def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args)>3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
with io.open(data_path,'r',encoding='utf-8') as f:
# for line in f:
# Xc = line.rstrip('\n')
# Xt.append(Xc[:MAX_LENGTH])
###Input dataset with tweet+emoji instead of just tweets
data = csv.reader(f, delimiter=',', quotechar="|")
for line in data:
Xc = line[0].rstrip('\n')
Xt.append(Xc[:MAX_LENGTH])
# Model
print("Loading model params...")
if len(args)>3:
params = load_params('%s/model_%d.npz' % (model_path,m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes, n_char)
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet,t_mask],predictions)
encode = theano.function([tweet,t_mask],embeddings)
# Test
print("Encoding...")
out_pred = []
out_emb = []
numbatches = len(Xt)/N_BATCH + 1
for i in range(numbatches):
xr = Xt[N_BATCH*i:N_BATCH*(i+1)]
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
p = predict(x,x_m)
e = encode(x,x_m)
ranks = np.argsort(p)[:,::-1]
for idx, item in enumerate(xr):
out_pred.append(' '.join([inverse_labeldict[r] if r in inverse_labeldict else 'UNK' for r in ranks[idx,:5]]))
out_emb.append(e[idx,:])
# Save
print("Saving...")
###Author - jagathshree
if not os.path.exists(save_path):
os.makedirs(save_path)
with io.open('%s/predicted_tags.txt'%save_path,'w') as f:
for item in out_pred:
f.write(item + '\n')
with open('%s/embeddings.npy'%save_path,'w') as f:
np.save(f,np.asarray(out_emb))
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args)>3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
yt = []
with io.open(data_path,'r',encoding='utf-8') as f:
for line in f:
(yc, Xc) = line.rstrip('\n').split('\t')
Xt.append(Xc[:MAX_LENGTH])
yt.append(yc.split(','))
# Model
print("Loading model params...")
if len(args)>3:
params = load_params('%s/model_%d.npz' % (model_path,m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# iterators
test_iter = batch.BatchTweets(Xt, yt, labeldict, batch_size=N_BATCH, max_classes=MAX_CLASSES, test=True)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
targets = T.imatrix()
# masks
t_mask = T.fmatrix()
# network for prediction
predictions = classify(tweet, t_mask, params, n_classes, n_char)[0]
embeddings = classify(tweet, t_mask, params, n_classes, n_char)[1]
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet,t_mask],predictions)
encode = theano.function([tweet,t_mask],embeddings)
# Test
print("Testing...")
out_data = []
out_pred = []
out_emb = []
out_target = []
for xr,y in test_iter:
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
p = predict(x,x_m)
e = encode(x,x_m)
ranks = np.argsort(p)[:,::-1]
for idx, item in enumerate(xr):
out_data.append(item)
out_pred.append(ranks[idx,:])
out_emb.append(e[idx,:])
out_target.append(y[idx])
# Save
print("Saving...")
with open('%s/data.pkl'%save_path,'w') as f:
pkl.dump(out_data,f)
with open('%s/predictions.npy'%save_path,'w') as f:
np.save(f,np.asarray(out_pred))
with open('%s/embeddings.npy'%save_path,'w') as f:
np.save(f,np.asarray(out_emb))
with open('%s/targets.pkl'%save_path,'w') as f:
pkl.dump(out_target,f)
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args)>3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
with io.open(data_path,'r',encoding='utf-8') as f:
for line in f:
Xc = line.rstrip('\n')
Xt.append(Xc[:MAX_LENGTH])
# Model
print("Loading model params...")
if len(args)>3:
params = load_params('%s/model_%d.npz' % (model_path,m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes, n_char)
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet,t_mask],predictions)
encode = theano.function([tweet,t_mask],embeddings)
# Test
print("Encoding...")
out_pred = []
out_emb = []
numbatches = len(Xt)/N_BATCH + 1
for i in range(numbatches):
xr = Xt[N_BATCH*i:N_BATCH*(i+1)]
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
p = predict(x,x_m)
e = encode(x,x_m)
ranks = np.argsort(p)[:,::-1]
for idx, item in enumerate(xr):
out_pred.append(' '.join([inverse_labeldict[r] for r in ranks[idx,:5]]))
out_emb.append(e[idx,:])
# Save
print("Saving...")
with io.open('%s/predicted_tags.txt'%save_path,'w') as f:
for item in out_pred:
f.write(item + '\n')
with open('%s/embeddings.npy'%save_path,'w') as f:
np.save(f,np.asarray(out_emb))
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args)>3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
with io.open(data_path,'r',encoding='utf-8') as f:
for line in f:
Xc = line.rstrip('\n')
Xt.append(Xc[:MAX_LENGTH])
# Model
print("Loading model params...")
if len(args)>3:
params = load_params('%s/model_%d.npz' % (model_path,m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes, n_char)
# Theano function
print("Compiling theano functions...")
# predict = theano.function([tweet,t_mask],predictions)
encode = theano.function([tweet,t_mask],embeddings)
# Test
print("Encoding...")
out_pred = []
out_emb = []
numbatches = len(Xt)/N_BATCH + 1
print("Num Batches: "+str(numbatches))
for i in range(int(numbatches)):
print("processing batch "+str(i))
xr = Xt[N_BATCH*i:N_BATCH*(i+1)]
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
# p = predict(x,x_m)
e = encode(x,x_m)
# ranks = np.argsort(p)[:,::-1]
for idx, item in enumerate(xr):
# out_pred.append(' '.join([inverse_labeldict[r] if r in inverse_labeldict else 'UNK' for r in ranks[idx,:5]]))
out_emb.append(e[idx,:])
print("saving")
stansFileName = save_path+"/embeddings_w266_"+str(i)+".npy"
stansNPArr = np.asarray(out_emb)
np.save(stansFileName,stansNPArr)
out_emb = []
print("DONE")
def annotate_s2s_text():
"""
テキストファイルにタグを付与する関数
:return:
"""
# path
model_path = MODEL_PATH
text_path = TEST_INPUT
save_path = SAVE_PATH
# seq2seq用regex
pattern = "(.+?)(\n|\r\n)"
r = re.compile(pattern)
print("Loading model params...")
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
tweet = T.itensor3()
t_mask = T.fmatrix()
predictions, embeddings = classify(tweet, t_mask, params, n_classes,
n_char)
print("Compiling theano functions...")
predict = theano.function([tweet, t_mask], predictions)
# Encoding cmnts
cmnts = []
Xt = []
for line in io.open(text_path, 'r', encoding='utf-8'):
m = r.search(line)
if m is not None:
cmnts.append(m.group(1))
Xc_cmnt = m.group(1).replace(' ',
'') # 半角スペースの除去(tweet2vecに入力するため)
Xt.append(Xc_cmnt[:MAX_LENGTH])
out_pred = []
numbatches = len(Xt) / N_BATCH + 1
print 'number of batches', numbatches
for i in range(numbatches):
xr = Xt[N_BATCH * i:N_BATCH * (i + 1)]
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
p = predict(x, x_m)
ranks = np.argsort(p)[:, ::-1]
for idx, item in enumerate(xr):
out_pred.append([
inverse_labeldict[r] if r in inverse_labeldict else 'UNK'
for r in ranks[idx, :5]
][0])
print i, 'batches end...'
# Save result
with io.open(save_path, 'w') as f:
for tag, cmnt in zip(out_pred, cmnts):
f.write(tag + '\t' + cmnt + '\n')
def generate_embeddings(args):
data_path = args[0]
model_path = args[1]
# save_path = args[2]
if len(args) > 2:
m_num = int(args[2])
print("Preparing Data...")
# Test data
# read tweet texts into an array
Xt = []
# read from file
with io.open(data_path, 'r', encoding='utf-8') as f:
for line in f:
Xc = line.rstrip('\n')
Xt.append(Xc[:MAX_LENGTH])
print "Tweets:", len(Xt)
print "Unique tweets:", len(set(Xt))
# Model
print("Loading model params...")
if len(args) > 3:
params = load_params('%s/model_%d.npz' % (model_path, m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes,
n_char)
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet, t_mask], predictions)
encode = theano.function([tweet, t_mask], embeddings)
# Test
print("Encoding...")
out_data = []
out_pred = []
out_emb = []
numbatches = len(Xt) / N_BATCH + 1
for i in range(numbatches):
xr = Xt[N_BATCH * i:N_BATCH * (i + 1)]
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
p = predict(x, x_m)
e = encode(x, x_m)
ranks = np.argsort(p)[:, ::-1]
for idx, item in enumerate(xr):
out_data.append(item)
# print [r for r in ranks[idx,:5]]
# out_pred.append(' '.join([inverse_labeldict[r] for r in ranks[idx,:5]]))
out_pred.append(ranks[idx, :])
out_emb.append(e[idx, :])
return out_emb
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args) > 3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
with io.open(data_path, 'r', encoding='utf-8') as f:
for line in f:
# JK/QUARTZ
# here taking the string only up to the tab character we added
# to every trump tweet line. Was:
# Xc = line.rstrip('\n')
Xc = re.match(r'^(.*)\t', line).group(1).rstrip('\n')
print(Xc)
Xt.append(Xc[:MAX_LENGTH])
# Model
print("Loading model params...")
if len(args) > 3:
params = load_params('%s/model_%d.npz' % (model_path, m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes,
n_char)
# Theano function
print("Compiling theano functions...")
# JK/QUARTZ Disabling the prediction function, because we just need the vectoring
# predict = theano.function([tweet,t_mask],predictions)
encode = theano.function([tweet, t_mask], embeddings)
# Test
print("Encoding...")
# JK/QUARTZ Disabling the prediction lines, because we just need the vectoring
# out_pred = []
out_emb = []
numbatches = len(Xt) / N_BATCH + 1
for i in range(numbatches):
xr = Xt[N_BATCH * i:N_BATCH * (i + 1)]
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
# JK/QUARTZ Disabling the prediction function, because we just need the vectoring
# p = predict(x,x_m)
e = encode(x, x_m)
# JK/QUARTZ Disabling the prediction lines, because we just need the vectoring
# ranks = np.argsort(p)[:,::-1]
for idx, item in enumerate(xr):
# JK/QUARTZ Disabling the prediction lines, because we just need the vectoring
# out_pred.append(' '.join([inverse_labeldict[r] if r in inverse_labeldict else 'UNK' for r in ranks[idx,:5]]))
out_emb.append(e[idx, :])
# Save
print("Saving...")
# JK/QUARTZ Disabling the prediction lines, because we just need the vectoring
# with io.open('%s/predicted_tags.txt'%save_path,'w') as f:
# for item in out_pred:
# f.write(item + '\n')
with open('%s/embeddings.npy' % save_path, 'w') as f:
np.save(f, np.asarray(out_emb))
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args) > 3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
yt = []
with io.open(data_path, 'r', encoding='utf-8') as f:
for line in f:
(yc, Xc) = line.rstrip('\n').split('\t')
Xt.append(Xc[:MAX_LENGTH])
yt.append(yc.split(','))
# Model
print("Loading model params...")
if len(args) > 3:
params = load_params('%s/model_%d.npz' % (model_path, m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = len(chardict.keys()) + 1
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# iterators
test_iter = batch.BatchTweets(Xt,
yt,
labeldict,
batch_size=N_BATCH,
max_classes=MAX_CLASSES,
test=True)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
targets = T.imatrix()
# masks
t_mask = T.fmatrix()
# network for prediction
predictions = classify(tweet, t_mask, params, n_classes, n_char)[0]
embeddings = classify(tweet, t_mask, params, n_classes, n_char)[1]
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet, t_mask], predictions)
encode = theano.function([tweet, t_mask], embeddings)
# Test
print("Testing...")
out_data = []
out_pred = []
out_emb = []
out_target = []
for xr, y in test_iter:
x, x_m = batch.prepare_data(xr, chardict, n_chars=n_char)
p = predict(x, x_m)
e = encode(x, x_m)
ranks = np.argsort(p)[:, ::-1]
for idx, item in enumerate(xr):
out_data.append(item)
out_pred.append(ranks[idx, :])
out_emb.append(e[idx, :])
out_target.append(y[idx])
# Save
print("Saving...")
with open('%s/data.pkl' % save_path, 'w') as f:
pkl.dump(out_data, f)
with open('%s/predictions.npy' % save_path, 'w') as f:
np.save(f, np.asarray(out_pred))
with open('%s/embeddings.npy' % save_path, 'w') as f:
np.save(f, np.asarray(out_emb))
with open('%s/targets.pkl' % save_path, 'w') as f:
pkl.dump(out_target, f)
