def xtest_print_all_srl_tag():
print '*' * 20
print 'print all srl tags of cornll 05'
home = os.path.expanduser('~')
filename = os.path.join(home,"data/conll05/training-set")
raw_corpora = Conll05.loadraw(filename)
all_tags = set()
for sent in raw_corpora:
iobsents = Conll05.sentence2iobsentece(sent)
for iobsent in iobsents:
tags = set([i[2] for i in iobsent[1]])
all_tags = all_tags.union(tags)
for tag in all_tags:
print '\'%s\',' % (tag)
def xtest_Cornll05():
print '*' * 20
print 'test cornll 05'
home = os.path.expanduser('~')
filename = os.path.join(home,"data/conll05/training-set")
raw_corpora = Conll05.loadraw(filename)
print 'raw corpora size=%d' % (len(raw_corpora))
max_sent_len = -1
sum_sent_len = 0
cnt = 0
srl_corpora_size = 0
'''
for sent in raw_corpora:
cnt += 1
sz = len(sent)
sum_sent_len += sz
if sz > max_sent_len:
max_sent_len = sz
#print sent
srl_corpora_size += len(sent[0]) - 2
iobsent = Conll05.sentence2iobsentece(sent)
#srl_corpora_size += len(iobsent)
print "maxium sentece length=%d" % (max_sent_len)
print 'sum sentece length=%d' % (sum_sent_len)
print 'avg sentece length=%d' % (sum_sent_len/cnt)
print 'srl corpora size=%d' % (srl_corpora_size)
'''
'''
ss = rawstr.split('\n')
sentence = list()
for line in ss:
sss = line.split()
if len(sss) == 0:
continue
sss = sss[:2] + sss[6:]
sentence.append(sss)
for i in Conll05.sentence2iobsentece(sentence):
print i
print
'''
print 'test cornll 05 done'
print '*' * 20
def test_srl_conv_network():
print '*' * 20
print 'test_srl_conv_network'
home = os.path.expanduser('~')
filename = os.path.join(home,'data/conll05/training-set')
raw_corpora = Conll05.loadraw(filename)
srl_sents = []
for sent in raw_corpora:
iobsent = Conll05.sentence2iobsentece(sent)
srl_sents += iobsent
words = Corpora(pading_lst=[Word.padding_word(),Word.padding_word2()])
pos = Corpora(pading_lst=[Word.padding_pos(),Word.padding_pos2()])
srl_problem = SrlProblem(words,pos,srl_sents)
max_term_per_sent = 141
window_size = 11
pos_conv_size = 15
max_size = max_term_per_sent + window_size - 1
print 'corpora has words',len(Conll05.words)
print 'corpora has pos',len(Conll05.pos)
print 'corpora has tags',len(Conll05.tags)
print 'window_size' , window_size
print 'pos_conv_size', pos_conv_size
print 'max_term_per_sent', max_term_per_sent
print 'max_size',max_size
validation_frequency = 100
model_params = dict()
model_params['L1_reg'] = 0.0
model_params['L2_reg'] = 0.1
# max_sentence_length = max_term_per_sent + window_size - 1
# which is the maximum length of each sentence with padding
model_params['max_sentence_length'] = max_size
# which is also conv window size
model_params['window_size'] = window_size
model_params['word_num'] = len(Conll05.words)
model_params['POS_num'] = len(Conll05.pos)
# how many pos should we consider in model
model_params['verbpos_num'] = window_size + 1
model_params['wordpos_num'] = window_size + 1
model_params['position_conv_half_window'] = (window_size - 1) / 2
# the dimension of word vector
model_params['word_feature_num'] = 30
# the dimension of POS vector
model_params['POS_feature_num'] = 30
# the dimension of word's position vector
model_params['wordpos_feature_num'] = 30
# the dimension of verb's position vector
model_params['verbpos_feature_num'] = 30
model_params['conv_window'] = window_size
model_params['conv_hidden_feature_num'] = 20
model_params['hidden_layer_size'] = 100
model_params['tags_num'] = len(SrlTypes.SRLTYPE_ID_MAP) + 1
model_params['learning_rate'] = 0.3
print 'model params'
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(model_params)
rng = np.random.RandomState(1234)
model = SentenceLevelNeuralModel(rng,model_params)
# exp params
n_epochs = 1000
batch_iter_num = 10
validation_frequency = 2
epoch = 0
done_looping = False
from collections import Counter
cnt = Counter()
while (epoch <= n_epochs) and (not done_looping):
iter = 1
for idx,data in enumerate(srl_problem.get_batch(batch_size = 10000,
window_size = window_size,
max_size = max_size)):
X,Y,sent_len,masks = data
print 'data %d, X shape %s,Y shape %s,sent_len shape %s,masks shape %s' % (idx,str(X.shape),str(Y.shape),str(sent_len.shape),str(masks.shape))
X = X.astype(np.int32)
Y = Y.astype(np.int32)
#np.savetxt('/home/kingsfield/data/Y',Y,delimiter=',')
#np.save('/home/kingsfield/data/Y',Y)
#np.save('/home/kingsfield/data/masks',masks)
sent_len = sent_len.astype(np.int32)
masks = masks.astype(np.int32)
for row,l in zip(Y,sent_len):
for idx in xrange(l):
cnt[row[idx]]+= 1
if iter % validation_frequency == 0:
error,pred,time_cost = model.valid(X,Y,sent_len,masks)
#np.savetxt('/home/kingsfield/data/pred',pred,delimiter=',')
#np.save('/home/kingsfield/data/pred',pred)
print >> sys.stderr, 'epoch %i, minibatch %i/%i, validation error %f %%,cost time %f' % \
(epoch, iter,100,error * 100.,time_cost)
pass
else:
minibatch_avg_cost,time_cost = model.fit_batch(X,Y,sent_len,masks)
print >> sys.stderr, 'epoch %i, minibatch %i/%i, minibatch cost %f,cost time %f' % \
(epoch,iter,100,minibatch_avg_cost,time_cost)
iter += 1
epoch += 1
s = sum(cnt.values())
idmap = dict([v,k] for k,v in SrlTypes.SRLTYPE_ID_MAP.items())
for k,v in cnt.most_common():
print '\t',k,idmap[k],v,v * 100. / s
print len(cnt),len(SrlTypes.SRLTYPE_ID_MAP)
s1 = set([idmap[i] for i in cnt.keys()])
s2 = set(SrlTypes.SRLTYPE_ID_MAP.keys())
print 's1-s2',s1-s2
print 's2-s1',s2-s1
