contextwin(x, window_size)
words = map(lambda x: numpy.asarray(x).astype('int32'), cwords)
labels = y
self.sentence_train(words, labels, learning_rate)
self.normalize()
def train(self, x, y, window_size, learning_rate):
cwords = contextwin(x, window_size)
words = map(lambda x: numpy.asarray(x).astype('int32'), cwords)
labels = y
self.sentence_train(words, labels, learning_rate)
self.normalize()
def main(param=None):
if not param:
param = {
'lr': 0.0970806646812754,
'verbose': 1,
'decay': True,
# decay on the learning rate if improvement stops
'win': 7,
# number of words in the context window
'nhidden': 200,
# number of hidden units
'seed': 345,
'emb_dimension': 50,
# dimension of word embedding
'nepochs': 100,
# 60 is recommended
'savemodel': False
}
print param
folder = "RelationExtraction"
if not os.path.exists(folder):
os.mkdir(folder)
#load dataset
pickle_file = 'semeval.pkl'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
dic = save['dicts']
del save # hint to help gc free up memory
print('Training set', train_dataset.shape, train_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
# In[5]:
train_dataset = [np.array(x, dtype=np.int32) for x in train_dataset]
train_labels = [np.array(x, dtype=np.int32) for x in train_labels]
x_test = [np.array(x, dtype=np.int32) for x in test_dataset]
y_test = [np.array(x, dtype=np.int32) for x in test_labels]
x_train = train_dataset[0:7200]
y_train = train_labels[0:7200]
x_valid = train_dataset[7201:8000]
y_valid = train_labels[7201:8000]
# In[6]:
#Raw input encoding -''' visualize a few sentences '''
w2idx, labels2idx = dic['words2idx'], dic['labels2idx']
idx2w = dict((v, k) for k, v in w2idx.iteritems())
idx2la = dict((v, k) for k, v in labels2idx.iteritems())
# In[10]:
vocsize = len(idx2w)
nclasses = len(idx2la)
nsentences = len(x_train)
groundtruth_valid = [map(lambda x: idx2la[x], y) for y in y_valid]
words_valid = [map(lambda x: idx2w[x], w) for w in x_valid]
groundtruth_test = [map(lambda x: idx2la[x], y) for y in y_test]
words_test = [map(lambda x: idx2w[x], w) for w in x_test]
# instanciate the model
np.random.seed(param['seed'])
random.seed(param['seed'])
rnn = GRUTheano(word_dim=param['emb_dimension'],
window_context_size=param['win'],
vocab_size=vocsize,
num_labels=nclasses,
hidden_dim=param['nhidden'])
#rnn = RNNSLU_LSTM(hidden_dim=param['nhidden'], num_labels=nclasses, vocab_size=vocsize, word_dim=param['emb_dimension'], window_context_size=param['win'])
# train with early stopping on validation set
best_f1 = -np.inf
param['clr'] = param['lr']
for e in xrange(param['nepochs']):
# shuffle
shuffle([x_train, y_train], param['seed'])
param['ce'] = e
tic = timeit.default_timer()
for i, (x, y) in enumerate(zip(x_train, y_train)):
rnn.train(x, y, param['win'], param['clr'])
print '[learning] epoch %i >> %2.2f%%' % (e, (i + 1) * 100. /
nsentences),
print 'completed in %.2f (sec) <<\r' % (timeit.default_timer() -
tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [
map(
lambda x: idx2la[x],
rnn.classify(
np.asarray(contextwin(x, param['win'])).astype('int32')))
for x in x_test
]
predictions_valid = [
map(
lambda x: idx2la[x],
rnn.classify(
np.asarray(contextwin(x, param['win'])).astype('int32')))
for x in x_valid
]
# evaluation // compute the accuracy using conlleval.pl
res_test = conlleval(predictions_test, groundtruth_test, words_test,
folder + '/current.test.txt', folder)
res_valid = conlleval(predictions_valid, groundtruth_valid,
words_valid, folder + '/current.valid.txt',
folder)
if res_valid['f1'] > best_f1:
if param['savemodel']:
rnn.save(folder)
best_rnn = copy.deepcopy(rnn)
best_f1 = res_valid['f1']
if param['verbose']:
print('NEW BEST: epoch', e, 'valid F1', res_valid['f1'],
'best test F1', res_test['f1'])
param['vf1'], param['tf1'] = res_valid['f1'], res_test['f1']
param['vp'], param['tp'] = res_valid['p'], res_test['p']
param['vr'], param['tr'] = res_valid['r'], res_test['r']
param['be'] = e
subprocess.call([
'mv', folder + '/current.test.txt', folder + '/best.test.txt'
])
subprocess.call([
'mv', folder + '/current.valid.txt', folder + '/best.valid.txt'
])
else:
if param['verbose']:
print ''
# learning rate decay if no improvement in 10 epochs
if param['decay'] and abs(param['be'] - param['ce']) >= 10:
param['clr'] *= 0.5
rnn = best_rnn
if param['clr'] < 1e-5:
break
print('BEST RESULT: epoch', param['be'], 'valid F1', param['vf1'],
'best test F1', param['tf1'], 'with the model', folder)
def main(param=None):
if not param:
param = {'lr': 0.0970806646812754,
'verbose': 1,
'decay': True,
# decay on the learning rate if improvement stops
'win': 7,
# number of words in the context window
'nhidden': 200,
# number of hidden units
'seed': 345,
'emb_dimension': 50,
# dimension of word embedding
'nepochs': 100,
# 60 is recommended
'savemodel': False}
print param
folder = "RelationExtraction"
if not os.path.exists(folder):
os.mkdir(folder)
#load dataset
pickle_file = 'semeval.pkl'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
dic=save['dicts']
del save # hint to help gc free up memory
print('Training set', train_dataset.shape, train_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
# In[5]:
train_dataset=[np.array(x,dtype=np.int32) for x in train_dataset]
train_labels=[np.array(x,dtype=np.int32) for x in train_labels]
x_test=[np.array(x,dtype=np.int32) for x in test_dataset]
y_test=[np.array(x,dtype=np.int32) for x in test_labels]
x_train=train_dataset[0:7200]
y_train=train_labels[0:7200]
x_valid=train_dataset[7201:8000]
y_valid=train_labels[7201:8000]
# In[6]:
#Raw input encoding -''' visualize a few sentences '''
w2idx,labels2idx = dic['words2idx'], dic['labels2idx']
idx2w = dict((v,k) for k,v in w2idx.iteritems())
idx2la = dict((v,k) for k,v in labels2idx.iteritems())
# In[10]:
vocsize = len(idx2w)
nclasses = len(idx2la)
nsentences = len(x_train)
groundtruth_valid = [map(lambda x: idx2la[x], y) for y in y_valid]
words_valid = [map(lambda x: idx2w[x], w) for w in x_valid]
groundtruth_test = [map(lambda x: idx2la[x], y) for y in y_test]
words_test = [map(lambda x: idx2w[x], w) for w in x_test]
# instanciate the model
np.random.seed(param['seed'])
random.seed(param['seed'])
rnn = GRUTheano(word_dim=param['emb_dimension'], window_context_size=param['win'], vocab_size=vocsize, num_labels=nclasses, hidden_dim=param['nhidden'])
#rnn = RNNSLU_LSTM(hidden_dim=param['nhidden'], num_labels=nclasses, vocab_size=vocsize, word_dim=param['emb_dimension'], window_context_size=param['win'])
# train with early stopping on validation set
best_f1 = -np.inf
param['clr'] = param['lr']
for e in xrange(param['nepochs']):
# shuffle
shuffle([x_train, y_train], param['seed'])
param['ce'] = e
tic = timeit.default_timer()
for i, (x, y) in enumerate(zip(x_train, y_train)):
rnn.train(x, y, param['win'], param['clr'])
print '[learning] epoch %i >> %2.2f%%' % (
e, (i + 1) * 100. / nsentences),
print 'completed in %.2f (sec) <<\r' % (timeit.default_timer() - tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [map(lambda x: idx2la[x],
rnn.classify(np.asarray(
contextwin(x, param['win'])).astype('int32')))
for x in x_test]
predictions_valid = [map(lambda x: idx2la[x],
rnn.classify(np.asarray(
contextwin(x, param['win'])).astype('int32')))
for x in x_valid]
# evaluation // compute the accuracy using conlleval.pl
res_test = conlleval(predictions_test,
groundtruth_test,
words_test,
folder + '/current.test.txt',
folder)
res_valid = conlleval(predictions_valid,
groundtruth_valid,
words_valid,
folder + '/current.valid.txt',
folder)
if res_valid['f1'] > best_f1:
if param['savemodel']:
rnn.save(folder)
best_rnn = copy.deepcopy(rnn)
best_f1 = res_valid['f1']
if param['verbose']:
print('NEW BEST: epoch', e,
'valid F1', res_valid['f1'],
'best test F1', res_test['f1'])
param['vf1'], param['tf1'] = res_valid['f1'], res_test['f1']
param['vp'], param['tp'] = res_valid['p'], res_test['p']
param['vr'], param['tr'] = res_valid['r'], res_test['r']
param['be'] = e
subprocess.call(['mv', folder + '/current.test.txt',
folder + '/best.test.txt'])
subprocess.call(['mv', folder + '/current.valid.txt',
folder + '/best.valid.txt'])
else:
if param['verbose']:
print ''
# learning rate decay if no improvement in 10 epochs
if param['decay'] and abs(param['be']-param['ce']) >= 10:
param['clr'] *= 0.5
rnn = best_rnn
if param['clr'] < 1e-5:
break
print('BEST RESULT: epoch', param['be'],
'valid F1', param['vf1'],
'best test F1', param['tf1'],
'with the model', folder)
