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ScDiVa

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  1. downstream/Annotation_FT/hpancreas/best_model.pt +3 -0
  2. downstream/Annotation_FT/ms/best_model.pt +3 -0
  3. downstream/Annotation_FT/myeloid/best_model.pt +3 -0
  4. downstream/Annotation_FT/myeloid_b/best_model.pt +3 -0
  5. downstream/Annotation_Zeroshot/Cell_Lines/best_model.pt +3 -0
  6. downstream/Annotation_Zeroshot/DC/best_model.pt +3 -0
  7. downstream/Annotation_Zeroshot/HumanPBMC/best_model.pt +3 -0
  8. downstream/Annotation_Zeroshot/Immune/best_model.pt +3 -0
  9. downstream/Annotation_Zeroshot/MCA/best_model.pt +3 -0
  10. downstream/Annotation_Zeroshot/Myeloid/best_model.pt +3 -0
  11. downstream/Annotation_Zeroshot/PBMC368K/best_model.pt +3 -0
  12. downstream/Annotation_Zeroshot/Pancrm/best_model.pt +3 -0
  13. downstream/Multi-batch_Integration/COVID19/covid19_best_avgbatch.pt +3 -0
  14. downstream/Multi-batch_Integration/COVID19/covid19_best_avgbio.pt +3 -0
  15. downstream/Multi-batch_Integration/COVID19/covid19_best_overall.pt +3 -0
  16. downstream/Multi-batch_Integration/bmmc/bmmc_best_avgbatch.pt +3 -0
  17. downstream/Multi-batch_Integration/bmmc/bmmc_best_avgbio.pt +3 -0
  18. downstream/Multi-batch_Integration/bmmc/bmmc_best_overall.pt +3 -0
  19. downstream/Multi-batch_Integration/immune/immune_best_avgbatch.pt +3 -0
  20. downstream/Multi-batch_Integration/immune/immune_best_avgbio.pt +3 -0
  21. downstream/Multi-batch_Integration/immune/immune_best_overall.pt +3 -0
  22. downstream/Multi-batch_Integration/pbmc12k/pbmc12k_best_avgbatch.pt +3 -0
  23. downstream/Multi-batch_Integration/pbmc12k/pbmc12k_best_avgbio.pt +3 -0
  24. downstream/Multi-batch_Integration/pbmc12k/pbmc12k_best_overall.pt +3 -0
  25. downstream/Multi-batch_Integration/perirhinal/perirhinal_best_avgbatch.pt +3 -0
  26. downstream/Multi-batch_Integration/perirhinal/perirhinal_best_avgbio.pt +3 -0
  27. downstream/Multi-batch_Integration/perirhinal/perirhinal_best_overall.pt +3 -0
  28. downstream/Perturbation/Adamson/best_model.pt +3 -0
  29. downstream/Perturbation/Adamson/run.log +159 -0
  30. downstream/Perturbation/Adamson/test_metrics.json +4 -0
  31. downstream/Perturbation/Norman/best_model.pt +3 -0
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1
+ Vocab size: 164860, Genes: 5060
2
+ | epoch 1 | 100/823 batches | lr 0.0000 | ms/batch 866.38 | loss 0.35 |
3
+ | epoch 1 | 200/823 batches | lr 0.0000 | ms/batch 851.95 | loss 0.11 |
4
+ | epoch 1 | 300/823 batches | lr 0.0000 | ms/batch 851.96 | loss 0.10 |
5
+ | epoch 1 | 400/823 batches | lr 0.0000 | ms/batch 851.79 | loss 0.09 |
6
+ | epoch 1 | 500/823 batches | lr 0.0000 | ms/batch 851.60 | loss 0.09 |
7
+ | epoch 1 | 600/823 batches | lr 0.0000 | ms/batch 851.58 | loss 0.09 |
8
+ | epoch 1 | 700/823 batches | lr 0.0000 | ms/batch 851.43 | loss 0.09 |
9
+ | epoch 1 | 800/823 batches | lr 0.0000 | ms/batch 851.43 | loss 0.08 |
10
+ val_metrics at epoch 1:
11
+ {'pearson': np.float32(0.9924684), 'pearson_de': np.float32(0.95944583), 'pearson_delta': np.float32(0.5730563), 'pearson_de_delta': np.float32(0.74848413)}
12
+ New best model with score 0.5731
13
+ | epoch 2 | 100/823 batches | lr 0.0000 | ms/batch 860.92 | loss 0.08 |
14
+ | epoch 2 | 200/823 batches | lr 0.0000 | ms/batch 851.65 | loss 0.08 |
15
+ | epoch 2 | 300/823 batches | lr 0.0000 | ms/batch 852.28 | loss 0.08 |
16
+ | epoch 2 | 400/823 batches | lr 0.0000 | ms/batch 851.74 | loss 0.08 |
17
+ | epoch 2 | 500/823 batches | lr 0.0000 | ms/batch 851.61 | loss 0.08 |
18
+ | epoch 2 | 600/823 batches | lr 0.0000 | ms/batch 851.47 | loss 0.09 |
19
+ | epoch 2 | 700/823 batches | lr 0.0000 | ms/batch 851.44 | loss 0.09 |
20
+ | epoch 2 | 800/823 batches | lr 0.0000 | ms/batch 851.81 | loss 0.08 |
21
+ val_metrics at epoch 2:
22
+ {'pearson': np.float32(0.9925733), 'pearson_de': np.float32(0.95483106), 'pearson_delta': np.float32(0.57153684), 'pearson_de_delta': np.float32(0.6202937)}
23
+ | epoch 3 | 100/823 batches | lr 0.0000 | ms/batch 861.31 | loss 0.09 |
24
+ | epoch 3 | 200/823 batches | lr 0.0000 | ms/batch 852.12 | loss 0.08 |
25
+ | epoch 3 | 300/823 batches | lr 0.0000 | ms/batch 851.89 | loss 0.08 |
26
+ | epoch 3 | 400/823 batches | lr 0.0000 | ms/batch 851.55 | loss 0.08 |
27
+ | epoch 3 | 500/823 batches | lr 0.0000 | ms/batch 851.67 | loss 0.08 |
28
+ | epoch 3 | 600/823 batches | lr 0.0000 | ms/batch 851.59 | loss 0.08 |
29
+ | epoch 3 | 700/823 batches | lr 0.0000 | ms/batch 851.45 | loss 0.08 |
30
+ | epoch 3 | 800/823 batches | lr 0.0000 | ms/batch 851.94 | loss 0.08 |
31
+ val_metrics at epoch 3:
32
+ {'pearson': np.float32(0.99233115), 'pearson_de': np.float32(0.95387167), 'pearson_delta': np.float32(0.5799667), 'pearson_de_delta': np.float32(0.6475007)}
33
+ New best model with score 0.5800
34
+ | epoch 4 | 100/823 batches | lr 0.0000 | ms/batch 862.03 | loss 0.08 |
35
+ | epoch 4 | 200/823 batches | lr 0.0000 | ms/batch 852.94 | loss 0.08 |
36
+ | epoch 4 | 300/823 batches | lr 0.0000 | ms/batch 852.67 | loss 0.08 |
37
+ | epoch 4 | 400/823 batches | lr 0.0000 | ms/batch 852.39 | loss 0.08 |
38
+ | epoch 4 | 500/823 batches | lr 0.0000 | ms/batch 851.72 | loss 0.08 |
39
+ | epoch 4 | 600/823 batches | lr 0.0000 | ms/batch 852.52 | loss 0.08 |
40
+ | epoch 4 | 700/823 batches | lr 0.0000 | ms/batch 851.72 | loss 0.08 |
41
+ | epoch 4 | 800/823 batches | lr 0.0000 | ms/batch 851.60 | loss 0.08 |
42
+ val_metrics at epoch 4:
43
+ {'pearson': np.float32(0.99184006), 'pearson_de': np.float32(0.95636386), 'pearson_delta': np.float32(0.5380967), 'pearson_de_delta': np.float32(0.63693255)}
44
+ | epoch 5 | 100/823 batches | lr 0.0000 | ms/batch 860.40 | loss 0.08 |
45
+ | epoch 5 | 200/823 batches | lr 0.0000 | ms/batch 851.48 | loss 0.08 |
46
+ | epoch 5 | 300/823 batches | lr 0.0000 | ms/batch 851.70 | loss 0.08 |
47
+ | epoch 5 | 400/823 batches | lr 0.0000 | ms/batch 851.98 | loss 0.08 |
48
+ | epoch 5 | 500/823 batches | lr 0.0000 | ms/batch 852.01 | loss 0.08 |
49
+ | epoch 5 | 600/823 batches | lr 0.0000 | ms/batch 851.52 | loss 0.08 |
50
+ | epoch 5 | 700/823 batches | lr 0.0000 | ms/batch 851.55 | loss 0.08 |
51
+ | epoch 5 | 800/823 batches | lr 0.0000 | ms/batch 851.58 | loss 0.08 |
52
+ val_metrics at epoch 5:
53
+ {'pearson': np.float32(0.9921126), 'pearson_de': np.float32(0.95745575), 'pearson_delta': np.float32(0.55699015), 'pearson_de_delta': np.float32(0.6832355)}
54
+ | epoch 6 | 100/823 batches | lr 0.0000 | ms/batch 860.15 | loss 0.08 |
55
+ | epoch 6 | 200/823 batches | lr 0.0000 | ms/batch 851.50 | loss 0.08 |
56
+ | epoch 6 | 300/823 batches | lr 0.0000 | ms/batch 851.58 | loss 0.08 |
57
+ | epoch 6 | 400/823 batches | lr 0.0000 | ms/batch 851.55 | loss 0.08 |
58
+ | epoch 6 | 500/823 batches | lr 0.0000 | ms/batch 851.54 | loss 0.08 |
59
+ | epoch 6 | 600/823 batches | lr 0.0000 | ms/batch 851.76 | loss 0.08 |
60
+ | epoch 6 | 700/823 batches | lr 0.0000 | ms/batch 852.14 | loss 0.08 |
61
+ | epoch 6 | 800/823 batches | lr 0.0000 | ms/batch 852.53 | loss 0.08 |
62
+ val_metrics at epoch 6:
63
+ {'pearson': np.float32(0.99280727), 'pearson_de': np.float32(0.9593009), 'pearson_delta': np.float32(0.6154502), 'pearson_de_delta': np.float32(0.68726575)}
64
+ New best model with score 0.6155
65
+ | epoch 7 | 100/823 batches | lr 0.0000 | ms/batch 862.07 | loss 0.08 |
66
+ | epoch 7 | 200/823 batches | lr 0.0000 | ms/batch 852.80 | loss 0.08 |
67
+ | epoch 7 | 300/823 batches | lr 0.0000 | ms/batch 852.35 | loss 0.08 |
68
+ | epoch 7 | 400/823 batches | lr 0.0000 | ms/batch 851.89 | loss 0.08 |
69
+ | epoch 7 | 500/823 batches | lr 0.0000 | ms/batch 851.80 | loss 0.08 |
70
+ | epoch 7 | 600/823 batches | lr 0.0000 | ms/batch 851.81 | loss 0.08 |
71
+ | epoch 7 | 700/823 batches | lr 0.0000 | ms/batch 851.80 | loss 0.08 |
72
+ | epoch 7 | 800/823 batches | lr 0.0000 | ms/batch 851.75 | loss 0.08 |
73
+ val_metrics at epoch 7:
74
+ {'pearson': np.float32(0.99339944), 'pearson_de': np.float32(0.96072394), 'pearson_delta': np.float32(0.65126127), 'pearson_de_delta': np.float32(0.7328208)}
75
+ New best model with score 0.6513
76
+ | epoch 8 | 100/823 batches | lr 0.0000 | ms/batch 860.90 | loss 0.08 |
77
+ | epoch 8 | 200/823 batches | lr 0.0000 | ms/batch 851.83 | loss 0.08 |
78
+ | epoch 8 | 300/823 batches | lr 0.0000 | ms/batch 851.93 | loss 0.08 |
79
+ | epoch 8 | 400/823 batches | lr 0.0000 | ms/batch 851.99 | loss 0.08 |
80
+ | epoch 8 | 500/823 batches | lr 0.0000 | ms/batch 851.98 | loss 0.08 |
81
+ | epoch 8 | 600/823 batches | lr 0.0000 | ms/batch 852.07 | loss 0.08 |
82
+ | epoch 8 | 700/823 batches | lr 0.0000 | ms/batch 852.66 | loss 0.08 |
83
+ | epoch 8 | 800/823 batches | lr 0.0000 | ms/batch 852.16 | loss 0.08 |
84
+ val_metrics at epoch 8:
85
+ {'pearson': np.float32(0.9930026), 'pearson_de': np.float32(0.95992076), 'pearson_delta': np.float32(0.5512114), 'pearson_de_delta': np.float32(0.7583458)}
86
+ | epoch 9 | 100/823 batches | lr 0.0000 | ms/batch 861.51 | loss 0.08 |
87
+ | epoch 9 | 200/823 batches | lr 0.0000 | ms/batch 852.32 | loss 0.08 |
88
+ | epoch 9 | 300/823 batches | lr 0.0000 | ms/batch 852.10 | loss 0.08 |
89
+ | epoch 9 | 400/823 batches | lr 0.0000 | ms/batch 852.07 | loss 0.08 |
90
+ | epoch 9 | 500/823 batches | lr 0.0000 | ms/batch 852.00 | loss 0.08 |
91
+ | epoch 9 | 600/823 batches | lr 0.0000 | ms/batch 851.92 | loss 0.08 |
92
+ | epoch 9 | 700/823 batches | lr 0.0000 | ms/batch 852.21 | loss 0.08 |
93
+ | epoch 9 | 800/823 batches | lr 0.0000 | ms/batch 852.21 | loss 0.08 |
94
+ val_metrics at epoch 9:
95
+ {'pearson': np.float32(0.99360716), 'pearson_de': np.float32(0.96092397), 'pearson_delta': np.float32(0.68548614), 'pearson_de_delta': np.float32(0.77148426)}
96
+ New best model with score 0.6855
97
+ | epoch 10 | 100/823 batches | lr 0.0000 | ms/batch 862.16 | loss 0.08 |
98
+ | epoch 10 | 200/823 batches | lr 0.0000 | ms/batch 852.36 | loss 0.08 |
99
+ | epoch 10 | 300/823 batches | lr 0.0000 | ms/batch 852.19 | loss 0.08 |
100
+ | epoch 10 | 400/823 batches | lr 0.0000 | ms/batch 852.10 | loss 0.08 |
101
+ | epoch 10 | 500/823 batches | lr 0.0000 | ms/batch 852.14 | loss 0.08 |
102
+ | epoch 10 | 600/823 batches | lr 0.0000 | ms/batch 852.13 | loss 0.08 |
103
+ | epoch 10 | 700/823 batches | lr 0.0000 | ms/batch 852.15 | loss 0.08 |
104
+ | epoch 10 | 800/823 batches | lr 0.0000 | ms/batch 852.48 | loss 0.08 |
105
+ val_metrics at epoch 10:
106
+ {'pearson': np.float32(0.993454), 'pearson_de': np.float32(0.9617061), 'pearson_delta': np.float32(0.6751247), 'pearson_de_delta': np.float32(0.79925287)}
107
+ | epoch 11 | 100/823 batches | lr 0.0000 | ms/batch 861.40 | loss 0.08 |
108
+ | epoch 11 | 200/823 batches | lr 0.0000 | ms/batch 852.38 | loss 0.08 |
109
+ | epoch 11 | 300/823 batches | lr 0.0000 | ms/batch 852.15 | loss 0.08 |
110
+ | epoch 11 | 400/823 batches | lr 0.0000 | ms/batch 852.14 | loss 0.08 |
111
+ | epoch 11 | 500/823 batches | lr 0.0000 | ms/batch 852.11 | loss 0.08 |
112
+ | epoch 11 | 600/823 batches | lr 0.0000 | ms/batch 852.16 | loss 0.08 |
113
+ | epoch 11 | 700/823 batches | lr 0.0000 | ms/batch 852.09 | loss 0.08 |
114
+ | epoch 11 | 800/823 batches | lr 0.0000 | ms/batch 852.04 | loss 0.08 |
115
+ val_metrics at epoch 11:
116
+ {'pearson': np.float32(0.9935574), 'pearson_de': np.float32(0.9613107), 'pearson_delta': np.float32(0.68756264), 'pearson_de_delta': np.float32(0.79851687)}
117
+ New best model with score 0.6876
118
+ | epoch 12 | 100/823 batches | lr 0.0000 | ms/batch 861.52 | loss 0.08 |
119
+ | epoch 12 | 200/823 batches | lr 0.0000 | ms/batch 852.18 | loss 0.08 |
120
+ | epoch 12 | 300/823 batches | lr 0.0000 | ms/batch 852.12 | loss 0.08 |
121
+ | epoch 12 | 400/823 batches | lr 0.0000 | ms/batch 852.10 | loss 0.08 |
122
+ | epoch 12 | 500/823 batches | lr 0.0000 | ms/batch 852.11 | loss 0.08 |
123
+ | epoch 12 | 600/823 batches | lr 0.0000 | ms/batch 852.08 | loss 0.08 |
124
+ | epoch 12 | 700/823 batches | lr 0.0000 | ms/batch 852.23 | loss 0.08 |
125
+ | epoch 12 | 800/823 batches | lr 0.0000 | ms/batch 852.21 | loss 0.08 |
126
+ val_metrics at epoch 12:
127
+ {'pearson': np.float32(0.9937715), 'pearson_de': np.float32(0.9615432), 'pearson_delta': np.float32(0.6854335), 'pearson_de_delta': np.float32(0.8031682)}
128
+ | epoch 13 | 100/823 batches | lr 0.0000 | ms/batch 861.72 | loss 0.08 |
129
+ | epoch 13 | 200/823 batches | lr 0.0000 | ms/batch 852.40 | loss 0.08 |
130
+ | epoch 13 | 300/823 batches | lr 0.0000 | ms/batch 852.31 | loss 0.08 |
131
+ | epoch 13 | 400/823 batches | lr 0.0000 | ms/batch 852.22 | loss 0.08 |
132
+ | epoch 13 | 500/823 batches | lr 0.0000 | ms/batch 852.21 | loss 0.08 |
133
+ | epoch 13 | 600/823 batches | lr 0.0000 | ms/batch 852.17 | loss 0.08 |
134
+ | epoch 13 | 700/823 batches | lr 0.0000 | ms/batch 852.07 | loss 0.08 |
135
+ | epoch 13 | 800/823 batches | lr 0.0000 | ms/batch 852.00 | loss 0.08 |
136
+ val_metrics at epoch 13:
137
+ {'pearson': np.float32(0.99420965), 'pearson_de': np.float32(0.961836), 'pearson_delta': np.float32(0.7315334), 'pearson_de_delta': np.float32(0.8087917)}
138
+ New best model with score 0.7315
139
+ | epoch 14 | 100/823 batches | lr 0.0000 | ms/batch 861.44 | loss 0.08 |
140
+ | epoch 14 | 200/823 batches | lr 0.0000 | ms/batch 852.25 | loss 0.08 |
141
+ | epoch 14 | 300/823 batches | lr 0.0000 | ms/batch 852.08 | loss 0.08 |
142
+ | epoch 14 | 400/823 batches | lr 0.0000 | ms/batch 852.06 | loss 0.08 |
143
+ | epoch 14 | 500/823 batches | lr 0.0000 | ms/batch 851.94 | loss 0.08 |
144
+ | epoch 14 | 600/823 batches | lr 0.0000 | ms/batch 852.04 | loss 0.08 |
145
+ | epoch 14 | 700/823 batches | lr 0.0000 | ms/batch 851.92 | loss 0.08 |
146
+ | epoch 14 | 800/823 batches | lr 0.0000 | ms/batch 851.99 | loss 0.08 |
147
+ val_metrics at epoch 14:
148
+ {'pearson': np.float32(0.99408436), 'pearson_de': np.float32(0.96195143), 'pearson_delta': np.float32(0.73480195), 'pearson_de_delta': np.float32(0.8150549)}
149
+ New best model with score 0.7348
150
+ | epoch 15 | 100/823 batches | lr 0.0000 | ms/batch 861.72 | loss 0.08 |
151
+ | epoch 15 | 200/823 batches | lr 0.0000 | ms/batch 852.33 | loss 0.08 |
152
+ | epoch 15 | 300/823 batches | lr 0.0000 | ms/batch 852.18 | loss 0.08 |
153
+ | epoch 15 | 400/823 batches | lr 0.0000 | ms/batch 852.12 | loss 0.08 |
154
+ | epoch 15 | 500/823 batches | lr 0.0000 | ms/batch 852.13 | loss 0.08 |
155
+ | epoch 15 | 600/823 batches | lr 0.0000 | ms/batch 852.12 | loss 0.08 |
156
+ | epoch 15 | 700/823 batches | lr 0.0000 | ms/batch 852.04 | loss 0.08 |
157
+ | epoch 15 | 800/823 batches | lr 0.0000 | ms/batch 852.06 | loss 0.08 |
158
+ val_metrics at epoch 15:
159
+ {'pearson': np.float32(0.9940652), 'pearson_de': np.float32(0.9621587), 'pearson_delta': np.float32(0.7347072), 'pearson_de_delta': np.float32(0.80805075)}
downstream/Perturbation/Adamson/test_metrics.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ "Delta": 0.7347
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+ }
downstream/Perturbation/Norman/best_model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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