def test(cws,filename,output_path):
def seg(char_seq,text):
lens = cws.forward(char_seq)
res, begin =[], 0
for wlen in lens:
res.append(''.join(text[begin:begin+wlen]))
begin+=wlen
return res
char_seqs = prepareData(cws.character_idx_map,filename,test=True)
fo = open(output_path,'wb')
seq_idx = 0
for line in open(filename).readlines():
sent = unicode(line.decode('utf8')).split()
Left = 0
output_sent = []
for idx,word in enumerate(sent):
if len(re.sub('\W','',word,flags=re.U))==0:
if idx>Left:
words =seg(char_seqs[seq_idx],list(''.join(sent[Left:idx])))
seq_idx += 1
output_sent.extend(words)
Left = idx+1
output_sent.append(word)
if Left!=len(sent):
words = seg(char_seqs[seq_idx],list(''.join(sent[Left:])))
seq_idx += 1
output_sent.extend(words)
output_sent = ' '.join(output_sent).encode('utf8')+'\r\n'
fo.write(output_sent)
fo.close()
def test(cws, filename, output_path):
def seg(char_seq, text):
lens = cws.forward(char_seq)
res, begin = [], 0
for wlen in lens:
res.append(''.join(text[begin:begin + wlen]))
begin += wlen
return res
char_seqs = prepareData(cws.character_idx_map, filename, test=True)
#fo = open(output_path,'wb')
fo = open(output_path, 'w')
seq_idx = 0
for line in open(filename).readlines():
#sent = str(line.decode('utf8')).split()
sent = line.split()
Left = 0
output_sent = []
for idx, word in enumerate(sent):
if len(re.sub('\W', '', word, flags=re.U)) == 0:
if idx > Left:
words = seg(char_seqs[seq_idx],
list(''.join(sent[Left:idx])))
seq_idx += 1
output_sent.extend(words)
Left = idx + 1
output_sent.append(word)
if Left != len(sent):
words = seg(char_seqs[seq_idx], list(''.join(sent[Left:])))
seq_idx += 1
output_sent.extend(words)
#output_sent = ' '.join(output_sent).encode('utf8')+'\r\n'
output_sent = ' '.join(output_sent) + '\r\n'
fo.write(output_sent)
fo.close()
def test(character_idx_map, options, params, path, filename, batch_size=512):
X = tools.prepareData(character_idx_map, path, test=True)
dropout = (1 - options['dropout_rate']) * np.ones(
(options['ndims'], ), dtype=theano.config.floatX)
start, n = 0, len(X)
idx_list = range(n)
lens = [len(x) for x in X]
idx_list = sorted(idx_list, cmp=lambda x, y: cmp(lens[x], lens[y]))
Y = []
print 'count_test_sentences', len(X)
for i in range(n // batch_size):
batch_idx = idx_list[start:start + batch_size]
x = [X[t] for t in batch_idx]
x_lens = [lens[t] for t in batch_idx]
x = tools.asMatrix(x)
sY = tools.segment(params, options, x, x_lens, dropout)
Y.extend(sY)
start += batch_size
if start != n:
batch_idx = idx_list[start:]
x = [X[t] for t in batch_idx]
x_lens = [lens[t] for t in batch_idx]
x = tools.asMatrix(x)
sY = tools.segment(params, options, x, x_lens, dropout)
Y.extend(sY)
table = {}
nb = 0
for idx in idx_list:
table[idx] = nb
nb += 1
output_result(Y, table, path, filename)
def main():
# Model Parameters
degree = 13
whis = 2.5
lambda_ = 0.0001
# Load the training data
print("Loading the training Datas...")
y, tX, ids = load_csv_data(DATA_TRAIN_PATH)
# Clean and prepare our data
print("Clean and prepare the training datas...")
y_train, tX_train, ids_train = prepareData(y, tX, ids, degree, whis)
# Train our models
print("Train the models...")
weights_0, loss_0 = ridge_regression(y_train[0], tX_train[0], lambda_)
weights_1, loss_1 = ridge_regression(y_train[1], tX_train[1], lambda_)
weights_2, loss_2 = ridge_regression(y_train[2], tX_train[2], lambda_)
weights_3, loss_3 = ridge_regression(y_train[3], tX_train[3], lambda_)
# Load the dataset to predict
print("Loading the testing Datas...")
y_test, tX_test, ids_test = load_csv_data(DATA_TEST_PATH)
# Prepare the data in the same way as the train dataset
print("Clean and prepare the testing datas...")
y_test, tX_test, ids_test = prepareData(y_test, tX_test, ids_test, degree,
whis)
# Predict each class
print("Predict the testing datas...")
y_pred_0 = predict_labels(weights_0, tX_test[0])
y_pred_1 = predict_labels(weights_1, tX_test[1])
y_pred_2 = predict_labels(weights_2, tX_test[2])
y_pred_3 = predict_labels(weights_3, tX_test[3])
# Concatenate the results
y_pred = np.concatenate([y_pred_0, y_pred_1, y_pred_2, y_pred_3])
ids_test = np.concatenate(
[ids_test[0], ids_test[1], ids_test[2], ids_test[3]])
# Write the results in a csv file
print("Writing the results...")
create_csv_submission(ids_test, y_pred, OUTPUT_PATH)
print("DONE!, your predictions are available in ", OUTPUT_PATH)
def test(character_idx_map,
options,
params,
path,
filename,
batch_size = 512
):
X = tools.prepareData(character_idx_map,path,test=True)
dropout = (1-options['dropout_rate'])*np.ones((options['ndims'],), dtype=theano.config.floatX)
start,n = 0,len(X)
idx_list = range(n)
lens = [len(x) for x in X]
idx_list = sorted(idx_list,cmp = lambda x,y: cmp(lens[x],lens[y]))
Y = []
print 'count_test_sentences',len(X)
for i in range(n//batch_size):
batch_idx = idx_list[start:start+batch_size]
x = [X[t] for t in batch_idx]
x_lens = [lens[t] for t in batch_idx]
x = tools.asMatrix(x)
sY = tools.segment(params,options,x,x_lens,dropout)
Y.extend(sY)
start+=batch_size
if start!=n:
batch_idx = idx_list[start:]
x = [X[t] for t in batch_idx]
x_lens = [lens[t] for t in batch_idx]
x = tools.asMatrix(x)
sY = tools.segment(params,options,x,x_lens,dropout)
Y.extend(sY)
table = {}
nb= 0
for idx in idx_list:
table[idx] = nb
nb+=1
output_result(Y,table,path,filename)
def dy_train_model(max_epochs=50,
batch_size=256,
ndims=50,
nhiddens=50,
dropout_rate=0.2,
regularization=0.000001,
margin_loss_discount=0.2,
max_word_len=4,
start_point=1,
load_params=None,
max_sent_len=60,
beam_size=4,
shuffle_data=True,
train_file='../data/train',
dev_file='../data/dev',
lr=0.2,
pre_training='../w2v/c_vecs_50'):
options = locals().copy()
print 'Model options:'
for kk, vv in options.iteritems():
print '\t', kk, '\t', vv
Cemb, character_idx_map = initCemb(ndims, train_file, pre_training)
cws = CWS(Cemb, character_idx_map, options)
if load_params is not None:
cws.load(load_params)
char_seq, _, truth = prepareData(character_idx_map, train_file)
if max_sent_len is not None:
survived = []
for idx, seq in enumerate(char_seq):
if len(seq) <= max_sent_len and len(seq) > 1:
survived.append(idx)
char_seq = [char_seq[idx] for idx in survived]
truth = [truth[idx] for idx in survived]
n = len(char_seq)
print 'Total number of training instances:', n
print 'Start training model'
start_time = time.time()
nsamples = 0
for eidx in xrange(max_epochs):
idx_list = range(n)
if shuffle_data:
random.shuffle(idx_list)
for idx in idx_list:
loss = cws.backward(char_seq[idx], truth[idx])
if np.isnan(loss):
print 'somthing went wrong, loss is nan.'
return
nsamples += 1
if nsamples % batch_size == 0:
cws.trainer.update(1. / batch_size)
cws.trainer.update_epoch(1.)
end_time = time.time()
print 'Trained %s epoch(s) (%d samples) took %.lfs per epoch' % (
eidx + 1, nsamples, (end_time - start_time) / (eidx + 1))
test(cws, dev_file, '../result/dev_result%d' % (eidx + start_point))
def dy_train_model(max_epochs=30,
batch_size=256,
char_dims=50,
word_dims=100,
nhiddens=50,
dropout_rate=0.2,
margin_loss_discount=0.2,
max_word_len=4,
load_params=None,
max_sent_len=60,
shuffle_data=True,
train_file='../data/train',
dev_file='../data/dev',
lr=0.5,
edecay=0.1,
momentum=0.5,
pre_trained='../w2v/char_vecs_100',
word_proportion=0.5):
options = locals().copy() # Copy the local parameters to options
print 'Model options:'
for kk, vv in options.iteritems():
print '\t', kk, '\t', vv
# Based on the train_file, get the most frequent characters to generate matrix
# Cemb: Character embedding matrix, {index, vector}
# character_idx_map: {character:index}
Cemb, character_idx_map = initCemb(char_dims, train_file, pre_trained)
# Define parameters, trainer
cws = CWS(Cemb, character_idx_map, options)
# Load prams adn test
if load_params is not None:
cws.load(load_params)
test(cws, dev_file, 'result')
# Convert word corpus to sentence, index list
# char_seq: [sentence[cha_idx]]
# truth: [sentence[char_label]]
char_seq, _, truth = prepareData(character_idx_map, train_file)
# Remove too long or null sentence
if max_sent_len is not None:
survived = []
for idx, seq in enumerate(char_seq):
if len(seq) <= max_sent_len and len(seq) > 1:
survived.append(idx)
char_seq = [char_seq[idx] for idx in survived]
truth = [truth[idx] for idx in survived]
# Generate frequent word matrix H
if word_proportion > 0:
word_counter = Counter()
# Loop characters in sentence
for chars, labels in zip(char_seq, truth):
# Loop labels from 1 to n
# Generate tuple word index combinations (idx)
# Count the number of occurrence
word_counter.update(
tuple(chars[idx - label:idx])
for idx, label in enumerate(labels, 1))
# Put most frequent word in list
known_word_count = int(word_proportion * len(word_counter))
known_words = dict(word_counter.most_common()
[:known_word_count]) # {idx: number of occurrence}
idx = 0
# Set know_words to {idx1:idx2}
for word in known_words:
known_words[word] = idx
idx += 1
# we keep a short list H of the most frequent words, generate parameter matrix H
# Add known_words and param['word_embed'] as lookup_parameters
cws.use_word_embed(known_words)
n = len(char_seq)
print 'Total number of training instances:', n
print 'Start training model'
start_time = time.time()
nsamples = 0
for eidx in xrange(max_epochs):
idx_list = range(n)
# Random the sentences
if shuffle_data:
np.random.shuffle(idx_list)
total_loss = 0
total_times = 0
for idx in idx_list:
loss = cws.backward(char_seq[idx],
truth[idx]) # Construct computation graph
total_loss += loss
if np.isnan(loss):
print 'somthing went wrong, loss is nan.'
return
nsamples += 1
if nsamples % batch_size == 0:
cws.trainer.update()
total_times += batch_size
print '%s/%s, average loss:%s' % (total_times, n,
total_loss / batch_size)
total_loss = 0
# edecay is not avaiable after dynet 1.0
# I have to manually update learning rate
cws.trainer.learning_rate /= 1 + options['edecay']
total_times = 0
# Deprecated
# cws.trainer.update_epoch(1.)
end_time = time.time()
print 'Trained %s epoch(s) (%d samples) took %.lfs per epoch' % (
eidx + 1, nsamples, (end_time - start_time) / (eidx + 1))
test(cws, dev_file, '../result/dev_result%d' % (eidx + 1))
os.system('python score.py %s %d %d' % (dev_file, eidx + 1, eidx + 1))
cws.save('epoch%d' % (eidx + 1))
print 'Current model saved'
def dy_train_model(
max_epochs = 50,
batch_size = 256,
ndims = 50,
nhiddens = 50,
dropout_rate = 0.2,
regularization = 0.000001,
margin_loss_discount = 0.2,
max_word_len = 4,
start_point = 1,
load_params = None,
max_sent_len = 60,
beam_size = 4,
shuffle_data = True,
train_file = '../data/train',
dev_file = '../data/dev',
lr = 0.2,
pre_training = '../w2v/c_vecs_50'
):
options = locals().copy()
print 'Model options:'
for kk,vv in options.iteritems():
print '\t',kk,'\t',vv
Cemb, character_idx_map = initCemb(ndims,train_file,pre_training)
cws = CWS(Cemb,character_idx_map,options)
if load_params is not None:
cws.load(load_params)
char_seq, _ , truth = prepareData(character_idx_map,train_file)
if max_sent_len is not None:
survived = []
for idx,seq in enumerate(char_seq):
if len(seq)<=max_sent_len and len(seq)>1:
survived.append(idx)
char_seq = [ char_seq[idx] for idx in survived]
truth = [ truth[idx] for idx in survived]
n = len(char_seq)
print 'Total number of training instances:',n
print 'Start training model'
start_time = time.time()
nsamples = 0
for eidx in xrange(max_epochs):
idx_list = range(n)
if shuffle_data:
random.shuffle(idx_list)
for idx in idx_list:
loss = cws.backward(char_seq[idx],truth[idx])
if np.isnan(loss):
print 'somthing went wrong, loss is nan.'
return
nsamples += 1
if nsamples % batch_size == 0:
cws.trainer.update(1./batch_size)
cws.trainer.update_epoch(1.)
end_time = time.time()
print 'Trained %s epoch(s) (%d samples) took %.lfs per epoch'%(eidx+1,nsamples,(end_time-start_time)/(eidx+1))
test(cws,dev_file,'../result/dev_result%d'%(eidx+start_point))
def dy_train_model(max_epochs=30,
batch_size=256,
char_dims=50,
word_dims=100,
nhiddens=50,
dropout_rate=0.2,
margin_loss_discount=0.2,
max_word_len=4,
load_params=None,
max_sent_len=60,
shuffle_data=True,
train_file='../data/train',
dev_file='../data/dev',
lr=0.5,
edecay=0.1,
momentum=0.5,
pre_trained='../w2v/char_vecs_100',
word_proportion=0.5):
options = locals().copy()
print 'Model options:'
for kk, vv in options.iteritems():
print '\t', kk, '\t', vv
Cemb, character_idx_map = initCemb(char_dims, train_file, pre_trained)
cws = CWS(Cemb, character_idx_map, options)
if load_params is not None:
cws.load(load_params)
test(cws, dev_file, 'result')
char_seq, _, truth = prepareData(character_idx_map, train_file)
if max_sent_len is not None:
survived = []
for idx, seq in enumerate(char_seq):
if len(seq) <= max_sent_len and len(seq) > 1:
survived.append(idx)
char_seq = [char_seq[idx] for idx in survived]
truth = [truth[idx] for idx in survived]
if word_proportion > 0:
word_counter = Counter()
for chars, labels in zip(char_seq, truth):
word_counter.update(
tuple(chars[idx - label:idx])
for idx, label in enumerate(labels, 1))
known_word_count = int(word_proportion * len(word_counter))
known_words = dict(word_counter.most_common()[:known_word_count])
idx = 0
for word in known_words:
known_words[word] = idx
idx += 1
cws.use_word_embed(known_words)
n = len(char_seq)
print 'Total number of training instances:', n
print 'Start training model'
start_time = time.time()
nsamples = 0
for eidx in xrange(max_epochs):
idx_list = range(n)
if shuffle_data:
np.random.shuffle(idx_list)
for idx in idx_list:
loss = cws.backward(char_seq[idx], truth[idx])
if np.isnan(loss):
print 'somthing went wrong, loss is nan.'
return
nsamples += 1
if nsamples % batch_size == 0:
cws.trainer.update(1.)
cws.trainer.update_epoch(1.)
end_time = time.time()
print 'Trained %s epoch(s) (%d samples) took %.lfs per epoch' % (
eidx + 1, nsamples, (end_time - start_time) / (eidx + 1))
test(cws, dev_file, '../result/dev_result%d' % (eidx + 1))
os.system('python score.py %s %d %d' % (dev_file, eidx + 1, eidx + 1))
cws.save('epoch%d' % (eidx + 1))
print 'Current model saved'
def train_model(max_epoches=30,
optimizer=adadelta,
batch_size=256,
ndims=100,
nhiddens=150,
dropout_rate=0.,
regularization=0.,
margin_loss_discount=0.2,
max_word_len=4,
start_point=1,
load_params=None,
resume_training=False,
max_sent_len=60,
beam_size=4,
shuffle_data=True,
train_file='../data/train',
dev_file='../data/dev',
lr=0.2,
pre_training='../w2v/c_vecs_100'):
options = locals().copy()
print 'model options:', options
print 'Building model'
Cemb, character_idx_map = tools.initCemb(ndims, train_file, pre_training)
print '%saving config file'
config = {}
config['options'] = options
config['options']['optimizer'] = optimizer.__name__
config['character_idx_map'] = character_idx_map
f = open('config', 'wb')
f.write(json.dumps(config))
f.close()
print '%resume model building'
params = initParams(Cemb, options)
if load_params is not None:
pp = np.load(load_params)
for kk, vv in params.iteritems():
if kk not in pp:
raise Warning('%s is not in the archive' % kk)
params[kk] = pp[kk]
tparams = initTparams(params)
if optimizer is adadelta:
ms_up, ms_grad = prepare_adadelta(tparams)
if optimizer is adagrad:
if resume_training:
ss_grad = initTparams(np.load('backup.npz'))
else:
ss_grad = prepare_adagrad(tparams)
T_x, T_dropout, T_y, T_yy, T_y_mask, T_yy_mask, T_cost = build_model(
tparams, options)
weight_decay = (tparams['U']**2).sum() + (tparams['Wy']**2).sum()
weight_decay *= regularization
T_cost += weight_decay
if optimizer is adadelta:
T_updates = optimizer(ms_up, ms_grad, tparams, T_cost)
elif optimizer is sgd:
LR, T_updates = optimizer(tparams, T_cost, lr)
elif optimizer is adagrad:
T_updates = optimizer(ss_grad, tparams, T_cost, lr)
f_update = theano.function(
[T_x, T_dropout, T_y, T_yy, T_y_mask, T_yy_mask],
T_cost,
updates=T_updates)
print 'Loading data'
seqs, lenss, tagss = tools.prepareData(character_idx_map, train_file)
if max_sent_len is not None:
survived = []
for idx, seq in enumerate(seqs):
if len(seq) <= max_sent_len and len(seq) > 1:
survived.append(idx)
seqs = [seqs[idx] for idx in survived]
lenss = [lenss[idx] for idx in survived]
tagss = [tagss[idx] for idx in survived]
tot_lens = [len(seq) for seq in seqs]
print 'count_training_sentences', len(seqs)
print 'Training model'
start_time = time.time()
for eidx in xrange(max_epoches):
batches_idx = get_minibatches_idx(seqs,
tot_lens,
batch_size,
shuffle=shuffle_data)
for batch_idx in batches_idx:
X = [seqs[t] for t in batch_idx]
Y = [lenss[t] for t in batch_idx]
Z = [tagss[t] for t in batch_idx]
X_lens = [tot_lens[t] for t in batch_idx]
params = get_params(tparams)
X = tools.asMatrix(X)
dropout = np.random.binomial(1, 1 - dropout_rate,
(X.shape[1], ndims)).astype(
theano.config.floatX)
#numpy_start = time.time()
YY = tools.segment(params, options, X, X_lens, dropout,
margin_loss_discount, Z)
#print 'numpy',time.time()-numpy_start
Y = tools.asMatrix(Y, transpose=True)
YY = tools.asMatrix(YY, transpose=True)
Y_mask = (Y / Y).astype(theano.config.floatX)
YY_mask = (YY / YY).astype(theano.config.floatX)
#theano_start = time.time()
f_update(X, dropout, Y, YY, Y_mask, YY_mask)
#print 'theano',time.time()-theano_start
if optimizer is sgd:
LR.set_value(numpy_floatX(LR.get_value() * 0.9))
params = get_params(tparams)
test(config['character_idx_map'], config['options'], params, dev_file,
'../result/dev_result%s' % (eidx + start_point, ))
np.savez('epoch_%s' % (eidx + start_point, ), **params)
if optimizer is adagrad:
np.savez('backup', **get_params(ss_grad))
end_time = time.time()
print 'Trained %s epoch(s) took %.lfs per epoch' % (
eidx + 1, (end_time - start_time) / (eidx + 1))
def train_model(
max_epochs = 30,
optimizer = adadelta,
batch_size = 256,
ndims = 100,
nhiddens = 150,
dropout_rate = 0.,
regularization = 0.,
margin_loss_discount = 0.2,
max_word_len = 4,
start_point = 1,
load_params = None,
resume_training = False,
max_sent_len = 60,
beam_size = 4,
shuffle_data = True,
train_file = '../data/train',
dev_file = '../data/dev',
lr = 0.2,
pre_training = '../w2v/c_vecs_100'
):
options = locals().copy()
print 'model options:',options
print 'Building model'
Cemb,character_idx_map = tools.initCemb(ndims,train_file,pre_training)
print '%saving config file'
config = {}
config['options'] = options
config['options']['optimizer'] = optimizer.__name__
config['character_idx_map'] = character_idx_map
f = open('config','wb')
f.write(json.dumps(config))
f.close()
print '%resume model building'
params = initParams(Cemb,options)
if load_params is not None:
pp = np.load(load_params)
for kk,vv in params.iteritems():
if kk not in pp:
raise Warning('%s is not in the archive' % kk)
params[kk] = pp[kk]
tparams = initTparams(params)
if optimizer is adadelta:
ms_up,ms_grad = prepare_adadelta(tparams)
if optimizer is adagrad:
if resume_training:
ss_grad = initTparams(np.load('backup.npz'))
else:
ss_grad = prepare_adagrad(tparams)
T_x,T_dropout,T_y,T_yy,T_y_mask,T_yy_mask,T_cost = build_model(tparams,options)
weight_decay = (tparams['U']**2).sum()+(tparams['Wy']**2).sum()
weight_decay *= regularization
T_cost += weight_decay
if optimizer is adadelta:
T_updates = optimizer(ms_up,ms_grad,tparams,T_cost)
elif optimizer is sgd:
LR,T_updates = optimizer(tparams,T_cost,lr)
elif optimizer is adagrad:
T_updates = optimizer(ss_grad,tparams,T_cost,lr)
f_update = theano.function([T_x,T_dropout,T_y,T_yy,T_y_mask,T_yy_mask],T_cost,updates=T_updates)
print 'Loading data'
seqs,lenss,tagss = tools.prepareData(character_idx_map,train_file)
if max_sent_len is not None:
survived = []
for idx,seq in enumerate(seqs):
if len(seq)<=max_sent_len and len(seq)>1:
survived.append(idx)
seqs = [ seqs[idx] for idx in survived]
lenss = [ lenss[idx] for idx in survived]
tagss = [ tagss[idx] for idx in survived]
tot_lens = [len(seq) for seq in seqs]
print 'count_training_sentences',len(seqs)
print 'Training model'
start_time = time.time()
for eidx in xrange(max_epochs):
batches_idx = get_minibatches_idx(seqs,tot_lens,batch_size,shuffle=shuffle_data)
for batch_idx in batches_idx:
X = [seqs[t] for t in batch_idx]
Y = [lenss[t] for t in batch_idx]
Z = [tagss[t] for t in batch_idx]
X_lens = [tot_lens[t] for t in batch_idx]
params = get_params(tparams)
X = tools.asMatrix(X)
dropout = np.random.binomial(1,1-dropout_rate,(X.shape[1],ndims)).astype(theano.config.floatX)
#numpy_start = time.time()
YY= tools.segment(params,options,X,X_lens,dropout,margin_loss_discount,Z)
#print 'numpy',time.time()-numpy_start
Y = tools.asMatrix(Y,transpose=True)
YY = tools.asMatrix(YY,transpose=True)
Y_mask = (Y/Y).astype(theano.config.floatX)
YY_mask =(YY/YY).astype(theano.config.floatX)
#theano_start = time.time()
f_update(X,dropout,Y,YY,Y_mask,YY_mask)
#print 'theano',time.time()-theano_start
if optimizer is sgd:
LR.set_value(numpy_floatX(LR.get_value()*0.9))
params = get_params(tparams)
test(config['character_idx_map'],config['options'],params,dev_file,'../result/dev_result%s'%(eidx+start_point,))
np.savez('epoch_%s'%(eidx+start_point,),**params)
if optimizer is adagrad:
np.savez('backup',**get_params(ss_grad))
end_time = time.time()
print 'Trained %s epoch(s) took %.lfs per epoch'%(eidx+1,(end_time-start_time)/(eidx+1))
