-
Notifications
You must be signed in to change notification settings - Fork 0
/
testnohup
320 lines (320 loc) · 7.64 KB
/
testnohup
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
Working on: gi50_screen_786_0
0.702471
Processing...
Done!
BOTDS(3504)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.581201940548284 accuracy:0.7034220532319392
1052
Confusion Matrix (Rows: True, Cols: Predicted):
[[328 147]
[165 412]]
Precision: 0.7370304114490162
Recall: 0.7140381282495667
F1-score: 0.7253521126760564
Accuracy: 0.7034220532319392
Working on: gi50_screen_A498
0.717433
Processing...
Done!
BOTDS(3478)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.5809104328411292 accuracy:0.7174329501915708
1044
Confusion Matrix (Rows: True, Cols: Predicted):
[[314 183]
[112 435]]
Precision: 0.7038834951456311
Recall: 0.7952468007312614
F1-score: 0.7467811158798283
Accuracy: 0.7174329501915708
Working on: gi50_screen_A549_ATCC
0.667857
Processing...
Done!
BOTDS(3732)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.6162416850349732 accuracy:0.6678571428571428
1120
Confusion Matrix (Rows: True, Cols: Predicted):
[[295 258]
[114 453]]
Precision: 0.6371308016877637
Recall: 0.798941798941799
F1-score: 0.7089201877934272
Accuracy: 0.6678571428571428
Working on: gi50_screen_ACHN
0.702550
Processing...
Done!
BOTDS(3529)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.5860599761475247 accuracy:0.7034938621340887
1059
Confusion Matrix (Rows: True, Cols: Predicted):
[[324 193]
[123 419]]
Precision: 0.684640522875817
Recall: 0.7730627306273062
F1-score: 0.7261698440207971
Accuracy: 0.7016052880075543
Working on: gi50_screen_BT_549
0.678271
Processing...
Done!
BOTDS(2776)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.6731560388389899 accuracy:0.6782713085234093
833
Confusion Matrix (Rows: True, Cols: Predicted):
[[271 129]
[139 294]]
Precision: 0.6950354609929078
Recall: 0.6789838337182448
F1-score: 0.6869158878504673
Accuracy: 0.6782713085234093
Working on: gi50_screen_CAKI_1
0.683426
Processing...
Done!
BOTDS(3578)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.6263771700903468 accuracy:0.6834264432029795
1074
Confusion Matrix (Rows: True, Cols: Predicted):
[[245 252]
[ 88 489]]
Precision: 0.659919028340081
Recall: 0.8474870017331022
F1-score: 0.7420333839150228
Accuracy: 0.6834264432029795
Working on: gi50_screen_CCRF_CEM
0.706897
Processing...
Done!
BOTDS(3478)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.581209243086106 accuracy:0.7078544061302682
1044
Confusion Matrix (Rows: True, Cols: Predicted):
[[204 179]
[126 535]]
Precision: 0.7492997198879552
Recall: 0.8093797276853253
F1-score: 0.7781818181818182
Accuracy: 0.7078544061302682
Working on: gi50_screen_COLO_205
0.688015
Processing...
Done!
BOTDS(3643)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.6151667657731764 accuracy:0.6880146386093321
1093
Confusion Matrix (Rows: True, Cols: Predicted):
[[328 154]
[187 424]]
Precision: 0.7335640138408305
Recall: 0.6939443535188216
F1-score: 0.7132043734230445
Accuracy: 0.6880146386093321
Working on: gi50_screen_DLD_1
0.691257
Processing...
Done!
BOTDS(1220)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.592429717951785 accuracy:0.6912568306010929
366
Confusion Matrix (Rows: True, Cols: Predicted):
[[ 0 113]
[ 0 253]]
Precision: 0.6912568306010929
Recall: 1.0
F1-score: 0.8174474959612278
Accuracy: 0.6912568306010929
Working on: gi50_screen_DMS_114
0.742627
Processing...
Done!
BOTDS(1241)
128
Net(
(conv1): SAGEConv(128, 128)
(pool1): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv2): SAGEConv(128, 128)
(pool2): SAGPool(
(score_layer): GCNConv(128, 1)
)
(conv3): SAGEConv(128, 128)
(pool3): SAGPool(
(score_layer): GCNConv(128, 1)
)
(lin1): Linear(in_features=256, out_features=128, bias=True)
(lin2): Linear(in_features=128, out_features=64, bias=True)
(lin3): Linear(in_features=64, out_features=2, bias=True)
)
Test:: loss:0.5295673747005156 accuracy:0.7426273458445041
373
Confusion Matrix (Rows: True, Cols: Predicted):
[[ 14 77]
[ 19 263]]
Precision: 0.7735294117647059
Recall: 0.9326241134751773
F1-score: 0.8456591639871383
Accuracy: 0.7426273458445041