-
Notifications
You must be signed in to change notification settings - Fork 0
/
baseline.py
207 lines (167 loc) · 6.81 KB
/
baseline.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
import torch.nn as nn
import torch.nn.functional as F
from data_loader import DataLoader
import torch
from random import shuffle
import time
from sklearn.metrics import f1_score
import seaborn as sb
import matplotlib.pyplot as plt
import pandas as pd
from transformers import BertTokenizer, BertModel
###The RNN code below is from the official PyTorch tutorial on RNNs by Sean Robertson (2017).###
###Tutorial link: https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html ###
class Baseline(nn.Module):
def __init__(self, edim, vocab, hidden_size, output_size, useBert=0):
super(Baseline, self).__init__()
self.useBert = useBert
self.hidden_size = hidden_size
if useBert==0:
self.embed = nn.Embedding(vocab, edim)
self.edim=edim
else: self.edim = 768
# print('1')
self.rnn = nn.RNN(input_size=self.edim, hidden_size=hidden_size, batch_first=True, num_layers=2)
# print('2')
self.nlinearity = nn.LeakyReLU(negative_slope=0.01)
self.linear = nn.Linear(hidden_size, output_size)
# print('6')
#self.softmax = F.oftmax(dim=1)
def forward(self, input):
if self.useBert == 0:
words = self.embed(input)
else: words = input
# print('3')
seq, _ = self.rnn(words)
# print('4')
outp = self.nlinearity(self.linear(seq[:,-1,:]))
outp = F.softmax(outp, dim=1)
return outp
def trainBaseline(trainPath, vocabPath, labelPath, epoch=1, useBert=0, load=0, modelPath = '', saveName = "baseline"):
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
dl = DataLoader(vocabPath, labelPath)
if useBert == 0:
trainArticles = dl.readData_ub(trainPath)
else:
trainArticles = dl.readData_bert(trainPath)
sample = list(torch.utils.data.WeightedRandomSampler(dl.indices, dl.noSample, replacement=False))
lenArt = dl.lenArt
#print('epoch: ', i)
baseline = Baseline(64, dl.voc.keysize, 32, 2, useBert = useBert)
# print('7')
docLoss = nn.CrossEntropyLoss()
# print('5')
opt = torch.optim.Adam(baseline.parameters(), lr = 0.001)
if load == 1:
checkpoint = torch.load(modelPath)
baseline.load_state_dict(checkpoint['baseline_model'])
#DGAT.load_state_dict(checkpoint['document_model'])
opt.load_state_dict(checkpoint['optimizer'])
if useBert==1:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert = BertModel.from_pretrained('bert-base-uncased').to(device)
##Iterate over articles in dataset
for e in range(0, epoch):
truelabels = []; predictions = []
for n, ind in enumerate(sample):
a = trainArticles[ind]
id, article, l = a
aTemp = torch.tensor([])
if l>0:
l=torch.tensor([1], dtype=torch.long) #.to('cuda')
truelabels.append(1)
else:
l=torch.tensor([0], dtype=torch.long) #.to('cuda')
truelabels.append(0)
for i, s in enumerate(article):
if useBert == 1:
sTemp = bert(torch.tensor(tokenizer(s[:510] if len(s)>510 else s)['input_ids'], device=device).unsqueeze(0))[0].detach().squeeze(dim=0).to('cpu')
else: sTemp = s
aTemp =torch.cat((aTemp, sTemp.float()), dim=0)
#if i == 0:
#h = torch.zeros((1, 32))
label = baseline(aTemp.unsqueeze(0))
loss = docLoss(label, l)
if l.item() == label.argmax().item():
if l.item() == 0:
# tneg += 1
predictions.append(0)
else:
# tpos += 1
predictions.append(1)
else:
if l.item() == 0:
# fpos += 1
predictions.append(1)
else:
# fneg += 1
predictions.append(0)
opt.zero_grad()
loss.backward()
opt.step()
print('f1 score: ', f1_score(truelabels, predictions, average= None))
# if debugMode == 0:
torch.save({'baseline_model': baseline.state_dict(),
'optimizer': opt.state_dict()
}, 'dataset/{}.tar'.format(saveName))
# print(a)
# break
def testBaseline(testPath, vocabPath, labelPath, useBert=0, modelPath = ''):
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
dl = DataLoader(vocabPath, labelPath)
if useBert == 0:
testArticles = dl.readData_ub(testPath)
else:
testArticles = dl.readData_bert(testPath)
#sample = list(torch.utils.data.WeightedRandomSampler(dl.indices, dl.noSample, replacement=False))
lenArt = dl.lenArt
#print('epoch: ', i)
baseline = Baseline(64, dl.voc.keysize, 32, 2, useBert = useBert)
# print('7')
docLoss = nn.CrossEntropyLoss()
# print('5')
# opt = torch.optim.Adam(baseline.parameters(), lr = 0.001)
# if load == 1:
checkpoint = torch.load(modelPath)
baseline.load_state_dict(checkpoint['baseline_model'])
#DGAT.load_state_dict(checkpoint['document_model'])
# opt.load_state_dict(checkpoint['optimizer'])
if useBert==1:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert = BertModel.from_pretrained('bert-base-uncased').to(device)
##Iterate over articles in dataset
#for e in range(0, epoch):
truelabels = []; predictions = []
for n, a in enumerate(testArticles):
id, article, l = a
aTemp = torch.tensor([])
if l>0:
l=torch.tensor([1], dtype=torch.long) #.to('cuda')
truelabels.append(1)
else:
l=torch.tensor([0], dtype=torch.long) #.to('cuda')
truelabels.append(0)
for i, s in enumerate(article):
if useBert == 1:
sTemp = bert(torch.tensor(tokenizer(s[:510] if len(s)>510 else s)['input_ids'], device=device).unsqueeze(0))[0].detach().squeeze(dim=0).to('cpu')
else: sTemp = s
aTemp =torch.cat((aTemp, sTemp.float()), dim=0)
#if i == 0:
#h = torch.zeros((1, 32))
label = baseline(aTemp.unsqueeze(0))
# loss = docLoss(label, l)
if l.item() == label.argmax().item():
if l.item() == 0:
# tneg += 1
predictions.append(0)
else:
# tpos += 1
predictions.append(1)
else:
if l.item() == 0:
# fpos += 1
predictions.append(1)
else:
# fneg += 1
predictions.append(0)
print('f1 score: ', f1_score(truelabels, predictions, average= None))