forked from biomed-AI/MiGCN
-
Notifications
You must be signed in to change notification settings - Fork 0
/
test.py
329 lines (268 loc) · 11.3 KB
/
test.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
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
import matplotlib.pylab as plt
import matplotlib
from matplotlib.pyplot import MultipleLocator
# import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
matplotlib.rcParams['savefig.dpi'] = 500
# fig, axs = plt.subplots(1,1)
matplotlib.rcParams['figure.figsize'] = [10, 7]
font0 = {'family' : 'Times New Roman',
'weight' : 'normal',
'size' : 11,
}
font1 = {'family' : 'Times New Roman',
'weight' : 'normal',
'size' : 12,
}
font2 = {'family' : 'Times New Roman',
'weight' : 'normal',
'size' : 13,
}
d = pd.DataFrame()
d['variant'] = ['GCN']*15 + ['Sub-Neighbor']*15 + ['Attentive']*15 + ['Informative']*15 + ['GCN']*15 + ['Sub-Neighbor']*15 + ['Attentive']*15 + ['Informative']*15
d['p'] = ['Recall@1','Recall@15','Pre@1','Precision@15', 'AUC'] * (len(d['variant']) // 5)
d['dataset'] = ['MiGCN']*60 + ['MiGCN' + '$\mathregular{_{sim}}$']*60
d['ret'] = [0.0291,0.2158,0.0324,0.0162,0.824]\
+ [0.0291+0.0025,0.2158+0.0044,0.0324+0.003,0.0162+0.0008,0.824+0.004]\
+ [0.0291-0.0025,0.2158-0.0044,0.0324-0.003,0.0162-0.0008,0.824-0.004]\
+ [0.0296,0.2142,0.0327,0.0162,0.825]\
+ [0.0296+0.003,0.2142+0.005,0.0327,0.0162+0.0009,0.825+0.005]\
+ [0.0296-0.003,0.2142-0.005,0.0327,0.0162-0.0009,0.825-0.005]\
+ [0.0320,0.2178,0.0335,0.0165,0.827]\
+ [0.0320+0.002,0.2178+0.0036,0.0335,0.0165+0.0007,0.827+0.004]\
+ [0.0320-0.002,0.2178-0.0036,0.0335,0.0165-0.0007,0.827-0.004]\
+ [0.0322,0.2194,0.0349,0.0169,0.831]\
+ [0.0322+0.0017,0.2194+0.0040,0.0349,0.0169+0.0007,0.831+0.004]\
+ [0.0322-0.0017,0.2194-0.0040,0.0349,0.0169-0.0007,0.831-0.004]\
+ [0.0293,0.1432,0.0307,0.0102,0.780]\
+ [0.0293-0.0015,0.1432+0.0021,0.0307,0.0102+0.0009,0.780+0.005]\
+ [0.0293+0.0015,0.1432-0.0021,0.0307,0.0102-0.0009,0.780-0.005]\
+ [0.0300+0.0023,0.1459-0.0025,0.0316,0.0103,0.778+0.006]\
+ [0.0300,0.1459+0.0025,0.0316,0.0103+0.0006,0.778-0.006]\
+ [0.0300-0.0023,0.1459,0.0316,0.0103-0.0006,0.778]\
+ [0.0321+0.0020,0.1563-0.0018,0.0350,0.0105+0.0007,0.782+0.004]\
+ [0.0321-0.0020,0.1563+0.0018,0.0350,0.0105-0.0007,0.782-0.004]\
+ [0.0321,0.1563,0.0350,0.0105,0.782]\
+ [0.0313,0.1608,0.0327,0.0119,0.787]\
+ [0.0313+0.0025,0.1608+0.002,0.0327,0.0119-0.0007,0.787+0.006]\
+ [0.0313-0.0025,0.1608-0.002,0.0327,0.0119+0.0007,0.787-0.006]
r1 = d[d['p'] == 'Recall@1']
r15 = d[d['p'] == 'Recall@15']
p1 = d[d['p'] == 'Pre@1']
p15 = d[d['p'] == 'Precision@15']
dauc = d[d['p'] == 'AUC']
fig = plt.figure()
ax1 = fig.add_subplot(221)
plt.tick_params(labelsize=14)
ax2 = fig.add_subplot(222)
plt.tick_params(labelsize=14)
ax3 = fig.add_subplot(223)
plt.tick_params(labelsize=14)
y_major_locator=MultipleLocator(0.005)
ax3.yaxis.set_major_locator(y_major_locator)
ax4 = fig.add_subplot(224)
plt.tick_params(labelsize=14)
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=None)
# sns.set_theme(style="whitegrid",font='Times New Roman',font_scale=100)
plt.rc('patch',force_edgecolor=True)
sns.barplot(x='dataset',y='ret',data=r1,hue='variant',palette='OrRd',ax=ax1, capsize=.05, errwidth=1.5)
sns.barplot(x='dataset',y='ret',data=r15,hue='variant',palette='OrRd',ax=ax2, capsize=.05, errwidth=1.5)
sns.barplot(x='dataset',y='ret',data=p15,hue='variant',palette='OrRd',ax=ax3, capsize=.05, errwidth=1.5)
sns.barplot(x='dataset',y='ret',data=dauc,hue='variant',palette='OrRd',ax=ax4, capsize=.05, errwidth=1.5)
ax1.set_ylim(0.01, 0.05)
ax1.set_ylabel('Recall@1',font2)
ax2.set_ylim(0.12, 0.23)
ax2.set_ylabel('Recall@15',font2)
ax3.set_ylim(0.005, 0.02)
ax3.set_ylabel('Precision@15',font2)
ax4.set_ylim(0.75, 0.85)
ax4.set_ylabel('AUC',font2)
ax1.set_xlabel(None)
ax2.set_xlabel(None)
ax3.set_xlabel(None)
ax4.set_xlabel(None)
ax1.legend(loc='upper left',prop=font0, ncol=2)
ax2.legend(loc='best',prop=font1, ncol=1)
ax3.legend(loc='best',prop=font1, ncol=1)
ax4.legend(loc='best',prop=font1, ncol=1)
plt.savefig('./mp1.jpg', dpi=500)
# from functools import reduce
# from scipy.stats.stats import mode
# from scipy.sparse import coo_matrix
# import scipy.sparse as sp
# from numpy.random import rand
# import numpy as np
# import math
# import matplotlib.pylab as plt
# import seaborn as sns
# import pandas as pd
# import matplotlib
# from matplotlib.pyplot import MultipleLocator
# matplotlib.rcParams['figure.figsize'] = [15, 5]
# matplotlib.rcParams['savefig.dpi'] = 500
# # fig, axs = plt.subplots(1,1)
# font1 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 15,
# }
# font2 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 15,
# }
# font3 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 20,
# }
# plt.rc('patch',force_edgecolor=True)
# x = np.array([1, 2, 3, 4, 5])
# y1 = np.array([0.782, 0.82260, 0.83155, 0.83112, 0.81987])
# y11 = np.array([0.782-0.003, 0.82260-0.003, 0.83155-0.0035, 0.83112-0.002, 0.81987-0.002])
# y12 = np.array([0.782+0.003, 0.82260+0.003, 0.83155+0.0035, 0.83112+0.002, 0.81987+0.002])
# y2 = np.array([0.751, 0.7742,0.7813,0.7641,0.7566])
# y21 = np.array([0.751-0.0015, 0.7742-0.002,0.7813-0.0035,0.7641-0.0025,0.7566-0.0025])
# y22 = np.array([0.751-0.0015, 0.7742+0.002,0.7813+0.0035,0.7641+0.0025,0.7566+0.0025])
# df = pd.DataFrame()
# df['collaborative graph'] = ['MI-GCN (Relation Graph)']* 15 + ['MI-GCN* (Similarity Graph)']*15
# # ['MI-GCN (Relation Graph)','MI-GCN (Relation Graph)','MI-GCN (Relation Graph)','MI-GCN (Relation Graph)','MI-GCN (Relation Graph)','MI-GCN* (Similarity Graph)','MI-GCN* (Similarity Graph)','MI-GCN* (Similarity Graph)','MI-GCN* (Similarity Graph)','MI-GCN* (Similarity Graph)']
# df['Layer'] = [1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5]
# df['AUC'] = np.concatenate((y1,y11,y12,y2,y21,y22))
# # sns.lineplot(x="Layer", y="AUC",hue="collaborative graph", style="collaborative graph", \
# # # markers={'MI-GCN (Relation Graph)':'o', 'MI-GCN* (Similarity Graph)':'o'},\
# # markers=True,\
# # dashes=True, data=df)
# # c1 = 'steelblue'
# # c2 = 'crimson'
# c1 = 'blue'
# c2 = 'red'
# plt.subplot(121)
# ax3 = plt.plot(x, y1, linestyle='-', marker='s',linewidth=2, color=c1, label='MiGCN')
# plt.errorbar(x, y1,
# yerr=[0.006,0.006,0.007,0.004,0.004],
# fmt='o',ecolor=c1,color=c1,elinewidth=2,capsize=4
# )
# ax4 = plt.plot(x, y2, linestyle='--', marker='o',linewidth=2, color=c2, label='MiGCN' + '$\mathregular{_{sim}}$')
# plt.errorbar(x, y2,
# yerr=[0.003,0.004,0.007,0.005,0.005],
# fmt='o',ecolor=c2,color=c2,elinewidth=2,capsize=4
# )
# for a,b in zip(x,y1):
# if a == 5:
# plt.text(a-0.15, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# else:
# plt.text(a+0.15, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# for a,b in zip(x,y2):
# if a == 5:
# plt.text(a-0.15, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# else:
# plt.text(a+0.15, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# plt.grid(True)
# x_major_locator=MultipleLocator(1)
# ax=plt.gca()
# ax.xaxis.set_major_locator(x_major_locator)
# plt.ylim(0.745, 0.86)
# plt.xlim(0.9, 5.1)
# plt.title('Model Depth ' + r'$L$',font3)
# plt.tick_params(labelsize=13)
# plt.xticks(x,['layer=0', 'layer=1', 'layer=2', 'layer=3', 'layer=4'],fontsize=14)
# plt.ylabel('AUC',font1)
# plt.legend(loc='upper right',prop=font2, ncol=2)
# ##############################################################################################################
# x = np.array([0.1,0.3,0.5,0.7,1.0,1.3,1.5,1.7,2.0])
# y1 = np.array([0.8281, 0.8323, 0.8283, 0.8315, 0.8291, 0.8294, 0.8273, 0.8261, 0.8253])
# # y11 = np.array([y1[0]-0.003, y1[1]-0.003, y1[2]-0.0035, y1[3]-0.002, y1[4]-0.002, y1[5], y1[6], y1[7], y1[8], y1[9]])
# # y12 = np.array([y1[0]+0.003, y1[1]+0.003, y1[2]+0.0035, y1[3]+0.002, y1[4]+0.002, y1[5], y1[6], y1[7], y1[8], y1[9]])
# y2 = np.array([0.7774, 0.7716,0.7813,0.7801,0.7711,0.7701, 0.7772, 0.7712, 0.7692])
# # y21 = np.array([y1[0]-0.003, y1[1]-0.003, y1[2]-0.0035, y1[3]-0.002, y1[4]-0.002, y1[5], y1[6], y1[7], y1[8], y1[9], y1[10]])
# # y22 = np.array([y1[0]+0.003, y1[1]+0.003, y1[2]+0.0035, y1[3]+0.002, y1[4]+0.002, y1[5], y1[6], y1[7], y1[8], y1[9], y1[10]])
# c1 = 'blue'
# c2 = 'red'
# plt.subplot(122)
# ax3 = plt.plot(x, y1, linestyle='-', marker='s',linewidth=2, color=c1, label='MI-GCN')
# # plt.errorbar(x, y1,
# # yerr=[0.006,0.006,0.007,0.004,0.004],
# # fmt='o',ecolor=c1,color=c1,elinewidth=2,capsize=4
# # )
# ax4 = plt.plot(x, y2, linestyle='--', marker='o',linewidth=2, color=c2, label='MiGCN' + '$\mathregular{_{sim}}$')
# # plt.errorbar(x, y2,
# # yerr=[0.003,0.004,0.007,0.005,0.005],
# # fmt='o',ecolor=c2,color=c2,elinewidth=2,capsize=4
# # )
# for a,b in zip(x,y1):
# if a == 2.0:
# plt.text(a-0.05, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# else:
# if a in [0.3,0.7,1.3,1.7]:
# plt.text(a+0.05, b-0.008, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# else:
# plt.text(a+0.05, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# for a,b in zip(x,y2):
# if a == 2.0:
# plt.text(a-0.05, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# else:
# if a in [0.3,0.7,1.3,1.7]:
# plt.text(a+0.05, b-0.008, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# else:
# plt.text(a+0.05, b+0.003, '%.3f' % b, ha='center', va= 'bottom',fontsize=12)
# plt.grid(True)
# x_major_locator=MultipleLocator(0.5)
# ax=plt.gca()
# ax.xaxis.set_major_locator(x_major_locator)
# plt.ylim(0.76, 0.85)
# plt.xlim(0,2.1)
# plt.title('Balancing Coefficient ' + r'$\lambda$',font3)
# # plt.xticks(x, [],fontsize=14)
# plt.ylabel('AUC',font1)
# plt.legend(loc='upper right',prop=font2, ncol=2)
# plt.tick_params(labelsize=13)
# plt.savefig('./layer.jpg', dpi=500)
import time
import torch
import random
import numpy as np
from tqdm import tqdm
import torch.nn
import torch.optim as optim
from metrics import *
from utilty import *
from load_data import *
from model import *
import warnings
warnings.filterwarnings("ignore")
random.seed(2022)
np.random.seed(2022)
torch.manual_seed(2022)
print(cmd_args)
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
print("loading data...")
cmd_args.dataset = './OMIM/bigraph/cross1'
cmd_args.emb_dim = 200
cmd_args.layer_size = "[200]"
cmd_args.batch_size = 512
cmd_args.learning_rate = 0.005
cmd_args.dropout = 0.3
cmd_args.reg=0.001
cmd_args.gamma=0.1
cmd_args.alpha=3
cmd_args.beta=0.1
cmd_args.neg_num=5
cmd_args.k=0.5
cmd_args.Ks = [1,5,10,15]
data_generator = DataLoading(args=cmd_args)
cnt1, cnt2 = 0, 0
for d in range(0,3209):
print(d)
d1_set = set(data_generator.adj.tocsr()[d].tocoo().col)
for d1 in d1_set:
g1_set = set(data_generator.A.tocsr()[d1].tocoo().col)
for g1 in g1_set:
g_set = set(data_generator.adj.tocsr()[g1].tocoo().col)
for g in g_set:
# print(d, d1, g1, g, '0')
cnt1 += 1
if data_generator.A.tocsr()[d,g] > 0:
cnt2 += 1
# print(d, d1, g1, g, '1')
print(cnt1)
print(cnt2)