-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathpet_experiment_gru.py
171 lines (147 loc) · 4.98 KB
/
pet_experiment_gru.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
# %%
import torch
import pandas
import datetime
import numpy as np
import torch.nn as nn
import scipy.io as sio
from GRU_rnn import GRU
from utility import train
import matplotlib.pyplot as plt
from torch.autograd import grad
from torch.autograd import Variable
from PET_graph_loader import proper_split
from PET_graph_loader import leave_one_out
path = '/home/kristoffer/scripts/data/PET_voistat_data_12_VOIs_n68_v7.mat'
#x_tr, y_tr, x_te, y_te = leave_one_out(path)
x_tr, y_tr, x_va, y_va, x_te, y_te = proper_split(path)
data = sio.loadmat(path)
names = []
for i in range(11):
names.append(data['VOInames'][i+1][0][0])
# %%
n_epochs = 500
n_inputs = 11
n_layers = 2
n_hidden = 20
n_outputs = 1
batch_size = 11
bidirectional = False
time_start = datetime.datetime.now()
gru = GRU(n_inputs, n_hidden, n_outputs, n_layers, batch_size, bidirectional)
all_losses = []
test_loss = []
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(gru.parameters())
print('Training started at:', time_start)
for epoch in range(1, n_epochs + 1):
all_losses.append(train(x_tr,
y_tr,
batch_size,
optimizer,
criterion,
gru,
False))
print(pandas.DataFrame([epoch,
all_losses[-1],
datetime.datetime.now()-time_start],
['Iteration', 'Cost', 'Elapsed time'],
['GRU']))
pred = gru.pred(Variable(torch.from_numpy(x_te[0:batch_size, :, :])))
test_loss.append(np.abs((pred.data.numpy()-y_te)).sum() / batch_size)
print(test_loss[-1])
plt.figure(8)
plt.plot(all_losses)
plt.plot(test_loss)
# %%
pred = gru.pred(Variable(torch.from_numpy(x_te)))
plt.figure(1, figsize=(10, 6))
plt.subplot(2, 3, 1)
plt.plot(pred.data.numpy()[0, :, :], label='pred')
plt.plot(y_te[0, :, :], label='true')
plt.legend()
plt.subplot(2, 3, 2)
plt.plot(pred.data.numpy()[1, :, :], label='pred')
plt.plot(y_te[1, :, :], label='true')
plt.legend()
plt.subplot(2, 3, 3)
plt.plot(pred.data.numpy()[2, :, :], label='pred')
plt.plot(y_te[2, :, :], label='true')
plt.legend()
plt.subplot(2, 3, 4)
plt.plot(pred.data.numpy()[3, :, :], label='pred')
plt.plot(y_te[3, :, :], label='true')
plt.legend()
plt.subplot(2, 3, 5)
plt.plot(pred.data.numpy()[4, :, :], label='pred')
plt.plot(y_te[4, :, :], label='true')
plt.legend()
plt.subplot(2, 3, 6)
plt.plot(pred.data.numpy()[5, :, :], label='pred')
plt.plot(y_te[5, :, :], label='true')
plt.legend()
# %%
X = Variable(torch.from_numpy(x_te), requires_grad=True).float()
output = gru(X)
pred = output[0].data.numpy()
one_hot = np.ones((pred.shape), dtype=np.float32)
one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True)
one_hot = torch.sum(one_hot*output[0])
gradient = grad(one_hot, X)[0].data.numpy()
# %%
plt.close()
idx = 7
plt.figure(3, figsize=(10, 6))
plt.subplot(3, 4, 1)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 0], color='r')
plt.title(data['VOInames'][1][0][0])
plt.subplot(3, 4, 2)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 1], color='b')
plt.title(data['VOInames'][2][0][0])
plt.subplot(3, 4, 3)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 2], color='g')
plt.title(data['VOInames'][3][0][0])
plt.subplot(3, 4, 4)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 3], color='y')
plt.title(data['VOInames'][4][0][0])
plt.subplot(3, 4, 5)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 4], color='c')
plt.title(data['VOInames'][5][0][0])
plt.subplot(3, 4, 6)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 5], color='m')
plt.title(data['VOInames'][6][0][0])
plt.subplot(3, 4, 7)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 6], color='k')
plt.title(data['VOInames'][7][0][0])
plt.subplot(3, 4, 8)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 7], color='pink')
plt.title(data['VOInames'][8][0][0])
plt.subplot(3, 4, 9)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 8], color='olive')
plt.title(data['VOInames'][9][0][0])
plt.subplot(3, 4, 10)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 9], color='gray')
plt.title(data['VOInames'][10][0][0])
plt.subplot(3, 4, 11)
plt.bar(np.arange(0, 44, 1), gradient[idx, :, 10], color='brown')
plt.title(data['VOInames'][11][0][0])
plt.tight_layout()
plt.subplot(3, 4, 12)
plt.plot(pred[idx], label='pred')
plt.plot(y_tr[idx], label='true')
plt.legend()
plt.show()
plt.figure(4, figsize=(10, 6))
plt.plot(x_tr[idx, :, 0], label=names[0], color='r')
plt.plot(x_tr[idx, :, 1], label=names[1], color='b')
plt.plot(x_tr[idx, :, 2], label=names[2], color='g')
plt.plot(x_tr[idx, :, 3], label=names[3], color='y')
plt.plot(x_tr[idx, :, 4], label=names[4], color='c')
plt.plot(x_tr[idx, :, 5], label=names[5], color='m')
plt.plot(x_tr[idx, :, 6], label=names[6], color='k')
plt.plot(x_tr[idx, :, 7], label=names[7], color='pink')
plt.plot(x_tr[idx, :, 8], label=names[8], color='olive')
plt.plot(x_tr[idx, :, 9], label=names[9], color='gray')
plt.plot(x_tr[idx, :, 10], label=names[10], color='brown')
plt.legend()
plt.show()