def test(teat_path, n):
ids = pickle.load(open('ids.pkl', 'rb'))
ids_len = len(ids)
feat_lab = []
for line in open(teat_path, 'r'):
sentence = line.strip('\n').lower()
phi = defaultdict(int)
phi = create_features(sentence, n, phi, ids)
feat_lab.append(phi)
feat_list = []
for feat in feat_lab:
feat_list.append(create_phi_list(feat, ids_len))
out = Output()
hid = pickle.load(open('hidden_model_9', 'rb'))
inp = pickle.load(open('input_model_9', 'rb'))
with open('my_answer', 'w') as f:
for i, phi in enumerate(feat_list):
forward_nn(phi, out, hid, inp)
result = out.result()
print(result, file=f)
def main():
with open('net', 'rb') as net_f,\
open('ids', 'rb') as ids_f:
net = dill.loads(net_f.read())
ids = dill.loads(ids_f.read())
text = []
with open('../../data/titles-en-test.word', 'r') as input_file:
for line in input_file:
words = line.strip().split(' ')
test_phi0 = [[0]] * len(ids)
phi0_dict = Counter(words)
for word, count in phi0_dict.items():
if f'UNI:{word}' in ids:
test_phi0[ids[f'UNI:{word}']] = [count]
test_phi = forward_nn(net, test_phi0)
print(test_phi[len(net)][0][0])
if test_phi[len(net)][0][0] >= 0:
text.append('1')
else:
text.append('-1')
with open('test_answer.txt', 'w') as out_file:
out_file.write('\n'.join(text))
import pickle
from train_nn import create_features, forward_nn
if __name__ == '__main__':
with open('weight_file.txt', 'rb') as w, open('id_file.txt', 'rb') as id_:
net = pickle.load(w)
ids = pickle.load(id_)
with open('../../data/titles-en-test.word') as test, open(
'my_answer.labeled', 'w') as answer:
for line in test:
phi0 = create_features(line, ids)
phi = forward_nn(net, phi0)
y = (1 if phi[len(net) - 1][0] >= 0 else -1)
answer.write('{}\n'.format(y))
import pickle
from train_nn import forward_nn
def create_features(x):
phi = [0 for i in range(len(ids))]
words = x.strip().split()
for word in words:
if 'UNI:' + word in ids:
phi[ids['UNI:' + word]] += 1
return phi
if __name__ == '__main__':
test_f = '../../data/titles-en-test.word'
answer = 'my_answer.nn'
with open('network5_s_l.dump', 'rb') as net_f:
net = pickle.load(net_f)
with open('ids5_s_l.dump', 'rb') as ids_f:
ids = pickle.load(ids_f)
with open(answer, 'w') as ans_f, open(test_f, 'r') as t_f:
for x in t_f:
x = x.strip()
x_l = x.lower()
phi_0 = create_features(x_l)
phiS = forward_nn(net, phi_0)
y_ = (1 if phiS[len(net)][0] >= 0 else -1)
ans_f.write('{}\t{}\n'.format(y_, x))
from train_nn import train,forward_nn
import sys
import numpy as np
network,d=train(open(sys.argv[1]))
for line in open(sys.argv[2]):
phi0=np.zeros(len(d))
for item in line.strip("\n").split(" "):
if item in d:
phi0[d[item]]+=1
phi=forward_nn(network,phi0)
if phi[-1]<0:
y="-1"
else:
y="+1"
print(y+"\t"+line.strip("\n"))
return 1
else:
return -1
def create_features(x):
phi0 = [0] * len(ids)
words = x.split()
for word in words:
if word in ids:
phi0[ids[word]] += 1
return phi0
if __name__ == '__main__':
with open('id_file.txt') as id_f:
ids = {}
for iddayo in id_f:
x, y = iddayo.split()
ids[x] = int(y)
with open('weight_file.txt', 'rb') as w_f:
weight = pickle.load(w_f)
with open('../../data/titles-en-test.word') as text:
with open('my_answer.txt', 'w') as answer:
for line in text:
phi0 = create_features(line)
phi = train_nn.forward_nn(weight, phi0)
ydash = sign(phi[-1][0])
print(ydash, file=answer)
from train_nn import forward_nn
import numpy as np
import pickle
with open('weight_file.dump', 'rb') as f:
network = pickle.load(f)
with open('id_file.dump', 'rb') as g:
ids = pickle.load(g)
for line in open('../../data/titles-en-test.word', 'r'):
features = np.zeros(len(ids))
for word in line.strip('\n').split():
if word in ids:
features[ids[word]] += 1
polarity = 1 if forward_nn(network, features)[2][0] > 0 else -1
print(str(polarity) + '\t' + line.strip('\n'))